JTAER, Vol. 19, Pages 272-296: A Two-Stage Nonlinear User Satisfaction Decision Model Based on Online Review Mining: Considering Non-Compensatory and Compensatory Stages

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010015

Authors: Shugang Li Boyi Zhu Yuqi Zhang Fang Liu Zhaoxu Yu

Mining user satisfaction decision stages from online reviews is helpful for understanding user preferences and conducting user-centered product improvements. Therefore, this study develops a two-stage nonlinear user satisfaction decision model (USDM). First, we use word2vec technology and lexicon-based sentiment analysis to mine the sentiment polarity of each product attribute in the reviews. Then, we develop KANO mapping rules using utility functions to classify consumer preferences based on attribute importance. Based on this, a two-stage nonlinear USDM is developed to describe post-purchase evaluation behavior. In the first non-compensatory stage, consumers determine their initial satisfaction level based on the performance of basic attributes. If the performance of these attributes is poor, it is almost impossible for users to be satisfied. In the compensatory stage, the performance of the remaining attributes collectively affects final satisfaction through participation in user utility calculation. With the use of reviews from JD.com, we develop a genetic algorithm to determine feasible solutions for the USDM and verify its validity and robustness. The USDM is proven to be effective in predicting user satisfaction compared to other classic models and machine learning algorithms. This study provides a universal pattern for user satisfaction decisions and extends the study on preference analysis.

JTAER, Vol. 19, Pages 249-271: Value Co-Creation on TV Talent Shows: Cases from Mainland China, Taiwan and Hong Kong

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010014

Authors: Wai-Kit Ng Cheng-Ming Yang Chun-Liang Chen

Through the actions and interactions of video platform users, talent shows have expanded from the entertainment sphere to the social sphere and become an everyday part of life. Watching talent shows on online platforms, especially through participation in multi-platform interaction, is an ever developing and innovative field in many regions. This study adopts a multiple case analysis approach. We analyze and compare three cases of talent shows, examining aspects of their value co-creation, digital platform, dynamic capability and value network through an exploration of a series of creative activities on digital video platforms. Talent shows provide a unique environment in which different actors interact, co-exist and co-create value, i.e., another form of O2O marketing. These actors include producers, entertainment companies, sponsors and fans, and fan value co-creation currently takes many different forms, which are experienced, engaged and interacted with through different platforms. The findings contribute to examining the underlying dynamics of TV talent shows, in addition to explaining how they are achieving sustainable advantages in the media market. Furthermore, this study aims to understand the service ecosystem of network talent shows from the perspective of industrial innovation strategy; consequently, this research can help to promote the implications of this new form of digital content services and its innovation strategies.

JTAER, Vol. 19, Pages 232-248: Demystifying the Combined Effect of Consistency and Seamlessness on the Omnichannel Customer Experience: A Polynomial Regression Analysis

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010013

Authors: Wei Gao Ning Jiang

Although channel consistency and seamlessness have been regarded as two critical factors in conducting omnichannel business, their combined effect has yet to be revealed. By employing a polynomial regression, this study disentangles the combined effect of channel consistency and seamlessness on customer experience in the omnichannel context. The results indicate that enhancing channel consistency and seamlessness simultaneously can improve the omnichannel customer experience. The combined effect of a high (low) level of channel consistency and a low (high) level of channel seamlessness on the omnichannel customer experience is also positive. Data vulnerability can strengthen the combined effect of channel consistency and seamlessness on customer experience in the omnichannel context. This study not only uncovers the complex influences of different combinations of channel consistency and seamlessness but also provides new insights into conducting omnichannel retail for practitioners.

JTAER, Vol. 19, Pages 209-231: Have Your Cake and Eat It? Price Discount Programs under the Membership Free Shipping Policy in Online Retailing

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010012

Authors: Zhipeng Tang Guowei Hua Tai Chiu Edwin Cheng Xiaowei Li Jingxin Dong

Online retailers offer free shipping services, such as threshold free shipping (TFS) and membership free shipping (MFS), to promote sales and provide a better shopping experience to consumers in online retailing. Although MFS attracts more member-consumers, it encourages consumers to place more small orders than TFS, which significantly increases the operational costs of the online retailer. To address this issue, we propose two price discount policies under the MFS service, namely the limited-time discount and the threshold discount. Then, we build analytical models under these two policies to explore the impacts of offering price discounts on the retailer’s profit and consumers’ welfare. We find that no matter which discount policy is adopted, consumers are more likely to consolidate several small orders from different time periods into a big one to obtain the discount. The economies of scale generated by consumers consolidating their orders under these discount policies can help reduce online retailers’ operational costs. Therefore, regardless of any discount policy offered by the online retailer under the MFS service, consumers will place more big orders and more member-consumers are attracted, i.e., the online retailer can have its cake and eat it too. Our research findings provide decision-making insights for practitioners who offer free shipping services and price discounts to consumers in online retailing.

JTAER, Vol. 19, Pages 172-187: The Future of Electronic Commerce in the IoT Environment

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010010

Authors: Antonina Lazić Saša Milić Dragan Vukmirović

The Internet of Things (IoT) was born from the fusion of virtual and physical space and became the initiator of many scientific fields. Economic sustainability is the key to further development and progress. To keep up with the changes, it is necessary to adapt economic models and concepts to meet the requirements of future smart environments. Today, the need for electronic commerce (e-commerce) has become an economic priority during the transition between Industry 4.0 and Industry 5.0. Unlike mass production in Industry 4.0, customized production in Industry 5.0 should gain additional benefits in vertical management and decision-making concepts. The authors’ research is focused on e-commerce in a three-layer vertical IoT environment. The vertical IoT concept is composed of edge, fog, and cloud layers. Given the ubiquity of artificial intelligence in data processing, economic analysis, and predictions, this paper presents a few state-of-the-art machine learning (ML) algorithms facilitating the transition from a flat to a vertical e-commerce concept. The authors also propose hands-on ML algorithms for a few e-commerce types: consumer–consumer and consumer–company–consumer relationships. These algorithms are mainly composed of convolutional neural networks (CNNs), natural language understanding (NLU), sequential pattern mining (SPM), reinforcement learning (RL for agent training), algorithms for clicking on the item prediction, consumer behavior learning, etc. All presented concepts, algorithms, and models are described in detail.

JTAER, Vol. 19, Pages 188-208: Evolution of Men’s Image in Fashion Advertising: Breaking Stereotypes and Embracing Diversity

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer19010011

Authors: María Jesús Carrasco-Santos Carmen Cristófol-Rodríguez Ismael Begdouri-Rodríguez

This research study explores the representation of men in fashion advertising and investigates whether societal and fashion evolution has contributed to a departure from traditional stereotypes. The research methodology comprised three phases: content analysis, surveys, and in-depth interviews with an expert panel, examining how men’s clothing has been communicated in fashion over a span of 50 years, with a focus on three renowned brands: Lacoste, Burberry, and Hugo Boss. The findings reveal a notable shift in fashion advertising targeting men, characterized by increased racial diversity among models and a more diverse depiction of attitudes and poses. However, homosexual or bisexual couples remain largely unrepresented. The study highlights the influence of advertising on shaping the image of the “new man”, evident through the diminishing gender boundaries in clothing and accessories and the persistent struggle to break free from stereotypes. The study underscores the significance of ongoing efforts to promote diversity and inclusivity in fashion advertising.

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 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 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 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.