JTAER, Vol. 19, Pages 863-879: Understanding the Adoption Dynamics of ChatGPT among Generation Z: Insights from a Modified UTAUT2 Model

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19020045

Authors: Antun Biloš Bruno Budimir

This research delves into the factors influencing the adoption of ChatGPT, a sophisticated AI-based chatbot, among Generation Z members in Croatia. Employing an extended UTAUT2 model, the impact of various factors on the behavioral intention to use ChatGPT is explored. The study included 694 Generation Z participants, and data were collected through an online survey featuring self-reporting questions. The analysis utilized statistical software packages for performing both confirmatory and exploratory factor analyses, in addition to hierarchical linear regression. Key findings reveal that performance expectancy, social influence, hedonic motivation, habit, and personal innovativeness significantly influence the behavioral intention to use ChatGPT. However, effort expectancy, facilitating conditions, and price value do not exhibit a significant impact. Notably, the study excludes the use behavior factor due to multicollinearity issues with behavioral intention. While the research does not focus on moderating factors, it reports that the adapted UTAUT2 model explains 65% of the variance in the adoption of ChatGPT by Generation Z users.

JTAER, Vol. 19, Pages 846-862: Simulation Modeling and Analysis on the Value-Added Service of the Third-Party E-Commerce Platform Supporting Multi-Value Chain Collaboration

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19020044

Authors: Wenbo Li Yajie Zhang Bin Dan Xumei Zhang Ronghua Sui

Service-oriented third-party e-commerce platforms have emerged as a new trend in the manufacturing industry. This paper aims to investigate the platforms’ value-added service (VAS) and charging strategies with a dynamic evolution analysis. Considering the change in the user numbers and characteristics of the e-commerce industry, this paper proposes a system dynamics model composed of multi-value chains and a third-party e-commerce platform. The simulation results indicate that the platform should reduce VAS investment and appropriately increase the VAS fee in the early development period. After the number of users stabilizes, the platform should appropriately increase its VAS investment and reduce the VAS fee. When the VAS fee is low, the platform profit first increases and then decreases as the VAS level increases. Differently, the platform profit will first decrease, then increase, and finally decrease as the VAS level improves when the VAS fee is low. This paper further finds that the strong cross-network effect of manufacturers is not always beneficial to the platform.

JTAER, Vol. 19, Pages 818-845: Factors Influencing User Favorability of Government Chatbots on Digital Government Interaction Platforms across Different Scenarios

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19020043

Authors: Yuanyuan Guo Peng Dong

This study investigates the direct and indirect influences of behavioral quality, social support, perceived system, emotional perception, and public expectation on user favorability regarding government chatbots in both government service and policy consultation contexts. The findings reveal that while behavioral quality, social support, and perceived system directly affect user favorability in both scenarios, public expectation uniquely impacts user favorability in policy consultation settings, but not in government service scenarios. Furthermore, the analysis indicates that social support, emotional perception, and public expectation all indirectly influence user favorability through their mediating effect on behavioral quality in both contexts. Notably, the significant distinction between the two scenarios is the presence of an indirect impact of perceived system on user favorability within policy consultation scenarios, which is absent in government service scenarios. This study sheds light on the intricate interplay of factors shaping user favorability with government chatbots, and provides valuable insights for improving user experiences and user favorability in different governmental service contexts.

JTAER, Vol. 19, Pages 797-817: The Use of Digital Channels in Omni-Channel Retail—An Empirical Study

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19020042

Authors: Iulia Diana Nagy Dan-Cristian Dabija Romana Emilia Cramarenco Monica Ioana Burcă-Voicu

This article aims to highlight the influencing factors on omni-channel consumer attitudes towards virtual shopping channels, providing the literature with a new conceptual model that studies the use of technology by omni-channel consumers. The research hypotheses were established based on the literature review, and a conceptual model was defined. Quantitative research was carried out on an emerging market through the survey technique to verify the relations between the investigated concepts. In total, 307 responses from Millennials and Generation Z members were analyzed using structural equations modeling in SmartPLS. The results show that both channel and consumer characteristics, alongside their media contexts, influence the attitude and willingness to access and use retail channels. To keep up with constantly changing consumer needs, companies are advised to continually analyze the target market and implement any necessary measures. The paper expands the studies investigating the behavior of technology users, enhancing the UTAUT2 model-based literature.

JTAER, Vol. 19, Pages 774-796: Order Distribution and Routing Optimization for Takeout Delivery under Drone–Rider Joint Delivery Mode

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19020041

Authors: Fuqiang Lu Runxue Jiang Hualing Bi Zhiyuan Gao

Order distribution and routing optimization of takeout delivery is a challenging research topic in the field of e-commerce. In this paper, we propose a drone–rider joint delivery mode with multi-distribution center collaboration for the problems of limited-service range, unreasonable distribution, high delivery cost, and tight time windows in the takeout delivery process. The model is constructed with the minimum delivery cost and the overall maximum customer satisfaction as the objective function, and a two-stage heuristic algorithm is designed to solve the model. In the first stage, Euclidean distance is used to classify customers into the regions belonging to different distribution centers, and the affinity propagation (AP) clustering algorithm is applied to allocate orders from different distribution centers. The second stage uses an improved tabu search algorithm for route optimization based on specifying the number of rider and drone calls. This paper takes China’s Ele.me and Meituan takeout as the reference object and uses the Solomon data set for research. The experimental results show that compared with the traditional rider delivery mode, the drone–rider joint delivery mode with multiple distribution center collaboration can effectively reduce the number of riders used, lower the delivery cost, and improve the overall customer satisfaction.

JTAER, Vol. 19, Pages 743-773: Exploring Tourists’ Behavioral Patterns in Bali’s Top-Rated Destinations: Perception and Mobility

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19020040

Authors: Dian Puteri Ramadhani Andry Alamsyah Mochamad Yudha Febrianta Lusiana Zulfa Amelia Damayanti

The tourism sector plays a crucial role in the global economy, encompassing both physical infrastructure and cultural engagement. Indonesia has a wide range of attractions and has experienced remarkable growth, with Bali as a notable example of this. With the rapid advancements in technology, travelers now have the freedom to explore independently, while online travel agencies (OTAs) serve as important resources. Reviews from tourists significantly impact the service quality and perception of destinations, and text mining is a valuable tool for extracting insights from unstructured review data. This research integrates multiclass text classification and a network analysis to uncover tourists’ behavioral patterns through their perceptions and movement. This study innovates beyond conventional sentiment and cognitive image analysis to the tourists’ perceptions of cognitive dimensions and explores the sentiment correlation between different cognitive dimensions. We find that destinations generally receive positive feedback, with 80.36% positive reviews, with natural attractions being the most positive aspect while infrastructure is the least positive aspect. We highlight that qualitative experiences do not always align with quantitative cost-effectiveness evaluations. Through a network analysis, we identify patterns in tourist mobility, highlighting three clusters of attractions that cater to diverse preferences. This research underscores the need for tourism destinations to strategically adapt to tourists’ varied expectations, enhancing their appeal and aligning their services with preferences to elevate destination competitiveness and increase tourist satisfaction.

JTAER, Vol. 19, Pages 725-742: How Social Presence Influences Consumer Well-Being in Live Video Commerce: The Mediating Role of Shopping Enjoyment and the Moderating Role of Familiarity

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19020039

Authors: Zhen Huang Xue Yan Jia Deng

In recent years, with the rapid development of live-streaming commerce, the social dynamics and psychological impact of such online activities merit further discussion. In this study, we investigate the sensory experiences of viewers watching live streaming and examine how these online experiences influence consumer well-being. We developed a conceptual model to understand this mechanism based on the relationship between social presence, shopping enjoyment, familiarity, and consumer well-being. The results of 410 samples indicate that (1) social presence in live-streaming commerce has a significant positive effect on consumer well-being; (2) shopping enjoyment plays a mediating role in the process of social presence predicting consumer well-being; and (3) familiarity plays a moderating role in the second half of the indirect effect of social presence on well-being. This study examines the relationship between social presence and consumer well-being in the context of live-streaming marketing, expanding the research scenario of consumer well-being and clarifying the psychological mechanisms and boundary conditions of the effect of social presence on consumers well-being, which has important implications for online interactive marketing enterprises to enhance social presence and promote consumers long-term well-being.

JTAER, Vol. 19, Pages 705-724: How Social Presence Influences Engagement in Short Video-Embedded Advertisements: The Serial Mediation Effect of Flow Experience and Advertising Avoidance

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19020038

Authors: Can Zheng Shuai Ling Dongmin Cho Yonggu Kim

Short video platforms have problems with increased competition and low advertising conversion rates. Although social presence is closely related to consumer engagement, research regarding the impact of social presence on consumer engagement in short video-embedded advertisements is sparse. We developed a theoretical model, namely a social presence–flow experience–advertising avoidance–advertising engagement model, and explored the mechanism underlying advertising engagement from a psychological and behavioral perspective. The analysis of 563 short video users revealed that the model exhibited excellent explanatory power for advertising engagement (R2 = 41.3%). Social presence can increase consumers’ advertising engagement by enhancing flow experience and reducing advertising avoidance. Meanwhile, the flow experience, by diminishing advertising avoidance, generates a serial mediation effect between social presence and advertising engagement. This study emphasizes social presence’s applicability and influence mechanism in short video-embedded advertisements, a unidirectional information delivery. It provides new theoretical perspectives and practical advice for relevant practitioners.

JTAER, Vol. 19, Pages 692-704: The Effect of AI Agent Gender on Trust and Grounding

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010037

Authors: Joo-Eon Jeon

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.

JTAER, Vol. 19, Pages 654-670: Unlocking the Potential of Artificial Intelligence in Fashion Design and E-Commerce Applications: The Case of Midjourney

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010035

Authors: Yanbo Zhang Chuanlan Liu

The fashion industry has shown increasing interest in applying artificial intelligence (AI), yet there is a significant gap in exploring the potential of emerging diffusion-modeling-based AI image-generation systems for fashion design and commerce. Therefore, this study aims to assess the effectiveness of Midjourney, one such AI system, in both fashion design and related commerce applications. We employed the action research approach with the Functional, Expressive, and Aesthetic (FEA) Consumer Needs Model as the theoretical framework. Our research comprised three stages: refining an initial idea into well-defined textual design concepts, facilitating concept development, and validating the preceding observations and reflections by creating a new line of hemp-based products that were evaluated by targeted consumers through an online survey. Findings reveal that this AI tool can assist fashion designers in creating both visually expressive attire and ready-to-wear products, meeting defined design criteria and consumer needs. Midjourney shows promise in streamlining the fashion design process by enhancing ideation and optimizing design details. Potential e-commercial applications of such AI systems were proposed, benefiting physical and digital fashion businesses. It is noted that, to date, the major limitations of using Midjourney encompass its restriction to only facilitating early fashion design stages and necessitating substantial involvement from designers.