Rasoul Mehdikhani, C. Valmohammadi, Roghayeh Taraz
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Furthermore, the study found that distinct facets of ML, namely, exploratory and exploitative learning, exerted a positive influence on SCA. Additionally, the investigation uncovered that the mechanisms of exploratory ML and exploitative ML play a partially mediating role in the relationship between BA and SCA.\n\nResearch limitations/implications\nIt is prudent to acknowledge that the study’s sampled entities were exclusively Iranian companies, potentially curtailing the extent of generalizability of our findings.\n\nOriginality/value\nThis research contributes valuable theoretical insights and practical implications to policymakers and top managers of organizations, particularly the surveyed organizations to formulate and implement an appropriate strategy to avail of BA techniques toward enhancing SCA. Also, this study provides significant insights into the determinants of SCA and demonstrates how organizations can leverage data analytics and ML to attain sustained growth and ambidexterity within the supply chain context.\n","PeriodicalId":45590,"journal":{"name":"VINE Journal of Information and Knowledge Management Systems","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The influence of business analytics on supply chain ambidexterity: the mediating role of market learning\",\"authors\":\"Rasoul Mehdikhani, C. 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引用次数: 0
摘要
本研究的主要目的是评估商业分析(BA)对作为发展中国家的伊朗的供应链灵活性(SCA)和市场学习(ML)的影响。研究对象包括一系列关键职位,如伊朗的高级经理、供应链经理、高级 IT 经理以及高级营销和营销研究经理。通过调查,设计了一份问卷来收集这些人员的数据。研究结果显示,BA 对 SCA 和 ML 有积极影响。此外,研究还发现,ML 的不同方面,即探索性学习和利用性学习,对 SCA 有积极影响。此外,调查还发现,探索性 ML 和利用性 ML 的机制在 BA 与 SCA 之间的关系中发挥了部分中介作用。研究局限性/影响需要谨慎承认的是,本研究的抽样实体仅为伊朗公司,这可能会降低我们研究结果的普遍性。此外,本研究还为 SCA 的决定因素提供了重要见解,并展示了组织如何利用数据分析和 ML 在供应链背景下实现持续增长和灵活性。
The influence of business analytics on supply chain ambidexterity: the mediating role of market learning
Purpose
The main purpose of this study is to assess the influence of business analytics (BA) on supply chain ambidexterity (SCA) and market learning (ML) in the context of Iran as a developing country.
Design/methodology/approach
The study population encompasses a range of key positions such as senior managers, supply chain managers, senior IT managers and senior marketing and marketing research managers in Iran. Through a survey, a questionnaire was designed to gather data from these individuals. The data collected from a total of 214 participants underwent rigorous analysis using structural equation modeling.
Findings
Findings revealed BA has a positive influence on SCA and ML. Furthermore, the study found that distinct facets of ML, namely, exploratory and exploitative learning, exerted a positive influence on SCA. Additionally, the investigation uncovered that the mechanisms of exploratory ML and exploitative ML play a partially mediating role in the relationship between BA and SCA.
Research limitations/implications
It is prudent to acknowledge that the study’s sampled entities were exclusively Iranian companies, potentially curtailing the extent of generalizability of our findings.
Originality/value
This research contributes valuable theoretical insights and practical implications to policymakers and top managers of organizations, particularly the surveyed organizations to formulate and implement an appropriate strategy to avail of BA techniques toward enhancing SCA. Also, this study provides significant insights into the determinants of SCA and demonstrates how organizations can leverage data analytics and ML to attain sustained growth and ambidexterity within the supply chain context.