The Influence of Employee Wellbeing, HRD Practices and Organizational Culture on Commitment and Turnover Intention in the Aviation and Airport Industry

Sulisthia Fitriani Dewi¹, Justine Tanuwijaya²*, Andreas Wahyu Gunawan³

^1,2,3Universitas Trisakti, West Jakarta, DKI Jakarta, Indonesia

Email: [email protected]^1*

Abstract

This study aims to analyze the influence of employee well-being, HRD practices, and organizational culture on commitment and turnover intention in the aviation and airport industry in Indonesia. The research method uses primary data collected through a Google Forms questionnaire and analyzed using Structural Equation Modeling (SEM) with the AMOS application. The results indicate that employee well-being positively affects commitment, while HRD practices and organizational culture do not positively affect commitment. Additionally, commitment positively affects turnover intention. The implications of this research suggest that companies in the aviation and airport industry should develop optimal strategies to reduce turnover intentions by enhancing employee well-being and supporting meaningful work experiences.

Keywords: Employee Wellbeing, HRD Practices, Organizational Culture, Commitment, Turnover Intention.

This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International

INTRODUCTION

Mergers and acquisitions (M&A) are business strategies in which two companies merge (merger) or one company buys another company (acquisition) (Junni & Teerikangas, 2019; Koi-Akrofi, 2016; Kumar & Kumar, 2019; Malik et al., 2014; Ray, 2022). M&A can have a major impact on various aspects of business, economy, and society. Jain et al. (2024) explained that a merger is the merger of two entities to form a new company, usually with the aim of strengthening competitiveness and expanding operational capacity. They emphasized the importance of cultural fit and vision in the successful merger. Meanwhile, an acquisition according to Assiri (2021) is a process in which one company takes over another company and establishes itself as the new owner. The acquisition can be friendly or hostile, depending on whether the target company agrees to the takeover.

PT Aviasi Pariwisata Indonesia (Persero) or InJourney officially formed two subholdings, namely PT Angkasa Pura Indonesia (InJourney Airports) and PT Integrasi Aviasi Solusi (InJourney Aviation Services/IAS) as a transformation step in the aviation and airport industry.

InJourney Aviation Services/IAS is the most comprehensive service provider in the aviation sector that offers efficient ground handling solutions, integrated logistics, premium hospitality services, and reliable operational support at airports. This company was inaugurated on January 4, 2024 which is the result of the transformation of PT Angkasa Pura Kargo and 8 subsidiaries under Angkasa Pura 1, Angkasa Pura 2, and Garuda Indonesia.

The merger and acquisition phase often creates uncertainty and concern among employees which can have a significant impact on their level of commitment to the organization as well as their intention to switch jobs (Angwin et al., 2016; Galpin, 2014; Kotter et al., 2021; Krug et al., 2014; Schuler & Jackson, 2017).

Employee wellbeing can affect employee productivity and satisfaction levels which in turn affects their commitment to the organization (Krekel et al., 2019; Maharani et al., 2020; Meyer & Maltin, 2010). HRD practices also play an important role in providing support and development for employees that can strengthen their commitment (Azzahra et al., 2024). Meanwhile, organizational culture can affect how employees respond to change and manage uncertainty, which ultimately affects the level of turnover intention (Cullen et al., 2014; Metwally et al., 2019; Peltokorpi et al., 2015).

This research could delve deeper into how these factors interact with each other and how management can manage them effectively during crucial phases such as mergers and acquisitions.

Based on the description above, the researcher will conduct a study entitled "The Influence of Employee Wellbeing, HRD Practices and Organizational Culture on Commitment and Turnover Intention in the Aviation and Airport Industry".

RESEARCH METHODS

Data Collection Methods

In this study, primary data is used, namely data collected on spot, analyzed and compiled to find solutions to the problems discussed in the research. The data collection method uses a Google Forms questionnaire (Sekaran & Bougie, 2016).

1. Field Research

The questionnaire was used to spread the questions of the variables tested in this study. The dissemination will be carried out online by sending a Google Forms link on social media.

2. Library Research

In literature research, secondary data or data sources are obtained in-spot/directly from various scientific journal information and previous research results that are related and relevant to the topic being researched.

Sample Withdrawal Method

The following study design uses a non-probability sampling technique, i.e. respondents in the population do not have the same opportunity to be selected as a sample subject. The technique used is purposive sampling, where the sample is determined based on the criteria set by the researcher (Sekaran & Bougie, 2016). The criteria for respondents in this study are employees who work at PT. Integration of Aviation Solutions Group and other companies engaged in Aviation and Airports. This study collects primary data obtained directly from primary sources (Sekaran & Bougie, 2016). Data collection began from July 16, 2024, to July 18, 2024, with a sample of 210 respondents. This is in accordance with what was stated by Jr. et al. (2017), that the sample should be 5 times the number of indicators for each variable or at least 200 respondents (Jr. et al., 2017). The questionnaire is created through Google Forms and shared via WhatsApp.

Data Testing Methods

The purpose of testing a research instrument is to ensure that the questions used in the study can calculate the variables being studied precisely and accurately (Sekaran & Bougie, 2016). Furthermore, a validity and reliability test was carried out on the questions used.

Data Analysis Methods

The data of this study was processed based on the results of a questionnaire that was distributed in a valid and reliable manner. Data processing was carried out using the Structural Equation Model (SEM) and processed using the Analysis of Moment Structures (AMOS) application.

SEM can check for measurement errors. This technique can also be used to analyze the influence of one variable on another variable or an equation called a structural equation.

Before conducting a hypothesis analysis, an overall model suitability test should be performed first to ensure that the model used provides a comprehensive picture of the causality influence. According to Jr. et al. (2017) goodness-of-fit testing can be done by measuring the following criteria:

1. Absolute fit measures, which are measurements of the entire fit model (both structural models and measurement models). The criteria are based on probability values, RMSEA (Root Mean Square Error of Approximation), and GFI (Goodness of Fit Index).

2. Incremental fit measures, which are measures used to compare the proposed model with other models identified by the researcher. The criteria are based on the values of NFI (Normed Fit Index), TLI (Tucker Lewis Index), RFI (Relative Fit Index), CFI (Comperative Fit Index), IFI (Incremental Fit Index).

3. Parsimonious Fit Measures, which are adjustments made to the fit size so that it can be compared between models with the recommended number of coefficients, namely the lower limit of 1 or the upper limit of 5. The criteria are based on the AGFI (Adjusted goodness-of-fit) value.

Figure 1. Diagram Structural Equation Modeling

Source: Supriyati (2021)

RESULT AND DISCUSSION

Description of Research Data

Table 1. Profile Response (n=210)

Profil Respond

Characteristic

Frequency

Percentage

Gender

Man

Woman

Total

139

210

33,8%

66,2%

100%

Education

Diploma

Sarjana

Postgraduate

Doctor

Total

155

210

5,2%

73,8%

20%

100%

Age

20 – 29 years old

30 – 39 Years

40 – 49 years old

> 50 Years

Total

110

210

52,4%

40,5%

7,1%

100%

Working Period

˂ 1 Year

1 – 3 Years

3 – 5 Years

> 5 Years

Total

159

210

1,4%

75,7%

16,7%

6,2%

100%

Sum

210

100%

Source: Data processed

The respondent profile table shows that the majority of respondents are women, with a percentage of 66.2% or 139 people. Meanwhile, male respondents amounted to 33.8% or 71 people.

The data processing results showed that most respondents had a bachelor's degree, which was 73.8% or 155 people. Respondents with the last postgraduate education reached 20% or 42 people, while those with a diploma education amounted to 5.2% or 11 people, and those who had a doctoral degree amounted to 1% or 2 people.

The results of data processing showed that the majority of respondents were in the 20- to 29-year-old range, 52.4% or 110 people. Respondents aged 30 to 39 reached 40.5% or 85 people, and those aged 40 to 49 were 7.1% or 15 people.

Based on the data processing results, 75.7% of respondents had a working period of between 1 and 3 years, or 159 people. Respondents with a working period of 3 to 5 years reached 16.7%, or 35 people, while those with a working period of more than 5 years were 6.2%, or 13 people. The respondents with a working period of less than 1 year were 1.4% or 3 people.

Descriptive Statistics

Descriptive statistical analysis was carried out to measure the average value, standard deviation, minimum value, and maximum value to see the respondents' perceptions, responses, and responses to the research variables. The following are the details of the descriptive analysis of the variables in this study.

Table 2. Descriptive Statistics of Employee Wellbeing

No.	Indicator	Mean	Std. Deviation
1	I felt that my boss was unfair to me.	1.7300	.66279
2	The salary increase received is too small and rare.	1.8050	.74143
3	I feel that my work is underappreciated.	1.7450	.69454
4	I feel that my efforts are not appreciated.	3.5600	.53651
5	I feel that the purpose of this company is not clear.	1.7500	.69996
6	I often feel that I am not informed about the company's development.	2.0700	.34012
7	Sometimes I feel like my work is less meaningful.	2.0800	.36643
8	I often have to work harder because of the incompetence of my coworkers.	3.5900	.53227
9	There are too many conflicts in the workplace.	1.7950	.68946
10	My workload is very high.	2.0400	.99162
11	I have a lot of administrative work.	3.6400	.55853
Total Mean		2.3459	.61942

Source: AMOS Output

Table 3. Descriptive Statistics of HRD Practices

No.	Indicator	Mean	Std. Deviation
1	The company provides personal development plans.	3.9550	.25158
2	The company provides training for career advancement.	4.6200	.58935
3	Training for employees is fully supported.	4.4000	.60151
4	Employee career management programs are supported by the company.	4.4900	.64962
5	The company has a systematic program to assess employees' abilities and interests.	4.4500	.57371
6	Employees get the training they need to advance their careers.	4.4550	.60813
7	The company provides counseling and career planning assistance for employees.	4.2150	.54797
Total Mean		4.3693	.54598

Source: AMOS Output

Table 4. Descriptive Statistics of Organizational Culture

No.	Indicator	Mean	Std. Deviation
1	Our company has a very close and familial culture, where team members support each other.	4.4100	.57755
2	Our company is very dynamic and encourages employees to take measurable risks.	4.4750	.57535
3	Our company is highly structured and controlled, with formal procedures governing the work of employees.	4.2450	.51605
Total Mean		4.3767	.55632

Source: AMOS Output

Table 5. Descriptive Statistics of Commitment

No.	Indicator	Mean	Std. Deviation
1	One of the main reasons I stayed with this company was because I believed in the importance of loyalty and felt a moral obligation to stay afloat.	4.4650	.55707
2	I was taught to appreciate the value of loyalty to one company.	4.5200	.54873
3	Things are better when employees stay in one company throughout their careers.	4.4300	.63015
4	I recommend this company to others as a great place to work.	3.9700	.19823
5	I am proud to be a part of this company.	4.5950	.52186
6	I want to continue working at this company and see it as a lifelong workplace.	4.4550	.57413
7	I am happy to choose this company as my place of work.	4.4900	.54901
8	If I am given the opportunity to choose a job again, this company will remain my priority.	3.9600	.19645
9	I consider the future and fate of this company as a part of me.	4.4350	.53592
10	I believe that this company is the best place to work for me.	3.9800	.14035
Total Mean		4.3300	.44519

Source: AMOS Output

Table 6. Descriptive Statistics of Turnover Intention

No.	Indicator	Mean	Std. Deviation
1	I feel that my future in this company may not be so promising.	2.0850	.38538
2	I often consider resigning from my current position.	1.5150	.73654
3	I am seriously considering resigning from my job.	1.6200	.75395
4	I will most likely leave this company and work elsewhere within the next year.	1.6600	.77291
5	I am actively looking for other job opportunities.	1.6450	.74952
6	Once I get a better job opportunity, I will leave this company.	1.5650	.76070
Total Mean		1.6817	.69317

Source: AMOS Output

Data Analysis

Table 7. Hypothesis Test Results

Hypothesis	Estimate	P-Value	Results
H1: There is a positive influence of Employee Wellbeing on Commitment	.959	***	H1 supported
H2: There is a positive influence of HRD Practices on Commitment	.309	.529	H2 is not supported
H3: There is a positive influence of Organizational Culture on Commitment	-.155	.760	H3 is not supported
Q4: There is a positive influence of Commitment on Turnover Intention	-.849	***	H4 supported

Source: AMOS Output

SEM can check for measurement errors. This technique can also be used to analyze the influence of one variable on another variable or an equation called a structural equation.

Before conducting a hypothesis analysis, an overall model suitability test should be performed first to ensure that the model used provides a comprehensive picture of the causality influence. According to goodness-of-fit testing, it can be done by measuring the following criteria (Jr. et al., 2017).

Absolute fit measures, which are measurements of the entire fit model (both structural models and measurement models). The criteria are based on probability values, RMSEA (Root Mean Square Error of Approximation), and GFI (Goodness of Fit Index).

1. Incremental fit measures, which are measures used to compare the proposed model with other models identified by the researcher. The criteria are based on the values of NFI (Normed Fit Index), TLI (Tucker Lewis Index), RFI (Relative Fit Index), CFI (Comperative Fit Index), IFI (Incremental Fit Index).

2. Parsimonious Fit Measures, which are adjustments made to the fit size so that it can be compared between models with the recommended number of coefficients, namely the lower limit of 1 or the upper limit of 5. The criteria are based on the AGFI (Adjusted goodness-of-fit) value.

Table 8. Hasil Goodness of Fit Model

Measurement Type	Measurement	Value	Recommended Admission Limits	Conclusion
Absolute fit measures	p-value	0,000	≥ 0,05	Poor Fit
	RMSEA	0,056	≤ 0.08	Goodness of Fit
	GFI	0,830	≥ 0,90	Marginal Fit
Incremental fit measures	NFI	0,789	≥ 0,90	Poor Fit
	TLI	0,894	≥ 0,90	Marginal Fit
	RFI	0,765	≥ 0,90	Poor Fit
	CFI	0,905	≥ 0,90	Goodness of Fit
	YOUTH	0,907	≥ 0,90	Goodness of Fit
Parsimonius fit measures	AGFI	0,798	GFI ≤	Goodness of Fit

Source: AMOS Output

Based on the results of the goodness-of-fit test, it can be seen that the type of measurement of absolute fit measures, the measurement value of the probability value of P-value shows the value of poor fit, RMSEA and GFI show the value of good-of-fit and marginal fit. For the type of measurement of incremental fit measures, the measurement from NFI and RFI get a poorfit fit value, TLI shows a marginal fit, while CFI and IFI get a good-of-fit value. The type of parsimonius fit measures is declared good-of-fit by looking at the value from AGFI and showing a value that meets the criteria below the GFI value. According to the hypothesis, if one of the criteria of goodness-of-fit is met, then the model can proceed to the hypothesis testing stage (Hair, Black, et al, 2019)

Discussion of Research Results

The Effect of Employee Wellbeing on Commitment

Based on the results of the hypothesis test in this study, the influence of Employee Wellbeing on Commitment gets a p-value of 0.000 or has a significance value of < 0.05 with an estimated value of 0.959. This result implies that Employee well-being has a positive effect on Commitment. This shows that the higher the employee well-being felt by employees working in the aviation and airport industry in Indonesia, the higher the commitment will be.

The Influence of HRD Practices on Commitment

Based on the hypothesis test results in this study, the influence of HRD Practices on Commitment gets a p-value of 0.529 or has a significance value of > 0.05 with an estimated value of 0.309. These results imply that HRD Practices do not have a positive effect on Commitment.

The Influence of Organizational Culture on Commitment

Based on the results of the hypothesis test in this study, the influence of Organizational Culture on Commitment gets a p-value of 0.760 or has a significance value of > 0.05 with an estimated value of -0.155. These results imply that Organizational Culture does not have a positive effect on Commitment.

The Effect of Commitment on Turnover Intention

Based on the results of the hypothesis test in this study, the influence of Commitment on Turnover Intention gets a p-value of 0.000 or has a significance value of < 0.05 with an estimated value of -0.849. This result implies that Commitment has a positive effect on Turnover Intention. This shows that the higher the Commitment felt by employees of the Aviation and Airports Industry in Indonesia, the more influential it will be on the Turnover Intention that will occur.

CONCLUSION

Based on the research results, it can be concluded that Employee well-being has a positive effect on Commitment. This indicates that the higher the Employee well-being experienced by employees working in the Aviation and Airport Industry in Indonesia, the higher the level of Commitment will be. However, HRD Practices and Organizational Culture do not have a positive effect on Commitment. Additionally, Commitment has a positive effect on Turnover Intention, indicating that the higher the Commitment felt by employees in the Aviation and Airport Industry in Indonesia, the more it will influence Turnover Intention.

The implications of this research analysis are expected to benefit interested parties, particularly companies in the aviation and airport industry, in developing optimal strategies to reduce turnover intentions. This research is an important knowledge source for companies and their management. Companies are often more focused on saving costs generated from HRD practices. This research provides crucial insights for managers to understand what factors affect employees' perception of Employee well-being and their meaningful work-related experiences so they can feel happier at work and have a strong commitment to their jobs and the company. Consistent support and control from superiors are effective work resources for improving Employee well-being. Furthermore, this research helps HR practitioners design an environment that optimizes employee development and commitment to the company. Employees are more likely to commit to their roles and duties when they find the company they work for interesting. Implementing a strict hiring procedure will aid in selecting suitable candidates for the job, increasing commitment by choosing individuals with the appropriate expertise, support, and personality traits.

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Copyright holders:

Sulisthia Fitriani Dewi, Justine Tanuwijaya, Andreas Wahyu Gunawan (2024)

First publication right:

AJEMB – American Journal of Economic and Management Business