ABSTRACT
According to the constitution of Pakistan, the state is responsible for the provision of necessities of life to its citizens whenever their livelihood is permanently or temporarily threatened. COVID-19 and its associated lockdowns were a series of events where amenities of life around the world were seriously endangered. Especially, hard hit were the small- and medium-sized entrepreneurs (SMEs) of rural Pakistan. To quantitatively assess the social and economic impact of COVID-19, we interviewed the local microenterprise owners in rural Pakistan from January to February 2021 and then June 2021. Mean comparison tests were estimated for pre- and post-COVID-19 periods. Results reveal that the COVID-19 pandemic has significantly and negatively affected wellbeing of micro and small entrepreneurs in the regions as the income of most of the sampled entrepreneurs significantly decreased during the pandemic. Disaggregated consumption analysis however revealed that nominal consumption of food, clothing, energy, health, and education all increased, except for communication, during the pandemic. Furthermore, the regression analysis revealed that changes in income, occupation, borrowing during COVID-19, and family type of the respondents were significant factors in mitigating the effects of COVID-19. Based on the findings, policy recommendations are also spelled out in the last section.
ABSTRACT
BACKGROUND: Patients face difficulties identifying appropriate physicians owing to the sizeable quantity and uneven quality of information in physician rating websites. Therefore, an increasing dependence of consumers on online platforms as a source of information for decision-making has given rise to the need for further research into the quality of information in the form of online physician reviews (OPRs). METHODS: Drawing on the signaling theory, this study develops a theoretical model to examine how linguistic signals (affective signals and informative signals) in physician rating websites affect consumers' decision making. The hypotheses are tested using 5521 physicians' six-month data drawn from two leading health rating platforms in the U.S (i.e., Healthgrades.com and Vitals.com) during the COVID-19 pandemic. A sentic computing-based sentiment analysis framework is used to implicitly analyze patients' opinions regarding their treatment choice. RESULTS: The results indicate that negative sentiment, review readability, review depth, review spelling, and information helpfulness play a significant role in inducing patients' decision-making. The influence of negative sentiment, review depth on patients' treatment choice was indirectly mediated by information helpfulness. CONCLUSIONS: This paper is a first step toward the understanding of the linguistic characteristics of information relating to the patient experience, particularly the emerging field of online health behavior and signaling theory. It is also the first effort to our knowledge that employs sentic computing-based sentiment analysis in this context and provides implications for practice.