JTAER, Vol. 19, Pages 340-361: Similarities and Disparities of e-Commerce in the European Union in the Post-Pandemic Period

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010018

Authors: Rodica Manuela Gogonea Liviu Cătălin Moraru Dumitru Alexandru Bodislav Loredana Maria Păunescu Carmen Florentina Vlăsceanu

The emergence of the COVID-19 pandemic has resulted in notable transformations of the commerce landscape, particularly in the realm of electronic commerce. This sector has experienced a precipitous advancement, characterized by substantial modifications of online business under-takings, encompassing both products and services. The aim of the current research was to explore the similarities and differences between European Union member states in the context of e-commerce in the post-pandemic period, taking into consideration the population’s level of education, the risk of poverty, as well as households’ access to the internet. The analysis was conducted for the year 2021, which represented the most recent year for which data were available, and was based on the application of the hierarchical cluster methodology, which included the Ward method and the Robust Tests of Equality of Means (Welch and Brown–Forsythe). Five clusters resulted, which included a minimum of three countries and a maximum of nine. The present study focused on examining the similarities and disparities within clusters, as well as among countries belonging to those clusters. These observed similarities and disparities are believed to be the outcome of various indicators that influence the realm of electronic commerce, and they are contingent upon the economic development level of each country and their ability to cope with the challenges posed by the COVID-19 pandemic. The information obtained in this study pertains to the future of electronic commerce in the sense of identifying premises that allow the development and application of development strategies.

JTAER, Vol. 19, Pages 315-339: A Game-Theoretic Analysis of the Adoption of Patient-Generated Health Data

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010017

Authors: M. Tolga Akçura Zafer D. Ozdemir Hakan Tarakci

Patient-generated health data (PGHD) have great potential to improve clinical outcomes. As providers consider whether and how to incorporate PGHD into their clinical workflows, platforms by Apple and Amazon stand to fundamentally alter the landscape. With the aim to examine the conditions under which providers would adopt PGHD and possibly sign on with a platform, we analyzed the incentives and optimal strategies of two healthcare providers, a monopoly platform, and consumers using stylized game-theoretic models and solve for potential equilibria. We found that consumer surplus always increased with PGHD adoption, but social welfare may drop. The larger provider had more incentive to adopt PGHD than the smaller provider, but these incentives were reversed in the case of platform adoption. Accordingly, the platform enrolled the smaller provider first and possibly both providers. The emergence of the platform raised provider surplus, potentially at the expense of the consumers, despite offering its service to them for free. These results illustrate the importance of economic incentives regarding whether and how PGHD could be incorporated into our current healthcare system.

JTAER, Vol. 19, Pages 297-314: Financial Anti-Fraud Based on Dual-Channel Graph Attention Network

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010016

Authors: Sizheng Wei Suan Lee

This article addresses the pervasive issue of fraud in financial transactions by introducing the Graph Attention Network (GAN) into graph neural networks. The article integrates Node Attention Networks and Semantic Attention Networks to construct a Dual-Head Attention Network module, enabling a comprehensive analysis of complex relationships in user transaction data. This approach adeptly handles non-linear features and intricate data interaction relationships. The article incorporates a Gradient-Boosting Decision Tree (GBDT) to enhance fraud identification to create the GBDT–Dual-channel Graph Attention Network (GBDT-DGAN). In a bid to ensure user privacy, this article introduces blockchain technology, culminating in the development of a financial anti-fraud model that fuses blockchain with the GBDT-DGAN algorithm. Experimental verification demonstrates the model’s accuracy, reaching 93.82%, a notable improvement of at least 5.76% compared to baseline algorithms such as Convolutional Neural Networks. The recall and F1 values stand at 89.5% and 81.66%, respectively. Additionally, the model exhibits superior network data transmission security, maintaining a packet loss rate below 7%. Consequently, the proposed model significantly outperforms traditional approaches in financial fraud detection accuracy and ensures excellent network data transmission security, offering an efficient and secure solution for fraud detection in the financial domain.

JTAER, Vol. 19, Pages 272-296: A Two-Stage Nonlinear User Satisfaction Decision Model Based on Online Review Mining: Considering Non-Compensatory and Compensatory Stages

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010015

Authors: Shugang Li Boyi Zhu Yuqi Zhang Fang Liu Zhaoxu Yu

Mining user satisfaction decision stages from online reviews is helpful for understanding user preferences and conducting user-centered product improvements. Therefore, this study develops a two-stage nonlinear user satisfaction decision model (USDM). First, we use word2vec technology and lexicon-based sentiment analysis to mine the sentiment polarity of each product attribute in the reviews. Then, we develop KANO mapping rules using utility functions to classify consumer preferences based on attribute importance. Based on this, a two-stage nonlinear USDM is developed to describe post-purchase evaluation behavior. In the first non-compensatory stage, consumers determine their initial satisfaction level based on the performance of basic attributes. If the performance of these attributes is poor, it is almost impossible for users to be satisfied. In the compensatory stage, the performance of the remaining attributes collectively affects final satisfaction through participation in user utility calculation. With the use of reviews from JD.com, we develop a genetic algorithm to determine feasible solutions for the USDM and verify its validity and robustness. The USDM is proven to be effective in predicting user satisfaction compared to other classic models and machine learning algorithms. This study provides a universal pattern for user satisfaction decisions and extends the study on preference analysis.

JTAER, Vol. 19, Pages 249-271: Value Co-Creation on TV Talent Shows: Cases from Mainland China, Taiwan and Hong Kong

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010014

Authors: Wai-Kit Ng Cheng-Ming Yang Chun-Liang Chen

Through the actions and interactions of video platform users, talent shows have expanded from the entertainment sphere to the social sphere and become an everyday part of life. Watching talent shows on online platforms, especially through participation in multi-platform interaction, is an ever developing and innovative field in many regions. This study adopts a multiple case analysis approach. We analyze and compare three cases of talent shows, examining aspects of their value co-creation, digital platform, dynamic capability and value network through an exploration of a series of creative activities on digital video platforms. Talent shows provide a unique environment in which different actors interact, co-exist and co-create value, i.e., another form of O2O marketing. These actors include producers, entertainment companies, sponsors and fans, and fan value co-creation currently takes many different forms, which are experienced, engaged and interacted with through different platforms. The findings contribute to examining the underlying dynamics of TV talent shows, in addition to explaining how they are achieving sustainable advantages in the media market. Furthermore, this study aims to understand the service ecosystem of network talent shows from the perspective of industrial innovation strategy; consequently, this research can help to promote the implications of this new form of digital content services and its innovation strategies.