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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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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Big data heeft niet alleen geleid tot uitdagende technische vraagstukken, ook gaat het gepaard met allerlei nieuwe ethische en morele kwesties. Om verantwoord met big data om te gaan, moet ook over deze kwesties worden nagedacht. Want slecht datagebruik kan nadelige gevolgen hebben voor grote groepen mensen en voor organisaties. In de slotaflevering van deze serie verkennen Klaas Jan Mollema en Niek van Antwerpen op een pragmatische manier de ethische kant van big data, zonder te blijven steken in de negatieve effecten ervan.
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Full tekst beschikbaar voor gebruikers van Linkedin. Driven by technological innovations such as cloud and mobile computing, big data, artificial intelligence, sensors, intelligent manufacturing, robots and drones, the foundations of organizations and sectors are changing rapidly. Many organizations do not yet have the skills needed to generate insights from data and to use data effectively. The success of analytics in an organization is not only determined by data scientists, but by cross-functional teams consisting of data engineers, data architects, data visualization experts, and ("perhaps most important"), Analytics Translators.
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Verslag van een presentatie. In onderzoeken naar de prioriteiten van HR-professionals staan analytics dan ook steevast onderaan het prioriteitenlijstje. Echter, nu elke dag meer data beschikbaar komen en alles is te meten, is dit niet langer een houdbaar standpunt. HR-professionals zullen op zijn minst moeten beseffen dat data waardevol zijn. Een Engelstalige definitie van People Analytics luidt: ‘The systematic identification and quantification of the people drivers of business outcomes, with the purpose of making better decisions.‘ Daarbij is het belangrijk om een goede businessvraag te stellen én – vervolgens –de resultaten van de analyse op overtuigende wijze over te brengen.
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presentatie over Publieke Waarde, Big Data en de rol van Finance in onderwijsveld
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De missie van mijn vakgebied is dat data analytics wordt toegepast om organisaties beter te maken. Ons onderzoek richt zich op de verbanden tussen het effectiever maken van organisaties, het verbeteren van individueel welzijn en maatschappelijke waarde. Onze faculteit wil duurzaam waarde realiseren voor organisaties, individu en maatschappij en de drie uitkomsten moeten in balans zijn. Daar staan we voor.
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During the past two decades the implementation and adoption of information technology has rapidly increased. As a consequence the way businesses operate has changed dramatically. For example, the amount of data has grown exponentially. Companies are looking for ways to use this data to add value to their business. This has implications for the manner in which (financial) governance needs to be organized. The main purpose of this study is to obtain insight in the changing role of controllers in order to add value to the business by means of data analytics. To answer the research question a literature study was performed to establish a theoretical foundation concerning data analytics and its potential use. Second, nineteen interviews were conducted with controllers, data scientists and academics in the financial domain. Thirdly, a focus group with experts was organized in which additional data were gathered. Based on the literature study and the participants responses it is clear that the challenge of the data explosion consist of converting data into information, knowledge and meaningful insights to support decision-making processes. Performing data analyses enables the controller to support rational decision making to complement the intuitive decision making by (senior) management. In this way, the controller has the opportunity to be in the lead of the information provision within an organization. However, controllers need to have more advanced data science and statistic competences to be able to provide management with effective analysis. Specifically, we found that an important skill regarding statistics is the visualization and communication of statistical analysis. This is needed for controllers in order to grow in their role as business partner..
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The hospitality industry, comprising diverse Small and Medium Enterprises (SMEs) such as restaurants, hotels, and catering facilities plays an important role in local and regional communities by providing employment opportunities, facilitating the organization of community events, and supporting local social activities and sports teams (Panteia, 2023). The hospitality industry thereby represents a major source of income in Europe, but also a commensurate burden on the environment because of its relatively high usage of water and energy consumption, and food waste, leading to the formulation of several initiatives to increase the sustainability of hotels, restaurants, and resorts, such as farm to fork and towel reuse (Bux & Amicarelli, 2023). Another avenue for hospitality organizations to make progress towards sustainability goals is through circular economy strategies (Bux & Amicarelli, 2023) based on the creation of small regenerative loops that require the involvement of multiple stakeholders (Tomassini & Cavagnaro, 2022). Nevertheless, hospitality operators need to track their progress towards sustainability goals while keep sight of their financial goals (Bux & Amicarelli, 2023), requiring a data-driven decision-making approach to sustainability and circularity. Big data analytics have therefore been identified as an enabler of the circular economy paradigm by reducing uncertainty and allowing organizations to predict results (Awan et al., 2021; Gupta et al., 2019). Hospitality organizations however remain behind in leveraging data analytics for decisionmaking (Mariani & Baggio, 2022). The purpose of the study is therefore to examine how hospitality organizations can leverage data analytics to make data-driven decisions regarding circularity. Using a multiple case study approach of three Dutch hospitality SMEs, enablers and inhibitors of data analytics for datadriven decisions regarding circularity are examined. This addresses the call by Tomassini and Cavagnaro (2022) for more exploration of the circularity paradigm in hospitality. Despite the ongoing interest in increasing the sustainability of the hospitality industry (European Commission, 2013), relatively little attention has been paid to the development of circularity strategies and what is needed to implement them.
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''New technologies are advancing at an unprecedentedly accelerating pace over the years. The distance humanity has covered in 2200 years, from the Antikythera mechanism of ancient Greek world, the oldest known analogue computer, to the 4-bit first microprocessor in 1971, is not even comparable to the advancement of technology in the last 50 years. This dazzling journey of technological development has impacted all aspects of modern life, including industry.Earthquake engineering is one of the disciplines that has embraced new technologies. Earthquake engineers, accustomed to dealing with highly nonlinear and dynamic problems that require complex mathematical and often iterative approaches, are called nowadays to summon dexterity on advanced coding, and masteries on statistics and handling of large amount of data. Artificial Intelligence, Sensing Technologies of all sorts, and Big Data Analytics emerge as essential tools for reducing uncertainty, facilitating engineering process and enhancing knowledge. This Special Issue is a manifestation of the fact that the new technologies can be useful for the most challenging problems of earthquake engineering, opening new prospects in the field.''
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