J urnal M anajemen (E disi E lektronik ) Performance of ASN (Government Employee) in Implementation of the Work from Home Policy in the Regional Secretariat of Bogor Regency

aims to see the effect of readiness on performance, readiness to change on readiness for acceptance of technology, acceptance of the technology on performance during from of governance employee in the Regional of Regency. This study 136 respondents, with the sampling method used in this study is non-probability sampling. The sampling technique used is Voluntary Sampling. The Structural Equation Model-PLS analysis results showed that readiness to change had a significant effect on performance, readiness to change had an effect on readiness for technology acceptance, readiness for technology acceptance had a significant effect on performance, and there was no difference between the performance of men and women at work from home


INTRODUCTION
Along with the rapid development of technology and knowledge, human work in various activities has become more effective and efficient. This is because information technology can process work quickly and accurately. Information technology is able to create convenience in working in various locations (Yuliana 2014). Modern technology makes working from home possible for many people. This is what drives the application of work from home or work from home (WFH) to begin to develop in various companies as a result of the development of information and communication technology. Work from home has been proven to be able to encourage employees to strike a balance between work commitments and personal life. Work from home also creates a number of advantages such as increasing employee productivity through the creation of a work atmosphere and a relaxed atmosphere (Rupietta et al. 2016). Work from home is considered as a new form of flexible work system for many organizations and companies.
Work from home is further strengthened by the coronavirus disease 2019 (Covid- 19) pandemic. The COVID-19 pandemic has forced most organizations, both companies and public organizations, to implement a work from home pattern for their employees. Work from home has been used by companies to ensure the safety of their employees and to provide continuity of economic activity. In government agencies, implementing Work from home is a new thing for state civil servants (ASN). Work from home is implemented due to the regulation of the Minister of State Apparatus Empowerment and Bureaucratic Reform through Circular Letter number 19 of 2020 concerning Adjustment of the Work System of State Civil Apparatus in Efforts to Prevent Covid-19 in Government Agencies. The regulation emphasizes every government agency at the national or regional level to implement Work from home on all state civil servants, including the Bogor district government.
The Work from Home policy was also implemented by the Bogor Regency Government in accordance with the Regent's Decree No. 16 of 2020 regarding the implementation of largescale social restrictions (PSBB) in handling the 2019 corona virus (covid-19) in Bogor Regency. The large number of employees in the local government of Bogor Regency and the demands of work with the application of work from home become a challenge in itself without disturbing the achievement of organizational productivity and employee performance. Employees in the Bogor Regency local government, especially in the Bogor Regency Regional Secretariat, are required to remain productive and have good performance despite changes in work patterns from work from office to work from home. This change certainly forces ASN to be ready to change and adapt to information technology which is the main factor in implementing work from home. employees tend to have more positive attitudes towards organizational change when there is alignment of vision, goals, values, and culture between employees and the organization (Cole et al. 2006). Readiness to change can determine readiness for technology acceptance and employee performance. This is indicated by the research results of Wraikat et.al. (2017) which revealed a positive correlation between employee psychological perceptions and employee technology acceptance. On the other hand, Winardi and Agus Prianto (2016) revealed a relationship between readiness to change and performance.
Readiness to accept technology is also one of the factors that determine the achievement of employee performance in the work from home work pattern. Someone who is able to use technology in their work can improve their performance. Technology is able to help and make it easier for someone to carry out their duties and obligations effectively and efficiently. The results of research by Claudia Quintanilla and Edgardo Ayala (2011) reveal that increasing the Technological Readiness Index (TRI) for employees can improve service quality performance to customers. The results of this study are supported by Hasan and Nadzar (2010) who also reveal that technology acceptance is positively and significantly correlated with work performance.
Employee performance is a key factor in organizational success (Ismyarto et al. 2015). In the work from home pattern applied by the local government of Bogor Regency, especially at the Regional Secretariat (SETDA), employee performance is an interesting issue to discuss. Some experience problems in their implementation, both in terms of quantity of work and quality of work achieved, the time period for achieving these results, attendance and activities during their presence at work, and the ability to cooperate among them. On the other hand, the implementation of WFH is also able to increase the efficiency and effectiveness of their performance. Employee performance can be defined from the perspective of behavior or results (Muindi and K'Obonyo 2015).
Demographic issues in employee performance have often been discussed in several previous studies. Research by Edgar et al. (2020) showed that employee demographics in terms of gender played an influential role, with ability found to be the most significant predictor of job performance for men and opportunity as the strongest predictor of job performance for women. There was no significant difference in employee performance in terms of gender. This gives rise to research gaps related to gender issues in employee performance. Based on the description of the background, it can be seen that there is a relationship between employee performance during the implementation of work from home with readiness to change and readiness to accept technology in employees. Therefore, researchers are interested in researching this related to the performance of ASN in implementing work from home policies at the Regional Secretariat of Bogor Regency.

Readiness to change
Readiness to change is defined as a comprehensive attitude that is simultaneously influenced by the content, process, context and attributes of individuals involved in change, reflecting the extent to which individuals tend to agree, accept and adopt specific plans aimed at changing circumstances. Indicators of readiness to change in this study use the opinion of Holt and Vardaman (2013) which include precontemplation and preparation, suitability, leadership support, effectiveness of change, and individual valence. Readiness to change is believed to have a correlation with the readiness of technology acceptance and the performance of the ASN of the Bogor Regency Regional Secretariat.

Technology Readiness
The concept of the Technology Readiness Index (TRI) comes from research that places a person's personality at the center of their acceptance of technology (Parasuraman & Colby, 2015). Technology acceptance readiness is defined as an individual's readiness to use new technology in general. The indicator of technology acceptance readiness in this study uses the Technological Readiness Index (TRI) according to Parasuraman and Colby (2015) which includes optimism, innovation, discomfort, and insecurity.

Performance
Employee performance can be defined from the perspective of behavior or results (Muindi and K'Obonyo 2015). Performance is also defined as the result of work and work behavior that has been achieved in completing the tasks and responsibilities given within a certain period (Kasmir, 2016). Individual performance will improve if the focus is on setting and monitoring goal attainment, and harmonizing individual development and rewards with potential for growth and the development of new skills (Stanciu 2015). The performance indicators used in this study use the opinion of Kasmir (2016) which includes quality, quantity, time period, cooperation between employees, cost suppression, and supervision.

The Effect of Readiness to Change on Employee Performance
Walinga (2008) emphasizes that all changes made by organizations or individuals, both externally and internally, ultimately depend on the determination or willingness of individuals to change. Walinga (2008) added that if an individual's attention focuses on efforts to reduce anxiety levels, his ability to solve operational problems will decrease. In such a situation, the ability of employees to be proactively involved in the change program will be reduced and also interfere with their ability to carry out their daily work routines. In the end they can deal a significant blow to the organization. Individual readiness in the face of change is an important thing that must be considered. Employee acceptance of change will affect acceptance of individual and organizational performance. Employees who have good readiness to change will be very helpful in achieving performance both individually and in organizations. Based on this study, it is clear that the pattern of the relationship between readiness to change and employee performance is clear.

H1: Readiness to change has a significant effect on the performance of ASN at the Bogor
The Effect of Readiness to Change on Technology Acceptance Readiness Holt et al. (2007) mentions that to change as a comprehensive attitude is stimulantly influenced by the content, process, context and attributes involved in a change and reflects the extent to which the individual's desire to agree, accept and adopt specific plans that aim to change the current state. There are five indicators to determine the level of readiness to face environmental change, namely: pre-contemplation and preparation, suitability, leadership support, change effectiveness, and valence. The results of research on readiness to change towards technology acceptance readiness shown by Wraikat et al. (2017) which revealed a positive correlation between employee psychological perceptions and employee technology acceptance. There is a positive correlation between employee technology acceptance and organizational climate. A positive correlation was also found between employees' technology acceptance and strategy implementation. Iqbal and Bhatti's (2015) research also shows that psychological skills and readiness among students greatly affect ease of use/perceived ease of use (PEU) and perceived usefulness/perception of usefulness (PU) of technology, while the two constructs are perceived ease of use (PEU) and perceived usefulness (PU) of technology positively influences their behavioral intention to use technology. Then the hypothesis can be formulated:

The Effect of Technology Acceptance Readiness on Performance
Information and Communication Technology (ICT) is one of the new technologies that has influenced the organizational and industrial environment around us. Until a few decades ago, it was hard to imagine that such technology could affect the business environment around us including the performance of individuals and organizations. The effect of technology on growth can be divided into two categories: First, the impact of technology has been manifested in the form of capital goods, resulting in an increase in the productivity of capital. Second, technology increases the productivity of labor and ultimately, overall productivity does not necessarily increase the productivity of labor or capital. In the classical sense, technology is knowledge embodied in the means and methods of production. Parasuraman (2000) reveals that technology acceptance readiness is an individual's readiness to use new technology in general. Readiness to accept technology is influenced by several factors including optimism, innovation, discomfort, and insecurity.
Several studies have shown that there is a relationship between technology acceptance and a person's performance. Yuvaraj and Nadheya (2018) revealed that the introduction of technology helps organizations improve the performance of their employees. The introduction of technology also affects the relationship between employees because every job is done through ICT thereby minimizing human interaction. Quintanilla and Ayala (2011) revealed that increasing the Technological Readiness Index (TRI) for employees can improve service quality performance to customers. Hasan and Nadzar (2010) also revealed that technology acceptance is positively and significantly correlated with work performance. The study concludes that the technology issues under study are very important in ensuring higher acceptance of technological changes, thereby improving the performance of employees in government offices. Then the hypothesis can be formulated: H3: Readiness to accept technology affects performance.

RESEARCH METHODS
This research will be carried out within the Bogor Regency Government, namely the Bogor Regency Regional Secretariat. The population in this study were all ASN in the Regional Secretariat of the Bogor Regency Government. The sampling method used in this study is non-probability sampling with the sampling technique used is Voluntary Sampling (Voluntary Sampling), which is sampling based on willingness to participate in research (Murairwa, 2015). The number of samples in this study is based on the theory of chin at al. (1998) that the minimum use of the sample is based on 10 times the size of the largest format indicator to measure 1 latent variable. Based on the provisions of the number of samples, with the most indicators, this study amounted to 6 indicators so that the minimum number of respondents needed was 6 x 10 = 60 respondents. However, to get more optimal research results, researchers will try to get a larger number of samples. Data analysis was performed using Structural Equation Model (SEM).

Measurement Model Evaluation (Outer Model)
Assessment of the measurement model is carried out by taking into account the ability of each indicator in measuring its latent variable by testing the validity and reliability of each indicator on its latent variable through testing of convergent validity, discriminant validity and composite reliability. Convergent validity testing is determined by taking into account the loading factor and AVE parameters. This analysis was conducted to see the relationship Readiness change (X) Technology Acceptence (Y1) Performance (Y2) between latent variables and their indicators. To see convergent validity, the coefficients of each indicator will be seen. Indicators that have a loading factor below 0.7 are considered inappropriate and should be excluded from the model. However, according to Abdillah and Hartono (2016) researchers should not delete indicators that have an outer loading of 0.5-0.7, as long as the AVE and communality values of these variables are more than 0.5. The loading factor value of each indicator can be seen in the following figure: The results of convergent validity in the initial model in Figure 2 show that of all indicators there is one indicator whose coefficient value does not meet the requirements and does not meet the standard value of convergent validity. Therefore, a re-specification was carried out by eliminating these indicators. One indicator that is excluded from the model is KB 1 (Readiness to change 1) from the readiness variable to change so that from all indicators, there are 14 indicators that meet the standard value. Next is the discriminant validity assessment stage by comparing the cross loadings values for each construct with the correlation between the constructs and other constructs in the model. The cross-loading value for each construct is shown in Table 1. The correlation value of the cross-loading construct with the measurement item must be greater than the size of the other constructs. The results of cross loading all indicators in this study show that there are no problems in discriminant validity.   Based on the table above, it is known that all CA and CR values are greater than 0.7 and AVE is also more than 0.5. Therefore, all variables have met the criteria.

Structural Model Evaluation (Inner Model)
The structural model was evaluated using R-square for endogenous constructs, Stone-Geiser Q-square test for relevant predictive. The value of R2 can be used to assess the effect of certain exogenous latent variables, whether the endogenous latent variables have a substantive effect. The higher the R2 value, the greater the ability of the endogenous latent variable to explain the exogenous latent variable. The results of the calculation of the R-square value for the technology acceptance readiness variable are 0.436 and the performance is 0.556. This can be interpreted that the technology acceptance readiness variable can be explained moderately by readiness to change by 43.6 percent and another 52.4 percent is influenced by variables outside the model. Meanwhile, the performance variable can be explained moderately by readiness to change and readiness to accept technology by 55.6 percent and another 44.4 percent influenced by variables outside the model.

Source: Original Data
The next step is testing predictive relevance Q-square (Q2) which serves to validate the predictive ability of the model, the model is also evaluated by looking at the predictive relevance Q-square for constructive models. The magnitude of Q2 has a value range of 0 < Q2 <1, where the closer to 1 means the better the model. The quantity of Q2 is equivalent to the coefficient of total determination in path analysis. The value of Q2 > 0 indicates that the model has predictive relevance, otherwise if the value of Q2 0 indicates the model lacks predictive relevance. Calculation of Q2 total variables is done by the formula: Based on the calculation results, the resulting goodness of fit value is 0.58, which is greater than 0.36. Therefore, it can be concluded that the goodness of fit of this research model is included in the large category, which means that the suitability of the model is high.
The last stage of SEM analysis is hypothesis testing or significance testing. The path coefficient value shows the significance level of the T-statistical value in hypothesis testing. The considerations used are the minimum significant value of = 0.05. The hypothesis can be accepted if the path coefficient value is greater than 0.1 and the P-values are less than 0.05 and the T-statistic value is greater than t-table at the level of = 0.05 (1.96). Hypothesis testing is done by resampling using the bootstrapping method on the sample with the aim of minimizing the problem of abnormal research data. change has a significant effect on performance. The third hypothesis is that technology acceptance readiness has a significant effect on performance.
The results of this study indicate that readiness to change has a significant effect on performance. This study is in line with Kameer et. al (2020) Based on the summarized statistical calculations, it can be concluded that readiness to change has a positive and significant effect on worker performance. Banjongprasert (2017) mentions from the results of his research that readiness has a positive effect on performance, readiness in the form of innovation and communication so that it affects performance.
The results also show that readiness to change has a significant effect on readiness to accept technology. The results of this study are in line with Wraikat et al. (2017) which revealed a positive correlation between employee psychological perceptions and employee technology acceptance. There is a positive correlation between employee technology acceptance and organizational climate. A positive correlation was also found between employees' technology acceptance and implementation strategy. Iqbal and Bhatti's (2015) research also shows that skills and readiness among students greatly affect the perceived ease of use (PEU) and perceived usefulness (PU) of technology, while the two constructs are perceived ease of use (PEU) and perceived usefulness (PU) of technology positively influences their behavioral intention to use technology.
Readiness to accept technology as intended in this study is an attitude of acceptance or rejection of the use of technology such as optimism, security and discomfort towards technology used for work from home activities. Based on the calculation results indicate that the readiness of technology acceptance has a significant effect on performance. The results of this study are in line with several previous studies such as Yuvaraj and Nadheya (2018) revealing that the introduction of technology helps organizations improve the performance of their employees.
The introduction of technology also affects the relationship between employees because every job is done through ICT thereby minimizing human interaction. Quintanilla and Ayala (2011) revealed that increasing the Technological Readiness Index (TRI) for employees can improve service quality performance to customers. Hasan and Nadzar (2010) also revealed that technology acceptance is positively and significantly correlated with work performance. The study concludes that the technology issues under study are very important in ensuring higher acceptance of technological changes, thereby improving the performance of employees in government offices.
Another finding is the fact that there is no difference between the performance of men and women's performance in this study, based on the comparison between the results of the chisquare test. This states that the WFH performance of male and female ASN employees at the regional secretariat of Bogor Regency shows the same performance and is in accordance with the research hypothesis proposed earlier. The results of research from Arntz et al. (2020) show that WFH reduces the wage gap, so WFH does not create a difference between the performance of male and female employees. Meanwhile, according to Feng and Savani (2020) there is no difference between job satisfaction and performance between men and women.

CONCLUSION AND SUGGESTION
ASN performance in the implementation of wfh at the Bogor Regency Regional Secretariat ASN is influenced by readiness to change and readiness to accept technology. Readiness to change has a significant effect on performance in the implementation of WFH at the Regional Secretariat of Bogor Regency. Readiness to change also affects the readiness of technology acceptance in the implementation of WFH at the Regional Secretariat of Bogor Regency, the readiness of technology acceptance affects performance in the implementation of WFH at the Regional Secretariat of Bogor Regency.
The implementation of WFH at the Bogor Regency Regional Secretariat is expected not to reduce the performance of ASN during the COVID-19 pandemic. From the results of the study, it shows that there are two factors that affect the performance of the Bogor Regency Secretariat ASN during WFH, namely, readiness to change and readiness to accept technology. Readiness to change and readiness to accept technology has a positive correlation to its performance. From the results of this study, there are strategies that can be carried out by the Bogor Regency Government, especially at the Bogor Regency Regional Secretariat in implementing WFH during the COVID-19 pandemic so that ASN performance does not decrease. Readiness to change affects the performance of ASN during WFH, including individual beliefs that WFH has benefits, effectiveness of change, leadership support and adjustment.
The strategy for improving performance in the implementation of wfh in terms of readiness to change for ASN is how the leadership provides full support to ASN when implementing wfh. The support can be in the form of physical support or mental support. Physical support such as providing WFH support facilities so that ASN remains optimal to work from home, mental support can be in the form of support and motivation for ASN so that performance can still be achieved even though working from home. Readiness to change also affects readiness for technology acceptance, meaning that the more ready ASNs are to accept changes, the more prepared ASNs will be to use various technologies during WFH.
Readiness to accept technology also turned out to have a significant effect on the performance of the Bogor Regency Secretariat ASN in the implementation of WFH. Readiness to accept technology can be seen in the results of the study that ASN feels optimistic about using technology during WFH and feels comfortable and safe in using WFH supporting technology. A practical strategy that can be carried out by local governments is to provide training in the IT field so that the readiness of ASN technology acceptance increases so that it will improve performance.
This shows that the implementation of the WFH policy can also be applied in the future. Teleworking work patterns or work by relying on networks and technology can be done even though the mention of the new work pattern is no longer called WFH. Based on the results of this study, local governments can try to implement a new work pattern where the work pattern does not reduce the performance of ASN.