IT-Enabled Services as Complex Adaptive Service Systems: A Co-Evolutionary View of Service Innovation

One specific type of service innovation of particular interest to IT and business professionals is IT-Enabled Services (IES). Previous studies have suggested many roles for IT in service innovations. IT has proven a useful tool in service innovation. IT is an important component of most services in many industries, including healthcare, financial services, engineering, and management consulting. However, little work has been conducted in IESs. Thus, there is considerable potential for researchers in IS, operations, marketing, and economics to make contributions to the emerging debates and challenges in IESs and service innovation. Two topics are critically important in both IES research and practice: what IESs are and how such services emerge and evolve. This research-in-progress attempts to offer a novel perspective on these two topics. Drawn upon complexity theory, we conceptualize services (IESs) as complex adaptive service systems (CASS) with such properties and behaviors as emergence, self-organization, adaptive learning, and nonlinearity, and service development or innovation as a co-evolutionary process composed of variation, selection, and retention (VSR). From this perspective, IESs produce and are reproduced by the environment (or by wide business networks). Based on this complexity theory perspective, we also provide propositions regarding what IESs are, how they emerge and evolve, and what strategies are effective for IT-enabled eservice innovation. The last section offers a research plan for a longitudinal case study of Business Analytics (BA) as an IES to qualify the proposed theoretical perspective

Big Data Ecosystem: Network Analysis and Community Structure Detection

There is “big data fever” in both industry and academia. Much of this fever has been on the analytic side of big data: big data as “large, diverse, and fast-moving datasets”, and processing and analyzing such datasets for challenging business and societal problems. The goal of this research is to shed a different light on big data—big data as a digital service innovation. This innovative side of big data pays attention to the novelty and evolution of big data ecosystems and of the diverse elements. This research reviews some recent literature on digital innovation and service innovation, and uses an evolutionary, ecosystem-based framework to understand big data as a digital service innovation. The study uses digital trace data. Over 260,000 Twitter data containing the hashtag #bigdata, collected in two phases (March 2013 and June 2014), are processed and analyzed

Corporate Social Responsibility (CSR): A Survey of Topics and Trends Using Twitter Data and Topic Modeling

Corporate social responsibility (CSR) is an essential business practice in industry and a popular topic in academic research. Several studies have attempted to understand topics or categories in CSR contexts and some have used qualitative techniques to analyze data from traditional communication channels such as corporate reports, newspapers, and websites. This study adopts computational content analysis for understanding themes or topics from CSR-related conversations in the Twitter-sphere, the largest microblogging social media platform. Specifically, a probabilistic topic modeling-based computational text analysis framework is introduced to answer three questions: (1) What CSR-related topics are being communicated in the Twitter-sphere and what are the prevalent topics or themes in CSR conversation? (topic prevalence); (2) How are those topics interrelated? (topic correlation); (3) How have those topics changed over time? (topic evolution). The topic modeling results are discussed, and the direction for future research is presented