IMPROVING EMPLOYEE PERFORMANCE EFFECTIVENESS STRATEGY IN IMPLEMENTING HYBRID WORKING AT SWITCHING SERVICE COMPANIES

One of the impacts of the COVID-19 pandemic on industries in Indonesia is the implementation of strict mobility restrictions, including in the IT industry. The increased demand for online services and information technology has driven development and innovation to create new ideas in the application of information technology across various fields, such as e-commerce, online learning, work from home (WFH)


INTRODUCTION
The COVID-19 pandemic has caused significant disruptions to the national economy, including the IT industry.The impact on the IT industry includes increased demand for online services and information technology.This has driven development and innovation to create new ideas in the application of information technology across various fields, such as e-commerce, online learning, work from home (WFH), and others (Elgohary dan Abdelazyz, 2022), Al-Sakhnini (2022) describes the challenges faced by the IT industry during the COVID-19 pandemic as follows: unclear implementation of digital transformation; unstable distribution of high-speed internet networks; and inadequate monitoring of quality standards and handling of information confidentiality.
Companies operating in the service sector, including switching companies or similar IT firms, have also been affected by long-term lockdown policies and mobility restrictions.Mangalaraj et al., (2023) demonstrate how the negative effects of the pandemic forced companies to change their technology and information capabilities, often referred to as digital transformation, to better adapt to turbulence.This digital transformation has required workers to quickly adapt to new systems implemented by companies.The life cycle phase of work systems has dramatically changed over the past two years due to the COVID-19 pandemic.It was never anticipated that work systems would have to shift from office-based work to work from home (WFH) or remote working.Since the beginning of the pandemic, more companies have announced plans to permanently adopt a hybrid work system, allowing some employees to work in the office and some remotely (Rappaport, 2022).According to a U.S. survey conducted by Pew Research in October 2020 (Parker et al., 2021), it was reported that before the pandemic, only 20% of workers worked from home, but during the pandemic, this number increased significantly to 71%.
A key finding of this study is that 54% of workers want to continue working from home after the pandemic ends.
Previous studies have shown various benefits of WFH for organizations.The most important benefits for companies include increased productivity, reduced fixed costs, and organizational flexibility; for employees, the most perceived benefit is more flexible working hours (Pérez et al., 2003).Implementing the WFH system provides employees with the opportunity to perform remote work while managing family affairs (Johnson et al., 2007).Additionally, WFH can save travel costs due to reduced time needed to commute from home to the office (Tremblay dan Genin, 2007).
Despite the benefits of WFH for organizations, Ralph et al., (2020) confirm that the COVID-19 pandemic has had negative effects on the well-being and productivity of IT workers implementing WFH.The paradigm shift due to COVID-19 from traditional office spaces to virtual workplaces practiced with Work from Home (WFH) has made lifestyle important for employees, as office work and home tasks overlap (Weerarathna et al., 2022).While some studies reveal positive and negative impacts of implementing the WFH system, researchers need to conduct a deeper evaluation to understand the impact of implementing a hybrid work system in switching service companies.http://eduvest.greenvest.co.id

RESEARCH METHOD
This research was conducted at a payment system infrastructure network company from January to February 2024.Both primary and secondary data were used in this study.The primary data were obtained through an online questionnaire (via Google Forms) filled out by respondents, while the secondary data were sourced from various literature relevant to the research topic.The literature used by the researchers includes journals, news articles, official reports, and various sources with verifiable accuracy.The population for this study consists of 464 employees working under a hybrid work system.The respondents were selected using probability sampling techniques, specifically simple random sampling.The employees chosen as samples were randomly selected, giving each employee an equal chance of being chosen.The sample size was determined using the Slovin method (Tejada et al., 2012) as follow: Based on the formula for determining the sample size, the number of samples or respondents must be at least 83 employees.This study uses the Confirmatory Factor Analysis (CFA) method, implemented with SPSS version 25, and Structural Equation Modeling (SEM) with Smart-PLS.Factor analysis is a method for identifying a number of indicator variables that can maximize the correlation between indicator variables (J.Sondakh, 2018).The CFA method is used to calculate the validity test.In this validity test, an indicator is considered valid if its factor loading value is greater than 0.5 and can form a single component.A reliable instrument is one that produces consistent data when used multiple times to measure the same item (Sugiyono, 2016).The research model using CFA is illustrated in Figure 1.Santosa (2018) mentions several types of SEM, with the path analysis model being the first.This model usually consists only of observed variables.Next is the confirmatory factor analysis model, often used to test the pattern of relationships between several latent constructs.In the PLS SEM path model, variables are divided into two categories: exogenous and endogenous.Exogenous variables are those that affect other variables and are not affected by other variables, while endogenous variables are those whose values are determined by the system or other model variables.In this study, the exogenous variables are work environment, technology infrastructure, work efficiency, and job autonomy, while the endogenous variables are intrinsic motivation and performance.Based on the research hypotheses, the Smart PLS hypothesis model can be described in Figure 2, along with the following statements: H1: Work Environment has a significant impact on Employee Performance Improving Employee Performance Effectiveness Strategy In Implementing Hybrid Working At Switching Service Companies.4094 H2: Technology Infrastructure has a significant impact on Employee Performance H3: Work Efficiency has a significant impact on Employee Performance H4: Job Autonomy has a significant impact on Employee Performance H5: Job Autonomy has a significant impact on Intrinsic Motivation H6: Intrinsic Motivation has a significant impact on Employee Performance H7: Job Autonomy has a significant impact on Employee Performance through Intrinsic Motivation   The first step in PLS-SEM analysis is to test whether the data validity and reliability requirements are met.The accuracy and reliability of the data can be seen through the results of external loading from the measurement model or outer model.The next step is to analyze the R2 value indicating the strengths and weaknesses of the model.In addition, at this stage hypothesis testing is carried out using a structural model (inner model) in accordance with the hypothesis proposed.Thus, the focus of evaluation of measurement models and structural models is on metrics that indicate the predictive ability of the proposed model.

Measurement Model Analysis (Outer Model)
The evaluation of the measurement model (Outer Model) involves assessing the relationship between variables and their indicators, represented by arrows from constructs (ellipses) to several indicators (boxes).The evaluation of PLS-SEM measurement quality and the structural model focuses on metrics that indicate the model's predictive ability.The evaluation of the measurement model metrics includes two stages of testing: convergent validity and discriminant validity.
Convergent Validity aims to measure the appropriateness between the indicator results of variable measurements and the theoretical concept that explains the existence of these indicators.The convergent validity test can be evaluated in three stages: outer loadings, composite reliability, and average variance extracted (AVE).Outer loading is a table that contains the loading factor to show the degree of correlation between indicators and latent variables.The weakest loading factor accepted for validity is 0.7.The outer loadings output can be obtained from the PLS Algorithm Report in SmartPLS.
According to the criteria of Hair et al., (2022), an indicator of a variable is considered valid if it has a minimum loading factor of 0.70.The analysis results show that all indicators for each variable have loading factors greater than 0.70 (Figure 3), indicating that all indicators are considered valid.This shows that the indicators used can adequately explain their latent variables.The results of the reliability test processing on the variables WE, TI, WE, JA, P, and IM show that the Composite Reliability (CR) value for all constructs is greater than 0.7, indicating that all constructs in the destination model meet the discriminant validity criteria.Thus, the results of the Composite Reliability (CR) test show reliability.Meanwhile, the Cronbach's Alpha (CA) value for all constructs is greater than 0.7.Therefore, the results of the Cronbach's Alpha (CA) test indicate reliability.According to Kalnadi (2013), an indicator is considered valid if it has an AVE value above 0.5 or shows that all outer loading dimensions of the variable have a loading value > 0.5, so it can be concluded that the measurement meets the convergent validity criteria.Therefore, the results of the Average Variance Extracted (AVE) test are all reliable.The table above shows that the results of all instrument tests for validity and reliability indicate that all statement items meet the requirements for use in the research instrument.

Structural model analysis (inner model)
After the estimated model meets the criteria of the Outer Model, the next is the structural model testing (Inner model).Here is the R-Square value on the construct:

The Impact of Work Environment on Performance
The coefficient parameter for the work environment variable on performance is 0.316, indicating a positive influence of the work environment on performance.This can be interpreted to mean that the higher the value of the work environment, the higher the performance.An increase of one unit in the work environment will increase performance by 31.6%.The p-value is 0.000<0.05,indicating a statistically significant direct effect of the work environment on performance.This suggests that supporting work facilities and good relationships with colleagues can drive employees to achieve their performance.Conversely, an unsupportive work environment can hinder employee performance.These findings are consistent with Onsardi (2021), who found that the work environment significantly affects employee performance.The work environment is a crucial factor in creating employee performance.http://eduvest.greenvest.co.id

The Impact of Technology Infrastructure on Performance
The coefficient parameter for the technology infrastructure variable on performance is 0.119, indicating a positive influence of technology infrastructure on performance.This can be interpreted to mean that the higher the value of technology infrastructure, the higher the performance.An increase of one unit in technology infrastructure will increase performance by 11.9%.The p-value is 0.001<0.05,indicating a statistically significant direct effect of technology infrastructure on performance.This indicates that good information security creates an environment where company data and information are protected from cyber threats and leakage risks.These research findings align with Primawanti dan Ali ( 2022), who found that information technology is one of the factors that can influence performance in a company.

The Impact of Work Efficiency on Performance
The coefficient parameter for the work efficiency variable on performance is 0.319, indicating a positive influence of work efficiency on performance.This can be interpreted to mean that the higher the value of work efficiency, the higher the performance.An increase of one unit in work efficiency will increase performance by 31.9%.The p-value is 0.000<0.05,indicating a statistically significant direct effect of work efficiency on performance.This suggests that understanding the background and purpose of the work can help individuals better comprehend their tasks and responsibilities.Additionally, targeted work duration according to the schedule will accelerate the achievement of work targets according to the planned timeline.These research findings are supported by Syam (2020), who stated that there is a significant effect of work efficiency on employee performance.

The Impact of Job Autonomy on Performance
The coefficient parameter for the job autonomy variable on performance is 0.032, indicating a positive influence of job autonomy on performance.This can be interpreted to mean that the higher the value of job autonomy, the higher the performance.An increase of one unit in job autonomy will increase performance by 3.2%.The p-value is 0.095<0.05,indicating that the effect of job autonomy on performance is not statistically significant.This shows that the increase in job autonomy experienced by employees does not affect their performance.Creating freedom and flexibility while implementing a hybrid work system does not motivate employees to achieve their performance.These findings contrast with Prasetyo (2022), who found a positive and significant relationship between job autonomy and performance.

The Impact of Intrinsic Motivation on Performance
The coefficient parameter for the intrinsic motivation variable on performance is 0.370, indicating a positive influence of intrinsic motivation on performance.This can be interpreted to mean that the higher the value of intrinsic motivation, the higher the performance.An increase of one unit in intrinsic motivation will increase performance by 37%.The p-value is 0.000<0.05,indicating a statistically significant direct effect of intrinsic motivation on performance.Intrinsic motivation includes internal drives such as personal satisfaction, interest, or personal achievement.When someone feels intrinsically engaged with their work, their performance tends to increase significantly.These findings are confirmed by Lee dan Hidayat (2018), who found that intrinsic motivation has a dominant effect on employee performance.

The Impact of Job Autonomy on Intrinsic Motivation
The coefficient parameter for the job autonomy variable on intrinsic motivation is 0.395, indicating a positive influence of job autonomy on intrinsic motivation.This can be interpreted to mean that the higher the value of job autonomy, the higher the intrinsic motivation.An increase of one unit in job autonomy will increase intrinsic motivation by 39.5%.The p-value is 0.000<0.05,indicating a statistically significant direct effect of job autonomy on intrinsic motivation.Employees are aware of the workload that needs to be completed according to the set goals and objectives.Therefore, employees strive to complete their tasks on target, whether at home or in the office.This is supported by the analysis of Diamantidis dan Chatzoglou (2019), which shows that job autonomy significantly influences intrinsic motivation as a factor related to employees.

The Impact of Job Autonomy on Performance through Intrinsic Motivation
The coefficient parameter for the job autonomy variable on performance through intrinsic motivation is 0.146, indicating a positive indirect influence of job autonomy on performance through intrinsic motivation.This means that the higher the intrinsic motivation, the higher the performance through intrinsic motivation.An increase of one unit in intrinsic motivation will increase performance through intrinsic motivation by 14.6%.The p-value is 0.001<0.05,indicating a statistically significant indirect effect of job autonomy on performance through intrinsic motivation.The analysis explains that intrinsic motivation can be fostered by satisfying individual autonomy needs and by employees experiencing control over their work.Previous research has stated that intrinsic motivation, as a factor related to employees, serves as a mediator between job autonomy as a job factor and employee performance (Diamantidis dan Chatzoglou, 2019).

Strategies for Enhancing Employee Performance Effectiveness
After screening titles, 105 studies were analyzed regarding theories, approaches, factors, relationships, impacts, and implementations related to strategies for enhancing employee performance effectiveness, with six journal articles aligning with the research topic.The following recommendations are made for PT XYZ or companies implementing a hybrid work system to enhance employee performance through positively influencing factors: a) Support employees in achieving job satisfaction.b) Implement effective workplace safety policies and practices.c) Develop employees' skills, competencies, and knowledge.d) Design fair and transparent compensation programs.e) Create a motivating work environment that encourages employees to contribute their best.
Managerial Implications http://eduvest.greenvest.co.id The results of this study show that the work environment, technology infrastructure, work efficiency, and intrinsic motivation are factors that must be considered to achieve performance in the implementation of a hybrid work system at PT XYZ.Management strategies that can be applied include listening, providing constructive feedback, and facilitating career development.These measures are expected to create conditions where employees feel valued and motivated.Flexibility in balancing work and personal life, empowering employees through responsibility and autonomy, and recognizing their achievements are all crucial in building a positive work culture.Additionally, supporting mental health, effective conflict management, and employee welfare policies are foundational for creating a supportive work environment and enhancing employee satisfaction.

CONCLUSION
Based on the results and discussion presented, this study concludes as follows: 1.The results of CFA in this study aimed to confirm the indicators/items already existing towards the variables of work-life balance and work stress.The results confirmed that all indicators/items can explain their latent variables.2. The results of SEM PLS analysis revealed that the work environment, technology infrastructure, work efficiency, and intrinsic motivation significantly influence employee performance in the implementation of hybrid work systems.Meanwhile, job autonomy does not directly affect performance, and job autonomy significantly influences performance through intrinsic motivation.3. Based on the results of the Systematic Literature Review using the PRISMA method, recommendations for strategies to improve employee performance effectiveness in the implementation of hybrid work systems include assisting employees in achieving job satisfaction, implementing effective workplace safety policies and practices, designing fair and transparent compensation programs, enhancing skills and competencies, and creating a work environment that motivates employees in achieving and improving performance.The suggestion in this study is for management or company leaders to provide flexible work options, such as more open working hours, to help employees better manage personal and professional life challenges.Furthermore, it is important to ensure that the tasks assigned to employees are within their capacity.Work overload can trigger high levels of stress.Since the hybrid work system will continue to be implemented, companies should regularly evaluate existing worklife balance programs and listen to feedback from employees.

Figure 1 .
Figure 1.Research model using Confirmatory Factor Analysis (CFA) In figure 1 it can be seen that the model involves two factors with five indicators in each factor and there are 10 statement items, with the following description: Variabel X1 = Work-life balance Indicator 1= Responsibilities between work and family X1.1= able to create a balance between work and personal life X1.2=able to behave professionally and try to maintain household harmony Indicator 2= Division of engagement between work and family X1.3= has limits on interaction in the work and family environment X1.4= set aside time outside of work for family Indicator 3= Personal contribution to work and family X1.5= committed to advancing the company and ensuring household needs are fulfilled X1.6= reliable for solving work and household problems Indicator 4= Feeling satisfied with the work role X1.7= satisfied with the work X1.8= get a proper reward for work achievements Indicator 5= Career improvement and family roles

Figure 2 .
Figure 2. SEM Hypothesis ModelIn figure2it can be seen that the model involves six factors with several indicators in each.The description of the indicator is as follows:The work environment factor denoted by LK has five indicators, namely LK1 workspace, LK2 infrastructure, LK3 work tools, LK 4 relationships between employees, and LK5 work interactions.Furthermore, the technology infrastructure factor denoted by IT has three indicators, namely IT1 system reliability, IT2 information security, and IT3 system financial efficiency.Next, the work efficiency

Table 3
shows an R-Square value of 0.974 for the Performance (Y) construct, indicating that the variables work environment, technology infrastructure, work Aprinda Efendi Daulay, Harianto, Zenal Asikin