3,805 research outputs found
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The impact of employees' working relations in creating and retaining trust: the case of the Bahrain Olympic Committee
Introduction: This thesis investigates the impact of employees’ working relations in creating, maintaining and retaining trust in the Bahrain Olympic Committee (BOC).
Aim: The main aim of this thesis is to determine how the three groups of Organisational Trust variables, namely Social System Elements (SSE), Factors of Trustworthiness (FoT) and Third-Party Gossip (TPG), affect employees’ Organisational Trust (OTR) in the BOC and promote Organisational Citizenship Behaviour (OCB). To answer this main aim, a conceptual framework was created that focused on exploring the following research aims: (1) the interrelationship between SSE and FoT, (2) the effect of SSE on OTR, (3) the impact of TPG on OTR and (4) the effect of OTR on overall OCB.
Methodology: The study uses a mixed-method case study research style that included in-depth semi-structured interviews with 17 managers, an online questionnaire survey with 320 employees of the BOC and an analysis of the BOC’s Annual Reports from 2015 to 2018.
Results: The qualitative and quantitative findings indicate, firstly, that there is a significant interrelationship between SSE and FoT, establishing that SSE’s perception of organisational justice (OJ), including that FoTs benevolence and integrity as the most important factors in yielding employees’ trust in the BOC. Secondly, it has been established that SSEs have significant direct and indirect effects on OTR. Thirdly, negative and positive TPG concurrently occurred in the BOC and the prevalence of negative TPG poses more impact on OTR. Finally, this study’s findings demonstrated OTR’s effect in generating OCB, including that Civic Virtue was rated as the most preferred of the five OCB themes; this indicates the managers’ and the employees’ strong emotional attachment and support of the activities taking place at the BOC.
Contributions: Overall, this thesis substantially contributes to OTR literature, particularly in the context of the Middle East. It also proposes several insightful recommendations for future research and practical implications for practitioners in the field of Organisational Trust
Question Decomposition Tree for Answering Complex Questions over Knowledge Bases
Knowledge base question answering (KBQA) has attracted a lot of interest in
recent years, especially for complex questions which require multiple facts to
answer. Question decomposition is a promising way to answer complex questions.
Existing decomposition methods split the question into sub-questions according
to a single compositionality type, which is not sufficient for questions
involving multiple compositionality types. In this paper, we propose Question
Decomposition Tree (QDT) to represent the structure of complex questions.
Inspired by recent advances in natural language generation (NLG), we present a
two-staged method called Clue-Decipher to generate QDT. It can leverage the
strong ability of NLG model and simultaneously preserve the original questions.
To verify that QDT can enhance KBQA task, we design a decomposition-based KBQA
system called QDTQA. Extensive experiments show that QDTQA outperforms previous
state-of-the-art methods on ComplexWebQuestions dataset. Besides, our
decomposition method improves an existing KBQA system by 12% and sets a new
state-of-the-art on LC-QuAD 1.0.Comment: Accepted by AAAI202
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Production networks in the cultural and creative sector: case studies from the publishing industry
The CICERONE project investigates cultural and creative industries through case study research, with a focus on production networks. This report, part of WP2, examines the publishing industry within this framework. It aims to understand the industry’s hidden aspects, address statistical issues in measurement, and explore the industry’s transformation and integration of cultural and economic values. The report provides an overview of the production network, explores statistical challenges, and presents qualitative analyses of two case studies. It concludes by highlighting the potential of the Global Production Network (GPN) approach for analyzing, researching, policymaking, and intervening in the European publishing network.
The CICERONE project’s case study research delves into the publishing industry, investigating its production networks and examining key aspects often unseen by the public. The report addresses statistical challenges in measuring the industry and sheds light on its ongoing transformations and integration of cultural and economic values. It presents an overview of the production network, explores statistical issues, and provides qualitative analyses of two case studies. The report emphasizes the potential of the GPN approach for analyzing and intervening in the European publishing network, ultimately contributing to research, policymaking, and understanding within the industry
Digitale Edition in Österreich
Between 2016 and 2020 the federally funded project "KONDE - Kompetenznetzwerk Digitale Edition" created a network of collaboration between Austrian institutions and researchers working on digital scholarly editions. With the present volume the editors provide a space where researchers and editors from Austrian institutions could theorize on their work and present their editing projects. The collection creates a snapshot of the interests and main research areas regarding digital scholarly editing in Austria at the time of the project
Use of Smart Technology Tools for Supporting Public Health Surveillance: From Development of a Mobile Health Platform to Application in Stress Prediction
BACKGROUND Traditional public health data collection methods are typically based on self-reported data and may be subject to limitations such as biases, delays between collection and reporting, costs, and logistics. These may affect the quality of collected information and the ability of public health agencies to monitor and improve the health of populations. An alternative may be the use of personal, off-the-shelf smart devices (e.g., smartphones and smartwatches) as additional data collection tools. These devices can collect passive, continuous, real-time and objective health-related data, mitigating some of the limitations of self-reported information. The novel data types can then be used to further study and predict a condition in a population through advanced analytics. In this context, this thesis’ goal is to investigate new ways to support public health through the use of consumer-level smart technologies as complementary survey, monitoring and analyses tools, with a focus on perceived stress. To this end, a mobile health platform (MHP) that collects data from devices connected to Apple Health was developed and tested in a pilot study collecting self-reported and objective stress-related information, and a number of Machine Learning (ML) models were developed based on these data to monitor and predict the stress levels of participants.
METHODS The mobile platform was created for iOS using the XCode software, allowing users to self-report their stress levels based on the stress subscale of the Depression, Anxiety and Stress Scale (DASS-21) as well as a single-item LIKERT-based scale. The platform also collects objective data from sensors that integrate with Apple Health, one of the most popular mobile health data repositories. A pilot study with 45 participants was conducted that uses the platform to collects stress self-reports and variables associated with stress from Apple Health, including heart rate, heart rate variability, ECG, sleep, blood pressure, weight, temperature, and steps. To this end, participants were given an iPhone with the platform installed as well as an Apple Watch, Withings Sleep, Withings Thermos, Withings BPM Connect, Withings Body+, and an Empatica E4 (the only device that does not connect to Apple Health but included due to its wide use in research). Participants were instructed to take device measurements and self-report stress levels 6 times per day for 14 days. Several experiments were conducted involving the development of ML models to predict stress based on the data, using Random Forests and Support Vector Machines. In each experiment, different subsets of the data from the full sample of 45 participants were used. 3 approaches to model development were followed: a) creating generalized models with all data; b) a hybrid approach using 80% of participants to train and 20% to test the model c) creating individualized user-specific models for each participant. In addition, statistical analyses of the data – specifically Spearman correlation and repeated measures ANOVA – were conducted.
RESULTS Statistical analyses did not find significant differences between groups and only weak significant correlations. Among the Machine Learning models, the approach of using generalized models performed well, with f1-macro scores above 60% for several of the samples and features investigated. User-specific models also showed promise, with 82% achieving accuracies higher than 60% (the bottom limit of the state-of-the-art). While the hybrid approach had lower f1-macro scores, suggesting the models could not predict the two classes well, the accuracy of several of these models was in line with the state-of-the-art. Apple Watch sleep features, as well as weight, blood pressure, and temperature, were shown to be important in building the models.
DISCUSSION AND CONCLUSION ML-based models built with data collected from the MHP in real-life conditions were able to predict stress with results often in line with state-of-the- art, showing that smart technology data can be a promising tool to support public health surveillance. In particular, the approaches of creating models for each participant or one generalized model were successful, although more validation is needed in future studies (e.g., with more purposeful sampling) for increased generalizability and validity on the use of these technologies in the real-world. The hybrid approach had good accuracy but lower f1-scores, indicating results could potentially be improved (e.g., possibly with less missing or noisy data, collected in more controlled conditions). For feature selection, important features included sleep data as well as weight, blood pressure and temperature from mobile and wearable devices. In summary, this study indicates that a platform such as the MHP, collecting data from smart technologies, could potentially be a novel tool to complement population-level public health data collection. The predictive stress modelling might be used to monitor stress levels in a population and provide personalized interventions. Although more validation may be needed, this work represents a step in this direction
Building the Hill City: Internal Improvements and Political Economy in Lynchburg, Virginia, 1791-1829
This research explores the economic and political considerations of internal improvements in antebellum southwest Virginia. Special emphasis is paid to local petitions submitted to the Virginia General Assembly between 1791 and 1829, as well as local newspaper editorials printed or reprinted in Lynchburg, Virginia during the same period. Questions of intentionality, self-interest, and sectionalism are raised in elucidating what role localities along the ‘old frontier’ played in connecting the underdeveloped western region of the state to the developed eastern region during the early 19th century. Border towns first settled in the colonial backcountry—specifically Lynchburg, Virginia—served as ‘first movers’ in implementing internal improvements in the backcountry for the explicit purposes of seizing emerging economic opportunity. Source records show that communities such as Lynchburg viewed an inevitable national improvement not as a centralized federal mechanism of public works, but rather as a decentralized collection of bridges, canals, and roads, forming collectively a national network of communication and travel for the benefit of communities. This research shows that a developmental model can be applied to help explain, in part, how improvement philosophies from the Scottish Enlightenment helped influence and reach the backcountry town of Lynchburg, Virginia
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