Artificial intelligence-based supply chain resilience for improving firm performance in emerging markets

IF 3.8 Q2 MANAGEMENT
Subhodeep Mukherjee, Manish Mohan Baral, Ramji Nagariya, Venkataiah Chittipaka, Surya Kant Pal
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引用次数: 0

Abstract

Purpose This paper aims to investigate the firm performance of micro, small and medium enterprises (MSMEs) by using artificial intelligence-based supply chain resilience strategies. A theoretical framework shows the relationship between artificial intelligence, supply chain resilience strategy and firm performance. Design/methodology/approach A questionnaire is developed to survey the MSMEs of India. A sample size of 307 is considered for the survey. The employees working in MSMEs are targeted responses. The conceptual model developed is tested empirically. Findings The study found that eight hypotheses were accepted and two were rejected. There are five mediating variables in the current study. Artificial intelligence, the independent variable, positively affects all five mediators. Then, according to the survey and analysis of the final 307 responses from MSMEs, the mediating variables significantly impact the dependent variable, firm performance. Research limitations/implications This study is limited to emerging markets only. Also this study used only cross sectional data collection methods. Practical implications This study is essential for supply chain managers and top management willing to adopt the latest technology in their organisation or firmfor a better efficient supply chain process. Originality/value This study investigated artificial intelligence-based supply chain resilience for improving firm performance in emerging countries like India. This study tried to fill the research gap in artificial intelligence and supply chain resilience.
基于人工智能的供应链弹性,提高新兴市场企业绩效
本文旨在利用基于人工智能的供应链弹性策略研究中小微企业(MSMEs)的企业绩效。一个理论框架展示了人工智能、供应链弹性战略和企业绩效之间的关系。设计/方法/方法制定了一份调查问卷来调查印度的中小微企业。本次调查的样本量为307人。在中小微企业工作的员工是有针对性的回应。对所建立的概念模型进行了实证检验。研究发现8个假设被接受,2个被拒绝。在本研究中有五个中介变量。作为自变量的人工智能对所有五个中介都有积极影响。然后,根据对307家中小微企业最终反馈的调查和分析,中介变量显著影响因变量——企业绩效。本研究仅限于新兴市场。本研究仅采用横截面数据收集方法。本研究对于愿意在其组织或公司中采用最新技术以实现更高效供应链流程的供应链经理和高层管理人员至关重要。原创性/价值本研究调查了印度等新兴国家基于人工智能的供应链弹性,以提高企业绩效。本研究试图填补人工智能和供应链弹性方面的研究空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.40
自引率
0.00%
发文量
31
期刊介绍: The Journal of Global Operations and Strategic Sourcing aims to foster and lead the international debate on global operations and strategic sourcing. It provides a central, authoritative and independent forum for the critical evaluation and dissemination of research and development, applications, processes and current practices relating to sourcing strategically for products, services, competences and resources on a global scale and to designing, implementing and managing the resulting global operations. Journal of Global Operations and Strategic Sourcing places a strong emphasis on applied research with relevant implications for both knowledge and practice. Also, the journal aims to facilitate the exchange of ideas and opinions on research projects and issues. As such, on top of a standard section publishing scientific articles, there will be two additional sections: "The Industry ViewPoint": in this section, industrial practitioners from around the world will be invited (max 2 contributions per issue) to present their point of view on a relevant subject area. This is intended to give the journal not just an academic focus, but a practical focus as well. In this way, we intend to reflect a trend that has characterised the past few decades, where interests and initiatives in research, academia and industry have been more and more converging to the point of collaborative relationships being a common practice. "Research Updates - Executive Summaries". In this section, researchers around the world will be given the opportunity to present their research projects in the area of global sourcing and outsourcing by means of an executive summary of their project. This will increase awareness of the on-going research projects in the area and it will attract interest from industry.
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