Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource-Based View and Big Data Culture

被引:486
作者
Dubey, Rameshwar [1 ]
Gunasekaran, Angappa [2 ]
Childe, Stephen J. [3 ]
Blome, Constantin [4 ]
Papadopoulos, Thanos [5 ]
机构
[1] Montpellier Business Sch, Montpellier Res Management, 2300 Ave Moulins, F-34185 Montpellier, France
[2] Calif State Univ, Sch Business & Publ Adm, 9001 Stockdale Highway, Bakersfield, CA 93311 USA
[3] Plymouth Univ, Plymouth Business Sch, Plymouth PL4 8AA, Devon, England
[4] Univ Sussex, Sch Business Management & Econ, Sussex House, Brighton BN1 9RH, E Sussex, England
[5] Univ Kent, Kent Business Sch, Hist Dockyard, Sail & Colour Loft, Chatham ME4 4TE, Kent, England
关键词
SUPPLY CHAIN MANAGEMENT; INFORMATION-TECHNOLOGY CAPABILITY; COMMON METHOD VARIANCE; PARTIAL LEAST-SQUARES; FIRM PERFORMANCE; ORGANIZATIONAL CULTURE; COMPETITIVE ADVANTAGE; MEASUREMENT SYSTEMS; CORPORATE CULTURE; MEDIATING ROLE;
D O I
10.1111/1467-8551.12355
中图分类号
F [经济];
学科分类号
02 ;
摘要
The importance of big data and predictive analytics has been at the forefront of research for operations and manufacturing management. The literature has reported the influence of big data and predictive analytics for improved supply chain and operational performance, but there has been a paucity of literature regarding the role of external institutional pressures on the resources of the organization to build big data capability. To address this gap, this paper draws on the resource-based view of the firm, institutional theory and organizational culture to develop and test a model that describes the importance of resources for building capabilities, skills and big data culture and subsequently improving cost and operational performance. We test our research hypotheses using 195 surveys, gathered using a pre-tested questionnaire. Our contribution lies in providing insights regarding the role of external pressures on the selection of resources under the moderating effect of big data culture and their utilization for capability building, and how this capability affects cost and operational performance.
引用
收藏
页码:341 / 361
页数:21
相关论文
共 123 条
  • [1] Endogeneity: How Failure to Correct for it can Cause Wrong Inferences and Some Remedies
    Abdallah, Wissam
    Goergen, Marc
    O'Sullivan, Noel
    [J]. BRITISH JOURNAL OF MANAGEMENT, 2015, 26 (04) : 791 - 804
  • [2] Essential Micro-foundations for Contemporary Business Operations: Top Management Tangible Competencies, Relationship-based Business Networks and Environmental Sustainability
    Akhtar, Pervaiz
    Khan, Zaheer
    Frynas, Jedrzej George
    Tse, Ying Kei
    Rao-Nicholson, Rekha
    [J]. BRITISH JOURNAL OF MANAGEMENT, 2018, 29 (01) : 43 - 62
  • [3] How to improve firm performance using big data analytics capability and business strategy alignment?
    Akter, Shahriar
    Wamba, Samuel Fosso
    Gunasekaran, Angappa
    Dubey, Rameshwar
    Childe, Stephen J.
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2016, 182 : 113 - 131
  • [4] THEORIES OF ORGANIZATIONAL CULTURE
    ALLAIRE, Y
    FIRSIROTU, ME
    [J]. ORGANIZATION STUDIES, 1984, 5 (03) : 193 - 226
  • [5] Institutions and the Diversity and Prevalence of Multinationals' Knowledge-Augmenting Subsidiaries
    Allen, Matthew M. C.
    Allen, Maria L.
    Lange, Knut
    [J]. BRITISH JOURNAL OF MANAGEMENT, 2018, 29 (03) : 483 - 496
  • [6] STRATEGIC ASSETS AND ORGANIZATIONAL RENT
    AMIT, R
    SCHOEMAKER, PJH
    [J]. STRATEGIC MANAGEMENT JOURNAL, 1993, 14 (01) : 33 - 46
  • [7] STRUCTURAL EQUATION MODELING IN PRACTICE - A REVIEW AND RECOMMENDED 2-STEP APPROACH
    ANDERSON, JC
    GERBING, DW
    [J]. PSYCHOLOGICAL BULLETIN, 1988, 103 (03) : 411 - 423
  • [8] ESTIMATING NONRESPONSE BIAS IN MAIL SURVEYS
    ARMSTRONG, JS
    OVERTON, TS
    [J]. JOURNAL OF MARKETING RESEARCH, 1977, 14 (03) : 396 - 402
  • [9] Auschitzky E, 2014, How big data can improve manufacturing?
  • [10] Business analytics and firm performance: The mediating role of business process performance
    Aydiner, Arafat Salih
    Tatoglu, Ekrem
    Bayraktar, Erkan
    Zaim, Selim
    Delen, Dursun
    [J]. JOURNAL OF BUSINESS RESEARCH, 2019, 96 : 228 - 237