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In recent years, the COVID-19 pandemic has become one of the most impactful disruptions which has imposed high levels of uncertainty on supply chains around the world. Supply chain integration (SCI) is highly recommended as an underlying mechanism that can facilitate the development of resilience and robustness as two dynamic capabilities. They can in turn positively influence firm performance and success during the disruptive conditions of COVID-19 era. The study aims to examine whether SCI as an enabler of resilience and robustness can improve firm performance during COVID-19 pandemic.

A theoretical model is developed to elaborate the relationship between SCI dimensions, resilience and robustness and firm’s operational and financial performance during the COVID-19 pandemic. A survey method is then used to empirically examine the model using a sample of 94 companies in the food industry in the province of Tehran, Iran, during the COVID-19 pandemic. This study makes several contributions. It provides a novel theoretical model on the relationship between SCI, resilience and robustness and firm performance and tests this model in a less-studied yet critical context (i.e. Iranian food industry) and during a disruptive era (i.e. COVID-19 pandemic).

The results support the positive effect of three SCI dimensions of internal, product and process integration on operational and financial performance during corona virus pandemic. Furthermore, internal and process integration have positive effects on resilience. Internal, product and process integration have positive effects on robustness. In addition, resilience mediates the effects of internal and product integration on both operational and financial performance, whereas robustness mediates the effect of internal and product integration on financial performance.

This study was conducted in the Province of Tehran. To test and generalize the results, it is recommended to conduct this study in other places and countries.

These results highlight the importance of SCI dimensions as vital enablers of resilience and robustness and their consequent impact on firm’s performance during the COVID-19 pandemic.

Destinations to be able to compete with each other need to equip themselves with as many competitive advantages as possible. Tourists' loyalty to a destination is considered as a prominent competitive tool for destinations. Tourists' loyalty manifests itself in recommendation of the destination to others, repeat visit of the destination and willingness to revisit the destination. Although a plethora of studies have tried to define models to show the relation between loyalty and the antecedent factors leading up to it, few of them have tried to integrate these models with mathematical approaches for better understanding of loyalty behavior. The purpose of this paper is to integrate a tourist destination model with Bayesian Network in order to predict the behaviour of destination loyalty and its antecedent factors.

This paper has developed a probability model by the integration of a destination loyalty model with a Bayesian network (BN) which enables to predict and analyze the behavior of loyalty and its influential factors. To demonstrate the application of this framework, Tehran, the capital of Iran, was chosen as a destination case study.

The outcome of this research will assist in identifying the weak key points in the tourist destination area for giving insights to the marketers, businesses and policy makers for making better decisions related to destination loyalty. In the analysis process, the most influential factors were recognized as the travel environment image, natural/historical attractions and, with a lower degree, infrastructure image which help the decision maker to detect and reinforce the weak factors and put more effort in focusing on improving the necessary parts rather than the irrelevant parts.

The research identified all critical factors that have the most influence on destination loyalty while driving the associate uncertainty which is significant for the tourism industry. This resulted in better decision-making which is used to identify the impact of tourism destination loyalty.

Nature-based tourism (NBT) blossoming requires sound monitoring models to maximize its potential in the tourism industry. Cooperation of different segments from nature to economy will lead to a sustainable NBT. Therefore, the qualitative and quantitative relation between these subdivisions has to be investigated.

This paper proposes an advanced NBT model for the design of an optimum tourism system. To this end, Bayesian network (BN) has been implemented to characterize the impact of each subsector on NBT.

The outcomes of this study can help the tourism managers, policymakers and related organizations to find the optimum approach to achieve a continuous improvement in the system. To demonstrate the applicability of the methodology, two cases of observations are considered.

The originality of the work is well demonstrated in the literature review of the paper.

The purpose of this study is to investigate heat transfer and electrokinetic non-Newtonian flow in a rectangular microchannel in the developed and transient states.

The Carreau–Yasuda model was considered to capture the non-Newtonian behavior of the fluid. The dimensionless forms of governing equations, including the continuity equation for the Carreau–Yasuda fluid, are numerically solved by considering the volumetric force term of electric current (DC).

The impact of pertinent parameters such as electrokinetic diameter (R), Brinkman number and Peclet number is examined graphically. It is observed that for increasing R, the bulk velocity decreases. The velocity of the bulk fluid reaches from the minimum to the maximum state across the microchannel over time. At the electrokinetic diameter of 400, the maximum velocity was obtained. Temperature graphs are plotted with changes in the various Brinkman number (0.1 < Br < 0.7) at different times, and local Nusselt are compared against changes in the Peclet number (0.1 < ℘e < 0.5). The results of this study show that by increasing the Brinkman number from 0.25 to 0.7, the temperature along the microchannel doubles. It was observed that increasing the Peclet number from 0.3 to 0.5 leads to 200% increment of the Nusselt number along the microchannel in some areas along the microchannel. The maximum temperature occurs at Brinkman number of 0.7 and the maximum value of the local Nusselt number is related to Peclet number 0.5. Over time in the transient mode, the Nusselt number also decreases along the microchannel. By the increasing of time, the temperature increases at given value of Brinkman, which is insignificant at Brinkman number of 0.1. The simulation results have been verified by Newtonian and non-Newtonian flows with adequate accuracy.

This study contributes to discovering the effects of transient flow of electroosmotic flow for non-Newtonian Carreau–Yasuda fluid and transient heat transfer through rectangular microchannel. To the authors’ knowledge, the said investigation is yet not available in existing literature.