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336 J. Global Business Advancement, Vol. 13, No. 3, 2020
Copyright © 2020 Inderscience Enterprises Ltd.
Retailer responsiveness: a total interpretive structural
modelling approach
Deepika Sharma, Rashi Taggar
and Sunali Bindra*
School of Business,
Shri Mata Vaishno Devi University,
Kakryal, Katra – 182320, Jammu and Kashmir, India
Email: deepika11july@gmail.com
Email: rashi.taggar@smvdu.ac.in
Email: sunalimay6@gmail.com
*Corresponding author
Sanjay Dhir
Department of Management Studies,
Indian Institute of Technology Delhi,
Hauz Khas, New Delhi 110016, India
Email: sanjaydhir.iitd@gmail.com
Abstract: Customer-driven markets and developing information technology
pressurise retailers to respond time effectively towards current and future
demand. Responsiveness has been explored in the extant literature of
manufacturing, operations and logistics from multiple facets. The paper
primarily focuses on the examination of responsiveness variables and their
inter-relationships in retail context. Total interpretive structural modelling
(TISM) has been adopted to determine and interpret the interactions among the
identified variables. The use of TISM has also revealed the important links and
hierarchical associations. The findings of the study indicate firm performance
and behavioural intentions to be privileged with highest dependence power and
lowest driving power. The results of the analysis can act as a vital impetus to
comprehend the level of driving power of different responsiveness variables.
Management researchers and policy-makers shall primarily emphasise on the
interactions and their interpretations between variables with high driving force.
The study offers a ground-breaking understanding for retailers to consider their
inherent capabilities that can be promoted to respond time-effectively. The
findings can be insightful and feasible to formulate advanced retail policies and
strategies in order to transform the retail paradigm. It specifically encapsulates
the role and potential of retailer’s responsive capabilities in entire supply chain,
which was not previously covered by any study through TISM.
Keywords: behavioural intentions; hierarchical relationship; performance;
resource-based view; responsiveness; retail supply chain; retailer
responsiveness; satisfaction; TISM; total interpretive structural modelling.
Reference to this paper should be made as follows: Sharma, D., Taggar, R.,
Bindra, S. and Dhir, S. (2020) ‘Retailer responsiveness: a total interpretive
structural modelling approach’, J. Global Business Advancement,
Vol. 13, No. 3, pp.336–358.
Retailer responsiveness: a TISM approach 337
Biographical notes: Deepika Sharma is a doctoral research scholar at
Shri Mata Vaishno Devi University, Katra, J&K, India, with particular
interest in responsive retailing. Her study particularly focuses on improvising
the abilities of retailers to increase responsiveness for customer satisfaction.
She is a gold medallist in Business Administration from the University of
Jammu.
Rashi Taggar, PhD, is an Assistant Professor of Supply Chain Management
and Marketing management in School of Business at Shri Mata Vaishno Devi
University, Katra, J&K, India. Her areas of research interest are supplier
management, behavioural management and consumer psychology.
Sunali Bindra is a doctoral research scholar at Shri Mata Vaishno Devi
University, Katra, J&K, India, with particular interest in strategic management.
Her study particularly focuses on knowledge-based view to the capabilities of
an organisation in pursuit of competitive advantage. She is a Bachelor in
commerce and a gold medalist in Masters of Commerce from the University of
Jammu.
Sanjay Dhir, PhD, is an Assistant Professor of Strategic Management in the
Department of Management Studies at the Indian Institute of Technology (IIT)
in New Delhi, India, and a Fellow of the Indian Institute of Management
Lucknow. He has published several research papers in leading international
journals. His research interests include strategic management, joint ventures,
alliances, international business, and creative problem-solving.
1 Introduction
Competitive intensity in the present market situation is impacting the life span of
products with respect to their quality, costs, and speed of delivery (O’Connor et al., 2018;
Shibin et al., 2016; Chopra, 2003). Addressing these dynamic changes promptly
requires various organisations to transform these challenges into opportunities (Gligor,
2016; Kozlenkova et al., 2015). Responsiveness as a subject area has acquired
much academic effort to meet these challenges of extremely demanding requirements in
the market (Chuang, 2019; Mark et al., 2019). It is a topic of intense research interest
contributing to sustainable competitive advantage in the disciplines of logistics,
operations, and production management literature (Singh and Garg, 2015). Theoretical
works from the past years related to different management fields have frequently
portrayed responsiveness as the dynamic capability of the organisations (Reichhart and
Holweg, 2007; Teece, 2007; Holweg, 2005). These studies include inventory
management (Hariga et al., 2019; Jerath et al., 2017), order management (Bodaghi et al.,
2018), agile manufacturing system (Gunasekaran et al., 2019; Shekarian et al., 2019),
market orientation (Francescucci et al., 2018; Ozdemir et al., 2017), and service quality
(Gong and Yi, 2018; Famiyeh et al., 2018).
Most of the authors have inclined their work towards defining the responsiveness
as an internal ability of the firms whereas others have defined the ways to improvise
the ability of being responsive at various supply chain stages (Reichhart and Holweg,
2007). It has been linked to the capability of the firm to work on improvement for
338 D. Sharma et al.
quickly responding to the market indicators and internalising the customer choices time
effectively (Catalan and Kotzab, 2003). Generally, based upon the previous literature,
manufacturing industry has been considered as the quintessence of responsiveness (Batur
et al., 2018; Jerath et al., 2017). Many studies similar to flexibility and agility have
claimed manufacturing as the foundation of enhancing responsive capability in the
complete supply chain (Ahrens et al., 2018; Shekarian et al., 2019). Other retail and
distribution settings for creating responsiveness in the supply chain have been usually
neglected in most of the research works (Jafari, 2015; Sohel Rana et al., 2015; Chaudhuri,
2008). Many studies proclaim that the inventive power in the supply chain has
been shifting towards the retailers being the downstream side (Sandberg, 2013; Geylani
et al., 2007).
Dynamic global market variabilities and evolving customer expectations demand
sudden shifts in the processes adopted by firms to satisfy the customers (Kaushik and
Dhir, 2019). Customers are no longer committed to the operations with longer response
times and inflexibility of choices. However, delivering the exceptional customer
experience by providing them sufficient information impose retailers to take the lead to
improve their capabilities in creating responsiveness (Sandberg and Jafari, 2018). The
patented position has been gained by the retailers to encounter the responses of the
customers in the form of their behavioural outcomes. Retailers in the whole supply chain
can systematically work towards customising the customer’s experiences and renewing
the services. Moreover, it is not wrong to conclude that the successful accomplishment of
the goals of entire supply chain relies on the abilities of retailers in meeting customer
expectations. As the orientation of companies has shifted towards customers, it generates
the research gap to identify and analyse the responsiveness with respect to retailers
(Adivar et al., 2019; Bendoly et al., 2018). Retailers can achieve their short-run and long-
run objectives by improving responsiveness more than ever at the store level.
The existing variables of responsiveness have nevertheless been fragmented in the
field and cannot meet the specific needs of practitioners. It is therefore important to
encapsulate these facilitators in an effective way to allow contemporary organisations
to implement responsive techniques and strategies. Hence, the study and evaluation of
responsiveness require a systematic and structural approach to be adopted (Rajan and
Dhir, 2020; Shaik and Dhir, 2020; Singh et al., 2020a). It is imperative to understand and
identify the significant variables of the subject domain to provide reliable grounds for
future studies. These reliable grounds can be formulated by using a structural modelling
technique.
For this purpose, TISM has been applied to formulate a structural model for
responsiveness. This is used to create a hierarchical composition of number of variables
related to a specific subject domain (Hasan et al., 2019; Parameswar and Dhir, 2019).
The TISM is concerned with the embedded object analysis through a systematic iterative
approach supported by graph theory (Dhir et al., 2020). Moreover, the effectively defined
and well-formulated structures are developed from badly formulated conceptual
models for further interpretations (Parameswar and Dhir, 2020; Singh and Sushil, 2013).
It is also known as the modern qualitative and conceptual modelling methodology which
has assisted many researchers around the globe for different fields of study (Srivastava
and Sushil, 2013). The extensive exploration of the responsiveness variables
has been completed by systematically observing and surveying the available literature.
Retailer responsiveness: a TISM approach 339
Variables of responsiveness in retail supply chain have been explored by conducting an
extensive literature survey. An initial set of twenty variables has been compiled by
exploring available literature and subsequently eight variables have been finalised as the
result of brainstorming from marketing experts and researchers. TISM has been applied
to these finalised variables for structural modelling of responsiveness with context to
retailing.
This study would contribute significantly to the existing efforts in the retail
responsiveness and influence future researchers through the comprehensive positioning
of the possible core areas within retail firms. The rationale for this study is that the
influence of certain variables has been categorically described on responsiveness with the
best possible utility. Attempting to build the win-win situation for both firms and
customers, management should utilise these explored relationships. With the help of
framework structured through TISM method, management is enabled to evaluate
complex responsiveness strategies and take corrective steps using driving and depending
inter-linkages to resolve any imbalances.
2 Literature review
Survey of available literature on responsiveness and brainstorming with marketing
experts in the academia generates the following eight variables which have been finalised
for identifying their inter-linkages and relationships for structural modelling.
(1) Responsiveness
Responsiveness paved its way in the marketing discipline around 1969 from the fields of
production, operations, and logistics management (Sharma et al., 2020; Singh and Garg,
2015). It has mostly been defined as the firms’ inherent capacity to continuously
minimise the response time by shortening the time-to-market and improving value of
experiences (Belvedere et al., 2010). The subject domain has gained attention of several
researchers striving to enhance the firm performance (Pehrsson, 2014; Francescucci et al.,
2018). The contribution of the subject in improving the performance encourages them
to examine the role of firms in improving customer satisfaction (Bendoly et al., 2018;
Cai et al., 2015). Similarly, it has also been studied as an antecedent towards
various behavioural outcomes resulting from customer experiences (Burklin, 2019;
Almutawa et al., 2018). To meet the rapidly changing demands and market requirements,
responsiveness acts as the vital catalyst to improve performance specifically in the market
of short lifecycle products (Shockley et al., 2015; Reichhart and Holweg, 2007).
(2) Information system and technology
Firm performance can easily be achieved across the retail supply chains by improving the
efficient mechanisms of information system and technology (Shin et al., 2015; Tsai et al.,
2013). Previous literature establishes the positive association of information exchange
techniques with the responsiveness thereby improving firm performance (Agarwal et al.,
2020; Sindhani et al., 2019; Singh et al., 2019a). The dynamic and competitive
situations call for retailers and researchers to excel on information system
340 D. Sharma et al.
and technologies to improve the responsiveness more than ever (Parameswar et al., 2019;
Masson et al., 2007). This enabler of responsiveness can be significantly focused to
enhance the accuracy, more effectiveness, quick responses, and reduced response times.
Virtual supply chain, virtual organisations, and supply chain collaboration exemplifies
the level of information system and technologies in the subject area (Gunasekaran et al.,
2008; Holweg, 2005).
(3) Customer orientation
Customer orientation has been studied at the grass root level to improve the
responsiveness of the firms with the aim of enhancing firm performance (Stanton and
Paolo, 2012; Blocker et al., 2011). It evolves around identification of the evolving
needs and thereby responding to the customers accordingly to create values. The firms
have concentrated on the achievement of their goals and objectives of improving
responsiveness towards customers through customer orientation (Homburg et al., 2007).
Previous literature present evidences of relationship of customer orientation
with customer satisfaction (Pekovic et al., 2016) and behavioural intentions (Kautish
and Sharma, 2018; Ladhari et al., 2017). Therefore, this generate the need to
understand the ultimate impact of customer orientation on firm performance through
responsiveness.
(4) Innovation
Innovation is one of the significant enablers of responsiveness has been highlighted
by many studies in the past (Pekovic et al., 2016; Dobrzykowski et al., 2015; Choi and
Krause, 2006). Responsive firms use their resources to engage in innovative approaches
to internalise the customer choices and maintain competitive edge successfully (Dinesh
and Sushil, 2019; Ongsakul et al., 2019; Sharma et al., 2018). The creation and
enhancement of responsiveness bring competitive strength and contributes to customer
satisfaction thereby improving firm performance and behavioural intentions (Shin et al.,
2015; Reinartz et al., 2011). Dynamic retail formats, value branding, assortment,
customer experience, information integration, use of media, handling the deliveries
with payments, and order management are the areas in which innovation can be
incorporated.
(5) Collaboration
Numerous studies have established the association between collaboration techniques
and responsiveness thereby improving the firm performance and customer satisfaction
goals (Tsimiklis and Makatsoris, 2019; Shin et al., 2015; Dobrzykowski et al., 2015;
Kim et al., 2013). Retailer’s collaboration and engagement with suppliers and
customers respectively is a critical determinant to strengthen the supply chain’s capacity
to respond appropriately on time. Due to the ever-changing demands of customers, the
retailers are necessitated to establish mutual targets with the upstream distributors and
downstream customers. Vendor management inventory, just in time, efficient consumer
response are certain techniques which have been adopted by the supply chain
downstream members to initiate for improving responsiveness successfully and time
effectively.
Retailer responsiveness: a TISM approach 341
(6) Firm performance
Firm performance has been measured in the form of customer retention (Shin et al.,
2015), financial performance (Batra and Dhir, 2019; Dhir et al., 2018; Wei and Wang,
2011), operating margins (Salvador et al., 2014), net income (Jaeger et al., 2016), sales
growth (Martin and Grbac, 2003), and return on assets (Pehrsson, 2014). Retail firms
need to strive with all their possibilities to convert satisfaction of customers into
behavioural intentions thereby improving the overall performance (Gupta et al., 2020;
Ganesan et al., 2009). Responsiveness in the retail industry enables improvement
of the firm performance and transform the inconspicuous benefits resulting from
exceptional customer experience into visible rewards for the firm (Chan et al., 2017;
Shin et al., 2015; Salvador et al., 2014).
(7) Customer satisfaction
Over the years, it has been crucial for the firms to ensure customer satisfaction. Focus
on customer satisfaction through the lens of responsiveness has assisted firms to have
an overall growing impact on their performance (Gorane and Kant, 2017). Customer
satisfaction entails the emotional response of customers to the services provided
by retailers by evaluating their experience and generating customer fulfilment response
(Rust and Oliver, 2000). The emotional response outcomes of retailer’s efforts centred
towards customers in the form of satisfaction or dissatisfaction serves as a connection to
the discipline of consume behaviour (Olorunniwo et al., 2006).
(8) Behavioural intentions
Responsiveness influence the behavioural intention of customers substantially in the form
of cross-buying, revisit intentions, loyalty, referral behaviour, complaining behaviour,
and word of mouth (Mukerjee and Shaikh, 2019; Kumar et al., 2020). Behavioural
intentions can be understood as the various behavioural outcomes of customers at a given
period of time based upon their decision of associating or disassociating themselves with
the firm in future (Olorunniwo et al., 2006). Customer satisfaction, good service
quality, exceptional customer experience, and high level of responsiveness leads to a
positive influence on behavioural intentions. Whereas, behavioural intention lead to have
a significant impact on the firm performance which remains to be the centre of attraction
for many researchers to study it as an impetus to the achieve performance significantly
(Baumann et al., 2007; Lee and Lin, 2005).
3 Theoretical background
The unique and distinguished resources and abilities of the competitive firms have been
considered as the indispensable means to create competitive strength by resource-based
view (RBV) (Gorovaia and Windsperger, 2018; Chan et al., 2017). Whereas many studies
claim that focusing on the resources which are distinct to the particular firms can hinder
their pace of facing the new competitive challenges (Yaprak et al., 2018). Therefore, in
order to defeat the difficulties arising from RBV, dynamic capability theory has been
conceived to be the forerunner of RBV with inventiveness. The competitive firms
can adopt dynamic capability view by capitalising and utilising the inherent capabilities
of the firm in various operational and managerial processes (Um et al., 2017). As a result,
342 D. Sharma et al.
this will enable the firms to swiftly reconfigure their resources and other coordination
procedures (Zhang and Wu, 2017). The corresponding impact of dynamic capability
approach on the enhancement of innovation within firms has been observed by
Lokshin et al. (2008) with context to sourcing decisions. Therefore, the firms have been
recommended to create an alignment among their sourcing operations both internally
and externally to improve the efficiency by minimising the response times. The theory
of responsive retailing in the present paper has gained its grounds from the dynamic
capability theory. Retailing firms have been proposed to create interactions between the
internal and external sources by strengthening and empowering their inherent abilities to
improve responsiveness. The study further elaborates the significance of collaboration,
innovation, and information technology to increase the retailer’s responsiveness by
minimising the response time towards customers and suppliers.
4 Methodology
On the basis of specific research problem, contextual relationships are created
among different variables. This technique is interpretive since the group’s judgement
determines the association of variables. However as one of the major limitations of ISM,
it is not able to claim justice for defining the structural relationships and maintaining
adequate transparency. Therefore, to address all the drawbacks resulting from ISM, TISM
has been adopted as an interpretive extension and advanced version of Warfield’s (1974)
ISM (Sushil, 2012). With the help of this technique, hierarchical relationships among
various variables related to a specific criterion can be established (Hasan et al., 2019;
Wasuja et al., 2012). It is also helpful for interpreting the different linkages and
inter-connections of different variables. The refined representation of the relations
developed between these variables can be clearly obtained by such interpretations
(Khatwani et al., 2015). The answers to ‘What’, ‘Why’ and ‘How’ are conceptualised
under TISM to develop a framework. ‘What’ pertains to the fundamental dimensions
of a specific concept which can be worked upon by identifying the variables determining
it through literature survey (Singh et al., 2019b, 2020b). The inter-linkages and
hierarchical relationships among various variables are represented by answering ‘How’
in this technique. Whereas, ‘Why’ elucidates the reasons behind the particular
relationships among variables identified (Hussain et al., 2016; Sushil, 2012). However,
numerous researchers have effectively and extensively applied the TISM in the
disciplines of lean concepts (Vinodh, 2020), strategic thinking (Dhir and Dhir, 2020;
Bindra et al., 2019), asymmetric motives (Hasan et al., 2019), joint venture
competitiveness (Bamel et al., 2019; Dhir et al., 2019), waste management (Singh and
Sushil, 2013), agile manufacturing system (Sindhwani and Malhotra, 2017),
strategic performance management (Yadav, 2014), organisational citizenship behaviour
(Yadav et al., 2016), cloud computing (Sagar et al., 2013), and management education
(Mahajan et al., 2016).
The study incorporates TISM technique to build comprehensive responsiveness
model by using identified variables as the basic foundation. Identification of the variables
for responsiveness and evaluating the linkages among them has been assisted by the
final structural modelling. Various researchers and academicians have used TISM
methodology in varied disciplines to produce the results in the form of conceptual models
(Sindhwani and Malhotra, 2017; Jain and Raj, 2015; Yadav, 2014; Sushil, 2012).
Retailer responsiveness: a TISM approach 343
The following research methodology for conducting TISM has been proposed in this
study based upon the understandings gained from past studies. Figure 1 depicts the steps
involved in the process of TISM. TISM starts with identifying the variables which hold a
relationship with each other and responsiveness. Under the next step, which distinguishes
TISM and ISM, an interpretive relationship is established.
Figure 1 TISM methodology
5 Results and discussion
5.1 Step 1: Identifying and defining variables
A set of 20 variables has been compiled with survey of previous literature on
responsiveness. With the help of discussions and brainstorming with researchers and
marketing experts from academia, eight variables have been finalised for developing
structural model through TISM. These variables have been assigned their designated
codes (refer Table 1) for further analysis.
344 D. Sharma et al.
5.2 Step 2: Establishing contextual relationships
Structural modelling of responsiveness requires identification of the form of contextual
relationship among its variables. The contextual relationships can be established
in the form of “A influences or enables B” or “B influences or enables A”. This step
is necessary to determine the structure to be followed. Therefore, in order to develop
these contextual relationships, research scholars and marketing experts from the
academia have been approached for enhancing the quality and reliability.
Table 1 Variable codes
S. No. Variables Variable codes
1 Responsiveness C1
2 Information system and technology C2
3 Customer orientation C3
4 Innovation C4
5 Collaboration C5
6 Firm performance C6
7 Customer satisfaction C7
8 Behavioural intentions C8
5.3 Step 3: Interpreting relationships
It serves as the differentiating node as it tends to explain the reasons and logical
background behind particular relationship between variables. The step elaborates that
how the variables interact and support one another and how specific variable becomes the
driving factors for others. The interpretive logic has been established for the interactions
between responsiveness variables (refer Table 2).
Table 2 Interpretive logic – knowledge base
S. No.
Variable
codes
Pairwise
comparison
Interpretation
References
1 C2–C4 Information system
and technology
positively influence
innovation
Innovation is mostly influenced
by information system and
technology by identifying the
different platforms to introduce
innovation
Agnihotri et al. (2016),
Pekovic et al. (2016),
Kim et al. (2013) and
Wei and Wang (2011)
2 C5–C4 Collaboration
positively influence
innovation
Collaboration has a direct
relationship with innovation.
With the help of collaboration,
sources of innovation are
increased
Tsimiklis and
Makatsoris (2019),
Shin et al. (2015),
Dobrzykowski et al.
(2015) and Kim et al.
(2013)
3 C3–C4 Customer
orientation
positively influence
innovation
Customer orientation positively
influences innovation. It
enhances the degree of
innovation by introducing more
avenues in the firm
Mukerjee and Shaikh
(2019), Genc and
De Giovanni (2018)
and Blocker et al.
(2011)
Retailer responsiveness: a TISM approach 345
Table 2 Interpretive logic – knowledge base (continued)
S. No.
Variable
codes
Pairwise
comparison
Interpretation
References
4 C4–C1 Innovation
positively influence
responsiveness
Responsiveness can be improved
by innovating the abilities
towards upstream and
downstream supply chain
Pekovic et al. (2016),
Dobrzykowski et al.
(2015) and Choi and
Krause (2006)
5 C1–C7 Responsiveness
positively influence
customer
satisfaction
Retailers create more
responsiveness in order to satisfy
customers more than ever. Thus,
responsiveness enhances
customer satisfaction
Cannella et al. (2018),
Gong and Yi (2018),
Gorane and Kant
(2017), Kant and
Jaiswal (2017) and
Agnihotri et al. (2016)
6 C7–C6 Customer
satisfaction
positively influence
firm performance
Better firm performance is based
upon the customer satisfaction
and is directly influenced by it
Gorane and Kant
(2017), Famiyeh et al.
(2018) and Grandey
et al. (2011)
7 C7–C8 Customer
satisfaction directly
impact behavioural
intentions
Customer satisfaction directly
impacts the behavioural
intentions in the form of referral
behaviour, cross-buying and up-
buying intentions
Gloor et al. (2017),
Ladhari et al. (2017),
Qin et al. (2010)
and Olorunniwo et al.
(2006)
8 C1–C6 Responsiveness
positively influence
firm performance
Transitive Chan et al. (2017),
Shockley et al. (2015),
Shin et al. (2015) and
Salvador et al. (2014)
9 C1–C8 Responsiveness
positively influence
behavioural
intentions
Transitive Kautish and Sharma
(2018), Ladhari et al.
(2017), Campbell and
Fairhurst (2016) and
Olorunniwo et al.
(2006)
5.4 Step 4: Pairwise comparisons
Under this, the variables of responsiveness have been compared pairwise in order to
identify the interpretive logic behind such interactions. For this purpose, self-structural
interaction matrix (SSIM) has been developed (refer Table 3). The matrix is prepared by
comparing each variable individually to the rest of the variables. Here, ‘Yes’ denotes
the interaction between two variables and ‘No’ denotes no interaction between the
variables based upon the strong theoretical grounds. Once the interactions have been
identified, ‘interpretive-logic-knowledge base’ is attained for the elucidation of
established logics.
5.5 Step 5: Transitivity check and reachability matrix
Interpretive logic-knowledge base has assisted to prepare reachability matrix. Cell in this
knowledge base representing ‘Yes’ has been replaced by ‘1’ and ‘No’ has been replaced
by ‘0’. Transitivity rule shall be applied after the preparation of reachability matrix.
Transitivity rule implies that if C3 is influencing C4 and C4 is influencing C1 then C3
346 D. Sharma et al.
must be influencing C1. Figure 2 evident the reachability matrix revealing the transitivity
among the same interactions. For the purpose of establishing links of transitivity
in the reachability matrix, experts and researchers have been approached for better
reliability. The overall responses carrying more than 60% weightage were regarded as the
significant transitive links.
Table 3 Structural self-interactive matrix (SSIM)
Variables C1 C2 C3 C4 C5 C6 C7 C8
C1 Yes No No No No Yes Yes Yes
C2 Yes Yes No Yes No Yes Yes Yes
C3 Yes No Yes Yes No Yes Yes Yes
C4 Yes No No Yes No Yes Yes Yes
C5 Yes No No Yes Yes Yes Yes Yes
C6 No No No No No Yes No No
C7 No No No No No Yes Yes Yes
C8 No No No No No No No Yes
Variables C1 C2 C3 C4 C5 C6 C7 C8
C1 1 0 0 0 0 1* 1 1*
C2 1* 1 0 1 0 1* 1* 1*
C3 1* 0 1 1 0 1* 1* 1*
C4 1 0 0 1 0 1* 1* 1*
C5 1* 0 0 1 1 1* 1* 1*
C6 0 0 0 0 0 1 0 0
C7 0 0 0 0 0 1 1 1
C8 0 0 0 0 0 0 0 1
Figure 2 Reachability and transitivity matrix
5.6 Step 6: Level partitioning
The next step assists in understanding the positioning of variables on the basis different
levels formed (Pandey and Garg, 2009). As the reachability and antecedent sets have
been formulated with the help of reachability matrix, therefore, it is required to identify
Retailer responsiveness: a TISM approach 347
the particular place of a variable in relation to others. For this purpose, the intersection of
all the variables is identified in order to make juxtaposition between sets of transitivity
and reachability (refer Table 4).
Table 4 Level partitioning
Variables Reachability set Antecedent set Intersection set Levels
(a): Iteration – 1
C1 1,6,7,8 1,2,3,4,5 1
C2 1,2,4,6,7,8 2 2
C3 1,3,4,6,7,8 3 3
C4 1,4,6,7,8 2,3,4,5 4
C5 1,4,5,6,7,8 5 5
C6 6 1,2,3,4,5,6,7 6 I
C7 6,7,8 1,2,3,4,5,7 7
C8 8 1,2,3,4,5,7,8 8 I
(b): Iteration – 2
C1 1, 7 1,2,3,4,5 1
C2 1,2,4,7 2 2
C3 1,3,4,7 3 3
C4 1,4,7 2,3,4,5 4
C5 1,4,5,7 5 5
C7 7 1,2,3,4,5,7 7
II
(c): Iteration-3
C1 1 1,2,3,4,5 1
III
C2 1,2,4 2 2
C3 1,3,4 3 3
C4 1,4 2,3,4,5 4
C5 1,4,5 5 5
(d): Iteration-4
C2 2,4 2 2
C3 3,4 3 3
C4 4 2,3,4,5 4 IV
C5 4,5 5 5
(e): Iteration-5
C2 2 2 2 V
C3 3 3 3 V
C5 5 5 5 V
348 D. Sharma et al.
The variables with common sets of intersection and reachability are ranked higher in the
hierarchy (refer Table 5). In the same manner, the positions of all the variables have been
determined until each variable is placed at its respective level.
Table 5 TISM levels
S. No. Assigned codes Variables TISM levels
1 C6 Firm performance I
2 C8 Behavioural intentions I
3 C7 Customer satisfaction II
4 C1 Responsiveness III
5 C4 Innovation IV
6 C2 Information system and technology V
7 C3 Customer orientation V
8 C5 Collaboration V
5.7 Step 7: Developing diagraph
As the position for each variable with respect to other is determined, the next step
involves to arrange all these variables by developing a diagraph based upon the links
identified in the reachability matrix (refer Figure 3). The transitive links with significant
interpretation have been considered in order to enable the structural modelling in the
most effective way.
Figure 3 Diagraph
5.8 Step 8: Total interpretive structural modelling
The final step includes the application of TISM on finalised variables. The TISM has
been developed for the variables by considering the interpretive matrix with the diagraph
prepared. The links present in the diagraph have been explained with the interpretive
logic behind them as discussed in Table 2. The explanation provided for the respective
link shave supported the relationship of variables and subsequently, the model
has been formulated (refer Figure 4). Finally, this helps to understand the driving and
reliant variables unequivocally in the proposed model based upon their hierarchical
relationships.
Retailer responsiveness: a TISM approach 349
Figure 4 Total interpretive structural model for responsiveness
6 Discussion and conclusion
The prime focus of the present study lies in identifying the variables related to
responsiveness. Prior studies on responsiveness have been surveyed systematically to
identify the variables and thereafter, the marketing experts and research scholars from the
same discipline have been involved to share their opinions on the inclusion and exclusion
of variables. The modelling of responsiveness variables is based upon the reliable
exploration of eight variables. These variables have been worked upon to create their
hierarchical relationship. For this purpose, TISM has been adopted to interpret the logics
behind the particular relationship and formulate a structural model. With the help of
TISM approach, the existing model reveals the effect of variables, their inter-
relationships, inter-linkages, and interpretive logic behind such relationships. As the
350 D. Sharma et al.
mutual relationship cannot be established through ISM, therefore, TISM has been given
due consideration for the structural modelling. Gradually, TISM has resulted in the
formulation of a comprehensive model of responsiveness by placing the variables
according to their level partitions.
The study however intends to examine the driving and dependency relations between
variables, which serve as a significant extension to the knowledge for studies in retail
chain. Management thinkers shall utilise the model as a platform to observe the
significant variables with their respective inter-dependence. Information system and
technology, collaboration, and customer orientations unveil the maximum driving
influence on rest of the variables. These variables have been placed at level V. On the
other hand, variables such as firm performance and behavioural intentions claim strong
dependence power but weak driving power, with their position at level I. These outcomes
depend upon the variables place at level II, III and IV. Furthermore, recognition of these
variables allows to determine their facilitating impact on responsiveness and hence
allows to pose questions to direct and indirect influence explored outcomes both for the
management and prospective researchers.
7 Managerial implications and future scope
Firms are switching their operational preferences towards quality, adaptability, and
shorter lead times in order to maintain and gain their competitive strength. Based upon
the perspectives shifted to bring more customer centricity, retailers have been
continuously working to improve responsiveness by reducing their response times. Such
a move necessitates monitoring of market sensitivity and orientation measures.
Consequently, concentrated and extensive purpose of improving responsiveness must
reflect the relevance of monitoring. However, it is necessary for retail companies to
integrate responsiveness enablers with context to customers and suppliers for long term
competitiveness and performance. The model developed must therefore enable retailers
to determine the performance of market sensing technologies and track the execution in
order to enhance responsiveness. This interpretive model can also be used to create an
effective framework in any retail organisation for planning and execution of
responsiveness strategies. These results help managers to use their resources wisely and
efficiently by concentrating on the most crucial variables.
Such findings give an outline of the factors that affect the preference of market
tactics, approaches and policies to be used in retail settings. This can also boost
profitability, customer loyalty and firm performance which lead to progressive retail
practices. It is crucial for the firms to consider the responsiveness variables that can
contribute to the performance and behavioural outcomes specifically for short life cycle
products. The measures required to successfully achieve the long-run and short-run
objectives of retail firms have been elucidated in the developed responsiveness model.
The inter-relationships in the proposed model therefore can act as a benchmark that
tracks the progress of responsiveness and provides the information to improve retailing
goals and targets. The results of this study will further enable the management to use their
resources competently to focus on the critical facilitators of responsiveness. Retailers and
managers have been recommended to focus on proactive approaches in relation to
sensing, reacting, and adopting the related market changes. The drivers and policies of
focal organisations which have demonstrated worldwide success with responsiveness are
Retailer responsiveness: a TISM approach 351
also very significant to be examined for improving the responsiveness as inherent
capability of the firms.
This research work, based upon the structural modelling of responsiveness can assist
the researchers, academicians, and policy makers in different ways. However, it is subject
to various limitations. The current research work is based upon the identification and
structural modelling of eight variables related to retailer responsiveness, whereas
additional variables can be considered for establishing such relationships. Also, the
research work conducted to develop TISM model has been focused on the few significant
expert opinions which opens the gateway to unavoidable amount of bias. Watson (1978)
proclaims the inflexibility of TISM to redefine and recombine the variables and the
structure of their relationships. Also, it lacks the description of weighted associations
and focuses only on creating hierarchical relationships (Kannan et al., 2008). Moreover,
future studies can be concentrated on statistically testing and validating the TISM model
of responsiveness through structural equation modelling (SEM) as the relationship among
variables has been established on justified theoretical grounds.
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