Data analytics dynamic capabilities for Triple-A supply chains
| DOI | https://doi.org/10.1108/IMDS-03-2022-0167 |
| Published date | 02 November 2022 |
| Date | 02 November 2022 |
| Pages | 534-555 |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
| Author | Mohammad Daneshvar Kakhki,Alan Rea,Mehdi Deiranlou |
Data analytics dynamic
capabilities for Triple-A
supply chains
Mohammad Daneshvar Kakhki and Alan Rea
Department of Business Information Systems, Haworth College of Business,
Western Michigan University, Kalamazoo, Michigan, USA, and
Mehdi Deiranlou
Department of Industrial Engineering, University of Bojnord, Bojnord, Iran
Abstract
Purpose –This study aims to analyze the mediating role of data analytics management capability (DAMC)in
the relationship between supply chain integration (SCI) and supply chain agility, adaptability and alignment
(Triple-A). It also studies the role of Triple-A supply chains in performance improvement. We develop and
present a model based on our proposition and informed by the dynamic capabilities perspective.
Design/methodology/approach –The authors employ meta-analytic structural equation modeling to test
the proposed model by analyzing reported statistics of 117 published studies.
Findings –This study’s results describe why some prior research findings are contradictory. For example,
researchers have posited mixed findings on the impact of SCI on agility. The results show that SCI and agility
do not have a significant direct association, and DAMC mediates their indirect relationship.
Originality/value –The impact of SCI on performance is debatable. SCI permits access to shared resources
for competitive advantage; conversely, SCI-induced rigidity may reduce supply chain agility and adaptability.
Informed by dynamic capabilities theory, the authors demonstrate that DAMC positively mediates the impact
of SCI on performance.
Keywords Data analytics management capability, Supply chain integration, Triple-A supply chains,
Meta-analysis structural equation modeling
Paper type Research paper
1. Introduction
Supply chain integration (SCI) offers competitive advantage when a company forms explicit
linkages to external resources that simultaneously exclude rivals from accessing the same
(Rungtusanatham et al., 2003). However, such competitive advantages are not maintainable
and eventually diminish (Mellat-Parast and Spillan, 2014). For example, when one or more
supply chain (SC) partners face business volatility, their commitment to the SC may falter and
disrupt the SCI. Since such failures are increasingly prevalent in today’s global marketplace
(Gulati et al., 2012), investigating methods that can improve the success of SCI is warranted
(Wiengarten et al., 2019). We argue that data analytics (DA) can enhance SCI; accordingly, we
study its mediating influence on SCI and SC performance.
DA is the “extensive use of data, statistical and quantitative analysis, explanatory and
predictive models, and fact-based management to drive decisions and actions”(Davenport
IMDS
123,2
534
Funding: The authors did not receive support from any organization for the submitted work.
Conflict of interest: All authors certify that they have no affiliations with or involvement in any
organization or entity with any financial interest or non-financial interest in the subject matter or
materials discussed in this manuscript.
Data availability statement: The datasets generated during and/or analyzed during the current study
are not publicly available because authors are still analyzing the data for another research. However, the
data are available from the corresponding author on reasonable request.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0263-5577.htm
Received 23 March 2022
Revised 18 June 2022
5 August 2022
9 September 2022
Accepted 11 October 2022
Industrial Management & Data
Systems
Vol. 123 No. 2, 2023
pp. 534-555
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-03-2022-0167
and Harris, 2007, p. 7). Because improving SCI-related decisions via DA can benefit SCI
(Razaghi and Shokouhyar, 2021), firms have heavily invested in DA (AMA, 2020).
Nevertheless, proper deployment of DA to benefit SCs has rema ined a significant
challenge (Chehbi-Gamoura et al., 2020).
The study of DA-enabled mechanisms for maintaining SCI is an emerging focus of
information systems (ISs) and operations management (OM) research (e.g. Torres et al., 2018;
Zsidisin et al., 2015). IS scholars have called for studying DA as mediators of performance
(Grover et al., 2018;Kohli and Grover, 2008); OM scholars have called for investigating
methods to improve SCI’s success (Ataseven and Nair, 2017;Wiengarten et al., 2019).
Therefore, our study focuses on investigating two research questions:
RQ1. How can DA mediate the relationship between SCI and performance?
RQ2. Can DA-mediation lessen the negative impacts of SCI on performance?
After the next section’s literature review, we inform our research with dynamic capabilities
theory and explain how DA provides the capabilities required to sustain SCI-enabled
competitive advantage. We then test the research model using a meta-analysis structural
equation modeling (metaSEM) approach. Next, we discuss the findings and explain our
contribution to the IS literature by empirically studying technology-mediated performance.
We also discuss our contribution to the OM literature explaining a mechanism for improving
SCI success. Next, we discuss the implications for practitioners, research limitations and a
proposed direction for future research. We conclude the paper with a brief review of the
findings.
2. Literature background
Research suggests that SC efficiency increases when managed as an integrated entity, and SC
partners benefit from mutual resources and capabilities (Agusa and Hassan, 2008). Although
the impact of SCI on performance has empirical support (Maheshwari et al., 2021), some
studies failed to find an association or suggest a negative impact (Tarifa-Fern
andez
et al., 2019).
SCI can promote undesirable qualities –lack of mobility in coupled systems, low flexibility
due to commitment, and high organizational inertia –that prevent reaction and change
(Gulati et al., 2012;Lavie, 2006). These qualities, coupled with the increasing volatility and
complexity of today’s business environment, make it challenging to benefit from SCI
(Arshinder et al., 2008;Chae and Olson, 2013). Subsequently, researchers search for
mechanisms to facilitate SCI benefits (Ataseven and Nair, 2017), and some suggest that DA
capabilities can be a potential remedy for the negative impacts of SCI (Srinivasan and Swink,
2018). There are two major streams of research that examine DA and its performance effect
in SCM.
2.1 DA impact on SC performance studies
The first stream studies the influence of DA on SC performance and establishes a positive
association between them (Chehbi-Gamoura et al., 2020). It suggests that DA can lead to
capabilities (flexibility and agility), operational performance (customer satisfaction and
inventory performance), and financial and strategic performance (e.g. Choi, 2018;Seddon
et al., 2017).
The second stream investigates how DA influences on SC performance. Researchers have
studied how DA capability impacts SC processes (Chae and Olson, 2013), its capabilities such
as resilience (Dubey et al., 2021;Mandal, 2018), flexibility (Srinivasan and Swink, 2018;Yu
et al., 2021b), agility (Ghasemaghaei et al., 2017;Srimarut and Mekhum, 2020;Wamba and
Analytics
capabilities for
Triple-A
supply chains
535
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