An Event-based Analysis Framework for Open Source Software Development Projects

The increasing popularity and success of Open Source Software (OSS) development projects has drawn significant attention of academics and open source participants over the last two decades. As one of the key areas in OSS research, assessing and predicting OSS performance is of great value to both OSS communities and organizations who are interested in investing in OSS projects. Most existing research, however, has considered OSS project performance as the outcome of static cross-sectional factors such as number of developers, project activity level, and license choice. While variance studies can identify some predictors of project outcomes, they tend to neglect the actual process of development. Without a closer examination of how events occur, an understanding of OSS projects is incomplete. This dissertation aims to combine both process and variance strategy, to investigate how OSS projects change over time through their development processes; and to explore how these changes affect project performance. I design, instantiate, and evaluate a framework and an artifact, EventMiner, to analyze OSS projects’ evolution through development activities. This framework integrates concepts from various theories such as distributed cognition (DCog) and complexity theory, applying data mining techniques such as decision trees, motif analysis, and hidden Markov modeling to automatically analyze and interpret the trace data of 103 OSS projects from an open source repository. The results support the construction of process theories on OSS development. The study contributes to literature in DCog, design routines, OSS development, and OSS performance. The resulting framework allows OSS researchers who are interested in OSS development processes to share and reuse data and data analysis processes in an open-source manner

Diversity in Software Development Routines are Attractive: A Preliminary Analysis of GitHub Repositories

Free, libre, open-source software projects (FLOSS) are known for their chaotic development style and unique collaboration model. How does such chaotic development produce high quality software and attract users and developers? To provide insight into this conundrum, this study explores the roles of diversity and change in design routines. It investigates the relationship between routine diversity and change on project attraction to users and developers. Various sequence-mining techniques such as motif analysis and hidden Markov models (HMM) are applied to examine design routines of 88 FLOSS projects on GitHub.com. Regression analysis reveals that development processes with high routine-diversity and relatively low change-magnitude attract more users and developers

Urban metabolic flow in China’s megacities doubled by material stock accumulation since the 21st century

Abstract Buildings, infrastructure, and durable goods play a critical role in urbanization, akin to bones and muscles that structure the human body. These stocks contribute to the exploitation of over half of the world’s resources and offer potential “urban mining” sources. However, the process of resource transformation regarding urban material stock growth and material flow alteration remains unclear. The metaphor of urban metabolism provides a new perspective to dissect this process, but current studies often spotlight only specific fragments, such as certain end-use types or materials. This study bridges this gap by establishing a comprehensive level-to-level analysis of urban “bone-muscle” metabolism in China’s megacities. This study presents a comprehensive analysis of urban metabolism in China’s megacities, tracking the lifecycle of material stock across over a hundred distinct end-use types and 12 categories of materials. Results indicate that annual metabolic flow in these cities has doubled since the early 21st century, reaching 264–737 Mt in 2018, with manufacturing, construction, and transportation as primary drivers. As accumulation intensifies, the material stock’s growth rate diminishes logarithmically, hinting at increased efficiency and a move towards a steady state. Concurrently, scrap flow is on the rise. Driven by population growth, per capita scrap is projected to reach 2.0–4.7 t/cap by 2035, and material stock is expected to rise 1.4–2 fold. Proactive population planning and coordinated development strategies can mitigate the risks associated with this growth and maintain urban system stability

Reporting Information Security Policy Violations – An Exploratory Study

Information security policy (ISP) violations have become a serious concern in organizations. Although prevention is the best option since it prevents ISP violations from occurring, incidents still occur. Such violations should be reported so that organizations can take immediate actions and reduce the negative impact. However, the current literature mainly focuses on factors that lead to violations of ISPs and our current understanding of what influences employees’ intention to report others’ ISP violations is limited. In this study, we attempt to fill this gap by conducting an explorative study to investigate the ISP violation reporting phenomenon. Six pilot interviews are conducted to investigate why or why not individuals report others’ ISP violations. Guided by literature on ISP violations and organizational citizenship behavior, our preliminary findings suggest that employees’ intention of reporting is motivated by the purpose and the consequence of the violation, as well as the severity of such consequences

Management Responses to Online Hotel Reviews: Text Mining to Lift Sales

In the era of electronic word-of-mouth, hotels are under the pressure to respond to online reviews more effectively and strategically, to maintain and enhance hotel reputation and financial viability. Little research is done to guide organizations to effectively operate management response strategies. Using data mining techniques such as text mining, sentiment analysis, and Latent Dirichlet Allocation, guided by the affect theory, this study develops and evaluates an “AAAA” framework that classifies management responses to online hotel reviews into four categories: acknowledgment, account, action, and affect. A training sample of 29,606 review responses collected from three U.S cities on TripAdvisor.com are mined and analyzed. The results will be further tested by a longitudinal, econometric sales model, to identify the effectiveness of management response strategies for hotels. This research-in- progress will contribute to emerging research in management responses to online hotel reviews and provide managerial implications for managing responses to enhance hotel performance

The Influence of Professional Embeddedness and Public Reputation on Critic Review Behavior

As one popular form of online word-of-mouth, critic reviews are provided by experts as a quality signal. In this research-in-progress, we consider two forms of social features of critics’ reviewing environment and their impact on critics’ reviews: critics’ professional reputations based on how embedded they are in their professional networks, and critics’ public reputations enabled by reviewing platforms. We collected a dataset of 6,8450 critic reviews posted by 716 critics for 3,654 movies from Rotten Tomatoes and constructed these critics’ professional networks based on the publications they work for. We used social network analysis techniques to obtain each critic’s embeddedness in their networks and used their “top critic” status to capture their public reputation. We discussed future analysis techniques and anticipated contributions

Managers’ Responses to Online Reviews for Improving Firm Performance: A Text Analytics Approach

In the era of electronic word-of-mouth, firms face pressure to respond to online reviews strategically to maintain and enhance their reputation and financial viability. With guidance from service recovery theory and affect theory, we developed a framework that classifies management responses to seek actionable opportunities to improve firm performance. Using 37,896 managerial responses to online reviews for 390 hotels in three U.S cities, we employed text-mining techniques such as sentiment analysis and topic modeling to develop a framework that classifies the responses into four categories: acknowledgment, account, action, and affect. We evaluated this framework’s effectiveness on subsequent reviews and hotel revenue. Among the management response characteristics, we found that acknowledgment and action were significantly associated with future review ratings. Furthermore, hotel class moderated the relationships between these characteristics and hotel revenue. This study provides recommendations to firms about how they can manage their resources to manage responses to online consumer reviews toward increased financial performance

An ultrahigh- throughput screening platform based on flow cytometric droplet sorting for mining novel enzymes from metagenomic libraries

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166418/1/emi15257_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/166418/2/emi15257.pd