The Effect of Self-Efficacy on Student's Work Readiness in the Tourism Study Program in Java-Bali

Work Readiness is becoming a prioritizing factor for students who graduated in the era of Society 5.0, especially in the tourism industry. One of the determinant elements is Higher Order Thinking Skills and Digital Dexterity. This study is intended to analyze the factors that affect student work readiness; these factors include the ability to think at a high level in the stages of thinking according to Taxonomy Bloom, having a Proactive Personality and mastering digital agility. Proactive Personality in this research was tested as the dominant factor of work readiness. The influence of these factors is strengthened by strong Self-Efficacy, which will help students to prepare for working in the industry. This study put focuses on Hospitality Self Efficacy and Using a descriptive quantitative method, analysis by SmartPLS 3.0, the data from 431 respondents of university students who are members of HILDIKTIPARI (Association of Indonesian Tourism Higher Education Institutions) in Java and Bali have been analyzed, and the results proved that Self-Efficacy as mediation, is able to play a full-mediation role in influencing the relationship between digital agility and student work readiness.


Introduction
Graduating students of tourism education currently face difficulties in having a career because there are fewer job vacancies in the tourism industry (Lidyana, 2021). This was triggered by the Covid 19 pandemic that collided with a declining economic sector and affected graduates looking for job vacancies (Holt-White & Montacute, 2020). The sector most affected by the COVID-19 pandemic is the hotel and restaurant industry; according to data from the Indonesian Hotel and Restaurant Association (PHRI), since March 2020, the number of hotel and restaurant employees who have been laid off has reached 90%, or around 8.1 million employees ( Wardoyo, 2020).
The Covid 19 pandemic also has an impact on applied of Human Resources management, especially in the field of recruitment and selection. During the Covid-19 crisis, most companies applied limited recruitment and selection processes and even did an employee layoff strategy (Mwita, 2020). Covid 19 poses new challenges for job seekers and also for human resource practitioners in carrying out the role of recruitment and selection (International Labour Organization, 2020). Based on a survey conducted in 2020 by YouGov, the United Kingdom's independent survey of employers, 61% of the company in the UK are close to hiring employees, and the rest continue to hire in limited numbers and emphasize the online job (Holt-White & Montacute, 2020). The delay in hiring employees in the Indonesian tourism sector during the pandemic happened as well; the company decided even to reduce the employee. According to the statement of the Minister of Manpower Ida Fauziyah, in 2020, 1.5 million workers were affected by COVID-19, approximately 10% (10 percent) or around 150,000 workers were terminated, and about 90 percent have been suspended employee (Fauzia, 2020).

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The hospitality industry in Indonesia is in a phase of stagnation; many hotels closed operations, as reported by CNBC Indonesia; the Hospitality industry (hotels and restaurants) is the most affected sector by the pandemic, according to Agustino et al. (2020), based on research on 100 respondents as employees of different companies and industries in Indonesia, 55 companies closed in recruiting process, and approximately 15% of recruitment, organizational development, and employee development have been cancelled. Besides the Covid-19 pandemic, the emergence of the era of the 'super-smart society' (Society 5.0), which is a technology-based society, is a challenge for tourism graduates who want to have a career in the tourism industry; they are required to have global competitive abilities, such as technology/digital skill, social skill (leadership and people management), higher cognitive skills, they are all becomes determinant factors of work readiness (Bughin et al., 2018).
The concept of Society 5.0 is collaborating between 'tangible space' and 'virtual space' by utilizing Information Communication and Technology (ICT). The tourism and Hospitality industry applied Technological innovations; to meet customer demand and increase industry competitiveness; the focus of technological innovation in the tourism and hospitality industry is to create customers memorable experiences (Lam & Law, 2019). According to research conducted by the McKinsey Global Institute in 2018, it was found that the required recruitment is for prospective employees who have technological skills. It is estimated that the demand for digital skills and digital mastery will increase to 55% by 2030 (Bughin et al., 2018). Technology and digital skills have become important requirements in employee recruitment in almost all industries, including the hospitality and tourism industries, because they are related to the products, work processes, and services provided by the company (Gilch & Sieweke, 2021).
The Hospitality and Tourism Industry in Indonesia is growing very rapidly with the support application of technology in the tourism sector. The activities of searching and sharing through digital devices have reached 70% of the total customer activities. Technology in the tourism sector can influence and conduct tourism activities since they have planned a trip, while on a trip, and when they return from the trip (Rizkinaswara, 2019).
The purpose of this study was to analyze the relationship between Higher Order Thinking Skills, Proactive Personality, and Digital Agility through Self-Efficacy on Job Readiness.

Method
This study uses a quantitative descriptive method with hypothesis testing. According to Ghozali (2018), descriptive quantitative methods provide an overview of descriptive statistics used to describe the sample data profile. Then the sample data is tested for hypotheses to determine the effect of the relationship between variables using statistical analysis tools. The analytical tool used in this research is SmartPLS version 3.0. The data collected was taken from the research subject (unit of analysis) students of the tourism study program. An online questionnaire was applied to obtain primary data from 431 students majoring in Tourism, using the Stratified Random Sampling method.
Data analysis methods used in this study are: • Descriptive Statistics (Univariate). According to Ghozali (2018), descriptive statistics are used to analyze data by providing a description or descriptive of data seen from the average value ( mean), maximum value, minimum value, and standard deviation. Descriptive Statistics is a data analysis technique that provides an initial description by describing the collected data, seen from the mean (average), maximum -minimum, and standard deviation values, without making conclusions that apply to the general public. is a multivariate analysis technique that consists of factor analysis and regression analysis to test the effect between variables on the research model. In this study, a partial correlation analysis (Partial Least Square) was conducted to measure the relationship between several independent variables and one dependent variable. One of the independent variables was the mediating variable (Sarstedt et al., 2017).

Validity test
This test is part of the instrument test, intended to measure the accuracy of an instrument in measurement so that it is known whether the existing indicators can represent the variable (Ghozali, 2021). Test the validity using SmartPLS version 3.0 by doing a test Convergent Validity to find out the results of the Factor Loading of each indicator on its Variables, with a minimum value of 0.7 and a minimum Average Variant Extracted (AVE) value of 0.5. While the Discriminant Validity test was carried out by looking at the results of the Fornell Larcker Criterion test, namely the square root value of AVE compared to the correlation value between constructs (the square root value of AVE must be higher than the correlation value between constructs), and Cross Loading, namely the discriminant validity approach by looking at the correlation between indicators and other indicators, compared to the correlation between indicators and the indicators themselves (the correlation value between indicators and their indicators must be greater than that of other constructs).

Reliability Test
Test Reliability is done to determine how effective and consistent the instrument used in this research model is to capture and reveal the actual condition of the object under study by looking at the Cronbach Alpha value (a value that indicates a correlation between one item and another). The value of Cronbach's alpha ranges from 0 to 1, and the closer Cronbach's alpha is to a value of 1 (one), the better the reliability of the measuring instrument. In addition to Cronbach's alpha, to be said to be Reliable by looking at the Composite Reliability value, both values must be above 0.70 (Hair et al., 2019)

Model Fit Test
This test is intended to determine how appropriate the model used in this study is. In SmartPLS version 3.0, to see the model's suitability by looking at the value of the Norm Index Fit (NFI) (Setiawan et al., 2021). In addition, by looking at the R-square value, which shows the independent variables' contribution simultaneously influencing the dependent variable's value, the R-square value ranges from 0 to 1; the closer the value to 1, the better the model used.

Multicollinearity test
This test was conducted to identify the measure of the degree of multicollinearity contained in a set of multiple regression variables by ensuring the Variance Inflation Factor (VIF) value is below 5.

Hypothesis Test
The test was conducted to explain the relationship between the variables in this research model by looking at the results of the statistical calculation of Path Coefficient, T-Statistic,

Validity Test and Reliability Test
Factor Loading values used in the validity test are those above 0.7, so indicator items with values below 0.7 will be omitted (Wr1, Wr3, Wr5, Wr6, Wr8).
While the Average Variant Extracted (AVE) value is at least 0.5, a measure of the degree of validity can be seen in the From the two tables, it is stated that the results of the validity and reliability tests can be stated that the instrument used has the required validity and reliability values.

Model Fit Test
The value of the Normed Fit Index (NFI) achieved in this study can be seen in the following table: The NFI calculation shows a value of 0.800 (close to number 1), so the model used in this study is declared feasible to explain the facts on the ground.
While the results of the calculation of R2, the R-square value ranges from 0 to 1, the closer the value to 1, the better the model used.

Multicollinearity test
The results of this test indicate that the model in this study is free from the issue of collinearity, where the Variance Inflation Factor (VIF) value is below the value of 5.

Direct Influence
The results of the direct influence analysis show that Higher Order Thinking Skill has a positive (0.177) and significant (3.5 43 ) effect on Self Efficacy, and Proactive Personality has a positive (0.452) and significant (7. 837 ) effect on Self Efficacy. Digital Dexterity has a positive effect (0.232 ) and significant ( 4.003 ) on Self Efficacy, so it can be concluded that the results of the analysis of the direct influence of the three independent variables (X) on the dependent variable/mediator (Z), support hypotheses 1, 2 and 3.

Indirect Influence
The indirect influence analysis shows that there is a positive (0.048) and significant (2.631) effect between Higher Order Thinking Skills through Self Efficacy, and between Proactive Personality on Work Readiness through Self Efficacy, there is a positive (0.124) and significant (4,588) effect. ), while the indirect effect between Digital Dexterity on Work Readiness through Self Efficacy there is a positive (0.063) and significant (0.003) effect.
By comparing the direct and indirect effects of Digital Dexterity on Work Readiness, it can be seen that the mediator variable (Self Efficacy) plays a role in changing the effect of significance. This is in line with the research of Ahmed et al. (2020), which states that Digital Agility will affect Work Readiness if put together through Self-Efficacy. So it can be concluded that Self Efficacy has a full role in the relationship between Digital Dexterity and Work Readiness.

Practical implications
This research is expected to have a practical impact on educational institutions/academics as providers of the workforce (graduates) who are ready to enter the world of work, especially related to non-technical skills that must be mastered by students. For industries that focus on developing quality human resources, the results of this study can assist companies in preparing workforce planning and employee development models focusing on competency (employee development competency-based) to help companies realize their highest achievements.

Theoretical Implications
This research is expected to be one of the references in preparing the curriculum, especially in the field of tourism, considering the demands of the industry in finding workers are getting tighter. It is hoped that the determinants of Work Readiness can be identified more carefully so that it becomes the basis for teachers in the tourism sector to be more creative in developing teaching modules.

Suggestion
• To use other independent variables to measure the effect on Work Readiness, such as Social Support.

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Moderator variables can also be used to determine the strength of the role of independent variables on dependent variables, such as the Hospitality Service Attitude variable. Publisher's Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.