The Effect of Perceived Complexity of Information Systems on User Performance with the Mediating Role of Frequency of Use

Hardani, Mehdi; Farhadpoor, Mohammad Reza

doi:10.22055/slis.2026.51439.2009

The Effect of Perceived Complexity of Information Systems on User Performance with the Mediating Role of Frequency of Use

Document Type : Original Article

Authors

Mehdi Hardani ¹

Mohammad Reza Farhadpoor ²

¹ Department of Information Technology Management, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.

² Department of Information Science and Knowledge, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.

https://doi.org/10.22055/slis.2026.51439.2009

Abstract

Background and Objectives: In today's IT-based workplaces, effective use of IT systems is crucial for enhancing user performance (Badda & Rahmouni, 2023). However, the inherent complexity of these systems can significantly affect how users interact with technology and, ultimately, their productivity (Verna et al., 2023). Perceived complexity refers to users' perceptions of the difficulty associated with understanding and using technology (Te'eni, 1989). Perceived IT complexity commonly occurs in organizational contexts characterized by distributed work environments, complex network structures, software projects involving multiple roles and interactions, high dependencies between subsystems, a diversity and multiplicity of applications, and changing and complex business needs (Storm & Scheepers, 2019). According to Stoica et al. (2024), organizations with networked structures, numerous applications, and high dependencies among IT components experience higher levels of perceived complexity. Accordingly, working across different locations and within distributed teams may further increase users' perceptions of IT complexity. Despite considerable attention to IT complexity and its potential consequences in previous research, the mechanisms through which perceived IT complexity translates into actual user performance have not yet been fully understood (Badda & Rahmouni, 2023). One potentially important mechanism is the frequency of information system use. While previous studies have examined system quality, service quality, user satisfaction, and performance, the mediating role of frequency of use in the relationship between perceived information system complexity and user performance requires further investigation. The existing evidence also suggests a complex relationship between these variables. On the one hand, perceived system complexity may discourage repeated use and consequently limit the development of users' skills and effective performance. On the other hand, frequent system use may increase users' familiarity with technology and thereby reduce some of the negative consequences associated with perceived complexity. Thus, frequency of use may serve as an important mechanism linking perceived complexity to user performance. Understanding this relationship is particularly important in educational organizations that rely on multiple information systems. Shahid Chamran University of Ahvaz, as one of the major universities in Iran, employs numerous information systems to support educational, research, student, cultural, financial, administrative, and knowledge-related activities. This multiplicity of systems may increase users' perceived complexity and influence their performance. Therefore, the present study aimed to investigate the effect of perceived IT complexity on the performance of users at Shahid Chamran University of Ahvaz, with particular emphasis on the mediating role of frequency of information system use.
Methodology: This study was applied in terms of purpose and employed a survey method with a causal-correlational approach to examine the relationships among the independent, dependent, and mediating variables. The statistical population consisted of 1,503 users of information systems at Shahid Chamran University of Ahvaz. Based on the criterion of 15 respondents per questionnaire item, the required sample size was estimated at 420 participants. Participants were selected using convenience sampling. Of the distributed questionnaires, 286 completed questionnaires were considered valid and included in the final analysis. The collected data were analyzed using SmartPLS software to examine the direct and indirect relationships among perceived IT complexity, frequency of information system use, and user performance.
Findings: The results showed that the mean score of perceived IT complexity was 3.33, indicating that users perceived the university's information technologies and information systems as moderately complex. This level of perceived complexity suggests that difficulties associated with understanding and using the systems may interfere with their effective utilization.
The hypothesis-testing results showed that perceived IT complexity had a negative effect on the performance of users at Shahid Chamran University of Ahvaz. A negative and significant relationship was observed between perceived complexity and user performance (reported coefficient = -0.221). Thus, higher levels of perceived complexity were associated with lower levels of user performance.
The results also showed that perceived IT complexity had a significant negative effect on the frequency of users' use of university information systems (β = -0.533). In other words, as users perceived information systems to be more complex, the frequency with which they used those systems decreased. This finding suggests that complexity may discourage continued interaction with information systems and consequently limit opportunities for users to develop familiarity and proficiency.
Furthermore, the analysis of the mediating relationship showed that perceived IT complexity had a significant negative indirect effect on user performance through frequency of information system use. The reported t-value for the indirect effect was 7.34, exceeding the critical value of 1.96 and indicating statistical significance at the 0.05 level. These findings demonstrate that frequency of use plays a significant mediating role in the relationship between perceived IT complexity and user performance. Accordingly, perceived complexity not only affects performance directly but may also reduce performance indirectly by decreasing users' frequency of interaction with information systems.
Discussion: The findings indicate that perceived IT complexity is an important factor affecting both the frequency of information system use and user performance. As users perceive organizational information systems to be more complex, they tend to use them less frequently, which in turn may negatively affect their performance. The significant mediating role of frequency of use highlights the importance of continuous interaction with information systems in reducing some of the adverse consequences associated with perceived complexity.
Therefore, improving the usability and simplicity of university information systems, reducing unnecessary complexity, enhancing integration among different systems, and providing appropriate user training may encourage more frequent system use and contribute to improved user performance. These findings can assist university managers and information system designers in developing more user-centered systems and improving the effective use of information technology in academic environments

Keywords

Perceived complexity of information technology

Performance of information systems'

users

Use of information systems

Information behavior

Subjects

Information Behavior

Abbas, T., Ujwal Gadiraju, Khan, V.-J., & Markopoulos, P. (2022). Understanding User Perceptions of Response Delays in Crowd-Powered Conversational Systems. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 1–42. https://doi.org/10.1145/3555765.

Abugabah, A., Sanzogni, L., & Poropat, A. (2009). The impact of information systems on user performance: A critical review and theoretical model. In International Conference on Computer Science and Engineering (ICCSE 2009). WASET.‏

Ali, A. & Money, W. (2005). A Study of Project Management System Acceptance. Proceedings of the 38th Annual Hawaii International Conference on System Sciences, 234c-234c. https://doi.org/10.1109/HICSS.2005.53

Badda, A. & Rahmouni, A. F. (2023). The Impact of the Complexity of Information Systems on User Performance Case of Moroccan Companies. International Journal of Scientific Research and Management, 11(3): 4714-4724. https://doi.org/10.18535/ijsrm/v11i03.em6.

Bessiere, K., Ceaparu, I., Lazar, J., Robinson, J., & Shneiderman, B. (2003). Understanding Computer User Frustration: Measuring and Modeling the Disruption from Poor Designs. University Libraries (University of Maryland).

Butt, S., Mahmood, A., Saleem, S., Murtaza, S. A., Hassan, S., & Molnár, E. (2023). The Contribution of Learner Characteristics and Perceived Learning to Students’ Satisfaction and Academic Performance during COVID-19. Sustainability, 15(2), 1348. https://doi.org/10.3390/su15021348.

Callimaci, A., & Fortin, A. (2022). Intended use of educational technology after the COVID-19 pandemic. Journal of Further and Higher Education, 1–15. https:// doi. org/ 10. 1080/ 0309877x. 2022.2079970.

Ceaparu, I., Lazar, J., Bessiere, K., Robinson, J., & Shneiderman, B. (2004). Determining Causes and Severity of End-User Frustration. International Journal of Human-Computer Interaction, 17(3), 333–356. https://doi.org/10.1207/s15327590ijhc1703_3.

Chan, S. H., Song, Q., & Yao, L. (2014). The Impact of Task Complexity, Task Motivation. Decision Support System (DSS) Motivation, and DSS Use on Performance. PACIS, 292, 1-19.‏

Collazo, N. A. J., Elen, J., & Clarebout, G. (2014). Towards a model on tool use in computer-based learning environments. International Journal of Technology Enhanced Learning, 6(2), 124. https://doi.org/10.1504/ijtel.2014.066859

Coskun, E., & Grabowski, M. (2005). Impacts of User Interface Complexity on User Acceptance and Performance in Safety-Critical Systems. Journal of Homeland Security and Emergency Management, 2(1). https://doi.org/10.2202/1547-7355.1109.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q., 13, 319-340. https://doi.org/10.2307/249008.

Ghasemaghaei, M., Hassanein, K., & Benbasat, I. (2019). Assessing the Design Choices for Online Recommendation Agents for Older Adults: Older Does Not Always Mean Simpler. MIS Quarterly, 43(1), 329–346. https://doi.org/10.25300/misq/2019/13947.

Gupta, A., Li, H., & Sharda, R. (2013). Should I send this message? Understanding the impact of interruptions, social hierarchy and perceived task complexity on user performance and perceived workload. Decision Support Systems, 55(1), 135–145. https://doi.org/10.1016/j.dss.2012.12.035.

Hasan, B., & Ali, J. M. H. (2009). The Impact of Computer Self-Efficacy and System Complexity on Acceptance of Information Technologies (pp. 264–275). IGI Global. https:// doi. org/ 10.4018/978-1-60566-128-5.CH015.

Hashim, B., Abbas, A. & Mohammed, B. B. (2016). Role of Information Technology (IT) in Managing Organizational Change and Organizational Interdependence. Researcher, 8(8): 1- 7.

Henim, S. R. & Sari, R. P. (2020). User Experience Evaluation of Student Academic Information System of Higher Education Using User Experience Questionnaire. Jurnal Komputer Terapan. https://doi.org/10.35143/JKT.V6I1.3582.

Hornbæk, K., & Hertzum, M. (2017). Technology acceptance and user experience: A review of the experiential component in HCI. ACM Transactions on Computer-Human Interaction (TOCHI), 24(5), 1-30.‏

Jaber Kadhim, M., & Ali Hassan, M. (2022). EFFECTS MODERATING ROLE OF INFORMATION TECHNOLOGY IN THE AUDIT PROFESSION. INTERNATIONAL JOURNAL of RESEARCH in SOCIAL SCIENCES & HUMANITIES, 12(04), 457–478. https://doi.org/10.37648/ijrssh.v12i04.025.

Kamis, A., Koufaris, M., & Stern, T. (2008). Using an Attribute-Based Decision Support System for User-Customized Products Online: An Experimental Investigation. MIS Quarterly, 32(1), 159. https://doi.org/10.2307/25148832.

Keikhosrokiani, P., Mustaffa, N., Zakaria, N., & Abdullah, R. (2019). Assessment of a medical information system: the mediating role of use and user satisfaction on the success of human interaction with the mobile healthcare system (iHeart). Cognition, Technology & Work, 22(2), 281–305. https://doi.org/10.1007/s10111-019-00565-4.

‌Lazar, J., Jones, A., Bessiere, K., Ceaparu, I., & Shneiderman, B. (2003). User Frustration with Technology in the Workplace. AIS Electronic Library (AISeL) (Association for Information Systems), 283.

Lazard, A. J., & King, A. J. (2019). Objective Design to Subjective Evaluations: Connecting Visual Complexity to Aesthetic and Usability Assessments of eHealth. International Journal of Human–Computer Interaction, 36(1), 95–104. https://doi.org/10.1080/10447318.2019.1606976

‌ Lee, S. C., & Ji, Y. G. (2019). Complexity of in-vehicle controllers and their effect on task performance. International Journal of Human–Computer Interaction, 35(1), 65-74.‏

Li, Y., You, F., You, X., & Ji, M. (2019). Smartphone text input: Effects of experience and phrase complexity on user performance, physiological reaction, and perceived usability. Applied Ergonomics, 80, 200–208. https://doi.org/10.1016/j.apergo.2019.05.019.

Lu, L., & Chen, C.-H. (2025). Diversity Analysis of Smart Home User Interface Design. AHFE International, 194. https://doi.org/10.54941/ahfe1006684.

‌Lunn, D., & Harper, S. (2010). Using galvanic skin response measures to identify areas of frustration for older web 2.0 users. https://doi.org/10.1145/1805986.1806032.

Lynch, B. D., Cerveny, R. P., Sanders, G. L., Krzystofiak, F. J., & Dansereau, F. E. (1996). Modeling information technology impacts in clerical work environments. CSUSB ScholarWorks (California State University, San Bernardino), 5(1), 7.

Marshall, T. E., Rainer, R. K., & Morris, S. (2003). Complexity and Control as Determinants of Performance with Information Technology Innovations. Journal of Computer Information Systems, 43(3), 1–9. https://doi.org/10.1080/08874417.2003.11647510

Mujeye, S., & Levy, Y. (2013). Complex passwords: How far is too far? The role of cognitive load on employee productivity. Online Journal of Applied Knowledge Management (OJAKM), 1(1), 122-132.‏

NADKARNI, S., & GUPTA, R. (2004). PERCEIVED WEBSITE COMPLEXITY, TELEPRESENCE AND USER ATTITUDES: THE MODERATING ROLE OF ONLINE USER TASKS. Academy of Management Proceedings, 2004(1), A1–A6. https://doi.org/10.5465/ambpp.2004.13862469.

Nah, F. F.-H., Zhang, P., & McCoy, S. (2005). Introduction: Human-Computer Interaction Studies in Management Information Systems. International Journal of Human-Computer Interaction, 19(1), 3–6. https://doi.org/10.1207/s15327590ijhc1901_2.

Nugroho, A. A., Astuti, D. S. P., & Kristianto, D. (2019). Pengaruh teknologi informasi, kemampuan teknik pemakai, dukungan manajemen puncak dan kompleksitas tugas terhadap kinerja sistem informasi akuntansi. 14(4). http://ejurnal.unisri.ac.id/index.php/Akuntansi/article/download/2967/2550

Odlyzko, A. (1999). The visible problems of the invisible computer: A skeptical look at information appliances. First Monday, 4(9). https://doi.org/10.5210/fm.v4i9.688.

Pacauskas, D., & Rajala, R. (2017). Information system users’ creativity. Information Technology & People, 30(1), 81–116. https://doi.org/10.1108/itp-04-2015-0090

Schilling, R. D., Beese, J., Haki, M. K., Aier, S., & Winter, R. (2017). Revisiting the Impact of Information Systems Architecture Complexity: A Complex Adaptive Systems Perspective. International Conference on Information Systems. https://www.alexandria.unisg.ch/252518/.

Schmitz, K., Teng, J. T. C., & Webb, K. J. (2016). Capturing the complexity of malleable IT use: adaptive structuration theory for individuals. Management Information Systems Quarterly, 40(3), 663–686. https://doi.org/10.25300/MISQ/2016/40.3.07

Soh, P.-H., & Subramanian, A. M. (2008). Is Usage a Missing Link in Explaining the Perceived Learning Outcome of Technology-Mediated Learning? IEEE Transactions on Engineering Management, 55(1), 50–66. https://doi.org/10.1109/tem.2007.912818.

Stoica, E., Moreira, J., Piest, J. P. S., & Bukhsh, F. (2024). Drivers and Metrics for Quantifying IT Landscape Complexity. In International Conference on Enterprise Design, Operations, and Computing (pp. 219-236). Cham: Springer Nature Switzerland.‏

Storm, C. P. & Scheepers, C. B. (2019). The impact of perceived work complexity and shared leadership on team performance of IT employees of South African firms. Information Systems Management, 36(3), 195-211.‏

Susanto, D. (2021). The Role of Mediation Intensity Utilization of Academic Information System and Ease of the Use on Task-Technology Fit Influence on Individual Performance (Empirical Study at Mercu Buana University, Yogyakarta). Jurnal Perilaku dan Strategi Bisnis, 9(2), 147-157.‏

Sweller, J. (1988). Cognitive Load during Problem Solving: Effects on Learning. Cogn. Sci., 12, 257-285. https://doi.org/10.1207/S15516709COG1202_4

Verna, E., Puttero, S., Genta, G., & Galetto, M. (2023). Exploring the effects of perceived complexity criteria on performance measures of human–robot collaborative assembly. Journal of Manufacturing Science and Engineering, 145(10), 101014.‏

Te'eni, D. (1989). Determinants and Consequences of Perceived Complexity in Human‐Computer Interaction. Decision Sciences, 20(1), 166–181. https://doi.org/10.1111/j.1540-5915.1989.tb01405.x.

‌ Uflacker, M. & Busse, D. K. (2007). Complexity in Enterprise Applications vs. Simplicity in User Experience. 778–787. https://doi.org/10.1007/978-3-540-73111-5_87

Wosny, M. (2025). Understanding the Root Causes of Healthcare Professionals’ Frustration with Digital Tools. Studies in Health Technology and Informatics. https://doi.org/10.3233/shti250109.

Xu, H., & Mahenthiran, S. (2021). Users’ perception of cybersecurity, trust and cloud computing providers’ performance. Information & Computer Security, ahead-of-print(ahead-of-print). https://doi.org/10.1108/ics-09-2020-0153.

Yulianandra, P. V., Wibirama, S., & Santosa, P. I. (2017, November 1). Examining the effect of website complexity and task complexity in web-based learning management system. IEEE Xplore. https://doi.org/10.1109/ICICOS.2017.8276348.

Zhang, P., & Galletta, D. (2006). Foundations of human-computer interaction in management information systems. Human-computer interaction and management information systems: Foundations, 5, 61-89.‏

Zheng, H., Xiao, D., & Zhou, J. (2024). Enhancing Perceived Value in Human-Computer Interaction: The Mediating Role of User Participation and the Moderating Role of Task Complexity. International Journal of Human–Computer Interaction, 1–10. https://doi.org/10.1080/10447318.2024.2350839.