Sentiment Analysis in Social Media Platforms: The Contribution of Social Relationships

The massive amount of data in social media platforms is a key source for companies to analyze customer sentiment and opinions. Many existing sentiment analysis approaches solely rely on textual contents of a sentence (e.g. words) for sentiment identification. Consequently, current sentiment analysis systems are ineffective for analyzing contents in social media because people may use non-standard language (e.g., abbreviations, misspellings, emoticons or multiple languages) in online platforms. Inspired by the attribution theory that is grounded in social psychology, we propose a sentiment analysis framework that considers the social relationships among users and contents. We conduct experiments to compare the proposed approach against the existing approaches on a dataset collected from Facebook. The results indicate that we can more accurately classify sentiment of sentences by utilizing social relationships

Towards Collaboration Virtualization Theory

With widespread use of collaboration technology and increasing dispersion of teams due to globalization of companies, more and more collaboration activities are being conducted virtually. However, virtual collaboration seems to work well for some cases, but not for others. This phenomenon motivates research questions: What factors determine the suitability of collaboration virtualization, and how do those factors affect the design of effective collaboration systems? Our literature study yielded little theoretical work in this regard. As such, we believe that research on collaboration virtualization theory (CVT) is critically needed. To this end, we present our preliminary findings on the purpose and composition of collaboration virtualization theory based on the literature. Essentially, our CVT contains three categories of constructs: task, team, and technology characteristics. Our main objective in this short paper is to initiate a new theoretical perspective for research in the field of collaboration technology and management

FORMATION AND EFFECT OF SOCIAL INTERACTIONS IN ONLINE BRAND COMMUNITY: AN EMPIRICAL INVESTIGATION

Online brand communities, enabled by social media technology, are being utilized by companies to improve marketing and sales. However, little is known about how to encourage customer interactions in an online brand community and whether the interactions can affect purchase behavior. To address these research questions, we explore factors that influence the formation of social interactions in an online brand community and assess the impact of different types of social interactions on customer purchase behavior, resulting in a set of theoretical hypotheses about social interactions for e- commerce. We test our hypotheses using a data set that includes customer social interactions and purchases in an online brand community. Our results show that homophily in certain customer characteristics (e.g,. member age, location, deal sensitivity) positively impacts the formation of social interaction while homophily in other customer characteristics (e.g,. share premium products) does not. We also find that social interactions with people who have purchased strongly influence customer purchase behavior. Furthermore, the effect of such social interactions is strengthened by geographical proximity. We discuss theoretical implications of our results and also offer practical guidelines for managers on how to manage customer relationships in online brand communities

Three Case Studies On Business Collaboration And Process Management

The importance of collaboration has been recognized for more than 2000 years. While recent improvement in technology creates vast opportunities for collaboration, effective collaboration remains challenging as ad hoc teams work across time, geographical, language, and technical boundaries, and suffer from process inefficiency. My dissertation addresses part of these challenges by proposing theoretical frameworks for business collaboration and process management. Case study is used as a research strategy for this thesis and it consists of three studies. The first study proposes a process modeling framework to support efficient process model design via model transformation and validation. First, we divide process modeling into three layers and formally define three layers of workflow models. Then, we develop a procedure for transforming a conceptual process model into its corresponding logical process model. Third, we create a validation procedure that can validate whether the derived logical model is consistent with its original conceptual model. The second study proposes a framework for analyzing the relationship between interaction processes and collaboration efficiency in software issue resolution in open source community. We first develop an algorithm to identify frequent interaction process structures referred to as interaction process patterns. Then, we assess patterns' impact through a time-dependent Cox regression model. By applying the interaction process analysis framework to software issue resolution processes, we identify several patterns that are significantly correlated with collaboration efficiency. We further conduct a case study to validate the findings of pattern efficiency in software issue resolution. The third study addresses the issue of suitability of virtual collaboration. Virtual collaboration seems to work well for some cases, but not for others. We define collaboration virtualization as the suitability for a task to be conducted virtually and propose a Collaboration Virtualization Theory (CVT) to explain collaboration virtualization. Three categories (i.e., task, technology, and team) of constructs that determine the suitability of collaboration virtualization are derived from a systematic literature review of related areas. In summary, this dissertation addresses challenges in collaboration and process management, and we believe that our research will have important theoretical and practical impacts on the development of collaboration management systems

Dual workflow nets: mixed control/data-flow representation for workflow modeling and verification

A WFMS(workflow management system) contains two basic elements: the workflow model and the workflow engine. It is important to verify workflow models before they are put to execution. Traditional workflow models mainly describe workflows either from the control perspective or from the data perspective. In fact, the control flow and the data flow are two important aspects for workflow modeling and they are not independent from each other. A new workflow modeling technique, named Dual Workflow Nets (DWF-nets), is proposed to explicitly model the control flow and data flow of workflow processes. Besides, the control/data flow interactions can be captured in DWF-nets. Moreover, the control/data inconsistency, which is neglected by traditional modeling techniques, can be detected by verification of DWF-nets

An Efficient Recommender System Using Locality Sensitive Hashing

Recommender systems are widely used for personalized recommendation in many business applications such as online shopping websites and social network platforms. However, with the tremendous growth of recommendation space (e.g., number of users, products, etc.), traditional systems suffer from time and space complexity issues and cannot make real-time recommendations when dealing with large-scale data. In this paper, we propose an efficient recommender system by incorporating the locality sensitive hashing (LSH) strategy. We show that LSH can approximately preserve similarities of data while significantly reducing data dimensions. We conduct experiments on synthetic and real-world datasets of various sizes and data types. The experiment results show that the proposed LSH-based system generally outperforms traditional item-based collaborative filtering in most cases in terms of statistical accuracy, decision support accuracy, and efficiency. This paper contributes to the fields of recommender systems and big data analytics by proposing a novel recommendation approach that can handle large-scale data efficiently

Integrating workflow and forum via event management

Workflows have been widely used for coordinating structured processes, whereas group support systems are used to facilitate ad hoc and unstructured collaboration activities. In many business settings, it is beneficial to use both workflows and group support systems. However, how to integrate workflow systems and group support systems have not been well studied in the literature. In this paper, we propose a scalable middleware framework, namely event management system, which can support high-degree decoupling between workflow and groupware. The event management architecture and its main functionalities as well as the implementation techniques are presented. We delineate the applicability of event-based integration by comparing with other integration paradigms

IMPACT OF PROMOTION ON ONLINE REVIEW RATINGS: THE MODERATING ROLE OF TEMPORAL DISTANCE AND DEAL PRONENESS

Promotion and online reviews — have been considerably studied separately. Although marketing literature has shown that promotions could influence customer postpurchase evaluative response, little is known about how promotions will affect customer behavior in online reviews. This research examines the effects of promotion on online review ratings. In addition, this study also explores how temporal distance between purchase and posting online review, and customer deal proneness moderate the relationship between promotion and online review ratings. We collect a unique dataset by combining customer transaction records with their behavior in online reviews, and use an ordered probit model to test our hypotheses. Our results show that coupon promotions have a positive impact on online review ratings, and the strength of this impact varies in reverse of the time delay between purchase and posting online reviews. And this relationship is enhanced when customers have high deal proneness