| dc.description.abstract | Indian apparel retail sector has shown tremendous growth rates over the last decade. Owning to tremendous growth rates and more stores getting added up, gives the customer a variety of choices in terms of store formats and stores within store format from where she can shop. With such varied tastes and demands of the customer and increased competition from other stores, it becomes imperative for the retailer to understand the capabilities he needs to develop within the store which helps in enhancing the sales and productivity based performance measures. Literature which is predominantly on front-end retail strategies has shown that price, promotion, location characteristics, convenience, merchandise mix and service quality are important strategic variables which impact customers’ decision to shop in a particular store, which may help in enhancing the sales of the store. Operations capability based literature which is relatively sparse in retail has shown that operational decisions taken by the retailer in terms of technology, supply side decisions may help in reducing the stock outs, reducing the manpower, lowering the inventory costs etc. Based on literature review, enhanced by resource based theory and discussions with several apparel retailers, we develop a framework which helps us answer the combined impact of how front-end service quality capabilities and back-end technological and supplier side capabilities help in improving the sales and productivity performance of Indian apparel retail stores. In this research we also develop a method to measure the capabilities of the store. This gives the retailer a tool to measure the service quality capability, technological capability and supplier side capability of the store. Operational capabilities are the knowledge based competencies which the store develops over a period of time. These are broadly divided into 3 constructs namely: front-end service quality capabilities and back-end technological capabilities, and supplier side capabilities. Service quality capabilities are classified into physical aspects of store, sales people capability, empathy and assurance related capabilities. Technological capabilities are broadly classified into Customer Interfacing Systems, Operation Support Systems and Customer Relationship Management systems. Supply side capabilities are classified as lead time, credit facility, returns capability, on-time delivery and communication trends of the retailer. Finally performance is measured using multiple measures which are primarily productivity based. These are annual inventory turnover, annual sales per employee, and annual sales per square Foot. Data was collected from 320 retail apparel stores from the store managers. This was done for each of the 5 types of retail store formats namely: Mom and Pop Outlets, Multi Brand Outlets, Exclusive Brand Outlets, Department Stores and Hyper-malts. Data was collected from four major cities across India to have good representation namely: Ahmedabad, Mumbai, Hyderabad and New-Delhi. After data collection, we used correlation analysis to drop some of the highly correlated variables. We used Principal component analysis to find the underlying components under each of the three dimensions of capability namely: Technological Capability Service Quality Capability and Supplier Side Capability. We convert these factors into capability indices on a scale to indicate the capability dimensions of the store. We saw that there was low inter construct correlation. We use these indices for further investigation. Empirical functional forms which have been studied in literature which relate capabilities to performance were assumed to be linear in nature. We argue and propose non-linear functional forms and use multiple non-linear regressions to explain the performance variables. Inventory Turnover of (Technological Capabilities, Service Quality capabilities, Supply side capabilities, store and city dummies)— Sales per Square Foot (Technological Capabilities, Service Quality capabilities, Supply side capabilities, tore and city dummies Sales per Employee of (Technological Capabilities, Service Quality capabilities, Supply side capabilities, Store and city dummies).Our data indicates that stores are similar in terms of location characteristics. Hence we have controlled for location characteristics. We have used store-format dummies to control for the product variety and pricing strategy related variables. We also have used city dummies to control for the impact of city characteristics like population demographics on the performance of store. Our results suggest that inventory turns are strongly influenced by technological and supplier side capability constructs. Within technological capabilities inventory related technologies and customer relationship related technologies are the major drivers whereas in supplier related capabilities lead times, percentage of on-time deliveries provided by the supplier are the major drivers. Sales per square Foot are strongly influenced by all three dimensions of service quality capabilities namely sales people and empathy related capability, assurance capability and physical aspects capability. Technological dimensions like customer relationship dimension and supplier dimension like communication trends of the supplier also drive sales per square Foot. Sales per employee is influenced by three dimensions of service quality capability, namely, sales people and empathy related capability, assurance policy capability and physical aspects of the store. It is also influenced by customer relationship dimension of technological capability. The store fronts dummies are also significant in explaining all the three performances related variables indicating format specific variables like product variety, pricing policies play a role in explaining performance of the store. Mumbai which has superior sales per square Foot and sales per employee because of higher disposable incomes shows significance in the model. For each performance variable we use multiple functional forms which we feel are appropriate to explain the performance of the store. We validate our models using the validation data set and choose the best model which has least root mean square error. Our research helps the retail store managers to position themselves in the market and understand to what extent one should develop operational capabilities to successfully compete in the market. The results are also consistent with the resource based theory as well as the literature in operations. They provide empirical evidence for the on how the front end seen ice quality capabilities and back end technological and supplier side capabilities together become more effective in producing business value to the retail stores. | en |
