Abstract-The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in personalization applications has been done before. In this paper, we study how important the contextual information is when predicting customer behavior and how to use it when building customer models. It is done by conducting an empirical study across a wide range of experimental conditions. The experimental results show that context does matter when modeling the behavior of individual customers and that it is possible to infer the context from the existing data with reasonable accuracy in certain cases. It is also shown that significant performance improvements can be achieved if the context is "cleverly" modeled, as described in this paper. These findings have significant implications for data miners and marketers. They show that contextual information does matter in personalization and companies have different opportunities to both make context valuable for improving predictive performance of customers' behavior and decreasing the costs of gathering contextual information.

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Purpose-This study proposes to model customer experience as a 'continuum', labelled Customer Experience Continuum (CEC). We adopt a customer experience quality construct and scale (EXQ) to determine the effect of customer experience on a bank's marketing outcomes. We discuss our study's theoretical and managerial implications, focusing on customer experience strategy design. Design/methodology/approach-We empirically test a scale to measure customer experience quality (EXQ) for a retail bank. We interview customers using a means-end-chain approach and soft-laddering to explore their customer experience perceptions with the bank. We classify their perceptions into the categories of 'brand experience' (pre-purchase), 'service experience' (during purchase), and 'post-purchase experience'. After a confirmatory factor analysis, we conduct a survey on a representative customer sample. We analyze the survey results with a statistical model based on the partial least squares method. We test three hypotheses: 1) Customers' perceptions of brand, service provider, and post-purchase experiences have a significant and positive effect on their experience quality (EXQ), 2) EXQ has a significant and positive effect on the marketing outcomes, namely share of wallet, satisfaction, and word-of-mouth, and 3) The overall effect of EXQ on marketing outcomes is greater than that of EXQ's individual dimensions. Practical implications-Banks should focus their customer experience (CE) strategies on the Customer Experience Continuum (CEC) and not on single encounters, tailoring marketing actions to specific stages in a customer's CE process. Different organisational units interacting with customers should be integrated into CE strategies, and marketing and communication budgets should be allocated according to CEC analysis. The model proposed in this paper enables the measurement of the quality of CE and its impact on marketing outcomes, thus enabling continuous improvement in customer experience. Findings-The results of the statistical analysis support the three hypotheses. Originality/value-The research proposes a different view of customer experience by modelling the interaction between company and customer as a continuum (CEC). It provides further empirical validation of the EXQ scale as a means of measuring customer experience. It also measures the impact of customer experience on a bank's marketing outcomes. It discusses the guidelines for designing an effective customer experience strategy in the banking industry.

Knowledge transfer is recognized as a fundamental issue for organizations. This research aims at investigating the knowledge transfer process, and finding out the most efficient and effective strategies to support it. Particularly, the research is focused on studying support strategies based on the use of technology. The paper proposes a model that describes both knowledge acquisition and practice. The model components are the actors’ cognitive systems, the processes of codification and interpretation, and the object transferred. The model predicts that a better understanding of knowledge transfer can be achieved by distinguishing organizational similarity from dissimilarity, training from fertilization, and autonomous from interactive practice. This is particularly helpful to discuss what the role and value of technology are in supporting knowledge transfer in organizations effectively.

Recently, there has been growing interest in recommender systems (RSs) and particularly in context-aware RSs. Methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper focuses on comparing the pre-filtering, the post-filtering, the contextual modeling and the un-contextual approaches and on identifying which method dominates the others and under which circumstances. Although some of these methods have been studied independently, no prior research compared the relative performance to determine which of them is better. This paper proposes an effective method of comparing the three methods to incorporate context and selecting the best alternatives. As a result, it provides analysts with a practical suggestion on how to pick a good approach in an effective manner to improve the performance of a context-aware recommender system.

Several approaches, processes and organizational forms to develop and properly manage knowledge in a company have been proposed in the literature. However, there is a lack of methodologies aimed at supporting the analysis and implementation of a knowledge management (KM) strategy, the organizational approach to manage and leverage the company's knowledge. In this paper, two main and opposite KM strategies are pointed out: the knowledge market and the knowledge community. A model is then proposed to assess the current KM strategy adopted by a company and its distance from the two extreme KM strategies. The model can support companies in the identification of suitable actions to better implement their KM strategy and to foster KM practices in the organization. The effectiveness of the model is also tested on a real case of application.