Examining Contextual Factors and Individual Value Dimensions of Healthcare Providers Intention to Adopt Electronic Health Technologies in Developing Countries

Part 5: Research in ProgressInternational audienceDespite substantial research on electronic health (e-Health) adoption, there still exist vast differences between resource-rich and resource-poor populations regarding Information Technology adoption. To help bridge the technological gulf between developed and developing countries, this research-in-progress paper examines healthcare providers’ intention to adopt e-health technologies from two perspectives 1) contextual factors (i.e. specific to developing world settings) and 2) individual value dimensions (i.e. cultural, utilitarian, social and personal). The primary output of this paper is a theoretical model merging both the contextual factors and value dimensions; this forms a strong baseline to examine and help ensure the successful adoption of e-Health technologies within developing countries. Future research will be performed to validate the model developed in this paper, with a specific focus on mobile Health in Malawi, Africa

Customer emotions in service failure and recovery encounters

Emotions play a significant role in the workplace, and considerable attention has been given to the study of employee emotions. Customers also play a central function in organizations, but much less is known about customer emotions. This chapter reviews the growing literature on customer emotions in employee–customer interfaces with a focus on service failure and recovery encounters, where emotions are heightened. It highlights emerging themes and key findings, addresses the measurement, modeling, and management of customer emotions, and identifies future research streams. Attention is given to emotional contagion, relationships between affective and cognitive processes, customer anger, customer rage, and individual differences

A spatial interaction model for deriving joint space maps of bundle compositions and market segments from pick-any/J data: An application to new product options

We propose an approach for deriving joint space maps of bundle compositions and market segments from three-way (e.g., consumers x product options/benefits/features x usage situations/scenarios/time periods) pick-any/J data. The proposed latent structure multidimensional scaling procedure simultaneously extracts market segment and product option positions in a joint space map such that the closer a product option is to a particlar segment, the higher the likelihood of its being chosen by that segment. A segment-level threshold parameter is estimated that spatially delineates the bundle of product options that are predicted to be chosen by each segment. Estimates of the probability of each consumer belonging to the derived segments are simultaneously obtained. Explicit treatment of product and consumer characteristics are allowed via optional model reparameterizations of the product option locations and segment memberships. We illustrate the use of the proposed approach using an actual commercial application involving pick-any/J data gathered by a major hi-tech firm for some 23 advanced technological options for new automobiles.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47207/1/11002_2004_Article_BF00434905.pd

Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

Background: Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients\u2019 clinical phenotypes and analyse the differential clinical course. Methods: We performed a hierarchical cluster analysis based on Ward\u2019s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results: A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients\u2019 prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P <.001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27\u20133.62; HR 3.42, 95%CI 2.72\u20134.31; HR 2.79, 95%CI 2.32\u20133.35), and Cluster 1 (HR 1.88, 95%CI 1.48\u20132.38; HR 2.50, 95%CI 1.98\u20133.15; HR 2.09, 95%CI 1.74\u20132.51) reported a higher risk for the three outcomes respectively. Conclusions: In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes