Hostname: page-component-7479d7b7d-qlrfm Total loading time: 0 Render date: 2024-07-11T12:04:23.411Z Has data issue: false hasContentIssue false

How do management factors influence digital adoption in the case of a large-scale digital transformation project – A process perspective

Published online by Cambridge University Press:  06 November 2023

Oliver Kohnke
Affiliation:
Department of Work and Organizational Psychology, University of Mannheim, Mannheim, Germany
Thea Nieland*
Affiliation:
Department of Work and Organizational Psychology, Osnabrueck University, Osnabrück, Germany
Tammo Straatmann
Affiliation:
Department of Work and Organizational Psychology, Osnabrueck University, Osnabrück, Germany
Karsten Mueller
Affiliation:
Department of Work and Organizational Psychology, Osnabrueck University, Osnabrück, Germany
*
Corresponding author: Thea Nieland; Email: thea.nieland@uni-osnabrueck.de

Abstract

Although practitioners and scientists agree that user adoption of new technologies is a key success factor in digital transformations, little is known about how specific management factors are related to user behavior. In particular, the temporal nature of digital transformation projects is largely neglected. Therefore, we propose a systematic, theory-based framework for the management of digital adoption (MDA) and derive specific process-oriented hypotheses for content-, process-, and context-related management factors, their relationships to user adoption, and underlying psychological processes (e.g., performance expectancy or social influence). We applied the MDA framework in the context of a large digital transformation project in a logistics company in a two-wave research design. We tested the process-oriented hypotheses based on latent change score analysis among 1,095 users. The results support the assumption that changes in management factors, largely mediated by changes in the psychological processes, lead to changes in user behavior.

Type
Research Article
Copyright
© The Author(s), 2023. Published by Cambridge University Press in association with the Australian and New Zealand Academy of Management

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

Abbasi, M. S., Tarhini, A., Hassouna, M., & Shah, F. (2015). Social, organizational, demography and individuals’ technology acceptance behaviour: A conceptual model. European Scientific Journal, 11(9), 4876.Google Scholar
Aguinis, H., Edwards, J. R., & Bradley, K. J. (2017). Improving our understanding of moderation and mediation in strategic management research. Organizational Research Methods, 20(4), 665685.10.1177/1094428115627498CrossRefGoogle Scholar
Ain, N., Vaia, G., DeLone, W. H., & Waheed, M. (2019). Two decades of research on business intelligence system adoption, utilization and success – A systematic literature review. Decision Support Systems, 125, .10.1016/j.dss.2019.113113CrossRefGoogle Scholar
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179211.10.1016/0749-5978(91)90020-TCrossRefGoogle Scholar
Amoako-Gyampah, K., & Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41(6), 731745.10.1016/j.im.2003.08.010CrossRefGoogle Scholar
Andriole, S. J., Cox, T., & Khin, K. M. (2018). The innovator’s imperative: Rapid technology adoption for digital transformation. Boca Raton, FL: CRC Press.Google Scholar
Armenakis, A. A., & Bedeian, A. G. (1999). Organizational change: A review of theory and research in the 1990s. Journal of Management, 25(3), 293315.10.1177/014920639902500303CrossRefGoogle Scholar
Armenakis, A. A., & Harris, S. G. (2002). Crafting a change message to create transformational readiness. Journal of Organizational Change Management, 15(2), 169183.10.1108/09534810210423080CrossRefGoogle Scholar
Armenakis, A. A., Harris, S. G., & Mossholder, K. W. (1993). Creating readiness for organizational change. Human Relations, 46(6), 681703.10.1177/001872679304600601CrossRefGoogle Scholar
Ayoko, O. B. (2021). Digital transformation, robotics, artificial intelligence, and innovation. Journal of Management & Organization, 27(5), 831835.10.1017/jmo.2021.64CrossRefGoogle 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), 11131140.10.25300/MISQ/2013/37.4.06CrossRefGoogle Scholar
Bala, H., Venkatesh, V., Ganster, D. C., & Rai, A. (2021). How does an enterprise system implementation change interpersonal relationships in organizations. Industrial Management & Data Systems, 121(8), 18241847.CrossRefGoogle Scholar
Berson, Y., Halevy, N., Shamir, B., & Erez, M. (2015). Leading from different psychological distances: A construal-level perspective on vision communication, goal setting, and follower motivation. The Leadership Quarterly, 26(2), 143155.10.1016/j.leaqua.2014.07.011CrossRefGoogle Scholar
Blut, M., Chong, A. Y. L., Tsigna, Z., & Venkatesh, V. (2022). 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), 1395.10.17705/1jais.00719CrossRefGoogle Scholar
Bouckenooghe, D., Schwarz, G. M., Kanar, A., & Sanders, K. (2021). Revisiting research on attitudes toward organizational change: Bibliometric analysis and content facet analysis. Journal of Business Research, 135, 137148.10.1016/j.jbusres.2021.06.028CrossRefGoogle Scholar
Brynjolfsson, E., & McAfee, A. (2011). Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy. Brynjolfsson and McAfee.Google Scholar
Bueno, S., & Salmeron, J. L. (2008). TAM-based success modeling in ERP. Interacting with Computers, 20(6), 515523.10.1016/j.intcom.2008.08.003CrossRefGoogle Scholar
Bughin, J., Holley, A., & Mellbye, A. (2015). Cracking the digital code: McKinsey global survey results. McKinsey & Company. Retrieved from www.mckinsey.com/business-functions/business-technology/our-insights/cracking-the-digital-code.Google Scholar
Cavalcanti, D. R., Oliveira, T., & de Oliveira Santini, F. (2022). Drivers of digital transformation adoption: A weight and meta-analysis. Heliyon, 8(2), .CrossRefGoogle ScholarPubMed
Chanias, S., Myers, M. D., & Hess, T. (2019). Digital transformation strategy making in pre-digital organizations: The case of a financial services provider. The Journal of Strategic Information Systems, 28(1), 1733.10.1016/j.jsis.2018.11.003CrossRefGoogle Scholar
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), .10.2307/249008CrossRefGoogle Scholar
Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475487.CrossRefGoogle Scholar
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 9821003.CrossRefGoogle Scholar
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 6095.CrossRefGoogle Scholar
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719734.CrossRefGoogle Scholar
Elving, W. J. L. (2005). The role of communication in organisational change. Corporate Communications: An International Journal, 10(2), 129138.CrossRefGoogle Scholar
Fitzgerald, M., Kruschwitz, N., Bonnet, D., & Welch, M. (2014). Embracing digital technology: A new strategic imperative. MIT Sloan Management Review, 55(2), .Google Scholar
Geiser, C., Keller, B. T., Lockhart, G., Eid, M., Cole, D. A., & Koch, T. (2015). Distinguishing state variability from trait change in longitudinal data: The role of measurement (non)invariance in latent state-trait analyses. Behavior Research Methods, 47(1), 172203.CrossRefGoogle ScholarPubMed
Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60(1), 549576.CrossRefGoogle ScholarPubMed
Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2021). A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. Journal of Management Studies 58(5), 11591197.CrossRefGoogle Scholar
Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440465.CrossRefGoogle Scholar
Henk, C. M., & Castro-Schilo, L. (2016). Preliminary detection of relations among dynamic processes with two-occasion data. Structural Equation Modeling: A Multidisciplinary Journal, 23(2), 180193.CrossRefGoogle Scholar
Holt, D. T., Armenakis, A. A., Feild, H. S., & Harris, S. G. (2007). Readiness for organizational change: The systematic development of a scale. The Journal of Applied Behavioral Science, 43(2), 232255.CrossRefGoogle Scholar
Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. M. (1997). Personal computing acceptance factors in small firms: A structural equation model. MIS Quarterly, 21(3), .CrossRefGoogle Scholar
Isiordia, M., & Ferrer, E. (2018). Curve of factors model: A latent growth modeling approach for educational research. Educational and Psychological Measurement, 78(2), 203231.CrossRefGoogle Scholar
Jackson, D. L., Gillaspy, J. A., & Purc-Stephenson, R. (2009). Reporting practices in confirmatory factor analysis: An overview and some recommendations. Psychological Methods, 14(1), 623.CrossRefGoogle ScholarPubMed
Jimmieson, N. L., Peach, M., & White, K. M. (2008). Utilizing the theory of planned behavior to inform change management: An investigation of employee intentions to support organizational change. The Journal of Applied Behavioral Science, 44(2), 237262.CrossRefGoogle Scholar
Jordan, P. J., & Troth, A. C. (2020). Common method bias in applied settings: The dilemma of researching in organizations. Australian Journal of Management, 45(1), 314.CrossRefGoogle Scholar
Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel, Y. (2021). semTools: Useful tools for structural equation modeling. Retrieved from https://CRAN.R-project.org/package=semTools.Google Scholar
Kane, G. C., Phillips, A. N., Copulsky, J. R., & Andrus, G. R. (2019). The technology fallacy: How people are the real key to digital transformation. MIT Press.Google Scholar
Karahanna, E., & Limayem, M. (2000). E-Mail and V-Mail usage: Generalizing across technologies. Journal of Organizational Computing and Electronic Commerce, 10(1), 4966.CrossRefGoogle Scholar
Kim, T. G., Hornung, S., & Rousseau, D. M. (2011). Change-supportive employee behavior: Antecedents and the moderating role of time. Journal of Management, 37(6), 16641693.CrossRefGoogle Scholar
Kohnke, O. (2017). It’s not just about technology: The people side of digitization. In Oswald, G. & Kleinemeier, M. (Eds.), Shaping the digital enterprise (pp. 6991). Cham: Springer International Publishing.Google Scholar
Kohnke, O., Wolf, T. R., & Mueller, K. (2011). Managing user acceptance: An empirical investigation in the context of business intelligence standard software. International Journal of Information Systems and Change Management, 5(4), .CrossRefGoogle Scholar
Kotter, J. P. (2012). Leading change. Boston, Mass: Harvard Business Review Press.Google Scholar
Lance, C. E., Dawson, B., Birkelbach, D., & Hoffman, B. J. (2010). Method effects, measurement error, and substantive conclusions. Organizational Research Methods, 13(3), 435455.CrossRefGoogle Scholar
Langley, A., Smallman, C., Tsoukas, H., & Van de Ven, A. H. (2013). Process studies of change in organization and management: Unveiling temporality, activity, and flow. Academy of Management Journal, 56(1), 113.CrossRefGoogle Scholar
Lee, C., & Wan, G. (2010). Including subjective norm and technology trust in the technology acceptance model: A case of e-ticketing in China. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 41(4), 4051.CrossRefGoogle Scholar
Lin, W.-C., & Tsai, C.-F. (2020). Missing value imputation: A review and analysis of the literature (2006–2017). Artificial Intelligence Review, 53(2), 14871509.CrossRefGoogle Scholar
Liu, L., & Ma, Q. (2006). Perceived system performance: A test of an extended technology acceptance model. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 37(2–3), 5159.CrossRefGoogle Scholar
Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 8195.CrossRefGoogle Scholar
McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60(1), 577605.CrossRefGoogle ScholarPubMed
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404414.CrossRefGoogle Scholar
Oreg, S., & Berson, Y. (2019). Leaders’ impact on organizational change: Bridging theoretical and methodological chasms. Academy of Management Annals, 13(1), 272307.CrossRefGoogle 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), 461524.CrossRefGoogle Scholar
Ployhart, R. E., & Vandenberg, R. J. (2010). Longitudinal research: The theory, design, and analysis of change. Journal of Management, 36(1), 94120.CrossRefGoogle Scholar
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63(1), 539569.CrossRefGoogle ScholarPubMed
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531544.CrossRefGoogle Scholar
Rafferty, A. E., Jimmieson, N. L., & Armenakis, A. A. (2013). Change readiness: A multilevel review. Journal of Management, 39(1), 110135.CrossRefGoogle Scholar
Rafferty, A. E., Jimmieson, N. L., & Restubog, S. L. D. (2013). When leadership meets organizational change: The influence of the top management team and supervisory leaders on change appraisals, change attitudes, and adjustment to change. In Oreg, S., Michel, A., & By, R. T. (Eds.), The psychology of organizational change (pp. 145172). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/.Google Scholar
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2 136).CrossRefGoogle Scholar
Rouibah, K., Hamdy, H. I., & Al‐Enezi, M. Z. (2009). Effect of management support, training, and user involvement on system usage and satisfaction in Kuwait. Industrial Management & Data Systems, 109(3), 338356.CrossRefGoogle Scholar
Saghafian, M., Laumann, K., & Skogstad, M. R. (2021). Stagewise overview of issues influencing organizational technology adoption and use. Frontiers in Psychology, 12, .CrossRefGoogle ScholarPubMed
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90103.CrossRefGoogle Scholar
Schepers, J., Wetzels, M., & de Ruyter, K. (2005). Leadership styles in technology acceptance: Do followers practice what leaders preach? Managing Service Quality: An International Journal, 15(6), 496508.CrossRefGoogle Scholar
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research, 8(2), 2374.Google Scholar
Schillewaert, N., Ahearne, M. J., Frambach, R. T., & Moenaert, R. K. (2005). The adoption of information technology in the sales force. Industrial Marketing Management, 34(4), 323336.CrossRefGoogle Scholar
Sonnentag, S. (2012). Time in organizational research: Catching up on a long neglected topic in order to improve theory. Organizational Psychology Review, 2(4), 361368.CrossRefGoogle Scholar
Straatmann, T., Kohnke, O., Hattrup, K., & Mueller, K. (2016). Assessing employees’ reactions to organizational change: An integrative framework of change-specific and psychological factors. The Journal of Applied Behavioral Science, 52(3), 265295.CrossRefGoogle Scholar
Sun, Y., Bhattacherjee, A., & Ma, Q. (2009). Extending technology usage to work settings: The role of perceived work compatibility in ERP implementation. Information & Management, 46(6), 351356.CrossRefGoogle Scholar
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144176.CrossRefGoogle 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), 342365.CrossRefGoogle Scholar
Venkatesh, V. (2006). Where to go from here? Thoughts on future directions for research on individual-level technology adoption with a focus on decision making. Decision Sciences, 37(4), 497518.CrossRefGoogle Scholar
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273315.CrossRefGoogle Scholar
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186204.CrossRefGoogle 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), .CrossRefGoogle Scholar
Venkatesh, V., Sykes, T. A., Aljafari, R., & Poole, M. S. (2021). The future is now: Calling for a focus on temporal issues in information system research. Industrial Management & Data Systems, 121(1), 3047.CrossRefGoogle Scholar
Venkatesh, V., Thong, J., & 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), 328376.CrossRefGoogle Scholar
Walker, H. J., Armenakis, A. A., & Bernerth, J. B. (2007). Factors influencing organizational change efforts: An integrative investigation of change content, context, process and individual differences. Journal of Organizational Change Management, 20(6), 761773.CrossRefGoogle Scholar
Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Boston, MA: Harvard Business Review Press.Google Scholar
Widaman, K. F., Ferrer, E., & Conger, R. D. (2010). Factorial invariance within longitudinal structural equation models: Measuring the same construct across time. Child Development Perspectives, 4(1), 1018.CrossRefGoogle ScholarPubMed
Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85102.CrossRefGoogle Scholar
World Economic Forum. (2020). Digital transformation: Powering the great reset. Retrieved from https://www3.weforum.org/docs/WEF_Digital_Transformation_Powering_the_Great_Reset_2020.pdf.Google Scholar
Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350363.CrossRefGoogle Scholar
Zheng, Z. (Eric), Pavlou, P. A., & Gu, B. (2014). Latent growth modeling for information systems: Theoretical extensions and practical applications. Information Systems Research, 25(3), 547568.CrossRefGoogle Scholar
Zobell, S. (2018). Why digital transformations fail: Closing the $900 billion hole in enterprise strategy. Forbes.Google Scholar