Clinicians adoption of the information systems deployed at hospitals is crucial to achieving the intended effects of the systems, yet many systems face substantial adoption barriers. In this study we analyse the adoption and use of an electronic medicati on record (EMR) 2‑4 years after its deployment. We investigate mid‑and‑lower‑level managers perception of (a) the extent to which clinicians have adopted the EMR and the work procedures associated with its use and (b) possible barriers toward adopt ing the EMR and work procedures, including the managers perception of the usefulness and ease of use of the EMR. The investigation consists of a questionnaire survey sent to the EMR managers in one Danish healthcare region, followed up with interviews at two hospital wards. The EMR is generally perceived as useful, yet respondents state that adoption of the EMR and related procedures is far from obtained. Eleven categories of barrier are identified with uncertainty about what the barriers concretely are as the prime barrier. This prime barrier is particularly noteworthy because the respondents are formally responsible for the adoption of the EMR. It is apparent that time alone has not led to consistent adoption of the EMR. We discuss implications of this finding for the organizational implementation of systems such as the EMR.

Abstract: Studies on the evaluation of information visualization techniques are flourishing, and related methodologies have been discussed in a growing number of recent studies. Yet, these evaluations concentrate mostly on usability measures and cognitive evaluations. In contrast, this contribution focuses on the various factors that drive the adoption of information visualization techniques. The Technology Acceptance Model and the Diffusion of Innovations theory are deployed to develop a framework for ev aluating information visualization adoption. These seminal theories are extended and adapted to Information Visualization, resulting into a framework with three main dimensions: perceived ease of use, perceived usefulness, and perceived authority. The app lication of this theoretically‑based evaluation framework is illustrated through positive and negative examples. As many visualization solutions have not achieved a wide use, the question of which factors foster their adoption seems to be a particularly r elevant yet under‑researched topic.

As mobile government (m‑government) services are a new field, there is limited research that identifies the factors that affect the acceptance of smart government (mobile‑application‑based) services. This study intends to explore the factors that seem to improve end‑users’ acceptance of mobile government services. Mobile technologies are one of the pillars of a smart city. Mobile technologies have huge potential to become one of governments’ most effective and efficient tools in offering their services to the public. However, the technologies that are available for use are not fully welcomed by end‑users. The key problem associated with the high failure rate of e‑government projects is the lack of awareness of the potential factors that may help citizens adopt m‑government services. By reviewing the existing work, this paper proposes a framework, as an extension of Davis’s technology acceptance model (1989), to address the factors that influence user acceptance of smart government services in the UAE and thereby guide the successful implementation of smart government. The literature reviewed led to ten propositions that present the main elements in ensuring end‑users’ adoption of smart government in the UAE. A framework is developed to highlight the key factors that influence the successful implementation of smart government services. The paper will help in understanding the key issues surrounding mobile applications that may support the successful operation of m‑government.

The growing repository of learning resources on the internet provides a ubiquitous opportunity for learning. Worldwide, millions of learners leverage open learning resources (OLRs) to acquire knowledge and skills. Students in academic settings are ardent users of OLRs such as MOOCs and OERs. The purpose of this paper is to identify the factors influencing voluntary acceptance of OLRs by higher education students for complementing learning in the Indian context. The study also proposes a measurement scale for OLRs self‑efficacy (OSE). This study extends and validates the innovation diffusion theory (IDT). The integrated research model reflects the effects of relative advantage, compatibility, OSE, subjective norm and facilitating conditions on acceptance of OLRs. Online survey data from 457 technical students in India were used for analysis using structural equation modeling. Results suggest that while relative advantage, compatibility, OSE and the subjective norm significantly impact intention to use OLRs, effect of facilitating conditions is not significant. Compatibility and OSE also significantly impact relative advantage. The proposed measure of OSE has valid psychometric properties. This study has advanced knowledge on the phenomenon of voluntary acceptance of OLRs from the perspective of students in traditional academic settings in a developing country context by extending and validating IDT. To the best of our knowledge, this is the first empirical study to understand the acceptance of OLRs by students in India. This study also provides a measure for OSE. The relative advantage mediating the effect of OSE on intention is confirmed by the results, which is scarcely tested before empirically. The study has practical implications for policy makers and teachers in a developing country to provide a more contextual learning environment to the students.

The purpose of this study is to propose a unified model integrating both the technology acceptance model (TAM) and task technology fit (TTF) model, and explore the organizational and environmental fit of the integrated model in order to investigate usage of Big Data Analytics and its effect on business performance. A questionnaire was used to collect data from 280 companies in CPG & Retail, Healthcare, Banking and Telecom in India. The data were analysed using exploratory and confirmatory factor analyses. Further, structural equation modelling was used to test the proposed model. The findings show that the research model for integrating the TAM for adoption and TTF model for utility provides a more comprehensive understanding of Big Data Analytics usage. The study identified task technology fit , individual technology fit, organizational data fit, organizational process fit, and business strategy fit as Tidd important variables for affecting Big Data Analytics usage using perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. Competitive fit and partner support/customer fit were also found to be directly affecting Big Data Analytics usage, which in turn has significant influence on business performance. The model explained 71.4 percent of Business performance. The integrated model may be used as a guideline to ensure a positive outcome of Big Data Analytics usage in organizations. This study combined both the key ideas of TAM and TTF to show that they were necessary in predicting Big Data Analytics usage and business performance.