Extending the story of IS success: a meta-analytic investigation of contingency factors at individual and organisational levels

References

• Al-Debei, M. M., Jalal, D., & Al-Lozi, E. (2013). Measuring web portals success: A respecification and validation of the DeLone and McLean information systems success model. International Journal of Business Information Systems, 14(1), 96–133. https://doi.org/10.1504/IJBIS.2013.055555
   Google Scholar
• Ali, N., Tretiakov, A., Whiddett, D., & Hunter, I. (2017). Knowledge management systems success in healthcare: Leadership matters. International Journal of Medical Informatics, 97, 331–340. https://doi.org/10.1016/j.ijmedinf.2016.11.004
   PubMed Web of Science ®Google Scholar
• Au, N., Ngai, E. W., & Cheng, T. E. (2002). A critical review of end-user information system satisfaction research and a new research framework. Omega, 30(6), 451–478. https://doi.org/10.1016/S0305-0483(02)00054-3
   Web of Science ®Google Scholar
• Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530–545. https://doi.org/10.1287/mnsc.29.5.530
   Web of Science ®Google Scholar
• Baum, J. A. C., Li, S. X., & Usher, J. M. (2000). Making the next move: How experiential and vicarious learning shape the locations of chains’ acquisitions. Administrative Science Quarterly, 45(4), 766–801. https://doi.org/10.2307/2667019
   Web of Science ®Google Scholar
• Belot, M., Duch, R., & Miller, L. (2015). A comprehensive comparison of students and non-students in classic experimental games. Journal of Economic Behavior & Organization, 113, 26–33. https://doi.org/10.1016/j.jebo.2015.02.007
   Web of Science ®Google Scholar
• Benner, M. J., & Tushman, M. L. (2003). Exploitation, exploration, and process management: The productivity dilemma revisited. Academy of Management Review, 2(2), 238–256. https://doi.org/10.5465/amr.2003.9416096
   Google Scholar
• Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2011). Introduction to meta-analysis. John Wiley & Sons.
   Google Scholar
• Burton-Jones, A., & Straub, D. W. (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
• Carpenter, J.P., Burks, S. and Verhoogen, E. (2005). Comparing students to workers: the effects of social framing on behavior in distribution games. In Harrison, G.W., Carpenter, J. and List, J.A. (Ed.) Field Experiments in Economics (Research in Experimental Economics,Vol. 10),Emerald Group Publishing Limited, Bingley, pp. 261–289. https://doi.org/10.1016/S0193–2306(04)10007–0
   Google Scholar
• Chadwick, I. C., & Raver, J. L. (2015). Motivating organizations to learn: Goal orientation and its influence on organizational learning. Journal of Management, 41(3), 957–986. https://doi.org/10.1177/0149206312443558
   Web of Science ®Google Scholar
• Chen, C. Y., Shih, B. Y., & Yu, S. H. (2012). Disaster prevention and reduction for exploring teachers’ technology acceptance using a virtual reality system and partial least squares techniques. Natural Hazards, 62(3), 1217–1231. https://doi.org/10.1007/s11069-012-0146-0
   Web of Science ®Google Scholar
• Chen, J. V., Jubilado, R. J. M., Capistrano, E. P. S., & Yen, D. C. (2015). Factors affecting online tax filing–An application of the IS Success Model and trust theory. Computers in Human Behavior, 43, 251–262. https://doi.org/10.1016/j.chb.2014.11.017
   Web of Science ®Google Scholar
• Chen, J. V., Rungruengsamrit, D., Rajkumar, T. M., & Yen, D. C. (2013). Success of electronic commerce Web sites: A comparative study in two countries. Information & Management, 50(6), 344–355. https://doi.org/10.1016/j.im.2013.02.007
   Web of Science ®Google Scholar
• Chen, Y. H., & Chengalur-Smith, I. (2015). Factors influencing students’ use of a library web portal: Applying course-integrated information literacy instruction as an intervention. The Internet and Higher Education, 26, 42–55. https://doi.org/10.1016/j.iheduc.2015.04.005
   Web of Science ®Google Scholar
• Chiu, P. S., Chao, I. C., Kao, C. C., Pu, Y. H., & Huang, Y. M. (2016). Implementation and evaluation of mobile e-books in a cloud bookcase using the information system success model. Library Hi Tech, 34(2), 207–223
   Web of Science ®Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed. ed.). Erlbaum.
   Google Scholar
• Conn, V. S., & Rantz, M. J. (2003). Research methods: Managing primary study quality in meta‐analyses. Research in Nursing & Health, 26(4), 322–333. https://doi.org/10.1002/nur.10092
   PubMed Web of Science ®Google Scholar
• Cooper, H., Hedges, L. V., & Valentine, J. C. (Eds.). (2019). The handbook of research synthesis and meta-analysis. Russell Sage Foundation.
   Google Scholar
• Corley, K. G. (2004). Defined by our strategy or our culture? Hierarchical differences in perceptions of organizational identity and change. Human Relations, 57(9), 1145–1177. https://doi.org/10.1177/0018726704047141
   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
• DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. https://doi.org/10.1287/isre.3.1.60
   Web of Science ®Google Scholar
• DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Devaraj, S., & Kohli, R. (2003). Performance impacts of information technology: Is actual usage the missing link? Management Science, 49(3), 273–289. https://doi.org/10.1287/mnsc.49.3.273.12736
   Web of Science ®Google Scholar
• Field, A. P. (2001). Meta-analysis of correlation coefficients: A Monte Carlo comparison of fixed-and random-effects methods. Psychological Methods, 6(2), 161. https://doi.org/10.1037/1082-989X.6.2.161
   PubMed Web of Science ®Google Scholar
• Fitzgerald, G., & Russo, N. L. (2005). The turnaround of the London ambulance service computer-aided dispatch system (LASCAD). European Journal of Information Systems, 14(3), 244–257. https://doi.org/10.1057/palgrave.ejis.3000541
   Web of Science ®Google Scholar
• Freeze, R. D., Alshare, K. A., Lane, P. L., & Wen, H. J. (2019). IS success model in e-learning context based on students’ perceptions. Journal of Information Systems Education, 21(2), 4. https://aisel.aisnet.org/jise/vol21/iss2/4
   Google Scholar
• Gharib, R. K., Philpott, E., & Duan, Y. (2017). Factors affecting active participation in B2B online communities: An empirical investigation. Information & Management, 54(4), 516–530
   Web of Science ®Google Scholar
• Griffith, T. L. (1999). Technology features as triggers for sensemaking. Academy of Management Review, 24(3), 472–488. https://doi.org/10.5465/amr.1999.2202132
   Web of Science ®Google Scholar
• Gully, S. M., Incalcaterra, K. A., Joshi, A., & Beaubien, J. M. (2002). A meta-analysis of team-efficacy, potency, and performance: Interdependence and level of analysis as moderators of observed relationships. Journal of Applied Psychology, 87(5), 819. https://doi.org/10.1037/0021-9010.87.5.819
   PubMed Web of Science ®Google Scholar
• Gupta, A. K., Smith, K. G., & Shalley, C. E. (2006). The interplay between exploration and exploitation. Academy of Management Journal, 49(4), 693–706. https://doi.org/10.5465/amj.2006.22083026
   Web of Science ®Google Scholar
• Halawi, L. A., McCarthy, R. V., & Aronson, J. E. (2007). An empirical investigation of knowledge-management systems’ success. The Journal of Computer Information Systems, 48(2), 121–135.
   Web of Science ®Google Scholar
• Hanseth, O., & Ciborra, C. (Eds.). (2007). Risk, complexity and ICT. Edward Elgar Publishing.
   Google Scholar
• Hedges, L. V., & Vevea, J. L. (1998). Fixed- and random-effects models in meta-analysis. Psychological Methods, 3(4), 486–504. https://doi.org/10.1037/1082-989X.3.4.486
   Web of Science ®Google Scholar
• Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–704. https://doi.org/10.2307/25148660
   Web of Science ®Google Scholar
• Hollenbeck, C. R., Mason, C. H., & Song, J. H. (2011). Enhancing student learning in marketing courses: An exploration of fundamental principles for website platforms. Journal of Marketing Education, 33(2), 171–182. https://doi.org/10.1177/0273475311410850
   Google Scholar
• Hong, H., Xu, D., Wang, G. A., & Fan, W. (2017). Understanding the determinants of online review helpfulness: A meta-analytic investigation. Decision Support Systems, 102, 1–11. https://doi.org/10.1016/j.dss.2017.06.007
   Web of Science ®Google Scholar
• Hossain, M., Moon, J., Yun, J., & Choe, Y. (2012). Impact of psychological traits on user performance in information systems delivering customer service: IS management perspective. Decision Support Systems, 54(1), 270–281. https://doi.org/10.1016/j.dss.2012.05.035
   Web of Science ®Google Scholar
• Hwang, Y., Kettinger, W. J., & Yi, M. Y. (2015). Personal information management effectiveness of knowledge workers: Conceptual development and empirical validation. European Journal of Information Systems, 24(6), 588–606. https://doi.org/10.1057/ejis.2014.24
   Web of Science ®Google Scholar
• IDC global ICT spending forecast (20182022). (2019). Available at https://www.idc.com/promo/global-ict-spending/forecast
   Google Scholar
• Ilie, V., Van Slyke, C., Parikh, M. A., & Courtney, J. F. (2009). Paper versus electronic medical records: The effects of access on physicians’ decisions to use complex information technologies. Decision Sciences, 40(2), 213–241. https://doi.org/10.1111/j.1540-5915.2009.00227.x
   Web of Science ®Google Scholar
• Isabella, L. A. (1990). Evolving interpretations as a change unfolds: How managers construe key organizational events. Academy of Management Journal, 33(1), 7–41. https://doi.org/10.5465/256350
   Web of Science ®Google Scholar
• King, W. R., & He, J. (2005). Understanding the role and methods of meta-analysis in IS research. Communications of the Association for Information Systems, 16(1), 32.
   Google Scholar
• Kirca, A. H., Jayachandran, S., & Bearden, W. O. (2005). Market orientation: A meta-analytic review and assessment of its antecedents and impact on performance. Journal of Marketing, 69(2), 24–41. https://doi.org/10.1509/jmkg.69.2.24.60761
   Web of Science ®Google Scholar
• Klein, K. J., & Kozlowski, S. W. J. (2000). From micro to meso: Critical steps in conceptualizing and conducting multilevel research. Organizational Research Methods, 3(3), 211–236. https://doi.org/10.1177/109442810033001
   Web of Science ®Google Scholar
• Larsen, M. A., & Myers, M. D. (1999). When success turns into failure: A package-driven business process re-engineering project in the financial services industry. The Journal of Strategic Information Systems, 8(4), 395–417. https://doi.org/10.1016/S0963-8687(00)00025-1
   Web of Science ®Google Scholar
• Lee, S. M., & Lee, S. H. (2012). Success factors of open-source enterprise information systems development. Industrial Management & Data Systems, 112(7), 1065–1084. https://doi.org/10.1108/02635571211255023
   Web of Science ®Google Scholar
• Liao, H., Toya, K., Lepak, D. P., & Hong, Y. (2009). Do they see eye to eye? Management and employee perspectives of high-performance work systems and influence processes on service quality. Journal of Applied Psychology, 94(2), 371. https://doi.org/10.1037/a0013504
   PubMed Web of Science ®Google Scholar
• Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. SAGE publications, Inc.
   Google Scholar
• March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87. https://doi.org/10.1287/orsc.2.1.71
   Web of Science ®Google Scholar
• Mason, R. O. (1978). Measuring information output: A communication systems approach. Information & Management, 1(4), 219–234. https://doi.org/10.1016/0378-7206(78)90028-9
   Google Scholar
• McGill, T., Hobbs, V., & Klobas, J. (2003). User-developed applications and information systems success: A test of DeLone and McLean’s model. Information Resources Management Journal, 16(1), 24–45. https://doi.org/10.4018/irmj.2003010103
   Google Scholar
• Newell, S., Swan, J. A., & Galliers, R. D. (2000). A knowledge-focused perspective on the diffusion and adoption of complex information technologies: The BPR example. Information Systems Journal, 10(3), 239–259. https://doi.org/10.1046/j.1365-2575.2000.00079.x
   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
• Peterson, R. A. (2001). On the use of college students in social science research: Insights from a second-order meta-analysis. Journal of Consumer Research, 28(3), 450–461. https://doi.org/10.1086/323732
   Web of Science ®Google Scholar
• Petter, S., DeLone, W., & McLean, E. R. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236–263. https://doi.org/10.1057/ejis.2008.15
   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., & McLean, E. R. (2009). A meta-analytic assessment of the DeLone and McLean IS success model: An examination of IS success at the individual level. Information & Management, 46(3), 159–166. https://doi.org/10.1016/j.im.2008.12.006
   Web of Science ®Google Scholar
• Pitt, L. F., Watson, R. T., & Kavan, C. B. (1995). Service quality: A measure of information systems effectiveness. MIS Quarterly, 19(2), 173. https://doi.org/10.2307/249687
   Web of Science ®Google Scholar
• Po-An Hsieh, J. J., & Wang, W. (2007). Explaining employees’ extended use of complex information systems. European Journal of Information Systems, 16(3), 216–227. https://doi.org/10.1057/palgrave.ejis.3000663
   Web of Science ®Google Scholar
• Rai, A., Lang, S. S., & Welker, R. B. (2002). Assessing the validity of IS success models: An empirical test and theoretical analysis. Information Systems Research, 13(1), 50–69. https://doi.org/10.1287/isre.13.1.50.96
   Web of Science ®Google Scholar
• Rana, N. P., Dwivedi, Y. K., Williams, M. D., & Lal, B. (2015). Examining the success of the online public grievance redressal systems: an extension of the IS success model. Information Systems Management, 32(1), 39–59
   Web of Science ®Google Scholar
• Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638. https://doi.org/10.1037/0033-2909.86.3.638
   Web of Science ®Google Scholar
• Sabherwal, R., & Jeyaraj, A. (2015). Information technology impacts on firm performance: An extension of Kohli and Devaraj (2003). MIS Quarterly, 39(4), 809–836. https://doi.org/10.25300/MISQ/2015/39.4.4
   Web of Science ®Google Scholar
• Sabherwal, R., Jeyaraj, A., & Chowa, C. (2006). Information system success: Individual and organizational determinants. Management Science, 52(12), 1849–1864. https://doi.org/10.1287/mnsc.1060.0583
   Web of Science ®Google Scholar
• Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007
   Web of Science ®Google Scholar
• Sears, D. O. (1986). College sophomores in the laboratory: Influences of a narrow data base on social psychology’s view of human nature. Journal of Personality and Social Psychology, 51(3), 515. https://doi.org/10.1037/0022-3514.51.3.515
   Web of Science ®Google Scholar
• Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3), 240–253. https://doi.org/10.1287/isre.8.3.240
   Web of Science ®Google Scholar
• Seddon, P. B., Staples, S., Patnayakuni, R., & Bowtell, M. (1999). Dimensions of information systems success. Communications of the Association for Information Systems, 2(1), 20. https://doi.org/10.17705/1CAIS.00220
   Google Scholar
• Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. University of Illinois Press.
   Google Scholar
• Siu, N. Y., & Lam, K. C. J. (2009). A comparative study of ethical perceptions of managers and non-managers. Journal of Business Ethics, 88(1), 167–183. https://doi.org/10.1007/s10551-008-9824-y
   Google Scholar
• Sørum, H., Medaglia, R., Normann Andersen, K., Scott, M., & DeLone, W. (2012). Perceptions of information system success in the public sector: Webmasters at the steering wheel? Transforming Government: People, Process & Policy, 6(3), 239–257. https://doi.org/10.1108/17506161211251254
   Google Scholar
• Stadin, M., Nordin, M., Broström, A., Hanson, L. L. M., Westerlund, H., & Fransson, E. I. (2020). Technostress operationalised as information and communication technology (ICT) demands among managers and other occupational groups–results from the Swedish longitudinal occupational survey of health (SLOSH). Computers in Human Behavior, 114, 106486. https://doi.org/10.1016/j.chb.2020.106486
   Web of Science ®Google Scholar
• Šumak, B., Heričko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067–2077. https://doi.org/10.1016/j.chb.2011.08.005
   Web of Science ®Google Scholar
• Stefanovic, D., Marjanovic, U., Delić, M., Culibrk, D., & Lalic, B. (2016). Assessing the success of e-government systems: An employee perspective. Information & Management, 53(6), 717–726
   Web of Science ®Google Scholar
• Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human Computer Studies, 64(2), 53–78. https://doi.org/10.1016/j.ijhcs.2005.04.013
   Web of Science ®Google Scholar
• Valaei, N., & Baroto, M. B. (2017). Modelling continuance intention of citizens in government Facebook page: A complementary PLS approach. Computers in Human Behavior, 73, 224–237. https://doi.org/10.1016/j.chb.2017.03.047
   Web of Science ®Google Scholar
• VandeWalle, D. (2003). A goal orientation model of feedback-seeking behavior. Human Resource Management Review, 13(4), 581–604. https://doi.org/10.1016/j.hrmr.2003.11.004
   Google 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), 497–518. https://doi.org/10.1111/j.1540-5414.2006.00136.x
   Web of Science ®Google Scholar
• Venkatraman, N. (1989). The concept of fit in strategy research: Toward verbal and statistical correspondence. Academy of Management Review, 14(3), 424–444. https://doi.org/10.5465/amr.1989.4279078
   Web of Science ®Google Scholar
• Wu, K., Zhao, Y., Zhu, Q., Tan, X., & Zheng, H. (2011). A meta-analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type. International Journal of Information Management, 31(6), 572–581. https://doi.org/10.1016/j.ijinfomgt.2011.03.004
   Web of Science ®Google Scholar
• Zyphur, M. J., Zammuto, R. F., & Zhang, Z. (2016). Multilevel latent polynomial regression for modeling (in) congruence across organizational groups: The case of organizational culture research. Organizational Research Methods, 19(1), 53–79. https://doi.org/10.1177/1094428115588570
   Web of Science ®Google Scholar