Plane Thermoelastic Waves in Infinite Half-Space Caused Decision Making: Applications in Management and Engineering ISSN: 2560-6018 eISSN: 2620-0104 DOI:_https://doi.org/10.31181/dmame0310112022j * Corresponding author. E-mail addresses: sudhanshu.joshi@uts.edu.au (S. joshi), manu.sharma@geu.ac.in (M. Sharma), dr.prasanjitchatterjee6@gmail.com (P. Chatterjee) OMNI- CHANNEL RETAILING FOR ENHANCING CUSTOMER ENGAGEMENT AMIDST SUPPLY CHAIN DISRUPTION: AN EMERGING MARKET PERSPECTIVE Sudhanshu Joshi1,2*, Manu Sharma3,4 and Prasanjit Chatterjee,5 1 Operations and Supply Chain Management Research Laboratory, School of Management, Doon University, Dehradun, India , sudhanshujoshi@doonuniversity.ac.in 2Australian Artificial Intelligence Institute (AAII), School of Computer Science, Faculty of Engineering & Information Technology, University of Technology Sydney, Sydney, Australia, sudhanshu.joshi@uts.edu.au 3 Department of Management Studies, Graphic Era Deemed to be University, Dehradun, India, manu.sharma@geu.ac.in 4Guidhall School of Business and Law, London Metropolitan University, London 5MCKV Institute of Engineering, West Bangal, India Received: 3 August 2022; Accepted: 18 October 2022; Available online: 10 November 2022. Original scientific paper Abstract: The research aims to explore the strength of enablers and adoption barriers in omnichannel retailing (OCR) and discuss how organizations may focus on redesigning their business models in emerging markets to manage the disruptive environment. The major enablers may enhance the omnichannel' performance to deliver a unified experience across all channels during the pandemic. The paper has used hybrid Multi-Criteria Decision- Making (MCDM) Methods. Organizations widely use these methods to explore the interrelationship among barriers and enablers affecting their performance. In the current study, 18 experts from different domains have examined and evaluated the 10 barriers and 7 enablers. The study reveals that integration, Visibility, internet accessibility, and advanced distribution centers are the primary enablers and driving the customer analytics enabler to strengthen their customer engagement and providing a unified experience to the. During the pandemic time the usage of the online channels have increased and thus retail channels may consider these enablers to enhance the unified experience level of the customers. The study also shows that inconsistency in price is the main adoption barrier followed by inconsistency in product discounts that should be minimized to engage customers effectively. The retail organizations need to understand the roadblocks in adopting OCR and should take relevant actions to minimize them. The retail organization or marketers Joshi et al./Decis. Mak. Appl. Manag. Eng. (2022) 2 may redesign their existing strategies based on price consistency, integration, Visibility, information systems, and coordination to develop a unified experience across channels during the pandemic situation. Key words: Omni-Channel Retailing (OCR); Supply Chain Disruption; Emerging Markets; Interpretative Structural Modeling (ISM); Fuzzy - MICMAC. 1. Introduction The global disruption, digital technologies progression, pandemic and enhanced usage of smart devices have reformed the countenance of retailing all across the world, subsequently engaging customers using multiple touch points and adopting OCR strategies to augment their experience (Li et al., 2020; Sharma et al., 2021a). The explosive growth rate of online customers in South Asia, Europe, UK and USA is carrying an irregular change to these marketplaces and giving a chance to companies to necessarily reimagine their business models. Developing economies like India and China are showing high encouragement through more engagement of online customers, and online purchases upsurge to $3.9 trillion value (WEF, 2020). Approximately 3 billion consumers from the emerging market will be online by 2022, exposing the opportunity for retail organizations to plan and target customer engagement appropriately (Nguyen et al., 2019). It is expected to achieve 1000 million target by 2030 (WEF, 2020). Moreover, the impact of digital influence can be understood by an example of Africa where e-commerce is limited to 1 % only but the digital influence is skyrocketing (BCG, 2018). In terms of growth perspective, online consumers in emerging markets represent an enormous opportunity. Technically, OCR raises to combine multiple points for online customers. The consumer decides where and when to shop and through which device (Ieva & Ziliani, 2018). If consumers do not purchase directly on the Internet, they still search for information on their mobile phones, which often influences their purchases. The value of digitally influenced spending in emerging markets is expected to reach $4 trillion (BCG, 2018). In the past years, emerging economies have shown exceptional growth. During 2000-2018, the share of these countries is from 11 to 28% in the world's gross domestic product and 11 to 24 % in global household consumption expenditures. The price fall of smartphones by 40% in emerging markets has impelled these devices into the hands of millions of people who were previously unable to afford them. The arrival of high-speed data networks has enabled these markets to achieve spectacular expansions in the connectivity and due to this half of the population is now connected to the Internet in emerging markets, mainly in the parts of Southeast Asia, Russia, Turkey and Brazil. The generations are spending time online, and thus marketers should explore new ways to reach digitally millennial consumers on modern platforms. Both online and offline retailers in emerging markets are highly motivated and aim to serve customers better through seamless experience and creating the appropriate content for each segment, communicating each segment through proper channels. Retailers in the emerging market need to adopt OCR as their businesses cannot be long-lasting if consumers are connected in a unidirectional way. With the advancement in information technology, consumers quickly disseminate and access information through multiple channels (Cai & Lo, 2020; Joshi et al., 2021; Joshi & Sharma, 2021). But, purchase actions happen as per consumer convenience and choices (Park & Lee, 2017; Chatterjee & Kumar, 2017). Thus, retail organizations need to understand what factors drive and restrict consumers' convenience during switching channels and completing their purchase actions. Previous research also Omni-channel retailing for enhancing customer engagement amidst supply chain disruption 3 highlights that discounts are the significant triggers for online purchases, but in emerging markets, discounts are not the only thing that matters (Chopra, 2016; Chopra et al., 2019; Arslan et al., 2021; Joshi et al., 2021). The other form of retail, i.e., Offline retail organization, has a limitation of consumer's time constraint that restricts him from visiting the store. Also, a limited range of product availability is overcome by online retailers. Thus, amidst the pandemic, retail organizations need to adopt a hybrid business model where online and offline formats will merge and engage consumers by giving them a choice to decide when, where, and how to shop. Online retail organizations have some limitations, such as tangibility, waiting for product delivery, delivery delay, dynamic pricing, etc. (Yang et al., 2019; Sharma et al., 2022a). There is still a population who believes in the visualization of products before purchase, and thus e-commerce companies need to open brick-and-mortar stores to capture this segment. Lens kart is one of the recent examples of OCR in the Indian market. Emerging market economies are expanding, boosted by educated younger, healthier populations with rising incomes, fueling a substantial increase in goods and services consumption. The spending by consumers in these economies is projected to be more than the developed nations. Thus, OCR is essential for retailers to reach targeted consumers efficiently by adopting digital technologies with minimum cost. Offline and online channels will complement each other to develop an efficient OCR system, breaking the wall between all the channels to provide a unified brand experience (Sharma et al., 2020a; Sharma & Joshi, 2020). The development of OCR will be dependent on the infrastructural development, technologies and digital transformations, and retailers' decisions to manage the current issues such as price inconsistency, order management, customer expectations, and others to provide a flawless experience across all the channels (Picot-Coupey et al., 2016; Ewerhard et al., 2019). There is a need to evaluate the existing scenario of the retailing industry in emerging markets as the future lies in the young and educated populations. There are research contributions in the area of OCR. Still, little attention has been given to the challenges or bottlenecks handled by the retail firms in the acceptance of OCR and also the influence of enablers enhancing its acceptability. This study is significant for mainly three reasons. First, prior research in the context of OCR in emerging markets is limited and focused only on the basic understanding and comparison with multi- channel retailing. Second, there are insufficient information regarding OCR's challenges and adoption barriers and enablers (Salvietti et al., 2022; Sharma et al., 2020a; Solem et al., 2022). The past research has not examined the challenges and adoption barriers existing in OCR influencing customer purchases. Lastly, the merit of inter-relationships among the identified adoption barriers and enablers of OCR is still unknown. Thus, to bridge the above research gaps, the present study intends to determine the enablers and adoption roadblocks in the OCR ecosystem influencing customers' journeys, choices, and unified experience. All the enablers and barriers are to be analyzed to explore the strength and weaknesses of the current channels of retail organizations that restrict or drive the customer to adopt OCR. The customers' switching of channels to complete their purchase action has questioned the organizations to investigate the reasons behind their behavior. This study provides the basis of acceptance and rejection of the omnichannel based on their attributes. This study also explores the intensity of enablers and barriers using hybrid MCDM methods for understanding the interrelationship among them. Thus, it helps the policymakers develop their OCR strategies based on the critical obstacles and enablers prioritized by the experts. Thus, the study framed key objectives to demarcate pressing strategic challenges that could set the pathway for Joshi et al./Decis. Mak. Appl. Manag. Eng. (2022) 4 retail management and can further contribute to existing theories. The theoretical background of the research is carried out from dynamic capabilities theory, the combination of the Technology acceptance model (TAM)- technology- organization- environment model(TOEM), and resource-based view theory. Retail chains' dynamic capabilities demonstrate their ability to develop and adopt the Omni channel framework for creating agile and responsive supply chains and improving operational excellence. Also, there is an urgent need to develop a strategic roadmap to bridge the implementation and research gaps. The research is an attempt to overall these issues. Based on these arguments, The study proposes the following objectives. RO1: Investigating the enablers and adoption barriers of OCR in emerging markets. RO2: To develop the hierarchical structure of enablers and barriers using the ISM approach. RO3: To investigate interrelationships among enablers and barriers using Fuzzy MICMAC and DEMATEL methods. The decision to design OCR is a complex problem. It has multiple levels, and thus hybrid MCDM approach has been employed to achieve the above objectives. The rest of the research work is structured as follows. Section 2 elaborates on existing works on OCR, enablers, and barriers. Section 3 explained the research methodology and steps for ISM, fuzzy MICMAC and DEMATEL methods. Section 4 presents the method applications, followed by the findings and discussions in Section 5. Section 6 elaborates the inferences and future work directions. Section 7 summarizes the study. 2. Literature review Based on the Scopus database, a comprehensive review of literature has been carried out on the relevant research works on omnichannel retailing and emerging markets in this domain. As depicted in Table 1, a search protocol was used using multiple keywords: "Omni- Channel Retailing" AND "Emerging Markets," AND "Pandemic” OR “COVID-19". A systematic literature review process was followed to evaluate the prominent publications on Omni Channel Strategy implementation challenges and the digitalization of retailing in emerging economies amidst the pandemic. For the SLR, the selected timeline was 2019–2022; the research results are articles. After the standard systematic literature review process, 37 papers were selected for final review. Table 1. Search Protocol for Systematic Literature Review Dimensions Detailed Explanation Keywords/ Terms used “Omni- Channel Retailing” AND “Emerging Markets” AND “Pandemic” OR “COVID-19" Timeline 2019-2022 Field Covered Title, Keywords, detailed abstract Inclusion Criteria SCOPUS database Exclusion Criteria Non-English Articles 2.1 Omni-Channel Retailing: Opportunities and Challenges OCR can be explained as a supply chain system where information, material, and fund flow happen by using several channels to coordinate, interact and fulfill customer Omni-channel retailing for enhancing customer engagement amidst supply chain disruption 5 demand (Rai et al., 2019; Chopra, 2016; Chopra, 2019; Galipoglu et al., 2018). It has caused interventions from many fields, such as Decision Sciences, Virtual Reality (VR), visual displays and merchandising decisions, engagement patterns, big data analytics, and profitability (Payne et al., 2017; Farah et al., 2019). Retail has evolved throug h multi-channel and has enhanced supply chain networks (Prabhuram et al., 2020). The traditional retailers of various products are extending their channels through virtual stores to derive the benefits of online media. Also, online retailers extend their reach through physical stores (Pan et al., 2017). This indicates the need for channel integration (Bayram & Cesaret, 2021). The integration in OCR has been categorized from three perspectives i) OCR stages ii) OCR types iii) OCR agents (Saghiri et al., 2017). The core enablers of integration and consistency in OCR strategy (Melero et al., 2016; Shen et al., 2018; Mirzabeiki & Saghiri, 2020).The Omnichannel need enablers, including broadband internet accessibility (Ye et al. 2018); well located and well - designed distribution centers (Melacini et al., 2018; Mkansi et al., 2018); efficient & extensive logistics (Kembro et al., 2018; Murfield et al., 2017; Saghiri et al., 2017; Daugherty et al., 2018; Hazen & Ellinger, 2019); customer analytics (Lekhwar et al., 2019;Vakhutinsky et al., 2019; Zaki & Neely, 2019); Visibility to customers (Ewerhard et al., 2019; Gawor & Hoberg, 2018); information system (Kembro et al., 2018; Kembro & Norrman, 2019) and product digitization (Cortiñas, et al., 2010; Ainsworth & Ballantine, 2017). Few researchers have underlined integration and Visibility as essential enablers for OCR (Ewerhard et al., 2019; Verhoef et al., 2015). The challenges in OCR are discussed by Picot-Coupey (2016) and divided into strategy-related and development-related levels. The organizational, managerial, and cultural were included in strategy-related and product mix, and information systems in development-related (Rai et al., 2019; Niranjan et al., 2019). Achieving demand, inventory, and in a single view is one of the most critical challenges for omnichannel. The objective of OCR is to transform the current business models, consumer behavior, and advancements in technology (Marchet et al., 2018). 2.2 Omnichannel Retailing in Emerging Markets OCR is changing the retailing landscape in emerging economies. The incremental growth of online retailing in association with small offline retailers is bringing profits for these economies. The integration of physical and online channels will create a win- win situation, such as a reduction in distribution costs and a wide variety of product availability. However, the success of this hybrid model depends on the development of complementary strengths of both channels to create a cost-efficient omnichannel and more responsive to consumer needs (Chopra, 2019). Retailers have put significant efforts into providing information access to customers using a number of channels and devices in the developed markets (Mrutzek-Hartmann, 2022). An e-retailer can send only sensory and digital information, which is a significant factor in the existence of physical retail organizations in emerging markets as still, many consumers use the offline channel for shopping (Asmare & Zewdie, 2022; Chopra, 2016; Lin et al., 2022; Yin et al., 2022). The omnichannel structure is not creating all the capabilities in each channel but rather assigns products and tasks to channels on the basis of effective handling (Chen et al., 2014;Chen et al., 2022). This structure is more appropriate in emerging markets where interim retailing models such as Borders and Circuit way have not yet been developed, and governments are still struggling to cope with the impact of online retailing on offline retailers (Ishfaq et al., 2022; Teixeira et al., 2022). The OCR will offer an exclusive opportunity that merges the online and offline model’s advantages to bring mutual benefits. Joshi et al./Decis. Mak. Appl. Manag. Eng. (2022) 6 2.3 Research Gaps The shift to OCR has been well familiar in the research literature (Park & Lee, 2017; Park & Kim, 2018). In the last decade, multi-channel retailing has grown into a standard approach (Schramm-Klein et al., 2011). Recent studies advocate the transition stage (Park & Kim, 2018; Zhang et al., 2019). Due to the prominent role of the physical retail format in the buying process, hybrid strategies for OCR have also been proposed (Huang & Jin, 2020).The previous research suggests customer wants a seamless experience during online purchases. Many studies have been conducted to understand the scenario of e-commerce in emerging markets in the context of dynamic pricing (Cavallo, 2017; Dan et al., 2012). The fulfillment and returns are also discussed by many researchers representing the consumers' view towards the e-commerce process in the context of the omnichannel environment (Bayram & Cesaret, 2021; Ewerhard et al., 2019). Moreover, the increasing synergy between both channels has been analyzed and highlights that channel integration is one of the key issues discussed (Zhang et al., 2019). Past research is limited to the understanding of Omnichannel. But what are the barriers and enablers that may affect the OCR framework that is still missing? The presence of enablers like coordination, infrastructure, analytics, etc., can enhance the OCR results, whereas the barriers like prince inconsistency and others may bring failure for omnichannel strategies. The literature also reveals that emerging markets like India and omnichannel are providing a wide variety of products to customers at a lesser cost, and thus, the strengths of both channels can be combined to develop a strong omnichannel structure (Chopra, 2016). This study establishes an OCR framework considering the barriers and enablers present in the retail environment that need to be considered by the retailers to build a strong and robust OCR framework where customers can be engaged and influenced to purchase products without any discrepancy among the channels. The conceptual framework is developed and illustrated in Figure 1. The literature review has identified seven main enablers and ten critical barriers exhibited in Table 2. The enablers are supporting OCR to enhance the customer's experience during purchase, including internet accessibility (Wang, 2013; Yu et al., 2016), Internet-enabled distribution centers (Chatterjee et al., 2002; Chen et al., 2014), Efficient and extensive logistics (Chen et al., 2022;Yan and Pei 2011; Blázquez, 2014), Customer analytics (Chatterjee et al., 2002), Visibility to customers (Agatz et al., 2008; Bahn & Fischer, 2003; Berman & Thelen,2018; Cassab & MacLachlan, 2009), Product digitization (Bernon et al., 2016; Verhoef et al., 2015), and integration (channel types, channel agents, and channel stages) (Saghiri et al., 2017). whereas the barriers are restricting the customer to use omnichannel including Low coordination among channel partners (Fulgoni, 2014; Hübner et al., 2016; Picot-Coupey et al., 2016), Variation in Pricing (Shankar et al., 2003; Neslin et al., 2006; Verhoef et al., 2015), Product Unavailability (Bernon et al., 2016; Chopra, 2016; Hübner et al., 2016; Ishfaq et al., 2022; Huang & Jin, 2020), Inconsistent contents (Verhoef et al., 2015), Central product (Balasubramanian et al, 2005; Verhoef et al., 2015), Data Security issues (Piotrowicz & Cuthbertson 2014) and Non-Understanding young customer habits (Verhoef et al., 2015), Order fulfillment (Chopra, 2016), Inconsistent Product discount (Sousa & Voss, 2006), and Time Constraint (Picot-Coupey et al., 2016; Neslin et al., 2006). Omni-channel retailing for enhancing customer engagement amidst supply chain disruption 7 Figure 1. Conceptual framework of Omnichannel Retailing The paper intends to discuss the influence of strong enablers on consumer’s experience towards OCR. The enablers and barriers need to be analyzed so that the decision- makers can enhance the organizational performance through omnichannel. Table 2. Enablers & barriers identified from literature Enablers References 1.Broadband internet accessibility (Wang et al., 2013; Ye et al., 2018; Yu et al., 2016) 2. Internet-enabled distribution centers (Chatterjee et al., 2002; Chopra, 2016; Sharma et al., 2020c) 3. Efficient and extensive logistics (Zhang et al., 2019; Yan and Pei 2011; Blázquez, 2014) 4. Customer analytics (Chatterjee & Kumar, 2017; Berman & Thelen, 2013) 5. Visibility to customers (Agatz et al., 2008; Bahn & Fischer, 2003; (Berman & Thelen, 2013; Cassab & MacLachlan, 2009) 6. Product digitization (Berman & Thelen, 2004; Verhoef et al., 2015) 7. Integration (channel types, channel agents, and channel stages) (Saghiri et al., 2018;Jocevski et al., 2019; Shanker et al., 2022;Sharma et al., 2020b; Sharma et al., 2022c). Omni-channels Retailing EnaB2 EnaB3 EnaB7 EnaB4 EnaB5 EnaB6 EnaB1 B3 B4 B5 B6 B7 B8 B9 B2 B10 B11 B1 B12 Joshi et al./Decis. Mak. Appl. Manag. Eng. (2022) 8 Enablers References Barriers References 1. Low coordination among channel partners (Fulgon 2014; Hübner et al.,2016; Picot-Coupey et al., 2016) 2. Variation in Pricing (Shankar et al., 2003; Neslin et al., 2006; Verhoef et al., 2015). 3. Product Unavailability (Bernon et al., 2016; Chopra, 2016; Hübner et al., 2016; Huang & Jin, 2020) 4. Inconsistent contents (Clinton and Whisnant, 2019; Sousa & Voss, 2006; Verhoef et al., 2015) 5. Central product (Balasubramanian et al, 2005; Verhoef et al., 2015) 6. Data Security issues (Piotrowicz and Cuthbertson 2014; Verhoef, & Agrawal, 2004) 7. Non-Understanding young customer habits (Verhoef et al., 2015; Picot-Coupey et al., 2016) 8. Order fulfillment (Chopra, 2016) 9 . Inconsistent Product discount (Chopra & Whisnant 2019) 10. Time Constraints (Neslin et al., 2006) 3. Research Methodology This study proposes a framework of enablers as well as barriers of OCR based on literature and experts’ responses. Data is collected through experts’ interviews, reviews, databases, and reports of the retailing industry. Various databases like WoS, Scopus, Emerald Insight, and Google scholar are extracted for identifying the enablers and barriers. The experts validated the enablers and barriers and evaluated them for developing hierarchical levels using ISM methodology. It is elaborated in two phases. Phase I includes the identification of enablers and barriers of OCR and employing Interpretative Structuring modeling (ISM) to develop a multi-level structure. The relationships among the variables vary, sometimes strong, weak equal, or not equal; thus, Fuzzy MICMAC and DEMATEL methods are used to compute the strengths of the enablers and barriers. 3.1 Phase I 3.1.1. Data collection The retail experts working in different capacities are selected for collecting data. The pool of experts includes consultants, CIOs, digital marketing, and supply chain management professionals. Three experts from the supply chain function of the retail organizations with a working experience of more than ten years, two academicians associated with the retail management program, two experts in the marketing domain from an online retail store, and three experts from operation management with five years of experience are selected for the panel. A questionnaire was circulated among the experts to collect data for this study. 3.1.2 Interpretative Structural Modeling (ISM) method This method is used to describe the relationship between the variables through hierarchical levels (Sharma et al., 2019). The steps of the ISM model, post- identification of barriers and enablers, are described as follows. Omni-channel retailing for enhancing customer engagement amidst supply chain disruption 9 i. Seven enablers and ten barriers are identified and validated by experts’ judgment. ii. Established a relationship among all the identified enablers and barriers. iii. A Structural Self Interaction Matrix (SSIM) is formed, and the relationship is represented in the form of four symbols. V: enabler i will ameliorate enabler j; A: enabler i will be ameliorated by enabler j; X: enablers i and j will ameliorate each other; and iv. An initial reachability matrix (IRM) is formed, and transitivity is checked v. The final reachability matrix is developed after checking for transitivity. vi. A digraph is made based on contextual relationships. vii. Nodal elements are then replaced by the statement. viii. The established model has assessed any conceptual inconsistencies. 3.2 Phase II 3.2.1 Fuzzy MICMAC and DEMATEL methods This phase includes Fuzzy MICMAC and DEMATEL to explore the strength of enablers and barriers. The fuzzy MICMAC method derives the driving and dependence value of the variables that help to understand the interrelationship among the variables. The relationships among the enablers or barriers vary, weak, equal, or sometimes stronger. Thus, this method helps to categorize the enablers and barriers on the basis of their driving and dependence powers. The following steps are used to obtain results (Sharma & Joshi, 2020). i. Establishing Binary Direct Relationship matrix ii. Developing Fuzzy Binary Direct Relationship matrix (FBDRM) iii. Developing Fuzzy-MICMAC stabilized matrix In the recent literature, multi-criteria decision methods are used for a variety of research in the area of marketing operations, viz online shopping for analyzing the change in purchasing behavior (Sharma et al., 2020a; Sharma et al., 2020b; Sharma et al., 2022a); to develop marketing strategies for alliance development (Tang et al., 2022), technological interventions in marketing and retailing (Kamble et al., 2019; Singh et al., 2020), waste management (Sharma et al., 2019; Sharma et al., 2020a; Sharma et al., 2020b) and product development and its supply chain management (Panchal and Kumar, 2017; Panchal et al., 2022; Sharma et al., 2020c; Tyagi et al., 2019). Specifically, the DEMATEL method has been employed in various domains such as marketing, supply chains, waste management, technology management, and reverse logistics (Chauhan et al., 2020;Mousavizade & Shakibazad, 2019; Sharma et al., 2020c). The method is described as follows: Step 1: Average matrix computation The experts are asked to rate the variables on the scale of 0 – 4, where 0 indicates ‘no influence’, 4 indicates ‘Very high influence’. A n x n matrix is developed as Xk= [𝑥𝑖𝑗 𝑘 ] on the basis of the expert responses. The responses are incorporated from h respondents, direct relation matric ‘aij’ is formed through equation 1. 𝑎𝑖𝑗 = 1 𝐻 ∑ 𝑥𝑖𝑗 𝑘 𝐻𝐾=1 (1) Where, K= number of respondent with 1≤ ik ≤ H N= number of criteria Joshi et al./Decis. Mak. Appl. Manag. Eng. (2022) 10 Step 2: Calculating the normalized initial direct- relation matrix D= M X B B = Min [ 1 𝑀𝑎𝑥 ∑ 𝑎𝑖𝑗 𝑛 𝑗=1 , 1 𝑀𝑎𝑥 ∑ 𝑎𝑖𝑗 𝑛 𝑖=1 ] (2) Step 3: Calculating the total relation matrix By the following equation T is calculated as 𝑇 = 𝑁(𝐼 − 𝑁)−1 (3) I denote the identity matrix. Step 4: Drawing the Diagraph The Sum of rows [Ri]n x and columns [Cj]1 x n denotes the vectors. Values of (Ri + Cj) and (Ri - Cj) are calculated. (If the value of (Ri -Ccj) is positive, then the enabler is categorized as causal group variables, and if the value of (Ri- Cj) is negative, then the enablers are categorized as effect group variables. 4. Models Application The integrated ISM-Fuzzy MICMAC-DEMATEL elaborated in section 3 is followed in this section for obtaining dependence and driving powers. The ten barriers are classified into six hierarchical levels and seven enablers after iterations shown in Tables 3 and 4. The hierarchical levels of enablers and barriers are exhibited in Figures 2 and 3. These barriers and enablers are taken into phase two for further analysis to explore the inter-relationships. Table 3. IRM -Enablers Enablers OCRE7 OCRE6 OCRE5 OCRE4 OCRE3 OCRE2 OCRE1 OCRE1 V X V V V V OCRE2 X V V A V OCRE3 O V O X OCRE4 V V V OCRE5 A A OCRE6 V OCRE7 Table 4. IRM- Barriers Ado B10 Ado B9 Ado B8 Ado B7 Ado B6 Ado B5 Ado B4 Ado B3 Ado B2 Ado B1 AdoB1 V V V V O V V V V AdoB2 O X O O O O X O AdoB3 A O V O O A V AdoB4 O A V A O V AdoB5 V V V V O AdoB6 O O O V AdoB7 O O V AdoB8 A A AdoB9 V AdoB10 Omni-channel retailing for enhancing customer engagement amidst supply chain disruption 11 Figure 2. Driving and dependence power diagram In phase II, a binary direct reachability matrix (BDRM) is obtained and the diagonal entries are converted to zero. FUZZY set theory (Eq. 4) is used to enhance the responsiveness of MICMAC. 𝐶 = 𝐴, 𝐵 = max 𝑘[(min(𝑎𝑖𝑘 , 𝑏𝑘𝑗 ))] where𝐴 = [𝑎𝑖𝑘 ] and 𝐵 = [𝑏𝑘𝑗 ] (4) The final matrix for enablers and barriers are obtained and exhibited in Table 5 and 6. Table 5. IRM- Enablers OCRE1 OCRE2 OCRE3 OCRE4 OCRE5 OCRE6 OCRE7 OCRE1 1 1 1 1 1 1 1 OCRE2 0 1 1 0 1 1 1 OCRE3 0 0 1 1 0 1 0 OCRE4 0 1 1 1 1 1 1 OCRE5 0 0 0 0 1 0 0 OCRE6 1 0 0 0 1 1 1 OCRE7 0 1 0 0 1 0 1 Enablers: broadband Internet accessibility ORCE1;well-located and well-designed distribution centers (OCRE2);efficient and extensive logistics network(ORCE3);cross-channel integration (ORCE4);customer analytics(ORCE5);Omni-channel visibility to customers (ORCE6);product digitization (ORCE7). Table 6. IRM- Barriers Ado B1 Ado B2 Ado B3 Ado B4 Ado B5 Ado B6 Ado B7 Ado B8 Ado B9 Ado B10 AdoB1 1 1 1 1 1 0 1 1 1 1 AdoB2 0 1 0 1 0 0 0 0 1 0 AdoB3 0 0 1 1 0 0 0 1 0 0 AdoB4 0 1 0 1 1 0 0 1 0 0 Joshi et al./Decis. Mak. Appl. Manag. Eng. (2022) 12 Ado B1 Ado B2 Ado B3 Ado B4 Ado B5 Ado B6 Ado B7 Ado B8 Ado B9 Ado B10 AdoB5 0 0 1 0 1 0 1 1 1 1 AdoB6 0 0 0 0 0 1 1 0 0 0 AdoB7 0 0 0 1 0 0 1 1 0 0 AdoB8 0 0 0 0 0 0 0 1 0 0 AdoB9 0 1 0 1 0 0 0 1 1 1 AdoB10 0 0 1 0 0 0 0 1 0 1 AdoB1: Lack of coordination and Information among channels; AdoB2: Price Inconsistency; AdoB3: Product Unavailability; AdoB4: Content Inconsistency; AdoB5:Lack of Centralized product assortment (CPA); AdoB6:Data privacy; AdoB7:Non-Understanding young customer habits; AdoB8: Order fulfillment; AdoB9:Inconsistent Product discount; AdoB10:Time Constraint. The relationship among the seven enablers, as well as the ten adoption barriers, have been developed using DEMATEL. By steps 1,2,3,4, and 5 of the DEMATEL process demonstrated in section 3, the direct influence matrix, normalized direct influence matrix, total relation matrix, and degree of influences are developed. Tables 7 and 8 represent the direct influences of enablers and barriers. Table 9 (a) and Table 9 (b) demonstrated Direct Influences – Enablers and Direct Influences – barriers using DEMATEL. Table 7. Transitivity matrix -Enablers OCRE1 OCRE2 OCRE3 OCRE4 OCRE5 OCRE6 OCRE7 OCRE1 1 1 1 1 1 1 1 OCRE2 *1 1 1 *1 1 1 1 OCRE3 *1 *1 1 1 1 1 *1 OCRE4 *1 1 1 1 1 1 1 OCRE5 0 0 0 0 1 0 0 OCRE6 1 1 *1 1 1 1 1 OCRE7 0 1 *1 0 1 *1 1 Enablers: broadband Internet accessibility ORCE1;well-located and well-designed distribution centers (OCRE2);efficient and extensive logistics network(ORCE3);cross-channel integration (ORCE4);customer analytics(ORCE5);Omni-channel visibility to customers (ORCE6);product digitization (ORCE7). Table 8. Transitivity matrix-Barriers Ado B1 Ado B2 AdoB 3 AdoB 4 Ado B5 Ado B6 Ado B7 Ado B8 Ado B9 AdoB 10 AdoB1 1 1 1 1 1 0 1 1 1 1 AdoB2 0 1 0 1 *1 0 0 *1 1 *1 AdoB3 0 *1 1 1 *1 0 0 1 0 0 AdoB4 0 1 0 1 1 0 *1 1 *1 *1 AdoB5 0 *1 1 *1 1 0 1 1 1 1 AdoB6 0 0 0 *1 0 1 1 *1 0 0 AdoB7 0 *1 0 1 *1 0 1 1 0 0 AdoB8 0 0 0 0 0 0 0 1 0 0 AdoB9 0 1 *1 1 *1 0 0 1 1 1 AdoB10 0 0 1 *1 0 0 0 1 0 1 AdoB1: Lack of coordination and Information among channels; AdoB2: Price Inconsistency; AdoB3: Product Unavailability; AdoB4: Content Inconsistency; AdoB5: Lack of Centralized product assortment (CPA); AdoB6: Data privacy; AdoB7: Non-Understanding young customer habits; AdoB8: Order fulfillment; AdoB9: Inconsistent Product discount; AdoB10: Time Constraint. Omni-channel retailing for enhancing customer engagement amidst supply chain disruption 13 Table 9 (a). Direct Influences - Enablers Row Total (D) Column Total (R) D+R Values D-R Values OCRE1 1.569 0.280 1.849 1.289 OCRE2 0.815 0.638 1.453 0.177 OCRE3 0.523 0.615 1.138 -0.093 OCRE4 0.974 0.390 1.364 0.585 OCRE5 0.000 1.384 1.384 -1.384 OCRE6 0.782 0.962 1.743 -0.180 OCRE7 0.402 0.796 1.199 -0.394 Table 9 (b). Direct Influences – Barriers Row Total (D) Column Total (R) D+R Values D-R Values AdoB1 0.294 0.000 0.294 0.294 AdoB2 0.032 0.204 0.236 -0.172 AdoB3 0.013 0.244 0.258 -0.231 AdoB4 0.128 0.239 0.367 -0.112 AdoB5 0.179 0.183 0.363 -0.004 AdoB6 0.001 0.000 0.001 0.001 AdoB7 0.013 0.125 0.138 -0.111 AdoB8 0.000 0.454 0.454 -0.454 AdoB9 0.175 0.199 0.373 -0.024 AdoB10 0.002 0.092 0.094 -0.090 5. Results and Discussion The ISM and Fuzzy MICMAC results demonstrate the hierarchical structure and categorization of the enablers and barriers. The enablers have shown a two-level structure from the ISM method application, whereas the barriers show a six-level structure in Figures 2 and 3. Customer analytics (OCRE5) is the top-level enabler at the hierarchical level. All other enablers are on the second level, which exhibits the two hierarchical levels structure for enablers, whereas there are six hierarchical levels in the adoption barriers. Order management (OCRB8) is on the top level, followed by three barriers, namely- inconsistency in content (OCRB4), inconsistency in product information (OCRB5), and lack of information about consumers (OCRB7). The third level of barriers has product unavailability (OCRB3) and time constraint (OCRB10). Data privacy and security (OCRB6) is present at the fourth level. The inconsistency in price (OCRB2) is the most critical adoption barrier in the OCR, followed by a lack of coordination & information sharing (OCRB1) and inconsistency in price discounts (OCRB9). The levels are exhibited in Figures 2 and 3, illustrating the multi-levels of the enablers and barriers. These levels are further validated by the Fuzzy MICMAC and DEMATEL and reveal that inconsistency in price, discounts, and information sharing are the most critical barriers in the OCR framework. The Fuzzy MICMAC results are exhibited in Figure 2, showing the four clusters consisting of enablers and barriers as per their dependence and driving powers. The Joshi et al./Decis. Mak. Appl. Manag. Eng. (2022) 14 seven enablers are categorized into two clusters only, whereas adoption barriers are classified into 3 clusters. Cluster I reflects weak driving and dependence power. The absence of any enablers or barriers in this cluster suggests that all the enablers and barriers undertaken in the study are significant. The enabler -customer analytics (OCRE5), and three barriers - order management (OCRB8), inconsistency in content (OCRB4) and inconsistency in product information (OCRB5), and lack of information about customers (OCRB7) are included in cluster II (Dependent barriers). This cluster has strong dependence and weak driving power. The strength of these variables (enablers & barriers) shows that the other variables need support to minimize their effect. These enablers and barriers are critical and need to be addressed by the retail organizations or decision-makers as a priority. The OCR should be more effective and efficient if the adoption barriers are minimized. No enabler is present in cluster III (linkage barriers), indicating that the enablers are either dependent or driving. This cluster has three barriers having high driving and dependence power, making it sensitive. These barriers are highly volatile and impede the execution of adoption among omnichannel. Data privacy and security (OCRB6), product unavailability (OCRB3) and time constraint (OCRB10) are linkage barriers. Cluster IV (Driving barriers) has high driving barriers and low dependence on power. This cluster includes enablers- Internet accessibility (OCRE1), well-located distribution centers (OCRE2), integration (OCRE3), integration across channels (OCRE4), visibility (OCRE6) and digitization (OCRE7). Omni-channel retailers should focus on integration among channels, Visibility to customers, and accessibility to understand and develop strategies for enhancing the customer's experience. The intention of retail firms is to provide a single view of products, services, and inventory to the customers, possible only when all the operations of channels are integrated and synchronized. Thus, retailers need to integrate their entire value chain, including supply chains, operations, e-commerce, and order fulfillment, which will lead to enhanced Visibility and transparency among the channels regarding customers' orders, information and purchase (Liu et al., 2020). The barriers present in this cluster are – inconsistency in price (OCRB2), lack of coordination & information sharing among channels (OCRB1), and inconsistency in product discounts (OCRB9). The adoption barriers of OCR, main inconsistency in price, discounts and information sharing, can deteriorate the unified experience of the customer (Sharma et al., 2019; Sharma & Joshi, 2020). The DEMATEL findings from Table 7 validate the results obtained from ISM and Fuzzy MICMAC methods application. The value for R-C shows that the single enabler customer analytics (-0.647) is the only dependent enabler and should be treated as the effect factor group, whereas the four barriers- order management (OCRB8); inconsistency in content (OCRB4), inconsistency in product information (OCRB5); lack of information about customers (OCRB7). DEMATEL results also signify that inconsistency in price (OCRB2), lack of coordination among channels & information sharing (OCRB1) and inconsistency in price discounts (OCRB9) are the cause group variables. The results of DEMATEL signify that the ISM results are valid and the levels of enablers and barriers should be considered by the retail organizations while designing their strategies. The firms should take action to remove inconsistencies among the price, discounts and information sharing, as on these driving barriers, the other barriers are dependent (Sharma et al., 2020a; Sharma et al., 2022b). For example, if the inconsistency in price and discount exists, it will affect the order management of the product. Three barriers, namely inconsistency in price (OCRB2), lack of coordination & information sharing among channels (OCRB1), and inconsistency in product discounts (OCRB9), are the driving barriers affecting all the other barriers. Inconsistency in price has the highest Omni-channel retailing for enhancing customer engagement amidst supply chain disruption 15 R-C value (1.162), driving all the other barriers, and thus organizations need to remove inconsistency among prices across their channels. This study tries to develop a theoretical understanding of the OCR in relation to the adoption barrier faced by retail firms as well as the enablers to enhance OCR adoption. The driving barriers (OCRB1, OCRB2, OCRB9) are validated integrated ISM-Fuzzy MICMAC-DEMATEL methods and proven that these are the most significant barriers that need to be minimized in the OCR framework. Also, the retail organization can perform better if they focus on the prominent enablers like integration, efficient logistics system and digitization (Cao and Li, 2015; Li et al., 2020). 6. Implications The emerging markets currently have the highest market potential, which may be targeted at young, millennial, and educated populations who are making their buying choices at their convenience through multiple channels like smartphones, the Internet, and mom-and-pop stores. Consumers are becoming agnostic today and want a unified experience across all the multiple points, but retailers are not upgraded yet and need much more integration in their back-end systems. Traditional retail firms are well aware that their systems, like merchandise planning, inventory planning, order management and others, are not compatible with omnichannel. Thus, enablers like integration, Visibility, robust information systems, information sharing, and the wide accessibility of the Internet need to be strengthened by the retail firms to engage their customers effectively and enhance their customer experience. Previously the studies have focused on the relationship between the physical shop's retail metrics and firm performance (Ailawadi and Farris, 2017; Sharma et al., 2019; Sharma et al., 2021). But, today, the turbulence in the current retail industry enhances the need to select the right metrics at the right time to predict product purchases (Caro and Sadr 2019; Caboni and Hagberg, 2019; Sharma et al., 2021; Sharma et al., 2022a; Hagberg et al., 2017). Thus, a more focused approach is needed to unravel the challenges in a more changing environment like OCR, which saves time and cost for the retailer and the customers (Larke et al., 2018; Galipoglu et al., 2018; Jocevski et al., 2019). The study also reveals customer analytics is the only dependent enabler. Analytics is not limited to online interactions only. Rather, physical stores can use advanced analytics through their robust information system that will help to learn how customers navigate their purchasing journey (Sharma et al., 2021). Moreover, analytics provides real-time information regarding the assortment and merchandise of the organizations and thus helps the retailer to improve the experience through optimized assortment decisions. The other benefit is personalization, one of the imperatives of the OCR and key to engaging customers effectively. It customizes the customer's experience by presenting only the most relevant choices, content, and offers. Data analytics is the emerging area of marketing to employ information management tools, which help retailers to engage consumers appropriately with more personalization. Cross-channel analytics seeks to correlate and analyze customer interaction across channels. It helps to track the performance of the multiple channels, i.e., how effective and attractive channels are performing at certain risks or generating positive results. Retailers need to develop their logistics systems through real-time monitoring, sensors etc., for better control of the supply chains. Also, a well- distributed warehouse helps retail organizations to control their order management and delivery. Integration and Visibility are the main enablers nowadays, as customers are more aware and access multiple devices. The retailers know it very well that Joshi et al./Decis. Mak. Appl. Manag. Eng. (2022) 16 without omnichannel transformation, the customers cannot be retained in the long run. The decision-makers and retailers can track customers' behavior at various touch points, indicating how organizations may improve the experience throughout the customer's journey through all these enablers. The analysis of clickstreams and product searches can also provide knowledge of the purchase journey, gauge demand, and upsell opportunities to the retailers for their future strategies. Nowadays, software is also employed, such as 'shopping assistant,' to ease and direct the customer journey. This study has focused on the critical role of cross-channel digital technologies, internet accessibility, distribution centers and inconsistency among price, discounts and content in the OCR structure. This study has indicated various key factors that need to be addressed by the managers and practitioners to transform the retail form into OCR. 6.1 Research limitations and future directions The results from ISM show the multi-level structure that can be further extended by TISM for exploring the strength of the enablers and the barriers. Firstly, OCR is an evolving area, and thus new approaches would be welcomed to analyze the effect on consumer engagement in emerging markets. Secondly, prominent adoption barriers and enablers identified in this study can be further built for industry-based studies such as consumer goods, automobiles, FMCG, and online retail. This study has undertaken the OCR as a whole, which can be further broken into distinct segments of products and services. Future studies may work on products and services that may help to redesign strategies specifically for them appropriately. Thirdly, the framework developed can be further empirically validated in future research works. 7. Conclusion The evolving OCR unifies all the customer touch points. There is a lack of theoretical embeddedness of research in OCR, and therefore, this study highlights the comprehensive structure of OCR considering enablers and adoption barriers influencing the performance of retail organizations in emerging markets like India Retail organizations can perform better if they focus on prominent enablers like integration, efficient logistics system and digitization. Advanced technologies like artificial intelligence, predictive modeling, machine learning, and real-time monitoring etc. may help retailers to develop their competitive advantage in engaging customers effectively. Digital technologies have challenged traditional retail organizations to transform their business models. Retail organizations need to adopt advanced analytical models to cope with the pandemic situation, as their traditional models are weak in handling customer's choices and expectations and managing customer's journeys efficiently. This study highlights the importance of integration across the channels with consistency, which may enhance the customer's intentions for future purchases. Due to the upsurge in mobile technology usage, new systems need to be developed with better integration and interchangeability. From the analysis of this study, inconsistent price (OCRB2) is the most crucial barrier in the OCR adoption process. Organizations should take action to eradicate inconsistency among prices across all channels. OCR does not aim to develop all the capabilities in each channel but rather assign products and tasks to channels on the basis of effective handling. The study also reveals that customer analytics is dependent on all the other enablers, which implies that the retailers need to upgrade their current sub-systems if they need Omni-channel retailing for enhancing customer engagement amidst supply chain disruption 17 to best fit the customer choices with multiple channels. The finding of the research work shall help in decision-making to the practitioners also as they can involve these enablers and barriers while adopting the OCR. More specifically, the practitioners should concentrate on the enablers and barriers to successfully adopting OCR. Both retail channels in emerging markets are highly motivated and aim to serve their customers better by facilitating them with a seamless purchasing experience. The digitization and hybrid business models are creating a competitive environment for firms where OCR combat the prevailing barriers and design strategies to strengthen their enablers. The implication of OCR suggests that retailers become ubiquitous. Author Contributions: Conceptualization, S.J and M.S.; methodology, M.S.; software, M.S. and P.C.; validation, S.J and M.S.; formal analysis, S.J.; investigation, S.J.; resources, S.J.; data curation, P.C.; writing—original draft preparation, M.S.; writing—review and editing, S.J and P.C..; visualization, M.S..; supervision, P.C..; project administration, M.S.. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. 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