Innovating with AI: How Innovative Attitude, Peer Influence, and Task-Technology Fit Shape AI Appropriation in Project Management

The rapid diffusion of generative artificial intelligence (AI) tools is reshaping project management practices, yet significant variation remains in how professionals creatively appropriate these technologies. This study investigates the drivers of AI appropriation in project management by integrating Adaptive Structuration Theory (AST) with Task–Technology Fit (TTF) theory. The research examines how innovation attitude, peer influence, and task–technology fit influence AI-enabled creative behaviour, while also assessing the moderating role of organizational culture. A quantitative cross-sectional survey was conducted among 234 project management professionals working in Pakistan’s information technology and logistics sectors. Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results indicate that task–technology fit is the strongest predictor of creative AI appropriation, followed by innovation attitude and peer influence. Furthermore, organizational culture significantly strengthens the relationships between these antecedents and creative behaviour, highlighting its role as a contextual amplifier of AI-enabled innovation. The model explains 71.5% of the variance in creative behaviour, demonstrating substantial explanatory power. The study contributes to the literature by extending AST to generative AI contexts, empirically integrating AST and TTF in a unified framework, and providing evidence from an emerging economy with collectivist cultural characteristics. Practically, the findings suggest that organizations seeking to leverage AI for innovative project outcomes should prioritize task-aligned AI tools, cultivate innovation-oriented mindsets among professionals, and develop supportive organizational cultures that encourage experimentation and collaborative learning. References Alam, I., Waheed, K. Z., & Rehman, M. S. (2024). The impact of cultural diversity on workforce efficiency in private banking sector of Pakistan. Journal of Workplace Behavior, 5(1), 65–81. https://doi.org/10.70580/jwb.05.01.0218 Balash, F., Ghooja, S., & Talab, A. S. (2018). The rankings of involvement and consistency in line with academic research ethics. In European proceedings of social and behavioural sciences (pp. 72–80). https://doi.org/10.15405/epsbs.2018.05.7 Barcaui, A., & Monat, A. (2023). Who is smarter? Generative artificial intelligence versus project managers in project risk identification. Project Management Journal, 54(5), 455–467. Bayaga, A. (2025). Leveraging AI-enhanced and emerging technologies for pedagogical innovations in higher education. Education and Information Technologies, 30(1), 1045–1072. Cao, G., Duan, Y., & Edwards, J. S. (2025). Organizational culture, digital transformation, and product innovation. Information & Management, 62(4), 104135. Chen, J., Xie, W., Xie, Q., Hu, A., Qiao, Y., Wan, R., & Liu, Y. (2025). A systematic review of user attitudes toward GenAI: Influencing factors and industry perspectives. Journal of Intelligence, 13(7), 78. Ciftci, O., Berezina, K., & Kang, M. (2021). Effect of personal innovativeness on technology adoption in hospitality and tourism: Meta-analysis. In Information and communication technologies in tourism 2021 (pp. 162–174). Cimino, A., Felicetti, A. M., Corvello, V., Ndou, V., & Longo, F. (2025). Generative artificial intelligence (AI) tools in innovation management: A study on the appropriation of ChatGPT by innovation managers. Management Decision, 63(10), 3431–3453. Denison, D. R. (1990). Corporate culture and organizational effectiveness. Wiley. Denison, D. R., & Mishra, A. K. (1995). Toward a theory of organizational culture and effectiveness. Organization Science, 6(2), 204–223. Dennis, A. R., Wixom, B. H., & Vandenberg, R. J. (2001). Understanding fit and appropriation effects in group support systems via meta-analysis. MIS Quarterly, 25(2), 167–193. DeSanctis, G., & Poole, M. S. (1994). Capturing the complexity in advanced technology use: Adaptive structuration theory. Organization Science, 5(2), 121–147. Dong, X., Tian, Y., He, M., & Wang, T. (2024). When knowledge workers meet AI? The double-edged sword effects of AI adoption on innovative work behavior. Journal of Knowledge Management, 29(1), 113–147. https://doi.org/10.1108/jkm-02-2024-0222 Felicetti, A. M., Cimino, A., Corvello, V., Ndou, V., & Longo, F. (2024). Appropriating ChatGPT in project management: A study among Italian project managers. International Journal of Project Management, 42(3), 102576. Giddens, A. (1979). Central problems in social theory: Action, structure, and contradiction in social analysis. University of California Press. Golden, J. H., & Shriner, M. (2017). Examining relationships between transformational leadership and employee creative performance: The moderator effects of organizational culture. The Journal of Creative Behavior, 53(3), 363–376. https://doi.org/10.1002/jocb.216 Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. Hartl, E., Nawrath, D., & Hess, T. (2018). Refining the influence of organizational culture on individual IS adoption. In Proceedings of the 26th European Conference on Information Systems (ECIS). Association for Information Systems. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. Hofstede, G. (1991). Cultures and organizations: Software of the mind. McGraw-Hill. Hossain, M. A., Akter, S., & Nagahashi, N. (2024). Generative AI in project management: A systematic literature review. International Journal of Project Management, 42(5), 102589. Hughes, L., Mavi, R. K., Aghajani, M., Fitzpatrick, K., Gunaratnege, S. M., Shekarabi, S. A. H., Hughes, R., Khanfar, A., Khatavakhotan, A., & Mavi, N. K. (2025). Impact of artificial intelligence on project management (PM): Multi-expert perspectives on advancing knowledge and driving innovation toward PM2030. Journal of Innovation & Knowledge, 10(5), 100772. Jain, R., Garg, N., & Khera, S. N. (2022). Adoption of AI-enabled tools in social development organizations in India: An extension of UTAUT model. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.893691 Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199–218. Jo, H., & Bang, Y. (2023). Analyzing ChatGPT adoption drivers with the TOEK framework. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-49710-0 Khan, A., Khan, A., Shah, T. A., Khattak, M. N., & Abukhait, R. (2023). Management's internal governance policies on flexible work practices and the mediating lens of work-life enrichment. Journal of Organizational Effectiveness: People and Performance, 11(3), 532–552. https://doi.org/10.1108/joepp-02-2023-0059 Lin, T.-C., & Huang, C.-C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410–417. Lu, Z., Li, P., Wang, W., & Yin, M. (2025). Understanding the effects of AI-based credibility indicators when people are influenced by both peers and experts. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1–19). Mahmood, S., Mehmood, H., & Waheed, K. Z. (2024). The influence of despotic leadership on counterproductive work behavior: The role of follower’s dispositional characteristics. Pakistan Journal of Humanities and Social Sciences, 12(2), 1826–1841. https://doi.org/10.52131/pjhss.2024.v12i2.2305 McClelland, G. H., & Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological Bulletin, 114(2), 376–390. Mohajan, H. K. (2020). Quantitative research: A successful investigation in natural and social sciences. Journal of Economic Development, Environment and People, 9(4). https://doi.org/10.26458/jedep.v9i4.679 Mokhchy, J., Chen, G., Ahmad, S., Khan, Y. A., Zhang, J., & Ahmed, M. (2025). Dynamic impact of leadership style, knowledge-sharing, and organizational culture on organizational performance. Current Psychology, 44(6), 4097–4112. Nenni, M. E., De Felice, F., De Luca, C., & Forcina, A. (2025). How artificial intelligence will transform project management in the age of digitization: A systematic literature review. Management Review Quarterly, 75(2), 1669–1716. Nevo, S., Nevo, D., & Pinsonneault, A. (2020). Exploring the role of IT in the front-end of innovation: An empirical study of IT-enabled creative behavior. Information and Organization, 30(4), 100322. Oh, K., Kho, H.-S., Choi, Y.-J., & Seogjun, L. (2022). Determinants for successful digital transformation. Sustainability, 14(3), 1215. https://doi.org/10.3390/su14031215 Pan, X., Wu, Y., & Liu, C. (2023). How peer influence shapes the adoption of AI-based recommendation systems: A social influence perspective. Computers in Human Behavior, 140, 107574. Park, Y. S., Konge, L., & Artino, A. R. (2020). The positivism paradigm of research. Academic Medicine, 95(5), 690–694. Patil, P. P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extendin