Modeling the Motivation of Users’ Sharing Option: A Case Study Based on A Car-Sharing Digital Platform

Sharing economy is associated with a series of economic activities in terms of renting, lending, trading, bartering and swapping of goods, service, space or money, that take place in organized systems or digital platforms. In this research, we interviewed 50 drivers, a kind of users type who provides cars and service from a car-sharing digital platform. Based on the prior studies of motivation, we combined Social Exchange Theory (SET) and Technology Acceptance Model (TAM) to identify salient motivators in this context. Furthermore, building on Self-determination Theory (SDT), we propose a motivation model of users’ sharing option in digital platform. The findings extend literatures of motivation researches in the context of sharing economy as well as help guide the development and operation of sharing economy digital platform

Exploring a Hybrid Algorithm for Price Volatility Prediction of Bitcoin

In recent years, the Bitcoin investment market has become increasingly popular. We collected existing literature on Bitcoin and found that predictions about the role of Bitcoin in investment portfolios and the volatility of Bitcoin price as well as return have become advanced research topics. This study shows our current work on the prediction of Bitcoin price volatility and proposes an idea for predicting the price volatility. We have designed an experiment that compares different combinations of machine learning algorithms with GARCH-type models, intending to compare the effects of these models in the prediction of Bitcoin time series and finally implement an optimized algorithm

A Conceptual Model of Trust Influencing Factors in Robo-Advisor Products: A Qualitative Study

As an integration of e-commerce and traditional financial service, robo-advisor is a promising product that recommends portfolios to individual investors based on modern technologies. However, this industry faces many challenges such as slow adoption and distrust from customers. This paper extends prior literatures in robo-advisor by exploring trust influencing factors and their detailed sub-factors from the perspective of five dimensions of trust. In this study, we not only validated previous factors of trust in the context of robo-advisor, but also found several new factors influencing customers’ feelings. A conceptual model is further proposed. The data analysis is based on semi-structured interviews with 27 investors. Understanding trust factors of robo-advisor helps the service vendors provide a better product for individual investors and facilitates faster adoption behavior from customers, which promotes further development of the industry

Understanding Continuance Usage of Social Networking Services: A Theoretical Model and Empirical Study of the Chinese Context

Social networking services (SNS) provide innovative online platforms for social interactions and communications. In order to understand users’ continuance intention of using SNS, we first propose a comprehensive research model based on the expectation-confirmation model (ECM) of IS continuance. Our model examines direct and indirect factors affecting users’ continuance intention of SNS usage. We then apply the model in an empirical study, in which we collect and analyze survey data from the users of a major Chinese SNS website. The results of the study reveal different effects of individual motivations such as perceived usefulness and perceived enjoyment on continued usage intention (CUI) in SNS. We also find significant impact of non-individual motivation (i.e., structural embeddedness) on CUI. This research not only extends the IS continuance theory into SNS studies, but also provides IS researchers and SNS practitioners empirical insights into CUI in SNS and its underlying factors in the Chinese context

The impact of extended global ransomware attacks on trust: How the attacker's competence and institutional trust influence the decision to pay

© 2018 Association for Information Systems. All rights reserved. The standardization, interconnectivity and pervasiveness of information systems, combined with the increasing ability to collect and utilize data, enhance the value they offer a user. These strengths however can also be turned into a weakness and vulnerability by ransomware (RW). RW can utilize the functionality of current systems both to infect them but also to increase the magnitude of the attack. This research proposes a model of the impact of the RW attack on the user's trust, which in turn has an effect on their decision to pay the ransom or follow the guidance from the relevant institutions. The model shows that the effectiveness of the attack, the trust in the competence of the attacker and ransomware demands that are reasonable and easy to fulfil, positively influence the intention to pay the ransom. The initial institutional response, institutional trust and institutional solution influence the intention to follow the institutional guidance

Introduction to IT Enabled Collaboration in Developing Countries Minitrack

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Investigating the Relationship among Characteristics of Social Commerce, Consumers’ Trust and Trust Performance

Social commerce as a subset of e-commerce, popularizes rapidly with an increasing number of users, and consumers’ trust has become a crucial factor in the success of social commerce firms, and impacts on their decision on purchasing. In this regard, the study tries to research the characteristics of social commerce (transaction safety, concentration and enjoyment, communication and information quality) that influence consumers’ trust and assess the effects of trust on trust performance (purchase and word-of-mouth intentions), and trust performance will provides a basis for consumers to decide to purchase, and put forward feasible suggestions to social commerce firms. The results of an empirical analysis based on a sample of 133 users indicate that all the characteristics of social commerce involved had significant effects on trust, and then will positively influence trust performance

Investigating Impulse Buying Behavior in Live Streaming Commerce: The Role of Social Presence

Live streaming is changing the paradigm of people’s entertainment and consumption. It has been adopted by many small individual sellers to improve their market performance, leading to the emergence of live streaming commerce. Although existing literature has paid attention to consumer purchase behavior in live streaming commerce, little knowledge on impulse buying can be available. Drawing on social presence theory and cognitive-affective framework, this paper attempts to develop a theoretical model to investigate how social presence affects consumers’ urge to buy impulsively through the mediating mechanism of cognitive state (i.e., product risk) and affective state (i.e., affective intensity). This paper is expected to advance knowledge on consumers’ impulse buying in live streaming commerce

Exploring Trust in Online Ride-sharing Platform in China: A Perspective of Time and Location

Trust is a key issue to be considered deliberately in the online ride-sharing platform to reduce risk and ensure transactions. In this paper, trust-in-platform is explored from these two perspectives to fill the research gaps. A ride-sharing platform in China was investigated. Results show that trust-in-platform in economically developing districts is slightly higher than that in economically developed districts. At the same time, trust-in-platform level differs in time, trust-in-platform levels are obviously lower between 19’o clock and 23’o clock. Moreover, machine learning is employed to predict the relationships between time/location and trust-in-platform. The result is that recall is 78.3%, precision is 57.3%, and F1 is 66.2%. The result shows trust-in-platform has an obvious correlation with time and location, thus further consolidates the findings. This study contributes to the existing knowledge on trust in the ride-sharing platforms and has practical implications for platform operators