Journal of Management Information Systems

Volume 35 Number 2 2018 pp. 383-387

Special Issue: Strategic Value of Big Data and Business Analytics

Chiang, Roger HL, Grover, Varun, Liang, Ting-Peng, and Zhang, Dongsong

ROGER CHIANG ([email protected]) is a professor of information systems (IS) at the University of Cincinnati. He received his Ph.D. in computers and information systems from the University of Rochester. His research interests are in business intelligence and analytics, data and knowledge management, and intelligent systems. He has served as the senior or associate editor of numerous IS journals. He has published over fifty refereed articles in journals and conference proceedings including ACM Transactions on Database Systems, ACM Transactions on Management Information Systems, Communications of the ACM, Journal of Management Information Systems, Marketing Science, MIS Quarterly, and others.

VARUN GROVER ([email protected]) is the David D. Glass Endowed Chair and Distinguished Professor of Information Systems at the Walton School of Business, University of Arkansas. His work focuses on the impacts of digitalization on individuals and organizations. He has published extensively in the information systems (IS) field, with over 400 publications, 220 of which are in major refereed journals. He was recognized as one of 100 Highly Cited Scholars in Business by Thompson Reuters (2013). He has been a senior or associate editor of several major IS journals, a recipient of numerous awards for his research and teaching, and is a Fellow of the Association for Information Systems. He has been invited to give numerous keynote addresses and talks at various institutions and forums around the world.

TING-PENG LIANG ([email protected]; corresponding author) is a Life-time National Chair Professor at National Sun Yat-Sen University in Taiwan and Fellow, Leo Awardee and president-elect of the Association for Information Systems. He received his Ph.D. degree from the Wharton School of the University of Pennsylvania and has taught at the University of Illinois, Purdue University, Chinese University of Hong Kong, and City University of Hong Kong. His research interests include electronic commerce, intelligent decision support systems, and strategic applications of information technologies. He has published extensively in Journal of Management Information Systems, MIS Quarterly, Management Science, Operations Research, International Journal of Electronic Commerce, and others. He is the editor in chief of the Pacific Asia Journal of AIS and has served on the editorial board of many other journals.

DONGSONG ZHANG ([email protected]) is a professor in the Department of Information Systems at the University of Maryland, Baltimore County and guest chair professor at the International Business School, Jinan University, China. He received his Ph.D. in management information systems from the University of Arizona. His research interests include social computing, mobile human–computer interaction, data analytics, health information technologies, and online communities. He has published over 140 papers in journals and conference proceedings, including Journal of Management Information Systems, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Software Engineering, and MIS Quarterly. His research has been funded by the U.S. National Science Foundation, U.S. National Institutes of Health, U.S. Department of Education, National Natural Science Foundation of China, Chinese Academy of Sciences, and Google, Inc.

The rapid accumulation of data in diverse forms and from various sources has been driving an increasing interest in big data and business analytics. Applications of a variety of analytical techniques have gained much attention in recent years. Businesses are exploring the new possibility of uncovering hidden knowledge, improving decision making, and supporting strategic planning from big data. Although substantial resources have been invested in big data and analytics (BDA) and anecdotal evidence of both success and failure have been reported, there has been little substantial research on the strategic contributions of BDA. The lingering question is whether BDA is a fad that will disappear in five years.

In addition to the four V’s (volume, velocity, variety, and veracity) characterization of big data, value has been considered the fifth key dimension in BDA. Analysis of data without generating value offers no contribution to an organization, regardless of whether data are big or small. The success of BDA projects requires not only infrastructure, data analysts, and knowledge and tools for dealing with big data, but also an understanding of how BDA translates to competitive advantages and strategic value. The challenges in deploying advanced data analytics to outperform competitors are real and exist in every aspect of analytics, including capturing, collecting, storing, processing, and managing big data, as well as drawing valuable insights from it. This Special Issue, therefore, invited information systems (IS) research that examined how BDA creates strategic value for organizations. We received 47 submissions of extended abstract. The studies published in this issue are the result of initial screening and three rounds of review. The articles examine issues ranging from the value creation mechanism to the impact of BDA on organizations.

The first paper, “Creating Strategic Business Value from Big Data Analytics: A Research Framework,” defines strategic business values to include functional and symbolic value. A framework for strategic value creation is proposed, which includes capability building and capability realization processes. Illustrative cases, five theoretical lenses for strategic value creation and potential research issues are discussed. This framing is complemented with a more pragmatic problem-oriented approach to BDA that raises a set of important research questions that need to be studied to advance this area.

The other five studies are mapped onto the value creation framework presented in the first article (see Table 1). 10.1080/07421222.2018.1451950-T0001Table 1.

The Relevance of the Studies

Title Big data and analytics capabilities Value creation mechanisms Value targets Impact How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service Four case studies of big data vision and BDA technologies Customization and service innovation Consumer experience and market enhancement Functional/symbolic value Leveraging Financial Social Media Data for Corporate Fraud Detection Social media text analytics Transparency and accessLearning and crowdsourcing Organizational performanceBusiness process improvement Functional/symbolic value The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics Data mining, machine learning, text mining, visualization, and so on Transparency and access (presumed) Firm productivity Functional value Measuring Customer Agility from Online Reviews Using Big Data Text Analytics Textual social media analytics Learning and crowdsourcingContinuous monitoring and proactive adaptation Innovation and product performance Functional value Advanced Customer Analytics: Strategic Value Through Integration of Relationship-Oriented Big Data Big data integration and kernel-based machine learning Discovery and prediction Business process improvement and customer experience Functional value

The second paper, “How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service,” develops a theoretical model from four case studies to explain how the flexible and reprogrammable nature of BDA technologies provides features of sourcing, storage, event recognition and prediction, behavior recognition and prediction, rule-based actions, and visualization. The model highlights how material agency (in the case of service automation) and the interplay of human and material agencies (in the case of human-material service practices) enable service individualization, as organizations draw on a service-dominant logic.

The third paper, “Leveraging Financial Social Media Data for Corporate Fraud Detection,” proposes an analytic framework that taps into unstructured data from financial social media platforms to assess the risk of corporate fraud. The framework is evaluated by 64 fraudulent firms and a matched sample of 64 nonfraudulent firms, as well as the social media data prior to the firm’s alleged fraud violation in Accounting and Auditing Enforcement Releases (AAERs). Their findings demonstrate the value of financial social media data complement traditional fraud detection methods.

The fourth paper, “The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics,” presents an econometric study that analyzes the relationship between BDA and firm performance based on the Cobb–Douglas production function framework and objective measurements of BDA assets. The authors used a panel data set that contained detailed information about BDA solutions of 814 companies and their financial performance. The BDA assets included three categories of analytics— foundational database technologies, data mining and machine learning solutions, and data visualization and presentation tools. They found that live BDA assets were associated with an average of 3–7 percent improvement in firm productivity. In addition, the results show that different industries vary in returns from BDA—firms in information technology–intensive or highly competitive industries seem more able to gain value from BDA assets than other investigated industries.

The fifth paper, “Measuring Customer Agility from Online Reviews Using Big Data Text Analytics,” adopts a big data analytical approach to investigate the impact of online customer reviews on customer agility and product performance. The authors developed a semantic keyword similarity method bsed on singular vector decomposition (SVD) to quantify customer agility by measuring the semantic similarity between aggregated customer review texts and product release notes. They evaluated the relationship between customer agility and review volume by using over 3 million online reviews of mobile apps collected from the U.S. Apple App store. The results show that review volume has a curvilinear relationship with customer agility, which in turn has a curvilinear relationship with product performance. A firm’s number of sibling products and the variance of product ratings moderate the review volume–customer agility relationship.

The sixth paper, “Advanced Customer Analytics: Strategic Value Through Integration of Relationship-Oriented Big Data,” examines how firms can achieve agility by combining rich data sources and deploying analytics that sense and respond to customers in a dynamic environment. A key challenge in achieving this agility lies in the identification, collection, and integration of data across functional silos both within and outside the organization. The authors propose a framework for identifying and evaluating various sources of big data to create a value-justified data infrastructure that enables focused and agile deployment of advanced customer analytics. Such analytics move beyond siloed transactional customer analytics approaches of the past and incorporate a variety of rich, relationship-oriented constructs to provide actionable and valuable insights.


The guest editors would like to thank Vladimir Zwass, editor in chief of the Journal of Management Information Systems for his support, and the following reviewers (in alphabetical order) for their invaluable help in reviewing submissions and providing constructive comments to the authors: Michelle Carter, Cecil Eng Huang Chau, Michael Chau, Xiao Fang, Yulin Fang, S.C. Jack Hsu, Yan Huang, San-Yih Hwang, Dan Kim, Rajiv Kohli, Raymond Y.K. Lau, Xin Li, Stephen Liao, Kai Lim, Kevin P. Scheibe, Ning Su, Heshan Sun, Yongqiang Sun, Sharon Swee-Lin Tan, Jiliang Tang, Eric T.G. Wang, Chih-Ping Wei, Jennifer Xu, Bin Zhang, Yilu Zhou, and others.