Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-76fb5796d-wq484 Total loading time: 0 Render date: 2024-04-30T00:30:11.266Z Has data issue: false hasContentIssue false

Chapter 6 - Employee Responses to Technological Change

A Retrospective Review

from Part III - Change in Context

Published online by Cambridge University Press:  28 September 2023

By
Katerina Gonzalez  and
Rouven Kanitz
Edited by
Shaul Oreg ,
Alexandra Michel  and
Rune Todnem By
Shaul Oreg
Affiliation:
Hebrew University of Jerusalem
Alexandra Michel
Affiliation:
Universität Heidelberg
Rune Todnem By
Affiliation:
Universitet i Stavanger, Norway
Get access

Summary

The rate of technological change within organizations has been fast-tracked by the recent global health crisis and shifting workplace dynamics. As many organizations decide how to best manage the implementation of new technologies, they must also consider the human response, which can facilitate the success of the overall implementation. Complementing the focus of this book (how is change perceived by recipients?), we focus on what is known about how employees respond to technological changes. In this review, we provide a retrospective account of the body of work on this topic. First, we review the types of technological changes that have been studied in relation to broader dimensions of organizational change. Second, we elaborate on theoretical perspectives that have been used, including comparing technology-specific models with broader theoretical approaches. Third, we summarize the antecedent-response relationships that have been examined. We hope that this bird’s-eye view of the field allows scholars to span disciplines and consider aspects related to the design and type of the technology, which have been largely treated as a setting, in future research.

Keywords

Human-technology interfacePsychology of technological changeTechnology implementationAdoption processesTechnology useTechnology acceptanceTechnology resistanceNew technologyResponses to technological changePerspectives on technology acceptance/resistance
Type
Chapter
Information
The Psychology of Organizational Change
New Insights on the Antecedents and Consequences of Individuals' Responses to Change
 , pp. 120 - 147
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aggarwal, R., Kryscynski, D., Midha, V., & Singh, H. (2015). Early to adopt and early to discontinue: The impact of self-perceived and actual IT knowledge on technology use behaviors of end users. Information Systems Research, 26(1), 127–144.Google Scholar
Ahearne, M., Lam, S. K., Mathieu, J. E., & Bolander, W. (2010). Why are some salespeople better at adapting to organizational change? Journal of Marketing, 74(3), 65–79.Google Scholar
Aiman-Smith, L., & Green, S. G. (2002). Implementing new manufacturing technology: The related effects of technology characteristics and user learning activities. Academy of Management Journal, 45(2), 421–430.Google Scholar
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.CrossRefGoogle Scholar
Alexy, O., Henkel, J., & Wallin, M. W. (2013). From closed to open: Job role changes, individual predispositions, and the adoption of commercial open source software development. Research Policy, 42(8), 1325–1340.CrossRefGoogle Scholar
Armenakis, A. A., & Bedeian, A. G. (1999). Organizational change: A review of theory and research in the 1990s. Journal of Management, 25(3), 293–315.CrossRefGoogle Scholar
Autry, C. W., Grawe, S. J., Daugherty, P. J., & Richey, R. G. (2010). The effects of technological turbulence and breadth on supply chain technology acceptance and adoption. Journal of Operations Management, 28(6), 522–536.Google Scholar
Avgar, A., Tambe, P., & Hitt, L. M. (2018). Built to learn: How work practices affect employee learning during healthcare information technology implementation. MIS Quarterly, 42(2), 645–660.Google Scholar
Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 244–254.CrossRefGoogle Scholar
Bala, H. (2013). The effects of IT-enabled supply chain process change on job and process outcomes: A longitudinal investigation. Journal of Operations Management, 31(6), 450–473.Google Scholar
Bala, H., & Venkatesh, V. (2013). Changes in employees’ job characteristics during an enterprise system implementation: A latent growth modeling perspective. MIS Quarterly, 37(4), 1113–1140.CrossRefGoogle Scholar
Bala, H., & Venkatesh, V. (2016). Adaptation to information technology: A holistic nomological network from implementation to job outcomes. Management Science, 62(1), 156–179.CrossRefGoogle Scholar
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Hoboken, NJ: Prentice Hall.Google Scholar
Beaudry, A., & Pinsonneault, A. (2010). The other side of acceptance: Studying the direct and indirect effects of emotions on information technology use. MIS Quarterly, 34(4), 689–710.CrossRefGoogle Scholar
Bendoly, E. (2014). System dynamics understanding in projects: Information sharing, psychological safety, and performance effects. Production and Operations Management, 23(8), 1352–1369.Google Scholar
Bendoly, E., & Cotteleer, M. J. (2008). Understanding behavioral sources of process variation following enterprise system deployment. Journal of Operations Management, 26(1), 23–44.Google Scholar
Blut, M., Chong, A., Tsiga, Z., & Venkatesh, V. (2021). Meta-analysis of the unified theory of acceptance and use of technology (UTAUT): Challenging its validity and charting a research agenda in the Red Ocean. Journal of the Association for Information Systems, 23(1), 13–95.CrossRefGoogle Scholar
Bostrom, R. P., & Heinen, J. S. (1977). MIS problems and failures: A socio-technical perspective. Part I: The causes. MIS Quarterly, 1(3), 17–32.CrossRefGoogle Scholar
Boudreau, M.-C., & Robey, D. (2005). Enacting integrated information technology: A human agency perspective. Organization Science, 16(1), 3–18.Google Scholar
Brown, S. A., Dennis, A. R., & Venkatesh, V. (2010). Predicting collaboration technology use: Integrating technology adoption and collaboration research. Journal of Management Information Systems, 27(2), 9–54.Google Scholar
Brown, S. A., Venkatesh, V., & Goyal, S. (2014). Expectation confirmation in information systems research: a test of six competing models. MIS Quarterly, 38(3), 729–756.Google Scholar
Brown, S. A., Venkatesh, V., Kuruzovich, J., & Massey, A. P. (2008). Expectation confirmation: An examination of three competing models. Organizational Behavior and Human Decision Processes, 105(1), 52–66.CrossRefGoogle Scholar
Bruque, S., Moyano, J., & Eisenberg, J. (2008). Individual adaptation to IT-induced change: The role of social networks. Journal of Management Information Systems, 25(3), 177–206.Google Scholar
Cadwallader, S., Jarvis, C. B., Bitner, M. J., & Ostrom, A. L. (2010). Frontline employee motivation to participate in service innovation implementation. Journal of the Academy of Marketing Science, 38(2), 219–239.Google Scholar
Chau, P. Y. K., & Hu, P. J. (2002). Examining a model of information technology acceptance by individual professionals: An exploratory study. Journal of Management Information Systems, 18(4), 191–229.CrossRefGoogle Scholar
Chreim, S. (2006). Managerial frames and institutional discourses of change: Employee appropriation and resistance. Organization Studies, 27(9), 1261–1287.CrossRefGoogle Scholar
Cua, K. O., McKone, K. E., & Schroeder, R. G. (2001). Relationships between implementation of TQM, JIT, and TPM and manufacturing performance. Journal of Operations Management, 19(6), 675–694.CrossRefGoogle Scholar
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.Google Scholar
Dawson, P., & Buchanan, D. (2005). The way it really happened: Competing narratives in the political process of technological change. Human Relations, 58(7), 845–865.CrossRefGoogle Scholar
Devaraj, S., Easley, R. F., & Crant, J. M. (2008). Research note: How does personality matter? Relating the five-factor model to technology acceptance and use. Information Systems Research, 19(1), 93–105.CrossRefGoogle Scholar
Edmondson, A. C., Bohmer, R. M., & Pisano, G. P. (2001). Disrupted routines: Team learning and new technology implementation in hospitals. Administrative Science Quarterly, 46(4), 685–716.Google Scholar
Ettlie, J. E., Bridges, W. P., & O’Keefe, R. D. (1984). Organization strategy and structural differences for radical versus incremental innovation. Management Science, 30(6), 682–695.Google Scholar
Festinger, L. (1957). A theory of cognitive dissonance. Evanston, IL: Row Peterson.Google Scholar
Furst, S. A., & Cable, D. M. (2008). Employee resistance to organizational change: Managerial influence tactics and leader-member exchange. Journal of Applied Psychology, 93(2), 453–462.Google Scholar
Gallivan, M. J., Spitler, V. K., & Koufaris, M. (2005). Does information technology training really matter? A social information processing analysis of coworkers’ influence on IT usage in the workplace. Journal of Management Information Systems, 22(1), 153–192.Google Scholar
Gefen, D. R., & Ridings, C. M. (2002). Implementation team responsiveness and user evaluation of customer relationship management: A quasi-experimental design study of social exchange theory. Journal of Management Information Systems, 19(1), 47–69.Google Scholar
Grint, K., & Woolgar, S. (2013). The machine at work: Technology, work and organization. Hoboken, NJ: John Wiley & Sons.Google Scholar
Hackman, J. R., Hackman, R. J., & Oldham, G. R. (1980). Work redesign. Reading, MA: Addison-Wesley.Google Scholar
Hardgrave, B. C., Davis, F. D., & Riemenschneider, C. K. (2003). Investigating determinants of software developers’ intentions to follow methodologies. Journal of Management Information Systems, 20(1), 123–151.Google Scholar
Heracleous, L., & Barrett, M. (2001). Organizational change as discourse: Communicative actions and deep structures in the context of information technology implementation. Academy of Management Journal, 44(4), 755–778.Google Scholar
Homburg, C., Wieseke, J., & Kuehnl, C. (2010). Social influence on salespeople’s adoption of sales technology: A multilevel analysis. Journal of the Academy of Marketing Science, 38(2), 159–168.Google Scholar
Hong, W., Thong, J. Y., Chasalow, L. C., & Dhillon, G. (2011). User acceptance of agile information systems: A model and empirical test. Journal of Management Information Systems, 28(1), 235–272.CrossRefGoogle Scholar
Hsieh, J. P.-A., Rai, A., & Xu, S. X. (2011). Extracting business value from IT: A sensemaking perspective of post-adoptive use. Management Science, 57(11), 2018–2039.CrossRefGoogle Scholar
Hu, P. J., Chau, P. Y., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91–112.CrossRefGoogle Scholar
Kaltiainen, J., Lipponen, J., & Holtz, B. C. (2017). Dynamic interplay between merger process justice and cognitive trust in top management: A longitudinal study. Journal of Applied Psychology, 102(4), 636–647.Google Scholar
Kanitz, R., & Gonzalez, K. (2021). Are we stuck in the predigital age? Embracing technology-mediated change management in organizational change research. The Journal of Applied Behavioral Science, 57(4), 447–458.CrossRefGoogle Scholar
Kaplan, D. M. (ed.). (2009). Readings in the philosophy of technology. Lanham, MD: Rowman & Littlefield Publishers.Google Scholar
Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183–213.Google Scholar
Kelman, H. C. (1958). Compliance, identification, and internalization three processes of attitude change. Journal of Conflict Resolution, 2(1), 51–60.Google Scholar
Kim, H.-W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. MIS Quarterly, 33(3), 567–582.CrossRefGoogle Scholar
Klein, K. J., Conn, A. B., & Sorra, J. S. (2001). Implementing computerized technology: An organizational analysis. Journal of Applied Psychology, 86(5), 811–824.Google Scholar
Landers, R. N., & Marin, S. (2021). Theory and technology in organizational psychology: A review of technology integration paradigms and their effects on the validity of theory. Annual Review of Organizational Psychology and Organizational Behavior, 8, 235–258.Google Scholar
Langley, A. (1999). Strategies for theorizing from process data. Academy of Management Review, 24(4), 691–710.CrossRefGoogle Scholar
Lapointe, L., & Rivard, S. (2005). A multilevel model of resistance to information technology implementation. MIS Quarterly, 29(3), 461–491.Google Scholar
Lapointe, L., & Rivard, S. (2007). A triple take on information system implementation. Organization Science, 18(1), 89–107.CrossRefGoogle Scholar
Lauterbach, J., Mueller, B., & Kahrau, F. (2020). Achieving effective use when digitalizing work: The role of representational complexity. MIS Quarterly, 44(3), 1023–1048.Google Scholar
Lazarus, R. S. (1991). Progress on a cognitive-motivational-relational theory of emotion. American Psychologist, 46(8), 819–834.CrossRefGoogle ScholarPubMed
Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York: Springer.Google Scholar
Leonardi, P. M. (2007). Activating the informational capabilities of information technology for organizational change. Organization Science, 18(5), 813–831.CrossRefGoogle Scholar
Malhotra, Y., & Galletta, D. (2005). A multidimensional commitment model of volitional systems adoption and usage behavior. Journal of Management Information Systems, 22(1), 117–151.CrossRefGoogle Scholar
Marler, J. H., Liang, X., & Dulebohn, J. H. (2006). Training and effective employee information technology use. Journal of Management, 32(5), 721–743.CrossRefGoogle Scholar
Morris, M. G., & Venkatesh, V. (2010). Job characteristics and job satisfaction: Understanding the role of enterprise resource. MIS Quarterly, 34(1), 143–161.CrossRefGoogle Scholar
Nadler, D. A., & Tushman, M. L. (1989). Organizational frame bending: Principles for managing reorientation. Academy of Management Perspectives, 3(3), 194–204.CrossRefGoogle Scholar
Narayanaswamy, R., Grover, V., & Henry, R. M. (2013). The impact of influence tactics in information system development projects: A control-loss perspective. Journal of Management Information Systems, 30(1), 191–226.Google Scholar
Oreg, S., & Sverdlik, N. (2011). Ambivalence toward imposed change: The conflict between dispositional resistance to change and the orientation toward the change agent. Journal of Applied Psychology, 96(2), 337–349.Google Scholar
Oreg, S., Vakola, M., & Armenakis, A. (2011). Change recipients’ reactions to organizational change: A 60-year review of quantitative studies. The Journal of Applied Behavioral Science, 47(4), 461–524.CrossRefGoogle Scholar
Petrou, P., Demerouti, E., & Schaufeli, W. B. (2018). Crafting the change: The role of employee job crafting behaviors for successful organizational change. Journal of Management, 44(5), 1766–1792.Google Scholar
Rafferty, A. E., Jimmieson, N. L., & Armenakis, A. A. (2013). Change readiness: A multilevel review. Journal of Management, 39(1), 110–135.CrossRefGoogle Scholar
Rivard, S., & Lapointe, L. (2012). Information technology implementers’ responses to user resistance: Nature and effects. MIS Quarterly, 36(3), 897–920.Google Scholar
Robey, D., Ross, J. W., & Boudreau, M.-C. (2002). Learning to implement enterprise systems: An exploratory study of the dialectics of change. Journal of Management Information Systems, 19(1), 17–46.Google Scholar
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.Google Scholar
Santhanam, R., Seligman, L., & Kang, D. (2007). Postimplementation knowledge transfers to users and information technology professionals. Journal of Management Information Systems, 24(1), 171–199.CrossRefGoogle Scholar
Seidel, S., Recker, J., & Vom Brocke, J. (2013). Sensemaking and sustainable practicing: Functional affordances of information systems in green transformations. MIS Quarterly, 37(4), 1275–1299.Google Scholar
Stensaker, I., & Falkenberg, J. (2007). Making sense of different responses to corporate change. Human Relations, 60(1), 137–177.Google Scholar
Stevens, M., & van Schaik, J. (2020). Implementing new technologies for complex care: The role of embeddedness factors in team learning. Journal of Operations Management, 66(1–2), 112–134.Google Scholar
Sutanto, J., Kankanhalli, A., Tay, J., Raman, K. S., & Tan, B. C. (2008). Change management in interorganizational systems for the public. Journal of Management Information Systems, 25(3), 133–176.CrossRefGoogle Scholar
Sykes, T. A. (2015). Support structures and their impacts on employee outcomes: A longitudinal field study of an enterprise system implementation. MIS Quarterly, 39(2), 473–496.Google Scholar
Sykes, T. A., Venkatesh, V., & Gosain, S. (2009). Model of acceptance with peer support: A social network perspective to understand employees’ system use. MIS Quarterly, 33(2), 371–393.CrossRefGoogle Scholar
Sykes, T. A., Venkatesh, V., & Johnson, J. L. (2014). Enterprise system implementation and employee job performance: Understanding the role of advice networks. MIS Quarterly, 38(1), 51–72.Google Scholar
Symon, G. (2005). Exploring resistance from a rhetorical perspective. Organization Studies, 26(11), 1641–1663.Google Scholar
Tong, Y., Tan, C.-H., & Teo, H.-H. (2017). Direct and indirect information system use: A multimethod exploration of social power antecedents in healthcare. Information Systems Research, 28(4), 690–710.Google Scholar
Venkatesh, V. (1999). Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Quarterly, 23(2), 239–260.Google Scholar
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.Google Scholar
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.Google Scholar
Venkatesh, V., Bala, H., & Sambamurthy, V. (2016). Implementation of an information and communication technology in a developing country: A multimethod longitudinal study in a bank in India. Information Systems Research, 27(3), 558–579.Google Scholar
Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly, 32(3), 483–502.CrossRefGoogle Scholar
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.Google Scholar
Venkatesh, V., & Goyal, S. (2010). Expectation disconfirmation and technology adoption: Polynomial modeling and response surface analysis. MIS Quarterly, 34(2), 281–303.Google Scholar
Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139.Google Scholar
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.Google Scholar
Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376.Google Scholar
Venkatesh, V., Zhang, X., & Sykes, T. A. (2011). “Doctors do too little technology”: A longitudinal field study of an electronic healthcare system implementation. Information Systems Research, 22(3), 523–546.CrossRefGoogle Scholar
Vroom, V. H. (1964). Work and motivation. Hoboken, NJ: Wiley.Google Scholar
Weick, K. E. (1995). Sensemaking in organizations, vol. 3. Thousand Oaks, CA: Sage.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×