Earticle

While a great body of information systems (IS) literature has discussed mobile banking (m-banking) services, most of these studies have focused on the adoption or acceptance phases of this technology; with little attention was given to users’ intension to continue using such technology. This paper aims at investigating the most important factors that predict users’ continuous intension to use m-banking services in the post-adoption phase. This paper presents a conceptualization and validation of an extended expectation-confirmation model (ECM). A total of 303 Kuwaiti users of m-banking services participated in this study. Partial least squares (PLS) of structure equation modelling (SEM) technique was used to analyze the data. The results mainly showed that users’ continuous intension to use m-banking services is significantly influenced by perceived trust, satisfaction, self-efficacy, performance expectancy and effort expectancy. Theoretical and practical contributions as well as the research limitations and future directions are discussed.

The paper aims to analyze and predict sales of liquor in the state of Iowa by applying machine learning algorithms to models built for prediction. We have taken recourse of Azure ML and Spark ML for our predictive analysis, which is legacy machine learning (ML) systems and Big Data ML, respectively. We have worked on the Iowa liquor sales dataset comprising of records from 2012 to 2019 in 24 columns and approximately 1.8 million rows. We have concluded by comparing the models with different algorithms applied and their accuracy in predicting the sales using both Azure ML and Spark ML. We find that the Linear Regression model has the highest precision and Decision Forest Regression has the fastest computing time with the sample data set using the legacy Azure ML systems. Decision Tree Regression model in Spark ML has the highest accuracy with the quickest computing time for the entire data set using the Big Data Spark systems.

Social media platforms have become prominent channels for e-commerce, and the role of social network sites’ (SNS) content marketing is expanding as a strategic marketing communication approach to attract and retain consumers and increase sales. In this study, we focused on South Korea market and explored the influence of linguistic complexity and informality on consumer engagement. In particular, we identified the importance of complexity, focusing on its negative effects, as well as the moderating effect of commerce features to minimize these effects. Specifically, content length, hashtags, long words, and average sentence length significantly and negatively impacted consumer engagement. The influence of emojis, an informality variable, was not statistically significant. Shoppable tags, a commerce feature that provides both advertising explicitness and shopping convenience, were a moderating factor in the influence of complexity. Our findings provide new insights for content marketing researchers, and have practical implications for social media managers and content developers.

Technology has been changing the travel experience of visitors. Particularly, augmented reality (AR) is one of the emerging technologies, which widely used in cultural heritage tourism sites. This study is based on a new technology acceptance model and future modified this model to examine the relationship between product beliefs, consumer satisfaction with AR, and destination loyalty in cultural heritage sites. Moreover, this paper examined the role of technology readiness (TR) forming travellers’ loyalty of destination with a kind of travel technology--AR. The results show that perceived usefulness and perceived ease of use have significant effects on the satisfaction of AR towards the travellers’ loyalty of destination. TR is found to have moderating effects on this model.

Online financial technology products are an important consumer finance innovation. While a large body of previous research has focused on initial adoption and consumer willingness to use these products, little research explores the continued use of these products beyond the initial adoption phase. In particular, special attention should be paid to how users‘ trust and perceptions of privacy and security affect continued use behavior. This paper integrates the expectation confirmation model of information system continuance (ECM-ISC), the information system success model (ISSM) and the security and trust literatures to investigate continued use of online financial technology. To test the research model, we collected 398 valid questionnaires from Ant Credit Pay users. The research results show that system and service quality positively impact users‘ expectation confirmation, while information quality has no significant impact. Expectation confirmation and perceived usefulness positively affect user satisfaction. Moreover, the user‘s perception of privacy and security plays a vital role in user satisfaction. Satisfaction and perceived trust jointly promote users‘ continuance behaviors. Findings of this study indicates the importance of the information system success factors and security factors due to their influence on the continued use of Fintech products. This conclusion has implications for enterprises in improving the product qualities and enhancing the degree of security to meet user needs.