How Much Digital is Too Much? A Study on Employees’ Hybrid Workplace Preferences

How post-pandemic workplaces will evolve is one challenging decision organizations must consider. Prior studies have explored remote work and digital workplace transformations. However, literature offers only little insight into the status quo of employees\u27 preferences for their future workplace and its consequences. This paper posits that employees\u27 openness to digital change influences hybrid workplace preference. Performance and personal outcome expectations further have a mediating role in this relationship. Finally, hybrid workplace preferences can lead to office resistance and the intention to leave. This paper draws on social cognitive theory and sheds light on the interplay of employees\u27 preferences and potential consequences for businesses. We empirically tested the proposed model with survey data from U.S. employees. Findings show that hybrid settings are critical to attracting talent open to digital change. The contribution to IS literature is manifold and contains implications on how to envision the future workplace successfully

State of the Art in Open Data Research: Insights from Existing Literature and a Research Agenda

Abstract With the proliferation of mobile network, mobile devices, and Web of things, many different industries including government departments, private firms, and research communities offer more transparency through releasing data. The resultant effort offers a new paradigm -open data -still at infancy stage though. Despite the rising research initiatives explaining its benefits and challenges, and demonstrating policy conception and project details, no systematic survey of extant literature on open data is performed yet. Such a study could examine open data from a holistic canvas, assess the current status of research and propose future direction. This study conducts a review of the extant literature in order to ascertain the current state of research on open data and present an extensive exploration for eleven different types of analyses: contexts, perspectives, level of analysis, research methods, the drivers, benefits, barriers, theory/model development, the most productive journals, authors, and institutions. Also, we present a number of future research agendas. This study also explains the implications to assist researchers, policy makers and journal editors

Exploring learner perceptions of and interaction behaviors using the Research Writing Tutor for research article Introduction section draft analysis

The swiftly escalating popularity of automated writing evaluation (AWE) software in recent years has compelled much study into its potential for effective pedagogical use (Chen & Cheng, 2008; Cotos, 2011; Warschauer & Ware, 2006). Research on the effectiveness of AWE tools has concentrated primarily on determining learners\u27 achieved output (Warschauer & Ware, 2006) and emphasized the attainment of linguistic goals (Escudier et al., 2011); however, in-process investigations of users\u27 interactions with and perceptions of AWE tools remain sparse (Shute, 2008; Ware, 2011). This dissertation employed a mixed-methods approach to investigate how 11 graduate student language learners interacted with and perceived the Research Writing Tutor (RWT), a web-based AWE tool which provides discourse-oriented, discipline-specific feedback on users\u27 section drafts of empirical research papers. A variety of data was collected and analyzed to capture a multidimensional depiction of learners\u27 first time interactions with the RWT; data comprised learners\u27 pre-task demographic survey responses, screen recordings of students\u27 interactions with the RWT, individual users\u27 interactional reports archived in the RWT database, instructor and researcher observations of students\u27 in-class RWT interactions, stimulated recall transcripts, and post-task survey responses. Descriptive statistics of the Likert-scale response data were calculated, and open-ended survey responses and stimulated recall transcripts were analyzed using open coding discourse analysis techniques or Systemic Functional Linguistic (SFL) appreciation resource analysis (Martin & Rose, 2003), prior to triangulating data for certain research questions. Results showed that participants found the RWT to be useful and were positive in their attitudes about helpfulness of the tool in the future if issues in feedback accuracy were improved. However, the participants\u27 also cited wavering trust in the RWT and its automated feedback, seemingly originating from learners\u27 observations of RWT feedback inaccuracies. Systematized observations of learners\u27 actual and reported RWT interaction behaviors showed both unique and patterned behaviors and strategies for using the RWT for draft revision. The participants\u27 cited learner variables, such as technological background and comfort levels using computers, personality, status as a non-native speaker of English, discipline of study, and preferences for certain forms of feedback, as impacting their experience with the RWT. Findings from this research may help enlighten potential pedagogical uses of AWE programs in the university writing classroom as well as help inform the design of AWE tasks and tools to facilitate individualized learning experiences for enhanced writing development

A user-centric framework to improve the reusability

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsOpen data has a profound effect in working environments in which information is created and shared at all levels. At the local government level, open-data initiatives have resulted in higher levels of transparency as regards policies. Greater engagement between decision-makers and citizens has changed the way data analysis and evidence are used to support local governance. Initiatives on open data are currently playing an essential role in local governments. However, the current challenge of local open data that authorities are facing has gradually changed from accessibility issues to measures of the impact of the ongoing open-data projects, from more data catalogs to sustainable and increasing levels of reuse of released data, and better reusability of open data. Despite an increasing amount of data being made open, few studies have looked into its level of reusability, and the barriers that hamper the reuse of open geodata from a data consumer’s perspective are an issue that most communities of data users are currently faced with. Some frameworks are showing how the level of maturity in national open-data initiatives is either increasing or decreasing, but there is still a need for a specific framework to guide local data authorities to engage their current users and also help them to move toward a bottom-up approach. This research contributes with three elements in this regard. The first is the current status of the level of reuse of open geodata in cities. This is followed by a taxonomy of the barriers faced by data users in Colombia and Spain, and the third is a set of elements that shape a user-centric framework to help data authorities improve the level of reuse of published open geodata in their ongoing local initiatives. The proposed taxonomy and framework are based on a literature review, an online survey, and a set of participatory workshops conducted in four selected cities (Bogotá, Medellín, Cali in Colombia and Valencia in Spain), with local data authorities and user communities from different backgrounds and with experience in the field of open data. The taxonomy presented in this research highlights a number of issues such as outdated data, low integration of data producers, and difficulty to access data, the most relevant from the data consumer’s point of view being misinterpretation and misuse of released data and their terms of use. Once the barriers had been identified and validated with data users across the selected cities, this research defined the elements included in a conceptual framework that local authorities could use as a guideline to improve the level of reuse in their ongoing open data initiatives. The core elements of this framework are what are defined as ’Impact Enablers’, which consist of three aspects considered by the literature reviewed as relevant to improve the positive impact of current initiatives. These three factors are: A) the requirements of data-user communities; B) open data at city level as a way to promote and engage users; and finally, C) a geographic approach to improving the level of reusability of released data due to its potential to engage more users. The second part of the proposed framework is made up of four connected elements: 1) The complete identification of data-user communities and their needs; 2) The community of reuse as a set of technological tools to promote the reusability of released data; 3) User-focused metadata; and 4) Reuse-focused legal terms. The elements mentioned earlier were compiled and included due to their relevance for data-user communities in the four use cases included in this research. This framework provides a clear path for local data authorities to reshape their current open data strategies so as to include data-user requirements and move toward a bottom-up approach. The research ends with a discussion and some concluding points, in addition to several limitations in the application of our findings. At the end of this dissertation, a roadmap for future research and implementations are presented, taking into account some reflections on the framework

Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey

Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogeneity of domain data, the sophistication of domain knowledge, the uniqueness of domain objectives, and the diversity of the constraints (e.g., various social norms, cultural conformity, religious beliefs, and ethical standards in the domain applications). Domain specification techniques are key to make large language models disruptive in many applications. Specifically, to solve these hurdles, there has been a notable increase in research and practices conducted in recent years on the domain specialization of LLMs. This emerging field of study, with its substantial potential for impact, necessitates a comprehensive and systematic review to better summarize and guide ongoing work in this area. In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications. First, we propose a systematic taxonomy that categorizes the LLM domain-specialization techniques based on the accessibility to LLMs and summarizes the framework for all the subcategories as well as their relations and differences to each other. Second, we present an extensive taxonomy of critical application domains that can benefit dramatically from specialized LLMs, discussing their practical significance and open challenges. Last, we offer our insights into the current research status and future trends in this area

Dung beetle (Coleoptera Scarabaeoidea) assemblages in the western Italian Alps: benchmark data for land use monitoring

Traditional agro-pastoral practices are in decline over much of the Alps (MacDonald et al. 2000), leading to shrub and tree encroachment, and this represents one of the main threats for the conservation of alpine biodiversity, as many plant and animal species are dependent on the presence of semi-natural open habitats. However, quantifying this environmental change and assessing its impact on biodiversity may be difficult, especially in the context of sparse historical survey data. The accessibility of contemporary data about local biodiversity surveys in general, and indicator taxa in particular, is an essential consideration for planning future evaluations of conservation status in the Alps and for conservation plans that use ecological indicators to monitor temporal changes in biodiversity. Dung beetles are important ecosystem service providers (Nichols et al. 2008) that have been assessed as a good ecological indicator taxon in several studies (reviewed by Nichols and Gardner 2011), and although the Alps is perhaps one of the best-studied regions in respect of dung beetles, there are still only eight readily-accessible publications. We have augmented and comprehensively reviewed the data from these publications

AFTER-SCHOOL PROGRAMMING: A VISUAL ARTS PERSPECTIVE

As unsupervised, after-school time increases for America’s youth, negative and risky opportunities await them. Recent studies find that as many as 15.1 million children in the United States are left unsupervised after school. Unsupervised children are significantly at risk for truancy, poor academics and risk-taking behavior. These negative forces have been targeted by many intervention efforts over the years, primarily through after-school programs. The literature defines quality programs as those with distinct elements connected to positive outcomes such as student achievement, motivation/engagement, critical/creative thinking, social competencies, and communication. Such outcomes are also evident in arts-related literature and connected to specific exposure to the visual arts. While benefits of arts programs are well documented, less is known about visual arts programs, especially those offered outside of school. To respond to this gap in the literature, this study investigated a visual-arts after-school program for middle school students. The research questions were a) what are the demographic characteristics of student participants in a visual arts-based after-school program? and b) what possible impact does attendance in an arts-based after-school program have on its mentors? To answer these questions, data were collected on participants’ gender, age, grade, ethnicity, free/reduced lunch, Title 1 eligibility, discipline records, family status, program and school attendance. Participating high school mentors’ perceptions were measured through a survey with scaled and open-ended items. When compared with all students in the district, participants were disproportionately female. On other demographic measures no significant differences were found. Mentors (n=16) described benefits including academic skill development, social and personal identity, intrapersonal and peer relations, positive environment, stress relief, and inspiration. Implications for the development of youths’ social capital, for future research and for practice are offered

Identifying Unmaintained Projects in GitHub

Background: Open source software has an increasing importance in modern software development. However, there is also a growing concern on the sustainability of such projects, which are usually managed by a small number of developers, frequently working as volunteers. Aims: In this paper, we propose an approach to identify GitHub projects that are not actively maintained. Our goal is to alert users about the risks of using these projects and possibly motivate other developers to assume the maintenance of the projects. Method: We train machine learning models to identify unmaintained or sparsely maintained projects, based on a set of features about project activity (commits, forks, issues, etc). We empirically validate the model with the best performance with the principal developers of 129 GitHub projects. Results: The proposed machine learning approach has a precision of 80%, based on the feedback of real open source developers; and a recall of 96%. We also show that our approach can be used to assess the risks of projects becoming unmaintained. Conclusions: The model proposed in this paper can be used by open source users and developers to identify GitHub projects that are not actively maintained anymore.Comment: Accepted at 12th International Symposium on Empirical Software Engineering and Measurement (ESEM), 10 pages, 201