Customer Relationships in Electronic Commerce

Editor


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Guest Editor
Department of Computer Science and Information Management, Providence University, Taichung City 43301, Taiwan
Interests: marketing; E-commerce; customer relationship management

Topical Collection Information

Dear Colleagues,

With the rapid growth of information technology, customer relationship management has attracted an increasing amount of attention as a new strategy for companies. To acquire new customers and retain old customers, many companies tend to use information technology, such as big data, mobile devices, social media, Internet of Things, Artificial Intelligence, Cloud computing, to improve their services for customers. Currently, information technology is a necessary enabler of customer relationships in most organizations to store and analyze huge amounts of customer data and provide better values for customers. Moreover, the interaction interfaces between companies and customers will be changed. Through mobile devices, social media, Internet of Things, Artificial Intelligence, and Cloud computing, the contact points with customers will be more effective and affordable.

Since many companies likely use information technology and its applications for improving customer relationships in their businesses. It is still unclear whether the strategy can produce positive feedback from customers or better performance for companies. Moreover, before such applications can be developed and the benefits realized, companies need to address the possible challenging tasks and the obstacles possibly arise. Therefore, this Special Issue focuses on customer relationships in electronic commerce to develop a better solution for researchers and practitioners.

The related topics for the Special Issue may include, but are not limited to, the following:

  • Customer value and customer relationships in electronic commerce.
  • Customer data analysis and customer relationship management by using big data or data mining.
  • Developing customer relationships by using social media.
  • Developing customer relationships by using Internet of Things.
  • Developing customer relationships by using AI technology.
  • Analyzing customer data and developing service platforms by using cloud computing.
  • Creating customer value by integrating the online and offline channels.
  • Developing customer relationships in omni-channel commerce.
  • The challenges or the obstacles of customer relationships in e-commerce.

Prof. Dr. Yung-Shen Yen
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Theoretical and Applied Electronic Commerce Research is an international peer-reviewed open access quarterly journal published by MDPI.

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Keywords

  • e-commerce 
  • customer value 
  • big data 
  • social media 
  • Internet of Things 
  • AI technology 
  • cloud computing 
  • online and offline channels 
  • omni-channel commerce

Published Papers (4 papers)

2024

Jump to: 2023, 2021

17 pages, 333 KiB  
Article
Technology and Innovation: Analyzing the Heterogeneity of the Hotel Guests’ Behavior
by Mariia Bordian, María Fuentes-Blasco, Irene Gil-Saura and Beatriz Moliner-Velázquez
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1599-1615; https://doi.org/10.3390/jtaer19020078 - 17 Jun 2024
Viewed by 191
Abstract
The study intends to identify and analyze different consumer segments. For this purpose, we examine why customers turn to electronic word-of-mouth (eWOM) before making a purchase and how they perceive a hotel’s information and communication technology (ICT) and relational innovation after making a [...] Read more.
The study intends to identify and analyze different consumer segments. For this purpose, we examine why customers turn to electronic word-of-mouth (eWOM) before making a purchase and how they perceive a hotel’s information and communication technology (ICT) and relational innovation after making a purchase. The objective was empirically tested with data from a panel of consumers who stayed at hotels during the post-pandemic recovery period in Spain. In total, 393 valid questionnaires were obtained. The estimation of a finite mix model was applied to identify guest profiles. Estimation identified three guest profiles where the perceptions of the hotel’s relational innovation and ICT present a high discriminant power in the first two segments. Moreover, compared to the second segment, the first group is characterized by the low impact level of these variables. On the other hand, the motivation to consult eWOM in the prebooking stage significantly influences all three groups; however, the guests of the third segment present less motivation than the rest. Hotel managers may consider ICT, relational innovation, and eWOM factors when segmenting consumers. Understanding this would enhance the company’s service delivery and the hotel’s competitiveness. The contribution of this study lies in considering ICT, relational innovation, and eWOM as novel factors that help identify different guest profiles. Full article
13 pages, 617 KiB  
Article
The Effect of AI Agent Gender on Trust and Grounding
by Joo-Eon Jeon
J. Theor. Appl. Electron. Commer. Res. 2024, 19(1), 692-704; https://doi.org/10.3390/jtaer19010037 - 21 Mar 2024
Viewed by 1090
Abstract
Artificial intelligence (AI) agents are widely used in the retail and distribution industry. The primary objective was to investigate whether the gender of AI agents influences trust and grounding. This paper examined the influence of AI agent gender and brand concepts on trust [...] Read more.
Artificial intelligence (AI) agents are widely used in the retail and distribution industry. The primary objective was to investigate whether the gender of AI agents influences trust and grounding. This paper examined the influence of AI agent gender and brand concepts on trust and grounding within virtual brand spaces. For this purpose, it used two independent variables: brand concept (functional vs. experiential) and AI agent gender (male vs. female). The dependent variables included AI agent trust and grounding. The study revealed that in virtual brand spaces centered around a functional concept, male AI agents generated higher levels of trust than female AI agents, whereas, when focused on an experiential concept, female AI agents induced higher levels of grounding than male AI agents. Furthermore, the findings indicate that the association between customers’ identification with AI agents and recommendations for actual brand purchases is mediated by trust and grounding. These findings support the idea that users who strongly identify with AI agents are more inclined to recommend brand products. By presenting alternatives that foster the establishment and sustenance of a meaningful, sustainable relationship between humans and AI, this study contributes to research on human–computer interactions. Full article
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2023

Jump to: 2024, 2021

12 pages, 2665 KiB  
Article
Sentiment and Emotion on Twitter: The Case of the Global Consumer Electronics Industry
by Claudia Pezoa-Fuentes, Danilo García-Rivera and Sebastián Matamoros-Rojas
J. Theor. Appl. Electron. Commer. Res. 2023, 18(2), 765-776; https://doi.org/10.3390/jtaer18020039 - 23 Mar 2023
Cited by 5 | Viewed by 2557
Abstract
Sentiment analysis is a new tool on new social media platforms, locations very attractive to the global consumer industry to investigate, due to their relevance and increased consumption in a pandemic. This study aims to determine the predominant sentiment and emotions on Twitter [...] Read more.
Sentiment analysis is a new tool on new social media platforms, locations very attractive to the global consumer industry to investigate, due to their relevance and increased consumption in a pandemic. This study aims to determine the predominant sentiment and emotions on Twitter through a sentiment analysis in the consumer electronics industry, according to the top 30 companies of the Consumer Electronic Show 2020, by analyzing 96,000 tweets with a total of 273,221 words. The methodology used is quantitative, of a descriptive type, that integrates the study of emotions and sentiment through a statistical analysis of the tweets with R. The main results identify that the predominant sentiment is of positive assessment and the emotions of anticipation and confidence were the most representative. The contribution of this research is to provide empirical evidence of the global consumer electronics industry for correct decision-making through a data language analysis procedure on Twitter. Full article
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2021

Jump to: 2024, 2023

13 pages, 1304 KiB  
Article
An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation
by Jiafu Su, Xu Chen, Fengting Zhang, Na Zhang and Fei Li
J. Theor. Appl. Electron. Commer. Res. 2021, 16(5), 1571-1583; https://doi.org/10.3390/jtaer16050088 - 12 May 2021
Cited by 11 | Viewed by 3301
Abstract
For customer collaborative product innovation (CCPI), lead users are powerful enablers of product innovation. Identifying lead users is vital to successfully carrying out CCPI. In this paper, in order to overcome the shortcomings of traditional evaluation methods, a novel intelligent method is proposed [...] Read more.
For customer collaborative product innovation (CCPI), lead users are powerful enablers of product innovation. Identifying lead users is vital to successfully carrying out CCPI. In this paper, in order to overcome the shortcomings of traditional evaluation methods, a novel intelligent method is proposed to identify lead users efficiently based on the cost-sensitive learning and support vector machine theory. To this end, the characteristics of lead users in CCPI are first analyzed and concluded in-depth. On its basis, considering the sample misidentification cost and identification accuracy rate, an improved cost-sensitive learning support vector machine (ICS-SVM) method for lead user identification in CCPI is further proposed. A real case is provided to illustrate the effectiveness and advantages of the ICS-SVM method on lead user identification in CCPI. The case results show that the ICS-SVM method can effectively identify lead users in CCPI. This work contributes to user innovation literature by proposing a new way of identifying highly valuable lead users and offers a decision support for the efficient user management in CCPI. Full article
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