The influence of business analytics on supply chain ambidexterity: the mediating role of market learning

Abstract

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.

Citation

Mehdikhani, R., Valmohammadi, C. and Taraz, R. (2024), "The influence of business analytics on supply chain ambidexterity: the mediating role of market learning", VINE Journal of Information and Knowledge Management Systems, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/VJIKMS-12-2023-0344