Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach.

Recent years have witnessed an increased demand for mobile health (mHealth) platforms owing to the COVID-19 pandemic and preference for doorstep delivery. However, factors impacting user experiences and satisfaction levels across these platforms, using customer reviews, are still largely unexplored in academic research. The empirical framework we proposed in this paper addressed this research gap by analysing unmonitored user comments for some popular mHealth platforms. Using topic-modelling techniques, we identified the impacting factors (predictors) and categorised them into two major dimensions based on strategic adoption and motivational association. Findings from our study suggest that time and money, convenience, responsiveness, and availability emerge as significant predictors for delivering a positive user experience on m-health platforms. Next, we identified substantial moderating effects of review polarity on the predictors related to brand association and hedonic motivation, such as online booking and video consultation. Further, we also identified the top predictors for successful user experience across these platforms. Recommendations from our study will benefit business managers by offering an improved service design leading to higher user satisfaction across these m-health platforms.

Impact of COVID-19 on supply chains: lessons learned and future research directions

Purpose>This study aims to review and organize the research articles which focused on the impact of COVID-19 pandemic in the supply chain (SC) domain through a bibliometric and network analysis.Design/methodology/approach>Initially, a total of 772 research articles with selected keywords were retrieved from the Scopus database for the year 2020 (with the commencement of COVID-19 outbreak). After the filtration and refinement, 484 research articles were found relevant and unique. Further, this study systematically reviews and evaluates the 484 research articles including influential authors, keys journals, influential research work, and collaboration among the countries and institutes with the help of bibliometric analysis tool. The emergent research clusters are identified and established.Findings>The findings reveal that the total number of related publications are steadily growing with the United States leading the way. European countries have made notable accomplishments as well. In addition, both the most cited publications and the keyword distribution provide research guidance for future research.Practical implications>This study focuses on the need and advancement of the literature on the impacts of the COVID-19 pandemic on SCs to frame a research agenda for researchers and practitioners.Originality/value>The present study offers future research directions in the area of SC under the pandemic situation.

Decision-making framework for identifying regions vulnerable to transmission of COVID-19 pandemic.

At the beginning of 2020, the World Health Organization (WHO) identified an unusual coronavirus and declared the associated COVID-19 disease as a global pandemic. We proposed a novel hybrid fuzzy decision-making framework to identify and analyze these transmission factors and conduct proactive decision-making in this context. We identified thirty factors from the extant literature and classified them into six major clusters (climate, hygiene and safety, responsiveness to decision-making, social and demographic, economic, and psychological) with the help of domain experts. We chose the most relevant twenty-five factors using the Fuzzy Delphi Method (FDM) screening from the initial thirty. We computed the weights of those clusters and their constituting factors and ranked them based on their criticality, applying the Fuzzy Analytic Hierarchy Process (FAHP). We found that the top five factors were global travel, delay in travel restriction, close contact, social cohesiveness, and asymptomatic. To evaluate our framework, we chose ten different geographically located cities and analyzed their exposure to COVID-19 pandemic by ranking them based on their vulnerability of transmission using Fuzzy Technique for Order of Preference by Similarity To Ideal Solution (FTOPSIS). Our study contributes to the disciplines of decision analytics and healthcare risk management during a pandemic through these novel findings. Policymakers and healthcare officials will benefit from our study by formulating and improving existing preventive measures to mitigate future global pandemics. Finally, we performed a sequence of sensitivity analyses to check for the robustness and generalizability of our proposed hybrid decision-making framework.

Channel coordination of a risk-averse supply chain: a mean– variance approach

In the age of rapid technological advancement and digitization, coordination strategy remains an important issue for the supply chain. Additionally, the uncertainty caused by the disruption often induces the risk aversion in the supply chain members. Motivated by this issue, here we propose a coordination mechanism for a risk-averse supply chain using mean-variance approach. Here, we consider both centralized and decentralized cases and show that our analysis holds good for a central planner as well as for a decentralized supply chain under channel coordinating contracts such as buyback and revenue-sharing schemes. With the help of theoretical and numerical analysis, we exhibit how an individual supply chain agent's risk aversion behavior can impact the contracts selection mechanism - from the profitability perspective. We extend our analysis to a dyadic setting to a single-supplier multiple-retailer network and confirm that pure strategy Nash equilibrium exists when all the retailers are risk-averse with varying risk attitude.