The moderating role of industry clockspeed on the relation between supply chain integration practices and new product flexibility.

This study assessed the firm's new product flexibility in the recovery phase of COVID-19. Using the theoretical lens of organizational information processing theory, it established the relationship between supply chain integration practices (supply chain planning, internal integration, and supplier involvement) and new product flexibility. It also explained the moderating effect of industry clockspeed on supply chain integration practices and new product flexibility with the help of contingency theory. This study used an online survey method to collect data from plant managers, and we received 256 useable responses. We conducted a confirmatory factor analysis to test the validity and reliability of the scales, and we tested hypotheses using moderated multiple regression technique. The results showed significant positive relationships between supply chain integration practices and new product flexibility. The moderating effects hypotheses showed that industry clockspeed significantly and positively moderated on supply chain planning-new product flexibility and internal integration-new product flexibility links. But it negatively moderated on supplier involvement and new product flexibility. Our study departs from earlier studies in the field that were conducted under normal circumstances. We conducted our study in the recovery phase of COVID-19 in Pakistan, when firms, after fourteen days of complete lockdown, resumed their operations and experienced a new business landscape.

Medical Students' Online Learning Perceptions, Online Learning Readiness, and Learning Outcomes during COVID-19: The Moderating Role of Teacher's Readiness to Teach Online.

This study determined the direct and indirect effects of medical students' online learning perceptions on learning outcomes via their readiness for online learning. It also determined the moderating effect of teachers' online teaching readiness on medical students' online learning perceptions and learning outcomes. We apply the theoretical lens of self-determination theory and constructivist theory to formulate hypotheses. We used self-administered and postal survey methods to collect data from fourth and fifth-year medical students on online learning perceptions, readiness for online learning, and learning outcomes in two waves. We also collected data from the teachers about their perceptions of online teaching readiness. We received 517 usable students' responses (Level-1) and 88 usable teachers' responses (Level-2). We tested Level-1 hypotheses about direct and indirect effects in Analysis of Moment Structures (AMOS), and a Level-2 hypothesis about moderating effect was tested using Hierarchical Linear Modeling (HLM). The results for the Level-1 hypotheses supported the positive effects of students' online learning perceptions and readiness for online learning on learning outcomes. Student readiness for online learning significantly mediated the relationship between online learning perceptions and learning outcomes. HLM results also supported a moderating effect of teachers' online teaching readiness on medical students' online learning perceptions and learning outcomes in such a way that learning outcomes were high when students' online learning perceptions and teachers' online teaching readiness were high. Based on the study's findings, we offer contributions to theory and practice.

Modeling the dynamics of novel coronavirus (COVID-19) via stochastic epidemic model

Novel coronavirus disease is a burning issue all over the world. Spreading of the novel coronavirus having the characteristic of rapid transmission whenever the air molecules or the freely existed virus includes in the surrounding and therefore the spread of virus follows a stochastic process instead of deterministic. We assume a stochastic model to investigate the transmission dynamics of the novel coronavirus. To do this, we formulate the model according to the charectersitics of the corona virus disease and then prove the existence as well as the uniqueness of the global positive solution to show the well posed-ness and feasibility of the problem. Following the theory of dynamical systems as well as by constructing a suitable stochastic Lyapunov function, we establish sufficient conditions of the extinction and the existence of stationary distribution. Finally, we carry out the large scale numerical simulations to demonstrate the verification of our analytical results.

A COVID-19 Supply Chain Management Strategy Based on Variable Production under Uncertain Environment Conditions.

The management of a controllable production in the manufacturing system is essential to achieve viable advantages, particularly during emergency conditions. Disasters, either man-made or natural, affect production and supply chains negatively with perilous effects. On the other hand, flexibility and resilience to manage the perpetuated risks in a manufacturing system are vital for achieving a controllable production rate. Still, these performances are strongly dependent on the multi-criteria decision making in the working environment with the policies launched during the crisis. Undoubtedly, health stability in a society generates ripple effects in the supply chain due to high demand fluctuation, likewise due to the Coronavirus disease-2019 (COVID-19) pandemic. Incorporation of dependent demand factors to manage the risk from uncertainty during this pandemic has been a challenge to achieve a viable profit for the supply chain partners. A non-linear supply chain management model is developed with a controllable production rate to provide an economic benefit to the manufacturing firm in terms of the optimized total cost of production and to deal with the different situations under variable demand. The costs in the model are set as fuzzy to cope up with the uncertain conditions created by lasting pandemic. A numerical experiment is performed by utilizing the data set of the multi-stage manufacturing firm. The optimal results provide support for the industrial managers based on the proactive plan by the optimal utilization of the resources and controllable production rate to cope with the emergencies in a pandemic.

Stability analysis and optimal control of covid-19 with convex incidence rate in Khyber Pakhtunkhawa (Pakistan).

The dynamic of covid-19 epidemic model with a convex incidence rate is studied in this article. First, we formulate the model without control and study all the basic properties and results including local and global stability. We show the global stability of disease free equilibrium using the method of Lyapunov function theory while for disease endemic, we use the method of geometrical approach. Furthermore, we develop a model with suitable optimal control strategies. Our aim is to minimize the infection in the host population. In order to do this, we use two control variables. Moreover, sensitivity analysis complemented by simulations are performed to determine how changes in parameters affect the dynamical behavior of the system. Taking into account the central manifold theory the bifurcation analysis is also incorporated. The numerical simulations are performed in order to show the feasibility of the control strategy and effectiveness of the theoretical results.