Showing papers by "Ting-Peng Liang published in 2014"

What can crowdsourcing do for decision support

Abstract: Crowdsourcing can be viewed as a method of distributing work to a large number of workers (the crowd) both inside and outside of an organization, for the purpose of improving decision making, completing cumbersome tasks, or co-creation of designs and other projects. Of the various applications of crowdsourcing, the one investigated in this paper is to support various phases of managerial decision-making and problem solving. To examine the research issues related to such support, we created a framework based on four major components of crowdsourcing: the task that is outsourced, the crowd which carries out the task, the crowdsourcing process, and the outcome evaluation. Each component is examined from the managerial, behavioral, and information technology aspects. This framework enables us to organize existing literature and identify key research issues. Suggested topics for future research are described.

Guidelines for Neuroscience Studies in Information Systems Research

Abstract: Neuroscience provides a new lens through which to study information systems (IS). These NeuroIS studies investigate the neurophysiological effects related to the design, use, and impact of IS. A major advantage of this new methodology is its ability to examine human behavior at the underlying neurophysiological level, which was not possible before, and to reduce self-reporting bias in behavior research. Previous studies that have revisited important IS concepts such as trust and distrust have challenged and extended our knowledge. An increasing number of neuroscience studies in IS have given researchers, editors, reviewers, and readers new challenges in terms of determining what makes a good NeuroIS study. While earlier papers focused on how to apply specific methods (e.g., functional magnetic resonance imaging), this paper takes an IS perspective in deriving six phases for conducting NeuroIS research and offers five guidelines for planning and evaluating NeuroIS studies: to advance IS research, to apply ...

A multi-theoretical kernel-based approach to social network-based recommendation

Abstract: Recommender systems are a critical component of e-commerce websites. The rapid development of online social networking services provides an opportunity to explore social networks together with information used in traditional recommender systems, such as customer demographics, product characteristics, and transactions. It also provides more applications for recommender systems. To tackle this social network-based recommendation problem, previous studies generally built trust models in light of the social influence theory. This study inspects a spectrum of social network theories to systematically model the multiple facets of a social network and infer user preferences. In order to effectively make use of these heterogonous theories, we take a kernel-based machine learning paradigm, design and select kernels describing individual similarities according to social network theories, and employ a non-linear multiple kernel learning algorithm to combine the kernels into a unified model. This design also enables us to consider multiple theories' interactions in assessing individual behaviors. We evaluate our proposed approach on a real-world movie review data set. The experiments show that our approach provides more accurate recommendations than trust-based methods and the collaborative filtering approach. Further analysis shows that kernels derived from contagion theory and homophily theory contribute a larger portion of the model.

We “line” because we’re having fun

Abstract: As smartphones become more and more popular, uses of social media on mobile devices have grown explosively. Previous studies have investigated usage behaviors of social networks in various ways, but few of them emphasized the activities on smartphones and social applications. In this study, we develop a research framework based on the network externalities and cognitive absorption to explain users’ actual utilization and satisfaction of a popular social mobile messaging app in Asia: LINE. Two different contexts information sharing and game playing were examined in this research. We tested our research model using 331 data collected via online survey and we found that (1) network externalities (the number of peers) had a positive effect on an individual’s actual usage; (2) cognitive absorption positively affected an individual’s actual usage of and satisfaction toward LINE; (3) LINE users’ actual utilization had a significant impact on their satisfaction; (4) LINE users’ intrinsic motivation (measured by cognitive absorption) is more important than extrinsic motivation (measured by network externalities); and (5) tasks with higher hedonic motives such as game playing may be a stronger trigger of LINE users’ actual utilization.