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Does Supply Chain Analytics Enhance Supply Chain Innovation and Robustness Capability? Cover

Does Supply Chain Analytics Enhance Supply Chain Innovation and Robustness Capability?

Open Access
|Jul 2019

References

  1. Abubakar, A. M., Behravesh, E., Rezapouraghdam, H., & Yildiz, S. B. (2019). Applying artificial intelligence technique to predict knowledge hiding behavior. International Journal of Information Management, 49, 45-57, https://doi.org/10.1016/j.ijinfomgt.2019.02.00610.1016/j.ijinfomgt.2019.02.006
  2. Abubakar, A. M., Elrehail, H., Alatailat, M. A., & Elçi, A. (2017). Knowledge management, decision-making style and organizational performance. Journal of Innovation & Knowledge, 4(2), 104-114, https://doi.org/10.1016/j.jik.2017.07.00310.1016/j.jik.2017.07.003
  3. Abubakar, A. M., Karadal, H., Bayighomog, S. W., & Merdan, E. (2018). Workplace injuries, safety climate and behaviors: application of an artificial neural network. International Journal of Occupational Safety and Ergonomics, 1-11, https://doi.org/10.1080/10803548.2018.145463510.1080/10803548.2018.1454635
  4. Abubakar, A. M., Yazdian, T. F., & Behravesh, E. (2018). A riposte to ostracism and tolerance to workplace in-civility: a generational perspective. Personnel Review, 47(2), 441-457. https://doi.org/10.1108/PR-07-2016-015310.1108/PR-07-2016-0153
  5. 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(1), 113-131, https://doi.org/10.1016/j.ijpe.2016.08.01810.1016/j.ijpe.2016.08.018
  6. Ambulkar, S., Blackhurst, J., & Grae, S. (2015). Firm’s resilience to supply chain disruptions: Scale development and empirical examination. Journal of Operations Management, 33-34, 111-122, https://doi.org/10.1016/j.jom.2014.11.00210.1016/j.jom.2014.11.002
  7. Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99-120, https://doi.org/10.1177/01492063910170010810.1177/014920639101700108
  8. Behravesh, E., Tanova, C., & Abubakar, A. M. (2019). Do high-performance work systems always help to retain employees or is there a dark side? The Service Industries Journal, https://doi.org/10.1080/02642069.2019.1572748 (in press).10.1080/02642069.2019.1572748()
  9. Blome, C., Schoenherr, T., & Eckstein, D. (2014). The impact of knowledge transfer and complexity on supply chain flexibility: A knowledge-based view. International Journal of Production Economics, 147(1), 307-316, https://doi.org/10.1016/j.ijpe.2013.02.02810.1016/j.ijpe.2013.02.028
  10. Bagozzi, R. P., & Heatherton, T. F. (1994). A general approach to representing multifaceted personality constructs: application to state self-esteem. Structural Equation Model: A Multidisciplinary Journal, 1 (1), 35–67, https://doi.org/10.1080/1070551940953996110.1080/10705519409539961
  11. Brandon-Jones, E., Squire, B., Autry, C.W., & Petersen, K. J. (2014). A contingent resource-based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55–73, https://doi.org/10.1111/jscm.1205010.1111/jscm.12050
  12. Chae, B., Olson, D., & Sheu, C. (2014). The impact of supply chain analytics on operational performance: a resource-based view. International Journal of Production Research, 52(16), 4695-4710, http://dx.doi.org/10.1080/00207543.2013.86161610.1080/00207543.2013.861616
  13. Chen, H., Chiang, R.H.L., & Storey, V.C. (2013). Special issue: business intelligence research business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4), 1165-1188, http://doi.org/10.2307/315131210.2307/3151312
  14. Christopher, M., & Lee, H. (2004). Mitigating supply chain risk through improved confidence. International Journal of Physical Distribution & Logistics Management, 34(5), 388-396, https://doi.org/10.1108/0960003041054543610.1108/09600030410545436
  15. Côrte-Real, N., Oliveira, T., & Ruivo, P. (2016). Assessing business value of big data analytics in European firms. Journal of Business Research, 70(1), 379-390, https://doi.org/10.1016/j.jbusres.2016.08.01110.1016/j.jbusres.2016.08.011
  16. Dehghani, M., Abubakar, A. M., & Pashna, M. (2018). Market-driven management of start-ups: The case of wearable technology. Applied Computing and Informatics, https://doi.org/10.1016/j.aci.2018.11.002 (in press)10.1016/j.aci.2018.11.002(
  17. Fernando, Y., Chidambaram, R.R.M., & Wahyuni-TD, I.S. (2018). The impact of Big Data analytics and data security practices on service supply chain performance. Benchmarking: An International Journal, 25(9), 4009-4034,https://doi.org/10.1108/BIJ-07-2017-019410.1108/BIJ-07-2017-0194
  18. Fornell, C., & Larcker, D., (1981). Evaluating structural equation models with unobservable and measurement error. Journal of Marketing Research, 18(1), 39–50.10.1177/002224378101800104
  19. Fosso, W.S., Gunasekaran, A., Akter, S., Ren, S.J.F., Dubey, R. & Childe, S.J. (2017). Big dataanalytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365, https://doi.org/10.1016/j.jbusres.2016.08.00910.1016/j.jbusres.2016.08.009
  20. Fosso, W.S., Gunasekaran, A., Papadopoulos, T. & Ngai, E. (2018). Big data analytics in logistics and supply chain management, The International Journal of Logistics Management, 29(2), 478-484, https://doi.org/10.1108/IJLM-02-2018-002610.1108/IJLM-02-2018-0026
  21. Galbraith, J.R. (2014). Organization design challenges resulting from big data. Journal of Organizational Design, 3(1), 2-13, https://doi.org/10.7146/jod.885610.7146/jod.8856
  22. Gao, D., Xu, Z., Ruan, Y. Z., & Lu, H. (2017). From a systematic literature review to integrated definition for sustainable supply chain innovation. Journal of Cleaner Production, 142, 1518-1538, https://doi.org/10.1016/j.jclepro.2016.11.15310.1016/j.jclepro.2016.11.153
  23. García-Sánchez, E., García-Morales, V. J., & Martín-Rojas, R. (2018). Influence of Technological Assets on Organizational Performance through Absorptive Capacity, Organizational Innovation and Internal Labor Flexibility. Sustainability, 10(3), 770,http://doi.org/10.3390/su1003077010.3390/su10030770
  24. Grant, R. (1991). The Resource-Based Theory of Competitive Advantage: Implications for Strategy Formulation, California Management Review, 33(3), 114-135, https://doi.org/10.2307/4116666410.2307/41166664
  25. Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(S2), 109-122, https://doi.org/10.1002/smj.425017111010.1002/smj.4250171110
  26. Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317, https://doi.org/10.1016/j.jbusres.2016.08.00410.1016/j.jbusres.2016.08.004
  27. Hair, J.F., Ringle, C.M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: rigorous apps, better results and higher acceptance. Long Range Planning, 46(1/2), 1-12, https://ssrn.com/abstract=223379510.1016/j.lrp.2013.01.001
  28. Hayes, A.F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford Press
  29. Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online Readings in Psychology and Culture, 2(1), https://doi.org/10.9707/2307-0919.101410.9707/2307-0919.1014
  30. Jahmani, K., Fadiya, S.O., Abubakar, A.M., & Elrehail, H. (2018). Knowledge content quality, perceived usefulness, KMS use for sharing and retrieval: A flock leadership application. VINE Journal of Information and Knowledge Management Systems, 48(4), 470-490. https://doi.org/10.1108/VJIKMS-08-2017-005410.1108/VJIKMS-08-2017-0054
  31. Jeble, S., Dubey, R., Childe, S.J., Papadopoulos, T., Rou-baud, D., & Prakash, A. (2018). Impact of big data and predictive analytics capability on supply chain sustainability. The International Journal of Logistics Management, 29(2), 513-538, https://doi.org/10.1108/IJLM-05-2017-013410.1108/IJLM-05-2017-0134
  32. Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations & Production Management, 37(1), 10-36, https://doi.org/10.1108/IJOPM-02-2015-007810.1108/IJOPM-02-2015-0078
  33. Klein-Schmeink, S., & Peisl, T. (2013). Supply chain innovation and risk assessment (SCIRA) model. In Supply Chain Safety Management (pp. 309-326). Springer, Berlin, Heidelberg.10.1007/978-3-642-32021-7_20
  34. Kwak, D. W., Seo, Y. J., & Mason, R. (2018). Investigating the relationship between supply chain innovation, risk management capabilities and competitive advantage in global supply chains. International Journal of Operations & Production Management, 38(1), 2-21, https://doi.org/10.1108/IJOPM-06-2015-039010.1108/IJOPM-06-2015-0390
  35. 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, https://doi.org/10.1108/IJLM-06-2017-015310.1108/IJLM-06-2017-0153
  36. Lee, S. M., Lee, D., & Schniederjans, M. J. (2011). Supply chain innovation and organizational performance in the healthcare industry. International Journal of Operations & Production Management, 31(11), 1193-1214, https://doi.org/10.1108/0144357111117849310.1108/01443571111178493
  37. Likoum, S.W.B., Shamout, M.D., Harazneh, I., & Abubakar, A.M. (2018). Market-Sensing Capability, Innovativeness, Brand Management Systems, Market Dynamism, Competitive Intensity, and Performance: An Integrative Review. Journal of the Knowledge Economy, 1-21, https://doi.org/10.1007/s13132-018-0561-x10.1007/s13132-018-0561-x
  38. Marijn, J., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision making quality. Journal of Business Research, 70(1), 338-345, https://doi.org/10.1016/j.jbusres.2016.08.00710.1016/j.jbusres.2016.08.007
  39. Matook, S., Lasch, R., & Tamaschke, R. (2009). Supplier development with benchmarking as part of a comprehensive supplier risk management framework. International Journal of Operations and Production Management, 29(3), 241-267, https://doi.org/10.1108/0144357091093898910.1108/01443570910938989
  40. Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S.J., & Fosso-Wamba, S. (2017). The role of big data in explaining disaster resilience in supply chains for sustainability, Journal of Cleaner Production, 142, 1108-1118, https://doi.org/10.1016/j.jclepro.2016.03.05910.1016/j.jclepro.2016.03.059
  41. Ramanathan, R., Philpott, E., Duan, Y., & Cao, G. (2017). Adoption of business analytics and impact on performance: a qualitative study in retail. Production Planning & Control, V28 (11/12), 985-998, https://doi.org/10.1080/09537287.2017.133680010.1080/09537287.2017.1336800
  42. Sahay, B.S., & Ranjan, J. (2008). Real time business intelligence in supply chain analytics. Information Management & Computer Security, 16(1), 28-48, https://doi.org/10.1108/0968522081086273310.1108/09685220810862733
  43. Schoenherr, T., & Speier-Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), 120–132, https://doi.org/10.1111/jbl.1208210.1111/jbl.12082
  44. Seo, Y.J., Dinwoodie, J., & Kwak, D.W. (2014), The impact of innovativeness on supply chain performance: is supply chain integration a missing link? Supply Chain Management: An International Journal, 19(5/6), 733-746, https://doi.org/10.1108/SCM-02-2014-005810.1108/SCM-02-2014-0058
  45. Tiwari, S., Wee, H.M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115, 319-330, https://doi.org/10.1016/j.cie.2017.11.01710.1016/j.cie.2017.11.017
  46. Verona, G. (1999). A resource-based view of product development. Academy of Management Review, 24(1), 132-142, http://doi.org/10.2307/25904110.2307/259041
  47. Wagner, S.M. (2008). Innovation management in the German transportation industry. Journal of Business Logistics, 29(2), 215-231, https://doi.org/10.1002/j.2158-1592.2008.tb00093.x10.1002/j.2158-1592.2008.tb00093.x
  48. Waller, M.A., & Fawcett, S.E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84, https://ssrn.com/abstract=227948210.1111/jbl.12010
  49. Wang, Y., & Byrd, T.A. (2017). Business analytics-enabled decision-making effectiveness through knowledge absorptive capacity in health care. Journal of Knowledge Management, 21(3), 517-539, https://doi.org/10.1108/JKM-08-2015-030110.1108/JKM-08-2015-0301
  50. Wang, G., Gunasekaran, A., Ngai, E.W.T., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: certain investigations for research and applications. International Journal of Production Economics, 176(1), 98-110, https://doi.org/10.1016/j.ijpe.2016.03.01410.1016/j.ijpe.2016.03.014
  51. Waters, D. (2007). Supply Chain Risk Management: Vulnerability and Resilience. The Chartered Institute of Logistics and Transportation, London, 35-50.
  52. Wieland, A., & Wallenburg, C.M. (2012). Dealing with supply chain risks: linking risk management practices and strategies to performance. International Journal of Physical Distribution & Logistics Management, 42(10), 887-905, https://doi.org/10.1108/0960003121128141110.1108/09600031211281411
  53. Xu, Z., Frankwick, G.L., & Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), 1562-1566, https://doi.org/10.1016/j.jbusres.2015.10.01710.1016/j.jbusres.2015.10.017
DOI: https://doi.org/10.2478/orga-2019-0007 | Journal eISSN: 1581-1832 | Journal ISSN: 1318-5454
Language: English
Page range: 95 - 106
Submitted on: Apr 6, 2019
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Accepted on: May 20, 2019
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Published on: Jul 9, 2019
Published by: University of Maribor
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2019 Mohamed Dawood Shamout, published by University of Maribor
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.