Journal of Theoretical and Applied Electronic Commerce Research

Why are men less likely to use electronic coupons than women? Previous studies have explained the gender difference in coupon usage by exploring roles within the household and personality traits of the sexes. However, this research offers a novel explanation for this phenomenon, that men’s reluctance to use e-coupons may derive from the prevalent stereotype that e-coupon users are feminine. Because of the feminine stereotype associated with e-coupon usage, acquiring and using e-coupons are inconsistent with men’s gender identity. Five studies combining real data analysis, an online survey and experiments are used to support the previous notion. Using e-coupon acquisition data from a platform, study 1 tests whether females are more likely to acquire and use e-coupons than males in the real world. Study 2 experimentally tests the coupon–feminine stereotype. Study 3 explores the mediation effect of gender identity threat. Study 4 and study 5 consider two boundary conditions under which male consumers may be motivated to use e-coupons: when male consumers’ masculine identity is affirmed, and when the association between e-coupons and femininity is weakened. The results explain the gender difference in e-coupon usage from the novel lens of the coupon–feminine stereotype, offering a new and important perspective to explore the effect of gender identity on coupon use. Practical implications such as breaking the coupon–feminine stereotype, adopting a masculine design and communicating an affirmation of gender identity are further discussed. Full article

It is crucial to explore the impact of digital financial inclusion on tourism for national economic development. This paper utilizes panel data from 256 prefecture-level cities in China between 2011 and 2019 to examine the influence of digital financial inclusion on tourism. The findings demonstrate that digital financial inclusion significantly contributes to the development of the tourism industry. Notably, its coverage breadth, depth of use, and level of digitalization also have positive effects. Mechanism analysis reveals that digital financial inclusion facilitates the growth of tourism by supporting the development of tourism enterprises and enhancing consumer spending. Heterogeneity analysis further reveals regional and urban disparities in the promotion of digital financial inclusion, with the effect being more pronounced in the eastern region and larger cities. In comparison to existing studies, this paper delves into the mechanisms through which digital financial inclusion impacts tourism, as well as investigates regional and city size discrepancies. Consequently, governments should strive to foster the development of digital financial inclusion to attract market players and promote the advancement of residents’ consumption, thereby bolstering tourism development. Full article

Live streaming e-commerce has emerged as a novel online marketing model. Drawing upon influencer marketing theory, this study examines the mechanisms through which influencers (live streamers) promote consumers’ purchase intention in the context of live streaming e-commerce. A sample of 449 valid questionnaires was utilized to test the proposed theoretical framework. The empirical research findings reveal that customer experience significantly and positively impacts both influencer trust and influencer attachment. Furthermore, trust and attachment established with live streamers are identified as two effective mechanisms influencing consumer decision-making. Notably, influencer attachment exhibits a stronger influence on consumer purchase intention compared to influencer trust. By comparing the effects of Taobao and Douyin live streamers on stimulating consumption and purchase intention, the study demonstrates that live streamers play a crucial mediating role between customer experience and consumer purchase intention. Specifically, the results indicate that consumer purchase intention influenced by top Taobao streamers is stronger than that of Douyin streamers, whereas influencer attachment for Taobao streamers is relatively weaker than that for Douyin streamers. These findings provide theoretical and managerial implications for platforms and live streamers seeking to stimulate robust purchase intentions among consumers by fostering attachment relationships. The establishment of an emotional connection between the live streamer and the audience proves particularly valuable in increasing purchase intention. This research contributes to the understanding of the underlying mechanisms driving consumer behavior in the context of live streaming e-commerce. It emphasizes the significance of customer experience, influencer trust, and influencer attachment as key drivers of consumer purchase intention. The findings offer valuable insights for platforms and live streamers to optimize their strategies and enrich user data labels in order to enhance consumer engagement and stimulate purchase intentions. Ultimately, this research contributes to the advancement of the live streaming e-commerce field, strengthens the application of data elements in live streaming e-commerce marketing, and guides effective decision-making by industry practitioners. Full article

The study aims to investigate how an individual’s technology awareness, subjective financial literacy and personal innovativeness characteristics impact the intention to use blockchain-based digital currencies such as cryptocurrency. The UTAUT 2 (Unified Theory of Acceptance and Use of Technology 2) model is extended with crucial constructs to develop the conceptual model. A total of 312 responses are analysed using Covariance-Based Structural Equation Modelling (CB-SEM). The moderation effects are assessed using multi-group analysis. The findings show a significant moderating effect of technology awareness and subjective financial literacy on the relationship between performance expectancy (PE) and behavioural intention to use cryptocurrency (BI). It further identified that performance expectancy (PE) mediates personal innovativeness (PI) and usage intentions (BI). The study adds to the growing literature of digital currency adoption by focusing on individual innovativeness, technology awareness and financial literacy. It also proposes a research model that can be generalised for new-age consumer-based financial technology adoption. Full article

The objective of this study is to improve the effectiveness of anti-piracy educational strategies by identifying unique digital pirate segments and delivering personalized campaign messages to the target audiences. In the first study, we introduced a segmentation study of digital pirates based on different types of risks involved in pirating activities. We identify four digital pirate segments (anti-pirates, hard-core pirates, performance-sensitive pirates, and finance-sensitive pirates), each demonstrating distinctive characteristics. Further profiling of the segments revealed different risk perceptions regarding gender and piracy experience. In the second study, we conduct an experiment to test the effects of targeted campaign messages for the newly identified pirating segments. Our results show that targeted piracy campaign messages have a significantly higher message persuasiveness, while they damage the attitude towards piracy. However, we found that the targeted piracy campaign messages have a marginal effect on changing the intention to pirate. Findings from this study offer useful implications for the design and implementation of anti-piracy educational campaigns. Full article

This study, based on the AISAS model, explores the impact of the interaction effect between visual and auditory signals on consumer purchase behavior. Using experimental methods, 120 participants were randomly assigned to four different visual and auditory signal combinations, and their purchase intentions and actual purchase behavior were measured. The results show that the interaction effect between visual and auditory signals has a significant impact on both purchase intentions and actual purchase behavior, and there is a significant positive relationship. Specifically, when visual and auditory signals are mutually consistent, consumers have the highest purchase intentions and actual purchase behavior; when both visual and auditory signals are absent, consumers have the lowest purchase intentions and actual purchase behavior; when either the visual or auditory signal is missing, consumers’ purchase intentions and actual purchase behavior are between the two extremes. This study provides a new perspective for understanding consumers’ decision-making processes in multi-sensory environments and offers valuable insights for the development of marketing strategies. Full article

Online food community purchases contributed to urban residents’ food security during the COVID-19 pandemic in Shanghai. The influence of the outbreak on the purchasing behavior of an online food community was examined. An innovative e-commerce model describes how the online community purchases facilitate integration of local food and agri-product resources, and provide consumers, especially residents of densely populated agglomerations, with convenient short-distance distribution. The survey data collected from 1168 residents show that the lockdown severity and food security concerns increased the frequency of residents’ online food purchases. Heterogeneity analysis indicated that the Omicron outbreak effected the online food purchases of those born before the 1990s, males, the less educated, and low-income earners through a community group effect. The internet provides a convenient means of disseminating information, promoting access to local foods, and assuring food access during public health emergencies. Purchasing food online can be further enhanced through standardized management of online communities. Full article

Against the backdrop of the fourth industrial revolution and the COVID-19 pandemic, digital transformation (DT) in the day-to-day operations of micro and small enterprises (MSEs) comes with challenges. Existing maturity models generally focus on advanced levels and are inappropriate for relatively immature companies (e.g., most hospitality MSEs). This study used online documents and in-depth interviews as data sources to develop a customized maturity model framework for hospitality MSEs. Through coding analysis, the research identified four key dimensions that constitute the digital maturity of hotels: strategy and organization, digital technology, digital capabilities, and integrated business. These enterprises have progressed in their digital maturity, moving from an IT-enabled transformation to adopting a brand-oriented approach. The selection of a digital transformation strategy depends on strategic alignment. The proposed model provides a comprehensive understanding of the maturity levels of these companies, thereby facilitating their successful integration into the ongoing modern industrial revolution. Full article

This paper presents an empirical analysis of e-commerce data obtained through Google Analytics (GA) from two small businesses’ perspectives: an IT components company and a tourism agency website located within the same county in Romania. The objective of our study is to examine the enduring effects of the COVID-19 pandemic and seasonal variations over the last four years. The data collection spanned from January 2019, predating the onset of the COVID-19 pandemic, until mid-February 2023. To facilitate our analysis, we categorize the GA metrics into groups that encompassed website performance, site accessibility, and user behavior for the IT company. As for the tourism agency, we focus on website accessibility, user behavior, and marketing campaigns. Our goal is to empirically group or associate GA metrics according to their intrinsic meaning and check if each group reflects a certain latent concept (such as user behavior or site accessibility). Furthermore, our study aims to formulate and test five hypotheses regarding the immediate and long-lasting impact of the COVID-19 pandemic on the operations of small businesses. Our contribution consists of formulating and verifying the five hypotheses by providing descriptive data from the results of the Pearson correlation test, empirically grouping the GA metrics and verifying whether they reflect certain latent factors or topics, interpreting the results from the application of the ANOVA technique and Scarpello’s adaptation of the one factor test, respectively. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

The increasing popularity of online job vacancies and machine learning methods has raised questions about their combination to enhance our understanding of labour markets and algorithms. However, the lack of comparable studies necessitates further investigation. This research aims to explore the effectiveness of Random Forest Regressor (RFR) and Support Vector Regressor (SVR) machine learning models in predicting online job vacancies compared to the auto-regressive ARIMA method. To answer this question, detailed sub-questions are posed in relation to the sub-samples of the main data provided by Birch Consultants, an external partner originally obtained by Jobdigger. Drawing upon previous research on time-series accuracy, this study combines various approaches to benefit society and the external partner. Using the walk-forward validation method, with a 91-day expanding window, it provides precise answers to the sub-questions. Findings suggest that RFR is suitable for forecasting larger samples, while SVR is preferred due to its capability to predict small series despite relatively small scoring benefits and computational costs. Both machine learning models outperform the baseline ARIMA model in capturing complex time-series. Further research should focus on exploring advanced auto-regressive, deep learning, and hybrid models for future investigations. Full article

The Lightning Network (LN), a second-layer protocol built on top of the Bitcoin blockchain, is an innovative digital payment solution that offers increased convenience, speed, and cost-effectiveness to consumers and businesses alike. However, there is limited literature available on the characteristics of this nascent technology, the depth and breadth of the various business LN-related applications as well as relevant adoption/implementation challenges. This study aims to contribute to the understanding of the LN’s characteristics, its potential in enhancing business operations and its applicability across different sectors, while taking into account adoption and implementation challenges. We apply a narrative review methodology using a semi-systematic approach to examine new and emerging business models empowered by the LN and its characteristics, topology, performance, privacy and security. We analyze the data to identify key themes and trends in the literature, offering a critical analysis of the strengths and weaknesses of the existing literature. Based on the findings, we provide several clusters of fruitful areas for future research directions. This study not only provides crucial insights for businesses contemplating the adoption of LN to improve their operations and customer experience, but it also represents a substantial academic contribution, offering valuable knowledge and fostering further research in the fields of blockchain technology, FinTech and cryptocurrencies. Full article

In light of the development of online travel agencies (OTAs), the rules of the entire tourism industry have changed. Due to the ease of finding information and comparing products, consumers can choose a hotel not only by room type, but also by rate, according to their preferences. The purpose of this study was to explore the effect of price discrimination on the fairness perception toward reservation intentions. The interaction effects of the brand familiarity and the type of consumers on the fairness perception were also examined. The study used an experimental design, with 2 price discriminations × 2 brand familiarities × 2 regulatory focuses, on a total of 320 valid subjects. The results showed that advantaged-price discriminations had higher fairness perceptions than equal-price discriminations, and that higher fairness perceptions had higher reservation intentions. The interaction effect of brand familiarity showed no significant impact on the fairness perceptions, while the regulatory focus had a mitigating effect on the price discrimination and on the fairness perceptions. This study provides insights into the relationship between online price discrimination and tourism, and it contributes to the literature on hospitality. It also provides the managerial implications for online hotels in developing pricing strategies. Full article

Digital content marketing (DCM) complements traditional marketing communication approaches and is a major focus of research. Uses and gratifications research posits that DCM only unfolds positive effects if it provides valuable content to consumers. However, there is limited evidence on what constitutes gratifying digital corporate content on company websites. This study aimed to elicit consumers’ preferences for key characteristics of digital corporate content on company websites and whether preferences differ among consumer subgroups. Best–worst scaling (BWS) was used to reveal preferences. To obtain BWS data, a cross-sectional survey was employed. The study sample comprised 1527 consumers from Germany, Switzerland, and Austria. Data were analyzed using counting analysis and conditional logit modeling. Subgroup comparisons were performed with t-tests and one-way ANOVA. The results consistently show that consumers prioritize information value as the most important content characteristic, followed by value in use, entertainment value, process value, and social value. Subgroup comparisons revealed generally similar priorities among consumers, with the greatest heterogeneity being found in assessments of the importance of social value. The study also suggests that consumers prioritize digital corporate content characteristics on company websites differently than they do on social media. These findings contribute to the evolving literature on DCM and provide insights that could help set evidence-based priorities in DCM practice. Full article

The COVID-19 pandemic has altered consumer behavior, making social commerce a viable alternative throughout the world. Europe is trailing the US and China in adopting this technology, but the prognosis is encouraging. Our goal is to contribute to this process by offering a literature review on social commerce in Europe for researchers, practitioners, and policymakers. We analyzed 4.764 articles published during the 2015–2023 period on the topic of social commerce in Europe utilizing the PRISMA flow diagram. After scrutinizing this large body of literature with various instruments including artificial intelligence (AI), we identified a final list of 45 articles that are most pertinent to our research questions. The emerging themes were that social media is shaping behavior and triggering buying intentions, that trust is paramount in buying impulses and behavior, and that success in social commerce is predicated upon relationships and engagement. Full article