Purpose
This research aims to identify halal attributes for Indonesian Muslim tourists that can create a destination image, revisit intention and recommendation intention. Indonesia has the largest Muslim population in the world and their Muslim tourists who frequently visit non-Muslim countries. This is a great opportunity for non-Muslim countries to improve halal tourism services.
Design/methodology/approach
Data was collected through a panel of recruited online sampling on 268 Indonesian Muslim tourists who had visited non-Muslim countries. Structural equation modeling analysis is used to investigate the impact of halal tourism attributes on destination images and behavioral intentions.
Findings
This study found the five halal tourism attributes that had a positive and significant impact on affective destination image, overall destination image, revisit intention and recommendation intention.
Practical implications
Halal tourism attributes can be used as a basis for marketing strategies of tourism bureaus to create a destination image, increase intention to revisit and provide effective word-of-mouth recommendations based on Muslim tourists needs.
Originality/value
To the best of the authors’ knowledge, this is the first study to analyze the main needs for halal tourism of Indonesian Muslim tourists when traveling to non-Muslim countries. Our study contributes to the halal tourism literature, along with having implications for non-Muslim tourism bureaus and halal tourism teaching and practice.
Covid-19 is an infectious illness caused by a newly identified form of coronavirus. This is a new virus and illness that was previously unknown before the December 2019 outbreak in Wuhan, China. The number of confirmed cases of Covid-19 and the number of deaths due to this virus in Southeast Asia are increasing and quite alarming. Therefore this study will discuss the grouping of Cases and Deaths of COVID-19 in Southeast Asia. The method used is the K-Means Clustering Data Mining. By using this method the data that has been obtained can be grouped into several clusters, where K-Means Clustering Process is applied using RapidMiner tools. Data used are Country statistics, Area of recorded laboratory-confirmed cases of COVID-19, and April 2020 deaths from WHO (World Health Organization). Data is divided into 3 clusters: high (C1), medium (C2) and low (C3). The results obtained are that there are four countries with a high level cluster (C1), one country with a moderate level cluster (C2), and 6 countries with a low level cluster (C3). This can be an input for each country to increase awareness of the transmission of Covid-19.
Until now, Covid-19 is a phenomenal problem faced by almost all over the world, especially countries on the Asian continent. Not only causing casualties, but this virus also affects the wheels of the country’s economy. The purpose of this paper is to view and map the spread of the Covid-19 virus in Asia based on Total Cases, Total Deaths, Total Cured and Active Cases from 49 Countries. The research data in this paper were obtained from the Worldometer website sourced from WHO, CDC, NHC and others. In this proposed paper, the algorithm used is X-Means Clustering with the help of Rapidminer. The results of this proposed paper are in the form of grouping or mapping the spread of the Covid-19 virus in Asia which is divided into 4 zones, including the Red Zone (the number of active cases of Covid -19 and the death rate is very high) which consists of 1 country, the Orange Zone (number of cases active covid -19 and the mortality rate is quite high) consisting of 1 countries, the Yellow Zone (active case rate of Covid -19 and moderate mortality) consisting of 39 countries, and the Blue Zone (active case rate of covid -19 and low mortality rate) which consists of 8 countries.
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