The differential impacts of top management support and transformational supervisory leadership on employees’ digital performance

References

• Accenture. (2015). Realizing digital transformation: Adapt to future work, stimulate employees’ enthusiasm. Online Available: https://www.accenture.com/documents.
   Google Scholar
• Agarwal, R., Sambamurthy, V., & Stair, R. M. (2000). The evolving relationship between general and specific computer self-efficacy—an empirical assessment. Information Systems Research, 11(4), 418–430. https://doi.org/10.1287/isre.11.4.418.11876
   Web of Science ®Google Scholar
• Ahuja, M. K., & Thatcher, J. B. (2005). Moving beyond intentions and toward the theory of trying: Effects of work environment and gender on post-adoption information technology use. MIS Quarterly, 29(3), 427–459. https://doi.org/10.2307/25148691
   Web of Science ®Google Scholar
• Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.
   Google Scholar
• Amabile, T. M. (1988). A model of creativity and innovation in organizations. Research in Organizational Behavior, 10(1), 123–167.
   Google Scholar
• Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
   Web of Science ®Google Scholar
• Armstrong, C. P., & Sambamurthy, V. (1999). Information technology assimilation in firms: The influence of senior leadership and IT infrastructures. Information Systems Research, 10(4), 304–327. https://doi.org/10.1287/isre.10.4.304
   Web of Science ®Google Scholar
• Bandura, A. (1978). Self-efficacy: Toward a unifying theory of behavioral change. Advances in Behaviour Research and Therapy, 1(4), 139–161. https://doi.org/10.1016/0146-6402(78)90002-4
   Google Scholar
• Bandura, A., & Locke, E. A. (2003). Negative self-efficacy and goal effects revisited. The Journal of Applied Psychology, 88(1), 87–99. https://doi.org/10.1037/0021-9010.88.1.87
   PubMed Web of Science ®Google Scholar
• Barrett, M., Davidson, E., Prabhu, J., & Vargo, S. L. (2015). Service innovation in the digital age: Key contributions and future directions. MIS Quarterly, 39(1), 135–154. https://doi.org/10.25300/MISQ/2015/39:1.03
   Web of Science ®Google Scholar
• Bass, B. M. (1985). Leadership and performance beyond expectations. The Free Press.
   Google Scholar
• Bass, B. M., & Avolio, B. J. (2000). MLQ, Multifactor Leadership Questionnaire sampler set: Technical report, leader form, rater form, and scoring key for MLQ form 5x-short. Mind Garden Inc.
   Google Scholar
• Bass, B. M., & Riggio, R. E. (2006). Transformational leadership. Psychology press.
   Google Scholar
• Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory is research. Information & Management, 57(2), 103168. https://doi.org/10.1016/j.im.2019.05.003
   Web of Science ®Google Scholar
• Benitez, J., Ruiz, L., Castillo, A., & Llorens, J. (2020). How corporate social responsibility activities influence employer reputation: The role of social media capability. Decision Support Systems, 129(1), 113223. https://doi.org/10.1016/j.dss.2019.113223
   Google Scholar
• Bono, J. E., Foldes, H. J., Vinson, G., & Muros, J. P. (2007). Workplace emotions: The role of supervision and leadership. The Journal of Applied Psychology, 92(5), 1357–1367. https://doi.org/10.1037/0021-9010.92.5.1357
   PubMed Web of Science ®Google Scholar
• Boynton, A. C., Zmud, R. W., & Jacobs, G. C. (1994). The influence of IT management practice on IT use in large organizations. MIS Quarterly, 18(3), 299–318. https://doi.org/10.2307/249620
   Web of Science ®Google Scholar
• Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
   Web of Science ®Google Scholar
• Burton-Jones, A., & Grange, C. (2013). From use to effective use: A representation theory perspective. Information Systems Research, 24(3), 632–658. https://doi.org/10.1287/isre.1120.0444
   Web of Science ®Google Scholar
• Burton-Jones, A., & Straub, D. W., Jr. (2006). Reconceptualizing system usage: An approach and empirical test. Information Systems Research, 17(3), 228–246. https://doi.org/10.1287/isre.1060.0096
   Web of Science ®Google Scholar
• Cao, G., & Duan, Y. (2014). A path model linking business analytics, data-driven culture, and competitive advantage. The Twenty Second European Conference on Information Systems, Tel Aviv, Israel, Israil.
   Google Scholar
• Chatterjee, D., Grewal, R., & Sambamurthy, V. (2002). Shaping up for e-commerce: Institutional enablers of the organizational assimilation of web technologies. MIS Quarterly, 26(2), 65–89. https://doi.org/10.2307/4132321
   Web of Science ®Google Scholar
• Chen, F., Curran, P. J., Bollen, K. A., Kirby, J., & Paxton, P. (2008). An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models. Sociological Methods & Research, 36(4), 462–494. https://doi.org/10.1177/0049124108314720
   PubMed Web of Science ®Google Scholar
• Chen, G., Farh, J.-L., Campbell-Bush, E. M., Wu, Z., & Wu, X. (2013). Teams as innovative systems: Multilevel motivational antecedents of innovation in R&D teams. The Journal of Applied Psychology, 98(6), 1018–1027. https://doi.org/10.1037/a0032663
   PubMed Web of Science ®Google Scholar
• Chen, A., & Karahanna, E. (2018). Life interrupted: The effects of technology-mediated work interruptions on work and nonwork outcomes. MIS Quarterly, 42(4), 1023–1042.
   Web of Science ®Google Scholar
• Chen, R. R., Ou, C. X., Wang, W., Peng, Z., & Davison, R. M. (2020). Moving beyond the direct impact of using CRM systems on frontline employees’ service performance: The mediating role of adaptive behaviour. Information Systems Journal, 30(3), 458–491. https://doi.org/10.1111/isj.12265
   Web of Science ®Google Scholar
• Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39. https://doi.org/10.1080/07421222.2015.1138364
   Web of Science ®Google Scholar
• Cheung, M. W. L. (2007). Comparison of Approaches to Constructing Confidence Intervals for Mediating Effects Using Structural Equation Models. Structural Equation Modeling, 14(2), 227–246. https://doi.org/10.1080/10705510709336745
   Web of Science ®Google Scholar
• Choe, J.-M. (1996). The relationships among performance of accounting information systems, influence factors, and evolution level of information systems. Journal of Management Information Systems, 12(4), 215–239. https://doi.org/10.1080/07421222.1996.11518107
   Google Scholar
• Choi, J. N., & Chang, J. Y. (2009). Innovation implementation in the public sector: An integration of institutional and collective dynamics. The Journal of Applied Psychology, 94(1), 245–253. https://doi.org/10.1037/a0012994
   PubMed Web of Science ®Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates.
   Google Scholar
• Colbert, A., Yee, N., & George, G. (2016). The digital workforce and the workplace of the future. Academy of Management Journal, 59(3), 731–739. https://doi.org/10.5465/amj.2016.4003
   Web of Science ®Google Scholar
• Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
   Web of Science ®Google Scholar
• Cooper, R. B., & Zmud, R. W. (1990). Information technology implementation research: A technological diffusion approach. Management science, 36(2), 123–139. https://doi.org/10.1287/mnsc.36.2.123
   Web of Science ®Google Scholar
• Côrte-Real, N., Ruivo, P., & Oliveira, T. (2020). Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value? Information & Management, 57(1), 103141. https://doi.org/10.1016/j.im.2019.01.003
   Web of Science ®Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• Doll, W. J., Xia, W., & Torkzadeh, G. A. (1994). Confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18(4), 453–461. https://doi.org/10.2307/249524
   Web of Science ®Google Scholar
• Dremel, C., Wulf, J., Herterich, M. M., Waizmann, J.-C., & Brenner, W. (2017). How AUDI AG established big data analytics in its digital transformation. MIS Quarterly Executive, 16(2), 81–100.
   Web of Science ®Google Scholar
• Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource‐based view and big data culture. British Journal of Management, 30(2), 341–361. https://doi.org/10.1111/1467-8551.12355
   Web of Science ®Google Scholar
• Elbanna, A. (2013). Top management support in multiple-project environments: An in-practice view. European Journal of Information Systems, 22(3), 278–294. https://doi.org/10.1057/ejis.2012.16
   Web of Science ®Google Scholar
• Essex, P. A., Magal, S. R., & Masteller, D. E. (1998). Determinants of information center success. Journal of Management Information Systems, 15(2), 95–117. https://doi.org/10.1080/07421222.1998.11518210
   Google Scholar
• Fichman, R. G., Dos Santos, B. L., & Zheng, Z. (2014). Digital innovation as a fundamental and powerful concept in the information systems curriculum. MIS Quarterly, 38(2), 329–353. https://doi.org/10.25300/MISQ/2014/38.2.01
   Web of Science ®Google Scholar
• Fichman, R. G., Nambisan, S., & Halpern, M. (2014). Configurational thinking and value creation from digital innovation: The case of product lifecycle management implementation. In F. Rowe & D. Te’eni (Eds.), Innovation and IT in an International Context (pp. 115–139). Palgrave macmillan.
   Google Scholar
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An Introduction to theory and research. Addison-Wesley.
   Google Scholar
• Fitzgerald, M., Kruschwitz, N., Bonnet, D., & Welch, M. (2014). Embracing digital technology: A new strategic imperative. MIT Sloan Management Review, 55(2), 1–12.
   Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
   Web of Science ®Google Scholar
• Frieder, R. E., Wang, G., & Oh, I.-S. (2018). Linking job-relevant personality traits, transformational leadership, and job performance via perceived meaningfulness at work: A moderated mediation model. The Journal of Applied Psychology, 103(3), 324–333. https://doi.org/10.1037/apl0000274
   PubMed Web of Science ®Google Scholar
• Grant, A. M. (2012). Leading with meaning: Beneficiary contact, prosocial impact, and the performance effects of transformational leadership. Academy of Management Journal, 55(2), 458–476. https://doi.org/10.5465/amj.2010.0588
   Web of Science ®Google Scholar
• Grover, V., Chiang, R. H., Liang, T.-P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), 388–423. https://doi.org/10.1080/07421222.2018.1451951
   Web of Science ®Google Scholar
• 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 Systems, 26(3), 191–209. https://doi.org/10.1016/j.jsis.2017.07.003
   Web of Science ®Google Scholar
• Gupta, S. (2018). Driving digital strategy: A guide to reimagining your business. Harvard Business Press.
   Google Scholar
• Gupta, G., & Bose, I. (2019). Digital transformation in entrepreneurial firms through information exchange with operating environment. Information & Management, 59(3), 103243. https://doi.org/10.1016/j.im.2019.103243
   Google Scholar
• Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.004
   Web of Science ®Google Scholar
• Gupta, M., George, J. F., & Xia, W. (2019). Relationships between IT department culture and agile software development practices: An empirical investigation. International Journal of Information Management, 44, 13–24. https://doi.org/10.1016/j.ijinfomgt.2018.09.006
   Web of Science ®Google Scholar
• Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.
   Web of Science ®Google Scholar
• Hanelt, A., Firk, S., Hildebrandt, B., & Kolbe, L. M. (2021). Digital M&A, digital innovation, and firm performance: An empirical investigation. European Journal of Information Systems, 31(1), 3–26. https://doi.org/10.1080/0960085X.2020.1747365
   Google Scholar
• Hannah, S. T., Schaubroeck, J. M., & Peng, A. C. (2016). Transforming followers’ value internalization and role self-efficacy: Dual processes promoting performance and peer norm-enforcement. The Journal of Applied Psychology, 101(2), 252–266. https://doi.org/10.1037/apl0000038
   PubMed Web of Science ®Google Scholar
• Hartog, D. N. D., & Belschak, F. D. (2012). When does transformational leadership enhance employee proactive behavior? The role of autonomy and role breadth self-efficacy. The Journal of Applied Psychology, 97(1), 194–202. https://doi.org/10.1037/a0024903
   PubMed Web of Science ®Google Scholar
• Hart, C. M., Ritchie, T. D., Hepper, E. G., & Gebauer, J. E. (2015). The balanced inventory of desirable responding short form (BIDR-16). Sage Open, 5(4), 1–9. https://doi.org/10.1177/2158244015621113
   Google Scholar
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8
   Web of Science ®Google Scholar
• Hess, T., Matt, C., Benlian, A., & Wiesböck, F. (2016). Options for formulating a digital transformation strategy. MIS Quarterly Executive, 15(2), 123–139.
   Web of Science ®Google Scholar
• Hill, T., Smith, N. D., & Mann, M. F. (1987). Role of efficacy expectations in predicting the decision to use advanced technologies: The case of computers. The Journal of Applied Psychology, 72(2), 307–313. https://doi.org/10.1037/0021-9010.72.2.307
   Web of Science ®Google Scholar
• Hoffman, B. J., Bynum, B. H., Piccolo, R. F., & Sutton, A. W. (2011). Person-organization value congruence: How transformational leaders influence work group effectiveness. Academy of Management Journal, 54(4), 779–796. https://doi.org/10.5465/amj.2011.64870139
   Web of Science ®Google 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. https://doi.org/10.1287/mnsc.1110.1398
   Web of Science ®Google Scholar
• Huang, P., Tafti, A. R., & Mithas, S. (2017). Platform sponsor’s investments and user contributions in knowledge communities: The role of knowledge seeding. Forthcoming in MIS Quarterly, Robert H. Smith School Research Paper No. RHS, 2913581.
   Google Scholar
• Hu, L.-T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037/1082-989X.3.4.424
   Web of Science ®Google Scholar
• Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of Management Information Systems, 11(4), 87–114. https://doi.org/10.1080/07421222.1995.11518061
   Google Scholar
• Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. (1997). Personal computing acceptance factors in small firms: A structural equation model. MIS Quarterly, 21(3), 279–305. https://doi.org/10.2307/249498
   Web of Science ®Google Scholar
• International Data Corporation. (2019). IDC reveals 2020 worldwide digital transformation predictions. Retrieved from https://www.idc.com/getdoc.jsp?containerId=prUS45617519
   Google Scholar
• James, T. L., Wallace, L., & Deane, J. K. (2019). Using organismic integration theory to explore the associations between users’ exercise motivations and fitness technology feature set use. MIS Quarterly, 43(1), 287–312. https://doi.org/10.25300/MISQ/2019/14128
   Web of Science ®Google Scholar
• Janssen, O. (2001). Fairness perceptions as a moderator in the curvilinear relationships between job demands, and job performance and job satisfaction. Academy of Management Journal, 44(5), 1039–1050. https://doi.org/10.2307/3069447
   Web of Science ®Google Scholar
• Jarvenpaa, S. L., & Ives, B. (1991). Executive involvement and participation in the management of information technology. MIS Quarterly, 15(2), 205–227. https://doi.org/10.2307/249382
   Web of Science ®Google Scholar
• Judge, T. A., Bono, J. E., & Locke, E. A. (2000). Personality and job satisfaction: The mediating role of job characteristics. The Journal of Applied Psychology, 85(2), 237–249. https://doi.org/10.1037/0021-9010.85.2.237
   PubMed Web of Science ®Google Scholar
• Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2015). Strategy, not technology, drives digital transformation. MIT Sloan Management Review and Deloitte University Press, 14(July), 1–25.
   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. https://doi.org/10.2307/249751
   Web of Science ®Google Scholar
• Kark, R., Katz-Navon, T., & Delegach, M. (2015). The dual effects of leading for safety: The mediating role of employee regulatory focus. The Journal of Applied Psychology, 100(5), 1332–1348. https://doi.org/10.1037/a0038818
   PubMed Web of Science ®Google Scholar
• Ke, W., & Wei, K. K. (2008). Organizational culture and leadership in ERP implementation. Decision Support Systems, 45(2), 208–218. https://doi.org/10.1016/j.dss.2007.02.002
   Web of Science ®Google Scholar
• Kiron, D., Prentice, P. K., & Ferguson, R. B. (2012). Innovating with analytics. MIT Sloan Management Review, 54(1), 47–52.
   Web of Science ®Google Scholar
• Kiron, D., & Shockley, R. (2011). Creating business value with analytics. MIT Sloan Management Review, 53(1), 57–66.
   Web of Science ®Google Scholar
• Klein, K. J., Conn, A. B., & Sorra, J. S. (2001). Implementing computerized technology: An organizational analysis. The Journal of Applied Psychology, 86(5), 811–824. https://doi.org/10.1037/0021-9010.86.5.811
   PubMed Web of Science ®Google Scholar
• Kline, R. B. (1998). Principles and Practice of Structural Equation Modeling. Guilford Press.
   Google Scholar
• Kuegler, M., Smolnik, S., & Kane, G. (2015). What’s in IT for employees? Understanding the relationship between use and performance in enterprise social software. The Journal of Strategic Information Systems, 24(2), 90–112. https://doi.org/10.1016/j.jsis.2015.04.001
   Web of Science ®Google Scholar
• Kwak, D.-H., Holtkamp, P., & Kim, S. S. (2019). Measuring and controlling social desirability bias: Applications in information systems research. Journal of the Association for Information Systems, 20(4), 317–345. https://doi.org/10.17705/1jais.00537
   Web of Science ®Google Scholar
• Lai, V. S., Lai, F., & Lowry, P. B. (2016). Technology evaluation and imitation: Do they have differential or dichotomous effects on ERP adoption and assimilation in China? Journal of Management Information Systems, 33(4), 1209–1251. https://doi.org/10.1080/07421222.2016.1267534
   Web of Science ®Google Scholar
• Lee, K. C. (2013). Digital creativity: Individuals, groups, and organizations. Springer.
   Google Scholar
• Lehmann‐willenbrock, N., Grohmann, A., & Kauffeld, S. (2013). Promoting multifoci citizenship behavior: Time‐lagged effects of procedural justice, trust, and commitment. Applied Psychology, 62(3), 454–485. https://doi.org/10.1111/j.1464-0597.2012.00488.x
   Web of Science ®Google Scholar
• Lehrer, C., Wieneke, A., Vom Brocke, J., Jung, R., & Seidel, S. (2018). How big data analytics enables service innovation: Materiality, affordance, and the individualization of service. Journal of Management Information Systems, 35(2), 424–460. https://doi.org/10.1080/07421222.2018.1451953
   Web of Science ®Google Scholar
• Leidner, D. E., & Kayworth, T. (2006). Review: A review of culture in information systems research: Toward a theory of information technology culture conflict. MIS Quarterly, 30(2), 357–399. https://doi.org/10.2307/25148735
   Web of Science ®Google Scholar
• Leonard-Barton, D., & Deschamps, I. (1988). Managerial influence in the implementation of new technology. Management science, 34(10), 1252–1265. https://doi.org/10.1287/mnsc.34.10.1252
   Web of Science ®Google Scholar
• Lewin, K. (1958). Group Decision and Social Change. In E. E. Maccoby, T. M. Newcomb, & E. L. Hartley (Eds.), Readings in social psychology. Holt, Rinehart and Winston, Inc, (pp.197–211).
   Google Scholar
• Lewis, W., Agarwal, R., & Sambamurthy, V. (2003). Sources of influence on beliefs about information technology use: An empirical study of knowledge workers. MIS Quarterly, 27(4), 657–678. https://doi.org/10.2307/30036552
   Web of Science ®Google Scholar
• Liang, H., Saraf, N., Hu, Q., & Xue, Y. (2007). Assimilation of enterprise systems: The effect of institutional pressures and the mediating role of top management. MIS Quarterly, 31(1), 59–87. https://doi.org/10.2307/25148781
   Web of Science ®Google Scholar
• Li, X., & Hsieh, J.P.-A. (2007). Impact of transformational leadership on system exploration in the mandatory organizational context. Twenty Eighth International Conference on Information Systems, Montreal, Canada: Association for Information Systems.
   Google Scholar
• Li, X., Hsieh, J.P.-A, & Rai, A. (2013). Motivational differences across post-acceptance information system usage behaviors: An investigation in the business intelligence systems context. Information Systems Research, 24(3), 659–682. https://doi.org/10.1287/isre.1120.0456
   Web of Science ®Google Scholar
• Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. The Journal of Applied Psychology, 86(1), 114–121. https://doi.org/10.1037/0021-9010.86.1.114
   PubMed Web of Science ®Google Scholar
• MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7(1), 83. https://doi.org/10.1037/1082-989X.7.1.83
   PubMed Web of Science ®Google Scholar
• Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2015). Giving too much social support: Social overload on social networking sites. European Journal of Information Systems, 24(5), 447–464. https://doi.org/10.1057/ejis.2014.3
   Web of Science ®Google Scholar
• Maier, J. L., Rainer, R. K., Jr., & Snyder, C. A. (1997). Environmental scanning for information technology: An empirical investigation. Journal of Management Information Systems, 14(2), 177–200. https://doi.org/10.1080/07421222.1997.11518170
   Google Scholar
• Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in is research: A comparison of alternative approaches and a reanalysis of past research. Management science, 52(12), 1865–1883. https://doi.org/10.1287/mnsc.1060.0597
   Web of Science ®Google Scholar
• Mandviwalla, M., & Flanagan, R. (2021). Small business digital transformation in the context of the pandemic. European Journal of Information Systems, 30(4), 359–375. forthcoming. https://doi.org/10.1080/0960085X.2021.1891004
   Web of Science ®Google Scholar
• Marchand, D. A., & Peppard, J. (2013). Why IT fumbles analytics. Harvard Business Review, 91(1), 104–112.
   Web of Science ®Google Scholar
• McFarland, L. A., & Ployhart, R. E. (2015). Social media: A contextual framework to guide research and practice. The Journal of Applied Psychology, 100(6), 1653–1677. https://doi.org/10.1037/a0039244
   PubMed Web of Science ®Google Scholar
• Mikalef, P., & Krogstie, J. (2020). Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities. European Journal of Information Systems, 29(3), 260–287. https://doi.org/10.1080/0960085X.2020.1740618
   Web of Science ®Google Scholar
• Moores, T. T., & Chang, J.C.J. (2009). Self-efficacy, overconfidence, and the negative effect on subsequent performance: A field study. Information & Management, 46(2), 69–76. https://doi.org/10.1016/j.im.2008.11.006
   Web of Science ®Google Scholar
• Morris, M. G., & Venkatesh, V. (2010). Job characteristics and job satisfaction: Understanding the role of enterprise resource planning system implementation. MIS Quarterly, 34(1), 143–161. https://doi.org/10.2307/20721418
   Web of Science ®Google Scholar
• Muthén, L. K., & Muthén, B. O. (2011). Mplus user’s guide (6^th). Muthén and Muthén.
   Google Scholar
• Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2017). Digital Innovation Management: Reinventing innovation management research in a digital world. MIS Quarterly, 41(1), 223–238. https://doi.org/10.25300/MISQ/2017/41:1.03
   Web of Science ®Google Scholar
• Nunnally, J. C. (1994). Psychometric theory 3E. Tata McGraw-Hill Education.
   Google Scholar
• Ogbanufe, O., & Gerhart, N. (2020). The mediating influence of smartwatch identity on deep use and innovative individual performance. Information Systems Journal, 30(6), 977–1009. https://doi.org/10.1111/isj.12288
   Web of Science ®Google Scholar
• Orlikowski, W. J. (1992). The duality of technology: Rethinking the concept of technology in organizations. Organization Science, 3(3), 398–427. https://doi.org/10.1287/orsc.3.3.398
   Web of Science ®Google Scholar
• Orlikowski, W. J., Yates, J., Okamura, K., & Fujimoto, M. (1995). Shaping electronic communication: The metastructuring of technology in the context of use. Organization Science, 6(4), 423–444. https://doi.org/10.1287/orsc.6.4.423
   Web of Science ®Google Scholar
• Paulhus, D. L. (1984). Two-component models of socially desirable responding. Journal of Personality and Social Psychology, 46(3), 598. https://doi.org/10.1037/0022-3514.46.3.598
   Web of Science ®Google Scholar
• Peng, Z., & Guo, X. (2019). A multilevel investigation on antecedents for employees’ exploration of enterprise systems. European Journal of Information Systems, 28(4), 439–456. https://doi.org/10.1080/0960085X.2019.1589964
   Web of Science ®Google Scholar
• Petter, S., DeLone, W., & McLean, E. R. (2013). Information systems success: The quest for the independent variables. Journal of Management Information Systems, 29(4), 7–62. https://doi.org/10.2753/MIS0742-1222290401
   Web of Science ®Google Scholar
• Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 31(4), 623–656. https://doi.org/10.2307/25148814
   Web of Science ®Google Scholar
• Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
   PubMed Web of Science ®Google Scholar
• Porter, M. E., & Heppelmann, J. E. (2015). How smart, connected products are transforming companies. Harvard Business Review, 93(10), 96–114.
   Web of Science ®Google Scholar
• Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.
   PubMed Web of Science ®Google Scholar
• Purvis, R. L., Sambamurthy, V., & Zmud, R. W. (2001). The assimilation of knowledge platforms in organizations: An empirical investigation. Organization Science, 12(2), 117–135.
   Web of Science ®Google Scholar
• Ragu-Nathan, B. S., Apigian, C. H., Ragu-Nathan, T., & Tu, Q. (2004). A path analytic study of the effect of top management support for information systems performance. Omega, 32(6), 459–471.
   Web of Science ®Google Scholar
• Rai, A., Brown, P., & Tang, X. (2009). Organizational assimilation of electronic procurement innovations. Journal of Management Information Systems, 26(1), 257–296.
   Web of Science ®Google Scholar
• Ross, J. W., Beath, C. M., & Quaadgras, A. (2013). You may not need big data after all. Harvard Business Review, 91(12), 90–98.
   PubMed Web of Science ®Google Scholar
• Saga, V., & Zmud, R. (1994). The nature and determinants of IT acceptance, routinization, and infusion. In, L. Levine, (Ed.), Proceedings of the IFIP TC8 Working Conference on Diffusion, Transfer and Implementation of Information Technology, New York, 67–86.
   Google Scholar
• Saldanha, T. (2019). Why digital transformations fail: The surprising disciplines of how to take off and stay ahead. Berrett-Koehler Publishers.
   Google Scholar
• Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197–211.
   Web of Science ®Google Scholar
• Schaubroeck, J., Lam, S. S., & Peng, A. C. (2011). Cognition-based and affect-based trust as mediators of leader behavior influences on team performance. The Journal of Applied Psychology, 96(4), 863–871.
   PubMed Web of Science ®Google Scholar
• Schmitz, K. W., Teng, J. T., & Webb, K. J. (2016). Capturing the complexity of malleable IT use: Adaptive structuration theory for individuals. MIS Quarterly, 40(3), 663–686.
   Web of Science ®Google Scholar
• Scott, S. G., & Bruce, R. A. (1994). Determinants of innovative behavior: A path model of individual innovation in the workplace. Academy of Management Journal, 37(3), 580–607.
   Web of Science ®Google Scholar
• Shah, S., Horne, A., & Capellá, J. (2012). Good data won’t guarantee good decisions. Harvard Business Review, 90(4), 23–25.
   PubMed Web of Science ®Google Scholar
• Shamim, S., Zeng, J., Khan, Z., & Zia, N. U. (2020). Big data analytics capability and decision making performance in emerging market firms: The role of contractual and relational governance mechanisms. Technological Forecasting and Social Change, 161(12), 120315.
   Web of Science ®Google Scholar
• Shamir, B., House, R. J., & Arthur, M. B. (1993). The motivational effects of charismatic leadership: A self-concept based theory. Organization Science, 4(4), 577–594.
   Web of Science ®Google Scholar
• Shao, Z. (2019). Impact mechanism of direct supervisor’s leadership behaviors on employees’ extended use of information technologies. Journal of Enterprise Information Management, 32(4), 626–645.
   Web of Science ®Google Scholar
• Shao, Z., Benitez, J., Zhang, J., Zheng, H., & Ajamieh, A. (2022). Antecedents and performance outcomes of employees’ data analytics skills: An adaptation structuration theory-based empirical investigation. European Journal of Information Systems, 1–20.
   Web of Science ®Google Scholar
• Shao, Z., Feng, Y., & Hu, Q. (2016). Effectiveness of top management support in enterprise systems success: A contingency perspective of fit between leadership style and system life-cycle. European Journal of Information Systems, 25(2), 131–153.
   Web of Science ®Google Scholar
• Shao, Z., Feng, Y., & Hu, Q. (2017). Impact of top management leadership styles on ERP assimilation and the role of organizational learning. Information & Management, 54(7), 902–919.
   Web of Science ®Google Scholar
• Shao, Z., & Li, X. (2022). The influences of three task characteristics on innovative use of malleable it: An extension of adaptive structuration theory for individuals. Information & Management, 59(3), 103597.
   Web of Science ®Google Scholar
• Sharma, R., & Yetton, P. (2003). The contingent effects of management support and task interdependence on successful information systems implementation. MIS Quarterly, 27(4), 533–556.
   Web of Science ®Google Scholar
• Singh, D., Pattnaik, C., Lee, J. Y., & Gaur, A. S. (2019). Subsidiary staffing, cultural friction, and subsidiary performance: Evidence from Korean subsidiaries in 63 countries. Human Resource Management, 58(2), 219–234.
   Web of Science ®Google Scholar
• Soluk, J., & Kammerlander, N. (2021). Digital transformation in family owned Mittelstand firms: A dynamic capabilities perspective. European Journal of Information Systems, forthcoming. https://doi.org/10.1080/0960085X.2020.1857666
   Web of Science ®Google Scholar
• Stajkovic, A. D., & Luthans, F. (1998). Self-efficacy and work-related performance: A meta-analysis. Psychological Bulletin, 124(2), 240–261.
   Web of Science ®Google Scholar
• Suseno, Y., Laurell, C., & Sick, N. (2018). Assessing value creation in digital innovation ecosystems: A Social Media Analytics approach. The Journal of Strategic Information Systems, 27(4), 335–349.
   Web of Science ®Google Scholar
• Sykes, T. A., & Venkatesh, V. (2017). Explaining post-implementation employee system use and job performance: Impacts of the content and source of social network ties. MIS Quarterly, 41(3), 917–936.
   Web of Science ®Google Scholar
• Taylor, A. B., Mackinnon, D. P., & Tein, J. Y. (2008). Tests of the Three-Path Mediated Effect. Organizational Research Methods, 11(2), 241–269.
   Web of Science ®Google Scholar
• Tepper, B. J., Dimotakis, N., Lambert, L. S., Koopman, J., Matta, F. K., Man Park, H., & Goo, W. (2018). Examining follower responses to transformational leadership from a dynamic, person–environment fit perspective. Academy of Management Journal, 61(4), 1343–1368.
   Web of Science ®Google Scholar
• Tierney, P., & Farmer, S. M. (2002). Creative self-efficacy: Its potential antecedents and relationship to creative performance. Academy of Management Journal, 45(6), 1137–1148.
   Web of Science ®Google Scholar
• Triandis, H. C. (1980). Values, attitudes, and interpersonal behavior. In H. H. M. Page (Ed.), Nebraska Symposium on Motivation (pp. 195–260). University of Nebraska Press.
   Google Scholar
• Upadhyay, P., & Kumar, A. (2020). The intermediating role of organizational culture and internal analytical knowledge between the capability of big data analytics and a firm’s performance. International Journal of Information Management, 52(6), 102100.
   Web of Science ®Google Scholar
• Vancouver, J. B., More, K. M., & Yoder, R. J. (2008). Self-efficacy and resource allocation: Support for a nonmonotonic, discontinuous model. The Journal of Applied Psychology, 93(1), 35–47.
   PubMed Web of Science ®Google Scholar
• VanYperen, N. W. (1998). Informational support, equity and burnout: The moderating effect of self‐efficacy. Journal of Occupational and Organizational Psychology, 71(1), 29–33.
   Google Scholar
• Vega, A., & Chiasson, M. (2019). A comprehensive framework to research digital innovation: The joint use of the systems of innovation and critical realism. The Journal of Strategic Information Systems, 28(3), 242–256.
   Web of Science ®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.
   Web of Science ®Google Scholar
• Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144.
   Web of Science ®Google Scholar
• Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119–134.
   Web of Science ®Google Scholar
• West, M. A., & Farr, J. L. (1990). Innovation at Work. In M. A. W. In & J. L. Farr (Eds.), Innovation and Creativity at Work: Psychological and Organizational Strategies (pp. 3–13). John Wiley & Sons.
   Google Scholar
• Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177–195.
   Web of Science ®Google Scholar
• Wixom, B. H., & Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. MIS Quarterly, 25(1), 17–41.
   Web of Science ®Google Scholar
• Yoon, Y., & Guimaraes, T. (1995). Assessing expert systems impact on users’ jobs. Journal of Management Information Systems, 12(1), 225–249.
   Google Scholar
• Yoon, Y., Guimaraes, T., & O’Neal, Q. (1995). Exploring the factors associated with expert systems success. MIS Quarterly, 19(1), 83–106.
   Web of Science ®Google Scholar
• Zhang, X. (2017). Knowledge Management System Use and Job Performance: A Multilevel Contingency Model. MIS Quarterly, 41(3), 811–840.
   Web of Science ®Google Scholar
• Zhang, M., & Gable, G. G. (2017). A systematic framework for multilevel theorizing in information systems research. Information Systems Research, 28(2), 203–224.
   Web of Science ®Google Scholar
• Zhang, Z., & Peterson, S. J. (2011). Advice networks in teams: The role of transformational leadership and members’ core self-evaluations. The Journal of Applied Psychology, 96(5), 1004–1017.
   PubMed Web of Science ®Google Scholar
• Zhao, H. H., Seibert, S. E., Taylor, M. S., Lee, C., & Lam, W. (2016). Not even the past: The joint influence of former leader and new leader during leader succession in the midst of organizational change. The Journal of Applied Psychology, 101(12), 1730–1738.
   PubMed Web of Science ®Google Scholar