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Single males and females are an under-studied segment of grocery shoppers. This study aims to compare the shopping habits of single males with single females and couples.

Structural equation modeling is used to analyze purchase history data for testing the effect of household type on shopping trip metrics and budget allocation to product categories.

Although single male shoppers differ on some trip metrics from females, differences are more pronounced between single shoppers, male or female, and couples. The study also confirms the mediating effect of trip metrics on the relationship between demographics and allocation of budget to grocery product categories.

The study contributes to the literature by identifying the differences and similarities between shopping behaviors of single males, single females and couples, developing an understanding of their budget allocation decisions to different product categories and testing and confirming that trip characteristics mediate the relationship between demographics and budget allocated to grocery products.

Outlines the foundations and dimensions of marketing myopia and proposes a two‐dimensional classification of its several types. Presents an integrated analysis which is intended to enrich, rather than replace, the existing explanations and to clarify and reinforce a caveat proposed 30 years ago. Makes a recommendation for seeking innovative marketing strategies which are designed to remove some of the myopia‐caused restrictions on the range of strategic options.

Examines the concept of marketing myopia, its different explanations and types. Organizes the four types of marketing myopia into a classification scheme, suggesting a new perspective which can produce innovative marketing strategies. Recommends five steps towards becoming an innovative firm: a generic firm/industry view, other‐industry monitoring, benchmarking, recruitment of marketers, and a flexible approach to problems.

Addresses two shortcomings of service quality empirical research. Investigates the importance of service quality as a predictor of actual choice behaviour and examines the importance of process and outcome quality attributes as predictors of choice. Uses regression analysis to investigate the importance of service quality attributes on choice. Suggests that consumers utilise multiple process and outcome quality attributes in their choices.

The marriage of new scanner‐type data sources and new computing and analysis methods is allowing a new approach to the development and use of models for decision support and product line management. Data‐driven modeling describes a process of model‐building wherein models are created that fit the dynamics of the data rather than assuming a priori relationships among brands and their marketing mix elements. Based on a combination of time‐series and econometric modeling methods, these models can significantly improve a modeler’s ability to capture marketplace structure and dynamics. Although more complex than their predecessors, the capabilities of these new data‐driven decision support models make them potentially very powerful tools, improving intuition and managerial understanding while suggesting improved decision alternatives. Develops such a model using detailed multiproduct retail data and demonstrates its capabilities.

Despite the proliferation of retail loyalty programs, little is known about differences in the behavior patterns of the consumers within them. There may be several unique segments within a loyalty program, and significant managerial implications may accrue from identification of these segments and analysis of differences among them. This paper aims to investigate the potential for deriving meaningful, managerially relevant customer segments within a retail loyalty‐type program.

Data from the one‐year test of a retail loyalty card program by a major US retailer were used to derive patronage‐based segments and explore determinants of segment differences and strategy implications. A numerical taxonomy process was used to group 57,650 loyalty‐card program members into distinct segments. Cluster analysis was used to generate a range of potential market structures on a set of managerially relevant variables. The most appropriate market structure was selected using scree testing and discriminant analysis. Each of the resulting six segments was named profiled. Finally, a set of patronage‐related variables was regressed on to the clusters using multinomial logistic regression.

The results indicate that, at least for this card program, clearly defined segments with unique patronage profiles do exist both descriptively and statistically; only a small percentage of loyalty card program members demonstrate behaviors that can be considered truly loyal; and different marketing strategies appear appropriate to try and increase patronage among the segments based on their profile characteristics.

This study should prove valuable to academic researchers as well as managers. It is the first effort to generate meaningful, rigorously derived segments within a large loyalty program. It shows that behavioral variables can yield managerially relevant segments, and that the segments appear to call for individualized strategy initiatives.

A new literature is emerging around the role of self‐service technologies (SSTs) such as airline ticketing machines, automatic teller machines, and computer‐based shopping services in the strategic offering of service providers. SSTs allow (or force) consumers to help produce their own service encounters via machine interaction rather than by interacting with a firm’s service personnel. Firms which introduce SSTs wish to gain rapid acceptance and usage of these technologies by potential consumers. This study investigates whether the provision of more personal control to consumers can reduce their perceived risk, enhance the perceived value of the SST, and induce greater adoption intention associated with the innovation. Propositions are tested using an experiment. Multiple analysis of covariance and follow‐up tests either fully or partially supported 11 out of 12 hypotheses. A set of managerial implications and recommendations is provided.

The purpose of this paper is to measure consumer‐based brand equity in the supermarket industry and to identify the strategy drivers associated with levels of brand equity for consumers' typically patronized supermarkets.

A nine state survey of consumers was conducted to provide brand equity ratings of 22 national, regional, and specialty supermarket brands.

Factor analysis yields two brand equity outcome dimensions and eight brand equity drivers. A large proportion of consumers clearly have strong feelings about the supermarkets they patronize, and that effort expended in keeping customers, service level, and product quality and assortment appear to be basic requirements for achieving high levels of consumer‐based brand equity. The top supermarket brands typically score highly on at least one other key driver of equity. Supermarket brands that use formal loyalty programs to drive patronage in general have lower levels of customer‐based brand equity.

Selection of designated supermarkets was limited by spatial distribution in the geographic area. The sample is more affluent and educated than the general US population.

As retailers search for ways to compete more effectively for consumer dollars and loyalty, they need to explore in more detail the customer‐based brand equity and the drivers of customer equity associated with their retail brands.

This paper is the first to link consumer‐based brand equity and the supermarket branding efforts that drive it for specific retail brands. In an industry with numerous choices in nearly all market areas and low switching costs, successful branding can translate into emotional commitment, shopping loyalty, and even person‐to‐person promotion of the brand to others.

New internet technologies are not always readily accepted by target users – especially by small companies. More effective identification of target market characteristics and requirements associated with the adoption and ongoing usage of internet‐based service systems (NCSSs) is therefore needed. Drawing on adoption theory, this research aims to assess pre‐launch survey, legacy and longitudinal data to evaluate factors that encourage small companies to use NCSSs.

The roll out and ongoing usage of a new NCSS are tracked within a single channel of dental offices over a 185‐week period. Logistic and multiple regression analyses are utilized to examine the influences of system attributes, attributes of the target user group, supplier promotional activity and system completeness on ongoing usage.

Pre‐launch survey data is an ineffective predictor of ongoing small company NCSS usage. The best predictors are indicators of organizational readiness and organizational resources drawn from the supplier firm's legacy database. Synergistic coordination of promotional activities and new system features generate the largest number of ongoing users.

Findings are not generalizable to the population of small service provider firms because the sample consisted purely of regional dental offices.

Pre‐launch promotional efforts should concentrate on capturing specific attributes from supplier firm legacy databases that more accurately predict ongoing customer usage.

As far as can be determined, this study is the first reported longitudinal examination of NCSS usage across an entire customer base. The examination of pre‐launch and legacy data as predictors of ongoing usage and the assessment of ongoing usage rather than intention to adopt or try out a technological innovation extends the adoption literature and responds to calls for research that improves current understanding of the determinants of e‐business acceptance and usage across organizations.

The aim of this paper is to develop a measurement scale that encompasses a wide array of product characteristics. In addition, a comprehensive model is developed and tested illustrating the relationship among product characteristics and with adoption.

Utilizing 628 respondents, a measurement scale is developed and a structural equation model is tested through a multi‐stage series of surveys. The scope of the research is consumer durable products.

This paper is successful in developing a 43‐item scale that measures 15 unique innovation characteristics. This scale is then used to test a second order model illustrating the relationships innovation characteristics have with each other and ultimately innovation adoption.

The major limitation this research suffers from is its lack of variety in products under analysis. For the four consumer durable products studied, the research finds significant results. However, these findings would have greater impact if they reflected a broader array of products and product classes.

To date there have been very few attempts to model and test in an exhaustive fashion the role innovation characteristics play during the adoption process. This current research advances Holak and Lehmann and empirically tests first and second order characteristics within the context of a structural equation model.