How to cite this paper
Juma, L & Kilani, S. (2022). Adoption enablers of big data analytics in supply chain management practices: the moderating role of innovation culture.Uncertain Supply Chain Management, 10(3), 711-720.
Refrences
Aggestam, V., Fleiß, E., & Posch, A. (2017). Scaling-up short food supply chains? A survey study on the drivers behind the intention of food producers. Journal of rural studies, 51, 64-72.
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment?. International Journal of Production Economics, 182, 113-131.
Ali-Hassan, H., Nevo, D., & Wade, M. (2015). Linking dimensions of social media use to job performance: The role of social capital. The Journal of Strategic Information Systems, 24(2), 65-89.
Sharifirad, M. S., & Ataei, V. (2012). Organizational culture and innovation culture: exploring the relationships between constructs. Leadership & Organization Development Journal, 33(5), 494-517.
Bahrami, M., & Shokouhyar, S. (2021). The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view. Information Technology & People.
Benoit, D. F., Lessmann, S., & Verbeke, W. (2020). On realising the utopian potential of big data analytics for maximising return on marketing investments. Journal of Marketing Management, 36(3-4), 233-247.
Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative science quarterly, 36(3), 421-458.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4), 1165-1188.
Creswell, J. W. (2009). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (Third Edition). Sage publications.
Dobni, C.B. (2008). Measuring innovation culture in organizations: the development of a generalized innovation culture construct using exploratory factor analysis. European Journal of Innovation Management, 11(4), 539-559.
Eldridge, J., & Crombie, A. (2013). A Sociology of Organisations (RLE: Organizations). Routledge, London.
Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, 18(3), 382–388.
Fosso Wamba, S., Gunasekaran, A., Akter, S., Ren, S.J., Dubey, R., & Childe, S.J. (2017). Big data analytics and firm performance: effects of dynamic capabilities. Journal of Business Research, 70(1), 356-365.
Gandomi, A. and Haider, M. (2015). Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B. and Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70(1), 308-317.
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.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems, 116(1).
Horibe, F. (2016), Creating the Innovation Culture: Leveraging Visionaries, Dissenters and Other Useful Troublemakers in Your Organisation, Wiley and Sons, Toronto.
Hsu, P.F., Ray, S. and Li-Hsieh, Y.Y. (2014). Examining cloud computing adoption intention, pricing mechanism, and deployment model. International Journal of Information Management, 34(4), 474-488.
Jere, J. N., & Ngidi, N. (2020). A technology, organisation and environment framework analysis of information and communication technology adoption by small and medium enterprises in Pietermaritzburg. South African Journal of Information Management, 22(1), 1-9.
Jum’a, L. (2020). The Effect of Value-Added Activities of Key Suppliers on the Performance of Manufacturing Firms. Polish Journal of Management Studies, 22(1), 231–246. https://doi.org/10.17512/pjms.2020.22.1.15
Jum’a, L., Ikram, M., Alkalha, Z., & Alaraj, M. (2022). Factors affecting managers’ intention to adopt green supply chain management practices: evidence from manufacturing firms in Jordan. Environmental Science and Pollution Research, 29(4), 5605-5621.
Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation. The International Journal of Logistics Management, 29(2), 676-703.
Maduku, D.K., Mpinganjira, M., & Duh, H. (2016). Understanding mobile marketing adoption intention by South African SEMs: a multi-perspective framework. International Journal of Information Management, 36(5), 711-723.
Maheshwari, S., Gautam, P., & Jaggi, C. K. (2021). Role of Big Data Analytics in supply chain management: current trends and future perspectives. International Journal of Production Research, 59(6), 1875-1900.
Mikalef, P., Pappas, I., Krogstie, J., & Pavlou, P. A. (Eds.). (2020). Big data and business analytics: A research agenda for realizing business value. Elsevier.
Mishra, D., Akman, I., & Mishra, A. (2014). Theory of reasoned action application for green information technology acceptance. Computers in Human Behavior, 36(2), 29-40.
Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Sage publications.
Queiroz, M.M., & Telles, R. (2018). Big data analytics in supply chain and logistics: an empirical approach. The International Journal of Logistics Management, 29(2), 767-783. https://doi.org/10.1108/IJLM-05-2017-0116
Rai, A., Patnayakuni, R., & Seth, N. (2006). Firm performance impacts of digitally enabled supply chain integration capabilities. MIS Quarterly, 30(2), 225-246.
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3 (Version 3) [Computer software]. SmartPLS GmbH. http://www.smartpls.com
Ruppel, C.P., & Harrington, S.J. (2000). The relationship of communication, ethical work climate, and trust to commitment and innovation. Journal of Business Ethics, 25(4), 313-328.
Sarstedt, M., Hair Jr, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal (AMJ), 27(3), 197-211.
Shanker, R., Bhanugopan, R., van der Heijden, B.I.J.M., & Farrell, M. (2017), Organizational climate for innovation and organizational performance: the mediating effect of innovative work behavior. Journal of Vocational Behavior, 100, 67-77.
Sivarajah, U., Kamal, M.M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70(1), 263-286.
Tan, K.H., Zhan, Y., Ji, G., Ye, F., & Chang, C. (2015). Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph. International Journal of Production Economics, 165(1), 223-233.
Tsai, M.C., Wen, L., & Wu, H.C. (2010). Determinants of REID adoption intention: evidence from Taiwanese retail chains. Information & Management, 47(5/6), 255-261.
Wamba, S.F., Gunasekaran, A., Bhattacharya, M., & Dubey, R. (2016). Determinants of RFID adoption intention by SMEs: an empirical investigation. Production Planning & Control, 27(12), 979-990.
Yasmin, M., Tatoglu, E., Kilic, H. S., Zaim, S., & Delen, D. (2020). Big data analytics capabilities and firm performance: An integrated MCDM approach. Journal of Business Research, 114, 1-15.
Yu, K.P., Song, J.H., Yoon, S.W., & Kim, J. (2014). Learning organization and innovative behavior. European Journal of Training and Development, 38(1), 75-94.
Zhong, R.Y., Newman, S.T., Huang, G.Q., & Lan, S. (2016). Big data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Computers and Industrial Engineering, 101(1), 572-591.
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment?. International Journal of Production Economics, 182, 113-131.
Ali-Hassan, H., Nevo, D., & Wade, M. (2015). Linking dimensions of social media use to job performance: The role of social capital. The Journal of Strategic Information Systems, 24(2), 65-89.
Sharifirad, M. S., & Ataei, V. (2012). Organizational culture and innovation culture: exploring the relationships between constructs. Leadership & Organization Development Journal, 33(5), 494-517.
Bahrami, M., & Shokouhyar, S. (2021). The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view. Information Technology & People.
Benoit, D. F., Lessmann, S., & Verbeke, W. (2020). On realising the utopian potential of big data analytics for maximising return on marketing investments. Journal of Marketing Management, 36(3-4), 233-247.
Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative science quarterly, 36(3), 421-458.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4), 1165-1188.
Creswell, J. W. (2009). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (Third Edition). Sage publications.
Dobni, C.B. (2008). Measuring innovation culture in organizations: the development of a generalized innovation culture construct using exploratory factor analysis. European Journal of Innovation Management, 11(4), 539-559.
Eldridge, J., & Crombie, A. (2013). A Sociology of Organisations (RLE: Organizations). Routledge, London.
Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, 18(3), 382–388.
Fosso Wamba, S., Gunasekaran, A., Akter, S., Ren, S.J., Dubey, R., & Childe, S.J. (2017). Big data analytics and firm performance: effects of dynamic capabilities. Journal of Business Research, 70(1), 356-365.
Gandomi, A. and Haider, M. (2015). Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B. and Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70(1), 308-317.
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.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems, 116(1).
Horibe, F. (2016), Creating the Innovation Culture: Leveraging Visionaries, Dissenters and Other Useful Troublemakers in Your Organisation, Wiley and Sons, Toronto.
Hsu, P.F., Ray, S. and Li-Hsieh, Y.Y. (2014). Examining cloud computing adoption intention, pricing mechanism, and deployment model. International Journal of Information Management, 34(4), 474-488.
Jere, J. N., & Ngidi, N. (2020). A technology, organisation and environment framework analysis of information and communication technology adoption by small and medium enterprises in Pietermaritzburg. South African Journal of Information Management, 22(1), 1-9.
Jum’a, L. (2020). The Effect of Value-Added Activities of Key Suppliers on the Performance of Manufacturing Firms. Polish Journal of Management Studies, 22(1), 231–246. https://doi.org/10.17512/pjms.2020.22.1.15
Jum’a, L., Ikram, M., Alkalha, Z., & Alaraj, M. (2022). Factors affecting managers’ intention to adopt green supply chain management practices: evidence from manufacturing firms in Jordan. Environmental Science and Pollution Research, 29(4), 5605-5621.
Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation. The International Journal of Logistics Management, 29(2), 676-703.
Maduku, D.K., Mpinganjira, M., & Duh, H. (2016). Understanding mobile marketing adoption intention by South African SEMs: a multi-perspective framework. International Journal of Information Management, 36(5), 711-723.
Maheshwari, S., Gautam, P., & Jaggi, C. K. (2021). Role of Big Data Analytics in supply chain management: current trends and future perspectives. International Journal of Production Research, 59(6), 1875-1900.
Mikalef, P., Pappas, I., Krogstie, J., & Pavlou, P. A. (Eds.). (2020). Big data and business analytics: A research agenda for realizing business value. Elsevier.
Mishra, D., Akman, I., & Mishra, A. (2014). Theory of reasoned action application for green information technology acceptance. Computers in Human Behavior, 36(2), 29-40.
Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Sage publications.
Queiroz, M.M., & Telles, R. (2018). Big data analytics in supply chain and logistics: an empirical approach. The International Journal of Logistics Management, 29(2), 767-783. https://doi.org/10.1108/IJLM-05-2017-0116
Rai, A., Patnayakuni, R., & Seth, N. (2006). Firm performance impacts of digitally enabled supply chain integration capabilities. MIS Quarterly, 30(2), 225-246.
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3 (Version 3) [Computer software]. SmartPLS GmbH. http://www.smartpls.com
Ruppel, C.P., & Harrington, S.J. (2000). The relationship of communication, ethical work climate, and trust to commitment and innovation. Journal of Business Ethics, 25(4), 313-328.
Sarstedt, M., Hair Jr, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal (AMJ), 27(3), 197-211.
Shanker, R., Bhanugopan, R., van der Heijden, B.I.J.M., & Farrell, M. (2017), Organizational climate for innovation and organizational performance: the mediating effect of innovative work behavior. Journal of Vocational Behavior, 100, 67-77.
Sivarajah, U., Kamal, M.M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70(1), 263-286.
Tan, K.H., Zhan, Y., Ji, G., Ye, F., & Chang, C. (2015). Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph. International Journal of Production Economics, 165(1), 223-233.
Tsai, M.C., Wen, L., & Wu, H.C. (2010). Determinants of REID adoption intention: evidence from Taiwanese retail chains. Information & Management, 47(5/6), 255-261.
Wamba, S.F., Gunasekaran, A., Bhattacharya, M., & Dubey, R. (2016). Determinants of RFID adoption intention by SMEs: an empirical investigation. Production Planning & Control, 27(12), 979-990.
Yasmin, M., Tatoglu, E., Kilic, H. S., Zaim, S., & Delen, D. (2020). Big data analytics capabilities and firm performance: An integrated MCDM approach. Journal of Business Research, 114, 1-15.
Yu, K.P., Song, J.H., Yoon, S.W., & Kim, J. (2014). Learning organization and innovative behavior. European Journal of Training and Development, 38(1), 75-94.
Zhong, R.Y., Newman, S.T., Huang, G.Q., & Lan, S. (2016). Big data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Computers and Industrial Engineering, 101(1), 572-591.