Supply chain management professionals’ proficiency in big data analytics: Antecedents and impact on performance

Tobias Schoenherr

Transportation Research Part E: Logistics and Transportation Review, 2023, vol. 169, issue C

Abstract: While big data analytics (BDA) carries great potential for supply chain management (SCM), actual advanced applications of these approaches are still limited in practice. Within this context, we seek to identify what can foster an SCM professional’s proficient use of BDA, and define BDA proficiency as an individual’s ability to effectively use BDA. Grounding our research in the theoretical framework of technology acceptance, we consider perceived ease of use, perceived usefulness, attitude toward, and behavioral intention to use BDA as potential influential antecedents. Using primary survey data from 212 SCM professionals, we find that all but one of these hypothesized relationships are confirmed among the sample. This offers valuable insight on how to drive the intention of SCM professionals to use BDA, which serves as a foundation to become proficient in it. In addition, we hypothesize about the impact of BDA proficiency on generating business value in terms of both quality and cost performance, two important outcome variables in SCM research. While the impact on cost performance is confirmed, the impact on quality performance is non-significant, bringing greater clarity to the differentiated performance impacts of BDA proficiency. When further scrutinizing these links to performance under the moderating effect of environmental competitiveness, we not only find the impact of BDA proficiency on cost performance to be greater under higher levels of environmental competitiveness, but also that under such greater levels the link from BDA proficiency to quality performance becomes significant. Overall, our research provides insight not only into what can foster an SCM professional’s BDA proficiency, but also into its impact on performance under various levels of environmental competitiveness.

Keywords: Big data analytics; Proficiency; Technology acceptance model; Environmental competitiveness; Empirical research (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.tre.2022.102972

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