| dc.description.abstract | Omnichannel retailing is an integrated experience that melds the advantages of physical stores with information-rich online shopping. To offer a seamless omnichannel shopping experience, retailers need to understand what drives customers towards omnichannel and what dissuades them away from omnichannel. Also, retailers need to build cross channel synergies with effective decision-making regarding order fulfillment. Thus, with this premise, this study addresses a class of problems related to omnichannel with specific research objectives to (a) understand customer-specific drivers and barriers for the adoption of omnichannel (b) rank those drivers and barriers according to their importance (c) classify omnichannel customers according to sociodemographic characteristics and buying behavior, and (d) propose a solution methodology for fulfillment decision problem in omnichannel. To address these objectives, this study is confined to the apparel industry in India and the required primary data is collected from Mumbai and Bangalore as they are among top five Indian cities in terms of omnichannel orders and their price-value.
For addressing the first research objective, 10 unique drivers for the adoption of omnichannel retailing in the Indian apparel industry (D-AOCRIAI): Improved Shopping Experience, Reduced Effort, Social Influence, Habit, Hedonic Motivation, Technology Development, Enhanced Promotion, Sporadic Event, Personalization, and Integrated Supply chain and 9 unique barriers (B-AOCRIAI): Inconsistency in Offering, Channel Discord, Lack of Trust, Data Privacy Concern, Lack of Infrastructure and Resources, Psychological Hinderance, Poor Customer Support, Inefficient Order Fulfillment, and Difficulty due to Sporadic Events and their measurement variables are identified. Total Interpretive Structural Modelling (TISM) and Decision Making Trial and Evaluation Laboratory (DEMATEL) approaches are used to propose an initial conceptual framework for each of D-AOCRIAI and B-AOCRIAI using data from 12 domain experts and 23 customers.
The proposed conceptual framework for each of D-AOCRIAI and B-AOCRIAI is statistically validated and finalized by following descriptive research methods including confirmatory factor analysis and structural equation modeling. The data required for statistically finalizing the proposed framework is collected from 850 customers comprising of 448 adopters and 402 non-adopters of omnichannel by developing an appropriate questionnaire. From the total variance (R2) explained with respect to D-AOCRIAI (R2 = 0.86) and B-AOCRIAI (R2 = 0.71), final version for each of the frameworks is constructed. Further, it is evident that Drivers: Improved Shopping Experience, Reduced Efforts, and Social Influence directly and positively impact omnichannel adoption. Also, Barriers: Lack of Trust, Psychological Hindrance, and Lack of Infrastructure and Resources directly and negatively impact omnichannel adoption.
To address the second research objective, the identified unique drivers and barriers are ranked using MCDM methods: Analytical Hierarchy Process (AHP), and Best Worst Method (BWM) respectively using data collected from the group of 36 customers by developing suitable questionnaire for each of these MCDM methods. From the results obtained, it is observed that the most important drivers are: Reduced Effort, Improved Shopping Experience and Enhanced Promotion. Similarly, the most important barriers are Data Privacy Concerns, Psychological Hinderance, and Inconsistency in Offering.
Classification models are developed with approaches: decision tree, random forest, and adaptive boosting to address third objective – that is classifying the customers into adopters and non-adopters of omnichannel based on their sociodemographic characteristics using data from all 850 respondents. It appears from the results that Age, Profession, and Income were found to be the most significant sociodemographic characteristics impacting the adoption of omnichannel. Further, adopters of omnichannel are segmented based on the buying behavior (Recency, Frequency, and Monetary Value) using K-Means clustering into 4 different clusters: Omni-connected, Omni-consistent, Omni-spenders, and Omni-hesitant.
Finally, to address the fourth research objective of the research, a solution methodology for the decision problem of online order fulfillment in omnichannel is developed by formulating the problem as a mixed integer linear programming (MILP) model with the objective of cost-minimization. The problem configuration considers a deterministic demand over multiple periods and computes product flow across various locations in a retailer’s network. From the optimal solution obtained, it appears that fulfillment of online orders through ‘warehouse’ and ‘direct-to-customer center’ is more cost effective.
The research problems considered in this study can serve as empirical evidence to assist retailers in developing cluster-wise omnichannel strategies. Retailers should focus on offering an improved, personalized, and convenient shopping experience while ensuring data privacy and mitigating inconsistencies. Though this research has achieved all the planned objectives, there are certain limitations. The inferences of the study cannot be generalized at the pan-India level across different product categories. The proposed classification models for clustering use self-reported ordinal values from customers regarding their buying behavior. The proposed mathematical model for order fulfillment uses deterministic demand. Future research could validate the proposed models for across different geographies and product categories. Also, the independent decision problem of ‘omnichannel fulfillment’ can be treated as an integrated decision problem with pricing, inventory, and last-mile delivery. | en_US |
