Impact of big data and predictive analytics capability on supply chain sustainability

• Published date: 14 May 2018
• DOI: https://doi.org/10.1108/IJLM-05-2017-0134
• Pages: 513-538
• Date: 14 May 2018
• Author: Shirish Jeble,Rameshwar Dubey,Stephen J. Childe,Thanos Papadopoulos,David Roubaud,Anand Prakash
• Subject Matter: Management science & operations,Logistics

Impact of big data and predictive

analytics capability on supply

chain sustainability

Shirish Jeble

Symbiosis Centre for Research and Innovation, Symbiosis International University,

Pune, India

Rameshwar Dubey

Montpellier Business School, Montpellier, France

Stephen J. Childe

Plymouth Business School, Plymouth University, Plymouth, UK

Thanos Papadopoulos

Kent Business School, University of Kent, Kent, UK

David Roubaud

Montpellier Research in Management, Montpellier Business School, Montpellier,

France, and

Anand Prakash

School of General Management,

National Institute of Construction Management and Research, Pune, India

Abstract

Purpose –The purpose of this paper is to develop a theoretical model to explain the impact of big data and

predictive analytics (BDPA) on sustainable business development goal of the organization.

Design/methodology/approach –The authors have developed the theoretical model using resource-based

view logic and contingency theory. The model was further tested using partial least squares-structural

equation modeling (PLS-SEM) following Peng and Lai (2012) arguments. The authors gathered 205 responses

using survey-based instrument for PLS-SEM.

Findings –The statistical results suggest that out of four research hypotheses, the authors found support for

three hypotheses (H1-H3) and the authors did not find support for H4. Although the authors did not find

support for H4 (moderating role of supply base complexity (SBC)), however, in future the relationship

between BDPA, SBC and sustainable supply chain performance measures remain interesting research

questions for further studies.

Originality/value –This study makes some original contribution to the operations and supply chain

management literature. The authors provide theory-driven and empirically proven results which extend

previous studies which have focused on single performance measures (i.e.economic or environmental). Hence,

by studying the impact of BDPA on three performance measures the authors have attempted to answer some

of the unresolved questions. The authors also offer numerous guidance to the practitioners and policy makers,

based on empirical results.

Keywords India, Sustainability, Partial least squares (PLS), Structural equation modeling,

Supply chain management (SCM), Big data and predictive analytics (BDPA), Contingency theory (CT),

Resource-based view (RBV), Supply base complexity (SBC)

Paper type Research paper

1. Introduction

In the recent years, big data analytics has been considered as the next big thing for

organizations to gain competitive advantage (Wamba et al., 2015; Akter et al., 2016). With

the increasing digitalization of every aspect of business and government, large data sets are

available for analysis. Big data has been defined primarily with 5 Vs: volume, variety,

The International Journal of

Logistics Management

Vol. 29 No. 2, 2018

pp. 513-538

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-05-2017-0134

Received 29 May 2017

Accepted 3 June 2017

The current issue and full text archive of this journal is available on Emerald Insight at:

www.emeraldinsight.com/0957-4093.htm

513

Big data and

predictive

analytics

capability

velocity, veracity and value (Wamba et al., 2015). Big data analytics is a field which consists

of big data, analytical tools and techniques to derive actionable insights from the big data

for delivering sustainable value, improving business performance and providing

competitive advantage (Wamba et al., 2017). Predictive analytics is defined as the process

of discovering meaningful patterns of data using pattern recognition techniques, statistics,

machine learning, artificial intelligence and data mining (Abbott, 2014).

Big data and predictive analytics (BDPA) is an emerging field which uses various

statistical techniques and computer algorithms to derive insights, patterns from large data

sets. Analytics is considered as the next big frontier of innovation, competition and

productivity (Manyika et al., 2011, p. 1). While next generation information technology

techniques (such as smart phones, digital devices, scanning devices, cloud computing,

Internet of Things, etc.) help in improving productivity, these generate variety of large data

sets which help in building analytics capabilities for the firms.

Business firm’s primary goal is to make profits for long-term economic sustainability.

With globalization, improved communication and arrival of social media, firms are

competing as never. Despite the challenging business environment, going forward keeping

profit alone as a goal may not be sustainable, considering the long-term impact of

commercial activities on environment and society. Thus, in addition to profit maximization,

social and environmental sustainability goals are necessary for businesses as per Elkington

(1994). Environmental sustainability has gained significant attention in recent years due to

growing concern for environment. Extreme weather, rising temperature, scarcity of natural

resources –all these call for a different strategy toward environment (Winston, 2014).

To preserve natural resources for future generations, sustainability needs to be considered

in every aspect of business, supply chains and executive decision making.

Businesses strivefor creating value for the stakeholders such as shareholders and society.

Although, livingconditions in most developed anddeveloping countries have improved,there

are several regions which are challenged to meet their basic needs. Brundtland and Khalid

(1987) have acknowledged the need for attention to social issues along with environmental

concerns in their report to United Nations. There are several measures designed to assess

economic and environmental performance (EP)of the firm, however, socialperformance (SP)

does not get measureddue to intangible nature of these issuesand complexity in assessment

(Maniet al., 2014). There are severalinstances when organizationsin developed countrieshave

come under scrutiny due to untenable social practices of their suppliers located in distant

regions (Goldberg and Yagan, 2007; Plambeck et al., 2012). With improved communication,

awareness about social sustainability is improving amongst manufacturing companies

(Wu and Pagell, 2011). As a result, many companies have started publishing their corporate

social responsibility reports that share company’s track record on social issues. Automobile

industry is oneof the fastest growing industriesin India and provides large-scaleemployment

(Chandra Shukla et al., 2009). This industry generates significant level of carbon footprint

across entire product life cycle which includes manufacturing process, movement of goods

across supply chain and use of automobiles by consumer (Luthra et al., 2016). Thus,

environmental, social and economic impact of automobile industry is significant (Kushwaha

and Sharma, 2016). The supply chains of the automotive industry arehighly complex (Thun

and Hoenig, 2011).Hence, the major challenges of the automotive industry supply chains are

visibility, cost containmen t, risk management, increasing customer d emands and

globalization. The information sharing among the partners in complex supply chains

network is highly challenging (Wu and Pagell, 2011).

Considering the revolutionary role of big data analytics in several domains, there has

been a trend of research in big data and sustainability for firms in auto industry (Bughin

et al., 2010). However, most of these studies offer conceptual and anecdotal evidences. The

empirical studies focusing on BDPA capability and its impact on three dimensions of the

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