literature review on employee engagement and organisational performance

• Open access
• Published: 27 February 2024

Effects of employee engagement on organizational performance: case of public universities in Ethiopia

• Dawit Udessa Gede   ORCID: orcid.org/0000-0003-2032-016X 1 &
• Admassu Tesso Huluka   ORCID: orcid.org/0000-0002-1946-0977 2  

Future Business Journal volume  10 , Article number:  32 ( 2024 ) Cite this article

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The objective of this research is to examine the impact of Employee Engagement on Organizational Performance within Public Universities in Ethiopia. It aims to explore the relationship between employee engagement and the overall performance of these institutions, specifically focusing on public universities. By providing significant insights and recommendations, this research will contribute towards the development of strategies that can enhance employee engagement and improve the overall performance of Ethiopian public universities. The study utilized both quantitative and qualitative approaches, employing descriptive and explanatory research designs. To gather data, three Ethiopian universities were selected based on their establishment date, and a random selection technique was used to include 365 personnel in the sample. Descriptive statistical tools like mean and standard deviation were employed, while structural equation models were utilized for confirmatory factor analysis and path analysis. The study's findings indicate that vigor, dedication, and absorption all have a significant and favorable impact on organizational performance in higher education. The study findings also indicate that the performance of study institutions differs based on the extent of employee involvement. This research introduces a fresh perspective of the relationship between employee engagement and organizational performance by concentrating specifically on the context of public universities in Ethiopia. This sheds light on the distinctive dynamics and obstacles encountered by these institutions. Furthermore, the study adds to the current body of knowledge by exploring the relationship between employee engagement and organizational performance, offering valuable insights and suggestions to enhance performance in the setting of Ethiopian public universities.

Introduction

In today's rapidly evolving business landscape, organizations are increasingly recognizing the pivotal role that employee engagement plays in driving sustainable success and achieving competitive advantage [ 45 ]. Employee engagement refers to the emotional commitment and active involvement of employees towards their work, organization, and its goals, while organizational performance represents the overall effectiveness, productivity, and success of the organization [ 50 ]. For this research, the concept of employee engagement pertains to the emotional and psychological bond that exists between an employee and their work, organization, as well as its objectives. On the other hand, organizational performance measures the extent to which an organization successfully attains its intended goals and desired outcomes. Over the past decades, extensive research has been conducted to understand the dynamics of employee engagement and its impact on organizational outcomes. Zada and Ismael [ 58 ] concluded that organizations with highly engaged employees tend to experience lower turnover rates, reduced absenteeism, and enhanced employee retention. They suggested further that Engaged employees are more likely to be motivated, satisfied, and committed to their work, resulting in increased loyalty towards the organization. According to Kurniawati and Raharja [ 21 ] higher levels of employee engagement are associated with improved customer satisfaction and loyalty, as engaged employees are more likely to deliver exceptional customer service and foster strong customer relationships. There is evidence to suggest that engaged employees are more innovative, creative, and willing to go above and beyond their job requirements, leading to higher levels of productivity and organizational performance [ 22 , 23 ]. Despite the wealth of existing research, there are still gaps in our understanding of employee engagement and its consequences for organizational performance. To address these gaps, further exploration of the underlying mechanisms and specific impacts is necessary. This will enable organizations to develop tailored strategies and create an environment that fosters engagement, ultimately leading to improved performance outcomes.

Extensive studies have been conducted to investigate the relationship between employee engagement and the overall performance of organizations [ 14 , 21 , 22 ]. However, there are still several gaps and areas that require further investigation. According to Lemon and Macklin [ 39 ], the process of establishing a causal link between employee engagement and organizational performance is intricate and influenced by various contextual elements specific to different institutions. These factors have often been overlooked or neglected in previous studies [ 8 ] which can be termed as contextual or situational gaps. The relationship between engagement and performance can vary across industries, organizations, and contexts [ 3 ]. Factors such as organizational culture, leadership style, job characteristics, and industry dynamics can influence the strength and direction of this relationship [ 10 ]. Consequently, a standardized relationship may not uniformly apply to all organizational settings [ 1 , 15 ]. Moreover, while research suggests a positive association between employee engagement and organizational performance, it is challenging to determine whether engagement leads to performance improvements or if high-performing employees are more likely to be engaged [ 44 ]. Other factors, such as job satisfaction, motivation, and organizational support, can also influence both engagement and performance [ 26 ]. There are also geographical and population gaps in developing countries like Ethiopia where no sufficient investigation has been undertaken about employee engagement level and effects on organizational performance [ 36 ]. Almost all empirical evidences about employee engagement and effects from western and from industry developed countries. In Ethiopia, even though different scholars attempted to study about employee engagement and effects on organizational performance [ 13 , 16 , 17 , 30 , 52 ]. Rate of engaged employees have not been yet explored and identified. But there is only study conducted by Yallew [ 57 ] on higher education in Ethiopia recent development and challenges. The author found out that low level of motivation and engagement of academic staff in Ethiopian universities. As a consequence of the situation, academic staff in university tends to migrate to other sectors of the economy for the search of better opportunities and working condition [ 57 ]. The focus of that study is only on identification of reason for disengagement of higher educational staff. It not included in turn effects on organizational performance.

Moreover, the evidences from Ethiopian higher educational institutions show that government aspiration to increase the quality of the overall system through quality enhancement of graduates and research output backed by different guidelines and rules, loosen lecturer engagement [5, 23]. Specifically, guidelines during Covid-19 pandemic affects educational delivery system and forced it to online and distance learning, which in turn negatively affects engagement level of the lecturers. Top challenging issue of online teaching is decline in lecturer moral that lead to low engagement level as the consequences of covid-19 pandemic [ 53 ].

Hence, this study was to provide fresh contextual understanding within the field of management literature specifically related to Ethiopian higher educational institutions, where such evidence has not yet been documented. The main focus of this research was to employ a combination of condensed viewpoints derived from various theories in order to address and bridge the current gaps in knowledge. Specifically, the theories employed include the Job Demands-Resources Model, Self-Determination Theory, Job Characteristics Theory, and Kahn's Engagement Theory. These theoretical frameworks provide distinct and valuable understandings of the relationship between employee engagement and organizational performance [ 55 ]. They highlight the importance of job attributes, social interactions, psychological requirements, and positive emotional states in fostering engagement and improving performance results.

Therefore, the primary objective of this study was to examine the impact of employee engagement on the performance of Ethiopian public universities. It is worth noting that this particular area has been overlooked and lacks previous research on the subject matter. The outcomes of this investigation hold great value for the field of management and theory as they shed light on employee engagement practices and their direct influence on the overall performance of universities in Ethiopia.

Literature review

Theoretical framework and hypothesis, theoretical review.

Theoretical perspectives and arguments were utilized to explore the association between employee engagement and organizational performance. Various theories, including the Job Demands-Resources Model, Self-Determination Theory, Job Characteristics Theory, and Kahn's Engagement Theory, were employed to examine the impact of employee engagement on organizational performance.

Job Demands-Resources Model This model suggests that engagement is influenced by job demands (such as workload and time pressure) and job resources (such as autonomy, social support, and opportunities for growth). Engaged employees perceive their job resources as sufficient to meet the demands, leading to positive outcomes such as increased job satisfaction, motivation, and performance [ 20 , 33 , 42 ]. According to the JD-R model, work engagement is shaped by the equilibrium achieved between the demands and resources present in a job [ 42 ]. This equilibrium ultimately manifests as vigor, dedication, and absorption, which can be considered as dimensions of employee engagement [ 25 ].

The Self-Determination Theory (SDT) revolves around the inherent drive and psychological requirements of individuals [ 5 ]. When it comes to involvement, SDT proposes that the levels of devotion and engagement can be understood through the fulfillment of psychological needs such as autonomy, competence, and relatedness [ 56 ]. If employees perceive a sense of independence in their work, feel skilled and accomplished, and enjoy positive social interactions and connections, they are more inclined to exhibit dedication and complete absorption in their assigned tasks [ 18 ].

Job Characteristics Theory This theory argues that certain job characteristics influence engagement and performance [ 2 ]. According to this theory view, key job characteristics include skill variety, task identity, task significance, autonomy, and feedback [ 37 ]. Engaged employees are more likely to experience meaningful and challenging work, leading to higher levels of performance and satisfaction [ 24 ]. The Job Characteristics Model places great emphasis on the significance of particular job characteristics in promoting employee engagement. Vigor, dedication, and absorption are regarded as the end results of meaningful and stimulating work [ 32 ]. According to this model, there are five essential job characteristics such as skill variety, task identity, task significance, autonomy, and feedback that have an impact on individuals' psychological states, ultimately influencing their level of engagement [ 2 ].

Kahn's Engagement Theory, developed by William A. Kahn, provides a comprehensive framework for understanding employee engagement [ 46 ]. According to Kahn, engagement is a psychological state that occurs when individuals bring their full selves, both physically and emotionally, to their work roles [ 29 ]. It goes beyond mere job satisfaction and involves a deep sense of connection, fulfillment, and involvement in one's work. Kahn, provides a framework for understanding employee engagement and its dimensions, including vigor, dedication, and absorption [ 19 ]. According to Kahn, employee engagement is a state of "psychological presence" in which individuals bring their full selves, both physically and cognitively, to their work roles. Within this theory, vigor, dedication, and absorption are key components of engagement [ 43 ]. These theories provide different perspectives on the underlying mechanisms and factors that contribute to vigor, dedication, and absorption within the broader context of employee engagement.

The theories reviewed here above are essentially provide a foundation for organizing knowledge, explaining phenomena, generating hypotheses, making predictions, integrating findings, and guiding practical applications of this research. From this theoretical basis that researchers enabled to formulate hypothesis. They help us to identify study variables, specify the expected relationships between them, and generate testable predictions.

After reviewing the theoretical framework discussed above, we have formulated a set of study hypotheses to establish a clear focus and guide the design and analysis of our research. These hypotheses are as follows:

Hypothesis 1: Vigor exerts a statistically significant influence on organizational performance. Hypothesis 2: Dedication exerts a statistically significant influence on organizational performance. Hypothesis 3: Absorption exerts a statistically significant influence on organizational performance.

Empirical literature review

Several researchers have conducted extensive research on the impacts of employee engagement on organizational performance and have indicated a statistically significant positive correlation [ 11 , 19 , 31 , 35 , 47 , 49 ]. However, the concept of engagement remains perplexing, and its relationship with organizational performance is intricate and multifaceted.

Researchers have proposed that despite the intricate nature of the relationship between employee engagement and organizational performance, there are positive and substantial effects of employee engagement on performance. Gupta and Sharma [ 19 ] concluded in his study on impact of employee engagement on performance that when employees are engaged, they are more likely to be motivated, committed, and satisfied with their work, which can lead to several positive outcomes. He posit further that engaged employees are more focused, proactive, and willing to exert extra effort, leading to higher levels of productivity and efficiency. According to Ikon and Chika [ 31 ] research findings engaged employees are driven to achieve their goals and contribute to the organization's success. They suggested further that engaged employees are willing to go beyond their job descriptions, share ideas, and take risks, leading to improved problem-solving, process improvement, and innovation within the organization.

However, measuring and defining employee engagement can be a daunting task due to its intricate and multi-dimensional nature [ 12 , 54 ]. Moreover, the relationship between engagement and performance can differ depending on the industry, organizations, and situations, which restricts the ability to apply findings universally [ 6 ]. Additionally, maintaining consistently high levels of employee engagement in the long run is challenging for companies. It is crucial to identify and overcome any potential obstacles or difficulties that may hinder the maintenance and improvement of engagement levels [ 48 ].

In a recent study exploring employee engagement, Lopez-Zafra et al. [ 40 ] uncovered significant benefits of enthusiasm and energy, commonly referred to as vigor, on organizational performance. Their research specifically examined the role of vigor at work as a mediator in this relationship. Additionally Corbeanu and Iliescu [ 8 ] conducted a separate investigation and similarly established the positive impact of vigor on organizational performance. Kurniawati and Raharja [ 21 ] conducted a comprehensive review of existing literature to investigate the various factors that affect the effect of employee engagement on the performance of organizations. Study aimed to gain insights into the relationship between employee engagement and organizational performance by thoroughly analyzing relevant research articles published in reputable journals between 2010 and 2022. The findings strongly indicated a substantial relationship between employee engagement and organizational performance, supported by a p value of less than 0.01. These results suggest that higher levels of employee engagement are linked to enhanced organizational performance. Jaya and Ariyanto [ 32 ] conducted a study aiming to examine the impact of vigor, dedication, and absorption on the performance of employees at PT Garuda Indonesia Cargo. The findings of their research revealed that vigor, dedication, and absorption positively and significantly influence employee performance. However, it is important to acknowledge that this study has certain limitations. These limitations include a small sample size, limited context as it focused solely on the sector head office, and an undisclosed data collection period. To improve the reliability and applicability of the obtained results, it is recommended that future research be conducted using larger and more diverse samples. Additionally, employing longitudinal designs and considering additional variables would contribute to enhancing the strength and generalizability of the findings.

Methodology and materials

The study utilized both quantitative and quantitative research methodology with an explanatory design to explore the relationship between independent variables and dependent variables. A cross-sectional survey design was implemented for this purpose. The Ethiopian public universities were chosen for examination based on the assumption that they are relevant institutions for this study. Conducting thorough investigations to explore the impact of employee engagement on the performance of public universities in Ethiopia carries great importance. These institutions have the potential to serve as vital sources for skilled personnel, thereby playing a pivotal role in driving economic transformation and national development. This research is essential in order to enhance the overall effectiveness of these organizations, foster the well-being and satisfaction of employees, enhance student outcomes, contribute to the development of the nation, and provide valid information for making decisions based on evidence.

In order to select the universities to be included in the study, three public universities in Ethiopia were chosen based on their reputation and level of achievement. Among the universities in the south direction, Hawasa University was found to meet the criteria of being a first-generation university as well as a research university, and thus it was selected for the study. Similarly, Wolaita Sodo University meets the requirements of a second-generation and applied university and has been chosen accordingly. Moreover, Bule Hora University has been selected as a third-generation and comprehensive university. According to the human resource records of these three universities, the combined population of university staff amounts to 19,470 individuals, out of which 17,875 were specifically considered based on their employment status. The target population for this study comprised permanent employees working at the sampled universities, excluding expatriate staff and temporary employees.

The instance being examined is a component of the chosen and analyzed target populations, which is done to make inferences about the overall population [ 38 ]. Due to the substantial size of the target population, it is recommended to utilize Cochran's [ 7 ] formula to determine the necessary sample size. To calculate the required sample size, the maximum variability is considered to be 50% ( p  = 0.5), and a confidence level of 95% is chosen with a precision of ± 5%. The calculations for the sample size are as follows: p  = 0.5, thus q = 1 − 0.5 = 0.5; e = 0.05; z = 1.96.

The given formula can be expressed as follows: no = (z 2 pq)/e 2 , where no represents the sample size, z is the critical value chosen for the desired confidence level, p is the estimated proportion of a specific attribute present in the population, q is equal to 1 minus p, and e denotes the desired level of precision. By substituting the given values, we obtain: no = ((1.96) 2 (0.5) (0.5))/(0.05) 2  = 384.

According to Cochran, if the population is finite and the sample size exceeds 5% of the population (which is 894 in this case), a slight adjustment to the sample size is necessary. Cochran [ 7 ] proposed a correction formula to calculate the final sample size in such scenarios. The formula is as follows: n = no/(1 + ((n o -1))/N), where n represents the adjusted sample size, N is the population size, and no is the initial sample size. Applying this formula, we find: n = 384/(1 + ((384 − 1))/17,875) = 376.

Finally, we have distributed the sample size of 376 among three universities using the probability proportion to size (PPS) method. A total of 376 individuals were chosen to represent a sample group from three different universities. The selection process ensured that the sample size from each university was proportional to the overall population of that university. To achieve this, the method of proportional quota sampling was employed, taking into consideration the four essential aspects of the universities: good governance, teaching and learning, research, and community services. In line with this approach, 25% of the sample quota was allocated to administrative staff, while the remaining 75% was assigned to academic staff. To randomly select the sample group from each university, a simple random sampling technique utilizing a random number table was implemented. This method ensured an equal opportunity for all university staff within their respective quota to be included in the sample group.

The data collection process involved using a 5-point Likert scale and open-ended questions. The utilization of a Likert scale is preferred due to its ability to minimize the influence of respondents' opinions being solely based on one or two aspects of the situation. In order to ensure clarity and ease of comprehension for both survey administrators and respondents, the Likert scale used for this study comprised of five-point scale questions. The selection of sample sizes in each university was then conducted in a manner that maintained a proportional rate. The survey data was gathered in English as all the participants were university staff who possess the proficiency to comprehend and respond in English. It is worth noting that English serves as the primary instructional language in Ethiopian Universities. Hence, there is no necessity to translate the data collection questions into local languages. The survey was conducted within 4 months from December 2021 to March 2022. The survey questions utilized to gather the data were examined to determine the pattern of responses provided by participants (unidimensionality) through item-to-rest correlation. All of the survey items utilized for data collection exhibited item-to-rest correlation coefficients surpassing 0.3, which is deemed an acceptable threshold according to the general guideline. The content validity of the instruments was assessed through rigorous peer review, involving soliciting feedback from experienced researchers and esteemed professors, and subsequently obtained their approval. A structural equation model was employed to analyze the data through confirmatory factor analysis and path analysis models. Confirmatory factor analysis (CFA) was used to measure model fitness by checking data validity and reliability and testing model goodness of fit. Whereas, the path analysis model was used to examine the relation between constructs. Qualitative data was used as evidence for quantitative results in the discussion section and used for triangulation.

In order to ensure legality and social acceptability, researchers obtain consent from the top management of the university. They also clarify the objective and significance of the study to the deans, directors, departments, and offices included in the sample frame. All participants are informed that the collected data will be used solely for academic research purposes, and their consent is obtained. It is emphasized to the participants that they are under no obligation to answer the questions unless they willingly choose to do so. Once the data collection process is completed, the information is double-checked with the relevant authority to enhance their confidence in the provided information and to ensure the overall acceptability of the research findings.

Data analysis and presentation

In this section, we will focus on the examination and representation of the original data obtained from respondents through written and interview questionnaires. We proposed a sample size of 376 and distributed the questionnaires accordingly. Out of the distributed questionnaires, we were able to collect 365, with 11 questionnaires remaining unreturned. Therefore, the response rate of the distributed questionnaires was approximately 97%, indicating a significantly high rate of response that is adequate for further data analysis.

A comparative analysis on the implementation of concepts across educational institutions

Descriptive analysis was employed to depict the fundamental attributes of the study's primary variables, from which research data was gathered. Additionally, it was utilized to elucidate the level of practical implementation of the study's concepts within the relevant institutions. The average mean of participants' responses was used to determine the application level. A higher average mean was assumed to signify a commendable application of the issue being investigated, while a lower average mean indicated a lower level of application. The values presented in Table  1 below provide a concise summary of the comparative analysis results among the various institutions involved in the study.

The data presented in Table  1 demonstrates the level of implementation of the study's concepts, which ranged from low to satisfactory. The average mean of the participants' responses indicated that the variables of the study fell between 2.60 and 3.48, with variations observed among the universities involved in the study. Employee engagement levels were found to range from 2.6 to 3.2, while organizational performance spanned from 2.95 to 3.48. The measurement of employee engagement considered three parameters: vigor, dedication, and absorption. The responses of the participants exhibited a consistent level of variability across all variables, with a standard deviation ranging from 0.54 to 0.64. This suggests that there was relatively little variation in the participants' responses. Additionally, the overall average mean of the study institutions indicates that organizational performance is directly influenced by employee engagement.

Measurement model

Employee engagement construct

To evaluate the degree of employee engagement as a predictor of organizational performance, three specific aspects were considered: Vigor, Dedication, and Absorption. The items used for this assessment underwent thorough tests to ensure their validity and reliability, as demonstrated in Table  2 below. The values presented in Table  2 indicate that all items possess Cronbach's alpha values greater than 0.83, which signifies a high level of internal consistency among the items. Additionally, all items exhibited factor loadings above or equal to the minimum requirement of 0.7, with average variance extracted values exceeding 0.60. These findings serve as evidence for the presence of both composite reliability and convergent validity. Furthermore, the correlation values between each variable are significantly lower than the square root values of the average variance extracted (AVE) for each construct. Similarly, the squared values of the correlations between constructs are also considerably below the AVE values for each construct. This confirms that the construct possesses discriminant validity, thereby establishing its appropriateness.

In summary, the values presented in Table  2 provide a comprehensive overview of the measurement aspects related to the employee engagement construct. These values were obtained through confirmatory factor analysis, which serves as a robust method for testing both validity and reliability.

Assessments of model fitness for employee engagement construct

The RMSEA value for this particular model is 0.066, which falls within the range of close fit, as a value below 0.08 is considered to indicate a good fit according to Cudeck et al. [ 9 ] and Steiger [ 51 ]. Additionally, the precision of the RMSEA estimate can be assessed through the point estimate, which represents the lower boundary of the confidence interval. In this case, the lower bound of the model is 0.046, which is below the suggested close fit value of 0.05 according to MacCallum et al. [ 41 ]. Consequently, the model is considered to be a close fit for the given data.

Another measure of model fit is the Standardized Mean Square Residual (SRMR), which provides an overall assessment based on squared residuals. In this model, the SRMR value is 0.028, which is lower than the minimum cutoff points suggested by the rule of thumb proposed by Hu et al. [ 28 ]. Furthermore, the comparative fit index is used to evaluate the fitness of the model of interest in comparison to a baseline model. The CFI and TLI are two comparative fit indexes, with values of 0.975 and 0.963, respectively, for this particular model. Both of these values exceed the minimum cutoff point of 0.95, as suggested by Kaplan [ 34 ]. Therefore, the model is considered to be the best fit based on both absolute and comparative fit indexes.

The Table  3 below displays the summary results of the model goodness-of-fit test for the employee engagement construct.

Organizational performance construct

The results of the assessment of construct validity and reliability revealed that all the items used to measure concepts are suitable. The data presented in Table  4 below demonstrate that all items have Cronbach's alpha values exceeding 0.89, which indicates a high level of internal consistency among the items. Additionally, the factor loadings of all items were above or equal to the minimum required level, with average variance extracted values surpassing 0.50. These findings indicate the presence of composite reliability and convergent validity.

Furthermore, the correlation values between each variable are significantly lower than the square root values of the average variance extracted (AVE) of each construct. Similarly, the squared values of the correlation between constructs are also considerably below the AVE values of each construct. As a result, it can be concluded that the construct exhibits discriminant validity.

The figures presented in Table  4 provide a comprehensive summary of the confirmatory factor analysis conducted.

Assessments of model fitness for organizational performance construct

The model's fitness was evaluated using descriptive fit indices, which compared the sample covariance matrix to the model's expected covariance matrix. The RMSEA value for this model is 0.018, falling within the acceptable range of less than 0.05, which is considered the best fit. According to Hu and Bentler [ 27 ], a value below 0.06 is recommended as a cutoff criterion, making this model acceptable. Additionally, the SRMR value for this model is 0.028, which is lower than the minimum cut points typically used as a rule of thumb and indicates a good fit for the data [ 28 ].

The comparative fit indexes (CFI: 0.998, TLI: 0.997) also suggest a good fit for the model when compared to the data. Table  5 provides a summary of the goodness-of-fit indices for the organizational performance construct.

Structural model and hypothesis testing

The aim of this study was to evaluate the overall fitness of the structural model. To accomplish this, we utilized the same goodness of fit indices that were employed in the measurement model. Additionally, we examined the estimated parameters to ensure their statistical significance. Below, Fig.  1 illustrates the conceptual relationship between the latent construct and its respective indicators.

The RMSEA value for this particular model is 0.068, which falls within the acceptable range of less than 0.08. This level of fit is considered adequate based on the suggestions of Cudeck et al. [ 9 ] and Steiger [ 51 ]. Additionally, the precision of the RMSEA estimate can be assessed through the point estimate, which helps evaluate the model fitness. MacCallum et al. [ 41 ] proposed that for a close fit, the lower boundary (left side) of the confidence interval should be less than 0.05, and for an exact fit, it should contain zero. In this case, the lower bound of the model is 0.034, which aligns with the suggested close fit value of 0.05. Therefore, the model demonstrates a close fit for the given data. The SRMR value for the model is 0.036, which is lower than the minimum cut points suggested by Hu et al. [ 28 ]. This indicates a good fit of the model for the data. The comparative index (CFI) value for this study is 0.982, which is a good indicator of comparative fit and surpasses the minimum cut points. Additionally, the TLI value is 0.966, exceeding the minimum acceptable level of 0.90 and indicating a comparative fitness of the model. Hence, the model is considered the best fit in terms of both absolute and comparative indexes. The overall coefficient of determination (CD) for the model is 0.973. Since the CD value is approaching 1, it further confirms that the model is the best fit for the data. In summary, the table below presents the values of the goodness-of-fit test for the structural model (Table  6 ).

Path analysis

The degree to which the model fits the theoretical one in path analysis is assessed by considering the collective impact of all exogenous variables on the endogenous variables, known as R 2 . In path analysis, the R 2 value represents the coefficient of determination, also known as the association index. This value serves as a scale to measure the magnitude of the combined effect of all exogenous variables on the endogenous variables simultaneously. Figure  2 illustrates the causal relationship between the explanatory variables and the predicted variable.

The data presented in Table  7 indicates that there is a significant relationship between the exogenous variable (employee engagement) and the endogenous variable (organizational performance). All three parameters used to measure employee engagement (Vigor, Dedication, and Absorption) significantly contribute to the variability in the dependent variable of the study. The model fits the data well, with a value of 82% representing the magnitude of the effect of the exogenous variable on the endogenous variable. The coefficient of determination (R 2 ) in the model is 0.8212, which indicates the proportion of the organizational performance's variation that can be explained by employee engagement. According to the association index, approximately 0.18 percent of the variability in organizational performance is attributed to factors not included in the model. The summarized results of the path analysis can be found in Table  7 below.

Summary of hypothesis testing

The assessment result of the hypothesis formulated for this study was presented as follows.

Hypothesis 1: Vigor exerts a statistically significant influence on organizational performance.

The variable have a p value of 0.000, indicating their significant impact. The regression coefficients are 0.19, which is much lower than the 5% significance level. As a result, we reject the null hypothesis and accept the alternative hypothesis proposed by the researcher. Consequently, vigor has a noteworthy and direct influence on organizational performance. In this context, the regression coefficient signifies that a one standard deviation increase in vigor results in a 0.19 standard deviation improvement in organizational performance, assuming all other variables remain constant.

Hypothesis 2: Dedication exerts a statistically significant influence on organizational performance.

The importance of the variable has been determined to have a p value of 0.002, along with regression coefficients of 0.12, which is considerably lower than the 5% significance level. As a result, the null hypothesis is rejected, and the alternative hypothesis, proposed by the researcher, is accepted. Consequently, it can be concluded that employee dedication has a significant and direct impact on organizational performance. In this context, it can be inferred that a one standard deviation increase in employee dedication leads to a corresponding 0.12 standard deviation improvement in organizational performance, assuming all other variables remain constant.

Hypothesis 3: Absorption exerts a statistically significant influence on organizational performance.

The variable has a significance ( p ) value of 0.001, indicating a strong statistical relationship. The regression coefficients, which measure the strength of this relationship, are 0.16. This value is significantly lower than the 5% level of significance, providing evidence to reject the null hypothesis and accept the alternative hypothesis proposed by the researcher. Consequently, employee absorption plays a crucial role in influencing organizational performance.

To further understand the impact of this relationship, we can interpret the regression coefficient for the variable. A one standard deviation increase in employee absorption is associated with a 0.16 standard deviation improvement in organizational performance, assuming all other variables remain constant. This suggests that enhancing employee absorption can lead to notable enhancements in organizational performance.

Summary of findings and discussions

The primary objective of this study is to explore the impact of employee engagement on the performance of Ethiopian universities. To achieve this, a combination of qualitative and quantitative data was gathered from a sample of three selected universities. The quantitative data was analyzed using stata14 software, while the qualitative information was utilized for corroborating the findings obtained through questionnaires. Both descriptive and inferential statistics were employed during the analysis process.

The descriptive analysis of the study indicates that the application levels of the variables under investigation in the respective institutions were moderately implemented, although there were variations among the institutions. The descriptive analysis of these variables reveals that in the institution where employee engagement is low, the organizational performance is also lower compared to institutions where employee engagement is relatively higher, and vice versa. This characteristic of application implies the presence of cause-and-effect relationships between the variables. All three variables used to examine the influence of employee engagement on organizational performance exhibit strong and significant correlations and effects. The results from both the confirmatory factor analysis and path analysis demonstrate that the models are statistically appropriate for the given data. The study focuses on employee engagement, specifically exploring the variables of vigor, dedication, and absorption. These variables demonstrate noteworthy correlations and effects, which are consistent with the findings of investigation by Jaya and Ariyanto [ 32 ]. While the study of Jaya and Ariyanto provides insights into the relationship between vigor, dedication, absorption, and employee performance in the context of PT Garuda Indonesia Cargo, its limitations should be considered when interpreting the findings and applying them to other settings. The paper has limitations include a small sample size, limited context as it focused solely on the sector head office, and an undisclosed data collection period.

The outcomes of this research fulfill the assumption of the AMO theory, which suggests that enhancing the ability, motivation, and opportunity of employees to participate in the organizational interests is beneficial. This aligns with the concept of employee engagement. Moreover, it is in line with the findings of Nabhan and Munajat [ 23 ], whose findings suggest that work engagement and organizational commitment strengthen the influence of organizational identification and Islamic work ethic on job performance. The findings underscore the significance of being actively involved in one's work and having a strong sense of dedication towards the organization for enhancing job performance. However, it is important to acknowledge that the results have certain limitations due to factors such as the size of the sample, contextual elements, and missing variables. To enhance the validity of these findings and gain a deeper understanding of the topic, future researchers are encouraged to employ larger and more diverse samples, utilize longitudinal designs, and consider incorporating additional variables. By doing so, the findings can be strengthened, leading to a more comprehensive and reliable understanding of the subject matter. Furthermore, it substantiates the findings established by Mansor et al. [ 22 ], who conducted a thorough examination and established a noteworthy correlation between employee engagement and organizational performance. However, the outcomes are constrained due to the exclusive use of closed questionnaires, the narrow concentration on private companies, and the recommendation for additional research to overcome these limitations and gain a more extensive understanding of the factors that impact employee engagement and the strategies that effectively promote it. Consequently, the present study endeavored to tackle at least one or more of the constraints proposed by each analyzed study.

Furthermore, the study explored the respondents' perspectives, emotions, and attitudes regarding the implementation level of study concepts, as well as its impact on organizational performance. In order to accomplish this, quantitative data was utilized and analyzed. The purpose of the inquiry was to evaluate the outlook and understanding of university employees regarding their level of engagement with their respective institutions. The combined and condensed outcomes derived from the participants' qualitative feedback indicate that the level of employee engagement in higher education is moderately satisfactory, despite considerable differences among the institutions under study. To provide further insight, the following statement from one of the respondents is presented.

From a logical standpoint, there exists a positive correlation between employee engagement and organizational performance. At present, the engagement level of academic staff falls within the medium range, and its impact can be observed through the quality of education and the competence of students in the business market.

The findings presented here bear resemblance to the results obtained through descriptive statistics, which are summarized in Table  1 . In the table, it is observed that the average figure for men is 2.98, with a standard deviation of 0.64. This figure provides a cumulative overview of the responses obtained from all three university staff members. When examining the comparative descriptive summary, it becomes evident that there is a significant degree of variability among the different institutions in terms of employee engagement levels. Similarly, the qualitative descriptions provided by the respondents also highlight this variability. At an extreme level, the views expressed by the respondents regarding employee engagement levels differ greatly between the two institutions under study. In one university, the majority of respondents reported low levels of employee engagement, whereas in another university, respondents stated that employee engagement levels were high. However, when considering the cumulative responses from all three universities, it can be concluded that there is a somewhat moderate level of employee engagement present, despite the variability observed among the different institutions.

Conclusion, implication and limitation

Conclusion and implication.

The study findings reveal that employee engagement dimensions such as vigor, dedication, and absorption have a notable and favorable impact on the performance of organizations in higher education. Additionally, the research highlights that the level of employee involvement affects the performance of institutions differently. This study aims to bridge the gaps in understanding the dynamics of employee engagement and its consequences on organizational performance. It emphasizes the importance of exploring contextual elements and specific influences that shape the relationship between engagement and performance. Factors like organizational culture, leadership style, job characteristics, and industry dynamics play a role in shaping this relationship. By focusing on the distinct dynamics and challenges faced by public universities in Ethiopia, this study contributes to the existing knowledge in this field. It offers valuable insights and recommendations to enhance performance in the higher education sector. The research underlines the significance of tailored strategies and cultivating an environment that promotes employee engagement to improve performance outcomes. Furthermore, it sheds light on the importance of employee engagement in driving organizational performance and underscores the need for further studies and interventions within the context of Ethiopian public universities. Although there may be certain limitations to this article, as suggested in the limitations and suggestions for future research, it still contributes to the existing literature by emphasizing the significance of employee engagement in Ethiopian public universities. It lays the groundwork for future research and provides practical implications for managers and leaders aiming to enhance organizational performance through employee engagement initiatives.

The study makes a significant contribution to the management literature by combining various theoretical frameworks, including the Job Demands-Resources Model, Self-Determination Theory, Job Characteristics Theory, and Kahn's Engagement Theory. This integration offers a comprehensive comprehension of the connection between employee engagement and organizational performance. The study's results underscore the significance of particular aspects of employee engagement, specifically vigor, dedication, and absorption, in influencing organizational performance within the higher education sector. These findings emphasize the necessity for further research to delve more deeply into these dimensions and their effects on performance outcomes.

The research offers valuable insights for managers and leaders in public universities in Ethiopia, shedding light on the importance of nurturing employee engagement to enhance organizational performance. Managers should prioritize the implementation of strategies that cultivate enthusiasm, commitment, and absorption among their workforce. The study suggests that organizations should establish a work environment that is supportive and positive, fostering employee engagement. This can be accomplished by providing opportunities for professional growth, promoting a healthy work-life balance, and acknowledging and rewarding employee contributions. In essence, the study presents actionable implications for managers and leaders in public universities, underlining the significance of employee engagement in driving organizational performance. By adopting approaches to fortify employee engagement, organizations can enhance productivity, retention rates, and overall success.

Limitation and suggestion for future researchers

The research conducted on the impact of employee engagement on the performance of public universities in Ethiopia offers valuable insights and suggestions. However, it is important to acknowledge certain limitations of the study, including the presence of potential confounding variables. The study employed a cross-sectional design, which restricts the ability to establish a causal relationship between employee engagement and organizational performance. To gain a more comprehensive understanding of this relationship, future research should employ longitudinal designs. Additionally, it should be noted that the sample size of 365 personnel from three selected universities may not accurately represent the entire population of public universities in Ethiopia. Hence, caution should be exercised when generalizing the findings. Furthermore, the study primarily focused on the dimensions of vigor, dedication, and absorption as indicators of employee engagement. It did not extensively examine other dimensions or factors that could potentially influence engagement and performance, such as job satisfaction, motivation, and organizational support. To enhance the findings, future research should thoroughly explore these additional dimensions and factors. Additionally, it is worth mentioning that the study solely relied on self-reported data, which may be susceptible to common method bias and social desirability bias. To strengthen the findings, future research could incorporate multiple data sources and objective measures. Lastly, the study did not extensively explore the potential influence of external factors, such as economic conditions or government policies, on employee engagement and organizational performance. These external factors could act as important confounding variables and should be taken into account in future studies.

Abbreviations

Employee engagement

Organizational performance

Quality in services

Quality in research output

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Detecting causal relationships between work motivation and job performance: a meta-analytic review of cross-lagged studies

• Nan Wang 1   na1 ,
• Yuxiang Luan 2   na1 &

Humanities and Social Sciences Communications volume  11 , Article number:  595 ( 2024 ) Cite this article

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Given that competing hypotheses about the causal relationship between work motivation and job performance exist, the current research utilized meta-analytic structural equation modeling (MASEM) methodology to detect the causal relationships between work motivation and job performance. In particular, completing hypotheses were checked by applying longitudinal data that include 84 correlations ( n  = 4389) from 11 independent studies measuring both work motivation and job performance over two waves. We find that the effect of motivation (T1) on performance (T2), with performance (T1) controlled, was positive and significant ( β  = 0.143). However, the effect of performance (T1) on motivation (T2), with motivation (T1) controlled, was not significant. These findings remain stable and robust across different measures of job performance (task performance versus organizational citizenship behavior), different measures of work motivation (engagement versus other motivations), and different time lags (1–6 months versus 7–12 months), suggesting that work motivation is more likely to cause job performance than vice versa. Practical and theoretical contributions are discussed.

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Introduction.

Job performance is defined as “scalable actions, behavior, and outcomes that employees engage in or bring about that are linked with and contribute to organizational goals” (Viswesvaran and Ones, 2000 , p. 216), is a core concept in the applied psychological field (Campbell and Wiernik, 2015 ; Choi et al., 2022 ; Giancaspro et al., 2022 ; Hermanto and Srimulyani, 2022 ; Motowidlo, 2003 ). Employees’ job performance is important for both organization and the employee. For an organization, job performance is the vital antecedent of organizational performance (Almatrooshi et al., 2016 ); for an employee, job performance is a predictor of turnover (Bycio et al., 1990 ; Martin et al., 1981 ), and wellbeing (Bakker and Oerlemans, 2011 ; Ford et al., 2011 ). Considering the importance of job performance in the applied psychological field, it is not surprising that researchers have devoted significant effort to researching job performance, especially its antecedents.

Prior meta-analyses identified a series of antecedents of job performance, such as job satisfaction (Iaffaldano and Muchinsky, 1985 ; Judge et al., 2001 ; LePine et al., 2002 ), organizational commitment (Jaramillo et al., 2005 ; Mathieu and Zajac, 1990 ), and work motivation (Cerasoli et al., 2014 ; Van den Broeck et al., 2021 ; Van Iddekinge et al., 2018 ). Among these factors, motivation, which refers to the force that drives the direction, intensity, and persistence of employee behavior (Pinder, 2014 ), is a medium to strong predictor of performance (Cerasoli et al. 2014 ). Although the early meta-analyses (e.g., Cerasoli et al., 2014 ; Van Iddekinge et al., 2018 ) confirmed the significant correlations between work motivation and job performance, the accurate causal relationship between work motivation and job performance remains unclear. Does work motivation cause job performance? Does reverse causality exist? Or there is a reciprocal relationship between them? Unfortunately, previous meta-analyses (e.g., Cerasoli et al., 2014 ; Van Iddekinge et al., 2018 ), which are based on cross-temporal data rather than longitudinal cross-lagged panel data, could not address this research gap.

We propose four competing hypotheses to explain the causal relationship between them. First, work motivation causes job performance. Second, job performance causes work motivation. Third, work motivation causes job performance and vice versa (reciprocal model). Finally, work motivation and job performance are causally unrelated. In the Theory and Hypotheses part, we will describe these hypotheses in detail.

By checking all four hypotheses, the current study aims to reveal the causal relationship between work motivation and job performance. A single primary study could not accomplish our research goal due to the distorting of statistical artifacts (e.g., sampling error and measurement error; Hunter and Schmidt, 2004 ). For instance, the relationships of interest may vary when sampling from different organizations because of sampling error, which would harm the accuracy of the results. Fortunately, the meta-analysis methodology could help us to correct the statistical artifacts and thereby provide solid and reliable empirical evidence for the theory. As such, we utilize a meta-analysis methodology that allows us to aggregate cross-lagged panel data to test the four hypotheses.

This article provides the first meta-analysis that estimates the longitudinal effects between work motivation and job performance, contributing to both theory and practice. In terms of theory, this study will provide solid evidence for the causal relationship between work motivation and job performance, contributing to motivation and performance literature. In relation to practice, the results of our study will provide guidance for human resource management. For instance, if we find that motivation causes performance, using human resource practice (e.g., performance appraisal and training) that will influence motivation to improve performance will be reasonable; whereas if other results were found, perhaps we will reconsider the effectiveness of the current human resource practices.

Theory and hypotheses

In this part, we will review work motivation and job performance and their measurements. Then, we will develop the hypotheses between them. Finally, as a meta-analysis, we will propose a research question about the moderators that might influence the relationships between motivation and performance.

Before the 1970s, organizational psychologists primarily directed their attention toward job satisfaction, often sidelining the exploration of work performance (Organ, 2018 ). However, the tide turned in the 1980s, when scholars began conceptualizing individual job performance as a distinct construct (Campbell and Wiernik, 2015 ). Job performance is commonly characterized by two key forms: task performance and organizational citizenship behavior (OCB), providing a structured framework for evaluating employee contributions (Hoffman et al., 2007 ; Sidorenkov and Borokhovski, 2021 ; Young et al., 2021 ). Notably, performance should not be conflated with efficiency and productivity. While performance encompasses a broader term, often associated with achieving various levels or outcomes potentially under myriad conditions, both efficiency and productivity are intricately tied to the concept of optimizing resource utilization and maximizing output production (Campbell and Wiernik, 2015 ).

Task performance refers to the effectiveness with which job incumbents perform activities that contribute to the organization’s technical core (Borman and Motowidlo, 1997 , p. 99). Notably, this concept is also identified as “in-role performance/behavior” in the literature (Koopmans et al., 2011 ; Raja and Johns, 2010 ). In-role performance essentially encapsulates behaviors aimed at fulfilling formal tasks, duties, and responsibilities, often detailed in job descriptions (Becker and Kernan, 2003 ; Williams and Anderson, 1991 ). Contrarily, early meta-analyses have amalgamated related concepts, acknowledging their overlapping domains (Riketta, 2008 ; Young et al., 2021 ). OCB is delineated as “individual behavior that is discretionary, not directly or explicitly recognized by the formal reward system, and that in the aggregate promotes the effective functioning of the organization” (Organ, 1988 , p. 4). Contextual performance, reflecting actions extending beyond formal job descriptions and enhancing organizational effectiveness (MacKenzie et al., 1991 ), is frequently paralleled with OCB in meta-analytic practices (Riketta, 2008 ; Young et al., 2021 ). A noteworthy correlation between task performance and OCB (ρ = 0.74) is illuminated through a meta-analysis by Hoffman et al. ( 2007 ). While some scholars propose that performance can exhibit counterproductive facets (Campbell and Wiernik, 2015 ), meta-analysis unveils only a moderate relationship between OCB and counterproductive work behavior and reveals somewhat disparate relationship patterns with their antecedents (Dalal, 2005 ). Therefore, in this study, we study two fundamental dimensions of job performance: task performance and OCB.

Motivation reflects why people do something. It is widely researched in the work and educational psychological field (Anesukanjanakul et al., 2019 ; Christenson et al., 2012 ; Fishbach and Woolley, 2022 ; Hartinah et al., 2020 ; Muawanah et al., 2020 ). Work motivation stands distinct amidst a spectrum of related concepts. Firstly, it is imperative to differentiate motivation from personality. Personality, defined as a construct embodying a set of “traits and styles displayed by an individual, represents (a) dispositions, that is, natural tendencies or personal inclinations of the person, and (b) aspects wherein the individual deviates from the ‘standard normal person’ in their society” (Bergner, 2020 , p.4). Personality acts as a distal antecedent to performance, influencing it indirectly through the medium of motivation (Judge and Ilies, 2002 ; Kanfer et al., 2017 ). Secondly, while interrelated, goal pursuit and motivation are distinctive concepts. For example, if employees aim to earn money, their motivations are characterized as external. Conversely, intrinsically motivated employees engage in work for the enjoyment derived from the process itself, potentially without being driven by explicit work goals (Deci et al., 2017 ). Thirdly, motivation is different from attitude. Job attitudes (e.g., job satisfaction) reflect the evaluations of one’s job (Judge and Kammeyer-Mueller, 2012 ). Motivation may not necessarily include the evaluation of the job. For instance, engaged people, who usually put a great deal of effort into their work (Bakker et al., 2014 ), may not include the evaluation of the job. Actually, attitudes may likely be influenced by motivations, indicating they are different concepts (Judge and Kammeyer-Mueller, 2012 ).

As work motivation is a very grand concept, many psychological and organizational theories try to measure motivation by using different scales. For instance, in the perspective of the Job Demands–Resources (JD-R) Theory (Bakker, 2011 ; Bakker and Demerouti, 2017 ), work engagement is regarded as the motivation factor that links job resources and job performance; in the perspective of the Self-determination Theory (SDT), motivation (e.g., intrinsic motivation and extrinsic motivation) is the antecedent of job performance (Deci et al., 2017 ; Deci and Ryan, 2000 ). In the review process, we notice that work engagement is one of the most widely-used measurements of motivation when researching the work motivation-job performance linkage.

Hypotheses between motivation and performance

The first potential causal relationship is that work motivation causes job performance. This argument is shown in Fig. 1 . This Argument is supported by many well-established theories and empirical evidence. To start, in the JD-R theory (Bakker, 2011 ; Bakker and Demerouti, 2007 ; Bakker and Demerouti, 2017 ), engaged (well-motivated) people will accomplish job performance because they will experience more positive emotions which may increase the creation of new ideas and resources and they will be healthy and be energetic at work. The correlational relationship was confirmed by a prior meta-analysis as it found a medium correlation ( ρ  = 0.48) between engagement and job performance (Neuber et al., 2021 ). Then, from the perspective of SDT (Deci et al., 2017 ; Deci and Ryan, 2000 ; Gagné and Deci, 2005 ), motivation also influences performance. In particular, intrinsically motivated employees will be creative and productive, increasing their job performance. An early meta-analysis finds a moderate correlation between intrinsic motivation and performance ( ρ  = 0.28) (Cerasoli et al., 2014 ). Finally, motivation may influence performance directly by determining the level of effort and persistence an individual will exert in the face of obstacles (Kanfer, 1990 ). Motivation may also influence performance indirectly, as motivated individuals are more likely to set challenging goals and commit to achieving them, leading to higher performance (Locke and Latham, 2006 ). Together, it seems obvious that work motivation will cause subsequent job performance. When using the cross-lagged panel research design to test this hypothesis, the subsequent performance will be predicted by the previous motivation after controlling the auto-correlation effect. As such, the following hypothesis is proposed:

An illustration of arguments for a “motivation-causing-performance” process. This figure is covered by the Creative Commons Attribution 4.0 International License.

Hypothesis 1 : Work motivation causes job performance. In particular, work motivation (T1) is the significant predictor of job performance (T2) after controlling the auto-correlation effect of job performance (T1).

As illustrated in Fig. 2 , the second potential causal relationship is that performance causes motivation. As SDT suggested, feedback will influence motivation (Deci et al., 1999 ). Employees who achieve job performance may receive positive feedback (e.g., pay and recognition) from their organizations and leaders (Riketta, 2008 ), increasing their work motivation. Applying longitudinal data, Presbitero ( 2017 ) provided indirect evidence that improvements in reward management yielded a positive change in the level of motivation (measured by engagement). Therefore, we hypothesize the following:

An illustration of arguments for a “performance-causing-motivation” process. This figure is covered by the Creative Commons Attribution 4.0 International License.

Hypothesis 2 : Job performance causes work motivation. In particular, job performance (T1) is the significant predictor of work motivation (T2) after controlling the auto-correlation effect of work motivation (T1).

According to Fig. 3 , the third hypothesis is that motivation causes performance and performance causes motivation simultaneously. Combining Hypotheses 1 and 2, we could conclude this reciprocal hypothesis. Utilizing cross-lagged panel data, early studies found reciprocal relationships between (a) self-efficacy and academic performance (Talsma et al., 2018 ) and (b) job characteristics and emotional exhaustion (Konze et al., 2017 ). That is to say, there might be a reciprocal relationship between variables. Thus, we derive the following hypotheses:

An illustration of arguments for a simultaneous reciprocity between work motivation and job performance. This figure is covered by the Creative Commons Attribution 4.0 International License.

Hypothesis 3 : There is a reciprocal causal relationship between work motivation and job performance. In particular, work motivation (T1) is the significant predictor of job performance (T2) after controlling the auto-correlation effect of job performance (T1) and vice versa.

As presented in Fig. 4 , the final potential causal relationship is that performance and motivation are causally unrelated. Performance and motivation may be causally unrelated due to cross-temporal research design and common method bias (Podsakoff et al., 2003 ). For instance, when work motivation and job performance are measured at the same time point and rated by one person, their correlation may inflate due to common method bias and thereby draw inaccurate causality. Therefore, we put the following hypothesis:

An illustration of arguments for a causally unrelated relationship between work motivation and job performance. This figure is covered by the Creative Commons Attribution 4.0 International License.

Hypothesis 4 : Work motivation and job performance are causally unrelated. In particular, work motivation (T1) is not a significant predictor of job performance (T2) after controlling the auto-correlation effect of job performance (T1), whereas job performance (T1) is also not the significant predictor of work motivation (T2) after controlling the auto-correlation effect of work motivation (T1).

We also propose a research question about the potential moderators that may influence the relationship of interest. Following early longitudinal meta-analyses (Riketta, 2008 ; Talsma et al., 2018 ), three moderators are considered, namely, performance measurements, motivation measurements, and length of time lag (shorter vs. longer time lags between two waves).

Firstly, as we illustrated in the Introduction part, there are two measurements of work performance, namely, task performance and OCB. We would like to explore the potential moderating role of job performance measurements (task performance versus OCB). This exploration is pivotal. Theoretically, performance should envelop two dimensions: task performance and OCB (Koopmans et al., 2011 ). However, a disparity exists in organizational recognition and reward systems, wherein task performance is formally acknowledged, while OCB is not (Organ, 2018 ). The impact of such discrepancies on their respective relationships with performance remains nebulous. Undertaking a meta-analysis to probe into these moderating variables will not only deepen our understanding of the nexus between motivation and performance but also furnish supplementary evidence to buttress their interconnection.

Secondly, the motivation measurement is taken into consideration. In particular, many longitudinal studies (e.g., Shimazu et al., 2018 ; Nawrocka et al., 2021 ) use work engagement to measure motivation. Although theoretical frameworks suggest that these measures might reflect motivation, various measures of motivation may exhibit distinct relationships with performance. Despite the absence of cross-lagged meta-analyses, insights can potentially be derived from cross-temporal meta-analyses. For example, Cerasoli et al. ( 2014 ) identified a correlation of 0.26 between intrinsic motivation and performance, while Corbeanu and Iliescu ( 2023 ) observed a correlation of 0.37 between work engagement and performance. Consequently, we question whether the measurement of motivation exerts a significant moderating effect. Given that work engagement is the most prevalently utilized measure, we draw comparisons between the results pertaining to work engagement and those associated with other forms of motivation.

Finally, it is unclear how long the time lag process (i.e., the length of time between two measurement waves) will influence the relationship of interest. In the present study, time lags varied from 1 to 12 months (refer to the coding information for details). On the one hand, the relationship between motivation and performance may depend on time. For instance, even with strong motivation, employees may require time to learn and adapt to new tasks, affecting performance enhancement. Furthermore, the delay in receiving feedback or recognition, especially in long-term projects, may decelerate the positive influence of performance on motivation.

On the other hand, there may exist an optimal time lag interval in cross-lagged analysis, as suggested by Dormann and Griffin ( 2015 ). When the time lag falls short of this optimal point, the cross-lagged effect size diminishes sharply; inversely, if the time lag exceeds it, the effect size likewise declines. Aligning with prior meta-analysis efforts (Riketta, 2008 ), we categorize the time lag into two groups, namely, 1–6 months and 7–12 months, to explore the possible moderating influence of the time lag. The efficacy of a 6-month time lag design remains uncertain. Nevertheless, a design that maintains a 6-month interval at each end—presenting a symmetrical six-month span—prompts a subgroup analysis within the meta-analysis, increasing the likelihood of discerning potential moderating impacts. To sum up, we seek to answer the following research question:

Research Question 1 : Do the causal relationship between work motivation and job performance vary due to (a) job performance measurement (task performance versus OCB), (b) work motivation measurement (work commitment versus other motivations), and (c) time lag (1–6 months versus 7–12 months)?

Literature search

To locate the studies that might include the cross-lagged data about work motivation and job performance, following early meta-analyses (Neuber et al., 2021 ; Riketta, 2008 ; Van Iddekinge et al., 2018 ), the authors searched the following keywords: (a) motivation ( motivation or engagement ), (b) performance ( performance , job performance , task performance , or organization citizenship behavior ), and (c) cross-lagged ( longitudinal or cross-lagged) utilizing Web of Science and Google Scholar databases. The authors (W and L) seek to include studies published from 2000 to 2022. The search was conducted in January 2023 and encompassed English-language research materials. We did not restrict the types of research sources, including journal articles, book chapters, and dissertations. Authors W and L performed the search using the Title, Abstract, and Keywords. After removing duplicates, the authors initially obtained 120 potential articles that used longitudinal data.

Inclusion criteria and coding

After reviewing some early published longitudinal meta-analyses (Maricuțoiu et al., 2017 ; Riketta, 2008 ; Talsma et al., 2018 ), the authors made the following inclusion criteria. First, samples should come from organizations because the current study focuses on work motivation and job performance. As such, students’ or athletes’ samples were removed.

Second, studies should provide a full correlation matrix that includes six correlations and measure motivation and performance at two (or more) measurement waves. Six correlations are two synchronous correlations, the two cross-lagged correlations, and the two stabilities correlations (Kenny, 1975 ). In particular, two synchronous correlations are correlations (a) between motivation (T1) and performance (T1) and (b) between motivation (T2) and performance (T2). Two cross-lagged correlations are correlations (a) between motivation (T1) and performance (T2) and (b) between performance (T1) and motivation (T2). Two stabilities correlations are correlations (a) between motivation (T1) and motivation (T2) and (b) between performance (T1) and performance (T2).

After reading all potential studies ( k  = 120) and excluding studies that were not able to meet the inclusion criteria, the final database contained 11 studies that included 84 correlations ( n  = 4389). Considering the challenges in obtaining samples and findings from early meta-analyses (Riketta, 2008 , with 16 studies; Talsma et al., 2018 , with 11 studies), a sample of 11 studies is likely sufficient for conducting a cross-lagged meta-analysis. Two authors coded the following information: bibliographic references (authors and publication year), sample description (sample size and country), research design (interval between two measurement waves), effect sizes, and the reliabilities (i.e., Cronbach’s α) of all scales. The authors discussed the differences in the coding information until the intercoder agreement was researched 100%. Among the examined studies, 8 utilized a self-reported method for measuring performance, 2 adopted a leader-reported method, and 1 study employed an objective indicator, specifically the results of performance appraisals. The majority of these studies ( k  = 10) originated from companies, with only one emanating from an educational organization. The samples in the 11 studies encompass a wide range of industries, including banking, auditing, and social services. The diversity in this study stems from the primary authors’ intentional strategy to collect data from a variety of industries. This approach enables a comprehensive insight into the nature of professional settings and employee motivation across different sectors. Geographically, most samples were drawn from Europe (k = 9), while the remaining were from East Asia (k = 2). A PRISMA flowchart (see Fig. 5 ) presents the process of literature search.

An illustrative demonstration of literature search procedures and inclusion criteria. This figure is covered by the Creative Commons Attribution 4.0 International License.

Before analyzing, publication bias is taken into consideration. We used the Trim-and-Fill method and Eggs’ Regression method to detect potential publication bias. This analysis was conducted utilizing metafor package (Viechtbauer, 2010 ) in R. The results were shown in Table 1 .

Generally speaking, there are two steps in a meta-analytic structural equation modeling analysis (Bergh et al., 2016 ; Viswesvaran and Ones, 1995 ). The first one is to build a meta-analytic correlation matrix. The second one is to use this matrix to conduct path analysis. In the current study, to build a meta-analytic correlation matrix, we employed the Hunter-Schmidt methods’ meta-analysis technology to aggregate effect sizes (Hunter and Schmidt, 2004 ). In particular, reliabilities (i.e., Cronbach’s α) were used to correct measurement errors. The random effect meta-analysis method was utilized to correct sampling errors. This analysis was accomplished using the psychmeta package (Dahlke and Wiernik, 2019 ) in R. The results of the meta-analytic correlation matrix for path analysis were shown in Table 2 . To answer research question 1, Table 2 also includes correlations that are grouped by performance measurements, motivation measurements, and time lags.

Then, this meta-analytic correlation matrix was used to conduct path analysis, the results were shown in Table 3 . This analysis was accomplished using MPLUS software (Muthén and Muthén, 2017 ). Specifically, to conduct path analysis, the maximum likelihood estimation (MLE) was used. Besides, the sum of the sample sizes was employed as the inputted sample size (Riketta, 2008 ).

As Table 1 shows, the results suggest there is not a significant publication bias. First, using the Trim-and-Fill method, only one asymmetric effect size was located (i.e., the correlation between performance T1 and performance T2). After inputting this “missed” correlation, the averaged correlation only decreased by 0.02, suggesting the publication bias is not serious. Second, utilizing the Eggs’ Regression method, all the p-values are bigger than 0.05, confirming the publication bias is not significant. Together, the overall publication bias is not serious.

Table 2 depicts the averaged correlation (r) and true score correlation (ρ) of interest. For instance, the ρ between motivation (T1) and motivation (T2) is 0.80, whereas the ρ between performance (T1) and performance (T2) is 0.54.

As Table 3 presents, overall, work motivation appears to be a predictor of job performance, whereas job performance appears to be a predictor of work motivation. In particular, the path coefficient (i.e., M1 → P2) from motivation (T1) to performance (P2) is positive and significant ( β  = 0.143, p  < 0.001). However, the path coefficient (i.e., P1 → M2) from performance (T1) to motivation (P2) is not significant ( β  = −0.014, p  > 0.050). As such, H1 was supported, whereas H2, H3, and H4 were rejected. We draw Fig. 6 to explain the causal relationship between work motivation and job performance.

An illustration of estimated causal relationship between work motivation and job performance following MASEM analysis. This figure is covered by the Creative Commons Attribution 4.0 International License.

To answer research question 1, as Table 3 shows, neither the performance measure, motivation measure, nor time lag influence the causal relationship between motivation and performance. In particular, all the path coefficients (i.e., M1 → P2) from motivation (T1) to performance (P2) are positive and significant. However, the path coefficients (i.e., P1 → M2) from performance (T1) to motivation (P2) are negative or insignificant, supporting H1. The moderating effect was determined using z-tests to compare the two effect sizes. For example, when examining the moderating role of the performance measure, there was no significant difference in path coefficients for M1 → P2 (β1 = 0.129, β2 = 0.085; z = 1.4, p  = 0.08). Similarly, for path coefficients P1 → M2, no significant difference was observed (β1 = −0.016, β2 = −0.052; z  = 1.14, p  = 0.13). Additionally, we did not observe any significant moderating effect for either motivation measures or time lag. Together, the causal relationship is motivation causes subsequent performance rather than vice versa. Besides, this relationship is not influenced by the three potential moderators.

In this part, we will first discuss our findings. Then, we will discuss the theoretical and practical implications. Finally, the limitations and future directions will be discussed.

To start, we will discuss the magnitude of correlations. Cohen ( 2013 ) suggested that a correlation at 0.1 is small, at 0.3 is medium, whereas at 0.5 is large. Applying this standard, we find that the magnitudes of correlations of interest are from medium to large. For instance, the ρ between motivation (T1) and motivation (T2) is 0.80 which is large, whereas the ρ between performance (T1) and performance (T2) is 0.54 which is medium. Besides, the correlation ( ρ  = 0.34) between motivation (T1) and performance (T1) is bigger than the correlation ( ρ  = 0.31) between motivation (T1) and performance (T2). One plausible explanation is that the former is measured at the same time point whereas the latter is measured at different time points. Two constructs measuring at the same time point may suffer from common method bias and their correlation may inflate (Podsakoff et al., 2003 ). Besides, early meta-analyses also found the correlations between motivation and performance are medium. For instance, Cerasoli et al. ( 2014 ) found a correlation between intrinsic motivation and performance is 0.26. Similarly, Borst et al. ( 2019 ) found medium correlations between engagement and in-role performance and ex-role performance (range from 0.31 to 0.46). To sum up, the overall correlations between motivation and performance are medium.

Then, we found that work motivation causes job performance rather than vice versa. This finding rejects the reciprocal and causally unrelated model. This finding is in line with many experiment studies (e.g., Amabile, 1985 ; Hendijani et al., 2016 ; Kovjanic et al., 2013 ) which found that motivation influenced performance. Combining the findings of both longitudinal and experimental studies, evidence suggests that work motivation appears to be a predictor of job performance.

However, what makes us surprised is that job performance cannot predict work motivation based on cross-lagged data. One possible explanation is there might be mediators that fully mediate the relationship between job performance and subsequent work motivation. For instance, in the perspective of SDT (Deci et al., 2017 ; Deci and Ryan, 2000 ), basic psychological needs (i.e., competence, autonomy, and relatedness) are the antecedents of motivation. Employees who accomplished their job performance are likely to fulfill the need for competence and thereby influence motivation. Thus, job performance (T1) may not directly influence work motivation (T2) but through the mediating role of basic psychological needs. In the JD-R theory (Bakker, 2011 ; Bakker and Demerouti, 2017 ), there could also have mediators between performance and motivation. These mediators are job resources (e.g., leader support). Employees who achieve performance may influence job resources (e.g., leader support) and thereby influence their motivation. In the current cross-lagged panel meta-analysis, these potential mediators (e.g., basic psychological needs and leader support) could not be tested. Therefore, we do not find job performance (T1) causes work motivation (T2).

Finally, three moderators (i.e., performance measure, motivation measure, and time lag) do not influence the causal relationship between motivation and performance. First, for performance measures, one explanation is that both task performance and OCB captured the nature of job performance. Second, for motivation measures, one explanation is that different measures of motivation both reflect the definition of motivation (Pinder, 2014 ). For instance, employees could work hard by being driven by both work engagement (Bakker, 2011 ) and intrinsic motivation (Deci et al., 2017 ). In other words, despite different measures of motivation being used, these concepts all capture the characteristics of motivation, indicating a consensus conclusion.

It’s important to acknowledge that various studies have employed distinct measures to gauge motivation, including psychological capital and self-efficacy, among others. Psychological capital can indeed serve as a reflection of motivation. Comprising four subdimensions—self-efficacy, hope, resilience, and optimism—psychological capital embodies the internal forces (motivation) that drive individuals to confront challenges (Newman et al., 2014 ). These components collectively capture the essence of motivation by epitomizing the underlying reasons that initiate and direct behavior. Therefore, they are integral in understanding the multifaceted nature of motivation. Additionally, our moderation analysis contributes further insights, suggesting that despite the nuanced complexities of motivation measures, they didn’t exhibit a substantial moderating impact on the outcomes. This finding underscores the importance of considering these motivational aspects not just as isolated factors but as integral components that interact with other elements in human behavior and response mechanisms.

For time lag, an early meta-analysis study finds a significant moderating role in the length of time lag (Riketta, 2008 ) which is different from the current study. In the current study, we noticed that the length of time lag is between 1 month and 12 months. However, we still lack the knowledge of whether this causal relationship will change over a longer period of time (e.g., more than 12 months). Together, three moderators do not influence the causal relationship between work motivation and job performance, strengthening the confidence in our findings.

Theoretical and practical implications

The current study is the first meta-analysis that uses longitudinal data to test the causal relationship between work motivation and job performance, making some theoretical implications. First, utilizing meta-analysis methodology, we reconciled four competing hypotheses about the causal relationship between work motivation and job performance, contributing to work motivation and job performance literature. Second, the current study contributes to SDT literature. SDT suggests that work motivation will influence human behavior and job performance (Deci et al., 2017 ). The current study provides solid evidence for the argument of SDT by using longitudinal data. Besides, the current study collected data from multiple organizations, making the findings have high external validity. Finally, the current study provided evidence for the JD-R theory, as we found engagement causes job performance rather than vice versa using a cross-lagged research design. Drawing on this finding, some results (e.g., Yu et al., 2020 ; Almawali et al., 2021 ), in JD-R literature using a cross-temporal research design, should be explained with caution.

The current study is also essential to practice. First, as the current study provides solid causal evidence for the motivation-performance linkage, it provides knowledge for human performance management. That is, human performance practices (e.g., compensation management and performance management) that influence employee motivation, will influence employee performance. Second, our knowledge suggests that some motivation-based leadership (e.g., empowering leadership) is useful as motivation predicts job performance in the long run. Finally, since we do not find job performance could predict subsequent work performance, practitioners should try to find some try practices to strengthen feedback mechanisms between them, making employees increase their performance continuously.

Limitations and future directions

There are some limitations in the current study. First, in the current study, both motivation and performance are measured by self-reported scales, which may trigger common method bias (Podsakoff et al., 2003 ). This effect is stronger when two constructs are measured at the same time point. For instance, the ρ between job performance (T1) and work motivation (T1) may inflate due to common method bias. Future studies could try to measure performance utilizing more objective indicators. Second, due to the cross-lagged research design, it allows for only tentative causal conclusions and cannot rule out some alternative causal explanations (Riketta, 2008 ). Future studies could try to use instrumental variables to rule out alternative causal explanations (Saridakis et al., 2020 ). Third, the present study employed the MASEM method to carry out path analyses. However, the generalizability of this method to other populations may be limited when dealing with heterogeneous correlation matrices (Cheung, 2018 ). Upon the accumulation of more homogeneous evidence, future research could replicate this study. Fourth, during our search process, we did not impose geographical constraints on the origin of primary studies. However, we observed that the majority of the samples predominantly come from Europe ( k  = 9). This brings to light the potential influence of culture on the relationship between motivation and performance. In countries characterized by high individualism, values such as personal achievement and autonomy are emphasized (Hofstede et al. 2010 ). In such cultures, motivation is frequently linked to personal goals and achievements, which may intensify the association between personal-focused motivation and performance. Nonetheless, our current dataset limits our ability to definitively assess these cultural effects. Future research should aim to explore the impact of cultural factors on the motivation-performance dynamics. Finally, our study faced certain constraints regarding data availability, particularly concerning specific motivation metrics such as extrinsic motivation, which were not obtainable from the primary studies. Future research could enhance and validate the findings of this study by employing a broader range of motivation measures. This expanded approach will not only reinforce the comprehensiveness and reliability of the results but also provide a more nuanced understanding of motivational dynamics.

Conclusions

This meta-analysis is the first one to detect the accurate causal relationship between work motivation and job performance using longitudinal data. The evidence supports the effects of work motivation on job performance and does not support the reverse effects. The reciprocal model and causally unrelated model are also not supported. The results appear reasonably robust, as the finding that work motivation predicts job performance was consistent across the examined moderators of job performance measure, motivation measure, and time lag length. This study contributes to motivation and performance literature. Besides, our findings are important for human resource management and leadership. Future studies could try to use instrumental variables to get a more accurate causal relationship.

Data availability

All data used to conduct the meta-analytic review are included in the supplemental file.

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School of Business, North Minzu University, Yinchuan, Ningxia, China

School of Labor and Human Resources, Renmin University of China, Beijing, China

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Wang, N., Luan, Y. & Ma, R. Detecting causal relationships between work motivation and job performance: a meta-analytic review of cross-lagged studies. Humanit Soc Sci Commun 11 , 595 (2024). https://doi.org/10.1057/s41599-024-03038-w

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Impact Of Employee Engagement On Organisational Performance: Systematic Review of 2014-2019

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Firm’s performance has been well affected by engagement of the employees with their work. An employee who is engaged with his work is one who is taking interest in the work of the organization and showing some sort of initiatives in the benefits of the organization. There are total 17 articles were published from 2014 to 2019 and all of these are presented in this paper for systematic review on “impact of employee engagement on Organizational performance”. These articles revealed the existence of positive linkage between employee engagement with work and Organizational performance. As the total organizational outcomes while individual well-being is, doing the welfare of individual employee can be defined as Organizational Performance. Review of 17 articles from Google scholar interpret that interest in the topic of employee engagement with their work and the impact of it has increased potentially during the last few years and remains a productive area for the academic research in th...

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The impact of communication software usage on work engagement in remote work: the mediating role of distraction and FOMO

• Published: 10 May 2024

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• Wan Jin 1 ,
• Pingping Li 2 ,
• Hang Ma 1 &
• Mingyue Qin 1  

With the increasing demand for work-life balance and the development of information technology, remote work has become a widely adopted work mode, which has made communication software the primary means of communication for employees. However, previous research has not fully explored the impact of communication software usage (CSU) in remote work on work engagement, which is a crucial factor influencing employee performance. Therefore, this study, based on the Job Demands-Resources Theory, investigated the effects and mechanisms of CSU on work engagement in remote work. We collected a single time-point data set of 519 individuals with remote working experience and a dual time-point data set of 325 individuals in China. The results obtained from both datasets are consistent and indicate the following: (1) CSU in remote work has a significant negative impact on employee work engagement. (2) Distraction and fear of missing out (FOMO) mediate the relationship between CSU in remote work and work engagement respectively. (3) Intrinsic motivation moderates the negative effects of distraction and FOMO on work engagement. When employees have high intrinsic motivation, the negative effects of CSU in remote work through distraction and FOMO on work engagement diminishes. This study contributes to the research on the outcomes of CSU in remote work, deepens the understanding of the underlying mechanisms linking CSU and work engagement, and expands the knowledge of their boundary conditions.

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This research is supported by the National Natural Science Foundation of China (72161014; 72162017), and the Social Science Foundation of Jiangxi Province(22JY23).

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Wan Jin contributed to the design of the framework, and the collection of primary materials. Pingping Li contributed to the empirical work and the analysis of the results. Hang Ma contributed to the writing of the first draft. Mingyue Qin contributed to the investigation and data analysis.

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Jin, W., Li, P., Ma, H. et al. The impact of communication software usage on work engagement in remote work: the mediating role of distraction and FOMO. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06035-z

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• Bhaskar Chakravorti

As AI becomes more powerful, it faces a major trust problem. Consider 12 leading concerns: disinformation, safety and security, the black box problem, ethical concerns, bias, instability, hallucinations in LLMs, unknown unknowns, potential job losses and social inequalities, environmental impact, industry concentration, and state overreach. Each of these issues is complex — and not easy to solve. But there is one consistent approach to addressing the trust gap: training, empowering, and including humans to manage AI tools.

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• Bhaskar Chakravorti is the Dean of Global Business at The Fletcher School at Tufts University and founding Executive Director of Fletcher’s Institute for Business in the Global Context . He is the author of The Slow Pace of Fast Change .

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