JTAER, Vol. 19, Pages 597-614: Fog Computing-Based Smart Consumer Recommender Systems

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010032

Authors: Jacob Hornik Chezy Ofir Matti Rachamim Sergei Graguer

The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC “smart” applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the “fog-based recommender systems” opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper.

JTAER, Vol. 19, Pages 581-596: How Personality Traits Affect Customer Empathy Expression of Social Media Ads and Purchasing Intention: A Psychological Perspective

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010031

Authors: Serhan Demirci Chia-Ju Ling Dai-Rong Lee Chien-Wen Chen

Consumers’ personality traits significantly influence their perceptions regarding social media advertising. While prior research on consumers’ purchasing intentions in social networking sites advertising has mainly focused on advertising valence antecedents, it is crucial to recognize that consumers’ susceptibility to advertising persuasion, particularly in terms of empathic expression, varies based on a key criterion: whether consumers are driven to attain a specific desired state or are more inclined to avoid an undesirable state. Regulatory Focus Theory (RFT) posits that individuals operate under distinct motivational mechanisms that govern their determination to achieve desired goals, influencing how they process and evaluate advertising messages. In light of RFT, we conducted an online survey with 524 valid responses, utilizing partial least squares (PLS) for research model analysis. The findings revealed that promotion-focused individuals have positively influenced perceptions of social media ad effectiveness (informativeness, ad creativity, perceived relevance, and emotional appeal). In contrast, prevention-focused individuals negatively perceived social media ad effectiveness. Furthermore, this study highlighted that perceived relevance and emotional appeal have a more significant impact on attitudes toward expressing empathy than informativeness and ad creativity.

JTAER, Vol. 19, Pages 561-580: Do Dynamic Signals Affect High-Quality Solvers’ Participation Behavior? Evidence from the Crowdsourcing Platform

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010030

Authors: Xue Liu Xiaoling Hao

The emergence of the crowdsourcing platform enables seekers to obtain higher-quality services at lower costs. High-quality services are often provided by high-quality solvers, which is the key to the sustainable development of crowdsourcing platforms. Therefore, how to attract more high-quality solvers to participate needs to be focused on. Most previous studies that used stock data to measure crowdsourcing performance failed to describe the contest process of high-quality solvers’ behavior. Different from the previous study, this paper explores the information signals that influence the participation of high-quality solvers in the dynamic process of crowdsourcing contests. Based on the creative projects of the Winvk platform, dynamic models affecting the participation of high-quality solvers are constructed from the perspective of reducing information asymmetry, and the effects of quality signals and intention signals are explored in depth. The results show that for logo design projects, clear information display and monetary mechanisms have a significant impact on alleviating information asymmetry and attracting the participation of high-quality solvers. Interestingly, the effect of market competition on high-quality solvers shows a U-shaped change. The research results provide a reference for enterprises to reduce information asymmetry, obtain high-quality solutions, and enrich the theoretical application in the field of crowdsourcing.

JTAER, Vol. 19, Pages 538-560: The Impact of Academic Publications over the Last Decade on Historical Bitcoin Prices Using Generative Models

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010029

Authors: Adela Bâra Simona-Vasilica Oprea

Since 2012, researchers have explored various factors influencing Bitcoin prices. Up until the end of July 2023, more than 9100 research papers on cryptocurrencies were published and indexed in the Web of Science Clarivate platform. The objective of this paper is to analyze the impact of publications on Bitcoin prices. This study aims to uncover significant themes within these research articles, focusing on cryptocurrencies in general and Bitcoin specifically. The research employs latent Dirichlet allocation to identify key topics from the unstructured abstracts. To determine the optimal number of topics, perplexity and topic coherence metrics are calculated. Additionally, the abstracts are processed using BERT-transformers and Word2Vec and their potential to predict Bitcoin prices is assessed. Based on the results, while the research helps in understanding cryptocurrencies, the potential of academic publications to influence Bitcoin prices is not significant, demonstrating a weak connection. In other words, the movements of Bitcoin prices are not influenced by the scientific writing in this specific field. The primary topics emerging from the analysis are the blockchain, market dynamics, transactions, pricing trends, network security, and the mining process. These findings suggest that future research should pay closer attention to issues like the energy demands and environmental impacts of mining, anti-money laundering measures, and behavioral aspects related to cryptocurrencies.