Socio-demographic analysis of destination selection factors for Himalayan Hill destinations

Research objectives and hypothesis development

Based on the above literature review, this study aims to fulfill the following objectives and examine the related hypotheses:

Objective 1 To identify the selection factors to visit Himalayan region destination.

Objective 2 To identify the relationship between sociodemographic variables and destination selection factors.

Objective 2 is analyzed and fulfilled with the help of following related Hypothesis-

Sociodemographic analysis

To fulfil the research objectives, responses collected from 748 tourists from selected Indian hill destinations were analyzed using a structured questionnaire. The respondents were selected based on their travel experience to a specific destination. Sociodemographic dimensions, such as gender, age, occupation, and income, were purposely included in the questionnaire to fulfil the research objectives. The distribution of the data is presented in Table 1.

Respondents’ sociodemographic analysis represented 57.5% of the youth tourist age ranging from to 25-35 years followed by 23% of respondents aged below 25 years. This indicates that about 80% of tourists belong to the young age group, that is, not more than 35 years. The maximum number of tourists was 71%, and 56% of the respondents belonged to private sector jobs, with incomes ranging from 20,000 Rs. to 50,000 Rs. Only 29% of the respondents were female as they were reluctant to participate in the survey. This behavior shows that the motivation of Indian females to travel to a destination still depends on their counterparts, whether their husbands, fathers, brothers, or friends.

Inferential Analysis

To identify the destination attributes or destination selection factors, a reliability analysis was performed, and the Cronbach’s alpha value of 0.909 showed that the questionnaire achieved high internal consistency. Before verifying the hypotheses set out in this study, an exploratory factorial analysis was conducted. The factors extracted by this method are uncorrelated and arranged in the order of decreasing variance. Bartlett’s test of sphericity and the calculation of Kaiser-Meyer-Olkin statistics indicate whether data are suitable for identifying orthogonal factor dimensions. Variables with loading equal to or greater than 0.4 were included in a given factor to decrease the probability of misclassification (Hair et al., 1995). Forty-nine items were loaded saliently, and any factor that emerged with eigenvalues greater than one was considered for further analysis. The final factor distribution was allocated to forty-three items, other items with values below the threshold limit were not used for the final analysis. The total variance explained by factor analysis was 60%. The results of Cronbach’s alpha coefficients, KMO, Bartlett’s test of sphericity, and factor analysis are shown in Table 2.

Table 2 shows the factors considered motivating and satisfactory for destination selection. Factors such as ‘Destination Image’, ‘Value for Money,’ ‘Infrastructure Facilities,’ ‘Beauty, Culture & Heritage’, ‘Tour & Travel Connections’, ‘Value added Services,’ Destination Brand Value’ are independent factors and able to create destination selection factor. The table also shows that the Cronbach alphas’ value reported on factor 7, i.e. ‘Destination Brand Value’ is low. This could be a consequence of this factor because the maximum number of respondents belonged to the income group of Rs. 20,000 to Rs. 50,000; for such respondents’ destinations, brand value would not be a motivating factor in selecting a destination. However, it was considered suitable to include this item because destination image would be helpful in creating destination brand value, and hence, becomes a motivating factor for the selection of a destination. The table also shows the dependent variable, destination satisfaction and intention to revisit.

The possible relationship between tourists’ socio-demographic characteristics and the selection factors of a destination was analyzed using ANOVA, checking its significance by means of the F statistic and p value (confidence level 95%).

The relationship between the sociodemographic variables of respondents and the factors of destination selection decisions of the respondents are shown in Table 3 with the help of ANOVA.

The results show that there is a significant similarity between gender and destination selection factors, except for one factor, Value-added Services (VaS). This result justifies the fact that women tend to assess the value-added services of a destination more favorably than men. For all other factors, DI, Vm, Infra, BCH, TTC, and DBV, there were no significant differences irrespective of the gender of the respondents. Therefore, we confirm hypothesis H1 that Destination selection factors are significantly similar irrespective of the gender of tourists.

Table 4 shows the relationship between the age of respondents and destination selection factors. The results show that there is a significant difference among the selection factors for different age groups of the respondents.

Factors such as destination image, value for money, tour and travel connections, and destination brand value are considered differently by different age groups of tourists. For other factors, including infrastructure facilities, ‘Beauty, Culture and Heritage’, and ‘Value-added Services’, the results show no significant difference with the age of the respondents. Results implies that the selection factor ‘destination image’ or ‘Destination Brand Value’ are significantly different for different age categories of the tourists. Young tourists tend to be motivated by destinations with a grand brand value rather than older tourists. Similarly, a destination would have different age tourist footfall due to the difference in value for money. On the basis of these results, we partially confirm hypothesis H2 that Destination selection factors are significantly similar irrespective of the age of the tourists but only for factors ‘Infrastructure facilities’, ‘Beauty, Culture and Heritage,’ and ‘Value-added Services.’

Table 5 shows the result of occupation and reveals a significant difference between occupation and selection factors for destination, but only for the factors ‘Destination Image’, and ‘Value for Money’. The results reveal similarities among various occupation respondents and their selection factors for destinations, such as ‘Beauty, Culture & Heritage’, ‘Infrastructure facilities’, ‘tour & travel connections’, ‘value-added services’, and ‘destination brand value’. Results implies that the factor ‘Destination Image’ or ‘Value for money’ are significantly different for various occupational categories of the tourists. Tourists who are private sector employees motivate destinations that provide more value for money than other tourists. These results confirm hypothesis H3 that destination selection factors are significantly similar, irrespective of the occupation of the tourists.

The relationship between the income of the respondents and the selection factors of the destination is shown in Table 6. The result shows there is no similarity between income and selection factors for destination only for the factors ‘Destination Image,’ ‘Value for Money,’ ‘Beauty, Culture & Heritage’. For other factors, ‘Infrastructure facilities,’ ‘Tour-Travel Connections’, ‘Value-added Services,’ and ‘Destination Brand Value’ showed significant similarity with the diverse income of respondents. Results implies that the factor ‘destination image’ or ‘Value for money’ are significantly different for different income categories of the tourists. Tourists with middle-income levels motivate and select destinations that provide more value for money than do other tourists. Thus, tourists with various income groups look for value for money and varied destination images rather than other destination selection criteria. These results partially confirm hypothesis H4 Destination selection factors are significantly similar irrespective of tourists’ income.

Implications and Limitations

Destination selection factors are important for any destination to improve tourist footfall and gain a competitive advantage. Socio-demographic variables play an important role in such decisions and hence need to be taken care of by the DMOs. Destination marketing organizations should identify their tourists and their choices. Any tourist, if impacted by their socio-demographic variable, will try to select destination suits for their personality and pocket. Young tourists would love to visit Goa, whereas senior-aged tourists would like to travel to a destination with religious beliefs. Thus, destination marketing organizations should analyze their tourist choices and market their products according to the needs of tourists.

Not every study has been completed without limitations, and this research also has some limitations. The first and foremost limitation of this study is the selection of socio-demographic variables. This research used only four socio-demographic variables: Gender, Age, Occupation and Income. Other sociodemographic variables were beyond the scope of this study. The results may vary if other variables are included in future studies. The other limitation is the study area, which is related to the Himalayan region and included a few destinations of Uttarakhand and Himachal Pradesh with random selection. Future studies should include other destinations. A comparison between these destinations could also be performed with respect to sociodemographic variables. This study covers the Himalayan region, and other hill regions of India can also be studied with the help of similar analysis, and further comparisons can be drawn.