The mediating role of knowledge management and information systems selection management capability on Big Data Analytics quality and firm performance

References

• Abawajy, J., Kelarev, A., & Chowdhury, M. (2014). Automatic generation of meta classifiers with large levels for distributed computing and networking. Journal of Networks, 9(9), 2259–2268. https://doi.org/10.4304/jnw.9.9.2259-2268
   Google Scholar
• Abubakar, A.M., Elrehail, H., Alatailat, M.A., & Elçi, A. (2019). Knowledge management, decision-making style and organizational performance. Journal of Innovation & Knowledge, 4(2), 104–114. https://doi.org/10.1016/j.jik.2017.07.003
   Web of Science ®Google Scholar
• Adams, F.G., & Graham, K.W. (2017). Integration, knowledge creation and B2B governance: The role of resource hierarchies in financial performance. Industrial Marketing Management, 63, 179–191. https://doi.org/10.1016/j.indmarman.2016.10.009
   Web of Science ®Google Scholar
• Alaskar, T.H., Mezghani, K., & Alsadi, A.K. (2020). Examining the adoption of Big data analytics in supply chain management under competitive pressure: Evidence from Saudi Arabia. Journal of Decision Systems, 1–21. https://doi.org/10.1080/12460125.2020.1859714
   Google Scholar
• Alavi, M., & Leidner, D.E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136. https://doi.org/10.2307/3250961
   Web of Science ®Google Scholar
• Al-Mashari, M., Al-Mudimigh, A., & Zairi, M. (2003). Enterprise resource planning: A taxonomy of critical factors. European Journal of Operational Research, 146(2), 352–364. https://doi.org/10.1016/S0377-2217(02)00554-4
   Web of Science ®Google Scholar
• Ambrosini, V., & Bowman, C. (2009). What are dynamic capabilities and are they a useful construct in strategic management? International Journal of Management Reviews, 11(1), 29–49. https://doi.org/10.1111/j.1468-2370.2008.00251.x
   Web of Science ®Google Scholar
• Ardito, L., Scuotto, V., Del Giudice, M., & Petruzzelli, A.M. (2019). A bibliometric analysis of research on Big Data analytics for business and management. Management Decision, 57(8), 1993–2009. https://doi.org/10.1108/MD-07-2018-0754
   Google Scholar
• Argote, L. (2011). Organizational learning research: Past, present and future. Management Learning, 42(4), 439–446. https://doi.org/10.1177/1350507611408217
   Web of Science ®Google Scholar
• Argote, L., & Miron-Spektor, E. (2011). Organizational learning: From experience to knowledge. Organization Science, 22(5), 1123–1137. https://doi.org/10.1287/orsc.1100.0621
   Web of Science ®Google Scholar
• Auster, E., & Choo, C.W. (1996). Managing information for the competitive edge. Neal-Schuman. Retrieved December 29, 2008.
   Google Scholar
• Barchiesi, M.A., & Colladon, A.F. (2019). Big data and big values: When companies need to rethink themselves. Journal of Business Research.
   Web of Science ®Google Scholar
• Baum, J., & Wally, S. (2003). Strategic decision speed and firm performance. Strategic Management Journal, 24(11), 1107–1129. https://doi.org/10.1002/smj.343
   Web of Science ®Google Scholar
• Bi, Z., & Cochran, D. (2014). Big data analytics with applications. Journal of Management Analytics, 1(4), 249–265. https://doi.org/10.1080/23270012.2014.992985
   Google Scholar
• Brix, J. (2017). Exploring knowledge creation processes as a source of organizational learning: A longitudinal case study of a public innovation project. Scandinavian Journal of Management, 33(2), 113–127. https://doi.org/10.1016/j.scaman.2017.05.001
   Web of Science ®Google Scholar
• Brix, J., & Peters, L.S. (2015). Exploring an innovation project as a source of change in organization design. Journal of Organization Design, 4(1), 29–43. https://doi.org/10.7146/jod.17574
   Google Scholar
• Brown, J.S., & Duguid, P. (1991). Organizational learning and communities-of-practice: Toward a unified view of working, learning, and innovation. Organization Science, 2(1), 40–57. https://doi.org/10.1287/orsc.2.1.40
   Web of Science ®Google Scholar
• Buhalis, D., & Volchek, K. (2021). Bridging marketing theory and big data analytics: The taxonomy of marketing attribution. International Journal of Information Management, 56, 102253. https://doi.org/10.1016/j.ijinfomgt.2020.102253
   Web of Science ®Google Scholar
• Burton, R., Obel, B., & Haakonsson, D. (2015). How to get the matrix organization to work. Journal of Organization Design JOD, 4(3), 37–45. https://doi.org/10.7146/jod.22549
   Google Scholar
• Buyya, R., Ramamohanarao, K., Leckie, C., Calheiros, R.N., Dastjerdi, A.V., & Versteeg, S. (2015, December). Big data analytics-enhanced cloud computing: Challenges, architectural elements, and future directions. In 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS) (pp. 75–84). IEEE.
   Google Scholar
• Cao, G., Duan, Y., & Li, G. (2015). Linking business analytics to decision making effectiveness: A path model analysis. IEEE Transactions on Engineering Management, 62(3), 384–395. https://doi.org/10.1109/TEM.2015.2441875
   Web of Science ®Google Scholar
• Castaneda, D.I., Manrique, L.F., & Cuellar, S. (2018). Is organizational learning being absorbed by knowledge management? A systematic review. Journal of Knowledge Management, 22(2), 299–325. https://doi.org/10.1108/JKM-01-2017-0041
   Google Scholar
• Cataldo, A.J., McQueen, R.J., & Sepulveda, M. (2011). Big it for small companies: Quick-scan method makes up for lack of resources. Industrial Engineer, 43(5), 48–54.
   Google Scholar
• Chai, J., Liu, J.N., & Ngai, E.W. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications, 40(10), 3872–3885. https://doi.org/10.1016/j.eswa.2012.12.040
   Web of Science ®Google Scholar
• Cheung, F.K., Kuen, J.L.F., & Skitmore, M. (2002). Multi-criteria evaluation model for the selection of architectural consultants. Construction Management & Economics, 20(7), 569–580. https://doi.org/10.1080/01446190210159818
   Google Scholar
• Chin, W.W., Marcolin, B.L., & Newsted, P.R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217. https://doi.org/10.1287/isre.14.2.189.16018
   Web of Science ®Google Scholar
• Ciampi, F., Demi, S., Magrini, A., Marzi, G., & Papa, A. (2021). Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation. Journal of Business Research, 123, 1–13. https://doi.org/10.1016/j.jbusres.2020.09.023
   Web of Science ®Google Scholar
• Citroen, C.L. (2011). The role of information in strategic decision-making. International Journal of Information Management, 31(6), 493–501. https://doi.org/10.1016/j.ijinfomgt.2011.02.005
   Web of Science ®Google Scholar
• Côrte-Real, N., Ruivo, P., & Oliveira, T. (2020). Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value? Information & Management, 57(1), 103141. https://doi.org/10.1016/j.im.2019.01.003
   Web of Science ®Google Scholar
• Crossan, M.M., Lane, H.W., & White, R.E. (1999). An organizational learning framework: From intuition to institution. Academy of Management Review, 24(3), 522–537. https://doi.org/10.5465/amr.1999.2202135
   Web of Science ®Google Scholar
• Crossan, M.M., Maurer, C.C., & White, R.E. (2011). Reflections on the 2009 AMR decade award: Do we have a theory of organizational learning? Academy of Management Review, 36(3), 446–460. https://doi.org/10.5465/amr.2010.0544
   Web of Science ®Google Scholar
• Cyert, R.M., & March, J.G. (1963). A Behavioral Theory of the Firm, 2(4), 169–187.
   Google Scholar
• Davenport, T. (2014). Big data at work: Dispelling the myths, uncovering the opportunities. Harvard Business Review Press.
   Google Scholar
• De Long, D. (1997). Building the knowledge-based organization: How culture drives knowledge behaviors. Centers for Business Innovation–Working Paper, 1–29.
   Google Scholar
• Delen, D., & Demirkan, H. (2013). Data, information and analytics as services.
   Google Scholar
• Dong, J.Q., & Yang, C.H. (2020). Business value of big data analytics: A systems-theoretic approach and empirical test. Information & Management, 57(1), 103124. https://doi.org/10.1016/j.im.2018.11.001
   Web of Science ®Google Scholar
• Drnevich, P.L., & Kriauciunas, A.P. (2011). Clarifying the conditions and limits of the contributions of ordinary and dynamic capabilities to relative firm performance. Strategic Management Journal, 32(3), 254–279. https://doi.org/10.1002/smj.882
   Web of Science ®Google Scholar
• Du Plessis, M. (2007). The role of knowledge management in innovation. Journal of Knowledge Management, 11(4), 20–29. https://doi.org/10.1108/13673270710762684
   Google Scholar
• Duarte, P.O., Alves, H.B., & Raposo, M.B. (2010). Understanding university image: A structural equation model approach. International Review on Public and Nonprofit Marketing, 7(1), 21–36. https://doi.org/10.1007/s12208-009-0042-9
   Google Scholar
• Eisenfuhr, F. (2011). Decision making. springer.
   Google Scholar
• Elgendy, N., Elragal, A., & Päivärinta, T. (2021). DECAS: A modern data-driven decision theory for big data and analytics. Journal of Decision Systems, 1–37. https://doi.org/10.1080/12460125.2021.1894674
   Web of Science ®Google Scholar
• Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: Impact on firm performance. Management Decision, 57(8), 1923–1936. https://doi.org/10.1108/MD-07-2018-0825
   Google Scholar
• Fiol, C.M., & Lyles, M.A. (1985). Organizational learning. Academy of Management Review, 10(4), 803–813. https://doi.org/10.5465/amr.1985.4279103
   Web of Science ®Google Scholar
• Flores, L.G., Zheng, W., Rau, D., & Thomas, C.H. (2012). Organizational learning: Subprocess identification, construct validation, and an empirical test of cultural antecedents. Journal of Management, 38(2), 640–667. https://doi.org/10.1177/0149206310384631
   Web of Science ®Google Scholar
• Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
   Web of Science ®Google Scholar
• Galetsi, P., Katsaliaki, K., & Kumar, S. (2020). Big data analytics in health sector: Theoretical framework, techniques and prospects. International Journal of Information Management, 50, 206–216. https://doi.org/10.1016/j.ijinfomgt.2019.05.003
   Web of Science ®Google Scholar
• Garvin, D.A., Edmondson, A.C., & Gino, F. (2008). Is yours a learning organization? Harvard Business Review, 86(3), 109.
   PubMed Web of Science ®Google Scholar
• George, G., Haas, M.R., & Pentland, A. (2014). Big data and management.
   Google Scholar
• Ghasemaghaei, M. (2020). The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage. International Journal of Information Management, 50, 395–404. https://doi.org/10.1016/j.ijinfomgt.2018.12.011
   Web of Science ®Google Scholar
• Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of management information systems,18(1), 185–214.
   Web of Science ®Google Scholar
• Grover, V., Chiang, R. H., Liang, T. P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems,35(2), 388–423.
   Web of Science ®Google Scholar
• Gupta, M., & George, J.F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.004
   Web of Science ®Google Scholar
• 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.2013.01.001
   Web of Science ®Google Scholar
• Hajli, N., Tajvidi, M., Gbadamosi, A., & Nadeem, W. (2020). Understanding market agility for new product success with big data analytics. Industrial Marketing Management, 86, 135–143. https://doi.org/10.1016/j.indmarman.2019.09.010
   Web of Science ®Google Scholar
• Hall, R., & Andriani, P. (2003). Managing knowledge associated with innovation. Journal of Business Research, 56(2), 145–152. https://doi.org/10.1016/S0148-2963(01)00287-9
   Web of Science ®Google Scholar
• Harrison, E.F. (1996). A process perspective on strategic decision making. Management Decision, 34(1), 46–53. https://doi.org/10.1108/00251749610106972
   Google Scholar
• Henseler, J., Ringle, C.M., & Sinkovics, R.R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Group Publishing Limited.
   Google Scholar
• Holmlund, M., Van Vaerenbergh, Y., Ciuchita, R., Ravald, A., Sarantopoulos, P., Ordenes, F.V., & Zaki, M. (2020). Customer experience management in the age of big data analytics: A strategic framework. Journal of Business Research, 116, 356–365. https://doi.org/10.1016/j.jbusres.2020.01.022
   Web of Science ®Google Scholar
• Huber, G.P. (1991). Organizational learning: The contributing processes and the literatures. Organization Science, 2(1), 88–115. https://doi.org/10.1287/orsc.2.1.88
   Web of Science ®Google Scholar
• Iqbal, A., Latif, F., Marimon, F., Sahibzada, U.F., & Hussain, S. (2019). From knowledge management to organizational performance. Journal of Enterprise Information Management, 32(1), 36–59. https://doi.org/10.1108/JEIM-04-2018-0083
   Google Scholar
• Işık, Ö., Jones, M.C., & Sidorova, A. (2013). Business intelligence success: The roles of BI capabilities and decision environments. Information & Management, 50(1), 13–23. https://doi.org/10.1016/j.im.2012.12.001
   Web of Science ®Google Scholar
• Jou, J., Shanteau, J., & Harris, R.J. (1996). An information processing view of framing effects: The role of causal schemas in decision making. Memory & Cognition, 24(1), 1–15. https://doi.org/10.3758/BF03197268
   PubMed Web of Science ®Google Scholar
• Kerlinger, F.N., Lee, H.B., & Bhanthumnavin, D. (2000). Foundations of Behavioral Research: The Most Sustainable Popular Textbook By Kerlinger & Lee (2000).
   Google Scholar
• Ko, D.G. (2010). Consultant competence trust doesn’t pay off, but benevolent trust does! Managing knowledge with care. Journal of Knowledge Management, 14(2), 202–213. https://doi.org/10.1108/13673271011032355
   Google Scholar
• Kumar, V., Maheshwari, B., & Kumar, U. (2003). An investigation of critical management issues in ERP implementation: Empirical evidence from Canadian organizations. Technovation, 23(10), 793–807. https://doi.org/10.1016/S0166-4972(02)00015-9
   Web of Science ®Google Scholar
• Lai, Y.L., & Ishizaka, A. (2020). The application of multi-criteria decision analysis methods into talent identification process: A social psychological perspective. Journal of Business Research, 109, 637–647. https://doi.org/10.1016/j.jbusres.2019.08.027
   Web of Science ®Google Scholar
• Lin, H.F. (2014). Contextual factors affecting knowledge management diffusion in SMEs. Industrial Management & Data Systems, 114(9), 1415–1437. https://doi.org/10.1108/IMDS-08-2014-0232
   Google Scholar
• Luo, W., & Liberatore, M.J. (2009). Achieving it consultant objectives through client project success. Information & Management, 46(5), 259–266. https://doi.org/10.1016/j.im.2009.03.003
   Web of Science ®Google Scholar
• Maroufkhani, P., Tseng, M.L., Iranmanesh, M., Ismail, W.K.W., & Khalid, H. (2020). Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International Journal of Information Management, 54, 102190. https://doi.org/10.1016/j.ijinfomgt.2020.102190
   Web of Science ®Google Scholar
• Marr, B. (2015). Big Data: Using SMART big data, analytics and metrics to make better decisions and improve performance. John Wiley & Sons.
   Google Scholar
• Merendino, A., Dibb, S., Meadows, M., Quinn, L., Wilson, D., Simkin, L., & Canhoto, A. (2018). Big data, big decisions: The impact of big data on board level decision-making. Journal of Business Research, 93, 67–78. https://doi.org/10.1016/j.jbusres.2018.08.029
   Web of Science ®Google Scholar
• Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261–276. https://doi.org/10.1016/j.jbusres.2019.01.044
   Web of Science ®Google Scholar
• Mikalef, P., Krogstie, J., Pappas, I.O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169. https://doi.org/10.1016/j.im.2019.05.004
   Web of Science ®Google Scholar
• Moores, T.T., & Chang, J.C.J. (2006). Ethical decision making in software piracy: Initial development and test of a four-component model. Mis Quarterly, 30(1), 167–180. https://doi.org/10.2307/25148722
   Web of Science ®Google Scholar
• Neaga, I., Liu, S., Xu, L., Chen, H., & Hao, Y. (2015, May). Cloud enabled big data business platform for logistics services: A research and development agenda. In International Conference on Decision Support System Technology (pp. 22–33). Springer
   Google Scholar
• Nelson, B., & Olovsson, T., “Security and privacy for big data: A systematic literature review”, Big Data (Big Data) 2016 IEEE International Conference on, pp. 3693–3702, 2016.
   Google Scholar
• Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford university press.
   Google Scholar
• Noorderhaven, N.G. (1995). Strategic decision making.
   Google Scholar
• Nutt, P.C. (2005). Search during decision making. European Journal of Operational Research, 160(3), 851–876. https://doi.org/10.1016/j.ejor.2003.07.009
   Web of Science ®Google Scholar
• Peteraf, M.A., & Barney, J.B. (2003). Unraveling the resource‐based tangle. Managerial and Decision Economics, 24(4), 309–323. https://doi.org/10.1002/mde.1126
   Google Scholar
• Podsakoff, P.M., & Organ, D.W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531–544. https://doi.org/10.1177/014920638601200408
   Web of Science ®Google Scholar
• Popova-Nowak, I.V., & Cseh, M. (2015). The meaning of organizational learning: A meta-paradigm perspective. Human Resource Development Review, 14(3), 299–331. https://doi.org/10.1177/1534484315596856
   Web of Science ®Google Scholar
• Popovič, A., Hackney, R., Coelho, P.S., & Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54(1), 729–739. https://doi.org/10.1016/j.dss.2012.08.017
   Web of Science ®Google Scholar
• Pradhananga, Y., Karande, S., & Karande, C. (2015, February). CBA: Cloud-based big data analytics. In 2015 International Conference on Computing Communication Control and Automation (pp. 47–51). IEEE.
   Google Scholar
• Ranjan, J., & Foropon, C. (2021). Big data analytics in building the competitive intelligence of organizations. International Journal of Information Management, 56, 102231. https://doi.org/10.1016/j.ijinfomgt.2020.102231
   Web of Science ®Google Scholar
• Rouhani, S., & Rotbei, S. (2021). Big data platforms: In the lens of selection and evaluation approach. Journal of Decision Systems, 1–30. https://doi.org/10.1080/12460125.2020.1869432
   Google Scholar
• Schilke, O. (2014). On the contingent value of dynamic capabilities for competitive advantage: The nonlinear moderating effect of environmental dynamism. Strategic Management Journal, 35(2), 179–203. https://doi.org/10.1002/smj.2099
   Web of Science ®Google Scholar
• Schniederjans, D.G., Curado, C., & Khalajhedayati, M. (2020). Supply chain digitization trends: An integration of knowledge management. International Journal of Production Economics, 220, 107439. https://doi.org/10.1016/j.ijpe.2019.07.012
   Web of Science ®Google Scholar
• Scott, S.G., & Bruce, R.A. (1995). Decision-making style: The development and assessment of a new measure. Educational and Psychological Measurement, 55(5), 818–831. https://doi.org/10.1177/0013164495055005017
   Web of Science ®Google Scholar
• Sharma, R., & Yetton, P. (2003). The contingent effects of management support and task interdependence on successful information systems implementation. MIS Quarterly, 27(4), 533–556. https://doi.org/10.2307/30036548
   Web of Science ®Google Scholar
• Smys, S., & Raj, J.S. (2019). Internet of things and big data analytics for health care with cloud computing. Journal of Information Technology, 1(1), 9–18.
   Google Scholar
• Somers, T.M., & Nelson, K.G. (2004). A taxonomy of players and activities across the ERP project life cycle. Information & Management, 41(3), 257–278. https://doi.org/10.1016/S0378-7206(03)00023-5
   Web of Science ®Google Scholar
• Tabesh, P., & Vera, D.M. (2020). Top managers’ improvisational decision-making in crisis: A paradox perspective. Management Decision, 58(10), 2235–2256. https://doi.org/10.1108/MD-08-2020-1060
   Google Scholar
• Tarafdar, M., & Zhang, J. (2005). Analysis of critical website characteristics: A cross-category study of successful websites. Journal of Computer Information Systems, 46(2), 14–24.
   Web of Science ®Google Scholar
• Tene, O., & Polonetsky, J. (2011). Privacy in the age of big data: A time for big decisions. Stanford Law Review Online, 64, 63.
   Google Scholar
• Tsai, W.H., Lee, P.L., Chen, S.P., Hsu, W., & Lin, T.W. (2009). A study of the selection criteria for enterprise resource planning systems. International Journal of Business and Systems Research, 3(4), 456–480. https://doi.org/10.1504/IJBSR.2009.027200
   Google Scholar
• Tsai, W.H., Lee, P.L., Shen, Y.S., & Lin, H.L. (2012). A comprehensive study of the relationship between enterprise resource planning selection criteria and enterprise resource planning system success. Information & Management, 49(1), 36–46. https://doi.org/10.1016/j.im.2011.09.007
   Web of Science ®Google Scholar
• Tsai, W.H., Shaw, M.J., Fan, Y.W., Liu, J.Y., Lee, K.C., & Chen, H.C. (2011). An empirical investigation of the impacts of internal/external facilitators on the project success of ERP: A structural equation model. Decision Support Systems, 50(2), 480–490. https://doi.org/10.1016/j.dss.2010.11.005
   Web of Science ®Google Scholar
• Upadhyay, P., & Kumar, A. (2020). The intermediating role of organizational culture and internal analytical knowledge between the capability of big data analytics and a firm’s performance. International Journal of Information Management, 52, 102100. https://doi.org/10.1016/j.ijinfomgt.2020.102100
   Web of Science ®Google Scholar
• Veríssimo, J., & Lacerda, T. (2015). The impact of CEOS’ transformational leadership and ethical integrity on strategic orientation to corporate social responsibility. European Journal of Management Studies, 20(2), 95–114.
   Google Scholar
• Verville, J., & Halingten, A. (2003). A six-stage model of the buying process for ERP software. Industrial Marketing Management, 32(7), 585–594. https://doi.org/10.1016/S0019-8501(03)00007-5
   Web of Science ®Google Scholar
• Villamizar Reyes, M.M., & Castañeda Zapata, D.I. (2014). Relation between organizational climate and its dimensions and knowledge-sharing behavior among knowledge workers. International Journal of Psychological Research, 7(2), 64–75. https://doi.org/10.21500/20112084.659
   Google Scholar
• Wang, E.T., & Chen, J.H. (2006). Effects of internal support and consultant quality on the consulting process and ERP system quality. Decision Support Systems, 42(2), 1029–1041. https://doi.org/10.1016/j.dss.2005.08.005
   Web of Science ®Google Scholar
• Weirich, P. (2004). Realistic decision theory: Rules for nonideal agents in nonideal circumstances. Oxford University Press.
   Google Scholar
• Wixom, B.H., & Watson, H.J. (2001). An empirical investigation of the factors affecting data warehousing success. MIS Quarterly, 25(1), 17–41. https://doi.org/10.2307/3250957
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.jbusres.2020.03.028
   Web of Science ®Google Scholar
• Yi, X., Liu, F., Liu, J., & Jin, H. (2014). Building a network highway for big data: Architecture and challenges. Ieee Network, 28(4), 5–13. https://doi.org/10.1109/MNET.2014.6863125
   Web of Science ®Google Scholar
• Youakim, B., & Jean, S. (2007). Security and risk management in supply chins. Journal of Information Assurance and Security, 2, 288–296.
   Google Scholar
• Yu, C., & Wong, T.N. (2015). An agent-based negotiation model for supplier selection of multiple products with synergy effect. Expert Systems with Applications, 42(1), 223–237. https://doi.org/10.1016/j.eswa.2014.07.057
   Web of Science ®Google Scholar
• Zareravasan, A., & Ashrafi, A. (2019). An empirical investigation on big data analytics (BDA) and innovation performance. In Proceedings of the 3rd International Conference on Business and Information Management (pp. 97–101).
   Google Scholar
• Zeydan, M., Çolpan, C., & Çobanoğlu, C. (2011). A combined methodology for supplier selection and performance evaluation. Expert Systems with Applications, 38(3), 2741–2751. https://doi.org/10.1016/j.eswa.2010.08.064
   Web of Science ®Google Scholar
• Zheng, X., Fu, M., & Chugh, M. (2017). Big data storage and management in SaaS applications.
   Google Scholar