thesis on advancement of technology

Technological Advancement, Globalization, and Developing Countries

A Case Study of Travel Behavior and Familial Settings in Sub-Saharan West Africa

• Living reference work entry
• First Online: 03 August 2023
• Cite this living reference work entry

• Sunday Olutayo Fakunle   ORCID: orcid.org/0000-0002-0053-0082 7 ,
• John Lola Okunola   ORCID: orcid.org/0000-0002-9870-3462 7 &
• Bukunmi Kehinde Ajani   ORCID: orcid.org/0000-0002-3358-890X 8  

72 Accesses

The twenty-first century has witnessed an unprecedented advancement in technology as one of the major components of globalization. This advancement has led to the invention of the Internet and Android mobile phones. The use of these invented devices has resulted in significant changes in how people conduct their social activities and construct their daily lives. Moreover, globalization has influenced social institutions such as the economy, politics, education, technology, health, and family life through several adjustments that have occurred in each of these social institutions. This section is mainly concerned with the interplay among various concepts such as globalization, the advancement of technology, neoliberalism, and social change. Therefore, this section shows the extent to which people in sub-Saharan West Africa have been incorporated into globalization through the Internet and mobile phone use and the significant alterations or modifications that globalization has brought to travel behavior and the traditional familial settings in the region. The section begins with a succinct discussion of the contextual meaning of each of these concepts, while the significant impacts of globalization in relation to neoliberalism and change are succinctly highlighted, in particular, travel behavior and familial settings in the sub-Saharan African region.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Adenuga, K. I., Iahad, N. A., & Miskon, S. (2017). Towards reinforcing telemedicine adoption amongst clinicians in Nigeria. International Journal of Medical Informatics, 104 , 84–96. https://doi.org/10.1016/j.ijmedinf.2017.05.008

Article   Google Scholar  

Adenuga, K. I., Iahad, N. A., & Miskon, S. (2020). Telemedicine system: Service adoption and implementation issues in Nigeria. Indian Journal of Science and Technology, 13 , 1321–1327.

Ajani, O. A., & Fakunle, S. O. (2018). Mobile phone use and travel behaviour of adult residents Ile-Ife, South-western Nigeria. Research on Humanities and Social Sciences, 8 (14), 71–77.

Google Scholar  

Ajani, O. A., & Fakunle, S. O. (2021a). Gender differential in mobile phone use and travel behaviour. Discovery, 57 (306), 519–529.

Ajani, O. A., & Fakunle, S. O. (2021b). Globalisation and trends of changes in family institution in Nigerian society. American International Journal of Supply Chain Management, 2 (1), 37–45.

Aremu, A. D. (2006). Culture and some basic theories in anthropology. In O. A. Ogunbameru & R. A. Rotimi (Eds.), Man and his social environment: A textbook of sociology . Spectrum Books Limited.

Asongu, S. A., & Le, S. (2017). Technological forecasting and social change enhancing ICT for inclusive human development in Sub-Saharan Africa. Technological Forecasting and Social Change, 118 (5), 44–54. https://doi.org/10.1016/j.techfore.2017.01.026

Babalola, D., Anayo, M., & Itoya, D. A. (2021). Telehealth during COVID-19: Why sub-Saharan Africa is yet to log-in to virtual healthcare? AIMS Medical Science, 8 (1), 46–55. https://doi.org/10.3934/medsci.2021006

Bond, E. (2010). Managing mobile relationships: Children’s perception of the impact of the mobile phone on relationships in their everyday lives. Childhood, 17 , 514–529.

Busari, D. A. M., Owojuyigbe, R. A., & Okunola, I. (2017). Cultural concepts employed in child discipline within rural Yoruba households: The Ayetoro-Oke African community. Rural Society, 26 (2), 161–177.

Fakunle, S. O., & Ajani, B. K. (2021). Peculiarities of ICT adoption in Nigeria. Insights into Regional Development, 3 (4), 51–61. https://doi.org/10.9770/IRD.2021.3.4(4)

Familusi, O. O., & Ajayi, D. S. (2019). Effects of globalization on Yoruba family values. Nigerian Journal of Applied Psychology, 21 (1), 207–227.

Harry, O. N., Okoye, C. A. F., & Ugwu-Oju, A. C. (2013). Positive family dynamics: The anti thesis of domestic violence. Global Journal of Arts Humanities and Social Sciences, 1 (4), 6–14.

Igyuve, A. I., Akilla, O. M., Oriola, O. M., & Agbele, J. D. (2018). Smartphone adoption in Nigeria: Issues and discourse. Saudi Journal of Humanities and Social Sciences (SJHSS), 3 (1A), 11–20. https://doi.org/10.21276/sjhss.2018.3.1.3

Imam-Tamim, M. K., Zin, N. M., & Ibrahim, N. (2016). Impact of globalisation on domestic family law: Multi-tiered marriage in Nigeria as a case study. The Journal of Legal Pluralism and Unofficial Law, 48 (2), 256–272.

Ime, N. G., & David, E. U. (2013). Combating the 21st century family challenges in Nigeria for social stability through family counselling services. Developing Country Studies, 3 (4), 52–58.

Keengwe, J., Schnellert, G., & Jonas, D. (2012). Mobile phones in education: Challenges and opportunities for learning. Education, Information and Technology, 11 (12), 1–10. https://doi.org/10.1007/s10639012-9235-7

Kyalo, P. (2012). A reflection on the African traditional values of marriage and sexuality. International Journal of Academic Research in Progressive education and Development, 1 (2), 211–219.

Manyati, T. K., & Mutsau, M. (2020). Exploring the effectiveness of telehealth interventions for diagnosis, contact tracing and care of Corona virus disease of 2019 (COVID19) patients in sub Saharan Africa: A rapid review. Health and Technology, 11 , 341–348. https://doi.org/10.1007/s12553-020-00485-8

Mars, M. (2012). Building the capacity to build capacity in e-health in sub-Saharan Africa: The KwaZulu natal experience. Telemedicine Journal of e-Health, 18 (1), 32–37.

Mawere, M., & Mawere, A. (2010). The changing philosophy of African marriage: The relevance of Shona customary marriage practice of Kukumbira. Journal of African Studies and Development, 2 (9), 224–233.

Montgomery, N. D., Liomba, N. G., Kampani, C., Krysiak, R., Stanley, C. C., Tomoka, T., Kamiza, S., Dhungel, B. M., Gopal, S., & Fedoriw, Y. (2016). Accurate real-time diagnosis of lymphoproliferative disorders in Malawi through clinicopathologic teleconferences: A model for pathology services in sub-Saharan Africa. American Journal of Clinical Pathology, 146 (16), 423–430. https://doi.org/10.1093/ajcp/aqw118

Muoghalu, C. O., & Fakunle, S. O. (2021). Socio-cultural binoculars view of telemedicine in sub Saharan Africa: Potency, prospect, defect and danger. American International Journal of Biology and Life Sciences, 3 (1), 18–27.

Obaro, O. A., & Agbontaen-Eghafona, K. A. (2003). Changes in the family institution in Nigeria. Ibom Journal of Social Issues, 7 (1), 27–44.

Obasola, O. I., Mabawonku, I., & Lagunju, I. (2015). A review of e-health interventions for maternal and child health in sub-Sahara Africa. Maternal and Child Health Journal, 19 , 1813–1824.

Ojua, T. A. (2013). Child rights protection and sustainable social development programme in contemporary Nigeria: A sociological overview. Modern Social Science Journal, 2 (2).

Ojua, T. A., & Omono, C. K. (2012). African sacrificial ceremonies and issues in socio-cultural development. British Journal of Arts and Social Sciences, 4 (1).

Ojua, T. A., Lukpata, F. E., & Atama, C. (2014). Exploring the neglect of African family value systems and its effects on sustainable development. American Journal of Human Ecology, 3 (3), 43–50.

Olorunfemi, D. Y. (2009). Family-work conflict, information use, and social competence: A case study of married postgraduate students in the faculty of education, University of Ibadan, Nigeria. Library Philosophy and Practice, 9 , 1–7.

Oloya, B. O. (2013). A social change and the family institution in Africa. In V. T. Jike (Ed.), Nigeria for tertiary institutions (p. 180). Progress Printing Associates.

Omobowale, A. O., Omobowale, M. O., & Falase, O. S. (2018). The context of children in Yoruba popular culture. Global Studies of Childhood, 9 (1), 18–28.

Omotayo, F. O. (2016). Adoption and use of information and communication technologies by Pentecostal churches in Ibadan, Nigeria. Journal of Librarianship and Information Science, 49 (2), 22–233. https://doi.org/10.1177/0961000616662517

Otsen, B., & Agyei-baffour, P. (2016). Cost-effectiveness analysis of telemedicine for primary healthcare delivery in the Amansie-west District, Ghana. African Journal of Health Economics, 5 , 1–11.

Oyefara, J. L. (2017). Social dynamics of marital dissolution among ethno-endogamous marriages in contemporary Nigerian society: The Yoruba example. African Journal for the Psychological Studies of Social, 20 (3), 27–56.

Rahman, K. M. A., & Zhang, D. (2017). Globalization and family values: Eroding trends. International Journal of Social and Administrative Sciences, Asian Economic and Social Society, 2 (2), 63–74.

Ritzer, G. (2011). Sociological theory (8th ed.). Mc Graw-Hill Higher Education.

Sam, A. M., & Ibekwe, P. (2017). Contemporary family choice: Areas of Nigerian families’ change lag. European Journal of Research and Reflection in Educational Sciences, 5 (1), 66–70.

Sarfo, F. S., Adamu, S., Awuah, D., & Ovbiagele, B. (2017). Tele-neurology in sub-Saharan Africa: A systematic review of the literature. Journal of Neurological Science, 380 (17), 196–199. https://doi.org/10.1016/j.jns.2017.07.037

Steve, B., & Steven, Y. (2006). The sage dictionary of sociology . Sage.

Wahab, E., Odunsi, S., & Ajiboye, O. (2012). Causes and consequences of rapid erosion of cultural values in traditional society. Journal of Anthropology, 10 (1), 10–17.

World Health Organization. (2019). Contraception. www.who.int/reproductivehealth

Wusu, O., & Isiugo-Abanihe, U. C. (2003). Family structure and reproductive health decision making among the Ogu of southwestern Nigeria: A qualitative study. African Population Studies, 18 (2), 27–45.

Wusu, O., & Isiugo-Abanihe, U. C. (2006). Interconnections among changing family structure, childrearing and fertility behaviour among the Ogu, Southwestern Nigeria: A qualitative study. Demographic Research, 14 (8), 139–156.

Download references

Author information

Authors and affiliations.

Department of Behavioural Studies, Redeemer’s University, Ede, Nigeria

Sunday Olutayo Fakunle & John Lola Okunola

Department of Anthropology, Georgia State University, Atlanta, Georgia, United States of America

Bukunmi Kehinde Ajani

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Sunday Olutayo Fakunle .

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive licence to Springer Nature Switzerland AG

About this entry

Cite this entry.

Fakunle, S.O., Okunola, J.L., Ajani, B.K. (2023). Technological Advancement, Globalization, and Developing Countries. In: The Palgrave Handbook of Global Social Change. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-87624-1_160-1

Download citation

DOI : https://doi.org/10.1007/978-3-030-87624-1_160-1

Received : 02 November 2022

Accepted : 19 June 2023

Published : 03 August 2023

Publisher Name : Palgrave Macmillan, Cham

Print ISBN : 978-3-030-87624-1

Online ISBN : 978-3-030-87624-1

eBook Packages : Springer Reference Social Sciences Reference Module Humanities and Social Sciences Reference Module Business, Economics and Social Sciences

• Publish with us

Policies and ethics

• Find a journal
• Track your research

SYSTEMATIC REVIEW article

The effects of technological developments on work and their implications for continuous vocational education and training: a systematic review.

• Faculty of Human Sciences, University of Regensburg, Regensburg, Germany

Technology is changing the way organizations and their employees need to accomplish their work. Empirical evidence on this topic is scarce. The aim of this study is to provide an overview of the effects of technological developments on work characteristics and to derive the implications for work demands and continuous vocational education and training (CVET). The following research questions are answered: What are the effects of new technologies on work characteristics? What are the implications thereof for continuous vocational education and training? Technologies, defined as digital, electrical or mechanical tools that affect the accomplishment of work tasks, are considered in various disciplines, such as sociology or psychology. A theoretical framework based on theories from these disciplines (e.g., upskilling, task-based approach) was developed and statements on the relationships between technology and work characteristics, such as complexity, autonomy, or meaningfulness, were derived. A systematic literature review was conducted by searching databases from the fields of psychology, sociology, economics and educational science. Twenty-one studies met the inclusion criteria. Empirical evidence was extracted and its implications for work demands and CVET were derived by using a model that illustrates the components of learning environments. Evidence indicates an increase in complexity and mental work, especially while working with automated systems and robots. Manual work is reported to decrease on many occasions. Workload and workflow interruptions increase simultaneously with autonomy, especially with regard to digital communication devices. Role expectations and opportunities for development depend on how the profession and the technology relate to each other, especially when working with automated systems. The implications for the work demands necessary to deal with changes in work characteristics include knowledge about technology, openness toward change and technology, skills for self- and time management and for further professional and career development. Implications for the design of formal learning environments (i.e., the content, method, assessment, and guidance) include that the work demands mentioned must be part of the content of the trainings, the teachers/trainers must be equipped to promote those work demands, and that instruction models used for the learning environments must be flexible in their application.

Introduction

In the face of technology-driven disruptive changes in societal and organizational practices, continuous vocational education and training (CVET) lacks information on how the impact of technologies on work must be considered from an educational perspective ( Cascio and Montealegre, 2016 ). Research on workplace technologies, i.e., tools or systems that have the potential to replace or supplement work tasks, typically are concerned with one out of two areas of interest: First, economic and sociological research repeatedly raises the question on technological mass-unemployment and societal inequality as a result of technological advances ( Brynjolfsson and McAfee, 2014 ; Ford, 2015 ; Frey and Osborne, 2017 ). And second, management literature questions the suitability of prevailing organizational structures in the face of the so-called “fourth industrial revolution” ( Schwab, 2017 ), taking visionary leaps into a fully automated future of digital value creation ( Roblek et al., 2016 ).

Many of the contributions of scholars discuss the enormous potential of new technologies for work and society at a hypothetical level, which led to a large number of position papers. Moreover, the question on what consequences recent developments, such as working with robots, automated systems or artificial intelligence will have for different professions remain largely unclear. By examining what workplace technologies actually “do” in the work environment, it was suggested that work tasks change because of technological developments ( Autor et al., 2003 ; Autor, 2015 ). This is due to technologies substituting different operations or entire tasks and thus leave room for other activities. Jobs are defined by the work tasks and the conditions under which the tasks have to be performed. This in turn defines the necessary competences, that is the potential capacity to carry out a job (e.g., Ellström, 1997 ). Therefore, CVET needs to be informed on the changes that technology causes in work tasks and the consequential characteristics of work. Only then CVET is able to derive the required competences of employees and organize learning environments that foster the acquirement of these competences. These insights can be used to determine the implications thereof for the components of formal learning environments: content, didactics, trainer behavior, assessment, and resources (e.g., Mulder et al., 2015 ).

The aim of this systematic literature review is to get insight into the effects of new technological developments on work characteristics in order to derive the necessary work demands and their implications for the design of formal learning environments in CVET.

Therefore, the following research questions will be answered:

RQ 1 : What are the effects of new technologies on work characteristics?

RQ 2 : What are the implications thereof for continuous vocational education and training?

Theoretical considerations on the relationships between technology and work characteristics are presented before the methods for searching, selecting and analyzing suitable studies are described. Regarding the results section, the structure is based on the three main steps of analyzing the included studies: First, the variables identified within the selected studies are clustered and defined in terms of work characteristics. Second, a comprehensive overview of evidence on the relationships between technologies and work characteristics is displayed. Third, the evidence is evaluated regarding the work demands that result from technologies changing work characteristics. Finally, the implications for CVET and future research as well as the limitations of this study will be discussed.

Theoretical Framework

In this section, a conceptualization of technology and theoretical assumptions on relationships between technology and work characteristics will be outlined. Research within various disciplines, such as sociology, management, economics, educational science, and psychology was considered to inform us on the role of technology within work. Completing this section, an overview of the various components of learning environments is provided to be used as a basis for the analyses of the empirical evidence.

Outlining Technology and Recent Technological Developments

A clear definition of technology often lacks in studies, what may be due to the fact that the word itself is an “equivoque” ( Weick, 1990 , p. 1) and a “repository of overlapping inconsistent meanings” ( McOmber, 1999 , p. 149). A suitable definition can be provided by analyzing what technologies actually “do” ( Autor et al., 2003 , p. 1,280). The primary goal of technology at work is to save or enhance labor in the form of work tasks, defined as “a unit of work activity that produces output” ( Autor, 2013 , p. 186). Technology can therefore be defined as mechanical or digital devices, tools or systems. These are used to replace work tasks or complement the execution of work tasks (e.g., McOmber, 1999 ; Autor et al., 2003 ). According to this view, technology is conceptualized according to “its status as a tool” (“instrumentality”; McOmber, 1999 , p. 141). Alternatively, technology is understood as “the product of a specific historical time and place,” reflecting a stage of development within a predefined historical process (“industrialization”; McOmber, 1999 , p. 143) or as the “newest or latest instrumental products of human imagination” (“novelty”; McOmber, 1999 , p. 143), reflecting its nature that is rapidly replacing and “outdating” its predecessors. The definition according to “instrumentality” is particularly suitable for this research, as the interest focuses on individual-level effects of technologies and its use for accomplishing work. Therefore, the technology needs to be mentioned explicitly (e.g., “robot” instead of “digital transformation”) and described specifically in the form with which the employee is confronted at the workplace. Different definitions may reflect different perspectives on the role of technology for society and work. These perspectives in the form of paradigmatic views ( Liker et al., 1999 ) include philosophical and cultural beliefs as well as ideas on organizational design and labor relations. They differ with regard to the complexity in which the social context is believed to determine the impact of technology on society. Listed in accordance to increasing social complexity, the impact may be determined by technology itself (i.e., “technological determinism”), established power relations (i.e., “political interest”), managerial decisions (i.e., “management of technology”), or the interaction between technology and its social context (i.e., “interpretivist”) ( Liker et al., 1999 ). Later research added an even more complex perspective, according to which the effects of technology on society and organizations are determined by the relations between the actors themselves (i.e., “sociomateriality”; Orlikowski and Scott, 2008 ). Paradigmatic views may guide research in terms of content, purpose and goals, which in turn is likely to affect the methods and approach to research and may be specific to disciplines. For instance, Marxist sociological research following the view of “political interest” or research in information systems following the view of “management of technology.”

New technological developments are widely discussed in various disciplines. For instance, Ghobakhloo (2018) summarizes the expected areas of application of various technological concepts within the “smart factory” in the manufacturing industry: The internet of things as an umbrella term for independent communication of physical objects, big data as procedure to analyse enormous amounts of data to predict the consequences of operative, administrative, and strategic actions, blockchain as the basis for independent, transparent, secure, and trustworthy transaction executed by humans or machines, and cloud computing as an internet-based flexible infrastructure to manage all these processes simultaneously ( Cascio and Montealegre, 2016 ; Ghobakhloo, 2018 ). The central question to guide the next section is to what extent these new technologies, and also well-established technologies such as information and communication technologies (ICT), which are constantly being expanded with new functions, could influence work characteristics on a theoretical basis.

Theories on the Relationships Between Technology and Work Characteristics

A central discussion on technology can be found in the sociological literature on deskilling vs. upgrading ( Heisig, 2009 ). The definition of “skill” in empirical studies on this subject varies regarding its content by describing either the level of complexity that an employee is faced with at work, or the level of autonomy that employees are able to make use of Spenner (1990) . Theories advocating the deskilling of work (e.g., labor process theory; Braverman, 1998 ) propose that technology is used to undermine workers' skill, sense of control, and freedom. Employees need to support a mechanized workflow under constant surveillance in order to maximize production efficiency ( Braverman, 1998 ). Other authors, advocating “upskilling” ( Blauner, 1967 ; Bell, 1976 ; Zuboff, 1988 ), propose the opposite by claiming that technology frees employee's from strenuous tasks, leaving them with more challenging and fulfilling tasks ( Francis, 1986 ). In addition, issues of identity at work were raised by Blauner (1967) who acknowledged that employees may feel “alienated” as soon as technologies change or substitute work that is meaningful to them, leaving them with a feeling of powerlessness, meaninglessness, or self-estrangement ( Shepard, 1977 ). In sum, sociological theories suggest that technology has an impact on the level of freedom, power and privacy of employees, determining their identity at work and the level of alienation they experience.

According to contingency theories ( Burns and Stalker, 1994 ; Liker et al., 1999 ) technology is a means to reduce uncertainty and increase competitiveness for organizations ( Parker et al., 2017 ). Therefore, the effects of technology on the employee depend on strategic decisions that fit the organizational environment best. When operational uncertainty is high, organizations get more competitive by using technology to enhance the flexibility of employees in order to enable a self-organized adaption to the changing environment ( Cherns, 1976 ). This increases employee's flexibility by allowing them to identify and decide on new ways to add value to the organization (“organic organization”; Burns and Stalker, 1994 ). When operational uncertainty is low, organizations formalize and standardize procedures in order to optimize the workflow and make outputs more calculable (“mechanistic organization”; Burns and Stalker, 1994 ). This leads to less opportunities for individual decision-making and less flexibility for the employees. In sum, contingency theories suggest, that the effects of technology depend on the uncertainty and competitiveness in the external environment and may increase or decrease employee's flexibility and opportunities for decision-making and self-organization.

Economic research following the task-based approach from Autor et al. (2003) suggests, that technology substitutes routine tasks and complements complex (or “non-routine”) ones. Routine manual and cognitive tasks usually follow a defined set of explicit rules, which makes them susceptible to automation. By analyzing qualification requirements in relation to employment rates and wage development, it was argued that workplace automation substitutes routine and low-skill tasks and thus favors individuals who can carry out high-skilled complex work due to their education and cognitive abilities ( Card and DiNardo, 2002 ; Autor et al., 2003 ). This means, that the accomplishment of tasks “demanding flexibility, creativity, generalized problem-solving, and complex communications” ( Autor et al., 2003 , p. 1,284) becomes more important. Complex tasks, so far, posed a challenge for automation, because they required procedural and often implicit knowledge ( Polanyi, 1966 ; Autor, 2015 ). However, recent technological developments such as machine learning, are capable of delivering heuristic responses to complex cognitive tasks by applying inductive thinking or big data analysis ( Autor, 2015 ). Regarding complex manual tasks, mobile robots are increasingly equipped with advanced sensors which enable them to navigate through dynamic environments and interactively collaborate with human employees ( Cascio and Montealegre, 2016 ). In sum, economic research following the task-based approach argues that technology affects the routineness and complexity of work by substituting routine tasks. However, new technologies may be able to increasingly substitute and complement not only routine tasks, but complex tasks as well. According to the theories, this will again increase the complexity of work by creating new demands for problem-solving and reviewing the technology's activity.

Useful insights can be gained from psychological theories that explicitly take the role of work characteristics into account. Work characteristics are often mentioned by for instance sociological theories (e.g., autonomy and meaningfulness) without clearly defining the concepts. Particularly the job characteristics model of Hackman and Oldham (1975) and the job-demand-control model of Karasek (1979) and Karasek et al. (1998) are consulted to further clarify the meaning of autonomy and meaningfulness at work. With regard to autonomy, Hackman and Oldham's model 1975 conceptualizes autonomy as a work characteristic, defined as “the degree to which the job provides substantial freedom, independence, and discretion to the employee in scheduling the work and in determining the procedures to be used in carrying it out” ( Hackman and Oldham, 1975 , p. 162). According to the authors, autonomy facilitates various work outcomes, such as motivation and performance. In a similar vein, Karasek et al. (1998) stress the role of autonomy in the form of “decision authority” that interacts with more demanding work characteristics, such as workload or frequent interruptions and therefore enables a prediction of job strain and stress ( Karasek et al., 1998 ). With regard to meaningfulness, Hackman and Oldham (1975) clarify that different core job dimensions, such as the significance of one's own work results for the work and lives of other people, the direct contribution to a common goal with visible outcomes, and the employment of various skills, talents and activities all enhance the perception of meaningfulness at work. In sum, psychological theories on employee motivation and stress clarify the concepts of autonomy and meaningfulness by illustrating the factors that contribute to their experience in relation to challenging and rewarding aspects of work.

Components of CVET

In order to formulate the implications for CVET of the studied effects of technology on work characteristics, a framework with the different components of CVET is needed. The objective of the VET system and continuous education is to qualify people by supporting the acquirement of required competences, for instance by providing training. Competences refer to the potential capacity of an individual in order to successfully carry out work tasks ( Ellström, 1997 ). They contain various components such as work-related knowledge and social skills (e.g., Sonntag, 1992 ). Competences are considered here as “the combination of knowledge, skills and attitude, in relation to one another and in relation to (future) jobs” ( Mulder and Baumann, 2005 , p. 106; e.g., Baartman and de Bruijn, 2011 ).

Participants in CVET enter the system with competences, such as prior knowledge, motivation, and expectations. It is argued that these have to be considered when designing learning environments for CVET. Next to making the distinction between the different components of learning environments content, guidance, method, and assessment, it is considered important that these components are coherent and consistent ( Mulder et al., 2015 ). For instance, the content of the training needs to fit to the objectives and the background of the participants. The same goes for the method or didactics used (e.g., co-operative learning, frontal instruction) and the guidance of teachers, mentors or trainers. In addition, assessment needs to be consistent with all these components. For instance, problem based learning or competence based training requires other forms of assessment than more classical teacher centered forms of didactics, which makes a classic multiple choice test not fitting ( Gulikers et al., 2004 ). Figure 1 contains an overview of the components of learning environments for CVET.

Figure 1 . Components of CVET learning environments (adapted from Mulder et al., 2015 , p. 501).

Three steps are necessary to answer the research questions. Firstly, a systematic search and review of empirical studies reporting evidence on the direct relationships between new technologies and work characteristics. Secondly, an analysis of the evidence with regard to its implications for work demands. Thirdly, deriving the work demands and their implications for CVET.

Systematic Search Strategy

Due to the interdisciplinary nature of our research, specific databases were selected for each of the disciplines involved: Business Source Premier (business and management research) and PsycArticles (psychology) were searched via EBSCOhost, and ERIC (educational science), and Sociological Abstracts (sociology) were searched via ProQuest.

Identifying suitable keywords for technological concepts is challenging due to the rapidly changing and inconsistent terminology and the nested nature of technological concepts ( Huang et al., 2015 ). Therefore, technological terms were systematically mapped by using the different thesauri provided by each of the chosen databases. After exploding a basic term within a thesaurus, the resulting narrower terms and related terms were documented and examined within the following procedure: (a) Checking the compatibility with our definition of technology reflecting its instrumentality, (b) Adjustment of keywords that are too broad or too narrow, (c) Disassembling nested concepts. The procedure was repeated stepwise for each of the databases. Finally, 45 terms that reflect new technologies were documented and used for the database search.

Keywords reflecting work characteristics are derived from the theoretical conceptualizations previously outlined. Synonyms for different concepts within the relevant theories were identified and included. In order to narrow our search results, additionally operators for empirical studies conducted in a workplace setting were added.

In order to avoid unnecessary redundancy, the use of asterisks was carefully considered, provided that the search results did not lose significantly in precision or the number of hits did not grow to an unmanageable number of studies. The final search string is shown in Table 1 .

Table 1 . Final search string.

Eligibility Criteria and Study Selection

Technical criteria included methodological adequacy. This was ensured by only including studies published in peer-reviewed journals. In addition, the studies had to provide quantitative or qualitative data on relationships between technology and work characteristics. Only English-language studies were considered, because most of the studies are published in English and therefore the most complete overview of the existing knowledge on this topic can be obtained. This also enables as many readers as possible to have access to the original studies and analyse the findings of the empirical studies themselves.

Concerning technology, variables had to express the direct consequence or interaction with a certain technology (e.g., the amount of computer-use or experience with robots in the workplace) and indirect psychological states that conceptually resulted from the presence of the technology (e.g., a feeling of increased expectations concerning availability). Regarding work characteristics, variables had to describe work-related aspects associated with our conceptualization of work characteristics (e.g., a change in flexibility or the perception of complexity).

Regarding the direction of effects, only studies that focused on the implementation or use of technologies for work-related purposes were included. Studies were excluded, if they (a) tested particular designs or features of technologies and evaluated them without considering effects on work characteristics, (b) regarded technology not as a specific tool but an abstract process (e.g., “digital transformation”), (c) were published before 1990 due to the fact that the extent of usability and usefulness of technologies before that time should be substantially limited compared to today (e.g., Gattiker et al., 1988 ), and (d) investigated the impact of technologies on society in general without a specific relation to professional contexts (e.g., McClure, 2018 ).

Studies that were found but that did not report empirical findings on the relationships between technology and work characteristics, but rather on the relationships between technology and work demands (e.g., specific knowledge or skills) or work outcomes (e.g., performance, job satisfaction) were documented. Since the aim for this study was to derive the work demands from the work characteristics in any case, the studies that reported a direct empirical relationship between technology and work demands were analyzed separately ( N = 7).

Data Extraction

The variables expressing technology and work characteristics were listed in a table, including the quantitative or qualitative data on the relationships. Pearson's r correlations were preferred over regression results to ensure comparability. For qualitative data, the relevant passages documenting data were included. Finally, methodological information as well as sample characteristics and size are listed.

Analysis of the Results

Firstly, the variables containing work-related aspects are clustered thematically into a comprehensive final set of work characteristics. This is necessary to reduce complexity due to variations in naming, operationalization and measurement and to make any patterns in the data more visible. Deviations from the theoretically expected clusters are noted and discussed before synthesizing the evidence narratively in accordance to the research questions ( Rodgers et al., 2009 ). As proposed, the evidence on changing work characteristics is analyzed with respect to the resulting work demands in the sense of knowledge, skills, attitude and behavior, which in turn are used to determine the implications for the different components of CVET.

Figure 2 depicts a flowchart documenting the literature search. In sum, 21 studies providing evidence on relationships between technology and work characteristics were included. In addition, seven supplementary studies containing empirical evidence on relationships between technology and specific work demands were identified. These studies are taken into account when deriving the work requirements. Next, the descriptive characteristics of the included studies will be reported. After that, the evidence on relationships between technologies and work characteristics of the 21 included studies will be summarized, before finally deriving the work demands based on the evidence found.

Figure 2 . Flowchart of literature search process.

Characteristics of Studies

Table 2 contains an overview of the characteristics of selected studies. Most of the studies were published between 2015 and 2019 (52%). Nearly half of the studies were conducted in Europe (48%), followed by North America (33%). Most of the studies reported qualitative data collected with methods such as interviews (62%).

Table 2 . Characteristics of the studies.

The studies investigated a variety of technologies, such as computers (1, 7), various forms of Information and Communication technologies (ICTs; 2, 3, 17, 18, 21) in a broad sense, including specific examples of work-extending technologies and other tools for digital communication, information technology (IT) systems supporting information dissemination and retrieval within organizations (4, 9), automated systems supporting predominantly physical work procedures (5, 6, 11, 12, 13, 14, 20), robots (15, 19), social media enabling professional networking and participation in organizational and societal practices (8, 16), and more domain-specific technologies such as clinical technology supporting professional decisions (9) and field technology for labor management (10).

Relationships Between Technology and Work Characteristics

In sum, nine work characteristics were identified and defined distinctively. Table 3 contains the operational definitions of the final work characteristics and the work-related aspects they consist of. The final work characteristics are: Workflow interruptions, workload, manual work, mental work, privacy, autonomy, complexity, role expectations, and opportunities for development.

Table 3 . Overview for final work characteristics and the exemplary work-related aspects assigned to them.

The complete overview of the selected studies and results for the relationships between technology and work characteristics is provided in Table 4 (for quantitative data) and Table 5 (for qualitative data). To further increase comprehensibility, the variables within the tables were labeled according to their function in the respective study (e.g., independent variable, mediating variable, dependent variable; see notes).

Table 4 . Studies providing quantitative evidence for the relationship between technology and work-related aspects.

Table 5 . Studies providing qualitative evidence for the relationship between technology and work-related aspects.

There is quantitative evidence on positive relationships between IT system use and complexity reported by two studies (4, 9). On a similar note, qualitative evidence suggests lower situational awareness within automated systems indicating an increase in complexity (12), and clinical technology being associated with an increase in complexity for nurses (9).

There is mixed quantitative evidence on the relationships between computer work and autonomy (1). The amount of computer work is positively related to autonomy, while technological pacing is negatively related to autonomy. Working within automated systems is negatively (5, 6) or not related (6) to different measures of autonomy. ICT use shows mixed relationships with job decision latitude (3) depending on ICT features that describe negative or positive effects of use. Evidence indicates a positive relationship between social media use and autonomy. Qualitative evidence suggests that ICT use increases autonomy (21) and flexibility (17, 18, 21).

Quantitative studies indicate strong positive relationships between computer work (1) and ICT use (2) and workload. The relationships are not consistent due to the fact that certain ICT features differ in their effects on workload. ICT characteristics such as presenteeism and pace of change are positively related to feelings of increasing workload, while a feeling of anonymity is negatively associated with workload. Evidence indicates positive relationships between time or workload pressure in the context of computer work (7), working in an automated system (5), as well as social media use (8) and provide evidence for positive relationships between various technologies and workload. Qualitative studies report similar outcomes. ICT use (18), automated systems (12, 13) as well as clinical technology (9) are reported to increase the workload.

Workflow Interruptions

Quantitative evidence indicates positive relationships between computer work and increasing levels of interruptions as well as an increasing demand for multitasking (7). Qualitative evidence suggests that ICT use is positively associated with an increased level of interruptions on the one hand and workflow support on the other hand (21). Further qualitative evidence suggests that robots at the workplace have positive effects on workflow support (19), and automated systems seem to increase the level of multitasking required in general (12).

Manual Work

Qualitative evidence suggests a decrease in the amount of physically demanding tasks when working with automated systems (11) and robots (15). In one study, qualitative evidence suggests an increase in manual work for technical jobs where automated systems are used (14).

Mental Work

Quantitative evidence indicates no relationships between monitoring tasks or problem-solving demands for technical jobs within automated systems (6). Qualitative evidence however suggests positive relationships between work within automated systems and various cognitive tasks and demands, such as problem-solving and monitoring (11, 13), while working with robots increases the amount of new and challenging mental tasks (15).

Quantitative evidence indicates that different ICT characteristics show different relationships with invasion of privacy (2). Some features are negatively related to invasion of privacy (anonymity) and others are positively related to it (presenteeism, pace of change). Qualitative evidence suggests that IT systems are not related to the perception of managerial surveillance (9), while social media is positively related to peer-monitoring (16), and field technology is negatively related to employee data control (10).

Role Expectations

Quantitative evidence indicates that ICT use is inconsistently related to role ambiguity depending on specific characteristics of the technology (2). Regarding automated systems, quantitative evidence indicates no relationship between working in an automated system and opportunities for role expansion in the form of an increased perceived responsibility (6). Qualitative evidence suggests that ICT use increases the expectations for availability and connectivity (21), and social media positively affects networking pressure (16). Qualitative evidence suggests that IT systems (9) decrease meaningful job content and role expansion. Qualitative evidence suggests that automated systems vary with regard to enhancing meaningfulness at work, dependent on whether the work tasks are complemented by the system or revolve around maintaining the system (20).

Opportunities for Development

Qualitative evidence suggests that ICT use (12) as well as working with an automated system (17) increase the demands for continuing qualification. Qualitative evidence suggests that opportunities for learning and development are prevalent with clinical technology (9) and absent when working with robots (19). Mixed qualitative evidence regarding automated systems and learning opportunities suggests that the effects depend on the differences in work roles in relation to being supported by the system or supporting the system (20).

A comprehensive summary of the outcomes can be found in Table 6 . The information in this table gives a summary of the evidence found for the different technologies and their relationships to work characteristics, more specifically to work related aspects. Important distinctive characteristics such as sample characteristics are listed in Tables 4 , 5 .

Table 6 . Overview over identified relationships between technology and work characteristics.

Subsequently, the results shown are now used as a basis for the identification of work demands that lead to the need for adapting to changes in work characteristics.

Relationships Between Technologies and Work Demands

Three sources are considered for the identification of work demands: Work demands mentioned in the studies on technology and work characteristics, work demands mentioned by the supplementary studies found during the database search ( N = 7), and work demands analytically derived from the results.

Some studies that examined the effects of technology on work characteristics also reported concrete work demands. Regarding the increasing complexity and the associated mental work, qualitative evidence suggests an increasing demand for cognitive as well as digital skills (11) in automated systems. With regard to IT systems, quantitative evidence indicates positive relationships with computer literacy (9), and analytical skills (4). With regard to the increase in workflow interruptions and the role expectations for constant availability and connectivity, time and attention management strategies are proposed in order to cope with the intrusive features of technology (2). Other strategies mentioned in the studies include self-discipline for disengaging from the ubiquitous availability resulting from mobile communication devices (18, 8) as well as the need for reflecting on individual responsiveness when working overtime due to self-imposed pressure to be available at all times (18, 21). Concerning opportunities for development, the willingness and ability to learn and adapt to technological changes and the associated changes in work (15, 4, 12) is emphasized. Moreover, employability is facilitated by using technological tools for professional networking (16).

The supplementary studies provide evidence on the direct relationships between technologies and work demands without the mediating consideration of work characteristics. This evidence is listed in Table 7 .

Table 7 . Supplementary studies on the relationship between technology and work-related demands.

There is quantitative evidence for positive relationships between the perception of controllability and exploratory use of computers (22), first-hand experience with robots and readiness for robotization (23, 24), and perceived usefulness and positive attitudes toward telemedicine technology (25), blockchain technology (26), and IT systems in general (27). Further quantitative evidence indicates mixed effects of perceived ease of use. Evidence indicates a positive relationship between perceived ease of use and perceived technological control with regard to telemedicine (25), no relationship between ease of use and attitude regarding blockchain technology (26), and a positive relationship between ease of use and attitude toward using IT systems (27). Quantitative evidence indicates that information processing enabled by technology is positively related to an increasing demand of cognitive skills (e.g., synthesizing and interpreting data) and interpersonal skills (e.g., coordinating and monitoring other people), but not related to an increasing demand in psychomotor skills (e.g., manual producing and precise assembling) (28). The level of standardization of work is positively related to interpersonal skills, but not related to cognitive and psychomotor skills (28). A high variety of tasks is positively related to the demand for cognitive skills and interpersonal skills and not related to psychomotor skills (28).

By analyzing the evidence on relationships between technology and work characteristics, further work demands can be derived. Knowledge about the specific technology at hand may be useful to decrease the perception of complexity as new technologies are introduced. This seems evident when comparing the effects of a simple computer with the effects of work within an automated system. For instance, while evidence indicates no relationship between computer work and complexity (6), work within an automated system is suggested to be associated with increasing complexity (12). Moreover, problem-solving skills (13) and cognitive skills such as diagnosing and monitoring (11, 15) increase when employees work within automated systems. Increasing autonomy suggests the need for personal skills regarding self-organizing and self-management due to greater flexibility and the associated possibilities for structuring work in many ways, particularly when working with ICTs (18, 21). Workflow interruptions and an increasing workload also increases the importance of communication skills for explicating the boundaries of one's own engagement to colleagues and leaders (17, 18, 21). Furthermore, reflecting the professional role at work may be critical due to changes in role expectations. The example of self-imposed need for availability underlines this argument (21). All this has implications for self-regulatory activities, such as reflection, and could benefit from experimenting and monitoring one's own strategies for time and attention management.

Implications for CVET: Objectives and Characteristics

The aforementioned studies describe several required behavioral aspects that are considered important due to technology at work. Emphasized is the need for components related to the organization of one's own work, namely self-discipline and time and attention management.

The identified need for reflection on one's own professional actions, for experimentation, and also for professional networking (for instance by using tools) can be seen as parts of further professional development by oneself or in interaction with others. In addition, the need for demonstrating employability is mentioned. From all these professional and career development aspects can be derived that problem-solving skills, self-regulation skills, and communication skills are required as well as proactive work behavior and coping and reflection strategies.

Various relevant skills, such as psychomotor skills, analytical skills, management skills, and interpersonal skills are mentioned. In addition, the need for diagnostic and monitoring skills as well as digital skills is emphasized. All these components can be used in relation to two explicitly mentioned needs: ability to learn and computer literacy. The demand for generic and transferable skills is emphasized. As a basis for the skills, knowledge is required, for instance on the technology itself, although not explicitly discussed in the studies. In contrast, several components of attitude are explicitly mentioned and considered to be a requirement for the ability to deal with challenges caused by new technologies at work. Firstly, the more generic willingness to learn, adaptability, and perceived behavioral control. Secondly, attitudes that are directly linked to technology, namely a positive attitude and trust, especially toward technology (e.g., robots), and technological readiness and acceptance.

Next to the opportunity of acquiring the mentioned components of competences at work, CVET can organize training interventions in the form of adequate learning environments to foster these. The ability of employees to carry out, develop and use the mentioned behavioral aspects, skills, knowledge, and attitudes, can be considered as required objectives of CVET and have concrete consequences for the characteristics of the learning environments.

As for the content of the learning environments, derived from the aforementioned requirements, it can be argued that attention should be paid to different categories of learning objectives: acquiring knowledge about and learning how to use technology, how to manage work and oneself, and how to continue one's own professional development. In addition, the relevance of attitude tells us that these components need to be fostered in the training and therefore need to be part of the content of the learning environments as well.

In relation to the methods or the didactics, only one study explicitly mentioned a suggestion, namely experience based learning for fostering adaptability (12). In relation to the guidance of trainers or teachers no suggestions are provided. The same goes for assessment, diagnoses or monitoring, and the coherence of components of the learning environments.

This systematic literature review aimed at identifying effects of new technological developments on work characteristics, identifying associated work demands, and determining their implications for the design of formal CVET learning environments.

Effects of New Technologies on Work Characteristics and Word Demands

Based on a systematic review focusing on empirical evidence, several effects of technology on work characteristics were found, thus answering RQ 1. Evidence suggests that complexity and mental work increases with ongoing automation and robotization of work, for instance due to the automatization of procedures which “hides” certain processes from employees. The automatization of tasks introduces new mental tasks, such as monitoring the machine's activities and solving problems. A decrease in manual work depends on the relation between the job and the technology in use (supporting vs. being supported).

Workload and workflow interruptions increase as a general consequence of the ubiquity of technology, mainly due to a higher level of job speed and the associated time and workload pressure. A higher level of autonomy seems to be associated with a higher workload and more workflow interruptions. This applies in particular to work with ICTs and domain-specific technologies, such as field technology.

Role expectations and opportunities for development depend on the relation between the job and the technology in use (supporting vs. being supported). With regard to role expectations, the need for being available or connected via digital devices and a new division of responsibilities between employees and technology are repeatedly mentioned in the studies. This applies particularly to work with automated systems, robots, and domain-specific technologies such as clinical technology.

With regard to work demands, employees need strategies to deal with higher levels of workload, autonomy, and complexity. Required skill demands contain mental, analytical, cognitive, and self-regulatory demands. In addition, opportunities for role expansion and learning, which do not seem to automatically result from the implementation and use of new technologies, need to be created (pro)actively by the employees. Employees need to take more responsibility with regard to their own development and professional work identity (for instance considering the pressure for constant availability). They need to be able to effectively deal with a high workload and number of interruptions, increasing flexibility, complexity, and autonomy, a demand for constant availability, changes in meaningfulness of tasks, changes in work roles, and the need to create and use learning opportunities. In the light of ongoing changes and challenges, skills to further develop and adapt one's own skills gain in importance. Regarding attitudes, the willingness to learn, adapt and experiment may be a central work demand.

Implications for the Practice of CVET

Various required objectives of CVET can be concluded from the reported results. For instance, developing the ability of employees to carry out the mentioned behaviors, as well as the skills, knowledge and attitudes that are necessary for those behaviors. These objectives have consequences for the content of CVET learning environments. From the empirical studies on the relationships between technology and work, we derived the need for employees to organize their own work, for instance through time management. Furthermore, many issues relating to own professional development and career development are important, to acquire individually and independently as well as by interacting with others. Ultimately, this refers to the skills of self-initiated learning and development. With regard to fostering helpful attitudes, raising awareness of the relevance of trust or training the social skills to promote trust in the workplace can be included in the content of CVET learning environments. In research on creating trust within organizations, regularly giving and receiving relevant information was shown to be important for creating trust toward co-workers, supervisors and top-management, which in turn fostered the perception of organizational openness and employee involvement as a result ( Thomas et al., 2009 ). In the research on creating trust in virtual teams, the importance of frequent interaction was important to develop trust on a cognitive as well as an affective level (e.g., Germain, 2011 ). These research results however need to be adapted to the context of technology at work.

Although there is no information provided on the guidance of employees, informal guidance through leadership ( Bass and Avolio, 1994 ) as well as formal guidance by trainers and teachers during interventions contain possibilities for fostering the required competences. Attention should be paid not only to acquiring relevant knowledge (digital literacy), but also to skills in applying the knowledge and therefore dealing with technology. Even more challenging might be the task of supporting attitude development (e.g., technological acceptance and openness to changes), fostering transfer of skills, and preparation for future development. Especially future professional development, which includes the ability to learn in relation to current and future changes, needs to be focused on. Teachers, trainers and mentors need to be equipped to be able to foster these competences.

In relation to the use of didactical methods, methods that do not merely focus on knowledge acquisition but also provide opportunities for skill acquisition and changes in attitude need to be applied. For example, one study explicitly suggested experience based learning for fostering the adaptability of employees when faced with ongoing technological developments. Other solutions for instruction models as a profound basis for learning environments may be found in more flexible approaches, for instance according to the cognitive flexibility theory ( Spiro et al., 2003 ), where learners are meant to find their own learning paths in ill-structured domains. By applying such models, that are often based on constructivist learning theories, in a coherent way, the development of strategies for self-organizing and self-regulation may be facilitated.

Furthermore, the use of technology within learning environments may have the potential to increase participants interactions, which are focused in for instance collaborative and co-operative learning ( Dillenbourg et al., 2009 ). Next to increasing interactions in learning and being able to co-operate, technology in learning environments can used to foster the other required competences, if adequately designed ( Vosniadou et al., 1996 ; Littlejohn and Margaryan, 2014 ).

When keeping in mind, that the coherence of components is an important requirement for the design of learning environments ( Mulder et al., 2015 ), the component that describes assessment needs further attention. There is evidence supporting the idea, that the type of assessment has an impact on how learning takes place ( Gulikers et al., 2004 ; Dolmans et al., 2005 ). Therefore, it can be used to deliberatively support and direct learning processes.

Only when all these aspects are considered can CVET interventions effectively and sustainably foster the mentioned objectives, such as promoting a willingness to change in relation to technologies, the effective use of technology, and personal development in the context of technological developments.

Limitations and Implications for Future Research

Regarding the search methods, the use of databases is challenging when investigating technologies ( Huang et al., 2015 ). Technological and technical terms are widespread outside the research in which they are regarded as the object of investigation. Therefore, it produces a large amount of studies that concern technology with diverse research objectives that can be difficult to sort. An interesting focus for future research would be the systematic mapping of journals dealing specifically with technology in order to identify research that could complement the results of the present study as well as consider specificities regarding the domains in which the data is collected and disciplines by which the research is conducted. For instance, domain-specific databases from healthcare or manufacturing might provide additional insights into the effects of technology on work. Another limitation is the absence of innovative new technologies, such as artificial intelligence, blockchain, or the internet of things as object of investigation. Broad technological categories, such as ICTs and social media have received some attention in research, especially in relation to questions beyond the scope of this review. Newer technological developments as discussed by Ghobakhloo (2018) are virtually not present in current research. This gap in empirical research needs to be filled. In addition, future research should ensure that it does not miss opportunities for research where effects of these innovative technologies can be examined in detail, for instance by conducting an accompanying case study of the implementation process. Research investigating changes over time regarding the use of technology and its effects is needed. In doing so, research could capture the actual dynamics of change and development of processes as they happen in order to inform truly effective interventions in practice. Moreover, a classification of technological characteristics according to their effects may be valuable by enabling a more in-depth analysis of new technologies and their effects on specific groups of employees and different types of organizations. These analyses will also allow a breakdown of effects in relation to differences in jobs, hierarchy levels and levels of qualification, which could be very important for organizations and employers in order to adapt the CVET strategy to the specific demands of specific groups of employees. The present review takes a first step in this direction by identifying work characteristics that are affected by different technologies. In addition, future research could also take into account non-English language research, which might increase insight in for instance cultural differences in the use and the effects of technology at work.

Regarding theory, some of the relevant theories considering technology stem from sociology (e.g., Braverman, 1998 ) or economics ( Autor et al., 2003 ). For instance, the task-based approach ( Autor et al., 2003 ) showed some explanatory value by suggesting that complexity may increase as a consequence of technology. Furthermore, it suggested that this effect may depend on job specifics. Those propositions are reflected in the aforementioned empirical evidence. Psychological theories on work characteristics do not conceptualize technology explicitly (e.g., Hackman and Oldham, 1975 ; Karasek, 1979 ). As of the present study, the large variation regarding the concepts and variables derived from theory might limit the comparability of results. To foster systematic research, further theory development needs to more explicitly consider the role of technology at multiple levels (i.e., individual level, team level, organizational level) and with regard to the characteristics and demands of work. In the context of theory, the paradigmatic views also deserve attention (e.g., Liker et al., 1999 ; Orlikowski and Scott, 2008 ). These views could be reflected in the subject of research, as exemplified for instance in the study of field technologies and its effects on privacy from a managerial control and power perspective, potentially reflecting the view of political interest ( Tranvik and Bråten, 2017 ). Most of the studies, however, do not take a clear stand on what exactly they mean when they investigate technology. This complicates interdisciplinary inquiry and integration, as it is not always clear which understanding of technology is prevalent. We therefore encourage future research to explicitly define technology, for instance as in the present paper using the proposed framework of McOmber (1999) . In doing so, characteristics of technology may be defined more clearly and distinctive which in turn would enable the formation of the strongly needed categorization of technologies, as was proposed earlier.

And, although there are theories and models on the use of technology in education (e.g., E-Learning, Technology enhanced learning), they are not focussing on fostering the competences required to deal with new technologies in a sustainable manner. In general, the same gap needs to be filled for instruction models and instructional design models, for instance to promote changes in attitude and professional development. In addition, there is hardly any attention for the consequences of new technologies at work for CVET yet ( Harteis, 2017 ). All this requires more systematic evaluation studies. The research gaps identified need to be filled in order to provide evidence-based support to employees in dealing with new technologies at work in a sustainable manner, taking charge of their own performance and health, as well as seeking and using opportunities for their own professional and career development.

Data Availability Statement

All datasets generated for this study are included in the article/supplementary material.

Author Contributions

PB and RM have jointly developed the article, and to a greater or lesser extent both have participated in all parts of the study (design, development of the theoretical framework, search, analyses, and writing). The authors approved this version and take full responsibility for the originality of the research.

Conflict of Interest

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

* Amick, B. C., and Celentano, D. D. (1991). Structural determinants of the psychosocial work environment: introducing technology in the work stress framework. Ergonomics 34, 625–646. doi: 10.1080/00140139108967341

PubMed Abstract | CrossRef Full Text | Google Scholar

Autor, D. H. (2013). The “task approach” to labor markets: an overview. J. Labour Market Res. 46, 185–199. doi: 10.1007/s12651-013-0128-z

CrossRef Full Text | Google Scholar

Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. J. Econ. Perspect. 29, 3–30. doi: 10.1257/jep.29.3.3

Autor, D. H., Levy, F., and Murnane, R. J. (2003). The skill content of recent technological change: an empirical exploration. Q. J. Econ. 118, 1279–1333. doi: 10.1162/003355303322552801

* Ayyagari, R., Grover, V., and Purvis, R. (2011). Technostress: technological antecedents and implications. MIS Q. 35, 831–858. doi: 10.2307/41409963

Baartman, L. K. J., and de Bruijn, E. (2011). Integrating knowledge, skills and attitudes: conceptualising learning processes towards vocational competence. Educ. Res. Rev. 6, 125–134. doi: 10.1016/j.edurev.2011.03.001

Bass, B. M., and Avolio, B. J. (1994). Transformational leadership and organizational culture. Int. J. Public Administr. 17, 541–554. doi: 10.1080/01900699408524907

Bell, D. (1976). The coming of the post-industrial society. Educ. Forum 40, 574–579. doi: 10.1080/00131727609336501

Blauner, R. (1967). Alienation and Freedom: The Factory Worker and His Industry. Oxford: Chicago University of Press.

Google Scholar

* Bordi, L., Okkonen, J., Mäkiniemi, J.-P., and Heikkilä-Tammi, K. (2018). Communication in the digital work environment: implications for wellbeing at work. NJWLS 8, 29–48. doi: 10.18291/njwls.v8iS3.105275

Braverman, H. (1998). Labor and Monopoly Capital: The Degradation of Work in the Twentieth Century. New York, NY: Monthly Review Press.

Brynjolfsson, E., and McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York, NY: Norton.

Burns, T., and Stalker, G. M. (1994). The Management of Innovation. Oxford: Oxford University Press.

* Calitz, A. P., Poisat, P., and Cullen, M. (2017). The future African workplace: the use of collaborative robots in manufacturing. SA J. Hum. Resour. Manag. 15, 1–11. doi: 10.4102/sajhrm.v15i0.901

Card, D., and DiNardo, J. E. (2002). Skill-biased technological change and rising wage inequality: some problems and puzzles. J. Labor Econ. 20, 733–783. doi: 10.1086/342055

Cascio, W. F., and Montealegre, R. (2016). How technology is changing work and organizations. Annu. Rev. Organ. Psychol. Organ. Behav. 3, 349–375. doi: 10.1146/annurev-orgpsych-041015-062352

** Chau, P. Y.K., and Hu, P. J.-H. (2002). Investigating healthcare professionals' decisions to accept telemedicine technology: an empirical test of competing theories. Inform. Manage. 39, 297–311. doi: 10.1016/S0378-7206(01)00098-2

* Chen, W., and McDonald, S. (2015). Do networked workers have more control? The implications of teamwork, telework, ICTs, and social capital for job decision latitude. Am. Behav. Sci. 59, 492–507. doi: 10.1177/0002764214556808

Cherns, A. (1976). The principles of sociotechnical design. Hum. Relations 29, 783–792. doi: 10.1177/001872677602900806

* Chesley, N. (2014). Information and communication technology use, work intensification and employee strain and distress. Work Employ. Soc. 28, 589–610. doi: 10.1177/0950017013500112

** Chow, S. K., Chin, W.-Y., Lee, H.-Y., Leung, H.-C., and Tang, F.-H. (2012). Nurses' perceptions and attitudes towards computerisation in a private hospital. J. Clin. Nurs. 21, 1685–1696. doi: 10.1111/j.1365-2702.2011.03905.x

Dillenbourg, P., Järvela, S., and Fischer, F. (2009). “The evolution of research on computer-supported collaborative learning: from design to orchestration,” in Technology-Enhanced Learning , ed N. Balacheff, S. Ludvigsen, T. de Jong, A. Lazonder, and S. Barnes (Dordrecht: Springer Netherlands), 3–19.

Dolmans, D. H., De Grave, W., Wolfhagen, I. H., and van der Vleuten, C. P. (2005). Problem-based learning: future challenges for educational practice and research. Med. Educ. 39, 732–741. doi: 10.1111/j.1365-2929.2005.02205.x

* Dvash, A., and Mannheim, B. (2001). Technological coupling, job characteristics and operators' well-being as moderated by desirability of control. Behav. Inform. Technol. 20, 225–236. doi: 10.1080/01449290116930

Ellström, P.-E. (1997). The many meanings of occupational competence and qualification. J. Euro Industr. Train. 21, 266–273. doi: 10.1108/03090599710171567

* Findlay, P., Lindsay, C., McQuarrie, J., Bennie, M., Corcoran, E. D., and van der Meer, R. (2017). Employer choice and job quality. Work Occupat. 44, 113–136. doi: 10.1177/0730888416678038

Ford, M. (2015). Rise of the Robots: Technology and the Threat of a Jobless Future. New York, NY: Basic Books.

Francis, A. (1986). New Technology at Work. Oxford: Clarendon Press.

Frey, C. B., and Osborne, M. A. (2017). The future of employment: how susceptible are jobs to computerisation? Technol. Forecast. Soc. Change 114, 254–280. doi: 10.1016/j.techfore.2016.08.019

Gattiker, U. E., Gutek, B. A., and Berger, D. E. (1988). Office technology and employee attitudes. Soc. Sci. Comp. Rev. 6, 327–340. doi: 10.1177/089443938800600301

* Gekara, V. O., and Thanh Nguyen, V.-X. (2018). New technologies and the transformation of work and skills: a study of computerisation and automation of Australian container terminals. N. Technol. Work Employ. 33, 219–233. doi: 10.1111/ntwe.12118

Germain, M.-L. (2011). Developing trust in virtual teams. Perf. Improvement Q. 24, 29–54. doi: 10.1002/piq.20119

** Ghani, J. A., and Deshpande, S. P. (1994). Task characteristics and the experience of optimal flow in human—computer interaction. J. Psychol. 128, 381–391. doi: 10.1080/00223980.1994.9712742

Ghobakhloo, M. (2018). The future of manufacturing industry: a strategic roadmap toward industry 4.0. J. Manuf. Tech. Manage. 29, 910–936. doi: 10.1108/JMTM-02-2018-0057

* Gough, R., Ballardie, R., and Brewer, P. (2014). New technology and nurses. Labour Industry 24, 9–25. doi: 10.1080/10301763.2013.877118

Gulikers, J. T. M., Bastiaens, T. J., and Kirschner, P. A. (2004). A five-dimensional framework for authentic assessment. ETR&D 52, 67–86. doi: 10.1007/BF02504676

Hackman, J. R., and Oldham, G. R. (1975). Development of the job diagnostic survey. J. Appl. Psychol. 60, 159–170. doi: 10.1037/h0076546

Harteis, C. (2017). “Machines, change and work: an educational view on the digitalization of work,” in The Impact Digitalization in the Workplace: An Educational View , ed C. Harteis (New York: Springer), 1–10.

Heisig, U. (2009). “The deskilling and upskilling debate,” in International Handbook of Education for the Changing World of Work: Bridging Academic and Vocational Learning , ed R. Maclean and D. Wilson (Dordrecht: Springer Netherlands), 1639–1651.

* Holden, R. J., Rivera-Rodriguez, A. J., Faye, H., Scanlon, M. C., and Karsh, B.-T. (2013). Automation and adaptation: Nurses' problem-solving behavior following the implementation of bar coded medication administration technology. Cognition Technol. Work 15, 283–296. doi: 10.1007/s10111-012-0229-4

Huang, Y., Schuehle, J., Porter, A. L., and Youtie, J. (2015). A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’. Scientometrics 105, 2005–2022. doi: 10.1007/s11192-015-1638-y

* James, K. L., Barlow, D., Bithell, A., Hiom, S., Lord, S., Oakley, P., et al. (2013). The impact of automation on pharmacy staff experience of workplace stressors. Int. J. Pharm. Pract. 21, 105–116. doi: 10.1111/j.2042-7174.2012.00231.x

** Kamble, S., Gunasekaran, A., and Arha, H. (2019). Understanding the Blockchain technology adoption in supply chains-Indian context. Int. J. Product. Res. 57, 2009–2033. doi: 10.1080/00207543.2018.1518610

Karasek, R., Brisson, C., Kawakami, N., Houtman, I., Bongers, P., and Amick, B. (1998). The Job Content Questionnaire (JCQ): an instrument for internationally comparative assessments of psychosocial job characteristics. J. Occupat. Health Psychol. 3, 322–355. doi: 10.1037/1076-8998.3.4.322

Karasek, R. A. (1979). Job demands, job decision latitude, and mental strain: implications for job redesign. Administr. Sci. Q. 24, 285–308. doi: 10.2307/2392498

* Körner, U., Müller-Thur, K., Lunau, T., Dragano, N., Angerer, P., and Buchner, A. (2019). Perceived stress in human-machine interaction in modern manufacturing environments-results of a qualitative interview study. Stress Health 35, 187–199. doi: 10.1002/smi.2853

* Kraan, K. O., Dhondt, S., Houtman, I. L.D., Batenburg, R. S., Kompier, M. A.J., and Taris, T. W. (2014). Computers and types of control in relation to work stress and learning. Behav. Inform. Technol. 33, 1013–1026. doi: 10.1080/0144929X.2014.916351

Liker, J. K., Haddad, C. J., and Karlin, J. (1999). Perspectives on technology and work organization. Annu. Rev. Sociol. 25, 575–596. doi: 10.1146/annurev.soc.25.1.575

Littlejohn, A., and Margaryan, A., (eds.). (2014). Technology-Enhanced Professional Learning: Processes, Practices and Tools. New York, NY: Routledge.

* Marler, J. H., and Liang, X. (2012). Information technology change, work complexity and service jobs: a contingent perspective. N. Technol. Work Employ. 27, 133–146. doi: 10.1111/j.1468-005X.2012.00280.x

McClure, P. K. (2018). ‘You're Fired,’ says the robot: the rise of automation in the workplace, technophobes, and fears of unemployment. Soc. Sci. Comput. Rev. 36, 139–156. doi: 10.1177/0894439317698637

McOmber, J. B. (1999). Technological autonomy and three definitions of technology. J. Commun. 49, 137–153. doi: 10.1111/j.1460-2466.1999.tb02809.x

Mulder, R. H., and Baumann, S. (2005). “Competencies of in-company trainers,” in Bridging Individual, Organisational, and Cultural Aspects of Professional Learning , eds H. Gruber, C. Harteis, R. H. Mulder, and M. Rehrl (Regensburg: S. Roderer Verlag), 105–109.

Mulder, R. H., Messmann, G., and König, C. (2015). Vocational education and training: researching the relationship between school and work. Eur. J. Educ. 50, 497–512. doi: 10.1111/ejed.12147

* Ninaus, K., Diehl, S., Terlutter, R., Chan, K., and Huang, A. (2015). Benefits and stressors - perceived effects of ICT use on employee health and work stress: an exploratory study from Austria and Hong Kong. Int. J. Qual. Stud. Health Well Being 10:28838. doi: 10.3402/qhw.v10.28838

Orlikowski, W. J., and Scott, S. V. (2008). 10 sociomateriality: challenging the separation of technology, work and organization. ANNALS 2, 433–474. doi: 10.1080/19416520802211644

Parker, S. K., van den Broeck, A., and Holman, D. (2017). Work design influences: a synthesis of multilevel factors that affect the design of jobs. Acad. Manage. Ann. 11, 267–308. doi: 10.5465/annals.2014.0054

Polanyi, M. (1966). The Tacit Dimension. New Work, NY: Doubleday.

Roblek, V., Meško, M., and KrapeŽ, A. (2016). A complex view of industry 4.0. SAGE Open 6:215824401665398. doi: 10.1177/2158244016653987

Rodgers, M., Sowden, A., Petticrew, M., Arai, L., Roberts, H., Britten, N., and Popay, J. (2009). Testing methodological guidance on the conduct of narrative synthesis in systematic reviews. Evaluation 15, 49–73. doi: 10.1177/1356389008097871

Schwab, K. (2017). The Fourth Industrial Revolution. London: Portfolio Penguin.

Shepard, J. M. (1977). Technology, alienation, and job satisfaction. Ann. Rev. Sociol. 3, 1–21.

Sonntag, K. (1992). Personalentwicklung in Organisationen [Human Resource Development in Companies. Psychological Basics, Methods, and Strategies]. Göttingen: Verlag für Psychologie.

** Spell, C. S. (2001). Organizational technologies and human resource management. Hum. Relat. 54, 193–213. doi: 10.1177/0018726701542003

Spenner, K. I. (1990). Skill: meanings, methods, and measures. Work Occupat. 17, 399–421. doi: 10.1177/0730888490017004002

Spiro, R. J., Collins, B. P., Thota, J. J., and Feltovich, P. J. (2003). Cognitive flexibility theory: hypermedia for complex learning, adaptive knowledge application, and experience acceleration. Educ. Technol. 43, 5–10. Available online at: https://www.jstor.org/stable/44429454 (accessed April 28, 2020).

Thomas, G. F., Zolin, R., and Hartman, J. L. (2009). The central role of communication in developing trust and its effect on employee involvement. J. Bus. Commun. 46, 287–310. doi: 10.1177/0021943609333522

* Towers, I., Duxbury, L., Higgins, C., and Thomas, J. (2006). Time thieves and space invaders: technology, work and the organization. J. Org. Change Manage. 19, 593–618. doi: 10.1108/09534810610686076

* Tranvik, T., and Bråten, M. (2017). The visible employee - technological governance and control of the mobile workforce. Socio Econ. Stud. 28, 319–337. doi: 10.5771/0935-9915-2017-3-319

** Turja, T., and Oksanen, A. (2019). Robot acceptance at work: a multilevel analysis based on 27 EU countries. Int. J. Soc. Robot. 11, 679–689. doi: 10.1007/s12369-019-00526-x

** Turja, T., Taipale, S., Kaakinen, M., and Oksanen, A. (2019). Care workers' readiness for robotization: identifying psychological and socio-demographic determinants. Int. J. Soc. Robot. 4:67. doi: 10.1007/s12369-019-00544-9

* van Zoonen, W., and Rice, R. E. (2017). Paradoxical implications of personal social media use for work. N. Technol. Work Employ. 32, 228–246. doi: 10.1111/ntwe.12098

Vosniadou, S., Corte, E. de, Glaser, R., and Mandl, H., (eds.). (1996). International Perspectives on the Design of Technology-Supported Learning Environments. Mahwah, NJ: Erlbaum.

* Walden, J. A. (2016). Integrating social media into the workplace: a study of shifting technology use repertoires. J. Broadcast. Electr. Med. 60, 347–363. doi: 10.1080/08838151.2016.1164163

Weick, K. E. (1990). “Technology as equivoque,” in Technology and Organizations , ed P. S. Goodman and L. Sproull (San Francisco, CA: Jossey-Bass), 1–44.

Zuboff, S. (1988). In the Age of the Smart Machine: The Future of Work and Power. New York, NY: Basic Books.

* Zubrycki, I., and Granosik, G. (2016). Understanding therapists' needs and attitudes towards robotic support. The roboterapia project. Int. J. Soc. Robot. 8, 553–563. doi: 10.1007/s12369-016-0372-9

* Studies included in the systematic review.

** Supplementary studies.

Keywords: technology, work characteristics, continuous vocational education and training, automation, work demands, systematic review

Citation: Beer P and Mulder RH (2020) The Effects of Technological Developments on Work and Their Implications for Continuous Vocational Education and Training: A Systematic Review. Front. Psychol. 11:918. doi: 10.3389/fpsyg.2020.00918

Received: 14 February 2020; Accepted: 14 April 2020; Published: 08 May 2020.

Reviewed by:

Copyright © 2020 Beer and Mulder. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Patrick Beer, patrick.beer@ur.de

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

Preparing for the Revolution: Information Technology and the Future of the Research University (2002)

Chapter: 1. introduction, 1 introduction.

O ur society is now being reshaped by rapid advances in information technologies—computers, telecommunications networks, and other digital systems—that have vastly increased our capacity to know, achieve, and collaborate (Attali, 1992; Brown, 2000; Deming and Metcalfe, 1997; Kurzweil, 1999). These technologies allow us to transmit information quickly and widely, linking distant places and diverse areas of endeavor in productive new ways, and to create communities that just a decade ago were unimaginable.

Of course, our society has been through other periods of dramatic change before, driven by such innovations as the steam engine, railroad, telephone, and automobile. But never before have we experienced technologies that are evolving so rapidly (increasing in power by a hundredfold every decade), altering the constraints of space and time, and reshaping the way we communicate, learn, and think.

The rapid evolution of digital technologies is creating not only new opportunities for our society but challenges to it as well, 1 and institutions of every stripe are grappling to respond by adapting their strategies and activities. Corporations and governments are reorganizing to enhance productivity, improve quality, and control costs. Entire industries have been restructured to better align themselves with the realities of the digital age. It is no great exaggeration to say that information technology is fundamentally changing the relationship between people and knowledge.

Yet ironically, at the most knowledge-based entities of all— our colleges and universities—the pace of transformation has been relatively modest in key areas. Although research has in many ways been transformed by information technology, and it

is increasingly used for student and faculty communications, other higher-education functions have remained more or less unchanged. Teaching, for example, largely continues to follow a classroom-centered, seat-based paradigm.

Nevertheless, some major technology-aided teaching experiments are beginning to emerge, and several factors suggest that digital technologies may eventually drive significant change throughout academia (Newman and Scurry, 2000; Hanna, 2000; Noble, 2001). Because these technologies are expanding by orders of magnitude our ability to create, transfer, and apply information, they will have a profound impact on how universities define and fulfill their missions. In particular, the ability of information technology to facilitate new forms of human interaction may allow the transformation of universities toward a greater focus on learning. 2

American academia has undergone significant change before, beginning with the establishment of secular education during the 18 th century (Rudolph, 1991). Another transformation resulted from the Land-Grant College Act of 1862 (Morrill Act), which created institutions that served agriculture and industries; academia was no longer just for the wealthy but charged with providing educational opportunities to the working class as well. Around 1900, the introduction of graduate education began to expand the role of the university in training students for careers both scholarly and professional. The middle of the twentieth century saw two important changes: the G. I. Bill, which provided educational opportunities for millions of returning veterans; and the research partnership between the federal government and universities, which stimulated the evolution of the research university. Looking back, each of these changes seems natural. But at the time, each involved some reassessment both of the structure and mission of the university (Wulf, 1995).

Already, higher education has experienced significant technology-based change, particularly in research, 3 even though it presently lags other sectors in some respects. And we expect that the new technology will eventually also have a profound impact on one of the university’s primary activities—teaching— by freeing the classroom from its physical and temporal bounds and by providing students with access to original source materials (Gilbert, 1995). The situations that students will encounter as citizens and professionals can increasingly be

simulated and modeled for teaching and learning, and new learning communities driven by information technology will allow universities to better teach students how to be critical analyzers and consumers of information.

The information society has greatly expanded the need for university-level education; lifelong learning is not only a private good for those who pursue it but also a social good in terms of our nation’s ability to maintain a vibrant democracy and support a competitive workforce.

But while information technology has the capacity to enhance and enrich teaching and scholarship, it also appears to pose certain threats to our colleges and universities (Duderstadt, 2000a; Katz, 1999) in their current manifestations. We can now use powerful computers and networks to deliver educational services to anyone—any place, any time. Technology can create an open learning environment in which the student, no longer compelled to travel to a particular location in order to participate in a pedagogical process involving tightly integrated studies based mostly on lectures or seminars by local experts, is evolving into an active and demanding consumer of educational services. 4

Similarly, faculty’s scholarly communities are shifting from physical campuses to virtual ones, globally distributed in cyberspace. And technological innovations are stimulating the growth of powerful markets for educational services and the emergence of new for-profit competitors, which could also help reshape the higher-education enterprise (Goldstein, 2000; Shea, 2001).

Technological change also has the potential for transforming how the research university accomplishes its social mission. In an increasingly global culture linked together by technology, with no single cultural context to provide a “filter,” the role of traditional disciplinary canons is changing.

It is clear that the digital age poses many questions for academia. For example, what will it mean to be “educated” in the twenty-first century? How will academic research be organized and financed? As the constraints of time and space are relaxed by information technology, how will the role of the university’s physical campus change?

In the near term it seems likely that the campus, a geographically concentrated community of scholars and a center of culture, will continue to play a central role, though the current

manifestations of higher education may shift. For example, students may choose to distribute their college experience among residential campuses, commuter colleges, and online (virtual) universities. They may also assume more responsibility for, and control over, their education. 5 The scholarly activities of faculty will more frequently involve technology to access distant resources and enhance interaction with colleagues around the world. The boundaries between the university and broader society may blur, just as its many roles will become ever more complex and intertwined with those of other components of the knowledge and learning enterprise (Brown and Duguid, 1996).

Thus we must take care not simply to extrapolate the past but instead to examine the full range of options for the future, even though their precise impacts on society and its institutions will be difficult to predict. In any case, we must be ready for disruption. Just as these technologies have driven rapid, significant, and frequently discontinuous and unforeseen change in other sectors of our society, so too will they present university decision makers not only with exciting prospects but a decidedly bumpy ride.

CONTEXT FOR THE STUDY

Given their mandate from Congress to advise the federal government on scientific and technological matters, the presidents of the National Academies (National Academy of Sciences, National Academy of Engineering, and Institute of Medicine) acted on the above concerns. They launched a project in early 2000, through the National Research Council (NRC), to better understand the implications of information technology for the research university. This institution is a key element of the national research enterprise, a prime mover of the economy, and a critical source of scientists and engineers. Its wide range of academic functions also makes it an important model for analysis, with broad applicability elsewhere in the university community.

Primary support for the National Academies project was provided by the National Research Council, with additional support from the W.K. Kellogg Foundation, the National Science Foundation, and the Woodrow Wilson Fellowship Foundation.

The project was organized under the Policy and Global Affairs Division of the NRC, with staff and program support from the Government-University-Industry Research Roundtable.

The premise of this study was simple. Although the rapid evolution of digital technology will present numerous challenges and opportunities to the research university, there is a sense that many of the most significant issues are not well understood by academic administrators, their faculty, and those who support or depend on the institution’s activities.

The study had two objectives:

To identify those information technologies likely to evolve in the near term (a decade or less) that could ultimately have major impact on the research university.

To examine the possible implications of these technologies for the research university—its activities (teaching, research, service, outreach) and its organization, management, and financing—and the impacts on the broader higher-education enterprise.

In addressing the second point, the panel examined those functions, values, and characteristics of the research university most likely to change as well as those most important to preserve.

In pursuit of these ends, a panel was formed consisting of leaders from industry, higher education, and foundations with expertise in the areas of information technology, the research university, and public policy. Since first convening in February 2000, the Steering Committee has held a number of meetings— including site visits to major technology-development centers such as Lucent (Bell) Laboratories and IBM Research Laboratories—to identify and discuss trends, issues, and options. The major themes addressed by these activities were:

The pace of evolution of information technology.

The ubiquitous character of the Internet.

The relaxation of the conventional constraints of space, time, and institution.

The pervasive character of information technology (the potential for near-universal access to information, education, and research).

The changing ways in which we handle digital data, information, and knowledge.

The growing importance of intellectual capital relative to physical or financial capital.

In January 2001 a two-day workshop was held at the National Academies—with the invited participation of about 80 leaders from higher education, industry, and government—to explore possible strategies for the research university and its various stakeholders and to provide input on possible follow-up initiatives. The presentations and discussions of the workshop were videotaped and broadcast on the Research Channel, and they are currently being videostreamed from its web site (programs.researchchannel.com) to help stimulate public discussion. Members of the panel also participated in a discussion of the project at the June 2001 meeting of the Government-University-Industry Research Roundtable.

This report, finalized through a series of conference calls and email exchanges during the second half of 2001, discusses what the panel learned during the study process. Chapter 2 describes the likely near-future of information technology;

Chapter 3 discusses the implications of this technology for the research university; and Chapter 4 summarizes the panel’s findings and calls for a continued dialogue between the research university and its stakeholders on these issues.

The panel has tried to maintain a clear and focused presentation of the issues. In a number of places, it makes assertions based on its collective judgment, while taking care to alert readers and appropriately qualify those assertions. Where possible, the report references the growing literature on information technology and education in order to complement the panel’s opinions. Yet change is occurring so rapidly there is high risk that any specific assertion made by individual experts or a panel such as this one may be proved wrong within a few years. Indeed, a central theme of the report is that the research university must be prepared to cope with constant shifts and continued uncertainty regarding information technology and its implications.

In addition, while this report focuses on the 261 U.S. doctoral/research universities, one of the inevitable consequences of the march of information technology is that these universities will become much more interconnected with the rest of higher education. Therefore much of the discussion deals with the broader academic context, of which the research university is but one component.

However, in seeking to gain a broad view of the issues facing the research university and information technology, the panel was unable (given the available time and resources) to examine several issues in the depth it would have liked. Therefore some important topics, such as the service mission of the university, are discussed but briefly.

Finally, although its original charge was to provide specific conclusions and recommendations on a range of policy issues— including some, such as the altered funding environment for the research university and the changes to intellectual-property protection wrought by the digital revolution that are spurring legislative actions, roiling campuses, and finding their way to court—the panel ultimately decided that specificity at this point would be inappropriate and premature. Digital technology is evolving so rapidly that an overly prescriptive set of conclusions and recommendations would be in danger of becoming irrelevant soon after the report’s publication. However, the

priorities for action that the panel identified are in areas that institutions and the overall higher-education enterprise can themselves consider and begin to address. And academia might get some assistance in that regard. The digital revolution will undoubtedly create barriers and opportunities that permit new federal and state approaches to provide significant leverage in helping the research university anticipate and manage change.

The rapid evolution of information technology (IT) is transforming our society and its institutions. For the most knowledge-intensive entities of all, research universities, profound IT-related challenges and opportunities will emerge in the next decade or so. Yet, there is a sense that some of the most significant issues are not well understood by academic administrators, faculty, and those who support or depend on the institution's activities. This study identifies those information technologies likely to evolve in the near term (a decade or less) that could ultimately have a major impact on the research university. It also examines the possible implications of these technologies for the research university—its activities (learning, research, outreach) and its organization, management, and financing—and for the broader higher education enterprise. The authoring committee urges research universities and their constituents to develop new strategies to ensure that they survive and thrive in the digital age.

READ FREE ONLINE

Welcome to OpenBook!

You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

Do you want to take a quick tour of the OpenBook's features?

Show this book's table of contents , where you can jump to any chapter by name.

...or use these buttons to go back to the previous chapter or skip to the next one.

Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

To search the entire text of this book, type in your search term here and press Enter .

Share a link to this book page on your preferred social network or via email.

View our suggested citation for this chapter.

Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

Get Email Updates

Do you enjoy reading reports from the Academies online for free ? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.

Technological Advancement Essay

Searching for a technological advancement essay? Look no further! This simple essay on breakthrough technologies describes all the benefits and drawbacks of the issue.

Introduction

Why write about technology advancement, breakthrough technologies in various sectors, technological advancement essay faq.

Technological advancement has taken major strides in bringing liberation to the divergent human wants and gratifications. After keen observation, I have come to realize that technological advancement plays a critical role in solving the major crisis of food shortages in the modern world. In the state of Virginia during the 17th century, human labor was imperative due to the pressing need to grow enough food to serve the people in the community during the winter spell hence the need to hire slaves from Africa to work on their farms (Brush, 1988).

This has since changed partly due to the technological advancements over the years that have led to the replacement of human and animal labor with more efficient energy sources as wind power, hydroelectric and steam energies that ultimately led to a significant increase in productivity. Thus, the thesis statement for this essay is to analyze the impact of technological advancement on people’s lives from ancient times to the present modern world.

It is evident that technology is the backbone of the industrial revolution process that has occurred over the years and leads to a total overhaul from crude systems to modern efficient machinery. With this in mind, we cannot overlook the role that technology has played on the social and economic fronts of many societies hence the need to have a deeper insight and research on this particular topic. The transformation brought about by technological advancement has helped many societies in Africa and the world at large to alleviate poverty and improve their standards of living through the increased food supply and significant growth in the economy and this integrates with the research question: Is technology liberating?

The three academic disciplines from which this research has drawn insight from include: agriculture, sociology and communication sectors.

Technology Advancement in Agriculture

In the ancient world, the main source of power was human labor obtained mainly from slaves. In North America for example, during the early 17th century, most whites purchased slaves as a chief source of labor to work on their farms but with the emancipation proclamation by President Abraham Lincoln during the civil war of 1863 that declared all slaves to be set free from bondage, their masters had no choice but to source for another alternative source of labor.

This act spearheaded the advancement of the agricultural revolution, which was also boosted by the industrial revolution that led to the development of more efficient agricultural machinery that required very few workers and resulted in higher farm production. Examples of some of the medieval technologies used in the ancient world included: water wheel, four-field crop rotation system, the horse collar and selective breeding of livestock with good traits.

In 1750, engineer John Smeaton working on the water wheel significantly increased its efficiency hence boosting its productivity. It was during this period that technological advancement, revolution, and innovation in agriculture were at its peak and it led to the emergence of new farm machinery like cultivators, combine harvesters and mowers that were pulled by oxen, mules, and horses. These machines were later powered by steam energy than a more efficient diesel fuel that led to a remarkable increase in farm output (Kedar, 2009). Previously, the land was prepared by a man using traditional mattocks and hoes made from raw materials obtained locally like wood and scrap metals.

With the mechanization of agriculture, farmers could now make use of the machinery like combine harvesters and petrol powered tractors to prepare large acres of land within a short period with minimum input on human labor to clear, plow and plant on their expansive farms. Technology has led to hybridization, selective breeding and inbreeding in livestock to obtain or maintain all the good qualities in their animals as high milk production, quality wool production, quality meat production, and other desirable animal traits.

Robert Bakewell and Thomas Coke doing their research on selective breeding crossed Lincoln and Longhorn sheep, to produce a hybrid that exhibited all the good qualities of both Lincoln and Longhorn and was referred to as New Leicester variety. This has helped in alleviating the crisis of food shortages through maximization of farm output.

Technology Advancement in Everyday Lives

Technology has been indispensable in bettering the social lives of many people in society. Technological advancements have led to the development in infrastructure and social amenities which has in turn positively impacted on the general livelihood of many individuals. It was until the Roman era in the 18th century that good roads were constructed, during those days, slaves were also used to carry loads and farm produce from the farms to storage warehouses and vice versa. They also used canoes and boats to carry farm products from North America between the Appalachian Mountains and Mississippi River during the early periods of the 19th century.

During this period, the transport system was still archaic and underdeveloped and people found it difficult to navigate from one region to another or carry heavy luggage over long distances because of poor roads and crude modes of transport. The canals preceded the construction of railroads that marked the beginning of the industrial revolution and from there we had significant developments in the transport sector with the construction of the first transcontinental railroad in 1869 and the subsequent construction of tarmac roads, sea canals and subway systems (Butler, 1996).

These developments made it easier for people to move around hence positively impacting on their social lives by enhancing communication, trade, and farming. This indirectly led to improved living standards as a result of the increased food supply by farmers and the development of business firms. Farmers could now effectively carry their farm inputs and fertilizers to the farm and farm products to the market without difficulties. Businesses also thrived because of the efficient transport system and in no time firms began proliferating from every sector of the economy. This enabled them to diversify their economic activities as they no longer depended on the agricultural sector for their daily provision but also ventured into the business sector within the community.

With the recent development in infrastructure, it paved the way to the development of social amenities as schools, hospitals, public toilets, shops and market centers that increased in number as more and more investors joined the market. These amenities played a critical role in the development of the economy and elevating the living standards of the people in the community as they could now easily access all the essential resources. Hence technology played a vital role in liberating the lives of many from the bondage of hunger and scarcity to a point of abundance and stable food supply.

Technology Advancement in Communication

Communication is the act of conveying information from one person to another either face to face or by means of a communication medium. According to Scruton (1996), during the ancient times, slaves used to communicate through hymns, quilts or underground railroads while others used drums to convey coded information since most had originated from Africa and drum beating was their cultural way of communicating. These primitive modes of communications were not very reliable as the information could at times be distorted or misinterpreted by the recipient leading to a communication breakdown.

During the ancient period, people used to communicate through messages carved on stone pillars but this type of communication had limitations as the recipients had to travel miles to receive them and the message could only be read within a certain reading range. Others like the American Indians used smoke to convey a particular message to the community while others used bonfires lit on hilltops but such signals were limited to conveying specific information like looming danger, war or victory.

Communication then developed to more elaborate form which included writing on portable materials like reeds and papyrus. This medium of communication was much more reliable than the earlier archaic communication system. With the emergence of technological advancement and innovations, the transmission of signals from one person to another through a more sophisticated medium like communication cables took center stage. In the early 1830s, the electrical communication system made significant progress in this industry as people could now get in touch through electronic devices like a telephone.

In the year 1833, scientists Carl Friedrich and Wilhelm Eduard Weber researching on the electric transmission devices, made use of the principle of “electromagnetic technology” that later acted as the fundamental basis or a prerequisite for the innovation of telephones (Williams, 1993). Subsequent experiments done by Alexander Bell and Thomas Watson worked to optimize its efficiency and could now be used for commercial purposes. This was later followed by other technological developments and innovations by telecommunication engineers and scientists that led to the production of the carbon microphone, telephone exchange, data storage devices, wireless phones, and computers.

At this point, we can only appreciate the technological advancements that the communication industry has taken overtime to come up with sophisticated and very efficient gadgets that can serve multiple purposes other than communication. Such progress in technology has acted as a remedy to the many communication snarl-ups that people in the ancient world had to contend with but now people can freely share information, ideas, thoughts, opinions, photos, video clips on very many communication platforms using the sophisticated devices and handsets.

For example, use of the internet on computers and mobile phones to share information and ideas across the globe hence making the world a small village and enabling the free flow of information that is objective and informative. Hence this technology could be used to positively impact the lives of people by making them more informed and educated.

In conclusion, technology has had quite a significant impact on people’s lives over the years by making life more bearable through the production of efficient systems that require little labor but produce a significantly high output. One significant finding from the above research is that African culture and tradition has been greatly revolutionized over the years from the archaic, crude and barbaric practices to sophisticated and more efficient processes through technological innovations and advancement. The introduction of western culture has worked to raise the living standards of many African communities that were previously languishing in hunger and poverty.

• What is technological advancement? Technological advancement implies the emergence and development of technical devices that affect various spheres of peoples’ life. It affects economic, political, social, and other sectors.
• How does technology affect the advancement of science? Modern technologies make it easier to share information and knowledge, allow scientists from different countries to interact effectively, and also involve the development of new methods of analysis.
• How does the advancement of technology affect society? Modern technologies influence various spheres of public life. They have significantly changed the labor market, transport and communications. People’s daily lives have become easier and more efficient.
• How do I start an essay about technology It is a good idea to start your technology advancement with a hook. One option is to use a quote, like the following one by Albert Einstein: “It has become appallingly obvious that our technology has exceeded our humanity.” One more option is to use an exciting fact like the following one: Over 6,000 new computer viruses are created and released every month.

Brush, S. G. (1988). The History of Modern Science. A Guide to the Second Scientific Revolution, 35 (10), 5-8.

Butler, G. (1996). A History of Information Technology and Systems. Chicago, IL: University of Chicago Press.

Kedar, S. (2009). Database Management Systems . Washington, DC: American Psychological Association.

Scruton, R. (1996 ). The Art of Communication Over the Years. The New Criterion, 15 (30), 9-13.

Williams, T. (1993). A Short History of Technology: From the Earliest Times . New York: Dover Publications.

• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2020, May 7). Technological Advancement Essay. https://ivypanda.com/essays/technological-advancement-essay/

"Technological Advancement Essay." IvyPanda , 7 May 2020, ivypanda.com/essays/technological-advancement-essay/.

IvyPanda . (2020) 'Technological Advancement Essay'. 7 May.

IvyPanda . 2020. "Technological Advancement Essay." May 7, 2020. https://ivypanda.com/essays/technological-advancement-essay/.

1. IvyPanda . "Technological Advancement Essay." May 7, 2020. https://ivypanda.com/essays/technological-advancement-essay/.

Bibliography

IvyPanda . "Technological Advancement Essay." May 7, 2020. https://ivypanda.com/essays/technological-advancement-essay/.

• New Product: Laptop Assembling Machinery Nokia Corporation
• Domination in the Book "Animal Farm"
• The US Farm Bills and Policy Reforms
• Industrialization and Changes of Western History
• Slavery in Islamic Civilisation
• World History: Is Competition Good?
• Imperialism' Impacts on Africa, Latin America and India
• Enlightenment and Romantic Age

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Dialogues Clin Neurosci
• v.22(2); 2020 Jun

Language: English | Spanish | French

The impact of digital technology use on adolescent well-being


El impacto del empleo de la tecnología digital en el bienestar de los adolescents, impact de l’usage des technologies numériques sur le bien-être de l’adolescent, tobias dienlin.

School of Communication, University of Hohenheim, Germany

Niklas Johannes

Institute of Neuroscience and Psychology, University of Glasgow, UK

This review provides an overview of the literature regarding digital technology use and adolescent well-being. Overall, findings imply that the general effects are on the negative end of the spectrum but very small. Effects differ depending on the type of use: whereas procrastination and passive use are related to more negative effects, social and active use are related to more positive effects. Digital technology use has stronger effects on short-term markers of hedonic well-being (eg, negative affect) than long-term measures of eudaimonic well-being (eg, life satisfaction). Although adolescents are more vulnerable, effects are comparable for both adolescents and adults. It appears that both low and excessive use are related to decreased well-being, whereas moderate use is related to increased well-being. The current research still has many limitations: High-quality studies with large-scale samples, objective measures of digital technology use, and experience sampling of well-being are missing.


Esta revisión entrega una panorámica de la literatura acerca del empleo de la tecnología digital y el bienestar de los adolescentes. En general, los resultados traducen que los efectos globales son negativos, aunque muy insignificantes. Los efectos difieren según el tipo de empleo: la procastinación y el empleo pasivo están relacionados con efectos más negativos; en cambio, el empleo social y activo se asocia con efectos más positivos. El empleo de la tecnología digital tiene efectos más potentes en los indicadores de corto plazo del bienestar hedónico (como los afectos negativos) que las mediciones a largo plazo del bienestar eudaimónico (como la satisfacción con la vida). Aunque los adolescentes son más vulnerables, los efectos son comparables para adolescentes y adultos. Parece que tanto el empleo reducido como el excesivo están relacionados con una disminución del bienestar, mientras que el empleo moderado se vincula con un mayor bienestar. La investigación actual todavía tiene muchas limitaciones: faltan estudios de alta calidad con muestras numerosas, mediciones objetivas del empleo de tecnología digital y muestras de experiencia de bienestar.

Nous proposons ici une revue de la littérature sur la pratique des technologies numériques et le bien-être de l’adolescent. Les données générales sont en faveur d’un effet négatif mais qui reste négligeable. L’usage définit la nature de l’effet : la procrastination et la passivité sont associées à un effet plus négatif alors qu’une pratique active et tournée vers la socialisation s’associe à un effet plus positif. Les effets sont plus importants sur les marqueurs à court terme du bien-être hédonique (comme les affects négatifs) que sur ceux à long terme du bien-être eudémonique (épanouissement personnel) ; ils sont comparables chez les adultes et les adolescents, même si ces derniers sont plus fragiles. Une utilisation excessive ou à l’inverse insuffisante semble diminuer le bien-être, alors qu’une pratique modérée l’augmenterait. Cependant, la recherche actuelle manque encore d’études de qualité élevée à grande échelle, de mesures objectives de la pratique des technologies numériques et d’expérience d’échantillonnage du bien-être.

With each new technology come concerns about its potential impact on (young) people’s well-being. 1 In recent years, both scholars and the public have voiced concerns about the rise of digital technology, with a focus on smartphones and social media. 2 To ascertain whether or not these concerns are justified, this review provides an overview of the literature regarding digital technology use and adolescent well-being. 


Digital technology use and well-being are broad and complex concepts. To understand how technology use might affect well-being, we first define and describe both concepts. Furthermore, adolescence is a distinct stage of life. To obtain a better picture of the context in which potential effects unfold, we then examine the psychological development of adolescents. Afterward, we present current empirical findings about the relation between digital technology use and adolescent well-being. Because the empirical evidence is mixed, we then formulate six implications in order to provide some general guidelines, and end with a brief conclusion.


Digital technology use


Digital technology use is an umbrella term that encompasses various devices, services, and types of use. Most adolescent digital technology use nowadays takes place on mobile devices. 3 , 4 Offering the functions and affordances of several other media, smartphones play a pivotal role in adolescent media use and are thus considered a “metamedium.” 5 Smartphones and other digital devices can host a vast range of different services. A representative survey of teens in the US showed that the most commonly used digital services are YouTube (85%), closely followed by the social media Instagram (72%), and Snapchat (69%). Notably, there exist two different types of social media: social networking sites such as Instagram or TikTok and instant messengers such as WhatsApp or Signal.


All devices and services offer different functionalities and affordances, which result in different types of use . 6 When on social media, adolescents can chat with others, post, like, or share. Such uses are generally considered active . In contrast, adolescents can also engage in passive use, merely lurking and watching the content of others. The binary distinction between active and passive use does not yet address whether behavior is considered as procrastination or goal-directed. 7 , 8 For example, chatting with others can be considered procrastination if it means delaying work on a more important task. Observing, but not interacting with others’ content can be considered to be goal-directed if the goal is to stay up to date with the lives of friends. Finally, there is another important distinction between different types of use: whether use is social or nonsocial. 9 Social use captures all kinds of active interpersonal communication, such as chatting and texting, but also liking photos or sharing posts. Nonsocial use includes (specific types of) reading and playing, but also listening to music or watching videos.


When conceptualizing and measuring these different types of digital technology use, there are several challenges. Collapsing all digital behaviors into a single predictor of well-being will inevitably decrease precision, both conceptually and empirically. Conceptually, subsuming all these activities and types of use under one umbrella term fails to acknowledge that they serve different functions and show different effects. 10 Understanding digital technology use as a general behavior neglects the many forms such behavior can take. Therefore, when asking about the impact of digital technology use on adolescent well-being, we need to be aware that digital technology use is not a monolithic concept.


Empirically, a lack of validated measures of technology use adds to this imprecision. 11 Most work relies on self-reports of technology use. Self-reports, however, have been shown to be imprecise and of low validity because they correlate poorly with objective measures of technology use. 12 In the case of smartphones, self-reported duration of use correlated moderately, at best, with objectively logged use. 13 These findings are mirrored when comparing self-reports of general internet use with objectively measured use. 14 Taken together, in addition to losing precision by subsuming all types of technology use under one behavioral category, the measurement of this category contributes to a lack of precision. To gain precision, it is necessary that we look at effects for different types of use, ideally objectively measured.


Well-being


Well-being is a subcategory of mental health. Mental health is generally considered to consist of two parts: negative and positive mental health. 15 Negative mental health includes subclinical negative mental health, such as stress or negative affect, and psychopathology, such as depression or schizophrenia. 16 Positive mental health is a synonym for well-being; it comprises hedonic well-being and eudaimonic well-being. 17 Whereas hedonic well-being is affective, focusing on emotions, pleasure, or need satisfaction, eudaimonic well-being is cognitive, addressing meaning, self-esteem, or fulfillment.


Somewhat surprisingly, worldwide mental health problems have not increased in recent decades. 18 Similarly, levels of general life satisfaction remained stable during the last 20 years. 19 , 20 Worth noting, the increase in mental health problems that has been reported 21 could merely reflect increased awareness of psychosocial problems. 22 , 23 In other words, an increase in diagnoses might not mean an increase in psychopathology.


Which part of mental health is the most likely to be affected by digital technology use? Empirically, eudaimonic well-being, such as life satisfaction, is stable. Although some researchers maintain that 40% of happiness is volatile and therefore malleable, 24 more recent investigations argued that the influences of potentially stabilizing factors such as genes and life circumstances are substantially larger. 25 These results are aligned with the so-called set-point hypothesis, which posits that life satisfaction varies around a fixed level, showing much interpersonal but little intrapersonal variance. 26 The hypothesis has repeatedly found support in empirical studies, which demonstrate the stability of life satisfaction measures. 27 , 28 Consequently, digital technology use is not likely to be a strong predictor of eudaimonic well-being. In contrast, hedonic well-being such as positive and negative affect is volatile and subject to substantial fluctuations. 17 Therefore, digital technology use might well be a driver of hedonic well-being: Watching entertaining content can make us laugh and raise our spirits, while reading hostile comments makes us angry and causes bad mood. In sum, life satisfaction is stable, and technology use is more likely to affect temporary measures of hedonic well-being instead of more robust eudaimonic well-being. If this is the case, we should expect small to medium-sized effects on short-term affect, but small to negligible effects on both long-term affect and life satisfaction.


Adolescents


Adolescence is defined as “the time between puberty and adult independence,” 29 during which adolescents actively develop their personalities. Compared with adults, adolescents are more open-minded, more social-oriented, less agreeable, and less conscientious 30 ; more impulsive and less capable of inhibiting behavior 31 ; more risk-taking and sensation seeking 29 ; and derive larger parts of their well-being and life satisfaction from other peers. 32 During adolescence, general levels of life satisfaction and self-esteem drop and are often at their all-time lowest. 33 , 34 At the same time, media use increases and reaches a first peak in late adolescence. 3 Analyzing the development of several well-being-related variables across the last two decades, the answers of 46 817 European adolescents and young adults show that, whereas overall internet use has risen strongly, both life satisfaction and health problems remained stable. 19 Hence, although adolescence is a critical life stage with substantial intrapersonal fluctuations related to well-being, the current generation does not seem to do better or worse than those before.


Does adolescent development make them particularly susceptible to the influence of digital technology? Several scholars argue that combining the naturally occurring trends of low self-esteem, a spike in technology use, and higher suggestibility into a causal narrative can take the form of a foregone conclusion. 35 For one, although adolescents are in a phase of development, there might be more similarities between adolescents and adults than differences. 30 Concerns about the effects of a new technology on an allegedly vulnerable group has historically often taken the form of paternalization. 36 For example, and maybe in contrast to popular opinion, adolescents already possess much media literacy or privacy literacy. 3 


This has two implications. First, asking what technology does to adolescents ascribes an unduly passive role to adolescents, putting them in the place of simply responding to technology stimuli. Recent theoretical developments challenge such a one-directional perspective and advise to rather ask what adolescents do with digital technology , including their type of use. 37 Second, in order to understand the effects of digital technology use on well-being, it might not be necessary to focus on adolescents. It is likely that similar effects can be found for both adolescents and adults. True, in light of the generally decreased life satisfaction and the generally increased suggestibility, results might be more pronounced for adolescents; however, it seems implausible that they are fundamentally different. When assessing how technology might affect adolescents compared with adults, we can think of adolescents as “canaries in the coalmine.” 38 If digital technology is indeed harmful, it will affect people from all ages, but adolescents are potentially more vulnerable.


Effects


What is the effect of digital technology use on well-being? If we ask US adolescents directly, 31% are of the opinion that the effects are mostly positive, 45% estimate the effects to be neither positive nor negative, and 24% believe that effects are mostly negative. 4 Teens who considered the effects to be positive stated that social media help (i) connect with friend; (ii) obtain information; and (c) find like-minded people. 4 Those who considered the effects to be negative explained that social media increase the risks of (i) bullying; (ii) neglecting face-to-face contacts; (iii) obtaining unrealistic impressions of other people’s lives. 4 


Myriad studies lend empirical support to adolescents’ mixed feelings, reporting a wide range of positive, 39 neutral, 40 or negative 41 relations between specific measures of digital technology use and well-being. Aligned with these mixed results of individual studies, several meta-analyses support the lack of a clear effect. 42 In an analysis of 43 studies on the effects of online technology use on adolescent mental well-being, Best et al 43 found that “[t]he majority of studies reported either mixed or no effect(s) of online social technologies on adolescent wellbeing.” Analyzing eleven studies on the relation between social media use and depressive symptoms, McCrae et al 44 report a small positive relationship. Similarly, Lissak 45 reports positive relations between excessive screen time and insufficient sleep, physiological stress, mind wandering, attention deficit-hyperactivity disorder (ADHD)-related behavior, nonadaptive/negative thinking styles, decreased life satisfaction, and potential health risks in adulthood. On the basis of 12 articles, Wu et al 46 find that “the use of [i]nternet technology leads to an increased sense of connectedness to friend[s] and school, while at the same time increasing levels of anxiety and loneliness among adolescents.” Relatedly, meta-analyses on the relation between social media use and adolescent academic performance find no or negligible effects. 47 


It is important to note that the overall quality of the literature these meta-analyses rely upon has been criticized. 48 This is problematic because low quality of individual studies biases meta-analyses. 49 To achieve higher quality, scholars have called for more large-scale studies using longitudinal designs, objective measures of digital technology use that differentiate types of use, experience sampling measures of well-being (ie, in-the-moment measures of well-being; also known as ambulant assessment or in situ assessment), and a statistical separation of between-person variance and within-person variance. 50 In addition, much research cannot be reproduced because the data and the analysis scripts are not shared. 51 In what follows, we look at studies that implemented some of these suggestions.


Longitudinal studies generally find a complex pattern of effects. In an 8 year study of 500 adolescents in the US, time spent on social media was positively related to anxiety and depression on the between-person level. 52 At the within-person level, these relationships disappeared. The study concludes that those who use social media more often might also be those with lower mental health; however, there does not seem to be a causal link between the two. A study on 1157 Croatians in late adolescence supports these findings. Over a period of 3 years, changes in social media use and life satisfaction were unrelated, speaking to the stability of life satisfaction. 40 In a sample of 1749 Australian adolescents, Houghton et al 53 distinguished between screen activities (eg, web browsing or gaming) and found overall low within-person relations between total screen time and depressive symptoms. Out of all activities, only web surfing was a significant within-person predictor of depressive symptoms. However, the authors argue that this effect might not survive corrections for multiple testing. Combining a longitudinal design with experience sampling in a sample of 388 US adolescents, Jensen et al 54 did not find a between-person association between baseline technology use and mental health. Interestingly, they only observed few and small within-person effects. Heffer et al 55 found no relation between screen use and depressive symptoms in 594 Canadian adolescents over 2 years. These results emphasize the growing need for more robust and transparent methods and analysis. In large adolescent samples from the UK and the US, a specification curve analysis, which provides an overview of many different plausible analyses, found small, negligible relations between screen use and well-being, both cross-sectionally and longitudinally. 56 Employing a similar analytical approach, Orben, Dienlin, and Przybylski 57 found small negative between-person relations between social media use and life satisfaction in a large UK sample of adolescents over 7 years. However, there was no robust within-person effect. Similarly, negligible effect sizes between adolescent screen use and well-being are found in cross-sectional data sets representative of the population in the UK and US. 58 In analyzing the potential effects of social media abstinence on well-being, two large-scale studies using adult samples found small positive effects of abstinence on well-being. 59 , 60 Two studies with smaller and mostly student samples instead found mixed 61 or no effects of abstinence on well-being. 62 


The aforementioned studies often relied on composite measures of screen use, possibly explaining the overall small effects. In contrast, work distinguishing between different types of use shows that active use likely has different effects than passive use. Specifically, active use may contribute to making meaningful social connections, whereas passive use does not. 9 For example, meaningful social interactions have been shown to increase social gratification in adults, 63 , 64 whereas passive media use or media use as procrastination has been negatively related to well-being. 6 , 8 This distinction should also apply to adolescents. 6 The first evidence for this proposition already exists. In a large sample of Icelandic adolescents, passive social media use was positively related to anxiety and depressive symptoms; the opposite was the case for active use. 65 


Furthermore, longitudinal work so far relies on self-reports of media use. Self-reported media use has been shown to be inaccurate compared with objectively measured use. 14 Unfortunately, there is little work employing objective measures to test whether the results of longitudinal studies using self-reports hold up when objective use is examined. The limited existing evidence suggests that effects remain small. In a convenience sample of adults, only phone use at night negatively predicted well-being. 66 Another study that combined objective measures of social smartphone applications with experience sampling in young adults found a weak negative relation between objective use and well-being. 67 


Effects might also not be linear. Whereas both low and high levels of internet use have been shown to be associated with slightly decreased life satisfaction, moderate use has been shown to be related to slightly increased life satisfaction. 10 , 35 , 68 However, evidence for this position is mixed; other empirical studies did not find this pattern of effects. 53 , 54 


Taken together, do the positive or the negative effects prevail? The literature implies that the relationship between technology use and adolescent well-being is more complicated than an overall negative linear effect. In line with meta-analyses on adults, effects of digital technology use in general are mostly neutral to small. In their meta-review of 34 meta-analyses and systematic reviews, Meier and Reinecke 42 summarize that “[f]indings suggest an overall (very) small negative association between using SNS [social networking sites], the most researched CMC [computer mediated communication] application, and mental health.” In conclusion, the current literature is mostly ambivalent, although slightly emphasizing the negative effects of digital tech use.


Implications


Although there are several conflicting positions and research findings, some general implications emerge:


1. The general effects of digital technology use on well-being are likely in the negative spectrum, but very small—potentially too small to matter.


2. No screen time is created equal; different uses will lead to different effects.


3. Digital technology use is more likely to affect short-term positive or negative affect than long-term life satisfaction.


4. The dose makes the poison; it appears that both low and excessive use are related to decreased well-being, whereas moderate use is related to increased well-being.


5. Adolescents are likely more vulnerable to effects of digital technology use on well-being, but it is important not to patronize adolescents—effects are comparable and adolescents not powerless.


6. The current empirical research has several limitations: high-quality studies with large-scale samples, objective measures of digital technology use, and experience sampling of well-being are still missing.


Conclusion


Despite almost 30 years of research on digital technology, there is still no coherent empirical evidence as to whether digital technology hampers or fosters well-being. Most likely, general effects are small at best and probably in the negative spectrum. As soon as we take other factors into account, this conclusion does not hold up. Active use that aims to establish meaningful social connections can have positive effects. Passive use likely has negative effects. Both might follow a nonlinear trend. However, research showing causal effects of general digital technology use on well-being is scarce. In light of these limitations, several scholars argue that technology use has a mediating role69: already existing problems increase maladapted technology use, which then decreases life satisfaction. Extreme digital technology use is more likely to be a symptom of an underlying sociopsychological problem than vice versa. In sum, when assessing the effects of technology use on adolescent well-being, one of the best answers is that it’s complicated.


This lack of evidence is not surprising, because there is no consensus on central definitions, measures, and methods. 42 Specifically, digital technology use is an umbrella term that encompasses many different behaviors. Furthermore, it is theoretically unclear as to why adolescents in particular should be susceptible to the effects of technology and what forms of well-being are candidates for effects. At the same time, little research adopts longitudinal designs, differentiates different types of technology use, or measures technology use objectively. Much work in the field has also been criticized for a lack of transparency and rigor. 51 Last, research (including this review) is strongly biased toward a Western perspective. In other cultures, adolescents use markedly different services (such as WeChat or Renren, etc). Although we assume most effects to be comparable, problems seem to differ somewhat. For example, online gaming addiction is more prevalent in Asian than Western cultures. 70 


Adults have always criticized the younger generation, and media (novels, rock music, comic books, or computer games) have often been one of the culprits. 1 Media panics are cyclical, and we should refrain from simply blaming the unknown and the novel. 1 In view of the public debate, we should rather emphasize that digital technology is not good or bad per se. Digital technology does not “happen” to individuals. Individuals, instead, actively use technology, often with much competence. 3 The current evidence suggests that typical digital technology use will not harm a typical adolescent. That is not to say there are no individual cases and scenarios in which effects might be negative and large. Let’s be wary, but not alarmist.


Acknowledgments

Both authors declare no conflicts of interest. Both authors contributed equally to this manuscript. Tobias Dienlin receives funding from the Volkswagen Foundation. We would like to thank Amy Orben for valuable feedback and comments

Technology Thesis Statement

The dynamic world of technology continually shapes our daily lives and future. Writing a compelling thesis statement about technology means delving deep into the nuances of innovation, foreseeing its implications, and presenting a clear, concise perspective. Crafting the perfect statement requires a keen understanding of your topic, its relevance, and the message you wish to convey. Below, we will explore examples of technology-related thesis statements, provide tips on how to hone them, and guide you in encapsulating the essence of your research.

What is the Technology Thesis Statement? – Definition

A technology thesis statement is a concise summary or main point of a research paper, essay, or dissertation related to a technology-focused topic. It establishes the central theme, position, or argument that the author intends to communicate, providing readers with a clear overview of what the subsequent content will address. This research paper thesis statement is essential in guiding the flow and coherence of the piece, ensuring that the content remains relevant to the proposed topic.

What is an example of a Technology thesis statement?

“With the rapid evolution of wearable technology, there is a compelling need to address the associated privacy concerns, arguing that without comprehensive regulations, users’ personal data could be at significant risk.”  You should also take a look at our  middle school thesis statement .

100 Technology Statement Examples

Size: 224 KB

Technology concise thesis statements encapsulate the essence of tech-focused research papers or essays, presenting a concise argument or perspective on a specific technological development, trend, or challenge. These statements guide the reader’s understanding, giving clarity and direction to the narrative.

• Artificial Intelligence : “The integration of AI in healthcare can revolutionize patient diagnosis, but ethical constraints need addressing.”
• Virtual Reality : “Virtual reality’s potential in education extends beyond immersion, offering tailored learning experiences.”
• Blockchain : “Blockchain technology, while disruptive, promises to make financial transactions more transparent and secure.”
• Cybersecurity : “The rise of IoT devices demands stronger cybersecurity measures to prevent unprecedented breaches.”
• Biotechnology : “CRISPR technology might hold the key to genetic disorders, yet its ethical implications are vast.”
• E-Commerce : “The shift to e-commerce has fundamentally changed consumer behavior, prioritizing convenience over brand loyalty.”
• 5G Technology : “The deployment of 5G will enhance IoT capabilities, but infrastructure challenges persist.”
• Green Technology : “Solar panel advancements are crucial for sustainable energy but require policy support for widespread adoption.”
• Robotics : “Robotic automation in manufacturing accelerates production but poses employment challenges.”
• Wearable Tech : “Wearables are transforming health monitoring, but data privacy remains a significant concern.”
• Quantum Computing : “While quantum computers promise to solve complex problems in seconds, they also pose threats to current encryption methods.”
• Space Exploration : “The commercialization of space travel opens new frontiers for tourism but also raises environmental and safety concerns.”
• Augmented Reality : “Augmented reality in retail can enhance customer experience, yet it challenges traditional shopping norms.”
• Drones : “The proliferation of drone technology in delivery services improves efficiency but brings forth airspace regulation issues.”
• Nano-Technology : “Nanotechnology in medicine offers targeted drug delivery but has unexplored long-term effects on human health.”
• Self-Driving Cars : “Autonomous vehicles could drastically reduce traffic accidents, but their integration requires comprehensive legal frameworks.”
• Smart Cities : “Smart cities optimize urban living conditions; however, they highlight disparities in digital access.”
• Edge Computing : “Edge computing decentralizes data processing, enhancing IoT performance, but it raises concerns about localized data breaches.”
• 3D Printing : “3D printing revolutionizes manufacturing and healthcare but challenges intellectual property rights.”
• Digital Assistants : “Voice-activated digital assistants streamline daily tasks but provoke debates on user surveillance and privacy.”
• Telemedicine : “Telemedicine democratizes healthcare access, yet questions arise about its efficacy compared to in-person consultations.”
• Big Data : “Big data analytics can transform industries, but the potential misuse of information is a growing concern.”
• Cloud Computing : “Cloud adoption offers businesses scalability and flexibility, though it introduces unique cybersecurity challenges.”
• Digital Currency : “Cryptocurrencies like Bitcoin could redefine financial systems, but their volatility and regulatory gray areas persist.”
• Gaming Technology : “Esports and gaming technology foster global communities, but they also spotlight issues of digital addiction.”
• Neural Networks : “Neural networks enhance machine learning capabilities but make algorithm decision-making processes more opaque.”
• Mixed Reality : “Mixed reality blends the best of AR and VR, offering innovative solutions in training but requires significant hardware investments.”
• Social Media Algorithms : “Algorithms on social platforms shape user behavior, leading to questions about influence and manipulation.”
• Broadband Technology : “Universal broadband access can bridge educational gaps, but infrastructural and cost barriers remain.”
• Digital Learning Platforms : “Online education platforms democratize learning but challenge traditional educational paradigms.”
• Agricultural Tech : “Smart farming through tech can optimize yields, but its cost can exclude small-scale farmers.”
• Mobile Banking : “Mobile banking boosts financial inclusion in developing nations but raises issues of digital literacy.”
• Chatbots : “Chatbots in customer service optimize responsiveness but can depersonalize the user experience.”
• Facial Recognition : “Facial recognition tech can enhance security measures but has sparked debates on privacy and misuse.”
• Deepfakes : “Deepfake technology, while impressive, poses significant threats to misinformation and trust in media.”
• Health Tech : “Wearable health devices offer real-time monitoring, yet there’s growing concern over data security and interpretation accuracy.”
• Marine Technology : “Underwater drones present opportunities for oceanic exploration, but their use raises environmental concerns.”
• Sustainable Tech : “Technological solutions to waste management are crucial for urban sustainability, but require societal behavior changes for maximum effectiveness.”
• Language Translation : “Real-time translation tools are bridging communication gaps, but can’t replace the nuance of human translators.”
• Online Privacy : “VPN services enhance online privacy, yet they introduce challenges in legal jurisdictions and data accountability.”
• Internet of Things (IoT) : “While IoT connects everyday devices, it also increases potential points of cyber vulnerabilities.”
• Haptic Technology : “Haptic tech holds potential in virtual training environments but demands rigorous testing for consistent real-world replication.”
• Renewable Energy Tech : “Wind energy is a clean alternative, yet its land use and noise pollution issues remain unresolved.”
• Genomic Editing : “While genomic editing can prevent hereditary diseases, its potential misuse in ‘designer babies’ raises ethical debates.”
• E-Learning : “Digital classrooms can provide education continuity during crises, but highlight inequalities in tech accessibility.”
• Wireless Charging : “The evolution of wireless charging technology promotes convenience but necessitates universal standardization.”
• Retail Tech : “Smart mirrors in retail enhance consumer experience but can potentially infringe on privacy rights if misused.”
• Data Storage : “Quantum data storage could revolutionize information keeping, yet the transition from classical methods is fraught with challenges.”
• Livestreaming Tech : “The growth of livestreaming platforms boosts creator economies, but presents issues of content moderation.”
• Digital Twins : “Digital twins in manufacturing optimize production processes, but require significant data management and interpretation efforts.”
• Animal Tech : “RFID tags in wildlife conservation assist in species monitoring but raise concerns about animal welfare and interference.”
• Thermal Imaging : “Thermal imaging in public spaces can enhance security, but its widespread use prompts privacy debates.”
• Financial Tech (FinTech) : “Digital-only banks provide unparalleled convenience, yet face skepticism over their ability to handle financial crises.”
• Audio Tech : “Spatial a in headphones creates immersive experiences, but its effects on auditory health are under-researched.”
• Nano-Biotechnology : “Nano-biotech in targeted drug delivery holds promise, but its long-term interactions with biological systems remain unknown.”
• Location-Based Services : “Geolocation tools in apps enhance user experience, but inadvertently contribute to data surveillance concerns.”
• Human-Machine Interface : “Brain-computer interfaces might redefine communication for the differently-abled, but they also present neuroethical dilemmas.”
• Gig Economy Platforms : “Tech-driven gig economies offer flexible employment, but often at the cost of job security and benefits.”
• Environmental Monitoring : “Satellite technology for environmental monitoring is crucial for climate change mitigation, but depends on international collaboration and data-sharing.”
• Entertainment Tech : “Augmented reality in entertainment redefines audience engagement, but challenges traditional content creation paradigms.”
• Food Technology : “Lab-grown meats could significantly reduce the environmental impact of livestock, but their societal acceptance and taste equivalency remain under scrutiny.”
• Telecommunication : “The transition to satellite-based internet services can enhance global connectivity but introduces space debris management challenges.”
• Digital Art and Media : “Digital art platforms democratize artistic expression, though they raise concerns over copyright and originality.”
• Fitness Tech : “Smart gyms utilize AI to personalize workout regimens, but their reliance on user data raises privacy issues.”
• Medical Imaging : “AI-driven medical imaging can enhance diagnostic precision, yet its integration demands rigorous validation against traditional methods.”
• Urban Mobility : “Electric scooters in urban centers promote green mobility, but their indiscriminate use poses pedestrian safety risks.”
• Adaptive Tech : “Adaptive technologies for the differently-abled democratize access, but their high costs can limit widespread adoption.”
• Cryptographic Tech : “Post-quantum cryptography aims to secure data against future quantum attacks, but its practical implementation remains challenging.”
• Travel and Navigation : “AR-based navigation tools can revolutionize travel experiences, but they demand robust infrastructure to prevent inaccuracies.”
• Event Technology : “Virtual event platforms offer global outreach, but they challenge the conventional understanding of networking and engagement.”
• Consumer Electronics : “Flexible electronics pave the way for innovative gadgets, yet their durability and recyclability are concerns.”
• Space Mining : “Space mining could answer Earth’s resource scarcity, but its feasibility and impact on space ecosystems are contentious.”
• Fashion Tech : “Smart fabrics offer dynamic design possibilities, but their production processes raise environmental questions.”
• Elderly Tech : “Tech solutions for the elderly improve quality of life, but require intuitive designs to ensure ease of use.”
• Cyber Physical Systems : “Integrating physical processes with computer-based algorithms promises efficiency, but challenges real-time adaptability.”
• Rehabilitation Tech : “VR in physical rehabilitation offers immersive therapy, but its long-term efficacy compared to traditional methods is under exploration.”
• Collaborative Platforms : “Cloud-based collaborative tools redefine workplace productivity, but their over-reliance can risk centralizing data control.”
• Quantum Sensing : “Quantum sensors could redefine detection limits in various fields, but their scalability in real-world applications remains a hurdle.”
• Learning Management Systems (LMS) : “LMS platforms facilitate organized e-learning, but their design must prioritize user-friendliness for diverse user groups.”
• Aerospace Tech : “Electric aircraft represent the future of eco-friendly travel, but the transition requires breakthroughs in battery technology.”
• Hydroponic Farming : “Tech-driven hydroponic systems can increase agricultural yield in urban areas, but the initial setup costs and energy consumption are deterrents.”
• Waste Management Tech : “Automated waste sorting can significantly enhance recycling rates, but its success demands public awareness and participation.”
• Digital Publishing : “E-books and digital publications increase accessibility, but they also challenge traditional publishing economics.”
• Therapeutic Tech : “Biofeedback apps promise personalized stress management, but their recommendations need backing by robust clinical research.”
• Molecular Electronics : “Molecular-scale electronics could miniaturize devices further, but their stability and manufacturing pose significant challenges.”
• Industrial IoT : “Integrating IoT in industries optimizes production and maintenance, but its seamless functioning demands strong cybersecurity protocols.”
• Photonics : “Photonics in data transmission offers higher speeds, but its integration into current infrastructure is complex.”
• Marine Energy : “Harnessing oceanic energy can be a renewable power solution, but its impact on marine ecosystems needs careful evaluation.”
• Prosthetics Tech : “Advanced prosthetics with AI integration promise life-changing mobility, but the cost of development and acquisition challenges their accessibility.”
• Resilient Infrastructure : “Smart materials in construction adapt to environmental changes, but the long-term sustainability and economic feasibility remain subjects of research.”
• Optogenetics : “Optogenetics holds transformative potential for neurological disorders, but its ethical application in humans is still debated.”
• Entertainment Streaming : “Streaming platforms are reshaping entertainment consumption, but they also spotlight issues of digital rights and royalties.”
• Water Purification Tech : “Nanotechnology in water purification can address global water crises, but its ecological impact requires close monitoring.”
• Transportation Tech : “Hyperloop transportation promises rapid transits, but the infrastructural and safety challenges are monumental.”
• Pedagogical Tools : “AI-driven pedagogical tools individualize learning, but there’s a risk of over-reliance and diminished human interaction.”
• Remote Work Tech : “Advanced collaborative tools enable effective remote work, but they also blur the lines between professional and personal boundaries.”
• Sensor Technology : “Smart sensors in agriculture optimize irrigation and reduce water wastage, but their implementation costs can be prohibitive for small-scale farmers.”
• Food Preservation : “Innovative food preservation technologies can reduce global food wastage, but their energy consumption and efficiency need optimization.”
• Gaming Interfaces : “Brain-computer interfaces in gaming promise immersive experiences, but their long-term effects on neurological health are underexplored.”
• Material Science : “Meta-materials can revolutionize optics and telecommunications, but their large-scale production and integration pose significant challenges.”

Technology Thesis Statement Examples for Argumentative Essay

As the digital age progresses, there’s a growing consensus about the pros and cons of technology’s integration into our daily lives. Argumentative essays thesis statement on technology often delve into the ethical and societal implications, pushing the boundaries of the debates even further.

• Social Media’s Impact : “While some argue that social media strengthens interpersonal relationships, it can also be held responsible for eroding face-to-face interactions and deepening feelings of social isolation.”
• Digital Dependency : “The increasing reliance on smartphones has jeopardized our cognitive abilities, leading to diminished memory recall and reduced attention spans.”
• Online Privacy : “In the digital age, online privacy has become an illusion, with corporations and governments frequently infringing upon personal data rights.”
• Virtual Reality : “Despite the immersive experiences offered by virtual reality, its overuse can blur the distinction between the real and virtual worlds, leading to psychological implications.”
• Technological Progress vs. Job Security : “Technological advancements, while driving efficiency and progress, also threaten traditional jobs, potentially leading to economic disparities.”
• Digital Currency : “Cryptocurrencies, despite their volatile nature, represent a significant shift in the financial landscape and have the potential to decentralize traditional banking systems.”
• E-books vs. Traditional Books : “While e-books offer convenience and accessibility, they can never replace the tactile experience and emotional connection readers have with physical books.”
• The Internet and Democracy : “The internet, although hailed as a tool for democratizing information, also presents threats like misinformation campaigns that can undermine democratic processes.”
• Tech Giants and Monopoly : “The unchecked rise of tech giants poses a threat to competition, potentially stifling innovation and enabling monopolistic behaviors.”
• Green Technology : “Investing in green technologies is not merely an environmental imperative but also an economic opportunity that promises both sustainable growth and job creation.”

Thesis Statement Examples for Technology in Education

Education has undergone tremendous transformation thanks to technology. The intersection of technology and education raises questions about equity, effectiveness, and the shaping of future minds.

• Digital Literacy : “Incorporating digital literacy in education is crucial, not just for technological proficiency but for navigating the modern world responsibly and critically.”
• Online Learning : “Online education, while offering flexibility and accessibility, can lack the personal touch and hands-on experiences that traditional classrooms provide.”
• EdTech in Early Childhood : “Introducing technology in early childhood education can foster creativity and adaptability, but it must not overshadow foundational learning experiences.”
• Gamification of Learning : “Gamifying education can increase student engagement, but there’s a risk of prioritizing rewards over actual knowledge acquisition.”
• Tech in Special Education : “Technology has the potential to revolutionize special education, offering tailored learning experiences to cater to individual needs.”
• Digital Distractions : “The integration of technology in classrooms, while beneficial, also brings the challenge of combating digital distractions and ensuring focused learning.”
• Open Source Learning : “Open-source educational resources can democratize education, but there’s a need to ensure the quality and credibility of these materials.”
• AR and VR in Education : “Augmented and virtual reality tools in education can offer immersive learning experiences, but their efficacy compared to traditional methods remains to be thoroughly evaluated.”
• Adaptive Learning Systems : “Adaptive learning technologies promise personalized education, but reliance on them must be balanced with human mentorship.”
• Digital Divide : “The push for technology in education must also address the digital divide, ensuring that students from all socioeconomic backgrounds have equal access.”

Thesis Statement Examples on Technology in Artificial Intelligence

The realm of artificial intelligence is a marvel of modern science and engineering, but it brings forth numerous concerns and speculations. Essays on AI and technology focus on the potential of machines surpassing human intelligence and the societal repercussions of such a possibility.

• Ethical AI : “As AI systems grow in complexity, there’s an urgent necessity to establish ethical guidelines that prioritize human values and safety.”
• AI in Warfare : “The integration of AI in military operations, while enhancing precision, raises alarming concerns about the lack of human judgment in life-and-death decisions.”
• Bias in Machine Learning : “Unchecked, machine learning models can perpetuate and amplify societal biases, necessitating rigorous audit processes before deployment.”
• AI and Employment : “The rise of automation and AI in industries risks a significant displacement of the workforce, highlighting the need for societal adaptation and job retraining.”
• Emotion AI : “Artificial Intelligence designed to recognize and respond to human emotions could revolutionize industries, but also brings concerns about privacy and emotional manipulation.”
• Singularity : “The potential for an AI singularity, where AI surpasses human intelligence, necessitates preemptive safeguards to ensure the alignment of AI goals with humanity’s best interests.”
• AI in Healthcare : “While AI in healthcare can lead to more accurate diagnoses, it must complement, not replace, the critical thinking and empathy of medical professionals.”
• Deepfakes and Reality : “The advent of deepfake technology, driven by AI, challenges our trust in visual content, pressing for the development of verification tools.”
• AI and Creativity : “The surge of AI in creative fields, from art to music, questions the uniqueness of human creativity and the future role of AI as co-creators.”
• General AI vs. Narrow AI : “While narrow AI excels in specific tasks, the pursuit of general AI, mirroring human intelligence, presents unprecedented challenges and ethical dilemmas.”

Thesis Statement Examples on Medical Technology

The medical field has seen rapid technological advancements, leading to breakthroughs in treatment and patient care. Discussing medical technology often centers around its impact on the patient-doctor relationship and health outcomes.

• Telemedicine : “Telemedicine, while increasing healthcare accessibility, requires rigorous regulation to ensure the quality of care and the privacy of patient data.”
• Gene Editing : “CRISPR and other gene-editing technologies hold promise for eradicating genetic diseases, but they also raise ethical concerns about the potential misuse in creating ‘designer babies’.”
• Wearable Health Tech : “Wearable health devices empower individuals to monitor their health, but also bring concerns about data privacy and the accuracy of health information.”
• 3D Printed Organs : “3D printing of organs could revolutionize transplants, but the technology must first overcome challenges in biocompatibility and functionality.”
• Robot-Assisted Surgery : “Robot-assisted surgeries promise precision and minimized invasiveness, yet the high costs and training requirements present hurdles for widespread adoption.”
• Mental Health Apps : “Digital tools for mental health can democratize access to resources, but they cannot replace the nuanced care provided by human professionals.”
• Nanotechnology in Medicine : “The integration of nanotechnology in medicine offers targeted treatments and drug delivery, but long-term effects on the human body remain largely unknown.”
• Virtual Reality in Therapy : “VR therapies hold potential for treating phobias and PTSD, but research must ensure that virtual experiences translate to real-world recovery.”
• EHR (Electronic Health Records) : “While EHRs streamline medical data management, concerns arise about patient data security and system interoperabilities.”
• AI-driven Diagnosis : “AI-driven diagnostic tools can analyze vast data quickly, but they should act as aides to human clinicians, not replacements.”

Thesis Statement Examples for Technology Essay

General technology essays touch on the overarching theme of how technology shapes society, cultures, and personal interactions. These essays dive deep into both the boons and banes of technological innovation.

• Digital Age and Mental Health : “The digital age, while connecting the world, has also escalated mental health issues, prompting a deeper examination of our relationship with technology.”
• Augmented Humanity : “Biohacking and body augmentations, powered by tech, are pushing the boundaries of human capabilities but also raise ethical questions about self-modification and societal implications.”
• Cybersecurity : “In a hyper-connected world, cybersecurity is not just a technical challenge but a fundamental aspect of ensuring personal rights and national security.”
• Sustainable Technologies : “The rise of sustainable technologies is not a mere trend but a necessity to ensure the future survival and prosperity of our planet.”
• Digital Nomadism : “The evolution of remote work technologies has birthed the digital nomad culture, reshaping traditional perceptions of work-life balance and productivity.”
• Space Technologies : “Emerging space technologies, from satellite constellations to interplanetary exploration, hold the promise of reshaping our understanding of the universe and our place in it.”
• Tech and Pop Culture : “The infusion of technology into pop culture, from movies to music, reflects society’s struggles, aspirations, and dreams in the digital age.”
• Digital Archiving : “The practice of digital archiving is crucial not just for preserving history but for ensuring accountability in the digital era.”
• The Right to Disconnect : “As work and personal life boundaries blur due to technology, there’s a rising demand for the ‘right to disconnect’, ensuring mental well-being.”
• Tech in Urban Planning : “Smart cities, driven by technology, promise enhanced living experiences, but they also raise concerns about surveillance and the loss of privacy.”

Thesis Statement Examples for Technology in the Classroom

Classroom technology has redefined traditional teaching methodologies, leading to a new age of learning. Essays in this category often grapple with the balance between technology and traditional pedagogies.

• Digital Collaboration : “Collaborative tools in classrooms foster teamwork and communication but necessitate guidelines to ensure productive and respectful engagements.”
• Interactive Learning : “Interactive whiteboards and digital simulations can enhance understanding and retention, but educators must ensure they don’t become mere entertainment.”
• Classroom Analytics : “The use of analytics in classrooms promises personalized feedback and interventions, but raises concerns about student privacy and data misuse.”
• Digital Textbooks : “While digital textbooks offer dynamic content and portability, the potential loss of traditional reading skills and tactile learning must be addressed.”
• Flipped Classrooms : “Flipped classrooms, facilitated by technology, encourage student-centered learning at home, but require a redefinition of classroom roles and responsibilities.”
• Tech and Special Needs : “Assistive technologies in classrooms have democratized education for students with special needs, but teachers need training to utilize them effectively.”
• Student Engagement : “Gamified learning platforms can significantly increase student engagement, but there’s a risk of overemphasis on rewards over actual learning outcomes.”
• Distance Learning : “Technology has made distance learning feasible and expansive, yet the challenges of student isolation and self-regulation need addressing.”
• Digital Citizenship : “Teaching digital citizenship in classrooms is essential in the modern age to ensure students use technology responsibly and ethically.”
• Classroom VR : “Introducing virtual reality in classrooms can offer immersive educational experiences, but its efficacy and potential overstimulation issues need thorough research.”

What is a good thesis statement for technology?

A good thesis statement for technology succinctly captures your main argument or perspective on a specific technological issue. Such a statement should exhibit:

• Precision : Clearly articulate your viewpoint on the technological matter, ensuring it isn’t vague.
• Debate Potential : Present a point open to discussion or counterargument, not just a plain fact.
• Current Relevance : Address up-to-date technological advancements or concerns.
• Conciseness : Stay direct and avoid broad overviews.

Example: “Artificial intelligence in healthcare, while promising enhanced patient care, raises pressing ethical concerns.”

How do you write a Technology Thesis Statement? – Step by Step Guide

• Pinpoint a Specific Tech Area : Instead of a broad area like “technology,” zoom into niches: e.g., “Blockchain’s role in data security” or “Virtual Reality in education.”
• Undertake Preliminary Research : Grasp the current scenario of your selected area. Identify ongoing debates, breakthroughs, and challenges.
• State Your Assertion : Your research will guide you to a specific stance. This becomes your thesis’s foundation.
• Check for Debate Potential : Ensure that your assertion isn’t just stating the obvious but invites discussion.
• Maintain Brevity : Keep it succinct—usually, one to two sentences will suffice.
• Iterate : As your research or essay progresses, you might find the need to fine-tune your statement.

Tips for Writing a Thesis Statement on Technology Topics

• Stay Informed : With technology’s rapid pace, being up-to-date is essential. Your thesis should resonate with current technological dialogues.
• Steer Clear of Jargons : If your audience isn’t tech-centric, simplify or explain tech terms for clarity.
• Dive into Ethical Angles : Tech topics often interweave with ethical considerations. Tackling these adds depth.
• Solicit Feedback : Sharing your thesis with colleagues or mentors can offer new viewpoints or refinements.
• Employ Assertive Language : Words like “should,” “must,” or “will” give your statement authority.
• Remain Adaptable : If new evidence emerges as you write, be open to reworking your thesis slightly.
• Link to Broader Implications : Relating your tech topic to wider societal or global issues can offer added layers of significance.
• Ensure Clarity : Your thesis should have one clear interpretation to avoid reader confusion.

By honing these techniques and tips, you’ll be adept at formulating impactful thesis statements tailored to technology-centric topics. As technology continues to shape our world, the ability to critically and concisely discuss its implications is invaluable.  You may also be interested in our Analytical Essay thesis statement .

AI Generator

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

From the world wide web to AI: 11 technology milestones that changed our lives

The world wide web is a key technological milestone in the past 40 years. Image:  Unsplash/Ales Nesetril

.chakra .wef-1c7l3mo{-webkit-transition:all 0.15s ease-out;transition:all 0.15s ease-out;cursor:pointer;-webkit-text-decoration:none;text-decoration:none;outline:none;color:inherit;}.chakra .wef-1c7l3mo:hover,.chakra .wef-1c7l3mo[data-hover]{-webkit-text-decoration:underline;text-decoration:underline;}.chakra .wef-1c7l3mo:focus,.chakra .wef-1c7l3mo[data-focus]{box-shadow:0 0 0 3px rgba(168,203,251,0.5);} Stephen Holroyd

.chakra .wef-9dduvl{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-9dduvl{font-size:1.125rem;}} Explore and monitor how .chakra .wef-15eoq1r{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;color:#F7DB5E;}@media screen and (min-width:56.5rem){.chakra .wef-15eoq1r{font-size:1.125rem;}} Artificial Intelligence is affecting economies, industries and global issues

.chakra .wef-1nk5u5d{margin-top:16px;margin-bottom:16px;line-height:1.388;color:#2846F8;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-1nk5u5d{font-size:1.125rem;}} Get involved with our crowdsourced digital platform to deliver impact at scale

Stay up to date:, emerging technologies.

• It’s been 40 years since the launch of the Apple Macintosh personal computer.
• Since then, technological innovation has accelerated – here are some of the most notable tech milestones over the past four decades.
• The World Economic Forum’s EDISON Alliance aims to digitally connect 1 billion people to essential services like healthcare, education and finance by 2025.

On 24 January 1984, Apple unveiled the Macintosh 128K and changed the face of personal computers forever.

Steve Jobs’ compact, user-friendly computer introduced the graphical user interface to the world, marking a pivotal moment in the evolution of personal technology.

Since that day, the rate of technological innovation has exploded, with developments in computing, communication, connectivity and machine learning expanding at an astonishing rate.

Here are some of the key technological milestones that have changed our lives over the past 40 years.

Have you read?

9 ways ai is helping tackle climate change, driving trust: paving the road for autonomous vehicles, these are the top 10 emerging technologies of 2023: here's how they can impact the world, 1993: the world wide web.

Although the internet’s official birthday is often debated, it was the invention of the world wide web that drove the democratization of information access and shaped the modern internet we use today.

Created by British scientist Tim Berners-Lee, the World Wide Web was launched to the public in 1993 and brought with it the dawn of online communication, e-commerce and the beginning of the digital economy.

Despite the enormous progress since its invention, 2.6 billion people still lack internet access and global digital inclusion is considered a priority. The World Economic Forum’s EDISON Alliance aims to bridge this gap and digitally connect 1 billion people to essential services like healthcare, education and finance by 2025.

1997: Wi-Fi

The emergence of publicly available Wi-Fi in 1997 changed the face of internet access – removing the need to tether to a network via a cable. Without Wi-Fi, the smartphone and the ever-present internet connection we’ve come to rely on, wouldn’t have been possible, and it has become an indispensable part of our modern, connected world.

1998: Google

The launch of Google’s search engine in 1998 marked the beginning of efficient web search, transforming how people across the globe accessed and navigated online information . Today, there are many others to choose from – Bing, Yahoo!, Baidu – but Google remains the world’s most-used search engine.

2004: Social media

Over the past two decades, the rise of social media and social networking has dominated our connected lives. In 2004, MySpace became the first social media site to reach one million monthly active users. Since then, platforms like Facebook, Instagram and TikTok have reshaped communication and social interaction , nurturing global connectivity and information sharing on an enormous scale, albeit not without controversy .

2007: The iPhone

More than a decade after the first smartphone had been introduced, the iPhone redefined mobile technology by combining a phone, music player, camera and internet communicator in one sleek device. It set new standards for smartphones and ultimately accelerated the explosion of smartphone usage we see across the planet today.

2009: Bitcoin

The foundations for modern digital payments were laid in the late 1950s with the introduction of the first credit and debit cards, but it was the invention of Bitcoin in 2009 that set the stage for a new era of secure digital transactions. The first decentralized cryptocurrency, Bitcoin introduced a new form of digital payment system that operates independently of traditional banking systems. Its underlying technology, blockchain, revolutionized the concept of digital transactions by providing a secure, transparent, and decentralized method for peer-to-peer payments. Bitcoin has not only influenced the development of other cryptocurrencies but has also sparked discussions about the future of money in the digital age.

2014: Virtual reality

2014 was a pivotal year in the development of virtual reality (VR) for commercial applications. Facebook acquired the Oculus VR company for $2 billion and kickstarted a drive for high-quality VR experiences to be made accessible to consumers. Samsung and Sony also announced VR products, and Google released the now discontinued Cardboard – a low-cost, do-it-yourself viewer for smartphones. The first batch of Oculus Rift headsets began shipping to consumers in 2016.

2015: Autonomous vehicles

Autonomous vehicles have gone from science fiction to science fact in the past two decades, and predictions suggest that almost two-thirds of registered passenger cars worldwide will feature partly-assisted driving and steering by 2025 . In 2015, the introduction of Tesla’s Autopilot brought autonomous features to consumer vehicles, contributing to the mainstream adoption of self-driving technology.

2019: Quantum computing

A significant moment in the history of quantum computing was achieved in October 2019 when Google’s Sycamore processor demonstrated “quantum supremacy” by solving a complex problem faster than the world’s most powerful supercomputers. Quantum technologies can be used in a variety of applications and offer transformative impacts across industries. The World Economic Forum’s Quantum Economy Blueprint provides a framework for value-led, democratic access to quantum resources to help ensure an equitable global distribution and avoid a quantum divide.

2020: The COVID-19 pandemic

The COVID-19 pandemic accelerated digital transformation on an unprecedented scale . With almost every aspect of human life impacted by the spread of the virus – from communicating with loved ones to how and where we work – the rate of innovation and uptake of technology across the globe emphasized the importance of remote work, video conferencing, telemedicine and e-commerce in our daily lives.

In response to the uncertainties surrounding generative AI and the need for robust AI governance frameworks to ensure responsible and beneficial outcomes for all, the Forum’s Centre for the Fourth Industrial Revolution (C4IR) has launched the AI Governance Alliance .

The Alliance will unite industry leaders, governments, academic institutions, and civil society organizations to champion responsible global design and release of transparent and inclusive AI systems.

2022: Artificial intelligence

Artificial intelligence (AI) technology has been around for some time and AI-powered consumer electronics, from smart home devices to personalized assistants, have become commonplace. However, the emergence of mainstream applications of generative AI has dominated the sector in recent years.

In 2022, OpenAI unveiled its chatbot, ChatGPT. Within a week, it had gained over one million users and become the fastest-growing consumer app in history . In the same year, DALL-E 2, a text-to-image generative AI tool, also launched.

Don't miss any update on this topic

Create a free account and access your personalized content collection with our latest publications and analyses.

License and Republishing

World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

The Agenda .chakra .wef-n7bacu{margin-top:16px;margin-bottom:16px;line-height:1.388;font-weight:400;} Weekly

A weekly update of the most important issues driving the global agenda

.chakra .wef-1dtnjt5{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;} More on Emerging Technologies .chakra .wef-17xejub{-webkit-flex:1;-ms-flex:1;flex:1;justify-self:stretch;-webkit-align-self:stretch;-ms-flex-item-align:stretch;align-self:stretch;} .chakra .wef-nr1rr4{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;white-space:normal;vertical-align:middle;text-transform:uppercase;font-size:0.75rem;border-radius:0.25rem;font-weight:700;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;line-height:1.2;-webkit-letter-spacing:1.25px;-moz-letter-spacing:1.25px;-ms-letter-spacing:1.25px;letter-spacing:1.25px;background:none;padding:0px;color:#B3B3B3;-webkit-box-decoration-break:clone;box-decoration-break:clone;-webkit-box-decoration-break:clone;}@media screen and (min-width:37.5rem){.chakra .wef-nr1rr4{font-size:0.875rem;}}@media screen and (min-width:56.5rem){.chakra .wef-nr1rr4{font-size:1rem;}} See all

Solar storms hit tech equipment, and other technology news you need to know

Sebastian Buckup

May 17, 2024

Generative AI is trained on just a few of the world’s 7,000 languages. Here’s why that’s a problem – and what’s being done about it

Madeleine North

Critical minerals demand has doubled in the past five years – here are some solutions to the supply crunch

Emma Charlton

May 16, 2024

6 ways satellites are helping to monitor our changing planet from space

Andrea Willige

How can GenAI be optimized for people and processes?

May 15, 2024

This is how AI can empower women and achieve gender equality, according to the founder of Girls Who Code and Moms First

Kate Whiting

May 14, 2024

OpenAI and Google's AI Advancements Highlight a Week of Technological Progress

In the realm of technology, certain moments mark significant shifts, often referred to as "inflection points." These pivotal times redefine possibilities, bringing new threats and opportunities. However, in recent years, what used to be seen as extraordinary leaps now seem almost routine. This week was a testament to that, as two tech giants, OpenAI and Google, unveiled groundbreaking advancements that promise to reshape our interaction with technology and, ultimately, our daily lives.

OpenAI's Latest Marvel: GPT-4o

On Monday, OpenAI, amidst swirling rumors about new AI products, made a startling announcement. Contrary to expectations of revealing an AI-powered search product or a next-generation model GPT-5, OpenAI introduced GPT-4o, a new flagship model available for free. GPT-4o stands out for its ability to process and respond in multiple modes—text, speech, and vision—creating an unnervingly natural interaction with users.

The demonstration of GPT-4o captivated many, highlighting the model's playful and provocative nature. This emotionally expressive chatbot is not just a conversational partner; it possesses an encyclopedic knowledge derived from vast datasets. OpenAI's CEO, Sam Altman, succinctly captured the essence of this breakthrough in a one-word tweet : "Her." The reference to the movie "Her," where a man falls in love with an AI, is particularly poignant coming from someone whose company has essentially created such a sophisticated AI.

Adding to the spectacle, OpenAI's co-founder Greg Brockman showcased a scene where one chatbot scanned a room with a camera while another asked it questions. This interaction led to the two AI entities humorously critiquing Brockman's fashion and decor choices, even serenading him with songs about it. The demo was not just a technical marvel but also a glimpse into a future where AI's role in our personal lives could become deeply integrated and, at times, hilariously intrusive.

Google's AI Innovations: Gemini Pro and Project Astra

The following day, Google hosted its annual I/O developers conference, marking another significant moment in AI's rapid evolution. Google introduced several AI advancements, including a new version of its powerful AI model, Gemini Pro, and a revolutionary product in development, Project Astra. This new multimodal chatbot mirrors OpenAI's GPT-4o in its ability to process continuous visual and auditory information, offering sophisticated responses about its observations.

In a captivating demo, Project Astra demonstrated its practical applications. When asked, "Where did I leave my glasses?" it accurately identified their location, showcasing its potential to become an invaluable assistant in daily tasks. Google hinted at future developments where Astra could be integrated into smart glasses, enabling a level of life-logging and recall far beyond human capabilities. Imagine asking your smart glasses about a conversation you had months ago or the cause of a peculiar noise your car made last week—such scenarios highlight the profound impact AI could have on our memory and daily interactions.

The Debate: Genuine Transformation or Overblown Hype?

Despite these impressive advancements, the AI revolution is not without its detractors. As the initial shock of technologies like ChatGPT wears off, some critics argue that the progress of large language models (LLMs) has stagnated. They claim that while these models were groundbreaking initially, we should not expect significant improvements in the near future.

A prominent voice in this debate is Julia Angwin, a respected tech journalist who recently published an essay in The New York Times titled "Press Pause on the Silicon Valley Hype Machine." Angwin's critique recalls Clifford Stoll's infamous 1995 Newsweek column, where he dismissed the internet as a fleeting trend. Angwin's skepticism centers on claims that AI's progress has been exaggerated, pointing to analyses suggesting that GPT-4's performance on the Uniform Bar Exam was overstated by OpenAI.

However, dismissing AI's potential based on current limitations may be shortsighted. The rapid pace of AI development suggests that today's shortcomings could be swiftly overcome. Demis Hassabis, co-founder of DeepMind and Google's AI czar, emphasized this acceleration in a post-keynote interview at the I/O conference. He noted that AI is advancing three to four times faster than previous technological revolutions like the internet and mobile phones.

Google search VP Liz Reid echoed this sentiment, highlighting the challenge of keeping pace with such rapid innovation. She pointed out that the technology evolves so quickly that even researchers working on the same project can have vastly different perspectives on what is currently possible.

The Future: AI's Transformative Impact

There is a consensus in the tech world that AI is a transformative force, perhaps even surpassing the internet in its potential impact. This belief is shared not only by tech insiders but also by those who experience AI's capabilities firsthand. For instance, President Joe Biden became a believer after a demo of ChatGPT in March 2023. This growing acceptance is why major companies like Microsoft, Meta, Amazon, and Apple are heavily investing in AI, striving to lead in this rapidly advancing field.

Critics may argue that this fervor is driven by the promise of massive profits. However, the tangible advancements and demonstrations suggest otherwise. Just as smartphones transitioned from exotic gadgets to essential tools, AI's extraordinary capabilities may soon become commonplace. While AI's magic might fade into normalcy over time, its impact on our lives and society will be profound and enduring.

As we stand on the brink of this AI revolution, the potential for both positive and negative outcomes is vast. The developments of this week alone underscore that we are merely at the beginning of a transformative era. And as we await the unveiling of future models like GPT-5, one thing is certain: AI will continue to push boundaries, reshaping our world in ways we are only beginning to understand.

Revisiting the Past: Lessons from the Internet Revolution

Reflecting on the AI revolution, it's instructive to consider the transformative impact of the internet, a revolution that began in earnest in the mid-1990s. In 1995, the internet was beginning to revolutionize communication, commerce, and entertainment. Microsoft VP Paul Moritz emphasized the internet's role in democratizing access to information, while Kevin Kelly of WIRED highlighted its explosive growth. The internet mediated human interaction in unprecedented ways, from emails and newsgroups to the burgeoning World Wide Web.

Despite initial skepticism, the internet's transformative power became undeniable. Similarly, AI's potential to reshape our lives is immense, though it comes with its own set of challenges and uncertainties.

Like our content? Follow Financial World on MSN.

The Post OpenAI and Google's AI Advancements Highlight a Week of Technological Progress appeared first on Financial World

• Related: Neuralink Seeks New Human Trial Participant for Revolutionary Brain Implant

Suggestions or feedback?

MIT News | Massachusetts Institute of Technology

• Machine learning
• Social justice
• Black holes
• Classes and programs

Departments

• Aeronautics and Astronautics
• Brain and Cognitive Sciences
• Architecture
• Political Science
• Mechanical Engineering

Centers, Labs, & Programs

• Abdul Latif Jameel Poverty Action Lab (J-PAL)
• Picower Institute for Learning and Memory
• Lincoln Laboratory
• School of Architecture + Planning
• School of Engineering
• School of Humanities, Arts, and Social Sciences
• Sloan School of Management
• School of Science
• MIT Schwarzman College of Computing

A better way to control shape-shifting soft robots

Press contact :, media download.

*Terms of Use:

Images for download on the MIT News office website are made available to non-commercial entities, press and the general public under a Creative Commons Attribution Non-Commercial No Derivatives license . You may not alter the images provided, other than to crop them to size. A credit line must be used when reproducing images; if one is not provided below, credit the images to "MIT."

Previous image Next image

Imagine a slime-like robot that can seamlessly change its shape to squeeze through narrow spaces, which could be deployed inside the human body to remove an unwanted item.

While such a robot does not yet exist outside a laboratory, researchers are working to develop reconfigurable soft robots for applications in health care, wearable devices, and industrial systems.

But how can one control a squishy robot that doesn’t have joints, limbs, or fingers that can be manipulated, and instead can drastically alter its entire shape at will? MIT researchers are working to answer that question.

They developed a control algorithm that can autonomously learn how to move, stretch, and shape a reconfigurable robot to complete a specific task, even when that task requires the robot to change its morphology multiple times. The team also built a simulator to test control algorithms for deformable soft robots on a series of challenging, shape-changing tasks.

Their method completed each of the eight tasks they evaluated while outperforming other algorithms. The technique worked especially well on multifaceted tasks. For instance, in one test, the robot had to reduce its height while growing two tiny legs to squeeze through a narrow pipe, and then un-grow those legs and extend its torso to open the pipe’s lid.

While reconfigurable soft robots are still in their infancy, such a technique could someday enable general-purpose robots that can adapt their shapes to accomplish diverse tasks.

“When people think about soft robots, they tend to think about robots that are elastic, but return to their original shape. Our robot is like slime and can actually change its morphology. It is very striking that our method worked so well because we are dealing with something very new,” says Boyuan Chen, an electrical engineering and computer science (EECS) graduate student and co-author of a paper on this approach .

Chen’s co-authors include lead author Suning Huang, an undergraduate student at Tsinghua University in China who completed this work while a visiting student at MIT; Huazhe Xu, an assistant professor at Tsinghua University; and senior author Vincent Sitzmann, an assistant professor of EECS at MIT who leads the Scene Representation Group in the Computer Science and Artificial Intelligence Laboratory. The research will be presented at the International Conference on Learning Representations.

Controlling dynamic motion

Scientists often teach robots to complete tasks using a machine-learning approach known as reinforcement learning, which is a trial-and-error process in which the robot is rewarded for actions that move it closer to a goal.

This can be effective when the robot’s moving parts are consistent and well-defined, like a gripper with three fingers. With a robotic gripper, a reinforcement learning algorithm might move one finger slightly, learning by trial and error whether that motion earns it a reward. Then it would move on to the next finger, and so on.

But shape-shifting robots, which are controlled by magnetic fields, can dynamically squish, bend, or elongate their entire bodies.

“Such a robot could have thousands of small pieces of muscle to control, so it is very hard to learn in a traditional way,” says Chen.

To solve this problem, he and his collaborators had to think about it differently. Rather than moving each tiny muscle individually, their reinforcement learning algorithm begins by learning to control groups of adjacent muscles that work together.

Then, after the algorithm has explored the space of possible actions by focusing on groups of muscles, it drills down into finer detail to optimize the policy, or action plan, it has learned. In this way, the control algorithm follows a coarse-to-fine methodology.

“Coarse-to-fine means that when you take a random action, that random action is likely to make a difference. The change in the outcome is likely very significant because you coarsely control several muscles at the same time,” Sitzmann says.

To enable this, the researchers treat a robot’s action space, or how it can move in a certain area, like an image.

Their machine-learning model uses images of the robot’s environment to generate a 2D action space, which includes the robot and the area around it. They simulate robot motion using what is known as the material-point-method, where the action space is covered by points, like image pixels, and overlayed with a grid.

The same way nearby pixels in an image are related (like the pixels that form a tree in a photo), they built their algorithm to understand that nearby action points have stronger correlations. Points around the robot’s “shoulder” will move similarly when it changes shape, while points on the robot’s “leg” will also move similarly, but in a different way than those on the “shoulder.”

In addition, the researchers use the same machine-learning model to look at the environment and predict the actions the robot should take, which makes it more efficient.

Building a simulator

After developing this approach, the researchers needed a way to test it, so they created a simulation environment called DittoGym.

DittoGym features eight tasks that evaluate a reconfigurable robot’s ability to dynamically change shape. In one, the robot must elongate and curve its body so it can weave around obstacles to reach a target point. In another, it must change its shape to mimic letters of the alphabet.

“Our task selection in DittoGym follows both generic reinforcement learning benchmark design principles and the specific needs of reconfigurable robots. Each task is designed to represent certain properties that we deem important, such as the capability to navigate through long-horizon explorations, the ability to analyze the environment, and interact with external objects,” Huang says. “We believe they together can give users a comprehensive understanding of the flexibility of reconfigurable robots and the effectiveness of our reinforcement learning scheme.”

Their algorithm outperformed baseline methods and was the only technique suitable for completing multistage tasks that required several shape changes.

“We have a stronger correlation between action points that are closer to each other, and I think that is key to making this work so well,” says Chen.

While it may be many years before shape-shifting robots are deployed in the real world, Chen and his collaborators hope their work inspires other scientists not only to study reconfigurable soft robots but also to think about leveraging 2D action spaces for other complex control problems.

Share this news article on:

Related links.

• Project website
• Suning Huang
• Boyuan Chen
• Vincent Sitzmann
• Scene Representation Group
• Computer Science and Artificial Intelligence Laboratory
• Department of Electrical Engineering and Computer Science

Related Topics

• Computer science and technology
• Artificial intelligence
• Computer Science and Artificial Intelligence Laboratory (CSAIL)
• Electrical Engineering & Computer Science (eecs)

Related Articles

An easier way to teach robots new skills

MIT engineers design flexible “skeletons” for soft, muscle-powered robots

A system for designing and training intelligent soft robots

Helping soft robots turn rigid on demand

Previous item Next item

More MIT News

Janabel Xia: Algorithms, dance rhythms, and the drive to succeed

Read full story →

Jonathan Byrnes, MIT Center for Transportation and Logistics senior lecturer and visionary in supply chain management, dies at 75

Researchers develop a detector for continuously monitoring toxic gases

The beauty of biology

Navigating longevity with industry leaders at MIT AgeLab PLAN Forum

Jeong Min Park earns 2024 Schmidt Science Fellowship

• More news on MIT News homepage →

Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA

• Map (opens in new window)
• Events (opens in new window)
• People (opens in new window)
• Careers (opens in new window)
• Accessibility
• Social Media Hub
• MIT on Facebook
• MIT on YouTube
• MIT on Instagram

From start-up to scale-up: Accelerating growth in construction technology

Construction sites in 2023 might in many ways resemble those in 1923, with manual bricklaying, paper blueprints, and scaffold towers. At $12 trillion, 1 Oxford Economics, March 2023. architecture, engineering, and construction (AEC) is one of the biggest industries in the world, but historically it has been among the slowest to digitize and innovate.

This, however, is changing fast: strong demand for infrastructure, a shortage of skilled labor, and increased stakeholder pressure for data transparency and integration are all accelerating digital adoption. As a result, the AEC tech ecosystem has experienced an explosion of investment and a wave of start-up launches. An estimated $50 billion was invested in AEC tech between 2020 to 2022, 85 percent higher than the previous three years. During the same period, the number of deals in the industry increased 30 percent to 1,229 (Exhibit 1).

Although the AEC tech industry is maturing, it is not yet at the scale and sophistication of more established software markets like logistics, manufacturing, and agriculture. The industry boasts fewer scale-ups and unicorns relative to its size. And it is hard for AEC tech companies to grow efficiently due to several dynamics among AEC customers, including fragmentation, low IT spend (relative to other industries), and entrenched analog ways of working.

In this environment, how can AEC tech companies accelerate adoption and sales and achieve scale? To answer this question, we surveyed approximately 100 investors and AEC tech players in 2022 and interviewed founders, investors, and large software companies in the industry. Using primary research and publicly available data, we also mapped and analyzed more than 3,000 AEC tech companies. 2 PitchBook, November 15, 2022. In this article, we review the findings of that research. We outline the investment trends that are accelerating the digitization of the industry, and we suggest how tech businesses, and their investors, can address challenges to get on a path of efficient growth.

TABLE OF CONTENTS

Trends accelerating the digitization of aec, hurdles to scale aec tech investments remain, strategies for scaling aec tech businesses.

Digitization of the AEC industry started gathering steam a decade ago, but the pace has accelerated over the past three years—and a number of trends suggest it will continue to do so (see sidebar, “What do we mean by architecture, engineering, and construction tech?”).

Economic factors and regulation are prompting investment

What do we mean by architecture, engineering, and construction tech.

A variety of software and tech is used across the architecture, engineering, and construction (AEC) industry. It includes design software, robotics, and tools for the planning, scheduling, budgeting, and performance management of projects (exhibit). Companies in the AEC tech industry range from multibillion-dollar software giants to one-person start-ups.

A combination of supply-and-demand factors are prompting investment in AEC tech. On one hand, global demand for long-term construction is strong, in part because of increased stimulus by governments, such as the $1.2 trillion Bipartisan Infrastructure Law in the United States and the €800 billion NextGenerationEU fund in Europe. More asset owners are also investing sizeable capital to decarbonize their portfolios to make them climate resilient. On the other hand, there is a shortage of skilled workers as more retire or transition to other industries. The United States has 440,000 vacancies in AEC, compared with around 300,000 in 2019, whereas the United Kingdom’s vacancies have nearly doubled since 2019. 3 “Construction: NAICS 23,” US Bureau of Labor Statistics, 2023; “UK job vacancies (thousand): Construction,” UK Office for National Statistics, March 2023. The industry is deploying digital technology to help increase productivity and bridge this gap between supply and demand.

Meanwhile, regulatory changes aimed at creating a more connected industry are reinforcing this wave of digitization. For example, the United Kingdom’s Building Safety Act requires a digital ledger of all building data for new residential buildings, and Sweden’s ID06 requires digital records of all the construction workers on a construction site.

Investor optimism is high

Investment in AEC tech has grown multifold and, based on our research, more and more investors are recognizing AEC tech’s potential to fundamentally change the structure of the construction industry and redistribute value pools at scale. This momentum is likely to continue. Seventy-seven percent of the respondents to our survey expect to invest in AEC tech at similar or higher levels in 2023, and 64 percent see it generating higher returns versus other verticals.

Seventy-seven percent of the respondents to our survey expect to invest in AEC tech at similar or higher levels in 2023.

The tech scene is maturing

The proportion of late-stage venture capital in total AEC tech investment totaled $11.5 billion between 2020 and 2022, more than triple that of the previous three years (Exhibit 2). Meanwhile, M&A continues to be the largest source of funding for AEC tech ventures, accounting for 48 percent of all investments and 68 percent of all exits. The growth of the industry is further reflected in the fact that the median deal size and post-money valuation 4 Post-money valuation is a measure of a company’s valuation that includes all external investments. in the industry has more than doubled since 2017 (Exhibit 3).

Companies and customers are still seeking interoperability

In 2020, we observed  that AEC tech players were targeting multiple use cases to address customer pain points. 5 “ Rise of the platform era: The next chapter in construction technology ,” McKinsey, October 30, 2020. This trend has continued, led by customer demand for interoperability—either through virtual platforms built using open standards and workflows, such as openBIM, or with one-stop-shop platforms such as those developed by some of the largest AEC tech companies. Indeed, nearly half of the companies we analyzed offer customers solutions that address three or more use cases.

AEC technology and property technology are converging

Until now, AEC tech and property technology (proptech) have evolved as separate ecosystems. AEC tech has focused on the design and construction of assets, while proptech has focused on the financing, planning, operation, and maintenance aspects of assets. This is starting to change, as customers and technology players see value in connecting the two. Our analysis shows that 20 percent of AEC tech companies also address at least one proptech use case: for example, linking the design and operation of building management systems using a digital twin.

While the trends above have helped expand the ecosystem of AEC-focused tech businesses and start-ups, investors and founders still wonder how best to pursue efficient growth—defined as the ability to grow annual recurring revenues (ARR) and to generate free cash flow (FCF) from those revenues. 6 Annual recurring revenue is the revenue that a company (often businesses that operate on a subscription-based model) expects to receive from customers on an annual basis. Free cash flow is the cash generated by a company after paying operating expenses and capital expenditures. Our analysis across industries shows that as software companies expand, efficient growth increasingly correlates strongly with valuations (Exhibit 4).

Within the AEC technology industry, however, our research also indicates that efficient growth is particularly tough to achieve for four reasons:

• Customer fragmentation. The average construction company employs fewer than ten people. The average project involves more than 100 different suppliers and subcontractors. So achieving scale requires selling to a large number of companies. This means that sales growth can be labor intensive and slow. As one investor noted, “This is a risk-averse and fragmented sector at its core, so growth is slow, but it is extremely sticky.”
• Multiple customer personas. Founders frequently tell us that identifying the real customer is tough because they lack a clear understanding of user versus buyer personas. Depending on the project, for example, the customer could be the project manager, IT manager, or procurement manager. And often, purchase decisions are made at the project level, not the enterprise level. As a result, companies need to resell the product again to the next project, which drives down net retention and raises acquisition costs. As one investor said, “The most successful companies have a plan to sell to the enterprise, not just the project.”
• Low margins and economic headwinds. Making the case for spending on software can be tough for AEC companies when there is limited capacity for investment. The industry has low margins and increasing economic headwinds, including materials cost inflation. Moreover, the typical IT spend for AEC companies is 1 to 2 percent of the revenue, compared with the 3 to 5 percent average across industries. 7 “Gartner top strategic technology trends for 2022,” Gartner, October 2021. Against this backdrop, solutions must come with a business case. Although ROI can be high, until recently players have not been effective at quantifying benefits. As one investor said, “In a low-margin industry, and in this market environment in particular, it is important that companies can clearly demonstrate and measure the cost-saving benefits of their product.”
• Adoption and scaling challenges. Driving tech adoption in a projects business like construction poses several challenges: users often switch products among different projects—sometimes they need to adopt different tools depending on client preferences, and staff come and go. Furthermore, the industry has traditionally had limited digital capabilities, although this is changing as workers become accustomed to using digital technology in their everyday lives. And as one AEC company executive said, “The pandemic forced us to accelerate adoption from the office to the site overnight.”

For companies that can overcome these barriers, there is a big prize up for grabs: a customer base that is larger than most other industries. So what does it take? Our analysis of tech companies in AEC, as well as other industries like manufacturing, travel, and logistics, shows five common growth characteristics.

Pursue a big total addressable market and a bold vision

As one investor told us, “If the extent of your vision is to sell tools to solve a niche problem, then we’re not excited. We are looking for founders with vision and mission to improve outcomes for big swathes of the market.” Having a bold vision—and being able to effectively articulate how it benefits the user and the broader industry—helps attract talent, investors, and customers, and allows companies to move faster as they continually course-correct toward a North Star. For example, one AEC tech company focuses on improving predictability of project outcomes and uses that simple vision to expand the total addressable market (TAM) beyond contractors and planners to cover a far broader customer set, including project owners, banks, and insurance companies.

A bold vision usually means founders are thinking about the entire AEC tech ecosystem and figuring out ways in which their company can work with other providers to create a seamless user experience and unlock newfound value for a broader set of customers. For example, one AEC design platform expanded its core offering beyond architects and engineers to connect to product suppliers, and thus monetize transactions for building products used in designs.

Achieve a great product market fit

Finding the right product market fit is a key part of the investment decision-making process for investors in most industries, but AEC tech companies often do not get it right. In fact, as our survey indicates, the most common issues observed by AEC tech investors are an overfocus on engineering (rather than product and market fit) and product fragmentation (Exhibit 5).

As one AEC tech player noted, “Niche, technical design tools are often built by self-taught developers and construction professionals who built the tool to solve a specific problem or fill a gap in their workflow. As such, the very nature of those tools focuses on the tech and not the user experience.” In our discussions with start-ups and investors, three common themes emerged that can help create a better product market fit. All three elements require strong product management capabilities .

First is focus. Since customer needs differ across segments, companies would do well to focus on one or a few specific segments, whether they are targeting architects or subcontractors or distributors. As one founder put it, “I have potential customers in manufacturing, retail, construction, and facilities management across more than ten geographies, but we have to focus, or we will achieve nothing.”

Second is feedback. As one investor told us, “Many contech [construction technology] firms are founded by industry professionals who launched their business to solve a problem, so they have huge product focus. We need to see more founders with a balanced product and market/customer focus.” One way to sharpen market focus is to build a network of customers and collaborators. Most successful players do this through their investors’ networks and beta customers, who benefit from low-cost early releases in return for investment in testing and development feedback. And a side benefit is that they can provide access to a critical mass of other customers (Exhibit 6).

Third is flexibility. Nearly every start-up and scale-up we have spoken to has seen a big shift in their product proposition because they responded to market views and kept evolving to optimize the product market fit. For example, one start-up developed an app to measure material waste from construction sites but eventually evolved it to measure embodied carbon in materials.

Build a customer acquisition engine with a scalable revenue and distribution model

Valuations for start-ups are tied strongly with the ARR growth metric. In a fragmented market like AEC, customer acquisition is difficult and expensive. Based on our research, leading players differentiate themselves with three moves to maximize the ARR bang for each buck spent on marketing and R&D:

• Deliver a scalable revenue model. As one investor said, “Some products require so much customization that the software company becomes a consultancy.” Successful businesses have a product that can be deployed with minimal customization and training (and that usually means software rather than hardware). And where customization or training is required, they invest time only in high-potential customers and often partner with independent software vendors to deliver at scale.
• Find creative routes to market. You’re never going to crack the market one customer at a time. Successful players use their investors and existing customers to open new routes to market. They also lock in users early. For example, one design software player gave away free copies of its software to architecture students, who then took it to their new employers. Moreover, these players have a channel strategy aligned with customer tiers, and that includes not only value-added resellers (VARs) and distributors but also low-cost remote channels (including multilingual remote inside-sales centers) and self-serve, web shop, and e-commerce.
• Supercharge the sales team. Successful software companies incentivize their direct-sales teams to cross-sell and upsell and drive key account management capabilities. One leading player with multiple brands centralized its go to market across brands to accelerate cross-sell and upsell and capped bonuses on some established products to incentivize sales of new products. The best sales organizations are underpinned by data that allows them to see the relationship between specific, often siloed, sales and marketing activities and overall growth outcomes.

Improve net retention with customer success

Our analysis shows that as software companies grow, the most important driver of valuation shifts from pure growth, often measured by ARR, to include the ability to generate FCF from ARR. In short, it’s not enough to just have customers; you need to earn money from them. In what is commonly referred  to as the “rule of 40,” the sum of percentage growth and the FCF rate should equal 40 percent or higher. 8 Paul Roche and Sid Tandon, “ SaaS and the Rule of 40: Keys to the critical value creation metric ,” McKinsey, August 3, 2021.

Achieving strong FCF is in large part about optimizing the payback period—that is, how long does it take to recover your customer acquisition costs. This means acquiring new customers efficiently, retaining customers, and upselling and cross-selling to them. This is measured by net retention rate (NRR), 9 Net retention rate is a metric that shows how effective a company is at driving growth in its existing customer base while keeping the churn low. which requires a laser focus on customer success. Across sectors, companies with high NRRs demonstrate three common characteristics:

• They know their numbers. At the heart of customer success is a data-driven understanding of how customers obtain value from a specific product. Maximizing NRR is a game of inches, so leaders analyze the many drivers of growth and churn (upsell, contract cancellation, additional licenses, and so on) at a customer level and respond with targeted interventions (for example, offering bundles for additional “seats” as usage reaches contract limits).
• They set up a dedicated customer success function. A team that can work with customers to get maximum value from the product is particularly important in AEC, where customers are less digitally mature and solutions are less well established. For example, the largest AEC technology companies have customer success teams and run conferences and training for their users. One software company hired a retired construction contractor for its customer success function to better understand customer needs.
• They deliver customer success at low cost. Customer success does not have to mean dedicated (and expensive) customer support. It can often be delivered at lower cost by cultivating user communities and promoting the use of online tutorials, for example. One AEC tech company gained thousands of users on zero-marketing spend by leveraging its community forums and industry networks—effectively putting its own customers to work.

Build functional maturity as you scale

As software companies grow beyond the start-up and scale-up stages, growth rates slow, and FCF (and hence, valuation) is increasingly driven by operational efficiency. This typically comes down to optimizing NRR as well as marketing and sales spend (which can be 50 percent or more of the revenues of typical software companies). At-scale software companies in the top quartile for valuation typically exhibit the following characteristics 10 “SaaS and the Rule of 40,” 2021. :

As software companies grow beyond the start-up and scale-up stages, growth rates slow, and free cash flow (and hence, valuation) is increasingly driven by operational efficiency.

• Optimize marketing and sales spend. Leading software players allocate marketing and sales spend against future, not past, revenue opportunities to give high-growth accounts the biggest coverage. They also continuously segment customers, targeting lower-potential customers through web sales/e-commerce and inside sales while increasing spend on the highest-potential customers.
• Continuously optimize pricing and track impact. Leading players build customer business cases to link pricing to the value generated for customers. They also track the impact of pricing changes in near real time and optimize accordingly. Companies would also do well to make sure their payment terms are right. As one investor explained, AEC tech players often price based on a project or milestone. “This is not ARR, even though some may call it that. And because construction is often subject to delays, this means the risk attached to these revenue streams is very high, which puts off potential investors.”
• Lean on data and automate processes. Successful software companies leverage data, AI, and automated processes  across the business in a variety of ways, including identifying leads and proactively targeting cross-sell and upsell opportunities, leveraging usage information in pricing and product decisions, and assessing developer velocity .
• Strengthen the business-building muscle. Tech companies of every size often reach the tip of a growth curve without a market-ready venture or offering that can pick up the slack, so their growth dips. Leading players maintain momentum by launching net-new businesses more quickly. They incubate new businesses thoughtfully, with dedicated resourcing for product development and go to market.

Several tailwinds are powering growth in the AEC tech industry despite the near-term challenges of the economic slowdown. To capitalize on the investment opportunities and achieve efficient growth, investors and tech companies can learn from the most successful AEC tech companies and catch the wave in this exciting industry.

The authors wish to thank Daniele Di Mattia, Julien Gagnon, Josh Johnson, and Adam Singer for their contributions to this article.

This article was edited by Arshiya Khullar, an editor in the Gurugram office.

Explore a career with us

Related articles.

Reducing embodied carbon in new construction

Rise of the platform era: The next chapter in construction technology

Artificial intelligence: Construction technology’s next frontier