An Investigation of the Relationship between Performance Appraisal and Work Engagement

Employees are very important to the success of any organisation. They are the fulcrum that holds the organisation together and they have also been described as the major assets of the organisation. Thus, the main aim of this study is to investigate the relationship between Performance Appraisal and work engagement among SMEs in Malaysia. Drawing upon Job demands-resources theory, this empirical study developed a conceptual framework tested using quantitative method. The study focuses on small and medium enterprises in Malaysia. The unit of analysis is at the level of individual employees who work with these SMEs. While the Independent variable is Performance Appraisal, the dependent variable is Work engagement measured using three constructs namely: Vigour, Absorption and Dedication. This study utilised confirmatory factor analysis (CFA) and structural equation modeling (PLS-SEM) using both SPSS and SmartPLS software. The findings show that Performance Appraisal has a positive significant relationship with work engagement. This study contributes significantly to the body of literature in the areas of employee retention and engagement. Theoretically, the study further expands the SET and JD-R theories by building a framework that empirically tested the relationship between performance appraisal and work engagement.

• Work Engagement
• Performance Appraisal

1. Introduction

Performance evaluations serve two purposes: first, to provide a precise and useful assessment of an individual’s performance; second, to help them enhance their abilities in accordance with job duties ( Cespedes, 2022 ). Many employees are discontented with performance reviews in their organisations due to the perception that they are time-consuming, discouraging, unjust, biased, and erroneous. Employees are very important to the success of any organisation. They are the fulcrum that holds the organisation together and they have also been described as the major assets of the organisation. Deregularisation, digitization and technological innovation have made Human Resource specialists and leaders understand the criticality of human resources in organisational success ( Bharadwaj, Khan, & Yameen, 2022 ). Performance appraisal can either positively or negatively impact the employee’ behaviour and motivation and in this regard, work engagement plays a key role. A well-engaged employee will be highly motivated to give his/her best contribution towards organisation growth and the overriding impact of this will reflect on both the financial and non-financial performance of the organisation. On the other hand, when employees find performance appraisal in their organisations to be unfavourable or inadequate, the overriding impact could be an intention to leave, thereby causing retention problems for these organisations. Work engagement is seen as one of the most efficient ways to improve retention rates ( Nel & Linde, 2019 ). Evidence indicates that high work engagement leads to lower voluntary turnover ( Bailey, Madden, Alfes, & Fletcher, 2017 ). Reports suggest that poor engagement is a global phenomenon ( Albrecht, Bakker, Gruman, Macey, & Saks, 2015 ). Empirical evidence showed that HRM practices including performance appraisal led to enhancement of employees’ level of work engagement (e.g., Muduli, Verma, & Datta, 2016 ; Turner, 2020 ). Empirical evidence has also shown that employees of a particular organisation tend to compare their work experience (work environment, reward and pay, leadership style, training, appraisal, opportunity for career development and decision making, etc.) with that of another organisation. According to the informal psychological contract, employees consciously expect their employers to fulfil some obligations in exchange for their commitment and labour. It is an unwritten mutual belief, common ground and perceptions between the employers and the employees.

Researchers had previously investigated the relationship between performance appraisal and work/employee engagement albeit from different contexts. The researchers suggested that performance appraisal could create engaged or disengaged employees with varying implications for the organisation including impact on firm performance, employee motivation and job satisfaction, intention to quit, etc. Researchers such as Memon et al. (2021) and Gim et al. (2022) in Malaysia examined the HRM practice-performance appraisal and concluded that it is one of the key drivers of employee engagement at work. In other countries such as Bangladesh ( Aktar & Pangil, 2018 ), Egypt ( Saad et al., 2021 ), Bahrain ( Alzyoud, 2018 ), Iran ( Sepahvand & Bagherzadeh Khodashahri, 2021 ), etc., researchers also found that HRM practice-performance appraisal had a positive significant impact on employee engagement. In other words, when employees perceive the performance appraisal to be an effective appraisal system with attributes such as clear appraisal objectives, well defined appraisal criteria, constructive post appraisal interview, continuous feedback, etc., then they exhibit positive behaviour or a positive state of mind at work leading to positive work-related outcomes. Employees who are satisfied with the performance appraisal systems provided by their organisations tend to have high levels of work engagement and are energetic, dedicated and immersed in their work. Empirical evidence has demonstrated that a high level of work engagement reduces voluntary turnover ( Albrecht et al., 2015 ; Bailey et al., 2017 ). Work engagement leads to personally fulfilling work-related experiences, good health and a state of mind that is positively correlated with progressive work efforts ( Memon et al., 2021 ). These positive experiences and emotions improve work-related outcomes, cause the employee to positively regard their employer and increase their commitment to the organisation ( Shuck, Twyford, Reio Jr., & Shuck, 2014 ). Positively engaged individuals tend to be “more satisfied with their jobs, feel more committed to the organisation and do not intend to leave the organisation” ( Schaufeli & Salanova, 2008 : p. 388). Recently, organisations began to adopt a more open approach to work engagement by considering it as a substantial psychological adaptation and involvement on the part of employees in the organisation. This shift can be attributed to how the engagement notion has quickly evolved within the practitioner community, hampering the understanding of work engagement for practical purposes ( Trabucchi et al., 2020 ). The concept of engagement, given the advent of the Fourth Industrial Revolution ( Klaus, 2016 ), has passed from the definition of mere physical exploitation of the employees to a desirable active espousal of the entire “person” to the work sphere in modern organisations. Thus, nowadays, engagement can be considered an essential condition for employees and the organisation they work for and indeed, researchers interpret engagement as a property of organisations, that is, employees throughout the organisation may share perceptions that members of the organisation collectively invest their full selves into their work roles. For example, motivational states such as engagement are highly transferrable to other members of the organisation ( Karanika-Murray, Duncan, Pontes, & Griffiths, 2015 ). This study defines performance appraisal from the perspectives of intention of the management, frequency of appraisal, perception of employee towards the system and the overall goal of evaluating an employee’s skills, achievements, and growth, or lack thereof. Researchers such as Memon et al. (2021) and Gim et al. (2022) in Malaysia examined the HRM practice-performance appraisal and concluded that it is of the key drivers of employee engagement at work. In other countries such as Bangladesh ( Aktar & Pangil, 2018 ), Egypt ( Saad et al., 2021 ), Bahrain ( Alzyoud, 2018 ), Iran ( Sepahvand & Bagherzadeh Khodashahri, 2021 ), etc., researchers also found that HRM practice-performance appraisal had a positive significant impact on employee engagement. In other words, when employees perceive the performance appraisal to be an effective appraisal system with attributes such as clear appraisal objectives, well defined appraisal criteria, constructive post appraisal interview, continuous feedback, etc., then they exhibit positive behaviour or a positive state of mind at work leading to positive work-related outcomes.

Scope of the Study

The scope of a study defines its purpose, the population size and characteristics, geographical location, the timeframe within which the study will be conducted, the theories to be utilized, etc. The main purpose of this study is to investigate the relationship between HRM practices and work engagement among SMEs in Malaysia. The theories utilized in this study are the SET and JD-R theories. The study population comprises SMEs throughout Malaysia. SMEs are selected because of their importance and contribution to the Gross Domestic Product (GDP), employment and overall economy of Malaysia ( Mohamad, Mustapa, & Razak, 2021 ). In Malaysia, the definition of an SME is based on sales turnover and the number of full-time employees. For the manufacturing sector, SMEs are defined as firms with sales turnover not exceeding RM50 million OR number of full-time employees not exceeding 200 and for the services and other sectors, SMEs are defined as firms with sales turnover not exceeding RM20 million OR number of full-time employees not exceeding 75 ( SME Corp Malaysia, 2020 ). Under the new definition, all SMEs must be entities registered with SSM or other equivalent bodies ( SME Corp Malaysia, 2020 ). The landscape of SMEs has grown considerably well since 2016 until 2021. Based on the latest data in Malaysia Statistical Business Register (MSBR) published by the Department of Statistics, Malaysia (DOSM), there were altogether 1,226,494 SMEs in 2021 which accounts for 97.4% of overall establishments in Malaysia. There has been an increment of about 140,000 firms as compared to a total of 1,086,533 MSMEs in 2016, thus registering an average growth rate of 5.2% per annum during the six-year period ( SME Corp Malaysia, 2022 ).

2.1. Research Aim and Objective

The main aim of this study is to investigate the relationship between HRM practices and work engagement among SMEs in Malaysia.

RO1: To investigate the relationships between HRM practices and work engagement among SMEs in Malaysia.

2.2. Research Question

RQ1: Is there any relationship between HRM practices and work engagement among SMEs in Malaysia?

2.3. Definitions of Key Terms

The process of assessing and recording an employee’s performance is known as a performance appraisal ( Puspita Dewi et al., 2024 ). It is a component of an organisation’s performance management scheme and is based on how well an employee is performing in relation to annual goals that were set with their management ( Ohemeng et al., 2018 ).

There are so many definitions of work engagement available in literature, however, the definition given by Schaufeli et al. (2002) , has been the most extensively used and cited in the literature, used by 86% of the studies reviewed by Bailey et al. (2017) . According to Schaufeli et al. (2002: p. 702) , work engagement is defined as “a positive fulfilling work-related state of mind characterised by vigour, dedication and absorption”.

1) Vigour

Vigour is defined as “mental resilience while working, persistence when faced with issues and a willingness to invest effort in one’s role performance” ( Strom et al., 2014 : p. 71).

2) Dedication

Dedication is defined and characterised as “one’s enthusiasm, sense of inspiration and response to challenges at work” ( Strom et al., 2014 : p. 71).

3) Absorption

Absorption is defined and concerned with “being happy, fully concentrated, and deeply engrossed in one’s work, with trouble detaching from work” ( Strom et al., 2014 : p. 71).

3. Problem Statement

At this precarious moment and unprecedented time of disruption to organisational workflows, the role of work engagement and employee retention cannot be overemphasised. Many employers rate work engagement as a top objective and they think it is important to increase employees’ understanding of work engagement and its benefits. Business units with high employee engagement achieve higher productivity, higher customer loyalty/engagement, better safety, lower turnover, and higher profitability, among other positive business outcomes ( Harter et al., 2020 ). Though work engagement has received considerable attention among researchers in the last decades, evidence suggests a low level of work engagement among employees globally and in Malaysia in particular ( Memon et al., 2021 ). Even before coronavirus (COVID-19) related crises, organisations were wrestling with finding ways to engage employees ( Sońta, 2021 ). It has been widely reported that there is a prevalence of low engagement among employees globally. According to the Gallup (2021) report on state of the global workplace, global employee engagement declined by 2% from 22% to 20% between 2019 and 2020 with reported high rate of worry, stress, anger, and sadness. Collectively, in Southeast Asia comprising Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand and Vietnam, employee engagement was only 23% in 2020 down by 4% in comparison to 27% in 2019. The region recorded a high rate of job loss as a result of COVID-19 pandemic at 42% in comparison to global rate of 32% and employees received less pay than usual from their employers and businesses at 67% in comparison to global 50% rate. About 42% of people in the region believe that they were affected by COVID-19 pandemic ( Gallup, 2021 ). In sharp contrast to the report by Gallup (2021) , Qualtrics (2021) in their report titled “2020 employee experience trends in Malaysia”, found the average employee engagement score across Malaysia is 54%. This is above the global average of 53%, with India (79%), Thailand (72%) and Hong Kong SAR (China) (63%) returning the highest scores. The study found that the drivers of employee engagement in 2020 in Malaysia in order of their importance are: recognition for good work (62%), there is a clear link between my work and the company’s strategic objectives (62%), opportunities for learning and development (67%), my manager helps me in my career development (59%) and confidence in senior leadership to make the right decisions for the company (62%). Employee engagement rises significantly to 79% when employee feedback is well-received and acted on. It falls to 47% when feedback is not acted on. In contrast, last year’s engagement drivers were receiving recognition for good work, seeing a clear link between work and strategic objectives, opportunities for learning and development, manager support in career development, and confidence in senior leadership to make the right decisions. Having a high number of disengaged employees increases the risk of voluntary turnover. Therefore, much of the current debate revolves around what drives employees to be engaged in order to reduce the high rate of voluntary turnover. Small and medium enterprises (SMEs) are of particular importance in Malaysia due to their contributions to gross domestic product (GDP), employment and overall economy of the country ( Hanifah, Abd Halim, Vafaei-Zadeh, & Nawaser, 2022 ). Work engagement is “a positive, fulfilling, work-related state of mind that is characterised by vigour, dedication, and absorption” ( Schaufeli et al., 2002 : p. 74), has emerged as a significant construct in the management literature because it has been shown to promote a variety of not only the employee but also organisational outcomes such as job satisfaction, organisational citizenship behaviour, organisational engagement and employee retention ( Aboramadan et al., 2019 ; Albrecht & Marty, 2020 ; Rai & Maheshwari, 2020 ). The growing interest in work engagement gives rise to the need for a better understanding of its antecedents ( Matsuo, 2022 ). Although numerous factors have been examined as antecedents (e.g., personality traits, learning goal orientation, personal resources and job autonomy), performance-related antecedents of work engagement have so far received very little attention in the existing literature. Only recently, research began to address questions concerning whether employees facing demands for high performance increase or decrease their work engagement ( Dorta-Afonso, Romero-Domínguez, & Benítez-Núñez, 2023 ; Stirpe & Revilla, 2024 ). Thus, only a narrow spectrum of the performance-related antecedents of work engagement has been considered, especially in terms of performance pressure.

4. Literature Review

4.1. Performance Appraisal and Work Engagement

Recent studies by Uraon & Kumarasamy (2024) and Sharif et al. (2024) revealed that performance appraisal significantly influences work engagement. While Uraon & Kumarasamy (2024) in their study particularly examined the direct impact of justice perceptions of performance appraisal practices (procedural, distributive, interpersonal and informational justice) on job satisfaction, intention to stay and job engagement, Sharif et al. (2024) investigated the effects of HRM practices on administrative and faculty members’ work engagement and organizational commitment. The study measured HRM practices using variables—selection and recruitment, rewards and compensation, job security training and development, employee participation and performance appraisal. Similarly, Zreen et al. (2024) in their study conducted to examine the influence of performance appraisal on Innovative work behavior of employees in public sector universities of Pakistan, found a positive correlation between performance appraisal and innovative work behaviour. Furthermore, researchers such as Memon et al. (2021) and Gim et al. (2022) in Malaysia examined the HRM practice-performance appraisal and concluded that it is one of the key drivers of employee engagement at work. In other countries such as Bangladesh ( Aktar & Pangil, 2018 ), Egypt ( Saad et al., 2021 ), Bahrain ( Alzyoud, 2018 ), Iran ( Sepahvand & Bagherzadeh Khodashahri, 2021 ), etc., researchers also found that HRM practice-performance appraisal had a positive significant impact on employee engagement. In other words, when employees perceive the performance appraisal to be an effective appraisal system with attributes such as clear appraisal objectives, well defined appraisal criteria, constructive post appraisal interview, continuous feedback, etc., then they exhibit positive behaviour or a positive state of mind at work leading to positive work-related outcomes. Employees who are satisfied with the performance appraisal systems provided by their organisations tend to have high levels of work engagement and are energetic, dedicated and immersed in their work.

4.2. Gap in Literature

According to Gim et al. (2022) , despite the fact that work engagement comprises three underlying dimensions, UWES-9 has been consistently used to measure WE as a unidimensional scale as recommended because the three underlying dimensions of vigour, dedication and absorption were found to be highly correlated and thus lacking discriminant validity ( Schaufeli et al., 2006 ). Similar operationalisation was observed in recent studies ( Albrecht, Breidahl, & Marty, 2018 ; Karatepe, Rezapouraghdam, & Hassannia, 2020 ; Teo, Bentley, & Nguyen, 2020 ). Therefore, conceptual gap still exists in defining a uniform measurement for work engagement in research. Similarly, there is a gap literature in the area of dimensions used in measuring HRM practices, given the lack of clarity and universally accepted dimensions. The growing interest in work engagement gives rise to the need for a better understanding of its antecedents ( Matsuo, 2022 ). Although numerous factors have been examined as antecedents (e.g., personality traits, learning goal orientation, personal resources and job autonomy), performance-related antecedents of work engagement have so far received very little attention in the existing literature. Only recently, research began to address questions concerning whether employees facing demands for high performance increase or decrease their work engagement ( Zhang et al., 2017 ; Mitchell et al., 2019 ). Thus, only a narrow spectrum of the performance-related antecedents of work engagement has been considered, especially in terms of performance pressure. Empirical studies on the HRM practices and employee retention relevant to SMEs in Malaysia are less than encouraging; therefore, more empirical studies are required to fill this obvious gap. Theoretically, three theories underpin employee/work engagement research in literature, namely job demands-resources (JD-R) ( Bakker & Demerouti, 2007, 2017 ), conservation of resources (COR) ( Hobfoll, 2002, 2011 ) as well as social exchange theory. These theories share a common view that more employer-provided resources enable greater employee engagement. Theories such as job demands-resources (JD-R) theory and social exchange theory have been utilised in empirical research to investigate the relationship between performance appraisal (a construct of HRM practices) and work engagement. The JD-R model has subsequently matured into a theory because of its flexibility and wide acceptance in the research literature ( Bakker & Demerouti, 2014 ). The JD-R theory assumes that each occupation is associated with a unique pattern of job stress, and these stresses assume two basic forms: job demands and job resources ( Bakker & Demerouti, 2014 ). According to Turner (2020: p. 48) , “the job demands-resources model has emerged as one of the more prominent models in explaining the antecedents and outcomes of employee engagement.” Social exchange theory assumes that one’s actions are contingent upon the reactions of others ( Blau, 2017 ).

4.3. Measurement of Study Variables

Uraon & Kumarasamy (2024) in their study measured performance appraisal using the JPPA practices scale developed by Thurston & McNall (2010) . JPPA practices scale had four dimensions, namely, procedural justice perceptions (α = 0.892), distributive justice perceptions (α = 0.869), interpersonal justice perceptions (α = 0.906), and informational justice perceptions (α = 0.960). Procedural justice had three components such as assigning a rater (5 items), setting criteria (4 items), and seeking appeals (5 items). In their study, Sharif et al. (2024) measured PA using 5 items including “How fair do you feel your last performance appraisal was”? Please check for possible phrasing issues. The study measured PA using the six (6) items adopted from Iqbal et al. (2019) , as shown in Table 1 below.

Table 1

Table 1. Performance.

S/N	Performance Appraisal	Number of Items
1	Performance Appraisal (PA)	6

In terms of multi-dimensional constructs, researchers have investigated work engagement mainly as either a dependent variable or a mediating variable. According to Schaufeli et al. (2002) and Wang et al. (2021) , work engagement has three dimensions, i.e., vigour, absorption and dedication, which tend to correlate significantly with each other and usually load together. Items were rated on a 7-point scale (1 = not at all and 7 = extremely). According to Montgomery (2020), work engagement has also been expressed in literature as either physical engagement, cognitive engagement, or emotional engagement as shown in Figure 1 below.

Figure 1

Figure 1. Facets of employee engagement: Source: Shuck, Adelson, & Reio (2017: p. 954) .

According to Wang et al. (2021) , work engagement has three dimensions, i.e., vigour absorption and dedication. Utrecht Work Engagement Scale (UWES)-9 has been consistently used to measure work engagement as a unidimensional scale as recommended because the three underlying dimensions of vigour, dedication and absorption were found to be highly correlated and thus lacking discriminant validity. In the 9-item version of the UWES each dimension is covered by three items, while in the 17-item version vigour is covered with six items, dedication with five items and absorption with six items. While majority of the studies ( Van Wingerden & Poell, 2019 ; Bannay et al., 2020 ; Memon et al., 2021 ; Permata & Mangundjaya, 2021 ; Yucel et al., 2023 ) measured work engagement using the nine-item, Dutch version of the Utrecht Work Engagement Scale (UWES) by Schaufeli et al. (2006) , others such as Rosario-Hernández & Millán, 2018 ; Ni et al., 2020 ; Zhang et al., 2017 utilised the 17-items developed Schaufeli et al. (2002) . The newest three-item version of the UWES covers all three dimensions of work engagement with one item each ( Schaufeli & De Witte, 2017 ). This study measured work engagement using 9 constructs, as shown in Table 2 below.

Table 2

Table 2. Work engagement.

S/N	Dimensions of work engagement	Number of items
1	Vigor	3
2	Dedication	3
3	Absorption	3
	Total	9

4.4. Conceptual Framework

Figure 2 below depicts the conceptual framework. It shows a single direct relationship between performance appraisal and work engagement.

Figure 2

Figure 2. Conceptual framework.

4.5. Hypothesis Development

Recent studies by Uraon & Kumarasamy (2024) and Sharif et al. (2024) revealed that performance appraisal significantly influences work engagement. While Uraon & Kumarasamy (2024) in their study particularly examined the direct impact of justice perceptions of performance appraisal practices (procedural, distributive, interpersonal and informational justice) on job satisfaction, intention to stay and job engagement, Sharif et al. (2024) investigated the effects of HRM practices on administrative and faculty members’ work engagement and organizational commitment. The study measured HRM practices using variables—selection and recruitment, rewards and compensation, job security training and development, employee participation and performance appraisal. Similarly, Zreen et al. (2024) in their study conducted to examine the influence of performance appraisal on Innovative work behavior of employees in public sector universities of Pakistan, found a positive correlation between performance appraisal and innovative work behaviour. Researchers such as Memon et al. (2021) and Gim et al. (2022) in Malaysia examined the HRM practice-performance appraisal and concluded that it is one of the key drivers of employee engagement at work. In other countries such as Bangladesh ( Aktar & Pangil, 2018 ), Egypt ( Saad et al., 2021 ), Bahrain ( Alzyoud, 2018 ), Iran ( Sepahvand & Bagherzadeh Khodashahri, 2021 ), etc., researchers also found that HRM practice-performance appraisal had a positive significant impact on employee engagement. In other words, when employees perceive the performance appraisal to be an effective appraisal system with attributes such as clear appraisal objectives, well defined appraisal criteria, constructive post appraisal interview, continuous feedback, etc., then they exhibit positive behaviour or a positive state of mind at work leading to positive work-related outcomes. Employees who are satisfied with the performance appraisal systems provided by their organisations tend to have high levels of work engagement and are energetic, dedicated and immersed in their work. Therefore, hypothesis H1 below is postulated:

H1: There is a positive significant relationship between performance appraisal dimension of HRM practices and work engagement.

5. Methodology

5.1. Population Size and Sampling Technique

The research population for this study comprises 7.31 million people employed by SMEs in Malaysia by 2021. Small and Medium Enterprises are the backbone of Malaysian economy, representing 97.4% of overall business establishments in the year 2021. In Malaysia, sales turnover and number of full-time employees are the two criteria used in determining the definition of SME with the “OR” basis as follows:

1) For the manufacturing sector, SMEs are defined as firms with sales turnover not exceeding RM50 million OR number of full-time employees not exceeding 200.

2) For the services and other sectors, SMEs are defined as firms with sales turnover not exceeding RM20 million OR number of full-time employees not exceeding 75 ( SME Corp Malaysia, 2021 ).

Under the new definition, all SMEs must be entities registered with SSM or other equivalent bodies. It, however, excludes:

1) Entities that are public listed on the main board;

2) Subsidiaries of:

a) Publicly listed companies on the main board.

b) Multinational corporations (MNCs);

c) Government-linked companies (GLCs);

d) Syarikat Menteri Kewangan Diperbadankan (MKDs);

e) State-owned enterprises ( SME Corp Malaysia, 2021 ).

Figure 3 below shows the detailed categorisation of Malaysian SMEs.

Figure 3

Figure 3. Detailed categorisation of Malaysian SMEs. Source: SME Corp Malaysia (2021) .

SMEs’ employment in Agriculture sector continued to register an increase of 1.5 per cent to record 791 thousand persons (2020: 779 thousand persons). SMEs’ employment in the agriculture sector contributed 42.2 per cent of overall employment in this sector in 2021 (2020: 41.8%). Meanwhile, the contribution of SMEs’ employment in Mining & quarrying sector was 27.9 per cent, with 21 thousand workers in 2021. SMEs’ employment in this sector rebounded marginally by 0.3 per cent from negative 1.3 per cent in 2020 ( Department of Statistics Malaysia, 2022 ). There were 1.21 million workers in the MSMEs’ Manufacturing sector, which comprised 46.2 per cent of Manufacturing’s total employment (2020: 46.5%). Although the share of SMEs’ employment in this sector declined in 2021, the number of employments increased by 2.0 per cent (2020: −0.4%). It was contributed by two sub-sectors, namely Food, beverages and tobacco (2020: 1.0%; 2021: 4.2%) and Petroleum, chemical, rubber and plastic products (2020: 1.5%; 2021: 3.5%) ( Department of Statistics Malaysia, 2022 ). Stratified random sampling was used to extract samples from the overall population because the method highlights the differences between groups in a population, as opposed to simple random sampling, which treats all members of a population as equal, with an equal likelihood of being sampled ( Rahman, Zahid, Khan, Al‐Faryan, & Hussainey 2024 ). This study employs proportionate stratified random sampling. Strata were formed based on sub-sectors (shared attributes) in the main two sectors namely Manufacturing and Services & Other Sectors. The sample size is based on the four sub-sectors of Manufacturing Sector and Services & Other Sectors. There were no available data for the breakdown of Services & Other Sectors into sub-sectors. Therefore, the whole sector is categorised into one. The number of employees working in the sub-sectors of manufacturing sector is Agriculture (791,000, Mining & Quarrying (21,000), Manufacturing (1,211,000) and Construction (668,000). The total number of employees working in the Services & Other Sectors is 4,620,000. The proportionate stratified random sample was obtained using the following formula: (Sample size/Population size) * Stratum size ( Bryman, 2016 ; Mukaram, Rathore, Khan, Danish, & Zubair, 2021 ). Sample size for this study is 385 employees. Table 3 below illustrates the proportionate stratified random sampling.

Table 3

Table 3. Population and sample.

S/N	Sectors/Strata	Population	Percentage	Sample
1	Agriculture	791,000	10.82%	42
2	Mining & Quarrying	21,000	0.29%	1
3	Manufacturing	1,211,000	16.56%	64
4	Construction	668,000	9.14%	35
5	Services & Other Sectors	4,620,000	63.19%	243
	Total	7,311,000	100%	385
	Formula = (Sample size/Population size) * Stratum size	385

5.2. Respondents

The population for the study comprises employees working in SMEs located in thirteen states and three federal territories in Malaysia. The total number of questionnaires distributed was 755. The highest number of questionnaires (111) were distributed in Kula Lumpur, and this followed closely by Selangor (96). The least represented state was PERLIS, which had only 18 participants. Furthermore, the 755 participants were drawn from service, construction, manufacturing and agriculture sectors. The most represented sector was service with 378 participants followed closely by construction with 265. The least represented sector was agriculture with only 8 participants. The number of participants who completed their questionnaires correctly was 481, representing 62% response rate. During the process of data screening, it was found that 35 of the obtained questionnaires cannot be used due to ineligible handwriting and other issues. The total number of questionnaires used for data analysis was 446. The diagrammatical representation of the analysis is shown in Table 4 below.

Table 4

Table 4. Analysis of study population.

Description	Total Number	Percentage
Questionnaire distributed	775	100%
Questionnaire received	481	62%
Ineligible questionnaire	35	5%
Eligible questionnaire	446	58%

The method adopted in this study is quantitative. The data to be collected are numerical in nature using Likert 7-point scale where 1 = Strongly disagree, 2 = Disagree 3 = Somewhat disagree, 4 = Neither agree or disagree, 5 = Somewhat agree, 6 = Agree and 7 = Strongly agree. There data are the scientifically manipulated using the statistical measures. Quantitative methods are suitable because of the nature of the research questions and objectives as well as the hypotheses developed for this study. In quantitative studies, the researcher uses standardised questionnaires or experiments to collect numeric data. Quantitative research is conducted in a more structured environment that often allows the researcher to have control over study variables, environment, and research questions. Quantitative research may be used to determine relationships between variables and outcomes. Quantitative research involves the development of a hypothesis—a description of the anticipated result, relationship, or expected outcome from the question being researched.

5.3. Data Collection

This study utilised primary data source via survey questionnaire. Data was collected via self-administered questionnaire distributed face-to-face to the target population. This researcher personally visited the SMEs and obtained the consent of the owners for the survey questionnaire. It was emphasised to the employees that participation in the survey is voluntary. Printed questionnaires were distributed to employees, and pictures were taken for reference. The final data collection process began on the 20^th of November, 2023 and ended on the 14^th of March, 2024. Data were collected from employees working in the thirteen states (13) and one of the three federal capital territories that made up Malaysia. Table 5 below shows the total number of questionnaires distributed per state and sector.

Table 5

Table 5. Number of employees per state and sector.

STATE	SERVICE	CONSTRUCION	MANIFACTURING	AGRICULTURE	MINING	TOTAL
SELANGOR	75	7	6	8	0	96
W/P KL	55	49	7	0	0	111
JOHOR	42	35	10	0	0	87
PULAU PINANG	28	24	8	0	0	60
SARAWAK	27	24	6	0	0	57
PERAK	26	21	9	0	0	56
SABAH	27	23	7	0	0	57
KEDAH	18	15	8	0	0	41
KELANTAN	17	15	7	0	0	39
PAHANG	16	13	9	0	0	38
NEGERI SEMBILAN	13	11	8	0	0	32
TERENGGANU	12	10	9	0	0	31
MELAKA	12	10	10	0	0	32
PERLIS	10	8	0	0	0	18
W/P LABUAN	0	0	0	0	0	0
W/P PUTRAJAYA	0	0	0	0	0	0
TOTAL	378	265	104	8	0	755

5.4. Data Analysis Plan

This study utilised confirmatory factor analysis (CFA) and structural equation modeling (SEM). The latter represents a theory-driven data analytical approach for the evaluation of a priori specified hypotheses about causal relations among measured and/or latent variables. Such hypotheses may be expressed in a variety of forms, with the most common being measured variable path analysis models, confirmatory factor analysis models, and latent variable path analysis models. For analysing models of these as well as more complex types, SEM is not viewed as a mere statistical technique but rather as an analytical process involving model conceptualization, parameter identification and estimation, data-model fit assessment, and potential model re-specification. PLS-SEM has now become a popular analytical tool with many international journals or scientific research using this method ( Purwanto & Sudargini, 2021 ). Partial Least Square abbreviated is a type of component-based SEM analysis with formative construct properties. PLS was first used to process data in the field of econometrics as an alternative to SEM techniques with a weak theoretical basis. PLS only serves as a predictor analysis tool, not a model test ( Purwanto & Sudargini, 2021 ). Statistical analyses were conducted using IBM SPSS and SmartPLS 4.0 software.

5.5. Theoretical Background

The job demands-resources (JD-R) model is a heuristic model that specifies how employee wellbeing can be produced by two sets of working conditions. Job demands are the characteristics of the job that can evoke strain if they exceed an employee’s adaptive capabilities, and job resources are the working conditions that the job at hand offers to individual employees ( Bakker & Demerouti, 2017 ). The basic idea behind the JD-R model is that while job demands can lead to exhausting employees’ physical and mental resources, job resources are motivational and can lead to positive attitudes, behaviours and well-being ( Bakker & Demerouti, 2017 ). One of the central hypotheses in the JD-R model is that job resources can alleviate the impact of job demands on employees’ well-being ( Toth, Heinänen, & Puumalainen, 2021 ). In the organisational context, social and organisational support serves as the most important resources. Such support may take the form of actions implemented by the organisation and the employee’s superiors, which are often referred to as perceived organisational support (POS). POS denotes employees’ perceptions of how the organisation values their contributions and cares for their well-being ( Turek, 2020 ). Currently, the JD-R model is the most widely used to define the antecedents and consequences of work engagement. Another modification to the model is the inclusion of personal resources as antecedents of work engagement ( Toth et al., 2021 ). According to the theory, an increase in job demands (JD) could lead to exhaustion and burnout ( Bakker & Demerouti, 2017 ; Teo et al., 2020 ). As an example of resources, JR could foster personal growth, work engagement and employee wellbeing ( Bakker & Demerouti, 2017 ; Teo et al., 2020 ). One example of JR is job autonomy. Job autonomy refers to the degree of independence and discretion in employees’ jobs that allow them to autonomously decide what they can do and how they can carry out their work. When employees experience an on-going and continuous period of high JD and low JR, it could motivate them to acquire and use job-related resources to overcome stressful situations ( Bakker & Demerouti, 2017 ; Teo et al., 2020 ). Low JR and high JD can lead to a depletion of energy level, low work identification and lack of perceived efficiency at work, and these situational events start a spiral of resource loss, which signifies the burnout process ( Hobfoll, Halbesleben, Neveu, & Westman, 2018 ). In summary, JD-R perspective postulates that adequate resources could lead to positive outcomes and allow employees to invest and attain more resources ( Hobfoll et al., 2018 ). According to the JD-R model, job resources might be located in the organisation at large, in interpersonal and social relations, at task level or at organisation of work level ( Katou, Koupkas, & Triantafillidou, 2022 ). The main message of the JD-R model is that it distinguishes two parallel mediating processes. The first, called the stress process, argues that high job demands lead to negative work outcomes through work burnout. The se cond, called the motivational process, argues that job resources lead to positive work outcomes through work engagement. The resource dimension of this basic JD-R model has been extended by including personal resources in combination with job resources by Borst, Kruyen & Lako (2019) and Bakker & Demerouti (2017) . JD-R theory explains how working conditions influence employees, and how employees influence their own working conditions ( Bakker & Demerouti, 2018 ). High job demands reflect a process where the organization is requiring from employees continued efforts to accomplish specific goals. However, this process may exhaust employees and produce health problems ( Katou et al., 2022 ).

6. Findings

6.1. Demographic Analysis

Table 6 below shows the demographic analysis of the respondents in eleven (11) categories ranging from voluntary participation of the sampled participants to ownership structure of the SMEs they work for. All participants agreed to voluntarily participate in the study. While female participants represented 54% of the total sampled, 46% of the respondents were males. Participants between the ages of 21 and 30 were the highest, representing 34% of the total sample. Married employees were also the highest at 54% of the total sampled.

Table 6

Table 6. Demographic analysis.

S/N	Demographics	Items	Frequency	Percentage	Cumulative Percentage
1	Voluntary participation	I agree to participate voluntarily	446	100%	100%
		I do not agree	0	0%	100%
		Total	446	100%
2	Age group	Below 21 years of age	45	10%	10%
		21 - 30 Years Old	153	34%	44%
		31 - 40 Years Old	148	33%	78%
		Above 40 Years Old	100	22%	100%
		Total	446	100%
3	Gender	Male	205	46%	46%
		Female	241	54%	100%
		Total	446	100%
4	Marital status	Single	186	42%	42%
		Married	240	54%	96%
		Widowed	7	2%	97%
		Divorced	13	3%	100%
		Total	446	100%
5	Education qualification	Diploma	197	44%	44%
		Bachelor	146	33%	77%
		Masters	65	15%	91%
		PhD	24	5%	97%
		Professional qualifications	14	3%	100%
		Total	446	100%
6	Income range/month	Less than RM 2,000	87	20%	20%
		RM 2,001 - 3,000	131	29%	49%
		RM 3,001 to 5,000	145	33%	81%
		RM 5,000 above	83	19%	100%
		Total	446	100%
7	Designation	Clerical officer	153	34%	34%
		Supervisor	143	32%	66%
		Manager	92	21%	87%
		Senior Manager	42	9%	96%
		Director	16	4%	100%
		Total	446	100%
8	Length of service	Less than 2 years	112	25%	25%
		2 - 5 years	138	31%	56%
		6 - 10 years	109	24%	80%
		Above 10 years	87	20%	100%
		Total	446	100%
9	Employment type	Contract	69	15%	15%
		Part-time	85	19%	35%
		Full-time	292	65%	100%
		Total	446	100%
10	Company status	Micro	117	26%	26%
		Small	143	32%	58%
		Medium	186	42%	100%
		Total	446	100%
11	Ownership structure	Local	290	65%	65%
		Foreigner	87	20%	85%
		Mixed	69	15%	100%
		Total	446	100%

6.2. Validity and Reliability

The Cronbach’s alpha (α) coefficients of the variables are shown in Table 7 below. While Cronbach’s alpha (α) coefficient performance appraisal is 0.899, that of work engagement is 0.887, depicting very good reliability coefficients. Therefore, all measurement items are considered reliable.

6.3. CFA

In SmartPLS, results are highlighted depending on whether a certain threshold is reached for a particular analysis. As shown in Table 8 below, the standardized factor loadings of all items are greater than 0.5, except for one out of seven constructs used to measure Compensation & Benefits (CB). However, this was not removed in accordance with Stevens (2002) , who proposed that a factor loading’s value should be more than 0.4 for interpretational purposes. Two items out of nine used to measure work engagement were greater than 0.5 but less than 0.7; however, this is acceptable in accordance with Hair et al. (2022) . Similarly, all items have AVE in the range of 0.531 and 0.666 and CR between 0.910 and 0.923. Therefore, all AVE and CR met the required rule of thumb having exceeded the minimum value of 0.50 and 0.70, respectively. The Cronbach’s Alpha (CA) coefficients of both variables also exceeded the threshold of 0.7. Therefore, it can be concluded that the data used in this study are reliable and valid.

Table 7

Table 7. Cronbach’s alpha coefficients.

S/N	Constructs	Number of items	Cronbach’s Alpha Coefficient
2	Performance Appraisal (PA)	6	0.899
3	Work Engagement (WE)	9	0.887

Table 8

Table 8. Factor loadings.

Constructs	Items	Loadings	AVE	CR	CA
Performance Appraisal (PA)	PA1	0.781	0.666	0.923	0.899
	PA2	0.825
	PA3	0.850
	PA4	0.842
	PA5	0.800
	PA6	0.796
Work Engagement (WE)	WE1	0.761	0.531	0.910	0.889
	WE2	0.795
	WE3	0.790
	WE4	0.730
	WE5	0.602
	WE6	0.652
	WE7	0.720
	WE8	0.768
	WE9	0.718

6.4. SEM

Bootstrapping is employed to determine the path coefficient and t-value for the relationship between constructs. The bootstrapping procedure’s start dialog allows you to pick the significance level (e.g., 0.05). In the bootstrapping result report, p values over this value appear in red, while all bootstrap p values less than or equal to this value appear in green. In order to ascertain the significance of the correlations, Hair et al. (2022) recommend using a bootstrapping approach with 5000 resamples to obtain the path coefficients and corresponding t-values. After that, at a predetermined significance level, the resultant t-value is contrasted with the crucial t-value. This study utilised two-tailed because a two-tailed test uses both the positive and negative tails of the distribution. In other words, it tests for the possibility of positive or negative differences. t-value is simply the calculated difference represented in units of standard error. The greater the magnitude of t-value, the greater the evidence against the null hypothesis. When interpreting t-value, the researcher can reject the null hypothesis (H0: β = 0) if the t-value is greater than 1.96 in absolute value with a level of significance of 0.05 ( Hair et al., 2014 ). The study utilised 95% power (0.05). The p-value for each term tests the null hypothesis that the coefficient is equal to zero (no effect). A low p-value (< 0.05) indicates that you can reject the null hypothesis. If 95% of the t distribution is closer to the mean than the t-value on the coefficient the researcher is looking at, then the p-value is 5%. This is also referred to a significance level of 5%. This study utilised 95% of the t distribution ( Hair et al., 2014 ). A p-value of 5% or less is the generally accepted point at which to reject the null hypothesis. With a p-value of 5% (or 0.05), there is only a 5% chance that results the researcher generates would have come up in a random distribution, so the researcher can conclude that with a 95% probability of being correct that the variable is having some effect, assuming the model is specified correctly ( Hair et al., 2014 ).

The results of this study, as shown in Table 9 below, show that the R² value for the direct relationships between Performance Appraisal (PA) and Work Engagement (WE) is 0.418 indicating that the independent variable (PA) contributed 41.8% to the variation in the dependent variable—Work Engagement. In other words, the remaining 58.2% (WE) are explained by other HRM factors such as Training & Development, Compensation & Benefits, Career Development, Recruitment & Selection, etc., that are not tested in this study. Table 10 below depicts the summary of hypothesis.

Figure 4 depicts the confirmatory factor analysis (path model) carried out using the SMARTPLS software and shows the factor loading of each item.

Figure 5 depicts the structural equation modelling carried out using the SMARTPLS software and shows the factor loading of each item.

Figure 4

Figure 4. CFA.

Figure 5

Figure 5. SEM.

Table 9

Table 9. Summary of coefficient of determination values.

Predictor Construct	Target Construct	R2	Predictive Accuracy
PA → WE	Work Engagement (WE)	0.418	Moderate

Table 10

Table 10. Summary of path coefficient and t-values.

S/N	Hypotheses	Relationship	Path Coefficient (β)	t-value	p-value	Decision
H1	There is a positive significant relationship between performance appraisal dimension of HRM practices and work engagement.	PA → WE	0.630	19.334	0.000	Supported

6.5. Hypotheses Testing

H1: There is a positive significant relationship between effective performance appraisal system dimension of HRM practices and work engagement.

This hypothesis assumes that, as effective performance appraisal system increases in SMEs, work engagement among the employees also increases. In other words, effective performance appraisal system influences work engagement on in a positive direction. From Table 10 above and looking at the three statistical measures, it can be deduced that the hypothesis is statistically supported by the PLS model. The decision is arrived at having checked that the t-value and p-value which both met the required rules of thumb. The t-value of 19.334 is higher than 1.96 (95% distribution) and the p-value of 0.000 is lower than 0.05. There is also a positive significant relationship between the two variables. This can be interpreted that the null hypothesis is statistically rejected. Simply put, effective performance appraisal system dimension of HRM practices significantly influenced work engagement (WE) among SME employees in Malaysia. The Beta value of 0.630 showed a positive relationship of 63.0% strength between the variables. Hence, the hypothesis 1 which states there is a positive significant relationship between effective performance appraisal system dimension of HRM practices and work engagement is statistically accepted. The findings of this study correspond with that of other researchers who found positive significant relationship between the variables. For example, Aktar & Pangil (2018) , Alzyoud (2018) , Memon et al. (2021) , Gim et al. (2022) , Saad et al. (2021) , Sepahvand & Bagherzadeh Khodashahri (2021) all found that the HRM practice-performance appraisal had a positive significant impact on employee engagement. In other words, when employees perceive the performance appraisal to be an effective appraisal system with attributes such as clear appraisal objectives, well defined appraisal criteria, constructive post appraisal interview, continuous feedback, etc., then they exhibit positive behaviour or a positive state of mind at work leading to positive work-related outcomes. Employees who are satisfied with the performance appraisal systems provided by their organisations tend to have high levels of work engagement and are energetic, dedicated and immersed in their work.

7. Conclusion

This study investigated the relationships between performance appraisal and work engagement among SMEs in Malaysia. The independent variable in this study is performance appraisal, while the dependent variable is work engagement. There is only one direct hypothesis associated with the research objective—H1: There is a positive significant relationship between the effective performance appraisal system dimension of HRM practices and work engagement. While Performance Appraisal (PA) was measured using six constructs, the dependent variable—work engagement was measured using nine constructs (3 each for vigour, absorption and dedication). Likert 7-Scale was utilised in this study where: 1 = Strongly disagree, 2 = Disagree 3 = Somewhat disagree, 4 = Neither agree or disagree, 5 = Somewhat agree, 6 = Agree and 7 = Strongly agree. The validity and reliability of the constructs were tested using Cronbach’s alpha coefficients and the assessment of the measurement model was measured using the Outer Loadings, Average and composite reliability. The discriminant validity was measured based on the multitrait-multimethod matrix: the heterotrait-monotrait ratio of correlations (HTMT). Bootstrapping is employed to determine the path coefficient and t-value for the relationship between constructs. The bootstrapping procedure’s start dialog allows you to pick the significance level (e.g., 0.05). In the bootstrapping result report, p values over this value appear in red, while all bootstrap p values less than or equal to this value appear in green. In order to ascertain the significance of the correlations, a bootstrapping approach with 5000 resamples was recommended to obtain the path coefficients and corresponding t-values. After that, at a predetermined significance level, the resultant t-value is contrasted with the crucial t-value. This study utilises two-tailed because a two-tailed test uses both the positive and negative tails of the distribution. The results of this study showed that the R² value for the direct relationships between Performance Appraisal and Work Engagement (WE) is 0.418, indicating that the independent variable contributed 41.8% to the variation in the dependent variable—Work Engagement (WE). In other words, the remaining 58.2% (WE) are explained by other factors that are not tested in this study. Overall, the only hypothesis (PA → WE) attached to this research objective was statistically accepted. The results agreed with the findings of many researchers. The findings were based on the relevant rule of thumb, including the T-value, p-value and β-value.

8. Significance, Contribution and Limitations

8.1. Research Significance and Contribution

The findings of the study show that Performance Appraisal plays a significant role in employee work engagement. The study further proves that Performance Appraisal plays a role in aligning sustainability with the organisation’s internal practices, which contributes to the firm’s competitiveness. Additionally, through compensation & benefits, strategic and operational support can be provided by integrating the area with organisationl sustainability and producing synergistic results. Adequate and competitive compensation & benefits increase positive attitudinal and behavioural outcomes and investment in employees, and providing them with a voice, support and engaging work in a positive environment fosters work-related well-being. Theoretically, this study contributes and advances emerging literature on work engagement and employee retention which has previously produced conflicting results. While some researchers found that Performance Appraisal significantly influenced work engagements, others argued otherwise. Based on the tenets of J-D and SET theories, the primary contribution of this study lies in exploring the paradoxical consequences of Performance Appraisal on Work engagement. Practically, the findings of the study are significant in enhancing HRM practices and work engagement among SMEs in Malaysia. This should have a positive impact on employee retention and thereby help these SMEs to retain knowledge within the organisations. The findings of this study specifically contribute to HRM strategies for SMEs through formulation of objective and effective performance appraisal strategies that address biased evaluation, unclear expectations, lack of constructive feedback and not linking employee’s performance appraisal to actionable development and rewards.

8.2. Limitations of the Study

This study is not without some limitations, but these did not significantly impact the results of the research and or intrinsic to the current state of body knowledge. This study focuses on SMEs in Malaysia; however, it was not an easy task getting the list of total SMEs in Malaysia and this researcher faced many challenges before the data were realised by the body responsible for collating SMEs data. The researcher was unable to visit all states in Malaysia due to some constraints. Furthermore, language created a barrier during data collection. The questionnaire was converted to Bahasa Melayu for easy understanding; however, some meanings were lost in translation. This resulted in some missing values and outliers; however, this limitation was addressed during data analysis. The study relied on self-administered questionnaires, which may have created a social desirability bias and misinterpretation among the participants. Additionally, 35 questionnaires were excluded from the study due to many missing data and outliers. However, this did not impact the overall generalizability of the study. Lastly, the service sector accounts for the majority of responses due to the fact that the majority of Malaysian SMEs are in the service sector, according to data from the SME Corporation Malaysia. Approximately 89.2% of all Malaysian SMEs are operating in the service sector.

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