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The tourism industry has bounced back post-COVID-19 pandemic from 2021 onwards, especially in Yogyakarta. As part of the marketing strategy of a service company, 7P’s and customer behavior analysis are separately studied by several researchers. However, it is important to find direct connections between the detailed qualities of 7P’s with re-visit intention or purchase intention. This study uses a combination of 7P’s as a base framework approached by customer behavior as a stimulus. Re-visit intention for experienced customers is influenced by customer satisfaction of previous experiences. The study shows that among several factors, a firm should ensure previous customer satisfaction from service hospitality and gain trust from product quality. To gain broad and new customers, a firm shall intensify its promotional program while simultaneously focusing on trust building and information satisfaction on the website quality. Website quality, e-service quality, and aggressive promotional programs will boost customers’ purchase intention. Information satisfaction might be slightly held to avoid adverse effects and keep the customer curious. The results of the customer study will be a strong foundation for the analysis proposed to define new STP and new 7P’s as the new Marketing Strategy. This will be applied to the firm’s organizational vision and operating philosophy and seamlessly communicated to the website or any digital content. However, there shall be further investigation of other customer behavior to complete the approach to 7P’s framework.

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Introduction

The tourism industry is one of the most influenced sectors due to Covid-19 pandemic. Mckinsey & Company estimates that the recovery of tourism in the top 10 global cities will be a full recovery after the COVID-19 pandemic in 2023. That is why the government of Indonesia has pushed the tourism industry forward and supported the top 5 national Indonesian destinations as a super priority. One of them is Borobudur temple near Yogyakarta city. As there are many competitions in tourism industry, this paper studies small tourism company (CV. Jogja Jalan Jalan or CV.3J) in Yogyakarta which is struggling to increase revisit intention and purchase intention, despites increasing demands and competitions as the same time.

Local statistics (yogyakarta.bps.go.id) show significant increase of transportation traffic and hotel occupation in Yogyakarta during recent years after pandemic. As per October 2023, international passenger arrival in Yogyakarta International Airport (YIA) increases 341% (y-o-y) and domestic passenger arrival increases 26% (y-o-y). Star hotels occupancy is not much different from 2022 to 2023 (y-on-y declining 3.49%), but still rise from 2021. This hotel occupancy is a combination of all visitors for both local and foreign visitors. The train’s number of passengers is also showing a yearly rising trend 30.31% from 2022 to 2023. Yogyakarta’s tourism board has targeted this year (2023), hoping that 6.6 million tourists will come to this city and adjacent area, an increase of 6.69% from 2022 tourist visits (6.17 million).

CV.3J has 2 main divisions: tourism and car rental. From the given company internal report, the performance of CV.3J to gather more tourists is struggling despite the tourism industry recovery. In 2022, most of the order is from the car rental (94%) and very minimum order in tourist visit (6%). That’s why tourist purchase intention needs to be increased. Overall, there are 84% of private orders and 16% are corporate orders.

Corporate orders have a significant number of days and number of customers. But this corporate order has not happened on many occasions. When the order is dominated with single day order, this means the order is dominated with transportation rent. If the customer stays more and with many tourists in single order will give additional revenue for the company within the same marketing effort. Non-repeat order quantity is dominating a whole year. Around 71% of the customer orders are non-repeat orders for their service. In one year, from a total of 919 orders, 270 orders were repeat orders, which is around 29%. This means that customers revisit intention still has room to be improved.

CV.3J has several business issues such as natural disaster issue, low customer acquisition, ineffective marketing channel, revenue imbalance and tour package bundling. This paper studies marketing strategies to increase revisit intention and purchase intention for the company. Natural disaster concern is not part of this study.

Reflecting to the business issue analysis above, the problem definition could be stated as following:

- Customer acquisition is relatively low compared to total visitor statistical data.

- Existing marketing channel and strategy does not effective to boost purchase intention and would lead to in-effectiveness of revenue generation.

- Many of the tourisms package bundling is not selected by customers, and many destinations are not visited.

Literature Review

7P’s in Service Companies

Marketing mix generally refers to a tool used by many marketing companies and experts to define the offer of a product. Previously, the original model introduced by McCarthy (1964) was the “4Ps” (Product, Place, Price, promotion). Further in 1981, Booms and Bitner (1981) add more 3Ps to the original 4Ps and currently known as 7Ps applicable for services companies. Marketing mix in the holistic marketing was also introduced by Kotler and Keller (2006). Floraet al.(2019) found that marketing mix elements (7P’s), including product, price, promotion, location, physical evidence, people, and process, all have a significant influence on tourist decision-making, including alternative models of marketing strategy. It was suggested that marketing mix is essential for tourism organizations to attract tourists, differentiate their tourism product, and implement sustainable tourism practices.

Ciriković (2014), argues that some marketing mix elements are crucial for tourism organizations to differentiate their tourism product. In this view, can be understood that understanding the elements is an important step to develop a competitive strategy. That’s why Hoet al. (2022) investigated 7P’s framework effect on purchase intention and found that they need an intermediate variable to influence purchase intention as a response.

Stimulus Variables

Behavioral studies are separately conducted by many researchers especially related with the path to the purchase intention and revisit intention. To further elaborate on the detailed quality of 7P’s, the detailed variables are used in this research as an approach to the 7P’s framework. These 7P’s, stimulus variables, and revisit intention/purchase intention as a response is collected in a general S-O-R (Stimulus-Organism-Response) framework.

There are two main groups of customers in this study. The first group is the experienced customers with services from CV.3J, and the expected response or objective is to increase revisit intention. The second group of customers is the broad customer’s candidate on the website and generally never use services from CV.3J, and the expected response or objective is to increase purchase intention during online browsing or booking.

Table I shows the 7P framework is aproached by below stimulus variables.

7P framework Stimulus variable approach References
Product Destination image Thipsingh et al . (2022)
Hospitality image Nugroho et al . (2021)
Perceived quality Qalatiet al. (2021), Liuet al. (2022)
Price Perceived value Thipsingh et al . (2022)
Place Destination image Thipsingh et al . (2022)
Website quality Khumalo-Ncube and Motala (2021)
People Tourist motivation Santoso (2019), López-Sanzet al. (2021)
Novelty seeking Thipsingh et al . (2022)
Hospitality image Nugroho et al . (2021)
Perceived quality Qalati et al . (2021)
- Product Quality Liu et al . (2022)
- E-Service Quality Brahmanto (2015)
Process Caesar et al . (2022)
- Online Process
- On-Site Service Process
Promotion Promotion Ho et al . (2022)
Physical evidence Physical evidence Ho et al . (2022)
Table I. 7P’s Framework and Related Stimulus Variables Approach

Destination Images

Destination image is an appealing system of ideas, opinions, feelings, visualizations, and intentions towards a destination (Tasci & Gartner, 2007). According to Thipsinghet al. (2022) show that destination images have a positive and significant effect on tourist satisfaction.

H1: The destination image has a positive impact on tourist satisfaction.

Hospitality Images

Nugrohoet al. (2021) studied tourism in Yogyakarta and summarizes that variables and indicators of hospitality are Invites, Empathy, and Comfort (adopted by Pijlset al., 2017). The study concludes that Hospitality or hospitality image affects satisfaction and then satisfaction affects revisit intention.

H2: Hospitality image/experiences has a positive impact on tourist satisfaction.

Perceived Quality

This research views the perceived quality from two perspectives. First is the quality of the destination as product and second, quality of the website as e-service. Kusumawatiet al. (2021), doing a study on 450 international tourists in Bali, found that destination quality in Bali has a significant effect on trust. Qalatiet al. (2021), found that there is a direct effect of perceived e-service quality on purchase intention and trust in online shopping.

H3: Perceived quality has a significant influence on trust.

Perceived Value

Thipsinghet al. (2022), shows that perceived value has a positive effect on tourist satisfaction. According to Waheed and Hassan (2016), there are five dimensions associated with customer perceived value affecting tourist satisfaction. But this research uses only functional value, where perceived values is the result of a comparison of an offering’s benefits and cost (Thipsinghet al., 2022).

H4: The perceived value has a positive impact on tourist satisfaction.

Website Quality

Khumalo-Ncube and Motala (2021) studies website quality features on travel agent or customer information satisfaction and purchase intention. The study results indicate that there is a positive relationship between website quality and customer information satisfaction, and between customer information satisfaction and purchase intention.

H6: Website quality has positive impact on customer information satisfaction.

Tourist Motivation

Santoso (2019) hypothesized that “Tourist Motivation construct has a positive and significant effect on Tourist Satisfaction construct”. The study shows that tourists whose have positive image from certain tourist destination, he can be satisfied than he has a big motivation to visit.

H7: Tourist internal motivation has significant influence on tourist satisfaction.

Novelty Seeking

Novelty seeking is part of the external motivation for tourists which characterized by scholars as “Pull” factors (Tanget al., 2022; Yeet al., 2021). Thipsinghet al. (2022), found that novelty seeking has a positive effect on tourist satisfaction, which is the strongest relationship of the factors.

H8: Novelty seeking has a positive impact on tourist satisfaction.

Process

Caesaret al. (2022), analyses 7Ps in the service marketing to purchase intention in tourism, they found that the ease of process shall determines purchase intention. For this research, the process shall be divided into 2 categories, first is the process on the website service or online booking reservation and second is process during visit such as transportation, accommodation, destinations tours etc.

H15: The ease of process determines satisfaction and purchase intention.

Promotion

Promotion in this research can be both short-term incentives and broadcast of information to attract customers. Hoet al. (2022) investigates hypothesis of promotion effect on life streaming shopping to purchase intention and found that it has positive effect although insignificance in term of short-term incentives.

H5: The promotion offered on the website is positively associated with customers purchase intention.

Physical Evidence

Physical evidence focuses on the physical condition of the business premises including its surrounding area (Khan, 2014). For tourism with online purchasing, physical means the actual destination locations, people, service, accommodations, logistics etc. All those physicals are blend in product evidence enjoyed by the customers. According to Hoet al. (2022), who investigate live streaming shopping, physical evidence can also all features, ornaments, information etc. on the website in the form of photo, text, or video to show the customers evidence of the product.

H9: The physical evidence on the website is positively associated with customer’s purchase intention.

Organism Variables

Organism variables are the variables as the effect of the stimulus behavior as a construct in 7P’s frameworks. This study investigates several organism variables such as customer satisfaction, trust, and information satisfaction.

Customer Satisfaction

Thipsinghet al. (2022) studied tourism in Yogyakarta and found a strong relationship between tourist satisfaction and revisit intention. Some researchers found that satisfaction is highly significant and has a positive effect on revisit intention (Waheed & Hassan, 2016).

H10: Customer satisfaction has a positive impact on revisit intention.

Trust

Qalatiet al. (2021) investigates the effect of website quality on online purchase intention through the ‘Trust’ as mediating role. Trust significantly mediates the relationship between perceived service quality, website quality reputation, and online purchase intention. The study found a significant mediating role of trust between perceived service quality, website quality and purchase intention.

H12: Trust mediates the relationship between perceived quality and telepresence to the purchase intention.

Information Satisfaction

Khumalo-Ncube and Motala (2021), studied the information quality on the website. Website quality dimensions namely ease of use, information quality and visual appearance, as well as customer satisfaction and purchase intention. The result shows that there is a positive relationship between customer satisfaction and related on the website information with purchase intention.

H11: Information satisfaction has a positive impact on purchase intention.

Response Variables

Response variables are the expected outcome of the customers behavior to fulfill the objectives of the firm strategy. For the experienced customers with services from CV.3J, the expected response is to increase revisit intention by increasing customer satisfaction (Waheed & Hassan, 2016). With some other variables also can be by increasing trust. For broad customers without previous experiences with services from CV.3J, the expected response is to increase purchase intention during website browsing or online booking process. To increase purchase intention could be by.

Revisit Intention

Nugrohoet al. (2021) explore revisit intention as the final response from the purchaser’s sustainable hospitality and customer satisfaction. It was founded that hospitality does not directly affect visit intention. It uses satisfaction as the mediating role to the visit intention of the tourist.

H13: Re-visit intention is influenced by customer satisfaction.

Purchase Intention

Purchase intention is the final response to all the strategies applied by the company since it could add revenue to the company. Purchase intention is an approach behavior manifested by positive actions, while abandoning a purchase is an avoidance behavior manifested by negative actions (Chang & Chen, 2008; Erogluet al., 2001).

H14: Purchase intention is influenced by Information Satisfaction and Trust.

Conceptual Framework

According to Camp (2001), a conceptual framework is a structure that best explains the natural progression of the phenomenon to be studies. Summarizing the above literature review, the conceptual framework uses in this study is as following:

The conceptual framework shows the relation between stimulus behavior as 7P’s, with intermediate variables expecting 2 final responses as company’s strategic objectives (revisit intention and purchase intention).

Fig. 2 shows the conceptual framework showing relationship between stimulus, intermediate variable and final responses.

Research Method

This quantitative research is designed for causal survey that collected data from questionnaires based on research objectives of identifying the important factors to increase revisit intention and purchase intention of tourist in Yogyakarta, Indonesia.

Data Collection

This research will use quantitative model approach. There are some steps to collect the data. This design of quantitative research is a survey that collected data from questionnaires based on research objectives structures indicated in the conceptual framework for tourist or prospective customers to visit Yogyakarta, Indonesia. The primary data is gathered from a questionnaire and the secondary data is gathered from literature review, company internal report, government statistics etc. The questionnaire is in the google form format and collected from contact list of people who already being or not a customer of CV Jogja Jalan Jalan in previous services (Link of google form:  http://bit.ly/45v1nMp).

Sampling Method

The size of the data sample will use Slovin’s formula. This research target population is people who visited Yogyakarta with or without CV.3J. A minimum confidence level of 10% is a significant indication of the variable. However, this research is used 90% of confidence level (Nunnally, 1975).

Defining sample size by using Slovin’s formula with a degree of error of 10% will be as following: (1)n=N1+Ne2where n is sample size, N is population for tourist visited Yogyakarta in general 2022, and e is level of significance 10%.

According to the Yogyakarta Tourism Board, there are 6.17 million tourists visited Yogyakarta in 2022. So, the value of N will be 6,170,000 people. With e equals to 0.10, then sample size (n) will be around 100 samples as minimum: n=6.170.0001+(6.170.000×0.12)=99.998≈100

A minimum of 100 samples is required for this company, but the actual data will collect 200 samples of respondent’s answers.

Research Design

Research design framework is a method or technique to resolve the research objective chosen by researcher. Research design is the implementation of theoretical tools in the research framework. The foundation, step, and methodology help the author seek problem solving. Research design is shown in the Fig. 3 below.

The quantitative survey will be part of the consumer analysis and will be part of internal analysis. All internal and external analysis will be combined into SWOT analysis and the strategy will be reformulated in the new STP and new 7P’s.

Data Analysis

To analyze the statistical data, SPSS will be used. SPSS will analyze demographic information related to gender, age etc. including description analysis. SPSS is also used to analyze the statistical result of the data such as reliability test, normality test and linear regression. The linear regression results will correlate dependent variables and construct variables in the conceptual framework. The analysis will also use average score of the Likert scale on each category.

Research Questions

The research questions shall be divided into several parts. There are three parts of this research question. The first and second part shall be referring to and some modified from Thipsinghet al. (2022). The third part shall refer to individual variables. The third part will use 5 level of Likert scale to measure the respondence level or answer. The first part is the customer data or demographic information such as age, gender, occupation, education, etc. The second data is related to the travelling behavior such as number of visits, purpose of traveling, transportation used etc, and the last part is the 7Ps and variables related with tourist behavior. Detailed research questions are implemented in the google form.

Business Solution

Internal Analysis

Segmentation, Targeting, Positioning

The existing market segmentation shall be based on geographic, demographic, psychographic, behavioral and entity (Kotler, 2023). Geographic-based segmentation has made no difference with the other as other competition would do the same easily. This is also the same case with demographic based segmentation. “Entity” is an additional segment category which divides the market based on their affiliations. Personal segment is commonly served by this company through the year 2022. The corporate segment is also interesting as it can be accessed through a campaign via corporate contact person. Every corporate has unique needs and can be so different from each other.

The targeting is selecting which one or more of those segments previously analyzed to be served and focusing the entire strategy based on the selected target. According to the study of existing marketing program, website etc., the existing targeting are generally identified as targeting the segments such as Indonesians, Personal and Corporate segments. The adventure segment is normally embedded in persona or corporate segments. There is no specific target based on the demographic data. Fig. 1 shows tourism division and corporate type has lower number of orders. Since tourism corporate has significant revenue per day or service, this shall be the selected target of the strategy.

Fig. 1. Number of orders based on division and customer type.

Fig. 2. Conceptual framework for tour and travel tourism marketing strategy. Adapted from Hoet al. (2022).

Fig. 3. Research design flowchart.

According to Gary T. Armstrong, a product position is the way the product is positioned uniquely in the customer’s mind that makes it different to the competition. The keyword here is “unique position in the customer’s mind”. For CV. 3J, the simple positioning that can be kept in the customer’s mind shall be developed. The existing selected positioning was “Best Service yet Affordable”. This positioning should be in customer’s mind because it was the young company and trying to catch the existing market share.

7P’s Framework

Caesaret al. (2022) studied the effect of 7Ps of Service Marketing and Digital Media on Purchase Intention in Tourism. They concluded that 7P’s of service marketing has positive impact on Purchase Intention.

The internal analysis refers to 7Ps framework shall be as per Table II.

No 7P’s framework Analysis Remarks
1 Product Many destinations redundancy,
Less repeated order,
Many destinations not ordered
2 Price Tour high price,
Tight cost margin
3 Place Add favorite/alternative destination,
Website less attractive
4 People Hired per order,
Service quality not standardized
5 Process Online booking problem,
Tour schedule problem
6 Promotion Less promotional incentives,
Less advertisement
7 Physical evidence Less evidence perception built on website With media
Table II. Internal 7P’s Analysis

External Analysis

PESTEL Analysis

PESTEL model is a framework that categorizes and analyses an important set of external factors such as Political, Economic, Sociocultural, Technology, Ecology and Legal that might be affecting a firm (Rothaermel, 2021). Related to the tourism industry or tour and travel company, PESTEL analysis is the analysis aimed to examine the macro factors that affect how the company operates directly or indirectly. See Table III for PESTEL analysis.

Item Factors Analysis Remarks
P Political Good government support on top 5 destinations, Borobudur/Yogyakarta included
General election effect,
E Economical Good 5.17% economic growth
S Socio-cultural Rich heritage and cultural values
T Technology Gadget and social media growth
E Ecology Ecotourism demand, natural disaster threat, local waste treatment problem
L Legal Good government regulatory support
Table III. PESTEL Analysis

Porter 5 Forces Analysis

Porter’s five forces analysis is the analysis of 5 main competitive forces that affect the industry, especially the company. Understanding all these factors will enhance long-term strategy development. The strongest competitive force determines the profit and strategy formulation. By combining theory from industrial organization economics with detailed case studies, Porter derived two key insights that form the basis of his seminal five forces model, (Rothaermel, 2021). See Table IV for Porter 5 Forces Analysis.

Force Factors Analysis Summary
1 Threat of new entry Medium entry barriers lead to potential on newcomers Medium
2 Power of suppliers Suppliers have more bargaining power to the firm Medium
3 Power of buyers High switching potential to the competitors High
4 Threat of substitutes Yogyakarta as destination has no substitutes, Medium
Potential of similar natural destinations may rise
5 Rivalry among competitors Growth of many similar services in Yogyakarta is high High
Table IV. Porter’s 5 Forces Analysis

Competition Analysis

Currently there are more than 17 direct competitions in the tourism industry in Yogyakarta. The tourism industry is growing fast since the yearly visit is more than 6 million and increasing. The tourism service provider is also increasing in line with the increasing number of tourists visiting the city. Table V shows the top 5 from the closest competitor.

No Name Car rent Tour with hotel Honey moon Other*
1  jogjaliburan.com (Own)
2  visit-jogja.com
3  widyalokawisata.com
4  nagantour.com
5  niagatour.com
6  alodiatour.com
Table V. Competitor Services Table

Below is a graph showing the pricing comparation for tour with and without hotel among top 5 competitors.

Fig. 4 above shows price range of the closest competitions  jogjaliburan.com (CV.3J). The tour package (without hotel) of  Jogjaliburan.com has maximum price closer to top 4 of most the pricy among others. Below IDR 55,000 will be the most competitive range.

Fig. 4. Tour Package without hotel price per itemized facilities. The price range of CV.3J lies in the top range among the competitions.

Below is Fig. 5 which shows that the pricing for tour package with hotel lies in the competitive range among top 5 competitors. This service group shall increase hospitality and other services to be more competitive.

Fig. 5. Tour package with hotel price per itemized facilities. The price range of CV.3J lies on the competitive range among the competitions.

The price positioning also confirms that the existing strategy is struggling with the competition to attract more customers with competitive pricing.

Costumer Analysis

The questionnaires were taken between august until November 2023. There are 200 respondent’s answers collected and shortlisted. The respondents are a mix for those who never uses service from CV.3J or at least once uses services from CV.3J. Below is the demographic background and traveling habits according to the questionnaire.

Demographic Background

Table VI shows that the majority of respondents are men, with a total of 126 respondents (63.2%). 14% of respondents (28) were between the ages of 20 and 29. 54% of respondents (108) were above 50 years old. The majority of respondents (72.9%) had undergraduate university study and higher. Where 1.9% respondents were less than high school education, 10.3% being at university, 15% respondents had high school graduated. Related with occupancy, most respondents, (50.5%) were employee, 12.1% were businessman, 10.3% were freelancer, and 18.7% are other occupancy. The respondent's hometown were 59.8% was from Jakarta and its surrounding area (Jabodetabek), 30.8% were from java island but outside Jabodetabek, while 6.5% are coming from outside Java island. Related with salary, 60% are ranged from 5 to 10 million IDR per month (315–629 USD) while 23.8% had salary more than 20 million IDR per month (1259 USD).

No Categories Results Percentage (%)
1 Total respondents 200 respondents
2 Gender Male 63.2
Female 36.8
3 Age <20 years 0
20–29 years 14.2
30–39 years 26.4
40–49 years 5.7
>50 years 53.8
4 Education Less than high school 1.9
High School Graduated 15.0
Being in Strata 1 10.3
S1 graduated and more 72.9
5 Occupation Business 12.1
Freelancer 10.3
Employee 50.5
Student 4.7
Retirement 3.7
Others 18.7
6 Hometown Jabodetabek 59.8
Java outside Jabodetabek 30.8
Outside Java 6.5
Others 2.8
7 Salary 5–10 Mi IDR 60.0
10–20 Mi IDR 16.2
>20 Mi IDR 23.8
Table VI. Demographic Background

Traveling Habits

Table VII shows related with traveling habit, 83% (majority) of respondent had more than three times visiting Yogyakarta before, while 9% of respondents had the second time visiting Yogyakarta. The more respondents experienced to visit Yogyakarta will give more accurate opinion about the tourism in Yogyakarta.

No Categories Results Percentage (%)
1 Number of visit to Yogyakarta Never 1
First time 7
Second time 9
More than 3 times 83
2 To travel, which one are preferred? By own cars 37
Rent a car 10
By public transport or mixed 16.5
By travel agent 36.5
3 Situation to choose travel agent Office activity 54.5
Not understand local tourism potential places 29
Not want to be busy 33
More efficient 31.5
Others 2.5
Table VII. Customer Traveling Habits

Validity and Reliability

The validity test is used by researchers to test whether the questionnaire that will be given to respondents is valid. Validity was tested using a two-sided significance value of 5% based on the criteria: (i) declared valid, the calculated r-value > r table, and (ii) declared invalid, the calculated r-value < r table. See Table VIII for Validity Test results.

Question code R count R table Status Question code R count R table Status
DI-1 0.849 0.166 Valid TR-1 0.858 0.166 Valid
DI-2 0.842 0.166 Valid HI-1 0.931 0.166 Valid
PQ-2 0.862 0.166 Valid HI-3 0.942 0.166 Valid
TM-2 0.672 0.166 Valid DS-1 0.946 0.166 Valid
TM-3 0.749 0.166 Valid PRo-4 0.935 0.166 Valid
PV-1 0.717 0.166 Valid PRo-5 0.950 0.166 Valid
NS-1 0.719 0.166 Valid PRo-6 0.947 0.166 Valid
PRo-1 0.761 0.166 Valid CS-1 0.949 0.166 Valid
PQ-1 0.761 0.166 Valid CS-2 0.890 0.166 Valid
PQ-4 0.516 0.166 Valid CS-3 0.895 0.166 Valid
PQ-5 0.468 0.166 Valid CS-5 0.946 0.166 Valid
PRm-1 0.606 0.166 Valid CS-6 0.897 0.166 Valid
PRm-2 0.658 0.166 Valid CS-7 0.860 0.166 Valid
PRm-3 0.794 0.166 Valid CS-9 0.941 0.166 Valid
WQ-2 0.833 0.166 Valid CS-10 0.867 0.166 Valid
PE-4 0.786 0.166 Valid VI-1 0.833 0.166 Valid
IS-1 0.773 0.166 Valid VI-2 0.690 0.166 Valid
TR-2 0.809 0.166 Valid VI-3 0.783 0.166 Valid
PI-2 0.758 0.166 Valid VI-4 0.905 0.166 Valid
PI-1 0.564 0.166 Valid
Table VIII. Validity Test

A reliability test is a test that aims to find out how far a measuring instrument can be relied upon or trusted. Latan and Ghozali (2016) The reliability test in this research was carried out using the Cronbach’s Alpha statistical test (α). If Cronbach’s Alpha (α) > 0.60 then the instrument is said to be reliable. Cronbach alpha for all above question codes is 0.889 which is reliable ((α) > 0.60).

Results

Multicollinearity Test

Multicollinearity test is designed to check whether the regression model found a correlation between dependent or independent variables (Ghozali, 2016). Good regression model should have no correlation between independent variables. Limit valuetolerance and valuevariance inflation factor (VIF) is 10, which means that when the valuetolerance is smaller than 10 and the VIF value is greater than 10, it can be said that multicollinearity occurs.

The multicollinearity test in Table IX shows that the VIF value of each independent variable is less than 10. Therefore, it can be concluded that there is no multicollinearity in the independent variables.

Coefficientsa
Model Collinearity statistics
Tolerance VIF
1 (Constant)
Destination Image 0.607 1.647
Hospitality Image 0.477 2.097
Tourist Motivation 0.661 1.514
Perceived Value 0.577 1.733
Novelty Seeking 0.617 1.622
Product Quality 0.778 1.286
E-Service Quality 0.791 1.264
Online Process 0.409 2.443
On Site Service Process 0.478 2.093
Table IX. Multicollinearity Test

Hypothesis Testing

This hypothesis testing checks the supporting relation between organism variables, revisit intention, and purchase intention.

F Test

The F test is used to measure simultaneously (together) the influence of the independent variable on the dependent variable. F test is carried out by comparing the calculated F value with the F table and a significance value of 0.05 in the following way:

If F_count > F_table or probability < significant value (Sig < 0.05), then the research model can be used.

If F_count < F_table or probability > significant value (Sig > 0.05), then the research model cannot be used.

In Table X, the F Test on Revisit Intention, the significance value (sig) of 0.000 is smaller than 0.05. Therefore, the decision to reject H0 was obtained with the conclusion that there is a significant influence of the customer satisfaction variable on revisit intention.

ANOVAa
Model Sum of squares df Mean square F Sig.
1 Regression 139.208 1 139.208 37.758 0.000b
Residual 678.383 184 3.687
Total 817.591 185
Table X. F Test on Re-Visit Intention

In Table XI, F Test on Purchase Intention, the significance value (sig) of 0.000 is smaller than 0.05. Therefore, the decision to reject H0 was obtained with the conclusion that there was a significant influence of the information satisfaction, trust, online process and promotion variables on purchase intention.

ANOVAa
Model Sum of squares df Mean square F Sig.
1 Regression 100.793 3 33.598 6.409 0.000b
Residual 954.111 182 5.242
Total 1054.903 185
Table XI. F Test on Purchase Intention

T Test

The T test is used to determine the effect of each independent variable on the dependent variable. Testing was carried out using a significance of 0.05 (α = 5%). Acceptance or rejection of the hypothesis is carried out according to the following criteria:

If the significant value is >0.05 then the hypothesis is rejected (the regression coefficient is not significant). This means that the independent variable does not have a significant influence on the dependent variable.

If the significant value is <0.05 then the hypothesis is accepted (significant regression coefficient). This means that the independent variable has a significant influence on the dependent variable.

According to T test in Table XII, the significance value (sig) of customer satisfaction is 0.000, which is smaller than α (0.05), therefore the decision to reject H0 is obtained with the conclusion that customer satisfaction has a significant influence on increasing revisit intention.

Coefficientsa
Model Unstandardized coefficients Standardized coefficients t Sig.
B Std. error Beta
1 (Constant) 9.843 0.920 10.699 0.000
Customer satisfaction 0.369 0.060 0.413 6.145 0.000
Table XII. T Test on Revisit Intention

According to T test in Table XIII, shows several results as follows:

Coefficientsa
Model Unstandardized coefficients Standardized coefficients t Sig.
B Std. error Beta
1 (Constant) 7.926 1.665 4.760 0.000
Information satisfaction 0.031 0.107 0.026 0.286 0.775
Trust 0.113 0.099 0.110 2.150 0.025
Online process 0.192 0.105 0.134 2.083 0.027
Promotion 0.221 0.080 0.221 2.771 0.006
Table XIII. T Test on Purchase Intention

i) The significance value (sig) of information satisfaction is 0.775 which is greater than ɑ (0.05), therefore the decision to accept H0 is obtained with the conclusion that information satisfaction does not have a significant influence on increasing purchase intention.

ii) The significance value (sig) of trust is 0.025, which is less than ɑ (0.05), therefore the decision to reject H0 is obtained with the conclusion that trust has a significant influence on increasing purchase intention.

iii) The significance value (sig) of Online Process is 0.027, which is smaller than ɑ (0.05), therefore the decision to reject H0 is obtained with the conclusion that online-process has a significant influence on increasing purchase intention.

iv) The significance value (sig) of promotion is 0.006, which is smaller than ɑ (0.05), therefore the decision to reject H0 is obtained with the conclusion that promotion has a significant influence on increasing purchase intention.

Multiple Regression Linear Model

This research uses multiple linear regression analysis (multiple regression analysis) with the aim of finding out how the independent variables influence the dependent variable (introductory accounting learning outcomes).

Customer Satisfaction

Data analysis results in Table XIV would be expressed into the following formula model:

Coefficientsa
Model Unstandardized coefficients Standardized coefficients
B Std. error Beta
1 (Constant) 6.897 1.922
Destination image 0.068 0.091 0.065
Hospitality image 0.037 0.097 0.037
Tourist motivation 0.086 0.095 0.075
Perceived value −0.091 0.081 −0.099
Novelty seeking −0.017 0.071 −0.020
Product quality 0.469 0.114 0.312
e-Service quality 0.307 0.137 0.169
Online process −0.010 0.148 −0.007
On site service process 0.173 0.167 0.100
Table XIV. Influence on Customer Satisfaction

(2)CustomerSatisfaction=6.897+0.068DestinationImage+0.037HospitalityImage+0.086TouristMotivation−0.091PerceivedValue−0.017NoveltySeeking+0.469ProductQuality+0.307eServiceQuality−0.010OnlineProcess+0.173OnSiteServiceProcess

The multiple linear regression model above shows that a one unit increase in the destination image factor can increase customer satisfaction by 0.068, a one unit increase in the hospitality image factor can increase customer satisfaction by 0.037, a one unit increase in the tourist motivation factor can increase customer satisfaction by 0.086, an increase of one One unit of the perceived value factor can reduce customer satisfaction by 0.091, one unit increase in the novelty seeking factor can reduce customer satisfaction by 0.017, one unit increase in the product quality factor can increase customer satisfaction by 0.469, one unit increase in the e-Service quality factor can increase customer satisfaction by 0.307, increasing one unit of online process factors can reduce customer satisfaction by 0.010 and increasing one unit of on site service process factors can increase customer satisfaction by 0.173.

Information Satisfaction

Data analysis results in Table XV would be expressed into the following formula model:(3)Information Satisfaction=12.019+0.21Website Quality

Coefficientsa
Model Unstandardized coefficients Standardized coefficients t Say
B Std. error Beta
1 (Constant) 12.019 0.846 14.204 0.000
Website quality 0.210 0.056 0.269 3.787 0.000
Table XV. Influence on Information Satisfaction

The multiple linear regression model above shows that one unit increase in the website quality factor can increase Information Satisfaction by 0.210.

Trust

Data analysis results in Table XVI would be expressed into the following formula model:

Coefficientsa
Model Unstandardized coefficients Standardized coefficients t Sig.
B Std. error Beta
1 (Constant) 6.850 1.046 6.549 0.000
Product quality 0.111 0.107 0.075 3.034 0.030
E-service quality 0.248 0.135 0.139 1.840 0.067
Physical evidence 0.369 0.072 0.390 5.148 0.000
Table XVI. Influence on Trust

(4)Trust=6.850+0.111Product Quality+0.248eServiceQuality+0.369Physical Evidence

The linear regression model above shows that a one unit increase in the product quality factor can increase trust by 0.111, one unit increase in the e-sercice quality factor can increase trust by 0.248 and a one unit increase in the physical evidence factor can increase trust by 0.369.

Revisit Intention

Data analysis results in Table XVII would be expressed into the following formula model:

Coefficientsa
Model Unstandardized coefficients Standardized coefficients t Sig.
B Std. error Beta
1 (Constant) 9.843 0.920 10.699 0.000
Customer satisfaction 0.369 0.060 0.413 6.145 0.000
Table XVII. Influence on Revisit Intention

(5)Revisit Intention=9.843+0.369Customer Satisfaction

Purchase Intention

Data analysis results in Table XVIII would be expressed into the following formula model.

Coefficientsa
Model Unstandardized coefficients Standardized coefficients t Sig.
B Std. error Beta
1 (Constant) 7.926 1.665 4.760 0.000
Information satisfaction 0.031 0.107 0.026 0.286 0.775
Trust 0.113 0.099 0.110 2.150 0.025
Promotion 0.221 0.080 0.221 2.771 0.006
Online process 0.192 0.105 0.134 2.083 0.027
Table XVIII. Influence on Purchase Intention

(6)Purchase Intention=7.926−0.031InformationSatisfaction+0.113Trust+0.192OnlineProcess+0.221Promotion

The linear regression model above shows that a one unit increase in the information satisfaction factor can reduce purchase intention by 0.031, a one unit increase in the trust factor can increase purchase intention by 0.113 and a one unit increase in the promotion factor can increase purchase intention by 0.221, and one unit increase in the online process factor can increase purchase intention by 0.192.

Summary of Likert Score

Table XIX is the summary average of some constructs that combined into the 7P’s components such as product, price, place, process, people, promotions, and physical evidence. The 5 levels of Likert score on the questionnaire were collected and averaged based on the variables group.

7P framework Stimulus variable approach Average scores
Product Destination image 4.47
Hospitality image 3.99
Product quality 4.10
E-service quality 3.34
Price Perceived value 4.24
Place Destination image 4.47
Website quality 3.95
People Tourist motivation 3.97
Novelty seeking 4.32
Hospitality image 3.99
Product quality 4.10
E-Service quality 3.34
Process Online process 4.00
On site process 3.97
Promotion Promotion 3.86
Physical evidence Physical evidence 3.96
Table XIX. 7P’s Average Likert Score on the Questionnaire

The average Likert score on several aspects summarizes the customer’s mind and preferences. The maximum score was given to the destination image (4.47). This confirms that Yogyakarta is well-known as a good place to visit.

The minimum score is given to the perceived quality. Perceived quality has several components, such as product quality and e-service quality. The major contribution to the lower score is derived from e-service quality 3.3. This is due to the website having no customer service to handle any orders, which leads to the loss of potential customers.

The promotion aspect is scored second lowest (3.86). This shows that many customers were not reached by any promotional incentives or advertisements. This suggests that promotional media should be intensified.

SWOT Analysis and Matrix

SWOT analysis is the combination of external and internal analysis of the system, including result of the customer behavior survey. SWOT analysis is a framework that allows company’s management team to synthesize an insight obtained from analysis of company’s internal Strength and Weakness (S and W) with those from external analysis of Opportunities and Threat (O and T) to derive strategic formulation (Rothaermel, 2021). Collecting information from previous internal and external analysis, below is the summary of the SWOT of CV. 3J.

According to Rothaermel (2021), SWOT matrix is to develop strategic alternative to the firm. Table XX is the SWOT analysis which is summarized from previously internal and external factor analysis. This SWOT analysis will be constructed further in SWOT matrix in Table XXI to develop firm's strategy.

Item Code Analysis
Strength S1 Offers complete service range
S2 Bundling pricing/discount with suppliers
S3 Long experiences/understand local industry
S4 Wide local networking
S5 Good customer satisfaction on the service
Weakness W1 e-service quality is not good, loss potential customers
W2 Team hired by project/order. No service standards
W3 Organization not well established
W4 Website appearance not attractive
W5 Less marketing campaign on media
W6 Redundancy and complicated package structure
W7 Low purchase intention and revisit intention
Opportunity O1 Government program of top 5 destination as market potential
O2 Many interesting new destination potentials
O3 Plenty of room to improve customer trust and satisfaction
Threat T1 The potential of rapid growing of newcomers in the same industry will cannibalize market share
T2 Retaliation of big players to increase their market share with their big marketing budget and better networking system.
T3 Political situation potential to give negative effect on the tourism industry
T4 Thread from natural disaster about surrounding of Yogyakarta from volcanic eruption, earthquake, and tsunamis
Table XX. SWOT Analysis
External to firm
Opportunities Threads
Strength-Opportunities Strength-Threads
Internal to firm Strength SO1 Promoting its competitive advantage to reach or capture more potential market and demand S1, S2, S3, S5, O1, O2, O3 ST1 Use the differentiation strategy as competitive advantage to face thread from competition S1, S2, S3, T1, T2
SO2 Use the networking and referral to provide advocacy in the community and potential market S4, O1, O2 ST2 Use all the competitiveness strategy to find new potential opportunities in spite of the thread situation S1, S2, S3, S4, T3, T4
Weakness-Opportunities Weakness-Threads
Weakness WO1 Training to improve team quality of services hospitality, trust, satisfaction, and organizing to capture more market share W3, O2, O2 WT1 To create new Marketing Divisions to develop dedicated marketing program and facing any threat from external and competitions W1, W2, T1, T2
WO2 To expand promotion and advertising program to reach and capture more market potentials W1, W2, O2, O2 WT2 Tour package re-design and improvements to face competitions and other threats W4, T1, T2, T3, T4
WO3 Improve website quality to reach more customer and opportunity W1, W4, O1, O2 WT3 Re-organizing the Firm to create robust, creative and agile team to face threat from any external factors W3, T1, T2, T3, T4
Table XXI. SWOT Matrix

New STP

Segmentation, Targeting and Positioning Matrix is developed in Table XXII. The new segmentations have not changed since the market pool is still the same as the original. The general selected targeting is Indonesian, mostly living in Java Island, who worked as employee or corporate segments with mostly having mid to higher incomes, which love to do adventure or having company programs such as gathering, training etc.

No Market segment New segment New target Survey population Accessed by
Geographic
1 Nationality
Indonesia 100% Website, network
Foreigner X 0%
2 Location
Java 90.6% Website, network
Outside Java X 9.4%
Demographic
1 Age, gender, occupation, religion, etc.
Age > 20 Yo 100%
Employee + Self employee + Business 72% Website, ads
2 Incomes
Low-Middle incomes X 60%
Mid-Higher incomes 40% Pricing
Psychographic
1 Lifestyle, personality, etc. X
2 Adventure 29.5% Tour Pkg
Behavioral
1 Occasions
General holiday 48.5% Holiday program
Religious holiday X 9%
2 Loyalty status Discount
Entity
1 Personal
General Website, ads
Honeymoon, etc.
2 Corporate
General tour Tour package, website, corporate marketing
Gathering
3 School
Educational School marketing
Table XXII. The Segmentation and Targeting Matrix

The new positioning and differentiation shall be based on the survey and analysis of the market available. The survey shows that the customers expect satisfaction from their previous set values and motivation before visiting. From the study, the major keywords of customer are service quality and reliability. Corporate level is demanding in service quality and reliability. Therefore, the new positioning and differentiation shall be “Quality Service and Reliable”.

Based on this positioning, the marketing strategy shall be categorized as Need Based Positioning, by selecting specific needs (corporate, adventure, gathering, honeymoon, educational, etc.), improving hospitality services and special customer cares program.

New 7P’s

According to the analysis and the survey result, the new 7P’s shall be developed to have an overall view of the company business. Table XXIII is the development of the new 7P's. This new 7P's are developed based on the summarizes of previous analysis (internal and external). Among those factors, well trained People with hospitality is the most important factor for re-visit intention. Promotional and massive advertising is also important to reach broad customer casidates.

No 7P’s framework Proposed
1 Product Specific need destination,
New potential destination,
Improving services
2 Price Middle-up pricing range
3 Place Improving website appearance
4 People Well trained team and standardized
5 Process Dedicated SOP for scheduling, etc.
6 Promotion Promotional discount and intensive advertising
7 Physical evidence Remote presence effect on the website
Table XXIII. Proposed New 7P’s

Implementation Plan

As the result of new marketing strategy, the new proposed program shall be developed as a solution to the problems. The implementation program shall cover all previous problems, concerns, target, and positioning strategy.

There are 5 proposed implementation programs such as: (1) To improve team quality, (2) Create new division focuses on Corporate Services, (3) Tour package redesign, (4) Improve Website Quality and (5) To expand promotion and advertising program.

According to the survey, increasing revisit intention could be by improving customer satisfaction. This means previous experience is the key for customers to come and visit again next time. Ensuring good service is important and can be achieved by improving team quality. Improving team quality through team development programs such as training or apprenticeship. This is to ensure better and standardized hospitality services, reduced un-interrupted process with SOP and ensure service reliability.

As the strategy is to enter special needs for corporate segments, a special team or division shall be established to handle and take care of corporate service requirements.

Tour package redesign is the improvement of existing tour package program, by adding specific needs and adding co-creation template. Co-creation means customers can select the preferred destinations and number of days based on their budget preferences.

Improving website quality will lead to information satisfaction and give clear trusted content to the customers. The website design and philosophy shall reflect previous marketing program and implementation, including employee quality and reliability, tour co-creation and attractive destinations, and customer focused services.

According to the survey, promotion has a significant effect on purchase intention. So, the promotion program is an important strategy to increase purchase intention. Advertising will increase brand awareness and create brand positioning in the customer’s mind. Promotions such as discounts etc. will increase customers’ intention to purchase.

Conclusion and Recommendation

Conclusion

For customers who already visit Yogyakarta with CV.3J, the most important factor to increase revisit intention is ensuring customer satisfaction on previous visit by improving hospitality according to the selected pricing within the targeting segments.

For broad customer to increase purchase intention on the website, the important factor is to be more aggressive on promotion, as well as improving e-service quality, to engage customer with trust. Another interesting finding is that information satisfaction needs a mild ‘hold’ since it has an adverse effect on purchase intention. Keep the customer a little bit curious so that they are willing to purchase. The website shall include important factors such as physical evidence (telepresence effect) in the form of photos and video or customer testimonials.

It is concluded that to increase market share or visit intention/purchase intention for customers, CV.3J shall improve their service quality (human resources), website quality and broader promotional programs to gain more attractions, and then targeting the correct segment on the corporate basis to speed up the company revenue.

Recommendation

It is recommended to improve this research in other types of behaviors as stimulus that meet all the 7P’s framework. Several parts of 7P’s framework need to explore more customer behavior related with Process, Promotions and Physical Evidence. Perhaps there are still other factors that might be important to increase re-visit intention and purchase intention.

References

  1. Booms, B. H., & Bitner, M. J. (1981). Marketing strategies and organization structures for service firm. In J. H. Donelly, W. R. George (Eds.), Marketing of Service (pp. 47–51). Chicago: American Marketing Association.
     Google Scholar
  2. Brahmanto, E. (2015 Nov). Magnet paket wisata dalam menarik kunjungan wisatawan asing berkunjung ke yogyakarta. Akademi pariwisata BSI yogyakarta. Jurnal Media Wisata, 13(2).
     Google Scholar
  3. Camp, W. G. (2001). Formulating and evaluating theoretical frameworks for career and technical education research. Journal of Vocational Educational Research. 26(1), 27–39.
     Google Scholar
  4. Caesar, L. A. Y., Larasati, A., Vincent, N. S. W., & Nabila, N. A. (2022). 7Ps of service marketing and digital media on purchase intention in tourism. 2022 13th International Conference on E-business. https://doi.org/10.1145/3556089.3556162.
     Google Scholar
  5. Chang, H. H., & Chen, S. W. (2008). The impact of online store environment cues on purchase intention: Trust and perceived risk as a mediator. Online Information Review, 32(6), 818–841.
     Google Scholar
  6. Cirikovic ́, E. (2014, June). Marketing mix in tourism. In Academic journal of interdisciplinary studies (vol. 3, No. 2). Rome-Italy: MCSER Publishing. ISSN 2281-3993. https://doi.org/10.5901/ajis.2014.v3n2 p111.
     Google Scholar
  7. Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2001). Atmospheric qualities of online retailing. Journal of Business Research, 54(2), 177–184.
     Google Scholar
  8. Flora, V. A. S. M., Tandilino, S. B., Nugraha, Y. E. (2019). Asembling of marketing mix model in management strategy of tourism destination in Lasiana beach Kupang city East Nusa Tenggara. ICESC 2019. https://doi.org/10.4108/eai.18-10-2019.2290001.
     Google Scholar
  9. Ghozali, I. (2016). Aplikasi Analisis Multivariete Dengan Program IBM SPSS 23. 8th ed. Badan Penerbit Universitas Diponegoro, ISBN: 979-704-015-1.
     Google Scholar
  10. Ho, C.-I., Liu, Y., & Chen, M.-C. (2022). Factors influencing watching and purchase intentions on live streaming platforms: From a 7Ps marketing mix perspective. Information-An International Interdisciplinary Journal, 13, 239. https://doi.org/10.3390/info13050239.
     Google Scholar
  11. Khan, M. (2014). The Concept of Marketing Mix and its Elements (A. Conceptual Review). International Journal of Information, Business and Management, 6(2), 95–107.
     Google Scholar
  12. Khumalo-Ncube, S., & Motala, T. (2021). Hotel booking website quality, travel agent satisfaction and purchase intention. African Journal of Hospitality, Tourism and Leisure, 10(6), 1932–1943. https://doi.org/10.46222/ajhtl.19770720.201.
     Google Scholar
  13. Kotler, P., & Keller, K. L. (2006). Marketing Management. 12th ed. Upper Saddle River, NJ: Prentice Hall.
     Google Scholar
  14. Kotler, P. (2023). Principles of Marketing, Global Edition. 19th ed. Pearson International Content. https://bookshelf.vitalsource.com/books/9781292449333.
     Google Scholar
  15. Kusumawati, A., Utomo, H. S., Suharyono, S., & Sunarti, S. (2021). The antecedents of behavioural intention for island tourism across traveller generations: A case of Bali. https://doi.org/10.1080/14927713.2021.1872405.
     Google Scholar
  16. Latan, & Ghozali, H. d. I. (2016). Partial Least Squares Konsep, Metode dan Aplikasi Menggunakan Program WarpPls 5.0 (Edisi 3). Semarang: Badan Penerbit Universitas Diponegoro.
     Google Scholar
  17. Liu, X., Zhang, L., & Chen, Q. (2022). The effects of tourism e-commerce live streaming features on consumer purchase intention: The mediating roles of flow experience and trust. Frontiers in Psychology, 13, 995129. https://doi.org/10.3389/fpsyg.2022.995129.
     Google Scholar
  18. López-Sanz, J. M., Penelas-Leguía, A., Gutiérrez-Rodríguez, P., & Cuesta-Valiño, P. (2021). Rural tourism and the sustainable development goals. A study of the variables that most influence the behavior of the tourist. Frontiers in Psychology, 12, 722973. https://doi.org/10.3389/fpsyg.2021.722973.
     Google Scholar
  19. McCarthy, J. E. (1964). Basic Marketing. A Managerial Approach. Home- wood, IL: Irwin.
     Google Scholar
  20. Nugroho, I., Hanafie, R., Rahayu, Y. I., Sudiyono, Suprihana, Yuniar, H. R., et al. (2021). Sustainable hospitality and revisit intention in tourism services. Journal of Physics.: Conference Series, 1908, 012004. https://doi.org/10.1088/1742-6596/1908/1/012004.
     Google Scholar
  21. Nunnally, J. C. (1975). Psychometric theory—25 years ago and now. Edu- cational Researcher, 4(10), 7–21. https://doi.org/10.3102/0013189X004010007.
     Google Scholar
  22. Pijls, R., Groen, B. H., Galetzka, M., & Pruyn, A. T. H. (2017). Measuring the experience of hospitality: Scale development and validation. International Journal of Hospitality Management, 67, 125–133. https://doi.org/10.1016/j.ijhm.2017.07.008.
     Google Scholar
  23. Qalati, S. A., Vela, E. G., Li, W., Dakhan, S. A., Thuy, T. T. H., & Merani, S. H. (2021). Effects of perceived service quality, website quality, and reputation on purchase intention: The mediating and moderating roles of trust and perceived risk in online shopping. Cogent Business & Management, 8(1), 1869363. https://doi.org/10.1080/23311975.2020.1869363.
     Google Scholar
  24. Rothaermel, F. T. (2021). Strategic Management. 5th ed. New York, USA: McGraw-Hill Education.
     Google Scholar
  25. Santoso, S. (2019). Examining relationships between destination image, tourist motivation, satisfaction, and visit intention in Yogyakarta. Expert Journal of Business and Management, 7(1), 82–90. http://www.zbw.eu/econis-archiv/handle/11159/3847.
     Google Scholar
  26. Tang, H., Wang, R., Jin, X., & Zhang, Z. (2022). The effects of motivation, destination image and satisfaction on rural tourism tourists’ willingness to revisit. Sustainability, 14, 11938. https://doi.org/10.3390/su141911938.
     Google Scholar
  27. Tasci, A. D. A., & Gartner, W. C. (2007). Destination image and its functional relationships. Journal of Travel Research, 45(4), 413–425. https://doi.org/10.1177/ignorespaces0047287507299569.
     Google Scholar
  28. Thipsingh, S., Srisathan, W. A., Wongsaichia, S., Ketkaew, C., Naruetharadhol, P., & Hengboriboon, L. (2022). Social and sustainable determinants of the tourist satisfaction and temporal revisit intention: A case of Yogyakarta. Indonesia Cogent Social Sciences, 8(1), 2068269. https://doi.org/10.1080/23311886.2022.2068269.
     Google Scholar
  29. Waheed, N., & Hassan, Z. (2016). Influence of customer perceived value on tourist satisfaction and revisit intention: A study on guesthouses in Maldives. International Journal of Accounting, Business and Management, 4(1), 101– 123. https://doi.org/10.24924/ijabm/2016.04/v4.iss1/98.119.
     Google Scholar
  30. Ye, J., He, M., Yuan, J., Zhu, X., & Wang, Y. (2021). A study on the motivation, tourism involvement and post-tourism behavior mechanism of rural tourism: A case study of Turpan. Arid Land. Resources and Environment, 35, 203–208.
     Google Scholar


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