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

The Impact of Exploration and Exploitation Activities and Market Agility on the Relationship between Big Data Analytics Capability and Firms’ Performance

부제만|정희경
한국산업경영시스템학회 (KOREAN SOCIETY OF INDUSTRIAL AND SYSTEMS ENGINEERING)|2022.09|한국산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)|제45권 제3호|pp. 150-162

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참고문헌

키워드

ExplorationExploitationBig data analytics capability(BDAC)Market agility
주제분류산업공학

초록

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 organiza- tional 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, con- sequently, 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.

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 organiza- tional 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, con- sequently, 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.

참고문헌 (66)

  1. Akter, S, "How to improve firm performance using big dataanalytics capability and business strategy alignment?", Int. J. Production Economics, 182, pp.113~131 (2016)
  2. "Death by digital: Good-bye to finance as you know it" (2015)[URL]
  3. Bedford, D, "Management control systems across different modes of innovation: Implications for firm performance", Management Accounting Research, 28, pp.12~30 (2015)
  4. Bhatt, G.D, "Types of information technology capabilities and their role in competitive advantage:An empirical study", Journal of Management Information Systems, 22(2), pp.253~277 (2005)
  5. Brynjolfsson, E, "The productivity paradox of information technology", Communications of the ACM, 36(12), pp.66~77 (1993)
  6. Cegarra-Navarro, J. G, "Structured knowledge processes and firm performance: the role of organizational agility", Journal of Business Research, 69, pp.1544~1549 (2016)
  7. Chae, K.M, "Advanced statistics by using SPSS and AMOS" (2018), Yangseowon
  8. Chenhall, H.R, "Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: An exploratory study", Accounting, Organizations and Society, 30(5), pp.395~422 (2005)
  9. 천민경, "빅데이터 역량 평가를 위한 참조모델 및 수준진단시스템 개발", 한국산업경영시스템학회지, 39(2), pp.54~63 (2016), 한국산업경영시스템학회
  10. 최종산, "소셜 빅데이터를 이용한 외국인의 한식에 대한 인식 분석", 예술인문사회 융합 멀티미디어 논문지, 7(8), pp.427~437 (2017), 사단법인 인문사회과학기술융합학회[DOI]
  11. Constantiou, I.D, "New games, new rules: Big data and the changing context of strategy", Journal of Information Technology, 30(1), pp.44~57 (2015)
  12. Davenport, T.H, "How ‘Big Data’is different", MIT Sloan Management Review, 54(1), pp.22~24 (2012)
  13. De Mauro, A, "Human resources for big data professions: a systematic classification of job roles and required skill sets", Information Processing & Management, 54(5), pp.807~817 (2018)
  14. Dyer, J.H, "The innovator’s DNA", Harvard Business Review, 87(12), pp.60~67 (2009)
  15. Erevelles, S, "Big data consumer analytics and the transformation of marketing", Journal of Business Research, 69(2), pp.897~904 (2016)
  16. Ferraris, A, "Big data analytics capabilities and knowledge management: impact on firm performance", Management Decision, 57(8), pp.1923~1936 (2018)
  17. George, G, "Big data and management", Academy Management Journa, 57(2), pp.321~326 (2014)
  18. Gerbing, D.W, "An updated paradigm for scale development incorporating unidimensionality and its assessment", Journal of Marketing Research, 25(2), pp.186~192 (1988)
  19. Gualandris, J, "Exploration and exploitation within supply networks", International Journal of Operations & Production Management, 38(3), pp.667~689 (2018)
  20. Gupta, A. K, "The interplay between exploration and exploitation", The Academy of Management Journal, 49(4), pp.693~706 (2006)
  21. Gupta, M, "Toward the development of a big data analytics capability", Information &Management, 53(8), pp.1049~1064 (2016)
  22. Gupta, S, "Practical Enterprise Data Lake Insights", pp.261~295 (2018), Apress
  23. He, Z.-L, "Exploration vs. exploitation:an empirical test of the ambidexterity hypothesis", Organization Science, 15(4), pp.481~494 (2004)
  24. Henseler, J, "A new criterion for assessing discriminant validity in variance-based structural equation modeling", Journal of the Academy of Marketing Science, 43(1), pp.115~135 (2015)
  25. 홍지숙, "소셜미디어 빅데이터를 활용한 항공사 이미지 변화 분석: K항공의 램프리턴을 중심으로", 관광연구저널, 30(6), pp.119~133 (2016), 한국관광연구학회[DOI]
  26. Hong, P, "Managing sustainability orientation: An empirical investigation of manufacturing firms", International Journal of Production Economics, 211, pp.71~81 (2019)
  27. Hong, S.H, "Criteria for Selecting Appropriate Fit Indices in Structural Equation Modeling and Their Rationales", Korean Journal of Clinical Psychology, 19(1), pp.161~177 (2000)
  28. Jansen, J.J.P, "Exploratory innovation, exploitative innovation, and performance: effects of organizational antecedents and environmental moderators", Management Science, 52(11), pp.1661~1674 (2006)
  29. Janssen, M, "Factors influencing big data decision-making quality,", Journal of Business Research, 70, pp.338~345 (2017)
  30. 정재헌, "빅데이타 분석을 통한 자동차산업에서의 클러스터 형성과 생태계 연구", 산업경제연구, 30(5), pp.1615~1642 (2017), 한국산업경제학회
  31. 김지대, "중소제조기업에서 외부 제조 빅데이터 기술이전 성과에 영향을 끼치는 요인에 관한 연구: 한국전자통신연구원의 기술이전 사례", 정보화연구, 15(3), pp.307~327 (2018), 한국엔터프라이즈아키텍처학회[DOI]
  32. 김기환, "공공부문 빅데이터의 활용성과 위험성", 정책분석평가학회보, 23(2), pp.1~27 (2013), 한국정책분석평가학회[DOI]
  33. Kim, S.E, "One pass paper: AMOS structural equation utilization and SPSS advanced analysis" (2018), Hanbit Academy
  34. Kristal, M.M, "The effect of an ambidextrous supply chain strategy on combinative competitive capabilities and business performance", Journal of Operations Management, 28(5), pp.415~429 (2010)
  35. 권혁준, "Big Data의 효과적 활용을 위한 관리회계 교육의 변화", 연세경영연구, 58(2), pp.1~33 (2021), 경영연구소[DOI]
  36. Lawson, R, "Management Accounting competencies: fit for purpose in a digital age?" (2018), Institute of Management Accountants
  37. 이기광, "A Business Application of the Business Intelligence and the Big Data Analytics", 한국산업경영시스템학회지, 42(4), pp.84~90 (2019), 한국산업경영시스템학회[DOI]
  38. Lee, S.M, "Ambidextrous supply chain as a dynamic capability: Building a resilient supply chain", Management Decision, 54(1), pp.2~23 (2016)
  39. Lennerts, S, "The asymmetric effects of exploitation and exploration on radical and incremental innovation performance: An uneven affair", European Management Journal, 38(1), pp.121~134 (2020)
  40. March, J.G, "Exploration and exploitation in organizational learning", Organization Science, 2(1), pp.71~87 (1991)
  41. McAfee, A, "Big data: The management revolution", Harvard Business Review, 90(10), pp.1~9 (2012)
  42. Mikalef, P, "Big data analytics capabilities and innovation: The mediating role of dynamic capabilities and moderating effect of the environment", British Journal of Management, 30(2), pp.272~298 (2019)
  43. Mikalef, P, "Exploring the relationship between big data analytics capability and competitive performance: The mediating reles of dynamic and operational capabilities", Information & Management, 57(2), pp.103169~ (2020)
  44. Mikalef, P, "Big data analytics capabilities: A systematic literature review and research agenda", Information System e-Business Management, 16, pp.1~32 (2018)
  45. 노미진, "빅데이터 분석능력과 가치가 비즈니스 성과에 미치는 영향", 스마트미디어저널, 10(1), pp.108~115 (2021), (사)한국스마트미디어학회[DOI]
  46. Ojha, D, "Supply chain organizational learning, exploration, exploitation, and firm performance: A creation-dispersion perspective", International Journal of Production Economics, 204, pp.70~82 (2018)
  47. Pauleen, D.J, "Does big data mean big knowledge? KM perspectives on big data and ana-lytics", Journal of Knowledge Management, 21(1), pp.1~6 (2017)
  48. Raguseo, E, "Investments in big data analytics and firm performance: An empirical investigation of direct and mediating effects", International Journal of Production Research, 56(15), pp.5206~5221 (2018)
  49. Riabacke, A, "Managerial decision making under risk and uncertainty", IAENG International Journal of Computer Science, 32(4), pp.1~7 (2006)
  50. Rialti, R, "Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model", Technological Forecasting & Social Change, 149, pp.119781~ (2019)
  51. Roh, J, "Implementation of a responsive supply chain strategy in global complexity:The case of manufacturing firms", International Journal of Production Economics, 147, pp.198~210 (2014)
  52. Sharma, R, "Transforming decision-making processes: A research agenda for understanding the impact of business analytics on organizations", European Journal of Information System, 23(4), pp.433~441 (2014)
  53. Simsek, Z, "Organizational ambidexterity: Towards a multilevel understanding", Journal of Management Studies, 46(4), pp.597~624 (2009)
  54. Singh, N.P, "Impact of strategic and operational risk management practices on firm performance: An empirical investigation", European Management Journal, 38, pp.723~735 (2020)
  55. Soto-Acosta, P, "Information technology, knowledge management and environmental dynamism as drivers of innovation ambidexterity: A study in SMEs", Journal of Knowledge Management, 22(4), pp.824~849 (2018)
  56. Spanos, Y.E, "An examination into the causal logic of rent generation contrasting porter’s competitive strategy framework and the resource‐based perspective", Strategic Management Journal, 22(10), pp.907~934 (2001)
  57. Subramani, M, "How do suppliers benefit from information technology use in supply chain Relationships", MIS Quarterly, 28(1), pp.45~73 (2004)
  58. Tallon, P.P, "Competing perspectives on the link between strategic information technology alignment and organizational agility: Insights from a mediation model", MIS Quarterly, 35(2), pp.463~486 (2011)
  59. Tan, K. H, "Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph", International Journal of Production Economics, 165, pp.223~233 (2015)
  60. Urciuoli, L, "Differences in security risk perceptions between logistics companies and cargo owners,", The International Journal of Logistics Management, 27(2), pp.418~437 (2016)
  61. Wamba, S.F, "Big data integration with business processes: A literature review", Business Process Management Journal, 23(3), pp.477~492 (2017)
  62. Wamba, S.F, "Big data analytics and firm performance: Effects of dynamic capabilities", Journal of Business Research, 70, pp.356~365 (2017)
  63. Wang, Y, "Big data analytics:understanding its capabilities and potential benefits for healthcare organizations", Technological Forecasting and Social Change, 126, pp.3~13 (2018)
  64. Yiu, C, "The big data opportunity: Making government faster, smarter and more personal" (2012), Policy exchange
  65. 육근효, "산업 4.0 시대의 관리회계 전망과 과제", 관리회계연구, 19(1), pp.33~57 (2019), 한국관리회계학회[DOI]
  66. Yoon, H.J, "A Study on the Effect of the Organizational, Technical and Environmental Recognition and Utilization of Big Data", Journal of Business Management, 7(2), pp.153~178 (2014)

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