1,720,974 research outputs found
Designing an Effective Governance Model for Data Collaboratives
No full textOverview: Data Collaboratives have gained traction as interorganizational partnerships centered on data exchange. They enhance the collective capacity of responding to contemporary societal challenges using data, while also providing participating organizations with innovation capabilities and reputational benefits. Unfortunately, data collaboratives often fail to advance beyond the pilot stage and are therefore limited in their capacity to deliver systemic change. The governance setting adopted by a data collaborative affects how it acts over the short and long term. We present a governance design model to develop context-dependent data collaboratives. Practitioners can use the proposed model and list of key reflective questions to evaluate the critical aspects of designing a governance model for their data collaboratives. Practitioner Takeaways:Practitioners can develop a detailed understanding of the governance factors that influence the successful creation of data collaboratives.The governance design process model offers practitioners a tangible roadmap and concrete activities to help them revamp an existing data collaborative or create a new one.The list of reflective questions can guide practitioners' initial design efforts and help them assess the robustness of their data collaboratives' current governance models
Fostering Data Collaboratives’ systematisation through models’ definition and research priorities setting
Full text linkData collaboratives (DC) [12, 18] have gained increasing attention in recent years benefitting and nurturing the momentum around the use of Data for Good [11]. However, research on the topic, derived and built upon the fields of collaborative governance, information sharing, and open data [9, 17] is still unmature, lacking a systematic body of knowledge, grounded in empirical evidence [11]. Except few studies, specifically referred to DC [31, 36, 37, 40, 42] [15, 18, 19, 24], most of the literature used in the field is encompassing broader concepts as such DataSharing, DataforGood, or Cross Sectoral Partnership. Given that the empirical field has matured sufficiently to permit more quantitative analysis, the research seeks to go beyond existing qualitative classifications and inductively define data collaborative archetypes, emphasizing their distinctions and peculiarities as a foundation for future research on the topic. The research started from a literature review on DCs, their definition and the dimensions identifying different DC's models. The dataset provided on datacollaboratives.org has been filtered based on the literature review, excluding those instances that do not meet the DCs criteria or for whom online data collection is not feasible. Once the empirical setting was defined, a phase of variables selection and population has been conducted according to different variables. The evaluation of different clustering solutions, using both qualitative and quantitative methodologies, brought to identify five mutually exclusive clusters. Each cluster is described according to 18 variables, allowing the emergence of cluster's specific peculiarities and challenges. Findings are consistent with prior classifications and taxonomies [18, 29] with additional views afforded by a larger number of instances, the use of quantitative methodologies and the analysis of additional variables. Findings demonstrate the coexistence of quite different entities under the concept of DC, each of whose challenges and progress should be examined independently by researchers. Responding to the objective to foster DCs long term sustainability, different research priorities are specified according to identified clusters and an empirical setting for conducting this research is made available. From a practitioner perspective, research's findings may enable those interested in the topic to obtain more comprehensive information about benchmark examples, which is a valuable resource for industry growth. Additionally, the research illustrates the efficacy of categorical variable clustering analysis for inductive exploratory studies in a novel field of research
Determinants for university students’ location data sharing with public institutions during COVID-19: The Italian case
Full text linkData on real-time individuals’ location may provide significant opportunities for managing emergency situations. For example, in the case of outbreaks, besides informing on the proximity of people, hence supporting contact tracing activities, location data can be used to understand spatial heterogeneity in virus transmission. However, individuals’ low consent to share their data, proved by the low penetration rate of contact tracing apps in several countries during the coronavirus disease-2019 (COVID-19) pandemic, re-opened the scientific and practitioners’ discussion on factors and conditions triggering citizens to share their positioning data. Following the Antecedents → Privacy Concerns → Outcomes (APCO) model, and based on Privacy Calculus and Reasoned Action Theories, the study investigates factors that cause university students to share their location data with public institutions during outbreaks. To this end, an explanatory survey was conducted in Italy during the second wave of COVID-19, collecting 245 questionnaire responses. Structural equations modeling was used to contemporary investigate the role of trust, perceived benefit, and perceived risk as determinants of the intention to share location data during outbreaks. Results show that respondents’ trust in public institutions, the perceived benefits, and the perceived risk are significant predictor of the intention to disclose personal tracking data with public institutions. Results indicate that the latter two factors impact university students’ willingness to share data more than trust, prompting public institutions to rethink how they launch and manage the adoption process for these technological applications
Technological Innovation as an Enabler of Social Value Creation in the Museum Sector: Evidences from the InnovaMusei Experience
No full textDesigning data collaboratives’ governance dimensions for long-term stability: an empirical analysis
Full text linkThe momentum surrounding the use of data for the public good has grown over the past few years, resulting in several
initiatives, and rising interest from public bodies, intergovernmental organizations, and private organizations. The
potential benefits of data collaboratives (DCs) have been proved in several contexts, including health, migration,
pandemics, and public transport. However, these cross-sectoral partnerships have frequently not progressed beyond
the pilot level, a condition hindering their ability to generate long-term societal benefits and scale their impact.
Governance models play an important role in ensuring DCs’ stability over time; however, existing models do not
address this issue. Our research investigates DCs’ governance settings to determine governance dimensions’ design
settings enhancing DCs’ long-term stability. The research identifies through the literature on collaborative governance
and DCs seven key governance dimensions for the long-term stability of DCs. Then, through the analysis of
16 heterogeneous case studies, it outlines the optimal design configurations for each dimension. Findings make a
significant contribution to academic discourse by shedding light on the governance aspects that bolster the long-term
stability of DCs. Additionally, this research offers practical insights and evidence-based guidelines for practitioners,
aiding in the creation and maintenance of enduring DCs
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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