Web Mining Functions in an Academic Search Application

This paper deals with Web mining and the different categories of Web mining like content, structure and usage mining. The application of Web mining in an academic search application has been discussed. The paper concludes with open problems related to Web mining. The present work can be a useful input to Web users, Web Administrators in a university environment.Database, HITS, IR, NLP, Web mining

Control enactment in global virtual teams

textThis dissertation examines how control is enacted in global virtual teams. Literature on virtual teams asserts this phenomenon has features, such as limited physical observation of behavior, that diminish the usefulness of control. Theories about formal control support this prediction, although little is known empirically about the development of any form of control in such a context. Global virtual teams are distributed work groups whose members focus on a global task, span multiple boundaries, and interact primarily via communication technologies. Control enactment refers to the development of processes and structures that attempt to influence members to engage in behaviors that accomplish collective goals. Background literature for this study examines small groups, information technology, and control, revealing the need to examine processes and structures internal and external to the team and consider the development of control over time. This dissertation presents a longitudinal, qualitative analysis of the communication archives for three virtual teams. The results suggest that control enactment includes team processes such as specifying control structures, pressuring teammates, terminating team membership, as well as team and member monitoring. Team processes, along with team structures and external processes and structures, are integrated in a framework for control enactment in global virtual teams; this framework differs from much of the literature that has adopted (or actively rejected) cybernetic conceptions of control theory. Also, the results suggest that, although members frequently relied on their teammates for information about their activities, members in some instances were able to monitor the behaviors of other members based on their electronic communication and work products. Specifying task activities to combine task coordination with technology appropriation enabled this process. As such, the concept of behavior observability may need to be reconceptualized for virtual work. These findings are based on analyses of teams formed for an eight-week student exercise coordinated by the author. Teams in field settings or with different external environments may have occasioned different control processes from those observed here. Further, the data were primarily archival in nature, so access to member perceptions was somewhat limited. The reader should examine the appropriateness of generalization to other settings.Information, Risk, and Operations Management (IROM

Reimagining the Journal Editorial Process: An AI-Augmented Versus an AI-Driven Future

The editorial process at our leading information systems journals has been pivotal in shaping and growing our field. But this process has grown long in the tooth and is increasingly frustrating and challenging its various stakeholders: editors, reviewers, and authors. The sudden and explosive spread of AI tools, including advances in language models, make them a tempting fit in our efforts to ease and advance the editorial process. But we must carefully consider how the goals and methods of AI tools fit with the core purpose of the editorial process. We present a thought experiment exploring the implications of two distinct futures for the information systems powering today’s journal editorial process: an AI-augmented and an AI-driven one. The AI-augmented scenario envisions systems providing algorithmic predictions and recommendations to enhance human decision-making, offering enhanced efficiency while maintaining human judgment and accountability. However, it also requires debate over algorithm transparency, appropriate machine learning methods, and data privacy and security. The AI-driven scenario, meanwhile, imagines a fully autonomous and iterative AI. While potentially even more efficient, this future risks failing to align with academic values and norms, perpetuating data biases, and neglecting the important social bonds and community practices embedded in and strengthened by the human-led editorial process. We consider and contrast the two scenarios in terms of their usefulness and dangers to authors, reviewers, editors, and publishers. We conclude by cautioning against the lure of an AI-driven, metric-focused approach, advocating instead for a future where AI serves as a tool to augment human capacity and strengthen the quality of academic discourse. But more broadly, this thought experiment allows us to distill what the editorial process is about: the building of a premier research community instead of chasing metrics and efficiency. It is up to us to guard these values

Measuring the Economic Impact of High Speed Rail Construction for California and the Central Valley Region

The nation’s first high-speed rail project is under construction in California’s Central Valley as of the date of this report. This research analyzes the immediate economic impacts, focused on employment and spending generated by California High-Speed Rail (HSR) Construction Package 1 (CP1) in the Central Valley and the rest of California. The authors use a two-pronged approach that combines original economic analysis and modeling with case study vignettes that explore the economic impacts through the lens of a sample of businesses and individuals directly impacted by this phase of HSR development. Overall, the economic analysis suggests that CP1-related spending (forecasted through to 2019) will lead to more than 31,500 additional jobs (both part-time and full-time) by the year 2029. Growth is concentrated in Fresno County, with the number of additional jobs estimated at more than 15,500. The analysis considers job growth across a number of alternative scenarios, converting the raw jobs estimates to full-time equivalent job-years. Under the most conservative HSR spending scenario considered, over the 15-year period evaluated, more than 25,000 full-time equivalent job-years are created. This amount to 14,900 jobs per billion (real) dollars of spending, or a cost of approximately $67,200 per job-year

Predicting final user satisfaction based on user experience data using machine learning

東京都立大学Tokyo Metropolitan University博士（工学）doctoral thesi