Social network analysis of tourism data: A case study of quarantine decisions in COVID-19 pandemic

Analysis of an industrial tourism business network using social network approach: the case of Isfahan, Iran

Purpose

Industrial tourists can contribute to the local sites income. Apart from large and well-known industrial units, small industrial units can also introduce their products to the market by organizing tourism tours. This study aims to analyze an industrial tourism business network established in Isfahan, a large, industrial and historic city in central of Iran, as a case study.

Design/methodology/approach

Participants were managers and experts of organizations operated in the Industrial Tourism Network in Isfahan city. A sample of 30 participates was selected through purposive and snowball sampling approaches. Data was analyzed by qualitative content analysis and social network analysis using UCINET software.

Findings

Twenty-eight organizations participated in the Industrial Tourism Business Network of Isfahan. Macro indices of the network revealed that: density is relatively low; most of the relationships among network member organizations are reciprocal; transitivity is relatively weak indicating low network stability against environmental player; clustering is low, indicating a relatively low tendency of members to form different clusters. Micro indices indicated that in-degree and out-degree centralizations are low; overall centralization is poor. In addition, six actors were in the center and 22 actors were in the peripheral. Finally, the number of interactions of central organizations was triple than the peripheral organizations.

Research limitations/implications

Limitations of this study include: The data obtained in this study only reflected the relationships among the actors studied during a specific period of time; this paper only focused on actors engaged in an industrial tourism network. Therefore, the sample might involve some bias. The results of the study contribute to our understanding of the role of networking in tourism destinations, particularly industrial sites.

Practical implications

According to the results, the authors may be able to advise firms operating in the tourism industry, particularly actors operate in industrial tourism, regarding how to increase their collaboration with both internal and external environment to achieve better performance.

Originality/value

To the best of the authors’ knowledge, this is the first paper that has addressed an industrial tourism business network.

Prescriptive Analytics-Based SIRM Model for Predicting Covid-19 Outbreak

Predicting the outbreak of a pandemic is an important measure in order to help saving people lives threatened by Covid-19. Having information about the possible spread of the pandemic, authorities and people can make better decisions. For example, such analyses help developing better strategies for distributing vaccines and medicines. This paper has modified the original Susceptible-Infectious-Recovered (SIR) model to Susceptible-Immune-Infected-Recovered (SIRM) which includes the Immunity ratio as a parameter to enhance the prediction of the pandemic. SIR is a widely used model to predict the spread of a pandemic. Many types of pandemics imply many variants of the SIR models which make it very difficult to find out the best model that matches the running pandemic. The simulation of this paper used the published data about the spread of the pandemic in order to examine our new SIRM. The results showed clearly that our new SIRM covering the aspects of vaccine and medicine is an appropriate model to predict the behavior of the pandemic.