JTAER, Vol. 18, Pages 1463-1483: Internet Usage among Senior Citizens: Self-Efficacy and Social Influence Are More Important than Social Support

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer18030074

Authors: Mirjana Pejić Bach Lucija Ivančić Vesna Bosilj Vukšić Ana-Marija Stjepić Ljubica Milanović Glavan

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.

JTAER, Vol. 18, Pages 1446-1462: Deep Filter Context Network for Click-Through Rate Prediction

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer18030073

Authors: Mingting Yu Tingting Liu Jian Yin

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 models’ 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.

JTAER, Vol. 18, Pages 1431-1445: Exploring the Advantages of Using Social Media in the Romanian Retail Sector

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer18030072

Authors: Cristinel Vasiliu Mihai Felea Irina Albastroiu Nastase Mihaela Bucur Adrian Istrate-Scradeanu

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.

JTAER, Vol. 18, Pages 1419-1430: The Interplay of AI Adoption, IoT Edge, and Adaptive Resilience to Explain Digital Innovation: Evidence from German Family-Owned SMEs

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer18030071

Authors: Irfan Saleem Shah Md. Safiul Hoque Rubeena Tashfeen Manuela Weller

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.

JTAER, Vol. 18, Pages 1404-1418: TEE: Real-Time Purchase Prediction Using Time Extended Embeddings for Representing Customer Behavior

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer18030070

Authors: Miguel Alves Gomes Mark Wönkhaus Philipp Meisen Tobias Meisen

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.