Journal Description
Journal of Theoretical and Applied Electronic Commerce Research
Journal of Theoretical and Applied Electronic Commerce Research
published since 2006, is an international, peer-reviewed, scientific journal owned by the Faculty of Engineering of the Universidad de Talca, and MDPI provides publishing services for the journal since Volume 16, Issue 3 (2021).
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SSCI (Web of Science), dblp, and other databases.
- Journal Rank: JCR - Q2 (Business) / CiteScore - Q1 (General Business, Management and Accounting)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 38.4 days after submission; acceptance to publication is undertaken in 4.9 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually in the journal.
Impact Factor:
5.6 (2022);
5-Year Impact Factor:
5.4 (2022)
Latest Articles
Coordination of Online Shopping Supply Chain Considering Fresh Product Preservation Efforts and Cargo Damage Costs
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1337-1357; https://doi.org/10.3390/jtaer19020068 (registering DOI) - 31 May 2024
Abstract
To reduce the losses caused by insufficient preservation efforts during transportation, the preservation effort level has been the focus of research. In the fierce competition of online sales, it is particularly important to reduce the cost of damaged goods by improving the level
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To reduce the losses caused by insufficient preservation efforts during transportation, the preservation effort level has been the focus of research. In the fierce competition of online sales, it is particularly important to reduce the cost of damaged goods by improving the level of preservation efforts. Therefore, according to Stackelberg game theory, this article establishes five decision-making models and incorporates the damage rate and preservation effort level into the research. Finally, this article coordinates the online shipping supply chain (SC) through a joint contract. After comparing and analyzing the model results, research has found that: (1) in centralized model, the level of preservation effort reaches its optimal level and the system benefit is maximized; (2) under third-party logistics (TPL) leading decision-making, the different bearers of cargo damage costs will not affect the profits of both parties and the system; (3) among the four decentralized models, the level of preservation efforts and system profit are highest when the decision is led by online store and TPL bears the cost of damaged goods; and (4) under a given sharing ratio, when the logistics service quotation satisfies a certain range of condition, the online shopping SC can achieve Pareto improvement. This paper studies the differences and reasons for decision models in the supply and demand relationship between online stores and TPL, which provides fresh product e-commerce decision-makers with a theoretical basis.
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(This article belongs to the Collection Advances in Supply Chain Management in the Era of Electronic Commerce)
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Open AccessArticle
Information Collection and Personalized Service Strategy of Monopoly under Consumer Misrepresentation
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Mingyue Zhong, Yan Cheng, Shu-e Mei and Weijun Zhong
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1321-1336; https://doi.org/10.3390/jtaer19020067 (registering DOI) - 31 May 2024
Abstract
To decrease privacy risks, consumers may choose to misrepresent themselves when they are asked to offer personal information. Using a game theoretic model, this study examines the impact of consumer misrepresentation on both a monopolistic firm and consumers. The results show that consumer
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To decrease privacy risks, consumers may choose to misrepresent themselves when they are asked to offer personal information. Using a game theoretic model, this study examines the impact of consumer misrepresentation on both a monopolistic firm and consumers. The results show that consumer misrepresentation may benefit the firm, but hurt consumers under certain conditions. In addition, we find that when the unit cost of personalized service is low, consumer misrepresentation may encourage the firm to provide a higher personalized service level. Moreover, when consumers misrepresent themselves and the firm only covers part of the market, a greater unit value of consumer private information will reduce the firm’s profit, while a greater unit cost of personalized service will increase the firm’s profit. The analysis reported here provides important insights regarding the application of consumer information in online personalized marketing and consumer privacy protection.
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(This article belongs to the Topic Online User Behavior in the Context of Big Data)
Open AccessArticle
Risk Analysis of Bankruptcy in the U.S. Healthcare Industries Based on Financial Ratios: A Machine Learning Analysis
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Hadi Gholampoor and Majid Asadi
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1303-1320; https://doi.org/10.3390/jtaer19020066 (registering DOI) - 30 May 2024
Abstract
The prediction of bankruptcy risk poses a formidable challenge in the fields of economics and finance, particularly within the healthcare industry, where it carries significant economic implications. The burgeoning field of healthcare electronic commerce, continuously evolving through technological advancements and changing regulations, introduces
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The prediction of bankruptcy risk poses a formidable challenge in the fields of economics and finance, particularly within the healthcare industry, where it carries significant economic implications. The burgeoning field of healthcare electronic commerce, continuously evolving through technological advancements and changing regulations, introduces additional layers of complexity. We collected financial data from 1265 U.S. healthcare industries to predict bankruptcy based on 40 financial ratios using multi-class classification machine learning models across various industry subsectors and market capitalizations. The exceptionally high post-tuning accuracy rates, exceeding 90%, along with high-performance metrics solidified the robustness and exceptional predictive capability of the gradient boosting model in bankruptcy prediction. The results also demonstrate the power and sensitivity of financial ratios in predicting bankruptcy based on financial ratios. The Altman models highlight the return on investment (ROI) as the most important parameter for predicting bankruptcy risk in healthcare industries. The Ohlson model identifies return on assets (ROA) as an important ratio specifically for predicting bankruptcy risk within industry subsectors. Furthermore, it underscores the significance of both ROA and the enterprise value to earnings before interest and taxes (EV/EBIT) ratios as important parameters for predicting bankruptcy based on market capitalization. Recognizing these ratios enables proactive decision making that enhances resilience. Our findings contribute to informed risk management strategies, allowing for better management of healthcare industries in crises like those experienced in 2022 and even on a global scale.
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(This article belongs to the Topic Artificial Intelligence Applications in Financial Technology)
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Open AccessArticle
Dynamic Incentive Mechanisms for Collaborative Innovation of Green Supply Chain Considering Digital Capability and Consumer Green Preference
by
Wen Cheng, Qunqi Wu, Qian Li, Fei Ye and Lingling Tan
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1267-1302; https://doi.org/10.3390/jtaer19020065 (registering DOI) - 29 May 2024
Abstract
In light of increasingly prominent environmental issues, inspiring green supply chain (GSC) members to engage in collaborative innovation is crucial to improve environmental performance. In this paper, in relation to a two-level GSC consisting of manufacturers and suppliers, differential equations involving the greenness
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In light of increasingly prominent environmental issues, inspiring green supply chain (GSC) members to engage in collaborative innovation is crucial to improve environmental performance. In this paper, in relation to a two-level GSC consisting of manufacturers and suppliers, differential equations involving the greenness of intermediate and final products as state variables are constructed considering the effect of digital capability on green innovation. Subsequently, designs for three incentive mechanisms—the greenness reward, the R&D effort reward, and the digital construction reward—are presented, and their long-term dynamic effects on the economic, environmental, and social benefits are compared and analyzed. Finally, the impacts of consumer green preference and the contribution of digital capability to the advancement of green innovation are explored. The findings show that all these incentives can boost economic, environmental, and social benefits while motivating the supplier. To achieve the best incentive effect, the reward coefficient should fall within a specific range. The digital construction reward mechanism is the most favourable in the initial stage, while the R&D effort reward mechanism is the most appropriate in the long term. The promotion effects of digital capability on green innovation and consumer green preference have the potential to enhance economic, environmental, and social performance.
Full article
(This article belongs to the Collection Exploring the Future of Creative Economy: Transforming Creative Industries through Innovation, Technology and Enhanced Consumer Engagement)
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Open AccessArticle
Online Review Helpfulness and Information Overload: The Roles of Text, Image, and Video Elements
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Liang Wang, Gaofeng Che, Jiantuan Hu and Lin Chen
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1243-1266; https://doi.org/10.3390/jtaer19020064 (registering DOI) - 29 May 2024
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Online reviews have become an important source of information for consumers, significantly influencing their purchasing decisions. However, the abundance and variety of review formats, especially the mix of text, image, and video elements, can lead to information overload and hinder effective decision-making. This
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Online reviews have become an important source of information for consumers, significantly influencing their purchasing decisions. However, the abundance and variety of review formats, especially the mix of text, image, and video elements, can lead to information overload and hinder effective decision-making. This study investigates how different review formats and their combinations affect the perceived helpfulness of reviews. We develop a comprehensive framework to analyze the interactions between text, image, and video elements and their impact on the helpfulness of reviews. We collect and code 8693 online reviews from JingDong Mall Mallacross six product categories, including both experience products and search products, and use multiple regression analysis to test our hypotheses. Our results show that textual review elements significantly increase review helpfulness. However, their effectiveness decreases as the amount of information increases, indicating cognitive overload. Text reviews are more prone to contribute to information overload, while visual elements such as images and videos generally do not contribute to information overload in the coexistence of text, image, and video reviews. Imagery components have a minimal effect on review helpfulness. Video elements are relatively short, which may not be sufficient to convey useful information. We also find that the impact of review formats varies between experience products and search products, and that star ratings moderate the alignment of textual or imagery components with consumer expectations. We conclude that the hybrid of text, image, and video elements in online reviews plays a crucial role in shaping consumer decision-making and information overload. Our research contributes to the literature on online reviews and information overload while providing practical implications for online retailers, review platforms, and consumers to optimize review formats, star ratings, and product types to facilitate informed purchase decisions.
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Open AccessArticle
Digital Wallet, Happy Heart: An Analysis Based on the Economic–Social–Environmental Perspective
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Xiaomei Gong, Huirong Zhang and Jiaping Zhang
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1222-1242; https://doi.org/10.3390/jtaer19020063 - 25 May 2024
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While the prior mobile payment−subjective well-being (SWB) literature has mainly discussed its economic and social impacts, the present study supplements this body of research by introducing an economic–social–environmental perspective. Using two waves of representative Chinese national surveys, the instrumental variable (IV) estimator suggests
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While the prior mobile payment−subjective well-being (SWB) literature has mainly discussed its economic and social impacts, the present study supplements this body of research by introducing an economic–social–environmental perspective. Using two waves of representative Chinese national surveys, the instrumental variable (IV) estimator suggests that mobile payment is positively and statistically significantly correlated with SWB. Furthermore, the results reveal that the positive correlation comes from the compound influence of economic, social, and environmental channels. Specifically, it shows that mobile payment not only affects people’s economic and social performance but also mitigates the adverse effects of poor environmental conditions on SWB. Additionally, a further disaggregated analysis shows that mobile payment exerts a stronger positive influence on SWB for people from underdeveloped areas within the economic–social–environmental framework. These findings shed light on the role of financial technology in facilitating sustainable development.
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Open AccessArticle
Can Self-Presentation on Social Media Increase Individuals’ Conspicuous Consumption Tendency? A Mixed-Method Empirical Study on WeChat in China
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Yan Lu, Taiyang Zhao, Siying Song and Wei Song
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1206-1221; https://doi.org/10.3390/jtaer19020062 - 25 May 2024
Abstract
Previous studies have mainly focused on the impact of social media self-presentation on individuals’ happiness and mental health, whereas few have attended to its potential influence on consumer behavior. A mixed-method empirical study with an online survey (N = 408) and an
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Previous studies have mainly focused on the impact of social media self-presentation on individuals’ happiness and mental health, whereas few have attended to its potential influence on consumer behavior. A mixed-method empirical study with an online survey (N = 408) and an experimental study (N = 160) investigated the effects of social media self-presentation on individuals’ conspicuous consumption in China. We found that consumers’ self-presentation behavior on social media promoted tendencies for social comparison, thereby enhancing their conspicuous consumption. However, compared with individuals using social media less often, self-presentation had a more significant impact on the social comparison tendencies of users who used social media more often. Our study explains why individuals’ self-presentation on social media increases their conspicuous consumption tendencies. The study results contribute to the existing knowledge of how consumers’ behavior on social media affects their product preferences.
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(This article belongs to the Topic Consumer Psychology and Business Applications)
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Open AccessArticle
Impact of Exclusive Choice Policies on Platform Supply Chains: When Both Same-Side and Cross-Side Network Effects Exist
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Haijun Chen and Qi Xu
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1185-1205; https://doi.org/10.3390/jtaer19020061 - 25 May 2024
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This research investigates the effects of “choose-one-over-another” monopolistic strategies on two-sided platforms, analyzing the implications of such practices on network effects and platform economics through the Hotelling model. Our key findings include the following: (1) “Choose-one-over-another” policies enhance positive network effects, increasing multi-homing
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This research investigates the effects of “choose-one-over-another” monopolistic strategies on two-sided platforms, analyzing the implications of such practices on network effects and platform economics through the Hotelling model. Our key findings include the following: (1) “Choose-one-over-another” policies enhance positive network effects, increasing multi-homing on the demand side but reducing overall platform revenue. These policies also intensify negative network effects, leading to higher prices for supply-side users and thereby undermining the welfare of demand-side users. (2) After antitrust interventions, platforms adjust pricing dynamically, increasing for one side and decreasing for the other in response to changes in same-side network effects, which in turn influences multi-homing behaviors and revenue impacts differently before and after the enforcement of such policies. (3) Without exclusive selection mandates, platform pricing strategies tend to lower prices for supply-side users, especially under competitive pressures or weaker positive network effects, potentially increasing platform revenue and overall supply chain welfare under certain conditions. This study highlights the critical role of regulatory oversight in curbing monopolistic platform behaviors to protect user rights and ensure market health, offering strategic guidance for platform management amidst competitive and operational challenges.
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Open AccessArticle
Peak-Season Price Adjustments in Shared Accommodation: The Role of Platform-Certified Signals and User-Generated Signals
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Xiangyu Wang, Yipeng Liu, Shengli Li and Haoyu Wang
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1164-1184; https://doi.org/10.3390/jtaer19020060 - 23 May 2024
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This study investigates the factors influencing landlords’ price adjustments during peak seasons on accommodation-sharing platforms, focusing on the role of platform-certified and user-generated signals. Utilizing a comprehensive dataset of 11,795 observations from a leading Chinese accommodation-sharing platform, we employ binary logit models to
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This study investigates the factors influencing landlords’ price adjustments during peak seasons on accommodation-sharing platforms, focusing on the role of platform-certified and user-generated signals. Utilizing a comprehensive dataset of 11,795 observations from a leading Chinese accommodation-sharing platform, we employ binary logit models to investigate how these signals affect landlords’ pricing strategies during “festival” and “weekend” peak times. Our analysis reveals that both platform-certified signals (such as “Preferred House” badges) and user-generated signals (such as customer satisfaction ratings) significantly increase the probability and magnitude of price adjustments during both festival and weekend peak seasons. Specifically, houses with a “Preferred” status are up to 28 times more likely to have price hikes during weekends compared to non-preferred ones. Further analysis reveals that higher levels of landlord professionalism, measured by the number of properties managed, amplifies the impact of user-generated signals on both the probability and magnitude of price adjustments. However, as the level of professionalism increases, this effect diminishes, indicating that highly professional landlords may have less flexibility to adjust prices due to already-high baseline rates. Interestingly, landlord professionalism did not significantly influence the impact of platform-certified signals on price adjustments, suggesting that the influence of such signals remains consistent across different levels of landlord professionalism. These results underscore the significant roles that both types of signals and landlord professionalism play in shaping pricing strategies, offering valuable insights for platform management and policy formulation aimed at enhancing consumer trust and competitive dynamics in the sharing economy.
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Open AccessArticle
Understanding the Dynamics of Brand Love in the Automobile Industry
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Mohamad Hashem, Carla Ruiz and Rafael Curras-Perez
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1142-1163; https://doi.org/10.3390/jtaer19020059 - 22 May 2024
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Given the increasing competition and the impact of digital media in the automobile industry, dealerships need to understand the antecedents of customer happiness and brand love. The goals of the study are to analyse the combined influence of the cognitive and affective drivers
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Given the increasing competition and the impact of digital media in the automobile industry, dealerships need to understand the antecedents of customer happiness and brand love. The goals of the study are to analyse the combined influence of the cognitive and affective drivers of brand love for high-involvement products and its effects on behavioural intentions, paying special attention to the moderating role of susceptibility to information posted on social media. Using a sample of 317 Jordanian car buyers, a structural model is tested that confirms that the sales consultant’s empathy is a strong predictor of customer happiness during a car purchase and a stronger predictor of his/her trust in the car dealership. Happiness and trust translate into greater brand love, which in turn can generate resistance towards negative information posted on social media; positive electronic word-of-mouth; and willingness to pay more. Happiness fully mediated the relationship between empathy and car brand love. The effect of the impact of the perceived empathy of salespeople on customer happiness was stronger for consumers with low susceptibility to information posted on social media. This work expands the academic knowledge of the direct mediating and moderating effects of brand love.
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Open AccessArticle
Evaluating Arabic Emotion Recognition Task Using ChatGPT Models: A Comparative Analysis between Emotional Stimuli Prompt, Fine-Tuning, and In-Context Learning
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El Habib Nfaoui and Hanane Elfaik
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1118-1141; https://doi.org/10.3390/jtaer19020058 - 14 May 2024
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Textual emotion recognition (TER) has significant commercial potential since it can be used as an excellent tool to monitor a brand/business reputation, understand customer satisfaction, and personalize recommendations. It is considered a natural language processing task that can be used to understand and
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Textual emotion recognition (TER) has significant commercial potential since it can be used as an excellent tool to monitor a brand/business reputation, understand customer satisfaction, and personalize recommendations. It is considered a natural language processing task that can be used to understand and classify emotions such as anger, happiness, and surprise being conveyed in a piece of text (product reviews, tweets, and comments). Despite the advanced development of deep learning and particularly transformer architectures, Arabic-focused models for emotion classification have not achieved satisfactory accuracy. This is mainly due to the morphological richness, agglutination, dialectal variation, and low-resource datasets of the Arabic language, as well as the unique features of user-generated text such as noisiness, shortness, and informal language. This study aims to illustrate the effectiveness of large language models on Arabic multi-label emotion classification. We evaluated GPT-3.5 Turbo and GPT-4 using three different settings: in-context learning, emotional stimuli prompt, and fine-tuning. The ultimate objective of this research paper is to determine if these LLMs, which have multilingual capabilities, could contribute to enhancing the aforementioned task and encourage its use within the context of an e-commerce environment for example. The experimental results indicated that the fine-tuned GPT-3.5 Turbo model achieved an accuracy of 62.03%, a micro-averaged F1-score of 73%, and a macro-averaged F1-score of 62%, establishing a new state-of-the-art benchmark for the task of Arabic multi-label emotion recognition.
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Open AccessReview
Eye Tracking as an Instrument in Consumer Research to Investigate Food from A Marketing Perspective: A Bibliometric and Visual Analysis
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Tonia Ruppenthal and Nils Schweers
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1095-1117; https://doi.org/10.3390/jtaer19020057 - 13 May 2024
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Eye tracking plays a crucial role in consumer research. The aim of this work is to present the statuses of studies that used eye tracking as an instrument in consumer research to investigate food from a marketing perspective. For this purpose, a bibliometric
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Eye tracking plays a crucial role in consumer research. The aim of this work is to present the statuses of studies that used eye tracking as an instrument in consumer research to investigate food from a marketing perspective. For this purpose, a bibliometric review of 118 articles from the Business Source Premier and Web of Science Core Collection database was compiled. The bibliometric review provides information on publication trends, leading authors, collaborative networks, journals, institutions, countries, articles, keywords, and themes investigated. Publications in the research field have appeared since 2011, primarily in Europe, the United States, and Uruguay. Three areas of research streams were identified: (1) how consumers became aware of and chose food, (2) nutritional information and its impact, and (3) how food information and its visual attention led to certain consumer behavior. The bibliographic review summarized past research directions and, thus, identified possibilities for future research streams.
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Open AccessArticle
From a Penny to Self-Disgust: How Cashback Rewards for Favorable Comments and Relationship Norms Affect Consumers’ Post-Purchase Behavioral Intentions
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Qingqing Guo
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1079-1094; https://doi.org/10.3390/jtaer19020056 - 10 May 2024
Abstract
Sellers of platforms offering cashback rewards for favorable comments (hereinafter CRFC) to generate positive online reviews are ubiquitous. This research examines when and how a CRFC influences consumers’ post-purchase behavioral intentions regarding repurchase and recommendation. Anchoring on the relationship norms theory and casting
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Sellers of platforms offering cashback rewards for favorable comments (hereinafter CRFC) to generate positive online reviews are ubiquitous. This research examines when and how a CRFC influences consumers’ post-purchase behavioral intentions regarding repurchase and recommendation. Anchoring on the relationship norms theory and casting light on consumers’ self-perceptions, the effect of a CRFC on consumers’ post-purchase behavioral intentions is contingent on the relationship norms. The findings of a pilot study and two experimental studies show that after a CRFC offer, communal consumers experienced lower post-purchase behavioral intentions than exchange consumers, and that consumers’ feeling of self-disgust is the mechanism underlying this interactive effect. Specifically, a CRFC is effective for exchange consumers but not for communal consumers because it triggers self-disgust in communal consumers. This paper thus reveals the mediating role of self-disgust in the interactive effect of CRFC and relationship norms on post-purchase behavioral intentions. The implications for sellers, platforms and consumers are discussed.
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(This article belongs to the Topic Consumer Psychology and Business Applications)
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The Impact of Online Reviews on Consumers’ Purchase Intentions: Examining the Social Influence of Online Reviews, Group Similarity, and Self-Construal
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Yunjeong Ahn and Jieun Lee
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1060-1078; https://doi.org/10.3390/jtaer19020055 - 10 May 2024
Abstract
Consumers often rely on evaluations such as online reviews shared by other consumers when making purchasing decisions. Online reviews have emerged as a crucial marketing tool that offers a distinct advantage over traditional methods by fostering trust among consumers. Previous studies have identified
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Consumers often rely on evaluations such as online reviews shared by other consumers when making purchasing decisions. Online reviews have emerged as a crucial marketing tool that offers a distinct advantage over traditional methods by fostering trust among consumers. Previous studies have identified group similarity between consumers and reviewers as a key variable with a potential impact on consumer responses and purchase intention. However, the results remain inconclusive. In this study, we identify self-construal and group similarity as key factors in the influence of online review ratings on consumers’ purchase intentions. We further investigate the role of consumers’ self-construal in shaping consumers’ perceptions of online reviews in terms of belongingness and diagnosticity. To test the hypothesis, we conducted a 2 (online review rating) × 2 (group similarity) × 2 (self-construal) ANOVA on 276 subjects collected through Amazon Mechanical Turk (MTurk), and contrast analysis and PROCESS macro model 12 were used for the interaction effect analysis and moderated mediation analysis. Our findings reveal that consumers with an interdependent self-construal are sensitive to both review ratings and group similarity with regards to their purchase intentions. They demonstrate a positive purchase intention when both group similarity and online review ratings are high. However, their purchase intention is not influenced by review ratings when group similarity is low. Conversely, consumers with an independent self-construal exhibit a more positive purchase intention when the online review rating is high, irrespective of group similarity. Additionally, our study highlights the mediating roles of perceived diagnosticity and belongingness in the relationship between online review ratings, group similarity, self-construal, and purchase intentions. Results show significant indirect effects for perceived diagnosticity and belongingness, meaning that the impact of online review ratings on purchase intention is mediated by these two variables. The outcomes of our research offer theoretical and practical implications concerning online reviews and suggest new avenues for future research in the area of online consumer behavior.
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(This article belongs to the Section Digital Marketing and the Connected Consumer)
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Open AccessArticle
Research on B2C Cross-Border Electronic Commerce Return Logistics Model Selection Based on Estimated Return Rate
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Yi Li and Zhiyang Li
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1034-1059; https://doi.org/10.3390/jtaer19020054 - 10 May 2024
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This paper investigates the estimated return rate and optimal order quantity under three cross-border e-commerce return logistics modes: direct mail (from predecessor), in situ destruction (new), and insurance (new). The estimated return rate under each model was analyzed and it was found that
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This paper investigates the estimated return rate and optimal order quantity under three cross-border e-commerce return logistics modes: direct mail (from predecessor), in situ destruction (new), and insurance (new). The estimated return rate under each model was analyzed and it was found that different modes have different thresholds in delivery lead time (the time retailers need to deliver goods to customers), and within which the estimated return rate increases as the delivery lead time increases. And a size comparison of the estimated return rates for the three models was conducted. A profit model was constructed based on the estimated return rate model, the optimal order quantity was calculated, and the effects of different factors (tax, postage, and delivery lead time etc.) on it were analyzed. For the insurance model, the effect of bearing the insurance ratio between retailers and consumers on the optimal order quantity was examined. The goal of this paper was to construct a model of the estimated return rate for the two new modes and to compare the estimated return rate of the three modes, which provides a reference for retailers to choose among the diversified return logistics modes and then make the best ordering strategy according to the influence of different factors on the optimal order quantity.
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Open AccessArticle
Unlock Happy Interactions: Voice Assistants Enable Autonomy and Timeliness
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Linlin Mo, Liangbo Zhang, Xiaohui Sun and Zhimin Zhou
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1013-1033; https://doi.org/10.3390/jtaer19020053 - 30 Apr 2024
Abstract
This study examines the effects of three interactive voice assistant (VA) features (responsiveness, ubiquitous connectivity, and personalization) on consumer happiness. An online survey was administered to 316 VA consumers, and the data were analyzed using structural equation modeling with SmartPLS 4 software. The
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This study examines the effects of three interactive voice assistant (VA) features (responsiveness, ubiquitous connectivity, and personalization) on consumer happiness. An online survey was administered to 316 VA consumers, and the data were analyzed using structural equation modeling with SmartPLS 4 software. The results indicate that VA responsiveness, ubiquitous connectivity, and personalization have significant effects on consumer happiness. This study also provides evidence that consumer happiness is influenced by VA features through the mediating roles of autonomy and timeliness. Notably, perceived privacy risk has a dual effect, negatively affecting happiness but positively moderating the relationship between autonomy and happiness, suggesting a complex interplay between benefits and concerns in user interactions with VAs. This study highlights the need for VA businesses to consider both the enhancing and mitigating factors of technology for user experiences. Furthermore, our findings have significant implications for VA businesses and executives, suggesting that improved interactions through these VA features can better serve consumers and enhance their experiences.
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(This article belongs to the Topic Consumer Psychology and Business Applications)
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Open AccessArticle
Motivations toward Electronic Word-of-Mouth Sending Behavior Regarding Restaurant Experiences in the Millennial Generation
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Giovanny Haro-Sosa, Beatriz Moliner-Velázquez, Irene Gil-Saura and Maria Fuentes-Blasco
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 993-1012; https://doi.org/10.3390/jtaer19020052 - 27 Apr 2024
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A growing body of the literature on the study of online reviews presents interesting research opportunities, especially in services highly frequented by young consumer segments, such as restaurants. In this context, the present study examines the restaurant electronic word-of-mouth (EWOM) behavior of Millennial
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A growing body of the literature on the study of online reviews presents interesting research opportunities, especially in services highly frequented by young consumer segments, such as restaurants. In this context, the present study examines the restaurant electronic word-of-mouth (EWOM) behavior of Millennial consumers by addressing both review queries before the purchase decision and writing and sending after the purchase. Based on the theory of reasoned action, a double objective is pursued. On the one hand, the influence of motivations related to extroversion, social benefits, and altruism on EWOM sending behavior is analyzed. On the other hand, the moderating role of EWOM consultation in these relationships is studied. Using a sample of 341 Millennials from Ecuador, a structural model is constructed that confirms the contribution of two types of motivations in sending EWOM: those of extroversion and those of social benefits. The results also reveal the moderating role of EWOM consultation alone in the effects of extraversion and altruism motivations. Managerial implications for restaurants derived from this study include improvements in the design of digital communication strategies tailored to Millennial customers based on their motivations.
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Open AccessArticle
Interplay between Platform Providers and Complementors via Affordance, Autonomy, and Super-Modularity: The Empirical Investigation of the Korean Digital Industry
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Dongnyok Shim
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 975-992; https://doi.org/10.3390/jtaer19020051 - 26 Apr 2024
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This study examines the dynamic interplay between platform providers and complementors in the context of digital ecosystems, focusing on the complementary factors of affordance, autonomy, and super-modularity. Using national survey data from the Korean digital industry, the study applied multivariate ordered probit and
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This study examines the dynamic interplay between platform providers and complementors in the context of digital ecosystems, focusing on the complementary factors of affordance, autonomy, and super-modularity. Using national survey data from the Korean digital industry, the study applied multivariate ordered probit and k-mode clustering models to analyze what determines these factors and how these factors are interrelated from the perspective of platform providers and complementors, respectively. The results indicate that platform providers with open APIs promote affordance, but providing an SDK inhibits affordance. In terms of complementors, choosing a platform providing APIs increases super-modularity. And affordance increases when using the platform for logistics and new product development. In addition, we found that affordance and autonomy have a trade-off relationship from the perspective of both platform providers and complementors. Finally, we classified platforms and complementors into subgroups with respect to affordance, autonomy, and super-modularity using cluster analysis and found that the size of a complementor’s firm, such as revenue and number of employees, influences which platform it chooses. Conversely, the size of a platform provider also influences how much autonomy and collaboration it offers. This study contributes to the understanding of digital platform ecosystems and provides insights for practitioners on how to leverage platform dynamics to enhance competitive advantage.
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Open AccessArticle
Does Experience Matter? Unraveling the Drivers of Expert and Non-Expert Mobile Consumers
by
Simona Vinerean, Dan-Cristian Dabija and Gandolfo Dominici
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 958-974; https://doi.org/10.3390/jtaer19020050 - 22 Apr 2024
Abstract
The surge in mobile shopping faces a challenge as not all potential consumers are comfortable with this mode. Retailers need a deeper understanding of factors influencing user experience to enhance marketing strategies. Despite extensive research, a gap remains in comprehending this aspect. Using
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The surge in mobile shopping faces a challenge as not all potential consumers are comfortable with this mode. Retailers need a deeper understanding of factors influencing user experience to enhance marketing strategies. Despite extensive research, a gap remains in comprehending this aspect. Using a statistical PLS-SEM-ANN approach, this research aims to explore the psychological dimensions of expert and non-expert mobile shoppers for establishing better targeted marketing strategies in m-commerce settings. Analyzing experience levels in mobile commerce (m-commerce), key drivers like enjoyment, usefulness, subjective norms, and trust were scrutinized as interaction settings for consumers using mobile technologies. The findings reveal that, for less experienced m-shoppers, trust is the most significant driver of attitude and satisfaction, while, for experienced users, trust and usefulness are the primary antecedents. This research provides novel insights, aiding mobile marketers in refining targeting strategies based on consumer experience levels, emphasizing the importance of usefulness and trustworthiness for a seamless m-shopping experience.
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(This article belongs to the Collection Enhancing Consumer Experience through Mobile Commerce: Challenges and Opportunities)
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Open AccessArticle
Tailoring Garment Fit for Personalized Body Image Enhancement: Insights from Digital Fitting Research
by
Jiayin Li, Xing Su, Jiahao Liang, P. Y. Mok and Jintu Fan
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 942-957; https://doi.org/10.3390/jtaer19020049 - 22 Apr 2024
Abstract
In the context of the Fashion Apparel Industry 4.0, a transformative evolution is directed towards the Online Apparel Mass Customization (OAMC) strategy, which provides efficient and personalized apparel product solutions to consumers. A critical challenge within this customization process is the determination of
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In the context of the Fashion Apparel Industry 4.0, a transformative evolution is directed towards the Online Apparel Mass Customization (OAMC) strategy, which provides efficient and personalized apparel product solutions to consumers. A critical challenge within this customization process is the determination of sizes. While existing research addresses comfort evaluation in relation to wearer and garment fit, little attention has been given to how garment fit influences the wearer’s body image, which is also an important purchase consideration. This study investigates the impact of garment fit on the wearer’s body scale perception using quantitative research design. A digital dataset of avatars, clothed in varying sizes of T-shirts, were created for the body scale perception experiment, and an Artificial Neural Network (ANN) model was developed to predict the effect of T-shirt fit on body image. With only a small number of garments and body measurements as inputs, the ANN model can accurately predict the body scales of the clothed persons. It was found that the effect of apparel fit on body image varies depending on the wearer’s gender, body size, and shape. This model can be applied to enhance the online garment shopping experience with respect to personalized body image enhancement.
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(This article belongs to the Collection Exploring the Future of Creative Economy: Transforming Creative Industries through Innovation, Technology and Enhanced Consumer Engagement)
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