Distinguishing mHealth from other health care alternatives in developing countries: a study on service characteristics

Services in general and healthcare services in particular require proper planning and design so as to address patients concerns and improve outcomes. In this context, mobile phone s wide spread penetration coupled with its versatility is transforming it as a significant delivery channel for healthcare services. Mobile Health (mHealth- healthcare using mobile phones) is expected to enhance the access to healthcare especially, in the developing world. Following the House of Quality (HoQ) for service design, the literature search identified significant gaps in comparatively assessing mHealth with the other conventional services. Such an analysis is important for the large scale adoption of mHealth. To fill this gap, the current research has carried out a quantitative comparison of healthcare services, an important element of HoQ. The study explores the broad research questions: whether service alternatives are distinguishable from each other and if so, what factors contribute to the differentiation. A multiple discriminant analysis (MDA) is performed to understand patients perceptions of various healthcare services: public hospital (PH), general practitioner (GP), traditional medicine (TM) and B2C mHealth service in a developing country. Ubiquity, interaction quality and value have been identified to have significant influence on the patients attitude towards health care services. mHealth is perceived by the patients as far more easy to use, useful and valuable than other service alternatives. These insights are incorporated into the HoQ model for healthcare service design. mHealth is found to be an effective alternative to serve the developing world where populations are marginally deprived of even basic healthcare services. Theoretical and practical relevance of these findings are analysed and some directions are provided for future research

Cost Models for mHealth Intervention in Aged Care Diabetes Management

Governments across the globe are facing the challenges posed by ageing population. Diabetes is one of the leading causes of disease burden to the economies. A proactive management of diabetes for the elderly can offer benefits to all the stakeholders. Mobile Health (mHealth) can play a vital role to tackle the complexities associated with aged people who are living independently. While there have been several pilot studies of mHealth interventions in diabetes management, they have not made inroads into operational reality. The significant factors appear to be lack of comprehensive cost models and business case for mHealth interventions. The paper reviews some of the related research work and argues for the development of cost models for mHealth interventions in aged care diabetes management. It also presents the work-in-progress of creation of cost models and envisages that such a development will help the operational adoption of mHealth benefiting all the stakeholders

Distinguishing “mHealth” from Other Healthcare Services in a Developing Country: A Study from the Service Quality Perspective

Mobile phones’ exponential growth is fuelling the emergence of mobile health (mHealth), thus contributing to healthcare services’ innovative transformation in developing countries. mHealth’s ubiquitous personalised capabilities obviate the access barriers and dismal performance of conventional systems, therefore gaining popularity among patients. Researchers have focused on service quality―a vital element of service adoption―and sustainability. For mHealth to become a robust alternative, how patients perceive mHealth vis-à-vis conventional services must be understood. Comparative analysis studies between mHealth and conventional systems are scarce yet would contribute to theory and strengthen the antecedent phases to service quality, that is, design and operation. mHealth is a viable alternative for fulfiling the unmet goal of quality of life for all. Prompted by these insights, this study is the first attempt to discover the differentiating characteristics of mHealth. Patients’ perceptions were analyzed by multiple discriminant analysis, a classification technique. The findings show that, in distinguishing between healthcare services, mHealth is a favourable alternative: service differentiation occurs along the dimensions of ubiquity, information-quality, and value. The findings’ implications for theory and practice and future research guidelines are also discussed

Building Dynamic Service Analytics Capabilities for the Digital Marketplace

Service firms are now interacting with customers through a multitude of channels or touchpoints. This progression into the digital realm is leading to an explosion of data, and warranting advanced analytic methods to manage service systems. Known as big data analytics, these methods harness insights to deliver, serve, and enhance the customer experience in the digital marketplace. Although global economies are becoming service-oriented, little attention is paid to the role of analytics in service systems. As such, drawing on a systematic literature review and thematic analysis of 30 in-depth interviews, this study aims to understand the nature of service analytics to identify its capability dimensions. Integrating the diverse areas of research on service systems, big data and dynamic capability theories, we propose a dynamic service analytics capabilities (DSAC) framework consisting of management, technology, talent, data governance, model development, and service innovation capability. We also propose a future research agenda to advance DSAC research for the emerging service systems in the digital marketplace

Reflections on Artificial Intelligence – A Hermeneutic Journey

Science, engineering and technology have been moulding and placing an ever increasing pressure on society and in turn, on life styles. The inquisitive nature of man has led to the amazing development of computer. In just four decades the computer has changed its role from a mere data cruncher to decision aid. A reading of the Artificial Intelligence, 17 (1-3), January, 1991, Special Volume on Foundations of AI, has motivated me to transcribe some of my long persistent feelings in writing. Loose usage and blown up speculations may bring discredit to concepts. Not founded on the characteristic behaviour of computer and its numerical instability, I fear that the same thing has happened to computers and to the associated fields of study. The purpose of this autoethnographic article is to reflect on the lessons learnt from AI and search for a right perspective for research and practice