Although governments are investing heavily in big data analytics, reports show mixed results in terms of performance. Whilst big data analytics capability provided a valuable lens in business and seems useful for the public sector, there is little knowledge of its relationship with governmental performance. This study aims to explain how big data analytics capability led to governmental performance. Using a survey research methodology, an integrated conceptual model is proposed highlighting a comprehensive set of big data analytics resources influencing governmental performance. The conceptual model was developed based on prior literature. Using a PLS-SEM approach, the results strongly support the posited hypotheses. Big data analytics capability has a strong impact on governmental efficiency, effectiveness, and fairness. The findings of this paper confirmed the imperative role of big data analytics capability in governmental performance in the public sector, which earlier studies found in the private sector. This study also validated measures of governmental performance.
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To compete in a globalized world, organizations need develop their data analytic capability. Using a review of the literature and case studies, this study examines how organizations acquire and orchestrate the necessary resources to build their data analytic capability in a way that fits their organization and context. The literature review identified six categories of assets and resources needed for the development of data analytic capability, namely data, data analytics, technology, structure and processes, management, and knowledge and skills. The case study findings showed that their impact is interlinked, with the presence of technical knowledge in the organization being essential.
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Learning analytics can help higher educational institutions improve learning. Its adoption, however, is a complex undertaking. The Learning Analytics Capability Model describes what 34 organizational capabilities must be developed to support the successful adoption of learning analytics. This paper described the first iteration to evaluate and refine the current, theoretical model. During a case study, we conducted four semi-structured interviews and collected (internal) documentation at a Dutch university that is mature in the use of student data to improve learning. Based on the empirical data, we merged seven capabilities, renamed three capabilities, and improved the definitions of all others. Six capabilities absent in extant learning analytics models are present at the case organization, implying that they are important to learning analytics adoption. As a result, the new, refined Learning Analytics Capability Model comprises 31 capabilities. Finally, some challenges were identified, showing that even mature organizations still have issues to overcome.
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A practical framework for the implementation of digitalization entitled the “Data Analytic Capability Wheel” was presented. The aspects encompassed by this framework included data quality, data analytics, IT infrastructure, processes, employee knowledge and skills, and management.
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Implementation of the United Nations Convention on the rights of persons with disabilities (UN CRPD) requires countries to harmonise their legislative frameworks with it. This paper investigates the national legislative frameworks of four Asian countries to see the extent to which they provide support services in accordance with Article 19 of the UN CRPD. The UN CRPD requires persons with disabilities to have access to and choice and control over support services. To analyse the policy alignment with the UN CRPD, an analytical framework based on the Capability Approach (CA) was developed. The results show that most countries address support services, including assistive devices, only from the perspective of a social security measure for persons with disabilities living in poverty, failing to uphold the rights of those not meeting those eligibility criteria. However, while support services are inseparably linked to social security, they also are a right for persons with disabilities. Therefore, a paradigm shift is required in the approach of support services and the distributive systems of countries, from one that addresses persons with disabilities as those requiring care considered a burden, to one that considers them rights holders with equal opportunities, for which, support services are a pre-requisite.
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Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?” Our research is grounded in resource-based view theory and we extend the scope beyond the field of learning analytics and include capability frameworks for the more mature research fields of big data analytics and business analytics. This paper’s contribution is twofold: 1) it provides a literature review on known capabilities for big data analytics, business analytics, and learning analytics and 2) it introduces a capability model to support the implementation and uptake of learning analytics. During our study, we identified and analyzed 15 key studies. By synthesizing the results, we found 34 organizational capabilities important to the adoption of analytical activities within an institution and provide 461 ways to operationalize these capabilities. Five categories of capabilities can be distinguished – Data, Management, People, Technology, and Privacy & Ethics. Capabilities presently absent from existing learning analytics frameworks concern sourcing and integration, market, knowledge, training, automation, and connectivity. Based on the results of the review, we present the Learning Analytics Capability Model: a model that provides senior management and policymakers with concrete operationalizations to build the necessary capabilities for successful learning analytics adoption.
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In pursuit of competitive advantage in an increasingly globalized and complex environment, organizations are turning to continuous improvement and digitalization to achieve operational excellence. Viewed through the lens of Dynamic Capabilities Theory, the similarities complementarities, and synergies of continuous improvement capability and data analytic capability are examined. Bridging the gap between theory and practice, continuous improvement routines and practices that can be harnessed to accelerate the implementation of data analytical capability are identified. These include Hoshin Kanri to link digitalization projects to organizational strategic, training to develop organizational knowledge of digitalization, problem solving teams to break knowledge silos, and the use of PDCA-type processes for adopting and monitoring the performance of digital technologies.
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OBJECTIVE: Develop a generic trans-disciplinary, skills-based capability framework for health professionals providing care for people with OA.DESIGN: e-Delphi survey. An international inter-professional Delphi Panel (researchers; clinicians; consumer representatives) considered a draft framework (adapted from elsewhere) of 131 specific capabilities mapped to 14 broader capability areas across four domains (A: person-centred approaches; B: assessment, investigation and diagnosis; C: management, interventions and prevention; D: service and professional development). Over three rounds, the Panel rated their agreement (Likert or numerical rating scales) on whether each specific capability in Domains B and C was essential (core) for all health professionals when providing care for all people with OA. Those achieving consensus (≥80% of Panel) rating of ≥ seven out of ten (Round 3) were retained. Generic domains (A and D) were included in the final framework and amended based on Panel comments.RESULTS: 173 people from 31 countries, spanning 18 disciplines and including 26 consumer representatives, participated. The final framework comprised 70 specific capabilities across 13 broad areas i) communication; ii) person-centred care; iii) history-taking; iv) physical assessment; v) investigations and diagnosis; vi) interventions and care planning; vii) prevention and lifestyle interventions; viii) self-management and behaviour change; ix) rehabilitative interventions; x) pharmacotherapy; xi) surgical interventions; xii) referrals and collaborative working; and xiii) evidence-based practice and service development).CONCLUSION: Experts agree that health professionals require an array of skills in person-centred approaches; assessment, investigation and diagnosis; management, interventions and prevention; and service and professional development to provide optimal care for people with OA.
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