PurposeAs acquiring new customers is costly, putting effort into satisfying and keeping customers over the long term can improve profitability. Firms usually do not know how each individual customer is feeling at any time (their attitude to the firm), so typically a customer's likelihood of leaving (“churning”) is predicted from behavioural data. The purpose of this paper is to investigate how a firm can add attitudinal variables to these churning models by deriving proxy indicators of satisfaction and commitment from behavioural data. The paper tests whether adding these proxies improved predictions of churning compared to a typical model based on purchasing behaviour (PB).Design/methodology/approachAnalysing data from 6,000 regular customers from an Australian digital versatile disc rental company, logistic regression predicted membership termination (i.e. churning=1) versus continuation (=0). A baseline model used three traditional behavioural variables directly linked to members' PB. A second model including proxies for satisfaction and commitment from the customer database was compared against the baseline model to investigate improvement in churn prediction.FindingsThe most significant predictor of churn is an indicator of commitment: the uncertainty of a customer's commitment, indicated by number of times they changed their subscription plan.Practical implicationsThe more customers change their plan, the more likely they are to quit the relationship with the firm, most likely because they are uncertain about how they can benefit from a long‐term commitment to the firm. Monitoring uncertainty indicators, such as plan changing, allows firms to intervene with special offers for uncertain customers, and, therefore, increase the likelihood of them staying with the firm.Originality/valueThe paper discusses the use of customer behaviour recorded in databases to identify proxy indicators of attitude before this attitude translates into churning behaviour.

This study investigates the role of traditional and information and communication technology (ICT)-mediated leisure activities in consumer behaviour. An online survey of 558 members and 1,319 ex-members of an Australian DVD rental company gathered preferences for nine traditional leisure activities and seven ICT-mediated leisure activities. The results of a cluster analysis showed four clusters with significant cluster differences across leisure activities as well as across demographics and consumer behaviours. For practitioners, the study illustrates how profiling customers on their leisure preferences can increase advertising effectiveness, reflect loyalty and help predict customer lifetime value. For academia, the results reveal how another consumer dimension, leisure activities, relates to demographic and behavioural characteristics.

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Purpose-The purpose of this paper is to investigate how three organizational factorsaffiliation, sufficient capital and company agerelated to 323 Malaysian foodservice companies' diffusion of six information technology (IT) applications. The IT applications, basic or advanced, respectively, represent two innovation diffusion levels, adoption and implementation. Design/methodology/approach-This study drew on a survey of chief executive officers, owners, information system/technology managers, operations managers, and account/financial managers in 323 Kuala Lumpur and Selangor foodservice companies. The study conducted logistic regression to examine factors related to the adoption and implementation of IT applications. Findings-IT adoption and implementation related significantly to sufficient capital. Company age and affiliation showed an insignificant relation with adopting and implementing IT applications. Originality/value-To the authors' knowledge, this is the first hospitality study to examine simultaneously the diffusion of basic and advanced IT applications. Most studies investigate the adoption of one or two innovations, such as spreadsheets, web sites, and e-mail, without considering diffusion stages. This study demonstrates multiple innovations, multiple diffusion stages and multivariate analyses.

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