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The high rate of internet penetration has led to the proliferation of social media (SM) use, even at the workplace, including academia. This research attempts to develop a…
The high rate of internet penetration has led to the proliferation of social media (SM) use, even at the workplace, including academia. This research attempts to develop a topology and thereby determine the dominant use motive for faculty’s use of SM.
In this two-part study, a two-stage research design has been adopted for topology development based on the application of Uses and Gratifications Theory. In the second part, the Technology Acceptance Model is applied to discern the dominant motive for SM use in academia.
The work is able to develop a seven-item topology, conforming to the basic three use motives, namely, hedonic, utilitarian and social. The work shows faculty attach more value to the instrumental utility of SM, while the hedonic function is also significant.
Discerning dominant motive implies that SM use at the workplace should not be banned, rather effective regulated use will instil the faculty to enhance work outcomes. The conceptualisation of topology for SM use in academia at the workplace can aid in designing an effective organisation policy, and design of an internal SM platform.
The study is unique towards topology development for academic faculty and has many important implications for management and academia, especially towards policy design for SM use at the workplace.
The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels…
The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.