Abstract
IT use is the fundamental construct of IS research. However, despite its centrality, the literature lacks agreement on the nature of the construct and its antecedents, which has led to a fragmented nomological network. We provide a comprehensive review of the existing empirical IT use constructs, analyze their overlaps, and distill them into three dominant constructs: adoption, static IT use, and innovative IT use. We then develop an overarching framework for extant IT use research by developing a holistic model. Each IT use construct has its own set of organizational and technological antecedents as well as psychological drivers. Our model synthesizes existing research and is empirically validated using a web-based survey of business technology users. These findings resolve issues in the conceptualization of IT use, integrate the fragmented IT literature, and help avoid the illusion of knowledge accumulation. The outcomes of the paper provide guidelines for researchers on the conceptualization of IT use as well as for practitioners regarding purposeful use-based design. Areas for future research in IT use are also recommended.
Similar content being viewed by others
Data Availability
The data that support the findings of this study are available from the corresponding author upon request.
Notes
The jangle fallacy exists when two or more studies research the same phenomena under different labels. This fallacy may be identified by comparing measurement items and/or construct definitions (Wilhelm, 2009).
According to (Wilhelm, 2009, p. 146), “The jingle fallacy applies to any case where two constructs with identical names measure different latent constructs.”
References
Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.
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.
Akhlaghpour, S., Wu, J., Lapointe, L., & Pinsonneault, A. (2013). The ongoing quest for the IT artifact: Looking back, moving forward. Journal of Information Technology, 28(2), 150–166.
Al-Dhaen, F., Hou, J., Rana, N. P., & Weerakkody, V. (2023). Advancing the Understanding of the Role of Responsible AI in the Continued Use of IoMT in Healthcare. Information Systems Frontiers, 25, 2159–2178. https://doi.org/10.1007/s10796-021-10193-x
Alter, S. (2013). Work system theory: Overview of core concepts, extensions, and challenges for the future. Journal of the Association of Information Systems, 14(2), 72–121.
Bagayogo, F. F., Lapointe, L., & Bassellier, G. (2014). Enhanced use of IT: A new perspective on post-adoption. Journal of the Association for Information Systems, 15(7), 361–387.
Bagozzi, R. P., Tybout, A. M., Craig, C. S., & Sternthal, B. (1979). The construct validity of the tripartite classification of attitudes. Journal of Marketing Research, 16(1), 88–95.
Baird, A., & Maruping, L. M. (2021). The next generation of research on IS use: A theoretical framework of delegation to and from agentic IS artifacts. MIS Quarterly, 45(1), 315–341.
Bala, H., & Venkatesh, V. (2016). Adaptation to information technology: A holistic nomological network from implementation to job outcomes. Management Science, 62(1), 156–179.
Benbasat, I., & Barki, H. (2007). Quo vadis, TAM? Journal of the Association for Information Systems, 8(4), 212–218.
Benlian, A. (2015). IT feature use over time and its impact on individual task performance. Journal of the Association for Information Systems, 16(3), 144–173.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370.
Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 229–254.
Boella, G., & van der Torre, L. W. N. (2004). Regulative and constitutive norms in normative multiagent systems. KR, 4, 255–265.
Bostrom, R. P., Gupta, S., & Thomas, D. (2009). A meta-theory for understanding information systems within sociotechnical systems. Journal of Management Information Systems, 26(1), 17–47.
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
Burton-Jones, A., Stein, M.-K., & Mishra, A. (2017). IS Use. MIS Quarterly Research Curations. https://www.misqresearchcurations.org/blog/2017/12/1/is-use
Burton-Jones, A., & Straub, D. W. (2006). Reconceptualizing system usage: An approach and empirical test. Information Systems Research, 17(3), 228–246.
Carter, M., Petter, S., Grover, V., & Thatcher, J. B. (2020). Information technology identity: A Key determinant of IT feature and exploratory usage. MIS Quarterly, 44(3), 983–1021.
Chin, W., Cheah, J.-H., Liu, Y., Ting, H., Lim, X.-J., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research. Industrial Management & Data Systems, 120(12), 2161–2209.
Cho, J., & Park, I. (2022). Does information systems support for creativity enhance effective information systems use and job satisfaction in virtual work? Information Systems Frontiers, 24(6), 1865–1886.
Clements, J. A., & Boyle, R. (2018). Compulsive technology use: Compulsive use of mobile applications. Computers in Human Behavior, 87, 34–48.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.
Cooper, R. B., & Zmud, R. W. (1990). Information technology implementation research: A technological diffusion approach. Management Science, 36(2), 123–139.
Cui, T., Tong, Y., & Tan, C.-H. (2022). Open innovation and information technology use: Towards an operational alignment view. Information Systems Journal, 32(5), 932–972.
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.
DeSanctis, G., & Jackson, B. M. (1994). Coordination of information technology management: Team-based structures and computer-based communication systems. Journal of Management Information Systems, 10(4), 85–110.
DeSanctis, G., & Poole, M. S. (1994). Capturing the complexity in advanced technology use: Adaptive structuration theory. Organization Science, 5(2), 121–147.
Devaraj, S., Easley, R. F., & Crant, J. M. (2008). Research note: How does personality matter? Relating the five-factor model to technology acceptance and use. Information Systems Research, 19(1), 93–105.
Elie-Dit-Cosaque, C., Pallud, J., & Kalika, M. (2011). The influence of individual, contextual, and social factors on perceived behavioral control of information technology: A field theory approach. Journal of Management Information Systems, 28(3), 201–234.
Faulkner, P., & Runde, J. (2019). Theorizing the digital object. MIS Quarterly, 43(4), 1279–1302.
Fox, G., & James, T. L. (2021). Toward an understanding of the antecedents to health information privacy concern: A mixed methods study. Information Systems Frontiers, 23(6), 1537–1562.
Fuller, R. M., & Dennis, A. R. (2009). Does fit matter? The impact of task-technology fit and appropriation on team performance in repeated tasks. Information Systems Research, 20(1), 2–17.
Giddens, A. (1984). The constitution of society: Outline of the theory of structuration. University of California Press.
Goodhue, D. L. (1998). Development and measurement validity of a task-technology fit instrument for user evaluations of information system. Decision Sciences, 29(1), 105–138.
Goodhue, D. L. (2006). TASK-TECHNOLOGY FIT: A Critical (But Often Missing!) Construct in Models of Information Systems and Performance. In Human-computer Interaction and Management Information Systems: Foundations (pp. 184–204). Routledge.
Green, L. (2016). Understanding the life course: Sociological and psychological perspectives. Wiley.
Gregor, S. (2006). The nature of theory in information systems. MIS Quarterly(3), 611.
Grover, V., & Lyytinen, K. (2015). New state of play in information systems research: The push to the edges. MIS Quarterly, 39(2), 271-A275.
Gupta, S., & Bostrom, R. P. (2013). An investigation of the appropriation of technology-mediated training methods incorporating enactive and collaborative learning. Information Systems Research, 24(2), 454–469.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice Hall.
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage publications.
Hair, J. F., Hollingsworth, C. L., Randolph, A. B., & Alain Yee Loong, C. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced issues in partial least squares structural equation modeling. Sage Publications.
Hollenbeck, J. R., & Brief, A. P. (1987). The effects of individual differences and goal origin on goal setting and performance. Organizational Behavior and Human Decision Processes, 40(3), 392–414.
Hsieh, P.-J., & Lin, W.-S. (2020). Understanding the performance impact of the epidemic prevention cloud: An integrative model of the task-technology fit and status quo bias. Behaviour & Information Technology, 39(8), 899–916.
Jasperson, J., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work systems. MIS Quarterly, 29(3), 525–557.
Jones, M. R., & Karsten, H. (2008). Giddens’s structuration theory and information systems research. MIS Quarterly, 32(1), 127–157.
Joo, Y. J., Park, S., & Shin, E. K. (2017). Students’ expectation, satisfaction, and continuance intention to use digital textbooks. Computers in Human Behavior, 69, 83–90.
Kallinikos, J., Aaltonen, A., & Marton, A. (2013). The ambivalent ontology of digital artifacts. MIS Quarterly, 37(2), 357–370.
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.
Kari, T., Salo, M., & Frank, L. (2020). Role of situational context in use continuance after critical exergaming incidents. Information Systems Journal, 30(3), 596–633.
Karimikia, H., Safari, N., & Singh, H. (2020). Being useful: How information systems professionals influence the use of information systems in enterprises. Information Systems Frontiers, 22(2), 429–453.
Ke, W., Tan, C.-H., Sia, C.-L., & Wei, K.-K. (2012). Inducing intrinsic motivation to explore the enterprise system: The supremacy of organizational levers. Journal of Management Information Systems, 29(3), 257–290.
Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741–755.
Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Two competing perspectives on automatic use: A theoretical and empirical comparison. Information Systems Research, 16(4), 418–432.
Lambert, L. S., & Newman, D. A. (2022). Construct Development and Validation in Three Practical Steps: Recommendations for Reviewers, Editors, and Authors. Sage Publications. https://doi.org/10.1177/10944281221115374
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.
Lankton, N. K., Wilson, E. V., & Mao, E. (2010). Antecedents and determinants of information technology habit. Information & Management, 47, 300–307.
Lankton, N. K., McKnight, D. H., & Thatcher, J. B. (2014). Incorporating trust-in-technology into expectation disconfirmation theory. The Journal of Strategic Information Systems, 23(2), 128–145. https://doi.org/10.1016/j.jsis.2013.09.001
Larsen, K. R., & Bong, C. H. (2016). A tool for addressing construct identity in literature reviews and meta-analyses. MIS Quarterly, 40(3), 529-A520.
Larsen, T. J., Sørebø, A. M., & Sørebø, Ø. (2009). The role of task-technology fit as users’ motivation to continue information system use. Computers in Human Behavior, 25(3), 778–784.
Lauterbach, J., Mueller, B., Kahrau, F., & Maedche, A. (2020). Achieving effective use when digitalizing work: The role of representational complexity. MIS Quarterly, 44(3), 1023–1048.
Li, X., Hsieh, J.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.
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.
Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737.
Louis, M. R., & Sutton, R. I. (1991). Switching cognitive gears: From habits of mind to active thinking. Human Relations, 44(1), 55–76.
Lowry, P. B., Gaskin, J., & Moody, G. D. (2015). Proposing the multi-motive information systems continuance model (MISC) to better explain end-user system evaluations and continuance intentions. Journal of the Association for Information Systems, 16(7), 515–579.
Lu, H.-P., & Yang, Y.-W. (2014). Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34, 323–332.
MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542–555.
Marakas, G. M., Johnson, R. D., & Clay, P. F. (2007). The evolving nature of the computer self-efficacy construct: An empirical investigation of measurement construction, validity, reliability and stability over time. Journal of the Association for Information Systems, 8(1), 15.
March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.
Martins, L. L., & Shalley, C. E. (2011). Creativity in virtual work. Small Group Research, 42(5), 536–561.
Menzies, T. (2015). Card-sorting. In T. Zimmermann (Ed.), Perspectives on data science for software engineering (1st edition ed., pp. 137–141). https://doi.org/10.1016/B978-0-12-804206-9.00027-1
Milton, S. K., & Kazmierczak, E. (2006). Ontology as meta-theory: A perspective. Scandinavian Journal of Information Systems, 18(1), 85.
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.
Nambisan, S., Agarwal, R., & Tanniru, M. (1999). Organizational mechanisms for enhancing user innovation in information technology. MIS Quarterly, 23(3), 365–395.
Ogden, D., & Rose, R. A. (2005). Using Giddens’s structuration theory to examine the waning participation of African Americans in baseball. Journal of Black Studies, 35(4), 225–245.
Oh, W., & Pinsonneault, A. (2007). On the assessment of the strategic value of information technologies: Conceptual and analytical approaches. MIS Quarterly, 31(2), 239–265.
Orlikowski, W. J. (1992). The duality of technology: Rethinking the concept of technology in organizations. Organization Science, 3(3), 398.
Orlikowski, W. J. (2000). Using technology and constituting structures: A practice lens for studying technology in organizations. Organization Science, 11(4), 404–428.
Orlikowski, W. J., & Lacono, C. S. (2001). Research commentary: Desperately seeking the “IT” in IT research–a call to theorizing the IT artifact. Information Systems Research, 12(2), 121.
Ortiz de Guinea, A., & Markus, M. L. (2009). Why break the habit of a lifetime? Rethinking the roles of intention, habit, and emotion in continuing information technology use. MIS Quarterly, 33(3), 433–444.
Pearson, J. M., Bahmanziari, T., Crosby, L., & Conrad, E. (2002). An empirical investigation into the relationship between organizational culture and computer efficacy as moderated by age and gender. The Journal of Computer Information Systems, 43(2), 58–70.
Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 31(4), 623–656.
Polites, G. L., & Karahanna, E. (2012). Shackled to the status quo: The inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS Quarterly, 36(1), 21-A13.
Prat, N., Comyn-Wattiau, I., & Akoka, J. (2015). A taxonomy of evaluation methods for information systems artifacts. Journal of Management Information Systems, 32(3), 229–267.
Rahrovani, Y., & Pinsonneault, A. (2020). Innovative IT Use and innovating with IT: A study of the motivational antecedents of two different types of innovative behaviors. Journal of the Association for Information Systems, 21(4), 936–970.
Rezvani, A., Khosravi, P., & Dong, L. (2017). Motivating users toward continued usage of information systems: Self-determination theory perspective. Computers in Human Behavior, 76, 263–275.
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3: Boenningstedt: SmartPLS GmbH. Retrieved from http://www.smartpls.com
Saeed, K. A., & Abdinnour, S. (2013). Understanding post-adoption IS usage stages: An empirical assessment of self-service information systems. Information Systems Journal, 23(3), 219–244.
Sasidharan, S., & Santhanam, R. (2006). Technology-based training. Human-Computer Interaction and Management Information Systems: Applications, 6, 247.
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-B619.
See, B. P., Yap, C. S., & Ahmad, R. (2019). Antecedents of continued use and extended use of enterprise systems. Behaviour & Information Technology, 38(4), 384–400.
Seo, D., & Ray, S. (2019). Habit and addiction in the use of social networking sites: Their nature, antecedents, and consequences. Computers in Human Behavior, 99, 109–125.
Spreitzer, G. M. (1996). Social structural characteristics of psychological empowerment. Academy of Management Journal, 39(2), 483–504.
Stern, B. B. (2006). What does brand mean? Historical-analysis method and construct definition. Journal of the Academy of Marketing Science, 34(2), 216–223.
Straub, D., Limayem, M., & Karahanna-Evaristo, E. (1995). Measuring system usage: implications for IS theory testing. Management Science, 41(8), 1328.
Subramani, M. (2004). How do suppliers benefit from information technology use in supply chain relationships? MIS Quarterly, 28(1), 45–73.
Sun, H. (2012). Understanding user revisions when using information system features: Adaptive system use and triggers. MIS Quarterly, 36(2), 453–478.
Sun, J., & Teng, J. T. C. (2012). Information Systems Use: Construct conceptualization and scale development. Computers in Human Behavior, 28(5), 1564–1574.
Sun, H., Wright, R. T., & Thatcher, J. (2019). Revisiting the impact of system use on task performance: An exploitative-explorative system use framework. Journal of the Association for Information Systems, 20(4), 398–433.
Tams, S., Thatcher, J. B., & Craig, K. (2017). How and why trust matters in post-adoptive usage: The mediating roles of internal and external self-efficacy. The Journal of Strategic Information Systems, 27(2), 170–190. https://doi.org/10.1016/j.jsis.2017.07.004
Thatcher, J. B., Wright, R. T., Heshan, S., Zagenczyk, T. J., & Klein, R. (2018). Mindfulness in information technology use: Definitions, distinctions, and a new measure. MIS Quarterly, 42(3), 831–847.
Thomas, D. R. (2006). A general inductive approach for analyzing qualitative evaluation data. American Journal of Evaluation, 27(2), 237–246.
Tsai, H., Compeau, D., & Meister, D. (2017). Voluntary use of information technology: An analysis and synthesis of the literature. Journal of Information Technology, 32(2), 147.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186–204.
Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83(1), 33–60.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 237(3), 425–478.
Walsh, I., Gettler-Summa, M., & Kalika, M. (2016). Expectable use: An important facet of IT usage. The Journal of Strategic Information Systems, 25(3), 177–210.
Wilhelm, O. (2009). Issues in computerized ability measurement: Getting out of the jingle and jangle jungle. In F. Scheuermann & J. Björnsson (Eds.), The Transition to Computer-Based Assessment: New Approaches to Skills Assessment and Implications for Large-scale Testing (pp. 138–143). European Communities.
Williams, J. A., & Gupta, S. (2018). There and back again: the cyclical process of IT use. Paper presented at the Americas Conference on Information Systems, New Orleans, USA.
Willison, R., Warkentin, M., & Johnston, A. C. (2018). Examining employee computer abuse intentions: Insights from justice, deterrence and neutralization perspectives. Information Systems Journal, 28(2), 266–293.
Xiang, G., Cheung, C. M. K., Zhang, K. Z. K., Chongyang, C., & Lee, M. K. O. (2021). A dual-identity perspective of obsessive online social gaming. Journal of the Association for Information Systems, 22(5), 1245–1284.
Zamani, E. D., Pouloudi, N., Giaglis, G. M., & Wareham, J. (2022). Appropriating Information technology artefacts through trial and error: The case of the tablet. Information Systems Frontiers, 24(1), 97–119.
Zhang, X., & Venkatesh, V. (2017). A nomological network of knowledge management system use: Antecedents and consequences. MIS Quarterly, 41(4), 1275–1306.
Acknowledgements
The first author acknowledges and is grateful for the support of his family during this work.
Funding
Data collection for this work was supported by a grant from Coles College of Business, Kennesaw State University.
Author information
Authors and Affiliations
Contributions
Both authors contributed to the design and implementation of this research, to the data analysis, and to the writing of the manuscript.
Corresponding author
Ethics declarations
Ethics Approval and Consent to Participate
Data collection was approved by the Kennesaw State University IRB under Study #19-189.
Consent for Publication
Jason A. Williams and Saurabh Gupta, the authors, give our consent for all relevant information about this research to be published in Information Systems Frontiers.
Competing Interests
The authors have no financial or proprietary interests in any material discussed in this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Williams, J.A., Gupta, S. Integrating the IT Use Literature: Construct Validity and a Holistic Nomological Framework. Inf Syst Front (2023). https://doi.org/10.1007/s10796-023-10454-x
Accepted:
Published:
DOI: https://doi.org/10.1007/s10796-023-10454-x