Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management. An empirical investigation

• Pages: 676-703
• Date: 14 May 2018
• Published date: 14 May 2018
• DOI: https://doi.org/10.1108/IJLM-06-2017-0153
• Author: Yuanyuan Lai,Huifen Sun,Jifan Ren
• Subject Matter: Management science & operations,Logistics

Understanding the determinants

of big data analytics (BDA)

adoption in logistics and supply

chain management

An empirical investigation

Yuanyuan Lai, Huifen Sun and Jifan Ren

School of Economics and Management, Harbin Institute of Technology,

Shenzhen, China

Abstract

Purpose –Based on previous literature on big data analytics (BDA) and supply chain (SC) management, the

purpose of this paper is to address the factors determining firms’intention to adopt BDA in their daily

operations. Specifically, this study classifies potential factors into four ca tegories: technological,

organizational, environmental factors, and SC characteristics.

Design/methodology/approach –Drawing on the innovation diffusion theory, a model consisted of direct

technological and organizational factors as well as moderators was proposed. Subsequently, survey data was

collected from 210 organizations. Then we used SPSS and SmartPLS to analyze the collected data.

Findings –The empirical results revealed that perceived benefits and top management support can

significantly influence the adoption intention. And environmental factors, such as competitors’adoption,

government policy, and SC connectivity, can significantly moderate the direct relationships between driving

factors and the adoption intention.

Research limitations/implications –Given the fact that big data (BD) usage in logistics and SC management

is still in the start-up stage, the interpretations toward BDA might vary from different perspectives, thus causing

some ambiguity in understanding the meaning and potential BD has. In addition, we collected data through

questionnaires completed by IT managers, whose viewpoint may not fully represent that of an organization.

Practical implications –This paper tests the organizational adoption intention of BDA and extends the

literature streams of BD and SC management simultaneously.

Social implications –This research helps top managers assess the benefits of BDA as well as how to adjust

their business strategy along the changes of environment and SC maturity.

Originality/value –This paper contributes to the literature of organizational adoption intention of BDA and

extends the literature streams of BD and SC management simultaneously.

Keywords China, Survey, Decision-making, Information technology, Operational performance,

Supply chain innovation, Big data analytics, Logistics and supply chain management, TOE framework

Paper type Research paper

1. Introduction

The rapid development that is taking place in the information technology has changed

the competition territory in many industries. Among various technology innovations,

big data (BD), characterized by volume, variety, velocity, and value (Chen et al., 2013),

plays a central role. Along with the advance of BD age, BD analytics (BDA) has attracted

sufficient attention of researchers and practitioners, after realizing the great business

value BD can bring to an organization (Chen et al., 2013). BDA can be decomposed into two

parts: BD and business analytics (BA) (Wang et al., 2016). The former lays information

foundation for BDA, while the latter refers to the ability firms can use data to gain

The International Journal of

Logistics Management

Vol. 29 No. 2, 2018

pp. 676-703

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-06-2017-0153

Received 16 June 2017

Revised 13 July 2017

3 October 2017

Accepted 8 November 2017

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

www.emeraldinsight.com/0957-4093.htm

The authors thank the guest editor Professor Samuel Fosso Wamba and the anonymous referees

for their useful suggestions. The research is supported by the National Science Foundation of China

(Grant No. 71472056).

676

IJLM

29,2

business insights. Theoretically, there are numerous studies to investigate the mechanism

through which firms can use BDA to improve the firm performance (Akter et al., 2016;

Fosso Wamba et al., 2016; Côrte-Real et al., 2016). Practically, marketers have embraced

the powerful tool of BDA to conduct precision marketing, predict consumer’sbehavior

(Miah et al., 2016) and manage user-generated content (Salehan and Dan, 2016). However,

there is little empirical research to investigate the motivation for firms to adopt BDA,

or what factors will influence their decision to use BDA in daily operations. To our

knowledge, two articles are related. Kwon et al. (2014) proposed a model of data quality

management and data usage experience to test the acquisition intention of BDA. Also,

Ramanathan et al. (2017) examined nine retailers in the UK to check the link between TOE

elements and BA adoption as well as the mechanism of how BA influence performance.

Both provided us valuable insights on the theoretical perspectives, but given the

characteristicsof qualitative research and case studies,it is still very necessary to conduct an

empirical research to investigate the BDA adoption from logistics management perspectives.

In spite of the potential BDA has in improving marketing efficiency (Xu et al., 2016),

decision-making process (Marijn et al., 2017; Elgendy and Elragal, 2016), and firm performance

(Akter et al., 2016), the impact BDA has on supply chain (SC) management is still an uncharted

area. Since firm’s logistics and SC performance can significantly influence firms’overall

performance (Gunasekaran et al., 2017), it is vital to improving SC process (Hazen et al., 2014)

through BDA. Equipped with this new data analytics technique, firms can develop the ability

of SC analytics, which will, in turn, promote the strategic and operational performance in

logistics and SC management (LSCM) (Wang et al. , 2016). Nevertheless, theories suitable for

explaining BDA adoption and usage in operation and SC management are still needed research

attempt. Therefore, it is necessary to deepen our understanding of the determinants of BDA

adoption in LSCM. More clearly, our research aims to figure out what the technological,

organizational, environmental factors are in motivating firms to utilize BDA in daily operations.

To bridge the literature gap, we draw on the emerging literature on BDA, innovation

diffusion theory, and the TOE framework to build our conceptual model. The remaining

part of this paper proceeds as follows: First, we review the present literature on LSCM, BDA,

and innovation diffusion. Then, we build the conceptual model and the hypotheses.

The subsequent part explains the methodology, empirical results, discussions, limitations,

and directions for future research.

2. Literature review

2.1 LSCM

LSCM playsa pivotal role in improving firm’s overall business performance (Wang etal., 2016;

Gunasekaran et al., 2017) as well as maintaining competitive advantage (Chen et al., 2013).

SC refersto “a set of three or moreentities (organizations or individuals) directly involvedin the

upstream and downstream flows of products, finances, and/or information from a source to a

customer”(Mentzer et al., 2001, p. 4) and logistics contributes to the business process through

completing the missionof transportingmaterials and products by meansof land, water, and air

carriage. With regard to the significance of LSCM, some measurements of SC performance are

put forward along the process of enriching the stream of SC research, such as cost efficiency

and customer-service indicators (Tummala et al., 2006), resource efficiency (Arthur et al., 1999;

Katayama and Bennett, 1999; Tummala et al., 2006), SC flexibility and agility (Prater et al., 2001;

Chiang et al., 2012), SC resilience, and robustness (Brandon-Jones et al., 2014) and so on.

However, there are somecrucial challenges faced by LSCM, such as the inefficiencies and

waste in SCs (Wang et al., 2016). Specifically, the delayed order, ever-increasing customer

requirement,SC disruption, and informationasymmetry can all negatively affectthe business

operations. Moreover, in face of rapid changes, globalization, and environmentaluncertainty,

it is particularly urgent for firms to leverage the resources and knowledge of their suppliers

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BDA adoption

in LSCM