Scielo RSS <![CDATA[Journal of theoretical and applied electronic commerce research]]> vol. 16 num. 1 lang. n <![CDATA[SciELO Logo]]> <![CDATA[Editorial: Electronic Commerce in the Time of Covid-19 - Perspectives and Challenges]]> <![CDATA[Influence of Platform Characteristics on Purchase Intention in Social Commerce: Mechanism of Psychological Contracts]]> Abstract: Social commerce is an emerging platform that entails different social features to attract the attention of online consumers. Borrowing insights from the psychological contract theory, this study examines the characteristics of social commerce platforms, such as platform interactivity and rating and reviews that influence the consumers’ purchase intentions. An online survey of 430 consumers was conducted on a popular social commerce site in China. Partial least squares structural equation modeling and fuzzy sets qualitative comparative analysis techniques are used to analyze this mediation model. The results reveal the positive relationships between (1) psychological contract and purchase intention; (2) platform interactivity and relational contract; and (3) rating and reviews and transactional contract. Besides, partial mediating effects of relational contract, on the relationship between platform interactivity and purchase intention, and transactional contract, on the relationship between rating and reviews and purchase intention, are also observed. Moreover, fuzzy sets qualitative comparative analysis validates the robustness of platform characteristics with psychological contracts and purchase intentions. Overall findings suggest that the platform characteristics have the potential to transform consumers’ purchase intentions through psychological contracts. The study provides new insights on consumers' behavior in social commerce milieus, along with its theoretical and practical implications. <![CDATA[Indicators of Website Features in the User Experience of E-Tourism Search and Metasearch Engines]]> Abstract:The continuous growth of e-commerce, combined with new trends in the tourism sector, creates an imperative to conduct analyses and establish suitable models for improving the experience of users who seek tourism products online, using search and metasearch engines. However, few studies analyze web design variables and their impacts on the user experience. Therefore, the present study aims to investigate the influence of content, usability, functionality, and branding for determining user experiences with search engines and metasearch engines dedicated to tourism. The methodology used in this research followed a mixed approach to fulfill the proposed dual perspective, that is, to collect data from websites and evaluate the user experience. To determine the variables to be modeled, the authors use a General Additive Model. Main results reveal two determinant factors that enhance the user experience: usability and branding. With this foundation, this study proposes basic premises of the digital strategy that tourism platforms should follow. As implications for the tourism sector, suggest that e-commerce search and metasearch engines in the tourism industry should devote substantial efforts to implementing interactivity, memorability, personalization, privacy, and security. <![CDATA[Roles and Capabilities of Enterprise Architecture in Big Data Analytics Technology Adoption and Implementation]]> Abstract:Organizations are attempting to harness the power of big data analytics. Enterprise architecture can be used as an instrument to integrate big data analytics into the existing IT landscape and enabling the development of capabilities to create value from these technologies. Yet, there is limited research about the role of enterprise architecture in adopting big data analytics. This paper explores enterprise architecture roles and capabilities for the adoption of big data analytics by conducting a qualitative case study at the Dutch Tax and Customs Administration. The first attempt to adopt big data analytics was focused on integrating analytics into the current complex IT landscape, but this encountered many challenges and resulted in slow progress. To overcome these challenges, a separate department was created to quickly harness the potential of big data analytics. Enterprise architecture was used for impact analysis and to create a transition process. The findings suggest that enterprise architecture was used in different ways at the various stages of adoption and implementation, requiring different roles and a different set of capabilities. Enterprise architecture was found to be contingent on the type of technology and the situation at hand. We recommend more research into the role of the context in enterprise architecture research. <![CDATA[How Individual Investors React to Negative Events in the FinTech Era? Evidence from China’s Peer-to-Peer Lending Market]]> Abstract:Inexperienced individual investors are the main players in the emerging FinTech industry, and also suffer from frequent negative events in the markets. With 3,110 negative events and 467,594 transaction data of China’s peer-to-peer lending market from 2015 to 2018, this paper analyzes how different types of platform negative events affect the decision-making of individual investors. We find that individual investors only have a significant negative reaction to moral hazard exposure events such as platforms absconding, with relatively ignorance of other types of negative events. The negative effect is rapid but short-lived, and shows differences among platforms of different background and attributes. By the mediation analysis, we find that public attention can be a mediator and explain the pattern of the impact that absconding events have on individual investors. Related risk prevention, regulation, investor education issues are discussed and further suggestions are also put forward for both individual investors and regulators. <![CDATA[Social Recommendations for Facebook Brand Pages]]> Abstract:The objective of this research is to bridge the gap by proposing a content-based framework that is specifically designed to operate in social media environment. This study proposed a recommendation framework that integrated the features of brand pages and information on user behavior on brand pages. Data were obtained from 2,076 official brand pages in Taiwan, and a total of 500,000 interaction data were obtained and processed. Decision trees were used to classify brand pages and detect features that facilitate distinguishing among brand pages. Our method involved a simple training procedure with no restriction to a particular classifier learning algorithm and yields improved results on data sets extracted from a considerable number of Facebook brand pages. The results represented the diversity of social media user criteria in evaluating whether an activity is interesting to users. Moreover, these prevalent features indicate that brand pages distinguish themselves from others through their willingness to engage and interact with users. These findings not only enabled using brand page recommendation models for facilitating the selection of the most suitable brand page, but also useful for researchers seeking to develop a recommendation system for social media.