from the article: "In the Netherlands, housing corporations are increasingly adopting self-service technologies (SSTs) to support affairs their tenants need to arrange. The purpose of the study is to examine the customers’ motivations of using SSTs in the context of the Dutch public housing sector. An empirical investigation is presented based on a sample of 1,209 tenants. Using partial least squares (PLS), the acceptance model of Blut, Wang, and Schoefer is adopted and tested. The results show that especially the need for interaction negatively influence the adoption of SSTs by tenants. Positively, subjective norm and self-efficacy influence the adoption. Furthermore, playfulness negatively influences this adoption. Developers of SSTs should focus on its ulitalitarian function, rather then invest in its playfulness. Moreover, adoption is propelled by the encouragement of others. This can be enhanced by positive word-of mouth and should therefore stimulated."
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Abstract: This case study examines the use of an eHealth application for improving preoperative rehabilitation (prehabilitation). We have analysed healthcare professionals' motivators and drivers for adopting eHealth for a surgical procedure at academic medical facilities. The research focused on when and why healthcare professionals are inclined to adopt eHealth applications in their way of working? For this qualitative study, we selected 12 professionals involved in all levels of the organisation and stages of the medical process and conducted semi-structured interviews. Kotter’s transformational change model and the Technology Acceptance Model were used as analytical frameworks for the identification of the motivation of eHealth adoption. The findings suggest that contrary to Kotter’s change model, which argues that adoption of change is based on perceptions and feelings, the healthcare drivers are rational when it comes to deciding whether or not to adopt eHealth apps. This study further elaborates the observation made by the Dutch expertise centre on eHealth, Nictiz, that when the value of an eHealth pplication is clear for a stakeholder, the adoption process accelerates. Analysis of the motivations and drivers of the healthcare professionals show a strong relationship with an evidence-based grounding of usefulness and the responsibility these professionals have towards their patients. We found that healthcare professionals respond to the primary goal of improving healthcare. This is true if the eHealth application will innovate their work, but mainly when the application will improve the patient care they are responsible for. When eHealth applications are implemented, rational facts need to be collected in a study before deployment of eHealth applications on how these applications will improve the patient's health or wellbeing throughout their so-called medical journey for their treatment. Furthermore, the preference to learn about new eHealth applications from someone who speaks from authority through expertise on the subject matter, suggests adoption by healthcare professionals may be accelerated through peers. The result of this study may provide healthcare management with a different approach to their eHealth strategy. Future research is needed to validate the findings in different medical organisational settings such as regional healthcare facilities or for-profit centers which do not necessarily have an innovation focus but are driven by other strategic drivers.
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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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