Showing papers on "Customer relationship management published in 2015"

The customer relationship management process: its measurement and impact on performance

Abstract: Customer relationship management (CRM) systems track and measure marketing campaigns over different networks. There are literally many studies associated with the implementation of CRM in different industries. This paper presents an empirical investigation to determine effective factors influencing on the success of CRM implementation. The study designs a questionnaire in Likert scale on measuring various factors and distributes it among selected employees who work for the biggest software maker firm in city of Tehran, Iran. Using principal component analysis, the study has detected five factors influencing on the succes of CRM including customer relationship technologies, customer oriented, enterprise development strategies, customer services and business plan.

The Effect of Experience Quality on Perceived Value, Satisfaction, Image and Behavioral Intention of Water Park Patrons: New versus Repeat Visitors

Abstract: This study proposed a conceptual model to examine how customers' perceptions of the quality of experiences influence perceived value, water park image, customer satisfaction and behavioral intentions for first-time and repeat customers of the water park of the tourism industry This study finds that the quality of participants' experiences significantly affects perceived value, water park image and customer satisfaction Moreover, perceived value and water park image exert a direct influence on customer satisfaction, and they also positively affect behavioral intentions Finally, the impacts on customer satisfaction of experience quality and water park image significantly differ between first-time and repeat customers Copyright © 2013 John Wiley & Sons, Ltd

The Essentials of Business Research Methods

Abstract: Increasingly, managers must make decisions based on almost unlimited information. How can they navigate and organize this vast amount of data? Essentials of Business Research Methods provides research techniques for people who aren't data analysts. The authors offer a straightforward, hands-on approach to the vital managerial process of gathering and using data to make clear business decisions. They include critical topics, such as the increasing role of online research, ethical issues, data mining, customer relationship management, and how to conduct information-gathering activities more effectively in a rapidly changing business environment. This is the only text that includes a chapter on qualitative data analysis, and the coverage of quantitative data analysis is more extensive, and much easier to understand than in other texts. The book features a realistic continuing case throughout that enables students to see how business research information is used in the real world. It includes applied research examples in all chapters, as well as ethical dilemma mini cases, and exercises.

Information Technology Adoption in Small Business: Confirmation of a Proposed Framework

Abstract: This paper investigates which drivers affect information technology (IT) adoption and which factors relate to a successful IT implementation in small businesses, where the adoption rate is traditionally low and the failure rate is high. The findings from this study suggest that customers are the main driving force of IT adoption. When it comes to IT implementation, our results suggest that managers/owner–managers must engage with five factors: organization, internal IT resources, external IT consultants, supplier relations, and customer relations. These findings give further insight into IT adoption in small businesses and highlight the importance of customer relations in the adoption process.

The Squeaky Wheel Gets the Grease-An Empirical Analysis of Customer Voice and Firm Intervention on Twitter

Abstract: Firms are increasingly engaging with customers on social media. Despite this heightened interest, guidance for effective engagement is lacking. In this study, we investigate customers' compliments and complaints and firms' service interventions on social media. We develop a dynamic choice model that explicitly accounts for the evolutions of both customers' voicing decisions and their relationships with the firm. Voices are driven by both the customers' underlying relationships and other factors such as redress seeking. We estimate the model using a unique data set of customer voices and service interventions on Twitter. We find that redress seeking is a major driver of customer complaints, and although service intervention improves relationships, it also encourages more complaints later. Because of this dual effect, firms are likely to underestimate the returns on service intervention if measured using only voices. Furthermore, we find an "error-correction" effect in certain situations, where customers compliment or complain when others voice the opposite opinions. Finally, we characterize the distinct voicing tendencies in different relationship states, and show that uncovering the underlying relationship states enables effective targeting. We are among the first to analyze individual customer level voice dynamics and to evaluate the effects of service intervention on social media.

The Roles of Value, Satisfaction, and Commitment in the Effect of Service Quality on Customer Loyalty in Hong Kong–Style Tea Restaurants

Abstract: The purpose of this study is to evaluate the roles of perceived value, customer satisfaction, and affective commitment as they mediate the effect of service quality on customer loyalty at Hong Kong...

Modelling CRM in a social media age

Abstract: Customer relationship management is a continually evolving domain that has been particularly affected by social media, which have revolutionised the way businesses and consumers interact This pape

CRM/Social Media Technology: Impact on Customer Orientation Process and Organizational Sales Performance

Abstract: The importance of technology in managing relationships with customers has grown significantly, especially with the advent of innovations such as cloud computing and web-based technology. One of the more popular topics in both academic research and business discussions has been the use of Customer Relationship Management (CRM) technology to increase business and sales productivity through the ongoing development of relationships with buyers. A new phenomenon in understanding buyers’ needs and reaching new customers is social media. Organizations are capturing data from tools such as LinkedIn, Facebook and blogs, and attempting to integrate this information into their sales process. In order to extract value from technology, organizations need to build a framework and processes to deliver value to the customer.

Online profiling and clustering of Facebook users

Abstract: In a relatively short period of time, social media have acquired a prominent role in media and daily life. Although this development brought about several academic endeavors, the literature concerning the analysis of social media data to investigate one's customer base appears to be limited. In this paper, we show how data from the social network site Facebook can be operationalized to gain insight into the individuals connected to a company's Facebook site. In particular, we propose a data collection framework to obtain individual specific data and propose methodology to explore user profiles and identify segments based on these profiles. The proposed data collection framework can be used as an identification step in an analytical customer relationship management implementation that specifically focuses on potential customers. We illustrate our methodology by applying it to the Facebook page of an internationally well-known professional football (soccer) club. In our analysis, we identify four clusters of users that differ with respect to their indicated "liking" profiles.

Customer Mistreatment: A Review of Conceptualizations and a Multilevel Theoretical Model

Abstract: In this chapter, we summarize and build on the current state of the customer mistreatment literature in an effort to further future research on this topic. First, we detail the four primary conceptualizations of customer mistreatment. Second, we present a multilevel model of customer mistreatment, which distinguishes between the unfolding processes at the individual employee level and the service encounter level. In particular, we consider the antecedents and outcomes unique to each level of analysis as well as mediators and moderators. Finally, we discuss important methodological concerns and recommendations for future research.

Using Twitter to engage with customers: a data mining approach

Abstract: Purpose – The purpose of this paper is to explore customer engagement in Twitter via data mining. Design/methodology/approach – This study’s intended contributions are twofold: to find a clear connection among customer engagement, presumption, and Web 2.0 in a context of service-dominant (S-D) logic; and to identify social networks created by prosumers. To this end, the study employed data mining techniques. Tweets about IKEA were used as a sample. The resulting algorithm based on 300 tweets was applied to 4,000 tweets to identify the patterns of electronic word-of-mouth (eWOM). Findings – Social networks created in IKEA’s tweets consist of three forms of eWOM: objective statements, subjective statements, and knowledge sharing. Most objective statements are disseminated from satisfied or neutral customers, while subjective statements are disseminated from dissatisfied or neutral customers. Satisfied customers mainly carry out knowledge sharing, which seems to reflect presumption behavior. Research limitations/implications – This study provides partial evidence of customer engagement and presumption in IKEA’s tweets. The results indicate that there are three forms of eWOM in the networks: objective statements, subjective statements, and knowledge sharing. It seems that IKEA successfully engaged customers in knowledge sharing, while negative opinions were mainly disseminated in a limited circle. Practical implications – Firms should make more of an effort to identify prosumers via data mining, since these networks are hidden behind “self-proclaimed” followers. Prosumers differ from opinion leaders, since they actively participate in product development. Thus, firms should seek prosumers in order to more closely fit their products to consumer needs. As a practical strategy, firms could employ celebrities for promotional purposes and use them as a platform to convert their followers to prosumers. In addition, firms are encouraged to make public how they resolve problematic customer complaints so that customers can feel they are a part of firms’ service development. Originality/value – Theoretically, the study makes unique contributions by offering a synergic framework of S-D logic and Web 2.0. The conceptual framework collectively relates customer engagement, presumption, and Web 2.0 to social networks. In addition, the idea of examining social networks based on different forms of eWOM has seldom been touched in the literature. Methodologically, the study employed seven algorithms to choose the most robust model, which was later applied to 4,000 tweets.

The Predictive Ability of Different Customer Feedback Metrics for Retention

Abstract: This study systematically compares different customer feedback metrics (CFMs) — namely customer satisfaction, the Net Promoter Score, and the Customer Effort Score — to test their ability to predict retention across a wide range of industries. We classify the CFMs according to a time focus (past, present, or future) and whether the full scale of the CFM is used or whether the focus is only on the extremes (e.g., top-2-box customer satisfaction). The data for this study represent customers of 93 firms across 18 industries. Multi-level probit regression models, which control for self-selection bias of respondents, investigate firm-, customer-, and industry-level effects simultaneously. Overall, we find that the top-2-box customer satisfaction performs best for predicting customer retention and that focusing on the extremes is preferable to using the full scale. However the best CFM does differ depending on industry and the unit of analysis (i.e., comparing customers or firms with one another). Furthermore, combining CFMs, along with simultaneously investigating multiple dimensions of the customer relationship, improves predictions even further.

Social Selling: A Comparison of Social Media Usage Across Process Stage, Markets, and Sales Job Functions

Abstract: This research compares the extent to which sales personnel in B2C (business-to-consumer) and B2B (business-to-business) markets use relationship-oriented social media to accomplish job-related and selling process (i.e., social CRM [customer relationship management]) tasks. A sample of 395 sales professionals across a variety of industries responded to questions assessing their use of the tools. Results demonstrate that a substantial proportion find utility in the applications. Interaction effects regarding B2B managers are particularly noteworthy, as no prior research has shown that B2B managers are significantly and far more engaged with social CRM than other sales representatives. The frequency and extent of social CRM usage varies based on the specific tools used and the stage of the selling process. The findings carry important implications for salespeople, managers, trainers, and university educators.

Self-Congruity and Functional Congruity in Brand Loyalty:

Abstract: This study used image congruity theory to understand customer loyalty in name-brand coffee shops in Korea. The authors developed and tested a structural model that links image congruity (self-congr...