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• Published: 11 January 2022

Effect of sleep and mood on academic performance—at interface of physiology, psychology, and education

• Kosha J. Mehta   ORCID: orcid.org/0000-0002-0716-5081 1  

Humanities and Social Sciences Communications volume  9 , Article number:  16 ( 2022 ) Cite this article

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Academic achievement and cognitive functions are influenced by sleep and mood/emotion. In addition, several other factors affect learning. A coherent overview of the resultant interrelationships is essential but has not been presented till date. This unique and interdisciplinary review sits at the interface of physiology, psychology, and education. It compiles and critically examines the effects of sleep and mood on cognition and academic performance while including relevant conflicting observations. Moreover, it discusses the impact of several regulatory factors on learning, namely, age, gender, diet, hydration level, obesity, sex hormones, daytime nap, circadian rhythm, and genetics. Core physiological mechanisms that mediate the effects of these factors are described briefly and simplistically. The bidirectional relationship between sleep and mood is addressed. Contextual pictorial models that hypothesise learning on an emotion scale and emotion on a learning scale have been proposed. Essentially, convoluted associations between physiological and psychological factors, including sleep and mood that determine academic performance are recognised and affirmed. The emerged picture reveals far more complexity than perceived. It questions the currently adopted ‘one-size fits all’ approach in education and urges to envisage formulating bespoke strategies to optimise teaching-learning approaches while retaining uniformity in education. The information presented here can help improvise education strategies and provide better academic and pastoral support to students during their academic journey.

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Introduction.

Academic performance and cognitive activities like learning are influenced by sleep and mood or emotion. This review discusses the roles of sleep and mood/emotion in learning and academic performance.

Sleep, mood, and emotion: definitions and descriptions

Sleep duration refers to “total amount of sleep obtained, either during the nocturnal sleep episode or across the 24-hour period” (Kline, 2013a ). Sleep quality is defined as “one’s satisfaction of the sleep experience, integrating aspects of sleep initiation, sleep maintenance, sleep quantity, and refreshment upon awakening” (Kline, 2013b ). Along similar lines, it is thought to be “one’s perception that they fall asleep easily, get sufficient duration so as to wake up feeling rested, and can make it through their day without experiencing excessive daytime sleepiness” (Štefan et al., 2018 ). Sleep disturbance includes disorders of initiating and maintaining sleep (insomnias) and sleep–wake schedule, as well as dysfunctions associated with either sleep or stages of sleep or partial arousals (Cormier, 1990 ). Sleep deprivation is a term used loosely to describe a lack of appropriate/sufficient amount of sleep (Levesque, 2018 ). It is “abnormal sleep that can be described in measures of deficient sleep quantity, structure and/or sleep quality” (Banfi et al., 2019 ). In a study, sleep deprivation was defined as a sleep duration of 6 h or less (Roberts and Duong, 2014 ). Sleep disorder overarches disorders related to sleep. It has many classifications (B. Zhu et al., 2018 ). Sleep disorders or sleep-related problems include insomnia, hypersomnia, obstructive sleep apnoea, restless legs and periodic limb movement disorders, and circadian rhythm sleep disorders (Hershner and Chervin, 2014 ).

Mood is a pervasive and sustained feeling that is felt internally and affects all aspects of an individual’s behaviour (Sekhon and Gupta, 2021 ). However, by another definition, it is believed to be transient. It is low-intensity, nonspecific, and an affective state. Affective state is an overarching term that includes both emotions and moods. In addition to transient affective states of daily life, mood includes low-energy/activation states like fatigue or serenity (Kleinstäuber, 2013 ). Yet another definition of mood refers to mood as feelings that vary in intensity and duration, and that usually involves more than one emotion (Quartiroli et al., 2017 ). According to the American Psychological Association, mood is “any short-lived emotional state, usually of low intensity” and which lacks stimuli, whereas emotion is a “complex reaction pattern, involving experiential, behavioural and physiological elements”. Emotion is a certain level of pleasure or displeasure (X. Liu et al., 2018 ). It is “a response to external stimuli and internal mental representations” (L. Zhang et al., 2021 ). It is “a conscious mental reaction (such as anger or fear) which is subjectively experienced as a strong feeling usually deriving from one’s circumstances, mood, or relationships with others”. “This feeling is typically accompanied by physiological and behavioural changes in the body”. “This mental state is an instinctive or intuitive feeling which arises spontaneously as distinguished from reasoning or knowledge” (Thibaut, 2015 ).

Since there is some overlap between the descriptions of mood and emotion, in the context of the core content of this review, here, mood and emotion have not been differentiated based on their theoretical/psychological definitions. This is because the aim of the review is not to distinguish between the effects of mood and emotion on learning. Thus, these have been referred to as general affective states; essentially specific states of mind that affect learning. Also, these have been addressed in the context of the study being discussed and cited in that specific place in the review.

Rationale for the topic

Sleep is essential for normal physiological functionality. The panel of National Sleep Foundation suggests sleep durations for various age groups and agrees that the appropriate sleep duration for young adults and adults would be 7–9 hours, and for older adults would be 7–8 hours (Hirshkowitz et al., 2015 ). Today, people sleep for 1–2 hours less than that around 50–100 years ago (Roenneberg, 2013 ). Millions of adults frequently get insufficient sleep (Vecsey et al., 2009 ), including college and university students who often report poor and/or insufficient sleep (Bahammam et al., 2012 ; Curcio et al., 2006 ; Hershner and Chervin, 2014 ). During the COVID-19 pandemic, sleep problems have been highly prevalent in the general population (Gualano et al., 2020 ; Jahrami et al., 2021 ; Janati Idrissi et al., 2020 ) and the student community (Marelli et al., 2020 ). Poor and insufficient sleep is a public health issue because it increases the risk of developing chronic pathologies, and imparts negative social and economic outcomes (Hafner et al., 2017 ).

Like sleep, mood and emotions determine our physical and mental health. Depressive disorders have prevailed as one of the leading causes of health loss for nearly 30 years (James et al., 2018 ). Increased incidence of mood disorders amongst the general population has been observed (Walker et al., 2020 ), and there is an increase in such disorders amongst students (Auerbach et al., 2018 ). These have further risen during the COVID-19 pandemic (Son et al., 2020 ; Wang et al., 2020 ).

The relationship between sleep, mood and cognition/learning is far more complex than perceived. Therefore, this review aims to recognise the interrelationships between the aforementioned trio. It critically examines the effects of sleep and mood on cognition, learning and academic performance (Fig. 1 ). Furthermore, it discusses how various regulatory factors can directly or indirectly influence cognition and learning. Factors discussed here are age, gender, diet, hydration level, obesity, sex hormones, daytime nap, circadian rhythm, and genetics (Fig. 1 ). The effect of sleep and mood on each other is also addressed. Pictorial models that hypothesise learning on an emotion scale and vice-versa have been proposed.

Sleep and mood/emotion affect cognition and academic achievement. Their effects can be additionally influenced by other factors like diet, metabolic disorders (e.g., obesity), circadian rhythm, daytime nap, hydration level, age, gender, and genetics. The figure presents the interrelationships and highlights the complexity emerging from the interdependence between factors, action of multiple factors on a single factor or vice-versa and the bidirectional nature of some associations. These associations collectively determine learning and thereby, academic achievement. Direction of the arrow represents effect of a factor on another.

Effect of sleep on cognition and academic performance

Adequate sleep positively affects memory, learning, acquisition of skills and knowledge extraction (Fenn et al., 2003 ; Friedrich et al., 2020 ; Huber et al., 2004 ; Schönauer et al., 2017 ; Wagner et al., 2004 ). It allows the recall of previously gained knowledge despite the acquisition of new information and memories (Norman, 2006 ). Sleeping after learning acquisition regardless of the time of the day is thought to be beneficial for memory consolidation and performance (Hagewoud et al., 2010 ). Therefore, unperturbed sleep is essential for maintaining learning efficiency (Fattinger et al., 2017 ).

Sleep quality and quantity are strongly associated with academic achievement in college students (Curcio et al., 2006 ; Okano et al., 2019 ). Sufficient sleep positively affects grade point average, which is an indicator of academic performance (Abdulghani et al., 2012 ; Hershner and Chervin, 2014 ) and supports cognitive functionality in school-aged children (Gruber et al., 2010 ). As expected, insufficient sleep is associated with poor performance in school, college and university students (Bahammam et al., 2012 ; Hayley et al., 2017 ; Hedin et al., 2020 ; Kayaba et al., 2020 ; Perez-Chada et al., 2007 ; Shochat et al., 2014 ; Suardiaz-Muro et al., 2020 ; Taras and Potts-Datema, 2005 ). In adolescents aged 14–18 years, not only did sleep quality affect academic performance (Adelantado-Renau, Jiménez-Pavón, et al., 2019 ) but one night of total sleep deprivation negatively affected neurobehavioral performance-attention, reaction time and speed of cognitive processing, thereby putting them at risk of poor academic performance (Louca and Short, 2014 ). In university students aged 18–25 years, poor sleep quality has been strongly associated with daytime dysfunctionality (Assaad et al., 2014 ). Medical students tend to show poor sleep quality and quantity. In these students, not sleep duration but sleep quality has been shown to correlate with academic scores (Seoane et al., 2020 ; Toscano-Hermoso et al., 2020 ). Students may go through repeated cycles wherein the poor quality of sleep could lead to poor performance, which in turn may again lead to poor quality of sleep (Ahrberg et al., 2012 ). Sleep deprivation in surgical residents tends to decrease procedural skills, while in non-surgical residents it diminishes interpretational ability and performance (Veasey et al., 2002 ).

Such effects of sleep deprivation are obvious because it can impair procedural and declarative learning (Curcio et al., 2006 ; Kurniawan et al., 2016 ), decrease alertness (Alexandre et al., 2017 ), and impair memory consolidation (Hagewoud et al., 2010 ), attention and decision making (Alhola and Polo-Kantola, 2007 ). It can increase low-grade systemic inflammation and hinder cognitive functionality (Choshen-Hillel et al., 2020 ). Hippocampus is the region in the brain that plays the main role in learning, memory, social cognition, and emotion regulation (Y. Zhu et al., 2019 ). cAMP signalling plays an important role in several neural processes such as learning and memory, cellular excitability, motor function and pain (Lee, 2015 ). A brief 5-hour period of sleep deprivation interferes with cAMP signalling in the hippocampus and impairs its function (Vecsey et al., 2009 ). Thus, optimal academic performance is hindered, if there is a sleep disorder (Hershner and Chervin, 2014 ).

Caveats to affirming the impact of sleep on cognition and academic performance

Despite the clear significance of appropriate sleep quality and quantity in cognitive processes, there are some caveats to drawing definitive conclusions in certain areas. First, there are uncertainties around how much sleep is optimal and how to measure sleep quality. This is further confounded by the dependence of sleep quality and quantity on various genetic and environmental factors (Roenneberg, 2013 ). Moreover, although sleep enhances emotional memory, during laboratory investigations, this effect has been observed only under specific experimental conditions. Also, the experiments conducted have differed in the methods used and in considering parameters like timing and duration of sleep, age, gender and outcome measure (Lipinska et al., 2019 ). This orientates conclusions to be specific to those experimental conditions and prevents the formation of generic opinions that would be applicable to all circumstances.

Furthermore, some studies on the effects of sleep on learning and cognitive functions have shown either inconclusive or apparently unexpected results. For example, in a study, although college students at risk for sleeping disorders were thought to be at risk for academic failure, this association remained unclear (Gaultney, 2010 ). Other studies showed that the effect of sleep quality and duration on academic performance was trivial (Dewald et al., 2010 ) and did not significantly correlate with academic performance (Johnston et al., 2010 ; Sweileh et al., 2011 ). In yet another example, despite the reduction in sleep hours during stressful periods, pharmacy students did not show adversely affected academic performance (Mnatzaganian et al., 2020 ). Also, the premise underlining the significance of sleep hours in enhancing the performance of clinical duties was challenged when the average daily sleep did not affect burnout in clinical residents, where the optimal sleep hours that would maximise learning and improve performance remained unknown (Mendelsohn et al., 2019 ). In some other examples, poor sleep quality was associated with stress but not with academic performance that was measured as grade point average (Alotaibi et al., 2020 ), showed no significant impact on academic scores (Javaid et al., 2020 ) and there was no significant difference between high-grade and low-grade achievers based on sleep quality (Jalali et al., 2020 ). Insomnia reflects regularly experienced sleeping problems. Strangely, in adults aged 40–69 years, those with frequent insomnia showed slightly better cognitive performance than others (Kyle et al., 2017 ).

The reason for such inconclusive and unanticipated results could be that sleep is not the sole determinant of learning. Learning is affected by various other factors that may alter, exacerbate, or surpass the influence of sleep on learning (Fig. 1 ). These factors have been discussed in the subsequent sections.

Effect of mood/emotion on cognition and learning

Emotions reflect a certain level of pleasure or displeasure (X. Liu et al., 2018 ). Panksepp described seven basic types of emotions, whereby lust, seeking, play and care are positive emotions whereas anger, fear and sadness are negative emotions (Davis and Montag, 2019 ). Emotions influence all cognitive functions including memory, focus, problem-solving and reasoning (Tyng et al., 2017 ). Positive emotions such as hope, joy and pride positively correlate with students’ academic interest, effort and achievement (Valiente et al., 2012 ) and portend a flexible brain network that facilitates cognitive flexibility and learning (Betzel et al., 2017 ).

Mood deficit often precedes learning impairment (LeGates et al., 2012 ). In a study by Miller et al. ( 2018 ), the negative mood is referred to as negative emotional induction, as was achieved by watching six horror films by the subjects in that study. Other examples of negative emotions given by the authors were anxiety and shame. Negative mood can unfavourably affect the learning of an unfamiliar language by suppressing the processing of native language that would otherwise help make connections, thereby reiterating the link between emotions and cognitive processing (Miller et al., 2018 ). Likewise, worry and anxiety affect decision-making. High level of worry is associated with poor task performance and decreased foresight during decision-making (Worthy et al., 2014 ). State anxiety reflects a current mood state and trait anxiety reflects a stable personality trait. Both are associated with an increased tendency of “more negative or more threatening interpretation of ambiguous information”, as can be the case in clinically depressed individuals (Bisson and Sears, 2007 ). This could explain why some people who show the symptoms of depression and anxiety may complain of confusion and show an inability to focus and use cognition skills to appraise contextual clues. Patients with major depressive disorder have scored lower on working and verbal memory, motor speed and attention than healthy participants (Hidese et al., 2018 ). Similarly, apathy, anxiety, depression, and mood disorders in stroke patients can adversely affect the functional recovery of patients’ cognitive functions (Hama et al., 2020 ). These examples collectively present a positive correlation between good mood and cognitive processes.

Caveats to affirming the impact of mood/emotion on cognition and academic performance

Based on the examples and discussion so far, a direct relationship between emotions and learning could be hypothesised, whereby positive emotions would promote creative learning strategies and academic success, whereas negative emotions would lead to cognitive impairment (Fig. 2a ). However, this relationship is far more complex and different than perceived.

Emotions have been shown on a hypothetical learning scale. a Usually, positive and negative emotions are perceived to match with optimal and poor learning, respectively. b Emotions that lead to sub-optimal/poor and optimal/better learning have been shown on the hypothetical learning scale. Here, distinct from ( a ), both negative emotions and high arousal positive emotions have been implicated in poorer learning compared with low-intensity positive emotion like pleasantness; the latter is believed to lead to optimal learning. The question mark reflects that some negative emotions like shame might stimulate learning, but the exact intensity of such emotions and whether these would facilitate better or worse learning than high arousal positive emotions or pleasantness need to be investigated.

Although positive mood favours the recall of learnt words, it correlates with increased distraction and poor planning (Martin and Kerns, 2011 ). High levels of positive emotions like excitedness and elatedness may decrease achievement (Fig. 2b ) (Valiente et al., 2012 ). It may be surprising to know that negative emotions such as shame and anxiety can arouse cognitive activity (Miller et al., 2018 ). Along similar lines, it has been observed that participants exposed to sad and neutral moods performed similarly in visual statistical (learning) tasks but those who experienced sad stimuli showed high conscious access to the acquired statistical knowledge (Bertels et al., 2013 ). Dysphoria is a state of dissatisfaction that may be accompanied by anxiety and depression. Participants with dysphoria have shown more sensitivity to temporal shifts in outcome contingencies than those without dysphoria (Msetfi et al., 2012 ), and these participants reiterated the depressive realism effect and were quicker in endorsing the connection between negative words and ambiguous statements, demonstrating a negative bias (Hindash and Amir, 2012 ). Likewise, not the positive emotion but negative emotion has been shown to influence the learning outcomes, and it increased the efficiency and precision of learning morphosyntactic instructions involving morphology and syntax of a foreign language (X. Liu et al., 2018 ). Thus, negative emotions can allow, and at times, stimulate or facilitate learning (Figs. 2 and 3 ). Further investigation is needed on the intensity of these emotions, whether these would facilitate better or worse learning than high-intensity positive emotions and whether the results would be task specific.

The figure depicts that low-to-medium intensity positive emotion like pleasantness leads to optimal learning, whereas high-intensity emotions, either positive or negative, may lead to suboptimal or comparatively poorer learning. The model considers the apparently unexpected data that negative emotions may stimulate learning. However, which negative emotions these would be, their intensities and their corresponding level of learning are not known, and so these are not shown in the figure. Also, the figure shows bias towards positive emotions in mediating optimal learning. This information is based on the literature so far. Note that the figure represents concepts only and is not prescriptive. It shows inequality and differences between the impacts of high arousal positive and high arousal negative emotions. This concept needs to be investigated. Therefore, the figure may/may not be an accurate mathematical representation of learning with regards to the intensities of positive and negative emotions. In actuality, the scaling and intensities of emotions on the negative and positive sides of the scale may not be equal, particularly in reference to the position of optimal learning on the scale. Furthermore, upon plotting the 3rd dimension, which could be one or more of the regulatory factors discussed here might alter the position and shape of the optimal learning peak.

Moreover, the intensity of positive emotions does not show direct mathematical proportionality to learning/achievement. In other words, the concept of ‘higher the intensity of positive emotions, higher the achievement’ is not applicable. Low-intensity positive emotions such as satisfaction and relaxedness may be potentially dysregulating and high-intensity positive emotions may hamper achievement (Figs. 2b and 3 ). Optimal achievement is likely to be associated with low to medium level intensity of positive emotions like pleasantness (Valiente et al., 2012 ) (Fig. 3 ). Therefore, it has been proposed that both positive and negative high arousal emotions impair cognitive ability (Figs. 2b and 3 ) whereas low-arousal emotions could enhance behavioural performance (Miller et al., 2018 ).

Interestingly, some studies have indicated that emotions do not play a significant role in context. For example, a study showed that there was no evidence that negative emotions in depressed participants showed negative interpretations of ambiguous information (Bisson and Sears, 2007 ). In another study, improvements in visuomotor skills happened regardless of perturbation or mood states (Kaida et al., 2017 ). Thus, mood can either impair, enhance or have no effect on cognition. The effect of mood on cognition and learning can be variable and depend on the complexity of the task (Martin and Kerns, 2011 ) and/or other factors. Some of these factors have been discussed in the following section. The discrepancies in the data on the effects of mood on cognition and learning may be partly attributed to the influence of these factors on cognitive functions.

Factors affecting cognition and its relationships with sleep and mood/emotion

The relationship of cognition with sleep and mood is confounded by the influence of various factors (Tyng et al., 2017 ) such as diet, hydration level, metabolic disorders (e.g., obesity), sex hormones and gender, sleep, circadian rhythm, age and genetics (Fig. 1 ). These factors and their relationships with learning are discussed in this section.

A healthy diet is defined as eating many servings per day of fruits and vegetables, while maintaining a critical view of the consumption of saturated fat, sugar and salt (Healthy Diet—an Overview|ScienceDirect Topics, n.d.). It is also about adhering to two or more of the three healthy attributes with regards to food intake, namely, sufficiently low meat, high fish and high fruits and vegetables (Sarris et al., 2020 ). Another definition of a healthy diet is the total score of the healthy eating index >51 (Zhao et al., 2021 ).

The association between an unhealthy diet and the development of metabolic disorders has been long established. In addition, food affects both cognition and emotion (Fig. 1 ) (Spencer et al., 2017 ). Food and mood show a bidirectional relation whereby food affects mood and mood affects the choice of food made by the individual. Alongside, poor diet can lead to depression while a healthy diet reduces the risk of depression (Francis et al., 2019 ). A high-fat diet stimulates the hippocampus to initiate neuroinflammatory responses to minor immune challenges and this causes memory loss. Likewise, low intake of omega-3 polyunsaturated fatty acids can affect endocannabinoid and inflammatory pathways in the brain causing microglial phagocytosis, i.e., engulfment of synapses by the brain microglia in the hippocampus, eventually leading to memory deficits and depression. On the other hand, vegetables and fruits rich in polyphenolics can lower oxidative stress and inflammation, and thereby avert and/or reverse age-related cognitive dysfunctionality (Spencer et al., 2017 ). Fruits and vegetables, fish, eggs, nuts, and dairy products found in the Mediterranean diet can reduce the risk of developing depression and promote better mental health than sugar-sweetened beverages and high-fat food found in Western diets. Consumption of dietary antioxidants such as the polyphenols in green tea has shown a negative correlation with depression-like symptoms (Firth et al., 2020 ; Huang et al., 2019 ; Knüppel et al., 2017 ). Likewise, chocolate or its components have been found to reduce negative mood or enhance mood, and also enhance or alter cognitive functions temporarily (Scholey and Owen, 2013 ). Alcohol consumption is prevalent amongst university students including those who report feelings of sadness and hopelessness (Htet et al., 2020 ). It can lead to poor academic performance, hamper tasks that require a high degree of cognitive control, dampen emotional responsiveness, impair emotional processing, and generally cause emotional dysregulation (Euser and Franken, 2012 ). Further details on the effects of diet on mood have been discussed elsewhere (Singh, 2014 ). Diet also affects sleep (Binks et al., 2020 ), which in turn affects learning and academic performance. Thus, diet is linked with sleep, mood, and brain functionality (Fig. 1 ).

Water is a critical nutrient accounting for about 3/4th of the brain mass (N. Zhang et al., 2019 ). Unlike the previously thought deficit of 2% or more in body water levels, loss of about 1–2% can be detrimental and hinder normal cognitive functionality (Riebl and Davy, 2013 ). Thus, mild dehydration can disrupt cognitive functions and mood; particularly applicable to the very old, the very young and those living in hot climatic conditions or those exercising rigorously. Dehydration diminishes alertness, concentration, short-term memory, arithmetic ability, psychomotor skills and visuomotor tracking. This is possibly due to the dehydration-induced physiological stress which competes with cognitive processes. In children, voluntary water intake has been shown to improve visual attention, enhance memory performance (Popkin et al., 2010 ) and generally improve memory and attention (Benton, 2011 ). In adults, dehydration can elevate anger, fatigue and depression and impair short-term memory and attention, while rehydration can alleviate or significantly improve these parameters (Popkin et al., 2010 ; N. Zhang et al., 2019 ). Thus, dehydration causes alterations in cognition and emotions, thereby showcasing the impact of hydration levels on both learning and emotional status (Fig. 1 ).

Interestingly, when older persons are deprived of water, they are less thirsty and less likely to drink water than water-deprived younger persons. This can be due to the defective functionality of baroreceptors, osmoreceptors and opioid receptors that alter thirst regulation with aging (Popkin et al., 2010 ). Since water is essential for the maintenance of memory and cognitive performance, the decline of cognitive functionality in the elderly could be partly attributed to their lack of sufficient fluid/water intake when dehydrated.

Obesity and underweightness

Normal weight is defined as a body mass index between 18.5 and 25 kg/m 2 (McGee and Diverse Populations Collaboration, 2005 ) or between 22 and 26.99 kg/m 2 (Nösslinger et al., 2021 ). Being underweight reflects rapid weight loss or an inability to increase body mass and is defined through grades (1–3) of thinness. In children, these are associated with poor academic performance in reading and writing skills, and mathematics (Haywood and Pienaar, 2021 ). Basically, underweight children may have health issues and this could affect their academic abilities (Zavodny, 2013 ). Also, malnourished children tend to show low school attendance and may show poor concentration and impaired motor functioning and problem-solving skills that could collectively lead to poor academic performance at school (Haywood and Pienaar, 2021 ). Malnourished children can show poor performance on cognitive tasks that require executive function. Executive functions could be impaired in overweight children too and this may lead to poor academic performance (Ishihara et al., 2020 ). The negative relation between overweightness and academic performance also implies that the reverse may be true. Poor academic outcome may cause children to overeat and reduce exercise or play and this could lead to them being overweight (Zavodny, 2013 ).

The influence of weight on academic performance is reiterated in observations that in children independent of socioeconomic and other factors, weight loss in overweight/obese children and weight gain in underweight children positively influenced their academic performance (Ishihara et al., 2020 ). Interestingly, independent of the BMI classification, perceptions of underweight and overweight can predict poorer academic performance. In youth, not only larger body sizes but perceptions about deviating from the “correct weight” can impede academic success. This clearly indicates an impact of overweight and underweight perceptions on the emotional and physical health of adolescents (Fig. 1 ) (Livermore et al., 2020 ).

Cognitive and mood disorders are common co-morbidities associated with obesity. Compared to people with normal weight, obese individuals frequently show some dysfunction in learning, memory, and other executive functions. This has been partly attributed to an unhealthy diet, which causes a drift in the gut microbiota. In turn, the obesity-associated microbiota contributes to obesity-related complications including neurochemical, endocrine and inflammatory changes underlying obesity and its comorbidities (Agustí et al., 2018 ). The exacerbated inflammation in obesity may impair the functionality of the region in the brain that is associated with learning, memory, and mood regulation (Castanon et al., 2015 ).

Obesity and mood appear to have a reciprocal relationship whereby obesity is highly prevalent amongst individuals with major depressive disorder and obese individuals are at a high risk of developing anxiety, depression and cognitive malfunction (Restivo et al., 2017 ). In patients with major depressive disorder, obesity has been associated with reduced cognitive functions, likely due to the reduction in grey matter and impaired integrity of white matter in the brain, particularly in areas related to cognition (Hidese et al., 2018 ). Obesity has been shown to be a predictor of depression and the two are linked via psychobiological mechanisms (LaGrotte et al., 2016 ). Notably, sleep deprivation increases the risk of obesity (Beccuti and Pannain, 2011 ) and sleep helps evade obesity (Pearson, 2006 ). Collectively, this links cognition and academic achievement with sleep, obesity, and mood.

Sex hormones and gender

According to the Office of National Statistics, the UK government defines sex as that assigned at birth and which is generally male or female, whereas gender is where an individual may see themselves as having no gender or non-binary gender or on a spectrum between man and woman. The following section discusses both sex and gender in context, as addressed within the cited studies.

Studies show that females outperform males in most academic subjects (Okano et al., 2019 ) and show more sustained performance in tests than male peers (Balart and Oosterveen, 2019 ). This indicates that biological sex may play a role in academic performance. The hormone oestrogen helps develop and maintain female characteristics and the reproductive system. Oestrogen also affects hippocampal neurogenesis, which involves neural stem cells proliferation and survival, and this contributes to memory retention and cognitive processing. Generally, on average, females show higher levels of oestrogen than males. This may partly explain the observed sex-based differences in academic achievement. Administration of oestrogen in females has been proposed to positively affect cognitive behaviour as well as depressive-like and anxiety-like behaviours (Hiroi et al., 2016 ). Clinical trials can establish whether there are any sex-based differences in cognition following oestrogen administration in males and females.

Progesterone, the hormone released by ovaries in females is also produced by males to synthesise testosterone. It affects some non-reproduction functions in the central nervous system in both males and females such as neural circuits formation, and regulates memory, learning and mood (González-Orozco and Camacho-Arroyo, 2019 ). The menstrual cycle in females shows alterations in oestrogen and progesterone levels and is broadly divided into early follicular, mid ovulation and late luteal phase. It is believed that the low-oestrogen-low-progesterone early follicular phase relates to better spatial abilities and “male favouring” cognitive abilities, whereas the high-oestrogen-high-progesterone late follicular or mid-luteal phases relate to verbal fluency, memory and other “female favouring” cognitive abilities (Sundström Poromaa and Gingnell, 2014 ). Thus, sex-hormone derivatives (salivary oestrogen and salivary progesterone) can be used as predictors of cognitive behaviour (McNamara et al., 2014 ). These ovarian hormones decline with menopause, which may affect cognitive and somatosensory functions. However, ovariectomy of rats, which depleted ovarian hormones, caused depression-like behaviour in rats but did not affect spatial performance (Li et al., 2014 ). While this suggests a positive effect of these hormones on mood, it questions their function in cognition and proposes activity-specific functions, which need to be investigated.

Serotonin is a neurotransmitter. Reduced serotonin is correlated with cognitive dysfunctions. Tryptophan hydroxylase-2 is the rate-limiting enzyme in serotonin synthesis. Polymorphisms of this enzyme have been implicated in cognitive disorders. Women have a lower rate of serotonin synthesis and are more susceptible to such dysfunctions than men (Hiroi et al., 2016 ; Nishizawa et al., 1997 ), implying a greater impact of serotonin reduction on cognitive functions in women than in men. Central serotonin also helps to maintain the feeling of happiness and wellbeing, regulates behaviour, and suppresses appetite, thereby modulating nutrient intake. Additionally, it has the ability to promote the wake state and inhibit rapid eye movement sleep (Arnaldi et al., 2015 ; Yabut et al., 2019 ). Thus, any sex-based differences in serotonin levels may affect cognitive functions directly or indirectly via the aforementioned parameters.

Interestingly, data on the relationship between sex and sleep have been ambiguous. While in one study, female students at a university showed less sleep difficulties than male peers (Assaad et al., 2014 ), other studies showed that female students were at a higher risk of presenting sleep disorders related to nightmares (Toscano-Hermoso et al., 2020 ) and insomnia was significantly associated with the risk of poor academic performance only in females (Marta et al., 2020 ). Collectively, sex and gender may influence learning directly, or indirectly by affecting sleep and mood; the other two factors that affect cognitive functions (Fig. 1 ).

Circadian rhythm

Circadian rhythm is a biological phenomenon that lasts for ~24 hours and regulates various physiological processes in the body including the sleep–wake cycles. Circadian rhythm is linked with memory formation, learning (Gerstner and Yin, 2010 ), light, mood and brain circuits (Bedrosian and Nelson, 2017 ). We use light to distinguish between day and night. Interestingly, light stimulates the expression of microRNA-132, which is the sole known microRNA involved in photic regulation of circadian clock in mammals (Teodori and Albertini, 2019 ). The photosensitive retinal ganglions that express melanopsin in eyes not only orchestrate the circadian rhythm with the external light-dark cycle but also influence the impact of light on mood, learning and overall health (Patterson et al., 2020 ). For example, we frequently experience depression-like feelings during the dark winter months and pleasantness during bright summer months. This can be attributed to the circadian regulation of neural systems such as the limbic system, hypothalamic–pituitary–adrenal axis, and monoamine neurotransmitters. Mistimed light in the night disturbs our biological judgement leading to a negative impact on health and mood. Thus, increased incidence of mood disorders correlates with disruption of the circadian rhythm (Walker et al., 2020 ). Interestingly, a study involving university students showed the significance of short-wavelength light, specifically, blue-enriched LED light in reducing melatonin levels [best circadian marker rhythm (Arendt, 2019 )], and improved the perception of mood and alertness (Choi et al., 2019 ). While these examples depict the effect of circadian rhythm on mood, the reverse is also true. Individuals who demonstrate depression show altered circadian rhythm and disturbances in sleep (Fig. 1 ) (Germain and Kupfer, 2008 ). Also, since circadian rhythm regulates physiological and metabolic processes, disruption in circadian rhythm can cause metabolic dysfunctions like diabetes and obesity (Shimizu et al., 2016 ), eventually affecting cognition and learning (Fig. 1 ).

Delayed circadian preference including a tendency to sleep later in the night is common amongst young adults and university students (Hershner and Chervin, 2014 ). This delayed sleep phase disorder, often seen in adolescents, negatively impacts academic achievement and is frequently accompanied by depression (Bartlett et al., 2013 ; Sivertsen et al., 2015 ). Alongside, there is a positive correlation between sleep regularity and academic grades, implying that irregularity in sleep–wake cycles is associated with poor academic performance, delayed circadian rhythm and sleep and wake timings (Phillips et al., 2017 ). Even weekday-to-weekend discrepancy in sleeping patterns has been associated with impaired academic performance in adolescents (Sun et al., 2019 ). Further connection between sleep pattern, circadian rhythm, alertness, and the mood was observed in adolescents aged 13–18 where evening chronotypes showed poor sleep quality and low alertness. In turn, sleep quality was associated with poor outcomes including low daytime alertness and depressed mood. Evening chronotypes and those with poor sleep quality were more likely to report poor academic performance via association with depression. Strangely, sleep duration did not directly affect their functionality (Short et al., 2013 ). Contrastingly, in adults aged 40–69 years, the evening and morning chronotypes were associated with superior and poor cognitive performance, respectively, relative to intermediate chronotype (Kyle et al., 2017 ). In addition to this age-specific effect, the effect of chronotype can be subject-specific. For example, in subjects involving fluid cognition for example science, there was a significant correlation between grades and chronotype, implying that late chronotypes would be disadvantaged in exams of scientific subjects if examined early in the day. This was distinct from humanistic/linguistic subjects in which no correlation with chronotype was observed (Zerbini et al., 2017 ). These observations question the “one size fits all” approach of assessment strategies.

Daytime nap

The benefits of daytime napping in healthy adults have been discussed in detail elsewhere (Milner and Cote, 2009 ). In children, daytime nap facilitates generalisation of word meanings (Horváth et al., 2016 ) and explicit memory consolidation but not implicit perceptual learning (Giganti et al., 2014 ). A 90-min nap increases hippocampal activation, restores its function and improves declarative memory encoding (Ong et al., 2020 ). Generally, daytime napping has been found to be beneficial for memory, alertness, and abstraction of general concepts, i.e. creating relational memory networks (Lau et al., 2011 ). Delayed nap following a learning activity helps in the retention of declarative memory (Alger et al., 2010 ) and exercising before the daytime nap is thought to benefit memory more than napping or exercising alone (Mograss et al., 2020 ). Also, napping for 0.1–1 hour has been associated with a reduced prevalence of overweightness (Chen et al., 2019 ).

Contrastingly, in some studies, napping has been found to impart no substantial benefits to cognition. For example, despite the daytime nap of 1 hour, procedural performance remained impaired after total deprivation of night sleep (Kurniawan et al., 2016 ), indicating that daytime nap may not always be reparative. In other studies, 4 weeks of 90-minute nap intervention (napping or restriction) did not alter behavioural performance or brain activity during sleep in healthy adults aged 18–35 (McDevitt et al., 2018 ) and enhancements in visuomotor skills occurred regardless of daytime nap (Kaida et al., 2017 ). Age is a factor in relishing the benefits of napping. A 90-min nap can benefit episodic memory retention in young adults but these benefits decrease with age (Scullin et al., 2017 ) and may be harmful in the older population, particularly in those getting more than 9 hours of sleep (Mantua and Spencer, 2017 ; Mehra and Patel, 2012 ).

Napping can increase the risk for depression (Foley et al., 2007 ) and show a positive association with depression, i.e., napping is associated with greater likelihood of depression (Y. Liu et al., 2018 ). Cardiovascular diseases, cirrhosis and kidney disease have been linked with both daytime napping and depression (Abdel-Kader et al., 2009 ; Hare et al., 2014 ; Ko et al., 2013 ). While a previous study indicated that the time of nap, morning or afternoon, made no difference to its effect on mood (Gillin et al., 1989 ), a subsequent study suggested that the timing of nap influenced relapses into depression. Specifically, in depressed individuals, morning naps caused a higher propensity of relapse into depression than afternoon naps, thereby proposing the involvement of circadian rhythm in this process. Apart from depression, studies have struggled to identify the direct effect of nap on mood (Gillin et al., 1989 ; Wiegand et al., 1993 ). As daytime napping has been associated with poor sleep quality (Alotaibi et al., 2020 ), it may lead to irregular sleep–wake patterns and thereby alter circadian rhythm (Phillips et al., 2017 ). Also, nap duration was found to be important. In patients with affirmed depression, shorter naps were found to be more detrimental than longer naps (Wiegand et al., 1993 ), whereas, in the elderly, more and longer naps were associated with increased risk of mortality amongst the cognitively impaired individuals (Hays et al., 1996 ). Thus, daytime napping can affect cognitive processes directly or indirectly via its association with circadian rhythm, metabolic dysfunctions, mood, or sleep (Fig. 1 ).

Aging is associated with decreased neurogenesis and structural changes in the hippocampus amongst other neurophysiological effects. This in turn is associated with age-related mood and memory impairments (Kodali et al., 2015 ). Study on the effect of age on mood and emotion regulation in adults aged 20–70 years showed that older participants had a higher tendency to use cognitive reappraisal while reducing negative mood and enhancing positive mood. Interestingly, while women did not show correlations between age and reappraisal, men showed an increment in cognitive reappraisal with age. This indicates gender-based differences in the effect of aging on emotion regulation (Masumoto et al., 2016 ). The influence of age on sleep is well known. Older people that have sleep patterns like the young demonstrate stronger cognitive functions and lesser health issues than those whose sleep patterns match their age (Djonlagic et al., 2021 ). Collectively, this interlinks age, cognition, mood, and sleep.

Apparently, there is a genetic influence on learning and emotions. Approximately 148 independent genetic loci have been identified that influence and support the notion of heritability of general cognitive functions (Davies et al., 2018 ). This indicates the role of genetics in cognition (Fig. 1 ). The α-7 nicotinic acetylcholine receptor (encoded by the gene CHRNA7 ) is expressed in the central and peripheral nervous systems and other peripheral tissues. It has been implicated in various behavioural and psychiatric disorders (Yin et al., 2017 ) and recognised as an important receptor of the cholinergic anti-inflammatory pathway that exhibits a neuroprotective role. Its activation has been shown to improve learning, working memory and cognition (Ren et al., 2017 ). However, there have been some contrasting results related to this receptor. While its deletion has been linked with cognitive impairments, aggressive behaviours, decreased attention span and epilepsy, Chrna7 deficient mice have shown normal learning and memory, and the gene was not deemed essential for the control of emotions and behaviour in mice. Thus, the role of α-7 nicotinic acetylcholine receptor in maintaining mood and cognitive functions, although indicative, is yet to be fully deciphered in humans (Yin et al., 2017 ). Similarly, the gene Slitrk6 , which plays a role in the development of neural circuits in the inner ear may also play a role in some cognitive functions, but it does not appear to play a clear role in mood or memory (Matsumoto et al., 2011 ). Notably, inborn errors of metabolism, i.e., rare inherited disorders may show psychiatric manifestations even in the absence of obvious neurological symptoms. These manifestations could involve impairments in cognitive functions, and/or in the regulation of learning, mood and behaviour (Bonnot et al., 2015 ).

Other factors and associations

Indeed, optimal learning is additionally influenced by factors beyond those discussed here. These factors could be adequate meal frequency, physical activity and low screen time (Adelantado-Renau, Jiménez-Pavón, et al., 2019 ; Burns et al., 2018 ). In adolescents, the time of internet usage was identified as a factor that mediated the association between sleep quality (but not duration) and academic performance (Adelantado-Renau, Diez-Fernandez, et al., 2019 ; Evers et al., 2020 ). Self-perception is another determinant of performance. The American Psychological Association defines self-perception as “person’s view of his or herself or of any of the mental or physical attributes that constitute the self. Such a view may involve genuine self-knowledge or varying degrees of distortion”. Compared to other residents, surgery residents indicated the less perceived impact of sleep-loss on their performance (Woodrow et al., 2008 ). This may be related to specific work culture or profession where there is the reluctance of acceptance of natural human limitations posed by sleep deprivation. Whether there is real resistance to sleep deprivation amongst such professional groups or a misconception requires investigation. Exercise affects both sleep and mood; the latter probably affects in a sex-dependent manner. Thus, moderate exercise has been proposed as a therapy for treating mood disorders (Lalanza et al., 2015 ).

Sleep and mood: a bidirectional but unequal relationship

While the cause of the relationship between sleep and mood is not fully understood, adequate quality and quantity of sleep has shown physiological benefits and may enhance mood (Scully, 2013 ). Sleep encourages insightful behaviour (Wagner et al., 2004 ) and regulates mood (Vandekerckhove and Wang, 2017 ). Sleeping and dreaming activate emotional and reward systems that help process information, and consolidate memory “with high emotional or motivational value”. Inevitably, sleep disturbances can dysregulate these motivational and emotional processes and cause predisposition to mood disorders (Perogamvros et al., 2013 ). Sleep loss can reinforce negative emotions, reduce positive emotions, and increase the risk for psychiatric disorders. In children and adolescents, it can increase anger, depression, confusion and aggression (Vandekerckhove and Wang, 2017 ). Thus, sleep disorder has been associated with depression, where the former can predict the latter (LaGrotte et al., 2016 ). Sleep deprivation and daytime sleepiness amongst adolescents and college students cause mood deficits, negatively affect their mood and learning, and lead to poor academic performance (Hershner and Chervin, 2014 ; Short and Louca, 2015 ). Thus, disrupted sleep acts as a diagnostic factor for mood disorders, including post-traumatic stress disorder, major depression and anxiety (Walker et al., 2020 ).

In turn, mood affects sleep quality. Emotional events and stress during the daytime can affect sleep physiology. Negative states such as sadness, loneliness, and grief are related to sleep impairments, whereas positive states like love can be associated with lessened sleep duration but better sleep quality; the latter needs further evidence. Although dysregulation of emotion relates to poor sleep quality (Vandekerckhove and Wang, 2017 ), the effect of mood on sleep can be modulated by our approach of coping with our emotions (Vandekerckhove and Wang, 2017 ). Therefore, this effect is significantly smaller than the reverse (Triantafillou et al., 2019 ).

Summary and future direction

Sleep and mood influence cognitive functions and thereby affect academic performance. In turn, these are influenced by a network of regulatory factors that directly or indirectly affect learning. The compilation of observations clearly demonstrates the complexity and multifactorial dependence of academic achievement on students’ lifestyle and physiology, as discussed in the form of effectors like age, gender, diet, hydration level, obesity, sex hormones, circadian rhythm, and genetics (Fig. 1 ).

The emerged picture brings forth two points. First, it partly explains the ambiguous and conflicting data on the effects of sleep and mood on academic performance. Second, these revelations collectively question the ‘one-size fits all’ approach in implementing education strategies. It urges to explore formulating bespoke group-specific or subject-specific strategies to optimise teaching–learning approaches. Knowledge of these factors and their associations with each other can aid in forming these groups and improving educational strategies to better support students. However, it is essential to retain parity in education, and this would be the biggest challenge while formulating bespoke approaches.

In the context of sleep, studies could be conducted that first establish standardised means of measuring sleep quality and then measure sleep quality and quantity simultaneously in individuals of different ages groups, sex, and professions. This could then be related to their performance in their respective fields/professions; academic or otherwise. Such studies will help to better understand these interrelationships and address some discrepancies in the data.

Limitations

One limitation of this review is that it addresses only academic performance. Performance should be viewed broadly and be inclusive of all types, for example, athletic performance, dance performance or performance at work on a desk job that may include creative work or financial/mathematical calculations. It would be interesting to investigate the effect of alterations in sleep and mood on various types of performances and those results will be able to provide us with a much broader picture than the one depicted here. Notably, while learning can be assessed, it is difficult to quantify emotions (Ayaz‐Alkaya, 2018 ; Nieh et al., 2013 ). As such, it is believed that qualitative research is a better approach for studying emotional responses than quantitative research (Ayaz‐Alkaya, 2018 ).

Another point of limitation is related to the proposed models in Figs. 2 and 3 . These show hypothetical mathematical scales of learning and emotion where emotions are placed on a scale of learning, and learning is placed on the scale of emotions, respectively. While these models certainly help to better visualise and understand the interrelationships, these scales show only 2-dimensions. There could be a 3rd dimension, and this could be either one of the factors or a combination of the several factors discussed here (and beyond) that determine the effect of mood/emotion on learning/cognition. Additionally, the depicted scales and their interpretations may vary between individuals because the intensity of the same emotion felt by different individuals may differ. Figure 3 depicts emotions and learning. Based on the studies so far, here, negative emotions have been shown to stimulate learning, but which negative emotions these would be (for e.g., shame or anxiety), at what intensities these would stimulate optimal learning if at all, and how this compares with optimal learning induced by positive emotions remains to be investigated. Therefore, the extent to which these scales can be applied in real-life needs to be verified.

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Exploring the determinants of students’ academic performance at university level: The mediating role of internet usage continuance intention

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This study investigates the impact of integrating essential factors on academic performance in university students’ context. The proposed model examines the influence of continuance intention, satisfaction, information value, and Internet addiction on academic performance. Additionally, it investigates the mediating role of continuance intention on the relationship of satisfaction and information value on academic performance among university students. A survey questionnaire method was adopted to collect data from university students in Jordan. Data was collected from 476 voluntary participants, and the analysis was conducted using SPSS and AMOS. The analysis results show that continuance intention, satisfaction, information value have a significant positive influence on academic performance. Besides, the results show that satisfaction and information value positively and significantly influence continuance intention. While continuance intention full mediation the relationship between satisfaction and academic performance, it partial mediation the relationship between information value and academic performance. This study is the first to examine the integrating of continuance intention, satisfaction, information value, and Internet addiction on students’ academic performance. Furthermore, this study is also distinguished from other studies by investigating the mediating role of continuance intention gap.

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1 Introduction

There is a significantly increasing influence of using Internet and communication technology in the education industry. University students use the Internet daily to access information, gather data, and conduct research (Bagavadi Ellore et al. 2014 ). Moreover, they use the internet for entertainment and enjoyment fulfilment. In addition to the importance of the internet as an educational tool, students use the internet for entertainment and enjoyment fulfilment (Al-Fraihat et al. 2020 ). Students worldwide are reported to spend on average around two hours and 24 min per day on social media alone in 2019 (Statista 2019 ). The amount of time spent on the internet or social network sites (SNS) provoked researchers in the past to examine the antecedents or determinants of continuous intention. Various media tools have been examined to understand what drives users to spent more time on the Internet (Joorabchi et al. 2011 ) (Błachnio et al. 2019 ), Facebook (Karnik et al. 2013 )(Houghton et al. 2020 ), social networking sites (SNS) (Y. Kim et al. 2011 )(Marengo et al. 2020 ).

Previous research showed that using technology enhances academic performance (Basak and Calisir 2015 )(Choi 2016 )(Naqshbandi et al. 2017 ) (Bae 2018 )(Hou et al. 2020 ) (Çebi and Güyer 2020 ). For example, (Naqshbandi et al. 2017 ) found that Facebook mediates the relationship between different personality dimensions (extraversion, agreeableness and loneliness) and academic performance. Therefore, it is paramount to identify the antecedents of continuous intention. Enjoinment (Choi 2016 ), satisfaction (Bae 2018 ), entertainment and status-seeking (Basak and Calisir 2015 ) are among the many antecedents evidenced in the literature. (Hou et al. 2020 ) examined the impact of WeChat as a social network site on learning. Besides, it investigates how social network sites would influence university students’ academic performance. They found that WeChat usage played a significant positive role in students’ academic performance by engaging and enhancing sharing information and resources. Another study examined the impact of student interaction with different online learning activity on learning performance (Çebi and Güyer 2020 ). They found that spending longer time on learning activities enhance their academic performance.

Nowadays, Internet resources have become a very important component in educational systems (Salam and Farooq 2020 ). Students continuously and extensively use the Internet to interact online, search information, and perform specific tasks and activities. The use of the Internet implicates positive and negative effects on university students’ academic performance (Chang et al. 2019 ). Nowadays, students are using the Internet excessively to do various tasks and access social networking sites. The Internet’s intensive use is mainly for online communications, socializing, chatting, and gaming purposes (Byun et al. 2009 ). The overload of information can negatively influence students’ academic performance (Sinha et al. 2001 ). Students use the Internet to perform tasks related and non-task-related to their study, influencing students’ academic performance (Chang et al. 2019 ). For instance, (Kolek and Saunders 2011 ) had not found any association between Facebook use and students’ academic performance. On the contrary, (Kirschner and Karpinski 2010 ) found students without using Facebook had higher GPAs compare with students had extensive use of Facebook. Thus, the impact of using the Internet and social media network on students’ academic performance is varied. It depends on the type of websites they are visiting and the tools they are using (Michikyan et al. 2015 ). A research study revealed that Internet use for academic purposes was influence positively academic performance, whereas the Internet use for other purposes was influencing negatively academic performance (Kim et al. 2017 ). Recently, another research study conclude the use of Internet affecting negatively physical and mental health of people, while it provides people with information and improves timely work-related data transmission (Saini et al. 2020 ). Currently, adopting online learning in higher education during COVID-19 Pandemic had a significant impact on learners, educators and learning performance (Ustun 2020 ). Many research studies examined the impact of using online learning systems on university student’s satisfaction and academic performance (Kapasia et al. 2020 ) (Maqableh et al. 2015 ). However, there is a need to understand the factors that positively or negatively influence students’ academic performance from the use of the Internet. Therefore, it emerges a potential research direction to investigate the factors that influence students’ academic performance.

The purpose of this study is to investigate the positive and negative impact of integrating essential factors (continuance intention, satisfaction, information value, and Internet addiction) that influence students’ academic performance. Additionally, it investigates the mediating role of continuance intention on the relationship between satisfaction and academic performance and information value and academic performance gap. This study is the first to examine the relationship between integrating four essential factors and students’ academic performance. Additionally, it is distinguished from other studies by investigating the mediating role of continuance intention and Internet usage on students’ academic performance gap.

2 Literature review and hypotheses development

2.1 academic performance.

Academic performance is defined as students’ ability to carry out academic tasks, and it measures their achievement across different academic subjects using objective measures such as final course grades and grading point average (Busalim et al. 2019 ) (Anthonysamy et al. 2020 ). Researchers agree that the Internet is becoming more important for students. For example (Bagavadi Ellore et al. 2014 ) note that the Internet is an important part of college/university students’ lives. Similarly, (Naqshbandi et al. 2017 ) note that most students use Facebook daily, making it a significant component of their daily lives.

Many studies confirm the benefits that Internet users provide for students. For example: (Mccamey et al. 2015 ) argue that as a result of the expansion of the Internet, the college students are increasingly having more resources available to help them widen their knowledge. Similarly, (Emeka and Nyeche 2016 ) argue that the Internet is beneficial for students, which enhances their capabilities and skills which are helpful in their studies, which students use for research purposes, assignments, and presentations in their respective fields of study.

Several studies have examined the relationship between using the Internet’s resources/services and different foci’ academic performance. For example: (Sampath Kumar and Manjunath 2013 ) found that university teachers and researchers’ use of Internet sources and services positively impacted their academic performance. (Emeka and Nyeche 2016 ) found that the use of the Internet has a positive influence on undergraduate students’ academic performance in a university in Nigeria.

2.2 Continuance intention

Continuance intention refers to the user’s initial decision to reuse Internet sites (Al-Debei et al. 2013 ). According to (Amoroso and Lim 2017 ) continuance intention refers to the strength of an individual intends to perform a specific activity. Subsequently, in this study, continuance intention refers to Internet usage continuance intention. Many studies examined the initial intention to use technology in the information system (IS) literature based on the technology acceptance model (TAM) (Schierz et al. 2010 ). Some studies integrated serval constructs based on several theoretical perspectives with the TAM to better understand users continuance intention (Nysveen et al. 2005 ). Consequently, Innovation Diffusion Theory (IDF) (Shin et al. 2010 ) and Task Technology Fit (TTF) (Junglas et al. 2008 ) are introduced. Research results were crucial to the development of a better theoretical understanding of technology initial intention to use and the enhancement of different practical practices to encourage users to use technology.

However, the initial intention to use technology is not enough. It is essential also to explore and understand the continuance intention to use technology; aspects that would encourage users to stay loyal and keep using the technology (C. Kim et al. 2010 )(Alzougool 2019 )(Bölen 2020 ). Companies have invested their resources to develop technologies based on users’ needs and requirements. They need to protect their investment by applying measure for continuance intention to use the technology. Literature directed towards understanding the continuance use of technology is growing (Authors et al. 2016 ) (Pai et al. 2018 ) (Bölen 2020 ). However, the Internet is rich cases for studying as they have high levels of interactions between users and would help researchers explore the different factors that affect continuance intention to use technology (Gao et al. 2014 ) (Fang and Liu 2019 ). Consequently, it is necessary to do exploratory research to identify and measure factors affecting continuance intention to use Internet sites. Overall, continuance intention previous has mainly examined in the literature as dependent variable literature (Yang and Lin 2014 ; Yang et al. 2018 ; Yang; Zhang et al. 2017 ; Zong et al. 2019 ). However, we will examine its relationship with satisfaction, Internet addiction and students’ academic performance. Based on these arguments, it is expected that students’ continuance intention to use the Internet and its resources will help them improve their academic performance. Thus, the following hypothesis is proposed:

Continuance intention significantly influences students’ academic performance.

2.3 Satisfaction

User satisfaction refers to the general feeling of fulfilment resulting from using the internet (Patwardhan et al. 2011 ). Satisfaction is an old but contemporary construct that has been used by many researchers in different disciplines (Ki Hun Kim et al. 2019 ). It has been used in the work context to measure job satisfaction (Locke 1976 ) (Saari and Judge 2004 ) and in the organizational context to customer satisfaction (Oliver and Gerald 1981 ) (Barrett 2004 ). Satisfaction is measured in the IS literature as well as many theories have been deployed accordingly. An Expectation-Confirmation Model of continued IT usage (ECM-IT) developed by Odel and Bhattacherjee ( 2001 ) compares user continued IT decisions to consumer repeat purchase decision. The research found that continuous usage of an IT has three antecedents, one of which is satisfaction with the IT used (Odel and Bhattacherjee 2001 ). Chen et al. ( 2009 ) found that consumers’ satisfaction positively and significantly influences continuance intention to use self-service technologies (S. C. Chen et al. 2009 ). In relation to the reuse health information, Kim et al. ( 2010 ) found that customer satisfaction had a significant positive influence on the decision to reuse health information provided by the internet (Kyoung Hwan Kim 2010 ). Bae ( 2018 ) found satisfaction with social network sites to have a significant impact on continuance intention to use social network sites (Bae 2018 ).

Based on the Expectation Confirmation Model (EDM), satisfaction is analyzed to understand the relationship between satisfaction and experiences while using technology (Melone 1990 ) (Bhattacherjee 2014 ); customers usually expect the performance of a product or a service before the actual usage. If their expectations relatively match their experience, then they would be satisfied. Therefore, the positive customer experience at first glance is a crucial determinant of user satisfaction. (Kuo et al. 2009 ) suggested that satisfaction can also be the aggregated positive emotional states developed through several experiences with the product or the service. Users’ IT continuance use behaviour is positively influenced by their satisfaction with prior IT usage (Bhattacherjee and Lin 2015 ). The uses and gratification theory is also performed as a theoretical basis to ground a better understanding of satisfaction and its relationship with continuance intention to use social networking systems. (Chiu and Huang 2015 ) revealed that user satisfaction with contents and features of social networking systems had a positive relationship with continuance use. Another research study examined the relationship between students satisfaction from Internet usage and students performance (Goyal et al. 2011 ). They found that Internet usage satisfaction had a significant positive impact on students academic performance. (Samaha and Hawi 2016 ) found that a low level of life satisfaction were less likely to achieve satisfactory cumulative GPAs. Based on the significant influence of satisfaction on continuance usage intention and academic performance, the following hypotheses are proposed:

Satisfaction significantly influences continuous intention to use the Internet.

Satisfaction significantly influences students’ academic performance.

2.4 Information value

Some research studies proposed another antecedent to continuous usage of an IT product/service is perceived usefulness which is closely related to information value (Zhang et al. 2017 ) (S. Yang et al. 2018 ) (Wang et al. 2020 ). The benefit of acquiring useful information through using the internet determines information value, especially if the information helps the user solve problems of developing his skills and abilities (Zhang et al. 2017 ). The uses and gratifications theory (U&G theory) explains why users select and adopts certain medium to fulfil their social and psychological needs (Ku et al. 2013 ) (Ma and Lee 2012 ). This theory has been linked with continuous intention, factors that satisfy users’ gratification needs, such as information needs and social needs. As found by (Wei et al. 2015 ), those two needs are critical factors to motivate users to interact with each other and enhance their stickiness towards using social networking sites. Moreover, (Chiang 2013 ) found that informativeness, social interactivity and playfulness needs affect users’ continuance intention towards social networking sites.

Information value refers to the useful information acquired from friends or information providers (Zhang et al. 2017 ). (Chiang 2013 ) argues that website informativeness is a potential influence on a user’s intentions and behaviours. (Liao and Shi 2017 ) found that web content (i.e. the accuracy, usefulness and completeness, and website information) directly influences the continuance intention to use online tourism services. (Zheng et al. 2013 ) found that information quality directly affects user satisfaction which in-turn influences a user’s continuance intention to use information-exchange virtual communities. Similarly, (Valaei and Baroto 2017 ) found that information quality had a positive impact on continuance intention to follow a government’s Facebook page. (Jin et al. 2007 ) found that information usefulness positively and significantly affects the continuance intention of virtual communities for information adoption. Based on these results and arguments, the following hypotheses are proposed:

Information value significantly influences continuous intention to use the Internet.

Information value significantly influences students’ academic performance.

2.5 Internet addiction

Facebook addiction refers to the excessive use of Facebook due to being psychologically reliant on its use that somewhat hinders other essential actions that the user could perform and, in the process, yield negative results (Moqbel and Kock 2018 ). About 350 million Facebook users are between 16 and 25 years old showing Facebook addiction syndrome (Leong et al. 2019 ). Overall, previous literature has mainly examined the concept of continuance intention as a dependent variable (Yang and Lin 2014 ; Yang et al. 2018 ; Yang 2019 ; Zhang et al. 2017 ; Zong et al. 2019 ). However, we will examine its relationship with Facebook addiction. Numerous theories and findings have established the relationship between behavioural intention and actual behaviour (Obeidat et al. 2017 ; Pelling and White 2009 ; Turel et al. 2010 ). Consequently, if the continuance intention of Facebook use is present, the user will continue to do so, thereby increasing the chances of addiction to the website. Furthermore, previous studies found that when a certain behaviour is exhibited, and the person is willing to do it again, future behaviour becomes an automatic, aligned response (Ronis et al. 1989 ). Therefore, the more a person uses social media to communicate with others, the more likely it will become a habit and lead to addiction (Turel et al. 2010 ). (J. V. Chen et al. 2008 ) conducted a research study that confirms higher Internet addiction can lead to a high degree of Internet abuse. Also, (Samaha and Hawi 2016 ) conducted a research study that showed smartphone addiction had a negative impact on students’ academic performance.

Following the same logic, we propose that the Facebook continuance intention resulting from the perceived values will increase Facebook addiction. Thus, this study is the first study that investigates the relationship between continuance intention and addiction gap. Generally, this factor strongly influences the association between online purchase intention and actual behaviour (Miyazaki and Fernandez 2001 ; Nepomuceno et al. 2014 ). Thus:

Internet addiction significantly negative influences on students’ academic performance.

2.6 The mediating role of continuance intention

In research, mediating factors are used to understand the mechanism that establishes the underlying relationship between the independent and dependent variables. The mediating role of employees’ satisfaction on the relationship between Internet actual usage and performance impact was examined (Isaac et al. 2017 ). The analysis results confirmed the mediating role of satisfaction. Moreover, some researchers examined the mediating role of social interaction on the relationship between network externalities on perceived values (Zhang et al. 2017 ). Also, satisfaction has been considered a mediating variable for the relationship between perceived security and continuance intention (Ki Hun Kim et al. 2019 ). Finally, Another research study examined the mediating effect of perceived value between the relationship of security and continuance intention in mobile government service (Wang et al. 2020 ). In this research, it proposed to have continuance intention as a mediating variable to measure the following relationships:

Continuance intention mediates the relationship between satisfaction and academic performance.

Continuance intention mediates the relationship between information value and academic performance.

3 Research methodology

This section provides the methodology applied in the current study. It consists of the research model of the study’s independent and dependent variables, research hypotheses, besides data collection tool and research population and sample.

3.1 Research model

In this research, the proposed model examines the impact of continuance intention, satisfaction, information value, and Internet addiction on students’ academic performance gap. Moreover, it investigates the mediating role of continuance intention on the relationship between satisfaction and academic performance and information value and academic performance gap. Figure 1 shows the proposed research model.

Research model

3.2 Data collection and sample

Data were collected from targeted participants with Internet experience using an online survey. Participants were selected opportunely from 4000 bachelor students from the School of Business at the University of Jordan in the Hashemite Kingdom of Jordan. However, what constitutes an adequate sample size for regression analysis is uncertain among researchers. Some researchers (O’Rourke and Hatcher 2013 ) recommend that the sample size of a study that applies multiple linear regression should be 100 participants or more than five times the number of items measured. The questionnaire was made up of 22 items, so the sample size should be over 110 students. Also, (Joseph Hair et al. 2014 ) recommended between 100 and 200 while (Krejcie and Morgan 1970 ) required 351 from a population of 4000. Therefore, the number of returned surveys is 476 that meets the sample size requirement for a structural equation model and shows adequate representation with the highest probability assessment. In Table 1 , the respondents’ characteristics of this study are summarized.

The 476 valid responses compromised of 70.6% female student. The sample’s dominant age range was 20 to 23 years, with a percentage of 73.3%. The respondents were mainly in their second and third years at the university, with 65.6% of the sample. 44.7% of students spend 1 to 3 h daily on internet activities. Moreover, almost 33% uses the internet from 10 to 29 h weekly. The full respondent’s profiles are shown in Table 1 .

3.3 Measurement development

The 5-points Likert scale is used to explore the associations among the research variables. It varies between strongly disagree =1 and strongly agree =5. Reliability and validity analyses were conducted, descriptive analysis was used to describe the characteristics of the sample and the respondent to the questionnaires besides the independent and dependent variables. Besides, SEM analysis was employed to test the research hypotheses. Table 2 shows the measured constructs and the items measuring each construct.

4 Data analysis and results

4.1 validity and reliability.

To check for the research model validity, and since all the measures were previously established, confirmatory factor analysis (CFA) was conducted using SPSS 20.0 and AMOS 22.0. The standardized factor loading of the item was examined since 0.55 represent a good fit (Harrington 2008 ) any item with standardized factor loading less than 0.55 was eliminated. Accordingly, item (Academic Performance 1), (Addiction 1, 2, and 3), (Information Value 4) were excluded from any further calculations. The full-standardized factor loading values from the CFA are presented in Table 3 . The model fit was assessed relaying on the model fit summary results, the cut points used in this research were χ 2 /df < 5, Root Mean Square of Error Approximation (RMSEA) <0.08, while all the other indices (i.e. GFI, CFI, TLI, IFI and NFI) should be close to 1 where higher than 0.9 is acceptable (Harrington 2008 ). Results are shown in Table 3 .

To check the reliability of the scale, Cronbach’s-Alpha test was used to assess the internal consistency the cut point usually used in researches is 0.7, but it can be lowered to 0.6 (Joe Hair et al. 2011 ). Cronbach’s-Alpha results in this research were between 0.754 and 0.864. Results are shown in Table 3 .

4.2 Descriptive statistics and correlations

Pearson’s correlation coefficient results are presented in Table 4 . Pearson’s correlation coefficient indicates the existence of a linear association between the variables according to person correlations values. No significant linear effect was found between the demographic variables and the dependent variable except for the demographic variable using the Internet (Hours per week) was found to have a significant negative correlation with academic performance (r = −.114*, p  < 0.01).

The highest mean score for information value (3.73) indicates a high positive respondents’ attitude toward continuance intention regarding the descriptive statistics. In contrast, the lowest mean score was for satisfaction (2.69). The skewness and kurtosis values were within the range of −2 to +2 (Garson 2012 ), which indicates normally distributed data. The results are provided in Table 5 .

4.3 Hypotheses testing

Multiple linear regression was used to test Hypotheses 1, 3, 5 and 6, where continuous intention, satisfaction, information value, and Internet addiction were the independent variables, and academic performance was the dependent variable. The normality plot p-p indicates that most of the points are near the best fit line, and the scatter plot produces no pattern and no multicollinearity issue was not detected. The tolerance ranged between 0.755 and 0.987, which are >1, and the variance inflation factor (VIF) statistics ranged between 1.013 and 1.325, which are less than 4, respectively (Garson 2012 ). The results are shown in Table 6 , the overall model was significant (F = 32.323, p  = 0.000 < 0.05), the R-value indicates that the whole model is correlated with the dependent, R = 0.464, R 2 indicate the amount of variance in the dependent variable that is caused by the independent variables R 2  = 21.5%. The adjusted R 2  = 20.9% is an indicator of the variance caused by the independent variables if the whole population were tested, the differences between R2 and Adj-R2 are 0.006. The regression coefficients values revealed that continuous intention, information value, and satisfaction have a significant positive effect on academic performance with effect values of B = 0.153, p  = 0.003 < 0.05, B = 0.085, p  = 0.026 < 0.05, and B = 0.424 and p  = 0.000 < 0.05 respectively. Nevertheless, in this model, Internet addiction negatively affects academic performance B = −0.057, p  = 0.169 > 0.05. Accordingly, hypotheses 1, 3 and 5 were supported, while hypothesis 6 was not supported. Results are shown in Table 6 .

To test Hypotheses 2 and 4, multiple linear regression was used where satisfaction and information value were the independent variables, and the continuous intention was the dependent variable. The normality plot p-p indicates that most of the points are near the best fit line, and the scatter plot produces no pattern. No multicollinearity issue was not detected. The results are shown in Table 7 indicate that the overall model was significant (F = 76.564, p  = 0.000 < 0.05), the R-value indicates that the whole model is correlated with the dependent, R = 0.495 and R 2 indicate the amount of variance in the dependent variable that is caused by the independent variables R 2  = 24.5%. The adjusted R 2  = 24.1% is an indicator of the variance caused by the independent variables if the whole population were tested, the differences between R2 and Adj-R2 are 0.004. The regression coefficients values revealed that both information value and satisfaction have a significant positive effect on continuous intention. The effect values were B = 0.431, p  = 0.000 < 0.05 and B = 0.157, p = 0.000 < 0.05 respectively. Accordingly, both hypotheses 2 and 4 were supported. Results are shown in Table 7 .

To test Hypotheses 7, a multiple linear regression was used to test the mediation effect using PROCESS Macro by Hayes V 3.3. Using PROCESS, the mediation effect will be tested based on 5000 Bootstrapped sample. The results of the mediation paths are shown in Table 8 . Where C represents the effect of satisfaction on performance (i.e. Total effect), (a) represents the effect of satisfaction on continuous intention, b is the effect of continuous intention on performance in the presence of satisfaction and C′ is the effect of satisfaction on performance in the presence of continuance intention (i.e. Direct effect). The mediation path can be calculated either by multiplying path a coefficient with path b coefficient or by subtracting path C coefficient form path C′ coefficient (Hayes 2015 ).

Findings showed that 95% bias-corrected bootstrap confidence intervals based on 5000 bootstrap samples ((BootLLCI) and (BootULLCI)) for specific indirect effects through continuance intention do not include zero accordingly the mediation path was found to be significant. Additionally, since the direct effect is insignificant, continuance intention fully mediates the relationship between satisfaction and continuance intention, which indicates that satisfaction affects academic performance because of continuance intention.

To test Hypotheses 8, multiple linear regression was used to test the mediation effect using PROCESS Macro by Hayes V 3.3; using PROCESS, the mediation effect will be tested based on the 5000 bootstrapped sample. The results of the mediation paths are shown in Table 9 . Where C is the effect of information value on performance (i.e. Total effect), a is the effect of information value on Continuous intention, b is the effect of continuous intention on Performance in the presence of information value and C′ is the effect of information value on Performance in the presence of continuance intention (i.e. Direct effect). The mediation path can be calculated either by multiplying path a coefficient with path b coefficient or by subtracting path C coefficient form path C′ coefficient (Hayes 2015 ).

Findings showed that 95% bias-corrected bootstrap confidence intervals based on 5000 bootstrap samples ((BootLLCI) and (BootULLCI)) for specific indirect effects through continuance intention do not include zero accordingly the mediation path was found to be significant. Additionally, since the direct effect is significant, information value partially mediates the relationship between satisfaction and continuance intention, which indicate that information value affects academic performance directly and because of continuance intention. Table 10 show the results of tested hypotheses in this research.

5 Discussion and conclusion

Former research studies have not investigated the impact of integrating essential factors that influence students’ academic performance. Thus, this study investigates the impact of continuance intention satisfaction, information value, and Internet addiction on students’ academic performance gap. Moreover, it investigates the mediating role of continuance intention on the relationship between information value and academic performance and the relationship between satisfaction and the academic performance gap. Therefore, we also tested the relationships between satisfaction and continuance intention and information value and continuance intention. The analysis results in Tables 6 and 7 show that the overall model was significant, and the whole model is correlated with the dependents. The analysis results show that most of the proposed hypotheses are supported. It shows that continuance intention, satisfaction, and information value explain 21.5% of academic performance variance. It also shows that the independent variables of continuance intention cause 19% of variances.

The research results show that continuance intention to use the Internet has a significantly positive effect on students’ academic performance. This finding supports previous research such as (Emeka and Nyeche 2016 ) (Sampath Kumar and Manjunath 2013 ) that confirmed the advantages of using the Internet as students. Using the Internet can help students search for information related to their modules and assignment. In addition, using the Internet can help students working together as groups to connect and collaborate online. Many universities nowadays integrate online learning with traditional teaching methods to create more interactive student-centred learning. Another research study showed that Facebook usage increase students’ academic performance (Naqshbandi et al. 2017 ).

The analysis results confirmed the positive influence of satisfaction on students’ academic performance, which is aligned with previous research results (Goyal et al. 2011 ) (Samaha and Hawi 2016 ). Moreover, it also confirmed that information value has a positive and significant impact on students’ academic performance. Regarding the impact of Internet addiction, the results show that Internet addiction is insignificant influence academic performance. The analysis results show that Internet addiction has a negative but insignificant effect on academic performance B = -0.057, p  = 0.169 > 0.05, which is consistent with the finding of (Kolek and Saunders 2011 ). This can be explained as the type of the tools students are using and the type of the website would had a major role on the impact of the students’ academic performance. For instance, the students who use Internet tools that support their study might be improve their academic performance, whereas the extensive use of Internet on unrelated website to their study might be reduce academic performance. Instead, a balance use of Internet between related and unrelated websites might be not effect students’ academic performance. Therefore, the impact of extensive use of Internet on academic performance might be varied from one group to another based on the type of visited websites and time spent on each type of websites. Moreover, based on the Pearson correlation coefficient, there was no significant linear effect between the demographic variables and the dependent variable except for using the Internet (hours per week). It found that Internet usage has a negative significant correlation with academic performance (r = −.114*, p  < 0.01). This can be justified as the students spend a long time using the Internet; they will waste their time on irrelative contents to their academic study that negatively affects their academic performance. This finding supports the results of previous research (J. V. Chen et al. 2008 )(Samaha and Hawi 2016 ).

This study investigated the relationship between satisfaction and continence intention. The results confirmed that satisfaction has a significant positive impact on students’ Internet continuance intention. This finding supports previous research that found satisfaction with social network sites to have a significant impact on continuance intention to use social network sites (Bae 2018 ). Furthermore, this study examined the influence of information value on continuance intention. The research findings confirmed that information value exhibits a significant influence on continuous intention, which is consistent with (S. Yang et al. 2018 ). The descriptive statistics show the information value has the highest mean score (3.73), which indicate a high positive respondents attitude toward continuance intention.

The mediating role of continuance intention on the relationship between satisfaction and academic performance is examined. The analysis results show that while satisfaction has a significant effect on academic performance, the direct effect of satisfaction on student academic performance in the presence of continuance intention is insignificant. These results indicate that continuance intention is fully mediate the relation between satisfaction and continuance intention. Finally, this research examined the mediating role of continuance intention on the relationship between information value and academic performance. The results confirmed the significant direct effect of information value and the significant effect of information value on academic performance in the presence of continues intention. These findings confirmed the partially mediating role of continuance intention on the relationship between satisfaction and continuance intention.

To conclude, this study investigated the impact of integrating four main factors of Internet usage in students’ context that influence students’ academic performance. It investigated the effect of continuance intention, satisfaction, information value, and Internet addiction on academic performance. The analysis results showed that continuance intention, satisfaction, and information value are positively influencing the students’ academic performance. Moreover, the analysis results showed that satisfaction and information value significantly influence continuance intention to use the Internet. In addition, this study investigates the mediating role of continuance intention on the relationship of satisfaction and students’ academic performance and information value and academic performance gap. The results showed that while continuance intention partially mediates the relationship between information value and academic performance, it fully mediates the relationship between satisfaction and academic performance in university students. Finally, the analysis results showed that Internet addiction does not influence students’ academic performance. Still, it has a negative impact and the number of hours to use the Internet has a negative impact on academic performance. This research study contributes to the emerging body of knowledge by extending the associations between four main factors that influence academic performance. It also contributes to the evolving body of knowledge about the mediating role of continuance intention to use the Internet on the relationship of satisfaction and information value on students’ academic performance. The finding of this research can help educators to advice their students to use Internet appropriately for academic purpose especially for students with low academic performance and grades to improve their academic performance.

6 Limitations and future research

This study was conducted on undergraduate students at one university in Jordan, which would limit the generalizability to other contexts. Therefore, future research can investigate other demographic groups, for example, employees or students from different year levels (or postgraduates). Besides, future research can address cultural differences to investigate if culture can influence continuance intention and academic performance. Furthermore, future research can be applied across different countries to compare and contrast the findings considering contextual factors peculiar for each country or region. This research only focused on four integrating factors that would influence students’ academic performance. Thus, future research can investigate another variable, such as perceived enjoyment and perceived usefulness to enrich the current research. A noteworthy result is that against our expectation, Internet addiction is not a factor that determines academic performance. It can be suggested based on the literature that perceived enjoyment and emotional experience could affect Internet addiction. Therefore, further studies can examine the impact of Internet addiction with another group of variables to identify its effect on academic performance.

Data availability

( Not applicable )

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Maqableh, M., Jaradat, M. & Azzam, A. Exploring the determinants of students’ academic performance at university level: The mediating role of internet usage continuance intention. Educ Inf Technol 26 , 4003–4025 (2021). https://doi.org/10.1007/s10639-021-10453-y

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ORIGINAL RESEARCH article

Academic performance in adolescent students: the role of parenting styles and socio-demographic factors – a cross sectional study from peshawar, pakistan.

• 1 Institute of Public Health & Social Sciences, Khyber Medical University, Peshawar, Pakistan
• 2 Department of Medicine, Aga Khan University Hospital, Karachi, Pakistan

Academic performance is among the several components of academic success. Many factors, including socioeconomic status, student temperament and motivation, peer, and parental support influence academic performance. Our study aims to investigate the determinants of academic performance with emphasis on the role of parental styles in adolescent students in Peshawar, Pakistan. A total of 456 students from 4 public and 4 private schools were interviewed. Academic performance was assessed based on self-reported grades in the latest internal examinations. Parenting styles were assessed through the administration of the Parental Bonding Instrument (PBI). Regression analysis was conducted to assess the influence of socio-demographic factors and parenting styles on academic performance. Factors associated with and differences between “care” and “overprotection” scores of fathers and mothers were analyzed. Higher socio-economic status, father’s education level, and higher care scores were independently associated with better academic performance in adolescent students. Affectionless control was the most common parenting style for fathers and mothers. When adapted by the father, it was also the only parenting style independently improving academic performance. Overall, mean “care” scores were higher for mothers and mean “overprotection” scores were higher for fathers. Parenting workshops and school activities emphasizing the involvement of mothers and fathers in the parenting of adolescent students might have a positive influence on their academic performance. Affectionless control may be associated with improved academics but the emotional and psychosocial effects of this style of parenting need to be investigated before recommendations are made.

Introduction

Despite residual ambiguity in the term, definitions over time have identified several elements of “academic success” ( Kuh et al., 2006 ; York et al., 2015 ). Used interchangeably with “student success,” it encompasses academic achievement, attainment of learning objectives, acquisition of desired skills and competencies, satisfaction, persistence, and post-college performance ( Kuh et al., 2006 ; York et al., 2015 ). Linked to happiness in undergraduate students ( Flynn and MacLeod, 2015 ) and low health risk behavior in adolescents ( Hawkins, 1997 ), a vast amount of literature is available on the determinants of academic success. Studies have shown socioeconomic characteristics ( Vacha and McLaughlin, 1992 ; Ginsburg and Bronstein, 1993 ; Chow, 2000 ; McClelland et al., 2000 ; Tomul and Savasci, 2012 ), student characteristics including temperament, motivation and resilience ( Ginsburg and Bronstein, 1993 ; Linnenbrink and Pintrich, 2002 ; Farsides and Woodfield, 2003 ; Valiente et al., 2007 ; Beauvais et al., 2014 ) and peer ( Dennis et al., 2005 ), and parental support ( Cutrona et al., 1994 ; Sanders, 1998 ; Dennis et al., 2005 ; Bean et al., 2006 ) to have a bearing on academic performance in students.

The influence of parenting styles and parental involvement is particularly in focus when assessing determinants of academic success in adolescent children ( Shute et al., 2011 ; Rahimpour et al., 2015 ; Weis et al., 2016 ; Checa and Abundis-Gutierrez, 2017 ; Zhang et al., 2019 ). The influence may be of significance from infancy through adulthood ( Steinberg et al., 1989 ; Weiss and Schwarz, 1996 ; Zahedani et al., 2016 ) and can be appreciated across a range of ethnicities ( Desimone, 1999 ; Battle, 2002 ; Jeynes, 2007 ). Previously, the authoritative parenting style has been most frequently associated with better academic performance among adolescent students ( Steinberg et al., 1989 , 1992 ; Deslandes et al., 1997 , 1998 ; Aunola et al., 2000 ; Adeyemo, 2005 ; Checa et al., 2019 ), while purely restrictive and negligent styles have shown to have a negative influence on academic performance ( Hillstrom, 2009 ; Parsasirat et al., 2013 ; Osorio and González-Cámara, 2016 ). Parenting styles have also been linked to academic performance indirectly through regulation of emotion, self-expression ( Deslandes et al., 1997 ; Weis et al., 2016 ), and self-esteem ( Zakeri and Karimpour, 2011 ).

Significant efforts have been made to explore and integrate factors which influence parenting stress and behaviors ( Belsky, 1984 ; Abidin, 1992 ; Östberg and Hagekull, 2000 ). A number of factors, including parent personality and psychopathology (in terms of extraversion, neuroticism, agreeableness, depression and emotional stability), parenting beliefs, parent-child relationship, marital satisfaction, parenting style of spouse, work stress, child characteristics, education level, and socioeconomic status have been highlighted for their role in determining parenting styles ( Belsky, 1984 ; Simons et al., 1990 , 1993 ; Bluestone and Tamis-LeMonda, 1999 ; Huver et al., 2010 ; Smith, 2010 ; McCabe, 2014 ). Studies have also highlighted differences between fathers and mothers in how these factors influence them ( Simons et al., 1990 ; Ponnet et al., 2013 ).

Insight into determinants of academic success and the role of parenting styles can have significant impact on policy recommendations. However, most existing data comes from western cultures where individualistic themes predominate. While some studies highlight differences between the two ( Wang and Leichtman, 2000 ), evidence from eastern collectivist cultures, including Pakistan, is scarce ( Masud et al., 2015 ; Khalid et al., 2018 ).

The aim of this study is to identify the determinants of academic performance, including the influence of parenting styles, in adolescent students in Peshawar, Pakistan. We also aim to investigate the factors affecting parenting styles and the differences between parenting behaviors of father and mothers.

Materials and Methods

The manuscript has been reported in concordance with the STROBE checklist ( Vandenbroucke et al., 2014 ).

Study Design

A cross sectional study was conducted by interviewing school-going students (grades 8, 9, and 10) to assess determinants of academic grades including the influence of parenting styles.

The study took place in the city of Peshawar in Pakistan at eight schools, four from the public sector and four from the private sector. The data collection process began in January 2017 concluded in December 2017.

The prevalence of high grades (A and A plus) among adolescent students was between 42.6 and 57.4% in a previous study ( Cohen and Rice, 1997 #248). Based on this, a sample size of 376 students was calculated to study the determinants of high grades in adolescent students with a confidence level of 95%. Assuming a non-response rate of approximately 20%, we decided to target 500 students from four public and four private schools. A total of 456 students participated in our study.

Participants

Inclusion criteria.

From the eight schools which provided admin consent to conduct the study, students enrolled in grade 8, 9, or 10 were invited to take part in the study. Following consent from the parents and assent from the student, he or she was included in the study.

Exclusion Criteria

Any student unable to understand or fill out the interview pro forma or questionnaire independently.

Data Sources and Measurement

Data was collected through a one on one interaction between each student and the data collector individually. The following tools were used.

Demographic pro forma ( Supplementary Datasheet 1 )

A brief and simple pro forma was structured to address all demographic related variables needed for the study.

Parental Bonding Instrument (PBI) ( Supplementary Datasheet 2 )

The original version of the Parental Bonding Instrument ( Parker et al., 1979 ), previously validated for internal consistency, convergent validity, satisfactory construct, and independence from mood effects in several different populations, including Turkish and Chinese ( Parker et al., 1979 ; Parker, 1983 , 1990 ; Cavedo and Parker, 1994 ; Dudley and Wisbey, 2000 ; Wilhelm et al., 2005 ; Murphy et al., 2010 ; Liu et al., 2011 ; Behzadi and Parker, 2015 ), was employed in our study. This tool, composed of 25 questions, assesses parenting styles as two independent measures of “care” and “control” as perceived by the child. It is filled out separately for the father and the mother. It is available online for use without copyright. The use of PBI has been validated for British Pakistanis ( Mujtaba and Furnham, 2001 ) and Pakistani women ( Qadir et al., 2005 ). A paper by Qadir et al. on the validity of PBI for Pakistani women, reports the Cronbach alpha scores to be 0.91 and 0.80 for the “care” and “overprotection” scales, respectively ( Qadir et al., 2005 ).

The demographic pro forma and the parental bonding index were translated into Urdu by an individual fluent in both languages and validated with the help of an epidemiologist and two experts in the field ( Supplementary Datasheet 3 ). Pilot testing of translated versions was done with 20 students to ensure clarity and assess understanding and comprehension by the students. Both versions for the two tools were provided in hard copy to each student to fill out whichever one he/she preferred. The data collector first verbally explained the items on the demographic pro forma and the PBI to the student following which the student was allowed to fill it out independently.

Using the data sources mentioned above, data was collected for the following variables.

Student Related

Gender, type of school (public or private), class grade (8th, 9th, and 10th) and academic performance.

In Pakistan, public and private schools may differ in several aspects including fee structures, class strength and difficulty levels of internal examinations, with private schools being more expensive, with fewer students per classroom, and subjectively tougher internal examinations.

The academic performance was judged as the overall grade (a combination of all subjects including English, Mathematics and Science) in the latest internal examinations sat by the student as A+, A, B, C, or D.

Family Related

Family structure and type of accommodation (rented or owned).

Parent Related

Information on living status, education level, employment status, employment type and parenting styles was obtained from the student separately for the father and mother.

Quantitative Variables

Academic performance.

The grades A+, A were categorized as “high” grades and grades B, C, and D were categorized as “low” grades.

Socio-Economic Status

We used variables which adolescent students are expected to have knowledge of to calculate a score which categorized students as belonging to either a high or low socioeconomic status. The points assigned to each variable are show in Table 1 .

Table 1. Calculation of an estimated socioeconomic status.

Parenting Styles

The PBI is a 25 item questionnaire, with 12 items measuring “care” and 13 items measuring “overprotection.” All responses have a 4 point Likert scale ranging from 0 (very unlikely) to 3 (very likely). The responses are summed up to categorize each parent to exhibit low or high “care” and low or high “overprotection.” Based on these findings, each parent can then be put into one of the 4 quadrants representing parenting styles including “affectionate constraint,” “affectionless control,” “optimal parenting,” and “neglectful parenting.” This computation is explained in Figure 1 obtained from the information provided with the PBI ( Parker et al., 1979 ).

Figure 1. Assigining parenting styles using the PBI ( Parker, 1979 #192).

Students were allowed to fill in the pro forma and questionnaire independently to avoid bias during the data collection process. However, self-reporting of grades in latest examination may be subject to recall bias.

Statistical Methods

Statistical analysis was performed using SPSS v.23 (IBM Corp., Armonk, NY, United States). Descriptive analyses were conducted on all study variables including socio-demographic factors and parenting styles. Categorical variables were reported as proportions and continuous variables as measures of central tendency. All continuous variables were subjected to a normality test. Mean and median values were reported for variables with normally distributed and skewed data, respectively.

The summary t -test was used to study the differences between mean “care” and “overprotection” scores of fathers and mothers. The independent sample t -test was used to study the factors associated with “care” and “overprotection” scores of fathers and mothers. Threshold for significance was p = 0.05.

The determinants of high grades including the influence of parenting styles were assessed using regression analysis. The outcome variable, student grades, was treated as binary (high grades and low grades). The threshold for statistical significance was p = 0.05. Crude Odds Ratios were adjusted for gender, school type, socioeconomic status, family structure, class grade, parents’ employments and education status.

Ethics Statement

The study was approved by the Ethical Committee of the Khyber Medical University, Advance Studies and Research Board (KMU-AS&RB) in August 2016. Identifying information of students was not obtained. Permissions were obtained from the relevant authorities in the school administration before approaching the students and their parents. Written consent was obtained from the parents through the home-work diary of the students and verbal assent of each student was obtained.

Participants and Descriptive Data

A total of 456 students were interviewed, with 249 (54.6%) males and 207 (45.4%) females. The majority (52.5%) were students of grade 8. Despite including an equal number of public and private schools, 63.6% of the students belonged to a public sector school. The reason may be due to the larger class strength in public schools in comparison to private schools. The nuclear family structure was dominant (64.3%), with most students living in rented accommodation (70.4%) with 42.8% reporting to have obtained high grades (A plus or A) in their latest internal examinations ( Table 2 ).

Table 2. Participant and descriptive data.

Majority of the students had both parents alive at the time of the interview. While all students’ mothers were alive, 14 students reported their father to have passed away. Surprisingly, only 46% of the students were able to report their father’s level of education compared to 99.5% for their mother. 9.2% of students reported their father to have an education level of grade 12 or above compared to 26% regarding their mother’s qualification. This was in contrast to 90% of the fathers being employed compared to only 11% of the mothers ( Table 2 ).

A Total of 257 (56%) students reported their mother to exhibit a high level of “care” vs. only 9 (2%) students reporting the same for their father. In terms of “overprotection,” 343 (75%) and 296 (65%) students reported a high level for their father and mother, respectively. Based on combinations of these measures, the most common parenting style for both fathers (73%) and mothers (35%) was affectionless control and the least common for fathers was optimal parenting (0%) and neglectful parenting for mothers (9%). 121 (26%) students had both parents with the same parenting style, with 23% students having both parents show affectionless control and not a single student with both parents showing optimal parenting ( Figure 2 ).

Figure 2. “Care,” “overprotection” and parenting styles for fathers and mothers as reported by students ( n = 456). Green circles represent students with both parents showing the same parenting style – none of the students received “Optimal parenting” from both parents while 106 students received affectionless control from both parents.

Determinants of High Grades

Our results show that high socioeconomic status [adjusted OR 2.78 (1.03, 7.52)], father’s education level till undergrad or above [adjusted OR 4.58 (1.49, 14.09)], father’s high “care” [adjusted OR 1.09 (1.01, 1.18)] and father’s affectionless control style of parenting [adjusted OR 3.23 (1.30, 8.03)] are significant factors contributing to high grades ( Table 3 ).

Table 3. Academic performance: Determinants of “high” grades in the latest internal examinations.

Differences in “Care” and “Overprotection” Between Fathers and Mothers

The mean “care” score for mothers were significantly higher than fathers overall. The difference remained significant for male and female students, public and private schools, joint and nuclear family structures and low and high socioeconomic statuses ( Table 4 ).

Table 4. Differences between mean “care” and “overprotection” scores between fathers and mothers.

Overprotection

The mean “overprotection” score was significantly higher for fathers overall. The difference remained significant for female students, private schools, nuclear family structure, and low socioeconomic status. However, there was no significant difference in mean “overprotection” scores between fathers and mothers for male students, public schools, joint family structures and high socioeconomic status ( Table 4 ).

Factors Associated With “Care” and “Overprotection” in Fathers and Mothers

The mean “care” score was significantly higher for fathers as reported by children in public schools and with higher grades. There was no significant difference in mean care scores based on student gender, socioeconomic status or family structure ( Table 5 ).

Table 5. Factors associated with “care” and “overprotection” for mothers and fathers.

For “overprotection” the only factor associated with a significantly higher mean score was “high” grades ( Table 5 ).

A significantly higher mean “care” score for mothers was reported by female students and students in public schools. No significant differences were observed for the other factors ( Table 5 ).

A significantly higher mean “overprotection” score was reported by male students, students in public schools and those with “high” grades for mothers ( Table 5 ).

Summary of Findings

Results of regression analysis show that socioeconomic status, father’s education level and fathers’ care scores have a significantly positive influence on the academic performance of adolescent students in Peshawar, Pakistan. The most common parenting style for both fathers and mothers was affectionless control. However, affectionless control exhibited by the father was the only parenting style significantly contributing to improved academic performance.

Overall, the mean “care” score was higher for mothers and the mean “overprotection” score was higher for fathers. However, differences in “overprotection” were eliminated for male students, public schooling, joint family structures and high socioeconomic status.

Public schooling was associated with a significantly higher mean “care” score for both fathers and mothers and a significantly higher mean “overprotection” score for mothers. High grades were associated with a significantly higher mean “overprotection” score for both fathers and mothers and a significantly higher mean “care” score for fathers. For mothers, female students reported a significantly higher mean care score and male students reported a significantly higher mean “overprotection” score.

An additional interesting finding from the results of the study was that only about half the students were able to report their father’s level of education compared to almost a 100% for their mother. From amongst those who did report, less than 10% of the father’s had an education level equal or above grade 12 compared to a quarter of the mothers. However, only 11% of the mothers were employed in contrast to 90% of the fathers.

Previous Literature and Comparison of Main Findings

The results of our study have identified socioeconomic status, father’s education level and high care scores for fathers to be significant predictors of academic success in adolescent students. Previous literature has shown socioeconomic status to be a predictor of academic success ( Gamoran, 1996 ; Sander, 1999 ; Lubienski and Lubienski, 2006 ).

Parental education has been frequently associated with improved academic performance ( Dumka et al., 2008 ; Dubow et al., 2009 ; Masud et al., 2015 ). In 2011, a study by Farooq et al. described the factors affecting academic performance in 600 students at the secondary school level in a public school in Lahore, Pakistan. Results of their study also associate parental education level with academic success in students. However, their results are significant for the education level of the mother as well as the father. Additionally, they also reported significantly higher academic performance in females and in students belonging to a higher socioeconomic status, factors not significant in our study ( Farooq et al., 2011 ). Differences may be explained by cultural variations in Lahore and Peshawar within Pakistan, which should be explored further.

The description of parenting styles and behaviors has evolved over the years. With some variation in terminologies, the essence lies in a few common principles. Diana Baumrind initially described three main parenting styles based on variations in normal parenting behaviors: authoritative, authoritarian and permissive ( Baumrind, 1966 , 1967 ). Building on the concepts put forth by Baumrind, Maccoby and Martin identified two dimensions, “responsiveness” and “demandingness,” which could classify parenting styles into 4 types, three of those described by Baumrind with the addition of neglectful parenting ( Maccoby et al., 1983 ). The two dimensions, “responsiveness” and “demandingness,” often referred to as “warmth” and “control” in literature ( Lamborn et al., 1991 ; Tagliabue et al., 2014 ), are similar to the two measures, “care” and “overprotection” assessed by the parental bonding instrument ( Parker et al., 1979 ; Parker, 1989 ; Dudley and Wisbey, 2000 ). Based on this, the authoritative, authoritarian, permissive and neglectful parenting styles described by Baumrind and Maccoby are similar to the affectionate constraint, affectionless control, optimal, and neglectful styles as classified by the parental bonding instrument, respectively ( Baumrind, 1991 ; Cavedo and Parker, 1994 ).

Results of our study show that affectionless control, similar to the authoritarian style of parenting, adapted by the father is significantly associated with improved academic performance. This differs from the popularity of the authoritative parenting style, similar to affectionate constraint, in determining academic success in literature from western cultures ( Steinberg et al., 1989 , 1992 ; Deslandes et al., 1998 ; Aunola et al., 2000 ; Adeyemo, 2005 ; Masud et al., 2015 ; Pinquart, 2016 ; Checa et al., 2019 ). Evidence from societies with cultural similarities with Pakistan presents varied findings. A study from Iran shows support for the authoritarian parenting style similar to our study ( Rahimpour et al., 2015 ). A review of 39 studies published by Masud et al. (2015) in 2015 assesses the effect of parenting styles on academic performance ( Masud et al., 2015 #205). The review very aptly described how the authoritative parenting style is the dominant and most effective style in terms of determining academic performance in the West and European countries while Asian cultures show more promising results for academic success for the authoritarian style ( Dornbusch et al., 1987 ; Lin and Fu, 1990 ; Masud et al., 2015 ). The results of our study are in synchrony with these findings. However, our results also show that high father’s “care” scores are significant contributors to higher academic grades. Since no father showed optimal parenting and only 9 fathers had affectionate constraint, both parenting styles with high care scores, these results may be a reflection of the importance of father’s role in determining academic performance in Asian cultures. Findings supporting the authoritarian/affectionless control style may be due to the abundance of this parenting style. Perhaps a fairer comparison may be possible with a larger sample population with fathers showing all types of parenting styles equally.

Interpretation and Explanation of Other Findings

Observations of factors associated with and differences in “care” and “overprotection” between fathers and mothers may be attributed to reverse causality and should be used as hypothesis generating.

Our results show that mothers have higher mean “care” score and fathers have a higher mean “overprotection” score. Since these scores are based on perceptions of the child, part of these observations may be explained by the cultural norms of expression of love and concern by fathers and mothers. With the difference in “overprotection” being eliminated for male and female children, it is possible that mothers are more overprotective of their sons. Male gender preference in Pakistan may be an explanation for this ( Qadir et al., 2011 ).

Our results show lower employment rates for women despite higher education levels. The finding of higher education levels for females compared to males does not agree with national data, which reports findings from rural areas as well where education opportunities are limited for females ( Hussain, 2005 ; Chaudhry and Rahman, 2009 ). Our results provide a zoomed in look at an urban population, which may have progressed enough to improve women’s education but cultural norms, gender discrimination and lack of opportunity still prevent women from stepping into the workface ( Chaudhry, 2007 ; Begum and Sheikh, 2011 ).

Implications and Future Direction

The findings of our study may have implications for future research and policy making.

Affectionless control is associated with improved academic performance but further research investigating the effects of this style on other aspects of child development, particularly emotional and psychological health, is needed. Factors affecting care and overprotection need to be studied in more detail so that parenting workshops and interventions are tailored to our population. Results also suggest that fathers should play a stronger role in parenting of adolescent students. School policies should make it mandatory for both parents to attend parent-teacher meetings and assigned home activities should include both parents.

Limitations

Since the study is based on the urban population of Peshawar, results may not be generalizable to the adolescent students of the country which includes large rural populations. Academic performance was judged on latest internal examinations, the marking criteria for which may vary across schools. The use of external examinations would have standardized grades across schools but limited the sample to students of grade 9 and 10.

Our study concludes that socioeconomic status, father’s level of education and high care scores for fathers are associated with improved academic outcomes in adolescent students in Peshawar, Pakistan. Affectionless control is the most common parenting style as perceived by the students and when adapted by the father, contributes to better grades. Further research investigating the effects of demonstrating affectionless control on the emotional and psychological health of students needs to be conducted. Parenting workshops and school policies should include recommendations to increase involvement of fathers in the parenting of adolescent children.

Data Availability Statement

Data collected and stored as part of this study is available upon reasonable request.

The studies involving human participants were reviewed and approved by the Khyber Medical University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

SM contributed in conceiving, designing, data acquisition, grant submission, and manuscript review. SHM involved in data analysis and manuscript writing. NQ involved in manuscript writing. MK was the principal investigator and supervisor for the project. FK and SK contributed in literature review and data management. All authors proofread and agreed on the final draft and accept responsibility for the work.

This project was graciously funded by the Research Promotion and Development World Health Organization Regional Office for the Eastern Mediterranean (RPPH Grant 2016-2017, TSA reference: 2017/719467-0).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank Dr. Nazish Masud (King Saud bin Abdulaziz University), and Dr. Khabir Ahmad and Dr. Bilal Ahmad (The Aga Khan University) for their contributions to the project.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02497/full#supplementary-material

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Keywords : parenting styles, academic performance, adolescent students, Pakistan, care, overprotection, parental bonding instrument

Citation: Masud S, Mufarrih SH, Qureshi NQ, Khan F, Khan S and Khan MN (2019) Academic Performance in Adolescent Students: The Role of Parenting Styles and Socio-Demographic Factors – A Cross Sectional Study From Peshawar, Pakistan. Front. Psychol. 10:2497. doi: 10.3389/fpsyg.2019.02497

Received: 16 May 2019; Accepted: 22 October 2019; Published: 08 November 2019.

Reviewed by:

Copyright © 2019 Masud, Mufarrih, Qureshi, Khan, Khan and Khan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sarwat Masud, [email protected] ; Muhammad Naseem Khan, [email protected] ; [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Student health behavior and academic performance

Associated data.

The following information was supplied regarding data availability:

Raw data are available in the Supplemental Files .

To explore the association between health behaviors and habits of university students and academic achievement.

Participants

Six hundred fourteen undergraduate students at a state university in the United States.

Students were invited over a 2-year period to participate in an anonymous online survey that asked questions concerning a wide range of health behaviors and habits; participants were asked to report their current grade point average (GPA). Standard Least Squares Models were used to examine differences in self-reported GPA across the different health behaviors and habits, with individuals as replicates.

The study found positive associations between breakfast consumption, physical activity, and strength training and self-reported GPA, and negative associations between the hours of sleep per night, hours worked per week, fast food and energy drinks consumption, and use of marijuana, alcohol and electronic vaping products.

Conclusions

While there is an association for some of the studied health behaviors and habits with self-reported GPA, the effect sizes for these health behaviors were low. The significant effect of vaping on GPA as well as the increased use reported in this study indicates that the topic should be explored further. Furthermore, students should be educated on the potential positive and negative effects of health behavior choices to help them make better choices.

Introduction

While the terms ‘academic performance’ and ‘academic success’ are widely used, there is no easy or universally acknowledged definition of either term ( York, Gibson & Rankin, 2015 ). In the United States, and many other countries, grades earned and a grade point average (GPA) calculated from these grades are used as measures of academic success ( Şahin, Çekin & Yazıcılar Özçelik, 2018 ). Looking into factors that may impact college students’ academic success in a positive or negative way is important because the information gained can be used to educate students about these factors to enable them to make choices that minimize negative choices and maximize positive choices.

Although adherence to appropriate health behaviors can assist in facilitating an overall healthy lifestyle in adulthood, the transition to college is often accompanied by an increase in unhealthy behaviors that may influence students’ academic performance ( Ruthig et al., 2011 ; Office of Disease Prevention & Health Promotion, 2020 ). Published studies have shown that students who do follow public health recommendations for their lifestyle choices achieve higher GPAs ( Trockel, Barnes & Egget, 2000 ; Wald et al., 2014 ). Health-promoting behaviors, such as consumption of fruits and vegetables, regular sleep routines, adequate physical activity, and frequent breakfast intake are positive predictors of GPA ( Bellar et al., 2014 ; Wald et al., 2014 ; Hershner, 2020 ; Reuter, Forster & Brister, 2020 ). On the contrary, unhealthy behaviors such as smoking or vaping, use of alcohol or drugs, consumption of fast food, and working long hours have been identified as negative predictors of academic performance ( Tessema, Ready & Astani, 2014 ; Arria et al., 2015 ; Meda et al., 2017 ; Lee, Baring & Sta. Maria, 2018 ; Reuter, Forster & Brister, 2020 ).

Sleep is not only a necessity for biological functioning, but is a vital component for maintaining cognitive roles, memory consolidation, decision making, and learning in general ( Hershner, 2020 ). Sleep quality and quantity are among the most researched health behaviors in connection with academic performance of university students, and there is a consensus that both can affect students’ grades ( Gilbert & Weaver, 2010 ; Flueckiger et al., 2014 ; Gaultney, 2016 ; Gomez Fonseca & Genzel, 2020 ). In Gaultney’s study, for example, freshmen at risk for a sleeping disorder, or infrequent restful sleep, did receive lower grades ( Gaultney, 2016 ). Chen & Chen (2019) found that four out of ten college freshmen reported chronic sleep deprivation, and furthermore, that sleep deprivation in general was associated with a lower GPA.

Because of the rising costs of attending university as well as covering basic expenses while enrolled, there has been a sharp increase in the number of students relying on income from working while in college ( National Center for Education Statistics, 2020 ). Zhang, Shao Tarleton & Johnston (2019) found that seven out of ten students were working for the duration of the semester, while almost half were averaging over 20 h a week. Other studies reported that students who worked ten or less hours over the course of the week reported the highest GPAs, while working more than 10 h was linked with lower GPAs ( Tessema, Ready & Astani, 2014 ; Andemariam et al., 2015 ).

Although a less studied topic relating to university students’ academic performance, physical activity may have a positive effect on overall GPA. According to the Committee on Physical Activity and Physical Education in the School Environment of the Institute of Medicine, basic cognitive functions related to attention and memory are enhanced by physical activity ( Kohl & Cook, 2013 ). Bellar et al. (2014) reported that higher levels of physical activity are positively associated with a higher GPA, while Roddy, Phle-Krauza & Geltz (2017) reported that more frequent use of a university’s recreation center was associated with higher GPAs. On the other hand, Flueckiger et al. (2014) did not find a clear correlation of physical activity with increased learning goal achievement or exam grades.

Healthy eating habits have been shown to positively influence academic performance ( Kristjánsson, Sigfúsdóttir & Allegrante, 2010 ; Wald et al., 2014 ; Burrows et al., 2017 ). The quality of students’ diet appears to be the main factor for this effect. Florence, Asbridge & Veugelers (2008) reported an association of diet quality and academic performance in school children. They found that students with decreased overall diet quality were significantly more likely to perform poorly on structured assessments. Rampersaud et al. (2005) also expressed that a healthy diet is effective in improving cognitive functioning and academic performance. Not all studies, however, agree on which habits specifically have a positive effect on academic performance. For example, Burrows et al. (2017) looked at seven different studies and found that five of them reported higher academic achievement with increased fruit intake. In contrast, Trockel, Barnes & Egget (2000) and Reuter, Forster & Brister (2020) did not report an association between fruit intake and GPA. Regular consumption of breakfast has also shown to have a positive effect on GPA ( Chawla et al., 2019 ; Reuter, Forster & Brister, 2020 ). Conversely, unhealthy dietary behaviors adversely affect academic performance. Kobayashi (2009) and Reuter, Forster & Brister (2020) found that as consumption of fast food increased, GPA successively decreased, and consumption of energy drinks also has shown to have a negative effect on academic success ( Trunzo et al., 2014 ; Champlin, Pasch & Perry, 2016 ).

The two most commonly abused substances among college students are alcohol and marijuana ( Meda et al., 2017 ). Both have been shown to be related to decreased academic performance in a number of published studies ( Wallis et al., 2019 ). Drug use is considered a major factor associated with inferior academic success and more frequent alcohol consumption is negatively correlated with GPA ( Arria et al., 2015 ; Conway & DiPlacido, 2015 ; Meda et al., 2017 ; Souza, Hamilton & Wright, 2019 ). Marijuana use was found to be negatively correlated with GPA, with more frequent users having the lowest GPAs ( Suerken et al., 2016 ).

Limited studies exist concerning cigarette smoking and e-cigarette use, and their relationship with college students’ GPA. Lee, Baring & Sta. Maria (2018) reported lower GPAs in students who smoked compared to their peers who did not. Phillips et al. (2015) also found that smoking was a negative predictor of cumulative GPA. The use of e-cigarettes or other electronic vaping products is a fairly new phenomenon and, thus, there are only a few published studies on their use among university students and possible effects on academic performance. Onojighofia Tobore (2019) stressed that induced oxidative stress from e-cigarettes can be linked to cognitive impairment and attention deficit. Leas et al. (2020) reported that almost one-third of the young adults they surveyed had used at least one form of tobacco product, including e-cigarettes. However, the study did not look into an association with academic performance.

Finally, there is a long history of university students turning towards the use of stimulants to improve academic performance ( Arria & DuPont, 2010 ). However, Arria et al. (2015) reported that users of stimulants and analgesics had lower average GPAs than nonusers. The students also spent less time studying, went out more socially, and skipped class more frequently.

This article reports on 28 health behaviors and habits and their impact on the academic performance of undergraduate students at a public university in the southern United States. The behaviors selected can be categorized in five categories: (1) sleeping habits, (2) working, (3) physical activity, (4) eating habits, and (5) alcohol, tobacco, and drugs consumption ( Table 1 ). Our aim was to cover the spectrum of behaviors and habits customarily found in a student population. Therefore, we included basic behaviors, such as sleeping and eating habits, as well as behaviors and habits that only some students may engage in, such as alcohol consumption.

Standard Least Squares Model (Restricted Maximum Likelihood Method, REML; individuals as replicates; students’ biological sex used as a random effect).

The last study to look at a similar, although different number of factors in a comparable study population was published in 2000 ( Trockel, Barnes & Egget, 2000 ). The students in that study were late Generation X and/or early Millennial students; the students in our study are Generation Z students whose health behaviors and habits are different in many ways ( Rue, 2018 ; Schlee, Evelan & Harich, 2020 ). For example, vaping was not prevalent in 2000, while smoking was far more common. For this reason, the findings of this study are of particular importance because of gaps in our understanding of health-promoting behaviors and the academic performance of this new generation of college students.

Ethical research statement

The research protocol and its amendment were approved by an ethical review board (Institutional Review Board) at Florida Gulf Coast University (FGCU) prior to data collection (FGCU IRB 2018-17, March 30, 2018). All researchers involved in the study were trained in ethical data collection through the Collaborative Institutional Training Initiative (CITI). Data collection followed all laws relevant to the survey of university student populations.

Data collection

Data were collected over a 2-year period between April 1, 2018 and January 31, 2020 using an anonymous online survey. Students at all colleges at a regional state university in southwest Florida were invited via email to participate in the survey. The first page of the survey consisted of an approved online survey consent form; in other words, consent was obtained. Participation was voluntary and participants did not receive any compensation or extra credit. Some part of the data unrelated to current research purpose was published elsewhere ( Reuter, Forster & Brister, 2020 ).

The survey consisted of five groups of questions around health and wellness, requesting information–among other topics–regarding demographic information, such as gender, age, ethnicity/race, year at school, and current overall grade point average (GPA), eating and sleeping habits, and drugs and alcohol consumption (see Appendix 1 ). Most of the questions were modeled after questions used in the 2017 Standard High School Youth Risk Behavior Survey ( Centers for Disease Control & Prevention, 2017 ).

Data analyses

For questions with categorical answers, data are presented as a percentage of the total participant pool, or a portion of this pool. For questions with quantitative answers, data are presented as means with standard deviations. Sample sizes are indicated as they vary for different analyses due to the voluntary nature of the survey.

To examine differences in self-reported GPA across 28 different health behaviors and habits, Standard Least Squares Models were used (Restricted Maximum Likelihood Method, REML), with individuals as replicates. GPA differed significantly by cohort (Wilcoxon Rank Sums Test, Chi-square = 18.9057, DF = 3, p = 0.0003) and was therefore included as a random effect in all tests. GPA differed by biological sex (Wilcoxon Rank Sums Test, Chi-square = 4.5894, DF = 1, p = 0.0322) and biological sex of students was therefore included as a random effect in all tests.

Student’s T-Test All Pairwise Comparisons (Least Squares Means) were used as post-hoc tests. The dependent variable in all tests was student self-reported GPA (a continuous numerical variable ranges from 0.00 to 4.00). Independent variables are listed in Table 1 below or can be found on the survey in Appendix 1 . Given the number of statistical analyses performed (41 tests) we have used the Benjamini–Hochberg Procedure (with a false discovery rate of 5%) to determine significance (tests with a p -value < 0.0189 are being considered significant using this approach). Statistical analyses were performed using JMP software program Version 15 (JMP ® ; SAS Institute Inc., Cary, NC, USA).

Study population

Of 761 students who participated in the online survey, 147 respondents were excluded from analyses for a number of reasons, including: (a) respondents who indicated an age younger than 18 years of age or older than 25 years of age or failed to provide information on their current GPA; (b) part-time students, as the sample sizes was too small ( n = 15) and they had a lower GPA than full-time students (Wilcoxon Rank Sums Test, Chi-square = 4.61, DF = 1, p = 0.0317); (c) non-traditional students (graduate, non-degree seeking, and second-degree seeking students); and (d) students who failed to provide information about their biological sex. As such, 614 students remained in the dataset.

Study population profile

Biological sex : 78.5% female students, 21.5% male students.

Race/ethnicity : 58.3% Caucasian/White, 15.8% Hispanic, 5.9% African-American/Black, 1.1% East Asian, 1.1% Non-Hispanic, 17.3% more than one ethnicity/race or ethnicity/race other than listed, 0.5% no information.

Age : 19.6 ± 1.4 years (mean ± standard deviation; range: 18–25 years; median age = 19 years).

Student population : 30.9% freshman, 29.5% sophomore, 26.6% junior, 13.0% senior.

The average current GPA for all 614 respondents was 3.39 ± 0.51 (range: 0.40–4.0; median GPA = 3.5). Freshmen reported the highest average GPA (3.45 ± 0.64), followed by seniors (3.45 ± 0.42), juniors (3.36 ± 0.39), and sophomores (3.36 ± 0.48).

The average GPA for female students was 3.42 ± 0.52 compared with 3.34 ± 0.49 for male students.

Health behaviors and self-reported GPA

Differences in self-reported GPA across 28 different health behaviors and habits were examined ( Table 1 ).

Average self-reported GPA differed by the number of hours slept on average per night ( Fig. 1 ). Students who slept 4 h or less per night had lower GPAs than students who slept 6 h per night ( p -value = 0.0062), 7 h per night ( p -value = 0.0025), and 8 h per night ( p -value = 0.0004, Student’s T-Test All Pairwise Comparisons). Students who slept 9 h per night, had lower GPAs than students who slept 8 h per night ( p = 0.0006, Student’s T-Test All Pairwise Comparisons).

There was no significant difference in the average GPA of students based on when they went to sleep. Nine out of ten respondents went to bed between 10 pm and 2 am (87.3%). Students going to bed between 8 pm and 10 pm (4.1%) had a self-reported GPA of 3.46 (±0.33), students going to bed between 10 pm and midnight (44.1%) reported an average GPA of 3.45 (±0.47), and the average GPA for students who went to bed between midnight and 2 am (43.2%) was 3.37 (±0.53).

Almost equal numbers of students got up before 6 am (8.5%) or liked to sleep in and got up after 10 am on average (8.6%). The rest preferred to get up between 6 am and 8 am (42.0%) or between 8 am and 10 am (40.9%). But, there was no significant difference in the average GPA of students based on when they woke up.

Self-reported GPA for respondents differed depending on whether or not respondents worked, with a negative association between hours worked on average per week and self-reported GPA. Students who did not work/worked zero hours per week had a higher GPA than students who worked 20–30 h per week ( p = 0.0188), 30–40 h per week ( p = 0.0094), and more than 40 h per week ( p = 0.0014). Students who did not work or only up to 10 h per week reported average GPAs of above 3.45, whereas students who worked 10 h or more per week had GPAs of 3.38 or less ( Fig. 2 ).

Physical activity

One-sixth of respondents considered themselves as not physically active at all (17.0%). Their self-reported GPA was 3.28 ± 0.64 as compared to an average GPA of 3.43 ± 0.48 for respondents who were physically active (83.0%). There was a significant difference in the average GPA of students based on the number of days they were physically active in the week prior.

In addition, respondents who engaged in exercises to strengthen or tone their muscles, such as push-ups, sit-ups, or weight lifting, reported higher GPAs than students who did not engage in such exercises.

Eating habits

Consumption habits for vegetables, fruits, fruit juice, or green salad did not change among students with different GPAs. There was also no significant difference in the average GPA of students based on the number of times milk, soda, diet soda, or sports drinks were consumed in the last week either. Nevertheless, the difference in average GPA between respondents who did and those who did not consume energy drinks was significant with a p -value of 0.0084.

Self-reported GPA differed based on the days that students had eaten breakfast in the past seven days ( Fig. 3 ). Students who ate breakfast seven days a week/everyday were more likely to have higher GPAs than students who never ate breakfast (zero days of the last seven), ate breakfast one day per week, ate breakfast 2 days per week, or ate breakfast three days per week ( p < 0.0075).

Self-reported GPA differed based on the number of times that students had eaten fast food in the past 7 days ( Fig. 4 ). Students who ate no fast food had higher GPAs than students who reported eating fast food 7 to 10 times ( p < 0.0001).

Alcohol, smoking, and drugs consumption

The self-reported GPAs for students who had consumed alcoholic drinks, students who had tried cigarette smoking, and students who had used electronic vaping products were lower versus students who had not engaged in any of these behaviors before.

There was also a significant difference in self-reported GPA of students based on whether or not they had ever used marijuana at all, with students who had never used marijuana reporting higher GPAs than those who had used it at least once.

Conversely, there was no significant difference in the average GPA of students based on whether or not they considered themselves to be a smoker, the number of times they had used an electronic vaping product in the last week, the number of alcoholic beverages they had consumed in the last 30 days, and the number of times they had used marijuana in the past 7 days.

There was also no significant difference in the average GPA of students based whether or not they had ever taken prescription pain medication without a prescription or differently than prescribed, and whether or not they had ever consumed drugs other than marijuana.

The most commonly named drugs were hallucinogens ( n = 40) such as LSD and DMT, cocaine ( n = 34), ecstasy/molly/MDA ( n = 24), and mushrooms ( n = 17). Surprisingly, Xanax ® and ADHD medications, such as Adderall ® , were listed by five respondents each only, although they are reportedly used more frequently ( National Survey on Drug Use & Health, 2017 ).

The findings of this study are of particular importance because they fill in gaps of our understanding of health behaviors and their impact on the academic performance of Generation Z university students within a university in the southern United States of America. Given, however, that health behaviors and habits vary considerably within a region or country, and from university to university, it is nevertheless difficult to compare our results to the behavior of student populations at other institutions. For example, a study comparing health promoting behaviors and lifestyle characteristics of students in the United Kingdom found marked differences for the students at the seven universities included ( El Ansari et al., 2011 ). Trockel, Barnes & Egget (2000) acknowledged that some of their findings would probably have been different had they not collected data at a ‘dry’ campus, i.e., a campus where alcohol consumption was prohibited. Also, the arrival of Generation Z students (i.e., students born after 1996) on campus over the last 5 to 6 years has brought changes that make it more difficult to compare our results with the findings of studies published 15 or 20 years ago. For instance, according to the American Lung Association (2020) , in 1999, 38.4% of high school students were smokers; by 2018, that percentage had dropped down to 8.8%. Still, the results of our study align with previously published results.

Our findings that students who sleep less than 4 h or more than 9 h per night have lower GPAs than students who sleep 6, 7, or 8 h per night, are similar to what Vedaa et al. (2019) reported for Norwegian students. Chen & Chen (2019) reported that a lack of sleep has a negative association with GPA for college freshmen.

However, our data did not confirm the results from other studies that indicated the timing of sleep to be a better predictor for academic performance than actual hours slept ( Eliasson, Lettieri & Eliasson, 2010 ; Genzel et al., 2013 ). For example, Eliasson, Lettieri & Eliasson (2010) reported that students with earlier bed times and earlier wake times had higher GPAs, and concluded that timing of sleep is a better predictor for academic performance than actual sleep hours. In our study, however, it did not matter what time students went to bed as long as they got a sufficient amount of sleep. For instance, students who went to bed between midnight and 2 am reported an average GPA of 3.24 ± 0.60 if they slept 5 h or less but an average GPA of 3.47 ± 0.60 if they slept 8 h per night.

There is a consensus among published studies that the number of hours that full-time students work negatively affects their academic performance ( Miller, Danner & Staten, 2008 ; Pike, Kuh & Massa-McKinley, 2008 ; Darolia, 2014 ; Tessema, Ready & Astani, 2014 ; Andemariam et al., 2015 ). In line with our study, Tessema, Ready & Astani (2014) reported lower GPAs for students working more than 11 h per week, and students working over 31 h per week had the lowest GPA in the study by Andemariam et al. (2015) .

Additionally, the positive effect on average GPA we found for students working fewer than 10 h per week ( Fig. 2 ), is supported by the studies of Darolia (2014) , Andemariam et al. (2015) , and Tessema, Ready & Astani (2014) .

We found a positive effect for both strength training and physical activity in general on average self-reported GPA. Respondents who engaged in exercises to strengthen or tone their muscles, such as push-ups, sit-ups, or weight lifting, on four days or more during the past seven days had a higher GPA at 3.51 ± 0.44 than students exercising 1-3 days (3.43 ± 0.44) and students who did not exercise at all (3.28 ± 0.64).

However, Wald et al. (2014) reported a modestly higher GPA for physical activity only and no association between GPA and strength training. Trockel, Barnes & Egget (2000) did not find an association between GPA and either exercise or strength training; nor did Flueckiger et al. (2014) . Contrary to that, Bellar et al. (2014) reported that higher levels of physical activity are positively associated with higher GPAs. Roddy, Phle-Krauza & Geltz (2017) showed that students who used a university’s recreation center frequently and regularly were the students with the higher GPAs as compared to their peers who visited irregularly. However, they did not look at the different types of exercise and how they affected GPA.

We found a positive association between eating breakfast and GPA as well as a negative association between the number of times fast food was consumed per week and GPA. The positive effect of eating breakfast regularly has been reported in studies from different countries ( Trockel, Barnes & Egget, 2000 ; Phillips, 2005 ; Rampersaud et al., 2005 ; Adolphus, Lawton & Dye, 2013 ; Chawla et al., 2019 ; Reuter, Forster & Brister, 2020 ). Kobayashi (2009) , Deliens et al. (2013) and Reuter, Forster & Brister (2020) also found a negative association between fast food consumption and academic performance.

In agreement with previous studies ( Petit & DeBarr, 2011 ; Trunzo et al., 2014 ; Buchanan & Ickes, 2015 ; Champlin, Pasch & Perry, 2016 ), our study showed a significant negative association between energy drink consumption and GPA. Interestingly, 84.3% of our respondents reported not having consumed energy drinks at all over the past seven days. On the other hand, Petit & DeBarr (2011) reported that more than half the students in their study drank at least one energy drink in the past seven days, illustrating how this health behavior has changed over the last 10 years.

A negative relationship between alcohol consumption and GPA was reported by Conway & DiPlacido (2015) , Piazza-Gardner, Barry & Merianos (2016) as well as Meda et al. (2017) . However, we did not find an association between the number of alcoholic beverages consumed in the past 30 days and GPA. But, students in our study who indicated having consumed alcohol at least once so far reported lower GPAs than students who had never had an alcoholic drink.

Smoking clearly is not as popular with current college students as it used to be. Only one-fifth (19.5%) of students who answered the question “ Have you ever tried cigarette smoking? ” answered ‘yes’ and only 10 students (1.7%) considered themselves to be smokers. Still, students who had tried cigarette smoking reported lower GPAs than students who had never smoked. Phillips et al. (2015) and Meda et al. (2017) also reported that smoking was a negative predictor of cumulative GPA.

Vaping was much more common than smoking among our study population; one-third of respondents (35.9%) answered ‘yes’ to the question “ Have you ever used an electronic vapor product? ” and 15.1% of respondents had vaped during at least 1 day over the last 7 days. The fact that in our study only 22.5% of seniors but 39.5% of freshmen admitted to having used electronic vaping products demonstrates that vaping is still on the rise. Study respondents who reported having vaped before had lower GPAs than respondents who had never vaped.

The only drug used by a substantial number of respondents in this study was marijuana with 44.2% of participants responding with ‘yes’ to the question “ Have you ever used marijuana? ”. While we did not find an association between marijuana use frequency (i.e., number of days marijuana used in the past 7 days) and GPA, our data show that students who answered the question with ‘yes’ reported lower GPAs compared with students who answered with ‘no’. Arria et al. (2015) did not find an association between marijuana use and GPA, whereas Meda et al. (2017) , Suerken et al. (2016) , Souza, Hamilton & Wright (2019) , and Wallis et al. (2019) all reported a negative relationship between marijuana use and academic success.

We did not find an association between pain killer abuse (i.e., taking prescription pain medication without prescription or differently than prescribed) or the use of drugs other than marijuana, such as cocaine, heroin, methamphetamines, ecstasy, hallucinogenic drugs, or synthetic marijuana, and GPA. Pain killer abuse among our study population was at about the same level as reported for the age group 18–25 in the 2017 National Survey on Drug Use and Health (NSDUH) at 7.7% in our study vs. 7.2% in the NSDUH.

The three main limitations of our study were: (1) participant selection, (2) reliance on self-reported health behaviors and habits, and (3) reliance on self-reported GPAs. Although we invited students from all colleges across the university to participate, our final study population with 78.5% female students and 21.5% male students does not reflect the demographics of the student body at our university (53% female students and 47% male students) ( Florida Gulf Coast University, 2020 ). Research has shown that female students are more interested in or worried about health-related issues and diet, and are more likely to participate in online surveys in general ( Rozin, Bauer & Catanese, 2003 ; Smith, 2008 ).

Participants may have intentionally or unintentionally provided incorrect information about their lifestyle choices or their current GPA. For example, students may have given us the GPA they were hoping to achieve at end of the current semester instead of the actual GPA for completed classes. Also, students may have wanted to appear to be making better health behavior choices or may not remember accurately how often they had consumed fast food, for example. Moreover, some of the behaviors and habits included in the survey were not defined and participants may have interpreted them differently. For example, some participants may have considered walking to class as being physically active, while others may have set the bar higher. Additionally, we cannot rule out that students may have participated more than once. However, there is no reason to believe that inaccuracies such as these or repeat responses had a substantial impact on the findings of this study due to the number of participants being higher than 600.

Our survey was not designed to look into factors that affect student health behaviors and habits; it was designed to look at whether or not the included behaviors impact students’ academic performance. Therefore, the information received from participants is not suitable to make inferences as to potential mechanisms underlying the findings of our study. These questions should be addressed in future studies.

Our study involving more than 600 students found positive associations between breakfast consumption, physical activity, and strength training and self-reported grade point average. We also found negative associations between the hours of sleep per night, hours worked per week, fast food and energy drinks consumption, and use of marijuana, alcohol and electronic vaping products. However, the effect sizes for these health behaviors were low. In other words, even though there is a relationship to the average GPA, the variables do not explain the variation in GPA scores. For example, although self-reported GPA differs significantly by hours of sleep per night, only 5.0% of the variation in the GPA scores can be explained by this factor alone.

So far, the public—as well as the research community—has focused mainly on the physical damage using electronic vaping products causes, such as lung injuries. The significantly negative effect of vaping on GPA as well as the increased use found in our study indicates that the topic should be explored in further studies.

Supplemental Information

Supplemental information 1, supplemental information 2, acknowledgments.

We would like to thank Kim E. Reuter, PhD, for support with the statistical analysis of the data, and for her valued insights and contributions to the project. Many thanks to Sierra Brister for her support in designing the research project.

Funding Statement

The authors received no funding for this work.

Additional Information and Declarations

The authors declare that they have no competing interests.

Peter R. Reuter conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

Bridget L. Forster conceived and designed the experiments, performed the experiments, analyzed the data, authored or reviewed drafts of the paper, and approved the final draft.

The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):

The Florida Gulf Coast University Institutional Review Board approved the study on March 30, 2018 (Protocol ID # 2018-17).

• Regular article
• Open access
• Published: 24 April 2018

Academic performance and behavioral patterns

• Valentin Kassarnig   ORCID: orcid.org/0000-0001-9863-0390 1 ,
• Enys Mones 2 ,
• Andreas Bjerre-Nielsen 3 , 4 ,
• Piotr Sapiezynski 2 , 5 ,
• David Dreyer Lassen 3 , 4 &
• Sune Lehmann 2 , 4 , 6  

EPJ Data Science volume  7 , Article number:  10 ( 2018 ) Cite this article

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Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students.

1 Introduction

Since research on academic achievement began to emerge as a field in the 1960s, it has guided educational policies on admissions and dropout prevention [ 1 ]. Although much of the literature has focused on higher education, the knowledge obtained on behavioral phenomena observed in colleges and universities can potentially guide research on student behavior in primary and secondary schools. A number of behavioral patterns have been linked to academic performance, such as time allocation [ 2 ], active social ties [ 3 ], sleep duration and sleep quality [ 4 ], or participation in sport activity [ 5 ]. Most of the existing studies, however, suffer from biases and limitations often associated with surveys and self-reports [ 6 , 7 ], particularly when measuring social networks [ 8 – 11 ].

Here we investigate the performance of 538 students within a novel dataset collected as part of the Copenhagen Network Study (CNS), with data collection ongoing for more than two years [ 12 ]. Due to the scale of the CNS, and the inclusion of directly observed data from smartphones in place of self-reports, we are able to mitigate some of the limitations encountered in existing ‘traditional’ studies. The strength of the CNS data is the high-resolution multi-channel measures for social interactions, including person-to-person proximity (using Bluetooth scans), calls and text messages, activity on online social networks (Facebook), and mobility traces.

The aim of our study was to better understand the impact of individual and network factors on our ability to distinguish between groups of students based on their performance. That is, we wanted to identify the ways in which low performers are significantly different from high performers and vice versa. We divide this goal into three specific objectives:

Identify individual and network factors that correlate with students’ performances.

Analyze the importance of different sets of features for supervised learning models to classify students as low, moderate, or high performers.

Investigate significant differences among performance groups for the most important individual and network features.

2 Related work

2.1 individual behavior.

Through a variety of methods, a large number of studies have investigated the factors that determine academic performance. Vandamme et al. [ 13 ] analyzed a broad range of individual characteristics concerning personal history, behavior, and perception. Similarly, the StudentLife study [ 14 ] used smartphones to collect data on student activity, social behavior, personality, and mental health. Both research groups observed correlations between performance and all feature categories, building a case that factors influencing academic performance are not limited to a single aspect of an individual’s life. Nghe et al. [ 15 ] reframed the problem as a prediction task: using data to predict performance in a population of undergraduate and postgraduate students at two different institutions. Using a wide range of features, they predicted GPA after third year with high accuracy. One of the features included GPA after the second year; in this work we show that even without the knowledge of past achievements it is possible to explain the students’ performance levels to a large extent. Furthermore, prior research has emphasized the positive influence of attending classes [ 16 – 19 ]. The study by Crede et al. [ 19 ] concludes that attendance is the most accurate known predictor of academic performance; see [ 20 ] for a more detailed analysis of the impact of class attendance on academic performance based on the CNS data.

Cao et al. [ 21 ] analyzed behavioral data from the digital records of nearly 19,000 students’ smart cards, such as entering and leaving the library, having a meal in the cafeteria, or taking a shower in the dormitory. They conclude that the students’ orderness (regularity of daily activities) is a strong predictor of academic performance. Our approach shares some similarities with [ 21 ], but the key difference is that we have investigated not only individual behavior but also the students’ social environment.

2.2 Individual traits

A large body of research at the intersection of psychology and education investigated the relationship between personality and performance, as pioneered by [ 22 ]. Many personality traits were found to be linked to academic success: Among the dimensions of the well-studied Big-Five Inventory [ 23 ] Conscientiousness (positive) and Neuroticism (negative) displayed the strongest correlation with academic performance [ 24 – 52 ]. The other three dimensions showed only very weak or no correlation. Furthermore, the characteristics Self Esteem [ 53 ], Satisfaction with Life [ 54 , 55 ], and Positive Affect Schedule [ 56 ] were also found to be positively correlated, while Stress [ 57 , 58 ], Depression [ 59 – 61 ], and Locus of Control [ 54 , 55 ] showed a negative effect on academic achievements.

2.3 Online social media

Only a few prior studies have investigated the impact of social media activity on academic performance, despite the growing availability of such data and undisputed presence of these media in our daily lives. The majority of existing studies found a decrease in academic performance with increasing time spent on social media [ 62 – 69 ]. However, not all studies confirm this result. In some studies, time spent on social media was found to be unrelated to academic performance [ 70 , 71 ] or even a had positive effect on performance [ 72 , 73 ].

2.4 Social interactions

There is a growing interest in the relationship between social interactions (especially online social interactions) and academic performance [ 3 , 74 – 92 ]. In the relevant literature there exist two dominant approaches. The first approach focuses on the relation between own performance and that of peers [ 74 – 81 ], based on a hypothesis of similarity in peer achievement. The similarity between pairs of individuals connected via social ties are attributed to various aspects: selection into friendships by similarity (i.e., homophily); influence by social peers (also know as peer effect); and correlated shocks (e.g., being exposed to the same teacher). As noted by [ 74 , 93 ] the issue of separating these effects is inherently difficult. The second approach emphasizes the positive influence of having a central position in the social network between students [ 85 – 90 ]. The majority of results in the existing research which measure social networks are, however, based on self-reports and therefore subject to various biases [ 8 – 11 ] that are in many ways mitigated by using smartphones to measure the social network [ 94 ]. However, it should be noted that surveys and observational studies often measure very different aspects of reality. For instance, in the case of assessing tie strengths, observational studies may be more accurate in quantifying duration and frequency variables of a relationship, while surveys can provide qualitative insights into depth and intimacy [ 95 , 96 ].

3 Materials and methods

3.1 data collection and preprocessing.

Results presented in this paper are based on the data collected in the Copenhagen Network Study (CNS) [ 12 ]. In the CNS, dedicated smartphones where handed out to students at the Technical University of Denmark (DTU) and used as their primary phones for two years. During this period various data types were recorded: Bluetooth scans, call and text message meta data, Facebook activity logs, and mobility traces. Additionally, participating students answered a survey on personality at the beginning of the study. Due to the possibility to exit the experiment at any given point, the number of participants varied over time. We investigate the data from 538 undergraduate students for whom we have complete data.

The raw data records are cleaned and transformed to meaningful information before the analysis. Bluetooth scans are used to estimate person-to-person interactions corresponding to a physical distance of up to 10 m (30 ft) between participants. While physical proximity is not a perfect proxy for person-to-person interactions, there is evidence that the proximity interactions are predictive of friendship in online social networks and communication using phone calls and text messages [ 97 – 99 ].

Facebook data was obtained via the Facebook Graph API, and contains both static friendship connections as well as various interactions on the social network. All types of interactions are treated equally. Private messages, however, are unavailable since they cannot be obtained from Facebook using the official Graph API.

The location data on the smartphones has varying accuracy depending on the providing sensor. The accuracy of the collected position can vary between a few meters for GPS locations, to hundreds of meters for cell tower location. We group the location data into 15-minute bins and use the median location of all data points with an accuracy below 80 m. In order to compute attendance we combined the smartphone locations with the person-to-person proximity obtained from Bluetooth scans. A detailed description of the method can be found in a companion paper [ 20 ].

We considered social interactions of five different channels: proximity, Facebook (friendships + interactions), calls, and text messages. For each channel we created a network to model the social relations. Note that these models are based only on the interactions among participants of the CNS. Interactions with any people outside the study were not considered. Importantly, for the proximity networks we excluded all meetings that took place during class time in order to eliminate effects caused by class co-attendance. Section B in Additional file 1 discusses further details of the creation of these network models. In the remainder of this paper, the direct neighbors in those networks are refereed to as ‘peers’.

The students’ course grades were provided by DTU administration. Only courses using the Danish 7-point grading scale were considered. This scale consists of the grades 12, 10, 7, 4, 02, 00, and −3 with 12 being the best grade and 00 and −3 indicating that the student failed. The positive weighted mean grades (term or cumulative) were converted to the standard GPA scale ranging from 4.0 (best) to 0.0 (worst). Every negative mean grade was set to 0.0. Only students attending at least three courses were considered. Figure  1 illustrates the distribution of the 538 cumulative GPAs. It shows a left-skewed distribution with a mean GPA of 2.5. More information about the student population can be found in Section A of Additional file 1 .

Distribution of cumulative GPAs. Distribution of 538 cumulative GPAs. The histogram shows a left-skewed distribution with a mean GPA of 2.5

In order to increase the stability of the results we applied bootstrap resampling. Analyses were performed on 100 bootstrap samples, where each has the same size as the original sample. We report as results the mean of the bootstrap analyses with approximated standard errors described by the Standard Error of the Mean .

3.2 Feature sets

To account for the different explanatory power of the individual and network aspects, we constructed four feature sets, each representing a certain aspect of life and corresponding to a specific level of information: personality , individual , network and combined .

3.2.1 Personality features

The personality features contain 16 individual personality traits obtained from questionnaires that the study participants had to fill in before receiving a phone.

3.2.2 Individual features

The individual feature set combines the 16 personality traits with behavioral and personal variables. Behavioral variables include average class attendance and the Facebook activity level (log of average number of posts per week). In terms of personal information, we added the students’ gender and their study year to the feature set. Information about the sociological background of the students was not available to us.

3.2.3 Network features

For the network features we consider metrics from five different networks, each based on a different channel (texts, calls, proximity, Facebook interactions, and Facebook friendships). Despite the large number of possible features to extract from networks, we considered only the metrics that follow the main approaches found in the literature, such as the mean GPA of peers, centrality, and the fraction of low and high performing peers. However, further aspects, such as deviation, skewness, or entropy of peers’ GPAs, would undoubtedly be interesting for future investigations.

The structure of the interaction networks provide further insight into how students’ position in their social environment is correlated with performance. Therefore, we evaluated different centrality measures. Footnote 1 Overall, the degree centrality displayed the strongest correlation and was therefore used as feature in our analyses.

3.2.4 Combined features

The combined feature set contains all 20 individual features and all 20 network features yielding a total of 40 features. See Table  1 for a complete list of features in each category. More details including descriptive statistics can be found in Section E of Additional file 1 .

3.3 Approach

We use machine learning techniques to evaluate the importance of different factors on the academic performance of students. Specifically, we create supervised learning models and evaluate their performance on classifying students as low, moderate, or high performers. This framework allows us to compare our results to related work, in particular, the works by Vandamme et al. [ 13 ] and Nghe et al. [ 15 ]. Furthermore, this approach makes it easier to detect significant differences between the individual performance groups. In contrast to classical statistical modeling with test of significance, machine learning uses a hypothesis-free approach that allows us to model complex interactions driven by the data [ 100 ]. We evaluate the model performance based on the mean classification accuracy of 100 independent 10-fold cross-validations.

A key point to emphasize here is that while classifying students’ performance levels based on current behavior might be useful in a practical context (for example to identify students in need of extra support), it is not our primary reason for using machine learning in the current study. Rather, we use machine learning as a tool for ranking and comparing features. That is, the more predictive a given feature is, the more important it is for describing performance. By training our models on features arising from many categories, previously only studied independently, we can begin to understand their relative importance, as well as their interplay in terms of academic performance.

The following results are reported in three stages. First, we perform an ANOVA F-test on all features to identify the most important features for dividing students into performance groups. Then we utilize supervised learning models to investigate the importance and interplay of the different feature categories. Based on the results of the first two stages, we then conduct an in-depth analysis of the most expressive impact factors of each category. Our primary focus is on the social behavioral features which have only been considered to a limited extent in previous studies.

4.1 Analysis of variance

Figure  2 shows the feature importance for features achieving significance of \(p < 0.001\) obtained from an ANOVA F-test. Footnote 2 Although all feature categories are correlated with academic performance, the result indicates that features which describe the social networks of students have the highest explanatory power. In general, network properties dominate the results with more than half of the significant features corresponding to this category. A potential explanation for the high impact of social relations is that the network connections may act as a proxy for previous performance, since the network features include information on the grades of others. The fraction of low performing peers as well as the mean GPA of peers contacted over text messages and calls display the highest explanatory power. Footnote 3 Class attendance proves to be the most important individual feature and moreover, overall the most important one if we had no information on anyone’s grades. Centrality in the proximity network is also found to be a significant descriptor with moderate importance. Among personality traits, only self-esteem and conscientiousness have significant explanatory power.

Feature importance ranking. Results from ANOVA F-test for 3-class classification. Features which did not achieve sufficient significance ( \(p \geq0.001\) ) are omitted

4.2 Supervised learning

In order to better understand the importance and interplay of different factors on the academic performance we utilized supervised learning techniques. We created models based on the different feature sets to classify the students as low, moderate, and high performers according to their GPAs. Each of those three groups contains the same number of students, corresponding to a baseline accuracy of 33.33%.

We use Linear Discriminant Analysis (LDA) to find an optimal model that separates the three performance classes. Figure  3 illustrates the mean results of 100 independent 10-fold cross-validations. The results show that the LDA model solely based on personality features exceeds the baseline performance by about 9 pps. Adding the four additional individual features (behavior + background info) improves the model’s performance by further 5.2 pps. Using network features instead of individual features results in a performance of about 19 pps above baseline. Combining individual and network features yields a superior model with about 57.9% accuracy; roughly 25 pps above baseline. Figure  4 shows its achieved in-class precision and recall values along with the corresponding \(F_{1}\) values. As the results indicate, once the GPA class is provided, the model has high predictive power among the low and high performers (compared to that of the moderate performers) with \(F_{1}\) values of 0.649 and 0.626, respectively.

Model performances on the different feature sets. Bars show the classification accuracy of the different LDA models

Precision-recall curve. Dots represent the model performance in the low (red), moderate (green) and high (blue) performer classes. Dashed lines mark the profile of constant \(F_{1}\) corresponding to the measured values for the specific class

4.3 Feature analysis

4.3.1 individual behavior.

Among the considered individual effects, class attendance was found to have the highest impact on academic performance. A correlation coefficient of \(r_{S} = 0.294\) for cumulative GPAs was determined ( \(p < 0.001\) ). An in-depth analysis of the observed class attendance patterns along with a detailed description of the method to measure attendance in the CNS dataset is discussed in [ 20 ].

The Facebook activity level measures the average number of published posts. Since the activity levels change significantly over time we consider each semester separately and use the corresponding term GPAs as measure for academic performance. This gives us up to four data points per student (one for each semester of the data collection period) for this analysis. In Fig.  5 students are divided into three groups of equal size according to their activity levels. As Fig.  5 (a) shows, the distribution of posts among students is heavy-tailed and is described by the vast majority of the students having less than 3 posts in a typical week. The distribution of term GPA values in the different tertiles reveals that, on average, students with lower activity perform better (see Fig.  5 (b)). To statistically evaluate the variation in the distribution over the different tertiles, we performed a Kruskal–Wallis H-test. This test rejected the global null hypothesis with \(p<0.001\) that the medians of the groups are all equal. A follow-up Dunn multiple comparison test with Bonferroni correction revealed pair-wise differences among the tertiles: all pairs are significantly different from each other ( \(p<0.001\) ). Thus, groups with different levels of Facebook activity have significantly different academic performances.

Facebook usage and performance in the tertiles. ( a ) Division of students into three groups of equal size according to their active Facebook updates. Each box represents a single tertile, width corresponds to the span of Facebook activity in the specific group and the x -position shows the mean term GPA. ( b ) Grade distribution inside each Facebook activity class

4.3.2 Social interactions

Based on the results presented in Fig.  2 and Fig.  3 we conclude that a student’s performance can be accurately inferred from the achievements of their peers. This effect was consistently observed across different communication and interaction channels, as shown in Fig.  6 . There, each channel is represented by a separate line illustrating the mean correlation of the members of each performance group and their respective peers. We can observe that regardless of the channel considered, each curve shows a strong increasing trend. This is further quantified in Table  2 which displays the corresponding correlation coefficients on the individual level. The most pronounced effect is observed for calls and text messages, which are considered to be proxies for strong social ties because this type of connection requires effort to initiate and maintain [ 101 ].

Similarity in academic performance for social ties. Curves show the mean GPAs of every performance group and their peers from different communication channels

Interestingly, these channels are not dominant in the case of centrality measures. Here, proximity interactions displayed the strongest correlation among all channels. However, we found weak to moderate positive correlations in all social networks, in agreement with the existing literature [ 85 – 90 ].

We further assessed the validity of pairwise similarity in the network by focusing exclusively on social ties based on text messages. Figure  7 shows a scatter plot of the correlation between the own GPA and mean GPA of the texting peers for every student in the dataset. Once again, we observe a clear linear trend; the trend is especially strong in the region where the majority of the students is located (GPAs in the range between 2 and 3). In Fig.  8 we divided the population into tertiles based on the GPA and calculated the fraction of text messages exchanged with members of the different groups. Beyond the correlation, we can see that the students’ communication in each group is dominated by members of the same group. This observation further underlines the importance of the social environment for academic success.

Correlation between performance of strong peers. For each student, we show their cumulative GPA versus the mean GPA of their peers obtained by their text messages. Color denotes density of points in arbitrary units

Own academic performance and peers’ academic performance. Each histogram displays how students distribute their text messages exchanged with others over the various performance groups. Groups are defined by tertiles based on their cumulative GPA

5 Discussion

For the participants of the CNS, we found that the peers’ academic performance has a strong explanatory power for academic performance of individuals. We observed this effect across different channels of social interactions with calls and text messages showing the strongest correlations, further emphasizing the phenomena. As mentioned in the literature review, this effect could be caused by either peer effects (adaption) or homophily (selection). It should be noted that GPA information is used here as target and, in aggregated form, also as network feature. This allows us to analyze and understand the relationships among peers; but should be taken into account when framing the problem as prediction task.

We found network centrality to have a positive correlation with academic performance, in agreement with the literature [ 85 – 90 ]. However, among all types of interaction networks, only proximity networks exhibited a strong effect. A possible limitation in measuring centrality is that the mere physical proximity of two individuals does not necessarily involve direct communication. Nevertheless, it is reasonable to expect an increased level of information exchange in a group of individuals if they are in close proximity, which was the case in our dataset. Footnote 4

Consistent with findings in existing literature, we found that class attendance showed the strongest correlation with academic performance when we consider only individual effects [ 16 , 18 , 19 , 102 – 106 ]. We also found that Facebook activity has a negative relation to academic performance—also in agreement with the majority of the studies that investigated Facebook and social media usage [ 62 – 69 ]. We note, however, that our the data is limited to Facebook activities such as posting a status update or uploading a picture etc, and that we have no information regarding ‘passive’ Facebook usage, such as scrolling and reading. Also, our data does not include direct messages which may constitute a relevant fraction of communications performed via the social network site.

The analysis of the different personality traits revealed that two characteristics, namely conscientiousness and self-esteem, have considerable explanatory power for academic success. These two traits reached a correlation coefficient between 0.2 and 0.3 corresponding to the upper limit achievable for any correlation with a personality trait, according to Mischel [ 107 ]. The impact of other investigated characteristics could not be confirmed with proper significance. These results agree with existing literature [ 24 – 53 ].

In the supervised learning experiment we achieved a classification accuracy of around 25 percentage points above baseline, a result similar to that of Vandamme et al. [ 13 ] While the classification accuracy is similar, comparing our results with theirs is difficult because of the very different feature sets and experimental setups. Vandamme et al. [ 13 ] use nearly ten times as many features to build a model as we did. In addition, the accuracy of Vandamme et al. [ 13 ] is driven by using prior achievement (grades), which is known to be a strong predictor of performance (e.g. due to persistence of skill and motivation). We note here that a potential reason for the similarity in performance to Vandamme et al. [ 13 ] could be that the network features used in our study include the grades of others in the network. Thus, if the network homophily with respect to academic performance is sufficiently strong, the average performance of others could serve as a proxy for each individual’s academic achievements.

Networks originating from different channels were treated separately because each network provides different information. For future studies it could be interesting to combine them and create multiplex network models which capture interactions across multiple channels and provide more information about the actual tie strength.

In summary, our findings—together with the results in the literature—emphasize that there is a considerable dependence of academic performance on personality and social environment. This experiment is by no means an attempt to be exhaustive of the possibilities for impact factors. Rather, we hope that this demonstration will stir interest to further study the impact of the social environment on academic success, as well as the interplay of individual and network factors.

5.1 Limitations

Although we utilized wider and more detailed data than most other studies, our approach also has important limitations which need to be taken into account. First, we only observed students from a single, technical, Danish university. For this reason, the findings may not be generalizable to students at other institutions, of other academic disciplines or with other demographics. Furthermore, only a subset of all the students at DTU participated in our study—for first year students the rate was around 40%. Although we observed a high degree of variation with respect to behavioral and network measures as well as academic performance, our sample may not be representative of the whole student population. Our measures of ego-networks and model estimates reflect only the smaller (and not closed) community of students in the CNS within the larger population of students.

Although direct measures overcome a lot of the limitations of surveys and self-reports, they continue to be affected by standard concerns over observational data, including selection bias, information bias, and confounding [ 108 ]. In particular, confounding plays a big role in our study as there are many factors that we were unable to capture but provenly affect the academic performance directly or interplay with other observed factors. For instance, many socio-economic variables have been identified as good predictors for academic achievements [ 109 – 112 ] but unfortunately such data was not available to us. There was also some tendency of selection into the study as the average student in the study tends to achieve higher grades than non-participants [ 113 ]. Furthermore, investigations on the CNS data have revealed, that findings differ slightly for men and women [ 114 ].

Social network observations were limited to phone calls/texts, meetings, and Facebook activities. Although these are arguably some of the most important means of communication, some students may communicate via other smartphone apps. Our method of inferring attendance is also subject to some noise (as thoroughly discussed in [ 20 ]). Furthermore, it does not imply in-class participation nor attention to the taught material.

Although we have identified many factors that correlate with academic performance, we make no claims regarding causality. The question of establishing causality from purely observational data is far from trivial. Thus, while being beyond the scope of this work we consider this question as promising and interesting for future research.

Details on the evaluation can be found in Section C of Additional file 1 .

Note that F-test should not be interpreted literally here, as the assumption of identical independent draws of errors is likely to violated due to correlation of errors in the network. Rather, we use it only as a guide to select features.

The reliability of this observation has been validated by a permutation test—see Section D of Additional file 1 .

The CNS uses (thresholded) Bluetooth visibility as an indicator of person-to-person proximity.

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Kassarnig, V., Mones, E., Bjerre-Nielsen, A. et al. Academic performance and behavioral patterns. EPJ Data Sci. 7 , 10 (2018). https://doi.org/10.1140/epjds/s13688-018-0138-8

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Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level

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Physical activity has been proven to be beneficial for physical and psychological health as well as for academic achievement. However, especially university students are insufficiently physically active because of difficulties in time management regarding study, work, and social demands. As they are at a crucial life stage, it is of interest how physical activity affects university students' stress load and recovery as well as their academic performance.

Student´s behavior during home studying in times of COVID-19 was examined longitudinally on a daily basis during a ten-day study period ( N  = 57, aged M  = 23.5 years, SD  = 2.8, studying between the 1st to 13th semester ( M  = 5.8, SD  = 4.1)). Two-level regression models were conducted to predict daily variations in stress load, recovery and perceived academic performance depending on leisure-time physical activity and short physical activity breaks during studying periods. Parameters of the individual home studying behavior were also taken into account as covariates.

While physical activity breaks only positively affect stress load (functional stress b = 0.032, p  < 0.01) and perceived academic performance (b = 0.121, p  < 0.001), leisure-time physical activity affects parameters of stress load (functional stress: b = 0.003, p  < 0.001, dysfunctional stress: b = -0.002, p  < 0.01), recovery experience (b = -0.003, p  < 0.001) and perceived academic performance (b = 0.012, p  < 0.001). Home study behavior regarding the number of breaks and longest stretch of time also shows associations with recovery experience and perceived academic performance.

Conclusions

Study results confirm the importance of different physical activities for university students` stress load, recovery experience and perceived academic performance in home studying periods. Universities should promote physical activity to keep their students healthy and capable of performing well in academic study: On the one hand, they can offer opportunities to be physically active in leisure time. On the other hand, they can support physical activity breaks during the learning process and in the immediate location of study.

Peer Review reports

Introduction

Physical activity (PA) takes a particularly key position in health promotion and prevention. It reduces risks for several diseases, overweight, and all-cause mortality [ 1 ] and is beneficial for physical, psychological and social health [ 2 , 3 , 4 , 5 ] as well as for academic achievement [ 6 , 7 ]. However, PA levels decrease from childhood through adolescence and into adulthood [ 8 , 9 , 10 ]. Especially university students are insufficiently physically active according to health-oriented PA guidelines [ 11 ] because of academic workloads as well as difficulties in time management regarding study, work, and social demands [ 12 ]. Due to their independence and increasing self-responsibility, university students are at a crucial life stage. In this essential and still educational stage of the students´ development, it is important to study their PA behavior. Furthermore, PA as health behavior represents one influencing factor which is considered in the analytical framework of the impact of health and health behaviors on educational outcomes which was developed by the authors Suhrcke and de Paz Nieves [ 13 , 14 ]. In light of this, the present study examines how PA affects university students' academic situations.

Along with the promotion of PA, the reduction of sedentary behavior has also become a crucial part of modern health promotion and prevention strategies. Spending too much time sitting increases many health risks, including the risk of obesity [ 15 ], diabetes [ 16 ] and other chronic diseases [ 15 ], damage to muscular balances, bone metabolism and musculoskeletal system [ 17 ] and even early death [ 15 ]. University students are a population that has shown the greatest increase in sedentary behavior over the last two decades [ 18 ]. In Germany, they show the highest percentage of sitting time among all working professional groups [ 19 ]. Long times sitting in classes, self-study learning, and through smartphone use, all of which are connected to the university setting and its associated behaviors, might be the cause of this [ 20 , 21 ]. This goes along with technological advances which allow students to study in the comfort of their own homes without changing locations [ 22 ].

To counter a sedentary lifestyle, PA is crucial. In addition to its physical health advantages, PA is essential for coping with the intellectual and stress-related demands of academic life. PA shows positive associations with stress load and academic performance. It is positively associated with learning and educational success [ 6 ] and even shows stress-regulatory potential [ 23 ]. In contrast, sedentary behavior is associated with lower cognitive performance [ 24 ]. Moreover, theoretical derivations show that too much sitting could have a negative impact on brain health and diminish the positive effects of PA [ 16 ]. Given the theoretical background of the stressor detachment model [ 25 ] and the cybernetic approach to stress management in the workplace [ 26 ], PA can promote recovery experience, it can enhance academic performance, and it is a way to reduce the impact of study-related stressors on strain. Load-related stress response can be bilateral: On the one hand, it can be functional if it is beneficial to help cope with the study demands. On the other hand, it can be dysfunctional if it puts a strain on personal resources and can lead to load-related states of strain [ 27 ]. Thus, both, the promotion of PA and reduction of sedentary behavior are important for stress load, recovery, and performance in student life, which can be of particular importance for students in an academic context.

A simple but (presumably) effective way to integrate PA and reduce sedentary behavior in student life are short PA breaks. Due to the exercises' simplicity and short duration, students can perform them wherever they are — together in a lecture or alone at home. Short PA breaks could prevent an accumulation of negative stressors during the day and can help with prolonged sitting as well as inactivity. Especially in the university setting, evidence of the positive effects of PA breaks exists for self-perceived physical and psychological well-being of the university students [ 28 ]. PA breaks buffer university students’ perceived stress [ 29 ] and show positive impacts on recovery need [ 30 ] and better mood ratings [ 31 , 32 ]. In addition, there is evidence for reduction in tension [ 30 ], overall muscular discomfort [ 33 ], daytime sleepiness or fatigue [ 33 , 34 ] and increase in vigor [ 34 ] and experienced energy [ 30 ]. This is in line with cognitive, affective, behavioral, and biological effects of PA, all categorized as palliative-regenerative coping strategies, which addresses the consequences of stress-generating appraisal processes aiming to alleviate these consequences (palliative) or restore the baseline of the relevant reaction parameter (regenerative) [ 35 , 36 ]. This is achieved by, for example, reducing stress-induced cortisol release or tension through physical activity (reaction reduction) [ 35 ]. Such mechanisms are also in accordance with the previously mentioned stressor detachment model [ 25 ]. Lastly, there is a health-strengthening effect that impacts the entire stress-coping-health process, relying on the compensatory effects of PA which is in accordance to the stress-buffering effect of exercise [ 37 ]. Health, in turn, effects educational outcomes [ 13 , 14 ]. Therefore, stress regulating effects are also accompanied with the before mentioned analytical framework of the impact of health and health behaviors on educational outcomes [ 13 , 14 ].

Focusing on the effects of PA, this study is guided by an inquiry into how PA affects university students' stress load and recovery as well as their perceived academic performance. For that reason, the student´s behavior during home studying in times of COVID-19 is examined, a time in which reinforced prolonged sitting, inactivity, and a negative stress load response was at a high [ 38 , 39 , 40 , 41 , 42 ]. Looking separately on the relation of PA with different parameters based on the mentioned evidence, we assume that PA has a positive impact on stress load, recovery, and perceived academic performance-related parameters. Furthermore, a side effect of the home study behavior on the mentioned parameters is assumed regarding the accumulation of negative stressors during home studying. These associations are presented in Fig.  1 and summarized in the following hypotheses:

Overview of the assumed effects and investigated hypotheses of physical activity (PA) behavior on variables of stress load and recovery and perceived academic performance-related parameters

Hypothesis 1 (path 1): Given that stress load always occurs as a duality—beneficial if it is functional for coping, or exhausting if it puts a strain on personal resources [ 27 ] – we consider two variables for stress load: functional stress and dysfunctional stress. In order to reduce the length of the daily surveys, we focused the measure of recovery only on the most obvious and accessible component of recovery experience, namely psychological detachment. PA (whether performed in leisure-time or during PA breaks) encourages functional stress and reduce dysfunctional stress (1.A) and has a positive effect on recovery experience through psychological detachment (1.B).

Hypothesis 2 (path 2): The academic performance-related parameters attention difficulties and study ability are positively influenced by PA (whether done in leisure-time or during PA breaks). We have chosen to assess attention difficulties for a cognitive parameter because poor control over the stream of occurring stimuli have been associated with impairment in executive functions or academic failure [ 43 , 44 , 45 , 46 ]. Furthermore, we have assessed the study ability to refer to the self-perceived feeling of functionality regarding the demands of students. PA reduces self-reported attention difficulties (2.A) and improves perceived study ability, indicating that a student feels capable of performing well in academic study (2.B).

Hypothesis 3: We assume that a longer time spent on studying at home (so called home studying) could result in higher accumulation of stressors throughout the day which could elicit immediate stress responses, while breaks in general could reduce the influence of work-related stressors on strain and well-being [ 47 , 48 ]. Therefore, the following covariates are considered for secondary effects:

the daily longest stretch of time without a break spent on home studying

the daily number of breaks during home studying

Study setting

The study was carried out during the COVID-19 pandemic containment phase. It took place in the middle of the lecture period between 25th of November and 4th of December 2020. Student life was characterized by home studying and digital learning. A so called “digital semester” was in effect at the University of Tübingen when the study took place. Hence, courses were mainly taught online (e.g., live or via a recorded lecture). Other events and actions at the university were not permitted. As such, the university sports department closed in-person sports activities. For leisure time in general, there were contact restrictions (social distancing), the performance of sports activities in groups was not permitted, and sports facilities were closed.

Thus, the university sports department of the University of Tübingen launched various online sports courses and the student health management introduced an opportunity for a new digital form of PA breaks. This opportunity provided PA breaks via videos with guided physical exercises and health-promoting explanations for a PA break for everyday home studying: the so called “Bewegungssnack digital” [in English “exercise snack digital” (ESD)] [ 49 ]. The ESD videos took 5–7 min and were categorized into three thematic foci: activation, relaxation, and coordination. Exercises were demonstrated by one or two student exercise leaders, accompanied by textual descriptions of the relevant execution features of each exercise.

Participants

Participants were recruited within the framework of an intervention study, which was conducted to investigate whether a digital nudging intervention has a beneficial effect on taking PA breaks during home study periods [ 49 ]. Students at the University of Tübingen which counts 27,532 enrolled students were approached for participation through a variety of digital means: via an email sent to those who registered for ESD course on the homepage of the university sports department and to all students via the university email distribution list; via advertisement on social media of the university sports department (Facebook, Instagram, YouTube, homepage). Five tablets, two smart watches, and one iPad were raffled off to participants who engaged actively during the full study period in an effort to motivate them to stick with it to the end. In any case, participants knew that the study was voluntary and that they would not suffer any personal disadvantages should they opt out. There was a written informed consent prompt together with a prompt for the approval of the data protection regulations immediately within the first questionnaire (T0) presented in a mandatory selection field. Positive ethical approval for the study was given by the first author´s institution´s ethics committee of the faculty of the University of Tübingen.

Participants ( N  = 57) who completed the daily surveys on at least half of the days of the study period, were included in the sample (male = 6, female = 47, diverse = 1, not stated = 3). As not all subjects provided data on all ten study days, the total number of observations was between 468 and 540, depending on the variable under study (see Table  1 ). The average number of observations per subject was around eight. Their age was between 18 and 32 years ( M  = 23.52, SD  = 2.81) and they were studying between the 1st to 13th semester ( M  = 5.76, SD  = 4.11) within the following major courses of study: mathematical-scientific majors (34.0%), social science majors (22.6%), philosophical majors (18.9%), medicine (13.2%), theology (5.7%), economics (3.8%), or law (1.9%). 20.4% of the students had on-site classroom teaching on university campus for at least one day a week despite the mandated digital semester, as there were exceptions for special forms of teaching.

Design and procedures

To examine these hypothesized associations, a longitudinal study design with daily surveys was chosen following the suggestion of the day-level study of Feuerhahn et al. (2014) and also of Sonnentag (2001) measuring recovery potential of (exercise) activities during leisure time [ 50 , 51 ]. Considering that there are also differences between people at the beginning of the study period, initial base-line value variables respective to the outcomes measured before the study period were considered as independent covariates. Therefore, the well-being at baseline serves as a control for stress load (2.A), the psychological detachment at baseline serves as a control for daily psychological detachment (2.B), the perception of study demands serves as a control for self-reported attention difficulties (1.A), and the perceived study ability at baseline serves as a control for daily study ability (2.B).

Subjects were asked to continue with their normal home study routine and additionally perform ESD at any time in their daily routine. Data were collected one to two days before (T0) as well as daily during the ten-day study period (Wednesday to Friday). The daily surveys (t 1 -t 10 ) were sent by email at 7 p.m. every evening. Each day, subjects were asked to answer questions about their home studying behavior, study related requirements, recovery experience from study tasks, attention, and PA, including ESD participation. The surveys were conducted online using the UNIPARK software and were recorded and analyzed anonymously.

Measures and covariates

In total, five outcome variables, two independent variables, and seven covariates were included in different analyses: three variables were used for stress load and recovery parameters, two variables for academic performance-related parameters, two variables for PA behavior, two variables for study behavior, four variables for outcome specific baseline values and one variable for age.

Outcome variables

Stress load & recovery parameters (hypothesis 1).

Stress load was included in the analysis with two variables: functional stress and dysfunctional stress. Followingly, a questionnaire containing a word list of adjectives for the recording of emotions and stress during work (called “Erfassung von Emotionen und Beanspruchung “ in German, also known as EEB [ 52 ]) was used. It is an instrument which were developed and validated in the context of occupational health promotion. The items are based on mental-workload research and the assessment of the stress potential of work organization [ 52 ]. Within the questionnaire, four mental and motivational stress items were combined to form a functional stress scale (energetic, willing to perform, attentive, focused) (α = 0.89) and four negative emotional and physical stress items were combined to form dysfunctional stress scale (nervous, physically tensioned, excited, physically unwell) (α = 0.71). Participants rated the items according to how they felt about home studying in general on the following scale (adjustment from “work” to “home studying”): hardly, somewhat, to some extent, fairly, strongly, very strongly, exceptionally.

Recovery experience was measured via psychological detachment. Therefore, the dimension “detachment” of the Recovery Experience Questionnaire (RECQ [ 53 ]) was adjusted to home studying. The introductory question was "How did you experience your free time (including short breaks between learning) during home studying today?". Students responded to four statements based on the extent to which they agreed or disagreed (not at all true, somewhat true, moderately true, mostly true, completely true). The statements covered subjects such as forgetting about studying, not thinking about studying, detachment from studying, and keeping a distance from student tasks. The four items were combined into a score for psychological detachment (α = 0.94).

Academic performance-related parameters (hypothesis 2)

Attention was assessed via the subscale “difficulty maintaining focused attention performance” of the “Attention and Performance Self-Assessment” (ASPA, AP-F2 [ 54 ]). It contains nine items with statements about disturbing situations regarding concentration (e.g. “Even a small noise from the environment could disturb me while reading.”). Participants had to answer how often such situations happened to them on a given day on the following scale: never, rarely, sometimes, often, always. The nine items were combined into the AP-F2 score (α = 0.87).

The perceived study ability was assessed using the study ability index (SAI [ 55 ]). The study ability index captures the current state of perceived functioning in studying. It is based on the Work Ability Index by Hasselhorn and Freude ([ 56 ]) and consists of an adjusted short scale of three adapted items in the context of studying. Firstly, (a) the perceived academic performance was asked after in comparison to the best study-related academic performance ever achieved (from 0 = completely unable to function to 10 = currently best functioning). Secondly, the other two items were aimed at assessing current study-related performance in relation to (b) study tasks that have to be mastered cognitively and (c) the psychological demands of studying. Both items were answered on a five-point Likert scale (1 = very poor, 2 = rather poor, 3 = moderate, 4 = rather good, 5 = very good). A sum index, the SAI, was formed which can indicate values between 2 and 20, with higher values corresponding to higher assessed functioning in studies (α = 0.86). In a previous study it already showed satisfying reliability (α = 0.72) [ 55 ].

Independent variables

Pa behavior.

Two indicators for PA behavior were included via self-reports: the time spent on ESD and the time spent on leisure-time PA (LTPA). Participants were asked the following overarching question daily: “How much time did you spend on physical activity today and in what context”. For the independent variable time spent on PA breaks, participants could answer the option “I participated in the Bewegungssnack digital” with the amount of time they spent on it (in minutes). To assess the time spent on LTPA besides PA breaks, participants could report their time for four different contexts of PA which comprised two forms: Firstly, structured supervised exercise was reported via time spent on (a) university sports courses and (b) other organized sports activities. Secondly, self-organized PA was indicated via (c) independent PA at home, such as a workout or other physically demanding activity such as cleaning or tidying up, as well as via (d) independent PA outside, like walking, cycling, jogging, a workout or something similar. Referring to the different domains of health enhancing PA [ 57 ], the reported minutes of these four types of PA were summed up to a total LTPA value. The total LTPA value was included in the analysis as a metric variable in minutes.

Covariates (hypothesis 3)

Regarding hypothesis 3 and home study behavior, the longest daily stretch of time without a break spent on home studying (in hours) and the daily number of breaks during home studying was assessed. Therein, participants had to answer the overarching question “How much time did you spend on your home studying today?” and give responses to the items: (1) longest stretch of time for home studying (without a break), and (2) number of short and long breaks you took during home studying.

In principle, efforts were made to control for potential confounders at the individual level (level 2) either by including the baseline measure (T0) of the respective variable or by including variables assessing related trait-like characteristics for respective outcomes. The reason why related trait-like characteristics were used for the outcomes was because brief assessments were used for daily surveys that were not concurrently employed in the baseline assessment. To enable the continued use of controlling for person-specific baseline characteristics in the analysis of daily associations, trait-like characteristics available from the baseline assessment were utilized as the best possible approximation.To sum up, four outcome specific baseline value variables were measured before the study period (at T0). The psychological detachment with the RECQ (α = 0.87) [ 53 ] was assessed at the beginning to monitor daily psychological detachment. Further, the SAI [ 55 ] was assessed at the beginning of the study period to monitor daily study ability. To monitor daily stress load, which in part measures mental stress aspects and negative emotional stress aspects, the well-being was assessed at the beginning using the WHO-Five Well-being Index (WHO-5 [ 58 ]). It is a one-dimensional self-report measure with five items. The index value is the sum of all items, with higher values indicating better well-being. As the well-being and stress load tolerance may linked with each other, this variable was assumed to be a good fit with the daily stress load indicating mental and emotional stress aspects. With respect to student life, daily academic performance-related attention was monitored with an instrument for the perception of study demands and resources (termed “Berliner Anforderungen Ressourcen-Inventar – Studierende” in German, the so-called BARI-S [ 59 ]). It contains eight items which capture overwork in studies, time pressure during studies, and the incompatibility of studies and private life. All together they form the BARI-S demand scale (α = 0.85) which was included in the analysis. As overwork and time pressure may result in attention difficulties (e.g. Elfering et al., 2013), this variable was assumed to have a good fit with academic performance-related attention [ 60 ]. Additionally, age in years at T0 was considered as a sociodemographic factor.

Statistical analysis

Since the study design provided ten measurement points for various people, the hierarchical structure of the nested data called for two-level analyses. Pre-analyses of Random-Intercept-Only models for each of the outcome variables (hypothesis 1 to 3) revealed an Intra-Class-Correlation ( ICC ) of at least 0.10 (range 0.26 – 0.64) and confirmed the necessity to perform multilevel analyses [ 61 ]. Specifically, the day-level variables belong to Level 1 (ESD time, LTPA time, longest stretch of time without a break spent on home studying, daily number of breaks during home studying). To analyze day-specific effects within the person, these variables were centered on the person mean (cw = centered within) [ 50 , 62 , 63 , 64 ]. This means that the analyses’ findings are based on a person’s deviations from their average values. The variables assessed at T0 belong to Level 2, which describe the person level (psychological detachment baseline, SAI baseline, well-being, study demands scale, age). These covariates on person level were centered around the grand mean [ 50 ] indicating that the analyses’ findings are based how far an individual deviates from the sample's mean values. As a result, the models’ intercept reflects the outcome value of an average student in the sample at his/her daily average behavior in PA and home study when all parameters are zero. For descriptive statistics SPSS 28.0.1.1 (IBM) and for inferential statistics R (version 4.1.2) were used. The hierarchical models were calculated using the package lme4 with the lmer-function in R in the following steps [ 65 ]. The Null Model was analyzed for all models first, with the corresponding intercept as the only predictor. Afterwards, all variables were entered. The regression coefficient estimates (”b”) were considered for statistical significance for the models and the respective BIC was provided.

In total, five regression models with ‘PA break time’ and ‘LTPA time’ as independent variables were computed due to the five measured outcomes of the present study. Three models belonged to hypothesis 1 and two models to hypothesis 2.

Hypothesis 1: To test hypothesis 1.A two outcome variables were chosen for two separate models: ‘functional stress’ and ‘dysfunctional stress’. Besides the PA behavior variables, the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’, and the ‘well-being’ at the beginning of the study as corresponding baseline variable to the output variable were also included as independent variables in both models. The outcome variable ‘psychological detachment’ was utilized in conjunction with the aforementioned independent variables to test hypotheses 1.B, with one exception: psychological detachment at the start of the study was chosen as the corresponding baseline variable.

Hypothesis 2: To investigate hypothesis 2.A the outcome variable ‘attention difficulties’ was selected. Hypothesis 2.B was tested with the outcome variables ‘study ability’. Both models included both PA behavior variables as well as the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’ and one corresponding baseline variable each: the ‘study demand scale’ at the start of the study for ‘attention difficulties’ and the ‘SAI’ at the beginning of the study for the daily ‘study ability’.

Hypothesis 3: In addition to both PA behavior variables, age and one baseline variable that matched the outcome variable, the covariates ‘daily longest stretch of time spent on home studying’ and ‘daily number of breaks during home studying’ were included in the models for all five outcome variables.

Handling missing data

The dataset had up to 18% missing values (most exhibit the variables ‘daily longest stretch of time without a break spent on home studying’ with 17.89% followed by ‘daily number of breaks during homes studying’ with 16.67%, and ‘functional / dysfunctional stress’ with 12.45%). Therefore, a sensitivity analysis was performed using the multiple imputation mice-package in the statistical program R [ 66 ], the package howManyImputation based on Von Hippel (2020, [ 67 ]), and the additional broom package [ 68 ]. The results of the models remained the same, with one exception for the Attention Difficulties Model: The daily longest stretch of time without a break spent on home studying showed a significant association (Table  1 in supplement). Due to this almost perfect consistency of results between analyses based on the dataset with missing data and those with imputed data alongside the lack of information provided by the packages for imputed datasets, we decided to stick with the main analysis including the missing data. Thus, in the following the results of the main analysis without imputations are presented.

Table 1 shows the descriptive statistics of the variables used in the analysis. An overview of the analysed models is presented in Table  2 .

Effects on stress load and recovery (hypothesis 1)

Hypothesis 1.A: The Model Functional Stress explained 13% of the variance by fixed factors (marginal R 2  = 0.13), and 52% by both fixed and random factors (conditional R 2  = 0.52). The time spent on ESD as well as the time spent on PA in leisure showed a positive significant influence on functional stress (b = 0.032, p  < 0.01). The same applied to LTPA (b = 0.003, p  < 0.001). The Model Dysfunctional Stress (marginal R 2  = 0.027, conditional R 2  = 0.647) showed only one significant result. The dysfunctional stress was only significantly negatively influenced by the time spent on LTPA (b = 0.002, p  < 0.01).

Hypothesis 1.B: With the Model Detachment, fixed factors contributed 18% of the explained variance and fixed and random factors 46% of the explained variance for psychological detachment. Only the amount of time spent on LTPA revealed a positive impact on psychological detachment (b = 0.003, p  < 0.001).

Effects on academic performance-related parameters (hypothesis 2)

Hypothesis 2.A: The Model Attention Difficulties showed 13% of the variance explained by fixed factors, and 51% explained by both fixed and random factors. It showed a significant negative association only for the time spent on LTPA (b = 0.003, p  < 0.001).

Hypothesis 2.B: The Model SAI showed 18% of the variance explained by fixed factors, and 39% explained by both fixed and random factors. There were significant positive associations for time spent on ESD (b = 0.121, p  < 0.001) and time spent on LTPA (b = 0.012, p  < 0.001). The same applied to LTPA (b = 0.012, p  < 0.001).

Effects of home study behavior (hypothesis 3)

Regarding the independent covariates for the outcome variables functional and dysfunctional stress, there were no significant results for the number of breaks during homes studying or the longest stretch of time without a break spent on home studying. Considering the outcome variable ‘psychological detachment’, there were significant results with negative impact for both study behavior variables: breaks during home studying (b = 0.058, p  < 0.01) and daily longest stretch of time without a break (b = 0.120, p  < 0.01). Evaluating the outcome variables ‘attention difficulties’, there were no significant results for the number of breaks during home studying or the longest stretch of time without a break spent on home studying. Testing the independent study behavior variables for the SAI, it increased with increasing number in daily breaks during homes studying relative to the person´s mean (b = 0.183, p  < 0.05). No significant effect was found for the longest stretch of time without a break spent on home studying ( p  = 0.07).

The baseline covariates of the models showed expected associations and thus confirmed their inclusion. The baseline variables well-being showed a significant impact on functional stress (b = 0.089, p  < 0.001), psychological detachment showed a positive effect on the daily output variables psychological detachment (b = 0.471, p  < 0.001), study demand scale showed a positive association on difficulties in attention (b = 0.240, p  < 0.01), and baseline SAI had a positive effect on the daily SAI (b = 0.335, p  < 0.001).

The present study theorized that PA breaks and LTPA positively influence the academic situation of university students. Therefore, impact on stress load (‘functional stress’ and ‘dysfunctional stress’) and ‘psychological detachment’ as well as academic performance-related parameters ‘self-reported attention difficulties’ and ‘perceived study ability’ was taken into account. The first and second hypotheses assumed that both PA breaks and LTPA are positively associated with the aforementioned parameters and were confirmed for LTPA for all parameters and for PA breaks for functional stress and perceived study ability. The third hypothesis assumed that home study behavior regarding the daily number of breaks during home studying and longest stretch of time without a break spent on home studying has side effects. Detected negative effects for both covariates on psychological detachment and positive effects for the daily number of breaks on perceived study ability were partly unexpected in their direction. These results emphasize the key position of PA in the context of modern health promotion especially for students in an academic context.

Regarding hypothesis 1 and the detected positive associations for stress load and recovery parameters with PA, the results are in accordance with the stress-regulatory potential of PA from the state of research [ 23 ]. For hypothesis 1.A, there is a positive influence of PA breaks and LTPA on functional stress and a negative influence of LTPA on dysfunctional stress. Given the bilateral role of stress load, the results indicate that PA breaks and LTPA are beneficial for coping with study demands, and may help to promote feelings of joy, pride, and learning progress [ 27 ]. This is in line with previous evidence that PA breaks in lectures can buffer university students’ perceived stress [ 29 ], lead to better mood ratings [ 29 , 31 ], and increase in motivation [ 28 , 69 ], vigor [ 34 ], energy [ 30 ], and self-perceived physical and psychological well-being [ 28 ]. Looking at dysfunctional stress, the result point that LTPA counteract load-related states of strain such as inner tension, irritability and nervous restlessness or feelings of boredom [ 27 ]. In contrast, short PA breaks during the day could not have enough impact in countering dysfunctional stress at the end of the day regarding the accumulation of negative stressors during home studying which might have occurred after the participant took PA breaks. Other studies have been able to show a reduction in tension [ 30 ] and general muscular discomfort [ 33 ] after PA breaks. However, this was measured as an immediate effect of PA breaks and not with general evening surveys. Blasche and colleagues [ 34 ] measured effects immediately and 20 min after different kind of breaks and found that PA breaks led to an additional short‐ and medium‐term increase in vigor while the relaxation break lead to an additional medium‐term decrease in fatigue compared to an unstructured open break. This is consistent with the results of the present study that an effect of PA breaks is only observed for functional stress and not for dysfunctional stress. Furthermore, there is evidence that long sitting during lectures leads to increased fatigue and lower concentration [ 31 , 70 ], which could be counteracted by PA breaks. For both types of stress loads, functional and dysfunctional stress, there is an influence of students´ well-being in this study. This shows that the stress load is affected by the way students have mentally felt over the last two weeks. The relevance of monitoring this seems important especially in the time of COVID-19 as, for example, 65.3% of the students of a cross-sectional online survey at an Australian university reported low to very low well-being during that time [ 71 ]. However, since PA and well-being can support functional stress load, they should be of the highest priority—not only as regards the pandemic, but also in general.

Looking at hypothesis 1.B; while there is a positive influence of LTPA on experienced psychological detachment, no significant influence for PA breaks was detected. The fact that only LTPA has a positive effect can be explained by the voluntary character of the activity [ 50 ]. The voluntary character ensures that stressors no longer affect the student and, thus, recovery as detachment can take place. Home studying is not present in leisure times, and thus detachment from study is easier. The PA break videos, on the other hand, were shot in a university setting, which would have made it more difficult to detach from study. In order to further understand how PA breaks affect recovery and whether there is a distinction between PA breaks and LTPA, future research should also consider other types of recovery (e.g. relaxation, mastery, and control). Additionally, different types of PA breaks, such as group PA breaks taken on-site versus video-based PA breaks, should be taken into account.

Considering the confirmed positive associations for academic performance-related parameters of hypothesis 2, the results are in accordance with the evidence of positive associations between PA and learning and educational success [ 6 ], as well as between PA breaks and better cognitive functioning [ 28 ]. Looking at the self-reported attention difficulties of hypothesis 2.A, only LTPA can counteract it. PA breaks showed no effects, contrary to the results of a study of Löffler and collegues (2011, [ 31 ]), in which acute effects of PA breaks could be found for higher attention and cognitive performance. Furthermore, the perception of study demands before the study periods has a positive impact on difficulties in attention. That means that overload in studies, time pressure during studies, and incompatibility of studies and private life leads to higher difficulties with attention in home studying. In these conditions, PA breaks might have been seen as interfering, resulting in the expected beneficial effects of exercise on attention and task-related participation behavior [ 72 , 73 ] therefore remaining undetected. With respect to the COVID-19 pandemic, accompanying education changes, and an increase in student´s worries [ 74 , 75 ], the perception of study demands could be affected. This suggests that especially in times of constraint and changes, it is important to promote PA in order to counteract attention difficulties. This also applies to post-pandemic phase.

Regarding the perceived academic performance of hypothesis 2.B, both PA breaks and LTPA have a positive effect on perceived study ability. This result confirms the positive short-term effects on cognition tasks [ 76 ]. It is also in line with the positive function of PA breaks in interrupting sedentary behavior and therefore counteracting the negative association between sitting behavior and lower cognitive performance [ 24 ]. Additionally, this result also fits with the previously mentioned positive relationship between LTPA and functional stress and between PA breaks and functional stress.

According to hypothesis 3, in relation to the mentioned stress load and recovery parameters, there are negative effects of the daily number of breaks during home studying and the longest stretch of time without a break spent on home studying on psychological detachment. As stressors result in negative activation, which impede psychological detachment from study during non-studying time [ 25 ], it was expected and confirmed that the longest stretch of time without a break spent on home studying has a negative effect on detachment. Initially unexpected, the number of breaks has a negative influence on psychological detachment, as breaks could prevent the accumulation of strain reactions. However, if the breaks had no recovery effect through successful detachment, the number might not have any influence on recovery via detachment. This is indicated by the PA breaks, which had no impact on psychological detachment. Since there are other ways to recover from stress besides psychological detachment, such as relaxation, mastery, and control [ 53 ], PA breaks must have had an additional impact in relation to the positive results for functional stress.

In relation to the mentioned academic performance-related parameters, only the number of breaks has a positive influence on the perceived study ability. This indicates that not only PA breaks but also breaks in general lead to better perceived functionality in studying. Paulus and colleagues (2021) found out that an increase in cognitive skills is not only attributed to PA breaks and standing breaks, but also to open breaks with no special instructions [ 28 ]. Either way, they found better improvement in self-perceived physical and psychological well-being of the university students with PA breaks than with open breaks. This is also reflected in the present study with the aforementioned positive effects of PA breaks on functional stress, which does not apply to the number of breaks.

Overall, it must be considered that the there is a more complex network of associations between the examined parameters. The hypothesized separate relation of PA with different parameters do not consider associations between parameters of stress load / recovery and academic performance although there might be a interdependency. Furthermore, moderation aspects were not examined. For example, PA could be a moderator which buffer negative effects of stress on the study ability [ 55 ]. Moreover, perceived study ability might moderate stress levels and academic performance. Further studies should try to approach and understand the different relationships between the parameters in its complexity.

Limitations

Certain limitations must be taken into account. Regarding the imbalanced design toward more female students in the sample (47 female versus 6 male), possible sampling bias cannot be excluded. Gender research on students' emotional states during COVID-19, when this study took place, or students´ acceptance of PA breaks is diverse and only partially supplied with inconsistent findings. For example, during the COVID-19 pandemic, some studies reported that female students were associated with lower well-being [ 71 ] or worse mental health trajectories [ 75 , 77 ]. Another study with a large sample of students from 62 countries reported that male students were more strongly affected by the pandemic because they were significantly less satisfied with their academic life [ 74 ]. However, Keating and colleges (2020) discovered that, despite the COVID-19 pandemic, females rated some aspects of PA breaks during lectures more positively than male students did. However, this was also based on a female slanted sample [ 78 ]. Further studies are needed to get more insights into gender bias.

Furthermore, the small sample size combined with up to 16% missing values comprises a significant short-coming. There were a lot of possibilities which could cause such missing data, like refused, forgotten or missed participation, technical problems, or deviation of the personal code for the questionnaire between survey times. Although the effects could be excluded by sensitive analysis due to missing data, the sample is still small. To generalize the findings, future replication studies are needed.

Additionally, PA breaks were only captured through participation in the ESD, the specially instructed PA break via video. Effects of other short PA breaks were not include in the study. However, participants were called to participate in ESD whenever possible, so the likelihood that they did take part in PA breaks in addition to the ESD could be ignored.

With respect to the baseline variables, it must be considered that two variables (stress load, attention difficulties) were adjusted not with their identical variable in T0, but with other conceptually associated variables (well-being index, BARI-S). Indeed, contrary to the assumption the well-being index does only show an association with functional stress, indicating that it does not control dysfunctional stress. Although the other three assumed associations were confirmed there might be a discrepancy between the daily measured variables and the variables measured in T0. Further studies should either proof the association between these used variables or measure the same variables in T0 for control the daily value of these variables.

Moreover, the measuring instruments comprised the self-assessed perception of the students and thus do not provide an objective information. This must be considered, especially for measuring cognitive and academic-performance-related measures. Here, existing objective tests, such as multiple choice exams after a video-taped lecture [ 72 ] might have also been used. Nevertheless, such methods were mostly used in a lab setting and do not reflect reality. Due to economic reasons and the natural learning environment, such procedures were not applied in this study. However, the circumstances of COVID-19 pandemic allowed a kind of lab setting in real life, as there were a lot of restrictions in daily life which limited the influence of other covariates. The study design provides a real natural home studying environment, producing results that are applicable to the healthy way that students learn in the real world. As this study took place under the conditions of COVID-19, new transformations in studying were also taken into account, as home studying and digital learning are increasingly part of everyday study.

However, the restrictions during the COVID-19 pandemic could result in a greater extent of leisure time per se. As the available leisure time in general was not measured on daily level, it is not possible to distinguish if the examined effects on the outcomes are purely attributable to PA. It is possible that being more physical active is the result of having a greater extent of leisure time and not that PA but the leisure time itself effected the examined outcomes. To address this issue in future studies, it is necessary to measure the proportion of PA in relation to the leisure time available.

Furthermore, due to the retrospective nature of the daily assessments of the variables, there may be overstated associations which must be taken into account. Anyway, the daily level of the study design provides advantages regarding the ability to observe changes in an individual's characteristics over the period of the study. This design made it possible to find out the necessity to analyze the hierarchical structure of the intraindividual data nested within the interindividual data. The performed multilevel analyses made it possible to reflect the outcome of an average student in the sample at his/her daily average behavior in PA and home study.

Conclusion and practical implications

The current findings confirm the importance of PA for university students` stress load, recovery experience, and academic performance-related parameters in home studying. Briefly summarized, it can be concluded that PA breaks positively affect stress load and perceived study ability. LTPA has a positive impact on stress load, recovery experience, and academic performance-related parameters regarding attention difficulties and perceived study ability. Following these results, universities should promote PA in both fashions in order to keep their students healthy and functioning: On the one hand, they should offer opportunities to be physically active in leisure time. This includes time, environment, and structural aspects. The university sport department, which offers sport courses and provides sport facilities on university campuses for students´ leisure time, is one good example. On the other hand, they should support PA breaks during the learning process and in the immediate location of study. This includes, for example, providing instructor videos for PA breaks to use while home studying, and furthermore having instructors to lead in-person PA breaks in on-site learning settings like universities´ libraries or even lectures and seminars. This not only promotes PA, but also reduces sedentary behavior and thereby reduces many other health risks. Further research should focus not only on the effect of PA behavior but also of sedentary behavior as well as the amount of leisure time per se. They should also try to implement objective measures for example on academic performance parameters and investigate different effect directions and possible moderation effects to get a deeper understanding of the complex network of associations in which PA plays a crucial role.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Attention and Performance Self-Assessment

"Berliner Anforderungen Ressourcen-Inventar – Studierende" (instrument for the perception of study demands and resources)

Centered within

Grand centered

“Erfassung von Emotionen und Beanspruchung “ (questionnaire containing a word list of adjectives for the recording of emotions and stress during work)

Exercise snack digital (special physical activity break offer)

Intra-Class-Correlation

Leisure time physical activity

• Physical activity

Recovery Experience Questionnaire

Study ability index

World Health Organization-Five Well-being index

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Acknowledgements

We would like to thank Juliane Moll, research associate of the Student Health Management of University of Tübingen, for the support in the coordination and realization study. We would like to express our thanks also to Ingrid Arzberger, Head of University Sports at the University of Tübingen, for providing the resources and co-applying for the funding. We acknowledge support by Open Access Publishing Fund of University of Tübingen.

Open Access funding enabled and organized by Projekt DEAL. This research regarding the conduction of the study was funded by the Techniker Krankenkasse, health insurance fund.

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M.T. and G.S. designed the study. M.T. coordinated and carried out participant recruitment and data collection. M.T. analyzed the data and M.T. and D.L. interpreted the data. M.T. drafted the initial version of the manuscript and prepared the figure and all tables. All authors contributed to reviewing and editing the manuscript and have read and agreed to the final version of the manuscript.

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Teuber, M., Leyhr, D. & Sudeck, G. Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level. BMC Public Health 24 , 598 (2024). https://doi.org/10.1186/s12889-024-18082-z

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by Julian Koplin, The Conversation

If you search Google Scholar for the phrase " as an AI language model ," you'll find plenty of AI research literature and also some rather suspicious results. For example, one paper on agricultural technology says,

"As an AI language model, I don't have direct access to current research articles or studies. However, I can provide you with an overview of some recent trends and advancements …"

Obvious gaffes like this aren't the only signs that researchers are increasingly turning to generative AI tools when writing up their research. A recent study examined the frequency of certain words in academic writing (such as "commendable," "meticulously" and "intricate"), and found they became far more common after the launch of ChatGPT—so much so that 1% of all journal articles published in 2023 may have contained AI-generated text.

(Why do AI models overuse these words? There is speculation it's because they are more common in English as spoken in Nigeria, where key elements of model training often occur.)

The aforementioned study also looks at preliminary data from 2024, which indicates that AI writing assistance is only becoming more common. Is this a crisis for modern scholarship, or a boon for academic productivity?

Who should take credit for AI writing?

Many people are worried by the use of AI in academic papers. Indeed, the practice has been described as " contaminating " scholarly literature.

Some argue that using AI output amounts to plagiarism. If your ideas are copy-pasted from ChatGPT, it is questionable whether you really deserve credit for them.

But there are important differences between "plagiarizing" text authored by humans and text authored by AI. Those who plagiarize humans' work receive credit for ideas that ought to have gone to the original author.

By contrast, it is debatable whether AI systems like ChatGPT can have ideas, let alone deserve credit for them. An AI tool is more like your phone's autocomplete function than a human researcher.

The question of bias

Another worry is that AI outputs might be biased in ways that could seep into the scholarly record. Infamously, older language models tended to portray people who are female, black and/or gay in distinctly unflattering ways, compared with people who are male, white and/or straight.

This kind of bias is less pronounced in the current version of ChatGPT.

However, other studies have found a different kind of bias in ChatGPT and other large language models : a tendency to reflect a left-liberal political ideology.

Any such bias could subtly distort scholarly writing produced using these tools.

The hallucination problem

The most serious worry relates to a well-known limitation of generative AI systems: that they often make serious mistakes.

For example, when I asked ChatGPT-4 to generate an ASCII image of a mushroom, it provided me with the following output.

It then confidently told me I could use this image of a "mushroom" for my own purposes.

These kinds of overconfident mistakes have been referred to as "AI hallucinations" and " AI bullshit ." While it is easy to spot that the above ASCII image looks nothing like a mushroom (and quite a bit like a snail), it may be much harder to identify any mistakes ChatGPT makes when surveying scientific literature or describing the state of a philosophical debate.

Unlike (most) humans, AI systems are fundamentally unconcerned with the truth of what they say. If used carelessly, their hallucinations could corrupt the scholarly record.

Should AI-produced text be banned?

One response to the rise of text generators has been to ban them outright. For example, Science—one of the world's most influential academic journals—disallows any use of AI-generated text .

I see two problems with this approach.

The first problem is a practical one: current tools for detecting AI-generated text are highly unreliable. This includes the detector created by ChatGPT's own developers, which was taken offline after it was found to have only a 26% accuracy rate (and a 9% false positive rate ). Humans also make mistakes when assessing whether something was written by AI.

It is also possible to circumvent AI text detectors. Online communities are actively exploring how to prompt ChatGPT in ways that allow the user to evade detection. Human users can also superficially rewrite AI outputs, effectively scrubbing away the traces of AI (like its overuse of the words "commendable," "meticulously" and "intricate").

The second problem is that banning generative AI outright prevents us from realizing these technologies' benefits. Used well, generative AI can boost academic productivity by streamlining the writing process. In this way, it could help further human knowledge. Ideally, we should try to reap these benefits while avoiding the problems.

The problem is poor quality control, not AI

The most serious problem with AI is the risk of introducing unnoticed errors, leading to sloppy scholarship. Instead of banning AI, we should try to ensure that mistaken, implausible or biased claims cannot make it onto the academic record.

After all, humans can also produce writing with serious errors, and mechanisms such as peer review often fail to prevent its publication.

We need to get better at ensuring academic papers are free from serious mistakes, regardless of whether these mistakes are caused by careless use of AI or sloppy human scholarship. Not only is this more achievable than policing AI usage, it will improve the standards of academic research as a whole.

This would be (as ChatGPT might say) a commendable and meticulously intricate solution.

Provided by The Conversation

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Cultural Relativity and Acceptance of Embryonic Stem Cell Research

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Main Article Content

There is a debate about the ethical implications of using human embryos in stem cell research, which can be influenced by cultural, moral, and social values. This paper argues for an adaptable framework to accommodate diverse cultural and religious perspectives. By using an adaptive ethics model, research protections can reflect various populations and foster growth in stem cell research possibilities.

INTRODUCTION

Stem cell research combines biology, medicine, and technology, promising to alter health care and the understanding of human development. Yet, ethical contention exists because of individuals’ perceptions of using human embryos based on their various cultural, moral, and social values. While these disagreements concerning policy, use, and general acceptance have prompted the development of an international ethics policy, such a uniform approach can overlook the nuanced ethical landscapes between cultures. With diverse viewpoints in public health, a single global policy, especially one reflecting Western ethics or the ethics prevalent in high-income countries, is impractical. This paper argues for a culturally sensitive, adaptable framework for the use of embryonic stem cells. Stem cell policy should accommodate varying ethical viewpoints and promote an effective global dialogue. With an extension of an ethics model that can adapt to various cultures, we recommend localized guidelines that reflect the moral views of the people those guidelines serve.

Stem cells, characterized by their unique ability to differentiate into various cell types, enable the repair or replacement of damaged tissues. Two primary types of stem cells are somatic stem cells (adult stem cells) and embryonic stem cells. Adult stem cells exist in developed tissues and maintain the body’s repair processes. [1] Embryonic stem cells (ESC) are remarkably pluripotent or versatile, making them valuable in research. [2] However, the use of ESCs has sparked ethics debates. Considering the potential of embryonic stem cells, research guidelines are essential. The International Society for Stem Cell Research (ISSCR) provides international stem cell research guidelines. They call for “public conversations touching on the scientific significance as well as the societal and ethical issues raised by ESC research.” [3] The ISSCR also publishes updates about culturing human embryos 14 days post fertilization, suggesting local policies and regulations should continue to evolve as ESC research develops. [4]  Like the ISSCR, which calls for local law and policy to adapt to developing stem cell research given cultural acceptance, this paper highlights the importance of local social factors such as religion and culture.

I.     Global Cultural Perspective of Embryonic Stem Cells

Views on ESCs vary throughout the world. Some countries readily embrace stem cell research and therapies, while others have stricter regulations due to ethical concerns surrounding embryonic stem cells and when an embryo becomes entitled to moral consideration. The philosophical issue of when the “someone” begins to be a human after fertilization, in the morally relevant sense, [5] impacts when an embryo becomes not just worthy of protection but morally entitled to it. The process of creating embryonic stem cell lines involves the destruction of the embryos for research. [6] Consequently, global engagement in ESC research depends on social-cultural acceptability.

a.     US and Rights-Based Cultures

In the United States, attitudes toward stem cell therapies are diverse. The ethics and social approaches, which value individualism, [7] trigger debates regarding the destruction of human embryos, creating a complex regulatory environment. For example, the 1996 Dickey-Wicker Amendment prohibited federal funding for the creation of embryos for research and the destruction of embryos for “more than allowed for research on fetuses in utero.” [8] Following suit, in 2001, the Bush Administration heavily restricted stem cell lines for research. However, the Stem Cell Research Enhancement Act of 2005 was proposed to help develop ESC research but was ultimately vetoed. [9] Under the Obama administration, in 2009, an executive order lifted restrictions allowing for more development in this field. [10] The flux of research capacity and funding parallels the different cultural perceptions of human dignity of the embryo and how it is socially presented within the country’s research culture. [11]

b.     Ubuntu and Collective Cultures

African bioethics differs from Western individualism because of the different traditions and values. African traditions, as described by individuals from South Africa and supported by some studies in other African countries, including Ghana and Kenya, follow the African moral philosophies of Ubuntu or Botho and Ukama , which “advocates for a form of wholeness that comes through one’s relationship and connectedness with other people in the society,” [12] making autonomy a socially collective concept. In this context, for the community to act autonomously, individuals would come together to decide what is best for the collective. Thus, stem cell research would require examining the value of the research to society as a whole and the use of the embryos as a collective societal resource. If society views the source as part of the collective whole, and opposes using stem cells, compromising the cultural values to pursue research may cause social detachment and stunt research growth. [13] Based on local culture and moral philosophy, the permissibility of stem cell research depends on how embryo, stem cell, and cell line therapies relate to the community as a whole . Ubuntu is the expression of humanness, with the person’s identity drawn from the “’I am because we are’” value. [14] The decision in a collectivistic culture becomes one born of cultural context, and individual decisions give deference to others in the society.

Consent differs in cultures where thought and moral philosophy are based on a collective paradigm. So, applying Western bioethical concepts is unrealistic. For one, Africa is a diverse continent with many countries with different belief systems, access to health care, and reliance on traditional or Western medicines. Where traditional medicine is the primary treatment, the “’restrictive focus on biomedically-related bioethics’” [is] problematic in African contexts because it neglects bioethical issues raised by traditional systems.” [15] No single approach applies in all areas or contexts. Rather than evaluating the permissibility of ESC research according to Western concepts such as the four principles approach, different ethics approaches should prevail.

Another consideration is the socio-economic standing of countries. In parts of South Africa, researchers have not focused heavily on contributing to the stem cell discourse, either because it is not considered health care or a health science priority or because resources are unavailable. [16] Each country’s priorities differ given different social, political, and economic factors. In South Africa, for instance, areas such as maternal mortality, non-communicable diseases, telemedicine, and the strength of health systems need improvement and require more focus. [17] Stem cell research could benefit the population, but it also could divert resources from basic medical care. Researchers in South Africa adhere to the National Health Act and Medicines Control Act in South Africa and international guidelines; however, the Act is not strictly enforced, and there is no clear legislation for research conduct or ethical guidelines. [18]

Some parts of Africa condemn stem cell research. For example, 98.2 percent of the Tunisian population is Muslim. [19] Tunisia does not permit stem cell research because of moral conflict with a Fatwa. Religion heavily saturates the regulation and direction of research. [20] Stem cell use became permissible for reproductive purposes only recently, with tight restrictions preventing cells from being used in any research other than procedures concerning ART/IVF.  Their use is conditioned on consent, and available only to married couples. [21] The community's receptiveness to stem cell research depends on including communitarian African ethics.

c.     Asia

Some Asian countries also have a collective model of ethics and decision making. [22] In China, the ethics model promotes a sincere respect for life or human dignity, [23] based on protective medicine. This model, influenced by Traditional Chinese Medicine (TCM), [24] recognizes Qi as the vital energy delivered via the meridians of the body; it connects illness to body systems, the body’s entire constitution, and the universe for a holistic bond of nature, health, and quality of life. [25] Following a protective ethics model, and traditional customs of wholeness, investment in stem cell research is heavily desired for its applications in regenerative therapies, disease modeling, and protective medicines. In a survey of medical students and healthcare practitioners, 30.8 percent considered stem cell research morally unacceptable while 63.5 percent accepted medical research using human embryonic stem cells. Of these individuals, 89.9 percent supported increased funding for stem cell research. [26] The scientific community might not reflect the overall population. From 1997 to 2019, China spent a total of $576 million (USD) on stem cell research at 8,050 stem cell programs, increased published presence from 0.6 percent to 14.01 percent of total global stem cell publications as of 2014, and made significant strides in cell-based therapies for various medical conditions. [27] However, while China has made substantial investments in stem cell research and achieved notable progress in clinical applications, concerns linger regarding ethical oversight and transparency. [28] For example, the China Biosecurity Law, promoted by the National Health Commission and China Hospital Association, attempted to mitigate risks by introducing an institutional review board (IRB) in the regulatory bodies. 5800 IRBs registered with the Chinese Clinical Trial Registry since 2021. [29] However, issues still need to be addressed in implementing effective IRB review and approval procedures.

The substantial government funding and focus on scientific advancement have sometimes overshadowed considerations of regional cultures, ethnic minorities, and individual perspectives, particularly evident during the one-child policy era. As government policy adapts to promote public stability, such as the change from the one-child to the two-child policy, [30] research ethics should also adapt to ensure respect for the values of its represented peoples.

Japan is also relatively supportive of stem cell research and therapies. Japan has a more transparent regulatory framework, allowing for faster approval of regenerative medicine products, which has led to several advanced clinical trials and therapies. [31] South Korea is also actively engaged in stem cell research and has a history of breakthroughs in cloning and embryonic stem cells. [32] However, the field is controversial, and there are issues of scientific integrity. For example, the Korean FDA fast-tracked products for approval, [33] and in another instance, the oocyte source was unclear and possibly violated ethical standards. [34] Trust is important in research, as it builds collaborative foundations between colleagues, trial participant comfort, open-mindedness for complicated and sensitive discussions, and supports regulatory procedures for stakeholders. There is a need to respect the culture’s interest, engagement, and for research and clinical trials to be transparent and have ethical oversight to promote global research discourse and trust.

d.     Middle East

Countries in the Middle East have varying degrees of acceptance of or restrictions to policies related to using embryonic stem cells due to cultural and religious influences. Saudi Arabia has made significant contributions to stem cell research, and conducts research based on international guidelines for ethical conduct and under strict adherence to guidelines in accordance with Islamic principles. Specifically, the Saudi government and people require ESC research to adhere to Sharia law. In addition to umbilical and placental stem cells, [35] Saudi Arabia permits the use of embryonic stem cells as long as they come from miscarriages, therapeutic abortions permissible by Sharia law, or are left over from in vitro fertilization and donated to research. [36] Laws and ethical guidelines for stem cell research allow the development of research institutions such as the King Abdullah International Medical Research Center, which has a cord blood bank and a stem cell registry with nearly 10,000 donors. [37] Such volume and acceptance are due to the ethical ‘permissibility’ of the donor sources, which do not conflict with religious pillars. However, some researchers err on the side of caution, choosing not to use embryos or fetal tissue as they feel it is unethical to do so. [38]

Jordan has a positive research ethics culture. [39] However, there is a significant issue of lack of trust in researchers, with 45.23 percent (38.66 percent agreeing and 6.57 percent strongly agreeing) of Jordanians holding a low level of trust in researchers, compared to 81.34 percent of Jordanians agreeing that they feel safe to participate in a research trial. [40] Safety testifies to the feeling of confidence that adequate measures are in place to protect participants from harm, whereas trust in researchers could represent the confidence in researchers to act in the participants’ best interests, adhere to ethical guidelines, provide accurate information, and respect participants’ rights and dignity. One method to improve trust would be to address communication issues relevant to ESC. Legislation surrounding stem cell research has adopted specific language, especially concerning clarification “between ‘stem cells’ and ‘embryonic stem cells’” in translation. [41] Furthermore, legislation “mandates the creation of a national committee… laying out specific regulations for stem-cell banking in accordance with international standards.” [42] This broad regulation opens the door for future global engagement and maintains transparency. However, these regulations may also constrain the influence of research direction, pace, and accessibility of research outcomes.

e.     Europe

In the European Union (EU), ethics is also principle-based, but the principles of autonomy, dignity, integrity, and vulnerability are interconnected. [43] As such, the opportunity for cohesion and concessions between individuals’ thoughts and ideals allows for a more adaptable ethics model due to the flexible principles that relate to the human experience The EU has put forth a framework in its Convention for the Protection of Human Rights and Dignity of the Human Being allowing member states to take different approaches. Each European state applies these principles to its specific conventions, leading to or reflecting different acceptance levels of stem cell research. [44]

For example, in Germany, Lebenzusammenhang , or the coherence of life, references integrity in the unity of human culture. Namely, the personal sphere “should not be subject to external intervention.” [45]  Stem cell interventions could affect this concept of bodily completeness, leading to heavy restrictions. Under the Grundgesetz, human dignity and the right to life with physical integrity are paramount. [46] The Embryo Protection Act of 1991 made producing cell lines illegal. Cell lines can be imported if approved by the Central Ethics Commission for Stem Cell Research only if they were derived before May 2007. [47] Stem cell research respects the integrity of life for the embryo with heavy specifications and intense oversight. This is vastly different in Finland, where the regulatory bodies find research more permissible in IVF excess, but only up to 14 days after fertilization. [48] Spain’s approach differs still, with a comprehensive regulatory framework. [49] Thus, research regulation can be culture-specific due to variations in applied principles. Diverse cultures call for various approaches to ethical permissibility. [50] Only an adaptive-deliberative model can address the cultural constructions of self and achieve positive, culturally sensitive stem cell research practices. [51]

II.     Religious Perspectives on ESC

Embryonic stem cell sources are the main consideration within religious contexts. While individuals may not regard their own religious texts as authoritative or factual, religion can shape their foundations or perspectives.

The Qur'an states:

“And indeed We created man from a quintessence of clay. Then We placed within him a small quantity of nutfa (sperm to fertilize) in a safe place. Then We have fashioned the nutfa into an ‘alaqa (clinging clot or cell cluster), then We developed the ‘alaqa into mudgha (a lump of flesh), and We made mudgha into bones, and clothed the bones with flesh, then We brought it into being as a new creation. So Blessed is Allah, the Best of Creators.” [52]

Many scholars of Islam estimate the time of soul installment, marked by the angel breathing in the soul to bring the individual into creation, as 120 days from conception. [53] Personhood begins at this point, and the value of life would prohibit research or experimentation that could harm the individual. If the fetus is more than 120 days old, the time ensoulment is interpreted to occur according to Islamic law, abortion is no longer permissible. [54] There are a few opposing opinions about early embryos in Islamic traditions. According to some Islamic theologians, there is no ensoulment of the early embryo, which is the source of stem cells for ESC research. [55]

In Buddhism, the stance on stem cell research is not settled. The main tenets, the prohibition against harming or destroying others (ahimsa) and the pursuit of knowledge (prajña) and compassion (karuna), leave Buddhist scholars and communities divided. [56] Some scholars argue stem cell research is in accordance with the Buddhist tenet of seeking knowledge and ending human suffering. Others feel it violates the principle of not harming others. Finding the balance between these two points relies on the karmic burden of Buddhist morality. In trying to prevent ahimsa towards the embryo, Buddhist scholars suggest that to comply with Buddhist tenets, research cannot be done as the embryo has personhood at the moment of conception and would reincarnate immediately, harming the individual's ability to build their karmic burden. [57] On the other hand, the Bodhisattvas, those considered to be on the path to enlightenment or Nirvana, have given organs and flesh to others to help alleviate grieving and to benefit all. [58] Acceptance varies on applied beliefs and interpretations.

Catholicism does not support embryonic stem cell research, as it entails creation or destruction of human embryos. This destruction conflicts with the belief in the sanctity of life. For example, in the Old Testament, Genesis describes humanity as being created in God’s image and multiplying on the Earth, referencing the sacred rights to human conception and the purpose of development and life. In the Ten Commandments, the tenet that one should not kill has numerous interpretations where killing could mean murder or shedding of the sanctity of life, demonstrating the high value of human personhood. In other books, the theological conception of when life begins is interpreted as in utero, [59] highlighting the inviolability of life and its formation in vivo to make a religious point for accepting such research as relatively limited, if at all. [60] The Vatican has released ethical directives to help apply a theological basis to modern-day conflicts. The Magisterium of the Church states that “unless there is a moral certainty of not causing harm,” experimentation on fetuses, fertilized cells, stem cells, or embryos constitutes a crime. [61] Such procedures would not respect the human person who exists at these stages, according to Catholicism. Damages to the embryo are considered gravely immoral and illicit. [62] Although the Catholic Church officially opposes abortion, surveys demonstrate that many Catholic people hold pro-choice views, whether due to the context of conception, stage of pregnancy, threat to the mother’s life, or for other reasons, demonstrating that practicing members can also accept some but not all tenets. [63]

Some major Jewish denominations, such as the Reform, Conservative, and Reconstructionist movements, are open to supporting ESC use or research as long as it is for saving a life. [64] Within Judaism, the Talmud, or study, gives personhood to the child at birth and emphasizes that life does not begin at conception: [65]

“If she is found pregnant, until the fortieth day it is mere fluid,” [66]

Whereas most religions prioritize the status of human embryos, the Halakah (Jewish religious law) states that to save one life, most other religious laws can be ignored because it is in pursuit of preservation. [67] Stem cell research is accepted due to application of these religious laws.

We recognize that all religions contain subsets and sects. The variety of environmental and cultural differences within religious groups requires further analysis to respect the flexibility of religious thoughts and practices. We make no presumptions that all cultures require notions of autonomy or morality as under the common morality theory , which asserts a set of universal moral norms that all individuals share provides moral reasoning and guides ethical decisions. [68] We only wish to show that the interaction with morality varies between cultures and countries.

III.     A Flexible Ethical Approach

The plurality of different moral approaches described above demonstrates that there can be no universally acceptable uniform law for ESC on a global scale. Instead of developing one standard, flexible ethical applications must be continued. We recommend local guidelines that incorporate important cultural and ethical priorities.

While the Declaration of Helsinki is more relevant to people in clinical trials receiving ESC products, in keeping with the tradition of protections for research subjects, consent of the donor is an ethical requirement for ESC donation in many jurisdictions including the US, Canada, and Europe. [69] The Declaration of Helsinki provides a reference point for regulatory standards and could potentially be used as a universal baseline for obtaining consent prior to gamete or embryo donation.

For instance, in Columbia University’s egg donor program for stem cell research, donors followed standard screening protocols and “underwent counseling sessions that included information as to the purpose of oocyte donation for research, what the oocytes would be used for, the risks and benefits of donation, and process of oocyte stimulation” to ensure transparency for consent. [70] The program helped advance stem cell research and provided clear and safe research methods with paid participants. Though paid participation or covering costs of incidental expenses may not be socially acceptable in every culture or context, [71] and creating embryos for ESC research is illegal in many jurisdictions, Columbia’s program was effective because of the clear and honest communications with donors, IRBs, and related stakeholders.  This example demonstrates that cultural acceptance of scientific research and of the idea that an egg or embryo does not have personhood is likely behind societal acceptance of donating eggs for ESC research. As noted, many countries do not permit the creation of embryos for research.

Proper communication and education regarding the process and purpose of stem cell research may bolster comprehension and garner more acceptance. “Given the sensitive subject material, a complete consent process can support voluntary participation through trust, understanding, and ethical norms from the cultures and morals participants value. This can be hard for researchers entering countries of different socioeconomic stability, with different languages and different societal values. [72]

An adequate moral foundation in medical ethics is derived from the cultural and religious basis that informs knowledge and actions. [73] Understanding local cultural and religious values and their impact on research could help researchers develop humility and promote inclusion.

IV.     Concerns

Some may argue that if researchers all adhere to one ethics standard, protection will be satisfied across all borders, and the global public will trust researchers. However, defining what needs to be protected and how to define such research standards is very specific to the people to which standards are applied. We suggest that applying one uniform guide cannot accurately protect each individual because we all possess our own perceptions and interpretations of social values. [74] Therefore, the issue of not adjusting to the moral pluralism between peoples in applying one standard of ethics can be resolved by building out ethics models that can be adapted to different cultures and religions.

Other concerns include medical tourism, which may promote health inequities. [75] Some countries may develop and approve products derived from ESC research before others, compromising research ethics or drug approval processes. There are also concerns about the sale of unauthorized stem cell treatments, for example, those without FDA approval in the United States. Countries with robust research infrastructures may be tempted to attract medical tourists, and some customers will have false hopes based on aggressive publicity of unproven treatments. [76]

For example, in China, stem cell clinics can market to foreign clients who are not protected under the regulatory regimes. Companies employ a marketing strategy of “ethically friendly” therapies. Specifically, in the case of Beike, China’s leading stem cell tourism company and sprouting network, ethical oversight of administrators or health bureaus at one site has “the unintended consequence of shifting questionable activities to another node in Beike's diffuse network.” [77] In contrast, Jordan is aware of stem cell research’s potential abuse and its own status as a “health-care hub.” Jordan’s expanded regulations include preserving the interests of individuals in clinical trials and banning private companies from ESC research to preserve transparency and the integrity of research practices. [78]

The social priorities of the community are also a concern. The ISSCR explicitly states that guidelines “should be periodically revised to accommodate scientific advances, new challenges, and evolving social priorities.” [79] The adaptable ethics model extends this consideration further by addressing whether research is warranted given the varying degrees of socioeconomic conditions, political stability, and healthcare accessibilities and limitations. An ethical approach would require discussion about resource allocation and appropriate distribution of funds. [80]

While some religions emphasize the sanctity of life from conception, which may lead to public opposition to ESC research, others encourage ESC research due to its potential for healing and alleviating human pain. Many countries have special regulations that balance local views on embryonic personhood, the benefits of research as individual or societal goods, and the protection of human research subjects. To foster understanding and constructive dialogue, global policy frameworks should prioritize the protection of universal human rights, transparency, and informed consent. In addition to these foundational global policies, we recommend tailoring local guidelines to reflect the diverse cultural and religious perspectives of the populations they govern. Ethics models should be adapted to local populations to effectively establish research protections, growth, and possibilities of stem cell research.

For example, in countries with strong beliefs in the moral sanctity of embryos or heavy religious restrictions, an adaptive model can allow for discussion instead of immediate rejection. In countries with limited individual rights and voice in science policy, an adaptive model ensures cultural, moral, and religious views are taken into consideration, thereby building social inclusion. While this ethical consideration by the government may not give a complete voice to every individual, it will help balance policies and maintain the diverse perspectives of those it affects. Embracing an adaptive ethics model of ESC research promotes open-minded dialogue and respect for the importance of human belief and tradition. By actively engaging with cultural and religious values, researchers can better handle disagreements and promote ethical research practices that benefit each society.

This brief exploration of the religious and cultural differences that impact ESC research reveals the nuances of relative ethics and highlights a need for local policymakers to apply a more intense adaptive model.

[1] Poliwoda, S., Noor, N., Downs, E., Schaaf, A., Cantwell, A., Ganti, L., Kaye, A. D., Mosel, L. I., Carroll, C. B., Viswanath, O., & Urits, I. (2022). Stem cells: a comprehensive review of origins and emerging clinical roles in medical practice.  Orthopedic reviews ,  14 (3), 37498. https://doi.org/10.52965/001c.37498

[2] Poliwoda, S., Noor, N., Downs, E., Schaaf, A., Cantwell, A., Ganti, L., Kaye, A. D., Mosel, L. I., Carroll, C. B., Viswanath, O., & Urits, I. (2022). Stem cells: a comprehensive review of origins and emerging clinical roles in medical practice.  Orthopedic reviews ,  14 (3), 37498. https://doi.org/10.52965/001c.37498

[3] International Society for Stem Cell Research. (2023). Laboratory-based human embryonic stem cell research, embryo research, and related research activities . International Society for Stem Cell Research. https://www.isscr.org/guidelines/blog-post-title-one-ed2td-6fcdk ; Kimmelman, J., Hyun, I., Benvenisty, N.  et al.  Policy: Global standards for stem-cell research.  Nature   533 , 311–313 (2016). https://doi.org/10.1038/533311a

[4] International Society for Stem Cell Research. (2023). Laboratory-based human embryonic stem cell research, embryo research, and related research activities . International Society for Stem Cell Research. https://www.isscr.org/guidelines/blog-post-title-one-ed2td-6fcdk

[5] Concerning the moral philosophies of stem cell research, our paper does not posit a personal moral stance nor delve into the “when” of human life begins. To read further about the philosophical debate, consider the following sources:

Sandel M. J. (2004). Embryo ethics--the moral logic of stem-cell research.  The New England journal of medicine ,  351 (3), 207–209. https://doi.org/10.1056/NEJMp048145 ; George, R. P., & Lee, P. (2020, September 26). Acorns and Embryos . The New Atlantis. https://www.thenewatlantis.com/publications/acorns-and-embryos ; Sagan, A., & Singer, P. (2007). The moral status of stem cells. Metaphilosophy , 38 (2/3), 264–284. http://www.jstor.org/stable/24439776 ; McHugh P. R. (2004). Zygote and "clonote"--the ethical use of embryonic stem cells.  The New England journal of medicine ,  351 (3), 209–211. https://doi.org/10.1056/NEJMp048147 ; Kurjak, A., & Tripalo, A. (2004). The facts and doubts about beginning of the human life and personality.  Bosnian journal of basic medical sciences ,  4 (1), 5–14. https://doi.org/10.17305/bjbms.2004.3453

[6] Vazin, T., & Freed, W. J. (2010). Human embryonic stem cells: derivation, culture, and differentiation: a review.  Restorative neurology and neuroscience ,  28 (4), 589–603. https://doi.org/10.3233/RNN-2010-0543

[7] Socially, at its core, the Western approach to ethics is widely principle-based, autonomy being one of the key factors to ensure a fundamental respect for persons within research. For information regarding autonomy in research, see: Department of Health, Education, and Welfare, & National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (1978). The Belmont Report. Ethical principles and guidelines for the protection of human subjects of research.; For a more in-depth review of autonomy within the US, see: Beauchamp, T. L., & Childress, J. F. (1994). Principles of Biomedical Ethics . Oxford University Press.

[8] Sherley v. Sebelius , 644 F.3d 388 (D.C. Cir. 2011), citing 45 C.F.R. 46.204(b) and [42 U.S.C. § 289g(b)]. https://www.cadc.uscourts.gov/internet/opinions.nsf/6c690438a9b43dd685257a64004ebf99/$file/11-5241-1391178.pdf

[9] Stem Cell Research Enhancement Act of 2005, H. R. 810, 109 th Cong. (2001). https://www.govtrack.us/congress/bills/109/hr810/text ; Bush, G. W. (2006, July 19). Message to the House of Representatives . National Archives and Records Administration. https://georgewbush-whitehouse.archives.gov/news/releases/2006/07/20060719-5.html

[10] National Archives and Records Administration. (2009, March 9). Executive order 13505 -- removing barriers to responsible scientific research involving human stem cells . National Archives and Records Administration. https://obamawhitehouse.archives.gov/the-press-office/removing-barriers-responsible-scientific-research-involving-human-stem-cells

[11] Hurlbut, W. B. (2006). Science, Religion, and the Politics of Stem Cells.  Social Research ,  73 (3), 819–834. http://www.jstor.org/stable/40971854

[12] Akpa-Inyang, Francis & Chima, Sylvester. (2021). South African traditional values and beliefs regarding informed consent and limitations of the principle of respect for autonomy in African communities: a cross-cultural qualitative study. BMC Medical Ethics . 22. 10.1186/s12910-021-00678-4.

[13] Source for further reading: Tangwa G. B. (2007). Moral status of embryonic stem cells: perspective of an African villager. Bioethics , 21(8), 449–457. https://doi.org/10.1111/j.1467-8519.2007.00582.x , see also Mnisi, F. M. (2020). An African analysis based on ethics of Ubuntu - are human embryonic stem cell patents morally justifiable? African Insight , 49 (4).

[14] Jecker, N. S., & Atuire, C. (2021). Bioethics in Africa: A contextually enlightened analysis of three cases. Developing World Bioethics , 22 (2), 112–122. https://doi.org/10.1111/dewb.12324

[15] Jecker, N. S., & Atuire, C. (2021). Bioethics in Africa: A contextually enlightened analysis of three cases. Developing World Bioethics, 22(2), 112–122. https://doi.org/10.1111/dewb.12324

[16] Jackson, C.S., Pepper, M.S. Opportunities and barriers to establishing a cell therapy programme in South Africa.  Stem Cell Res Ther   4 , 54 (2013). https://doi.org/10.1186/scrt204 ; Pew Research Center. (2014, May 1). Public health a major priority in African nations . Pew Research Center’s Global Attitudes Project. https://www.pewresearch.org/global/2014/05/01/public-health-a-major-priority-in-african-nations/

[17] Department of Health Republic of South Africa. (2021). Health Research Priorities (revised) for South Africa 2021-2024 . National Health Research Strategy. https://www.health.gov.za/wp-content/uploads/2022/05/National-Health-Research-Priorities-2021-2024.pdf

[18] Oosthuizen, H. (2013). Legal and Ethical Issues in Stem Cell Research in South Africa. In: Beran, R. (eds) Legal and Forensic Medicine. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32338-6_80 , see also: Gaobotse G (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[19] United States Bureau of Citizenship and Immigration Services. (1998). Tunisia: Information on the status of Christian conversions in Tunisia . UNHCR Web Archive. https://webarchive.archive.unhcr.org/20230522142618/https://www.refworld.org/docid/3df0be9a2.html

[20] Gaobotse, G. (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[21] Kooli, C. Review of assisted reproduction techniques, laws, and regulations in Muslim countries.  Middle East Fertil Soc J   24 , 8 (2020). https://doi.org/10.1186/s43043-019-0011-0 ; Gaobotse, G. (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[22] Pang M. C. (1999). Protective truthfulness: the Chinese way of safeguarding patients in informed treatment decisions. Journal of medical ethics , 25(3), 247–253. https://doi.org/10.1136/jme.25.3.247

[23] Wang, L., Wang, F., & Zhang, W. (2021). Bioethics in China’s biosecurity law: Forms, effects, and unsettled issues. Journal of law and the biosciences , 8(1).  https://doi.org/10.1093/jlb/lsab019 https://academic.oup.com/jlb/article/8/1/lsab019/6299199

[24] Wang, Y., Xue, Y., & Guo, H. D. (2022). Intervention effects of traditional Chinese medicine on stem cell therapy of myocardial infarction.  Frontiers in pharmacology ,  13 , 1013740. https://doi.org/10.3389/fphar.2022.1013740

[25] Li, X.-T., & Zhao, J. (2012). Chapter 4: An Approach to the Nature of Qi in TCM- Qi and Bioenergy. In Recent Advances in Theories and Practice of Chinese Medicine (p. 79). InTech.

[26] Luo, D., Xu, Z., Wang, Z., & Ran, W. (2021). China's Stem Cell Research and Knowledge Levels of Medical Practitioners and Students.  Stem cells international ,  2021 , 6667743. https://doi.org/10.1155/2021/6667743

[27] Luo, D., Xu, Z., Wang, Z., & Ran, W. (2021). China's Stem Cell Research and Knowledge Levels of Medical Practitioners and Students.  Stem cells international ,  2021 , 6667743. https://doi.org/10.1155/2021/6667743

[28] Zhang, J. Y. (2017). Lost in translation? accountability and governance of Clinical Stem Cell Research in China. Regenerative Medicine , 12 (6), 647–656. https://doi.org/10.2217/rme-2017-0035

[29] Wang, L., Wang, F., & Zhang, W. (2021). Bioethics in China’s biosecurity law: Forms, effects, and unsettled issues. Journal of law and the biosciences , 8(1).  https://doi.org/10.1093/jlb/lsab019 https://academic.oup.com/jlb/article/8/1/lsab019/6299199

[30] Chen, H., Wei, T., Wang, H.  et al.  Association of China’s two-child policy with changes in number of births and birth defects rate, 2008–2017.  BMC Public Health   22 , 434 (2022). https://doi.org/10.1186/s12889-022-12839-0

[31] Azuma, K. Regulatory Landscape of Regenerative Medicine in Japan.  Curr Stem Cell Rep   1 , 118–128 (2015). https://doi.org/10.1007/s40778-015-0012-6

[32] Harris, R. (2005, May 19). Researchers Report Advance in Stem Cell Production . NPR. https://www.npr.org/2005/05/19/4658967/researchers-report-advance-in-stem-cell-production

[33] Park, S. (2012). South Korea steps up stem-cell work.  Nature . https://doi.org/10.1038/nature.2012.10565

[34] Resnik, D. B., Shamoo, A. E., & Krimsky, S. (2006). Fraudulent human embryonic stem cell research in South Korea: lessons learned.  Accountability in research ,  13 (1), 101–109. https://doi.org/10.1080/08989620600634193 .

[35] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: interviews with researchers from Saudi Arabia. BMC medical ethics, 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6

[36] Association for the Advancement of Blood and Biotherapies.  https://www.aabb.org/regulatory-and-advocacy/regulatory-affairs/regulatory-for-cellular-therapies/international-competent-authorities/saudi-arabia

[37] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: Interviews with researchers from Saudi Arabia.  BMC medical ethics ,  21 (1), 35. https://doi.org/10.1186/s12910-020-00482-6

[38] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: Interviews with researchers from Saudi Arabia. BMC medical ethics , 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6

Culturally, autonomy practices follow a relational autonomy approach based on a paternalistic deontological health care model. The adherence to strict international research policies and religious pillars within the regulatory environment is a great foundation for research ethics. However, there is a need to develop locally targeted ethics approaches for research (as called for in Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: interviews with researchers from Saudi Arabia. BMC medical ethics, 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6), this decision-making approach may help advise a research decision model. For more on the clinical cultural autonomy approaches, see: Alabdullah, Y. Y., Alzaid, E., Alsaad, S., Alamri, T., Alolayan, S. W., Bah, S., & Aljoudi, A. S. (2022). Autonomy and paternalism in Shared decision‐making in a Saudi Arabian tertiary hospital: A cross‐sectional study. Developing World Bioethics , 23 (3), 260–268. https://doi.org/10.1111/dewb.12355 ; Bukhari, A. A. (2017). Universal Principles of Bioethics and Patient Rights in Saudi Arabia (Doctoral dissertation, Duquesne University). https://dsc.duq.edu/etd/124; Ladha, S., Nakshawani, S. A., Alzaidy, A., & Tarab, B. (2023, October 26). Islam and Bioethics: What We All Need to Know . Columbia University School of Professional Studies. https://sps.columbia.edu/events/islam-and-bioethics-what-we-all-need-know

[39] Ababneh, M. A., Al-Azzam, S. I., Alzoubi, K., Rababa’h, A., & Al Demour, S. (2021). Understanding and attitudes of the Jordanian public about clinical research ethics.  Research Ethics ,  17 (2), 228-241.  https://doi.org/10.1177/1747016120966779

[40] Ababneh, M. A., Al-Azzam, S. I., Alzoubi, K., Rababa’h, A., & Al Demour, S. (2021). Understanding and attitudes of the Jordanian public about clinical research ethics.  Research Ethics ,  17 (2), 228-241.  https://doi.org/10.1177/1747016120966779

[41] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[42] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[43] The EU’s definition of autonomy relates to the capacity for creating ideas, moral insight, decisions, and actions without constraint, personal responsibility, and informed consent. However, the EU views autonomy as not completely able to protect individuals and depends on other principles, such as dignity, which “expresses the intrinsic worth and fundamental equality of all human beings.” Rendtorff, J.D., Kemp, P. (2019). Four Ethical Principles in European Bioethics and Biolaw: Autonomy, Dignity, Integrity and Vulnerability. In: Valdés, E., Lecaros, J. (eds) Biolaw and Policy in the Twenty-First Century. International Library of Ethics, Law, and the New Medicine, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-05903-3_3

[44] Council of Europe. Convention for the protection of Human Rights and Dignity of the Human Being with regard to the Application of Biology and Medicine: Convention on Human Rights and Biomedicine (ETS No. 164) https://www.coe.int/en/web/conventions/full-list?module=treaty-detail&treatynum=164 (forbidding the creation of embryos for research purposes only, and suggests embryos in vitro have protections.); Also see Drabiak-Syed B. K. (2013). New President, New Human Embryonic Stem Cell Research Policy: Comparative International Perspectives and Embryonic Stem Cell Research Laws in France.  Biotechnology Law Report ,  32 (6), 349–356. https://doi.org/10.1089/blr.2013.9865

[45] Rendtorff, J.D., Kemp, P. (2019). Four Ethical Principles in European Bioethics and Biolaw: Autonomy, Dignity, Integrity and Vulnerability. In: Valdés, E., Lecaros, J. (eds) Biolaw and Policy in the Twenty-First Century. International Library of Ethics, Law, and the New Medicine, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-05903-3_3

[46] Tomuschat, C., Currie, D. P., Kommers, D. P., & Kerr, R. (Trans.). (1949, May 23). Basic law for the Federal Republic of Germany. https://www.btg-bestellservice.de/pdf/80201000.pdf

[47] Regulation of Stem Cell Research in Germany . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-germany

[48] Regulation of Stem Cell Research in Finland . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-finland

[49] Regulation of Stem Cell Research in Spain . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-spain

[50] Some sources to consider regarding ethics models or regulatory oversights of other cultures not covered:

Kara MA. Applicability of the principle of respect for autonomy: the perspective of Turkey. J Med Ethics. 2007 Nov;33(11):627-30. doi: 10.1136/jme.2006.017400. PMID: 17971462; PMCID: PMC2598110.

Ugarte, O. N., & Acioly, M. A. (2014). The principle of autonomy in Brazil: one needs to discuss it ...  Revista do Colegio Brasileiro de Cirurgioes ,  41 (5), 374–377. https://doi.org/10.1590/0100-69912014005013

Bharadwaj, A., & Glasner, P. E. (2012). Local cells, global science: The rise of embryonic stem cell research in India . Routledge.

For further research on specific European countries regarding ethical and regulatory framework, we recommend this database: Regulation of Stem Cell Research in Europe . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-europe   

[51] Klitzman, R. (2006). Complications of culture in obtaining informed consent. The American Journal of Bioethics, 6(1), 20–21. https://doi.org/10.1080/15265160500394671 see also: Ekmekci, P. E., & Arda, B. (2017). Interculturalism and Informed Consent: Respecting Cultural Differences without Breaching Human Rights.  Cultura (Iasi, Romania) ,  14 (2), 159–172.; For why trust is important in research, see also: Gray, B., Hilder, J., Macdonald, L., Tester, R., Dowell, A., & Stubbe, M. (2017). Are research ethics guidelines culturally competent?  Research Ethics ,  13 (1), 23-41.  https://doi.org/10.1177/1747016116650235

[52] The Qur'an  (M. Khattab, Trans.). (1965). Al-Mu’minun, 23: 12-14. https://quran.com/23

[53] Lenfest, Y. (2017, December 8). Islam and the beginning of human life . Bill of Health. https://blog.petrieflom.law.harvard.edu/2017/12/08/islam-and-the-beginning-of-human-life/

[54] Aksoy, S. (2005). Making regulations and drawing up legislation in Islamic countries under conditions of uncertainty, with special reference to embryonic stem cell research. Journal of Medical Ethics , 31: 399-403.; see also: Mahmoud, Azza. "Islamic Bioethics: National Regulations and Guidelines of Human Stem Cell Research in the Muslim World." Master's thesis, Chapman University, 2022. https://doi.org/10.36837/ chapman.000386

[55] Rashid, R. (2022). When does Ensoulment occur in the Human Foetus. Journal of the British Islamic Medical Association , 12 (4). ISSN 2634 8071. https://www.jbima.com/wp-content/uploads/2023/01/2-Ethics-3_-Ensoulment_Rafaqat.pdf.

[56] Sivaraman, M. & Noor, S. (2017). Ethics of embryonic stem cell research according to Buddhist, Hindu, Catholic, and Islamic religions: perspective from Malaysia. Asian Biomedicine,8(1) 43-52.  https://doi.org/10.5372/1905-7415.0801.260

[57] Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[58] Lecso, P. A. (1991). The Bodhisattva Ideal and Organ Transplantation.  Journal of Religion and Health ,  30 (1), 35–41. http://www.jstor.org/stable/27510629 ; Bodhisattva, S. (n.d.). The Key of Becoming a Bodhisattva . A Guide to the Bodhisattva Way of Life. http://www.buddhism.org/Sutras/2/BodhisattvaWay.htm

[59] There is no explicit religious reference to when life begins or how to conduct research that interacts with the concept of life. However, these are relevant verses pertaining to how the fetus is viewed. (( King James Bible . (1999). Oxford University Press. (original work published 1769))

Jerimiah 1: 5 “Before I formed thee in the belly I knew thee; and before thou camest forth out of the womb I sanctified thee…”

In prophet Jerimiah’s insight, God set him apart as a person known before childbirth, a theme carried within the Psalm of David.

Psalm 139: 13-14 “…Thou hast covered me in my mother's womb. I will praise thee; for I am fearfully and wonderfully made…”

These verses demonstrate David’s respect for God as an entity that would know of all man’s thoughts and doings even before birth.

[60] It should be noted that abortion is not supported as well.

[61] The Vatican. (1987, February 22). Instruction on Respect for Human Life in Its Origin and on the Dignity of Procreation Replies to Certain Questions of the Day . Congregation For the Doctrine of the Faith. https://www.vatican.va/roman_curia/congregations/cfaith/documents/rc_con_cfaith_doc_19870222_respect-for-human-life_en.html

[62] The Vatican. (2000, August 25). Declaration On the Production and the Scientific and Therapeutic Use of Human Embryonic Stem Cells . Pontifical Academy for Life. https://www.vatican.va/roman_curia/pontifical_academies/acdlife/documents/rc_pa_acdlife_doc_20000824_cellule-staminali_en.html ; Ohara, N. (2003). Ethical Consideration of Experimentation Using Living Human Embryos: The Catholic Church’s Position on Human Embryonic Stem Cell Research and Human Cloning. Department of Obstetrics and Gynecology . Retrieved from https://article.imrpress.com/journal/CEOG/30/2-3/pii/2003018/77-81.pdf.

[63] Smith, G. A. (2022, May 23). Like Americans overall, Catholics vary in their abortion views, with regular mass attenders most opposed . Pew Research Center. https://www.pewresearch.org/short-reads/2022/05/23/like-americans-overall-catholics-vary-in-their-abortion-views-with-regular-mass-attenders-most-opposed/

[64] Rosner, F., & Reichman, E. (2002). Embryonic stem cell research in Jewish law. Journal of halacha and contemporary society , (43), 49–68.; Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[65] Schenker J. G. (2008). The beginning of human life: status of embryo. Perspectives in Halakha (Jewish Religious Law).  Journal of assisted reproduction and genetics ,  25 (6), 271–276. https://doi.org/10.1007/s10815-008-9221-6

[66] Ruttenberg, D. (2020, May 5). The Torah of Abortion Justice (annotated source sheet) . Sefaria. https://www.sefaria.org/sheets/234926.7?lang=bi&with=all&lang2=en

[67] Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[68] Gert, B. (2007). Common morality: Deciding what to do . Oxford Univ. Press.

[69] World Medical Association (2013). World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA , 310(20), 2191–2194. https://doi.org/10.1001/jama.2013.281053 Declaration of Helsinki – WMA – The World Medical Association .; see also: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979).  The Belmont report: Ethical principles and guidelines for the protection of human subjects of research . U.S. Department of Health and Human Services.  https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.html

[70] Zakarin Safier, L., Gumer, A., Kline, M., Egli, D., & Sauer, M. V. (2018). Compensating human subjects providing oocytes for stem cell research: 9-year experience and outcomes.  Journal of assisted reproduction and genetics ,  35 (7), 1219–1225. https://doi.org/10.1007/s10815-018-1171-z https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6063839/ see also: Riordan, N. H., & Paz Rodríguez, J. (2021). Addressing concerns regarding associated costs, transparency, and integrity of research in recent stem cell trial. Stem Cells Translational Medicine , 10 (12), 1715–1716. https://doi.org/10.1002/sctm.21-0234

[71] Klitzman, R., & Sauer, M. V. (2009). Payment of egg donors in stem cell research in the USA.  Reproductive biomedicine online ,  18 (5), 603–608. https://doi.org/10.1016/s1472-6483(10)60002-8

[72] Krosin, M. T., Klitzman, R., Levin, B., Cheng, J., & Ranney, M. L. (2006). Problems in comprehension of informed consent in rural and peri-urban Mali, West Africa.  Clinical trials (London, England) ,  3 (3), 306–313. https://doi.org/10.1191/1740774506cn150oa

[73] Veatch, Robert M.  Hippocratic, Religious, and Secular Medical Ethics: The Points of Conflict . Georgetown University Press, 2012.

[74] Msoroka, M. S., & Amundsen, D. (2018). One size fits not quite all: Universal research ethics with diversity.  Research Ethics ,  14 (3), 1-17.  https://doi.org/10.1177/1747016117739939

[75] Pirzada, N. (2022). The Expansion of Turkey’s Medical Tourism Industry.  Voices in Bioethics ,  8 . https://doi.org/10.52214/vib.v8i.9894

[76] Stem Cell Tourism: False Hope for Real Money . Harvard Stem Cell Institute (HSCI). (2023). https://hsci.harvard.edu/stem-cell-tourism , See also: Bissassar, M. (2017). Transnational Stem Cell Tourism: An ethical analysis.  Voices in Bioethics ,  3 . https://doi.org/10.7916/vib.v3i.6027

[77] Song, P. (2011) The proliferation of stem cell therapies in post-Mao China: problematizing ethical regulation,  New Genetics and Society , 30:2, 141-153, DOI:  10.1080/14636778.2011.574375

[78] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[79] International Society for Stem Cell Research. (2024). Standards in stem cell research . International Society for Stem Cell Research. https://www.isscr.org/guidelines/5-standards-in-stem-cell-research

[80] Benjamin, R. (2013). People’s science bodies and rights on the Stem Cell Frontier . Stanford University Press.

Mifrah Hayath

SM Candidate Harvard Medical School, MS Biotechnology Johns Hopkins University

Olivia Bowers

MS Bioethics Columbia University (Disclosure: affiliated with Voices in Bioethics)

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Author Barb Wixom reads this research briefing as part of our audio edition of the series. Follow the series on SoundCloud.

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Today, everybody across the organization is hungry to know more about AI. What is it good for? Should I trust it? Will it take my job? Business leaders are investing in massive training programs, partnering with promising vendors and consultants, and collaborating with peers to identify ways to benefit from AI and avoid the risk of AI missteps. They are trying to understand how to manage AI responsibly and at scale.

Our book Data Is Everybody’s Business: The Fundamentals of Data Monetization describes how organizations make money using their data.[foot]Barbara H. Wixom, Cynthia M. Beath, and Leslie Owens, Data Is Everybody's Business: The Fundamentals of Data Monetization , (Cambridge: The MIT Press, 2023), https://mitpress.mit.edu/9780262048217/data-is-everybodys-business/ .[/foot] We wrote the book to clarify what data monetization is (the conversion of data into financial returns) and how to do it (by using data to improve work, wrap products and experiences, and sell informational solutions). AI technology’s role in this is to help data monetization project teams use data in ways that humans cannot, usually because of big complexity or scope or required speed. In our data monetization research, we have regularly seen leaders use AI effectively to realize extraordinary business goals. In this briefing, we explain how such leaders achieve big AI wins and maximize financial returns.

Using AI in Data Monetization

AI refers to the ability of machines to perform human-like cognitive tasks.[foot]See Hind Benbya, Thomas H. Davenport, and Stella Pachidi, “Special Issue Editorial: Artificial Intelligence in Organizations: Current State and Future Opportunities , ” MIS Quarterly Executive 19, no. 4 (December 2020), https://aisel.aisnet.org/misqe/vol19/iss4/4 .[/foot] Since 2019, MIT CISR researchers have been studying deployed data monetization initiatives that rely on machine learning and predictive algorithms, commonly referred to as predictive AI.[foot]This research draws on a Q1 to Q2 2019 asynchronous discussion about AI-related challenges with fifty-three data executives from the MIT CISR Data Research Advisory Board; more than one hundred structured interviews with AI professionals regarding fifty-two AI projects from Q3 2019 to Q2 2020; and ten AI project narratives published by MIT CISR between 2020 and 2023.[/foot] Such initiatives use large data repositories to recognize patterns across time, draw inferences, and predict outcomes and future trends. For example, the Australian Taxation Office (ATO) used machine learning, neural nets, and decision trees to understand citizen tax-filing behaviors and produce respectful nudges that helped citizens abide by Australia’s work-related expense policies. In 2018, the nudging resulted in AUD$113 million in changed claim amounts.[foot]I. A. Someh, B. H. Wixom, and R. W. Gregory, “The Australian Taxation Office: Creating Value with Advanced Analytics,” MIT CISR Working Paper No. 447, November 2020, https://cisr.mit.edu/publication/MIT_CISRwp447_ATOAdvancedAnalytics_SomehWixomGregory .[/foot]

In 2023, we began exploring data monetization initiatives that rely on generative AI.[foot]This research draws on two asynchronous generative AI discussions (Q3 2023, N=35; Q1 2024, N=34) regarding investments and capabilities and roles and skills, respectively, with data executives from the MIT CISR Data Research Advisory Board. It also draws on in-progress case studies with large organizations in the publishing, building materials, and equipment manufacturing industries.[/foot] This type of AI analyzes vast amounts of text or image data to discern patterns in them. Using these patterns, generative AI can create new text, software code, images, or videos, usually in response to user prompts. Organizations are now beginning to openly discuss data monetization initiative deployments that include generative AI technologies. For example, used vehicle retailer CarMax reported using OpenAI’s ChatGPT chatbot to help aggregate customer reviews and other car information from multiple data sets to create helpful, easy-to-read summaries about individual used cars for its online shoppers. At any point in time, CarMax has on average 50,000 cars on its website, so to produce such content without AI the company would require hundreds of content writers and years of time; using ChatGPT, the company’s content team can generate summaries in hours.[foot]Paula Rooney, “CarMax drives business value with GPT-3.5,” CIO , May 5, 2023, https://www.cio.com/article/475487/carmax-drives-business-value-with-gpt-3-5.html ; Hayete Gallot and Shamim Mohammad, “Taking the car-buying experience to the max with AI,” January 2, 2024, in Pivotal with Hayete Gallot, produced by Larj Media, podcast, MP3 audio, https://podcasts.apple.com/us/podcast/taking-the-car-buying-experience-to-the-max-with-ai/id1667013760?i=1000640365455 .[/foot]

Big advancements in machine learning, generative tools, and other AI technologies inspire big investments when leaders believe the technologies can help satisfy pent-up demand for solutions that previously seemed out of reach. However, there is a lot to learn about novel technologies before we can properly manage them. In this year’s MIT CISR research, we are studying predictive and generative AI from several angles. This briefing is the first in a series; in future briefings we will present management advice specific to machine learning and generative tools. For now, we present three principles supported by our data monetization research to guide business leaders when making AI investments of any kind: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects.

Principle 1: Invest in Practices That Build Capabilities Required for AI

Succeeding with AI depends on having deep data science skills that help teams successfully build and validate effective models. In fact, organizations need deep data science skills even when the models they are using are embedded in tools and partner solutions, including to evaluate their risks; only then can their teams make informed decisions about how to incorporate AI effectively into work practices. We worry that some leaders view buying AI products from providers as an opportunity to use AI without deep data science skills; we do not advise this.

But deep data science skills are not enough. Leaders often hire new talent and offer AI literacy training without making adequate investments in building complementary skills that are just as important. Our research shows that an organization’s progress in AI is dependent on having not only an advanced data science capability, but on having equally advanced capabilities in data management, data platform, acceptable data use, and customer understanding.[foot]In the June 2022 MIT CISR research briefing, we described why and how organizations build the five advanced data monetization capabilities for AI. See B. H. Wixom, I. A. Someh, and C. M. Beath, “Building Advanced Data Monetization Capabilities for the AI-Powered Organization,” MIT CISR Research Briefing, Vol. XXII, No. 6, June 2022, https://cisr.mit.edu/publication/2022_0601_AdvancedAICapabilities_WixomSomehBeath .[/foot] Think about it. Without the ability to curate data (an advanced data management capability), teams cannot effectively incorporate a diverse set of features into their models. Without the ability to oversee the legality and ethics of partners’ data use (an advanced acceptable data use capability), teams cannot responsibly deploy AI solutions into production.

It’s no surprise that ATO’s AI journey evolved in conjunction with the organization’s Smarter Data Program, which ATO established to build world-class data analytics capabilities, and that CarMax emphasizes that its governance, talent, and other data investments have been core to its generative AI progress.

Capabilities come mainly from learning by doing, so they are shaped by new practices in the form of training programs, policies, processes, or tools. As organizations undertake more and more sophisticated practices, their capabilities get more robust. Do invest in AI training—but also invest in practices that will boost the organization’s ability to manage data (such as adopting a data cataloging tool), make data accessible cost effectively (such as adopting cloud policies), improve data governance (such as establishing an ethical oversight committee), and solidify your customer understanding (such as mapping customer journeys). In particular, adopt policies and processes that will improve your data governance, so that data is only used in AI initiatives in ways that are consonant with your organization's values and its regulatory environment.

Principle 2: Involve All Your People in Your AI Journey

Data monetization initiatives require a variety of stakeholders—people doing the work, developing products, and offering solutions—to inform project requirements and to ensure the adoption and confident use of new data tools and behaviors.[foot]Ida Someh, Barbara Wixom, Michael Davern, and Graeme Shanks, “Configuring Relationships between Analytics and Business Domain Groups for Knowledge Integration, ” Journal of the Association for Information Systems 24, no. 2 (2023): 592-618, https://cisr.mit.edu/publication/configuring-relationships-between-analytics-and-business-domain-groups-knowledge .[/foot] With AI, involving a variety of stakeholders in initiatives helps non-data scientists become knowledgeable about what AI can and cannot do, how long it takes to deliver certain kinds of functionality, and what AI solutions cost. This, in turn, helps organizations in building trustworthy models, an important AI capability we call AI explanation (AIX).[foot]Ida Someh, Barbara H. Wixom, Cynthia M. Beath, and Angela Zutavern, “Building an Artificial Intelligence Explanation Capability,” MIS Quarterly Executive 21, no. 2 (2022), https://cisr.mit.edu/publication/building-artificial-intelligence-explanation-capability .[/foot]

For example, at ATO, data scientists educated business colleagues on the mechanics and results of models they created. Business colleagues provided feedback on the logic used in the models and helped to fine-tune them, and this interaction helped everyone understand how the AI made decisions. The data scientists provided their model results to ATO auditors, who also served as a feedback loop to the data scientists for improving the model. The data scientists regularly reported on initiative progress to senior management, regulators, and other stakeholders, which ensured that the AI team was proactively creating positive benefits without neglecting negative external factors that might surface.

Given the consumerization of generative AI tools, we believe that pervasive worker involvement in ideating, building, refining, using, and testing AI models and tools will become even more crucial to deploying fruitful AI projects—and building trust that AI will do the right thing in the right way at the right time.

Principle 3: Focus on Realizing Value From Your AI Projects

AI is costly—just add up your organization’s expenses in tools, talent, and training. AI needs to pay off, yet some organizations become distracted with endless experimentation. Others get caught up in finding the sweet spot of the technology, ignoring the sweet spot of their business model. For example, it is easy to become enamored of using generative AI to improve worker productivity, rolling out tools for employees to write better emails and capture what happened in meetings. But unless those activities materially impact how your organization makes money, there likely are better ways to spend your time and money.

Leaders with data monetization experience will make sure their AI projects realize value in the form of increased revenues or reduced expenses by backing initiatives that are clearly aligned with real challenges and opportunities. That is step one. In our research, the leaders that realize value from their data monetization initiatives measure and track their outcomes, especially their financial outcomes, and they hold someone accountable for achieving the desired financial returns. At CarMax, a cross-functional team owned the mission to provide better website information for used car shoppers, a mission important to the company’s sales goals. Starting with sales goals in mind, the team experimented with and then chose a generative AI solution that would enhance the shopper experience and increase sales.

Figure 1: Three Principles for Getting Value from AI Investments

The three principles are based on the following concepts from MIT CISR data research: 1. Data liquidity: the ease of data asset recombination and reuse 2. Data democracy: an organization that empowers employees in the access and use of data 3. Data monetization: the generation of financial returns from data assets

Managing AI Using a Data Monetization Mindset

AI has and always will play a big role in data monetization. It’s not a matter of whether to incorporate AI, but a matter of how to best use it. To figure this out, quantify the outcomes of some of your organization’s recent AI projects. How much money has the organization realized from them? If the answer disappoints, then make sure the AI technology value proposition is a fit for your organization’s most important goals. Then assign accountability for ensuring that AI technology is applied in use cases that impact your income statements. If the AI technology is not a fit for your organization, then don’t be distracted by media reports of the AI du jour.

Understanding your AI technology investments can be hard if your organization is using AI tools that are bundled in software you purchase or are built for you by a consultant. To set yourself up for success, ask your partners to be transparent with you about the quality of data they used to train their AI models and the data practices they relied on. Do their answers persuade you that their tools are trustworthy? Is it obvious that your partner is using data compliantly and is safeguarding the model from producing bad or undesired outcomes? If so, make sure this good news is shared with the people in your organization and those your organization serves. If not, rethink whether to break with your partner and find another way to incorporate the AI technology into your organization, such as by hiring people to build it in-house.

To paraphrase our book’s conclusion: When people actively engage in data monetization initiatives using AI , they learn, and they help their organization learn. Their engagement creates momentum that initiates a virtuous cycle in which people’s engagement leads to better data and more bottom-line value, which in turn leads to new ideas and more engagement, which further improves data and delivers more value, and so on. Imagine this happening across your organization as all people everywhere make it their business to find ways to use AI to monetize data.

This is why AI, like data, is everybody’s business.

© 2024 MIT Center for Information Systems Research, Wixom and Beath. MIT CISR Research Briefings are published monthly to update the center’s member organizations on current research projects.

Related Publications

Talking Points

Ai, like data, is everybody's business.

Working Paper: Vignette

The australian taxation office: creating value with advanced analytics.

Research Briefing

Building advanced data monetization capabilities for the ai-powered organization.

Building AI Explanation Capability for the AI-Powered Organization

What is Data Monetization?

About the researchers.

Barbara H. Wixom, Principal Research Scientist, MIT Center for Information Systems Research (CISR)

Cynthia M. Beath, Professor Emerita, University of Texas and Academic Research Fellow, MIT CISR

Mit center for information systems research (cisr).

Founded in 1974 and grounded in MIT's tradition of combining academic knowledge and practical purpose, MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

MIT CISR Associate Members

MIT CISR wishes to thank all of our associate members for their support and contributions.

MIT CISR's Mission Expand

MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We provide insights on how organizations effectively realize value from approaches such as digital business transformation, data monetization, business ecosystems, and the digital workplace. Founded in 1974 and grounded in MIT’s tradition of combining academic knowledge and practical purpose, we work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.