Keywords

Regulatory Effect, Human Resource Management, Performance Appraisal Reactions, Employee Engagement, Legal Employee Performance, Mediating Variable, Electronic Performance Appraisal, Local Government, Civil Servant.

Introduction

Design and good performance appraisal practices affect the improvement of employee and organizational performances. Previous studies have highlighted the importance of performance appraisal in organizations and have shown that performance appraisal has a very central role in managing resources in the organisation (Judge & Ferris, 1993; Cardy & Dobbins, 1994; Boswell & Boudreau, 2002). Provides information that helps managers improve performance (Denisi & Pritchard, 2006), in well-managed organizations, is the single most important and influential management tool in the career and work lives (Grote, 2002).

Even though performance appraisal is considered important, if it is not designed and practised properly, employee performance appraisal can harm the organisation through employee dissatisfaction (Kuvaas, 2006), demotivating employees (Rizvi, 2017), and not improving or even declining organisational performance (Denisi & Pritchard, 2006; Bouskila-Yam & Kluger, 2011; Budworth & Mann, 2011; Spence & Keeping, 2011; Kondrasuk, 2012; Kruse, 2012).

The employee’s reactions to the performance appraisal that the company conducts is a significant aspect of employee performance appraisal. Good performance appraisal reactions affect its effectiveness in managing employee performance (Murphy & Cleveland, 1995; Keeping & Levy, 2000; Anseel et al., 2011; Pichler, 2012).

Furthermore, based on a review of previous study findings on the relationship between performance appraisal reactions and employee performance, employee engagement variables have been found to act as a mediating variable in the relationship between those variables. Previous studies have also found that performance appraisal reactions affected employee engagement (Volpone et al., 2012; Levan, 2017) and that employee engagement affected employee performance (Bakker & Bal, 2010). Rich et al. (2010) Have reported that employee engagement has become a key determinant variable in the relationship between certain variables and employee performance (Arnold, 2009).

This research is important because employee engagement has been identified as a variable that can fill the theoretical gap in the relationship between performance appraisal reaction and employee performance. No studies that test the model of relationships between variables proposed in this study have been identified in the literature review (Lind, 1994).

This study focused on an electronic performance appraisal method, which is one of the most transformative developments in human resource management in recent decades. Arnold (2009) reported that a shift in modern organisations has taken place in the use of electronic performance management systems to increase the objectivity of assessments, reduce assessment bias and increase efficiency. These opinions are supported by many organizations utilizing software for various aspects of performance appraisal and management and have been used or will be used by 81% of 1,636 companies based on a survey conducted by Sierra-Cedar in 2013– 2018 (Payne & Mendoza, 2020). This change occurs because of the need to replace the traditional performance appraisal system (Adler et al., 2016) and to address the demands of the 4.0 industrial revolution where public employees must have the capacity to adapt to new technology (Schwab, 2016).

Another factor to consider is the paucity of literature on electronic performance appraisal. Stone & Dulebohn (2013) stated that studies dedicated to e-performance appraisal are still lacking. Furthermore, according to the literature review, no published studies on employee reactions to electronic performance appraisals can be found, especially studies that focused on local governments in developing countries.

Literature Review

Effect of Performance Appraisal Reactions on Employee Engagement

Performance appraisal reactions can be interpreted as employees perceptions of the work appraisal process carried out by the organization towards itself (Levan, 2017). This term is in line with the term rate reaction, appraisal reaction or feedback reaction, which is defined as the level of attitudes, assessments and individual responses to the performance appraisal process (Pichler, 2012).

The effect of performance appraisal reactions on employee engagement in organizations is theoretically related to organizational justice theory. This relation is based on the opinion of experts that organizational justice cannot ignore the contribution of performance appraisal and human resource management (Folger & Konovsky, 1989; Fryxell & Gordon, 1989; Lind, 1994).

The concept of organizational justice states that employees will react positively if they think the organization has provided justice and will react negatively if they think that the organization does not provide justice for themselves (French, 1964). Hence, employees will be more engaged and express themselves physically, cognitively, and emotionally during role performances if they are satisfied with the company’s performance appraisal. Employee engagement will be lower the performance appraisal carried out by the organization is viewed as not providing a sense of self-satisfaction. This effect can be seen from the presence of exhaustion, cynicism, and inefficacy (Maslach et al., 2001).

Based on this description, the following hypothesis is formulated:

H1: Performance appraisal reactions affect employee engagement.

Effect of Employee Engagement on Employee Performance

Employee engagement can predict employee performance (Anitha, 2014; Bakker & Bal, 2010; Cesário & Chambel, 2017; Kim et al., 2013; Merrill et al., 2013). Employees who feel bound to the organisation will have attitudes and behaviours and use their potential for the organization resulting in increased employee performance. Employee engagement is a factor that decides whether an employee performs well or poorly. The more engaged an employee is, the better the employee’s performance. In contrast, employee performance suffers when employee engagement is poor.

Based on this description, the following hypothesis is formulated:

H2: Employee engagement affects employee performance.

Effect of Performance Appraisal Reactions on Employee Performance

Employee performance is defined as the result of work and processes or employee behaviour during work (Mathis & Jackson, 2011; Gomes, 2003; Corvellec, 1996; Robbins, 2008). Based on organizational justice theory, employee performance is determined by performance appraisal reactions. The performance appraisal reaction is an important element because it is a crucial mechanism for improving performance (Anseel et al., 2011; Jawahar, 2010; Denisi & Pritchard, 2006). If the employee is more satisfied with their performance appraisal, then the employee’s performance will improve, and if the employee is increasingly dissatisfied with the performance appraisal, the employee’s performance will deteriorate.

Based on this description, the following hypothesis is formulated:

H3: Performance appraisal reactions affect employee performance.

Effect of Performance Appraisal Reactions on Employee Performance Mediated by Employee Engagement

Employee engagement variables have a potential role as a mediating variable between the relationship between performance appraisal reactions and employee performance.

Although previous studies have stated that the reaction of employee performance appraisal is an important factor to improve employee performance (Anseel et al., 2011; Jawahar, 2010; Denisi & Pritchard, 2006), some inconsistencies have been observed. For instance, Kuvaas (2011) reported that performance appraisal reactions affect employee performance significantly only for employees who receive regular feedback.

Rich et al. (2010) have stated that employee engagement has become a mediating variable on the relationship between certain variables and employee performance. Thus, employee performance will improve when the employee is engaged as a result of being satisfied with the performance appraisal. As a result, the authors believe that employee engagement plays a role in mediating the connection between performance appraisal reactions and employee performance.

Based on this description, the following hypothesis is formulated:

H4: Performance appraisal reactions affect employee performance mediated by employee engagement.

Based on the previously discussed hypotheses, the authors suggested the following hypothetical model as shown in (Figure 1).

The data is taken from civil servants in Ciamis Regency, West Java Province, Indonesia, who use an electronic performance appraisal known as E-Kinerja. In contrast to traditional performance appraisals that are filled out manually, E- kinerja is a performance appraisal application that is computerized, online, Internet-based and connected to other performance management processes.

Participants and Procedures

The sample was taken using systematic random sampling of 96 people from 2,808 employees. Using the Slovin formula with a 90% confidence level with 29 intervals, the sample was taken from numbers 29, 58.87, 2784 until a complete sample of 96 people was collected.

The respondents’ average working time was 19.7 years. In terms of gender, 63% of respondents were male, 35% were female and 2% were unknown. The respondents consist of 13% grade II, 73% grade III, 13% grade IV and 2% unknown, according to the grade. Executors accounted for 68% of the positions, with 16% structural, 15% functional and 2% unknown. As for education, 4% were junior high school graduates, 21% were high school graduate, 52% had a bachelor’s degree, 20% had a master’s degree and 1% had a doctoral degree.

The questionnaire was arranged based on the indicators of the research variables. Enumerators distributed the questionnaire with guidance on how to fill it out, reassured the respondents of the confidentiality of their data, and that their answers to the questionnaire will not affect their job. The questionnaires were collected after the respondents had filled in all the answers to the questions.

Measures

Indicators for evaluating performance appraisal reactions were compiled based on Keeping and Levy (2000), who were the first to introduce the concept of performance appraisal reactions. The results of their study found that performance appraisal reactions were composed of 1) Satisfaction with the Appraisal Session; 2) Satisfaction with the Appraisal System; 3) Perceived Utility of the Appraisal; 4) Perceived Accuracy of the Appraisal; 5) Procedural Justice; 6) Distributive Justice. A total of 26 statement items were arranged based on these six indicators. The following is an example: 1) I felt quite satisfied with my last appraisal discussion; 2) I felt good about the way the last appraisal discussion was conducted.

Employee engagement was measured using Rich et al. (2010), which are considered as the most appropriate measurement for determining employee engagement as the original concept first introduced by Kahn. The indicators consist of 1) Physical Engagement; 2) Emotional Engagement; 3) Cognitive Engagement. A total of 16 statement items were arranged based on these indicators. An example statement is: 1) I am enthusiastic in my job; 2) I feel energetic at my job.

Meanwhile, indicators based on Gomes (2003) were used to assess employee performance. These indicators are most appropriate for use in this study because they have many similarities with the performance indicators of civil servants in Indonesia who are the object of research. These indicators include 1) Quantity of work; 2) Quality of work; 3) Job Knowledge; 4) Creativeness; 5) Cooperation; 6) Dependability; 7) Initiative; 8) Personal Quality. A total of 17 statement items were arranged based on these eight indicators. Examples of statements are 1) My job volume matches the set workload; 2) I complete various types of tasks in the workplace.

The model uses second-order CPA by using a repeated indicator approach (Wold, 1985), in which the reaction performance appraisal reactions, employee engagement and employee performance serve as a higher-order factor, while the respective indicator serves as a secondorder indicator. The model is based on the approach taken by Wetzels et al. (2009).

The measurement scale uses a Likert scale with a five-point scale (Malhotra & Dash, 2016), namely 5=strongly agree, 4=agree 3=disagree, 2=disagree, and 1=strongly disagree. Score interpretation refers to Arikunto (2006), where 1.00–1.80=Very Low, >1.80–2.60=low, >2.60– 3.40=moderate, >3.40–4.20=high and>4.20–5.00=very high.

Data Analysis and Result

Data were analysed using descriptive and inferential analysis. A descriptive study of respondent’s responses to research variables focusing on the frequency distribution and average meaning of respondents responses. Inferential analysis using partial least square-structural equation modelling (SEM-PLS) assisted by Smart-PLS 3.0 software version 3.2.8.

Analysis on Structural Equation Modelling consists of the following: a. Measurement models, which include 1) convergent validity (loading factor and AVE); 2) discriminant validity (cross-loading and the square root of AVE); 3) reliability (composite reliability and Cronbach’s alpha) and b. Structural (inner) models include 1) R-Square value; 2) Q2 predictive relevant; 3) Goodness of Fit (GOF); 4) T statistics of the value.

Descriptive Analysis

Table 1 shows that the distribution of respondents’ answers is mostly in the high category followed by the medium, very high and low categories, and no answers in the very low category. Meanwhile, the average value obtained from Table 2 indicates that all variables are in the high category and all indicators also have an average value in the high category except for the initiative indicator, which has an average value in the medium category.

Table 1 Frequency Distribution of Respondents’ Answers
CATEGORY	VARIABLES
	Performance Appraisal Reactions		Employee Engagement		Employee Performance
	amount	(%)	amount	(%)	amount	(%)
Very high	17	18	10	10	15	16
High	55	57	60	63	46	48
Moderate	20	21	21	22	22	23
Low	4	4	5	5	13	14
Very low	0	0	0	0	0	0
Total	96	100	96	100	96	100

Table 2 SEM-PLS Test Result
VARIABLE	MEAN	OUTER LOADING	AVE	COMPOSITE RELIABILITY	CRONBACH’S ALPHA	IND	OUTER LOADING	CROSS LOADING
Performance appraisal reactions	3.71		0.607	0.976	0.974			0.779
Session Satisfaction	3.64	0.691	0.71	0.926	0.880	RP1	0.732	0.732
						RP2	0.764	0.764
						RP3	0.741	0.741
System Satisfaction	3.75	0.847	0.65	0.919	0.883	RP4	0.802	0.802
						RP5	0.753	0.753
						RP6	0.787	0.787
						RP7	0.821	0.821
Perceived Accuracy	3.88	0.912	0.67	0.946	0.933	RP8	0.777	0.777
						RP9	0.838	0.838
						RP10	0.794	0.794
						RP11	0.785	0.785
						RP12	0.778	0.778
						RP13	0.843	0.843
						RP14	0.827	0.827
Perceived Utility	3.62	0.783	0.68	0.927	0.895	RP15	0.788	0.788
						RP16	0.804	0.804
						RP17	0.728	0.728
						RP18	0.765	0.765
Procedural Justice	3.71	0.766	0.67	0.926	0.893	RP19	0.716	0.716
						RP20	0.775	0.775
						RP21	0.774	0.774
						RP22	0.776	0.776
Distributive Justice	3.64	0.771	0.69	0.931	0.901	RP23	0.753	0.753
						RP24	0.771	0.771
						RP25	0.751	0.751
						RP26	0.806	0.806
Employee Engagement	3.63		0.549	0.951	0.945			0.741
Physical Engagement	3.63	0.838	0.65	0.937	0.919	KP1	0.701	0.701
						KP2	0.705	0.705
						KP3	0.707	0.707
						KP4	0.701	0.701
						KP5	0.703	0.703
						KP6	0.724	0.724
Emotional Engagement	3.61	0.917	0.63	0.931	0.911	KP7	0.737	0.737
						KP8	0.761	0.761
						KP9	0.754	0.754
						KP10	0.752	0.752
						KP11	0.799	0.799
						KP12	0.774	0.774
Cognitive Engagement	3.66	0.892	0.63	0.911	0.870	KP13	0.783	0.783
						KP14	0.784	0.784
						KP15	0.729	0.729
						KP16	0.726	0.726
Employee Performance	3.51		0.549	0.951	0.945			0.741
Quantity of Work	3.54	0.899	0.62	0.893	0.760	KI1	0.802	0.802
						KI2	0.813	0.813
Quality of Work	3.46	0.858	0.7	0.918	0.822	KI3	0.791	0.791
						KI4	0.791	0.791
Job Knowledge	3.54	0.854	0.63	0.897	0.772	KI5	0.716	0.716
						KI6	0.819	0.819
Creativeness	3.48	0.859	0.64	0.900	0.778	KI7	0.753	0.753
						KI8	0.801	0.801
Cooperation	3.49	0.877	0.77	0.940	0.872	KI9	0.818	0.818
						KI10	0.833	0.833
Dependability	3.53	0.880	0.76	0.937	0.864	KI11	0.826	0.826
						KI12	0.826	0.826
Initiatives	3.39	0.851	0.8	0.946	0.886	KI13	0.780	0.780
						KI14	0.831	0.831
Personal Quality	3.60	0.888	0.66	0.919	0.868	KI15	0.815	0.815
						KI16	0.781	0.781
						KI17	0.769	0.769

Inferential Analysis

The results of the outer model test in Table 2 show that all dimensions and indicators have good convergent validity where the outer loading value of each dimension is more than 0.70 and the AVE value of each dimension is more than 0.50. Discriminant validity is classified as good when each dimension and variable has a cross-loading value of more than 0.70. Reliability is classified as good as indicated in the value of the composite reliability and Cronbach’s alpha for each dimension and the indicator is more than 0.70 (Table 3).

Table 3 R Square Value
Variables	R Square	R Square Adjusted
Employee Engagement	0.583	0.579
Employee Performance	0.504	0.493

The inner model test results show the R Square value for the employee engagement variable is 0.583 and the R Square value for the employee performance variable is 0.504. The value of R Square for each variable is more than 0.50, indicating that the model is included in the moderate category (Figure 2). From the data above, we have the following:

Q² = 1- (1- R1²) (1- R²)

Q² = 1- (1- 0.583) (1- 0.504)

Q² = 1- (0.417) (0.496)

Q² = 1- 0.207

Q² = 0.793

From the above calculations, it can be seen that the value of Q 2 >0.50 which indicates that the proposed model has a large predictive relevance.

Tenenhaus (2004) states that GoF is calculated by the formula:

The GoF value is stated to be large (more than 0.38), and thus, the proposed model proposed is very good.

The relationship between variables is determined from the T statistics value and the path coefficient value (Table 4). The relationship between variables can be explained as follows (Figure 3).

Table 4 T Statistics Value
Variables	Original Sample (O)	T statisticss (|O/STDEV|)	P Values
Employee Engagement -> Employee Performance	0.482	4,995	0
Performance Assessment Reaction -> Employee Engagement	0.764	14,358	0
Performance Assessment Reaction -> Employee Performance	0.269	2,856	0.002

Effect of Performance Appraisal Reactions on Employee Engagement (H1)

The results of the bootstrapping test show that the path coefficient value between the performance appraisal reactions variable on employee engagement was 0.764 (positive) with a T statistics value of 2.856 (above the t-table value of 1.96) (Table 5). Thus, performance appraisal reactions have a positive and significant effect on employee engagement, which means the better the employee’s reaction to the performance appraisal the more it will increase employee engagement. In line with this finding, the following hypothesis is proposed:

Table 5 Path Coefficient Value
Variables	Original Sample(O)	Sample Mean (M)	Standard Deviation (STDEV)	T statistics (|O/STDEV|)	P Values
Employee Engagement->Employee Performance	0.482	0.485	0.093	5.203	0
Performance Assessment Reaction->Employee Engagement	0.764	0.765	0.051	14.859	0
Performance Assessment Reaction->Employee Performance	0.269	0.267	0.091	2.975	0.002

H1: performance appraisal reactions affect employee engagement.

Have empirical evidence to be accepted.

Effect of Employee Engagement on Employee Performance (H₂)

The path coefficient value between employee engagement variables to employee performance is 0.482 (positive) with a T statistics value of 2.856 (above the t-table value of 1.96). It can be concluded that employee engagement has a positive and significant effect on employee performance, which means higher employee engagement will improve employee performance. Hence, the second hypothesis is proposed:

H2: Employee engagement affects employee performance.

Have empirical evidence to be accepted.

Effect of Performance Appraisal Reactions on Employee Performance (H₃)

Path coefficient value between performance appraisal reaction variables is 0269 (positive) to the value of T statistics 2,856 (on top of the t-table value 1.96). Therefore, the performance appraisal reactions have a positive and significant effect on employee performance, which means that the better the employee’s reaction to the performance appraisal the more improved the employee’s performance. The hypothesis proposed in this study is as follows:

H3: Performance appraisal reactions affect employee performance.

Have empirical evidence to be accepted.

Effect of Employee Engagement in Mediating the Relationship between Performance Appraisal Reactions and Employee Performance

We can use the procedure developed by Baron & Kenny (1986) to determine the effect of mediation on the above research model. From the results of bootstrapping on the SMART-PLS software, the indirect effect value on the relationship between performance appraisal reactions and employee performance is 0.37 (positive) with T statistics of 5.02 (more than 1.96). Because the indirect effect is positive and the T statistics value meets the requirements, it indicates that employee engagement mediates the relationship between the performance appraisal reaction variables and employee performance.

According to the rule of thumb, if the effect of exogenous variables on the mediating variable and the effect of the mediating variable on endogenous variables is positive and significant, while the effect of exogenous variables on endogenous variables is both positive and significant, indicating that employee engagement partially mediates the relationship between performance appraisal reactions with employee performance. Thus, the following hypothesis is proposed:

H4: Performance appraisal reactions affect employee performance mediated by employee engagement.

Have empirical evidence to be accepted.

Discussion

This study aims to determine the effects of employee engagement in mediating the relationship between performance appraisal reactions and employee performance. Following the proposed hypothesis that employee engagement mediates the relationship between performance appraisal reactions and employee performance, the results of this study add to the current literature by presenting empirical evidence through a model of the relationship between research variables that have never been studied previously. This research also provides findings that support previous findings that performance appraisal reactions affect employee engagement (Volpone et al., 2012; Levan, 2017), employee engagement affects employee performance and performance appraisal reactions affect employee performance (Bakker & Bal, 2010). Affect employee performance (Denisi & Pritchard, 2006; Jawahar, 2010; Anseel et al., 2011). This finding is in line with who posited that the success of performance appraisal is also determined by the social context of performance appraisal in addition to measurement, psychometric and cognitive aspects.

Whereas in the context of the performance appraisal system, the high average value of the variables indicates that electronic performance appraisal is a good performance appraisal system. The advantages of electronics performance appraisal include 1) employees feel the performance appraisal is fairer and more accurate (Payne et al., 2009); 2) managers are encouraged to develop behaviour management sustainability performance better (Hunt, 2011); 3) more positive behaviour towards performance reviews are encouraged (Gueutal, 2003).

Contribution to Practice

The results of this study have implications for stakeholders in terms of planning and executing performance appraisals that will be better perceived by employees. According to the variable relationship model used in this research, better perception improves employee engagement, which affects employee performance. Factors that must be considered are satisfaction with the performance evaluation session, satisfaction with the performance appraisal system, accuracy of the performance appraisal, usefulness of the performance appraisal, procedural justice and distributive justice (Keeping & Levy, 2000). The factors that affect employee acceptance of performance appraisals are leadership credibility, a sophisticated and well-planned system (Gabris & Ihrke, 2000), the use of performance appraisals as performance improvement, and capacity building (Kim, 2014). Thus, carrying out education and training on performance appraisal for supervisors and applying an integrated electronic performance appraisal system with human resource management that focuses on developing employee capacity are necessary.

The research findings also show an Initiative indicator of the employee performance variable which has the lowest average value and is in the medium category. As a result, improvement to the design and the implementation of performance appraisals that facilitate employee effort is required. Employees with high initiative values, for instance, could be rewarded based on the outcomes of their performance appraisals.

Limitation and Further Research Opportunities

This research was conducted on civil servants in local government who employ electronic performance appraisal. Further research can be carried out on research objects and/or loci with different characteristics to strengthen theoretical findings. Examples include private organizations or different appraisal systems, and comparisons of electronic performance appraisal systems with traditional performance appraisal systems.

This study used a cross-sectional design. Its strength is that it allows the researcher to observe all variables at the same time. The drawback is that the unit of analysis is observed only at the time of the survey and not continuously. As a result, further longitudinal research, which is conducted over a longer period, is necessary.

References

Adler, S., Campion, M., Colquitt, A., Grubb, A., Murphy, K., Ollander-Krane, R., & Pulakos, E.D. (2016). Getting rid of performance ratings: Genius or folly? A debate. Industrial and Organizational Psychology, 9(2), 219-252.

Indexed at, Google Scholar, Cross ref

Anitha, J. (2014). Determinants of employee engagement and their impact on employee performance. International Journal of Productivity and Performance Management.

Indexed at, Google Scholar, Cross ref

Anseel, F., Van-Yperen, N.W., Janssen, O., & Duyck, W. (2011). Feedback type as a moderator of the relationship between achievement goals and feedback reactions. Journal of Occupational and Organizational Psychology, 84(4), 703-722.

Indexed at, Google Scholar, Cross ref

Arnold, V.J. (2009). Leadership assessment and development in the mid-East. In Advances in global leadership. Emerald Group Publishing Limited.

Indexed at, Google Scholar, Cross ref

Bakker, A.B., & Bal, M.P. (2010). Weekly work engagement and performance: A study among starting teachers. Journal of Occupational and Organizational Psychology, 83(1), 189-206.

Indexed at, Google Scholar, Cross ref

Baron, R.M., & Kenny, D.A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173.

Indexed at, Google Scholar, Cross ref

Boswell, W.R., & Boudreau, J.W. (2002). Separating the developmental and evaluative performance appraisal uses. Journal of Business and Psychology, 16(3), 391-412.

Indexed at, Google Scholar

Bouskila-Yam, O., & Kluger, A.N. (2011). Strength-based performance appraisal and goal setting. Human Resource Management Review, 21(2), 137-147.

Indexed at, Google Scholar, Cross ref

Budworth, M.H., & Mann, S.L. (2011). Performance management: Where do we go from here? Human Resource Management Review, 21(2), 1-9.

Google Scholar

Cardy, R.L., & Dobbins, G.H. (1994). Performance appraisal: Alternative perspectives. Cincinnati, OH, South. Western Publishing.

Google Scholar, Cross ref

Cesário, F., & Chambel, M.J. (2017). Linking organizational commitment and work engagement to employee performance. Knowledge and Process Management, 24(2), 152-158.

Indexed at, Google Scholar, Cross ref

Corvellec, H. (1996). Stories of achievements-narrative features of organizational performance.

Indexed at, Google Scholar

Denisi, A.S., & Pritchard, R.D. (2006). Performance appraisal, performance management and improving individual performance: A motivational framework. Management and organization review, 2(2), 253-277.

Indexed at, Google Scholar, Cross ref

Folger, R., & Konovsky, M.A. (1989). Effects of procedural and distributive justice on reactions to pay raise decisions. Academy of Management journal, 32(1), 115-130.

Indexed at, Google Scholar

French, W. (1964). The nature and problems of organizational justice. In Academy of Management Proceedings Briarcliff Manor, NY 10510: Academy of Management.

Indexed at, Google Scholar, Cross ref

Fryxell, G.E., & Gordon, M.E. (1989). Workplace justice and job satisfaction as predictors of satisfaction with union and management. Academy of Management Journal, 32(4), 851-866.

Indexed at, Google Scholar, Cross ref

Gabris, G.T., & Ihrke, D.M. (2000). Improving employee acceptance toward performance appraisal and merit pay systems: The role of leadership credibility. Review of Public Personnel Administration, 20(1), 41-53.

Indexed at, Google Scholar, Cross ref

Gomes, F.C. (2003). Human resources management. Yogyakarta. Andi Offset.

Grote, R.C. (2002). The performance appraisal question and answer book: A survival guide for managers. AMACOM/American Management Association.

Indexed at, Google Scholar

Gueutal, H.G. (2003).  The brave new world of eHR. In Advances in human performance and cognitive engineering research. Emerald Group Publishing Limited.

Indexed at, Google Scholar, Cross ref

Hunt, S.T. (2011). Technology is transforming the nature of performance management. Industrial and Organizational Psychology, 4(2), 188-189.

Indexed at, Google Scholar, Cross ref

Jawahar, I.M. (2010). The mediating role of appraisal feedback reactions on the relationship between rater feedback-related behaviors and ratee performance. Group & Organization Management, 35(4), 494-526.

Indexed at, Google Scholar, Cross ref

Judge, T.A., & Ferris, G.R. (1993). Social context of performance evaluation decisions. Academy of Management Journal, 36(1), 80-105.

Indexed at, Google Scholar, Cross ref

Keeping, L.M., & Levy, P.E. (2000). Performance appraisal reactions: Measurement, modelling, and method bias. Journal of Applied Psychology, 85(5), 708-724.

Indexed at, Google Scholar, Cross ref

Kim, T. (2014). Performance appraisal: Determinants of public employees' acceptance. Doctoral dissertation, Rutgers University-Graduate School-Newark.

Indexed at, Google Scholar

Kim, W., Kolb, J.A., & Kim, T. (2013). The relationship between work engagement and performance: A review of an empirical literature and a proposed research agenda. Human Resource Development Review, 12(3), 248–276.

Indexed at, Google Scholar, Cross ref

Kondrasuk, J.N. (2012). The ideal performance appraisal is a format, not a form. Academy of Strategic Management Journal, 11(1), 115-120.

Indexed at, Google Scholar

Kruse, K. (2012). Continuous coaching outperforms annual reviews. Health Care Registration: The Newsletter for Health Care Registration Professionals, 22(1), 3-5.

Google Scholar

Kuvaas, B. (2006). Performance appraisal satisfaction and employee outcomes: Mediating and moderating roles of work motivation. The International Journal of Human Resource Management, 17(3), 504-522.

Indexed at, Google Scholar, Cross ref

Kuvaas, B. (2011). The interactive role of performance appraisal reactions and regular feedback. Journal of Managerial Psychology, 26(2), 123-137.

Indexed at, Google Scholar, Cross ref

Levan, K.B. (2017). Examining the relationships between performance appraisal reactions and employee engagement.

Indexed at, Google Scholar

Lind, E.A. (1994). Justice and authority relations in organizations (No. 9420). American Bar Foundation.

Indexed at 

Malhotra, N.K., & Dash, S. (2016). Marketing research: An applied orientation. Uttar Pradesh: Pearson India Education Services Pvt. Ltd.

Maslach, C., Schaufeli, W.B., & Leiter, M.P. (2001). Job burnout. Annual Review of Psychology, 52(1), 397-422.

Indexed at, Google Scholar, Cross ref

Mathis, R.L., & Jackson, J.H. (2011). Human resource management. Mason, OH, USA: South-Western Cengage Learning.

Indexed at

Merrill, R.M., Aldana, S.G., Pope, J.E., Anderson, D.R., Coberley, C.R., Grossmeier, J.J., & Whitmer, R.W. (2013). Self-rated job performance and absenteeism according to employee engagement, health behaviors, and physical health. Journal of Occupational and Environmental Medicine, 55(1), 10-18.

Indexed at, Google Scholar, Cross ref

Murphy, K.R., & Cleveland, J.N. (1995). Understanding performance appraisal: Social, organizational, and goal-based perspectives. Sage.

Indexed at, Google Scholar, Cross ref

Payne, S.C., & Mendoza, A.M. (2020). E-performance management. Encyclopedia of Electronic HRM.

Indexed at, Google Scholar, Cross ref

Payne, S.C., Horner, M.T., Boswell, W.R., Schroeder, A.N., & Stine‐Cheyne, K.J. (2009). Comparison of online and traditional performance appraisal systems. Journal of Managerial Psychology, 24(6), 526-544.

Indexed at, Google Scholar, Cross ref

Pichler, S. (2012). The social context of performance appraisal and appraisal reactions: A meta‐analysis. Human Resource Management, 51(5), 709-732.

Google Scholar,  Cross ref

Rich, B.L., Lepine, J.A., & Crawford, E.R. (2010). Job engagement: Antecedents and effects on job performance. Academy of Management Journal, 53(3), 617-635.

Indexed at, Google Scholar, Cross ref

Rizvi, M.A. (2017). A feedback model for an effective performance appraisal system. Journal for Global Business Advancement, 10(2), 140-157.

Indexed at,  Google Scholar, Cross ref

Robbins, S.P., & Judge, T.A. (2008). Organizational behaviour. Jakarta: Four Salemba.

Schwab, K. (2016). The fourth industrial revolution. New York: Crown Business.

Indexed at, Google Scholar

Spence, J.R., & Keeping, L. (2011). Conscious rating distortion in performance appraisal: A review, commentary, and proposed framework for research. Human Resource Management Review, 21(2), 85-95.

Indexed at, Google Scholar, Cross ref

Stone, D.L., & Dulebohn, J.H. (2013). Emerging issues in theory and research on electronic human resource management (eHRM). Human Resource Management Review, 23(1), 1-5.

Indexed at, Google Scholar, Cross ref

Volpone, S.D., Avery, D.R., & McKay, P.F. (2012). Linkages between racioethnicity, appraisal reactions, and employee engagement. Journal of Applied Social Psychology, 42(1), 252-270.

Indexed at, Google Scholar, Cross ref

Wetzels, M., Odekerken-Schröder, G., & Van-Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS quarterly, 177-195.

Indexed at, Google Scholar, Cross ref

Wold, H. (1985). Encyclopaedia of statistical sciences. Partial least squares. Wiley, New York.

Received: 28-Feb-2022, Manuscript No. JLERI-22-11401; Editor assigned: 03-Mar-2022, PreQC No. JLERI-22-11401(PQ); Reviewed: 17- Mar-2022, QC No. JLERI-21-11401; Revised: 06-Apr-2022, Manuscript No. JLERI-21-11401(R); Published: 12-Apr-2022