Micro, Small, and Medium Enterprises’ Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering

Revisiting Cluster Vulnerabilities towards Information and Communication Technologies in the Eastern Island of Indonesia Using Fuzzy C Means

Design/methodology/approach: In the present digital era, technology infrastructure plays an important role in the development of digital literacy in various sectors that can provide various important information on a large scale. Purpose: The use of information and communication technology (ICT) in Indonesia in the last five years has shown a massive development of ICT indicators. The population using the internet also experienced an increase during the period 2016–2020, as indicated by the increasing percentage of the population accessing the internet in 2016 from around 25.37 percent to 53.73 percent in 2020. This study led to a review of the level of ICT vulnerability in eastern Indonesia through a machine learning-based cluster analysis approach. Implications: Data were collected in this study from Badan Pusat Statistik (BPS) through SUSENAS to obtain an overview of the socioeconomic level and SAKERNAS to capture the employment side. This study uses 15 variables based on aspects of business vulnerability covering 174 districts/cities. Practical implications: Cluster analysis using Fuzzy C Means (FCM) was used to obtain a profile of ICT level vulnerability in eastern Indonesia by selecting the best model. The best model is obtained by selecting the validation value such as Silhouette Index, Partition Entropy, Partition Coefficient, and Modified Partition Coefficient. Social implication: For some areas with a very high level of vulnerability, special attention is needed for the central or local government to support the improvement of information technology through careful planning. Socio-economic and occupational aspects have been reflected in this very vulnerable cluster, and the impact of the increase in ICT will provide a positive value for community development. Originality/value: From the modelling results, the best cluster model is two clusters, which are categorized as high vulnerability and low vulnerability. For each cluster member who has a similarity or proximity to each other, there will be one cluster member.

Roles of Economic Development Level and Other Human System Factors in COVID-19 Spread in the Early Stage of the Pandemic

We identified four distinct clusters of 151 countries based on COVID-19 prevalence rate from 1 February 2020 to 29 May 2021 by performing nonparametric K-means cluster analysis (KmL). We forecasted future development of the clusters by using a nonlinear 3-parameter logistic (3PL) model, and found that peak points of development are the latest for Cluster I and earliest for Cluster IV. Based on partial least squares structural equation modeling (PLS-SEM) for the first twenty weeks after 1 February 2020, we found that the prevalence rate of COVID-19 has been significantly influenced by major elements of human systems. Better health infrastructure, more restriction of human mobility, higher urban population density, and less urban environmental degradation are associated with lower levels of prevalence rate (PR) of COVID-19. The most striking discovery of this study is that economic development hindered the control of COVID-19 spread among countries in the early stage of the pandemic. Highlights: While richer countries have advantages in health and other urban infrastructures that may alleviate the prevalence rate of COVID-19, the combination of high economic development level and low restriction on human mobility has led to faster spread of the virus in the first 20 weeks after 1 February 2020.

The Impact of Social Media Influencers Raffi Ahmad and Nagita Slavina on Tourism Visit Intentions across Millennials and Zoomers Using a Hierarchical Likelihood Structural Equation Model

Background: In this paper, we examine how social media influencers can influence visit intention, especially in the case of Raffi Ahmad and Nagita Slavina, a top influencer who by 2 September 2021 had reached 21.3 M subscribers on YouTube and 54.9 m followers on Instagram with an engagement rate of 0.42%. The focus of this study is Generation Y or Millennials (born 1981–1996) and Generation Z (born 1997–2012). Design/methodology/approach: Snowball sampling was performed to arrive at a representative group of Millennials. Data analysis was performed using hierarchical likelihood via structural equation modeling. Findings: The study results are helpful for a comprehensive understanding of factors affecting visit intention. Effects of the study results summary, tourists from Generations Y and Z are thriving within the internet of things and the digital age, an era in which information can be accessed via various forms of technology across multiple platforms. Practical implications: We discuss and identify the relative importance of each factor through the use of logistics with variational approximation and structural equation models using hierarchical likelihood. Originality: The technique we use is an integrated and extended version of the structural equation model with hierarchical likelihood estimation and features selection using logistics variational approximation.

The Efficiency Score of Small Accommodation Businesses in Non-Coastal Rural Areas in Greece

Small accommodation businesses dominate the rural hospitality industry, producing simple or complex tourist products and services in order to be sustainable and competitive. In this paper, a two-stage data envelopment analysis (DEA) model was applied in a representative sample of 151 small accommodation businesses in non-coastal areas in the region of Central Macedonia in Greece. In the first stage, DEA-bootstrapping is applied to estimate point and interval efficiency ratios of accommodation businesses and identify the benchmark accommodations. The double bootstrapping truncated procedure of Simar and Wilson is implemented in the second stage to investigate the role of five business factors in terms of efficiency. The findings suggest that small accommodation businesses, although they are based in areas where tourist resources abound, are inefficient. Moreover, the results of the truncated regression method showed that the business’s size, the operating days, and the variety of activities (simple/complex) affect business’s inefficiency. On the contrary, the business’s age and their engagement in agriculture or not do not affect business’s efficiency. The results are important for rural entrepreneurs and policy makers, and they will also be useful for the adaptation of businesses to increase their efficiency.

Sustainable Development Goals in Early COVID-19 Prevention and Control

Recent failures in COVID-19 prevention and control in some of the richest countries raise questions about the relevance of Sustainable Development Goals (SDGs) in the fight against pandemics. To examine this issue, we adopted the measure of countries’ progress for the SDGs in the SDG Index Scores (SDGS) and employed two analytical devices. The first was regression-aided adjustment of the number of deaths and confirmed cases. The second was the use of robust regressions to control the undue influence of outliers. The results are mixed. Between the SDGS and the adjusted infection rates, we found no significant correlation; however, between the SDGS and the adjusted death rates, the correlation was negative and statistically significant. These results provide a nuanced contrast to the hasty conclusions some of us might be tempted to draw from apparent positive correlations between SDGS and the cases and the deaths. The SDGs represent the fruit of painstaking global efforts to encourage and coordinate international action to enhance sustainability. We find the results reassuring, in that they suggest that the countries with higher SDGS have been able to control the devastation of deaths from COVID-19 more effectively, despite being unable to control the propagation of infections.