Top Management Support and Project Performance: Parallel Mediation through AI Adoption and Environmental Responsiveness
Keywords:
Top management support; Project performance; AI adoption; Environmental responsiveness; Malaysia oil and gas.Abstract
Project performance shortfalls persist in Malaysia’s oil and gas sector, implying executive support improves outcomes only when it activates enabling capabilities. Using TOE (with RBV and DOI support), this study tests whether top management support (TMS) influences project performance (PP) indirectly through parallel mediation via AI adoption (AIA) and environmental responsiveness (ER). Survey data from 412 professionals were analyzed using PLS-SEM with bootstrapping. TMS significantly increases AIA and ER; AIA significantly improves PP, while ER shows a marginal effect. The direct TMS→PP relationship is not significant, but the total indirect effect is significant, indicating indirect-only mediation. The findings clarify how executive support translates into performance through digital and adaptive capabilities; practically, leaders should operationalize support via AI governance and investment, alongside routines to sense and respond to regulatory and stakeholder pressures.
References
[1] J. B. Barney, “Firm resources and sustained competitive advantage,” Journal of Management, vol. 17, no. 1, pp. 99–120, 1991, doi: 10.1177/014920639101700108.
[2] D. J. Teece, G. Pisano, and A. Shuen, “Dynamic capabilities and strategic management,” Strategic Management Journal, vol. 18, no. 7, pp. 509– 533, 1997, doi: 10.1002/(SICI)1097-0266(199708)18:7<509::AID- SMJ882>3.0.CO;2-Z.
[3] L. G. Tornatzky and M. Fleischer, The Processes of Technological Innovation. Lexington, MA, USA: Lexington Books, 1990.
[4] E. M. Rogers, Diffusion of Innovations, 5th ed. New York, NY, USA: Free Press, 2003.
[5] R. Young and E. Jordan, “Top management support: Mantra or necessity?,” International Journal of Project Management, vol. 26, no. 7, pp. 713–725, 2008, doi: 10.1016/j.ijproman.2008.06.001.
[6] V. Uren and J. S. Edwards, “Technology readiness and the organizational journey towards AI adoption: An empirical study,” International Journal of Information Management, vol. 68, Art. no. 102588, 2023, doi: 10.1016/j.ijinfomgt.2022.102588.
[7] P. Mikalef and M. Gupta, “Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance,” Information & Management, vol. 58, no. 3, Art. no. 103434, 2021, doi: 10.1016/j.im.2021.103434.
[8] P. M. Almeida, G. Fernandes, and J. M. R. C. A. Santos, “Artificial intelligence tools for project management: A knowledge-based perspective,” Project Leadership and Society, vol. 6, Art. no. 100196, 2025, doi: 10.1016/j.plas.2025.100196.
[9] G. F. de Oliveira and R. Rabechini Jr., “Stakeholder management influence on trust in a project: A quantitative study,” International Journal of Project Management, vol. 37, no. 1, pp. 131–144, 2019, doi: 10.1016/j.ijproman.2018.11.001.
[10] M. B. Shaukat, K. F. Latif, A. Sajjad, and G. Eweje, “Revisiting the relationship between sustainable project management and project success: The moderating role of stakeholder engagement and team building,” Sustainable Development, vol. 30, no. 1, pp. 58–75, 2022, doi: 10.1002/sd.2228.
[11] U. Sekaran and R. Bougie, Research Methods for Business: A Skill- Building Approach, 7th ed. Chichester, U.K.: Wiley, 2016.
[12] P. M. Podsakoff, S. B. MacKenzie, J.-Y. Lee, and N. P. Podsakoff, “Common method biases in behavioral research: A critical review of the literature and recommended remedies,” Journal of Applied Psychology, vol. 88, no. 5, pp. 879–903, 2003.
[13] R. F. DeVellis, Scale Development: Theory and Applications, 4th ed. Thousand Oaks, CA, USA: Sage, 2017.
[14] D. A. Cook and T. J. Beckman, “Current concepts in validity and reliability for psychometric instruments: Theory and application,” The American Journal of Medicine, vol. 119, no. 2, pp. 166.e7–166.e16, 2006, doi: 10.1016/j.amjmed.2005.10.036.
[15] J. F. Hair, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed. Thousand Oaks, CA, USA: Sage, 2022.
[16] O. Zwikael, “Top management involvement in project management: Exclusive support practices for different project scenarios,” International Journal of Managing Projects in Business, vol. 1, no. 4, pp. 498–511, 2008, doi: 10.1108/17538370810906228.
[17] O. Neumann, K. Guirguis, and R. Steiner, “Exploring artificial intelligence adoption in public organizations: A comparative case study,” Public Management Review, vol. 26, pp. 114–141, 2022, doi: 10.1080/14719037.2022.2048685.
[18] J. A. Aragón-Correa and S. Sharma, “A contingent resource-based view of proactive corporate environmental strategy,” Academy of Management Review, vol. 28, no. 1, pp. 71–88, 2003.
[19] K. Buysse and A. Verbeke, “Proactive environmental strategies: A stakeholder management perspective,” Strategic Management Journal, vol. 24, no. 5, pp. 453–470, 2003, doi: 10.1002/smj.299.
[20] A. J. Shenhar, D. Dvir, O. Levy, and A. C. Maltz, “Project success: A multidimensional strategic concept,” Long Range Planning, vol. 34, no. 6, pp. 699–725, 2001.
[21] B. G. Tabachnick and L. S. Fidell, Using Multivariate Statistics, 6th ed. Boston, MA, USA: Pearson, 2013.
[22] N. Kock, “Common method bias in PLS-SEM: A full collinearity assessment approach,” International Journal of e-Collaboration, vol. 11, no. 4, pp. 1–10, 2015, doi: 10.4018/ijec.2015100101.
[23] C. Fornell and D. F. Larcker, “Evaluating structural equation models with unobservable variables and measurement error,” Journal of Marketing Research, vol. 18, no. 1, pp. 39–50, 1981.
[24] J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” Journal of the Academy of Marketing Science, vol. 43, no. 1, pp. 115– 135, 2015.
[25] J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ, USA: Lawrence Erlbaum Associates, 1988.
[26] O. Zwikael, “Top management involvement in project management: Exclusive support practices for different project scenarios,” Int. J. Manag. Proj. Bus., vol. 1, no. 3, pp. 387–403, 2008.
[27] J. Baker, “The Technology–Organization–Environment Framework,” in Information Systems Theory: Explaining and Predicting Our Digital Society, Vol. 1, Y. K. Dwivedi, M. R. Wade, and S. L. Schneberger, Eds. New York, NY, USA: Springer, 2011, pp. 231–245, doi: 10.1007/978-1- 4419-6108-2_12.
[28] D. S. Adamantiadou and L. Tsironis, “Leveraging artificial intelligence in project management: A systematic review of applications, challenges, and future directions,” Applied Sciences, vol. 14, no. 2, Art. no. 66, 2025.
[29] S. L. Hart, “A natural-resource-based view of the firm,” Acad. Manag. Rev., vol. 20, no. 4, pp. 986–1014, 1995.
[30] M. I. Merhi, “An evaluation of the critical success factors impacting artificial intelligence implementation,” Int. J. Inf. Manag., vol. 69, Art. no. 102545, 2023, doi: 10.1016/j.ijinfomgt.2022.102545.
