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 22.6 days after submission; acceptance to publication is undertaken in 3.9 days (median values for papers published in this journal in the first 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
Internet Usage among Senior Citizens: Self-Efficacy and Social Influence Are More Important than Social Support
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1463-1483; https://doi.org/10.3390/jtaer18030074 (registering DOI) - 31 Aug 2023
Abstract
For more than two decades, developed countries have been confronted with two trends that have implications for the emergence of engaging senior citizens in the digital environment. On the one hand, there is an increasing proportion of senior citizens in the total population.
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For more than two decades, developed countries have been confronted with two trends that have implications for the emergence of engaging senior citizens in the digital environment. On the one hand, there is an increasing proportion of senior citizens in the total population. On the other hand, the application of ICT in all areas of life and business is accelerating. This paper investigates the relationship between self-efficacy, social support, and social influence on Internet usage among senior citizens in Croatia. Survey research was conducted on a sample of Croatian senior citizens, and a structural equation mode was developed for testing the research hypothesis. Self-efficacy influenced both the Intensity and obstacles of Internet usage in a positive and negative manner, respectively. Social influence directly decreased the obstacles to Internet usage, while the relationship with the Intensity of the Internet was indirect through self-efficacy. Social support had only an indirect association with Intensity of Internet usage. Results have relevant implications for programmes aiming to enhance Internet usage among senior citizens, which should focus on the educational programmes fostering perceived self-efficacy of Internet usage among senior citizens.
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(This article belongs to the Section Digital Business Organization)
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Deep Filter Context Network for Click-Through Rate Prediction
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1446-1462; https://doi.org/10.3390/jtaer18030073 - 22 Aug 2023
Abstract
The growth of e-commerce has led to the widespread use of DeepCTR technology. Among the various types, the deep interest network (DIN), deep interest evolution network (DIEN), and deep session interest network (DSIN) developed by Alibaba have achieved good results in practice. However,
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The growth of e-commerce has led to the widespread use of DeepCTR technology. Among the various types, the deep interest network (DIN), deep interest evolution network (DIEN), and deep session interest network (DSIN) developed by Alibaba have achieved good results in practice. However, the above model’s use of filtering for the user’s own historical behavior sequences and the insufficient use of context features lead to reduced recommendation effectiveness. To address these issues, this paper proposes a novel article model: the deep filter context network (DFCN). This improves the efficiency of the attention mechanism by adding a filter to filter out data in the user’s historical behavior sequence that differs greatly from the target advertisement. The DFCN pays attention to the context features through two local activation units. This model greatly improves the expressiveness of the model, offering strong environment-related attributes and the adaptive capability of the model, with a significant improvement of up to 0.0652 in the AUC metric when compared with our previously proposed DICN under different datasets.
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(This article belongs to the Collection Utilizing Models for e-Business Decision-Making: From Data to Wisdom)
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Exploring the Advantages of Using Social Media in the Romanian Retail Sector
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J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1431-1445; https://doi.org/10.3390/jtaer18030072 - 21 Aug 2023
Abstract
The emergence of social media led to major changes in the manner in which retailers accomplish their daily profession, particularly since they provide traders with platforms for business development and brand improvement. In spite of this, little is known about their impact and
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The emergence of social media led to major changes in the manner in which retailers accomplish their daily profession, particularly since they provide traders with platforms for business development and brand improvement. In spite of this, little is known about their impact and influence on retail businesses. Research on retailers’ perceptions concerning social media is scarce and fragmented, which justifies the current increasing focus of scholars and practitioners on this subject. In this study, a quantitative research design was utilized, aiming to identify the advantages of social media as perceived by retailers in Romania. The findings confirm the hypotheses, acknowledging that Romanian retailers perceive social media as offering great advantages for individuals employed in the retail sector. The practical implications of our research were grouped according to the analyzed aspects, as follows: gathering information, content creation, and customer communication, approached as advantages of adopting social media in retail. This study contributes to the limited literature on social media and the perceived advantages of Romanian retailers, which has implications for further research in this field of knowledge.
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(This article belongs to the Section Digital Marketing and the Connected Consumer)
Open AccessArticle
The Interplay of AI Adoption, IoT Edge, and Adaptive Resilience to Explain Digital Innovation: Evidence from German Family-Owned SMEs
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1419-1430; https://doi.org/10.3390/jtaer18030071 - 17 Aug 2023
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This study aims to discover how artificial intelligence adoption in notion (AI) plays a role in digital innovation using the theoretical foundation of diffusion of innovations and effectuation theories. The current research also investigates the moderating role of other edge Internet of Things
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This study aims to discover how artificial intelligence adoption in notion (AI) plays a role in digital innovation using the theoretical foundation of diffusion of innovations and effectuation theories. The current research also investigates the moderating role of other edge Internet of Things (IoT) and the mediating role of adaptive resilience. The data collection is performed using a survey conducted among employees of family-owned SMEs. The findings reveal that AI forecasts digital innovation through adaptive resilience. The results also confirm the moderating role of threat to IoT edge and the mediating role of adaptive resilience, but moderated mediating is not supported. We conclude that family-owned SMEs intend to adopt AI, but SMEs face challenges using IoT edge. This study has implications for family firms specifically and technology adopters in general.
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TEE: Real-Time Purchase Prediction Using Time Extended Embeddings for Representing Customer Behavior
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1404-1418; https://doi.org/10.3390/jtaer18030070 - 17 Aug 2023
Abstract
Real-time customer purchase prediction tries to predict which products a customer will buy next. Depending on the approach used, this involves using data such as the customer’s past purchases, his or her search queries, the time spent on a product page, the customer’s
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Real-time customer purchase prediction tries to predict which products a customer will buy next. Depending on the approach used, this involves using data such as the customer’s past purchases, his or her search queries, the time spent on a product page, the customer’s age and gender, and other demographic information. These predictions are then used to generate personalized recommendations and offers for the customer. A variety of approaches already exist for real-time customer purchase prediction. However, these typically require expertise to create customer representations. Recently, embedding-based approaches have shown that customer representations can be effectively learned. In this regard, however, the current state-of-the-art does not consider activity time. In this work, we propose an extended embedding approach to represent the customer behavior of a session for both known and unknown customers by including the activity time. We train a long short-term memory with our representation. We show with empirical experiments on three different real-world datasets that encoding activity time into the embedding increases the performance of the prediction and outperforms the current approaches used.
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(This article belongs to the Topic Online User Behavior in the Context of Big Data)
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Unveiling the Power of ARIMA, Support Vector and Random Forest Regressors for the Future of the Dutch Employment Market
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1365-1403; https://doi.org/10.3390/jtaer18030069 - 08 Aug 2023
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The increasing popularity of online job vacancies and machine learning methods has raised questions about their combination to enhance our understanding of labour markets and algorithms. However, the lack of comparable studies necessitates further investigation. This research aims to explore the effectiveness of
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The increasing popularity of online job vacancies and machine learning methods has raised questions about their combination to enhance our understanding of labour markets and algorithms. However, the lack of comparable studies necessitates further investigation. This research aims to explore the effectiveness of Random Forest Regressor (RFR) and Support Vector Regressor (SVR) machine learning models in predicting online job vacancies compared to the auto-regressive ARIMA method. To answer this question, detailed sub-questions are posed in relation to the sub-samples of the main data provided by Birch Consultants, an external partner originally obtained by Jobdigger. Drawing upon previous research on time-series accuracy, this study combines various approaches to benefit society and the external partner. Using the walk-forward validation method, with a 91-day expanding window, it provides precise answers to the sub-questions. Findings suggest that RFR is suitable for forecasting larger samples, while SVR is preferred due to its capability to predict small series despite relatively small scoring benefits and computational costs. Both machine learning models outperform the baseline ARIMA model in capturing complex time-series. Further research should focus on exploring advanced auto-regressive, deep learning, and hybrid models for future investigations.
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A Review of the Lightning Network’s Evolution: Unraveling Its Present State and the Emergence of Disruptive Digital Business Models
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1338-1364; https://doi.org/10.3390/jtaer18030068 - 01 Aug 2023
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The Lightning Network (LN), a second-layer protocol built on top of the Bitcoin blockchain, is an innovative digital payment solution that offers increased convenience, speed, and cost-effectiveness to consumers and businesses alike. However, there is limited literature available on the characteristics of this
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The Lightning Network (LN), a second-layer protocol built on top of the Bitcoin blockchain, is an innovative digital payment solution that offers increased convenience, speed, and cost-effectiveness to consumers and businesses alike. However, there is limited literature available on the characteristics of this nascent technology, the depth and breadth of the various business LN-related applications as well as relevant adoption/implementation challenges. This study aims to contribute to the understanding of the LN’s characteristics, its potential in enhancing business operations and its applicability across different sectors, while taking into account adoption and implementation challenges. We apply a narrative review methodology using a semi-systematic approach to examine new and emerging business models empowered by the LN and its characteristics, topology, performance, privacy and security. We analyze the data to identify key themes and trends in the literature, offering a critical analysis of the strengths and weaknesses of the existing literature. Based on the findings, we provide several clusters of fruitful areas for future research directions. This study not only provides crucial insights for businesses contemplating the adoption of LN to improve their operations and customer experience, but it also represents a substantial academic contribution, offering valuable knowledge and fostering further research in the fields of blockchain technology, FinTech and cryptocurrencies.
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The Effect of Price Discrimination on Fairness Perception and Online Hotel Reservation Intention
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1320-1337; https://doi.org/10.3390/jtaer18030067 - 01 Aug 2023
Abstract
In light of the development of online travel agencies (OTAs), the rules of the entire tourism industry have changed. Due to the ease of finding information and comparing products, consumers can choose a hotel not only by room type, but also by rate,
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In light of the development of online travel agencies (OTAs), the rules of the entire tourism industry have changed. Due to the ease of finding information and comparing products, consumers can choose a hotel not only by room type, but also by rate, according to their preferences. The purpose of this study was to explore the effect of price discrimination on the fairness perception toward reservation intentions. The interaction effects of the brand familiarity and the type of consumers on the fairness perception were also examined. The study used an experimental design, with 2 price discriminations × 2 brand familiarities × 2 regulatory focuses, on a total of 320 valid subjects. The results showed that advantaged-price discriminations had higher fairness perceptions than equal-price discriminations, and that higher fairness perceptions had higher reservation intentions. The interaction effect of brand familiarity showed no significant impact on the fairness perceptions, while the regulatory focus had a mitigating effect on the price discrimination and on the fairness perceptions. This study provides insights into the relationship between online price discrimination and tourism, and it contributes to the literature on hospitality. It also provides the managerial implications for online hotels in developing pricing strategies.
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(This article belongs to the Section Digital Marketing and the Connected Consumer)
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Consumers’ Preferences for Digital Corporate Content on Company Websites: A Best–Worst Scaling Analysis
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J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1301-1319; https://doi.org/10.3390/jtaer18030066 - 25 Jul 2023
Abstract
Digital content marketing (DCM) complements traditional marketing communication approaches and is a major focus of research. Uses and gratifications research posits that DCM only unfolds positive effects if it provides valuable content to consumers. However, there is limited evidence on what constitutes gratifying
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Digital content marketing (DCM) complements traditional marketing communication approaches and is a major focus of research. Uses and gratifications research posits that DCM only unfolds positive effects if it provides valuable content to consumers. However, there is limited evidence on what constitutes gratifying digital corporate content on company websites. This study aimed to elicit consumers’ preferences for key characteristics of digital corporate content on company websites and whether preferences differ among consumer subgroups. Best–worst scaling (BWS) was used to reveal preferences. To obtain BWS data, a cross-sectional survey was employed. The study sample comprised 1527 consumers from Germany, Switzerland, and Austria. Data were analyzed using counting analysis and conditional logit modeling. Subgroup comparisons were performed with t-tests and one-way ANOVA. The results consistently show that consumers prioritize information value as the most important content characteristic, followed by value in use, entertainment value, process value, and social value. Subgroup comparisons revealed generally similar priorities among consumers, with the greatest heterogeneity being found in assessments of the importance of social value. The study also suggests that consumers prioritize digital corporate content characteristics on company websites differently than they do on social media. These findings contribute to the evolving literature on DCM and provide insights that could help set evidence-based priorities in DCM practice.
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(This article belongs to the Section Digital Marketing and the Connected Consumer)
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Social Commerce in Europe: A Literature Review and Implications for Researchers, Practitioners, and Policymakers
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1283-1300; https://doi.org/10.3390/jtaer18030065 - 20 Jul 2023
Abstract
The COVID-19 pandemic has altered consumer behavior, making social commerce a viable alternative throughout the world. Europe is trailing the US and China in adopting this technology, but the prognosis is encouraging. Our goal is to contribute to this process by offering a
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The COVID-19 pandemic has altered consumer behavior, making social commerce a viable alternative throughout the world. Europe is trailing the US and China in adopting this technology, but the prognosis is encouraging. Our goal is to contribute to this process by offering a literature review on social commerce in Europe for researchers, practitioners, and policymakers. We analyzed 4.764 articles published during the 2015–2023 period on the topic of social commerce in Europe utilizing the PRISMA flow diagram. After scrutinizing this large body of literature with various instruments including artificial intelligence (AI), we identified a final list of 45 articles that are most pertinent to our research questions. The emerging themes were that social media is shaping behavior and triggering buying intentions, that trust is paramount in buying impulses and behavior, and that success in social commerce is predicated upon relationships and engagement.
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(This article belongs to the Special Issue Social Commerce and the Recent Changes)
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“Customer Reviews or Vlogger Reviews?” The Impact of Cross-Platform UGC on the Sales of Experiential Products on E-Commerce Platforms
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1257-1282; https://doi.org/10.3390/jtaer18030064 - 10 Jul 2023
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User-generated content (UGC) from e-commerce platforms and third-party platforms can impact customer-perceived risk and influence product sales in online stores. However, the understanding of UGC from which platform type yields a stronger effect on product sales and how the effects interact across the
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User-generated content (UGC) from e-commerce platforms and third-party platforms can impact customer-perceived risk and influence product sales in online stores. However, the understanding of UGC from which platform type yields a stronger effect on product sales and how the effects interact across the platforms remains limited. This limitation arises from the complexity of consumer purchasing behavior and information processing, as well as the heterogeneity of UGC features across different platforms and the uncertainty surrounding causal relationships. This study constructs a novel cross-platform framework using the elaboration likelihood model (ELM) to investigate the underlying mechanism of how cross-platform UGC affects online sales of experiential products. Additionally, it examines the mediating effect of purchase intention in the relationship between cross-platform UGC and product sales, as well as the moderating effect of product price. Taking the e-commerce platform Tmall and third-party platform Bilibili as a cross-platform example, we analyzed customer reviews on Tmall and vlogger reviews on Bilibili for 300 cosmetic products, using text sentiment analysis and multiple regression. Results show that the number of product evaluations from third-party platforms positively impacts sales, but this impact is weaker compared to the influence of UGC originating from e-commerce platforms on sales. The underlying mechanism refers to the process by which UGC on an e-commerce platform directly impacts sales and also influences sales through purchase intention. In contrast, UGC on third-party platforms only influences sales through purchase intention. Furthermore, the product price has no significant moderating effect on the positive relationship between review length and sales. This study provides a cross-platform UGC research framework that can guide effective cross-platform marketing management by shedding light on the role of UGC in reducing customer-perceived risk and its impact on online sales of experiential products.
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Understanding Antecedents That Affect Customer Evaluations of Head-Mounted Display VR Devices through Text Mining and Deep Neural Network
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1238-1256; https://doi.org/10.3390/jtaer18030063 - 10 Jul 2023
Abstract
Although the market for Head-Mounted Display Virtual Reality (HMD VR) devices has been growing along with the metaverse trend, the product has not been as widespread as initially expected. As each user has different purposes for use and prefers different features, various factors
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Although the market for Head-Mounted Display Virtual Reality (HMD VR) devices has been growing along with the metaverse trend, the product has not been as widespread as initially expected. As each user has different purposes for use and prefers different features, various factors are expected to influence customer evaluations. Therefore, the present study aims to: (1) analyze customer reviews of hands-on HMD VR devices, provided with new user experience (UX), using text mining, and artificial neural network techniques; (2) comprehensively examine variables that affect user evaluations of VR devices; and (3) suggest major implications for the future development of VR devices. The research procedure consisted of four steps. First, customer reviews on HMD VR devices were collected from Amazon.com. Second, candidate variables were selected based on a literature review, and sentiment scores were extracted. Third, variables were determined through topic modeling, in-depth interviews, and a review of previous studies. Fourth, an artificial neural network analysis was performed by setting customer evaluation as a dependent variable, and the influence of each variable was checked through feature importance. The results indicate that feature importance can be derived from variables, and actionable implications can be identified, unlike in general sentiment analysis.
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(This article belongs to the Special Issue Social Commerce and the Recent Changes)
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Explaining Policyholders’ Chatbot Acceptance with an Unified Technology Acceptance and Use of Technology-Based Model
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1217-1237; https://doi.org/10.3390/jtaer18030062 - 07 Jul 2023
Abstract
Conversational robots powered by artificial intelligence (AI) are intensively implemented in the insurance industry. This paper aims to determine the current level of acceptance among consumers regarding the use of conversational robots for interacting with insurers and seeks to identify the factors that
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Conversational robots powered by artificial intelligence (AI) are intensively implemented in the insurance industry. This paper aims to determine the current level of acceptance among consumers regarding the use of conversational robots for interacting with insurers and seeks to identify the factors that influence individuals’ behavioral intention to engage with chatbots. To explain behavioral intention, we tested a structural equation model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. It was supposed that behavioral intention is influenced by performance expectancy, effort expectancy, social influence, and trust, and by the moderating effect of insurance literacy on performance expectancy and effort expectancy. The study reveals a significant overall rejection of robotic technology among respondents. The technology acceptance model tested demonstrates a strong ability to fit the data, explaining nearly 70% of the variance in behavioral intention. Social influence emerges as the most influential variable in explaining the intention to use conversational robots. Furthermore, effort expectancy and trust significantly impact behavioral intention in a positive manner. For chatbots to gain acceptance as a technology, it is crucial to enhance their usability, establish trust, and increase social acceptance among users.
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(This article belongs to the Topic Artificial Intelligence Applications in Financial Technology)
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How Streamers Foster Consumer Stickiness in Live Streaming Sales
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1196-1216; https://doi.org/10.3390/jtaer18030061 - 06 Jul 2023
Abstract
Streamers play a critical role in fostering consumer stickiness in live streaming sales. Thus, it is necessary to make clear the mechanism of how streamers influence consumer stickiness. Based upon the theories of social support, social identification and consumer stickiness, this study investigates
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Streamers play a critical role in fostering consumer stickiness in live streaming sales. Thus, it is necessary to make clear the mechanism of how streamers influence consumer stickiness. Based upon the theories of social support, social identification and consumer stickiness, this study investigates the effects of consumers’ perceived emotional support, informational support, financial support, affectionate support and social network support from streamers on consumer–streamer identification, which in turn affects consumer–streamer stickiness and consumer–brand stickiness in live streaming sales settings. Based on the structural equation modeling analysis of 280 online questionnaires, using the software of Smart PLS 3.0, the results demonstrate that perceived emotional support, perceived informational support, perceived financial support and perceived affectionate support enhance consumer–streamer identification, thereby enhancing consumer–streamer stickiness and consumer–brand stickiness, and thus, consumer–streamer stickiness also enhances consumer–brand stickiness. This study not only extends the theories of live streaming sales, but also provides practical implications for enterprises’ improving consumer–streamer stickiness and consumer–brand stickiness in live streaming sales.
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(This article belongs to the Collection The New Era of Digital Marketing)
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Pricing Game Models of Hybrid Channel Supply Chain: A Strategic Consumer Behavior Perspective
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1177-1195; https://doi.org/10.3390/jtaer18030060 - 06 Jul 2023
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The current sales model combining online and offline channels meets the diverse requirements of consumers. However, consumers’ inter-channel switching behavior and strategic behavior also pose significant challenges to pricing decisions in the hybrid channel. Using game theory and consumer utility theory, a retailer-driven
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The current sales model combining online and offline channels meets the diverse requirements of consumers. However, consumers’ inter-channel switching behavior and strategic behavior also pose significant challenges to pricing decisions in the hybrid channel. Using game theory and consumer utility theory, a retailer-driven pricing model is developed to study the optimal pricing problem for each channel in a mixed-channel supply chain considering the characteristics of channel competition and the waiting behavior of strategic consumers. Study results show there is a negative correlation between the proportion of strategic consumers and the optimal pricing and profit of each channel, and as the proportion of strategic consumers rises, the optimal pricing and profit of manufacturers and retailers all trend downward. Incorporating strategic consumers into the pricing model will assist the supply chain in elucidating the behavior of consumer heterogeneity during various decision-making periods and in making reasonable pricing decisions. Effective guiding strategies, such as pre-discount and purchase restrictions, can reduce the profit loss caused by strategic consumer behavior. The optimal combination of pre-announcement discount and strategic consumer ratio can generate the greatest profit for retailers and the supply chain.
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Open AccessArticle
Strata Fee Management in Condominiums via Smart Contracts
J. Theor. Appl. Electron. Commer. Res. 2023, 18(3), 1157-1176; https://doi.org/10.3390/jtaer18030059 - 04 Jul 2023
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Condominiums and similar properties use a stratum to manage daily operations, and owners fund it through strata fees. While existing strata fee management systems may be able to handle such funds, such systems could be more inherently transparent. It is possible to leverage
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Condominiums and similar properties use a stratum to manage daily operations, and owners fund it through strata fees. While existing strata fee management systems may be able to handle such funds, such systems could be more inherently transparent. It is possible to leverage the digital ledger from blockchain networks and smart contracts to build a fully transparent strata fee management system. This paper proposes designing a strata fee management system based on a smart contract in the Ethereum network. Both strata corporations and homeowners can interact with the smart contract to execute common procedures such as paying strata fees and handling expenses. Using smart contracts for strata fee management, it is believed that the chance of fraud by strata corporations is lowered compared to other systems.
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Open AccessArticle
Fear of COVID-19 Effect on Stock Markets: A Proposal for an Algorithmic Trading System Based on Fear
J. Theor. Appl. Electron. Commer. Res. 2023, 18(2), 1142-1156; https://doi.org/10.3390/jtaer18020058 - 12 Jun 2023
Abstract
This study analyzes the fear of COVID-19 effect on European stock market returns. For this purpose, the search volumes (SV) collected by Google Trends (GT) and Wikipedia were used as proxies of fear of COVID-19. In a sample from 13 European stock markets,
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This study analyzes the fear of COVID-19 effect on European stock market returns. For this purpose, the search volumes (SV) collected by Google Trends (GT) and Wikipedia were used as proxies of fear of COVID-19. In a sample from 13 European stock markets, fear of COVID-19 was found to be associated with negative European stock returns. Our research employed this observation to propose an algorithmic trading system based on fear of COVID-19. Back-testing results show the possibility of extraordinary returns based on this system. These findings have important implications for political authorities, the mass media, and investors.
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(This article belongs to the Section Entrepreneurship, Innovation, FinTech Accounting and Industry 4.0)
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A Multi-Criteria Group Decision-Making Method for Risk Assessment of Live-Streaming E-Commerce Platform
J. Theor. Appl. Electron. Commer. Res. 2023, 18(2), 1126-1141; https://doi.org/10.3390/jtaer18020057 - 12 Jun 2023
Abstract
Live-streaming e-commerce is the future development direction of the retail industry. When retailers choose a live-streaming e-commerce platform, they face the test of various risks of the platform, such as insecure control of capital flow, insufficient support of public domain traffic, etc. Therefore,
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Live-streaming e-commerce is the future development direction of the retail industry. When retailers choose a live-streaming e-commerce platform, they face the test of various risks of the platform, such as insecure control of capital flow, insufficient support of public domain traffic, etc. Therefore, it is necessary to evaluate the risks of the platform to help retailers identify the platform with the lowest risk. Considering the complexity of the risks of live-streaming e-commerce platforms and the ambiguity of the decision-makers thinking, the current method for multi-criteria group decision-making (MCGDM) method in a fuzzy environment rarely discusses the decision-makers weight for the criterion. This paper proposes interval-valued intuitionistic fuzzy multi-criteria group decision-making based on the decision-makers’ professionalism to evaluate the platform’s risks. This method determines the decision-maker’s weight for the criterion based on the professionalism of the decision-maker and uses the technique for order preference by similarity to an ideal solution (TOPSIS) method to rank the alternative platforms. Finally, a risk assessment of the agricultural product live-streaming e-commerce platforms is used as a case study to demonstrate the feasibility and effectiveness of the proposed method. This research will not only provide practical guidance for retailers to choose the live-streaming e-commerce platform with the lowest comprehensive risk but also provide ideas for the research of live-streaming e-commerce from the perspective of risk assessment.
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(This article belongs to the Collection Utilizing Models for e-Business Decision-Making: From Data to Wisdom)
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Open AccessArticle
What Drives VOD Purchases in Mobile TV Services? Exploring Utilization, Motivations, and Personality Traits
J. Theor. Appl. Electron. Commer. Res. 2023, 18(2), 1107-1125; https://doi.org/10.3390/jtaer18020056 - 12 Jun 2023
Abstract
Mobile streaming is increasingly viewed as a major advancement in the wireless industry, as it enables users to consume content without any time or space restrictions. Mobile TV serves as an excellent example of streaming, providing services for watching TV content on mobile
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Mobile streaming is increasingly viewed as a major advancement in the wireless industry, as it enables users to consume content without any time or space restrictions. Mobile TV serves as an excellent example of streaming, providing services for watching TV content on mobile devices. While previous studies have explored video-on-demand (VOD) purchasing factors in mobile TV, it is rare to find research examining differences based on users’ mobile TV usage types, such as subscribers and free users. Consequently, we investigated VOD purchase factors for 310 subscribers and 311 free mobile TV users. In other words, using 621 survey responses, we analyze the influence of personality traits, intrinsic and extrinsic motivations, and mobile-related factors on users’ VOD purchase intentions and behavior. Our findings indicate that mobile TV utilization, hedonic needs, and subjective norms are positively related to VOD purchases, and that neuroticism, extraversion, openness, and conscientiousness positively impact mobile TV utilization. We also examine the relationships between the constructs within two sub-groups to highlight the differing perceptions and behavioral patterns of these groups regarding mobile TV utilization and VOD purchases. Theoretical and practical implications are discussed as well.
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(This article belongs to the Section Digital Marketing and the Connected Consumer)
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Optimal Recommendation Strategies for AI-Powered E-Commerce Platforms: A Study of Duopoly Manufacturers and Market Competition
J. Theor. Appl. Electron. Commer. Res. 2023, 18(2), 1086-1106; https://doi.org/10.3390/jtaer18020055 - 07 Jun 2023
Abstract
Artificial intelligence-powered recommendation systems have gained popularity as a tool to enhance user experience and boost sales. Platforms often need to make decisions about which seller to recommend and the strength of the recommendation when conducting recommendations. Therefore, it is necessary to explore
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Artificial intelligence-powered recommendation systems have gained popularity as a tool to enhance user experience and boost sales. Platforms often need to make decisions about which seller to recommend and the strength of the recommendation when conducting recommendations. Therefore, it is necessary to explore the recommendation strategy of the platform in the case of duopoly competition. We develop a game model where two competing manufacturers sell products through an agency contract on a common platform, and they can decide whether or not to provide recommendations to the manufacturers. Our highlight lies in the endogenous recommendation strength of the platform. The findings suggest that it is optimal for the platform to offer recommendation services when the commission rate is high. The platform also prefers to only recommend one manufacturer in the market with low or high competition, but it prefers to recommend both manufacturers in moderately competitive markets. From the view of manufacturers, they can benefit from the recommendation service as long as the commission rate is not too low. Moreover, recommending only one manufacturer consistently yields stronger recommendations compared to recommending multiple manufacturers. However, the impact of recommendation on prices is influenced by the commission rate and product substitutability. These results have significant implications for platform decision making and provide valuable insights into the trade-offs involved in the development of recommendation systems.
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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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