JTAER, Vol. 19, Pages 152-171: A Consumer Behavior Analysis Framework toward Improving Market Performance Indicators: Saudi’s Retail Sector as a Case Study

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010009

Authors: Monerah Alawadh Ahmed Barnawi

Studying customer behavior and anticipating future trends is a challenging task, as customer behavior is complex and constantly evolving. To effectively anticipate future trends, businesses need to analyze large amounts of data, use sophisticated analytical techniques, and stay up-to-date with the latest research and industry trends. In this paper, we propose a comprehensive framework to identify trends in consumer behavior using multiple layers of processing, including clustering, classification, and association rule learning. The aim is to help a major retailer in Saudi Arabia better understand customer behavior by utilizing the power of big data analysis. The proposed framework is presented as being generalized to gain insight into the generated big data and enable data-driven decision-making in other relevant domains. We developed this framework in collaboration with a large supermarket chain in Saudi Arabia, which provided us with over 1,000,000 sales transaction records belonging to around 30,000 of their loyal customers. In this study, we apply our proposed framework to those data as a case study and present our initial results of consumer clustering and association rules for each cluster. Moreover, we analyze our findings to figure out how we can further utilize intelligence to predict customer behavior in clustered groups.

JTAER, Vol. 19, Pages 135-151: It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding Representation

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010008

Authors: Miguel Alves Gomes Richard Meyes Philipp Meisen Tobias Meisen

Alongside natural language processing and computer vision, large learning models have found their way into e-commerce. Especially, for recommender systems and click-through rate prediction, these models have shown great predictive power. In this work, we aim to predict the probability that a customer will click on a given recommendation, given only its current session. Therefore, we propose a two-stage approach consisting of a customer behavior-embedding representation and a recurrent neural network. In the first stage, we train a self-supervised skip-gram embedding on customer activity data. The resulting embedding representation is used in the second stage to encode the customer sequences which are then used as input to the learning model. Our proposed approach diverges from the prevailing trend of utilizing extensive end-to-end models for click-through rate prediction. The experiments, which incorporate a real-world industrial use case and a widely used as well as openly available benchmark dataset, demonstrate that our approach outperforms the current state-of-the-art models. Our approach predicts customers’ click intention with an average F1 accuracy of 94% for the industrial use case which is one percentage point higher than the state-of-the-art baseline and an average F1 accuracy of 79% for the benchmark dataset, which outperforms the best tested state-of-the-art baseline by more than seven percentage points. The results show that, contrary to current trends in that field, large end-to-end models are not always needed. The analysis of our experiments suggests that the reason for the performance of our approach is the self-supervised pre-trained embedding of customer behavior that we use as the customer representation.

JTAER, Vol. 19, Pages 116-134: Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010007

Authors: Bassant A. Abdelfattah Saad M. Darwish Saleh M. Elkaffas

Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.

JTAER, Vol. 19, Pages 95-115: Effects of Prior Negative Experience and Personality Traits on WeChat and TikTok Ad Avoidance among Chinese Gen Y and Gen Z

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010006

Authors: Ningyan Cao Normalisa Md Isa Selvan Perumal

While numerous people use social mobile applications, ads within these apps are often avoided. Although the significance of prior negative experience and personality traits in impacting consumers’ perceptions and behaviors has been acknowledged, limited research has explored their influence on ad perceptions and avoidance. This study aims to examine the effects of prior negative experience and personality traits on ad perceptions and ad avoidance of Generation Y (Gen Y) and Generation Z (Gen Z) within two prominent mobile social apps: WeChat and TikTok. An online survey was used to gather data from 353 Chinese Gen Y and Gen Zers who were active users of WeChat and TikTok. Findings from several regression analyses show that prior negative experience is an essential determinant of ad avoidance, influencing not just directly but indirectly by diminishing perceived ad personalization and intensifying perceived goal impediment and ad clutter. Personality traits also significantly affect ad avoidance, with conscientiousness exerting a positive effect, whereas agreeableness has a negative impact. Notably, agreeableness, emotional stability, and openness to experience moderate the associations between ad perceptions and avoidance. Intriguingly, the effects of these factors are platform-specific, with WeChat’s main factor for ad avoidance being erceived goal impediment and TikTok’s main factor being ad clutter. Based on these findings, the theoretical and practical implications are discussed.

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.