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This study focuses on the examination of the effect of customer engagement practices on the customers' loyalty. It then considers the investigation of the intermediating role of customer experience and customer identification factors in the connections between customer engagement dimensions and his/her brand loyalty. Thus, a total of approximately 352 usable responses from those life insurance customers in Jordan were evaluated after their responses were subjected to partial least squares structural equation modeling analysis. The research's outcomes gave a prediction that purchaser engagement significantly influences purchaser experience and customer identification. The attained outcomes also uncovered that purchaser experience and shopper identification meaningfully improve customer loyalty and the intermediated connection between customer engagement and customer loyalty. Additionally, these attained outcomes indicate the why practices that boost customer experience and identification result in customer loyalty and engagement also. [ABSTRACT FROM AUTHOR]

Customers' value co-creation behavior plays an essential role on brand pages, which many hospitality firms adopt to create value with customers. However, current understanding of the elements, drivers, and dynamics of such behavior is limited. Based on the value co-creation literature, motivation–opportunity–ability theory, and the dual identification framework, this study sought to bridge this knowledge gap. Specifically, this research clarified customer engagement behavior (CEB) and customer citizenship behavior (CCB) as respective in-role and extra-role behaviors of value co-creation and revealed their positive relationship. Structural equation modeling indicated that motivations (seeking assistance and diversion), social presence, and brand knowledge positively influenced CEB, which then affected CCB directly and indirectly through customer–brand identification. Moreover, functional benefits did not trigger meaningful CEB, and customer–community identification had a minimal impact on CCB. Overall, these insights can help practitioners manage their brand pages more effectively to co-create value with customers. [ABSTRACT FROM AUTHOR]

The purpose of this research is to examine the notion of salesperson moral identity as a prosocial individual trait and its associated effects on customer and coworker relationships. In addition, this study examines the underlying processes in which these effects occur as well as the moderating role of internal competitive climate. Our empirical investigation of business-to-business (B2B) sales professionals reveals that moral identity has both direct and indirect effects on a salesperson's customer- and team-directed outcomes. Specifically, our results demonstrate that salesperson moral identity positively affects both salesperson-customer identification and organizational identification, which, in turn, impact customer service provision and teamwork. Our findings also indicate that internal competitive climate exacerbates the positive effects of salesperson moral identity on customer service provision and teamwork. [ABSTRACT FROM AUTHOR]

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This study analyzes the influence of corporate social responsibility (CSR) from a multidimensional sustainable development approach on customer loyalty and satisfaction through the mediation of customer identification. CSR is a key concept for banks to attract, retain, satisfy and increase the loyalty of customers. Structural equation modeling was conducted to test the proposed relationship. The questionnaire was filled out by 389 banking sector customers. The findings suggested that customer identification mediates the relationship between the economic development dimension of CSR and customer satisfaction and loyalty. However, customer identification did not show a mediating effect between social equity and environmental protection dimension of CSR and customer loyalty and satisfaction. The findings are expected to provide insights into the importance of CSR for the banking industry in order to enhance favorable customer attitudes. [ABSTRACT FROM AUTHOR]

The study aims to analyze behavioral intentions influenced by customer engagement, experience, and identification moderated by competitive choices in the granite sector of Pakistan. The study has been carried out through primary data analysis of cross-sectional approach in the transition to a sustainable economy. In total, 400 questionnaires were distributed, for which only 216 were filled and usable with a response rate of 54%—collected data from the production managers and units. In contrast, missed mine holders and labor analyzed the data in SPSS and AMOS to run various tests, i.e., reliability, correlation analysis, regression, moderation regression, and confirmatory factor analysis. The study findings indicate a positive and significant relationship and effect among the variables. The reviews might contain some biases. Therefore, this study recommended adopting a probability sampling technique for future studies. The study results in a positive manner indicating customer service involvement as a significant factor in behavioral intention despite competitive options.

This study investigated the effect of numerical customer identification (i.e., assigning numbers to identify customers) in the service context on the numbered customers' reaction to service failures. We manipulated numerical identification in different ways (room number, customer number, table number, and order number) and measured customers' tolerance of services across various settings (in a restaurant, a spa, and a café) in four studies. The results demonstrated that after being identified by a number, customers tend to exhibit a higher tolerance of service failures (Studies 1 and 2), and this effect is mediated by a sense of self‐dehumanization among the numerically identified customers (Study 3). Moreover, the investigated effect diminished when customers had heightened individuation (e.g., by disclosing personal information) to buffer against dehumanization (Study 4). Our findings contribute to the underexplored research area on customer identification, broaden the numerical research and dehumanization literature in marketing, and bring practical implications for firms to mitigate the negative effects of service failures and decrease customer dissatisfaction. [ABSTRACT FROM AUTHOR]

This study explores how customer-based corporate reputation (CBR) influences customer engagement behaviors (CEB). It incorporates customer identification and brand love as mediators between CBR and CEB, and the industry type as a moderator, to investigate the direct and indirect effects of CBR on CEB through a moderated mediation analysis. The hypotheses were tested through PLS-SEM and PROCESS. Results confirmed the mediation effects of customer identification and brand love between CBR and CEB, and the positive direct impact of CBR on CEB. Furthermore, this study found that five aspects of CBR have different impacts on four dimensions of CEB. Lastly, the CBR–customer identification–CEB mechanism is stronger in the service than in the product industry, whereas there is no difference in the mediation mechanism of brand love between the product and service. [ABSTRACT FROM AUTHOR]

Over the last few years, the financial sector has undergone a digital revolution that has had a severe impact on different related areas such as, the entities, the cybersecurity of systems, regulations and, of course, customers. SOTER project takes the challenge providing a complete set of tools to enhance the cybersecurity levels by implementing, in addition to non-technological tools, an innovative onboarding process has been implemented with the goals of increasing security, improving the user experience and integrity in the sector, and facilitating the customer entry into the digital marketplace by combining a set of breakthrough technologies. The cybersecurity research plays a crucial role in the conception and implementation of the onboarding process and for this kind of processes it has to be studied as an individual area by itself, as it is necessary to analyze the possible threats that can affect them, their origin and the solution(s) that can be taken to address them. Therefore, the SOTER project presents a fully digital onboarding process, innovative, adaptable to both current and future market needs and to possible changes in regulation, based on the most advanced technologies available today, and above all guaranteeing the cybersecurity of both entities and end users.

Given that customer voice behaviors are confused with customer complaint behaviors in usage, this study thoroughly explains the essential differences between the two constructs. On that basis, this study investigates how employee–customer interaction (ECI) quality affects customers' prohibitive voice behaviors, which is an crucial type of customer voice behaviors, by examining customer trust and identification as mediators. Data from 395 restaurant customers are collected and analyzed using structural equation modeling. Results show that ECI quality positively affects customers' prohibitive voice behaviors. In this effect, customer trust and identification play direct and sequential mediating roles. This study contributes theoretically to the current knowledge by clearly distinguishing customer voice behaviors from customer complaint behaviors and by providing new insights into the mechanism of customers' prohibitive voice behaviors from the perspectives of service interaction and relational benefit enhancement. The practical implications of this study can help pointedly foster customers' prohibitive voice behaviors. [ABSTRACT FROM AUTHOR]

The current digital transformation provides great prospects for both businesses and consumers in the field of data collection and processing. The current development of data collection and processing systems opens not just new, but breakthrough opportunities for business in general and marketing in particular. Now not only structured data is being processed, but also unstructured data is becoming a part of marketing analytics.The aim of this research is to consider and present current aspects, tasks and trends of Customer Intelligence (CI) as the part of Marketing Intelligence. Today any organization can use digital tools and methods in their marketing activity. Companies have got huge ability through a lot of internal and external resources to gather and alter (processing) customer data, including personal feedback from customers. In their turn customers get more and more possibility to contact brands and companies directly. The involvement of consumers in a dialogue with a business and their influence on all areas of business activity have already become an integral part of our life.Authors did a literature review of 28 scientific and business resources to determine the nature and the role of CI in marketing activity through a prism of marketing intelligence and its four components - market, product, competitive and customers. Nowadays Customer Intelligence is one of the key marketing activity components with a huge capacity which makes a real impact on marketing performance.

Given that customer voice behaviors are confused with customer complaint behaviors in usage, this study thoroughly explains the essential differences between the two constructs. On that basis, this study investigates how employee–customer interaction (ECI) quality affects customers’ prohibitive voice behaviors, which is an crucial type of customer voice behaviors, by examining customer trust and identification as mediators. Data from 395 restaurant customers are collected and analyzed using structural equation modeling. Results show that ECI quality positively affects customers’ prohibitive voice behaviors. In this effect, customer trust and identification play direct and sequential mediating roles. This study contributes theoretically to the current knowledge by clearly distinguishing customer voice behaviors from customer complaint behaviors and by providing new insights into the mechanism of customers’ prohibitive voice behaviors from the perspectives of service interaction and relational benefit enhancement. The practical implications of this study can help pointedly foster customers’ prohibitive voice behaviors.

Recently, cross-border transfers using blockchain-based virtual assets (cryptocurrency) have been increasing. However, due to the anonymity of blockchain, there is a problem related to money laundering because the virtual asset service providers cannot identify the originators and the beneficiaries. In addition, the international anti-money-laundering organization (the Financial Action Task Force, FATF) has placed anti-money-laundering obligations on virtual asset service providers through anti-money-laundering guidance for virtual assets issued in June 2019. This paper proposes a customer identification service model based on distributed ledger technology (DLT) that enables virtual asset service providers to verify the identity of the originators and beneficiaries. [ABSTRACT FROM AUTHOR]

The article focuses on the relatively recent phenomenon for both traders and researchers studying the adaptation of trade organizations to the conditions of the modern market which is the use of omnichannel technologies for sale. The existing approaches in defining the concept of omnichannel business are presented, areas of application (B2B, B2C) are identified, as well as the principles and elements of omnichannel sale technologies in retail trade, advantages and risks of their use in commercial entrepreneurship, and the business’s readiness to use these technologies. [ABSTRACT FROM AUTHOR]

Class imbalance brings great challenges to feature selection in customer identification, and most of the current feature selection approaches cannot produce good prediction on the minority class. A number of studies have attempted to solve this issue by using resampling techniques. However, resampling techniques only use the in-domain information and they cannot achieve good performance when the imbalance is caused by the absolute rarity of the minority class. In this paper, we focus on the issue of feature selection with class imbalance caused by absolute rarity. By introducing the idea of transfer learning, we develop a transferred feature selection method based on the group method of data handling neural networks. The proposed ensemble neural network extracts information of similar customers from related domains to deal with the information scarcity of the minority class in the target domain. Experiments are done on a real-world application using data from a cigarette company. The results indicate that the new method gives better predictive performance than other benchmark feature selection methods, especially in terms of the predictive accuracy of the minority high-value customers. At the same time, the new algorithm can help to identify important features that distinguish high-value customers from low-value ones. [ABSTRACT FROM AUTHOR]

This study pertains to whether and how employees’ organizational citizenship behaviors toward customers (OCB-C) influence customers’ citizenship behaviors (CCB) directed toward the firm, employees, and other customers. Drawing on a social exchange perspective, this study proposes that a dual identification mechanism—spanning customer-employee identification (C-EI) and customer-firm identification (C-FI)—mediates the social exchange relationship between OCB toward customers (OCB-C) and CCB. Service climate as a key contextual factor moderates the mediating mechanisms of identification. With data collected from a field survey and an experiment, the findings confirm that the dual identification mechanism mediates the effect of OCB-C on customers’ reciprocation with CCB. The results also reveal a moderating effect of service climate, such that the positive effect of OCB-C on C-EI and C-FI grows stronger when the service climate is at low and high levels, respectively. In addition, the empirical results demonstrate that the underlying motive attribution explains the moderating effect of service climate. This work paints a more nuanced picture of the missing link in the OCB-C-CCB interface by identifying a mediating mechanism and boundary condition. To promote CCB, managers should leverage their employees’ OCB-C as well as their firms’ service climate. [ABSTRACT FROM AUTHOR]

This study examines how first-class airline travellers' perception of luxury goods influences the formation of their loyalty to first-class flights. Based on the proposed hypotheses, a conceptual model was developed and tested using empirical data collected from a sample of 202 first-class airline travellers in the USA. The results indicate that the five types of perceptions of luxury goods (e.g. quality, hedonism, conspicuousness, exclusivity, and the extended self) were generally associated with consumer attitudes towards first-class flights and thus led to well-being perceptions and perceived price fairness. In addition, well-being perceptions enhanced perceived price fairness, customer identification, and loyalty. Finally, alternative attractiveness moderated the relationship between consumer attitudes towards first-class flights and perceived price fairness. The results have important theoretical and managerial implications. [ABSTRACT FROM AUTHOR]

The task of re-identification consists of linking records for individuals with no identifying information to records with identifying information (i.e., name or social security number) in order to identify individuals within the anonymous data. This task is important for business since firms want to precisely identify consumers for several reasons, such as targeting advertisements to them or labeling them as fraudulent users. For these reasons, companies strive to improve their re-identification techniques. In addition, the re-identification task is relevant from a research prospective, and many algorithms and techniques have been proposed to improve existing re-identification models. However, no previous research has studied whether the use of contextual variables can improve re-identification performance. Context can be defined as the circumstances under which transactions take place. To date, contextual information (i.e., the time of day when or the location where digital data was created) has been used successfully in other modeling tasks such as in the recommender system domain, where its ability to improve the accuracy of lists of items suggested to website users has been demonstrated. Including contextual information in a re-identification model is not a trivial task for several reasons. In this paper, we discuss the main issues regarding the use of context for the re-identification task, namely, when incorporating context is expected to help re-identification and when it is expected to hurt. We propose contextual re-identification models and a framework for deciding when to use these and determining the best performing contextual method for the re-identification task. We test our contextual models using three different case studies. Our findings have a significant impact on expert and intelligent systems since they provide the first evidence of the possibility of including contextual variables for improving the results of the re-identification process. The results also have a relevant impact for businesses since they can help managers decide when and how to include a contextual variable into the re-identification task and contextualize subsequent actions after the re-identification task. [ABSTRACT FROM AUTHOR]

With the advancesin Web-based oriented technologies, experts are able to capture user activities on the Web. Users' Web browsing behavior is used for user identification. Identifying users during their activities is extremely important in electronic commerce (e-Commerce)as it has the potential to prevent illegal transactions or activities particularly for users who enter the system through the use of unknown methods.In addition, customer behavioral pattern identification provides a wide spectrum of applications such as personalized Web pages, product recommendations and present advertisements. In this research, a framework for users' behavioral profiling formation is presented and customer behavioral patternsare used for customer identification in the e-Commerce environment. Based on activity control, policies such as user restriction or blockingcan be applied. The neural network classification and the measure of similarity among behavioral patterns are two approaches applied in this research. The results of multi-layer perceptron with a back propagation learning algorithm indicate that there is less error and up to 15.12% more accuracy on average. The results imply that the accuracy of the neural network approach in customer pattern behavior recognition increases when the number of customers grows. In contrast, the accuracy of the similarity of pattern method decreases. [ABSTRACT FROM AUTHOR]