Erika Revida*
| Heri Kusmanto | Muhammad Arifin Nasution | Sukarman Purba
© 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
Tourist village quality is essential in the tourism sector. Enhancing such quality can be done through community behavior models. The models consist of cognitive, affective, and psychomotor levels. This research analyzes community behavior models to enhance the quality of tourist villages in Tomok Samosir, North Sumatra. The sample included 50 tourists who came to the Tomok tourist village. Data was collected using a questionnaire after being tested. The data was analyzed using multiple regression technique. The finding accepts the hypothesis. There was a contribution between community behavior models on the tourist village quality in Tomok Samosir, North Sumatra. After tracing community behavior models, it turned out that the psychomotor model had a more substantial contribution to the quality of the tourist village in Tomok Samosir, North Sumatra, followed by the affective and cognitive models. Enhancing the quality of the Tomok Samosir North Sumatra tourist village means improving the quality of community behavior models, namely psychomotor, affective, and cognitive.
the model community, community, behavior, tourism, tourist village, tourist village quality
Villages are the lowest governmental units. Most of Indonesia's population lives in villages. The progress of its village determines a developed country. The more developed the villages are, the more developed a nation will be. Therefore, President Joko Widodo set the third Nawacita to develop Indonesia from the periphery to strengthen the villages.
One of the development programs is tourism. The tourism sector was expected to increase the income or foreign exchange of the state, regions, and communities [1]. Tourism is temporary travel that a person takes from one place to another with the aim not to earn money but to enjoy the beauty of nature.
The tourism sector has multiple benefits needed by the community and government. Lee and Chang [2] said that economic growth was significantly impacted by tourism. The tourist village's objective was to enhance society's economy by expanding the citizens' benefits, eradicating poverty and unemployment, protecting the environment and culture, promoting the country's image, and strengthening national identity, unity, and friendship between countries [3]. Tourism is a critical factor in global economic development and a significant source of income in both developed and developing countries [4].
Tourist villages are part of tourism. A tourist village is the most essential place of community in a country that has a particular characteristic to become a tourist destination. In the tourist village, the community still has tradition and a relatively pure socio-culture. A resort is a parceled area with unique potential for specific tourist attractions and local communities that can be created with a mix of tourist attractions and support structures, including regional propositions to attract tourists [5].
Actually, a tourist village is a way to strengthen the village society. Tourist villages had multiple benefits for the community. Revida et al. [6] stated that the tourist villages aligned with the government's goals of creating jobs, avoiding urbanization, and eliminating community poverty. It means that a tourist village is one way of reducing poverty. Besides that, a tourist village will increase the presence of tourists, improve the quality of the environment, promote handicraft products, create new jobs, and improve the quality of rural life. It is why the quality of tourist villages needs to be enhanced. A tourist village's quality was a form of integrating attractions, accommodations, and amenities of tourist and community life with social processes and traditions.
In fact, the government has made several efforts to improve the tourist village quality with various programs and activities as well as financial assistance, but until now, the quality is still in the name; it has yet to show the expected results. One of the efforts was to improve the quality of the tourist village by improving community behavior models. Nevertheless, in reality, up to now, there are still many community behavior models that do not support the improvement of the tourist village quality, such as less caring, less hospitality, less honesty, and others. This is related to the research of Huang et al. [7] and Sutawa [8]. This is the main reason for researching "Enhancing Tourist Village Quality through Community Behavior Models". The location of the research was in Tomok Samosir, North Sumatra.
Community behavior is all the movements that are carried out by the community. Dickson [9] stated that forms of human or social behavior include knowledge of attitudes and behaviors or practical attitudes, often referred to as attitudinal knowledge. Community behavior is first formed through cognitive knowledge through reading or seeing and hearing to create new knowledge, creating an inner response, namely forming a new attitude towards a response. This new attitude will form further actions on the response whether to accept, reject, or remain silent.
Community behavior models consist of three levels, namely cognitive (knowledge), affective (attitude), and psychomotor (action) [10]. Cognitive is the result of perceiving or learning about a particular object through the senses. Affective is the closed response of a person to certain stimuli or objects that already contain ideas and emotional factors. At the same time, practice refers to behavior expressed in action, a natural form of knowledge and attitudes humans already have. Psychomotor deals with aspects of skills that involve the function of the nervous system and muscles (neuromuscular system) and psychic functions. Psychomotor consists of (a) readiness (set), (b) imitation, (c) getting used to (habitual), (d) adapting, and (e) creating (origination).
Community behavior models were equally important, with the government and the private sector as stakeholders in improving the quality of tourist villages [11]. Therefore, community behavior models are one of the motors in developing a tourist village's quality, starting from planning, implementing, monitoring, and evaluating a tourist village so that the quality of the tourist village gets better and more advanced. Several examples of quality definitions that are often used, including:
• Doing the right things since the beginning.
• Satisfied customers and make them happy.
• Continuous development.
• No defects.
• Meet customer needs at any time [12].
The quality was determined if it met the efficiency, effectiveness, responsiveness, and transparency requirements [13, 14]. Thus, the tourist village quality is when the attractions, accommodation, and accessibility in a tourist village fulfill the criteria of being efficient, effective, responsive, and transparent to tourists.
Community behavior models play an active role in developing and promoting tourist villages [15]. They show quality behavior such as being friendly, wise, and spontaneous, helping tourists, keeping the tourist village area clean, and actively supporting the development of the tourist village [16, 17]. This is why community behavior models have an essential role in improving the overall quality of the tourist village.
A quality tourist village becomes a unifier for village communities, leading to community behavior. Therefore, community behavior models make an important contribution to enhancing the quality of tourist villages.
Several studies prove a positive contribution between community behavior models and the quality of tourist villages. There is a positive community behavior in improving the quality of tourist villages. A quality tourist village will invite tourists to repeat visits and stay longer [18]. Therefore, community behavior models are essential in improving the quality of tourist villages, including hospitality, providing attractions and amenities, and maintaining accessibility and infrastructure. In addition, a model of friendly, polite, and helpful community behavior for tourists will be an effective promotional tool for other tourists. Therefore, it is crucial to improve the quality of tourist villages by modeling community behavior [19].
The tourist village quality refers to the efforts to develop and improve tourist facilities to meet the needs of tourists, ranging from attractions to accommodations and accessibility. Tourist villages have become one of the tourism sectors that increase the empowerment of rural communities [20].
This study used mixed-method research, combining quantitative and qualitative methods [21]. First, a quantitative research method was used, then a qualitative research method with interviews, observation, and focus group discussions (FGD). The sample for this research was 50 tourists visiting Tomok tourist village. Since post-COVID, it still turns out that only a few tourists have visited the Tomok tourist village, so the data obtained was only 50 people. The informants for this research were the head of the tourism service and the bureaucratic apparatus at the tourism service, the head of the Tomok Samosir tourist village, tourism activists, and community leaders. The technique applied the study of documentation, instruments, observation, focus group discussion (FGD), and triangulation.
The data collection techniques were documentary studies research instruments, observational interviews, focus group discussions (FGDs), and triangulation. The questionnaire was obtained from indicators of the community behavior model with five options.
Quantitative data analysis techniques included linear regression analysis, organization and reduction, interpreting data, and defining findings as conclusions. Multiple linear regression analysis techniques characterize quantitative analysis techniques. Validity and reliability tests were conducted before testing the hypothesis. Qualitative analytical methods are characterized by standard data reduction, data interpretation, and derivation of research findings.
4.1 Community behavior models (X)
According to the results of the responses, an overview of the community behavior model was shown from 50 respondents through descriptive statistical analysis, namely the lowest score of 86, the highest score of 132, and the mean of 108.50, mode 107, standard deviation 10.87, the highest ideal score was 150, the lowest ideal score was 30, the average ideal score was 90, and the ideal standard deviation was 20. Furthermore, the results of the descriptive analysis were explained through the study of the frequency distribution of group data with Sturges’ rule [22]; there were 7 classes with 7 interval lengths, as shown in Table 1.
Table 1. Frequency distribution of community behavior models
|
Class |
Interval Class |
Absolute Frequency |
Relative Frequency (%) |
Cumulative Frequency (%) |
|
1 |
86-92 |
4 |
8.00 |
8.00 |
|
2 |
93-99 |
6 |
12.00 |
20.00 |
|
3 |
100-106 |
9 |
18.00 |
38.00 |
|
4 |
107-113 |
17 |
34.00 |
72.00 |
|
5 |
114-120 |
8 |
16.00 |
88.00 |
|
6 |
121-127 |
2 |
4.00 |
92.00 |
|
7 |
128-134 |
4 |
8.00 |
100.00 |
|
Total |
|
50 |
100.00 |
|
The frequency distribution table showed that the acquisition of an average score of 116.49 was in class 4, and the smallest percentage of values was in the class 121-127 interval, with as many as 2 people (4%). So, 19 respondents (38%) were below the average score, and as many as 31 respondents (62%) were on-average and above-average scores. Based on the data, the trend status criteria of community behavior model variables are shown in Table 2.
Table 2. Tendency level of community behavior models data (X)
|
Class |
Class Interval |
Observation Frequency |
Relative Frequency (%) |
Category |
|
1 |
122-150 |
6 |
12.00 |
Good |
|
2 |
91-121 |
43 |
86.00 |
Sufficient |
|
3 |
60-90 |
1 |
2.00 |
Less |
|
4 |
30-59 |
- |
- |
Low |
|
Total |
|
50 |
100.00 |
|
Based on Table 2, it was known that 6 respondents (12%) showed that community behavior models were in a good category, and there were 43 respondents (86%) who said community behavior models were in the sufficient category. There was 1 respondent (2%) who said that community behavior models were in the less category. So, the contribution of community behavior models to the tourist village quality at Tomok Samosir, North Sumatra, was in the average of good category.
4.2 The tourist village quality (Y)
The results of the descriptive analysis were explained through the study of the frequency distribution of group data with the Sturges rule [23]; there were 7 classes with 7 length intervals, as seen in Table 3.
According to the distribution of frequency table, it could be seen that the acquisition of an average score of 127.82 lies in class 3; the smallest percentage of values was in the 150-156 interval class with as many as 2 respondents (4%). So there were 17 respondents (34%) below the average score and as many as 34 respondents (66%) on average and above average scores. Based on these data, the criteria for the tendency level of the tourist village quality variables are shown in Table 4.
Table 4 showed that 9 respondents indicated that the quality of the tourist villages was in the good category, and 41 respondents indicated that the quality was in the satisfactory category. Thus, the tourist village quality was generally good.
Table 3. The frequency distribution of tourist village quality data (Y)
|
Class |
Interval Class |
Absolute Frequency |
Relative Frequency (%) |
Cumulative Frequency (%) |
|
1 |
108-114 |
8 |
16.00 |
16.00 |
|
2 |
115-121 |
9 |
18.00 |
34.00 |
|
3 |
122-128 |
11 |
22.00 |
56.00 |
|
4 |
129-135 |
9 |
18.00 |
74.00 |
|
5 |
136-142 |
6 |
12.00 |
86.00 |
|
6 |
143-149 |
5 |
10.00 |
96.00 |
|
7 |
150-156 |
2 |
4.00 |
100.00 |
|
Total |
|
50 |
100.00 |
|
Table 4. Tendency level of tourist village quality (Y)
|
Class |
Class Interval |
Observation Frequency |
Relative Frequency (%) |
Category |
|
1 |
142-175 |
9 |
18.00 |
Good |
|
2 |
106-141 |
41 |
82.00 |
Sufficient |
|
3 |
70-105 |
- |
- |
Less |
|
4 |
35-69 |
- |
- |
Low |
|
Total |
|
50 |
100.00* |
|
Hypothesis testing was done after meeting the analysis testing requirements. Testing for normality of each variable and linearity between pairs of variables. The results of the normality test using the Kolmogorov-Smirnov formula are summarized in Table 5.
Table 5. Summary of test of normality using Kolmogorov-Smirnov
|
|
Community Behavior Models (X) |
Tourist Village Quality (Y) |
|
|
N |
|
50 |
50 |
|
Normal Parametersa,b |
Mean |
108.50 |
127.82 |
|
|
Std. Deviation |
10.878 |
12.313 |
|
Most Extreme Differences |
Absolute |
.095 |
.119 |
|
|
Positive |
.095 |
.119 |
|
|
Negative |
-.065 |
-.060 |
|
Kolmogorov-Smirnov Z |
|
.672 |
.840 |
|
Asymp. Sig. (2-tailed) |
|
.757 |
.481 |
|
a. Test distribution was Normal. b. Calculated from data. |
|||
Table 6. Summary of the result of linearity results and significance test
|
No. |
Ratio Between Variables |
Linearity Test |
Test of Regression Significant |
||||
|
Fh |
Sig. |
Status |
Fh |
Sig. |
Status |
||
|
1 |
X with Y |
0.985 |
0.528 |
Linear |
51.477 |
0.000 |
Significant |
Table 7. The coefficient of regression equation Ŷ = 39.47 + 0.81X
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
||||
|
B |
Std. Error |
Beta |
t |
Sig. |
||
|
1 |
(Constant) |
39.473 |
12.374 |
|
3.190 |
.003 |
|
|
CommunityBehavior Models |
.814 |
.113 |
.719 |
7.175 |
.000 |
|
Dependent variable: tourist village quality |
||||||
Table 8. Test of ANOVA significance of regression equation Ŷ = 39.47 + 0.81X
|
Model |
Sum of Squares |
Df |
Mean Square |
F |
Sig. |
|
|
1 |
Regression |
3844.539 |
1 |
3844.539 |
51.477 |
.000 |
|
|
Residual |
3584.841 |
48 |
74.684 |
|
|
|
|
Total |
7429.380 |
49 |
|
|
|
|
Predictors: (constant) community behavior models Dependent variable: tourist village quality |
||||||
The results of the calculations can be seen in Table 5 and show no difference between the fluctuating community behavior models and the quality of the tourist village. Since Sig (two-tailed) > 0.05, we can conclude that the distribution of results for each research variable did not deviate from the normality distribution. This means that the assumption of normality was satisfied. Additionally, the linearity and repeatability test results are summarized in Table 6.
According to the results of the calculations for the linearity test, it was obtained that the value of Fcount = 0.985 and a significance value (Sig) > 0.05, or 0.528 > 0.05, pairs of variables are linearly related and provide a test of significance, the Fcount value had a significance value (Sig) < 0.05 or 0.00 < 0.05 so the form of a meaningful variable relationship can be specified to satisfy the linearity assumption. After meeting the test analysis requirements, the simple regression analysis computation continues. Table 7 shows the calculation results.
The equation of regression showed Ŷ = 39.47 + 0.81X. As the quality of tourist village variables improves, the variables of community behavior models also increase. Every time the tourist village quality increases by 1 point, the community behavior model score of 0.81 will increase by 39.47 points. Table 8 shows the test of significance for the coefficient of regression using the ANOVA formula.
The research hypothesis showed that community behavior models significantly and positively affected the tourist village quality in Tomok Samosir, North Sumatra. Based on Table 8, the significance test of the coefficient regression direction was highly significant because the value of Fcount = 51.47 was greater than Ftable = 8.96 at α = 0.01. The correlation coefficient rxy = 0.719. When compared with the rtable value at α = 0.01 obtained 2.36, then the tcount > ttable or 7.71 > 2.36 so that it could be summarized that the correlation coefficient was meaningful. According to these computational results, it could be said that Ho was rejected and Ha was accepted, or community behavior models had a direct positive and significant impact on the quality of tourist villages, was verified. The determination coefficient Rxy2 = (0.719)2 × 100% = 51.69%. The contribution of community behavior models (X) on the tourist village quality (Y) was 51.69%, or it could be stated that there was a 51.69% variation in the tourist village quality (Y) could be explained by variations of community behavior models (Y) and the rest 48.31% determined by other variables.
In order to find out the contribution of community behavior model patterns to the tourist village quality, the contribution of each model index to the tourist village quality is calculated. The results of the descriptive analysis of each variable are shown in Table 9.
Furthermore, the correlation between community behavior models to enhance the quality of the tourist village is shown in Figure 1.
Finding the magnitude of the contribution of each community behavior model on the tourist village quality was done by using multiple regression analysis, and the results of the calculations were presented in Table 10.
Figure 1. The variable of research
Table 9. The summary of descriptive analysis of each model of community behavior
|
|
Cognitive (I1) |
Affective (I2) |
Psychomotor (I3) |
Tourist Village Quality (Y) |
|
|
N |
Valid |
50 |
50 |
50 |
50 |
|
Mean |
35.38 |
37.12 |
36.00 |
127.82 |
|
|
Median |
36.00 |
38.00 |
36.00 |
126.00 |
|
|
Mode |
36 |
38 |
36 |
126 |
|
|
Std. Deviation |
6.321 |
5.652 |
5.806 |
12.313 |
|
|
Variance |
39.955 |
31.944 |
33.714 |
151.620 |
|
|
Range |
24 |
23 |
22 |
48 |
|
|
Minimum |
24 |
25 |
25 |
108 |
|
|
Maximum |
48 |
48 |
47 |
156 |
|
|
Sum |
1769 |
1856 |
1800 |
6391 |
|
Table 10. Multiple regression equation Ŷ = 40.05 + 0.64X1 + 0.75X2 + 1.02X3
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
||||
|
B |
Std. Error |
Beta |
t |
Sig. |
||
|
1 |
(Constant) |
40.058 |
12.650 |
|
3.167 |
.003 |
|
|
Cognitive (I1) |
.643 |
.202 |
.330 |
3.185 |
.003 |
|
|
Affective (I2) |
.756 |
.222 |
.347 |
3.402 |
.001 |
|
|
Psychomotor (I3) |
1.026 |
.220 |
.484 |
4.674 |
.000 |
|
Dependent Variable: Tourist village quality |
||||||
Table 11. Summary of community behavior models contribution to enhancing tourist village quality
|
Community Behavior Models |
Big Contribution |
Direct Donation Amount |
Rank |
|
Cognitive (I1) |
0.330 |
(0.330)2 × 100% = 10.89% |
3 |
|
Affective (I2) |
0.347 |
(0.347)2 × 100% = 12.04% |
2 |
|
Psychomotor (I3) |
0.484 |
(0.484)2 × 100% = 23.43% |
1 |
Based on Table 10, it seems that the contribution of the cognitive model on the tourist village quality was 0.330, the magnitude of the affective contribution model on the tourist village quality was 0.347, and the magnitude of the contribution of the psychomotor model on the tourist village quality was 0.484. The amount of direct contribution from each model of community behavior on the tourist village quality is shown in Table 11.
In Table 11, from the three community behavior models, it turned out that the psychomotor model had a solid contribution to enhancing the tourist village quality, followed by the affective and cognitive models.
The results of hypothesis testing showed that community behavior models positively contributed to the tourist village quality in Samosir, North Sumatra. The amount of contribution of community behavior models on the tourist village quality was 51.69%, or it could be stated that there was 51.69% of the variation in the quality of tourist village (Y) could be explained by variations in community behavior models (X), and other variables determined the remaining 48.31%. This research was supported by the studies of Revida et al. [23], Revida et al. [24], Sasmitha and Marhaeni [25], and Nugraha et al. [26].
After tracing the research results, it was found that of the three community behavior models, namely cognitive, affective, and psychomotor, it turned out that the three models significantly contributed to the quality of tourist village quality. It is related to the research of Bentri et al. [27] and Utami et al. [28]. The psychomotor model strongly contributed to the quality of tourist villages, followed by the affective and cognitive models.
However, research results show that most people's behavior was less concerned with the quality of tourist villages. They generally need to learn the benefits of quality tourist villages. In fact, the higher the community's behavior in improving the quality of the tourist village, namely with good attractions, amenities, and accessibility, the more tourist visits will be. Thus, it will ultimately increase the village community's income. This follows the findings of Kurniawati et al. [29]. People's behavior can be demonstrated by the many attractions and amenities and by improving the accessibility of tourist villages [30].
The findings showed that community behavior models positively and significantly enhanced the tourist village quality in Tomok Samosir, North Sumatra. It means that the better the behavior, the better the quality of the Samosir, North Sumatra tourist village will be. The contribution of community behavior models to enhance the tourist village quality was 51.69%. The quality of the tourist village can be enhanced by improving the quality of community behavior models, namely the quality of psychomotor, affective, and cognitive models. Of the three community behavior models, the psychomotor model had a more substantial contribution to enhancing the tourist village quality, followed by the affective and cognitive models.
The authors would like to thank the Rector of Universitas Sumatera Utara, the Chairman, and the Research Institute of Universitas Sumatera Utara staff for funding this research under the TALENTA research scheme. A special thanks goes to those who have supported this research.
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