Analytics and Data Science

The 2015 Business Analytics Congress (BAC) brought together academic professionals and industry representatives who share a common passion for research and education innovation in the field of analytics. This event was organized by the Association for Information System’s (AIS) Special Interest Group on Decision Support and Analytics (SIGDSA) and Teradata University Network (TUN) and held in conjunction with the International Conference on Information Systems (ICIS 2015) in Ft. Worth, Texas from December 12 to 16, 2015. The theme of BAC 2015 was Exploring the Analytics Frontier and was kept in alignment with the ICIS 2015 theme of Exploring the Information Frontier. In the spirit of open innovation, the goal of BAC 2015 was for the attendees to contribute their scientific and pedagogical contributions to the field of business analytics while brainstorming with the key industry and academic leaders for understanding latest innovation in business analytics as well as bridge industry-academic gap. This volume in the Annals of Information Systems reports the work originally reviewed for BAC 2015 and subsequently revised as chapters for this book.

Inspired by the theme “Exploring the Information Frontier” of the ICIS 2015 conference, the Pre-ICIS Business Analytics Congress workshop sought forward-thinking research in the areas of data science, business intelligence, analytics, and decision support with a special focus on the state of business analytics from the perspectives of organizations, faculty, and students. The research track aimed to promote comprehensive research or research-in-progress on the role of business intelligence and analytics in the creation, spread, and use of information. This work has been summarized in this chapter.

Business intelligence (BI) is emerging as a critical area of expertise for firms’ value proposition. Firms are trying to leverage BI as an inherent capability to create value. Considering an organizational systems view, BI extends beyond a tool or artifact to include a number of capabilities. We draw on IT capabilities and prior research on BI to uncover potential capabilities that BI bestows to an organization. A three category BI capability classification is suggested: BI innovation infrastructure capability, BI process capability and BI integration capability. We discuss the attributes of these three BI capabilities to provide insights into how the capabilities help organizations. This taxonomy will help decision-makers take informed decisions on how to effectively implement BI within their organization to improve performance.

Big data is at the pinnacle of its hype cycle, offering big promise. Everyone wants a piece of the pie, yet not many know how to start and get the most out of their big data initiatives. We suggest that realizing benefits with big data depends on having the right capabilities for the right problems. When there is a discrepancy between these, organizations struggle to make sense of their data. Based on information processing theory, in this research-in-progress we suggest that there needs to be a fit between big data processing requirements and big data processing capabilities, so that organizations can realize value from their big data initiative.

This paper presents a mapping of the business analytics (BA) capabilities of a firm from a value chain lens similar to Porter’s (Harv Bus Rev 79:62–78, 2001) internet capabilities framework. The generally accepted classification of analytics: descriptive, predictive and prescriptive, is used as basis for mapping BA capabilities. Using an extensive search of the academic and practitioner literature, analytics applications were analyzed and mapped onto the value chain framework. Given the increased interest and investment in BA, it is important to have a good understanding of what analytics capabilities firms use to enhance value through its value chain activities. We illustrate exemplar uses of BA applications, tools and technologies used by firms. Preliminary results suggest that organizations are focusing on application of analytics where the outcome is easily measurable compared to application of analytics in other activities where it is harder to measure a direct value.

Business Intelligence (BI) systems have been rated as a leading technology for the last several years. However, organizations have struggled to ensure that high quality information is provided to and from BI systems. This suggests that organizations have recognized the value of information and the potential opportunities available but are challenged by the lack of success in Business Intelligence Systems Implementation (BISI). Therefore, our research addresses the preponderance of failed BI system projects, promulgated by a lack of attention to Systems Quality (SQ) and Information Quality (IQ) in BISI. The main purpose of this study is to determine how an organization may gain benefits by uncovering the antecedents and critical value factors (CVFs) of SQ and IQ necessary to derive greater BISI success. We approached these issues through adopting ‘critical value factors’ (CVF) as a conceptual ‘lens’. Following an initial pilot study, we undertook an empirical analysis of 1300 survey invitations to BI analysts. We used exploratory factor analysis (EFA) techniques to uncover the CVFs of SQ and IQ of BISI. Our study demonstrates that there is a significant effect in the relationships of perceived IQ of BISI to perceived user information satisfaction thereby confirming the importance BI system users place on information and the output produced. Our study also reported that there is a significant effect in the relationships of perceived IQ of BISI to perceived user system satisfaction thereby confirming the importance BI system users place on system output. We believe our research will be of benefit to both academics and practitioners in attempting to ensure BI systems implementation success.

Chinese business has developed exponentially in the last few decades and Chinese firms are highly influential in world trade. Business intelligence (BI) systems are large-scale decision support systems (DSS) that analyze enterprise data to generate business insights. BI was developed in the West and is integral to contemporary Western management practices. It is generally assumed that western BI systems are useable and effective in a Chinese context. No study has been undertaken to investigate the use behavior of large-scale DSS in Chinese organizations. We conducted two exploratory case studies in large indigenous Chinese organizations. The case analysis shows that a complex cultural factor (provisionally termed Factor X) affects BI systems use in China. A set of propositions are formulated from the analysis. They will be used as a foundation for future research on Chinese BI.

Product innovation is important for firms to gain competitive advantages in a dynamic business environment. Traditionally, customers are not very much involved in product innovation processes. With the technology of Web 2.0, online users are enabled and motivated to provide reviews and discussions about product features and use experiences. User generated product reviews have been found to have a word-of-mouth effect as a new element of marketing communication. However, their implication on improving product innovation cycles have not been studied before. Guided by a persuasion theory, we extracted the central and peripheral persuasion cues from user generated reviews and examined their impact on mobile app developers’ product innovation decisions. Using data collected from the Google App store, our empirical study shows that long and easy-to-read user reviews with mildly negative reviews can increase the likelihood of a future mobile app update. Our findings highlight the need for researchers to explore user generated reviews in the context of customer-centered product innovation.

Using Twitter as the primary social media platform, we study the predictive relationship of social media buzz in quiet periods and the IPO’s first-day return, liquidity, and volatility. We compare social media buzz with conventional press news coverage and show that social media buzz is stronger at predicting the first-day returns than conventional press news.

The rapid and ongoing evolution of mobile devices allows for increasing ubiquity of online handhelds, yet boosting the recent growth of social platforms. This development facilitates participation in social media for an enormous amount of individuals independently from time and location. When navigating through a city and especially when following activities worthy to be shared with others, people uncover their traces in both geographical and temporal dimension. Using these traces to spot popular areas in a metropolitan region is valuable to a broad variety of applications, reaching from city planning to venue recommendation and investment. We propose a density-based method to determine the attractiveness of areas based solely on spatial and content characteristics of Twitter activity. Furthermore, we show the relation of attached images, videos, or linked places to the activity users are engaged in and assess the explanatory power of Twitter messages in a geographical context.

Google Trends, the service that illustrates the trends in Google search activity, has recently received attention form analytics researchers for the prediction of economic trends and consumer behavior. Previous studies used Google Trends to estimate consumption and sales for a particular business, or provide general trends for an economic sector or industry. This study reported here differs from these attempts as it aims to estimate the performance of a single player in an industry by not only trends related to that player, but also those of its competitors. Further, these trends have been modified by Twitter based sentiment scores. It is demonstrated that the incorporation of competitive factors results in better estimates by as much as 5% while the addition of a Twitter sentiment score is not beneficial. The Twitter related findings could be because the tweet volumes in the particular industry that was examined are low and volatile.

The availability of big data sources and developments in computational linguistics present an opportunity for IS researchers to pursue new areas of inquiry and to tackle existing challenges with new methods. In this paper, a novel way of developing measurement scales using big data (i.e., tweets) and associated methods (i.e., natural language processing) is proposed and tested. The development of a new scale, the technology hassles and delights scale (THDS), is used to demonstrate how a syntax aware filtering process can identify relevant information from a large corpus of tweets to improve the content validity of a scale. In comparing themes generated from analyzing 146 million tweets, with themes generated from semi-structured interviews, a reasonable overlap is observed. Further, the potential for identifying even more relevant themes from within subsets of the tweet dataset is uncovered.

In the age of big data where vast amounts of data are collected, stored, and analyzed from all possible sources, the growth of social media and the culture of sharing personal information have created privacy and security related issues. Drawing on the prospect theory and rational apathy theory, we present a research model to investigate why people disclose personal information on Online Social Networks. This paper analyzes the impact of situational factors such as information control, ownership of personal information, and apathy towards privacy concern of users on Online Social Network. We describe the proposed research design for collecting our data and analysis using structural equation modeling to analyze the data. The findings and conclusions will be presented after the data is analyzed. This work contributes to the network analytics by developing new constructs using the Prospect Theory and the Rational Apathy theory from the fields of behavioral economics and social psychology respectively.

This paper takes a multi-method approach, combining neuroscience methods and behavioral experiments to investigate emotions triggered by olfactory-related information and related consumer decision-making outcomes. In the online context, olfactory information is limited to visual forms of triggering olfactory sensations. The effectiveness of using sensory congruent brand names in online ads to trigger emotions, and the influence on attitudes toward the ad, brand and purchase intentions are examined. Moreover, individual differences in olfactory sensitivity were considered, revealing moderating effects on cognitive and emotional processes. Findings provide managerial and organizational implications for online advertising, branding decisions and market segmentation decisions.

Financial investors face an increasing information abundance when making their valuation decisions of financial assets. As Information Systems research demonstrated, valuation not only builds on quantitative facts, but also on qualitative information such as the language used in financial disclosures and the readability of the texts. As an originator of financial disclosures, e.g. a company, it is thus essential to thoughtfully steer the creation of new textual information. While regulators provide guidelines on what content to publish, corporate communication departments can flexibly steer how they communicate. We have developed an IS prototype that accounts for the importance of textual information and provides corporate communications with a decision-support tool to assure a high readability and a positive sentiment. Our IS prototype builds on a two-step process. First, we extract a dictionary with the most relevant words for investors from a large inventory of regulatory filings with Bayesian learning algorithms. Second, we use this dictionary as input for a Microsoft Word add-in that highlights positively or negatively connoted words and suggests alternative words with a more positive investor perception to corporate communications professionals.

Keeping with the “Exploring the Information Frontier” theme of the ICIS 2015 conference, the Pre-ICIS Business Analytics Congress workshop sought forward-thinking research in the areas of data science, business intelligence, analytics and decision support with a special focus on the state of business analytics from the perspectives of organizations, faculty, and students. The teaching track aimed to promote comprehensive research or research-in-progress in teaching and learning addressing topics including business analytics curriculum development, pedagogical innovation, organizational case studies, tutorial exercises, and the use of analytics software in the classroom. This work has been summarized in this chapter.

The trend towards big data and business intelligence & analytics (BI&A) is still continuing. In the upcoming years, thousands of new jobs for data scientists will be established by the economy. Therefore, there is a need for well-educated graduates with deep analytical skills. In order to prepare students for their later profession and to teach them in analytics tools relevant for practice, related academic education is required. The BI&A sub-domains and tool categories (like text mining and web analytics) correspond to popular skill profiles. Since the market for BI&A tools is very large and hence hard to survey, this paper identifies and evaluates a number of tools for each BI&A sub-domain. The tools are evaluated with regard to university-specific requirements (such as expenses and available learning resources) and BI&A category-specific requirements (such as functionality). Based on the evaluation results recommendations for each tool category are given.

When problems are complex and cannot be solved through conventional methods such as statistical or management science models, and when human expertise is not sufficient for efficiently finding high-quality solutions, we can consider the use of machine learning techniques. One such technique is the artificial neural network (neural net), which can be used for predictive modeling. This chapter provides a brief introduction to the topic of neural nets, along with a tutorial in which a working neural net is built and then used to make predictions.

Today’s business colleges are attempting to meet the industry demand by developing marketable ERP (Enterprise Resource Planning) skills and delivering exposure to the realities of modern business into the curricula. Role adaptions in real-world settings such as ERP systems use can enhance students’ ability to learn conceptual knowledge for practical application. The situated learning theory capitalizes on a specified context where the context extensively impacts learning. Education data text mining is emerging to produce new possibilities for gathering, analyzing, and presenting student learning outcomes. This chapter aims to reveal ERP learning patterns and themes as evidence of knowledge transfer in ERP role adaptions. The results demonstrate amplified learning through role play in a simulated ERP learning environment.

Infusing data literacy into a curriculum is an unrealized opportunity for higher education to truly make an impact on the current generation as they prepare to move into the workforce. This chapter describes the design and structure of a new, unique undergraduate elective course introduced into the curriculum of a large, public University in the Northeastern United States. The design of the course is designed to inspire an “evidence-based” mindset, encouraging students to identify and use data relevant to them in their field of study and the larger world around them. The chapter includes the course goals mapped to specific learning objectives, examples of exercises and assignments, a reading list, and a course syllabus. Instructors and institutions interested in bringing data science concepts to a broad audience can use this course as a foundation to build their own curriculum in this area.