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International Journal of Production Research
ISSN: 0020-7543 (Print) 1366-588X (Online) Journal homepage: https://www.tandfonline.com/loi/tprs20
A study on investments in the big data-driven
supply chain, performance measures and
organisational performance in Indian retail 4.0
context
Shradha A. Gawankar, Angappa Gunasekaran & Sachin Kamble
To cite this article: Shradha A. Gawankar, Angappa Gunasekaran & Sachin Kamble (2019):
A study on investments in the big data-driven supply chain, performance measures and
organisational performance in Indian retail 4.0 context, International Journal of Production
Research, DOI: 10.1080/00207543.2019.1668070
To link to this article: https://doi.org/10.1080/00207543.2019.1668070
Published online: 26 Sep 2019.
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International Journal of Production Research, 2019
https://doi.org/10.1080/00207543.2019.1668070
A study on investments in the big data-driven supply chain, performance measures and
organisational performance in Indian retail 4.0 context
Shradha A. Gawankara, Angappa Gunasekaranb∗and Sachin Kamblea
aOperations and Supply Chain Management, National Institute of Industrial Engineering (NITIE), Mumbai, India; bSchool of Business
and Public Administration, California State University, Bakersfield, CA, USA
(Received 26 August 2018; accepted 8 September 2019)
The use of digital technologies such as ‘internet of things’ and ‘big data analytics’ have transformed the traditional retail
supply chains into data-driven retail supply chains referred to as ‘Retail 4.0.’ These big data-driven retail supply chains have
the advantage of providing superior products and services and enhance the customers shopping experience. The retailing
industry in India is highly competitive and eager to transform into the environment of retail 4.0. The literature on big data
in the supply chain has mainly focused on the applications in manufacturing industries and therefore needs to be further
investigated on how the big data-driven retail supply chains influence the supply chain performance. Therefore, this study
investigates how the retailing 4.0 context in India is influencing the existing supply chain performance measures and what
effect it has on the organisational performance. The findings of the study provide valuable insights for retail supply chain
practitioners on planning BDA investments. Based on a survey of 380 respondents selected from retail organisations in India,
this study uses governance structure as the moderating variable. Implications for managers and future research possibilities
are presented.
Keywords: big data; supply chain management; performance measures; organisational performance; governance structure;
retail 4.0
1. Introduction
Retail 4.0, referred as the fourth transformation for the retail industry is the use of internet of things, big data analytics
(BDA) and related technologies by retail organisations to attract and retain customers (Srivastava 2008; Kamble et al.
2019). The Retail 4.0 environment supports the organisations to offer improved products, services, and customer experience
transforming them to a data-driven decision-making unit from a non-sequential information processing mode (McFarlane
and Sheffi 2003; Patil 2016; Lee and Lee 2018). The decision-makers in the big data-driven retail 4.0 organisation have
access to valuable insights on value proposition and creation, which provides them better opportunities to strengthen their
relationships with the customers and adopt more effective policy and practices as compared to the traditional retail supply
chains (Bressanelli, Perona, and Saccani 2018; Sharma, Sharma, and Mandal 2018; Kamble et al. 2019). The retail 4.0
organisations can provide numerous benefits that include traceability of products by both the consumer and the salesper-
sons, display of product details, and communicating the latest discounts and promotional offers. Retail 4.0 organisations
have the advantage of tracking the logistical movements of the retail goods in real-time and reduced delivery lead times that
significantly contributes to managing the product shelf-life more efficiently. The retail 4.0 environment provides an auto-
mated environment for managing temperature reducing the energy consumption, effective category management, efficient
design of store layouts, product replenishment, and inventory management (Pantano and Timmermans 2014). The recent
technological advancements in BDA is expected to transform the business processes in the Indian retail sector and enhance
organisational performance (Raman et al. 2018). Although the literature supports the contribution of the big data-driven
retail supply chain in improving the overall organisational performance of retail 4.0 organisations, the authors have not
come across any studies that have empirically analysed the amount of influence BDDSC has on the retail 4.0 organisational
performance.
The existing literature on BDDSC is focused on understanding the influence of BDA on the manufacturing supply chains.
The Indian retail sector is highly complex due to the presence of a high number of retail outlets, high product variety, and
logistical requirements. However, the substantial inherent potential of the Indian economy makes the retail sector highly
*Corresponding author. Email: agunasekaran@csub.edu
© 2019 Informa UK Limited, trading as Taylor & Francis Group
2S. A. Gawankar et al.
competitive, despite these complexities (Anbanandam, Banwet, and Shankar 2009; Sharma, Sharma, and Mandal 2018).
The literature reports that firms use only one-fourth of the structured data when making an important decision, while less
than 1% of information is analysed. Additionally, only 70% of the firm employees have authorised access to unstructured
data, while only 80% of the data scientist’s time is productively used to prepare data and decision-making process (Grover
and Kar 2017). Therefore, exploring the linkages between the BDDSC and the organisational performance is necessary in
Indian retail 4.0 context. Achieving a balanced growth throughout the supply chain and continuous improvement in the
supply chain performance is a significant concern for all the supply chain members (Anand and Grover 2015). Many of
the existing performance measures are non-operational and may not be relevant in the dynamic retailing 4.0 environment.
However, these measures remain embedded in the overall performance measurement system, even though, they may not
contribute efficiently to retail 4.0 performance management. Therefore, the practitioners must become conscious about
performance measures which have a significant influence on the retail 4.0 performance. The outcome of the present study
will guide the practitioners to select appropriate performance measures and plan their BDA investments to improve the retail
supply chain performance.
The above discussions motivated the researchers to empirically analyse the unnoticed linkages between the BDDSC,
supply chain performance measures, and retail organisation performance in a retail 4.0 environment. Our initial discussions
with the practitioners revealed that the organisations might not deploy BDA technologies for all the products procured from
different supplying organisations and may focus only on critical products to control their BDA investments. Therefore, we
decided to use the governance structure as the moderating variable to test the above relationships. The governance structure
is defined as ‘the institutional matrix within which transactions are negotiated and executed.’ In this study, we consider two
types of governance structure prevalent in the retailing industry viz., contractual, and relationship-based alliance.
More specifically, the study attempts to seek answers to the following research questions (RQ) in retail 4.0 environment.
RQ1: What is the impact of BDDSC on different performance measures?
RQ2: Which supply chain performance measures (SCPM) influence the retail organization performance?
RQ3: Do the governance structure moderate the relationship stated in RQ1 and RQ2?
The study is based on an empirical survey of 380 supply chain managers from the Indian retail industry and uses structural
equation modeling (SEM) for validating the results. The remaining of the paper is organised as follows: Section 2 develops
the theoretical background and hypothesis for the study. Section 3 presents the research design adopted in this study. The
analysis and results are presented in section 4, and section 5 discusses the implications, contributions, and limitations of the
study.
2. Theoretical background, constructs, and hypotheses development
2.1. Big data analytics in Indian retail 4.0 context
In the supply chain literature, BDA is conceptualised as a significant organisational resource because of its potential to
extract rich knowledge from large datasets (Schoenherr and Speier-Pero 2015; Tan et al. 2015; Kuo and Kusiak 2019). The
organisation needs to exploit its technological and human resources to develop BDA capabilities. Previous studies have
shown a positive relationship between the level of investments in BDA and organisational performance (Jeble et al. 2018;
Müller, Fay, and vom Brocke 2018; Ivanov, Dolgui, and Sokolov 2019). The Indian retail sector is the fastest-growing in
the world and is expected to surpass the $1 trillion mark by the year 2020. The high availability of internet users offers the
potential for using internet-based technologies, generating voluminous data from the consumers (Ekambaram 2017). The
need to develop robust supply chain network and optimal use of resources are driving both the large organised and small
unorganised retail organisations to adopt advanced information communication technology and digital systems (Chutia and
Baruah 2014). The adoption of big-data by Indian retail organisations has improved their decision-making performance
resulting in increased profits (Tara 2018). A BDDSC can predict demand more accurately and reduce the shipment delays,
which results in efficient retail operations aligned with the organisational objectives (Krishnan 2017). The pressure on the
organisations to provide rich shopping experience by unlocking the hidden information is compelling them to invest in
BDA.
2.2. Supply chain performance measures (SCPM)
Supply chain performance (SCP) measurement supports a firm vision and mission to the organisation through efficient
strategy, better tactics, and effective operational decisions. Analysis of SCP is essential to solve issues and challenges
before they impact the overall operations of the organisation. SCPM not only supports the organisations to reduce the
International Journal of Production Research 3
Table 1. Supply chain performance measures.
SCMP Description
Flexibility (FLEX) Blome, Schoenherr, and Eckstein (2014) define SCPMF as ‘the ability to rapidly reconfigure
essential supply chain resources in an attempt to maintain competitiveness.’ SCPMF is
a significant component of competitive advantage in the highly changing environment
(Beamon 1999;Umetal.2017).
Integration (INTG) INTG is defined as ‘a group of activities with coordination between the flow of products
and supply chain partners comprising transaction activities (cash and data), materials
movement (inward and outward), processes, procedures, and optimization methods,
considering the direction of data and information flow’ (Frohlich and Westbrook 2001;
Ataseven and Nair 2017; Vanpoucke, Vereecke, and Muylle 2017).
Responsiveness to customers (RESP) RESP and markets are an indispensable requirement for all industries, particularly the retail
sector. RESP is defined as ‘the probability of fulfilling customer orders within a promised
lead time.’ Responsiveness is a firm’s ability and flexibility to respond to the customer by
providing a fast solution to the problem and efficient service (Gupta et al. 2018).
Efficiency (EFFY) Martin and Patterson (2009) define efficiency in the supply chain as ‘the level to which a
firm’s process get collaborated with supply chain stakeholders to achieve a cost-competitive
between the major competitors.’ Efficiency is a measure of growth and an essential factor
for the firm to profit (e.g. total inventory turnover and cost of operation).
Quality (QUAL) Quality is a critical strategic tool that helps in differentiating an organisation with other
competitors. Quality is a qualifying criterion for efficient supply chains with eight
dimensions: performance, structures, consistency, conformance, robustness, serviceability,
aesthetics, and perceived value of quality (Teoman and Ulengin 2017). These measures
are inclusive and widespread, but it is difficult to measure their dimension based on the
characteristics (Cao and Zhang 2011). Previous literature supports quality factor as one of
the critical components of performance measurement that is used for SCP linking different
sub-processes (Gawankar, Kamble, and Raut 2017).
Innovation (INNOV) Zaefarian et al. (2017) relates INNOV to either functional requirements, new offerings, or
further improvements in existing products or services, which in turn help the firm increase
benefits for the loyal customer. Tung (2012) helps us understand that product innovation
improves the capacity of an organisation to be flexible enough to adjust to continuously
changing environmental uncertainty, and hence is important for a firm’s existence in the
competitive market.
Market Performance (MKTP) MKTP is the organisation’s ability to improve its sales and market share relative to
competitors. In SCM theory, market performance deals with financial factors based on
total market share, return on investment, total assets of firms, sales growth, and an annual
turnover (Deshpande 2012).
Partnership Quality (PARTQ) Vanichchinchai and Igel (2011) show that PARTQ depends on the mutual partnership,
resulting in enhanced SCP for every partner of the firms, including all stakeholders, based
on mutual trust, mutual understanding, solving the mutual conflict, and commitment to
achieving the set goal.
demand-supply gap, but also provides directions for competitive advantage and supply chain excellence (Asrol, Marimin,
and Machfud 2017). More specifically, an appropriate selection of SCPM helps to develop a strong BDA capability and
improved SCP (Stefanovic 2015; Kamble and Gunasekaran 2019). A good BDDSC performance measurement system can
predict future performance and support proactive decision-making (Tan et al. 2017; Kamble and Gunasekaran 2019). SCP
measurement is found to stimulate managerial behaviour and improve organisational performance. However, the extent
to which the SCPM influence the individuals and organisational performance is dependent on the type of performance
management system adopted by the organisation and its execution (Franco-Santos, Lucianetti, and Bourne 2012; Kamble
and Gunasekaran 2019). A brief description of the SCPM selected in the present study is provided in Table 1.
2.3. Influence of big data-driven supply chain on supply chain performance measures
BDDSC provides organisations enormous information, enabling real-time tracking of various performance-related parame-
ters (Wamba et al. 2017; Kamble and Gunasekaran 2019). There is a need for the manufacturing and service organisations
to improve their processes on a continuous basis by using appropriate measures to have improved OP (Gupta, Modgil, and
Gunasekaran 2019). The literature suggests that the performance measures used for evaluating the traditional supply chains
are based on historical information, isolated, static and are less efficient in delivering information to the decision-makers
4S. A. Gawankar et al.
(Lapide 2010; Stefanovic 2015). Kamble and Gunasekaran (2019) proposed a BDDSC performance measurement frame-
work that included three main components; BDDSC performance planning, BDA capability, and BDDSC performance
monitoring. New measures with predictive performance capabilities were found to be relevant for BDDSC performance
measurement.
Therefore, we hypothesise that:
H1: BDDSC has a positive and significant impact on SCPM.
2.4. Organisational performance (OP)
The main objective of using SCPM, such as partnership quality, integration, and financial measures is to enhance the OP
(Lakhal 2014). The main priorities of top management are to reduce the order cycle time and inventory costs to improve
firm performance (Huang et al. 2010). Besides the reduction in cost, OP also aims at improving customer satisfaction and
loyalty, thus increasing the market share and financial performance (Gorane and Kant 2017).
2.5. Effect of supply chain performance measures on organisational performance
Performance in a supply chain is measured by quality, dependability, flexibility, and costs. An improvement in the market
share and supply chain costs enhances the market and financial performance, respectively. Chen and Paulraj (2004) propose
‘supply chain cost’ and ‘delivery of quality products and services in precise quantities and precise times’ as SCPM that
can enhance a firm’s market position and OP (Li et al. 2006; Whitten, Green, and Zelbst 2012). SCPM is also found to
have a positive influence on implementing the flexibility strategy and reducing process risks (Tang and Tomlin 2008). In
retail supply chains, SCPM enables implementation of innovative and flexible retail SCM strategies leading to improved
performance (Gawankar, Kamble, and Raut 2016,2017). In a BDDSC context, the organisations are required to select
measures for evaluating the supply chain processes and BDA capability for improved supply chain performance (Kamble
and Gunasekaran 2019).
Therefore, we hypothesise that:
H2: There is a positive and significant relationship between SCPM and OP.
2.6. Governance structure as a moderating variable
Governance Structure (GS) plays a critical role in defining the firm’s capital structure. Previous studies have focused on
adopting suitable GS (Bocquet and Mothe 2015). Four GS are identified from the literature that includes: the spot market,
relation-based alliance, vertical integration, and contractual-based alliance. The spot-market structure is mainly based on
price and is focused on short-term relations; the stakeholder’s in a spot market frequently makes changes to the price. The
spot market GS is suitable for adaptation to price changes. In a vertical integration structure, a different segment of the
supply chain is managed by one player with each player integrated into their buyer’s or supplier’s firm. In the spot-market
type GS, the stakeholder holds their decision rights, while in vertical integration, the player does not retain their decision
authority. Our discussions with the supply chain practitioners reveals that formal contract-based and relationship-based
alliance are the two most used types of GS in Indian retail supply chains. The contract-based GS is often considered to
represent the fundamental structural dimension in relationships, and they are widely used as regulators in retail business
exchanges. Relation-based alliance GS is focused on how the relationships among partners are organised, governed, and
managed. Wamba et al. (2017) suggest that the accomplishment of a relation-based alliance depends on the operational and
strategic management of the relationship between the stakeholders of the supply chain. In the present study, we considered
the contract based and relationship-based alliance GS to have a moderating effect on the relationship between the BDDSC,
SCP, and OP.
2.6.1. Contract-based alliance
In a contract-based alliance, the inter-organisational transactions are governed by a formal and written contract. The roles
and responsibilities of each party are clearly defined and support in moderating the risk and uncertainty caused by the
environment in exchange relationships (Macaulay 1963). The contractual relationships may have an impact on the supply
chain flexibility, data exchange, trading, and overall firm performance as an outcome of conflicts in contracts (Ahimbisibwe
2014). Contractual relationships improve performance as it compels the partners to maintain the desired level of product and
International Journal of Production Research 5
Figure 1. Proposed research framework.
service quality. The contracts may be terminated if either of the partners fails to support the agreed standards (Ahimbisibwe
2014).
2.6.2. Relation-based alliance
The relationship-based alliance is built on the trust and commitment between the alliance partners. In the retail supply chains,
the relational power is an outcome of nurturing personal relationships, shared dependency, involvement, and appreciation
of each other. There exists a definite link between the relation-based alliance and improvement in the firm’s performance
(Arranz, Arroyabe, and Fdez. de Arroyabe 2016). O’Toole and Donaldson (2000) observed that measures such as market
performance and partnership quality, if not used correctly, can increase the functioning risk of supply chain and can affect OP
negatively. SCPM such as lead time, inventory, quality, customer requirement, order fulfilment, and customer satisfaction
are found to have a positive influence on alliance performance (Ramdas and Spekman 2000). The relationship-based alliance
is the most challenging GS because of the dominance of authority – the more one partner controls the alliance, the more
it leads to a poorly performing supply chain (Elofson and Robinson 2007). In the present study, we intend to measure
the influence of GS as a moderating variable on the relationships between BDDSC, SCPM, and the OP. Therefore, we
hypothesise that:
H3: GS has a moderating effect on the relationship between the BDDSC, SCPM, and OP.
2.7. Proposed research framework
The above discussions suggest that BDDSC is an essential asset for the retail supply chain. The organisations are required
to make a substantial investment in developing BDA capabilities for improved organisational performance and overall
functioning of the business, including satisfied customers and partners. In the research framework shown in Figure 1,itis
proposed that BDDSC influences the selection of relevant SCPM, which further leads to improvement in the retail supply
chain performance. The GS moderates these relationships.
3. Research method
3.1. Sampling and data collection
Even though India comprises extensive rural population, where online shopping and product delivery is a primary challenge
there exists a huge opportunity with more than 1.34 billion consumers eager to change their buying habits to experience
high shopping experience, obtain information on various products and services and indicate their preferences across different
digital platforms (Banerjee 2019). It is interesting to note that the consumers drive the change in the retail scenario in India
and not the retailers and it is, therefore, necessary that the retailers in India adopt a more proactive stance than being
reactive (Banerjee 2019). In the present study, the empirical context is restricted to the Indian retail industry. It is expected
6S. A. Gawankar et al.
that the results of this study will enable the retail supply chain managers to develop more proactive strategies to address
the growing expectations of the consumers. A survey instrument was used to collect the required information, which was
subjected to content validity by selected subject experts for enhanced clarity and accuracy. The instrument was further
tested for reliability by conducting a pilot study on 50 respondents. The final survey included 380 supply chain practitioners
representing 50 modern retail stores from India. The data was collected during a six-month duration (November 2017–April
2018), and on an average, twelve to fifteen supply chain practitioners from each retail store participated in the survey with
a response rate of 63% (380/600). The profile of the selected respondents is presented in Appendix 1.
3.2. Instrument development
The selection of the constructs and generation of the measurement items was an outcome of content validity process carried
in three stages. In the first stage, a thorough literature review was performed to ensure content validity. The literature
search was conducted on high impact journals covering the concept of SCP. The literature search was done on the Scopus
database using the keyword supply chain performance. Articles published during the year 2010–2018 were considered. The
search was restricted to academic peer-reviewed journals, and a total of 65 articles were selected by its relevance to the
present study. While selecting the SCPM, only those articles which were empirical and included at least one performance
measure was considered. Fifteen performance measures were selected in the first stage that included delivery lead time,
partnership quality, supply chain agility, supply chain flexibility, information-carrying cost, supply chain efficiency, supply
chain integration, market performance, product development cycle time, responsiveness to customer, delivery performance,
quality, order entry methods, product innovation, and order lead time. In the second stage, a preliminary questionnaire was
developed using these fifteen constructs on a seven-point Likert scale. To ensure instrument validity, the questionnaire was
discussed with a team of six experts, consisting of two academicians from supply chain management field, three senior
managers with more than ten years’ experience in the retail supply chain (i.e. sourcing, store management, and logistics)
and one senior consultant with expertise in digital transformation. The validation from the experts helped to obtain retailing
4.0 perspective, reframe ambiguous questions, eliminate redundant items, and add new items wherever necessary. The final
measurement instrument included eight SCPM namely: supply chain flexibility (FLEX) supply chain efficiency (EFFY),
supply chain integration (INTG), quality (QUAL), product innovation (INNOV), responsiveness to the customer (RESP),
partnership quality (PARTQ), and market performance (MARTP). In the third stage, a pilot study was conducted on a sample
of 50 respondents (profile similar to the respondents in the final survey) to ensure instrument reliability using the Cronbach
alpha (α) test (Creswell 1994). Items not confirming the suggested value (α≥0.70) were dropped from the study. The
instrument used for the final survey included 76 measurement items (refer to appendix 2 for the detailed list). All the items
corresponding to the constructs were grouped, and no negative meaning words were used to avoid any respondent bias
(Podsakoff, MacKenzie, and Podsakoff 2012).
4. Analysis and results
SEM, which is a widely used tool in the field of operations and supply chain management to hypothetical based interactions,
was used to test the validity of the proposed framework. However, before applying the SEM, the measurement scales were
subjected to statistical validation using convergent and discriminant validity tests.
4.1. Measurement model
Validity is defined as ‘the extent to which data collection methods accurately measure what they were intended to measure’
(Saunders, Lewis, and Thornhill 2003). The results of validity and reliability tests are presented below.
4.1.1. Convergent validity
All the constructs from the proposed research framework were tested for the presence of convergent validity. For a given
measurement construct to satisfy convergent validity, all the measurement items must have their factor loadings ≥0.5 (Hair
et al. 2014), congeneric reliability (ρC) of the measurement constructs must be ≥0.70 (Cho 2016) and construct’s average
variance extracted (AVE) must be ≥0.50 (Fornell and Larcker 1981;Hairetal.2014). Exploratory Factor Analysis (EFA)
was also performed to test the uni-dimensionality of the selected items. The values of Cronbach alpha (α) and factor loadings
obtained from EFA are presented in Table 2.
The results presented in Table 2reveals high Cronbach alpha values (>0.70) indicating the reliability of the selected
constructs. The Kaiser-Meyer-Olkin (KMO) test was performed to check the adequacy of the sample size. The KMO value
International Journal of Production Research 7
Table 2. Reliability and EFA loading.
Constructs (Cronbach’s alpha
(α)) Item details EFA factor loading
BDDSC (0.942) BDDSC1 0.809
BDDSC2 0.805
BDDSC3 0.711
BDDSC4 0.762
BDDSC5 0.723
BDDSC6 0.786
BDDSC7 0.672
BDDSC8 0.731
BDDSC9 0.736
BDDSC10 0.723
BDDSC11 0.640
BDDSC12 0.669
BDDSC13 0.738
BDDSC14 0.685
BDDSC15 0.694
FLEX (0.8 91) FLEX1 0.654
FLEX2 0.657
FLEX3 0.775
FLEX4 0.725
FLEX5 0.702
INTG (0.917) INTG1 0.689
INTG2 0.679
INTG3 0.771
INTG4 0.838
INTG5 0.800
RESP (0.785) RESP1 0.716
RESP2 0.750
RESP3 0.704
RESP4 0.720
RESP5 0.042
EFFY (0.755) EFFY1 0.907
EFFY2 0.727
EFFY3 0.523
EFFY4 0.813
EFFY5 0.805
QUAL (0.886) QUAL1 0.629
QUAL2 0.395
QUAL3 0.780
QUAL4 0.822
QUAL5 0.813
INNOV (0.741) INNOV1 0.943
INNOV2 0.641
INNOV3 0.734
INNOV4 0.809
INNOV5 0.600
MKTP (0.851) MKTP1 0.569
MKTP2 0.660
MKTP3 0.778
MKTP4 0.585
MKTP5 0.555
PARTQ (0.965) PARTQ1 0.784
PARTQ2 0.674
PARTQ3 0.789
PARTQ4 0.620
PARTQ5 0.738
PARTQ6 0.822
PARTQ7 0.827
PARTQ8 0.744
(Continued).
8S. A. Gawankar et al.
Table 2. Continued.
Constructs (Cronbach’s alpha
(α)) Item details EFA factor loading
PARTQ9 0.786
PARTQ10 0.781
PARTQ11 0.849
PARTQ12 0.830
PARTQ13 0.552
PARTQ14 0.597
PARTQ15 0.694
PARTQ16 0.712
OP (0.876) OP1 0.815
OP2 0.809
OP3 0.764
OP4 0.693
OP5 0.649
OP6 0.772
OP7 0.695
OP8 0.688
OP9 0.656
OP10 0.669
Note: all the factor loadings were found to be significant at p<0.01.
Table 3. Congeneric reliability and AVE values.
Constructs CR AVE
BDDSC 0.9551 0.5871
FLEX 0.9226 0.7048
INTG 0.9176 0.6903
RESP 0.7693 0.5060
EFFY 0.8464 0.5256
QUAL 0.8803 0.5955
INNOV 0.8707 0.5757
MKTP 0.8922 0.6238
PARTQ 0.9533 0.5647
OP 0.8916 0.6584
of 0.837 reveals that the chosen sample size was adequate. The factor loadings satisfied the cutoff value (<0.5) on all the
items except for RESP5 and QUAL2, which were dropped from the study reducing the total number of items to 74. The
congeneric reliability and AVE values presented in Table 3, were found to be higher than the suggested cut-off values of
0.70 and 0.50, respectively, indicating good reliability and convergent validity for the selected items.
4.1.2. Discriminant validity
The discriminant validity test was performed as per the established guidelines (Fornell and Larcker 1981; Cooper and Zmud
1990). Table 4reveals that the square root of AVE of the selected constructs was higher than the correlations between the
specific construct and all the other constructs in the model indicating discriminant validity. Further, the heterotrait–monotrait
(HTMT) ratio of correlations between the constructs (less than 0.90) also reported discriminant validity of the constructs
(Henseler, Ringle, and Sarstedt 2015; Albort-Morant, Leal-Millán, and Cepeda-Carrión 2016). The results of HTMT are
presented in Table 5.
4.1.3. Missing data and common method variance
The amount of missing data associated with the measurement items was less than two percent, indicating no severe threat to
the validity of the measurement items (Hair et al. 2017). Any missing values were imputed using the expectation minimiza-
tion (EM) technique. The confirmatory factor analysis (CFA) marker technique was used to test the presence of common
International Journal of Production Research 9
Table 4. Measurement model: discriminant validity.
Constructs BDDSC FLEX INTG RESP EFFY QUAL INNOV MKTP PARTQ OP
BDDSC 0.86
FLEX 0.62 0.83
INTG 0.62 0.62 0.83
RESP 0.60 0.54 0.69 0.91
EFFY 0.60 0.60 0.61 0.50 0.82
QUAL 0.60 0.63 0.63 0.60 0.68 0.86
INNOV 0.56 0.52 0.50 0.60 0.61 0.63 0.86
MKTP 0.61 0.55 0.57 0.56 0.65 0.66 0.65 0.89
PARTQ 0.61 0.61 0.68 0.60 0.59 0.59 0.63 0.55 0.89
OP 0.53 0.51 0.67 0.60 0.69 0.55 0.66 0.59 0.50 0.81
Table 5. Heterotrait–Monotrait (HTMT).
Constructs BDDSC FLEX INTG RESP EFFY QUAL INNOV MKTP PARTQ OP
BDDSC –
FLEX 0.80 –
INTG 0.82 0.82 –
RESP 0.79 0.81 0.65 –
EFFY 0.78 0.79 0.75 0.81 –
QUAL 0.80 0.77 0.68 0.82 0.78 –
INNOV 0.75 0.69 0.72 0.80 0.79 0.80 –
MKTP 0.71 0.72 0.79 0.83 0.77 0.81 0.75 –
PARTQ 0.82 0.84 0.82 0.79 0.75 0.78 0.78 0.74 –
OP 0.75 0.60 0.65 0.80 0.76 0.76 0.80 0.75 0.75 –
Table 6. Model fit and goodness-of-fit indices.
Model χ2df Cmin N RMSEA CFI AGFI SRMR
Measurement model 11537.770 2736 4.21 380 0.04 0.861 0.867 0.01
Marker (structural) model 15367.849 3864 3.94 380 0.03 0.894 0.888 0.03
method variance (CMV). CMV is defined as ‘systematic error variance shared among variables measured with and intro-
duced as a function of the same method and source.’ The presence of CMV may influence the intercorrelations between two
variables as a function of the method and constructs being measured (Williams, Hartman, and Cavazotte 2010).The results
shown in Table 6ensured that CMV had no significant influence on the relationships between the constructs.
4.1.4. Confirmatory factor analysis (CFA)
The confirmation of the hypothesised structure was obtained using CFA measurement model fit. CFA is used to estimate a
sample covariance matrix that is compared with the observed covariance matrix (Schreiber et al. 2006). The standardised
loading values for three items (EFFY1, OP1, and OP2) were less than the cutoff value (>0.5), and therefore, were dropped
from the final model analysis. The use of the Maximum Likelihood (ML) method for estimating the relationships resulted
in an acceptable solution, ensuring that minimum iteration was achieved without any issues of multicollinearity. The mea-
surement model resulted in an over-identified model with 2582 positive degrees of freedom. The results of the goodness
of fit indices presented in Table 7indicates that the model fits the data reasonably well. The Goodness of Fit Index (GFI),
Root-Mean-Square Error of Approximation (RMSEA), Adjusted Goodness of Fit Index (AGFI), Comparative Fit Index
(CFI), Normed Fit Index (NFI), Tucker Lewis Index (TLI) were found to be somewhat lower than the recommended cutoff
values but still can be considered exhibiting a considerable model fit (Browne et al. 2002; Prudon 2015).
4.2. Results of structural equation modeling
After validating the proposed measurement model, SEM was used to examine the relationship between BDDSC, SCPM,
and OP. The effect of the moderating variable (GS) was tested at a significance level of 0.05, using Z scores (Hair et al.
10 S. A. Gawankar et al.
Table 7. Model-fit statistics of the measurement model.
Model-fit statistic Recommended* Obtained
CMIN – 10113.927
df – 2582
CMIN/df ≤5 (Carmines and McIver 1981)
significance p≤0.05 0.000
CMIN/DF <5.0 3.91
GFI >0.90 0.815
AGFI >0.90 0.879
NFI >0.90 0.862
RFI >0.90 0.846
TLI >0.90 0.877
CFI >0.90 0.891
RMSEA <0.05 0.04
*Source: Hair et al. (2014).
Figure 2. Path diagram for the research framework.
2014). The structural model included ten unobserved constructs and 71 observed variables. Variance inflation factors (VIF)
were calculated separately to check for multicollinearity. All the relationships were found to have the VIF values in the
recommended threshold value of 1–10, indicating the absence of multicollinearity between the variables (Hair et al. 2014).
Figure 2shows the path diagram for the BDDSC, SCPMs (FLEX, INTG, RESP, EFFY, QUAL, SCPMPPI, MKTP, and
PARTQ), and OP along with their standardised coefficient values. The results exhibit that the BDDSC, all eight SCPM, and
the OP have significant loading with a good model fit.
The hypothesis H1 (BDDSC →SCPMs) was supported (β=0.90), suggesting that the retail 4.0 BDDSC influences
the performance measures of all the activities in the supply chain. The use of BDA is found to make the supply chains
efficient and promotes a culture of the data-driven predictive performance management system (Hedgebeth 2007;Trkman
et al. 2010). Further, it can be observed that SCPM influenced the overall OP. The hypothesis H2 (SCPMs→OP) was
supported (β=0.89), suggesting that SCPM has a positive impact on all antecedents of firms which help in knowledge
transfer and improved managers’ awareness of shared targets, which ultimately impact the OP (Bititci et al. 2012). The
SEM results showed an excellent model fit with CFI (0.909), GFI (0.917), NFI (0.937), RMR (0.071), and CMIN/DF
(4.189).
International Journal of Production Research 11
Table 8. Differences between the effects of Governance structure.
Estimates
Relationships Contractual based alliance Relationship-based alliance Z-scores
SCPM ←BDDSC 0.394 0.344 0.131
INNOV ←SCPM 1.681 1.823 −0.734
QUAL ←SCPM 2.431 2.964 0.438
MKTP ←SCPM 2.685 3.252 0.027
RESP ←SCPM 1.984 2.048 −0.633
FLEX ←SCPM 1.641 1.646 2.774*
INTG ←SCPM 2.085 1.825 1.674*
PARTQ ←SCPM 2.012 1.999 2.254*
EFFY ←SCPM 2.192 2.367 1.791
Notes: *p-value <0.01.
The highly dynamic retail environment in India has opened many opportunities for the retailers, forcing them to initiate
multichannel strategies to provide the customers with the rich shopping experience. However, due to the ongoing digitisa-
tion, the distinctions between offline and online channels are vanishing. Under such circumstances, multichannel retailing
is moving to omnichannel retailing, leading to the fourth generation retail revolution (Kamble et al. 2019). The model
explained 30.6% of the total variance in OP hence signifying the importance of BDDSC in improving the performance of
retail 4.0 organisations. The findings support the view that the use of relevant SCPM expedites the transformation of the
organisations to higher values (Fosso Wamba et al. 2018).
4.3. Analysing the moderating effect of governance structure on the relationships.
The path diagrams for the two GS are shown in Figure 3a and b. The results show that the relationships between the
BDDSC, SCMP, and OP are significant across both the GS. In comparison to contract-based alliance, the effect of the
relationship between SCMP and OP was found to be low (β=0.38) in the relationship-based alliance. The difference in
the relationships between the GS were tested using Z-score. The values presented in Table 8identified GS as a significant
moderating variable. Three sub-constructs viz., Integration (INTG), Flexibility (FLEX), and Partnership Quality (PARTQ)
with Z scores of 1.674, 2.774, and 1.280 were found to be insignificant (at p<0.01), whereas, significant differences existed
on the remaining SCPM.
It was found that BDDSC has a strong influence on the SCPM (β=0.89, p<0.001) in retail supply chain with con-
tractual based alliance. The finding reveals that organisations are achieving significant improvements in their performance
and gaining contextual intelligence using BDA. The influence of SCPM on OP was found to be high (β=0.77, p<0.001),
supporting the claim that SCPM impact decision-making when the firm’s processes are based on contractual relationship
(Grafton, Lillis, and Widener 2010; Franco-Santos, Lucianetti, and Bourne 2012). The SEM results showed an excellent
model fit with CFI (0.995), GFI (0.901), NFI (0.943), RMR (0.065), CMIN/DF (4.669). The results for relationship-based
alliance also revealed a strong influence of BDDSC on SCPM (β=0.76, p<0.001), suggesting that BDDSC provides end-
to-end visibility for the supply chains. Further, SCPM showed positive and significant effect on OP (β=0.38, p<0.001).
Although the effect was significant, the magnitude of this effect was low (0.38). This may be because of the presence of
multiple inputs and output factors in the relationship-based system, including negative externalities (Zhao et al. 2016; Zhang
et al. 2017).
The relationship-based alliance is based on the degree of integration, coordination, and control, whereas in a contract-
based alliance, the relationships are based on transaction-specific investments. Further, the relationship-based alliance offers
greater supply chain flexibility. Relational rules and joint activities help reduce transaction costs and encourage supply chain
partners to contribute routine transactions and establish extended long-term relationships. Relationship-based governance
can both enhance co-operation among players and minimise risk. When relational governance is of a higher level, it increases
the enthusiasm of supply chain members to interchange and modifies domain-specific and process-specific resource utility.
This leads to new development among the business processes and activities and redesigns the physical flows of material.
Outcomes of such initiatives help the supply chain activities to respond rapidly and get adjusted with uncertain environmen-
tal changes resulting in a more flexible supply chain. The study found that contractual-level work is more rigid regarding
flexibility compared to the relationship-based alliances. The third construct on which the differences exist is the partnership
quality. Trust is found to be a determinant of perceived partnership quality.
12 S. A. Gawankar et al.
(a)
(b)
Figure 3. (a) Path diagram for the contractual relationship and (b) Path diagram for relationship-based alliance.
5. Implications, limitations and future research directions
5.1. Theoretical implications
Retail 4.0 offers the convenience of making payments, returning or exchanging products, and rich assortment of products
to the customers. The retail 4.0 has posed a significant challenge for the traditional retail supply chains, those who have
not yet devised strategies to adopt emerging technologies. Retail 4.0 transforms the supply chains to a customer-focused
organisation, facilitating the swift flow of products and information across the channel with highly customised services to
their customers (Singh and Chandra 2016). The retail 4.0 provides an experience-based approach through all the channels of
International Journal of Production Research 13
customer’s engagements that include physical stores, online stores, social media, mobile communications and public spaces
(Beecham 2016; Kamble et al. 2019). The main component of the retail 4.0 is the consumer and market data, which is used
for understanding the trends and changing preferences of the consumers over the different retailing channels. The data is
analysed for profiling the customers, analysing store traffic, deciding the merchandise, designing the store layout, planning
inventory, etc. Those retailers not investing in the retail 4.0 will likely disappear from the retail space, as the retailers with
a data-centric approach will be the likely king in this sector (Beecham 2016).
The theoretical contributions of this study are fourfold. First, the study contributes to the literature on BDA and SCPM,
which is not sufficiently developed (Hofmann 2017). Furthermore, this is the first empirical study that investigates the
relationships between the BDDSC, SCPM, and OP in a retail 4.0 environment. The combination of the two (BDA and
SCPM) enriches and deepens the general connotation of each other on the overall OP. The results will help the practitioners
in the retail industry to increase focus on BDA. The study highlights that big data-driven decision-making contributes to the
improvements in supply chain processes, logistics, inventory control, and cost reduction (Tan et al. 2017; Gupta, Modgil,
and Gunasekaran 2019). Second, despite the increasing research on BDA and OP, there is a paucity of empirical evidence
and theoretical reflection on SCPM in developing and emerging economies (Kamalahmadi and Parast 2016). This study
is conducted in the Indian retail sector and provides empirical evidence for improving retail supply chain performance in
developing countries. This fills a significant research gap, as existing research so far has focused on developed countries
(Raman et al. 2018). The findings will help the practitioners from the retail industry in developing economies to guide their
transformation to retail 4.0 through appropriate BDA investments. Third, the findings identify the presence of both financial
and non-financial performance measurement in a complementary manner. However, there is a need for new approaches to the
management of retail supply chain performance. The findings of this study suggesting that BDA offers significant advantages
in reducing supply chain costs, rapid response to changing the market environment, improved control of relationships with
partner and suppliers, and increase sales and operations planning abilities are supported by previous literature (Schoenherr
and Speier-Pero 2015; Gupta, Modgil, and Gunasekaran 2019). Finally, the study identified GS as a significant moderating
variable suggesting that the retail supply chain with contractual relationships are more adaptable to retail 4.0 environment.
5.2. Managerial implications
The new technologies provide an opportunity for retail organisations to transform their retail supply chain to a retail 4.0
environment (Bird et al. 2012). These technologies include;
(1) Radio-frequency identification, used for inventory tracking and control, improve store security and enable self-
checkout
(2) Use of micropayment platforms
(3) Cloud computing
(4) Retailer-customer interface technologies such as Microsoft’s touchless Kinect or SIRI from Apple and;
(5) Using customers mobile phones to track the traffic inside the stores.
The retail 4.0 promises to integrate the suppliers, merchants, and customers with the use of emerging technologies. IoT is
one of the major components of building a retail 4.0 supply chain. However, it requires a robust infrastructure that includes
internet connectivity with high bandwidth, IoT sensors, and various devices (Beecham 2016). The outcomes of this research
will help the retail supply chain practitioners to devise strategies on implementation of BDA.
The study emphasises the importance of focusing on the entire supply chain rather than solely on internal operations.
The framework of this study provides a benchmark to the decision-makers for the integration of business processes across
the supply chain. While transforming to retail 4.0, the managers must overcome specific challenges such as convincing and
training the employees to adapt and learn new technology. The considerable amount of big data generated in the retail 4.0
needs to be stored, analysed, and shared across the supply chains. This requires the retailing firms to make investments
in storage, computing, and data sharing technologies and hire skilled workers to execute the tasks. The other challenges
that need to be addressed include; technology integration, privacy, security, and ownership of data. The findings from
this research will motivate the managers to devise suitable strategies to overcome these challenges and plan their BDA
investments. It is implied that the investments in the retail 4.0 will help the forward-thinking retailers to guide their customers
on what to purchase, when to buy (notifying discounts), when the out of stock item is available, resulting in improved service
levels and better inventory control. Based on the findings, we can expect that in the future, a data-driven retail 4.0 would
drive the consumer’s purchase and payment habits bringing a positive influence on the OP.
14 S. A. Gawankar et al.
5.3. Limitations and future directions of research
Although, the study provides considerable insights for scholars and industry practitioners a few limitations and future scope
of research are outlined. The first limitation was concerning the time frame. We may not have considered a few factors,
those constraining the implementation of supply chain management in the retail firms. For example, this study refrains from
examining the impact of channel conflict and the power of top management. These factors may influence the implementation
of BDA, which needs to be addressed in future studies. The second limitation was to keep the model at a manageable size,
and therefore, the results may be subjective to the aspects specific to the culture of the region under particular consideration
(Monczka, Trent, and Handfield 1998). Future research direction may be an expansion of the developed framework by
incorporating new factors identified from another sector. For example, considering electronic commerce and its influence
on supply chain management will be an interesting topic for investigation. Other possibilities could be considering a green
supply chain or sustainability SCPM and its impact on the OP. The study has a representation of supply chain practitioners
working in different domain of retail supply chains such as sourcing, warehouse management, store management, logistics,
supply chain planning, and customer relationship management. Therefore, the findings represent an aggregated view of
retail supply chain managers, not representing a specific function. Further, this study does not incorporate the views of other
stakeholders such as suppliers, third-party service providers, and customers and would have missed on some interesting
aspects. Future studies should capture the perceptions of these stakeholders to gain more valuable insights.
Disclosure statement
No potentialconflict of interest was reported by the authors.
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Appendices
Appendix 1: Details of respondents
The population used in the study Retail Sector
No. of retail stores 50 firms
Geographical area Metro and sub-metro cities in India
Data collection method Survey-based
Sampling technique used Purposive
Sample size 380
Timeframe Five months (November 2017 – April 2018)
Respondent profile Sourcing- 76 (20%)
Warehousing- 48 (12%)
Store management- 126 (33%)
Logistics- 38 (10%)
Supply chain plannning- 50 (13%)
Customer relationship management- 42(11%)
Response rate 63%
Level of education Graduate: 250 (66%)
Post-graduate: 100 (27%)
Doctorate: 30 (7%)
Years of experience <2 years: 82 (21%)
2–5 years:105 (27%)
6–10 years: 165 (45%)
>10 years: 28 (7%)
International Journal of Production Research 19
Appendix 2: Constructs details
Constructs (Cronbach’s α) Source Measures
Big data Driven Supply
Chain (BDDSC)
(0.942)
Wamba et al. (2018), Tseng et al.
(2018), Wang et al. (2018).
Our firm uses:
Complete, accurate and timely data (BDDSC1)
Data in standardised form (BDDSC2)
Intelligent adaptive devices for data collection (BDDSC3)
Secured data transmission network for data movement
(BDDSC4)
BDA storage media (BDDSC5)
New data parallelism and advanced models/algorithms for
data processing (BDDSC6)
Big data driven decision making models (BDDSC7)
Big data visualisation and application models (BDDSC8)
Integration BD with current systems (BDDSC9)
Trained and skilled workforce to manage BDA (BDDSC10)
Our firm has improved visibility across the supply chain
(BDDSC11)
Our firm has improved service quality (BDDSC12)
Our firm regularly updates the computing equipment to
process Big data. (BDDSC13)
Our firm determines optimal decision by utilising
accumulated knowledge? (BDDSC14)
Our firm uses IT-enabled processes for fact-driven
decision-making? (BDDSC15)
Supply Chain Flexibility
(SCF) (0.891)
Gawankar, Kamble, and Raut
(2017), Shibin et al. (2016).
Our retail outlet:
flexible to manage nonstandard orders related to structures
options, varying sizes and different colours (FLEX1)
frequently get adjusted with customer demand (FLEX2)
frequently introduces large number of product
improvements/variation (FLEX3)
manage the rapid introduction of new products (FLEX4)
reacts swiftly to achieve the target market(s) (FLEX5)
Supply Chain Integration
(SCI) (0.917)
Gawankar, Kamble, and Raut
(2017), Maestrini et al. (2017).
Our retail outlet:
connect and coordinate with all retailing functions (INTG1)
prefer cross-functional teams for process design(INTG2)
has information system integration (INTG3)
supplier activities usually cross-over each other (INTG4)
considers full transparent system (INTG5)
Responsiveness to
Customer (RTC)
(0.785)
Fosso Wamba et al. (2018),
Gawankar, Kamble, and Raut
(2017), Nguyen and Nguyen
(2017).
Our retail store:
makes on time fulfilment of customer order (RESP1)
has shortest order-to-delivery cycle time (RESP2)
has fast customer response time (RESP3)
provides order flexibility to the customer (RESP4)
has the ability to respond quickly and successfully to change
(RESP5)
Efficiency (E) (0.755) Wiengarten et al. (2018), Tseng
et al. (2018), Gawankar,
Kamble, and Raut (2017).
Our retail store:
sell more items per sales (EFFY1)
has transaction value on the higher side (EFFY2)
have more point of sales (POS) compare to a competitor
(EFFY3)
have full-time employees (EFFY4)
operating expenses are less compared to competitors
(EFFY5)
(Continued).
20 S. A. Gawankar et al.
Constructs (Cronbach’s α) Source Measures
Quality (Q) (0.886) Tseng et al. (2018), Gawankar,
Kamble, and Raut (2017),
Maestrini et al. (2017).
Our retail store:
contest based on quality (QUAL1)
offer highly trustworthy products (QUAL2)
offer highly robust products (QUAL3)
offer high quality of customer (QUAL4)
offer high-quality service (QUAL5)
Product Innovation (PI)
(0.741)
Gawankar, Kamble, and Raut
(2017).
Our retail store:
sells custom-made products (INNOV1)
alter product characteristics based on customer needs (INNOV2)
provide an extension of product range within the main product
field through technologically improved products (INNOV3)
sell environment-friendly products (INNOV4)
replace products being phased out (INNOV5)
Market Performance (MP)
(0.851)
Yoo an d S e o ( 2017), Gawankar,
Kamble, and Raut (2017),
Gunasekaran et al. (2015).
Our retail stores market position is determined by:
growth of market share (MKTP1)
growth of sales (MKTP2)
growth of cash flow per share (MKTP3)
growth of Price-to-Earnings (MKTP4)
growth of Market-to-Book (MKTP5)
Partnership Quality(PQ)
(0.965)
Migdadi and Elzzqaibeh (2018),
Gawankar, Kamble, and Raut
(2017).
Our retail store and suppliers:
mutually profitable (PARTQ1)
share risks (PARTQ2)
share benefits from SCM implementation (PARTQ3)
are marked by a high degree of coordination (PARTQ4)
relationship is satisfactory (PARTQ5)
transactions are open and honest (PARTQ6)
are reliable (PARTQ7)
keep the shared information confidential (PARTQ8)
wish to increase business in the future (PARTQ9)
invest much effort in a relationship (PARTQ10)
abide by agreements (PARTQ11)
keep promises (PARTQ12)
understand each other’s’ business policies and rules (PARTQ13)
understand the aims and objectives of the supply chain (PARTQ14)
understand the importance of collaboration across the supply chain
(PARTQ15)
understand the importance of improvements in the supply chain
(PARTQ16)
Organisational
Performance (OP)
(0.876)
Müller, Fay, and vom Brocke
(2018)
Our retail store performance is based on:
average value on return on invested assets (OP1)
average value of profit margin set (OP2)
average value of profits on sales (OP3)
average value of the total market share and its market growth
(OP4)
average value of total sales and capacity growth (OP5)
average value of sales growth. (the value used in dollars) (OP6)
the average value of the return of the stock. (OP7)
BDA value-added Customer preservation compare to competitors
(in last five years) (OP8)
BDAenhanced growth in sales compared to competitors (OP9)
Overall financial performance (in last five years) (OP10)
Total Items 76
