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 - Q1 (Business) / CiteScore - Q1 (General Business, Management and Accounting )
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 32 days after submission; acceptance to publication is undertaken in 4.5 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually in the journal.
Impact Factor:
5.1 (2023);
5-Year Impact Factor:
5.1 (2023)
Latest Articles
The Evolution of Price Discrimination in E-Commerce Platform Trading: A Perspective of Platform Corporate Social Responsibility
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1907-1921; https://doi.org/10.3390/jtaer19030094 - 26 Jul 2024
Abstract
The widespread use of data in e-commerce has facilitated the implementation of different pricing strategies for platforms and merchants. However, the excessive use of algorithms for differential pricing has sparked discussions about fairness and price discrimination, disrupting the platform trading system. To address
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The widespread use of data in e-commerce has facilitated the implementation of different pricing strategies for platforms and merchants. However, the excessive use of algorithms for differential pricing has sparked discussions about fairness and price discrimination, disrupting the platform trading system. To address this challenge, we adopt an evolutionary game approach to analyze the evolutionary strategies of all parties from the perspective of platform CSR. It is based on a special type of e-commerce platform trading in which major merchants have data analytics capabilities. We construct an evolutionary game model considering reputation and punishment, explore the impact of different situations and factors on the system’s evolutionary stability strategy, and conduct its verification via simulation experiments. The results show that long-term reputation is the internal driving force for platforms to fulfill responsibilities. The joint punishment of collusion is the external binding force. Consumer complaints are key to restricting merchants’ integrity operation. Moreover, penalties imposed by e-commerce platforms can help eradicate price discrimination. This study provides a new perspective to solve price discrimination in the digital era. Measures based on reputation and punishment can guide platforms to fulfill other social responsibilities.
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(This article belongs to the Topic Consumer Psychology and Business Applications)
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Open AccessArticle
The Role of Service Quality Attributes and Perceived Value in US Consumers’ Impulsive Buying Intentions for Fresh Food E-Commerce
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Jee-Won Kang and Young Namkung
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1893-1906; https://doi.org/10.3390/jtaer19030093 - 23 Jul 2024
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Given the widespread adoption of fresh food e-commerce, this study aimed to explore the service quality attributes influencing utilitarian value, hedonic value, and impulsive buying behavior. A survey was conducted with 263 participants who had experience in purchasing fresh food online. Their responses
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Given the widespread adoption of fresh food e-commerce, this study aimed to explore the service quality attributes influencing utilitarian value, hedonic value, and impulsive buying behavior. A survey was conducted with 263 participants who had experience in purchasing fresh food online. Their responses were analyzed to test hypotheses using structural equation modeling. The findings reveal significant influences of information quality, ease of use, and problem resolution on utilitarian value. Additionally, ease of use, problem resolution, and trendiness were found to impact hedonic value. Problem resolution was a quality factor that affected both practical value and hedonic value, and its influence was found to be greater than that of other service quality factors. Hedonic value was also found to significantly affect impulsive buying behavior; however, utilitarian value did not exhibit a significant impact on impulsive buying behavior. The results provide useful theoretical and managerial implications in the field of fresh food e-commerce business.
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Open AccessArticle
Optimizing Reserve Decisions in Relief Supply Chains with a Blockchain-Supported Second-Hand E-Commerce Platform
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Yingjie Ju, Yue Wang, Jianliang Yang, Yu Feng and Yuheng Ren
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1869-1892; https://doi.org/10.3390/jtaer19030092 - 18 Jul 2024
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This paper develops a novel government reserve strategy, employing a blockchain-supported second-hand E-commerce platform, specifically designed to mitigate the depreciation and expiration of disaster relief supplies. Utilizing the newsvendor model and convex optimization techniques, this study evaluates the efficacy of a rotational strategy
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This paper develops a novel government reserve strategy, employing a blockchain-supported second-hand E-commerce platform, specifically designed to mitigate the depreciation and expiration of disaster relief supplies. Utilizing the newsvendor model and convex optimization techniques, this study evaluates the efficacy of a rotational strategy for optimal pre-positioning of supplies, considering the dynamic conditions of supply chain performance. Additionally, the paper demonstrates how blockchain technology significantly enhances the traceability of supplies, which is crucial for effective supply management. Empirical data analysis reveals that exceeding a critical price threshold on the platform not only augments the government’s optimal reserve levels but also substantially decreases operational costs. In scenarios where the supply chain is well coordinated, optimal reserve quantities are affected by variables such as the likelihood of disaster events, the success rate of sales, and a supply traceability index. This research extends the application of blockchain and E-commerce technologies within disaster management supply chains and offers new insights and practical approaches for improving E-commerce practices in this context.
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Open AccessArticle
Simultaneous or Sequential? Supplier Product Launch Strategy through E-Commerce Channels with Different Models
by
Zhiwen Li, Baojiao Wang and Yeting Wu
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1848-1868; https://doi.org/10.3390/jtaer19030091 - 18 Jul 2024
Abstract
As the e-commerce landscape diversifies, suppliers are faced with the critical decision of how to effectively launch their products through e-commerce channels with varying business models. This study aims to explore the strategic considerations for a supplier launching products through two distinct e-commerce
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As the e-commerce landscape diversifies, suppliers are faced with the critical decision of how to effectively launch their products through e-commerce channels with varying business models. This study aims to explore the strategic considerations for a supplier launching products through two distinct e-commerce channels: one based on a direct sale model and the other on a reselling model. It builds a theoretical model to examine the supplier’s decision-making across three strategic options: a simultaneous launch through both channels, a sequential launch starting with the direct sale model followed by the reselling model, and vice versa. The equilibria of those options are derived through game analysis and further compared. The results reveal that for suppliers under a non-alliance pricing contract, a simultaneous product launch across both channels is the most advantageous approach. Conversely, in scenarios where an alliance pricing contract is in place, the optimal strategy shifts towards a sequential launch. The decision of which channel to ally with—whether the direct sale or the reselling model—hinges critically on the difference in service efficiency and the intensity of competition between the channels. This nuanced analysis highlights the importance of strategic flexibility and alignment with channel dynamics in maximizing product launch success in the evolving e-commerce environment.
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(This article belongs to the Collection Emerging Topics in Omni-Channel Operations)
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Open AccessArticle
Dynamic Mining of Consumer Demand via Online Hotel Reviews: A Hybrid Method
by
Weiping Yu, Fasheng Cui, Ping Wang and Xin Liao
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1831-1847; https://doi.org/10.3390/jtaer19030090 - 18 Jul 2024
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This study aims to dynamically mine the demands of hotel consumers. A total of 378,270 online reviews in the cities of Beijing, Chengdu, and Guangzhou in China were crawled using Python. Natural language processing (e.g., opinion mining and the BERT model) and an
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This study aims to dynamically mine the demands of hotel consumers. A total of 378,270 online reviews in the cities of Beijing, Chengdu, and Guangzhou in China were crawled using Python. Natural language processing (e.g., opinion mining and the BERT model) and an improved Kano model (containing One-dimensional, Attractive, Indifferent, and Must-be) were utilised to analyse online hotel reviews. The results indicate that the hotel attributes that consumers care about (e.g., Clean, Breakfast, and Front Desk) are dynamically fluctuating, and the attention and satisfaction of corresponding attributes will also change. This study classified consumer demand into eight types across cities and found that it changes over time. In addition, we also found that hotel attributes, satisfaction and attention, and consumer demands vary among different cities. Existing studies of capturing consumer demand are usually time-consuming and static, and the results are subjective. This study compared and analysed the consumer demands of hotels in different cities via a dynamic perspective, and used hybrid methods to improve the granularity of the analysis, expanding the general applicability of the Kano model. Hotel managers can refer to the results of this article to allocate resources for improvement and create competitive hotel services.
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Open AccessArticle
The Impact of Travel Scenarios and Perceptions on Choice Behavior towards Multi-Forms of Ride-Hailing Services: Case of Nanjing, China
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Ke Lu and Yunlin Wei
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1812-1830; https://doi.org/10.3390/jtaer19030089 - 16 Jul 2024
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The travel behavior of urban residents has gradually changed in response to the widespread adoption of ride-hailing services. This paper explores the travel mode choices made by individuals utilizing multiple forms of ride-hailing services. Eight scenarios were established, which considered combinations of activity
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The travel behavior of urban residents has gradually changed in response to the widespread adoption of ride-hailing services. This paper explores the travel mode choices made by individuals utilizing multiple forms of ride-hailing services. Eight scenarios were established, which considered combinations of activity types (commute or recreation), travel periods (peak or off-peak), and price levels (discounted or normal rates for ride-hailing). Moreover, socio-psychological variables such as perceived value, behavioral intention, and subjective norm were integrated into the analysis. The findings reveal that consumers of ride-hailing services generally exhibit characteristics such as being younger in age, having higher income, lack of car ownership, and having greater experience in using ride-hailing services. Furthermore, the inclusion of socio-psychological variables significantly improved the model’s fitness. Travelers exhibit a preference for ride-hailing services in scenarios involving recreational activities, normal travel periods, and discounted ride-hailing prices. In conclusion, this study sheds light on the evolving travel behavior of urban residents in light of the widespread availability of ride-hailing services. The incorporation of socio-psychological factors is essential in comprehending and predicting travel mode choices. The insights derived from this research contribute to a nuanced understanding of the factors influencing the adoption of and preference for ride-hailing services among urban commuters.
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Open AccessArticle
Comparative Study on Barriers of Supply Chain Management MOOCs in China: Online Review Analysis with a Novel TOPSIS-CoCoSo Approach
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Shupeng Huang, Hong Cheng and Meiling Luo
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1793-1811; https://doi.org/10.3390/jtaer19030088 - 16 Jul 2024
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To enhance the effectiveness of supply chain talent education, higher education institutions and other organisations have started to develop and use Massive Open Online Courses (MOOCs) in their training programs. However, the problem is that the design and delivery of supply chain management
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To enhance the effectiveness of supply chain talent education, higher education institutions and other organisations have started to develop and use Massive Open Online Courses (MOOCs) in their training programs. However, the problem is that the design and delivery of supply chain management MOOCs can be inappropriately presented and, thus, ineffective, especially for educational teams with fewer teaching experiences of MOOCs. This eventually makes it hard for the students’ learning outcomes to meet the industrial requirements of supply chain experts. Motivated by such a problem, this paper aims to improve the design and delivery of supply chain management MOOCs to enhance student learning outcomes. To achieve this goal, the research method adopted in this paper is to analyse online reviews in a widely-used Chinese MOOC platform with a novel TOPSIS-CoCoSo approach, aiming to identify the barriers to supply chain management MOOCs and their potential solutions. The results of this study show that 16 barriers to MOOCs are identified from the online reviews and then ranked based on their severity of reducing learning outcomes. The perceptions of the severity of the barriers to students and lecturers are compared, and the solutions to the barriers are then discussed. In addition, our comparison indicates that although students and lecturers have similar perceptions of severity for the majority of the barriers, they have significant disagreements on certain barriers. The significance of this study is that it can inform lecturers in supply chain management or relevant disciplines to better design and deliver their MOOC content, as well as contribute to the existing literature by providing new methodological tools for educational analysis. Also, this study highlights the necessity of comparative study in the MOOC online review analysis.
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Open AccessArticle
Text Mining Based Approach for Customer Sentiment and Product Competitiveness Using Composite Online Review Data
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Zhanming Wen, Yanjun Chen, Hongwei Liu and Zhouyang Liang
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1776-1792; https://doi.org/10.3390/jtaer19030087 - 15 Jul 2024
Abstract
We aimed to provide a realistic portrayal of customer sentiment and product competitiveness, as well as to inspire businesses to optimise their products and enhance their services. This paper uses 119,190 pairs of real composite review data as a corpus to examine customer
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We aimed to provide a realistic portrayal of customer sentiment and product competitiveness, as well as to inspire businesses to optimise their products and enhance their services. This paper uses 119,190 pairs of real composite review data as a corpus to examine customer sentiment analysis and product competitiveness. The research is conducted by combining TF-IDF text mining with a time-phase division through the k-means clustering method. The study identified ‘quality’, ‘taste’, ‘appearance packaging’, ‘logistics’, ‘prices’, ‘service’, ‘evaluations’, and ‘customer loyalty’ as the commodity dimensions that customers are most concerned about. These dimensions should therefore serve as the primary entry point for improving the commodity and understanding customers. A review of customer feedback reveals that the composite reviews can be divided into three time stages. Furthermore, the sentiment expressed by customers can become increasingly negative over time. The product competitiveness based on the composite review can be characterised by four regional quadrants, such as ‘Advantage Area’, ‘Struggle Area’, ‘Opportunity Area’, and ‘Waiting Area’, and merchants can target these areas to improve product competitiveness according to the dimensional distribution. In the future, it will also be possible to take customer demographics into account in order to gain a deeper understanding of the customer base.
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(This article belongs to the Section e-Commerce Analytics)
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Open AccessArticle
Quantitative Stock Selection Model Using Graph Learning and a Spatial–Temporal Encoder
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Tianyi Cao, Xinrui Wan, Huanhuan Wang, Xin Yu and Libo Xu
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1756-1775; https://doi.org/10.3390/jtaer19030086 - 15 Jul 2024
Abstract
In the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models. Yet, prevailing models frequently neglect the fluid dynamics of asset relationships and
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In the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models. Yet, prevailing models frequently neglect the fluid dynamics of asset relationships and market shifts, a gap that undermines their predictive and risk management efficacy. This oversight renders them vulnerable to market volatility, adversely affecting investment decision quality and return consistency. Addressing this critical gap, our study proposes the Graph Learning Spatial–Temporal Encoder Network (GL-STN), a pioneering model that seamlessly integrates graph theory and spatial–temporal encoding to navigate the intricacies and variabilities of financial markets. By harnessing the inherent structural knowledge of stock markets, the GL-STN model adeptly captures the nonlinear interactions and temporal shifts among assets. Our innovative approach amalgamates graph convolutional layers, attention mechanisms, and long short-term memory (LSTM) networks, offering a comprehensive analysis of spatial–temporal data features. This integration not only deciphers complex stock market interdependencies but also accentuates crucial market insights, enabling the model to forecast market trends with heightened precision. Rigorous evaluations across diverse market boards—Main Board, SME Board, STAR Market, and ChiNext—underscore the GL-STN model’s exceptional ability to withstand market turbulence and enhance profitability, affirming its substantial utility in quantitative stock selection.
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(This article belongs to the Topic Artificial Intelligence Applications in Financial Technology)
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Open AccessArticle
Alone or Mixed? The Effect of Digital Human Narrative Scenarios on Chinese Consumer Eco-Product Purchase Intention
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Chaohua Huang, Tong Song and Haijun Wang
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1734-1755; https://doi.org/10.3390/jtaer19030085 - 9 Jul 2024
Abstract
Digital human narrative transportation has proven to be an effective green brand marketing strategy. However, there is still a lack of in-depth research on the relationship between the role of different digital human narrative scenarios in consumer perceptions and behaviors. This research examined
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Digital human narrative transportation has proven to be an effective green brand marketing strategy. However, there is still a lack of in-depth research on the relationship between the role of different digital human narrative scenarios in consumer perceptions and behaviors. This research examined the impact of digital human narrative scenarios on eco-product purchase intention through four studies. Study 1 found that anime-like (vs. human-like) digital human narratives led to more positive emotional arousal and higher eco-product purchase intention through the use of encephalography (EEG) experiments. Studies 2–4 examined the effect of digital human narrative scenarios on eco-product purchase intentions and explored the mediating role of narrative presence and the moderating role of narrative type. The results showed that mixed (vs. single) narratives lead to more positive consumer purchase intentions. In addition, sharing-oriented (vs. persuasion-oriented) narratives also led to a more positive perception of narrative presence. These findings provide insights for marketers using digital human narratives to promote eco-product consumption.
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(This article belongs to the Topic Consumer Psychology and Business Applications)
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Open AccessArticle
Buyers’ Negative Ratings and Textual Comments on eBay: Reasons for Posting Ratings and Factors in Denouncing Sellers
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Xubo Zhang, Yanbin Tu, Mark H. Haney and Huawei Cheng
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1717-1733; https://doi.org/10.3390/jtaer19030084 - 4 Jul 2024
Abstract
In this study, we use a dataset collected from eBay to analyze buyers’ negative feedback ratings and associated textual comments. By using text mining and sentiment analysis, we identify seven key reasons why buyers post negative ratings: communication problems, shipping issues, product defects,
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In this study, we use a dataset collected from eBay to analyze buyers’ negative feedback ratings and associated textual comments. By using text mining and sentiment analysis, we identify seven key reasons why buyers post negative ratings: communication problems, shipping issues, product defects, payment refund problems, customer service issues, fraud, and product packaging. These seven reasons can be classified into three categories: (1) sellers’ malicious fraudulence toward buyers, (2) factors likely under the control of sellers, and (3) factors not likely under the control of sellers. Drawing on these categories, we discuss how sellers can effectively reduce the likelihood that buyers post negative ratings. The most important things sellers can do to avoid negative ratings are to improve communications with buyers and to handle product shipping issues properly. In addition to posting the reasons for their negative ratings of sellers, the textual comments associated with negative feedback ratings may also include direct denouncements of sellers, such as buyers explicitly claiming a seller is a liar and warning other buyers to be cautious of the seller. We collectively call these actions buyers’ denouncements against sellers. These denouncements have significant negative impacts on sellers’ reputations. In this study, we use correlation analysis and logistic regression to investigate the factors that motivate buyers to denounce sellers. We find that, of the three categories of reasons why buyers post negative ratings, sellers’ malicious fraudulence toward buyers and factors likely under the control of sellers are more likely to lead to buyers’ denouncements of sellers, while factors not likely under the control of sellers are not likely to lead to buyers’ denouncements of sellers. In addition, buyers’ strong negative sentiment is also more likely to lead to their denouncement of sellers. Managerial implications of these findings are discussed.
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(This article belongs to the Section Digital Marketing and the Connected Consumer)
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Open AccessArticle
Determinants of Digitalization in Unorganized Localized Neighborhood Retail Outlets in India
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Biplab Bhattacharjee, Shubham Kumar, Piyush Verma and Moinak Maiti
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1699-1716; https://doi.org/10.3390/jtaer19030083 - 1 Jul 2024
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The increase in digital disruptions and changing preferences of different stakeholders has led to digital adoption in all hierarchies of business ecosystem. This study focused on the identification of the determinants of digitalization in unorganized small, localized retail outlets (Kirana stores) of an
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The increase in digital disruptions and changing preferences of different stakeholders has led to digital adoption in all hierarchies of business ecosystem. This study focused on the identification of the determinants of digitalization in unorganized small, localized retail outlets (Kirana stores) of an emerging economy. A theoretical model was constructed with certain modifications based on technology adoption models such as Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to study the impact on business performance in general and as an effect of pandemic. A survey of 285 Unorganized Localized Retail Outlets Stores from different regions of India was used to validate this theoretical model, and structural equation modeling was then further employed. The findings underscore that cost, compatibility, perceived ease of use, and perceived usefulness significantly affect the intention to digitalize. By addressing the post-pandemic impact of digitalization within an unorganized sector in an emerging economy, this study adds to the scant literature that exists in this context.
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Open AccessArticle
“Will You Buy It If They Recommend It?” Exploring the Antecedents of Intention to Purchase Podcaster-Endorsed Items
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Yi-Ting Huang and An-Di Gong
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1682-1698; https://doi.org/10.3390/jtaer19030082 - 1 Jul 2024
Abstract
The diverse content of and ease of listening to podcasts have made podcasts popular, particularly during the COVID-19 pandemic. Advertisers have begun to recognize their marketing potential and are now hiring podcasters to recommend their products. This study sought to determine the factors
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The diverse content of and ease of listening to podcasts have made podcasts popular, particularly during the COVID-19 pandemic. Advertisers have begun to recognize their marketing potential and are now hiring podcasters to recommend their products. This study sought to determine the factors influencing podcast commitment, parasocial interaction (PSI), and the intention to purchase podcaster-endorsed items. It was conducted in Taiwan with a sample size of 578 participants and an online questionnaire. Structural equation modeling and mediation analysis were applied to the collected data from the perspective of uses and gratifications theory. We found that podcast commitment is positively related to edutainment, storytelling transportation, and social engagement. Social engagement is positively related to PSI, while storytelling transportation has a negative relationship with PSI. Additionally, there is a strong positive correlation between podcast commitment and PSI and both factors positively influence the intention to purchase podcaster-endorsed items. PSI also significantly moderates the positive relationship between podcast commitment and the intent to buy podcaster-endorsed items.
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(This article belongs to the Section Digital Marketing and the Connected Consumer)
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Open AccessArticle
Analyzing the Dynamics of Customer Behavior: A New Perspective on Personalized Marketing through Counterfactual Analysis
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Mona Ebadi Jalal and Adel Elmaghraby
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1660-1681; https://doi.org/10.3390/jtaer19030081 - 27 Jun 2024
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The existing body of research on dynamic customer segmentation has primarily focused on segment-level customer purchasing behavior (CPB) analysis to tailor marketing strategies for distinct customer groups. However, these approaches often lack the granularity required for personalized marketing at the individual level. Moreover,
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The existing body of research on dynamic customer segmentation has primarily focused on segment-level customer purchasing behavior (CPB) analysis to tailor marketing strategies for distinct customer groups. However, these approaches often lack the granularity required for personalized marketing at the individual level. Moreover, the analysis of customer transitions between different groups has largely been overlooked. This study addresses these gaps by developing an efficient framework that enables businesses to forecast customer behavior, assess the impact of various strategies on each customer separately, and analyze customer transition between segments. This can facilitate providing personalized marketing strategies, fostering a gradual transition toward a desired customer status, and enhancing the overall marketing precision. In this study, we employ time series feature vectors encompassing recency, frequency, monetary value, and lifespan, applying the K-means algorithm with a range of distance metrics for customer segmentation along with classification algorithms to predict customer behavior. Leveraging counterfactual analysis, we establish a solution for analyzing customer transitions between groups and evaluating personalized marketing strategies. Our findings underscore the superior performance of the Euclidean distance metric, closely followed by the Manhattan distance, in distinguishing the patterns in time series customer behavior, with logistic regression excelling in predicting customer status. This study enables decision-makers to forecast the impact of diverse marketing strategies on customer behavior which facilitates customer retention and engagement through well-informed decisions.
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Open AccessArticle
Is Smarter Better? A Moral Judgment Perspective on Consumer Attitudes about Different Types of AI Services
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Qingji Fan, Yan Dai and Xue Wen
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 1637-1659; https://doi.org/10.3390/jtaer19030080 - 27 Jun 2024
Abstract
AI is considered a key driver of industrial transformation and a strategic technology that will shape future development. With AI services continuing to permeate various sectors, concerns have emerged about the ethics of AI. This study investigates the effects of different types of
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AI is considered a key driver of industrial transformation and a strategic technology that will shape future development. With AI services continuing to permeate various sectors, concerns have emerged about the ethics of AI. This study investigates the effects of different types of AI services (mechanical, thinking, and affective AI services) on consumers’ attitudes through offline and online AI service experiments. We also construct a model to explore the mediating roles of identity threat and perceived control. The findings reveal that mechanical AI services negatively affect consumers’ attitudes while thinking and affective AI services have a positive effect. Additionally, we explore how consumers’ attitudes vary across different service scenarios and ethical judgments (utilitarianism and deontology). Our findings could offer practical guidance for enterprises providing AI services.
Full article
(This article belongs to the Topic Consumer Psychology and Business Applications)
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Open AccessArticle
Blockchain and Supply-Chain Financing: An Evolutionary Game Approach with Guarantee Considerations
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Jizhou Zhan, Gewei Zhang, Heap-Yih Chong and Xiangfeng Chen
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1616-1636; https://doi.org/10.3390/jtaer19020079 - 19 Jun 2024
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Blockchain technology enables innovative financing models in supply-chain finance. This research constructs a tripartite evolutionary game model that includes core enterprises as employers, small- and medium-sized enterprises (SMEs) as contractors, and banks as financial institutions, where they have been simulated for their impact
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Blockchain technology enables innovative financing models in supply-chain finance. This research constructs a tripartite evolutionary game model that includes core enterprises as employers, small- and medium-sized enterprises (SMEs) as contractors, and banks as financial institutions, where they have been simulated for their impact on blockchain technology, especially on the strategic choices of supply-chain financing behavior and the system’s evolutionary path under core enterprises’ guarantee mechanism. The findings show the application of blockchain technology can effectively reduce the regulatory and review costs for financial institutions, thereby enhancing the efficiency of supply-chain financing. Particularly, blockchain technology provides a more reliable credit endorsement platform for SMEs in reducing their tendency to default. The guarantee mechanism of core enterprises is more effective with the support of blockchain technology, which helps to build more solid supply-chain financial cooperation relationships. The research contributes to the theoretical research on the integration of blockchain technology into supply-chain finance, especially for improving the operational efficiency of financial services. It also highlights the need for blockchain-backed guarantees from core enterprises in optimizing supply-chain financial services.
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Open AccessArticle
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
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
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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.
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(This article belongs to the Collection Customer Relationships in Electronic Commerce)
Open AccessArticle
Impact of AI-Oriented Live-Streaming E-Commerce Service Failures on Consumer Disengagement—Empirical Evidence from China
by
Yuhong Peng, Yedi Wang, Jingpeng Li and Qiang Yang
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1580-1598; https://doi.org/10.3390/jtaer19020077 - 17 Jun 2024
Abstract
Despite the popularity of AI-oriented e-commerce live-streaming, the service failures that can result from real-time interaction and instant transactions have not been taken seriously. This study aims to assess the failure of AI-oriented live-streaming e-commerce services and help retailers identify various risks. Based
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Despite the popularity of AI-oriented e-commerce live-streaming, the service failures that can result from real-time interaction and instant transactions have not been taken seriously. This study aims to assess the failure of AI-oriented live-streaming e-commerce services and help retailers identify various risks. Based on expectancy disconfirmation theory and a stressor–strain–outcome framework, this study identified a comprehensive framework including information, functional, system, interaction, and aesthetic failures. The structural equation modeling (SEM) method is used to further examine its effect on consumers’ discontinuance behavior. Further research reveals the mediating role of consumer disappointment and emotional exhaustion, as well as the moderating role of the live-streaming platform type. These results shed light on the negative influence of AI-oriented live-streaming e-commerce service failures and contribute to the literature on live-streaming commerce, service failure, and virtual streamers.
Full article
(This article belongs to the Topic Consumer Psychology and Business Applications)
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Open AccessReview
E-Commerce in Brazil: An In-Depth Analysis of Digital Growth and Strategic Approaches for Online Retail
by
Claudimar Pereira da Veiga, Cássia Rita Pereira da Veiga, Júlia de Souza Silva Michel, Leandro Ferreira Di Iorio and Zhaohui Su
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1559-1579; https://doi.org/10.3390/jtaer19020076 - 15 Jun 2024
Abstract
This article delves into Brazil’s rapidly expanding e-commerce sector, emphasizing its significant growth and evolving dynamics. Employing a meta-narrative review and a convergence-coding matrix, this research systematically analyzes and integrates findings from the existing literature to reveal critical industry patterns. The analysis identifies
[...] Read more.
This article delves into Brazil’s rapidly expanding e-commerce sector, emphasizing its significant growth and evolving dynamics. Employing a meta-narrative review and a convergence-coding matrix, this research systematically analyzes and integrates findings from the existing literature to reveal critical industry patterns. The analysis identifies four pivotal clusters: consumer behavior, e-commerce structure, product distribution, and environmental sustainability. These elements collectively offer a comprehensive view of Brazil’s present and future e-commerce directions. This study underscores the imperative for strategies responsive to changing consumer behaviors, technological advancements, and environmental concerns. It also furnishes practical insights for enhancing online retail consumer engagement, logistical efficiency, and sustainability. Furthermore, this research advocates for e-commerce as a vehicle for digital inclusion, calling for policies that promote equitable access to online markets. This underscores its broader socio-economic importance, suggesting a path forward for stakeholders in shaping a more inclusive and sustainable e-commerce ecosystem.
Full article
(This article belongs to the Section e-Commerce Analytics)
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Figure 1
Open AccessArticle
Leveraging Stacking Framework for Fake Review Detection in the Hospitality Sector
by
Syed Abdullah Ashraf, Aariz Faizan Javed, Sreevatsa Bellary, Pradip Kumar Bala and Prabin Kumar Panigrahi
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1517-1558; https://doi.org/10.3390/jtaer19020075 - 15 Jun 2024
Abstract
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Driven by motives of profit and competition, fake reviews are increasingly used to manipulate product ratings. This trend has caught the attention of academic researchers and international regulatory bodies. Current methods for spotting fake reviews suffer from scalability and interpretability issues. This study
[...] Read more.
Driven by motives of profit and competition, fake reviews are increasingly used to manipulate product ratings. This trend has caught the attention of academic researchers and international regulatory bodies. Current methods for spotting fake reviews suffer from scalability and interpretability issues. This study focuses on identifying suspected fake reviews in the hospitality sector using a review aggregator platform. By combining features and leveraging various classifiers through a stacking architecture, we improve training outcomes. User-centric traits emerge as crucial in spotting fake reviews. Incorporating SHAP (Shapley Additive Explanations) enhances model interpretability. Our model consistently outperforms existing methods across diverse dataset sizes, proving its adaptable, explainable, and scalable nature. These findings hold implications for review platforms, decision-makers, and users, promoting transparency and reliability in reviews and decisions.
Full article
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