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This study aims to examine the direct and moderating effects of generative learning on customer performance.

The authors test the relationships between customer relationship management (CRM) capabilities, generative learning, customer performance, and financial performance with a cross industry survey of CEOs and senior marketing executives from 199 firms. Partial least squares are used to estimate the parameters of the resulting model.

The results reveal that generative learning affects customer performance directly. Moreover, the interaction of CRM capabilities and generative learning contributes to customer performance. This finding suggests that firms need a well-developed generative learning orientation to fully benefit from translating new insights resulting from CRM capabilities into establishing, maintaining, and enhancing long-term associations with customers, and vice versa.

The main limitations are those that typically apply to cross-sectional surveys. Although several steps were taken to reduce the concern of key informant bias and common method variance, dependent and independent variables were collected from the same source at a single moment in time.

Ceteris paribus, an increase of generative learning orientation by one unit (seven-point scale) can command an increase of up to 7 percent of the average customer performance due to its direct and interaction effect. Because even small changes in customer performance have a strong impact on financial performance, this finding indicates a remarkable and substantial result for managers.

Though previous research provides evidence of the adaptive learning consequences of CRM, a review of the literature reveals a lack of studies that analyze the importance of generative learning orientation for successful CRM.

Elder German grocery shoppers are a growing, heterogeneous, and highly relevant and attractive, but under-researched market segment. In order to understand them and their grocery shopping motivations better and target them efficiently and effectively, the purpose of this paper is to identify dimensions of their shopping motivations and segment them based on these dimensions.

In total, 26 grocery store-choice criteria were identified in a thorough literature review and focus-group interviews with 36 elder German consumers aged 65 and older. In a subsequent survey, the importance of these criteria was rated by 1,288 German shoppers of the same age group. A principal component and cluster analysis were performed to identify dimensions of store-choice criteria and segments of elder German grocery shoppers. Multivariate analysis of variance, analysis of variance and discriminant analysis were used to test for statistically significant differences between the clusters.

Basic quality considerations, shopping experience and social interaction, service and assistance, price consciousness, product orientation, convenient location and quick service and packaging requirements influence the grocery store choice of elder German consumers in decreasing order of variance explained. The cluster analysis revealed indifferent, leisure, convenience, assistance-oriented, no frills, product-oriented and service-oriented elder German shoppers, which differ in their shopping motivations statistically and significantly. These clusters are described and contrasted in detail to derive managerial implications.

This research provides the first store-choice component analysis and cluster analysis for elder German grocery shoppers. This can help food retail to reach this attractive target group more efficiently and effectively and improve the food supply of elder German consumers.