2,030
Views
3
CrossRef citations to date
0
Altmetric
Research Article

Antecedents and performance outcomes of employees’ data analytics skills: an adaptation structuration theory-based empirical investigation

, , , &
Pages 921-940 | Received 05 Jul 2021, Accepted 06 May 2022, Published online: 24 May 2022

References

  • Ahuja, M. K., Galletta, D. F., & Carley, K. M. (2003). Individual centrality and performance in virtual R&D groups: An empirical study. Management Science, 49(1), 21–38. https://doi.org/10.1287/mnsc.49.1.21.12756
  • Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182(1), 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018
  • Albergaria, M., & Jabbour, C. J. C. (2020). The role of big data analytics capabilities (BDAC) in understanding the challenges of service information and operations management in the sharing economy: Evidence of peer effects in libraries. International Journal of Information Management, 51(1), 102023. https://doi.org/10.1016/j.ijinfomgt.2019.https://doi.org/10.008
  • Bagozzi, R. P., & Yi, Y. (1991). Multitrait-multimethod matrices in consumer research. Journal of Consumer Research, 17(4), 426–439. https://doi.org/10.1086/208568
  • Bala, H., & Venkatesh, V. (2016). Adaptation to information technology: A holistic nomological network from implementation to job outcomes. Management Science, 62(1), 156–179. https://doi.org/10.1287/mnsc.2014.2111.
  • Barton, D., & Court, D. (2012). Making advanced analytics work for you. Harvard Business Review, 90(10), 78–83.
  • Benitez, J., Castillo, A., Llorens, J., & Braojos, J. (2018). IT-enabled knowledge ambidexterity and innovation performance in small US firms: The moderator role of social media capability. Information & Management, 55(1), 131–143. https://doi.org/10.1016/j.im.2017.09.004
  • Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020a). 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
  • Benitez, J., Ruiz, L., Castillo, A., & Llorens, J. (2020b). 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
  • Cao, G., & Duan, Y. (2014). A path model linking business analytics, data-driven culture, and competitive advantage. Proceedings of the Twenty Second European Conference on Information Systems. Tel Aviv, Israel, 1–17.
  • Carlo, J. L., Lyytinen, K., & Rose, G. M. (2012). A knowledge-based model of radical innovation in small software firms. MIS Quarterly, 36(3), 865–895. https://doi.org/10.2307/41703484
  • Castaneda, J. A., Munoz-Leiva, F., & Luque, T. (2007). Web acceptance model (WAM): Moderating effects of user experience. Information & Management, 44(4), 384–396. https://doi.org/10.1016/j.im.2007.02.003
  • Castillo, A., Benitez, J., Llorens, J., & Braojos, J. (2021). Impact of social media on the firm’s knowledge exploration and knowledge exploitation: The role of business analytics talent. Journal of the Association for Information Systems, 22(5), 1472–1508. https://doi.org/10.17705/1jais.00700
  • Chatterjee, S., Moody, G., Lowry, P. B., Chakraborty, S., & Hardin, A. (2015). Strategic relevance of organizational virtues enabled by information technology in organizational innovation. Journal of Management Information Systems, 32(3), 158–196. https://doi.org/10.1080/07421222.2015.1099180
  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
  • 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
  • Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a monte carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217. https://doi.org/10.1287/isre.14.2.189.16018
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Earlbaum Associates.
  • Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553
  • Collins-Camargo, C., & Royse, D. (2010). A study of the relationships among effective supervision, organizational culture promoting evidence-based practice, and worker self-efficacy in public child welfare. Journal of Public Child Welfare, 4(1), 1–24. https://doi.org/10.1080/15548730903563053
  • Constantiou, I. D., & Kallinikos, J. (2015). New games, new rules: Big data and the changing context of strategy. Journal of Information Technology, 30(1), 44–57. https://doi.org/10.1057/jit.2014.17
  • 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
  • Cui, T., Tong, Y., Teo, H. H., & Li, J. (2020). Managing knowledge distance: IT-enabled inter-firm knowledge capabilities in collaborative innovation. Journal of Management Information Systems, 37(1), 217–250. https://doi.org/10.1080/07421222.2019.1705504
  • Davison, R. M., & Ou, C. X. (2017). Digital work in a digitally challenged organization. Information & Management, 54(1), 129–137. https://doi.org/10.1016/j.im.2016.05.005
  • Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: putting analytics and big data in cloud. Decision Support Systems, 55(1), 412–421. https://doi.org/10.1016/j.dss.2012.05.048
  • Deng, X., Doll, W. J., & Cao, M. (2008). Exploring the absorptive capacity to innovation/productivity link for individual engineers engaged in IT enabled work. Information & Management, 45(2), 75–87. https://doi.org/10.1016/j.im.2007.12.001
  • DeSanctis, G., & Poole, M. S. (1994). Capturing the complexity in advanced technology use: Adaptive structuration theory. Organization Science, 5(2), 121–147. https://doi.org/10.1287/orsc.5.2.121
  • DeSanctis, G., Poole, M. S., & Zigurs, I. (2008). The Minnesota GDSS research project: group support systems, group processes, and outcomes. Journal of the Association for Information Systems, 9(10), 551–608. https://doi.org/10.17705/1jais.00177
  • Di Gangi, P. M., McAllister, C. P., Howard, J. L., Thatcher, J. B., & Ferris, G. R. (2022). Can you see opportunity knocking? An examination of technology-based political skill on opportunity recognition in online communities for MTurk workers. Internet Research. https://doi.org/10.1108/INTR-03-2021-0175
  • Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297–316. https://doi.org/10.25300/MISQ/2015/39.2.02
  • Dong, J., & Yang, C. (2020). Business value of big data analytics: A systems-theoretic approach and empirical test. Information & Management, 57(1), 103124. https://doi.org/10.1016/j.im.2018.11.001
  • Dremel, C., Herterich, M. M., Wulf, J., & Vom Brocke, J. (2020). Actualizing big data analytics affordances: A revelatory case study. Information & Management, 57(1), 103121. https://doi.org/10.1016/j.im.2018.https://doi.org/10.007
  • Duan, Y., Wang, W., & Zhou, W. (2020). The multiple mediation effect of absorptive capacity on the organizational slack and innovation performance of high-tech manufacturing firms: Evidence from Chinese firms. International Journal of Production Economics, 229(1), 107754. https://doi.org/10.1016/j.ijpe.2020.107754
  • 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
  • Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: guidelines for research practice. Communications of the Association for Information Systems, 4(1), 2–77. https://doi.org/10.17705/1CAIS.00407\
  • Ghasemaghaei, M., Hassanein, K., & Turel, O. (2017). Increasing firm agility through the use of data analytics: The role of fit. Decision Support Systems, 101(1), 95–105. https://doi.org/10.1016/j.dss.2017.06.004
  • Ghasemaghaei, M. (2019). Does data analytics use improve firm decision making quality? The role of knowledge sharing and data analytics competency. Decision Support Systems, 120(1), 14–24. https://doi.org/10.1016/j.dss.2019.03.004
  • Ghobadi, S., & Mathiassen, L. (2020). A generational perspective on the software workforce: Precocious users of social networking in software development. Journal of Management Information Systems, 37(1), 96–128. https://doi.org/10.1080/07421222.2019.1705508
  • Goetz, N., & Wald, A. (2021). Employee performance in temporary organizations: The effects of person-environment fit and temporariness on task performance and innovative performance. European Management Review, 18(2), 25–41. https://doi.org/10.1111/emre.12438
  • Gopal, C. (2021). How data culture fuels business value in data-driven organizations. International Data Corporation, https://www.tableau.com/sites/default/files/2021-05/Tableau_WhitePaper_US47605621_FINAL-2.pdf
  • Grant, R. M. (1991). The resource-based theory of competitive advantage: Implications for strategy formulation. California Management Review, 33(3), 114–135. https://doi.org/10.2307/41166664
  • 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
  • Guo, R. X., Dobson, T., & Petrina, S. (2008). Digital natives, digital immigrants: an analysis of age and ICT competency in teacher education. Journal of Educational Computing Research, 38(3), 235–254. https://doi.org/10.2190/EC.38.3.a
  • 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
  • Gupta, G., & Bose, I. (2022). 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
  • Hair, J. F., Jr, Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage.
  • Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford publications.
  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382
  • Heudecker, N., & Hare, J. (2016). Survey analysis: Big data investments begin tapering in 2016. Gartner Report. https://www.gartner.com/doc/3446724/survey-analysis-big-data-investments.
  • Hirst, G., Van Knippenberg, D., Zhou, Q., Zhu, C. J., & Tsai, P. C. F. (2018). Exploitation and exploration climates’ influence on performance and creativity: Diminishing returns as function of self-efficacy. Journal of Management, 44(3), 870–891. https://doi.org/10.1177/0149206315596814
  • Hoffmann, C. P., Lutz, C., & Meckel, M. (2014). Digital natives or digital immigrants? The impact of user characteristics on online trust. Journal of Management Information Systems, 31(3), 138–171. https://doi.org/10.1080/07421222.2014.995538
  • Huang, M., Bhattacherjee, A., & Wong, C. S. (2018). Gatekeepers’ innovative use of IT: An absorptive capacity model at the unit level. Information & Management, 55(2), 235–244. https://doi.org/10.1016/j.im.2017.06.001
  • Ilie, V., & Turel, O. (2020). Manipulating user resistance to large-scale information systems through influence tactics. Information & Management, 57(3), 103178. https://doi.org/10.1016/j.im.2019.103178
  • 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
  • Janssen, O., & Van Yperen, N. W. (2004). Employees’ goal orientations, the quality of leader-member exchange, and the outcomes of job performance and job satisfaction. Academy of Management Journal, 47(3), 368–384. https://doi.org/10.5465/20159587.
  • Jha, A., Agi, M., & Ngai, E. (2020). A note on big data analytics capability development in supply chain. Decision Support Systems, 138(1), 113382. https://doi.org/10.1016/j.dss.2020.113382
  • Jiang, W., Chai, H., Li, Y., & Feng, T. (2019). How workplace incivility influences job performance: The role of image outcome expectations. Asia Pacific Journal of Human Resources, 57(4), 445–469. https://doi.org/10.1111/1744-7941.12197
  • Kane, G. C. (2015). Enterprise social media: Current capabilities and future possibilities. MIS Quarterly Executive, 14(1), 1–16. https://aisel.aisnet.org/misqe/vol14/iss1/3.
  • Ke, W., Kang, L., Tan, C. H., & Peng, C. H. (2021). User competence with enterprise systems: The effects of work environment factors. Information Systems Research, 32(3), 860–875. https://doi.org/10.1287/isre.2020.0989
  • Keil, M., Tan, B. C., Wei, K. K., Saarinen, T., Tuunainen, V., & Wassenaar, A. (2000). A cross-cultural study on escalation of commitment behavior in software projects. MIS Quarterly, 24(2), 299–325. https://doi.org/10.2307/3250940
  • Kesharwani, A. (2020). Do (how) digital natives adopt a new technology differently than digital immigrants? A longitudinal study. Information & Management, 57(2), 103170. https://doi.org/10.1016/j.im.2019.103170
  • Kirk, C. P., Chiagouris, L., Lala, V., & Thomas, J. D. (2015). How do digital natives and digital immigrants respond differently to interactivity online? A model for predicting consumer attitudes and intentions to use digital information products. Journal of Advertising Research, 55(1), 81–94. https://doi.org/10.2501/JAR-55-1-081-094
  • Kiron, D., Prentice, P. K., & Ferguson, R. B. (2014). The analytics mandate. MIT Sloan Management Review, 55(4), 1–25. https://sloanreview.mit.edu/projects/analytics-mandate/.
  • 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
  • Leal-Rodríguez, A. L., Ariza-Montes, J. A., Roldán, J. L., & Leal-Millán, A. G. (2014). Absorptive capacity, innovation and cultural barriers: A conditional mediation model. Journal of Business Research, 67(5), 763–768. https://doi.org/10.1016/j.jbusres.2013.11.041
  • 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
  • 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
  • Liang, H., Peng, Z., Xue, Y., Guo, X., & Wang, N. (2015). Employees’ exploration of complex systems: An integrative view. Journal of Management Information Systems, 32(1), 322–357. https://doi.org/10.1080/07421222.2015.1029402
  • Lin, J., Li, L., Luo, X. R., & Benitez, J. (2020). How do agribusinesses thrive through complexity? The pivotal role of e-commerce capability and business agility. Decision Support Systems, 135(1), 113342. https://doi.org/10.1016/j.dss.2020.113342
  • Lin, X., & Wang, X. (2020). Examining gender differences in people’s information-sharing decisions on social networking sites. International Journal of Information Management, 50(1), 45–56. https://doi.org/10.1016/j.ijinfomgt.2019.05.004
  • Lin, J., Luo, Z., Benitez, J., Luo, X. R., & Popovič, A. (2021). Why do organizations leverage social media to create business value? An external factor-centric empirical investigation. Decision Support Systems, 151(1), 113628. https://doi.org/10.1016/j.dss.2021.113628
  • Liu, H., Ke, W., Wei, K. K., & Hua, Z. (2013). The impact of IT capabilities on firm performance: The mediating roles of absorptive capacity and supply chain agility. Decision Support Systems, 54(3), 1452–1462. https://doi.org/10.1016/j.dss.2012.12.016
  • Liu, Y. (2014). Big data and predictive business analytics. The Journal of Business Forecasting, 33(4), 40–42. https://doi.org/10.1016/j.dss.2012.12.016.
  • Lowik, S., Kraaijenbrink, J., & Groen, A. (2016). The team absorptive capacity triad: A configurational study of individual, enabling, and motivating factors. Journal of Knowledge Management, 20(5), 1083–1103. https://doi.org/10.1108/JKM-11-2015-0433
  • Lowik, S., Kraaijenbrink, J., & Groen, A. J. (2017). Antecedents and effects of individual absorptive capacity: A micro-foundational perspective on open innovation. Journal of Knowledge Management, 21(6), 1319–1341. https://doi.org/10.1108/JKM-09-2016-0410
  • Madsen, A. K. (2015). Between technical features and analytic capabilities: Charting a relational affordance space for digital social analytics. Big Data & Society, 2(1), 1–15. https://doi.org/10.1177/2053951714568727
  • Majhi, S. G., Snehvrat, S., Chaudhary, S., & Mukherjee, A. (2020). The synergistic role of individual absorptive capacity and individual ambidexterity in open innovation: A moderated-mediation model. International Journal of Innovation Management, 24(7), 2050083. https://doi.org/10.1142/S1363919620500838
  • Malhotra, A., Gosain, S., & Sawy, O. A. E. (2005). Absorptive capacity configurations in supply chains: Gearing for partner-enabled market knowledge creation. MIS Quarterly, 29(1), 145–187. https://doi.org/10.2307/25148671
  • Marchand, D. A., & Peppard, J. (2013). Why IT fumbles analytics. Harvard Business Review, 91(1), 104–112. https://hbr.org/2013/01/why-it-fumbles-analytics.
  • Margaryan, A., Littlejohn, A., & Vojt, G. (2011). Are digital natives a myth or reality? University students’ use of digital technologies. Computers & Education, 56(2), 429–440. https://doi.org/10.1016/j.compedu.20https://doi.org/10.09.004
  • Metallo, C., & Agrifoglio, R. (2015). The effects of generational differences on use continuance of Twitter: An investigation of digital natives and digital immigrants. Behaviour & Information Technology, 34(9), 869–881. https://doi.org/10.1080/0144929X.2015.1046928
  • 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
  • Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169. https://doi.org/10.1016/j.im.2019.05.004
  • Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192
  • Müller, O., Fay, M., & Vom Brocke, J. (2018). The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. Journal of Management Information Systems, 35(2), 488–509. https://doi.org/10.1080/07421222.2018.1451955
  • Nguyen, T., Chen, J. V., & Nguyen, T. P. H. (2021). Appropriation of accounting information system use under the new IFRS: Impacts on accounting process performance. Information & Management, 58(8), 103534. https://doi.org/10.1016/j.im.2021.103534
  • 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
  • Palfrey, J., & Gasser, U. (2008). Opening universities in a digital era. New England Journal of Higher Education, 23(1), 22–24. https://eric.ed.gov/?id=EJ850701.
  • Peng, Z., Sun, Y., & Guo, X. (2018). Antecedents of employees’ extended use of enterprise systems: An integrative view of person, environment, and technology. International Journal of Information Management, 39(1), 104–120. https://doi.org/10.1016/j.ijinfomgt.2017.11.007
  • 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
  • Phillips, G., Daly, M., & Burstein, F. (2021). Reconciling business intelligence, analytics and decision support systems: More data, deeper insight. Decision Support Systems, 146(1), 1–13. https://doi.org/10.1016/j.dss.2021.113560.
  • Phillips-Wren, G., Iyer, L. S., Kulkarni, U., & Ariyachandra, T. (2015). Business analytics in the context of big data: A roadmap for research. Communications of the Association for Information Systems, 37(1), 448–472. https://doi.org/10.17705/1CAIS.03723
  • Pitafi, A. H., Kanwal, S., Ali, A., Khan, A. N., & Ameen, M. W. (2018). Moderating roles of IT competency and work cooperation on employee work performance in an ESM environment. Technology in Society, 55(1), 199–208. https://doi.org/10.1016/j.techsoc.2018.08.002
  • 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. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-90https://doi.org/10.88.5.879
  • Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5), 1–6. https://doi.org/10.1108/10748120110424816.
  • Rialti, R., Zollo, L., Ferraris, A., & Alon, I. (2019). Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model. Technological Forecasting and Social Change, 149(1), 119781. https://doi.org/10.1016/j.techfore.2019.119781
  • Ross, J. W., Beath, C. M., & Quaadgras, A. (2013). You may not need big data after all. Harvard Business Review, 91(12), 90–98. https://hbr.org/2013/12/you-may-not-need-big-data-after-all.
  • Sarstedt, M., Henseler, J., & Ringle, C. M. (2011). Multi-group analysis in Partial Least Squares (PLS), path modeling: Alternative methods and empirical results. Advances in International Marketing, 22(1), 195–218. https://doi.org/10.1108/S1474-7979(2011)0000022012.
  • Schmitz, K. W., Teng, J. T. C., & Webb, K. J. (2016). Capturing the complexity of malleable it use: Adaptive structuration theory for individuals. MIS Quarterly, 40(3), 663–686. https://doi.org/10.25300/MISQ/2016/40.3.07
  • Schneckenberg, D., Benitez, J., Klos, C., Velamuri, V. K., & Spieth, P. (2021). Value creation and appropriation of software vendors: A digital innovation model for cloud computing. Information & Management, 58(4), 103463. https://doi.org/10.1016/j.im.2021.103463
  • Seo, Y. W., Chae, S. W., & Lee, K. C. (2015). The impact of absorptive capacity, exploration, and exploitation on individual creativity: Moderating effect of subjective well-being. Computers in Human Behavior, 42(1), 68–82. https://doi.org/10.1016/j.chb.2014.03.031
  • Shamim, S., Zeng, J., Shariq, S. M., & Khan, Z. (2019). Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view. Information & Management, 56(6), 103135. https://doi.org/10.1016/j.im.2018.12.003
  • 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(1), 120315. https://doi.org/10.1016/j.techfore.2020.120315
  • Shao, Z., Zhang, L., Li, X., & Guo, Y. (2019). Antecedents of trust and continuance intention in mobile payment platforms: The moderating effect of gender. Electronic Commerce Research and Applications, 33(1), 100823. https://doi.org/10.1016/j.elerap.2018.100823
  • Shao, Z., Guo, Y., Li, X., & Barnes, S. (2020). Sources of influences on customers’ trust in ride-sharing: Why use experience matters? Industrial Management & Data Systems, 120(8), 1459–1482. https://doi.org/10.1108/IMDS-12-2019-0651
  • 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. https://doi.org/10.1016/j.im.2022.103597.
  • Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: A research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23(4), 433–441. https://doi.org/10.1057/ejis.2014.17
  • Sia, C. L., Lim, K. H., Leung, K., Lee, M. K., Huang, W. W., & Benbasat, I. (2009). Web strategies to promote internet shopping: Is cultural-customization needed? MIS Quarterly, 33(3), 491–512. https://doi.org/10.2307/20650306
  • Soper, D. S. (2020). Post-hoc Statistical Power Calculator for Multiple Regression [Software]. Available from http://www.danielsoper.com/statcalc
  • Stephenson, D. (2018). Big data demystified: how to use big data, data science and AI to make better business decisions and gain competitive advantage. Pearson UK.
  • Stoermer, S., Hitotsuyanagi-Hansel, A., & Froese, F. J. (2019). Racial harassment and job satisfaction in South Africa: The moderating effects of career orientations and managerial rank. The International Journal of Human Resource Management, 30(3), 385–404. https://doi.org/10.1080/09585192.2016.1278254
  • Straub, D. W. (1989). Validating instruments in MIS research. MIS Quarterly, 13(2), 147–169. https://doi.org/10.2307/248922
  • Tams, S., Thatcher, J. B., & Grover, V. (2018). Concentration, competence, confidence, and capture: An experimental study of age, interruption-based technostress, and task performance. Journal of the Association for Information Systems, 19(9), 857–908. https://doi.org/10.17705/1jais.00511
  • Tams, S. (2022). Helping older workers realize their full organizational potential: A moderated mediation model of age and it-enabled task performance. MIS Quarterly, 46(1), 1–33. https://doi.org/10.25300/MISQ/2022/16359
  • Thomas, D. M., & Bostrom, R. P. (2010). Vital signs for virtual teams: An empirically developed trigger model for technology adaptation interventions. MIS Quarterly, 34(1), 115–142. https://doi.org/10.2307/20721417
  • Thompson, P. (2013). The digital natives as learners: Technology use patterns and approaches to learning. Computers & Education, 65(1), 12–33. https://doi.org/10.1016/j.compedu.2012.12.022
  • Tilvawala, K., Sundaram, D., & Myers, M. D. (2013). Design of organisational ubiquitous information systems: Digital native and digital immigrant perspectives. Proceedings of the Pacific Asia Conference on Information Systems 2013, Jeju Island, Korea, 1–14.
  • 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(1), 102100. https://doi.org/10.1016/j.ijinfomgt.2020.102100
  • Vodanovich, S., Sundaram, D., & Myers, M. (2010). Research commentary—digital natives and ubiquitous information systems. Information Systems Research, 21(4), 711–723. https://doi.org/10.1287/isre.1100.0324
  • Wamba, S., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70(1), 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
  • Wang, Y., Kung, L., Wang, W. Y. C., & Cegielski, C. G. (2018). An integrated big data analytics-enabled transformation model: Application to health care. Information & Management, 55(1), 64–79. https://doi.org/10.1016/j.im.2017.04.001
  • Wang, Z., Wang, N., Su, X., & Ge, S. (2020). An empirical study on business analytics affordances enhancing the management of cloud computing data security. International Journal of Information Management, 50(1), 387–394. https://doi.org/10.1016/j.ijinfomgt.2019.09.002
  • Warkentin, M., Johnston, A. C., & Shropshire, J. (2011). The influence of the informal social learning environment on information privacy policy compliance efficacy and intention. European Journal of Information Systems, 20(3), 267–284. https://doi.org/10.1057/ejis.20https://doi.org/10.72
  • Wu, L., Lou, B., & Hitt, L. (2019). Data analytics supports decentralized innovation. Management Science, 65(10), 4863–4877. https://doi.org/10.1287/mnsc.2019.3344
  • Wu, L., Hitt, L., & Lou, B. (2020). Data analytics, innovation, and firm productivity. Management Science, 66(5), 2017–2039. https://doi.org/10.1287/mnsc.2018.3281
  • Xu, Y., Tong, Y., Liao, S. S., Zhou, G., & Yu, Y. (2018). Understanding indirect system use of junior employees in the context of healthcare. Information & Management, 55(6), 759–770. https://doi.org/10.1016/j.im.2018.03.005
  • Zeng, D., Tim, Y., Yu, J., & Liu, W. (2020). Actualizing big data analytics for smart cities: A cascading affordance study. International Journal of Information Management, 54(1), 102156. https://doi.org/10.1016/j.ijinfomgt.2020.102156
  • Zhang, L., Shao, Z., Li, X., & Feng, Y. (2021). Gamification and online impulse buying: the moderating effect of gender and age. International Journal of Information Management, 61(1), 102267. https://doi.org/10.1016/j.ijinfomgt.2020.102267
  • Zhang, J. A., Chen, G., O’Kane, C., Xiang, S., & Wang, J. (2022). How employee exploration and exploitation affect task performance: The influence of organizational competitive orientation. The International Journal of Human Resource Management, 33(5), 930–964. https://doi.org/10.1080/09585192.2020.1745866
  • Zhao, L., Lu, Y., Huang, W., & Wang, Q. (2010). Internet inequality: The relationship between high school students’ Internet use in different locations and their internet self-efficacy. Computers & Education, 55(4), 1405–1423. https://doi.org/10.1016/j.compedu.20https://doi.org/10.05.010
  • Zhou, Z., Jin, X. L., & Fang, Y. (2014). Moderating role of gender in the relationships between perceived benefits and satisfaction in social virtual world continuance. Decision Support Systems, 65(1), 69–79. https://doi.org/10.1016/j.dss.2014.05.004
  • Zhou, S., Qiao, Z., Du, Q., Wang, G. A., Fan, W., & Yan, X. (2018). Measuring customer agility from online reviews using big data text analytics. Journal of Management Information Systems, 35(2), 510–539. https://doi.org/10.1080/07421222.2018.1451956

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.