While open government data (OGD) is increasingly recognized as a critical resource for economic growth and data-driven innovation, methods for proactively evaluating the potential utilization of these datasets remain underdeveloped. This study addresses this gap by investigating two key methodological questions: first, whether automated machine learning (AutoML) is an appropriate tool for measuring and evaluating OGD utilization, and second, how the composition of training data affects the performance of models designed to predict such utilization. This research specifically compares the efficacy of two distinct data strategies: models trained on integrated datasets spanning multiple domains versus those trained on domain-specific datasets. Using metadata from the South Korean government’s extensive OGD portal, this study employs AutoML to systematically build and evaluate predictive models under these different training conditions. The findings reveal that the training data strategy is a critical determinant of predictive accuracy, with the integrated-domain approach frequently yielding superior performance over domain-specific models. This research provides empirical evidence on the impact of data integration strategies in this context and establishes a methodological framework for the prospective assessment of OGD value, offering a more robust alternative to traditional retrospective evaluation metrics.
This article attempts to present the entire research output of a scientist in terms of his publication activity and its impact as comprehensively as possible. To this end, a bibliometric overview of the scope and structure of his research output is first provided. Furthermore, the visibility of this output in the various information services is analyzed. Christian Schlögl authored a total of 177 publications, including 77 journal articles. This set of publications is only visible through the use of personal publication lists in an institutional repository (Uni Graz Online). In contrast, only a fraction of these publications are included in the common information services; in the Web of Science, for example, not even a third exist. The publication output is then examined in more detail, including preferred topics, co-authors, and journals. Next, a citation analysis is conducted, revealing, for example, the temporal distribution of citations and the most frequently cited publications. Finally, the seven most important research areas are briefly presented. For each of these areas, a co-author was asked to comment on Christian Schlögl’s working methods and collaboration. Overall, this article could serve as a good illustrative example for analyzing the research performance of an individual researcher.
This study examines the predictors and consequences of artificial intelligence (AI) tool adoption among university students in Saudi Arabia, integrating the technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), as well as Cognitive Social Theory with AI-specific trust and ethical concern. Using a cross-sectional survey of 317 students from four geographically distributed public universities, we employed partial least square-structural equation modeling to test ten hypotheses. The results reveal that perceived usefulness, trust in AI, and social influence significantly predict AI adoption, while perceived ease of use and ethical concerns show no significant effects. Self-efficacy partially mediates the relationships between perceived usefulness and perceived ease of use with adoption, but not for trust. Notably, AI adoption significantly enhances academic performance and cognitive skill development. The findings contribute to technology acceptance literature by validating the combination of TAM, UTAUT, and social cognitive theory in AI adoption contexts, highlighting trust as a critical factor beyond traditional TAM constructs, and demonstrating AI’s positive educational outcomes. Practically, universities should emphasize AI’s usefulness, foster trust through transparency, and integrate self-efficacy training to maximize adoption benefits.
Biomechanics is an interdisciplinary field with varying citation patterns and centrality of journals where researchers publish. The audit culture of scientific publication has driven persistent, inconsistent, and questionable interpretation of citations and metrics in selecting and evaluating journals. This article extends the understanding of bias in citation patterns and journal metrics for eight biomechanics journals. Citations from three databases and several journal metrics were examined from 2019 to 2023. Long-term changes (1999-2023) were documented for the most prestigious journal in the field (Journal of Biomechanics). There was large variation and positive skew in citations and citation rates to top cited articles in the biomechanics journals from 2021 in all databases. The skew and variation in citations and subject categories/areas assigned by databases contributed to biased journal usage metrics for biomechanics journals. For the Journal of Biomechanics, citation patterns led to opposing changes in overall usage (Journal Impact Factor) and field-normalized usage (Source-Normalized Impact per Paper) journal metrics. Evidence-based interpretations of journal metrics are illustrated and recommendations made to limit bias in planning, publishing, and evaluating research in interdisciplinary fields such as biomechanics.
This study explores the applications and developments of the theory of information worlds (TIW), developed by Burnett and Jaeger (2008), Jaeger and Burnett (2010), in peer-reviewed research published between 2008 and 2023. Building on the previous systematic review by Park et al. (2022), a qualitative content analysis was conducted on 27 articles that employed TIW at substantial theoretical levels, including theory application, theory conversation, and theory generation. The findings reveal that TIW serves as a robust and scalable theoretical framework, applied across diverse contexts and methodological approaches, often integrated with qualitative and mixed methods. The core concepts of normative information behavior and information value were most frequently employed and concepts such as social types were less frequently applied, indicating potential areas for further theoretical refinement. TIW has also informed new theoretical contributions, including a framework of information access in local communities and a theory of local information landscapes, and has been used in conjunction with complementary theories across disciplines. While criticisms regarding its limited treatment of individual agency and affective dimensions have been raised, recent theoretical developments incorporating cognitive and signification domains into the framework have sought to address these limitations. This study contributes to understanding the role and evolution of LIS theory and highlights the potential of TIW for future theoretical advancement and interdisciplinary application.
