JTAER, Vol. 19, Pages 73-94: Strategic Third-Party Product Entry and Mode Choice under Self-Operating Channels and Marketplace Competition: A Game-Theoretical Analysis

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010005

Authors: Biao Xu Jinting Huang Xiaodan Zhang Thomas Brashear Alejandro

To bolster their competitiveness and profitability, prominent e-commerce platforms have embraced dual retailing channels: self-operating channels and online marketplaces. However, a discernible trend is emerging wherein e-commerce platforms are expanding their marketplaces to encompass competitive third-party suppliers. Motivated by this trend, this study sought to examine the strategic integration of a third-party product amidst the competition between a self-operating channel and a marketplace. This investigation involved the development of a game-theoretic model involving a platform and two representative suppliers—an incumbent supplier and a new entrant. Specifically, we delved into establishing an equilibrium partnership between the platform and the new entrant supplier while also evaluating the self-operating strategy of the established supplier. Our analysis uncovered a counterintuitive outcome: an escalation in the commission rate resulted in diminished profits for the established supplier. Furthermore, we ascertained that the economic implications of a competitive product entry pivot significantly on product quality. Lastly, we demonstrated that the revenue-sharing rate plays a pivotal role in influencing the self-operating strategy of the established supplier, and the market equilibrium hinges on the interplay among product quality, the commission rate, and the revenue-sharing rate. These insights provide invaluable guidance for marketers and e-commerce platforms in their strategic decision-making processes.

JTAER, Vol. 19, Pages 40-53: Application of a Microeconomic Approach for Explanation of Citizen Participation in Open Government

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010003

Authors: María Verónica Alderete

The digital economy and the sharing economy have changed the role citizens may acquire in society. Citizens can perform at least two roles from the open government perspective: on the one hand, they can be passive users/demanders of information and, on the other hand, they can provide or produce the information in an active manner. The objective of this paper is to offer a theoretical model to explain citizens’ incentives to participate in open government projects. Which is the opportunity cost of participation for the citizen? Which are the drivers of the preferences for the social good? This model is based on the utility function and consumption theory. We complement the theoretical framework with an exploratory–descriptive analysis based on a case study’s primary data about citizen participation. In democracy projects where citizens actively collaborate and could earn monetary gains or become entrepreneurs, the opportunity cost of participation is lower than in a passive type and the amount of the social good depends on the preferences. Preferences for social goods are related to community experiences and e-government and they also affect the decision to participate. Very few studies in the field of open government have pretended to explain citizens’ participation by using microeconomic foundations.

JTAER, Vol. 19, Pages 54-72: Can the Conditional Rebate Strategy Work? Signaling Quality via Induced Online Reviews

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010004

Authors: Lu Xiao Chen Qian Chaojie Wang Jun Wang

Online reviews are an important part of product information and have important effects on consumers’ purchasing decisions. Some sellers try to manipulate the market by inducing online reviews. In this study, a signal game model based on Bayesian conditional probability is constructed to analyze the preconditions, decision-making process, and effect on market demand and profit of this behavior. The results show that first, when consumer sensitivity to rebates reaches a certain threshold, low-quality sellers will adopt a conditional rebate strategy to induce consumers to give positive reviews. Second, the optimal rebate cost (β*) is obtained, where β* increases with the product price (p), but it is not necessarily monotonic in consumers’ sensitivity to rebates (ρ) or the proportion of high-quality products (α). Third, the conditional rebate strategy can only work in a market dominated by low-quality goods. Using the conditional rebate strategy in a market dominated by high-quality goods will not bring benefits to low-quality sellers but will harm their profits. This study proposes that some developing online markets have collusive behaviors owing to a lack of regulations and laws, as well as consumers’ concern for small interests. Ensuring the orderly development of online markets will require joint efforts by platform enterprises, government agencies, and consumers.

JTAER, Vol. 19, Pages 20-39: Analysis of Green Innovation of the E-Tailer and Supplier with a Drop Shipping Option in E-Commerce

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010002

Authors: Yuepeng Cheng Bo Li

As customer demand for green products increases in the digital economic era, this study analyses the green innovation of the e-tailer and supplier in drop shipping models. Moreover, drop shipping e-tailers and suppliers with a drop shipping option need to make choices regarding whether to provide green or normal products to the market. When a supplier with a drop shipping option produces green products, more fees may be invested in the production of green products than on normal products. The drop shipping e-tailers and suppliers with a drop shipping option can also choose to sell normal products at a low cost, as before. This study designs four models of drop shipping e-tailers and suppliers with a drop shipping option under different choices, analyzes their operational process in drop shipping models, and investigates five theorems. The optimal pricing decisions and green degree of drop shipping e-tailers and suppliers with a drop shipping option were evaluated in this study. The impacts of the green innovation factor, green elasticity coefficient, manufacturing and distribution costs on the drop shipping e-tailers and suppliers with a drop shipping option, and the effect of other environmental parameters on the green degree of green products are also analyzed through computer simulation. The findings of the simulation analysis provide valuable guidance for e-tailers and suppliers with green innovation in drop shipping models and offer important academic and practical implications for e-commerce and the digital economy.

JTAER, Vol. 19, Pages 1-19: Time-of-Day and Day-of-Week Effects on TV and OTT Media Choices: Evidence from South Korea

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010001

Authors: Yun-Woo Choi Changjun Lee

The objective of this manuscript is to investigate the determinants influencing the selection of over-the-top (OTT) platforms as opposed to traditional television mediums—cable, Internet protocol television (IPTV), and satellite broadcasting—for the consumption of content such as television shows and films. Employing data extracted from the 2020 Media Panel comprising 423,851 observations garnered from personal media diaries, this study scrutinizes the impacts of individual attributes, environmental conditions, and temporal factors on platform choice. The findings reveal a temporal influence characterized by a “Friday effect” and a heightened preference for OTT platforms during early afternoon (12:00–16:00) and late-night hours (00:00–04:00). Notably, the likelihood of selecting OTT platforms is significantly augmented during the late-night period in comparison to other time frames. In relation to individual characteristics, variables such as male gender, younger age, higher educational attainment, and elevated income levels were positively correlated with a predilection for OTT platforms. Additionally, environmental variables such as possession of an unlimited data plan and ownership of a tablet personal computer also emerged as significant predictors for OTT preference. Furthermore, the presence of a beam projector during late-night hours and residing in a household with multiple occupants during afternoon hours also served as contributing factors for OTT utilization. In conclusion, the study offers critical insights for stakeholders in both traditional television and burgeoning OTT markets, providing data-driven recommendations for the strategic allocation of resources in consideration of day-of-week and time-of-day variables.

JTAER, Vol. 18, Pages 2257-2272: Are eBay’s Feedback Ratings Consistent with the Sentiments Embedded in Textual Comments? An Empirical Study

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer18040114

Authors: Xubo Zhang Yanbin Tu Ke Zhong

eBay’s feedback rating system is currently widely used. In this study, we examine if eBay’s feedback rating types (+, 0, −) are consistent with the sentiments reflected in the textual comments posted by buyers. Using the datasets collected from eBay, we test the hypotheses associated with the research questions at three levels: individual, group, and total. Overall, the types of feedback ratings are consistent with the sentiments embedded in the textual comments. However, there are some issues with eBay’s current feedback rating system: (1) at the individual level, the correlation coefficient between the ratings and the comments’ sentiments is low at 0.4311 (<0.5). While the three types of ratings are symmetric, like (−1, 0, +1), buyers’ textual comments have asymmetric distributions of sentiments among these three types. The three simple feedback ratings (+, 0, −) are not fully aligned with the sentiments revealed in the textual comments posted by buyers. We propose expanding the current three ratings into five ratings such as (−2, −1, 0, +1, +2), which might help remedy the issue. We contribute to the literature by tapping into this less-studied area vital to improving the online marketplace’s efficiency.

JTAER, Vol. 18, Pages 2238-2256: Altruism in eWOM: Propensity to Write Reviews on Hotel Experience

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer18040113

Authors: Miguel Llorens-Marin Adolfo Hernandez Maria Puelles-Gallo

This research tests the relationship between aspects of customer influenceability at the time of booking a hotel with the propensity to write a review in electronic word-of-mouth communication. A valid sample of 739 online questionnaires was obtained. An Exploratory Factor Analysis was conducted in order to reduce the dimensions of the two critical variables, and a measurement model was built. Then a Path analysis was carried out. The novelty of this research lies in measuring the evolution from being a passive eWOM reader to a proactive eWOM writer. Results indicate a relationship between being influenced by reading reviews and the propensity to write reviews. The most important underlying motivation to write a review is altruistic. Managers should try to identify the most responsive customers and encourage them to write reviews on altruistic grounds. This study effectively validated the impact of being responsive to reading reviews on the inclination to, in turn, write them. Findings contribute to the evolving research landscape in eWOM within the hospitality and tourism sector, offering practical insights for industry practitioners to formulate more effective strategies in soliciting and managing customer reviews.

JTAER, Vol. 18, Pages 2233-2237: Comment on Gruntkowski, L.M.; Martinez, L.F. Online Grocery Shopping in Germany: Assessing the Impact of COVID-19. J. Theor. Appl. Electron. Commer. Res. 2022, 17, 984–1002

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer18040112

Authors: Leo Van Hove

Gruntkowski and Martinez examined the impact of factors such as perceived risk and perceived usefulness on German consumers’ intention to purchase groceries online once the COVID-19 pandemic had subsided. They also compared consumer perceptions before and during the COVID-19 outbreak. This comment shows that Gruntkowski and Martinez’s research suffers from a number of problems, the most important of which is the use of an unrepresentative sample. They should therefore have refrained from generalizing their findings to the German population.

JTAER, Vol. 18, Pages 2217-2232: Consumer Intentions to Switch On-Demand Food Delivery Platforms: A Perspective from Push-Pull-Mooring Theory

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer18040111

Authors: I-Chiu Chang Win-Ming Shiau Chih-Yu Lin Dong-Her Shih

With a burgeoning market and a multitude of on-demand food delivery (OFD) platforms offering diverse options, comprehending the reasons that drive consumers to switch between platforms is paramount. The push-pull-mooring (PPM) theory provides a comprehensive framework for assessing why and how consumers navigate, guiding strategic decisions for service providers seeking to optimize their offerings and retain their customer base. This research employs the PPM theory to rigorously analyze how these elements influence consumers’ intentions to switch between OFD platforms in Taiwan. Findings from a comprehensive survey of 441 OFD users reveal that both pull and mooring factors exert a significant influence on consumers’ inclination to switch platforms, collectively explaining about 42% of the switching intention. Recognizing these critical factors empowers managers to make judicious decisions aimed at enhancing platform offerings and refining marketing strategies, ultimately fortifying customer retention and bolstering satisfaction levels.

JTAER, Vol. 18, Pages 2188-2216: A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer18040110

Authors: Xue Zhang Fusen Guo Tao Chen Lei Pan Gleb Beliakov Jianzhang Wu

The rapid growth of e-commerce has significantly increased the demand for advanced techniques to address specific tasks in the e-commerce field. In this paper, we present a brief survey of machine learning and deep learning techniques in the context of e-commerce, focusing on the years 2018–2023 in a Google Scholar search, with the aim of identifying state-of-the-art approaches, main topics, and potential challenges in the field. We first introduce the applied machine learning and deep learning techniques, spanning from support vector machines, decision trees, and random forests to conventional neural networks, recurrent neural networks, generative adversarial networks, and beyond. Next, we summarize the main topics, including sentiment analysis, recommendation systems, fake review detection, fraud detection, customer churn prediction, customer purchase behavior prediction, prediction of sales, product classification, and image recognition. Finally, we discuss the main challenges and trends, which are related to imbalanced data, over-fitting and generalization, multi-modal learning, interpretability, personalization, chatbots, and virtual assistance. This survey offers a concise overview of the current state and future directions regarding the use of machine learning and deep learning techniques in the context of e-commerce. Further research and development will be necessary to address the evolving challenges and opportunities presented by the dynamic e-commerce landscape.