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How to Be a Smart Consumer of Social Science Research

Don’t rely too much on any one study.

Academic studies in the social sciences often find very different results. Given this variability, how should we consume evidence? The immediate answer is to not rely too much on any one study. Whenever possible, look for meta-analyses or systematic reviews that synthesize results from many studies, as they can provide more-credible evidence and sometimes suggest reasons that results differ. Second, when considering how much weight to give a study’s results, pay attention to its sample size. Similarly, consider peculiarities of the sample, context, and implementation. You may also have more confidence in the results of a study if there is some clear, causal mechanism that explains the findings and is constant across settings. Finally, if a study’s results sound too good to be true, they probably are.

Academic studies in the social sciences often find very different results. Even in disciplines like medicine, where one might imagine there to be a direct, physical relationship between the intervention being tested and its consequences, results can vary — but many think the situation is worse in the social sciences. This is because the relationship between an intervention and its effects may depend on multiple factors, and differences in context or implementation can have a large impact on the studies’ results.

• EV Eva Vivalt is a Research Fellow and Lecturer at the Australian National University.

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Article Contents

What does involving consumers in research mean.

• Article contents
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• Supplementary Data

Charlotte Williamson, What does involving consumers in research mean?, QJM: An International Journal of Medicine , Volume 94, Issue 12, December 2001, Pages 661–664, https://doi.org/10.1093/qjmed/94.12.661

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Consumers' concerns and priorities for research are different from those of clinical researchers. 1– 3 That is not surprising, since consumers' and health professionals' concerns and priorities for treatment and care are also different. 4– 6 So creating the means for trying to reach agreement between consumers and doctors is important. 7, 8 For research, this is just beginning.

Academic institutions and large medical charities have generally left the choice of topics and methodologies to their professional and scientific committees rather than including non‐professionals in making those decisions. 9 Some consumer groups have long been concerned by what they see as the lack of investigation of certain topics, poorly designed or unsafe research, and a disregard of research evidence from other countries. 10, 11 However, their lobbying of governments and professional bodies made little progress until the development of new flamboyant techniques by AIDS consumers. 12 Their methods, ranging from wearing red ribbons to civil disobedience, led to the routine consultation of consumers in the design of AIDS research. This success influenced the approach of consumer groups for other diseases, including breast cancer, Parkinson's disease, Alzheimer's disease and juvenile diabetes. 12 Members of such consumer groups have pressed research organizations to include their members or other consumers on their research committees, or they have initiated research themselves, formulating their research questions and hypotheses and inviting clinicians and researchers to join them. 13 Now consumers' active involvement in research is being promoted by some clinicians and researchers. 14 Moreover, the government endorses it. 15 So it is time to look at the meanings for research of consumers and their involvement.

The term consumer here means patients, past patients, prospective patients, long‐term users of health services, relatives caring for patients or users, and people who speak for these primary consumers through local and national support and activist groups, community organizations such as community health councils, local and national coalitions of such groups, and international networks. Health‐care consumerism, also called the patient movement, is the active extension of patienthood. It is a voice speaking for the perspectives, ideas, interests and values of patients, users and carers as they define them. The perceptions, reflections and judgements of patients, users and carers inform the work of active consumers, called consumer representatives or consumer advocates. 16 Active consumers work to secure changes to professional and institutional systems, policies and practices that will meet other consumers' interests and values. 17 Often, though not always, active consumers are or have been patients or carers or users of health care like those for whom they are now active. 18 The changes they secure, accepted into practice, gradually change the experiences of patients and the expectations of the public. Members of the public, indeed everyone who is not a health‐care provider, are also sometimes called consumers. 19, 20 But what matters for most medical research is that those counted as consumers are either patients, users or carers with experiential knowledge of the disease, condition or situation to be investigated, or are active consumers familiar with their perspectives and aligned with them. 18

Involvement in research means active involvement, not simply a role as participants or subjects. The ethical rationale for consumers' involvement in research is their interest or stake in how well it creates new knowledge that could help patients or future patients like themselves. Consumers' voices should be heard equally with those of clinicians and research scientists. 21 The pragmatic and instrumental rationale is that consumers' experiential knowledge and challenges to professional perspectives are valuable for bringing about improvements to the prevention of disease, to treatments and to the quality of care. 21 Consumers will argue for research that looks at questions that matter to patients. 22 Research that is relevant to patients' needs as they experience them will make a more effective contribution to health care than other research. 23 Drawing consumers into the design of research will make it more sensitive to prospective participants' or subjects' concerns and so encourage them to take part. 24 Using consumers to disseminate findings will prompt patients to request evidence‐based new treatments or procedures and so speed up their acceptance into clinical practice. 25 So hopes for involvement run high.

Involvement can take place in two main ways. The first is consultation. Examples are: asking consumers for their views on some specific topic through questionnaires or meetings; inviting consumers to explore issues in focus groups; 26 sending consumers research proposals to comment on 27 or published papers to review. 28 What the consumers say can be influential. But they usually have no direct part in decisions about what action to take as a consequence of their views. Consultation is important, however, because it can be used at any stage of the research process, in any combination of methods and on any scale to draw on wider views than any research group has on its own.

The second form of involvement is partnership. Partnership is face‐to‐face interaction in shared decision‐making, with agreement that decisions will not be changed unilaterally, and with attempts to ensure that concerned parties are not excluded through lack of information or inadequate representation. 29 This is an exacting standard. It can be applied to the doctor‐patient clinical relationship, the paradigm of partnerships in health care. In research it can be applied to groups: prioritizing, advisory, steering, design, data monitoring, evaluation and dissemination groups. Whether and how it is applied varies. In 16 out of 60 randomized trials in UK, for example, consumers were members of the steering committee, but none was involved in monitoring data. 30 In the USA, consumers take part at all levels in clinical trials, although not yet in all clinical trials. 31

Taking the first steps into partnership is difficult. Consumers who invite members of the medical and scientific research communities to join a research group know whom to approach, because those communities' members are easy to identify and categorize, e.g. consultant cardiologist, medical statistician. But health professionals often feel uncertain about which consumers to approach. The answer is that much depends on the level and scope of the group and its work, as it does for other working groups of professionals and consumers. 32 Consumers who are current or recent patients, users or carers can offer their knowledge and concerns from their immediate experience of the index disease, condition or situation, and from their treatment and care as it affected them. Those insights are indispensable. But each can usually speak only for himself or herself. The units of collective knowledge and action are consumer groups: their members can speak for the perspectives of consumers like themselves. Much of that will be of wide application, but it may not cover all the particular experiences and concerns of consumers in general. Consumer group members who have developed more extensive knowledge and can apply broad perspectives to policy and strategy are called consumer advocates. 7, 33 Though these categories overlap, a research group at national level prioritizing topics or overseeing a large clinical trial requires more consumer advocates than a group managing a local research project where consumer group members' local knowledge is pertinent. It should become easier to find the right mix of consumers as the number of disease‐, condition‐ or situation‐specific consumer groups continues to rise 12 and engenders more consumer advocates. To provide a mix, to prevent tokenism and to create partnership, several consumers should be appointed to each research group.

As for all appointments, inviting consumers to take part in consultations and appointing them to research groups should be done through open advertisements and transparent procedures. Invitations to apply can be issued through all relevant local and national consumer groups, mention in local and national media and notices in hospitals and general practitioners' surgeries. Prospective patients who take a precautionary interest in a specific disease or condition they fear may afflict them can have useful perspectives. So can prospective participants or subjects. 15 Special efforts will be needed to reach consumers who do not come forward readily, for whatever reason, 34 and for conditions, situations or diseases for which there are no consumer groups or consumer advocates. 31 But consumer advocates specializing in one disease, condition or situation should be able to apply general principles to another, 16 provided they brief themselves on matters and issues particular to the new field. The consumers consulted or appointed to research groups should be as similar as possible to those who will be participants or subjects in ethnicity, social background, etc. 35 For appointments, the selection panel should include two or three consumer members, 18 helping professionals to avoid the temptation to cherry‐pick consumers they think will always agree with them. 36

Partnership means including consumers in the group from the first so that they share in setting the agenda. It also means that they, like the professional members, will be expected to contribute as much as they can to every aspect of the group's work. Professionals naturally read professional journals. Consumer advocates read professional journals and consumer publications. They read the first for information, to pick up shades of professional opinion, and to identify conflicts and convergences with consumer perspectives. But finding parallel material in consumer publications can be difficult, because much consumer writing and debate is in the grey literature, not published in peer‐reviewed medical or scientific journals, and so not listed on MEDLINE or other databases. 37 Examples are the important consumer charters for research, statements of standards for the ethical conduct of research that supplement the guidelines drawn up by doctors and medical ethicists. 38, 39 So the consumers' tasks can be onerous. They also have many commitments, may be housebound themselves or caring for others; their convenience and comfort should be considered. 40 Honoraria as well as expenses should usually be offered to those who take part in consultations or in research groups. Time, knowledge and effort have costs.

The NHS organization promoting consumer involvement in research publishes guides for consumers thinking about taking part in research and for professionals thinking of inviting them to do so. 13, 25 Some consumer groups provide training for their members in scientific concepts, data analysis, how to present arguments, how to work with professionals. 34, 41 The aim is to give consumers confidence and enough knowledge of scientific concepts and of research processes to enable them to contribute consumer perspectives at every stage and level of the work. Such training is not yet matched by training for doctors and other professionals in how to work with consumers, though some is planned. 42

All partnerships are potentially fragile. Consumers' relation to the medical community is complex, at once admiring and critical, challenging and supportive. Experienced consumers know they must try to understand the profession's values and norms, and its dynamics of conservatism and change, recognize the differences between clinical and working relationships, manage their feelings of ambivalence, and draw on forbearance as well as on courage. Doctors' relation to the consumer community is similarly complex. They need to try to understand the consumer community, work collaboratively rather than authoritatively, manage their feelings of ambivalence, and respect consumers' expertises. Theirs is perhaps the harder task, for it is part of the cultural shift that the profession's leaders espouse. 43

In these early days of consumer involvement, the claims made for the benefits of such involvement tend to be more predictive or impressionistic than demonstrable. With some exceptions, it is difficult to find data on exactly how the processes and outcomes of research have been changed from what they would have been without consumer involvement. Issues never raised can be as important as those explored. Points rejected are less likely to be flagged up in reports than those accepted. Both researchers and consumers are likely to have unrecognized biases and gaps in their knowledge, as measured against some ideal symposium of all stakeholders' perceptions, values and interests. So research into the effects of consumer involvement must be sophisticated, with consumers and social scientists as well as clinicians and scientists asking the research questions and drawing up the methodologies to try to answer them.

Whatever the immediate effects of involving consumers in research, with thought and care, the benefits predicted, including that of encouraging patients to take part in research that seems to them worthwhile and sensitive to their fears and hopes, are probably achievable. Much will also depend on the wider environment, on the move from representative to participative democracy, 44 on research ethics committees, 45 on doctors with new and radical ideas 46 and on the Department of Health. Involving consumers in research is not the only way to work towards reaching consensus between consumers and clinical researchers about what research should be done and how it should be done. But it is a promising way.

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New Consumer Research Technology for Food Behaviour: Overview and Validity

Garmt dijksterhuis.

1 Wageningen Food and Biobased Research, Wageningen University and Research, P.O. Box 17, 6700 AA Wageningen, The Netherlands; [email protected]

René de Wijk

Marleen onwezen.

2 Wageningen Economic Research, Wageningen University and Research, P.O. Box 9101, 6700 HB Wageningen, The Netherlands; [email protected]

Background: the last decade has witnessed an explosion of new consumer behaviour research technology, and new methods are published almost monthly. To what extent are these methods applicable in the specific area of food consumer science, and if they are, are they any good? Methods: in this paper, we attempt to give an overview of the developments in this area. We distinguish between (‘input’) methods needed to shape the measurement context a consumer is brought in, e.g., by means of ‘immersive’ methods, and (‘output’) methods that perform measurements proper. Concerning the latter, we distinguish between methods focusing on neuro-science, on psychology, and on behaviour. In addition, we suggest a way to assess the validity of the methods, based on psychological theory, concerning biases resulting from consumer awareness of a measurement situation. The methods are evaluated on three summarising validity criteria; conclusions: the conclusion is that behavioural measures generally appear more valid than psychological or neuro-scientific methods. The main conclusion is that validity of a method should never be taken for granted, and it should be always be assessed in the context of the research question.

1. Introduction

In recent, years many new technological research methods have been proposed, tested, and published that enable the study of consumer food behaviour. The developments follow each other very rapidly, and by now, a very diverse palette of research methods has become available for consumer researchers to choose from. On the one hand, this results in new and promising possibilities to study consumer food behaviour; on the other hand, it may also result in unclarity for researchers. It can be difficult to decide which methods are available for specific research questions and how useful and valid they are. An overview of a wide range of methodologies is needed. For food consumer scientists specifically, it is of interest to find out whether new methods are applicable in their area and if they provide valid measurement results.

A main question regarding new technologies is how to rate the validity of the new technologies. In particular, some of the novel technologies may change the way consumers are aware of being part of a study. This paper concerns the validity of new consumer research technologies, as applied in a food behaviour context. Therefore, we introduce three validity criteria based on psychological theory concerning biases resulting from the awareness a consumer has of a measurement situation (see Appendix A ). The measurement methods introduced will be scored on these criteria in order to enable a view on the validity of the methods. First of all, the three criteria are introduced to spawn discussion concerning validity of consumer research. Such discussion on the validity of consumer research is needed (e.g., [ 1 ]), as was also pointed out by Dijksterhuis [ 2 ] in a critical review concerning the high failure rate of new food products.

1.1. Input and Output Technologies

The current study adds to the literature by providing an overview of novel technologies in the area of food consumer behaviour. We broadly differentiate between input and output technologies. Some technologies are used to bring the consumer into a certain situation in order to create a mind-set as close to real life as possible. Different contexts, operating at the input side of a study, are typically the independent variables of a study. Although we acknowledge the presence of different types of input, as noted, for example, by the recent so-called EPI-cube (Embodiment-Presence-Interactivity, [ 3 ]), we will not further differentiate between them.

Other new technologies are used to measure consumer related variables in order to get a grip on consumer behaviour or responses; these work at the output side of a study and typically are the dependent variables of a study.

Providing information to a consumer in an experiment (or to a respondent in a survey) can happen in many ways. The input side of consumer research consists of the experimental situation, which can include the screen of an online survey, the instruction by an experimenter, the physical surrounding during a measurement or any context provided by means of VR and related technologies.

At the output side of consumer behaviour, several types of new technologies can be used to measure behavioural outcomes. Here, we distinguish three types of methods aimed at collecting data on a neuroscience, a psychological, or a behavioural level.

Neuroscientific measurements, are measurements related to neural activity. They often refer to neurophysiological measurement, be they CNS (Central Nervous System) or ANS (Autonomic Nervous System) based. CNS-based measurements often employ such technologies as EEG, fMRI, or MEG, and they often point at some form of cognitive processing. Other psychophysiological measurements, i.e., of a wide range of ANS functions, often indicate the execution of tasks.

Psychological measurements inquire about psychological traits or states of a human subject—in our case, a ‘consumer’. Despite the seemingly broad range of methods that could fall under this heading, we define them as measurements of mental phenomena. Self-reported, past, or prospected behaviour is also considered a psychological measurement.

Behavioural measurements refer to the observable behaviour of a consumer. Any movement that is somehow monitored can constitute a ‘behavioural measurement’. Typically, these are motor measurements such as gait, movement, agitation, but hand movement, eye-movement, face movement, or chewing behaviour also fall under this heading. Response time measurements are also an example of a behavioural measurement.

One recent interesting innovation exists in voice assistants. Typically, one may think of voice assisted device operation (car navigation, phone number calling, (very) smart TV programming, [ 4 ]). One can also think of types of smart devices, such as Google Assistant or Siri. These devices can be used for consumer research by asking questions or giving instructions to consumers to operate their devices. At the same time, these smart devices may collect data in the form of the responses consumers give to their instructions or questions. Ethical problems obviously lurk, as it is not always clear if a consumer wants his/her voice commands and responses to be recorded for subsequent analysis, even if anonymously.

1.2. Implicit and Explicit Methodology

Explicit consumer research methods are those methods that require some form of answering from consumers, often using conscious reflection. Consumers have to make explicit what they mean, why they act as they do, why they make some choice, etc. Hereby, they are conscious of what they are answering, and they may ponder their answer before they give it. In contrast, implicit methods do not require consumers to do this. The implicitness, here, refers to the fact that information concerning consumer behaviour is inferred from a measurement without the consumer knowingly having control over the outcome (cf. [ 5 , 6 ]). As an example, the amount eaten and the speed with which food is consumed can be seen as implicit measures of acceptance of the food.

One may be tempted to think that implicit methods always require consumers being unaware of the measurement, but this is not true. It is possible to have consumers explicitly report on some matters but without the research question being addressed in this matter. The answers of the consumers can next be studied to provide evidence of their ideas, opinions, or attitudes concerning the matter. This type of measurement is implicit, although consumers are to explicitly answer some questions. In this case, the implicitness of a consumer measurement concerns the consumer being unaware of the underlying research question, rather than in the way consumers are to provide data (which appears explicit). In Section 4 , such implicitness will be coupled with a method’s validity.

In De Houwer and Moors [ 7 ], it is argued that a measurement procedure (what we may call a research method in our context of food related consumer studies) cannot be named ‘implicit’. They rather reserve the term ‘implicit’ for measurement outcomes. This means that we could not talk about ‘implicit methods’ but of ‘implicit measurement outcomes’. De Houwer [ 8 ] suggested to equate ‘implicit’, in this sense, to the term ‘automatic’. However, most of the context of their discussions of implicitness are done in the context of (implicit) attitudes. This means that the constructs-to-be-measured are ‘implicit’ to the subjects. This applies to the IAT (Implicit Association Test, ref. [ 9 ] revealing implicit stereotypes, often used in the context of racial prejudices. In the context of our current paper, being food related consumer behaviour, the constructs-to-be-measured, themselves, are not necessarily to remain unknown to the subjects. Subjects may e.g., know they are consuming too much unhealthy food, to name an example. What they do not know is the underlying motivation for their food choice, so this motivation remains implicit. The type of research methods we refer to aim to understand, to explain, and, ultimately, to predict consumer food related behaviour.

Some authors seem to equate implicit measurements with physiological measurements. Often, such methods are indeed implicit, but implicit measurement is by no means restricted to this. De Wijk and Noldus [ 10 ], in their overview of implicit and explicit measures of emotions, list four (implicit) measurement types:

• measures that reflect the activity of the central nervous system, such as EEG and fMRI,
• measures of activity of the autonomic nervous system such as skin conductance and heart rate,
• expressive measures, such as facial expressions,
• behavioural measures.

One could argue that specific facial expressions, such as surprise, can also be the result of emotional processes and as such, partly fall under the second type, an autonomous reaction. The same can be said about behavioural measures. Posture or walking speed can be the result of, or correlate to, autonomic activity. What De Wijk and Noldus [ 10 ] make clear is that there seems to be no sharp distinction between implicit and explicit measurement.

In this paper, we suggest not to define implicit measurements in terms of the measurement process itself but rather, in terms of the way a measurement situation is set up.

2. Input Side: Context Providing Technologies

Food products are seldom consumed in isolation. Instead, foods are typically consumed in specific consumption situations, such as at home, in a restaurant, or at a canteen. These situations tend to shape our food experiences. Consequently, food preferences measured in a sensory laboratory may be different from preferences for the same foods measured in a real-life situation, as demonstrated by numerous studies. Many of these studies report higher preference ratings in real-world situations [ 11 ] or differences between several contexts [ 12 , 13 ], even though there are also some studies that report no difference [ 14 ].

Testing consumers in real-life consumption situations is often difficult because these situations tend to be noisy and offer little control over conditions, which may have unwanted effects on the measurements. Instead, there is a growing trend to recreate consumption contexts in the laboratory. Using so-called ‘immersive technologies’, these recreated contexts offer a compromise between real-life consumer experiences and tightly controlled laboratory conditions. ‘Immersion’ is a term used for what is thought to happen to consumers in a research situation where they are provided with a situational context that totally captures their attention—ideally to the point that they forget that they are in an experimental context. As advantages of ‘immersive technologies’, some have been mentioned:

• overcoming the ‘respondent burden’ (the degree to which a consumer finds participation difficult, time consuming, or emotionally stressful [ 15 ],
• increasing some statistical quality (validity, reliability, power) of the collected data [ 16 ],
• adding control to an experimental situation, while keeping the situation ‘ecological’,
• adding context to (online) surveys to enable more truthful (and hence valid) answers or to increase commitment from respondents.

In a way, telling a good story to a consumer, in an otherwise quiet testing room, can also be seen as an immersive ‘technology’. Reading a story from a paper or listening to a story told is so low-tech that we will not cover this in more detail here [ 17 ] (p. 30 on ‘Story Telling’ and p. 31 on ‘Sketchy descriptions’). In short, anything to have the subject mentally ‘leave’ the testing surrounding and enter the realm of their imagination can be seen as an ‘immersive’ technology. Short, specialised surveys have been developed to probe how deep consumers felt ‘immersed’ while being in such a situation [ 16 ].

In the below, we will introduce some recent (input) context providing technologies, ordered from a relatively low level of ‘reality’ (or ‘immersiveness’) to a high level.

2.1. Tabletop Technology: A Tablet Computer as an Expensive Coaster

To provide consumers with a special eating context while dining, tablet computers have been used. They can provide a special context to the food, and it’s feasible to apply them in this manner in food testing ( Figure 1 , left panel). However, other ways may be possible that are less cumbersome or expensive. Other technology, e.g., sound, has been provided with food in a similar vein ( Figure 1 , right panel).

( Left panel ): a tablet computer as a plate to hold your food. ( Right panel ): sound provision during eating (from [ 18 ], p. 316 and 329, resp.).

2.2. Virtualised Food Products

Using photogrammetry, a food product is totally visually digitalised and digitally recreated. The recreation may follow a (factorial) design, systematically varying aspects of the food product (e.g., its size, shape, colour, surface roughness, etc.), to be presented to consumers. The consumers next assess sensory properties, of course restricted to visual aspects, but they could assess expectations of other aspects, as texture or flavour. Gouton et al. [ 19 ] present an application using chocolate chip cookies and a comparison of simulated cookies with real cookies. Some differences were found, which Gouton et al. [ 19 ] suggest may depend on specific photogrammetric software settings.

2.3. VR Technology

A plethora of VR-related technologies has seen the light in the last decade. VR-glasses have been around for a while, and they can now be obtained against relative low costs. Tools and devices exist where one can insert a cell phone in a device ( Figure 2 ), and dedicated apps on the cell phone will assure a VR-presentation when wearing the device. Cardboard -fold your own- versions exist for under €1. These lend themselves for being sent to large groups of consumers, for in home, online survey testing, providing context through dedicated apps. Dynamic context, the typical VR-experience where one can virtually move around inside a surrounding, can be provided through these means. Many tools include integrated sound and, often, spatial sound effects.

( Left panel ): VR glasses, wherein one can slide a cell phone; ( Middle panel ) (reprinted with permission from OVR technology (Copyright OVR technology)) and ( Right panel ) (reprinted with permission from Takuji Narumi (Copyright Takuji Narumi)): a VR system enabling olfactory stimulation.

Taufik, Kunz, and Onwezen [ 20 ] conclude that VR-provided contexts often lead to measurement results comparable to their real life counterparts. In addition, they conclude that the technology also seems promising to lead consumers to change their behaviour. Fang et al. [ 21 ] point out that VR methodology can also help reduce the hypothetical bias (the difference between a real experiment and an imagined one) by introducing a form of realism to the measurement situation.

Adding odour, relevant in a food context, to a virtual surrounding is more of a challenge. Several devices have been developed, but maybe some should be called contraptions instead (see Figure 2 , rightmost panel). Advertisements exist, boasting about their ability to produce 1 ms short odour pulses and a time to switch between odours of 20 ms. When one realises that it may take some 300 ms, depending on many circumstances, for an odourant to reach olfactory receptors [ 22 ], such numbers look a bit over-the-top.

Other VR applications exist that use vibratory devices to simulate felt textures on the hand [ 23 ]. Straightforward applications in food related consumer science may not be in sight, or it must be the possibility to deliver vibratory stimulation in-mouth to simulate oral texture. Although technically feasible at this moment, it’s probably currently restricted to laboratory environments [ 24 ].

Bone conducting devices have been applied to record auditory and/or vibratory stimulation. In particular, in specific food related studies, where one may want to record the chewing sound as perceived by the chewer her/himself, and we know that chewing sound is, to a large extent, bone conducted sound [ 25 , 26 ]. They could also be used to provide food related auditory or vibratory stimulation, e.g., to adapt the sound perceived (vibrations felt) by consumers when chewing food (although we have not found papers in this area). A vibrating straw technology exists where no food is sucked through the straw, but a vibratory device can deliver the illusion of food streaming into the mouth [ 18 ].

Another technology is the development of ‘Sensory Reality pods’ ( Figure 3 ). Inside something that is best described as a ‘phone booth’, a subject takes place, puts on VR-goggles, and can, from within the Pod, be stimulated with sound, smells, air flows, and heat radiation. It provides a multisensory immersive surrounding. Applications in food science may be a bit farfetched at the moment, but they are certainly feasible. At this moment, one person at a time can be immersed, but in theory, several pods can be used simultaneously, if only cost were no issue.

A subject sitting in a ‘Sensory Reality Pod’ (Reprinted with permission from SENSIKS. Copyright 2019 SENSIKS).

2.4. AR Technologies

In AR (Augmented Reality) parts of the real physical surrounding of a consumer is integrated into the virtual surrounding. VR knows limited application in eating research or other applications due to the fact that consumers cannot comfortably interact with a real food. Many VR devices do not allow smelling, eating or drinking, and will spoil the immersion. In AR the interaction is provided through integrating an object into the virtual surrounding. A fully natural interaction with ones surrounding, in particular with the (food) object, is still not possible. The interaction will likely still feel alienating.

In the development of packaging, AR applications are clearly envisionable, as they allow visual interaction of a consumer with a packaging. Coupling visual AR to a haptic vibratory device (providing manual package texture simulation), one can imagine that the interaction may reach a level close to reality.

2.5. (Serious) Gaming Applications

All above mentioned examples can also be used in gamification applications. A computer game aims to give a subject a lively sense of immersion by providing a virtual context. Reality is not implied in the type of context (the game can be about the weirdest of worlds), but it is provided by the interaction with the environment by the moving and handling virtual objects, reactions of other persons (or alien entities) in the game, etc. The original aim of computer games is entertainment, but a shift to consumer research applications is feasible. Gamification has also been applied to make surveys more engaging [ 27 ]. This is also an application area that can be of use to any type of consumer research.

Applications in (food) marketing exist in an application where visitors of an entertainment park can plan ahead their visit, enabling the park to optimally locate their services [ 28 ]. In particular, in the context of online purchasing, there may appear a future for such applications.

Giving smell or taste feedback in gaming applications has also been suggested [ 29 ], although this appears a farfetched application, as, at this moment, it is still at a distance from use in consumer research.

2.6. Wall Projections/CAVEs

The mentioned alienation in AR may result in it not yet being used much in food related applications or in typical consumer science applications where many consumers are tested. An alternative is the projection of a surrounding onto the walls of a room or in a ‘CAVE’. A CAVE (a recursive acronym: Cave Automatic Virtual Environment) is a (small) room, where, onto the walls, an environment is projected, either by back projection (where the walls need to be translucent), normal projections by means of beamers, or using very large LCD-screens. WFBR (Wageningen Food and Biobased Research, one of the research institutes of Wageningen Research, part of WUR.) employs a CAVE-like projection room, where eight beamers can project images onto the walls of a normal room ( Figure 4 a). In addition to the visual projections, sounds can be played, and an odour dispersing unit is installed as well.

( a ) WFBR ‘Experience room’. Lower: projections of a beach ( b ) and a sushi restaurant ( c ). Note that props are used in the room to add to the immersion. (Reprinted with permission from WFBR. Copyright 2019 WFBR).

Figure 4 b shows the ‘Experience Room’ in action in a recent study where a beach environment was created. Note that props (towels, sand-coloured flooring) are used to add to the level of immersion in the simulated surrounding. The (c) panel of Figure 4 shows a recreated sushi restaurant, including real tables with menus and some (plastic) plants to help increase the reality level of the simulation [ 30 , 31 ].

One can imagine that there is no limit to the possibilities of providing (near real) projections with sound and odour. Room temperature, air humidity, etc., could be added, provided the budget to develop the technology is high enough.

2.7. ‘Non-Virtual’ Reality

A special case is the immersion in a rebuilt environment, as was done by Holthuysen et al. [ 11 ], who rebuilt an airplane fuselage in a lab room, to test air catered food items, complete with airplane engine noise. It was immersion, but it is not the type of consumer technology we’re addressing here.

3. Output Side: Measurement Technologies

In the sections below, several ways of collecting consumer related data are introduced, based on them collecting variables from neurophysiological, psychological, or behavioural origin. We will present different methods in the three areas mentioned. Some will be mentioned only scantily, as they may be very new, hardly used, or are on the fringe of what we can see as promising new consumer measurement methods in the food area.

3.1. Neuro Scientific Measurement Technologies

Neuroscientific variables can be of an overwhelming complexity, in addition to them being plentiful. They have in common that they attempt to measure neuronal correlates of consumer behaviour relevant to the area at hand. We will not introduce such techniques as EEG, fMRI, MEG, ANS-measurements in some detail, nor psychophysiology in general, but we list specific ways in which some of these technologies have recently been put to use to understand consumer food related behaviour. All these measurement techniques have that they require a connection to the human body in common. Although they do often not have to penetrate the skin or require otherwise invasive medical procedures, their impact on normal functioning mostly makes them listed as potentially invasive techniques. They will, thus, require some form of Ethical Clearance.

Innovation in food consumer neuro science may not only lie in developing new technology, in addition to the many existing methods that exist, but also in their combination. According to Niedziela and Ambroze [ 32 ], these methods should be used in addition to, not instead of, established food consumer methodology. It is useless to employ an expensive and complex measurement that is equivalent to liking, when a simple liking question may provide the same result.

Another innovation is taking neuroscientific measurements outside the lab into the real world, which has to do with making the technology portable. EEG-caps have become, more-or-less, portable and allow for this. Ambient EEG measures may introduce additional noise, possibly rendering measurement results even more difficult to interpret. In addition, the validity of EEG brain activity assessments is not always known. A recent paper shows that an unequivocal interpretation of EEG measures is not always possible. Eijlers et al. [ 33 ] measure arousal, resulting from looking at magazine advertisements (including food ads) using EEG, and conclude that their findings cannot be taken to show ad effectivity.

A recent neurophysiological development is fNIRS (functional Near InfraRed Spectroscopy) applied to brain activity. It is a technology where NIR radiation is sent through the skull by an optode and picked up after it has been reflected by brain tissue. The hemodynamic response of the brain tissue affects what can be picked up, which is related to the activity of the brain tissue. This technology has been applied in consumer research. It is claimed that it is more portable and easier to handle than other neurophysiological measurements. However, it appears to be, to date, removed from practical (portable) applications in consumer science, and it is a laboratory tool still (but see [ 34 ].

Augmenting online measurements with neuro scientific measurements (including ANS) has also become feasible. Using the camera in respondents’ (laptop) computers, heart beats can be inferred from a colour change of the face or forehead. Other ANS-devices, or even EEG, can be coupled to respondents’ computers, but this will bring some additional complexity still and is not yet applicable for large consumer samples.

An example of an innovative ‘field’ application combining several above mentioned (near) portable technology can be found in Brouwer et al. [ 35 ]. They combined measuring EDA (Electro Dermal Activity), ECG (Electro CardioGraphy) and EEG, allowing for extraction of several parameters, while their subjects were cooking and tasting. The subjects were voice-instructed to follow a strict cooking and tasting protocol. They cooked a meal with either chicken or mealworms, which was only revealed to them during cooking. Brouwer et al. [ 35 ] report they can, from the neurophysiological data, predict, with 82% accuracy, what dish a subject was cooking (mealworms or chicken). The aim of this study was to provide an implicit measurement method of affect or emotion during an actual cooking experience. In a follow up study, Brouwer et al. [ 36 ] collected measurements of facial expressions and wrist accelerometry in addition. Figure 5 shows some subjects in the Brouwer et al. study [ 36 ], obviously in a laboratory setting, not in their own kitchens.

Subjects in the study of Brouwer et al. (2019): ( a ) subject cooking (photograph from [ 36 ], Figure 1, p. 5. Reprinted with permission from ref. [ 36 ]. Copyright 2019 A.-M. Brouwer); ( b ) dish with mealworms in the frying pan; ( c , d ) subjects’ faces on seeing the mealworms (Reprinted with permission from A.-M. Brouwer. Copyright 2019 A.-M. Brouwer).

Biological Measurement Technology

For sake of completeness, we’ll list some recent developments that may not strictly fit the moniker of ‘neurophysiology’ but are biological in their origin. One recent development is a portable glucose level sensor ( Figure 6 ). More of these types of sensors have become available, and they can link (via Bluetooth) to a smartphone app or to other devices. Although some say their device is ‘non-invasive’, a short needle is to penetrate the skin still. Nevertheless, consumers sometimes report that they forget they are wearing the device. Finding out if truly non-invasive versions [ 37 ] exist (and are reliable) will require additional literature search.

Wearable glucose sensor, to be worn on (and in) the arm. (Reprinted with permission. Copyright 2022 Marieke Ubachs).

Some ‘smart watches’ can provide biological or psychophysiological data as well. Obviously, wearers of these devices are not continuously aware of the measurements being made.

Breathalysers have also been deployed to non-invasively measure physiological parameters from subjects. To what extent this is applicable, or has been applied, in a food related consumer context is not known to the authors. Obviously, it can potentially provide relevant diet-related information, including that related to flavour release, or flavour retention, in mouth or throat.

Another recent development, of quite a different nature, is what some may call ‘consumer genomics’, as in the paper by Masih and Verbeke [ 38 ], which covers the relationship between the expression of certain opioid receptors and the results of individuals on the PANAS mood scale [ 39 ]. To what extent claims in this field can already be translated to real life consumer behaviour in the food area remains to be studied.

3.2. Psychological Measurement Technologies

Any survey, or set of questions, can be given to a group of consumers, thus constituting a ‘psychological measurement’. Even when new questionnaires or new psychological scales are developed, the technology is hardly to be called new. Perhaps, with adaptive on line surveys, e.g., of the conjoint type, one can speak of some (technological) innovation. Newer developments are found in Big Data and AI technology that enable ‘very’ interactive surveying, where a path through a set of items may depend on the answers of many other respondents.

In this section, we will present several rather different technologies that aim to obtain information of consumers’ attitudes towards, or reported choices of, food. Many of the newer technologies are often online extensions of earlier developments. Sensometrics, covering statistics, data collection, and experimental design, is a rapidly evolving field from which we list a few innovations. AI and Big Data oriented applications form such a vast and rapidly expanding field that we decided not to include this area.

3.2.1. Text and Web Scrape Technology

Automatic interpretation of texts, either from the web or otherwise, made available is a relatively new and currently expanding technology, also called text mining, or text analytics. It is defined by Hearst [ 40 ] as ‘the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources’. It has to be set apart from text search, as this refers to the finding of things you are looking for, so things that you already know something about (e.g., that they exist). Text mining aims to discover new things, previously unknown information, from any text source. It is already being applied in food consumer contexts [ 41 ]. In Figure 7 (taken from [ 41 ]), the size and quality of text sources is shown. It is presented here merely to provide an indication of the possibilities of the technology, as well as for consumer research in our field. The figure shows that the best quality is provided by the scientific publishers, and the lowest is by general social media sources. In between is the internet, where ‘anything’ can probably be found.

Text sources’ quality and size, for text mining purposes, in a food and nutrition context (Figure 2 from [ 41 ]).

These days, text mining will likely also be possible from recorded spoken text, enabling even greater ‘mining’ possibilities.

Web-scraping is becoming a standard tool, provided by several agencies in many different contexts. A relatively recent application is the automated mining of food and recipe related data on the web. Another related technology concerns the analysis of texts from consumers that have been asked to describe a certain product. This text can be automatically processed [ 42 ]. In this way, automatic analysis of consumer prompted, written comments on specific products may reveal underlying ideas and feelings that consumers have about specific products.

3.2.2. Surveys and Ecological Momentary Assessment (EMA)

Online survey research is not new. Every thinkable set of questions can be asked to consumers, both in offline and online surroundings. The easier it is to ask questions, the easier it is to answer them, and the more pregnant the following dictum becomes: “The biggest problem in asking consumers a question, is that you will get an answer” (after E.P. Köster, personal communication).

Additional measures of an implicit nature (mouse clicking, answering speed, etc.), can also be collected online, in addition to the answers given in the survey. These are behavioural/motor, rather than psychological, measurements.

Alerting consumers at certain moments to give an answer to a question, as is possible via smartphone technology, enables a new way of surveying. When consumers are required to list their activities or food consumption at random moments during the day, a bias that may arise from filling out questions at fixed moments on the day will be countered (e.g., using the Foodprofiler [ 43 ]) or the Traqq app, [ 44 ]). Another development is EMA (Ecological Momentary Assessment [ 45 , 46 ]), the sending of surveys to consumers’ smartphones when they are in a certain surrounding. Obviously, they have to first agree to their smartphone providing their location, and other parameters, to the investigators. If this is agreed, questions can be sent to consumers depending on a host of environmental parameters. Based on the location (in a supermarket, in a city, at a bus-station, etc.), time of day, weather conditions, previous activities (cycling, walking, shopping, relaxing), specific questions can be sent to the consumer. In this way, the investigators can ask questions appropriate to the real context the consumer is in. This technology also allows to interactively adapt a consumer’s set of questions, depending on the answers of another specific consumer or a consumer segment.

3.2.3. Online Interaction with Consumers

Many ways exist to interact with consumers that are not in physical proximity, but they are online. Currently, there are online versions of many consumer face-to-face research methods.

In Table 1 , traditional and online focus groups are compared (adapted from [ 47 ], Table 6.1, p. 137).

Comparison of traditional focus groups and their online version (adapted from [ 47 ], Table 6.1, p. 137).

Traditional focus groups are a well-established tool for inventorying consumer opinions in a range of areas. Online focus groups [ 47 ] are a relatively new development. The participants in such a focus group attend the meeting from behind their computer, as does the moderator. Nowadays many people have, at least, some experience with online meetings, so online focus groups will not be alienating to most consumers. It must be added that video/audio connections only cannot replace the full gamut of non-verbal communication that a moderator will use in face-to-face meetings. Validity issues—not uncommon in focus groups—remain, and they will have to be closely scrutinised. Special software has been developed to aid the interpretation of focus group transcriptions [ 48 ].

Netnography is ethnographical methodology applied to consumers’ internet behaviour. The online activities of consumers can be surveyed, or otherwise investigated, all within the limits of privacy legislation, which can be a complicating and contentious area. In addition to individual consumers’ net-behaviour, consumers subscribing to communities and services can be studied. When, say, a consumer is following recipe-sites that contain vegetarian recipes, in addition to traditional ones, a food producer may be interested in knowing the reaction of this consumer community on certain plant based products to replace dairy.

3.2.4. Sensometrical Methods

Sensometrics is the field that concerns itself with research methodology, statistics, and experimental design, especially aimed at collecting and analysing data in the sensory and consumer sciences ( www.sensometric.org , accessed on 4 January 2022). The remit of the field reaches beyond food to include all instances where humans (in trained panels, or as consumers) are used to collect data concerning their perception, or liking, of product-related stimulation. Originally, these products were limited to food products and food-related stimuli, and the panels typically were sensory panels. Over the years, the field included ‘sensory and consumer’ studies of non-food products, including a wide a range of products, spanning home and personal care to cars. The Sensometric Society organises a conference every second year, and the contributions are published in the Elsevier journal Food Quality and Preference. They are a rich source of methodology and statistics in the area of sensory and consumer science. New methods, both for data collection and for data analysis, are continuously published, too much to even attempt to summarise in this review.

An example of a typical new(ish) method from this area is the technology whereby consumers can sort items on a screen. The position of the items after the sorting can be totally free, in groups, or following a pre-specified criterion (‘Sort these bottles into three groups based on which you find belong together.’). The numerical position on the screen or the group structure can be analysed using the apparatus of multivariate methods [ 49 ]. In particular, MDS [ 50 ] or other methods, allowing mapping and matching of spatial configurations of stimuli [ 51 ], are apt for the analysis of this type of data.

Almost every week, a new AI-based method is published to find structure in large data sets. Additionally, consumer (food) related data is collected in ever increasing amounts and with increasing speeds. AI-methods may be necessary to enable analysis of these amounts [ 52 ], there’s simply too much data for human analysts to process. This also applies to neuro scientific data. The data sets collected during fMRI-experiments are so huge and complex that automated pre-processing has become necessary. Data sets collected from the combination of neuro scientific, psychophysiological, and other consumer (food) behaviour measurement methods are of the same ilk: huge and complex. Automated, AI-based analysis methodology may be needed: powerful, but for many a practitioner, beyond their control. This control appears to be transferred from the content based psychologist or neuro scientist to the statistician/AI-specialist who designs and develops the software needed to analyse the data. It is, therefore, becoming ever so important that the validity of the measurement method and the forthcoming data can be guaranteed.

3.3. Behavioural Measurement Technologies

Any behavioural output can be seen as a (muscle) motor reaction to external stimulation. We list very different types of behavioural measurement technologies, of which we found a few food consumer related applications. They cover measurements very close to an individual (oral sensing, food ingestion, chewing behaviour), somewhat more remote (online) measurements (clicking behaviour, face reading, eye-tracking, reaction times, voice analysis), and automatic tracking of consumers when navigating an area.

3.3.1. Food Ingestion and Automatic Eating Behaviour Assessment

Many innovations exist in the study of food consumption and food ingestion. This is the study of the amount, and types, of food that a consumer eats and digests. Food ingestion is notoriously hard to measure. See Willet [ 53 ] for the many different approaches in this area. When consumers report their own intake, it appears to be severely biased toward under reporting. Scientists in this field are always on the lookout for more objective, valid ways to measure actual intake. Many tools exist to measure ingestion, and they range from micro measurement instruments that may be inserted into a molar [ 54 ], to video registration of people eating, recording of muscle activity in the jaw, tongue, and/or throat, weighing plates while eating, or weighing the person after dinner.

A new development is the automatic recognition of what is on a plate, video capturing the plate, and submitting the images to software that is able to discern what is on the plate (and hopefully, indeed, will end up in the consumer). Software employing AI has been developed that can estimate the nutritional value of what is on the plate by processing the image [ 55 ].

The developers of the tooth mounted sensor [ 54 ] suggest it can intra-buccally monitor ingestion, to the point of sensing the nutritional quality of the material ingested.

Another innovation in this area is the extraction of additional, food consumption relevant parameters from the video capturing consumers’ faces. One of these parameters relates to the consumption duration, i.e., the time between putting the food in the mouth, and the last swallow. Other parameters that can be collected are chewing behaviour (duration of chewing, type (continuous or in bursts) of chewing, biting behaviour (estimated size of bite), etc. [ 56 , 57 ].

3.3.2. Consumer Food Choice

Any situation in which a consumer chooses an alternative from among a set of items constitutes the output of a choice process. In this review, we limit ourselves to relatively recent developments in measurement methods concerning food choice. Most valid food related consumer choice concerns real food. Many choices between food pictures can be made, or between descriptions of food, but such a reported choice would, in our view, be a psychological, rather than a behavioural, measurement.

Collected data about mouse clicks and their timing may also contain valuable information about the choice process. Many online survey tools allow for these types of measurements. Creative developments in food choice and ingestion measurement have been published recently. One such development is the computerised manipulation of portion size, by adjusting the portion on a plate, as it appears on a computer screen [ 58 , 59 ].

3.3.3. Face Reading

Automatic processing of facial expressions has become standard technology. Applications of this technology in food related consumer research are of a more recent nature. A consumer is seated in front of a camera, and often a computer screen. S/he can answer questions on the screen, look at pictures, or do other tasks, while the camera records the face of the consumer. Software is available to infer emotions from facial expressions. The emotion data can be used to study the reactions of consumers to the pictures or tasks provided on the screen.

A more recent innovation in this area is online face reading, where consumers are at home behind their own computers and perform the tasks, while the laptop camera is recording their faces [ 14 ]. Thomas et al. [ 60 ] summarised six points to take into account concerning automatic facial emotion reading. They refer to an earlier study by Mahieu et al. [ 61 ], where several items (perfumes, video advertisements, and chocolates) were studied using face reader technology. We have used the points put forward by Thomas et al. [ 60 ] to formulate five points of attention when performing online face reading studies:

• results depend on the type of product and product category,
• not all emotions show differences between products,
• an individual baseline emotion measurement is advised and appears stable,
• not all face reading software yields the same result,
• the face should not be obscured (e.g., by glasses).

In particular, the fourth point is troublesome. If different software systems for automatic facial emotion reading do not agree, one cannot be sure what it is that is assessed by the software. This is a serious threat of the validity of automatic emotional measurement through facial expression. It should be mentioned that the techniques are continuously evolving with regard to required lighting, interference by glasses/beards, and interference by movements during talking and eating. As a result of this, the differences between various techniques will likely become smaller.

3.3.4. Eye-Tracking

Eye-tracking is a well-established technology that is also in the consumer sciences. One of the assumptions often made, however, is that the object that is projected onto the fovea is also the object of greatest interest for the subject, as well as that this object has the greatest impact onto the behaviour (choice) of the subject. This assumption is not always granted, as parafoveal stimulation can also attract attention, and it can also affect perception outside awareness. Much research has been devoted to this in a reading context [ 62 ].

Eye-tracking can be carried out with a static subject, but the newest methodology enables eye-tracking, either from moving subjects [ 63 ] or from subjects in an immersive environment [ 64 ]. Developments in this area combine several measurements, such as eye-tracking and face reading [ 65 ].

3.3.5. Reaction Times

The time a subject takes before reacting on a stimulus, making a choice, or answering a question is since long known to carry information about the cognitive processes (‘elementary mental organisation’ [ 66 ]) intervening between the registration of the stimulus and the result of a behavioural reaction to it. Reaction times (RT’s) can be recorded, with high precision, in experimental laboratory settings. They can also be provided with online surveys, or online choice tasks. More noise will be present in such online RT’s than when lab-recorded, but provided the N is large enough, the RT’s can be as valuable as the lab-collected ones. Precautions will have to be taken in keeping the noise at acceptable levels by deleting very long and extremely short RT’s. This is no different from laboratory collected RT data. See Woods et al. [ 67 ] for an overview of the issues with collecting RT data over the internet. Kochari [ 68 ] mentions that the online RT’s collected, in numerical cognition studies, were comparable to those from lab-based studies.

3.3.6. Sentiment Analysis

Free text comments can be analysed to infer emotions of the provider of the comments [ 69 ]. More sources can be analysed for sentiment-content, using specially trained, machine learning, algorithms. An additional new field includes the automatic analysis of tone of voice, the speed of talking, and other speech properties. This allows for an emotion estimation based on voice utterings [ 70 ]. The authors could not find evidence that this is used in food consumer science, e.g., analysing the emotional connotation of consumers discussing food items or meals.

3.3.7. Tracking and Recording of Movement

Technology exists to track peoples’ position and movement while they navigate through a space. In consumer science this has been used in retail or mall environments. In addition, movement speed and gait tracking is possible while navigating in a retail environment (real or simulated), trying to find ones groceries or other products [ 71 ].

Relatively new behaviours are the movements one makes with a finger on a touch screen, or a mouse while pointing at positions on a computer screen, when navigating web-pages, or during online (food) ordering. Timing, trajectories followed, velocity of mouse or finger movements, and even pressure exerted, may reveal a lot about the interaction between the information on the computer or smartphone screen and the consumer doing the navigation.

3.3.8. Observation Technology

Observing consumers can be done in a host of circumstances, ranging from highly unnatural environments, where a consumer is in a lab to perform certain tasks, to very ‘ecological’, where consumers have no clue of being observed. The latter type of studies may run into ethical problems when consumers are filmed or photographed. When they are ‘just’ watched by observers, the impact onto their privacy may be limited. However, privacy legislation forbids the following of individuals to, e.g., find out what they choose in shops and how they may compare alternative products. New methodology exists where automatic tracking, more than just navigation through a shop or mall, is possible. The technology may even allow identification of subjects, by facial recognition and recognition of their gait, and monitor choice behaviour automatically.

4. Validity

New technologies clearly extend the possibilities to manipulate the testing environment and to measure consumer responses. With the increase in the amount of new measurement methods, another problem arises: viz., so the burden of the work shifts from the collection of the data to the analysis of the data and interpretation of the results. The dictum ‘Rubbish in, rubbish out’, still holds, so due attention to the measurement method and its validity is perhaps more needed than ever. We will follow the definition of validity as presented in Borsboom, Mellenbergh, and van Heerden [ 72 ], who state that a measurement of an attribute (an attribute being something in the real world that one desires to probe using a measurement method) is valid when the attribute exists in the real world and when variation in it causally affects variation in the measurement. The research problems in our area revolve around food choice, food consumption, sensory testing, consumer acceptance, etc. The question is when they can be expected to causally affect our measurements. In order to establish this, a theory is needed that is able to couple the attribute to the measurement outcome [ 72 ].

We will approach validity in its most simple guise, viz. ‘A measurement is valid when it measures what you intend it to measure’, i.e., when it clearly relates to the matter under study. We claim that our field aims to find research methods to address our main research questions, viz. to understand food related behaviour and perception, and we aim to address these in a valid manner. This means that, when a method is employed to ‘measure’ consumer behaviour, it should clearly relate to that behaviour and not just to what consumers self-report their behaviour to be. It may also mean that a brain imaging study may not be able to predict ultimate revealed preferences of consumers. We know that there are many external threats to valid measurements. In particular, as consumers never operate as automatons in an information vacuum, we know that there are many biases and unwanted influences affecting their responses. This is the reason that the three criteria that we reiterate below, introduced earlier by Dijksterhuis [ 73 ], explicitly address consumers in a measurement situation. These validity criteria distinguish between levels of awareness as being part of a measure. We argue that, especially these levels of awareness, may change due to the application of novel research tools, as they result in novel ways to engage with consumer. We add to the literature by applying these validity criteria on a range of novel technologies, thereby providing researchers with guidelines to support their choices on whether and how to use these technologies.

Three Criteria for Validity

The three criteria are not intended to disqualify any research method. They do not specify their strict application; rather, they are intended as a rule of thumb, enabling researchers to get an idea of the validity of a host of different methods. They may prompt them to find a method that best fits their specific behavioural research question. The criteria measure to what level of detail a consumer is aware of the measurement situation he or she is in. The idea being that such awareness may interfere with the result of the measurement. The psychological basis for this idea is briefly introduced in Appendix A .

The three criteria addressing validity are:

• Reflection: the research method requires the ‘person(a)’ of the consumer, i.e., he/she needs to think about his-/herself or his/her behaviour,
• Awareness: the method requires the consumer to know he or she is being tested,
• Informed: the method requires the consumer to know the underlying research question.

In Table 2 , we have listed several of the newer consumer science measurement (‘output’) technologies introduced in the above. For each of the presented research methods, a criterion applies or does not apply. This is shown in Table 2 , where a method is given a tick mark (✓) in the appropriate column when the criterion applies.

Selected ‘new’ consumer science technologies and an indication of their validity, based on the three criteria. When a criterion applies, a tick mark (✓) is shown.

Obviously, the scoring of the criteria is not an exact matter. It can be discussed, and it will depend on the exact way measurements are performed and research situations are constructed. This is also one of the main points in the whole exercise: that it should be discussed and that the validity of consumer measurements should never be taken for granted. The framework can, therefore, also be regarded as a guiding tool to reflect on and support decision making.

Some methods (criterion 1, ‘reflection’) require subjects to reflect on their own situation and past, future, or even hypothetical behaviour. This is the case in many survey or interview oriented methods. This is also underlying the well-known difference between stated and revealed preference in economics. In revealed preference theory, the measurement does not interfere with the consumer, as it only considers what is actually been bought or chosen.

In some methods, it is inescapable that the consumer knows he/she is being tested (criterion 2, ‘awareness’), e.g., it is hardly possible to measure psychophysiological parameters without the consumer knowing that a measurement is performed. Knowing to be in a test can influence the way consumers behave. However, the research question itself need not be disclosed in these measurements.

Regarding criterion 3 (‘informed’), there are types of methods, e.g., group discussions, in which consumers are directly interviewed about their view on the research question, so they have to know even the research question, or they can’t be part of the discussion.

Looking at Table 2 , a number of things may be concluded. Obviously, it is impossible to perform neuro scientific tests with consumers without them knowing that they are in a test situation. The only exception may be the possibility to assess heart rate from a visual image of the face. Other ANS measurements require an apparatus that is impossible to apply surreptitiously. However, recent developments enable sensors, such as smart watches, that need to be worn for longer periods but appear to not bother the subject, and they often even forget they are wearing a sensor.

Psychological methods are so ubiquitous that it is impossible to even attempt to summarise many of them. We limited ourselves to some of the newer methods. Finding novel information in available texts by means of ‘web-scraping’, typically texts available on the internet, does not need any consideration of the producers of the text. Netnography may also be employed without consumers knowing they are being tested. Surveys, focus groups, and other methods where questions are asked do require consumers to have at least some information about the question they are inquired about.

All behaviour based methods presented here do not require a consumer to think about him- or herself to enable a measurement of their responses, nor do they need to know the research question. For example, the way one eats (food ingestion, eating behaviour), or what one chooses to eat (food choice), can be assessed by video without the eating consumer knowing. Tracking consumers’ movement, e.g., through a retail environment, also does not necessarily require this. Face reading, eye-tracking, reaction time measurement, or choice outcomes do not require a consumer knowing about the measurement taking place, although with eye-tracking, it is probably difficult to perform measurements unobtrusively.

Overall, the tick marks in Table 2 seem to hint at the conclusion that behavioural measurements appear more valid than neuro scientific measurements and psychological measurements. We hasten to say that these numbers are based on our particular choice of consumer measurement technology and on our interpretation of the technology. The finding does not reflect any superiority of one method over another. Other authors may use other definitions of their specific research methodology, and they may be confronted with research questions that demand specific applications of methodology not taken into account in our scoring.

5. Conclusions

With the above mentioned provisos, we tentatively conclude that the behavioural based methods, in general, appear to enable valid results, concerning actual consumer behaviour. When behavioural research data are collected in order to predict future consumer behaviour, behaviour based data may be the preferred type to base predictive models on. Psychological research methods can back such models up with behavioural knowledge and knowledge about consumer segments, based on psychological traits. The neuro-science based research methods are probably best suited to study very specific research questions in a small group of consumers.

All in all, the validity of (food) consumer measurement methodology should never be taken for granted. The three criteria provide a means to suggest the validity of a method and, perhaps more importantly, they show that validity should be discussed and taken into account before the measurements proper take place.

Acknowledgments

The following colleagues have provided us with comments, answers on some of our questions, or have referred us to the right persons or locations for information: Anne-Marie Brouwer, Freek Daniels, Doris Dijksterhuis, Lonneke Janssen Duijghuijsen, Marvin Kunz, Marieke Meeusen, Saskia Meyboom, Görkem Simsek-Senel, Paul Smeets, Corrie Snijder, Marieke Ubachs, Shota Ushiama, Geertje van Bergen, Jos van de Puttelaar, Liesbeth Zandstra. Please note that acknowledgement does not imply endorsement of the viewpoints presented in this article.

Appendix A. A Theoretical Basis for the Biases Resulting from Consumer Awareness of a Measurement Situation

In the chapter called “The methods and snares of psychology.” William James writes about ‘the named state’ which should be distinguished from ‘the naming state’ [ 74 ]. This means that a verbal report of a certain psychological state, say a felt sadness (the ‘named state’), is made while the subject is in another state (the ‘naming state’). A subject has to interpret his/her bodily and/or psychological circumstances and internal state to be able to, after the fact, report something such as ‘I am sad’, or ‘I currently feel sadness’. The reporting is a certain psychological state in itself, which will exert an influence, e.g., via a memory, on the previous state which one tries to report. James’ conclusion is that the introspection and report of one’s own internal psychological states is a snare.

It has become more and more clear that behaviour is not always the result of consciously willing it. Wilson [ 75 ] writes about the ‘adaptive unconscious’, shaped by evolution, guiding us through a complicated environment, and helping us with decisions through intuition. This all happens without our explicit knowledge, and often even without the possibility of such knowledge. Many of the psychological processes responsible are inaccessible to conscious awareness. In this context Wilson [ 75 , 76 ] states “People can no more observe how they are unconsciously categorizing their environments, setting goals, and generating intuitions than they can observe how their kidneys work.”

Damasio [ 77 ] explains the difference between an emotion and a feeling, much as James [ 74 ] has introduced this distinction. The former is a bodily reaction to a stimulus, and the latter a conscious interpretation of that emotion by the subject. This means that the emotion is attributed a reason, but this does not mean that this attribution is the very cause of the emotion; reasons are not causes. The underlying, unknown, causes may provide a non-rational route to decision making, alongside the (often fabricated) reasons resulting from a rational route. Kahneman and Tversky [ 78 , 79 ] have amply illustrated that in many economical decision making contexts most people make decisions that can be shown to be sub-optimal from a strict rational, utilitarian, viewpoint. As a result subjects may actually gain less money in comparison with the alternative they do not choose. Damasio [ 80 ] suggests that this is the result of an ‘emotional route’ to decision making which may supersede the rational route.

Another line of work showing the importance of the unconscious in perception and appreciation is that of Zajonc [ 81 ]. Lazarus [ 82 ] argues that it is necessary first to analyse a situation, and to build some knowledge about it, in order for an affective response to occur, an approach known under the name of appraisal theory. Zajonc [ 83 ] argues the opposite, emotion/affect is primary, only afterwards may knowledge about a situation occur, but cognition is not a prerequisite for affective responses to occur. A respectable number of studies show that emotion has primacy over cognition [ 84 , 85 , 86 ]. In one telling experiment subjects were tachistoscopically presented with words, flashed too short to enable identification. However, this short presentation still allowed an appropriate affective response to the words. The affective value of the stimulus is somehow registered but the identity of the stimulus remained unknown to the subjects [ 81 ].

Author Contributions

Conceptualization, G.D.; methodology, G.D.; investigation, G.D., R.d.W., M.O.; writing-original draft preparation: G.D.; writing—review and editing, G.D., R.d.W., M.O.; visualisation, G.D.; supervision, G.D.; project administration, G.D.; funding acquisition, M.O. All authors have read and agreed to the published version of the manuscript.

The review is conducted in a funded project on behaviour and novel food contexts financed by the Dutch Ministry of Agriculture, Nature and Food Quality.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Consumer Behavior Research

Exploring the Depths of Consumer Insights for Strategic Business Growth

In an era where understanding consumer behavior is more than a competitive edge, it’s a survival imperative, NielsenIQ (NIQ) and GfK emerge as pivotal allies. This expertise is essential for businesses in B2C commerce, retail, and beyond, aiming to navigate the complex consumer landscape for informed, strategic decision-making.

Definition and Importance of Consumer Behavior Research

Consumer behavior research is the study of how individuals make decisions to spend their resources on consumption-related items. It involves understanding the what, why, when, and how of consumer purchases. This field is crucial for businesses as it sheds light on consumer preferences, buying patterns, and decision-making processes. By understanding these aspects, companies can tailor their products and marketing strategies effectively, ensuring alignment with consumer needs and market trends, ultimately leading to increased customer satisfaction and loyalty.

Overview of the Impact of Consumer Behavior Research on Marketing Strategies

The insights from consumer behavior research are instrumental in shaping targeted marketing strategies. By understanding consumer motivations and behaviors, businesses can create more relevant and engaging marketing messages, leading to improved customer engagement and retention. This research helps in segmenting the market, identifying potential customers, and understanding the factors that drive consumer decisions. It also aids in predicting future trends, enabling companies to stay ahead of the curve. Effective use of consumer behavior research can lead to the development of products and services that meet the evolving needs of consumers, thereby enhancing brand loyalty and market share.

Consumer and shopper insights

Understand consumer and shopper behavior, demographics, and loyalty with modern, representative consumer panels and customer survey capabilities.

Understanding Consumer Behavior

These diverse influences combine to form unique consumer profiles, which businesses must understand to effectively target their marketing efforts..

Factors Influencing Consumer Behavior

Consumer behavior is influenced by a complex interplay of psychological, social, cultural, and personal factors. Psychological factors include perceptions, attitudes, and motivation, which guide consumers’ emotional and cognitive responses. Social factors encompass family, friends, and societal norms that shape buying habits through peer influence and social trends. Cultural factors involve the broader societal beliefs, values, and customs that dictate consumer behavior in a particular region. Personal factors such as age, occupation, lifestyle, and economic status also significantly impact consumer choices. These diverse influences combine to form unique consumer profiles, which businesses must understand to effectively target their marketing efforts.

The Role of Consumer Behavior in Decision Making

Consumer behavior plays a critical role in the decision-making process. It involves understanding how consumers decide upon their needs and wants, choose among products and brands, and determine their purchase methods. This knowledge is vital for businesses to design and position their offerings in a way that resonates with the target audience. Understanding consumer behavior helps in predicting how consumers will respond to marketing messages and product features, enabling businesses to tailor their strategies to meet consumer needs effectively. It also assists in identifying opportunities for new product development and market expansion.

Consumer Behavior Theories and Models

Consumer behavior theories and models provide frameworks for understanding and predicting consumer actions. The Stimulus-Response Model, for instance, illustrates how marketing stimuli and environmental factors influence consumer responses. Maslow’s Hierarchy of Needs explains consumer motivation in terms of fulfilling basic to complex needs. The Theory of Reasoned Action and the Theory of Planned Behavior focus on the relationship between attitudes, intentions, and behaviors. The Consumer Decision Model outlines the cognitive process involving need recognition, information search, evaluation of alternatives, purchase decision, and post-purchase behavior. These models help businesses in developing strategies that align with consumer psychology and behavioral patterns. They also assist in segmenting the market and targeting consumers with personalized marketing approaches, enhancing the effectiveness of marketing campaigns and product offerings.

Research Methods in Consumer Behavior Research

Customer analytics is vital for businesses across various sectors, including FMCG, sales, and e-commerce. It enables companies to create personalized experiences, improve customer engagement, and boost retention, ultimately leading to increased revenue. By understanding consumer behavior through data analysis, businesses can make informed decisions that resonate with their target audience.

Quantitative Research Methods

Quantitative research methods in consumer behavior research involve structured techniques like surveys and questionnaires to collect numerical data. These methods are useful for gauging consumer attitudes, preferences, and behaviors across larger populations. Statistical analysis of this data helps in identifying trends, testing hypotheses, and making generalizations about consumer behavior. Quantitative research is valuable for businesses as it provides measurable and comparable insights that can guide strategic decision-making. It helps in understanding the magnitude of consumer responses to various marketing stimuli and in assessing the potential market size for new products or services.

Qualitative Research Methods

Qualitative research methods in consumer behavior focus on understanding the deeper motivations, thoughts, and feelings of consumers. Techniques like in-depth interviews, focus groups, and observational studies provide rich, detailed insights that are not typically captured through quantitative methods. This approach is crucial for exploring the underlying reasons behind consumer choices, preferences, and attitudes. Qualitative research helps businesses in gaining a deeper understanding of consumer experiences, emotions, and perceptions, which can be invaluable in developing more effective marketing strategies, product designs, and customer service approaches. It allows companies to explore new ideas and concepts with consumers, gaining insights that can lead to innovation and differentiation in the market.

Experimental Research in Consumer Behavior

Experimental research in consumer behavior involves manipulating one or more variables to observe the effect on another variable, typically consumer behavior or attitudes. This method is used to establish cause-and-effect relationships, providing insights into how changes in product features, pricing, or marketing strategies might influence consumer behavior. Controlled experiments, often conducted in laboratory settings or as field experiments, allow researchers to isolate the effects of specific variables. This type of research is particularly valuable for testing new products, pricing strategies, and marketing messages before full-scale implementation. It helps businesses in making informed decisions based on empirical evidence, reducing the risks associated with new initiatives.

Factors Affecting Consumer Behavior

Psychological factors.

Psychological factors play a significant role in shaping consumer behavior. These include individual motivations, perceptions, attitudes, and beliefs. Motivation drives consumers to fulfill their needs and desires, influencing their buying decisions. Perception, how consumers interpret information, can significantly impact their choices, as it shapes their understanding of products and brands. Attitudes and beliefs, formed through experiences and social influences, guide consumer preferences and loyalty. Understanding these psychological factors is crucial for businesses as they influence how consumers view and interact with products and services. By aligning marketing strategies with consumer psychology, businesses can more effectively influence purchasing decisions and build stronger customer relationships.

Social Factors

Social factors significantly influence consumer behavior, encompassing the impact of society, family, and peer groups. Family members and friends can influence buying decisions through recommendations or shared experiences. Social groups, including social networks and communities, also play a role in shaping consumer preferences and behaviors. The influence of social media has become particularly significant, as it not only connects consumers but also serves as a platform for sharing opinions and experiences about products and services. Understanding these social dynamics is important for businesses as they can leverage social influences through targeted marketing strategies, influencer partnerships, and social media campaigns. Recognizing the power of social factors can help businesses in building brand awareness and loyalty among consumer groups.

Cultural Factors

Cultural factors are deeply ingrained elements that influence consumer behavior, including values, beliefs, customs, and traditions. These factors vary across different regions and societies, affecting how consumers perceive and interact with products and services. Cultural influences can determine consumer preferences, buying habits, and brand perceptions. For instance, color symbolism, dietary preferences, and language can all vary significantly between cultures, impacting marketing strategies and product development. Businesses must understand and respect these cultural nuances to effectively cater to diverse consumer markets. Adapting products and marketing messages to align with cultural values and norms can significantly enhance a brand’s appeal and acceptance in different markets.

Personal Factors

Personal factors, including age, gender, occupation, lifestyle, and economic status, also significantly influence consumer behavior. These factors determine individual needs, preferences, and purchasing power. For example, younger consumers may prioritize trendy and innovative products, while older consumers might value functionality and durability. Lifestyle choices, such as health consciousness or environmental awareness, can also drive consumer preferences and choices. Economic factors, such as income and economic conditions, influence consumers’ ability to purchase and their sensitivity to price changes. Understanding these personal factors is crucial for businesses to segment their market effectively and tailor their products and marketing strategies to meet the specific needs of different consumer groups.

Consumer Purchase Decision Making

Stages of the consumer purchase decision-making process.

The consumer purchase decision-making process typically involves several key stages: problem recognition, information search, evaluation of alternatives, purchase decision, and post-purchase behavior.

In the problem recognition stage, consumers identify a need or desire.

During the information search, they seek out information about products or services that can fulfill their need. In the evaluation stage, consumers compare different options based on attributes such as price, quality, and brand reputation.

The purchase decision involves choosing a product and making the purchase. Finally, in the post-purchase stage, consumers evaluate their satisfaction with the purchase, which can influence future buying decisions and brand loyalty.

Understanding these stages is essential for businesses to effectively influence consumers at each step, from raising awareness to ensuring post-purchase satisfaction.

Influences on Consumer Purchase Decisions

Consumer purchase decisions are influenced by a multitude of factors, including product attributes, brand reputation, marketing messages, social influences, and personal preferences. Product features such as quality, price, and usability are key determinants of consumer choices. Brand reputation, built over time through consistent quality and marketing efforts, also significantly impacts purchase decisions. Marketing messages and advertising play a crucial role in shaping consumer perceptions and driving demand. Social influences, including recommendations from family and friends, as well as online reviews and influencer endorsements, can sway consumer decisions. Personal factors such as individual needs, preferences, and financial constraints also play a critical role. Businesses must consider these diverse influences when developing products and crafting marketing strategies to effectively appeal to their target audience.

Impulse Buying Behavior

Impulse buying behavior refers to unplanned purchases made by consumers, often driven by emotional factors rather than rational decision-making. This type of behavior is typically triggered by external stimuli such as attractive product displays, promotional offers, or persuasive sales tactics. Emotional responses, such as excitement or the desire for instant gratification, also play a significant role in impulse buying. Retailers often leverage this behavior by strategically placing impulse items near checkout areas or using limited-time offers to create a sense of urgency. Understanding the triggers of impulse buying can help businesses in designing marketing strategies and store layouts that encourage such purchases, potentially increasing sales and customer engagement.

Online Shopping and Consumer Behavior

Impact of online shopping on consumer behavior.

The rise of online shopping has significantly impacted consumer behavior, offering convenience, a wider selection of products, and often competitive pricing. Online shopping has changed the way consumers research products, compare prices, and make purchasing decisions. The ease of access to a vast array of products and the ability to shop at any time have increased the frequency and diversity of purchases. Online reviews and ratings have also become important factors in the decision-making process, as consumers increasingly rely on the opinions of others. Additionally, the personalized shopping experiences offered by many online retailers, through targeted recommendations and tailored marketing messages, have further influenced consumer buying habits. Understanding these shifts in consumer behavior is crucial for businesses to adapt their strategies for the digital marketplace, ensuring they meet the evolving needs and expectations of online shoppers.

Factors Influencing Online Buying Behavior

Several factors influence online buying behavior, including website usability, product variety, pricing, customer reviews, and the overall shopping experience. A user-friendly website with easy navigation and a seamless checkout process is crucial for attracting and retaining online shoppers. A diverse product range and competitive pricing are also key factors in attracting consumers. Customer reviews and ratings significantly impact purchase decisions, as they provide social proof and reduce perceived risk. The overall shopping experience, including customer service, delivery options, and return policies, also plays a vital role in influencing online buying behavior. Security and privacy concerns are additional considerations, as consumers are increasingly aware of data protection and online fraud. Businesses must address these factors to create a compelling online shopping experience that meets consumer expectations and drives online sales.

Comparison of Online and Offline Consumer Behavior

Online and offline consumer behaviors exhibit distinct differences, influenced by the unique aspects of each shopping environment. Online shopping offers convenience, a broader selection, and often more competitive pricing, leading to different purchasing patterns compared to offline shopping. Consumers tend to spend more time researching and comparing products online, while offline shopping is often driven by immediate needs and sensory experiences. The tactile experience and instant gratification of offline shopping are not replicable online, but the online environment offers personalized recommendations and a wealth of product information. Offline shopping also provides opportunities for personal interaction and immediate problem resolution, which can enhance customer satisfaction. Understanding these differences is crucial for businesses to tailor their strategies for each channel, ensuring a cohesive and complementary shopping experience that meets the needs and preferences of consumers in both online and offline environments.

Consumer Satisfaction and Loyalty

Importance of customer satisfaction in consumer behavior research.

Customer satisfaction is a critical component of consumer behavior research, as it directly impacts repeat purchases and brand loyalty. Satisfied customers are more likely to become repeat buyers, recommend the brand to others, and provide positive reviews. Customer satisfaction is influenced by various factors, including product quality, customer service, and overall shopping experience. Understanding and measuring customer satisfaction helps businesses identify areas for improvement, enhance customer experiences, and build long-term relationships with consumers. High levels of customer satisfaction lead to increased customer loyalty, which is essential for business growth and sustainability.

Factors Influencing Customer Satisfaction

Customer satisfaction is influenced by a range of factors, including product quality, price, service quality, brand image, and customer expectations. Product quality is a primary determinant of satisfaction, as consumers expect products to perform as advertised. Price also plays a role, as consumers evaluate the value they receive relative to the cost. Service quality, encompassing customer service interactions and the overall shopping experience, significantly impacts satisfaction levels. A positive, helpful, and efficient service experience can enhance satisfaction, while negative experiences can lead to dissatisfaction. Brand image, shaped by marketing communications and past experiences, influences consumer expectations and perceptions. Meeting or exceeding these expectations is key to achieving high levels of customer satisfaction. Additionally, personal factors such as individual needs, preferences, and past experiences also influence satisfaction. Businesses must consider these diverse factors to effectively meet consumer needs and enhance satisfaction levels.

Relationship Between Customer Satisfaction and Loyalty

The relationship between customer satisfaction and loyalty is strong and direct. Satisfied customers are more likely to develop a sense of loyalty to a brand, leading to repeat purchases and positive word-of-mouth recommendations. Loyalty is not just about repeat buying; it also involves an emotional connection and a preference for the brand over competitors. Satisfied customers are also more likely to be forgiving of minor issues and are less sensitive to price changes. Conversely, dissatisfied customers are more likely to switch to competitors and share negative experiences with others. Building customer loyalty requires consistently meeting or exceeding customer expectations, providing high-quality products and services, and maintaining positive customer relationships. Loyal customers are valuable assets to businesses, as they tend to have a higher lifetime value, lower acquisition costs, and can become brand advocates, promoting the brand through their networks.

Consumer Research and Marketing Strategies

Utilizing consumer research to develop effective marketing programs.

Consumer research is a vital tool for developing effective marketing programs. By understanding consumer needs, preferences, and behaviors, businesses can create targeted marketing strategies that resonate with their audience. Consumer research helps in identifying market segments, understanding consumer pain points, and uncovering opportunities for product development or enhancement. It also provides insights into the most effective channels and messages for reaching the target audience. Utilizing consumer research in marketing program development ensures that strategies are data-driven and customer-centric, increasing the likelihood of success. It enables businesses to tailor their marketing efforts to the specific needs and preferences of different consumer segments, improving engagement and response rates. Additionally, ongoing consumer research allows businesses to adapt their marketing strategies in response to changing consumer trends and market conditions, ensuring continued relevance and effectiveness.

Targeting Specific Consumer Segments Based on Research Findings

Targeting specific consumer segments based on research findings is a key strategy for effective marketing. Consumer research provides detailed insights into different consumer groups, including their demographics, psychographics, behaviors, and preferences. By analyzing this data, businesses can identify distinct segments within their target market, each with unique needs and characteristics. Targeting these segments with tailored marketing messages and product offerings increases the relevance and appeal of the brand to each group. For example, a segment characterized by health-conscious consumers would respond more positively to marketing messages emphasizing the health benefits of a product. Segment-specific targeting allows businesses to allocate marketing resources more efficiently, focusing on the most promising segments with the highest potential for conversion and loyalty. It also enhances the customer experience by providing consumers with products and marketing messages that are more closely aligned with their individual needs and preferences.

Adapting Marketing Strategies to Consumer Behavior Trends

Adapting marketing strategies to consumer behavior trends is essential for businesses to stay relevant and competitive. Consumer behavior is constantly evolving, influenced by factors such as technological advancements, cultural shifts, and economic changes. By staying attuned to these trends, businesses can anticipate changes in consumer needs and preferences, and adjust their marketing strategies accordingly. This may involve adopting new marketing channels, such as social media or influencer marketing, to reach consumers where they are most active. It could also mean developing new products or services that align with emerging consumer trends, such as sustainability or personalization. Adapting marketing strategies to consumer behavior trends requires a proactive approach, with ongoing research and analysis to identify emerging patterns. Businesses that successfully adapt to these trends can capture new market opportunities, enhance customer engagement, and maintain a competitive edge.

Case Studies in Consumer Behavior Research

Analysis of real-life examples and their implications.

Real-life case studies in consumer behavior research provide valuable insights into the practical application of theoretical concepts and the effectiveness of different marketing strategies. For example, a case study in the automotive industry might analyze how consumer preferences for eco-friendly vehicles have influenced car manufacturers’ product development and marketing strategies. In the retail sector, a case study could examine the impact of online shopping on brick-and-mortar stores and how these businesses have adapted to the digital era. These case studies offer concrete examples of how businesses have successfully navigated changes in consumer behavior, providing lessons and strategies that can be applied in other contexts. They also highlight the importance of consumer research in identifying market trends, understanding consumer needs, and developing effective marketing strategies. By analyzing real-life examples, businesses can gain a deeper understanding of consumer behavior, learn from the successes and challenges of others, and apply these insights to their own strategies.

Examination of Successful Marketing Campaigns Based on Consumer Behavior Research

Examining successful marketing campaigns that are based on consumer behavior research can provide valuable insights into effective marketing practices. These case studies demonstrate how a deep understanding of consumer needs, preferences, and behaviors can be leveraged to create impactful marketing campaigns. For instance, a campaign that effectively uses consumer data to personalize messages and offers can result in higher engagement and conversion rates. Another example might be a campaign that taps into current consumer trends, such as sustainability or wellness, to resonate with the target audience. Analyzing these successful campaigns can reveal key strategies and tactics that businesses can adopt, such as the use of specific channels, messaging techniques, or promotional offers. These case studies also highlight the importance of data-driven decision-making in marketing, showing how consumer research can inform and guide successful marketing initiatives.

Motivating Consumers and New Product Adoption

Strategies to motivate consumers to adopt new products.

Motivating consumers to adopt new products is a critical challenge for businesses. Effective strategies for encouraging new product adoption include leveraging social proof, offering free trials or samples, and creating educational content. Social proof, such as customer testimonials or influencer endorsements, can reduce perceived risk and increase consumer confidence in trying a new product. Free trials or samples allow consumers to experience the product firsthand, reducing barriers to adoption. Educational content, such as how-to guides or product demonstrations, can help consumers understand the value and benefits of the new product. Additionally, businesses can use targeted marketing campaigns to reach early adopters and innovators who are more likely to try new products and spread the word to others. Creating a sense of urgency or exclusivity around the new product, through limited-time offers or exclusive access, can also motivate consumers to adopt the product more quickly.

Innovations in Consumer Behavior Research for New Product Development

Innovations in consumer behavior research are playing a crucial role in new product development. Advanced analytics and data mining techniques allow businesses to analyze large datasets and uncover deep insights into consumer needs and preferences. Social listening tools enable companies to monitor social media and online conversations, gaining real-time insights into consumer opinions and trends. Virtual reality (VR) and augmented reality (AR) technologies are being used to test consumer reactions to new products in simulated environments, providing valuable feedback before market launch. Behavioral economics principles, such as understanding cognitive biases and decision-making processes, are also being applied to better predict consumer responses to new products. These innovations in consumer behavior research provide businesses with more accurate and comprehensive data, enabling them to develop products that are closely aligned with consumer needs and preferences, increasing the likelihood of market success.

Social Media and Consumer Behavior

Influence of social media on consumer behavior.

Social media has a profound influence on consumer behavior, shaping how consumers discover, research, and share information about products and services. Platforms like Facebook, Instagram, and Twitter serve as important channels for brand communication and engagement. Consumers use social media to seek recommendations, read reviews, and gather opinions from their networks, which significantly influences their purchasing decisions. Brands leverage social media for targeted advertising, influencer partnerships, and content marketing, creating opportunities for direct interaction and engagement with consumers. Social media also facilitates the spread of trends and viral content, quickly influencing consumer preferences and behaviors. The interactive and dynamic nature of social media means that consumer opinions and trends can rapidly change, requiring businesses to be agile and responsive in their social media strategies. Understanding the influence of social media on consumer behavior is essential for businesses to effectively engage with their audience and influence purchasing decisions.

Role of Social Media in Shaping Consumer Perceptions and Purchase Decisions

Recap of the importance of consumer behavior research.

Consumer behavior research is essential for businesses seeking to understand and effectively respond to the evolving needs and preferences of their target audience. It provides valuable insights into why consumers make certain choices, what influences their purchasing decisions, and how they interact with brands. This research is crucial for developing effective marketing strategies, creating products that meet consumer needs, and enhancing the overall customer experience. By staying informed about consumer behavior trends and applying these insights, businesses can improve customer engagement, increase brand loyalty, and drive growth. In today’s competitive marketplace, a deep understanding of consumer behavior is a key differentiator, enabling businesses to create more personalized, relevant, and impactful marketing initiatives.

Future Directions and Emerging Trends in Consumer Behavior Research

The future of consumer behavior research is marked by rapid advancements in technology and data analytics, leading to more sophisticated and nuanced understanding of consumer preferences and behaviors. Emerging trends include the use of artificial intelligence (AI) and machine learning to analyze consumer data, providing deeper and more predictive insights. The integration of biometric data, such as eye tracking and facial recognition, offers new ways to understand consumer responses to marketing stimuli. The growing importance of sustainability and ethical considerations is also influencing consumer behavior, leading to increased demand for eco-friendly and socially responsible products. Additionally, the rise of the experience economy is shifting focus from product features to customer experiences, requiring businesses to create more immersive and engaging customer interactions. Staying abreast of these trends and continuously innovating in consumer behavior research will be crucial for businesses to remain relevant and competitive in the changing market landscape.

How NIQ and GfK Can Help

In the complex world of consumer behavior, NIQ and GfK offer the expertise and tools necessary to navigate this landscape effectively. With comprehensive solutions like:

• NielsenIQ’s Homescan : Track, diagnose, and analyze consumer behavior from more than 250,000 households across 25 countries.
• Consumer analytics : Go deeper and create more clarity around shopper behavior with custom surveys and segmentation.
• Consumption moments : Reveal the true motivations behind customer consumption behavior and usage to guide product innovation and marketing strategy.
• gfknewron marke t : Create the right opportunities with gfknewron market
• gfknewron predict : Plan your future using the world’s most comprehensive sales tracking data for Tech & Durables.
• gfknewron Consumer : Understand your consumers’ behavior to redefine your success

By leveraging these tools, businesses can gain a competitive edge, adapting to market changes and consumer trends with agility and precision.

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11   ( ) Research producers vs consumers

In my view, Beth makes a very useful distinction between research producers and research consumers. Examples for research producers are researchers at universities, PhD students or undergraduate students conducting research projects. Examples for research consumers are clinical psychologists, educational psychologists, counsellors, organisational psychologists, and many other types of psychologists working in applied fields.

A somewhat recent survey (note that the BPS seems to have managed to break this link; I’ll leave it here for now in case it gets restored) conducted by the British Psychologcal Society (BPS) showed that only about 7% of psychology graduates work in scientific research and development:

If it is somewhat unlikely that you become a research producer, why should you care about research? Beth’s and my answer is that it is just as important to understand how research is produced if you are a research consumer. Why? In my view, applied psychology can only be as good as the research that underpins it. As an applied psychologist, you will need to make decisions, for example, you might need to decide what type of therapy to recommend to a client. (Even as a student, friends or family members might ask you for advice! 1 )

These recommendations should be based on scientific evidence. I would argue that as psychologists, we have a duty to make the best possible recommendations given current scientific evidence. To make these recommendations, a research consumer needs to be able to understand and critically analyse research. And to learn and improve on these skills, it is important to also have produced research. Therefore, we will focus on both critically analysing existing research as well as producing our own research in this module.

One of my teachers once said that hearing that you study psychology will divide people into two groups: Those who take a step forward, and those who take a step back. ↩︎

Evidence-Based Decision Making in State and Local Criminal Justice Systems

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• 4c. Becoming a Better Consumer of Research

Navigating the Roadmap

Activity 4: Understand and have the capacity to implement evidence-based practices.

Introduction

The EBDM Initiative seeks to help local policy teams find and understand evidence-based knowledge about effective justice practices and to design more effective responses to defendants and offenders. [1] Many stakeholders already know how to find and use research; others will appreciate these tips regarding how to quickly access reliable research and how to review and understand the findings and their applications. The evidence or empirical studies will be drawn from many fields: evidence-based practices in criminal justice, behavioral health interventions, organizational development, leadership and management, effective collaboration processes, and cost–benefit analyses.

Broadly speaking, the goal of this document is to increase policy officials’ and practitioners’ skills in finding the research that matters and in understanding and translating empirical findings for their use in improving policy and practice. Specifically, this document offers

• tips for finding research relevant to critical questions about evidence-based practice;
• a list of searchable databases on criminal justice topics; and
• advice on how to review and assess the quality of the findings in academic articles and the research literature.

Participants

This document was developed for EBDM policy teams, their work groups, and agency practitioners to enhance their ability to find and understand the best available research that may be applied to criminal justice problems and proposed solutions.

Instructions

Step 1: Look in the Right Places to Find the Evidence that Matters

Where should the discerning consumer begin the search for evidence-based policies and programs and answers to specific research questions? The answer is three-fold: the Web, written literature, and experienced colleagues from your local and state criminal justice systems and from national networks of professionals.

Websites that Filter the Information for You: Evidence-Based Program Databases

Websites designed specifically to summarize research in one or more criminal justice practice areas are an excellent place to begin the search for information on effective programs and policies. A growing number of government agencies, academic institutions, and professional groups maintain these databases as a service to criminal justice professionals and the public. These organizations

• formulate evaluation criteria for assessing the strength of research findings;
• employ experts to review multiple studies of research on programs in a single area; and
• indicate which programs are shown to be effective (and at what level of rigor or confidence).

Some of these websites specialize in “systematic reviews” (also called meta-analytic reviews) of the literature regarding specific research questions and program areas. As the Center for Evidence-Based Crime Policy at George Mason University explains, systematic reviews “summarize the best available evidence on a specific topic using transparent, comprehensive search strategies to find a broad range of published and unpublished research, explicit criteria for including comparable studies, systematic coding and analysis, and often quantitative methods for producing an overall indicator of effectiveness.” [2]

A partial list of evidence-based program databases in criminal justice follows: [3]

• The Campbell Collaboration, The Crime and Justice Coordinating Group (CCJG) is an international network of researchers that prepares and disseminates systematic reviews of high-quality research on methods to reduce crime and delinquency and to improve the quality of justice. https://campbellcollaboration.org/
• The Center for the Study of the Prevention of Violence, University of Colorado, maintains a website, Blueprints for Violence Prevention, on evaluated programs to prevent adolescent violence, aggression, and delinquency. https://www.ncjrs.gov/pdffiles1/ojjdp/187079.pdf  
• George Mason University’s Center for Evidence-based Crime Policy offers a number of services, including systematic reviews, research on crime and place, and a summary (matrix) of evidence-based policing practices. https://info.nicic.gov/ebdm/node/75/edit
• Substance Abuse and Metal Health Services Administration’s (SAMSHA) National Registry of Evidence-based Programs and Practices (NREPP) provides a database of more than 190 interventions supporting mental health promotion, substance abuse prevention, and mental health and substance abuse treatment.
• U.S. Department of Justice, Office of Justice Programs’ Crime Solutions’ website provides research on program effectiveness; easily understandable ratings (effective, promising, and no effects) that indicate whether a program achieves its goal; and key program information and research findings. https://crimesolutions.ojp.gov/

Websites that Provide Bibliographic Databases

These websites, which provide a listing of hundreds of studies, are often maintained by government agencies and universities. Prominent among these in the criminal justice field are the following:

• The National Criminal Justice Reference Service (NCJRS), supported by the U.S. Department of Justice, Office of Justice Programs. https://www.ncjrs.gov/
• The National Institute of Corrections Information Center. https://nicic.gov/
• Correctional Services of Canada. http://www.csc-scc.gc.ca/text/rsrch-eng.shtml

Websites that Provide Summaries of Research and Practical Guidance

Some universities, state criminal justice agencies, and professional organizations also run websites that summarize the research on effective criminal justice practice and/or provide guidance to users.  While not as extensive as bibliographic databases, these websites focus their publications on the critical issues of most concern to policymakers and practitioners. A partial list follows:

• Center for Evidence-Based Crime Policy. https://cebcp.org/
• Correctional Treatment Evaluations, Texas Christian University, Institute for Behavioral Research. This national research center for addiction treatment studies in community and correctional settings provides access to over 700 resources on its website.    ttps://ibr.tcu.edu
• National Implementation Research Network. This website contains research on the successful implementation of new processes within organizations and systems. http://nirn.fpg.unc.edu/
• Stanford University, Evidence-Based Management. This website specializes in evidence directly related to the management of agencies. https://www.cebma.org/
• University of Cincinnati School of Criminal Justice. This university-based site contains a number of research studies regarding the use of evidence in correctional interventions. https://cech.uc.edu/schools/criminaljustice.html
• Washington State Institute for Public Policy. This website contains a number of helpful studies on what is or is not an effective intervention for reducing recidivism and costs. It is perhaps best known for its cost–benefit studies. http://www.wsipp.wa.gov/

Your Colleagues

Often an efficient way to check out the results of web-based and library searches is to ask experienced colleagues in your state and local jurisdiction and in national networks for recommendations regarding the latest and most reliable research. This strategy helps triangulate or hone in on the best studies.

Further, when identifying a journal article that appears useful but for which a subscription is required, contact colleagues at nearby colleges and universities and inquire about their ability to access the article from their library and provide a single copy for your review. (Be careful to not copy, distribute, or otherwise violate copyright laws.)

An increasing number of states support websites that summarize evidence-based research and practical guidance that is directly relevant to their criminal justice constituents and agencies. The websites may be hosted by a state criminal justice agency or university. Your colleagues will know how to access these sites.

Step 2: Evaluate Research Quality

What criteria should be used to decide if program evidence has been collected and analyzed according to high quality research standards? As Hess and Savadsky (2009) emphasize in their article “Evaluation Research for Policy Development,” all evidence is not created equally. Familiarity with a few key concepts can help policymakers wade through the growing body of information and make better-informed decisions about what is reliable. Following are a few tips about how to read the research literature and evaluate its quality: [4]

• Understand the target population of the study and consider its relationship to the target population under consideration in your jurisdiction. Pay attention to sample size and sample selection. In general, larger samples provide more reliable data; however, there is no one hard and fast rule about sample size. The sample size may vary according to the purpose of the study, overall population, sampling error, and so forth.
• Consider the context. What works in one place or for one population may not work for another (e.g., a study completed in a small, rural state with unique characteristics may not be applicable to a large, densely populated state with a different offender profile and justice system challenges). In addition, the context of one study cannot necessarily be transferred to other settings. An often-quoted study examined successful program results and found that 15% of the outcome was derived from the intervention itself (e.g., cognitive program, didactic intervention, or therapeutic community) and 30% from the working alliance with the individual providing the service. [5] However, the study was not carried out with correctional clients. The results could be valid across populations but until that hypothesis is tested, caution must be exercised about its applicability to the correctional population.
• Be cautious about assertions of causality. Correlation does not mean causation; an intervention may be related to a certain outcome but may not be responsible for that outcome. For example, a significant portion of many communities’ offender population includes individuals with mental illness. A common assumption is that mental health treatment will reduce the likelihood of reoffense among this population. However, while a mental health condition should be treated, studies have shown that mental health treatment alone is unlikely to reduce recidivism.
• Recognize that changes in implementation can change the outcomes of an intervention. For instance, an effective probation intervention that relies on officers proficient in motivational interviewing, case planning, and problem solving with clients may not work as well if delivered by staff who do not possess these skills.
• Be sure the conclusions follow logically from the reported findings. The summaries or conclusions of some studies can be deceptive or take license in explaining the implications of findings. Consumers should look for research that “measures the impact of particular interventions on identifiable populations under controlled circumstances.” [6] These studies offer prescriptive guidance about actions that can be consistently replicated elsewhere.
• The issue of confidence in results is important . The research consumer needs to know if the results of the intervention are “statistically significant.” This refers to the likelihood that a result is caused by something other than mere chance. In general, a 5% or power p-value is considered statistically significant. While policymakers may not want to dig through the statistical results’ section in great detail, it is useful to check whether the article mentions that the findings are statistically significant. Other issues such as whether the person(s) conducting the research study has a vested interest in the outcome of the study and whether the study was replicated elsewhere should also be considered. [7]

Additional Resources/Readings

Hess, F. M. & Savadsky, H. (2009). Evaluating research for policy development.

Fink, A. (2008). The research consumer as detective: Investigating program and bibliographic databases. Practicing Research: Discovering Evidence that Matters (pp. 33–64). Retrieved from https://www.sagepub.com/sites/default/files/upm-binaries/19270_Chapter_2...

Wampold, B. E. (2001). The great psychotherapy debate: Models, methods, and findings. Mahwah, NJ: Lawrence Erlbaum Associates.

[1] In Appendix 3 of the Framework for Evidence-Based Decision Making in Local Criminal Justice Systems , the Initiative provides a matrix of research findings on reducing pretrial misbehavior and offender recidivism. EBDM policy teams are encouraged to review this resource; however, the EBDM Research Matrix can only provide a snapshot of the research at one point in time, as new research is continually conducted. Therefore, this Starter Kit document is intended to provide EBDM policy teams with additional guidance on how to keep current with the research on EBDM.

[3] Adapted from Fink, 2008.

[4] Adapted from Hess & Savadsky, 2009.

[5] Wampold, 2001.

[6] Hess & Zavadsky, 2009.

[7] See Hess & Zavadsky (2009) for more information on how to be a good consumer of research.

Starter Kit

• 1a: EBDM Checklist
• 1b: Collaboration Survey
• 1c: Creating a Vision
• 1d: Conducting a Stakeholder Analysis
• 1e: Establishing Team Leadership
• 1f: Setting Ground Rules
• 1g: Building a Collaborative Climate
• 1h: Establishing a Decision Making Process
• 1i: Developing a Mission for Your Policy Team
• 1j: Creating a Charter for Your Policy Team
• 1k: Establishing Clear Roles and Responsibilities
• 1l: Developing an Action Plan for the Policy Team’s Work
• 1m: Managing the Policy Team: The Local Coordinator
• 1n: Developing Meeting Goals and Agendas
• 1o: Creating Useful Meeting Records
• 2a: Readying Staff for Change
• 3a: Developing a System Map
• 3b: Conducting a Policy and Practice Analysis
• 3c: Creating a Resource Inventory
• 3d: Gathering Baseline Data
• 3e: Prioritizing Your Team’s Targets for Change
• 4a: Understanding Your Agency: Conducting an EBP Knowledge Survey
• 4b: Equipping Stakeholders to Apply Research Evidence
• 5a: Building Logic Models
• 6a: Measuring Your Performance
• 6b: Developing a System wide Scorecard
• 7a: Developing a Communications Strategy; Building Stakeholder and Community Engagement
• 8a: Building a Plan for Implementation
• NIC Micro-Sites

Copyright © 2024, Evidence-Based Decision Making

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The past, present, and future of consumer research

• Published: 13 June 2020
• Volume 31 , pages 137–149, ( 2020 )

Cite this article

• Maayan S. Malter   ORCID: orcid.org/0000-0003-0383-7925 1 ,
• Morris B. Holbrook 1 ,
• Barbara E. Kahn 2 ,
• Jeffrey R. Parker 3 &
• Donald R. Lehmann 1  

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In this article, we document the evolution of research trends (concepts, methods, and aims) within the field of consumer behavior, from the time of its early development to the present day, as a multidisciplinary area of research within marketing. We describe current changes in retailing and real-world consumption and offer suggestions on how to use observations of consumption phenomena to generate new and interesting consumer behavior research questions. Consumption continues to change with technological advancements and shifts in consumers’ values and goals. We cannot know the exact shape of things to come, but we polled a sample of leading scholars and summarize their predictions on where the field may be headed in the next twenty years.

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1 Introduction

Beginning in the late 1950s, business schools shifted from descriptive and practitioner-focused studies to more theoretically driven and academically rigorous research (Dahl et al. 1959 ). As the field expanded from an applied form of economics to embrace theories and methodologies from psychology, sociology, anthropology, and statistics, there was an increased emphasis on understanding the thoughts, desires, and experiences of individual consumers. For academic marketing, this meant that research not only focused on the decisions and strategies of marketing managers but also on the decisions and thought processes on the other side of the market—customers.

Since then, the academic study of consumer behavior has evolved and incorporated concepts and methods, not only from marketing at large but also from related social science disciplines, and from the ever-changing landscape of real-world consumption behavior. Its position as an area of study within a larger discipline that comprises researchers from diverse theoretical backgrounds and methodological training has stirred debates over its identity. One article describes consumer behavior as a multidisciplinary subdiscipline of marketing “characterized by the study of people operating in a consumer role involving acquisition, consumption, and disposition of marketplace products, services, and experiences” (MacInnis and Folkes 2009 , p. 900).

This article reviews the evolution of the field of consumer behavior over the past half century, describes its current status, and predicts how it may evolve over the next twenty years. Our review is by no means a comprehensive history of the field (see Schumann et al. 2008 ; Rapp and Hill 2015 ; Wang et al. 2015 ; Wilkie and Moore 2003 , to name a few) but rather focuses on a few key thematic developments. Though we observe many major shifts during this period, certain questions and debates have persisted: Does consumer behavior research need to be relevant to marketing managers or is there intrinsic value from studying the consumer as a project pursued for its own sake? What counts as consumption: only consumption from traditional marketplace transactions or also consumption in a broader sense of non-marketplace interactions? Which are the most appropriate theoretical traditions and methodological tools for addressing questions in consumer behavior research?

2 A brief history of consumer research over the past sixty years—1960 to 2020

In 1969, the Association for Consumer Research was founded and a yearly conference to share marketing research specifically from the consumer’s perspective was instituted. This event marked the culmination of the growing interest in the topic by formalizing it as an area of research within marketing (consumer psychology had become a formalized branch of psychology within the APA in 1960). So, what was consumer behavior before 1969? Scanning current consumer-behavior doctoral seminar syllabi reveals few works predating 1969, with most of those coming from psychology and economics, namely Herbert Simon’s A Behavioral Model of Rational Choice (1955), Abraham Maslow’s A Theory of Human Motivation (1943), and Ernest Dichter’s Handbook of Consumer Motivations (1964). In short, research that illuminated and informed our understanding of consumer behavior prior to 1969 rarely focused on marketing-specific topics, much less consumers or consumption (Dichter’s handbook being a notable exception). Yet, these works were crucial to the rise of consumer behavior research because, in the decades after 1969, there was a shift within academic marketing to thinking about research from a behavioral or decision science perspective (Wilkie and Moore 2003 ). The following section details some ways in which this shift occurred. We draw on a framework proposed by the philosopher Larry Laudan ( 1986 ), who distinguished among three inter-related aspects of scientific inquiry—namely, concepts (the relevant ideas, theories, hypotheses, and constructs); methods (the techniques employed to test and validate these concepts); and aims (the purposes or goals that motivate the investigation).

2.1 Key concepts in the late - 1960s

During the late-1960s, we tended to view the buyer as a computer-like machine for processing information according to various formal rules that embody economic rationality to form a preference for one or another option in order to arrive at a purchase decision. This view tended to manifest itself in a couple of conspicuous ways. The first was a model of buyer behavior introduced by John Howard in 1963 in the second edition of his marketing textbook and quickly adopted by virtually every theorist working in our field—including, Howard and Sheth (of course), Engel-Kollat-&-Blackwell, Franco Nicosia, Alan Andreasen, Jim Bettman, and Joel Cohen. Howard’s great innovation—which he based on a scheme that he had found in the work of Plato (namely, the linkages among Cognition, Affect, and Conation)—took the form of a boxes-and-arrows formulation heavily influenced by the approach to organizational behavior theory that Howard (University of Pittsburgh) had picked up from Herbert Simon (Carnegie Melon University). The model represented a chain of events

where I = inputs of information (from advertising, word-of-mouth, brand features, etc.); C = cognitions (beliefs or perceptions about a brand); A = Affect (liking or preference for the brand); B = behavior (purchase of the brand); and S = satisfaction (post-purchase evaluation of the brand that feeds back onto earlier stages of the sequence, according to a learning model in which reinforced behavior tends to be repeated). This formulation lay at the heart of Howard’s work, which he updated, elaborated on, and streamlined over the remainder of his career. Importantly, it informed virtually every buyer-behavior model that blossomed forth during the last half of the twentieth century.

To represent the link between cognitions and affect, buyer-behavior researchers used various forms of the multi-attribute attitude model (MAAM), originally proposed by psychologists such as Fishbein and Rosenberg as part of what Fishbein and Ajzen ( 1975 ) called the theory of reasoned action. Under MAAM, cognitions (beliefs about brand attributes) are weighted by their importance and summed to create an explanation or prediction of affect (liking for a brand or preference for one brand versus another), which in turn determines behavior (choice of a brand or intention to purchase a brand). This took the work of economist Kelvin Lancaster (with whom Howard interacted), which assumed attitude was based on objective attributes, and extended it to include subjective ones (Lancaster 1966 ; Ratchford 1975 ). Overall, the set of concepts that prevailed in the late-1960s assumed the buyer exhibited economic rationality and acted as a computer-like information-processing machine when making purchase decisions.

2.2 Favored methods in the late-1960s

The methods favored during the late-1960s tended to be almost exclusively neo-positivistic in nature. That is, buyer-behavior research adopted the kinds of methodological rigor that we associate with the physical sciences and the hypothetico-deductive approaches advocated by the neo-positivistic philosophers of science.

Thus, the accepted approaches tended to be either experimental or survey based. For example, numerous laboratory studies tested variations of the MAAM and focused on questions about how to measure beliefs, how to weight the beliefs, how to combine the weighted beliefs, and so forth (e.g., Beckwith and Lehmann 1973 ). Here again, these assumed a rational economic decision-maker who processed information something like a computer.

Seeking rigor, buyer-behavior studies tended to be quantitative in their analyses, employing multivariate statistics, structural equation models, multidimensional scaling, conjoint analysis, and other mathematically sophisticated techniques. For example, various attempts to test the ICABS formulation developed simultaneous (now called structural) equation models such as those deployed by Farley and Ring ( 1970 , 1974 ) to test the Howard and Sheth ( 1969 ) model and by Beckwith and Lehmann ( 1973 ) to measure halo effects.

2.3 Aims in the late-1960s

During this time period, buyer-behavior research was still considered a subdivision of marketing research, the purpose of which was to provide insights useful to marketing managers in making strategic decisions. Essentially, every paper concluded with a section on “Implications for Marketing Managers.” Authors who failed to conform to this expectation could generally count on having their work rejected by leading journals such as the Journal of Marketing Research ( JMR ) and the Journal of Marketing ( JM ).

2.4 Summary—the three R’s in the late-1960s

Starting in the late-1960s to the early-1980s, virtually every buyer-behavior researcher followed the traditional approach to concepts, methods, and aims, now encapsulated under what we might call the three R’s —namely, rationality , rigor , and relevance . However, as we transitioned into the 1980s and beyond, that changed as some (though by no means all) consumer researchers began to expand their approaches and to evolve different perspectives.

2.5 Concepts after 1980

In some circles, the traditional emphasis on the buyer’s rationality—that is, a view of the buyer as a rational-economic, decision-oriented, information-processing, computer-like machine for making choices—began to evolve in at least two primary ways.

First, behavioral economics (originally studied in marketing under the label Behavioral Decision Theory)—developed in psychology by Kahneman and Tversky, in economics by Thaler, and applied in marketing by a number of forward-thinking theorists (e.g., Eric Johnson, Jim Bettman, John Payne, Itamar Simonson, Jay Russo, Joel Huber, and more recently, Dan Ariely)—challenged the rationality of consumers as decision-makers. It was shown that numerous commonly used decision heuristics depart from rational choice and are exceptions to the traditional assumptions of economic rationality. This trend shed light on understanding consumer financial decision-making (Prelec and Loewenstein 1998 ; Gourville 1998 ; Lynch Jr 2011 ) and how to develop “nudges” to help consumers make better decisions for their personal finances (summarized in Johnson et al. 2012 ).

Second, the emerging experiential view (anticipated by Alderson, Levy, and others; developed by Holbrook and Hirschman, and embellished by Schmitt, Pine, and Gilmore, and countless followers) regarded consumers as flesh-and-blood human beings (rather than as information-processing computer-like machines), focused on hedonic aspects of consumption, and expanded the concepts embodied by ICABS (Table 1 ).

2.6 Methods after 1980

The two burgeoning areas of research—behavioral economics and experiential theories—differed in their methodological approaches. The former relied on controlled randomized experiments with a focus on decision strategies and behavioral outcomes. For example, experiments tested the process by which consumers evaluate options using information display boards and “Mouselab” matrices of aspects and attributes (Payne et al. 1988 ). This school of thought also focused on behavioral dependent measures, such as choice (Huber et al. 1982 ; Simonson 1989 ; Iyengar and Lepper 2000 ).

The latter was influenced by post-positivistic philosophers of science—such as Thomas Kuhn, Paul Feyerabend, and Richard Rorty—and approaches expanded to include various qualitative techniques (interpretive, ethnographic, humanistic, and even introspective methods) not previously prominent in the field of consumer research. These included:

Interpretive approaches —such as those drawing on semiotics and hermeneutics—in an effort to gain a richer understanding of the symbolic meanings involved in consumption experiences;

Ethnographic approaches — borrowed from cultural anthropology—such as those illustrated by the influential Consumer Behavior Odyssey (Belk et al. 1989 ) and its discoveries about phenomena related to sacred aspects of consumption or the deep meanings of collections and other possessions;

Humanistic approaches —such as those borrowed from cultural studies or from literary criticism and more recently gathered together under the general heading of consumer culture theory ( CCT );

Introspective or autoethnographic approaches —such as those associated with a method called subjective personal introspection ( SPI ) that various consumer researchers like Sidney Levy and Steve Gould have pursued to gain insights based on their own private lives.

These qualitative approaches tended not to appear in the more traditional journals such as the Journal of Marketing , Journal of Marketing Research , or Marketing Science . However, newer journals such as Consumption, Markets, & Culture and Marketing Theory began to publish papers that drew on the various interpretive, ethnographic, humanistic, or introspective methods.

2.7 Aims after 1980

In 1974, consumer research finally got its own journal with the launch of the Journal of Consumer Research ( JCR ). The early editors of JCR —especially Bob Ferber, Hal Kassarjian, and Jim Bettman—held a rather divergent attitude about the importance or even the desirability of managerial relevance as a key goal of consumer studies. Under their influence, some researchers began to believe that consumer behavior is a phenomenon worthy of study in its own right—purely for the purpose of understanding it better. The journal incorporated articles from an array of methodologies: quantitative (both secondary data analysis and experimental techniques) and qualitative. The “right” balance between theoretical insight and substantive relevance—which are not in inherent conflict—is a matter of debate to this day and will likely continue to be debated well into the future.

2.8 Summary—the three I’s after 1980

In sum, beginning in the early-1980s, consumer research branched out. Much of the work in consumer studies remained within the earlier tradition of the three R’s—that is, rationality (an information-processing decision-oriented buyer), rigor (neo-positivistic experimental designs and quantitative techniques), and relevance (usefulness to marketing managers). Nonetheless, many studies embraced enlarged views of the three major aspects that might be called the three I’s —that is, irrationality (broadened perspectives that incorporate illogical, heuristic, experiential, or hedonic aspects of consumption), interpretation (various qualitative or “postmodern” approaches), and intrinsic motivation (the joy of pursuing a managerially irrelevant consumer study purely for the sake of satisfying one’s own curiosity, without concern for whether it does or does not help a marketing practitioner make a bigger profit).

3 The present—the consumer behavior field today

3.1 present concepts.

In recent years, technological changes have significantly influenced the nature of consumption as the customer journey has transitioned to include more interaction on digital platforms that complements interaction in physical stores. This shift poses a major conceptual challenge in understanding if and how these technological changes affect consumption. Does the medium through which consumption occurs fundamentally alter the psychological and social processes identified in earlier research? In addition, this shift allows us to collect more data at different stages of the customer journey, which further allows us to analyze behavior in ways that were not previously available.

Revisiting the ICABS framework, many of the previous concepts are still present, but we are now addressing them through a lens of technological change (Table 2 )

. In recent years, a number of concepts (e.g., identity, beliefs/lay theories, affect as information, self-control, time, psychological ownership, search for meaning and happiness, social belonging, creativity, and status) have emerged as integral factors that influence and are influenced by consumption. To better understand these concepts, a number of influential theories from social psychology have been adopted into consumer behavior research. Self-construal (Markus and Kitayama 1991 ), regulatory focus (Higgins 1998 ), construal level (Trope and Liberman 2010 ), and goal systems (Kruglanski et al. 2002 ) all provide social-cognition frameworks through which consumer behavior researchers study the psychological processes behind consumer behavior. This “adoption” of social psychological theories into consumer behavior is a symbiotic relationship that further enhances the theories. Tory Higgins happily stated that he learned more about his own theories from the work of marketing academics (he cited Angela Lee and Michel Pham) in further testing and extending them.

3.2 Present Methods

Not only have technological advancements changed the nature of consumption but they have also significantly influenced the methods used in consumer research by adding both new sources of data and improved analytical tools (Ding et al. 2020 ). Researchers continue to use traditional methods from psychology in empirical research (scale development, laboratory experiments, quantitative analyses, etc.) and interpretive approaches in qualitative research. Additionally, online experiments using participants from panels such as Amazon Mechanical Turk and Prolific have become commonplace in the last decade. While they raise concerns about the quality of the data and about the external validity of the results, these online experiments have greatly increased the speed and decreased the cost of collecting data, so researchers continue to use them, albeit with some caution. Reminiscent of the discussion in the 1970s and 1980s about the use of student subjects, the projectability of the online responses and of an increasingly conditioned “professional” group of online respondents (MTurkers) is a major concern.

Technology has also changed research methodology. Currently, there is a large increase in the use of secondary data thanks to the availability of Big Data about online and offline behavior. Methods in computer science have advanced our ability to analyze large corpuses of unstructured data (text, voice, visual images) in an efficient and rigorous way and, thus, to tap into a wealth of nuanced thoughts, feelings, and behaviors heretofore only accessible to qualitative researchers through laboriously conducted content analyses. There are also new neuro-marketing techniques like eye-tracking, fMRI’s, body arousal measures (e.g., heart rate, sweat), and emotion detectors that allow us to measure automatic responses. Lastly, there has been an increase in large-scale field experiments that can be run in online B2C marketplaces.

3.3 Present Aims

Along with a focus on real-world observations and data, there is a renewed emphasis on managerial relevance. Countless conference addresses and editorials in JCR , JCP , and other journals have emphasized the importance of making consumer research useful outside of academia—that is, to help companies, policy makers, and consumers. For instance, understanding how the “new” consumer interacts over time with other consumers and companies in the current marketplace is a key area for future research. As global and social concerns become more salient in all aspects of life, issues of long-term sustainability, social equality, and ethical business practices have also become more central research topics. Fortunately, despite this emphasis on relevance, theoretical contributions and novel ideas are still highly valued. An appropriate balance of theory and practice has become the holy grail of consumer research.

The effects of the current trends in real-world consumption will increase in magnitude with time as more consumers are digitally native. Therefore, a better understanding of current consumer behavior can give us insights and help predict how it will continue to evolve in the years to come.

4 The future—the consumer behavior field in 2040

The other papers use 2030 as a target year but we asked our survey respondents to make predictions for 2040 and thus we have a different future target year.

Niels Bohr once said, “Prediction is very difficult, especially if it’s about the future.” Indeed, it would be a fool’s errand for a single person to hazard a guess about the state of the consumer behavior field twenty years from now. Therefore, predictions from 34 active consumer researchers were collected to address this task. Here, we briefly summarize those predictions.

4.1 Future Concepts

While few respondents proffered guesses regarding specific concepts that would be of interest twenty years from now, many suggested broad topics and trends they expected to see in the field. Expectations for topics could largely be grouped into three main areas. Many suspected that we will be examining essentially the same core topics, perhaps at a finer-grained level, from different perspectives or in ways that we currently cannot utilize due to methodological limitations (more on methods below). A second contingent predicted that much research would center on the impending crises the world faces today, most mentioning environmental and social issues (the COVID-19 pandemic had not yet begun when these predictions were collected and, unsurprisingly, was not anticipated by any of our respondents). The last group, citing the widely expected profound impact of AI on consumers’ lives, argued that AI and other technology-related topics will be dominant subjects in consumer research circa 2040.

While the topic of technology is likely to be focal in the field, our current expectations for the impact of technology on consumers’ lives are narrower than it should be. Rather than merely offering innumerable conveniences and experiences, it seems likely that technology will begin to be integrated into consumers’ thoughts, identities, and personal relationships—probably sooner than we collectively expect. The integration of machines into humans’ bodies and lives will present the field with an expanding list of research questions that do not exist today. For example, how will the concepts of the self, identity, privacy, and goal pursuit change when web-connected technology seamlessly integrates with human consciousness and cognition? Major questions will also need to be answered regarding philosophy of mind, ethics, and social inequality. We suspect that the impact of technology on consumers and consumer research will be far broader than most consumer-behavior researchers anticipate.

As for broader trends within consumer research, there were two camps: (1) those who expect (or hope) that dominant theories (both current and yet to be developed) will become more integrated and comprehensive and (2) those who expect theoretical contributions to become smaller and smaller, to the point of becoming trivial. Both groups felt that current researchers are filling smaller cracks than before, but disagreed on how this would ultimately be resolved.

4.2 Future Methods

As was the case with concepts, respondents’ expectations regarding consumer-research methodologies in 2030 can also be divided into three broad baskets. Unsurprisingly, many indicated that we would be using many technologies not currently available or in wide use. Perhaps more surprising was that most cited the use of technology such as AI, machine-learning algorithms, and robots in designing—as opposed to executing or analyzing—experiments. (Some did point to the use of technologies such as virtual reality in the actual execution of experiments.) The second camp indicated that a focus on reliable and replicable results (discussed further below) will encourage a greater tendency for pre-registering studies, more use of “Big Data,” and a demand for more studies per paper (versus more papers per topic, which some believe is a more fruitful direction). Finally, the third lot indicated that “real data” would be in high demand, thereby necessitating the use of incentive-compatible, consequential dependent variables and a greater prevalence of field studies in consumer research.

As a result, young scholars would benefit from developing a “toolkit” of methodologies for collecting and analyzing the abundant new data of interest to the field. This includes (but is not limited to) a deep understanding of designing and implementing field studies (Gerber and Green 2012 ), data analysis software (R, Python, etc.), text mining and analysis (Humphreys and Wang 2018 ), and analytical tools for other unstructured forms of data such as image and sound. The replication crisis in experimental research means that future scholars will also need to take a more critical approach to validity (internal, external, construct), statistical power, and significance in their work.

4.3 Future Aims

While there was an air of existential concern about the future of the field, most agreed that the trend will be toward increasing the relevance and reliability of consumer research. Specifically, echoing calls from journals and thought leaders, the respondents felt that papers will need to offer more actionable implications for consumers, managers, or policy makers. However, few thought that this increased focus would come at the expense of theoretical insights, suggesting a more demanding overall standard for consumer research in 2040. Likewise, most felt that methodological transparency, open access to data and materials, and study pre-registration will become the norm as the field seeks to allay concerns about the reliability and meaningfulness of its research findings.

4.4 Summary - Future research questions and directions

Despite some well-justified pessimism, the future of consumer research is as bright as ever. As we revised this paper amidst the COVID-19 pandemic, it was clear that many aspects of marketplace behavior, consumption, and life in general will change as a result of this unprecedented global crisis. Given this, and the radical technological, social, and environmental changes that loom on the horizon, consumer researchers will have a treasure trove of topics to tackle in the next ten years, many of which will carry profound substantive importance. While research approaches will evolve, the core goals will remain consistent—namely, to generate theoretically insightful, empirically supported, and substantively impactful research (Table 3 ).

5 Conclusion

At any given moment in time, the focal concepts, methods, and aims of consumer-behavior scholarship reflect both the prior development of the field and trends in the larger scientific community. However, despite shifting trends, the core of the field has remained constant—namely, to understand the motivations, thought processes, and experiences of individuals as they consume goods, services, information, and other offerings, and to use these insights to develop interventions to improve both marketing strategy for firms and consumer welfare for individuals and groups. Amidst the excitement of new technologies, social trends, and consumption experiences, it is important to look back and remind ourselves of the insights the field has already generated. Effectively integrating these past findings with new observations and fresh research will help the field advance our understanding of consumer behavior.

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Malter, M.S., Holbrook, M.B., Kahn, B.E. et al. The past, present, and future of consumer research. Mark Lett 31 , 137–149 (2020). https://doi.org/10.1007/s11002-020-09526-8

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Turning Research Results into Consumer-Friendly Claims

For consumer brand leaders, the intersection of clinical research and accessible communication is a place of great potential. When creating science-backed product claims, you face the challenge of conveying intricate clinical findings in a manner that resonates with consumers. We have compiled a guide for turning complex research results into appealing product claims. We include strategies to help your brand simplify your findings and translate them into relatable language.

The Challenge of Communicating Science

Clinical trials and other forms of research often involve technical jargon and statistical information that, if you just used as-is for marketing, would probably bore or confuse consumers. The best science communication is concise, clear, and informative without being dull. It may be easier said than done, though, which is why we’ve compiled some top tips for crafting engaging content:

Strategy 1: Storytelling with Data

Narrative interpretation.

It is incredibly useful to draw consumers in by turning your raw research into an engaging story. Instead of just throwing out statistics in isolation, create narratives that outline your product’s journey from research to revelation. For instance:

Unpack how your product worked to improve certain health concerns over a long period of time.

Explain the specific things your participants liked about your product right away. This might be the initial application, immediate effects, or anything else you heard in the feedback!

Discuss the quality of life improvements your product provided in order to humanize a prominent statistic. If 90% of participants noticed fewer wrinkles while using your serum, how did this affect their confidence? (Be sure to pull only from actual consumer feedback, and don’t fictionalize your results!)

Embrace Context

Contextualize research by illustrating its relevance to consumers' lives. Emphasize how findings directly impact their well-being or address their needs, fostering a deeper connection.

Strategy 2: Real-Life Analogies

Connect to everyday experiences.

Form a connection between science and relatability by relating research outcomes to real-life situations. Explain how your product and its proven performance address common concerns that many people will relate to.

Consumer Benefits First

It may seem obvious, but the most important thing in product claims is to ensure you’re targeting how a product impacts consumers - NOT just how scientifically valid it is. Yes, your research may have shown significant improvements on a specific symptom, but this is only meaningful to your audience within the context of how it impacts them.

Highlight outcomes that resonate with consumers, showcasing how your product can enrich their daily experiences. Also, consider elaborating on how conducting research demonstrates your brand’s values of providing a safe and effective consumer experience.

Strategy 3: Visualizing Insights

Infographics for simplification.

Transform intricate data into visual representations, such as infographics. These visual aids can distill complex concepts into easily digestible formats, improving accessibility. Plus, this can be a great opportunity to incorporate your branding into the research results.

Other Impactful Visuals

Employ before-and-after visuals to highlight the transformative power of your product. This works best for products such as skincare or cosmetics where there is a noticeable aesthetic change. By visually illustrating changes, you provide consumers with tangible evidence of your claims.

Strategy 4: Crafting Clear and Concise Claims

Focus on the essential messages.

Filter out the most critical information from your research. Your product claims will reach people more effectively when they are succinct and clear. This means you should also avoid technical jargon in your marketing efforts. Try to use a conversational tone that won’t alienate your audience, and be sure to explain any words or phrases that may be unclear.

Strategy 5: Engaging Emotion

Your product serves a practical purpose, but it also addresses emotional needs that you should touch on in your marketing. One great example is:

If you’re marketing a gut-health product, of course, it addresses digestion. But how does this impact your consumers emotionally? Maybe the freedom to eat their favorite foods without stomach upset offers them relief from stress in food-related settings.

You should discuss the ways that your product fulfills desires that people can relate to, as this goes beyond the clinical solutions and fosters a personal bond between brands and their customers.

Empowering Statements

Similarly, you should consider composing claims that showcase how consumers can take control of their well-being through your product. This is an empowering message that encourages people to try your product. Everyone likes to feel proactively in charge of their health!

Wrapping Up

Leaders of consumer brands can bridge the gap between scientific research and consumer engagement. By making your product claims clear and accessible, you market your formula more effectively. If you can translate complex research into consumer-friendly claims, you create a transparent and authentic brand image that consumers greatly value. This also makes your research (and, therefore, your product) easier to find!

Want to create your own product claims or participate in research?

Citruslabs provides a simple and affordable way to prove that your products actually work. At Citruslabs, we design a clinical trial that is right for you and your budget so you can start sharing research-backed product claims without spending a fortune. We offer the cohesive planning and management required to conduct successful clinical trials from start to finish. Ready to get started? Let's talk! You can contact us here .

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Academic consumer researchers: a bridge between consumers and researchers

Affiliation.

• 1 Centre for Mental Health Research, The Australian National University, Canberra 0200, Australia. [email protected]
• PMID: 15038796
• DOI: 10.1080/j.1440-1614.2004.01337.x

Objective: To describe the contributions that consumers, and academic consumer researchers in particular, can make to mental health research.

Method: A literature survey and a systematic consideration of the potential advantages of consumer and academic consumer researcher involvement in health research.

Results: Consumer researchers may contribute to better health outcomes, but there are significant barriers to their participation in the research process. To date, discussion has focused on the role of nonacademic consumers in the health research process. There has been little recognition of the particular contributions that consumers with formal academic qualifications and research experience can offer. Academic consumer researchers (ACRs) offer many of the advantages associated with lay consumer participation, as well as some unique advantages. These advantages include acceptance by other researchers as equal partners in the research process; skills in research; access to research funding; training in disseminating research findings within the scientific community; potential to influence research funding and research policy; capacity to influence the research culture; and potential to facilitate the involvement of lay consumers in the research process. In recognition of the value of a critical mass of ACRs in mental health, a new ACR unit (the Depression and Anxiety Consumer Research Unit [CRU]) has been established at the Centre for Mental Health Research at the Australian National University.

Conclusions: Academic consumer researchers have the potential to increase the relevance of mental health research to consumers, to bridge the gap between the academic and consumer communities and to contribute to the process of destigmatizing mental disorders.

Publication types

• Research Support, Non-U.S. Gov't
• Academic Medical Centers*
• Community Participation*
• Cooperative Behavior*
• Mental Disorders / psychology
• Mental Disorders / therapy
• Stereotyping

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Chapter 16: Reading and Understanding Social Research

16.2 Being a Responsible Consumer of Research

Being a responsible consumer of research requires that you take seriously your identity as a social scientist. Now that you are familiar with how to conduct research and how to read the results of others’ research, you have some responsibility to put your knowledge and skills to use. Doing so is in part a matter of being able to distinguish what you do know, based on the information provided by research findings, from what you do not know. It is also a matter of having some awareness about what you can and cannot reasonably know as you encounter research findings.

When assessing social scientific findings, think about what information has been provided to you. In a scholarly journal article, you will presumably be given a great deal of information about the researcher’s method of data collection, the sample, and information about how the researcher identified and recruited research participants. All these details provide important contextual information that can help you assess the researcher’s claims. If, on the other hand, you come across some discussion of social scientific research in a popular magazine or newspaper, chances are that you will not find the same level of detailed information that you would find in a scholarly journal article. In this case, what you do and do not know is more limited than in the case of a scholarly journal article.

Also take into account whatever information is provided about a study’s funding source. Most funders want, and in fact require, that recipients acknowledge them in publications. But more popular press may leave out a funding source. In this internet age, it can be relatively easy to obtain information about how a study was funded. If this information is not provided in the source from which you learned about a study, it might behoove you to do a quick search on the web to see if you can learn more about a researcher’s funding. Findings that seem to support a particular political agenda, for example, might have more or less weight once you know whether and by whom a study was funded.

There is some information that even the most responsible consumer of research cannot know. For example, because researchers are ethically bound to protect the identities of their subjects, we will never know exactly who participated in a given study. Researchers may also choose not to reveal any personal stakes they hold in the research they conduct. We cannot know for certain whether or how researchers are personally connected to their work unless they choose to share such details. Neither of these “unknowables” is necessarily problematic; however, having some awareness of what you may never know about a study does provide important contextual information from which to assess what one can take away from a given report of findings.

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Key findings about Americans and data privacy

Many Americans have endless digital tools at their fingertips. And each device, site or app collects, analyzes and uses personal data. What does this mean for Americans now that so much of their day-to-day life leaves a digital footprint?

Pew Research Center has a long record of studying Americans’ views of privacy and their personal data, as well as their online habits. This study sought to understand how people think about each of these things – and what, if anything, they do to manage their privacy online. ( Read the full report .)

This survey was conducted among 5,101 U.S. adults from May 15 to 21, 2023. Everyone who took part in the survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, partisan affiliation, education and other categories. Read more about the  ATP’s methodology .

Here are the questions used for this analysis , along with responses, and its methodology .

Here are nine takeaways from a new Pew Research Center report exploring these issues.

Americans, especially Republicans, are growing more concerned about how the government uses the data it collects about them. About seven-in-ten U.S. adults (71%) say they are very or somewhat concerned about this, up from 64% in 2019. Concern has grown among Republicans and those who lean Republican but has held steady among Democrats and Democratic leaners.

Many Americans have little trust in companies to use AI responsibly. Among those who have heard of artificial intelligence (AI):

• 70% say they have little to no trust in companies to make responsible decisions about how they use AI in their products.
•   81% say the information companies collect will be used in ways that people are not comfortable with
• 80% say it will be used in ways that were not originally intended.

Still, 62% of those who have heard of AI say companies using it to analyze personal details could make life easier.

Many trust themselves to make the right decisions but are skeptical their actions matter. About eight-in-ten (78%) say they trust themselves to make the right decisions to protect their personal information.

But a majority (61%) are skeptical anything they do will make much difference. And only about one-in-five are confident that those with access to their personal information will treat it responsibly.

More than half of Americans (56%) say they always, almost always or often click “agree” without reading privacy policies. Another 22% say they do this sometimes and 18% rarely or never do this.

People are also largely skeptical that privacy policies do what they’re intended to do. About six-in-ten Americans (61%) think they’re ineffective at explaining how companies use people’s data.

About seven-in-ten Americans are overwhelmed by the number of passwords they have to remember. And nearly half (45%) report feeling anxious about whether their passwords are strong and secure.

Despite these concerns, only half of adults say they typically choose passwords that are more secure, even if they are harder to remember. A slightly smaller share (46%) opts for passwords that are easier to remember, even if they are less secure.

Some Americans have been targets of data breaches and hacking. In the past 12 months:

• Roughly a quarter of Americans (26%) say someone put fraudulent charges on their debit or credit card.
• A smaller share say they have had someone take over their email or social media account without their permission (11%).
• And 7% have had someone attempt to open a line of credit or apply for a loan using their name.

In total, 34% of Americans have experienced at least one of these issues in the past year. However, Black Americans are more likely than members of other racial and ethnic groups to have faced this.

Americans have little faith that social media executives will protect user privacy. Some 77% of Americans have little or no trust in leaders of social media companies to publicly admit mistakes and take responsibility for data misuse.

They are no more optimistic about the government reining them in: 71% have little to no trust that tech leaders will be held accountable for their missteps.

There is bipartisan support for more regulation to protect personal information. Some 78% of Democrats and 68% of Republicans think there should be more government regulation of what companies can do with customers’ personal information.

These findings are largely similar to our 2019 survey , which also showed strong support for increased regulation across parties.

About nine-in-ten Americans (89%) are concerned about social media sites knowing personal information about children. Most Americans are also concerned about advertisers using data about children’s online activities to target ads to them (85%) and online games tracking children on the internet (84%).

When it comes to who should be responsible for protecting kids’ online privacy, a vast majority (85%) says parents should bear a great deal of the responsibility. Still, roughly six-in-ten say the same about technology companies, and just under half believe the government should have a great deal of responsibility.

Note: Here are the questions used for this analysis , along with responses, and its methodology .

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Michelle Faverio is a research analyst focusing on internet and technology research at Pew Research Center

Many Americans think generative AI programs should credit the sources they rely on

Americans’ use of chatgpt is ticking up, but few trust its election information, q&a: how we used large language models to identify guests on popular podcasts, striking findings from 2023, what the data says about americans’ views of artificial intelligence, most popular.

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Curiosity Rover Science

Landing at Gale Crater, Mars Science Laboratory is assessing whether Mars ever had an environment capable of supporting microbial life. Determining past habitability on Mars gives NASA and the scientific community a better understanding of whether life could have existed on the Red Planet and, if it could have existed, an idea of where to look for it in the future.

Science Objectives

To contribute to the four Mars exploration science goals and meet its specific goal of determining Mars' habitability, Curiosity has the following science objectives:

Biological objectives

Geological and geochemical objectives, planetary process objectives, surface radiation objective.

1. Determine the nature and inventory of organic carbon compounds 2. Inventory the chemical building blocks of life (carbon, hydrogen, nitrogen, oxygen, phosphorous, and sulfur) 3. Identify features that may represent the effects of biological processes

1. Investigate the chemical, isotopic, and mineralogical composition of the Martian surface and near-surface geological materials 2. Interpret the processes that have formed and modified rocks and soils

1. Assess long-timescale (i.e., 4-billion-year) atmospheric evolution processes 2. Determine present state, distribution, and cycling of water and carbon dioxide

Characterize the broad spectrum of surface radiation, including galactic cosmic radiation, solar proton events, and secondary neutrons

Science Highlights

With over a decade of exploration, Curiosity has unveiled the keys to some of science's most unanswered questions about Mars. Did Mars ever have the right environmental conditions to support small life forms called microbes? Early in its mission, Curiosity's scientific tools found chemical and mineral evidence of past habitable environments on Mars. It continues to explore the rock record from a time when Mars could have been home to microbial life.

Science Instruments

From cameras to environmental and atmospheric sensors, the Curiosity rover has a suite of state-of-the-art science instruments to achieve its goals.

Discover More Topics From NASA

James Webb Space Telescope

Perseverance Rover

Parker Solar Probe