Flexible Human Resource Management and Innovation in the Digital Economy: The Moderating Role of Work-Related Stress in Pakistan's IT Sector
DOI:
https://doi.org/10.55737/trt/v-iv.369Keywords:
Flexible Human Resource Management, Skill Flexibility, Behavioral Flexibility, HR Practices Flexibility, Innovation Generation and Adoption Resource-based View, JD-Resource, Information TechnologyAbstract
While there is general agreement that flexible human resource management (FHRM) is an enabler of organizational innovativeness, the processes by which the three-dimensional aspect of FHRM (skill flexibility, behavioral flexibility, and HR-practice flexibility) both positively and negatively influence innovation generation and innovation adoption, and the reasons why each aspect of FHRM interacts with employees' psychological experience of work-related stress are not coherent enough. Based on the Resource Based View, Dynamic Capabilities theory and Job Demands-Resources (JD-R) model, this research conceptualizes and examines the moderation model using primary data collection with questionnaire survey from employees of software houses working in Pakistan's Information Technology sector. The current study employed data analysis using PLS-SEM (Partial least squares sequential modeling) to investigate the reliability and validity of the data, discriminant validity through Heterotrait-Monotrait ratio, and bootstrapped significance. Skill and behavioral flexibility significantly predict both innovation generation and adoption, while HR-practice flexibility is generally less influential. Behavioral flexibility is the strongest predictor of innovation adoption. Work-related stress weakens the positive effect of skill flexibility on innovation outcomes but does not significantly affect behavioral flexibility. These findings show that Flexible HRM dimensions differ in their contribution to innovation and in their sensitivity to workplace stress. The study recommends further research in emerging economy technology sectors using PLS-predict, necessity-condition analysis, and longitudinal designs, while also encouraging deeper examination of Flexible HRM dimensions and stress as a boundary condition within the JD-R framework.
References
Antwi, C. O., Fan, C. J., Aboagye, M. O., Brobbey, P., Jababu, Y., Affum-Osei, E., & Avornyo, P. (2019). Job demand stressors and employees' creativity: A within-person approach. The Service Industries Journal, 39(3–4), 250–278. https://doi.org/10.1080/02642069.2018.1520220
Arulampalam, W., & Booth, A. L. (1998). Training and labour market flexibility: Is there a trade-off? British Journal of Industrial Relations, 36(4), 521–536. https://doi.org/10.1111/1467-8543.00106
Baer, M., & Oldham, G. R. (2006). The curvilinear relation between experienced creative time pressure and creativity: moderating effects of openness to experience and support for creativity. The Journal of Applied Psychology, 91(4), 963–970. https://doi.org/10.1037/0021-9010.91.4.963
Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. I. (2014). Burnout and work engagement: The JD–R approach. Annual Review of Organizational Psychology and Organizational Behavior, 1(1), 389–411. https://doi.org/10.1146/annurev-orgpsych-031413-091235
Bakker, Arnold B., & Demerouti, E. (2007). The Job Demands’ Resources model: state of the art. Journal of Managerial Psychology, 22(3), 309–328. https://doi.org/10.1108/02683940710733115
Bani-Melhem, S., Abukhait, R. M., & Mohd. Shamsudin, F. (2020). Does job stress affect innovative behaviors? Evidence from Dubai five-star hotels. Journal of Human Resources in Hospitality & Tourism, 19(3), 344–367. https://doi.org/10.1080/15332845.2020.1737769
Bhattacharya, M., Gibson, D. E., & Doty, D. H. (2005). The effects of flexibility in employee skills, employee behaviors, and human resource practices on firm performance. Journal of Management, 31(4), 622–640. https://doi.org/10.1177/0149206304272347
Camps, J., Oltra, V., Aldás-Manzano, J., Buenaventura-Vera, G., & Torres-Carballo, F. (2016). Individual performance in turbulent environments: The role of organizational learning capability and employee flexibility. Human Resource Management, 55(3), 363–383. https://doi.org/10.1002/hrm.21741
Chen, C.-J. (2009). Strategic human resource practices and innovation performance: the mediating role of knowledge management capacity. Strategic Direction, 25(6). https://doi.org/10.1108/sd.2009.05625fad.009
Crawford, E. R., Lepine, J. A., & Rich, B. L. (2010). Linking job demands and resources to employee engagement and burnout: a theoretical extension and meta-analytic test. The Journal of Applied Psychology, 95(5), 834–848. https://doi.org/10.1037/a0019364
Damanpour, F., & Daniel Wischnevsky, J. (2006). Research on innovation in organizations: Distinguishing innovation-generating from innovation-adopting organizations. Journal of Engineering and Technology Management, 23(4), 269–291. https://doi.org/10.1016/j.jengtecman.2006.08.002
De Clercq, D., Dayan, M., & Ng, P. Y. (2024). When performance demands enable change: How employees can overcome emotional dissonance and organizational underappreciation. European Management Journal, 42(4), 565–575. https://doi.org/10.1016/j.emj.2023.02.004
Dewar, R. D., & Dutton, J. E. (1986). The adoption of radical and incremental innovations: An empirical analysis. Management Science, 32(11), 1422–1433. https://doi.org/10.1287/mnsc.32.11.1422
Dost, M., Badir, Y. F., Ali, Z., & Tariq, A. (2016). The impact of intellectual capital on innovation generation and adoption. Journal of Intellectual Capital, 17(4), 675–695. https://doi.org/10.1108/jic-04-2016-0047
Easterby-Smith, M., Thorpe, R., & Jackson, P. R. (2012). Management research (4th ed.). Sage.
Eysenck, M. W. (1995). Anxiety and cognition: A unified theory. Psychology Press.
Forbes Councils. (2026). Flexibility & the future of work: Why HR leaders are designing for choice. Forbes.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. JMR, Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
Grant, R. M., & Baden-Fuller, C. (2004). A knowledge accessing theory of strategic alliances. The Journal of Management Studies, 41(1), 61–84. https://doi.org/10.1111/j.1467-6486.2004.00421.x
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
Hair, J. F., Jr, Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001
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. https://doi.org/10.1108/ebr-11-2018-0203
Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis (2nd ed.). Guilford Press.
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. https://doi.org/10.1007/s11747-014-0403-8
Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). Guilford Press.
Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131
Lado, A. A., & Wilson, M. C. (1994). Human resource systems and sustained competitive advantage: A competency-based perspective. Academy of Management Review, 19(4), 699. https://doi.org/10.2307/258742
Laursen, K. (2003). New human resource management practices, complementarities and the impact on innovation performance. Cambridge Journal of Economics, 27(2), 243–263. https://doi.org/10.1093/cje/27.2.243
Lepak, D. P., & Snell, S. A. (1999). The human resource architecture: Toward a theory of human capital allocation and development. Academy of Management Review, 24(1), 31. https://doi.org/10.2307/259035
Lepak, D. P., Liao, H., Chung, Y., & Harden, E. E. (2006). A conceptual review of human resource management systems in strategic human resource management research. In Research in Personnel and Human Resources Management (pp. 217–271). Emerald (MCB UP ).
Liengaard, B. D., Becker, J.-M., Bennedsen, M., Heiler, P., Taylor, L. N., & Ringle, C. M. (2025). Dealing with regression models' endogeneity by means of an adjusted estimator for the Gaussian copula approach. Journal of the Academy of Marketing Science, 53, 279–299. https://doi.org/10.1007/s11747-024-01055-4
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
Martínez-Sánchez, A., Vela-Jiménez, M.-J., Pérez-Pérez, M., & de-Luis-Carnicer, P. (2011). The dynamics of labour flexibility: Relationships between employment type and innovativeness: Labour flexibility and innovativeness. The Journal of Management Studies, 48(4), 715–736. https://doi.org/10.1111/j.1467-6486.2010.00935.x
Ministry of Finance, Government of Pakistan. (2026). Pakistan Economic Survey 2025-26. Ministry of Finance.
Molina-Azorin, J. F. (2016). Mixed methods research: An opportunity to improve our studies and our research skills. European Journal of Management and Business Economics, 25(2), 37–38. https://doi.org/10.1016/j.redeen.2016.05.001
Nesheim, T., Olsen, K. M., & Kalleberg, A. L. (2007). Externalizing the core: Firms’ use of employment intermediaries in the information and communication technology industries. Human Resource Management, 46(2), 247–264. https://doi.org/10.1002/hrm.20159
Ngo, H.-Y., & Loi, R. (2008). Human resource flexibility, organizational culture and firm performance: an investigation of multinational firms in Hong Kong. The International Journal of Human Resource Management, 19(9), 1654–1666. https://doi.org/10.1080/09585190802295082
Oldham, G. R., & Cummings, A. (1996). Employee creativity: Personal and contextual factors at work. Academy of Management Journal, 39(3), 607–634. https://doi.org/10.2307/256657
Pakistan Software Export Board. (2025). PSEB annual report 2024-25. PSEB.
Perez-Luno, A., Gopalakrishnan, S., & Cabrera, R. V. (2014). Innovation and performance: The role of environmental dynamism on the success of innovation choices. IEEE Transactions on Engineering Management, 61(3), 499–510. https://doi.org/10.1109/tem.2014.2318085
Pérez-Luño, A., Wiklund, J., & Cabrera, R. V. (2011). The dual nature of innovative activity: How entrepreneurial orientation influences innovation generation and adoption. Journal of Business Venturing, 26(5), 555–571. https://doi.org/10.1016/j.jbusvent.2010.03.001
Premkumar, G., & Potter, M. (1995). Adoption of computer-aided software engineering (CASE) technology: An innovation adoption perspective. ACM SIGMIS Database, 26(2–3), 105–124. https://doi.org/10.1145/217278.217291
Pulakos, E. D., Arad, S., Donovan, M. A., & Plamondon, K. E. (2000). Adaptability in the workplace: Development of a taxonomy of adaptive performance. Journal of Applied Psychology, 85(4), 612–624. https://doi.org/10.1037/0021-9010.85.4.612
Reader, S. M., & Laland, K. N. (2002). Social intelligence, innovation, and enhanced brain size in primates. Proceedings of the National Academy of Sciences of the United States of America, 99(7), 4436–4441. https://doi.org/10.1073/pnas.062041299
Richter, N. F., Schubring, S., Hauff, S., Ringle, C. M., & Sarstedt, M. (2020). When predictors of outcomes are necessary: guidelines for the combined use of PLS-SEM and NCA. Industrial Management + Data Systems, 120(12), 2243–2267. https://doi.org/10.1108/imds-11-2019-0638
Saunders, M. (2009). Research methods for business students (5th ed.). Pearson Education.
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/ejm-02-2019-0189
Sosik, J. J., & Godshalk, V. M. (2000). Leadership styles, mentoring functions received, and job-related stress: a conceptual model and preliminary study. Journal of Organizational Behavior, 21(4), 365–390. https://doi.org/10.1002/(sici)1099-1379(200006)21:4<365::aid-job14>3.0.co;2-h
Spender, J.-C. (1996). Making knowledge the basis of a dynamic theory of the firm: Making Knowledge. Strategic Management Journal, 17(S2), 45–62. https://doi.org/10.1002/smj.4250171106
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(sici)1097-0266(199708)18:7<509::aid-smj882>3.0.co;2-z
Van Dyne, L., Jehn, K. A., & Cummings, A. (2002). Differential effects of strain on two forms of work performance: individual employee sales and creativity. Journal of Organizational Behavior, 23(1), 57–74. https://doi.org/10.1002/job.127
West, M. A., & Anderson, N. R. (1996). Innovation in top management teams. Journal of Applied Psychology, 81(6), 680–693. https://doi.org/10.1037/0021-9010.81.6.680
Wright, P. M., & Snell, S. A. (1998). Toward a unifying framework for exploring fit and flexibility in strategic human resource management. Academy of Management Review, 23(4), 756–772. https://doi.org/10.5465/AMR.1998.1255637
Zhou, K. Z., & Wu, F. (2010). Technological capability, strategic flexibility, and product innovation. Strategic Management Journal, 31(5), 547–561. https://doi.org/10.1002/smj.830
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
