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This paper is based on a study conducted to examine the influence of the store image on the buyers’ perception of quality for durable, semi‐durable and non‐durable products. The study has revealed that for a durable product, like colour television, although the reputation of the brand and price are more important considerations, the buyer does pay attention to the store image as well. This is done to reduce their perceived risk and in expectation of getting good after‐sale services. He perceives a significant difference in the quality of the product sold at different stores. However, people prefer to buy from a reputed store located at a nearby area. Moreover, the buyers generally make up thei rmind about thee brand to be purchased before selecting the store. Therefore, they either look for the exclusive showroom dealing in their preferred brand or a reputed store selling that brand along with others. Buyers prefer to buy branded T‐shirts either from an exclusive showroom or a departmental store. For local brands, they take little care about the store image. Moreover, they select the store with which they had a good previous experience. A store located in a posh market is generally perceived to be carrying superior quality T‐shirts. While purchasing toothpaste, buyers pay least attention to the store image, and do not perceive a significant difference in the quality of the toothpaste sold at different types of stores. They do not believe that the more reputed the store, the better will be the quality of the toothpaste sold. They, generally, buy from the same store again and again, without taking any notice of the store reputation. The proximity of the store was found to be the most important factor influencing the store selection.

The study’s objective is to measure the importance consumers attach to AI-based attributes, namely, chatbots, face recognition, virtual fitting room, smart parking and cashier-free station in retail stores. The study also examines the specific purpose of using these attributes for shopping.

A conjoint experiment was conducted using fractional factorial design. Consumers were given 14 profiles (AI attributes and its levels) to rank according to their visiting preferences.

The results revealed that the retail chatbot was considered the most important attribute, followed by face recognition, virtual fitting room, smart parking system and cashier-free station. Moreover, consumers prefer to use chatbots for in-store shopping assistance over alerts and updates, customer support and feedback. Similarly, consumers wish a face recognition facility for greetings while entering the store over other services. In addition, cluster analyses revealed that customer groups significantly differ in their preferences for AI-based attributes.

The study guides retail managers to invest in AI technologies to provide consumers with a technology-oriented shopping experience.

Our results provide an insight into the receptivity of AI technologies that consumers would like to experience in their favorite retail stores. The present study contributes to the literature by investigating consumer preferences for various AI technologies and their specific uses for shopping.

This paper provides an analysis of the consumer buying decision process and discusses its impacts and implications for Internet retail store design. The paper begins with a brief overview of the consumer buying decision process, extracts desirable characteristics for an e‐commerce site, and evaluates a number of online stores and its corporate business characteristics. The implications for managers are to analyze the buying decision process and incorporate the requirements into the e‐commerce planning.

The purpose of this paper is to show that new‐style retail wine stores with features such as tasting rooms, lecture theatres and demonstration kitchens used to educate and engage customers have better retail efficiency than old‐style stores.

Sales dollars, labour hours and litres of inventory depletion from a paired sample of old‐style and new‐style facilities located in five different communities are submitted to a data envelopment analysis to determine the retail efficiency of the stores.

All the new‐build stores had higher retail efficiency than the older stores, and input reductions in older stores were unlikely to bring their performance up to the level of the new store concepts.

One of the shortcomings of this research is that the old and new stores in the paired samples are different in size and location within each municipality. While it is clear that the new store features (tasting rooms, seminars, cooking demonstrations, etc.) increase retail efficiency, it remains to know the contribution of each of feature to the improvement in retail performance.

Profiles heavy buyers of store brand products and compares them with light buyers in terms of demographics, socio‐economic, and attitudinal variables. The results suggest that younger, unmarried, and smaller sized households tend to avoid store brands. As compared with heavy buyers, light buyers of store brands are less familiar with them and perceive them to be of lower quality, less value for money and as riskier choices.

A study to determine whether consumers develop their images of small little‐known retailers from the images of the anchor(s) of the shopping complexes in which they are located. Results suggest that image transference does exist in the conveyance of an image to a small, little‐known retailer. It appears that the images assigned to such retailers in shopping complexes by consumers are determined, at least in part, by the images assigned to the anchor(s) of that centre.

Originally undertaken on behalf of a national multiple food retailing chain concerned about a 50 per cent fall off in sales at a new store within six months of its opening, this research includes a comparative study of two other supermarkets in the same town. Interviews were conducted with 321 store customers and 189 people at random locations elsewhere and a standard questionnaire completed to determine the store features important in attracting their patronage, including price, layout, selection and variety of goods, opening hours, parking facilities and convenience of location. It was established that there was nothing radically wrong with the client company′s store and subsequent implementation of recommended “fine tuning” of a number of its features has led to its developing a comparably favourable level of popularity. However, it is concluded that marketing research should be commissioned at the initial planning stage, with particular reference to suitability of location, rather than as a means of sorting out deficiencies which become apparent only after completion and opening.

Reveals that the People′s Republic of China is a market in transition. The Chinese Government wants to modernize the retail industry as part of the movement from a planned economy to a market economy. Reports that a series of interviews with managers of government‐owned department stores and co‐operatives, free market retailers and members of the municipal and federal commercial planning committees began in 1992. One year later, when a second series of interviews was conducted, the retail industry was vastly different. Joint ventures increased and store managers were given the right to choose their own form of operation. Consumers have experienced self‐service food stores and foreign department stores opened in Beijing and Shanghai. A third set of interviews conducted in March 1995 showed that many reforms had been rescinded because of high domestic inflation. Several new retail ventures aimed at the nouveaux riches in China have had to position downwards. Concludes that the People′s Republic of China′s change from a planned to a market economy is a lesson in social and market change.

Store Location Assessment Model (SLAM) was developed initially to assist retail management in the assessment of store locations. Used by many of the UK′s leading retailers has shown the benefit of using the model as a general retail performance evaluation tool in addition to its function of assessing store locations. The development of the model while concentrating on its key applications in setting performance targets for existing sites, rationalising property portfolios, enabling management to understand the interactions of their particular retailing environment, and being able to assess new store locations is discussed. SLAM is in use in various fields ranging from consumer electricals, dry cleaning, fast food, clothing and petroleum retailing. In each application the pertinent variables change as do their weightings.

Received wisdom is that retail change starts in America and later spreads to major western European countries. This was certainly true until the early 1970s when France took the lead in innovation, blinding the retail world with its dazzling regional shopping centres and its massive hypermarkets. To this day the Americans have not succeeded in selling food and non‐food under the same roof with the same panache as the French. But in recent years the Americans have revived their capacity for innovation, especially in the development of speciality chains, some of them sharply focussed in marketing terms and closely supported by the latest in automated systems. Then again, in supermarkets there has been the development of different formats within specific store type categories, of which the food‐drug combination store and the “hybrid” warehouse store are two interesting examples. Lastly there have been significant technological developments in cable TV and viewdata systems, and a number of test teleshopping systems are already in operation. In this first of an irregular series of articles on American retailing, Dr David Rogers outlines these changes, some of which will be discussed in more detail in subsequent features.