当前位置: X-MOL 学术Int. J. Prod. Econ. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations
International Journal of Production Economics ( IF 9.8 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.ijpe.2019.107599
Rameshwar Dubey , Angappa Gunasekaran , Stephen J. Childe , David J. Bryde , Mihalis Giannakis , Cyril Foropon , David Roubaud , Benjamin T. Hazen

The importance of big data analytics, artificial intelligence, and machine learning has been at the forefront of research for operations and supply chain management. Literature has reported the influence of big data analytics for improved operational performance, but there has been a paucity of research regarding the role of entrepreneurial orientation (EO) on the adoption of big data analytics. To address this gap, we draw on the dynamic capabilities view of firms and on contingency theory to develop and test a model that describes the role of EO on the adoption of big data analytics powered by artificial intelligence (BDA-AI) and operational performance (OP). We tested our research hypotheses using a survey of 256 responses gathered using a pre-tested questionnaire from manufacturing firms in India with the help of the National Association of Software and Services Companies (NASSCOM) and the Federation of Indian Chambers of Commerce and Industry (FICCI). The results from our analysis indicate that EO enables an organisation to exploit and further explore the BDA-AI capabilities to achieve superior OP. Further, our results provide empirical evidence based on data analysis that EO is strongly associated with higher order capabilities (such as BDA-AI) and OP under differential effects of environmental dynamism (ED). These findings extend the dynamic capability view and contingency theory to create better understanding of dynamic capabilities of the organisation while also providing theoretically grounded guidance to the managers to align their EO with their technological capabilities within their firms.

中文翻译:

创业导向和环境活力影响下的大数据分析和人工智能通往运营绩效的途径:对制造组织的研究

大数据分析、人工智能和机器学习的重要性一直处于运营和供应链管理研究的前沿。文献报道了大数据分析对提高运营绩效的影响,但关于创业导向 (EO) 对采用大数据分析的作用的研究很少。为了解决这一差距,我们利用公司的动态能力观点和权变理论来开发和测试一个模型,该模型描述了 EO 在采用人工智能 (BDA-AI) 和运营绩效支持的大数据分析方面的作用。操作)。在全国软件和服务公司协会 (NASSCOM) 和印度工商联合会 (FICCI) 的帮助下,我们使用来自印度制造公司的预先测试问卷收集的 256 份答复调查了我们的研究假设)。我们的分析结果表明,EO 使组织能够利用并进一步探索 BDA-AI 功能以实现卓越的 OP。此外,我们的结果提供了基于数据分析的经验证据,即 EO 与更高阶能力(如 BDA-AI)和环境动力(ED)不同影响下的 OP 密切相关。
更新日期:2020-08-01
down
wechat
bug