Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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The Impact of Exploration and Exploitation Activities and Market Agility on the Relationship between Big Data Analytics Capability and Firms' Performance

빅 데이터 분석능력과 기업 성과 간의 관계에서 혁신 및 개선 활동과 시장 민첩성의 영향

  • Received : 2022.08.05
  • Accepted : 2022.09.06
  • Published : 2022.09.30
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Abstract

This study investigated the impact of the latest developments in big data analytics capabilities (BDAC) on firm performance. The BDAC have the power to innovate existing management practices. Nevertheless, their impact on firm performance has not been fully is not yet fully elucidated. The BDAC relates to the flexibility of infrastructure as well as the skills of management and firm's personnel. Most studies have explored the phenomena from a theoretical perspective or based on factors such as organizational characteristics. However, this study extends the flow of previous research by proposing and testing a model which examines whether organizational exploration, exploitation and market agility mediate the relationship between the BDAC and firm performance. The proposed model was tested using survey data collected from the long-term employees over 10 years in 250 companies. The results analyzed through structural equation modeling show that a strong BDAC can help improve firm performance. An organization's ability to analyze big data affects its exploration and exploitation thereby affecting market agility, and, consequently, firm performance. These results also confirm the powerful mediating role of exploration, exploitation, and market agility in improving insights into big data utilization and improving firm performance.

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References

  1. Akter, S., Wamba, S.F., Gunasekaran, A., and Dubey, R., How to improve firm performance using big data analytics capability and business strategy alignment?, Int. J. Production Economics, 2016, Vol. 182, pp. 113-131. https://doi.org/10.1016/j.ijpe.2016.08.018
  2. Axson, D., Death by digital: Good-bye to finance as you know it, 2015, https://www.cfo.com/analytics/2015/10/death-digital-good-bye-finance-know/ (accessed 16 January 2022).
  3. Bedford, D., Management control systems across different modes of innovation: Implications for firm performance, Management Accounting Research, 2015, Vol. 28, No. Septem- ber, pp. 12-30. https://doi.org/10.1016/j.mar.2015.04.003
  4. Bhatt, G.D. and Grover, V., Types of information technology capabilities and their role in competitive advantage: An empirical study, Journal of Management Information Systems, 2005, Vol. 22, No. 2, pp. 253-277. https://doi.org/10.1080/07421222.2005.11045844
  5. Brynjolfsson, E., The productivity paradox of information technology, Communications of the ACM, 1993, Vol. 36, No. 12, pp. 66-77. https://doi.org/10.1145/163298.163309
  6. Cegarra-Navarro, J.G., Soto-Acosta, P., and Wensley, A.K., Structured knowledge processes and firm performance: the role of organizational agility, Journal of Business Research, 2016 Vol. 69, pp. 1544-1549. https://doi.org/10.1016/j.jbusres.2015.10.014
  7. Chae, K.M., Advanced statistics by using SPSS and AMOS (2ed), Yangseowon, Paju, Korea, 2018.
  8. Chenhall, H.R., Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: An exploratory study, Accounting, Organizations and Society, 2005, Vol. 30, No. 5, pp. 395-422. https://doi.org/10.1016/j.aos.2004.08.001
  9. Cheon, M.K. and Baek, D.H., An assessment system ofr evaluating big data capbility based on a reference model, Journal of the Society of Korea industrial and Systems Engineering, 2016, Vol. 39, No. 2, pp. 54-63. https://doi.org/10.11627/jkise.2016.39.2.054
  10. Choi, J.S., Analysis on Foreigners' perception of Korean Food Using Social Big Data, Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, 2017, Vol. 7, No. 8, pp. 427-437. https://doi.org/10.35873/AJMAHS.2017.7.8.040
  11. Constantiou, I.D. and Kallinikos, J., New games, new rules: Big data and the changing context of strategy, Journal of Information Technology, 2015, Vol. 30, No. 1, pp. 44-57. https://doi.org/10.1057/jit.2014.17
  12. Davenport, T.H., Paul, B., and Randy, B., How 'Big Data'is different, MIT Sloan Management Review, 2012, Vol. 54, No. 1, pp. 22-24.
  13. De Mauro, A., Greco, M., Grimaldi, M., and Ritala, P., Human resources for big data professions: a systematic classification of job roles and required skill sets, Information Processing & Management, 2018, Vol. 54, No. 5, pp. 807-817. https://doi.org/10.1016/j.ipm.2017.05.004
  14. Dyer, J.H., Gregersen, H.B. and Christensen, C.M., The innovator's DNA, Harvard Business Review, 2009, Vol. 87, No. 12, pp.60-67.
  15. Erevelles, S., Fukawa, N., and Swayne L., Big data consumer analytics and the transformation of marketing, Journal of Business Research, 2016, Vol. 69, No. 2, pp. 897-904. https://doi.org/10.1016/j.jbusres.2015.07.001
  16. Ferraris, A., Mazzoleni, A., Devalle, A., and Couturier, J., Big data analytics capabilities and knowledge management: impact on firm performance, Management Decision, 2018, Vol. 57, No. 8, pp. 1923-1936.
  17. George, G., Haas, M.R., and Pentland, A., Big data and management, Academy Management Journal, 2014, Vol. 57, No. 2, pp. 321-326.
  18. Gerbing, D.W. and Anderson, J.C., An updated paradigm for scale development incorporating unidimensionality and its assessment, Journal of Marketing Research, 1988, Vol. 25, No. 2, pp. 186-192. https://doi.org/10.1177/002224378802500207
  19. Gualandris, J., Legenvre, H., and Kalchschmidt, M., Exploration and exploitation within supply networks, International Journal of Operations & Production Management, 2018, Vol. 38, No. 3, pp. 667-689. https://doi.org/10.1108/IJOPM-03-2017-0162
  20. Gupta, A.K., Smith, K.G., and Shalley, C.E., The interplay between exploration and exploitation, The Academy of Management Journal, 2006, Vol. 49, No. 4, pp. 693-706.
  21. Gupta, M. and George, J.F., Toward the development of a big data analytics capability, Information & Management, 2016, Vol. 53, No. 8, pp. 1049-1064. https://doi.org/10.1016/j.im.2016.07.004
  22. Gupta, S. and Giri, V., Ensure high availability of data lake, In Gupta, S. and Giri, V., Practical Enterprise Data Lake Insights, Apress, Berkeley, CA, 2018, pp. 261-295.
  23. He, Z.-L. and Wong, P.-K., Exploration vs. exploitation: an empirical test of the ambidexterity hypothesis, Organization Science, 2004, Vol. 15, No. 4, pp. 481-494. https://doi.org/10.1287/orsc.1040.0078
  24. Henseler, J., Ringle, C.M., and Sarstedt, M., A new criterion for assessing discriminant validity in variance-based structural equation modeling, Journal of the Academy of Marketing Science, 2015, Vol. 43, No. 1, pp. 115-135. https://doi.org/10.1007/s11747-014-0403-8
  25. Hong, J.S. and Oh, I.K., Image difference of before and after an incident using social big data analysis: Focusing on a ramp return of "K" airline, International Journal of Tourism and Hospitality Research, 2016, Vol. 30, No. 6, pp. 119-133. https://doi.org/10.21298/IJTHR.2016.06.30.6.119
  26. Hong, P., Jagani, S., Kim, J.H., and Youn, S.H., Managing sustainability orientation: An empirical investigation of manufacturing firms, International Journal of Production Economics, 2019, Vol. 211, pp. 71-81. https://doi.org/10.1016/j.ijpe.2019.01.035
  27. Hong, S.H., Criteria for Selecting Appropriate Fit Indices in Structural Equation Modeling and Their Rationales, Korean Journal of Clinical Psychology, 2000, Vol. 19, No. 1, pp. 161-177.
  28. Jansen, J.J.P., Van den Bosch, F.A.J., and Volberda, H.W., Exploratory innovation, exploitative innovation, and performance: effects of organizational antecedents and environmental moderators, Management Science, 2006, Vol. 52, No. 11, pp. 1661-1674. https://doi.org/10.1287/mnsc.1060.0576
  29. Janssen, M., van der Voort, H., and Wahyudi, A., Factors influencing big data decision-making quality, Journal of Business Research, 2017, Vol. 70, pp. 338-345. https://doi.org/10.1016/j.jbusres.2016.08.007
  30. Jung, J.H., The Study On the Automobile Industry Ecosystem using Big Data Analysis, Journal of Industrial Economics and Business, 2017, Vol. 30, No. 5, pp. 1615-1642. https://doi.org/10.22558/jieb.2017.10.30.5.1615
  31. Kim, J.D., Ji, S.Y., and Ryu, K.H., A Study on Factors Affecting External Manufacturing Big Data Technology Transfer Performance in Small-and-Medium-Sized Manufacturing Firms: The Technology Transfer Cases of Electronics and Telecommunications Research Institute, Journal of Information Technology and Architecture, 2018, Vol. 15, No. 3, pp. 307-327. https://doi.org/10.22865/JITA.2018.15.3.307
  32. Kim, K.H., Usefulness and Riskiness of Big Data in Public Sector, Korean Journal of Policy Analysis and Evaluation, 2013, Vol. 23, No. 2, pp. 1-27. https://doi.org/10.23036/KAPAE.2013.23.2.001
  33. Kim, S.E., Jeong, K.H., Heo, Y.H., Woo, J.H., and Kim, K.H., One pass paper: AMOS structural equation utilization and SPSS advanced analysis, Hanbit Academy, Seoul, Korea, 2018.
  34. KOREA Data Agency, 2021 Data Industry White Paper, KOREA Data Agency, Seoul, Korea, 2021.
  35. Kristal, M.M., Huang, X., and Roth, A.V., The effect of an ambidextrous supply chain strategy on combinative competitive capabilities and business performance, Journal of Operations Management, 2010, Vol. 28, No. 5, pp. 415-429. https://doi.org/10.1016/j.jom.2009.12.002
  36. Kwon, H.J., Yoon, Y.M., and Kim, J.H., Changes in Management Accounting Education for Effective Utilization of Big Data, Yonsei Business Review, 2021, Vol. 58, No. 2, pp. 1-33. https://doi.org/10.55125/YBR.2021.06.58.2.1
  37. Lawson, R., Management Accounting competencies: fit for purpose in a digital age? Institute of Management Accountants, Montvale, N.J, 2018.
  38. Lee, K.K. and Kim, T.H., A business application of the business intelligence and the big data analytics, Journal of the Society of Korea Industrial and Systems Engineering, 2019, Vol. 42, No. 4, pp. 84-90. https://doi.org/10.11627/jkise.2019.42.4.084
  39. Lee, S.M. and Rha, J.S., Ambidextrous supply chain as a dynamic capability: Building a resilient supply chain, Management Decision, 2016, Vol. 54, No. 1, pp. 2-23. https://doi.org/10.1108/MD-12-2014-0674
  40. Lennerts, S., Schulze, A., and Tomczak, T., The asymmetric effects of exploitation and exploration on radical and incremental innovation performance: An uneven affair, European Management Journal, 2020, Vol. 38, No. 1, pp. 121-134. https://doi.org/10.1016/j.emj.2019.06.002
  41. March, J.G., Exploration and exploitation in organizational learning, Organization Science, 1991, Vol. 2, No. 1, pp. 71-87. https://doi.org/10.1287/orsc.2.1.71
  42. McAfee, A. and Brynjolfsson, E., Big data: The management revolution, Harvard Business Review, 2012, Vol. 90, No. 10, pp. 1-9.
  43. Mikalef, P., Boura, M., Lekakos, G., and Krogstie, J., Big data analytics capabilities and innovation: The mediating role of dynamic capabilities and moderating effect of the environment, British Journal of Management, 2019, Vol. 30, No. 2, pp. 272-298.
  44. Mikalef, P., Krogstie, J., Pappas, I.O., and Pavlou, P., Exploring the relationship between big data analytics capability and competitive performance: The mediating reles of dynamic and operational capabilities, Information & Management, 2020, Vol. 57, No. 2, 103169. https://doi.org/10.1016/j.im.2019.05.004
  45. Mikalef, P., Pappas, I.O., Krogstie, J., and Giannakos, M., Big data analytics capabilities: A systematic literature review and research agenda, Information System e-Business Management, 2018, Vol. 16, pp. 1-32. https://doi.org/10.1007/s10257-017-0339-x
  46. Noh, M.J. and Lee, C.K., The Impact of Big Data Analytics Capabilities and Values on Business Performance, Smart Media Journal, 2021, Vol. 10, No.1, pp. 108-115.
  47. Ojha, D., Struckell, E., Acharya, C., and Patel, P.C., Supply chain organizational learning, exploration, exploitation, and firm performance: A creation-dispersion perspective, International Journal of Production Economics, 2018, Vol. 204, No. October, pp. 70-82. https://doi.org/10.1016/j.ijpe.2018.07.025
  48. Pauleen, D.J. and Wang, W.Y., Does big data mean big knowledge? KM perspectives on big data and ana-lytics, Journal of Knowledge Management, 2017, Vol. 21, No. 1, pp. 1-6. https://doi.org/10.1108/JKM-08-2016-0339
  49. Raguseo, E. and Vitari, C., Investments in big data analytics and firm performance: An empirical investigation of direct and mediating effects, International Journal of Production Research, 2018, Vol. 56, No. 15, pp. 5206-5221. https://doi.org/10.1080/00207543.2018.1427900
  50. Riabacke, A., Managerial decision making under risk and uncertainty, IAENG International Journal of Computer Science, 2006, Vol. 32, No. 4, pp. 1-7.
  51. Rialti, R., Zollo, L., Ferraris, A., and Alon, I., Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model, Technological Forecasting & Social Change, 2019, Vol. 149, 119781. https://doi.org/10.1016/j.techfore.2019.119781
  52. Roh, J., Hong, P., and Min, H., Implementation of a responsive supply chain strategy in global complexity: The case of manufacturing firms, International Journal of Production Economics, 2014, Vol. 147, pp. 198-210. https://doi.org/10.1016/j.ijpe.2013.04.013
  53. Sharma, R., Mithas, S., and Kankanhalli, A., Transforming decision-making processes: A research agenda for understanding the impact of business analytics on organizations, European Journal of Information System, 2014, Vol. 23, No. 4, pp. 433-441. https://doi.org/10.1057/ejis.2014.17
  54. Simsek, Z., Organizational ambidexterity: Towards a multilevel understanding, Journal of Management Studies, 2009, Vol. 46, No. 4, pp. 597-624. https://doi.org/10.1111/j.1467-6486.2009.00828.x
  55. Singh, N.P. and Hong, P.C., Impact of strategic and operational risk management practices on firm performance: An empirical investigation, European Management Journal, 2020, Vol. 38, No. 5, pp. 723-735. https://doi.org/10.1016/j.emj.2020.03.003
  56. Soto-Acosta, P., Popa, S., and Martinez-Conesa, I., Information technology, knowledge management and environmental dynamism as drivers of innovation ambidexterity: A study in SMEs, Journal of Knowledge Management, 2018, Vol. 22, No. 4, pp. 824-849. https://doi.org/10.1108/JKM-10-2017-0448
  57. Spanos, Y.E. and Lioukas, S., An examination into the causal logic of rent generation contrasting porter's competitive strategy framework and the resource-based perspective, Strategic Management Journal, 2001, Vol. 22, No. 10, pp. 907-934. https://doi.org/10.1002/smj.174
  58. Subramani, M., How do suppliers benefit from information technology use in supply chain Relationships?, MIS Quarterly, 2004, Vol. 28, No. 1, pp. 45-73. https://doi.org/10.2307/25148624
  59. Tallon, P.P. and Pinsonneault, A., Competing perspectives on the link between strategic information technology alignment and organizational agility: Insights from a mediation model, MIS Quarterly, 2011, Vol. 35, No. 2, pp. 463-486. https://doi.org/10.2307/23044052
  60. Tan, K.H., Zhan, Y., Ji, G., Ye, F., and Chang, C., Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph, International Journal of Production Economics, 2015, Vol. 165, pp. 223-233. https://doi.org/10.1016/j.ijpe.2014.12.034
  61. Urciuoli, L. and Hintsa, J., Differences in security risk perceptions between logistics companies and cargo owners, The International Journal of Logistics Management, 2016, Vol. 27, No. 2, pp. 418-437. https://doi.org/10.1108/IJLM-02-2014-0034
  62. Wamba, S.F. and Mishra, D., Big data integration with business processes: A literature review, Business Process Management Journal, 2017, Vol. 23, No. 3, pp. 477-492. https://doi.org/10.1108/BPMJ-02-2017-0047
  63. Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J.F., Dubey, R., and Childe, S.J., Big data analytics and firm performance: Effects of dynamic capabilities, Journal of Business Research, 2017, Vol. 70, pp. 356-365. https://doi.org/10.1016/j.jbusres.2016.08.009
  64. Wang, Y., Kung, L., and Byrd, T.A., Big data analytics: understanding its capabilities and potential benefits for healthcare organizations, Technological Forecasting and Social Change, 2018, Vol. 126, pp. 3-13. https://doi.org/10.1016/j.techfore.2015.12.019
  65. Yiu, C., The big data opportunity: Making government faster, smarter and more personal, Policy exchange, London, 2012.
  66. Yook, K.H., Challenges and Prospect for Management Accounting in Industry 4.0, Korean Journal of Management Accounting Research, 2019, Vol. 19, No. 1, pp. 33-57. https://doi.org/10.31507/KJMAR.2019.4.19.1.33
  67. Yoon, H.J., A Study on the Effect of the Organizational, Technical and Environmental Recognition and Utilization of Big Data, Journal of Business Management, 2014, Vol. 7, No. 2, pp. 153-178