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.

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.

PurposeDespite 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.Design/methodology/approachData 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.FindingsThe 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.Originality/valueThis 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.

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PurposeThe 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.Design/methodology/approachUtilizing 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.FindingsThis 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.Research limitations/implicationsThe 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.Originality/valueTo 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.