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Research on AI in the Workplace


This update is dedicated to supporting the study of issues related to the multivalent involvement of artificial intelligence (AI) in the workplace. In discussing these issues I share some initial thoughts about one of my two research foci.

The potential for AI to augment and perform human tasks is no longer reserved for spectacular and futuristic demonstrations by IBM’s Watson or Alphabet’s Deepmind, but a present day reality changing the way we work, collaborate, and communicate. Google describes its focus as ‘AI first,’ while Microsoft says AI is the ultimate breakthrough in technology. These claims are backed by heavy investments in AI by Apple, Facebook, and Amazon; together these are the five highest valued companies in the world. Additionally, the Massachusetts Institute of Technology (MIT) is investing $1 billion to set up a college to educate a new generation of AI experts. Needless to say, there is a plethora of examples at individual, organisational, and country levels that testify to the rise of AI in almost every aspect of our daily lives. My aim here is not to provide a comprehensive picture of current AI adoptions in society, or more specifically in organisations. However, those of you who know my work, or me personally, know that I have always been interested in research at the intersection of technology and employee wellbeing. The exponential and impressive rise of artificial intelligence has raised exciting, and vexing, questions about our relationship with technology in the workplace. With this post I hope to instigate a broad range of contributions to facilitate a better understanding of the implications of AI for employee wellbeing.

What is AI?

At this point a brief explanation of my understanding of AI might be of use. I have realised AI is quite sexy, and therefore, a concept that is widely abused and misused to oversell fancy bits of code writing and scripted algorithms as AI. Scholarly definition of AI define these technologies as those dealing with simulation of intelligent behaviour in computers, mimicking the ‘cognitive’ functions associated with the human mind, including problem solving and learning. Put differently, AI can be defined as a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation. Notably, AI is a ‘general purpose technology’ like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities. AI is not one universal technology, rather it is an umbrella term that includes a multitude of different technologies such as machine learning, computer visions, natural language processing, that individually or in combination add intelligence to applications. As a result, there are many different types and levels of artificial intelligence with various ways in which they impact work, for instance depending on the organisational context, sophistication of the AI, the level of autonomy, or decision-making authority it has.

AI and Employee Wellbeing

The current (scientific) debate is dominated by de-contextualized discussions on the types of tasks AI might take over or is highly speculative and controversial in their attempts to label new technological realities. For instance, by making predictions about potential technological (un)employment (e.g., the dystopian perspective: man and machine will wage a Darwinian struggle that machines will win causing mass technological unemployment: versus, the utopian perspective intelligent machines will bring unprecedented wealth and do the work, a universal income will cover basic needs). This discussion distracts from more direct and imminent influences of AI on work and communication processes here and now. Meanwhile many industry leaders and legislative bodies focus on the need for ethical and human-centred AI. The European Commission recently published their guidelines about ethical development and implementation of AI in society. These guidelines propose a few key requirements for trustworthy AI including; I) human agency and oversight (opting for human-in-the-loop, human-on-the-loop, and human-in-command approaches), II) transparency (about the capabilities and limitations of AI), and III) accountability for AI systems and their outcomes (referring to the  auditability and assessment of algorithms and data processing). These guidelines explicitly anchor the importance of human wellbeing by requiring AI systems to benefit human beings including future generations. This is in line with initiatives from Google to give away 25 Million Dollar to ‘humane’ AI projects. Similarly, Microsoft formulated six principles (i.e., socially fair, reliable, safe, inclusive, transparent, and accountable) to ensure AI initiatives benefit individuals and society at large. In addition, Jack Ma CEO and founder of Alibaba, who notoriously claimed workers in his company should be ready to work 80 plus hours per week, recently suggested AI gives us the possibility to head toward a 12-hour work week and employees should focus on the tasks and jobs that make them happy. Finally, universities and scholarly initiatives attempt to emphasise the importance of human(e) AI, as evidenced for instance by the articulation of a new research priority area at the University of Amsterdam.

Although experts and commentators from business and academic spheres often emphasised the importance of the human experience, attention for the impact of AI is almost exclusively focused on the capabilities of the technology and the types of tasks AI is able to perform; overlooking AI’s impact on employee wellbeing. My aim shift our attention to the social implication of AI, by also considering the implications of AI for employee wellbeing.

Below is a list of references that may be of use to those interested in the impact of AI in the workplace. This list is not exhaustive, and given the rapid expansion of the field will soon need to be updated. Recently, my colleague Jeff Treem together with organisational communication scholars compiled a similar list for the study of communication and visibility. If you have any suggestions for material to add to this list, please contact me.


Agrawal, A., Gans, J. S., & Goldfarb, A. (2017). What to expect from artificial intelligence. MIT Sloan Management Review, 58(3), 22-27.

Ananny, M. (2016). Toward an ethics of algorithms: Convening, observation, probability, and timeliness. Science, Technology, & Human Values41(1), 93-117.

Anteby, M., & Chan, C. K. (2018). A self-fulfilling cycle of coercive surveillance: Workers’ invisibility practices and managerial justification. Organization Science29(2), 247-263.

Araujo, T., van Zoonen, W., & ter Hoeven, C. (2019). Automated 1-2-1 Communication. (SWOCC Publication No. 77). Amsterdam: Stichting Wetenschappelijk Onderzoek Commerciële Communicatie.

Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. The Journal of Economic Perspectives, 29(3), 3-30.

Bader, V., & Kaiser, S. (2019). Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence. Organization, 26(5), 655-672.

Bailey, C., Lips‐Wiersma, M., Madden, A., Yeoman, R., Thompson, M., & Chalofsky, N. (2019). The five paradoxes of meaningful work: Introduction to the special issue ‘meaningful work: Prospects for the 21st century’. Journal of Management Studies56(3), 481-499.

Bailey, D., Faraj, S., Hinds, P., von Krogh, G., & Leonardi, P. (2019). Special Issue of Organization Science: Emerging Technologies and Organizing. Organization Science30(3), 642-646.

Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. I. (2014). Burnout and work engagement: The JD–R approach. Annu. Rev. Organ. Psychol. Organ. Behav.1(1), 389-411.

Barker, J. R. (1993). Tightening the iron cage: Concertive control in self-managing teams. In; Organizational influence Processes. Eds. Allen, R. W., Porter, L. W., & Angle, H.L., (2016). Routledge

Barley, S. R., Bechky, B. A., & Milliken, F. J. (2017). The changing nature of work: Careers, identities, and work lives in the 21st century. Academy of Management Discoveries, 3(2), 111-115.

Brougham, D., & Haar, J. (2017). Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 1-19.

Brynjolfsson, E., & McAfee, A. (2012). Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy. Brynjolfsson and McAfee.

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.

Brynjolfsson, E., & Mcafee, A. (2017). The business of Artificial Intelligence: What it can–and cannot–do for your organization. Harvard Business Review Digital Articles, 7, 3-11.

Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530-1534.

Brynjolfsson, E., Rock, D., & Syverson, C. (2018). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In The Economics of Artificial Intelligence: An Agenda. University of Chicago Press.

Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society3(1), 1-12.

Carless, S. A., & De Paola, C. (2000). The measurement of cohesion in work teams. Small group research31(1), 71-88.

Carlström, V. (2016). This Finnish company just made an AI part of the management team, Nordic Business Insider. Retrieved from:

Cavazotte, F., Heloisa Lemos, A., & Villadsen, K. (2014). Corporate smart phones: Professionals’ conscious engagement in escalating work connectivity. New Technology, Work and Employment29(1), 72-87.

Chalofsky, N. (2003). An emerging construct for meaningful work. Human Resource Development International, 6(1), 69-83.

Christin, A. (2016). The hidden story of how metrics are being used in courtrooms and newsrooms to make more decisions. Ethnography Matters, Jun20. Retrieved from:

Christin, A. (2017). Algorithms in practice: Comparing web journalism and criminal justice. Big Data & Society4(2), 1-14.

Chui, M., Manyika, J., & Miremadi, M. (2015). Four fundamentals of workplace automation. McKinsey Quarterly, 29(3), 1-9.

Cohen, N. S. (2015). From pink slips to pink slime: Transforming media labor in a digital age. The Communication Review18(2), 98-122.

Daugherty, P. R., & Wilson, H. J. (2018). Human+ machine: reimagining work in the age of AI. Harvard Business Press.

Davidson A. and Kestenbaum, D. (2014). The future of work looks like a UPS truck. Retrieved from:

Demerouti, E., Bakker, A. B., & Bulters, A. J. (2004). The loss spiral of work pressure, work–home interference and exhaustion: Reciprocal relations in a three-wave study. Journal of Vocational behavior64(1), 131-149.

DeSanctis, G., & Poole, M. S. (1994). Capturing the complexity in advanced technology use: Adaptive structuration theory. Organization science5(2), 121-147.

Dogan, M., & Yildirim, P. (2017). Man vs. Machine: When is Automation Inferior to Human Labor?.

Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management48, 63-71.

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … & Galanos, V. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management.

Eisenberg, E. M., & Witten, M. G. (1987). Reconsidering openness in organizational communication. Academy of Management Review12(3), 418-426.

Espeland, W. N., & Stevens, M. L. (2008). A sociology of quantification. European Journal of Sociology/Archives Européennes de Sociologie49(3), 401-436.

European Commission (2019). Ethics Guidelines for Trustworthy AI. From

Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62-70.

Frey, C. B., & Osborne, M. A. (2017). The future of employment: how susceptible are jobs to computerisation?. Technological Forecasting and Social Change114, 254-280.

Gibbs, J. L., Rozaidi, N. A., & Eisenberg, J. (2013). Overcoming the “ideology of openness”: Probing the affordances of social media for organizational knowledge sharing. Journal of Computer-Mediated Communication19(1), 102-120.

Gibson, J. J. (1986). The ecological approach to visual perception. Mahwah, NJ: Erlbaum.

Giddens, A. 1984. The constitution of society. Berkeley, CA: University of California Press.

Google (2018). AI for social good: Working together to apply AI for social good. Retrieved from:

Günther, W. A., Mehrizi, M. H. R., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems26(3), 191-209.

Habermas, J. (1971). Technology and science as ‘ideology’.

Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: Test of a theory. Organizational Behavior and Human Performance, 16(2), 250-279.

Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review61(4), 5-14.

Halbesleben, J. R., & Wheeler, A. R. (2015). To invest or not? The role of coworker support and trust in daily reciprocal gain spirals of helping behavior. Journal of Management41(6), 1628-1650.

Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y., De Visser, E. J., & Parasuraman, R. (2011). A meta-analysis of factors affecting trust in human-robot interaction. Human Factors, 53(5), 517-527.

Harris, J. G., & Davenport, T. H. (2005). Automated decision making comes of age. MIT Sloan Management Review, 2-10.

Harrison, D. A., Price, K. H., & Bell, M. P. (1998). Beyond relational demography: Time and the effects of surface-and deep-level diversity on work group cohesion. Academy of management journal41(1), 96-107.

Hatch, M. J., & Erhlich, S. B. (1993). Spontaneous humour as an indicator of paradox and ambiguity in organizations. Organization Studies14(4), 505-526.

Healy, J., Nicholson, D., & Parker, J. (2017). Guest editors’ introduction: technological disruption and the future of employment relations. Labour and Industry, 27(3), 157-164.

Hinkin, T. R. (1995). A review of scale development practices in the study of organizations. Journal of management21(5), 967-988.

Hodson, R. (2014). Workers’ earnings and corporate economic structure. Academic Press.

Huang, M. H., Rust, R., & Maksimovic, V. (2019). The Feeling Economy: Managing in the Next Generation of Artificial Intelligence (AI). California Management Review61(4), 43-65.

Introna, L. D. (2016). Algorithms, governance, and governmentality: On governing academic writing. Science, Technology, & Human Values41(1), 17-49.

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Business Horizons61(4), 577-586.

Jarvenpaa, S. L., & Lang, K. R. (2005). Managing the paradoxes of mobile technology. Information systems management22(4), 7-23.

Joh, E. E. (2016). The new surveillance discretion: Automated suspicion, big data, and policing. Harvard Law and Policy Review, 10, 15–42.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

Kantor, J. (2014). Working Anything But 9 to 5. NYT. Retrieved from:

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons62(1), 15-25.

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons62(1), 15-25.

Korczak, A., ALarotu, A., Sipilä, J.,Alila, A., Saarinen, A., Lukander, J., Erolainen, J., & Sandqvist, J., (2018). Uncovering AI in Finland: 2018 Field guide to AI. Retrieved from:

Kushlev, K. (2018). Media technology and well-being: A complementarity-interference model. Handbook of well-being. Salt Lake City, UT: DEF Publishers.

Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015, April). Working with machines: The impact of algorithmic and data-driven management on human workers. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1603-1612). ACM

Leonardi, P. M. (2014). Social media, knowledge sharing, and innovation: Toward a theory of communication visibility. Information systems research25(4), 796-816.

Leonardi, P. M., & Barley, S. R. (2008). Materiality and change: Challenges to building better theory about technology and organizing. Information and organization18(3), 159-176.

Leonardi, P. M., Treem, J. W., & Jackson, M. H. (2010). The connectivity paradox: Using technology to both decrease and increase perceptions of distance in distributed work arrangements. Journal of Applied Communication Research38(1), 85-105.

Lupton, D., & Jutel, A. (2015). ‘It’s like having a physician in your pocket!’A critical analysis of self-diagnosis smartphone apps. Social Science & Medicine133, 128-135.

Lüscher, L. S., & Lewis, M. W. (2008). Organizational change and managerial sensemaking: Working through paradox. Academy of management Journal51(2), 221-240.

Manyika, J., (2017). A future that works: AI, Automation, Employment, and Productivity. McKinsey Global Institute.

Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., … & Sanghvi, S. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute.

Marabelli, M., & Newell, S. (2019, July). Algorithmic Decision-making in the US Healthcare Industry: Good for Whom?. In Academy of Management Proceedings (Vol. 2019, No. 1, p. 15581). Briarcliff Manor, NY 10510: Academy of Management.

Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job burnout. Annual review of psychology52(1), 397-422.

Mathiason, G., Cerilli, J., Gordon, P., Kennedy, P., Lee, T., Lotito, M., … & Weiner, P. (2016). The transformation of the workplace through robotics, artificial intelligence, and automation. The Littler Report, 1-63.

Mazmanian, M., Orlikowski, W. J., & Yates, J. (2013). The autonomy paradox: The implications of mobile email devices for knowledge professionals. Organization science24(5), 1337-1357.

McClelland, M. (2012). I Was a Warehouse Wage Slave. Mother Jones. Retrieverd from:

McFarland, D. A., & McFarland, H. R. (2015). Big data and the danger of being precisely inaccurate. Big Data & Society2(2), 1-4.

Microsoft, (2018). Microsoft AI principles. From

Mukherjee, S. (2017) A.I. versus M.D. What happens when diagnosis is automated? The New Yorker. Retrieved from

Nass, C., Fogg, B. J., & Moon, Y. (1996). Can computers be teammates?. International Journal of Human-Computer Studies, 45(6), 669-678.

Nelson, A. J., & Irwin, J. (2014). “Defining what we do—all over again”: Occupational identity, technological change, and the librarian/Internet-search relationship. Academy of Management Journal57(3), 892-928.

Ng, A. (2016). What artificial intelligence can and can’t do right now. Harvard Business Review, 9.

Norman, D. (2017). Design, Business Models, and Human-Technology Teamwork: As automation and artificial intelligence technologies develop, we need to think less about human-machine interfaces and more about human-machine teamwork. Research-Technology Management, 60(1), 26-30.

Nowak, A., Lukowicz, P., & Horodecki, P. (2018). Assessing Artificial Intelligence for Humanity: Will AI be the Our Biggest Ever Advance? or the Biggest Threat [Opinion]. IEEE Technology and Society Magazine37(4), 26-34.

Oldham, G. R., & Hackman, J. R. (2010). Not what it was and not what it will be: The future of job design research. Journal of Organizational Behavior, 31(2‐3), 463-479.

Orlikowski, W. J. (2007). Sociomaterial practices: Exploring technology at work. Organization studies28(9), 1435-1448.

Orlikowski, W. J., & Scott, S. V. (2015). Exploring material‐discursive practices. Journal of management studies52(5), 697-705.

Ouchi, W. G., & Johnson, J. B. (1978). Types of organizational control and their relationship to emotional well being. Administrative Science Quarterly, 293-317.

Parry, K., Cohen, M., & Bhattacharya, S. (2016). Rise of the machines: A critical consideration of automated leadership decision making in organizations. Group & Organization Management41(5), 571-594.

Pasquale, F. (2015). The black box society. Harvard University Press.

Phan, P., Wright, M., & Lee, S. H. (2017). Of robots, artificial intelligence, and work. Academy of Management perspectives, 31(4), 253-255. Doi: 10.5465/amp2017.0199

Piccolo, R. F., & Colquitt, J. A. (2006). Transformational leadership and job behaviors: The mediating role of core job characteristics. Academy of Management Journal, 49(2), 327-340.

Poole, M. S., & DeSanctis, G. (2004). Structuration theory in information systems research: Methods and controversies. The handbook of information systems research, 206-249.

Puranam, P., Alexy, O., & Reitzig, M. (2014). What’s “new” about new forms of organizing? Academy of Management Review, 39(2), 162-180.

Puranam, P., Shrestha, Y. R., He, V. F., & von Krogh, G. (2018). Algorithmic induction through machine learning: using predictions to theorize. INSEAD Working paper: From:

Putnam, L. L., Fairhurst, G. T., & Banghart, S. (2016). Contradictions, dialectics, and paradoxes in organizations: A constitutive approach. The Academy of Management Annals10(1), 65-171.

Putnam, L. L., Myers, K. K., & Gailliard, B. M. (2014). Examining the tensions in workplace flexibility and exploring options for new directions. Human Relations67(4), 413-440.

Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review59(1).

Reynolds, N.-S. (2015), ‘Making Sense of new Technology During Organisational Change’, New Technology, Work and Employment 30, 2, 145–157

Rogers, E.M. (2003), Diffusion of Innovations, 5th edn (New York, NY: Free Press).

Rosso, B. D., Dekas, K. H., & Wrzesniewski, A. (2010). On the meaning of work: A theoretical integration and review. Research in Organizational Behavior, 30, 91-127.

Salanova, M., Schaufeli, W. B., Xanthopoulou, D., & Bakker, A. B. (2010). The gain spiral of resources and work engagement: Sustaining a positive worklife. Work engagement: A handbook of essential theory and research, 118-131.

Schaufeli, W. B., & Bakker, A. B. (2004). Job demands, job resources, and their relationship with burnout and engagement: A multi‐sample study. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior25(3), 293-315.

Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire: A cross-national study. Educational and psychological measurement66(4), 701-716.

Schwartz, J., Collins, L., Stockton, H., Wagner, D., & Walsh, B. (2019). The Future of work: The augmented workforce. Retrieved from:

Shoot, B. (2018). MIT is investing $1 Billion in new college with computing, AI focus. Fortune, Retrieved from:

Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational Decision-Making Structures in the Age of Artificial Intelligence. California Management Review61(4), 66-83.

Spreitzer, G. M. (1995). Psychological empowerment in the workplace: Dimensions, measurement, and validation. Academy of management Journal38(5), 1442-1465.

Steers, R. M., Mowday, R. T., & Shapiro, D. L. (2004). The future of work motivation theory. Academy of Management review, 29(3), 379-387.

Stohl, C. Stohl, M., & Leonardi, P (2016). Managing opacity: Information visibility and the paradox of transparency in the digital age. International Journal of Communication, 10

Susskind, R., & Susskind, D. (2015). The future of the professions: How technology will transform the work of human experts. Oxford, UK: Oxford University Press

Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135-146.

Tegmark, M. (2017). Life 3.0: Being human in the age of artificial intelligence. Knopf Doubleday Publishingh Group.

Ter Hoeven, C. L., Van Zoonen, W., & Fonner, K. L. (2016). The practical paradox of technology: The influence of communication technology on employee well-being, Communication Monographs, 83(2), 239-263. Doi: 10.1080/03637751.2015.1133920

Van der Smissen, S., Fransen, M., & Abraimi, R. (2016). De impact van automatisering op het Nederlandse onderwijs. Een verkenning op basis van data-analyse. Retrieved from:

Van Zoonen, W. & Banghart, S. G. (2018). Talking engagement into being: A three-wave panel study linking boundary management preferences, work communication on social media, and employee engagement Journal of Computer-Mediated Communication. Doi: 10.1093/jcmc/zmy014

van Zoonen, W., & Rice, R. E. (2017). Paradoxical implications of personal social media use for work. New Technology, Work and Employment.

van Zoonen, W., Verhoeven, J. W., & Vliegenthart, R. (2017). Understanding the consequences of public social media use for work. European Management Journal35(5), 595-605.

von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries.

Waardenburg, L. Sergeeva, A., Huysman, M. (2018). Digitizing crime: How the use of predictive policing influences police work practices. Presented at 34th EGOS Colloquium, Tallinn, Estonia.

Wegman, L. A., Hoffman, B. J., Carter, N. T., Twenge, J. M., & Guenole, N. (2018). Placing job characteristics in context: Cross-temporal meta-analysis of changes in job characteristics since 1975. Journal of Management, 44(1), 352-386.

Weick, K. E. (1995). Sensemaking in organizations (Vol. 3). Thousand Oaks, California: Sage Publication Inc.

Woyke, E. (2018). Slack hopes its AI will keep you from hating Slack. MIT Technology Review, 12(2), 13-14.

Wright, S. A., & Schultz, A. E. (2018). The rising tide of artificial intelligence and business automation: Developing an ethical framework. Business Horizons61(6), 823-832.

Zammuto, R. F., Griffith, T. L., Majchrzak, A., Dougherty, D. J., & Faraj, S. (2007). Information technology and the changing fabric of organization. Organization science18(5), 749-762.

Zoller, H. M., & Fairhurst, G. T. (2007). Resistance leadership: The overlooked potential in critical organization and leadership studies. Human Relations60(9), 1331-1360.