TOURIST SATISFACTION IN ERA SOCIETY 5.0 AS A MARKETING STRATEGY

This research aims to develop a marketing strategy for the Society 5.0 era by increasing tourist satisfaction in the lodging industry's business sector. Utilizing a quantitative approach with online data distribution, the sample in this study is 128 respondents. The PLS-SEM data analysis technique was used to analyze the questionnaire data. The study's results indicate that technological innovation services and tourist engagement influenced tourist satisfaction. In contrast, human innovation services, which have no direct impact on tourist satisfaction, must first be filtered through the variable measuring tourist engagement. The engagement of tourists is a moderating variable that influences tourist satisfaction. As tourists directly experience technological and human innovation services, they will affect tourist satisfaction. This research allows business actors in the lodging establishment industry to develop marketing strategies that increase tourist satisfaction, thereby increasing tourist trust and repeated visits in the future


INTRODUCTION
An investment policy is crucial to economic sector growth since it fosters national and international economic competitiveness.One of the missions for economic development has been established by one of the regional governments in Indonesia, the Government of Sumedang Regency.Agriculture and tourism are the leading sectors of Sumedang Regency's regional economy, and to further boost regional economic growth, these leading sectors must be encouraged to attract domestic and foreign investment.
In previous research by Chiang and Trimi (2020), DiPietro and Wang (2010), Lee and Lee (2020), Kervenoael et al. (2020), and Kim et al. (2018), it was explained that many tourists are now considering the service technology offered by the hospitality industry in terms of self-service for check-in and check-out or other services that use robotic applications.However, these five studies have yet to describe how human services contribute to higher tourist involvement.Therefore, this research focuses on elements of human service combined with technological improvements to build tourist engagement to achieve tourist satisfaction in accordance with the regulatory goals of the tourist development paradigm in Indonesia.This research aims to ascertain whether the mediating variable of tourist engagement will affect tourist satisfaction as a marketing strategy.
One hundred twenty-eight respondents responded to online surveys sent to travelers from West Java, Indonesia, who had booked at lodgings other than hotels.PLS-SEM is the analytic method used after the data from the questionnaire is gathered.
It was found that service innovation technology and human service innovation have a direct effect on tourist engagement and satisfaction.Following the completion of quality control, subsequent findings become a mediating variable influencing tourist satisfaction.Tourists can directly feel the technology services and employee services provided by non-hotel lodging, which affects engagement and satisfaction in tourists.The research's innovations are: 1.As a result of research, tourist satisfaction only looks at research objects in non-hotel lodgings in West Java, Indonesia.
2. Tourist satisfaction in this study is measured directly by the tourist engagement variable as a mediating variable, and tourist satisfaction is directly affected by the technology and human service innovation variables.
3. The technology service innovation variable is specifically studied, directly affecting tourist satisfaction and influencing the tourist engagement variable as a mediating variable.
4. The human service innovation variable is specifically studied, directly affecting tourist satisfaction and influencing the tourist engagement variable as a mediating variable.
5. The model in this study, namely technology service innovation and human service innovation with the mediation of tourist engagement as a mediating variable, comprises several previously researched factors.
This study aims to create a marketing plan based on the degree of satisfaction of nonhotel lodging tourists in West Java, Indonesia.Tourist satisfaction is specifically researched in West Java, and the three variables studied are technology service innovation, human service innovation, and tourist engagement.The method used in this study is a quantitative method that involves distributing questionnaires online to 128 respondents who stay at non-hotel accommodations in West Java, resulting from collecting the questionnaires and processing the data using PLS-SEM.

LITERATURE REVIEW Tourist Satisfaction
Schiffman and Kanuk's (2004) definition of customer satisfaction is the perception that exists in customers' minds compared to their expectations for a product or service.Customer satisfaction is achieved if expectations are exceeded so that the company's profits will be fulfilled.It can be concluded that a positive effect arises from customer satisfaction and can encourage customer loyalty, which is then expected to lead to repurchases and positive word-of-mouth promotions by customers (Ganesh et al. 2000).Among those who have studied customer satisfaction levels in the lodging industry is Karatepe (2006).According to Gursoy et al. (2003 and2007) in their research, tourist satisfaction is a theme often researched in hospitality and tourism.Information on tourist design is needed for the hospitality sector and calls for service innovation.

Technology Service Innovation (TSI)
According to Tether (2005) and Toivonen and Tuominen (2009), the earliest studies demonstrate that new technologies are a significant driver of service innovation.The impact of advanced technologies on operational efficiency, customer communication quality, and service efficiency is evident.Based on the research results of Bitner et al. (2000), it can be concluded that service experience and customer satisfaction can be improved by increasing customization, service recovery, and spontaneous satisfaction using technology.According to Yang et al. (2003), clients can receive more individualized attention and customization with the aid of technology for service, increasing their satisfaction with their shopping experience.We, therefore, propose the following hypotheses: H1: Technology Service Innovation (TSI) has a significant effect on Tourist Engagement (TE) H2: Technology Service Innovation (TSI) has a significant effect on Tourist Satisfaction (TS) Human Service Innovation (HSI) Howells and Tether (2004) stated that service innovation includes technological and nontechnological innovations (i.e., organizational and relational change).Ottenbacher and Gnoth (2005, p. 218) claim that due to competitors' propensity to obtain comparable resources and technology, technology gives only a slight competitive advantage to the hotel industry.Miles (2010), Martin-Rios et al. (2019), Wang et al. (2015), andLuo et al. (2019) stated that human interaction is the most crucial aspect that influences the pleasure and enjoyment of consumers.As Berry and Carbone (2007) have stated, the most desired outcome of establishing sincere emotional connections with clients can be accomplished more successfully through positive human interactions.Human services can be better than technology because they can meet customers' emotional needs by giving them empathy, a sense of exclusivity through personal relationships, the social identity they want, and attention.This study's non-technological service innovation component refers specifically to exceptional service actions exemplified by the utmost professionalism of the service staff (both in their behavior and attitudes), extraordinary empathy and concern, and great assistance in providing one-stop services (Luo et al., 2019).Excellent service is distinguished from average by frontline employees' empathic and considerate attitudes, acute sensitivity, keen observational skills, elaborative thinking abilities, and proactive and prompt response abilities (Luo et al., 2019).Superior customer service can promote pleasure creation and ensure that customers are satisfied.Human service or human interaction has been suggested by numerous researchers to be the most influential factor in customer satisfaction, according to Arnould and Price (1993), as well as Hinkin and Tracy (1998).We propose the following hypotheses: H3: Human Service Innovation (HSI) has a significant effect on Tourist Engagement (TE) H4: Human Service Innovation (HSI) has a significant effect on Tourist Satisfaction (TS)

Relation of engagement to tourist engagement
The term "engagement" has numerous meanings and dimensions across numerous disciplines.Engagement in marketing terms relates to how customers engage with a good or service (Brodie et al., 2011).Employee engagement served as the foundation for earlier definitions of Consumer (or Customer) Engagement (CE) in a business setting (So et al., 2014).In addition, the authors have emphasized the significance of retaining employees to implement a sense of ownership that would ultimately lead to the organization's organic growth.Customer engagement has yet to be a fully developed concept in marketing.The concept of engagement initially existed in the employee engagement psychology field (Vivek et al., 2012).So et al. (2014b) stated that customer engagement is a type of marketing that focuses on how customers think and act.Brodie et al. (2011) found that this is reflected in how customers talk to other customers or companies in a forum to find out about products or predict the risks of using these products.Brodie et al., (2011) and Hollebeek (2011)  Some researchers implied customer engagement leads to satisfaction, trust, and loyalty (Brodie et al., 2011;Hollebeek, 2011).Some researchers (So et al., 2014a) also thought that customer satisfaction, trust, and loyalty come before and after customer engagement.The relationship between customers and businesses can serve as an alternative metric for customers to evaluate the quality of services provided by businesses (Brodie et al., 2011).The customer will feel satisfied if the company's service or response meets their expectations during the interaction, and vice versa (Hollebeek, 2011).This argument leads to the following hypothesis: H5: Tourist Engagement has a significant effect on Tourist Satisfaction.The PLS-SEM validity test employs both the convergent validity and discriminant validity approaches.A set of indicators representing a single latent variable and containing a single latent variable demonstrates convergence validity.The Average Variance Extracted (AVE) value is used in this study to gauge convergent validity.The AVE value must www.ieeca.org/journal881 satisfy a minimum point requirement of 0.5 (Sari, 2022;Fornell & Larcker, 1981).There must be sufficient differences between the two measured concepts for discriminant validity.Cross-loading criteria for measuring discriminant validity When each indicator's loading exceeds its corresponding crossloading, discriminant validity is met.
The structural model, which assesses how latent factors relate to one another, can also be called the internal model.The R2 used as a measurement of each endogenous latent variable in the PLS-SEM structural model is described as follows: (1) A significant variable is one with an R2 value of 0.67.; (2) a variable with a 0.33 R2 value is a moderate variable; and (3) a variable with an R2 value of 0.19 is an unstable variable.
The P values of a hypothesis support its importance.The P-value must be lower than or equivalent to 0.05 to reject the null hypothesis.This study analyzed PLS-SEM data using Smart PLS-SEM software, with the indicators shown in Table 1.

Table 1: Variable Measurement
Each indicator is scored on a Likert scale ranging from 1 to 5, which indicates that the customer strongly disagrees and strongly agrees, respectively.We utilized a descriptive verification design for their study, while quantitative methodology was employed.According to Hailu (2019), informative examination is a method to explain the causal factors considered when comparing one variable to another.There are two steps in the data collection.First, we launched surveys online and offered site item guidance that led to survey questions.Then, we filter the respondents who will be chosen based on respondents who visited the city of West Java and stayed at lodging establishments in West Java.

DISCUSSION
This study distributed questionnaires to as many as 200 participants.However, 72 respondents were excluded because they do not fit the respondent criteria.In Table 2, there are 128 responses.33.1% of the respondents were women, and 66.9% were men.
As shown in Table 2, most respondents in this study were between the ages of 25 and 29.Most respondents are entrepreneurs, and the most common level of education among respondents is a bachelor's degree.IDR 25.000.000 is the highest monthly income for while the highest allowance for students is IDR 5.000.000.In addition, 68.1% of respondents reported having stayed twice at lodging establishments in West Java.All indicators and variables in the measurement model analysis show that all validity and reliability criteria for further analysis have been satisfied.Pocket money (student) per month < IDR 2.000.00010,9% > IDR 2.000.000-IDR 3.000.00045% > IDR 4.000.000-IDR 5.000.00029,1% > IDR 5.000.00015% 7 History of staying at a lodging establishment in the city of West Java Once 13,4% Twice 68,1% More than twice 18,5% Table 3 shows the coefficients of associated latent variables.Cronbach's alpha greater than 0.70 concludes the analysis of all composite reliability in the reliability table.
This table also contains the AVE values for each variable When.All AVE values used to evaluate convergent validity were higher than the suggested cut-off point for adequate validity, which is 0.5.Figure 4 shows that all R-Square are more significant than 0.02.This result shows that the predictor has a primary or only marginally significant effect on the predicted variables (Fornell & Larcker, 1998).The discriminant validity of each hidden variable is shown in Table 5.The correlation between these latent variables is lower than the square base of the average variance.The suggested standard for assessing discriminant validity is that the variance must be higher than the correlation involving the column of latent variables (Fornell & Larcker, in Yogi. S 2021).In addition, the extracted mean's square root for any hidden variable diagonal number must be higher than the column's top and bottom values.Moreover, convergent validity is indicated by several 0.5 or higher (Luckyardi S., et al. (2022).6, the correlation between the indicator and the latent variable is considered valid if the number of loading factors is higher than 0.60 (Prasetio & Sari, 2022).The seventh table provides an overview of hypothesis testing.Alternatively, the P value for each hypothesis is less than 0.05 (Prasetio & Sari, 2022).These two variables have variable effects on tourist engagement and satisfaction.According to Kervenoael et al. (2020), human service innovation affects the engagement and satisfaction of tourist.Human service innovation has the most significant impact on tourist satisfaction compared to other factors.In addition, Piccoli et al. (2017) found that technology service innovation influences tourist engagement and satisfaction; the findings indicate that the service innovation technology variable influences tourist satisfaction.According to research (So et al., 2014a;Finn's 2005Finn's , 2012;;Wang, 2011), for stabilizing the hospitality industry's lodging business sector, the human service innovation factor should not be eliminated because the personal touch of human service cannot be replicated through technology.Nevertheless, following the vision espoused in the era of Society 5.0, humans must play a role in this technology.

CONCLUSION AND RECOMMENDATION
In the era of Society 5.0, the study's results indicate that technological innovation services and tourist engagement influenced tourist satisfaction.In contrast, human innovation services, which have no direct impact on tourist satisfaction, must first be filtered through the variable measuring tourist engagement.Engagement in tourists is a moderating variable that influences tourist satisfaction.As technological and human innovation services directly experienced by tourists, they will affect tourist satisfaction.This research allows business actors in lodging establishments to create marketing plans that can boost tourist satisfaction, thereby increasing tourist trust and future return visits.A quantitative method was used to back up the research results, which involved sending online surveys to guests of motels in West Java, Indonesia, and then using PLS-SEM to analyze the data.This study concludes that the role of employee service in the hospitality industry must be partially replaced by technology.Instead, the function of technology must encompass the function of humans.This concept is in line with the goals of Society 5.0.
have thought of multidimensional customer engagement in different ways.This study uses the idea of customer engagement from So et al. (2014b) because it focuses on the service industry and gives an excellent way to measure how the idea is used.So et al. (2014b) defined customer engagement as the customer's relationship with the brand, which is shown by cognitive, emotional, and behavioral responses that go beyond the point of purchase.So et al. (2014b) stated that customer engagement can be measured by their zeal, focus, absorption, and interaction.

Figure
Figure 1: Research Framework

Figure 2
Figure 2 depicts the model of output.The graph demonstrates that every increase in technology services increases tourist engagement by 0.336% and tourist satisfaction by 0.513.Each increase in human service innovation increases tourist engagement by 0.475% but decreases tourist satisfaction by -

Table 2 :
Participant Data

Table 4 :
Measurement Model Test Result

Table 7 :
Results of Hypothesis Research