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2015 | Buch

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Reshaping Society through Analytics, Collaboration, and Decision Support

Role of Business Intelligence and Social Media

herausgegeben von: Lakshmi S. Iyer, Daniel J. Power

Verlag: Springer International Publishing

Buchreihe : Annals of Information Systems

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This volume explores emerging research and pedagogy in analytics, collaboration, and decision support with an emphasis on business intelligence and social media. In general, the chapters help understand where technology involvement in human decisions is headed. Reading the chapters can help understand the opportunities and threats associated with the use of information technology in decision making. Computing and information technologies are reshaping our global society, but they can potentially reshape it in negative as well as positive ways. Analytics, collaboration and computerized decision support are powerful decision aiding and decision making tools that have enormous potential to impact crisis decision making, regulation of financial systems, healthcare decision making and many more important decision domains.

Many information technologies can potentially support, assist and even decide for human decision makers. Despite the potential, some researchers think that we know the answers to how these technologies will change society. The "Wisdom of Crowds" or "Big Data" become the topic of the day and are soon replaced with new marketing terms. In many ways, mobile technology is just another form factor to adapt decision support capabilities too and experiment with new capabilities. The cloud is a nebulous metaphor that adds to the mystery of information technology. Wireless technology enables the ubiquitous presence of analytics and decision support. With new networking capabilities, collaboration is possible anywhere and everywhere using voice, video and text. Documents can be widely shared and massive numbers of documents can be carried on a small tablet computer. Recent developments in technologies impact the processes organizations use to make decisions. In addition, academics are looking for ways to enhance their pedagogy to train students to be more adept in understanding how emerging technology will be used effectively for decision making in organizations.

The chapters are based on papers originally reviewed at the Special Interest Group on Decision Support Systems (SIGDSS) Workshop at the 2013 International Conference on Information Systems (ICIS 2013). Ultimately this volume endeavors to find a balance between systematizing what we know, so we can teach our findings from prior research better, and stimulating excitement to move the field in new directions.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

The Association for Information Systems (AIS) Special Interest Group on Decision Support Systems (SIGDSS) workshop was planned as an event associated with the International Conference on Information Systems (ICIS 2013) in Milan, Italy at Bocconi University from December 14–18, 2013. In keeping with the ICIS2013 theme of “Reshaping Society”, the 2013 SIGDSS Workshop aimed to bring together academic and industry professionals from around the world who have a passion for research and education innovation in “Reshaping Society through Analytics, Collaboration, and Decision Support: Role of BI and Social Media”. This volume in the Annals of Information Systems reports work originally reviewed for that workshop that were presented and subsequently revised and refined as chapters for this book.

Lakshmi S. Iyer, Daniel J. Power

Chapter 2. Big Data Panel at SIGDSS Pre-ICIS Conference 2013: A Swiss-Army Knife? The Profile of a Data Scientist

Abstract

The purpose of the big data panel was to provide a forum for exchange of ideas on curricula content in the area of data science and big data. The panelists were from a broad range of academic institutions designed to provide different perspectives. Industry perspectives are vital as they will the ones employing the graduates of these programs. Thus, the panel included an industry expert from a company that is a leader in data science and big data. Although there was agreement on selected skills as being foundational, it was recognized that a curriculum would not provide all the skills a data scientist would need for many big data projects—thus the team approach to projects.

Barbara Dinter, David Douglas, Roger H. L. Chiang, Francesco Mari, Sudha Ram, Detlef Schoder

Chapter 3. Creating a Data-Driven Global Society

Abstract

Data is captured and analyzed for many purposes including supporting decision making. The expansion of data collection and the increasing use of data-driven decision support is creating a data-driven, global political, economic and social environment. This emerging global society is highly interconnected and many people rely on information technology to support decision making. This chapter explores the impacts that have and might occur as decision support technologies improve and continue shaping global society in new directions. Better understanding of the decision support and analytics phenomenon may help predict future societal changes and consequences. The goal of this analysis is to formulate hypotheses about the impact of decision support for further testing and speculate about long-run consequences. The increasing volume, velocity and variety of data is important to building new decision support functionality. Data collection expansion is part of a self-reinforcing decision support cycle that results in collecting more data, doing more analyses, and providing more and hopefully better decision support. Overall, nine hypotheses are proposed and briefly explored. More research is needed to test and verify them, but anecdotal evidence indicates analytics, business intelligence and decision support are creating a global society that is a data centric, real-time, decision-oriented socio-economic system. Data and decision scientists and technologists should anticipate and ponder the consequences of creating a more pervasive, data-driven global society.

Daniel J. Power

Chapter 4. Agile Supply Chain Decision Support System

Abstract

Recently, many organizations intend to make their supply chains more responsive to the change in demand in terms of volume and variety and hence consider agility one of the most critical evaluation criteria in addition to other well-known criteria such as general management capability, manufacturing capability, and collaboration capability. This paper formulates the supplier evaluation and selection problem as a multi-criteria decision-making (MCDM) problem with subjective and fuzzy preferences of decision makers over available evaluation criteria and provides the decision maker with a decision support system that presents the Pareto fronts, a set of best possible high-quality suppliers and optimized business operation levels from such suppliers. In addition, this paper quantifies the importance of agility and its sub-criteria in the process of evaluating and selecting agile suppliers by measuring the magnitude of bullwhip effect as a measurement of the business impact of resulting agile supply chain. The proposed system based on fuzzy analytic hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) successfully determines the priority weights of multiple criteria and selects the best fitting supplier after taking the vagueness and imprecision of human assessments. More importantly, it presents approximated Pareto fronts of resulting supplier chains as the priority weights of agility criterion and sub-criteria within agility are varied.

Jaehun Lee, Hyunbo Cho, Yong Seog Kim

Chapter 5. Hawkes Point Processes for Social Media Analytics

Abstract

Online social networks (OSNs) produce a huge volume of content and clickstream data over time as a result of continuous social interactions between users. Because these social interactions are not fully observable, the mining of such social streams is more complex than traditional data streams. Stochastic point processes, as a promising approach, have recently received significant research attention in social network analysis, in attempts to discover latent network structure of online social networks and particularly understand human interactions and behavior within the social networks. The objective of this paper is to provide a tutorial to the point process framework and its implementation in social media analytics. It begins by providing a quick overview of the history of Hawkes point processes as the most widely used classes of point process models. We identify various capabilities and attributes of the Hawkes point processes and build a bridge between the theory and practice of point processes in social network analytics. Then the paper includes a brief description of some current research projects that demonstrate the potential of the proposed framework. We also conclude with a discussion of some research opportunities in online social network and clickstream point process data.

Amir Hassan Zadeh, Ramesh Sharda

Chapter 6. Using Academic Analytics to Predict Dropout Risk in E-Learning Courses

Abstract

Information technology is reshaping higher education globally and analytics can help provide insights into complex issues in higher education, such as student recruitment, enrollment, retention, student learning, and graduation. Student retention, in particular, is a major issue in higher education, since it has an impact on students, institutions, and society. With the rapid growth in online enrollment, coupled with a higher dropout rate, more students are at risk of dropping out of online courses. Early identification of students who are at risk to drop out is imperative for preventing student dropout. This study develops a model to predict real-time dropout risk for each student while an online course is being taught. The model developed in this research utilizes a combination of variables from the Student Information Systems (SIS) and Course Management System (CMS). SIS data consists of ten independent variables, which provide a baseline risk score for each student at the beginning of the course. CMS data consists of seven independent variables that provide a dynamic risk score as the course progresses. Furthermore, the study provides an evaluation of various data mining techniques for their predictive accuracy and performance to build the predictive model and risk scores. Based on predictive model, the study presents a recommender system framework, to generate alerts and recommendations for students, instructors, and staff to facilitate early and effective intervention. The study results show that the boosted C5.0 decision tree model achieves 90.97 % overall predictive accuracy in predicting student dropout in online courses.

Rajeev Bukralia, Amit V. Deokar, Surendra Sarnikar

Chapter 7. Membership Reconfiguration in Knowledge Sharing Network: A Simulation Study

Abstract

The purpose of this study is to propose a new approach that minimizes the negative impacts of structural barriers to knowledge sharing in the current of knowledge sharing networks by dynamically reconfiguring communities of practice (CoP) memberships. For this purpose, we develop several propositions to determine source CoPs, destination CoPs, rearrangement candidates, and recipient candidates to regulate the process of reconfiguring collaboration networks of source CoPs and reconstructing networks of destination CoPs after reallocating members from source CoPs to destination CoPs. To test the validity and usefulness of the proposed approach, we simulate two reconfiguration strategies that are different in the sense whether or not the distribution of expertise levels of CoP members is considered to determine the destination CoP. Our experimental results confirm that the proposed approach with either strategy effectively decreases potential threats to collaboration among CoP members and improves the structural healthiness of knowledge sharing networks of departments and organization. In particular, the number of CoPs in which knowledge creating is more active than knowledge sharing is significantly increased while the number of inactive CoPs is decreased. We attribute this finding to the fact that both experts and non-experts members are more evenly distributed across CoPs through rearrangement and these experts with light collaboration burden post their knowledge and practical skills to help non-experts in their CoPs.

Suchul Lee, Yong Seog Kim, Euiho Suh

Chapter 8. On the Role of Ontologies in Information Extraction

Abstract

The ubiquity of unstructured/semi-structured data in business decision-making presents a unique challenge as data management methods developed for structured data are not directly applicable. While such non-traditional data have already become part of many organizations’ product/service offerings; most data managers admit that they lack the capability to leverage such data assets to elicit meaningful information. In this context, we discuss the use of Information Extraction (IE) methodologies to aid in the decision making process that utilizes un/semi-structured data. We focus on knowledge-based IE methodologies that are particularly suitable for business domains characterized by few subject matter experts’ tacit and uncodified domain knowledge. Ontologies that encapsulate and represent domain knowledge can play a key role in enabling knowledge-based IE. In this article we conduct a comprehensive review of the extant literature on Ontology-Based Information Extraction (OBIE) and articulate four different roles ontologies play in such knowledge-based IE systems. We discuss these various roles of ontologies in relation to the various IE phases and illustrate them with a case study involving IT service contracts, which is an example of a OBIE system. Finally, we discuss open research issues related to the use of ontologies, evaluation metrics, and applications of IE in decision-making.

Sagnika Sen, Jie Tao, Amit V. Deokar

Chapter 9. A Quantitative Approach to Identify Synergistic IT Portfolios

Abstract

Healthcare organizations continue to make large investments in health information technology to improve quality of care and lower costs. Therefore, there is an ever-growing need to have an ever-clearer understanding of how IT investments impact these organizations. Past studies have explored the impact of individual technologies or aggregate all technologies based on overall investment, but do not explore the impact of specific portfolios of information technology and their synergistic effects on healthcare quality. Based on the past studies on portfolio theory, we introduce an approach, utilizing data mining techniques and logistical regression, to identify such optimal portfolios, and explore the presence of such synergistic effects among the components of the portfolio. This multi-step approach is then applied to publically-available datasets, and the resulting candidate IT portfolios are presented. Statistical analysis is then used to test these results and demonstrate the feasibility of this approach.

Ken Pinaire, Surendra Sarnikar

Chapter 10. Introduction: Research-in-Progress Studies

Abstract

Keeping with the “Reshaping Society” theme of the ICIS 2013 conference, the Pre-ICIS SIGDSS workshop sought forward-thinking research in the areas of analytics, collaboration and decision support with special focus on business intelligence and social media. The track aimed to promote theoretical, design science, behavioral research and emerging applications in innovative areas of analytics, collaboration and decision support. The Research-in-Progress work from the workshop, points to the potential of BI, DSS and Analytics technologies to influence quality of life and business such as improve consumer purchase decision making or patient decision making in healthcare. This work has been summarized in this chapter.

Thilini Ariyachandra, Amit V. Deokar

Chapter 11. Towards Attentive In-Store Recommender Systems

Abstract

We present research-in-progress on an attentive in-store mobile recommender system that is integrated into the user’s glasses and worn during purchase decisions. The system makes use of the Attentive Mobile Interactive Cognitive Assistant (AMICA) platform prototype designed as a ubiquitous technology that supports people in their everyday-life. This paper gives a short overview of the technology and presents results from a pre-study in which we collected real-life eye-tracking data during decision processes in a supermarket. The data helps us to characterize and identify the different decision contexts based on differences in the observed attentional processes. AMICA provides eye-tracking data that can be used to classify decision-making behavior in real-time to make a recommendation process context-aware.

Jella Pfeiffer, Thies Pfeiffer, Martin Meißner

Chapter 12. Engaging with Online Crowd: A Flow Theory Approach

Abstract

Online collaborative problem solving (OCPS) refers to the use of social web technologies to garner netizens’ collective effort for problem solving and innovation tasks. The model has enabled organizations to involve online users in organizational works at large scale. However, success of this kind of initiatives depends much on, among other things, user engagement, or the amount of effort online users voluntarily devote to what are requested in an OCPS initiative. We argue that an important influence on user engagement in OCPS events is their experience when participating in the events. We further argue that Flow Theory by Csikszentmihalyi and Csikszentmihalyi (1988) provides much insights on how to improve this experience. In addition, we propose to measure the psychological construct “flow” through a novel physiological-psychometric approach. In this paper, detailed discussion of our theoretical standpoint and the design of a lab experiment to validate our hypotheses are provided.

Cuong Nguyen, Onook Oh, Abdulrahman Alothaim, Triparna de Vreede, Gert Jan de Vreede

Chapter 13. Modeling Dynamic Organizational Network Structure

Abstract

The organizational social networks, where the creation and recombination of knowledge typically takes place, are recognized as a crucial enabler for improving the organizational innovation and performance. While the recent research endeavors have been insightful in explaining the effect of the organizational social networks, we may need more effective tools to investigate the dynamics of the evolving network structures within an organization. Agent-based modeling has been considered a powerful tool for thoroughly studying the dynamics of the system. In this study, we propose an agent-based simulation model to provide a deeper understanding the dynamics of organizational network structures along with its task environment.

Seokwoo Song, Seong-Hoon Choi

Chapter 14. Teaching Analytics, Decision Support, and Business Intelligence: Challenges and Trends

Abstract

Companies are increasingly embracing analytics to enhance business value. Academia is responding to this trend, with innovative curricula in DSS/BI/Analytics providing a variety of degree programs, minors, and certificate programs in online, traditional, and hybrid format. With BI field rapidly evolving, more universities are becoming interested in offering BI courses and programs. This necessitates innovations in BI pedagogy and materials that can best prepare students for the industry demands. Teaching material that incorporates real cases with real data from companies into the pedagogy provides the benefit to students to get high-level BI skills that companies need.

Babita Gupta, Uzma Raja

Chapter 15. Data Analysis of Retailer Orders to Improve Order Distribution

Abstract

Our paper attempts to improve the order distribution for a logistics service provider who accepts order from retailers for fast moving consumer goods. Due to the fluctuations in orders on a day to day basis, the logistics provider will need the maximum number of trucks to cater for the maximum order day, resulting in idle trucks on other days. By performing data analysis of the orders from the retailers, the inventory ordering policy of these retailers can be inferred and new order intervals proposed to smooth out the number of orders, so as to reduce the total number of trucks needed. An average of 20 % reduction of the total number of trips made can be achieved. Complementing the proposed order intervals, the corresponding new proposed order size is computed using moving average from historical order sizes, and shown to satisfy the retailers’ capacity constraints within reasonable limits. We have successfully demonstrated how insights can be obtained and new solutions can be proposed by integrating data analytics with decision analytics, to reduce distribution cost for a logistics company.

Michelle L. F. Cheong, Murphy Choy

Chapter 16. An Online Graduate Certificate Credential Program at the University of Arkansas

Abstract

Business Analytics and Big Data have become a very popular topics in recent years. Many universities are gearing up to meet the reported demand people with these skills. This paper shares background, principles, and processes in the development of an online Business Analytics Graduate Certificate Credential program consisting of four graduate courses (each three semester hours). Innovative use of technology is incorporated into all four of the courses to ensure consistency and quality content across courses. The four courses are (1) IT Toolkit – designed to level students (especially those students who do not have an adequate IT background), (2) Decision Support and Analytics – an introduction to statistical analytics with a focus on what the data is telling us, (3) Database Management Systems – a focus on sourcing, preparing, storing and retrieval for data and (4) Business Intelligence – a focus on the discovery of knowledge from data and model development using data mining including social media. Included are the efforts, activities, software, hardware, concepts, teaching philosophy, and desired outcomes for the graduate credential certificate program. The paper should be very valuable to all those teaching or planning to teach in the Business Analytics area.

Timothy Paul Cronan, David E. Douglas, Jeff Mullins

Chapter 17. Business Intelligence at Bharti Airtel Ltd

Abstract

Bharti Airtel is an early adaptor of business intelligence solution in Indian telecom industry. Over a period of time, the company undertook many IT initiatives. In order to align IT with organization’s business strategy, the company has selected and adopted appropriate IT infrastructure and enterprise information systems such as Enterprise Resource Planning (ERP), and Customer Relationship Management (CRM). Subsequently the company implemented Data Warehousing (DW) and Business Intelligence (BI) systems to leverage the IT systems implemented. The company achieved many benefits out of these systems. This Case describes how the company adopted several IT initiatives, leveraged the systems and derived business value by using BI systems.

Prabin Kumar Panigrahi

Backmatter

Titel: Reshaping Society through Analytics, Collaboration, and Decision Support
herausgegeben von: Lakshmi S. Iyer
Daniel J. Power
Verlag: Springer International Publishing
Electronic ISBN: 978-3-319-11575-7
Print ISBN: 978-3-319-11574-0
DOI: https://doi.org/10.1007/978-3-319-11575-7