Understanding Behavioral Drivers in Twitter Social Media Networks

As social media platforms facilitate user interactions, organizations increasingly use social media networks (SMNs) to build network ties. Studying user behavior on SMNs can help to uncover strategic information and improve situation awareness. However, there is a lack of understanding of behavioral drivers of SMN participants. This research developed a theoretically-based IS development framework for modeling user behavior in large evolving SMNs. To demonstrate the feasibility of our framework, we developed a proof-of-concept system for simulating user activities in the SMNs of Twitter social communities. Our system models the complex behavioral features in the SMNs by using a wide range of theoretically-driven features and machine-discovered features, and predicts user activities by using a pipeline of statistical and machine-learning techniques. Preliminary results of a simulation study provide insights of the importance of comprehensive network features to model SMN group behavior accurately and quality of commitment features to model SMN user behavior

Enhancing Business Intelligence Quality with Visualization: An Experiment on Stakeholder Network Analysis

Business intelligence (BI) has gained a strategic importance in today’s global competitive environment. However, high-quality BI is not easy to obtain on the Web due to information overload and difficulty to present complicated relationships among various types of business stakeholders. Unfortunately, existing BI tools lack the capability of analyzing and visualizing such relationships and research on BI systems is sparse. In this paper, we review the current market of BI tools and related research, describe an approach to support the development of tools that provide high-quality BI, and report the findings of a user evaluation study of the prototype developed based on the proposed approach. The approach combines information visualization and Web mining techniques with human knowledge to enable business analysts to analyze and visualize complicated business stakeholder relationships. Results of an experiment involving 62 subjects show that the prototype significantly outperformed a traditional method of BI analysis in terms of efficiency, quality of BI, and user satisfaction. The subjects provided favorable comments and expressed strong preferences toward the prototype in most applications. This research contributes to advancing BI research and to providing new empirical findings for BI systems evaluation. Available at: https://aisel.aisnet.org/pajais/vol1/iss1/9

Automatic Summarization of Customer Reviews: An Integrated Approach

The proliferation of interactivity between Web content producers and consumers underscores the development of the Internet in recent years. In particular, customer reviews posted on the Web have grown significantly. Because customers represent the primary stakeholder group of a company, understanding customers’ concerns expressed in these reviews could help marketers and business analysts to identify market trends and to provide better products and services. However, the large volume of textual reviews written in informal language makes it difficult to understand customers’ concerns. This paper describes an integrated approach to summarizing customer reviews. The approach consists of the steps of sentence extraction, aspect identification, sentiment classification, and review summarization. We report preliminary results of using our approach to summarize product reviews extracted from Amazon.com. Our work augments existing work by considering nonstandard input and by incorporating linguistic resources and clustering in automatic summarization

Evaluating the Use of a Visual Approach to Business Stakeholder Analysis

As businesses increasingly use the Web to share information with stakeholders, the problems arising from information overload and interconnected nature of the Web make it difficult to obtain business intelligence (BI). This research proposes a visual approach to business stakeholder analysis that integrates information visualization and Web mining techniques with human domain knowledge. A proof-of-concept prototype was developed based on the approach to assist in analyzing and visualizing complicated stakeholder networks on the Web. We report results of an empirical evaluation comparing the prototype with a traditional method of BI analysis and discuss the implications on HCI research and BI systems development

Searching for Non-English Web Content: An Empirical Study of the Spanish Business Intelligence Portal

As non-English-speaking online populations grow rapidly, there are increasing needs to support searching for non-English Web content. Prior research has assumed English to be the primary language for Web searching, but this is not the case for many non-English-speaking regions. For example, Latin America will have the fastest growing population in the coming decades but existing Spanish search engines lack search, browse, and analysis capabilities. In this paper, we have proposed a language-independent approach to supporting non-English Web searching. Based on the approach, we have developed the Spanish Business Intelligence Portal (SBizPort) to support searching, browsing, summarization, categorization, and visualization of Web information. Results from an empirical study involving Spanish subjects show that the portal achieved significantly better user ratings on information quality, cross-regional search capability, and overall satisfaction than the benchmark search portal. This study thus contributes to human-computer interaction research on non-English Web searching

Living In the KnowlEdge Society (LIKES) Initiative and iSchools' Focus on the Information Field

In this poster, we describe the similarities between the Living In the KnowlEdge Society (LIKES) project and iSchools – both focus on the information field. This might lead to future collaborations between the two. One of the LIKES objectives is to spread computational thinking, fundamental CS/IT paradigms, key computing concepts and ICT paradigms across the Knowledge Society. This is analogous to iSchools’ vision of education for thorough understanding of information, IT and their applications. In the previous three LIKES workshops, participants from various disciplines had an intense discussion about grand challenges to incorporate computing/IT in their disciplines. All iSchools have courses that teach computing and information-related topics. If those courses can be expanded for other non-computing disciplines on their campuses with support from experiences of LIKES, it would further empower professionals in the iField

Rockstar Effect in Distributed Project Management on GitHub Social Networks

The internet has become increasingly social, opening up new space for online collaboration and distributed project management. Decentralized management techniques such as open-source software, distributed development, and software-as-a-service allow software developers to easily connect online and to solve complex problems collaboratively. Online rockstars, who are well-respected in a community and are followed by numerous other users, often influence the decisions of project managers and clients in software development. Understanding the effects of these rockstars can greatly facilitate technology development and adoption in distributed project management. This paper presents a study of the GitHub social network to understand rockstar effect in distributed project management. In GitHub, developers often collaborate in distributed teams and interact in their online social networks, which evolve with the popularity of software repositories and actions of rockstars. To understand how rockstars influence the popularity of software repositories, this research constructed temporal social networks from 2015 to 2017 between 13.5 million software repositories and 2.6 million GitHub users and examined the evolvement of the behavior of 245,501 rockstar followers. The results show that the more followers a rockstar has, the more triadic events there are in his/her participated repository. And the difference of a number of events between top rockstar and other rockstars is much higher in participative events than in contributive events, indicating higher triadic influence from top rockstar in those events for technology development in distributed project management

A Dynamic Classification Approach to Churn Prediction in Banking Industry

Churn prediction is the process of using transaction data to identify customers who are likely to cease their relationship with a company. To date, most work in churn prediction focuses on sampling strategies and supervised modeling over a short period of time. Few have explored the area of mining customer behavior pattern in longitudinal data. This research developed a dynamic approach to optimizing model specifications by using time-series predictors, multiple time periods, and rare event detection to enable accurate churn prediction. The study used a unique three-year dataset consisting of 32,000 transaction records of a retail bank in Florida, USA. It uses trend modeling to capture the change of customer behavior over time. Results show that data from multiple time periods helped to improve model precision and recall. This dynamic churn prediction approach can be generalized to other fields for which mining long term customer data is necessary

Extracting Business Intelligence from Online Product Reviews: An Experiment of Automatic Rule-Induction

Online product reviews are a major source of business intelligence (BI) that helps managers and market researchers make important decisions on product development and promotion. However, the large volume of online product review data creates significant information overload problems, making it difficult to analyze users’ concerns. In this paper, we employ a design science paradigm to develop a new framework for designing BI systems that correlate the textual content and the numerical ratings of online product reviews. Based on the framework, we developed a prototype for extracting the relationship between the user ratings and their textual comments posted on Amazon.com’s Web site. Two data mining algorithms were implemented to extract automatically decision rules that guide the understanding of the relationship. We report on experimental results of using the prototype to extract rules from online reviews of three products and discuss the managerial implications

LIKES: Educating the Next Generation of Knowledge Society Builders

Although information technology (IT) is used extensively in the education of all disciplines, the computing-related fields are facing tremendous challenges, such as declining student enrollment and a lack of representation from minorities and women. Strengthening the connection between computing and other fields could help instructors to integrate IT in their teaching and to support the learning of students, who will become the next generation of Knowledge Society builders. Presently, this connection is weak due to the lack of interdisciplinary collaboration and mutual understanding among faculty in computing and other fields. Our ongoing effort entitled “Living in the KnowlEdge Society (LIKES) Community Building Project” aims to build a community that will define a socially-relevant way to make systemic changes in how computing and IT concepts are taught and applied in both computing and other fields. In this paper, we review previous efforts in this area and summarize our project’s achievements and lessons learned. We also provide recommendations on integrating IT into other curricula and on strengthening interdisciplinary collaborations