The term "ecosystem is used pervasively in industry, government, and academia to describe the complex, dynamic, hyperconnected nature of many social, economic, and technical systems that exist today. Ecosystems are characterized by a large, dynamic, and heterogeneous set of geospatially distributed entities that are interconnected through various types of relationships. This study describes the design and development of ecoxight, a web-based visualization platform that provides multiple coordinated views of multi-partite, multi-attribute, dynamic, and geospatial ecosystem data with novel and rich interaction capabilities, to augment decision makers ecosystem intelligence. The design of ecoxight was informed by an extensive multi-phase field study of executives. ecoxight not only provides capabilities to interactively explore and make sense of ecosystems, but also provides rich visual construction capabilities to help decision makers align their mental model. We demonstrate the usability, utility, and value of our system using multiple evaluation studies with practitioners using socially-curated data on the emerging API ecosystem. We report on our findings and conclude with research implications. Collectively, our study contributes to design science research at the intersection of IS and strategy and the rapidly emerging field of visual enterprise analytics.
Blockchain technology promises a sizable potential for executing inter-organizational business processes without requiring a central party serving as a single point of trust (and failure). This paper analyzes its impact on business process management (BPM). We structure the discussion using two BPM frameworks, namely the six BPM core capabilities and the BPM lifecycle. This paper provides research directions for investigating the application of blockchain technology to BPM.
Systems integration connecting software systems for cross-functional work is a significant concern in many large organizations, which continue to maintain hundreds, if not thousands, of independently evolving software systems. Current approaches in this space remain ad hoc, and closely tied to technology platforms. Following a design science approach, and via multiple design-evaluate cycles, we develop Systems Integration Requirements Engineering Modeling Language (SIRE-ML) to address this problem. SIRE-ML builds on the foundation of coordination theory, and incorporates important semantic information about the systems integration domain. The paper develops constructs in SIRE-ML, and a merge algorithm that allows both functional managers and integration professionals to contribute to building a systems integration solution. Integration models built with SIRE-ML provide benefits such as ensuring coverage and minimizing ambiguity, and can be used to drive implementation with different platforms such as middleware, services and distributed objects. We evaluate SIRE-ML for ontological expressiveness and report findings about applicability check with an expert panel. The paper discusses implications for future research such as tool building and empirical evaluation, as well as implications for practice.
Twitter has emerged as a major social media platform and generated great interest from sentiment analysis researchers. Despite this attention, state-of-the-art Twitter sentiment analysis approaches perform relatively poorly with reported classification accuracies often below 70%, adversely impacting applications of the derived sentiment information. In this research, we investigate the unique challenges presented by the Twitter sentiment analysis problem, and review the literature to determine how the devised approaches have addressed these challenges. To assess the state-of-the-art in Twitter sentiment analysis, we conduct a benchmark evaluation of 28 top academic and commercial systems in tweet sentiment classification across five distinctive data sets. We perform an error analysis to uncover the causes of commonly occurring classification errors. To further the evaluation, we apply select systems in an event detection case study. Finally, we summarize the key trends and takeaways from the review and benchmark evaluation, and provide suggestions to guide the design of the next generation of approaches.