Omni–channel retailing and its impacts on on supply chain initiatives, the case of fast moving consumer goods industry

Nội dung text: Omni–channel retailing and its impacts on on supply chain initiatives, the case of fast moving consumer goods industry

1. OMNI–CHANNEL RETAILING AND ITS IMPACTS ON ON SUPPLY CHAIN INITIATIVES, THE CASE OF FAST MOVING CONSUMER GOODS INDUSTRY Ph.D Duong Van Bay1 Abstract: The study is aimed at exploring the impacts of omni–channel retailing on e-commerce supply chain initiatives. The study was conducted with 207 fast moving consumer goods (FMCG) businesses through direct and online surveys. The partial least squares structural equation modelling (SmartPLS 3.0) was used to analyze the measurement model and test the hypotheses. The study identified that the omni–channel supply chains are markedly different from multichannel supply chains. The omni–channel retailing has strong positive impact on supply chain initiatives other than multi–channels do. A true omni–channel retailing experience entails a complete rethink of how a business runs. Integrated supply chain solutions that offer full visibility as well as supply chain agility and provide a seamless customer experience that matches or even improves customers' expectations. Accordingly, omni–channel forces retailers to streamline operations, integrate supply chain platforms and re–organize supply chains. Keywords: Omni–channel, multi–channel, supply chain, e-commerce. 1. INTRODUCTION The world is entering the early stages of smart commerce, with the technologies of Internet of Things (IoT) and Artificial Intelligence (AI) gradually becoming popular. Along with the development of technology, consumers have many options to meet their shopping needs, both online and offline. This study scrutinizes the latest developments in e-commerce and omni–channel strategy as a new way to meet the expectations of empowered customers. When supply chain is vital to economy as well as to the retail business, the logistics system must meet the conditions for transition to the entire channel. All retailers are required to be permanently present, not only on internet but also on ground, and both must communicate in an omni–channel ecosystem. This means that omni–channel is no longer a product of the store chain, but also a new commerce, including e-commerce businesses. Seamless supply chain is a solution that combines the best people, processes and technology, as well as strong leadership and commitment. Managers instill a customer–centric culture, the corresponding business processes need to be embedded in the organization's culture. This not only leads to a separate and consistent omni–channel customer experience, but also can significantly affect a company's profitability, market share, and competitive advantage. Stemming from the above reasons, the author has chosen the topic "Omni–channel retailing and its impacts on on supply chains, the case of fast moving consumer goods industry" to study the relationship between omni–channel retailing and supply chain initiatives. The businesses selected for sampling in this study are fast–moving consumer goods retailers. They include beverages, food, milk, home care products and personal care products. Secondary data used in the study are data of the last three years. The qualitative and quantitative survey samples took place in 2019. 1 International School of Management and Economics, National Economics University. Email: duong.bay@isneu.org 699
2. 2. LITERATURE REVIEW AND THEORETICAL FRAMEWORK 2.1. Physical channel By operating physical stores, retailers allow their customers to return products easily and conveniently, as well as provide them with immediate support in the decision–making process or with any installation or repair operation (Rigby, 2011; Grewal et al., 2004). Physical stores also allow retailers to reduce the costs associated with actions that customers can take by themselves such as picking products from shelves and taking them home (Grewal et al., 2004). In addition, physical stores allow customers to select a channel that better suits their needs such as cash payments and traditional face–to–face interaction (Zhang et al., 2010). H1a: Physical channel has a positive impact on omni–channel retailing H1b: Physical channel has a positive impact on supply chain initiatives 2.2. Online channel Customers have 24/7 access and have more products to choose from online channel than physical ones (Rigby, 2011). In addition, digital channels can reduce a customer's search costs by providing them with additional product information, product recommendations and reviews, pricing comparison as well as a quick and easy payment process (Rigby, 2011; Agatz et al., 2008; Webb, 2002). For time– constrained customers, these factors are of great value as they allow them to save time in the buying process (Grewal et al., 2004). Online shopping also allows customers to reflect on their buying experience and to show how comfortable they are when buying from their home (Zhang et al., 2010). H2a: Online channel has a positive impact on omni–channel retailing H2b: Online channel has a positive impact on supply chain initiatives 2.3. Mobile channel The use of smartphones as a standout retail channel is increasing rapidly as retailers develop more mobile versions with more advanced applications to meet customers’ needs (Brynjolfsson et al., 2013). Smartphones offer customers the ability to combine online and offline purchases in a new way because mobile internet access allows them to compare prices instantly or ignore customer reviews and review prices of non–digital ingredients in real stores (Piotrowicz and Cuthbertson, 2014). Retailers have come to realize that the online channel itself is not enough to meet the needs of customers who demand greater convenience and accessibility. This has led to seeing smartphones as a separate sales channel. H3a: Mobile channel has positive impact on omni–channel retailing H3b: Mobile channel has positive impact on supply chain initiatives 2.4. Catalog channel The catalog channel is a traditional retail channel, in which customers can order by phone or regular mail. Through this channel, retailers can reach a great number of customers and provide customers with a different experience from the website experience (Hansell, 2002). Moreover, the catalogs are convenient to use and not requiring internet access and thus allow for more flexible shopping (Wallace, Giese & Johnson, 2004). Catalogs also allow customers to remain anonymous as mobile and online. The drawback of this retail channel is that they are expensive to print and send to potential customers, and the catalog content quickly becomes obsolete (Gulati & Garino, 2000). H4a: Catalog channel has a positive impact on omni–channel retailing H4b: Catalog channel has a positive impact on supply chain initiatives 700
3. 2.5. Omni–channel retailing Omni–channel is a cross–channel business model that retailers use to improve customer experience. Omni–channel business is defined as seamless and effortless, high quality customer experiences which occur within and between contact channels (Frost and Sullivan, 2015). Omni– channel retailing is an expansion of multi–channel retailing (Verhoef et al., 2015). The goal of implementing omni–channel retailing is to combine the benefits of both digital and non–digital retailing in order to give customers a seamless retail experience (Rigby, 2011). Advantages related to online sales include price transparency, availability of reviews and unlimited selection of products while benefits related to offline retailing are for example face–to–face interaction, instant gratification and a hands–on product experience (Rigby, 2011). Consequently, an online channel can in many ways complement an offline channel, and vice versa, which has lead several authors to claim that increased integration of retail channels creates several types of synergies (Herhausen et al., 2015). This is something that many customers place great value on and hence, a successfully implemented omni– channel strategy with total integration, has potential to greatly enhance customer shopping experience (Herhausen et al., 2015). Omni–channel retailing includes brick and mortar stores, kiosk, outlet locations, retail DCs, Websites, pop–up stores, E-commerce DCs, tablets/ mobile, call center and catalog. H5: Omni–channel retailing has a positive impact on supply chain initiatives 2.6. Retail supply chain initiatives Due to increased competition, traditional retailers try to differentiate and provide more product categories and higher product availability in stores, while reducing total operating costs. (Dubey; Veeranmani, 2017). The digitalization process is ongoing, with the difference between offline and online channels disappearing, multi–channel retailing is shifting to omni–channel retailing (Lee, 2017). Omni multi–channel retailer are aimed at coordinating processes and technologies across all channels in order to provide more integrated, consistent and reliable services to customers (Saghiri et al., 2017). With the increasing diversity of channels on omni–channels, they have made the procurement process more convenient for buyers but of course harder to manage for upstream suppliers and downstream retailers (Ailawadi; Farris, 2017). Integration and visibility are two main elements of omni–channel structure to reduce uncertainty and variations (Saghiri et al., 2017). Integration is essential from three perspectives: integration of channels stages (pre–purchase, payment, delivery, return), channel types (online and offline) and agents of channels (Saghiri et al., 2017). Figure 1: Theoretical framework Physical channel Online channel Supply chain Omni-channel initiatives Mobile channel Retailing Catalog 701
4. The supply chain initiatives are measured by order management system, centralized inventory management, integrated CRM, predictive analytics, enhanced network, real time retail, unified commerce playtfrom and cloud based commerce playtform plans. H5: Omni–channel retailing has a positive impact on supply chain initiatives 3. METHOD 3.1. Research process The study used a combination of qualitative and quantitative research in two phases, preliminary research and formal study. Qualitative research was conducted by in–depth interview method with 10 FMCG businesses. The interview results helped the author adjust the model and the scales before conducting quantitative research and formal verification of the model. The quantitative research was conducted with 207 FMCG businesses through direct survey and online survey. Data collection was used to test the hypotheses about the significance of the relationships in the model. The partial least squares structural equation modelling (PLS–SEM) was used to analyze the measurement model and test the hypotheses. The software was used for this study was SmartPLS 3.0. 3.2. Data collection The population of research is the FMCG businesses in Vietnam. To achieve the research objectives, the author selected non–probability sampling method. Samples were selected by stratified random sampling and the 207 samples were obtained in Hanoi. The survey questionnaire was developed based on the literature review, which focused on evaluating the impacts of omni–channel retailing on supply chain initiatives. Specifically, the survey was designed in three parts. The first part covers questions about omni–channel retailing. The second part contains questions about impacts of omni–channel retailing on supply chain initiatives. The questions were answered with a five–point Likert scale. And the third part is the personal information of the respondents. 4. FINDINGS 4.1. Sample descriptive statistics The statistical results show that out of 207 surveyed enterprises, SOEs account for 6.3%; Private enterprises for 59.9%; Joint stock enterprises for 30.4% and other types of enterprises for 3.4%. Of which, enterprises with less than 100 employees account for 48.8%; Enterprises with 100–300 employees for 23.7%; Enterprises with between 301 and 500 for 6.8%; Enterprises with 500 employees or more for 20.1%. Moreover, food and beverage enterprises account for 45.4%; Personal and family care products for 37.7% and other related products for 16.9%. 4.2. Measurement model Partial least squares (PLS) is a statistical procedure for estimating simultaneous systems of equations referred to as structural equation modeling (SEM). This estimation procedure enables the study of inter–relationships between one or more dependent and independent variables. The measurement model using the SmartPLS software is executed to assess the reflective measurement model including (1) Estimate of Loadings and Significance, (2) Indicator Reliability, (3) Composite Reliability, (4) Average Variance Extracted (AVE), (5) Discriminant Validity. 702
5. Assessing the indicator loadings and their significance. The standardized loadings should have a value of at least 0.7 and should have a t–statistic in excess of +/–1.96 to be significant at the 5% level. T–statistics in PLS–SEM are obtained by executing a bootstrapping procedure. The results show that after removing the indicators having the low loadings, all the left loadings exceed the rule of thumb. All outer loadings of the concepts are higher than the value of the threshold. Given that the requirements of reliability and convergent validity have been met, the indicators with loadings of 0.6 and above (Rasoolimanesh et al., 2017) have been decided to retain. Squaring the individual indicator loadings provides a measure of amount of variance shared between the individual indicator variable and its associated construct. This is referred to indicator reliability. Accordingly, the reliability of the construct can be measured in two ways – Cronbach’s alpha (α) and composite reliability (ρc). The Table 3 demonstrates that the rule of thumb is that both need to be above 0.6. The composite reliability of all constructs have high intrinsic consistency reliability and the Cronbach’s Alpha value of all concepts are higher than the threshold of 0.6. Convergent validity is measured by the Average Variance Extracted (AVE). The AVE is obtained by averaging the indicator reliabilities of a construct. It measures the average variance shared between the construct and all of its indicators. The benchmark for AVE is at least 0.5. Table 3 shows the diagnostic validity and values of the AVE for all variables are greater than the desired level of 0.5. As to convergent validity, the AVE must be > 0.5 (Hair et al., 2017). The results in Table 3 show that all the constructs fit these criteria. Discriminant validity measures the distinctiveness of a construct. As shown in Table 3, discriminant validity is evidenced when the AVE of a construct exceeds the square of its correlation with any other construct. Discriminant validity is measured by two methods. Firstly, it is measured by comparing the correlation among constructs and the square root of the AVEs. Secondly, it’s measured by the heterotrait– monotrait (HTMT) ratio, which has been established as a superior criterion (Henseler, Ringle, & Sarstedt, 2015). In Table 3, it can be seen that in all the cases the square root of the AVEs is greater than their corresponding intercorrelations and that all results are below the critical value of 0.85. Accordingly, both criteria for achieving discriminant validity are satisfied. These results allow us to confirm that the measurement model is reliable and valid. Table 3: Construct reliability and validity Cronbach's Composite Average Variance Construct rho_A Alpha Reliability Extracted (AVE) Catalog 1 1 1 1 Mobile channel 0.608 1.178 0.802 0.679 Online channel 0.605 0.609 0.835 0.716 Physical channel 0.695 0.705 0.867 0.766 Omni–channel retailing 0.823 0.826 0.872 0.534 Supply chain initiatives 0.83 0.868 0.884 0.659 703
6. Table 4: Discriminant Variables Omni– Mobile Online Physical Supply chain Variable Catalog channel channel channel channel initiatives (IN) retailing Catalog 1 Mobile channel 0.467 0.824 Omni–channel retailing 0.777 0.63 0.731 Online channel 0.558 0.402 0.882 0.846 Physical channel 0.28 0.275 0.601 0.503 0.875 Supply chain initiatives (IN) 0.103 0.352 0.494 0.501 0.55 0.812 4.3. Assessment of the Structural Model The SmartPLS algorithm yields the standardized estimated path coefficients shown in Table 5. To examine whether empirical support exists for the specified hypotheses, the sign and magnitude of the path coefficients should be initially inspected. The results show that eight out of nine paths have signs in line with those hypothesized. Only one path has sign in negative relationship existing between Mobile channel and Supply chain initiatives. Figure 2 shows the conceptual modexl after analysis. The numbers written on + the lines of the beta coefficients are extracted from the regression equation coefficients of the variables or in other terms from the path analysis. The number in each circle indicates the value of R2. The coefficient R2 for the correlation between the amounts of variance is explained by the covariance of the measurement and is considered as a latent variable. The coefficient of R2 determination is to predict the dependent variable by variables to be assessed independently. Obviously, the value of this index is calculated only for the dependent variables. Figure 2: Measurement model for impacts of omni–channel retailing on supply chain initiatives 704
7. The structural model for collinearity between items is assessed using the variance inflection factor (VIF) values (Hair et al., 2017). The VIF values of this analysis are lower than 2.955 in all cases, so there are no multicollinearity. Blindfolding procedure is used to evaluate the predictive relevance of the path model with the value of Q2. If the Q2 value is positive and large enough, the predictability of the model is verified. Empiritically, the blindfolding results shows that the Q2 value of the latent variable Omni–channel retailing (0.504) and Supply chain initiatives (0.302) are positive so there’s the model's predictive relevance with the latent endogenous variable. To assess the significance of the path coefficients, t–value needs to be calculated. Table 5 represents the calculated t–value for the dependent and independent variables. The structural equation analysis of the t–model is to measure the impact of the independent variables on the dependent variable and can be used as a criterion to test the hypotheses. Theoretically, if the value obtained for each hypothesis is greater than the absolute value of 1.96, the hypothesis is confirmed, and the smaller values will result in the rejection of hypotheses. As shown in Figure 2, all the values calculated are greater than the assumption of 1.96 and the research hypotheses are approved as being related. In other words, the relationships between the variables, regardless of the quality of the relationships are approved. Some researchers believe that if the path coefficients are greater than 0.1, a certain amount of influence in the model can be verified (Hair et al. 2011). Also, the path coefficients should be at least 0.05. The results show that the model has the capacity to explain the supply chain initiatives. Overall, the variables (physical channel, online channel, mobile channel and catalog) explain 96.3% of the variation in omni–channel retailing (R2 = 0.963). For supply chain initiatives, the variable Omni– channel retailing explain 47.6% of the variation in supply chain initiatives (R2 is 0.476). Theoritically, the R2 values of 0.67, 0.33 and 0.19 can be considered respectively as substantial, moderate and weak (Chin, 1998). In this case, the research model “moderately” explains the variations. Thus, the study demonstrates that the model is appropriate to explain the impact of omni–channel retailing on supply chain initiatives and explains variations in omni–channel retailing and supply chain initiatives. Table 5: Total effects Original T–Statistics Supported/ Structural model hypothesis Sample (O) (|O/STDEV|) P–Values Rejected Catalog Omni–channel retailing 0.316 13.243 0.000 Supported Catalog Supply chain initiatives –0.381 5.717 0.000 Rejected Mobile channel Omni–channel retailing 0.194 7.251 0.000 Supported Mobile channel Supply chain initiatives 0.17 1.843 0.066 Rejected Omni–channel retailing Supply chain 0.715 3.006 0.003 Supported initiatives Online channel Omni–channel retailing 0.538 24.976 0.000 Supported Online channel Supply chain initiatives 0.476 5.825 0.000 Supported Physical channel Omni–channel retailing 0.199 10.468 0.000 Supported Physical channel Supply chain initiatives 0.355 6.809 0.000 Supported 705
8. The estimated results of the parameters in the model presented in Table 5 show that there are seven hypotheses having positive relationships, including the relationship between online channel and omni– channel retailing (β = 0.538, P = 0.000); Catalog and Omni–channel retailing (β = 0.316, P = 0.000); Physical channel and Omni–channel retailing (β = 0.199, P = 0.000); Mobile channel and Omni– channel retailing (β = 0.194, P = 0.000); Online channel and Supply chain initiatives (β = 0.476, P=0.000); Physical channel and Supply chain initiatives (β = 0.355, P = 0.000) and especially the positive relationship between Omni–channel retailing and supply chain initiatives (β = 0.713, P = 0.003). One has a negative relationship, that is the relationship between catalog and supply chain initiatives (β = –0.381, P = 0.000) and one relationship is not statistically significant because P values are greater than 0.05, that is Mobile channel and Supply chain initiatives. Accordingly, seven hypotheses are supported and the other two are rejected. 4.4. Assessment of Predictive Validity using PLSpredict With the objective of producing valid predictions of Omni–channel retailing and supply chain initiatives, the PLSpredict for the general model was used with the SmartPLS software version 3.2.7. In general, for the simple models with minimal theoretical constraints, PLS predict allows predictions very close to those obtained by using LM (Shmueli, Ray, Velasquez Estrada, & Chatla, 2016). This study follows this approach to assess the predictive performance of the PLS path model for the indicators and constructs. The mean absolute error (MAE) and the root mean squared error (RMSE) and the Q2 for indicators were obtained. Moreover, the Q2 for the two endogenous constructs, Omni–channel retailing (Q2 = 0.504) and Supply chain initiatives (Q2 = 0.302) were also obtained. In order to assess predictive performance, the benchmark procedures developed by the SmartPLS team were carried out (Ringle et al., 2015): “The Q2 value, which compares the prediction errors of the PLS path model against simple mean predictions. The Q2 values of this study are positive, then the prediction error of the PLS–SEM results is smaller than the prediction error of simply using the mean values. Therefore, the PLS–SEM model offers an appropriate predictive performance. 5. DISCUSSION AND CONCLUSIONS 5.1. Discussion The study was conducted to explore the impacts of omni–channel retailing on e-commerce supply chain initiatives with 207 fast moving consumer goods (FMCG) businesses through direct and online surveys. The partial least squares structural equation modelling (SmartPLS 3.0) was used to analyze the measurement model and test the hypotheses. The study identified that the omni–channel supply chains are markedly different from multichannel supply chains. The omni–channel retailing has strong positive impact on supply chain initiatives other than multi–channels do. Omni–channel business is defined as seamless and effortless, high quality customer experiences which occur within and between contact channels (Frost and Sullivan, 2015) and is a cross–channel business model that retailers use to improve customer experience. Omni–channel retailing is an expansion of multi–channel retailing (Verhoef et al., 2015). The goal of implementing omni–channel retailing is to combine the benefits of both digital and non–digital retailing in order to give customers a seamless retail experience (Rigby, 2011). It can be said that a omni–channel retailing is quite complicated and demanding for a retailer to implement. It requires not only financial and human resources, but also a deep understanding of the impact that business model will have on its performance. Therefore, it is important that the retailer must develop a plan on how to handle the challenges associated with this strategy. 706
9. Retailers' mindsets and priorities play an important role in determining the pace of implementation as well as the way that retailers choose to implement omni–channel retail. Perceptions of the benefits that omni–channel retailing can vary among retailers and therefore the priority also varies. Smartphones are becoming a more popular shopping channel and this is an integral part of the daily lives of today's customers (Brynjolfsson et al., 2013). Omni–channel retailing not only links existing channels with each other but also creates a meeting place for customers who like shopping. Therefore, without this sales channel in the business, customers might not have a sense of a real omni–channel. A true omni–channel retailing experience entails a complete rethink of how a business runs. Integrated supply chain solutions that offer full visibility as well as supply chain agility and provide a seamless customer experience that matches or even improves customers' expectations. Accordingly, omni– channel forces retailers to streamline operations, integrate supply chain platforms and re–organize supply chains. Differences between omni–channel and multichannel Although the terms multi–channel and omni–channel are often used interchangeably, there are clear differences. Multi–channel refers to many supply chains used to accommodate each type of shopping experience. Each channel is separate. The online catalog is different from items stored in physical stores, and prices may vary. Each store has its own, often closely guarded, online store is a seperate entity from the retail stores. Omni–channel supply chains are completely different in that there is only one supply chain. Information is freely shared and displayed. The online catalog is the same with the one used in physical stores, showing online and inventory in nearby stores. When items are sold, the overall inventory will be adjusted. Prices are the same and customers can visit the stores to view the items and then buy online. Agility of omni–channel supply chains Delivering inventory at the right place and at the right time is a critical factor for success. In the retail store context, an efficient demand forecasting system coupled with a flexible distribution system ensures retail stores having enough stock to meet demand and the ability to replenish stock rapidly. For online selling, having inventory available in locations as close to customers’ delivery address as possible would minimize logistics costs. It is costly and inefficient to transport excess stock. What is needed is the flexibility of the supply chain, coupled with the analytics that helps identify future needs with certain precision and the ability to balance conflicting needs while managing distribution costs. Streamline supply chain operations There are many variables to be considered with omni–channel supply chains. This is beyond the capabilities of standard business analytics reporting past performance. What is needed is reliable forward–looking information along with the ability to make informed, data–driven decision–making. This requirement can be met with predictive analytics that provide insight into what will happen in the future. Examples include sales forecasts and buying trends. But more importantly, omni–channel retailers need insights into descriptive analysis, internal and external data analysis, and optimization processes to determine the right decisions on supply chains. Integrate supply chain platforms It is impossible to manage an omni–channel supply chain without an integrated software platform that incorporates all aspects of the business. Each aspect of the supply chain is integrated as well as all functions within the entire organization from marketing, sales, procurement and logistics. Key elements include full visibility, inventory planning forecasting capabilities, order management, rapid stock replenishment strategy. 707
10. Re–organize supply chains Omni–channel supply chains need to be flexible and able to accommodate bulk orders for stores and individual items to online shoppers. These things don't need to be mutually exclusive as long as the supply chain network is considered. Key decisions include where inventory is stored and how orders are handled and shipped. Omni–channel tends to support more regional distribution centers rather than large centralized hubs, and are more dependent on third–party distributors for supplying stores and delivering directly to customers. There's a trade–off between agility, service and delivery costs. 5.2. Research contributions and conclusions Managerial contributions This study may serve as a starting point for retailers to implement a omni–channel retailing. Retailers who are currently implementing this omni–channel retailing can show both inspiration and guidance on what they expect from this initiative. Retailers might be motivated to go in this direction. This will raise awareness and motivate retailers to develop and implement omni–channel retailing. Keeping up to date with retail trends is one way to compete and thus can benefit retailers. Fast moving consumer goods are all products that can be touched and felt, although all of these products can be found online, most customers want to try, touch and feel the products before buying. Therefore, retailers selling this type of product should realize importance of serving their customers through physical stores and this great advantage should be created before online pure competitors do. If a retailer can integrate physical channel with online channel, it can better protect itself from external competition. Omni–channel integrates all business activities and is regarded by customers as a comprehensive company in their eyes. This can be a challenge if the retailers’background takes a more personalized approach, which tends towards the performance of its own store rather than the performance of the entire chain. Therefore, retailers operating this type of business model need to pay special attention to the impact that might have on the omni–channel strategy. When moving into omni– channel retailing, retailers need to recognize the importance of educating and disseminating to the entire organization about the changes and the reasons for this change. It is also important to establish policies and guidelines that facilitate and encourage the application of this integrated channel, both from customer's and retailer's perspective. Academic contributions Academic literature on omni–channel retailing and how to integrate existing channels is very limited. Through the model developed in this study, some theoretical contributions can be made. The study was conducted at exploring the impacts of omni–channel retailing on the supply chain initiatives. The omni–channel retailing found in the research theory have been proven and expanded to capture more overall incentives for supply chains. The omni–channel supply chains are markedly different from multichannel supply chains. The omni–channel retailing has strong positive impact on supply chain initiatives other than multi–channels do. A true omni–channel retailing experience entails a complete rethink of how a business runs. Integrated supply chain solutions that offer full visibility as well as supply chain agility and provide a seamless customer experience that matches or even improves customers' expectations. Limitations and recommendations for future research A lot of researches related to the omni–channel retail sector needs to be done. After completing this study, the author has found a number of areas that need pay special attention for further research. First of all, the omni–channel concept is still fuzzy and needs to be clarified. As indicated in the limitations section, more omni–channel efforts than those in this research still exist. Therefore, the 708
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