Does Use of Twitter by Political Leaders Matter in a Health Crisis? The Perspective of COVID-19

While extant literature has reached a consensus on the effectiveness of Twitter in crisis communication, this paper contextualizes the use of Twitter specifically by political leaders during the COVID-19 crisis. We first identify political leaders on Twitter using machine learning techniques and then examine the significant properties of their networks. The findings demonstrate that the network of political leaders on Twitter is relatively dense and well-connected. However, a few nodes are highly prominent and have a large number of connections. Our study detects twenty-three communities of political leaders and observes the evidence of political polarization in the network. We also find two large communities representing the Republican and Democratic parties at the national-level. The remaining communities are reasonably well-balanced in size and center at the state-level. In further analysis, we plan to investigate the patterns of COVID-19 crisis communications through this network and explore the association with COVID-19 outcomes using panel data

An mHealth Application to Promote Diabetes Self-care Behavior among Medically Underserved Population

Diabetes is a chronic illness that causes serious health complications such as kidney failure, limb amputations, and often leads to premature death. Adoption of self-care behavior among diabetes patients is known to improve their health conditions and quality of life. Medically underserved populations (MUP) are disproportionately affected by diabetes. In this research we use a design science approach to develop an mHealth app to promote diabetes self-care behavior among MUPs. Using design theories on behavior change and user-centered design we articulated five key design principles. A mobile app based on these principles and AADE7 self-care behavior framework has been implemented using an Android system. We are currently in the process of evaluating its effectiveness. Our research contributes to the discourse on design for behavior change and illustrates the effectiveness of mHealth apps in promoting healthy lifestyles

A Study on Job Satisfaction: Focus on Bankers of Bangladesh

Job Satisfaction is a universal issue. Human Resource approach of motivation firmly advocates the notion of job satisfaction with a view to ensuring higher productivity of the employees. Banking industry, as driving force of the economy, is playing crucial role to promote and facilitate growth of country. We here eagerly interest in finding out relative importance of variables what influence the level of satisfaction of the bankers. Job satisfaction is the self-contentment that employees enjoy from the organization through the trade-off between contribution and inducement. The study found that 76.5% of the bankers are satisfied over their jobs and only 9.5% are dissatisfied. The variables responsible for job satisfaction of bankers are Job Status & Security, Management Policy, Pay, Working Condition, Decision making process & Communication Pattern, Supervisor Behavior, Job Nature, Recognition & Promotion. It is evident from the study that Pay,  Recognition & Promotion and Working Condition are strongly co-related to over all job satisfaction scoring 0.596, .572 & .562 respectively. Regression model is able to express 59.4% of total variation.  Pay and Working condition are the most influencing variables in framing job satisfaction of bankers since coefficient beta scores .298 & .278 respectively. The other two influencing variables are Job security & Status and Promotion & Recognition as coefficient beta scores .216 & .208. Key words: Employee Performance. Human Resource Management, Job Satisfaction, Strategic Asset

Diabetes self-care management using mobile applications among medically underserved population

Patients with type-2 diabetes can benefit from self-care behaviors that include eating a healthy diet, exercising regularly, and monitoring health conditions periodically. Most patients fail to abide by this routine, thus resulting in serious health complications. This situation is more prevalent among medically underserved populations (MUP). MUPs, typically, are from low-income groups and often lack health insurance and access to medical care. Mobile technology is increasingly being used in chronic disease management. In this study, we use a mixed method research approach to investigate factors that influence the intention to adopt mobile technology for diabetes self-care management among MUPs. We extend the Technology Acceptance Model (TAM) with relevant constructs (e.g. illness representation and privacy concern) to contextualize the healthcare setting of MUPs. This study will contribute to our understanding of mobile technology adoption behavior of MUPs and help improve diabetes management among this patient population