The influence of the Digital Divide on Big Data generation within supply chain management

• Pages: 592-628
• Published date: 14 May 2018
• DOI: https://doi.org/10.1108/IJLM-06-2017-0175
• Date: 14 May 2018
• Author: Ginevra Gravili,Marco Benvenuto,Alexandru Avram,Carmine Viola
• Subject Matter: Management science & operations,Logistics

The influence of the Digital

Divide on Big Data generation

within supply chain management

Ginevra Gravili and Marco Benvenuto

Department of Economics, Universita del Salento, Lecce, Italy

Alexandru Avram

Department of Finance, Faculty of Economics and Business Administration,

Universitatea de Vest din Timisoara, Timisoara, Romania, and

Carmine Viola

Department of Economics, Universita del Salento, Lecce, Italy

Abstract

Purpose –The purpose of this paper is to examine the influence of the Digital Divide (DD) and digital

alphabetization (DA) on the Big Data (BD) generation process, to gain insight into how BD could become a

useful tool in the decision-making process of supply chain management (SCM). Similarly, the paper aims to

recognize and understand, from a value-creation perspective, the correlation between DD and BD generation

and between DD and SCM.

Design/methodology/approach –The approach utilized in the present study consists of two steps: first, a

systematic literature review was conducted aiming at finding out to determine the existing relationship

between “Big Data Analytics”(BDA), “SCM”and the “DD”. A total of 595 articles were considered, and

analysis showed a clear relationship among BDA, SCM, and DD. Next, the Vector autoregressive (VAR)

approach was applied in a case study to prove the correlation between DD (as part of internet usage) and

internet acquisitions, and in general terms the relationship between DD and Trade. Internet usage and

internet acquisition in imports and exports at the European level were considered as variables in an empirical

study of European trade. The novelty of this two-tiered approach consists in its application of a systematic

literature review, the first of its kind, to generate inputs for the longitudinal case study of imports and

exports at the EU level. In turn, the case study tested the accuracy of the theorized relationship among the

main variables.

Findings –By analyzing the connection between DD and internet acquisitions, a positive and long-lasting

impulse response function was revealed, followed by an ascending trend. This suggests that a

self-multiplying effect is being generated, and it is reasonable to assume that the more individuals use the

internet, the more electronic acquisitions occur. We can thus reasonably conclude that the improvement of the

BD and SCM process is strongly dependent on the quality of the human factor. Tackling DA is the new

reading key in the decision-making process: quantifying the added value of the human factor in SCM is

challenging and is an ongoing process, based on the opportunitycost between automation in decision-making

or relying on the complexity of human factors.

Research limitations/implications –One of the biggest limits in our research is the lack of the time series

available on consumer orientations and preferences. Data on the typology of customer preferences, and how

they are shaped, modified, or altered, were non-accessible, though big companies may have access to this

data. The present longitudinal study on European trade helps clarify how and to what extent BDA, SCM, and

DD are inter-related. The modeling of the theoretical framework likewise highlights several identifiable

benefits for companies of adopting BDA in their business processes.

Practical implications –Understanding the obst acles to DD in trade compani es and states, and

identifying their in fluence on firm performa nce, serves to orient t he decision-making pr ocess in SCM

toward reducing DD to gener ate important economic ben efits. Enhancing interne t usage may accelerate

longer-term investm ents in human resources, offeri ng developing countries unprece dented opportunities to

enhance their educat ional systems and to impr ove their economic poli cies, widening the range of

opportunities for busi nesses and poor states.

Social implications –BD generation will undeniably influence microeconomic decisions: they will become

evaluation tools of more efficient economic progress in small and/or large economies. However, an

economically efficient society will be achievable only in those countries in which qualified human resources

can generate and manage BD, to unlock its potential. This twofold effect will surely affect the socio-economic

and geopolitical situation. The economic progress of conventional countries may vacillate if it is not

The International Journal of

Logistics Management

Vol. 29 No. 2, 2018

pp. 592-628

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-06-2017-0175

Received 30 June 2017

Revised 2 September 2017

Accepted 8 September 2017

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

www.emeraldinsight.com/0957-4093.htm

592

IJLM

29,2

adequately flanked by qualified human resources able to progress the information and communication

technology (ICT) prevalent in contemporary economies. Consequently, the social impact of investments in ICT

capacity building will necessarily affect future socio-economic scenarios. New indicators will become

necessary to measure the conventional progress, and one of them will surely be DD.

Originality/value –The novelty of the present study is twofold: first, it is the first meticulous meta-analysis

developed using a very wide analysis of the published literatureto highlight a previously hidden relationship

among DD, BD, and SCM. This comparative approach made it possible to build a theoretical framework for

the real evaluation of the impact of BDA on different organizational elements, including SCM. Second, the

research emphasizes the need to reform and reshape the studies on BDA, convincing companies that it is

necessary to understand that the obstacles (DD and DA, i.e. internet usage) must be addressed with conscious

decision-making processes, strategically and resolutely, to transform points of weakness into opportunities.

Keywords Strategic management, Case study, Buyer-supplier relationships, Management research,

Europe, Supply chain innovation, Supply chain competences, Performance measurements

Paper type Case study

1. Introduction

Big Data (BD) plays a crucial role in supply chain management (SCM), and the robust

published literature confirms its centrality in transforming today’s business climate.

However, there i s a significant deb ate on evaluating B ig Data Analysis ( BDA) in SCM.

The spread of information and communication technology has enabled companies to

access terabytes of information that, if properly managed, can become an important

source for improving their strategic and business processes. Selecting and monitoring

the most relevant information through BDA offers vast prospects for developing a

collaborative business model that can create synergies, both inside and outside

the company. Fosso-Wamba et al. (2017) affirm that the opportunities offered by BDA

are indeed extremely promising, especially for improving organizational decisions

(Chen et al., 2014), ensuring strong customer relationships, implementing robust risk

management and operational efficiency (Chen et al., 2014), enhancing supply chain

innovation capabilities (Dutta and Bose, 2015), and creating a better overall customer

experience (Tweney, 2013).

Despite these attractive benefits, the practical use of BD in SCM processes is not always

feasible, as it requires a cultural change within companies. This approach is developing

patchily, with social, technological, and human consequences that need to be considered in

order to provide real long-term advantages for companies. The growing speed with which

information is disseminated can lead to a digital and technological gap, compromising firms’

production capacity and hindering their efficiency, which may exclude them from the

macro-economic framework of the industrial world.

There are several obstacles to using BD appropriately, including security, privacy

(O’Flaherty, 2016; Martin, 2015; Nunan and Di Domenico, 2013), ethics (Martin, 2015;

Miller, 2014; Nunan and Di Domenico, 2013), and governance issues (Miller, 2014;

Cole, 2016). In order to assist organizations in understanding the opportunities offered by

information and advanced analytics, it is necessary to inform them of these obstacles, and to

identify how and to what extent these obstacles can influence the company’s performance.

Within “BD Society”several tasks can be distinguished: “data generating”,thosewho

generate data (both consciously and by leaving digital footprints); “data collecting/

storage”, those having the right means to collect and store data, e.g.: EMC or NetApp; and

“data elaborating”, those having the appropriate skills and expertise to analyze/elaborate

data, e.g. Google. The first group includes virtually anyone using the web and/or

mobile phones; the second group is smaller; and the third group is much smaller still

(Manovich, 2011). The existing gap between “BD Rich”firms (those that can produce or

purchase and archive BD) and “BD Poor”firms (those excluded from data access) creates a

“BD Divide”(Boyd and Crawford, 2012). In order to exploit BD’s potentiality access to

expensive technology infrastructures is necessary, and specific abilities and skills are

593

Influence of the

Digital Divide

on Big Data

required to control and analyze it. Weinberger (2011) describes these skills as a

“new knowledge”because it requires “not just giant computers but a network to connect

them, to nourish them, and to make their work accessible. It exists at the network level, not

in the heads of individual human beings.”

The paper aims to inspect the influence of Digital Divides (DDs) and digital alphabetizing

on the BD generation process, to show how these data can represent a useful tool in the

decision-making process of SCM and to emphasize the existing correlation between DD and

BD generation. Due to the complexity of the subject matter, we followed a tiered approach,

dividing the research work into two steps. First, we identified a previously hidden

relationship among DD, BDA, and SCM through a meta-analysis of the published literature.

Our analysis adopted the methodology of Ngai and Wat (2002). This first step allowed us to

identify the variables needed to recognize the influence of DD on BD and SCM more

concretely, and to design a case study (step two) that could contextualize the revealed

relationship. Accordingly, a vector autoregressive (VAR) model was applied to conduct

empirical research on European trade (Luetkepohl, 2014).

Based on the above-mentioned approach, the paper is organized as follows: the

introduction (Section 1) presents the challenges and general goals of the present research,

while Section 2 offers working definitions of BDA, SCM, and DD as a preliminary research

step. Section 3 introduces the selected research methodology and presents the literature

meta-analysis, providing useful results to introduce the second research step and

contextualize the influence of DD on BDA and SCM. Next, Section 4 presents the results of a

longitudinal empirical study of European trade, using a VAR model for its implementation.

Section 5 presents the study’s limitations and conclusions, including research implications

for the practical use of the outputs, and lastly Section 6 suggests future research possibilities

on the paper’s topic.

2. Defining big data analytics, SCM, and DD from a comparative perspective

The aim of the present section is twofold: first, it is a preliminary survey intended to

introduce the main descriptors investigated in the present paper, and use them to perform a

successive systematic literature analysis. Similarly, it allows us to define and standardize

the relevant variables utilized by several research groups. This facilitates the understanding

of the information utilized in the meta-analysis.

In the information era, huge amounts of data are generated by companies,

governments and individuals in society (Elragal and Haddara, 2014), such that by 2020

scholars expect about 40,000 exabytes of data to exist. These data assets cannot be

managed by organizations with traditional methods (Zikopoulos and Eaton, 2011;

Manyika et al., 2011): they need efficient and advanced analytic techniques to turn high

volumes of fast-moving and diverse data into meaningful insights. Producing BD requires

two sub-processes: data management, which involves acquiring and storing data, then

preparing and retrieving the analysis; and analytics, which includes all the techniques

used to analyze and acquire BD intelligence, as well as awareness of the data’sstrengths

and limitations (Sangari and Razmi, 2015). The concept of BD, whose origins date back to

the mid-1990s, has been variously defined in the literature (Mayer-Schonberger and

Cukier, 2013; Song et al., 2016). It can be summarized using five cornerstone aspects:

volume (Brown et al., 2011), variety (Gandomi and Haider, 2015; Fosso-Wamba et al., 2015),

velocity (Russom, 2011; Gandomi and Haider, 2015), value (Dijcks, 2012; Forrester, 2012),

and veracity (White, 2012).

BD and Analytics are intertwined, but the concept of Analytics is not new. It includes

all applications that support decision-making. At the end of the 20th century,

business intelligence was defined as “a broad category of applications, technologies,

and processes for gathering, storing, accessing, and analyzing data to help business

594

IJLM

29,2