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No stone left unturned? Towards a framework for the impact of datafication technologies on organizational control

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Schafheitle, Simon Daniel, Weibel, Antoinette, Ebert, Isabel Laura, Kasper, Gabriel, Schank, Christoph, Leicht-Deobald, Ulrich·2020·Academy of Management Discoveries
DOI 10.5465/amd.2019.0002
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Abstract

The goal of this article is to develop an empirically grounded framework to analyze how new technologies, particularly those used in the realm of datafication, alter or expand traditional organizational control configurations. Datafication technologies for employee-related data-gathering, analysis, interpretation, and learning are increasingly applied in the workplace. Yet there remains a lack of detailed insight regarding the effects of these technologies on traditional control. To convey a better understanding of such datafication technologies in employee management and control, we used a three-step, exploratory, multi-method morphological analysis. In step 1, we developed a framework based on 26 semi-structured interviews with technological experts. In step 2, we refined and redefined the framework in [...] and redefined the framework in four workshops with scholars specializing in topics that emerged in step 1. In step 3, we evaluated and validated the framework using potential and actual users of datafication technology controls. As a result, our refined and validated “Datafication Technology Control Configuration” (DTCC) framework comprises 11 technology control dimensions and 36 technology control elements, offering the first insights into how datafication technologies can change our understanding of traditional control configurations.

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  • Narrow algorithmic monitoring systems lead to a greater emphasis on quantified work objectives at the expense of other and potentially meaningful aspects of one's job, reducing autonomy in the choice of work methods.

    “A similar consequence of narrow algorithmic monitoring systems is a greater emphasis on quantified work objectives at the expense of other and potentially meaningful aspects of one's job (Schafheitle et al., 2020), reducing the autonomy of workers in the choice of their work methods (Day, Paquet, Scott, & Hambley, 2012;Wang, Liu, & Parker, 2020)”

    From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Schafheitle et al., 2020

    In “Framework on Datafication Technology Control Configurations” (page not provided), the paper describes technology that tracks employee outputs against performance targets. It does not say that narrow monitoring increases emphasis on quantified objectives at the expense of other meaningful aspects of work. The autonomy clause is cited to different sources, so it is not judged here. (LLM, conf=0.82)

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  • Algorithmic management is a technological phenomenon that is not contingent on the type of organization and can be found in a large array of work contexts.

    “However, recent reviews and conceptualizations (Kellogg et al., 2019;Mateescu & Nguyen, 2019a;Schafheitle et al., 2020) consider algorithmic management as a technological phenomenon that is not contingent on the type of organization and can be found in a large array of work contexts.”

    From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Schafheitle et al., 2020

    The Introduction (page unavailable in our copy) presents datafication technologies as applicable to both easily automated tasks and “thinking work,” supporting the claim that technology-based control spans varied work contexts. The paper discusses algorithmic decision-making in organizational control, but the passages do not establish that algorithmic management is independent of organization type. (LLM, conf=0.78)

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  • Because data collected through monitoring devices is often used to assess performance, workers sometimes tend to work for data, focusing more on monitored tasks and eluding unmonitored ones, which can hamper task variety.

    “Because data collected through monitoring devices is often used to assess their performance, workers sometimes tend to”

    From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Schafheitle et al., 2020

    The Framework on Datafication Technology Control Configurations section describes sensor and GPS data linked to individual performance management at UPS; the ORION discussion also considers how those data might be used to rank drivers. Neither passage says workers focus on monitored tasks, evade unmonitored ones, or experience reduced task variety. Page numbers are unavailable in our copy. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Unreasonable monitoring systems foster the datafication of work, or the 'working for data' phenomenon, viewed as the orientation of workers' efforts and energy towards monitored or quantified aspects of the job, which are presumably not the most meaningful, complex or valued ones.

    “Unreasonable monitoring systems foster the datafication of work, or the”

    From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Schafheitle et al., 2020

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Research works printed but not linked: 120

  1. Adler, P. S., & Borys, B. 1996. Two types of bureaucracy: Enabling and coercive. Administrative Science Quarterly, 41(1): 61-89.
  2. Aiello, J. R., & Kolb, K. J. 1995. Electronic performance monitoring and social context: Impact on productivity and stress. Journal of Applied Psychology, 80(3): 339-353.
  3. Alder, G. S. 2001. Employee reactions to electronic performance monitoring: A consequence of organizational culture. The Journal of High Technology Management Research, 12(2): 323-342.
  4. Alder, G. S., & Ambrose, M. L. 2005. An examination of the effect of computerized performance monitoring feedback on monitoring fairness, performance, and satisfaction. Organizational Behavior and Human Decision Processes, 97(2): 161-177.
  5. Alder, P. S., & Borys, B. 1996. Two types of bureaucracy: Enabling and coercive. Administrative Science Quarterly, 41(1): 61-89.
  6. Alge, B. J. 2001. Effects of computer surveillance on perceptions of privacy and procedural justice. Journal of Applied Psychology, 86(4): 797-804.
  7. Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. 2016. HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1): 1-11.
  8. Bacharach, S. B. 1989. Organizational theories: Some criteria for evaluation. Academy of Management Review, 14(4): 496-515.
  9. Bain, P., & Taylor, P. 2000. Entrapped by the 'electronic panopticon'? Worker resistance in the call centre. New Technology, Work and Employment, 15(1): 2-18.
  10. Ball, K. 2010. Workplace surveillance: An overview. Labor History, 51(1): 87-106.
  11. Ball, K., & Margulis, S. T. 2011. Electronic monitoring and surveillance in call centres: A framework for investigation. New Technology, Work and Employment, 26(2): 113-126.
  12. Bamberger, P. A. 2018. AMD -Clarifying what we are about and where we are going. Academy of Management Discoveries, 4(1): 1-10.
  13. Barker, J. R. 1993. Tightening the iron cage: Concertive control in self-managing teams. In C. Grey, & H. Willmott (Eds.), Critical Management Studies. A Reader: 209-243. Oxford, UK: Oxford University Press.
  14. Bassi, L. 2011. Raging debates in HR analytics. People and Strategy, 34(2): 14-18.
  15. Bernstein, E. S. 2017. Making transparency transparent: The evolution of observation in management theory. Academy of Management Annals, 11(1): 217-266.
  16. Bhave, D. P., Teo, L. H., & Dalal, R. S. 2020. Privacy at work: A review and a research agenda for a contested terrain. Journal of Management, 46(1): 127-164.
  17. boyd, d., & Crawford, K. 2012. Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5): 662-679.
  18. Cardinal, L. B. 2001. Technological innovation in the pharmaceutical industry: The use of organizational control in managing research and development. Organization Science, 12(1): 19-36.
  19. Cardinal, L. B., Kreutzer, M., & Miller, C. 2017. An aspirational view of organizational control research: Re-invigorating empirical work to better meet the challenges of 21st century organizations. Academy of Management Annals, 11(2): 559-592.
  20. Cardinal, L. B., Sitkin, S. B., & Long, C. P. 2004. Balancing and rebalancing in the creation and evolution of organizational control. Organization Science, 15(4): 411-431.
  21. Cardinal, L. B., Sitkin, S. B., & Long, C. P. 2010. A configurational theory of control. In S. B. Sitkin, L. B. Cardinal, & K. Bijlsma-Frankema (Eds.), Organizational Control: 51-79. Cambridge, UK: Cambridge University Press.
  22. Cardinal, L. B., Sitkin, S. B., Long, C. P., & Miller, C. C. 2018. The genesis of control configurations during organizational founding. In B. Sitkin Sim (Ed.), Organization Design (Advances in Strategic Management), Vol. 40: 83-114: Emerald Publishing Limited.
  23. Cascio, W. F., & Montealegre, R. 2016. How technology is changing work and organizations. Annual Review of Organizational Psychology and Organizational Behavior, 3: 349-375.
  24. Chang, S. E., Liu, A. Y., & Lin, S. 2015. Exploring privacy and trust for employee monitoring. Industrial Management & Data Systems, 115(1): 88-106.
  25. Chory, R., Vela, L., & Avtgis, T. 2016. Organizational surveillance of computer-mediated workplace communication: Employee privacy concerns and responses. Employee Responsibilities & Rights Journal, 28(1): 23-43.
  26. Choudhury, P., Allen, R., & Endres, M. 2018. Developing theory using machine learning methods. Harvard Business School Working Paper 19-032 Available at SSRN 3251077: 1-48.
  27. Costa, A. C., & Bijlsma-Frankema, K. 2007. Trust and control interrelatedness: New perspectives on the trust-control nexus in organizational relations. Group & Organization Management, 32 (4): 392- 406.
  28. Crawford, K., & Schultz, J. 2014. Big data and due process: Towards a framework to redress predictive privacy harms. Boston College Law Review 55(1): 93-128.
  29. Cukier, K., & Mayer-Schoenberger, V. 2013. The rise of big data. Foreign Affairs, 92(3): 27-40.
  30. D'Urso, S. C. 2006. Who's watching us at work? Toward a structural-perceptual model of electronic monitoring and surveillance in organizations. Communication Theory, 16(3): 281-303.
  31. Davenport, T. H. 2013. Analytics 3.0. Harvard Business Review, 91(12): 64-72.
  32. DeHart-Davis, L. 2008. Green tape: A theory of effective organizational rules. Journal of Public Administration Research and Theory, 19(2): 361-384.
  33. Dess, G. G., Newport, S., & Rasheed, A. M. 1993. Configuration research in strategic management: Key issues and suggestions. Journal of Management, 19(4): 775-795.
  34. DeTienne, K. B. 1993. Big brother or friendly coach? Computer monitoring in the 21st century. The Futurist, 27(5): 33-37.
  35. Dougherty, D. 2016. Taking advantage of emergence: Productively innovating in complex innovation systems. Oxford: Oxford University Press.
  36. Eisenhardt, K. M. 1985. Control: Organizational and economic approaches. Management Science, 31(2): 134-149.
  37. Eisenhardt, K. M. 1989. Building theories from case study research. Academy of Management Review, 14(4): 532-550.
  38. Eisenhardt, K. M., & Graebner, M. E. 2007. Theory building from cases: Opportunities and challenges. Academy of Management Journal, 50(1): 25-32.
  39. Finlay, S. 2014. Predictive analytics, data mining and big data. Myths, misconceptions and methods. Hampshire (UK): Palgrave Macmillan UK.
  40. Flamholtz, E. G., Das, T. K., & Tsui, A. S. 1985. Toward an integrative framework of organizational control. Accounting, Organizations and Society, 10(1): 35-50.
  41. Fleming, D. E., & Artis, A. B. 2010. Measuring corporate affinity for technology: A scale for customers and employees. Journal of Personal Selling & Sales Management, 30(2): 167-179.
  42. Frow, P., Nenonen, S., Payne, A., & Storbacka, K. 2015. Managing co-creation design: A strategic approach to innovation. British Journal of Management, 26(3): 463-483.
  43. Gasser, U., & Almeida, V. A. 2017. A layered model for AI governance. IEEE Internet Computing(6): 58-62.
  44. Gläser, J., & Laudel, G. 2009. Experteninterviews und qualitative Inhaltsanalyse: Springer-Verlag.
  45. Grant, R., & Higgins, C. A. 1989. Monitoring service workers via computer: The effect on employees, productivity, and service. National Productivity Review, 8(2): 101-112.
  46. Grant, R., & Higgins, C. A. 1991. Computerized performance monitors: Factors affecting acceptance. IEEE Transactions on Engineering Management, 38(4): 306-315.
  47. Hendrickx, F. 2018. Privacy, data protection and measuring employee performance. The triggers of technology and smart work. European Labour Law Journal, 9(2): 99-100.
  48. Holland, C., Levis, J., Nuggehalli, R., Santilli, B., & Winters, J. 2017. UPS optimizes delivery routes. INFORMS Journal on Applied Analytics, 47(1): 8-23.
  49. Holton, C. F., & Fuller, R. M. 2008. Unintended consequences of electronic monitoring of instant messaging. IEEE Transactions on Professional Communication, 51(4): 381-395.
  50. Im, K., & Cho, H. 2013. A systematic approach for developing a new business model using morphological analysis and integrated fuzzy approach. Expert Systems with Applications, 40(11): 4463-4477.
  51. Jaworski, B. J. 1988. Toward a theory of marketing control: Environmental context, control types, and consequences. Journal of Marketing, 52(3): 23-39.
  52. Jeske, D., & Kapasi, I. 2017. Electronic Performance Monitoring: Lessons from the Past and Future Challenges, Annual Conference of the Italian Chapter of the Association of Information Systems. Milan, Italy.
  53. Jeske, D., & Santuzzi, A. M. 2015. Monitoring what and how: Psychological implications of electronic performance monitoring. New Technology, Work and Employment, 30(1): 62-78.
  54. Kepes, S., & Delery, J. 2007. HRM systems and the problem of internal fit. In P. F. Boxall, J. Purcell, & P. M. Wright (Eds.), The Oxford handbook of human resource management. Oxford: Oxford University Press.
  55. Kidwell, R. E., & Bennett, N. 1994. Employee reactions to electronic control systems: The role of procedural fairness. Group & Organization Management, 19(2): 203-218.
  56. Kirsch, L. J., Ko, D.-G., & Haney, M. H. 2010. Investigating the antecedents of team-based clan control: Adding social capital as a predictor. Organization Science, 21(2): 469-489.
  57. Krumeich, J., Werth, D., & Loos, P. 2016. Prescriptive control of business processes -New potentials through predictive analytics of big data in the process manufacturing industry. Business & Information Systems Engineering, 58(4): 261-280.
  58. Kuckartz, U., & Rädiker, S. 2019. Datenaufbereitung und Datenbereinigung in der qualitativen Sozialforschung. In N. Baur, & J. Blasius (Eds.), Handbuch Methoden der empirischen Sozialforschung: 441-456. Wiesbaden: Springer Fachmedien Wiesbaden.
  59. Kurasaki, K. S. 2000. Intercoder reliability for validating conclusions drawn from open-ended interview data. Field Methods, 12(3): 179-194.
  60. Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. 2019. The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity. Journal of Business Ethics, 160(2): 377-392.
  61. Long, C. P., & Sitkin, S. B. 2018. Control-trust dynamics in organizations: Identifying shared perspectives and charting conceptual fault lines. Academy of Management Annals, 12(2): 725-751.
  62. Lycett, M. 2013. 'Datafication': Making sense of (big) data in a complex world. European Journal of Information Systems, 22(4): 381-386.
  63. McNall, L. A., & Roch, S. G. 2009. A social exchange model of employee reactions to electronic performance monitoring. Human Performance, 22(3): 204-224.
  64. McNall, L. A., & Stanton, J. M. 2011. Private eyes are watching you: Reactions to location sensing technologies. Journal of Business and Psychology, 26(3): 299-309.
  65. Ménard, C. 2009. Oliver Williamson and the logic of hybrid organizations. In M. Morroni (Ed.), Corporate Governance, Organization and the Firm. Co-operation and Outsourcing in the Global Economy: 87-103. Cheltenham: Edward Elgar Publishing.
  66. Meyer, A. D., Tsui, A. S., & Hinings, C. R. 1993. Configurational approaches to organizational analysis. Academy of Management Journal, 36(6): 1175-1195.
  67. Miller, D. 1987. The genesis of configuration. Academy of Management Review, 12(4): 686-701.
  68. Misangyi, V. F., Greckhamer, T., Furnari, S., Fiss, P. C., Crilly, D., & Aguilera, R. 2017. Embracing causal complexity: The emergence of a neo-configurational perspective. Journal of Management, 43(1): 255-282.
  69. Neff, G., & Nafus, D. 2016. Self-tracking. Camebridge: MIT Press.
  70. Normann, R. 2001. Reframing business: When the map changes the landscape. Chichester: John Wiley & Sons.
  71. Ouchi, W. G. 1979. A conceptual framework for the design of organizational control mechanisms. Management Science, 25(9): 833-848.
  72. Ouchi, W. G. 1980. Markets, bureaucracies, and clans. Administrative Science Quarterly, 25(1): 129-141.
  73. Ouchi, W. G., & Maguire, M. A. 1975. Organizational control: Two functions. Administrative Science Quarterly, 20(4): 559-569.
  74. Pasquale, F. 2015. The black box society: The secret algorithms that control money and information. Camebridge, MA: Harvard University Press.
  75. Phan, P., Wright, M., & Lee, S.-H. 2017. Of robots, artificial intelligence, and work. Academy of Management Perspectives, 31(4): 253-255.
  76. Prassl, J. 2018. Humans as a service: The promise and perils of work in the gig economy: Oxford University Press.
  77. Prassl, J. 2019. What if your boss was an algorithm? Economic incentives, legal challenges, and the rise of artificial intelligence at work. Comparative Labor Law and Policy Journal(Special Issue "Automation, Artificial Intelligence and Labour Protection").
  78. Ragin, C. C. 2008. Redesigning social inquiry: Fuzzy sets and beyond. Chicago: University of Chicago Press.
  79. Ritchey, T. 2011. Wicked problems-social messes: Decision support modelling with morphological analysis. Berlin: Springer Science & Business Media.
  80. Ritchey, T. 2012. Outline for a morphology of modelling methods: Contribution to a General Theory of Modelling. Acta Morphologica Generalis, 1(1): 1-20.
  81. Roberts, J. 2010. Designing incentives in organizations. Journal of Institutional Economics, 6(1): 125- 132.
  82. Rogers, K. J. S., Smith, M. J., & Sainfort, P. C. 1990. Electronic performance monitoring, job design and psychological stress. Proceedings of the Human Factors Society Annual Meeting, 34(12): 854- 858.
  83. Sandelin, M. 2008. Operation of management control practices as a package-A case study on control system variety in a growth firm context. Management Accounting Research, 19(4): 324-343.
  84. Sandle, T. 2018. Q&A: Applying neuroscientific principles to AI, Digital Journal. Retrieved from: http://www.digitaljournal.com/tech-and-science/technology/q-a-applying-neuroscientific- principles-to-ai/article/526758 (last access: April 14th, 2020).
  85. Sapegina, A., & Weibel, A. 2017. The good, the not so bad, and the ugly of competitive human resource practices: A multidisciplinary conceptual framework. Group & Organization Management, 42(5): 707-747.
  86. Searle, R., den Hartog, D. N., Weibel, A., Gillespie, N., Six, F., Hatzakis, T., & Skinner, D. 2011. Trust in the employer: The role of high-involvement work practices and procedural justice in European organizations. The International Journal of Human Resource Management, 22(5): 1069-1092.
  87. Seidenstricker, S., Rauch, E., & Dallasega, P. 2017. Industrie-4.0-Geschäftsmodell-Innovation für KMU. ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, 112(9): 616-620.
  88. Sewell, G., & Barker, J. R. 2006. Coercion versus care: Using irony to make sense of organizational surveillance. Academy of Management Review, 31(4): 934-961.
  89. Sewell, G., Barker, J. R., & Nyberg, D. 2012. Working under intensive surveillance: When does 'measuring everything that moves' become intolerable? Human Relations, 65(2): 189-215.
  90. Short, J. C., Payne, G. T., & Ketchen Jr, D. J. 2008. Research on organizational configurations: Past accomplishments and future challenges. Journal of Management, 34(6): 1053-1079.
  91. Simons, R. 1994. How new top managers use control systems as levers of strategic renewal. Strategic Management Journal, 15(3): 169-189.
  92. Sitkin, S. B., Cardinal, L. B., & Bijlsma-Frankema, K. M. 2010. Organizational Control. Camebridge (UK): Camebridge University Press.
  93. Sitkin, S. B., Sutcliffe, K. M., & Schroeder, R. G. 1994. Distinguishing control from learning in total quality management: A contingency perspective. Academy of Management Review, 19(3): 537-564.
  94. Snell, S. A. 1992. Control theory in strategic human resource management: The mediating effect of administrative information. Academy of Management Journal, 35(2): 292-327.
  95. Spiggle, S. 1994. Analysis and interpretation of qualitative data in consumer research. Journal of Consumer Research, 21(3): 491-503.
  96. Spitzmüller, C., & Stanton, J. M. 2006. Examining employee compliance with organizational surveillance and monitoring. Journal of Occupational and Organizational Psychology, 79(2): 245-272.
  97. Stanton, J. M. 2000a. Reactions to employee performance monitoring: Framework, review, and research directions. Human Performance, 13(1): 85-113.
  98. Stanton, J. M. 2000b. Traditional and electronic monitoring from an organizational justice perspective. Journal of Business and Psychology, 15(1): 129-147.
  99. Stanton, J. M., & Barnes-Farrell, J. L. 1996. Effects of electronic performance monitoring on personal control, task satisfaction, and task performance. Journal of Applied Psychology, 81(6): 738-745.
  100. Stanton, J. M., & Weiss, E. M. 2000. Electronic monitoring in their own words: An exploratory study of employees' experiences with new types of surveillance. Computers in Human Behavior, 16(4): 423-440.
  101. Stone, D. L., & Kotch, D. A. 1989. Individuals' attitudes toward organizational drug testing policies and practices. Journal of Applied Psychology, 74(3): 518-521.
  102. Strauss, E., Kristandl, G., & Quinn, M. 2015. The effects of cloud technology on management accounting and decision-making. Management and Financial Accounting Report, 10(6): 1-12.
  103. Sutrop, M., & Laas-Mikko, K. 2012. From identity verification to behavior prediction: Ethical implications of second generation biometrics. Review of Policy Research, 29(1): 21-36.
  104. Tabak, F., & Smith, W. P. 2005. Privacy and electronic monitoring in the workplace: A model of managerial cognition and relational trust development. Employee Responsibilities and Rights Journal, 17(3): 173-189.
  105. Tannenbaum, A. S. 1962. Control in organizations: Individual adjustment and organizational performance. Administrative Science Quarterly, 7(2): 236-257.
  106. Tomczak, D. L., Lanzo, L. A., & Aguinis, H. 2018. Evidence-based recommendations for employee performance monitoring. Business Horizons, 61(2): 251-259.
  107. Turley, R. E., Richardson, W. C., & Hansen, J. V. 1975. Morphological analysis for health care systems planning. Socio-Economic Planning Sciences, 9(2): 83-88.
  108. Vlaar, P. W., Van den Bosch, F. A., & Volberda, H. W. 2007. On the evolution of trust, distrust, and formal coordination and control in interorganizational relationships: Toward an integrative framework. Group & Organization Management, 32(4): 407-428.
  109. von Krogh, G. 2018. Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4): 404-409.
  110. Vuokko, R. 2008. Surveillance at workplace and at home: Social issues in transforming care work with mobile technology. Journal of Information, Communication and Ethics in Society, 6(1): 60-75.
  111. Weber, R. H. 2013. Transborder data transfers: Concepts, regulatory approaches and new legislative initiatives. International Data Privacy Law, 3(2): 117-130.
  112. Weibel, A. 2007. Formal Control and Trustworthiness: Shall the Twain Never Meet? Group & Organization Management, 32(4): 500-517.
  113. Weibel, A., & Six, F. 2013. Trust and control: The role of intrinsic motivation. In R. Bachmann, & A. Zaheer (Eds.), Handbook of Advances in Trust: 57-81. Northampton: Edward Elgar Publishing inc.
  114. Wilding, R., Sharif, A. M., & Irani, Z. 2006. Applying a fuzzy-morphological approach to complexity within management decision making. Management Decision, 44(7): 930-961.
  115. Witzel, A., & Reiter, H. 2012. The problem-centred interview: Principles and practice. London: SAGE Publications Ltd. .
  116. Yin, R. K. 2018. Case study research and applications: Design and methods. Los Angeles: Sage Publications.
  117. Zott, C., & Huy, Q. N. 2007. How entrepreneurs use symbolic management to acquire resources. Administrative Science Quarterly, 52(1): 70-105.
  118. Zweig, D., & Scott, K. 2007. When unfairness matters most: Supervisory violations of electronic monitoring practices. Human Resource Management Journal, 17(3): 227-247.
  119. Zweig, D., & Webster, J. 2002. Where is the line between benign and invasive? An examination of psychological barriers to the acceptance of awareness monitoring systems. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 23(5): 605-633.
  120. Isabel Ebert (isabel.ebert@unisg.ch) is a Ph.D. candidate and Research Associate at the Institute of Business Ethics at the University of St. Gallen, and a visiting researcher at the Oxford Internet Institute, University of Oxford. She holds an M.A. in International Relations, Peace and Conflict Studies from Goethe University of Frankfurt. Her research interests include digital corporate responsibility, business and human rights in the data economy, as well as privacy and sustainability of digital business models. Address: University of St. Gallen, IWE-HSG, Girtannerstrasse 8, 9010 St. Gallen, Switzerland.

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Authors
Schafheitle, S. D., Weibel, A., Ebert, I. L., Kasper, G., Schank, C., & Leicht-Deobald, U.
Affiliations
  • University of St.Gallen (CH)
  • University of Saint Gallen, 27215, Institute for Business Ethics, Sankt Gallen, Switzerland;
ORCID
Venue
Academy of Management Discoveries
Publisher
Academy of Management
Published
2020-05-15
ISSN
2168-1007 (ISSN-L)
DOI
10.5465/amd.2019.0002
OpenAlex
W2993485464
Cited by
61
References
0
Citations by year
2026: 11 · 2025: 13 · 2024: 10 · 2023: 12 · 2022: 8 · 2021: 6 · 2020: 1
FWCI
7.29
Type
journal article
Language
en
Open access
Locations
  • Academy of Management Discoveries (publishedVersion)
  • Alexandria (UniSG) (University of St.Gallen) (acceptedVersion)
  • Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
  • Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Accounting and Organizational Management
Keywords
datafication, organizational control, personnel management, employee monitoring, control configuration, morphological analysis

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