Proposed Marketing Strategy to Increase Re-Visit Intention and Purchase Intention of Tourist in Yogyakarta: Case of CV. Jogja Jalan Jalan

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.

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.

Table I. 7P’s Framework and Related Stimulus Variables Approach

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)

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.

Table II. Internal 7P’s Analysis

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

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.

Table III. 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

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.

Table IV. Porter’s 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

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.

Table V. Competitor Services Table

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	–	√	√	√

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).

Table VI. Demographic Background

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

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.

Table VII. Customer Traveling Habits

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

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.

Table VIII. Validity Test

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

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.

Table IX. Multicollinearity Test

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

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.

Table X. F Test on Re-Visit 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

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.

Table XI. F Test 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

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.

Table XII. T Test on 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

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

Table XIII. T Test on Purchase Intention

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

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:

Table XIV. Influence on Customer Satisfaction

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

(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

Table XV. Influence on Information Satisfaction

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

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:

Table XVI. Influence on Trust

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

(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:

Table XVII. Influence on 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

(5)Revisit Intention=9.843+0.369Customer Satisfaction

Purchase Intention

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

Table XVIII. Influence on Purchase Intention

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

(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.

Table XIX. 7P’s Average Likert Score on the Questionnaire

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

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.

Table XX. SWOT Analysis

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 XXI. SWOT Matrix

		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

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.

Table XXII. The Segmentation and Targeting Matrix

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

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.

Table XXIII. Proposed New 7P’s

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

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.