Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...

Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...

Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...

Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...

Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...

This study aimed at providing a systematic method to analyze the characteristics of customers’ purchasing behavior in order to improve the performance of customer relationship management system. For this purpose, the improved model of LRFM (including Length, Recency, Frequency, and Monetary indices) was utilized which is now a more common model than the basic RFM model apt for analyzing the cus...

R in consumer psychology shows that customers seek reasons for service failures and that attributions of blame moderate the effects of failure on the level of customer satisfaction. This paper extends research on service operations failures by hypothesizing that attributions of blame also affect what matters to the customer during service failures. Specifically, we hypothesize that the relative...

Motivated by overcoming the existing utility-based choice modeling approaches, we present a novel conceptual framework of multidimensional network analysis (MNA) for modeling customer preferences in supporting design decisions. In the proposed multidimensional customer–product network (MCPN), customer–product interactions are viewed as a socio-technical system where separate entities of ‘custom...