References
- Ahmad, A. B., Butt, A. S., Chen, D., & Liu, B (2020). A mediated model of the effect of organizational culture on the intentions to engage in change-supportive behaviours: insights from the theory of planned behaviour. Journal of Management & Organization, 1-21. https://doi.org/10.1017/jmo.2020.4110.1017/jmo.2020.41
- Ajzen, I. (1985). From intentions to actions: A theory of planned behaviour. In Action control (pp. 11-39). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-69746-3_210.1007/978-3-642-69746-3_2
- Altalhi, M. (2021). Toward a model for acceptance of MOOCs in higher education: the modified UTAUT model for Saudi Arabia. Education and Information Technologies, 26(2), 1589-1605. https://doi.org/10.1007/s10639-020-10317-x10.1007/s10639-020-10317-x
- Ammenwerth, E. (2019). Technology acceptance models in health informatics: TAM and UTAUT. Stud Health Technol Inform, 263, 64-71. https://doi.org/10.3233/SHTI190111
- Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1-11. https://doi.org/10.1111/1748-8583.1209010.1111/1748-8583.12090
- Attuquayefio, S., & Addo, H. (2014). Using the UTAUT model to analyze students’ ICT adoption. International Journal of Education and Development Using ICT, 10(3). https://www.learntechlib.org/p/148478/
- Bankole, F. O., & Bankole, O. O. (2017). The effects of cultural dimension on ICT innovation: Empirical analysis of mobile phone services. Telematics and Informatics, 34(2), 490-505. https://doi.org/10.1016/j.tele.2016.08.00410.1016/j.tele.2016.08.004
- Ben-Gal, H. C. (2019). An ROI-based review of HR analytics: practical implementation tools. Personnel Review 48(6),1429-1448. https://doi.org/10.1108/PR-11-2017-036210.1108/PR-11-2017-0362
- Bindu, K. (2016). A Review of Poor HR Analytical Skills. International Journal of Innovative Research in Technology (IJIRT), 3 (1), 397-402.
- Borkovich, D. J., Skovira, R. J., & Breese-Vitelli, J. (2015). New technology adoption: embracing cultural influences. Issues in Information Systems, 16(3). https://doi.org/10.48009/3_iis_2015_138-14710.48009/3_iis_2015_138-147
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
- Chin, W.W. (2010). How to write up and report PLS analyses. In: Handbook of Partial Least Squares, pp.655–690, Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32827-8_2910.1007/978-3-540-32827-8_29
- Dwivedi, Y. K., Shareef, M. A., Simintiras, A. C., Lal, B., & Weerakkody, V. (2016). A generalised adoption model for services: A cross-country comparison of mobile health (m-health). Government Information Quarterly, 33(1), 174-187. https://doi.org/10.1016/j.giq.2015.06.00310.1016/j.giq.2015.06.003
- Eskiler, E., Ekici, S., Soyer, F., & Sari, I. (2016). The relationship between organizational culture and innovative work behaviour for sports services in tourism enterprises. Physical Culture and Sport. Studies and Research, 69(1), 53-64. http://doi.org/10.1515/pcssr-2016-000710.1515/pcssr-2016-0007
- Falk, R.F. and Miller, N.B. (1992). A Primer for Soft Modeling, University of Akron Press, Ohio, USA.
- Fernandez, V., & Gallardo-Gallardo, E. (2021). Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption. Competitiveness Review: An International Business Journal, 31(1) 162-187. https://doi.org/10.1108/CR-12-2019-016310.1108/CR-12-2019-0163
- Fisk, R. P., Patricio, L., Lin, J. S. C., & Chang, H. C. (2011). The role of technology readiness in self-service technology acceptance. Managing Service Quality: An International Journal, 21(4), 424-444. https://doi.org/10.1108/0960452111114628910.1108/09604521111146289
- Fitz-Enz, J. (2010). The new HR analytics: predicting the economicvalue of your company’s human capital investments. Amacom.
- Frambach, R.T. and Schillewaert, N. (2002). Organizational Innovation Adoption: A Multi-Level Framework of Determinants and Opportunities for Future Research. Journal of Business Research, 55, 163-176. https://doi.org/10.1016/S0148-2963(00)00152-110.1016/S0148-2963(00)00152-1
- Gordon, G. G., & DiTomaso, N. (1992). Predicting corporate performance from organizational culture. Journal of Management Studies, 29(6), 783-798. https://doi.org/10.1111/j.1467-6486.1992.tb00689.x10.1111/j.1467-6486.1992.tb00689.x
- Hair Jr., J.F., Sarstedt, M., Ringle, C.M. and Gudergan, S.P. (2017). Advanced Issues in Partial Least Squares Structural Equation Modeling, Sage Publications, USA.10.1007/978-3-319-05542-8_15-1
- Hair, J. F., 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.2013.01.00110.1016/j.lrp.2013.01.001
- Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998). Factorial Analysis. Multivariate Data Analysis, 5th ed., Prentice Hall, New Jersey.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd Ed., Sage: Thousand Oaks.10.1007/978-3-030-80519-7
- Halper, F. (2014). Predictive analytics for business advantage. TDWI Research, 1-32. Available from https://vods.dm.ux.sap.com/previewhub/ITAnalyticsContentHubANZ/downloadasset.2014-03-mar-17-21.predictive-analytics-for-business-advantage-pdf.pdf
- Hofstede, G. (2001). Culture’s recent consequences: Using dimension scores in theory and research. International Journal of Cross Cultural Management, 1(1), 11-17. https://doi.org/10.1177%2F14705958011100210.1177/147059580111002
- Kabra, G., Ramesh, A., Akhtar, P., & Dash, M. K. (2017). Understanding behavioural Intention to use information technology: Insights from humanitarian practitioners. Telematics and Informatics, 34(7), 1250-1261. https://doi.org/10.1016/j.tele.2017.05.01010.1016/j.tele.2017.05.010
- Kariyapperuma, K. A. S. K. (2016). Role of organisational culture in open innovation: an empirical study of service sector organisations in Sri Lanka. Vidyodaya Journal of Management, 2(2), 55-76.10.31357/vjm.v2i2.3643
- Khazanchi, S., Lewis, M. W., & Boyer, K. K. (2007). Innovation-supportive culture: The impact of organizational values on process innovation. Journal of Operations Management, 25(4), 871-884. https://doi.org/10.1016/j.jom.2006.08.00310.1016/j.jom.2006.08.003
- Kline, R.B. (2011). Principles and Practice of Structural Equation Modelling, 3rd. ed., The Guilford Press. New York.
- Kock, N. (2015). Common method bias in PLS-SEM: a full collinearity assessment approach, International Journal of e-Collaboration (IJEC), 11(4),.1–10. http://doi.org/10.4018/ijec.201510010110.4018/ijec.2015100101
- Liu, H., Ke, W., Wei, K. K., Gu, J., & Chen, H. (2010). The role of institutional pressures and organizational culture in the firm’s Intention to adopt internet-enabled supply chain management systems. Journal of Operations Management, 28(5), 372-384. https://doi.org/10.1016/j.jom.2009.11.01010.1016/j.jom.2009.11.010
- Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3-26. https://doi.org/10.1080/09585192.2016.124469910.1080/09585192.2016.1244699
- McAfee, A. (2002). The impact of enterprise information technology adoption on operational performance: An empirical investigation. Production and Operations Management, 11(1), 33-53. https://doi.org/10.1111/j.1937-5956.2002.tb00183.x10.1111/j.1937-5956.2002.tb00183.x
- Mohammed, D., & Quddus, A. (2019). HR Analytics: A modern tool in hr for predictive decision making. Journal of Management, 6(3). https://doi.org/10.34218/JOM.6.3.2019.00710.34218/JOM.6.3.2019.007
- Mohtaramzadeh, M., Ramayah, T., & Jun-Hwa, C. (2018). B2B e-commerce adoption in Iranian manufacturing companies: Analyzing the moderating role of organizational culture. International Journal of Human–Computer Interaction, 34(7), 621-639. https://doi.org/10.1080/10447318.2017.138521210.1080/10447318.2017.1385212
- O’Reilly III, C. A., Chatman, J., & Caldwell, D. F. (1991). People and organizational culture: A profile comparison approach to assessing person-organization fit. Academy of Management Journal, 34(3), 487-516. https://doi.org/10.5465/25640410.5465/256404
- Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioural research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879. https://doi.org/10.1037/0021-9010.88.5.87910.1037/0021-9010.88.5.879
- Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor’s comments: a critical look at the use of PLS-SEM in” MIS Quarterly”. MIS Quarterly, 36(1), iii-xiv. https://doi.org/10.2307/4141040210.2307/41410402
- Sarstedt, M., Hair Jr., J.F., Cheah, J.H., Becker, J.M. and Ringle, C.M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM, Australasian Marketing Journal (AMJ), 27(3), 197–211. https://doi.org/10.1016/j.ausmj.2019.05.00310.1016/j.ausmj.2019.05.003
- Srite, M. (2006). Culture as an explanation of technology acceptance differences: An empirical investigation of Chinese and US users. Australasian Journal of Information Systems, 14(1). https://doi.org/10.3127/ajis.v14i1.410.3127/ajis.v14i1.4
- Sunny, S., Patrick, L., & Rob, L. (2019). Impact of cultural values on technology acceptance and technology readiness. International Journal of Hospitality Management, 77, 89-96. https://doi.org/10.1016/j.ijhm.2018.06.01710.1016/j.ijhm.2018.06.017
- Talukder, M. (2012). Factors affecting the adoption of technological innovation by individual employees: An Australian Study. Procedia-Social and Behavioural Sciences, 40, 52-57. https://doi.org/10.1016/j.sbspro.2012.03.16010.1016/j.sbspro.2012.03.160
- Tseng, S. M. (2017). Investigating the moderating effects of organizational culture and leadership style on IT-adoption and knowledge-sharing Intention. Journal of Enterprise Information Management. https://doi.org/10.1108/JEIM-04-2016-008110.1108/JEIM-04-2016-0081
- Tsui, A. S., Wang, H., & Xin, K. R. (2006). Organizational culture in China: An analysis of culture dimensions and culture types. Management and Organization Review, 2(3), 345-376. https://doi.org/10.1111/j.1740-8784.2006.00050.x10.1111/j.1740-8784.2006.00050.x
- Van der Togt, J., & Rasmussen, T. H. (2017). Toward evidence-based HR. Journal of Organizational Effectiveness: People and Performance. https://doi.org/10.1108/JOEPP-02-2017-001310.1108/JOEPP-02-2017-0013
- Vargas, R., Yurova, Y. V., Ruppel, C. P., Tworoger, L. C., & Greenwood, R. (2018). Individual adoption of HR analytics: a fine grained view of the early stages leading to adoption. The International Journal of Human Resource Management, 29(22), 3046-3067. https://doi.org/10.1080/09585192.2018.144618110.1080/09585192.2018.1446181
- Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.1192610.1287/mnsc.46.2.186.11926
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478. https://doi.org/10.2307/3003654010.2307/30036540
- Wandhe, P. (2020). HR Analytics: A Tool for Strategic Approach to HR Productivity. Available at SSRN 3700502. https://dx.doi.org/10.2139/ssrn.370050210.2139/ssrn.3700502
- Wang, G., Lu, H., Hu, W., Gao, X., & Pishdad-Bozorgi, P. (2020). Understanding Behavioural Logic of Information and Communication Technology Adoption in Small-and Medium-Sized Construction Enterprises: Empirical Study from China. Journal of Management in Engineering, 36(6), 05020013. http://doi.org/10.1061/(asce)me.1943-5479.000084310.1061/(ASCE)ME.1943-5479.0000843
- Weerakkody, V., Kapoor, K., Balta, M. E., Irani, Z., & Dwivedi, Y. K. (2017). Factors influencing user acceptance of public sector big open data. Production Planning & Control, 28(11-12), 891-905. https://doi.org/10.1080/09537287.2017.133680210.1080/09537287.2017.1336802