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Article

Human Resource Management in Industry 4.0 Era: The Influence of Resilience and Self-Efficacy on the Relationship Between Emotional Intelligence and Formative Assessment: A Study of Public Primary Educational Organizations

by
Athanasia Panagiotidou
1,*,
Chryssoula Chatzigeorgiou
1,
Evangelos Christou
1 and
Ioannis Roussakis
2
1
Department of Organisation Management, Marketing and Tourism, International Hellenic University, 57400 Thessaloniki, Greece
2
Department of Educational Studies, National and Kapodistrian University of Athens, 15784 Athens, Greece
*
Author to whom correspondence should be addressed.
Societies 2025, 15(5), 138; https://doi.org/10.3390/soc15050138
Submission received: 1 April 2025 / Revised: 14 May 2025 / Accepted: 14 May 2025 / Published: 19 May 2025
(This article belongs to the Special Issue Employment Relations in the Era of Industry 4.0)
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Abstract

The Industry 4.0 era has brought significant changes in all areas of everyday life. This development has an impact on employment relations, making evaluation a core aspect of human resource management, especially in education, where new skills and knowledge are important. Thus, the role of educational leaders as managers and evaluators of the educational staff, and especially, the characteristics/skills they should possess for an effective employee evaluation, are essential for a positive work environment. This study explores the qualitative characteristics that the educational unit manager should have to implement formative assessment practices effectively, and specifically, the mediating role of resilience and self-efficacy in the relationship between emotional intelligence and formative assessment. The sample consisted of 258 educational unit managers serving in primary public schools in the prefectures of Pieria, Imathia, Pella, Kilkis, and Chalkidiki, Greece. A self-report questionnaire was administered online using four scales: TEIQue-SF for emotional intelligence, the Multidimensional Teachers’ Resilience Scale, the New Generalized Self-Efficacy Scale, and the Teacher Formative Assessment Practice Scale. The model was evaluated with SPSS.20 and PLS-SEM v. 4.1.0.6. The results indicate that resilience plays the most crucial role in the model, acting as a key mediator between emotional intelligence and formative assessment. While self-efficacy also has a mediating role, it is effective only when combined with resilience, rather than functioning independently. This research’s results suggest a necessity for implementing professional development programs within schools for both educational managers and teachers. These findings have practical implications for in-service training of educational leaders in emotional intelligence and resilience-based HR practices.

1. Introduction

1.1. Human Resource Management in the Industry 4.0 Era

Human resource management represents a systematic and integrative methodology for overseeing the most significant assets within an organization, the personnel employed therein, who, both individually and collectively, play a significant role in the fulfillment of organizational objectives [1]. Human resource management (HRM) constitutes an essential component in the formulation of public administration by fostering the efficiency, efficacy, and responsiveness of public entities [2]. In the context of Industry 4.0, HRM has been developed incorporating new technological applications and new approaches to shaping employee relations [3]. In addition, there is a trend towards the inclusion of psychological concepts and theories into human resource management studies called “psychologization” [4]. Thus, the role of emotional intelligence and self-efficacy within this new era is the basis for developing employment capabilities further [5].
The COVID-19 pandemic intensified antecedent trends and solidified a progression towards the Fourth Industrial Revolution (4IR) [6]. The world is presently undergoing the Fourth Industrial Revolution, commonly denoted as Industry 4.0. This period signifies that sophisticated technologies demand high expectations concerning the individuals’ educational attainment, professional expertise, and competencies. This is the reason why there is an emerging necessity for individuals to develop employability and digital competencies/skills to proficiently acquire and apply new technologies [7].
Technological advancements like AI are transforming corporate learning by providing benefits such as real-time data access [8]. However, there are increasing concerns about ensuring these technologies align with human-centric values, including empathy and ethics [9]. Leaders’ emotional intelligence is crucial in shaping these learning experiences [10,11]. Ultimately, corporate learning aims to enhance individual skills and competencies to improve organizational performance [12].

1.2. Educational Leadership in Post-COVID Era

The COVID-19 period has also significantly affected educational leadership. Thus, leaders need to cultivate behavioral traits that promote genuine relationships and flexibility in order to address challenges effectively within educational institutions [13]. In the post-pandemic era, educational leadership places growing importance on emotional intelligence, especially within digital environments, as leaders address emerging challenges, support the well-being of students and staff, and adjust to new teaching methods such as online learning [14].
Evaluation, as an element of human resource management, is a very important part of organizations that aim to evolve and continuously improve the quality of the services they provide. This is particularly relevant in sectors like education, where the effectiveness of human resources directly influences service outcomes [15]. Regarding the field of education, the process of assessing educational staff’s employee performance is very important in improving the quality of the education provided [16]. Formative assessment in education staff evaluation is an ongoing process that assesses educators’ practices and development needs to inform professional growth, rather than solely relying on summative judgments. This approach emphasizes continuous feedback, reflection, and targeted support to enhance teaching effectiveness and student outcomes [17]. Specifically, the evaluation of staff serving in public education started a few years ago in Greece. Although it was legislated several years ago, it was never put into practice, mainly due to the strong opposition of the entire educational community.
Individuals contribute distinct competencies, expertise, proclivities, and value systems to the employment relations. In terms of efficiency, the alignment between the individual’s skills, expertise, and proclivities with the content of the job is pivotal in ascertaining the individual’s capacity to execute work-related behaviors, including performance, attendance, and duration of employment [18]. Thus, in the educational context, the qualitative characteristics that the manager is required to have to effectively assess the educational staff are particularly challenging due to considerable obstacles, stemming from the absence of a clear legal framework and the instability of educational policies [19].
The literature review has highlighted that in order for the educational manager to be able to effectively assess the performance of educators, they must possess leadership qualities, such as clear vision [20] and a democratic way of exercising leadership [21], organizational skills, such as planning, implementing, and supervising the educational staff’s employee performance [21], and interpersonal skills, such as good communication quality and high levels of emotional intelligence [22]. Additional significant characteristics include an increased degree of self-efficacy regarding the educational manager’s knowledge of evaluation [23].
Individual factors influencing the implementation of formative evaluation practices are not yet well advanced as far as formative assessment practice is concerned. Therefore, the spectrum regarding the individual characteristics that are necessary for educational managers to have in order to implement formative evaluation practices for the educational staff’s performance is quite limited. This may be due to the fact that the implementation of formative evaluation practices is considered to be quite demanding, and in cases where formative evaluation is implemented in educational practice, it is not satisfactory [24]. Moreover, it seems that summative assessment is implemented in practice quite more often compared to formative assessment and, as a result, it often overshadows it [25].
In this context, the purpose of the present study was to investigate the relationship between emotional intelligence and formative assessment through the effect of resilience and self-efficacy of primary school educational managers on the evaluation of teaching. Thus, it attempts to bridge the gap between psychological competencies, such as emotional intelligence, resilience, and self-efficacy, technological progress, which characterizes the Industry 4.0 era, and methods used to evaluate educational staff performance, such as formative assessment. The novelty of this study lies in identifying the qualitative characteristics and skills that educational managers should possess in order to effectively evaluate educational staff using formative assessment methods.

2. Literature Review

2.1. Human Resource Management, Educational Leadership, and Formative Assessment

Scholarly research suggests that psychology has emerged as a prominent source of citations within the field of Human Resource Management (HRM), reflecting a notable trend toward the increasing psychologization of the discipline [26]. The assessment of leadership is increasingly relying on psychometric instruments, including behavioral simulations and advanced cognitive processing tests, to evaluate leadership potential beyond conventional evaluative criteria [27]. Psychologization, though beneficial, is criticized for oversimplifying behavior by prioritizing individual traits over sociocultural context, prompting calls for a more balanced analytical approach [28].
According to the Joint Committee on Standards for Educational Evaluation, assessment systematically examines the quality of programs, projects, etc., in order to make decisions, formulate judgements, and produce new knowledge in response to the needs of those interested in it, leading to improvement and accountability that both enhance their organizational and social value [29]. Formative assessment, which can be implemented in schools, programs, or material objects, refers to the judgment/evaluation of qualities that is taken into account and takes place during the design and development of educational materials, processes, curricula, or educational programs. This assessment is used with the purpose of transforming, shaping, or even developing these educational processes and programs [30].
The distinction between formative assessment (FA) in leadership contexts, such as staff evaluation, and instructional settings, like student assessment, lies primarily in their objectives. FA in leadership focuses on evaluating and improving teacher performance to enhance instructional quality. Principals prioritize teacher evaluation as a means to inform their instructional leadership, although its integration into practice is often limited [31]. In contrast, student assessment aims to measure and improve student learning outcomes. It often utilizes standardized tests to gauge achievement and inform teaching strategies, as seen in studies linking instructional leadership to student performance [32].
As for educational settings, effective human resource management plays a vital role in educational leadership by prioritizing the development of competencies in school administration, which are key to improving institutional quality and responding to the changing needs of the public sector [33]. Human resource management needs to respond to emerging challenges by emphasizing employee growth and engagement, fostering a culture of trust and empowerment in educational settings [34]. In this context, leaders who support formative assessment help build a collaborative and trusting environment, which is vital for successful human resource management in public education [35]. In addition, they can enhance the skills of their staff, leading to a more competent workforce and improved educational outcomes [36].
An important element in the effective implementation of formative assessment is feedback, which is a set of information regarding aspects of the performance and effectiveness of a person [37]. The role of feedback is considered to be important in the process of formative assessment [38], as it also promotes employee engagement. Employee engagement and participation are integral components of employee relations and human resource management [39]. They constitute a comprehensive concept that includes a variety of practices designed to facilitate the employees’ engagement in the decision-making processes regarding matters that have an impact on them, ultimately fostering a dedicated workforce [40]. Also, ongoing feedback strengthens their weaknesses, culminating in more applicable and enduring resolutions [41].
Summative assessment evaluates both the degree of effectiveness of an educational program and the conditions under which it is beneficial. Therefore, the type of evaluation that provides suggestions so that the educational program subject to evaluation can be improved is more of a formative assessment than a summative one [38].

2.2. Emotional Intelligence and Its Relationship with Formative Assessment, Resilience, and Self-Efficacy

Research indicates that emotional intelligence (EI) and resilience are interrelated traits that collectively enhance an individual’s ability to engage with formative assessments. Higher levels of EI facilitate emotional regulation and self-awareness [42], which, when combined with resilience, enable individuals to more effectively respond to feedback and challenges inherent in formative assessment processes [43,44]. Furthermore, EI has been shown to partially mediate the relationship between self-efficacy and resilience, suggesting a dynamic interplay between these psychological constructs in supporting adaptive learning behaviors [42]. This interaction aligns with the self-regulation theory, which emphasizes the interconnected roles of emotion, cognition, and motivation in guiding learning and behavioral adaptation [45].
Emotional intelligence provides remarkable results in employment relationships and consists of the following interpersonal skills: (a) self-awareness, (b) self-regulation, (c) self-motivation, (d) social awareness, and (e) social skills [46]. Emotional intelligence plays a key role in boosting employee motivation and managing the complexities of digital environments, ultimately contributing to improved organizational performance during times of uncertainty and continuous change [47]. There are few studies concerning the relationship between emotional intelligence and formative assessment. In particular, Jones [48] argues that by incorporating emotion-based approaches, such as emotional intelligence and relational skills (establishing positive relationships with others), formative assessment practices are strengthened and improved. Additionally, evaluators’ emotions during the evaluation process play a very important role, and therefore, their recognition is important. Thus, emotional intelligence contributes to the decision-making process by improving their understanding, which leads to improved results in the context of formative evaluation [49]. The link between emotional intelligence (EI) and formative assessment is anticipated because EI promotes self-regulation and self-awareness, allowing individuals to more effectively track and modify their learning approaches during assessments.
Therefore, the following research hypothesis is presented:
H1a. 
Emotional intelligence (EI) positively influences Formative Assessment (FA).
In synthesizing the findings from the studies on emotional intelligence (EI) and resilience, the evidence consistently supports the positive relationship between EI and resilience, particularly in the context of stress management. In the sense of an individual’s adaptation to difficult and stressful situations [50], it becomes essential for employees to respond effectively to these working environment demands. In addition, the positive relationship between emotional intelligence and resilience leads to better management of stress and challenges related to stressful situations arising from a stressful and demanding work environment [51]. In addition, research has shown that educational staff with a high index of emotional intelligence and mental resilience appear to be better equipped to manage stress and adapt to changing circumstances [52]. While the studies broadly support the connection between EI and resilience, their relationship warrants further investigation, particularly in educational settings, to better understand how these factors can be leveraged to improve outcomes. Therefore, the following research hypothesis is presented:
H1b. 
Emotional intelligence (EI) positively influences Resilience (R).
In their study, Ran et al. [53] found a positive correlation between emotional intelligence and general self-efficacy in higher education students. Furthermore, a positive interaction between these two variables was also found by Valente et al. [54] in a sample of educational staff which means that teachers who display high levels of competence in the perception, understanding, expression, classification, management, and regulation of their emotions appear to have a high degree of self-efficacy, which contributes positively, on the one hand, to the personal development of their students and, on the other hand, to the formation of a positive and self-regulatory learning environment. Similar results were also found in another study with a sample of novice and experienced foreign language educators, in which the positive relationship between emotional intelligence and self-efficacy is confirmed [55]. Therefore, the following research hypothesis is presented:
H1c. 
Emotional intelligence (EI) positively influences Self-Efficacy (SE).

2.3. Self-Efficacy and Its Relationship with Resilience

Self-efficacy, in general, includes all the convictions regarding an individual’s competence to excel in diverse prospective contexts. It encapsulates affirmative self-perceptions that significantly have an impact on motivation and fortitude in confronting adversities and attaining aspired results [56]. Self-efficacy and resilience are concepts that are closely related to each other, which is why in many cases they are studied together [57]. Ozdemir and Kaplan [58] attempted to investigate the relationship between these variables and concluded that self-efficacy directly affects resilience, which emphasizes the importance of self-efficacy in improving resilience. This means that the sense of efficacy that individuals have for themselves influences how resilient they are. Therefore, the following research hypothesis is presented:
H2. 
Self-efficacy (SE) positively influences Resilience (R).

2.4. Resilience and Its Relationship with Formative Assessment

The perspective of resilience as a dynamic process of positive adaptation of the individual in the face of adverse situations and under exposure to risk factors is a common denominator of much research [59,60]. Resilience, particularly in the educational context, is a skill [61] that promotes positive adaptation of the individual [62] in the context of managing daily challenges [63]. It is therefore of research interest to determine whether resilience can ultimately be included among the personal factors that influence the implementation of formative assessment, as an “umbrella” factor, according to the literature, that influences its implementation in the set of skills and abilities of the assessor [64]. Therefore, the following research hypothesis is presented:
H3. 
Resilience (R) positively influences Formative Assessment (FA).

2.5. Self-Efficacy and Its Relationship with Formative Assessment

Despite the widespread recognition of formative assessment as a very useful strategy in the teaching and learning process, the factors that contribute to the implementation of formative assessment and influence it have not been thoroughly explored, resulting in a limited number of relevant studies [65]. Yan and Cheng [66] argued that self-efficacy was one of the most important predictors of the intention to implement formative assessment in the sense that educational staff with high levels of self-efficacy are more likely to use formative assessment. Moreover, self-efficacy—an individual’s belief in their own ability to succeed in specific tasks—is a key factor influencing teachers’ willingness to adopt and sustain formative assessment practices. Educators with high self-efficacy are more likely to feel confident in their instructional decisions, take initiative in adapting assessment methods, and persist in the face of challenges, all of which are essential to effectively implementing formative assessment. Therefore, the following research hypothesis is presented:
H4. 
Self-efficacy positively influences Formative Assessment (FA).

2.6. Resilience and Self-Efficacy as Mediators Between Emotional Intelligence and Formative Assessment

The mediating effect of resilience and self-efficacy on the relationship between emotional intelligence and formative assessment has not been studied before. In this study, an attempt is made to explore the mediating role of self-efficacy and resilience, as the literature, including a great number of empirical studies, confirms the positive relationship between the variables mentioned [48,49].
For this reason, the variables of self-efficacy and resilience were placed between the variables of emotional intelligence and formative assessment in the formation of the structural model.
Mediation occurs when a mediator intervenes between two variables that are directly related [67]. Thus, the mediator constitutes an endogenous latent variable that links the predictor, which in our case is emotional intelligence, with the outcome, which is formative assessment. Therefore, mediation explains why and how the predictor (emotional intelligence) is associated with the dependent variable (formative assessment) [68]. Based on the above, it is expected that self-efficacy and resilience will have a mediating role in the relationship between emotional intelligence and formative assessment. Therefore, the following research hypotheses are presented:
H5. 
Resilience (R) mediates the relationship between Emotional Intelligence (EI) and Formative Assessment (FA).
H6. 
Self-efficacy (SE) and Resilience (R) mediate the relationship between Emotional Intelligence (EI) and Formative Assessment (FA).
The proposed conceptual model is shown in Figure 1:

3. Materials and Methods

3.1. Participants and Sample Descriptives

The purpose of the current study was to investigate the mediating effect/role of resilience and self-efficacy as factors influencing the relationship between emotional intelligence and formative assessment of educational managers in primary schools (primary education). The population of this study includes all educational managers working in primary schools in the region of Central Macedonia, specifically in the prefectures of Pieria, Imathia, Pella, Kilkis, Chalkidiki, Thessaloniki, and Serres. Table 1 presents the number of educational managers by type of educational organization in each prefecture separately, their total per prefecture, and their total number within the specific region. A total of 729 educational managers and headmasters work in primary educational units in the region of Central Macedonia, according to data obtained from the relevant Directorates of Primary Education of these prefectures.
The sampling method chosen for the purposes of this research is cluster sampling. It belongs to the probability-based sampling methods family and is a methodological approach used to extract a sample that accurately reflects a larger population that researchers have delineated into smaller, manageable segments. Each segment acts as a microcosm of the entire population, comprising individuals from different backgrounds. The different segments exhibit a high degree of homogeneity among themselves. Researchers do not extract samples from each segment, as each segment effectively incorporates the characteristics of the entire population and possesses sufficient similarity to facilitate interchangeability. This greatly streamlines the sampling process. Group sampling is particularly beneficial to researchers when both the population size and the required sample size are extremely large. In this study, single-stage cluster sampling was used, which means that each group cluster from the selected ones is defined as a sample. After dividing the total sample into a predetermined number of clusters of expected cluster size, researchers randomly select and extract samples from these clusters to collect data from each unit within the defined clusters.
The geographical area of interest in this research is the region of Northern Greece. The prefectures of Pieria, Imathia, Pella, Kilkis, and Chalkidiki (clusters) were randomly selected. Therefore, and in accordance with the principles of cluster sampling, the sample from these prefectures consisted of all primary school educational managers of these regions serving in four-seat elementary schools and above according to the official data on the functionality of school units in the school year 2023–2024 as posted on the official websites of the respective Primary Education Directorate of these prefectures. The sample of the survey consisted of 258 educational managers, out of which men constitute 60.1% (N = 155), while women constitute 39.9% (N = 103). In terms of age, it is divided into five categories, as follows: “23–33” which constitutes 7.8%, “34–44” which constitutes 26%, “45–55” which constitutes 27.1%, “56–65” which constitutes 38.4%, and “>65” which constitutes 0.8%. In terms of marital status, the largest proportion of the sample is made up of “Married” individuals, which reaches 81%, followed by “Unmarried” ones, with 15.5%, and those who reported “Other”, with 3.5%.

3.2. Measurement Tools and Data Collection

Four different measurement tools were used, which assess Emotional Intelligence, Resilience, Self-Efficacy, and Formative Assessment: 1. The Τrait Εmotional Ιntelligence Questionnaire-Short Form: ΤΕΙQue-SF is one of the most widely used questionnaires to measure emotional intelligence and belongs to the self-report questionnaires group. It is a scale assessing emotional intelligence as a personality trait and is designed to primarily measure general emotional intelligence [69,70]. This scale includes 4 dimensions (subscales) of emotional intelligence as a personality trait, which are (1) Well-being, (2) Self-control, (3) Emotionality, and (4) Sociability [71]. 2. The Multidimensional Teachers’ Resilience Scale: It was developed by Mansfield and Wosnitza [72] and it measures teachers’ mental resilience. In fact, it is one of the few scales developed to measure mental resilience in a population [73]. This scale has recently been translated into Greek [57]. 3. The New Generalized Self-Efficacy Scale: The New Generalized Self-Efficacy Scale measures, as its name suggests, the generalized self-efficacy of the individual [74]. It is a scale with good psychometric properties that is short, easy to use, and therefore suitable for research purposes [75]. 4. The Teacher Formative Assessment Practice Scale: The Teacher Formative Assessment Practice Scale was recently developed by Yan and Pastore [76] to assess the formative assessment practices teachers use in their teaching and their frequency. It is based on Wiliam and Thompson’s [77] theory of the five major formative assessment strategies and consists of ten statements. Although this is a relatively new scale, it has good psychometric properties [76].
The scales were translated into Greek using a standard forward–backward translation method involving independent translations, back-translation, and reconciliation of discrepancies to ensure conceptual and semantic equivalence. Minor cultural adjustments were made for appropriateness, and the final version was pilot tested to confirm clarity and relevance. The questionnaire was administered online using Google Forms and distributed via email to participating teachers. Completion time was approximately 15–20 min.
Before the main research, a pilot test was conducted with 15 participants to evaluate the clarity and reliability of the questionnaire items. Based on feedback and preliminary analysis, minor revisions were made to improve item wording.

3.3. Statistical Analysis

For the purpose of this study, structural equation modeling was employed, and the data were analyzed using the Smart PLS-SEM v. 4.1.0.6 software application, on the one hand, for the estimation of the measurement model in the context of its reliability and validity, and on the other hand, for the verification of the proposed research hypotheses. Regarding the sample size, the PLS-SEM literature states that the minimum size is obtained by multiplying the number of items by 10, while another view is that it should be at least 200 [78]. Also, some researchers argue that the minimum sample size is around 150 [79]. However, Hair et al. [80] consider that the more complex a model is, the larger the sample size should be. In the case of this study, the sample was 258 educational managers, which is considered a satisfactory number as it covers all the above considerations.

3.4. Reliability and Validity of the Measurement Model

First, the reliability of the indicators was measured by examining the outer loadings. In order to ensure the reliability of the indicators at acceptable levels, the reliability index for each one should exceed 0.7 [67]. Those indicators that showed a low reliability value at an unacceptable level were removed from the model, while the rest were retained (Table 2).
The reliability of a scale is tested by Cronbach’s alpha, rho A, and composite reliability coefficients [67]. Table 3 includes the coefficients that exhibit internal consistency. All values are above the threshold of 0.7, which indicates there is a sufficient level of reliability. Furthermore, there is a sufficient level of convergent validity as well, which is indicated by the Average Variance Extracted (AVE) values that are all above the threshold of 0.5.
There are three ways to examine the level of discriminant validity: the Fornell–Larcker criterion, the cross-loadings criterion, and the heterotrait–monotrait ratio of correlations (HTMT) criterion. In accordance with the Fornell–Larcker criterion, the correlation matrix encompassing all constructs is meticulously computed, and for each construct, the correlations must remain inferior to the square root of the average variance extracted (AVE). The cross-loadings criterion systematically evaluates the loadings of the indicators that denote a particular construct. It is imperative that each construct displays superior loadings relative to the indicators that signify the concept it embodies. The HTMT criterion assesses the mean correlations among indicators that gauge the same construct, in contrast to the mean correlations among indicators pertinent to disparate constructs. Values should remain below 0.9 to substantiate the presence of discriminant validity [67].
Table 4 delineates the findings pertinent to the Fornell–Larcker criterion. The requisite condition is satisfied, as all correlation coefficients are inferior to the square roots of the average variance extracted (AVE, emphasized in bold) corresponding to the relevant constructs.
The cross-loadings, as illustrated in Table 5, substantiate the presence of both discriminant and convergent validity. Each construct exhibits substantial loadings exclusively on the dimensions it is intended to represent.
Table 6 displays the HTMT values, all of which remain below the threshold of 0.9. Collectively, our analysis substantiates the confirmation of discriminant validity in accordance with all the aforementioned criteria.

4. Results

4.1. Structural Model Analysis

Structural Equation Modelling analysis (SEM) provides the ability to simultaneously model and estimate complex relationships between multiple independent and dependent variables, even with small sample sizes and non-normal data distribution [67]. Furthermore, the analysis includes the estimation of the model’s explanatory and predictive power. The path diagram is shown in Figure 2:
The predictive accuracy of the endogenous latent variables, which constitutes the explanatory power of the structural model, is provided by the R2 index [81]. The R2 index quantifies the variation in the results explained by the predictive factors, while its values depend on the context and are interpreted according to some intervals limits that range as follows: 0–0.10, 0.11–0.30, 0.30–0.50, and >0.50, interpreted from weak to strong explanatory power [67]. According to the results (Table 7), the highest R2 value was observed for resilience (R2 = 0.414, p = 0.000) which means that the variance in resilience is explained by emotional intelligence (β = 0.400, p = 0.000) and self-efficacy (β = 0.344, p = 0.000) by 41.4%, which indicates moderate explanatory power. This is followed by self-efficacy, the variance of which is explained by the remaining variables by 24.2% (R2 = 0.242, p = 0.000), and formative assessment, with 21.5% (R2 = 0.215, p = 0.000), values that are interpreted as and correspond to small explanatory power.
Collinearity assesses the level of correlation among the variables of the model using the VIF index [67]. Table 8 shows that all VIF values are acceptable as they are all below the threshold of 0.5 [82].

4.2. Hypothesis Testing Results

After the estimation of the measurement model, the evaluation of the structural model follows, which is based on the proposed research hypotheses. Table 9 shows the direct, indirect, and total effects of the model’s variables.
Among the six hypotheses posited concerning the direct effects, four received empirical support, whereas two hypotheses did not find validation. The bootstrapping method based on 10,000 sub-samples was used to assess the significance of the parameters [67]. The subsequent direct effects are characterized as positive and statistically significant: the results, therefore, showed that emotional intelligence has a positive and statistically significant relationship with resilience (β = 0.400, p = 0.000). Similarly, emotional intelligence has a positive and statistically significant relationship with self-efficacy (β = 0.492, p = 0.000). Thus, the research hypotheses H1b and H1c, respectively, are confirmed. Similarly, self-efficacy has a positive and statistically significant relationship with resilience (β = 0.344, p = 0.000), and resilience has a statistically significant relationship with formative assessment (β = 0.475, p = 0.000). This confirms the research hypotheses H2 and H3, respectively. However, emotional intelligence does not show a statistically significant relationship with formative assessment, just as self-efficacy does not show a statistically significant relationship with formative assessment, and for this reason, research hypotheses H1a and H4, respectively, are not confirmed.
In addition, indirect effects were analyzed. The results show that there is an indirect positive relationship that is statistically significant between emotional intelligence and formative assessment through resilience (β = 0.190, p = 0.001), as 97.5% of the confidence intervals do not include 0. Thus, the research hypothesis H5 is confirmed, which states that resilience fully mediates the relationship between emotional intelligence and formative assessment, as the direct relationship between them is not statistically significant. In addition, statistically significant indirect positive relationships were found between self-efficacy and formative assessment through resilience (β = 0.164, p = 0.000), emotional intelligence and formative assessment through self-efficacy and resilience (β = 0.081, p = 0.000), and emotional intelligence with resilience through self-efficacy (β = 0.169, p = 0.000), as 97.5% of the confidence intervals do not include 0. Despite the fact that there is no statistically significant indirect relationship between emotional intelligence and formative evaluation through self-efficacy, as mentioned above, the results show the existence of an indirect positive relationship that is statistically significant between emotional intelligence and formative assessment through self-efficacy and resilience (β = 0.081, p = 0.000), indicating multiple mediation, since there are two mediators, as 97.5% of the confidence intervals do not include 0 and the direct relationship is not statistically significant. Therefore, research hypothesis H6 is confirmed.
As for the total effects, the overall effect of emotional intelligence on formative assessment is positive and statistically significant (β = 0.273, p = 0.001), as is the overall effect of emotional intelligence on resilience (β = 0.569, p = 0.000). Similarly, a statistically significant overall positive effect exists from emotional intelligence on self-efficacy (β = 0.492, p = 0.000) and from resilience on formative assessment (β = 0.475, p = 0.000). Finally, the results demonstrate an overall positive and statistically significant effect of self-efficacy on formative assessment (β = 0.106, p = 0.000) and of self-efficacy on resilience (β = 0.344, p = 0.000). These results reinforce the confirmation of the research hypotheses mentioned above while showing that resilience is the most decisive factor in the implementation of formative assessment practices (β = 0.475, p = 0.000).
The justified research model is shown in Figure 3:
Unlike covariance-based SEM, PLS-SEM does not rely on global goodness-of-fit indices such as RMSEA, CFI, or TLI, as it does not aim to reproduce the covariance matrix. Instead, model evaluation in PLS-SEM is based on measures of explained variance (R2), path significance, effect sizes (f2), and predictive relevance (Q2), as analyzed above. Although PLS includes some model fit indices, it has been shown in practice that their application is considered inadequate in most situations by social science researchers, and for this reason, their use in PLS is not deemed necessary [49,67].

5. Discussion

The purpose of the present study was to investigate the relationship between emotional intelligence and formative assessment through the effect of resilience and self-efficacy of primary school educational managers on the evaluation of teaching staff using SEM analysis, specifically the PLS-SEM statistical model analysis tool.
The results of the data analysis for the four variables, emotional intelligence, resilience, self-efficacy, and formative assessment, showed that the construct reliability criteria as well as the convergent validity and discriminant validity criteria were met. It is therefore a powerful and accurate tool in the sense that the results are consistent and can therefore lead to important conclusions regarding the factors influencing the implementation of formative evaluation. These factors are associated with skills that are important to be enhanced for an effective organizational performance, indicating a tendency towards the psychologization of employment relations.
Perhaps the most important finding that emerged from the analysis of the present research model was the dual role of resilience. Firstly, mental resilience has a direct and positive effect on formative evaluation, being in fact the most determinant factor influencing it, which means that educational managers who have a high degree of resilience to new and/or stressful situations seem to implement formative evaluation practices more often for their teaching staff. This finding is in line with related research that argues that resilience optimizes the positive adaptation of the individual [83] in order to be able to manage daily challenges in a more effective way [63]. Formative assessment, since it is a new method that is not often applied in educational practice, is logical to create anxiety, and therefore, mental resilience, with the importance of adaptation works as a counterbalance.
The second role of psychological resilience in the present research model refers to its mediating role between emotional intelligence and formative assessment. Thus, educational managers with a high degree of emotional intelligence who are in direct contact with their emotions and who are able to understand and manage them are better able to adapt to demanding and stressful working conditions, and this, in turn, leads them to apply formative assessment more often as a primary way of evaluating teaching staff.
The direct positive effect of emotional intelligence on resilience confirmed in the research model is consistent with findings of similar research. More specifically, increased levels of emotional intelligence result in more effective management of the stress and challenges resulting from demanding work conditions [51] and adaptation to changing situations [52]. Furthermore, the perception and understanding of the emotions that make up emotional intelligence lead to increased mental resilience, as they increase the degree of psychological well-being.
Emotional intelligence, in addition to its direct positive relationship with resilience mentioned above, shows a direct positive relationship with self-efficacy. High levels of emotional intelligence seem to contribute in a positive way to an individual’s perception/beliefs about themselves. This finding aligns with the findings of previous research using a sample of teachers [55], which have shown that high levels of competencies regarding the perception, understanding, expression, classification, management, and regulation of their emotions have a positive effect on their sense of efficacy by contributing to the personal development of their students on the one hand, and to the formation of a positive and self-regulatory learning environment on the other [54]. Furthermore, at the same time, this positive relationship leads to the improvement of their employee performance [84], which is particularly important in the educational context where conditions are constantly changing.
However, the results of the research model analysis did not show the existence of a statistically significant relationship between self-efficacy and formative assessment, which contradicts similar research conducted regarding this issue and argues that self-efficacy is one of the most important personal factors influencing formative evaluation [66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85]. This may be due to the fact that formative assessment practices are not implemented broadly in the Greek educational system, and this, consequently, affects their self-belief about applying it. Another possible reason regards the existence of other contextual factors that may have a greater influence on formative assessment than self-efficacy, masking its potential effect, such as the educational environment.
Furthermore, since formative assessment is not often implemented in educational practice, or even if it is implemented it is not carried out consciously by teacher-educational managers, there is limited knowledge regarding what competencies are included in it and how these competencies are applied in the specific context, resulting in a sense of insecurity regarding its implementation. In agreement with this, the results showed that self-efficacy does not function as a mediating factor between emotional intelligence and formative assessment.
Self-efficacy, nevertheless, shows a direct positive relationship with resilience in the sense that the more an individual trusts himself/herself and his/her abilities, the more effectively he/she adapts to different situations. This finding is in line with the results of other research in the educational field, which emphasizes the effect of self-efficacy on resilience in the sense of coping more effectively with stressful situations and job demands [86,87]. Thus, in this context, any difficulties and critical situations encountered by teachers are perceived by themselves more as opportunities to develop themselves and not so much as an obstacle to performing their job duties [87].
Regarding emotional intelligence and its effect on formative assessment, the results showed that there is no direct statistically significant positive relationship between these two variables. This contradicts related research studies, which support the influential role of emotional intelligence in formative assessment. In particular, Jones [48] highlights that the inclusion of emotional intelligence in formative assessment helps to improve the practices that lead to the creation of a supportive learning environment.
Similarly, it has been found that the recognition of the emotions of the person being assessed during this process is very important because it influences the decision-making process, thus improving both their understanding and the results of formative assessment itself [49]. In fact, understanding complex emotional dynamics resulting from the combination of emotions and beliefs positively influences formative assessment, which means that emotional intelligence holds an important role in the implementation of formative assessment strategies [88].
Another important finding of the present research, which again extols the role of resilience in the model, is that emotional intelligence positively influences formative assessment through the simultaneous mediation of self-efficacy and resilience. More specifically, a high degree of affective intelligence positively influences the individual’s confidence in his or her abilities, resulting in better adaptation to different situations and a more frequent use of formative assessment practices. This means that self-efficacy alone is not enough for the individual to apply this type of assessment, but the simultaneous presence of resilience is required. Besides, that research has shown that both emotional intelligence and self-efficacy are coping abilities that lead to increased levels of resilience in the face of challenges that occur in an individual’s life [68].
The findings of the present study offer theoretical implications for the post-COVID-19-era educational leadership. This study is the first to show that resilience, rather than self-efficacy alone, is the most significant factor/characteristic an educational manager should possess, mediating the relationship between emotional intelligence and formative assessment implementation. Simultaneously, it positively affects the implementation of formative assessment practices.
The most remarkable practical implications in the educational leadership context include training programs regarding resilience, such as stress management workshops, emotional intelligence improvement, such as emotion management workshops, as well as formative assessment methods implementation. Thus, educational managers could evaluate the educational staff more effectively, contributing to better educational management, further professional growth, and high-level operation of the educational institutions.
In conclusion, there has been extensive discussion recently about Industry 4.0 [89] and the psychologization of employment relations [90], and in this context, educational evaluation, as part of human resource management, is very important for the effective functioning of the educational organization. In terms of efficiency, the alignment between the individual’s skills, expertise, and proclivities in the content of employment is significant in ascertaining the individual’s capacity to execute work-related behaviors, including performance, attendance, and duration of employment [18]. Thus, the educational manager’s skills are considered to be important not only in order to evaluate the educational staff but also to respond effectively to the new demands and needs that are brought by the Industry 4.0 era. These shifts demand a reevaluation of leadership roles, as managers must now guide institutions through digital transformation, foster technologically adaptive teaching environments, and assess educators not only on traditional performance metrics but also on their capacity to innovate and adapt.
Like any research, this research is subject to certain limitations. Firstly, the survey data are derived from self-reports of the subjects who participated and formed the sample of the study, thus, their reliability is subject to possible biases. Furthermore, the present study includes only personal factors that influence the implementation of formative assessment practices and not contextual factors—external factors such as school support, school environment, etc.
The results obtained from this research can form the basis for a deeper exploration and understanding of the factors that influence the implementation of formative assessment practices that can be carried out through longitudinal studies. Also, a more comprehensive exploration of these factors can be performed by combining quantitative and qualitative research methods in order to highlight different nuances. Finally, it would be interesting to incorporate external factors influencing formative evaluation into the model in order to make the model more complete.

6. Conclusions

The contribution of this study to the fields of education and management is related to the qualitative characteristics of educational managers in order to evaluate their educational staff using formative assessment practices. The main goal was to investigate the mediating role of resilience and self-efficacy in the relationship between the educational managers’ emotional intelligence and formative assessment. In the context of Industry 4.0, the aforementioned findings assume heightened significance, as technological advancements exert influence on the dynamics of employee relations and employment relations. Innovations such as Artificial Intelligence, data-centric methodologies, and automation necessitate attributes including emotional intelligence, resilience, and self-efficacy for effective adaptation, indicating the psychologization of employment relations. Accepting the uncertainty that current educational leadership brings enables leaders to adjust their approaches in response to changing educational demands and ongoing technological progress [91]. Within this framework, formative assessment emerges as a crucial component of employee skills development in the context of corporate learning, offering direction for enhancement in performance. To the best of our knowledge, this study is the first concise investigation regarding the personal characteristics that educational managers should possess in order to evaluate the educational staff effectively. In addition, the findings emphasize the significant role of resilience as the most influential variable of the model, as well as the need for effective staff evaluation and training programs designed for emotional intelligence and resilience improvement. Overall, this research’s findings indicate that there is a need for in-school professional training programs for educational managers as well as teachers in order to further develop their levels of emotional intelligence, resilience, and self-efficacy and to better understand the methods of formative assessment. Future research should explore the integration of emotional intelligence development in professional learning frameworks for educational leaders.

Author Contributions

Conceptualization, A.P. and C.C.; methodology, C.C.; software, A.P.; validation, C.C., E.C. and I.R.; formal analysis, A.P.; investigation, A.P.; resources, A.P.; data curation, A.P.; writing—original draft preparation, A.P.; writing—review and editing, A.P.; visualization, A.P.; supervision, C.C., E.C. and I.R.; project administration, C.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The research is a part of the first author’s PhD thesis. The whole study was conducted in accordance with the Declaration of Helsinki and approved by the Department of Organizations Marketing and Tourism of the International Hellenic University (IHU) (protocol code 1/7-01-21 and 25 November 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The proposed conceptual model (mediators’ variables are marked in red).
Figure 1. The proposed conceptual model (mediators’ variables are marked in red).
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Figure 2. The path diagram.
Figure 2. The path diagram.
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Figure 3. The justified research model (mediators’ variables are marked in red).
Figure 3. The justified research model (mediators’ variables are marked in red).
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Table 1. Number of primary school educational managers in Central Macedonia by prefecture.
Table 1. Number of primary school educational managers in Central Macedonia by prefecture.
PERFECTURE1/S2/S3/S4/S5/S6/S7/S8/S9/S10/S11/S12/S13/S14/S15/S16/S17/STOTAL
PIERIA1763-20341--16-----61
IMATHIA-33127277224-10-----77
PELLA91491283161-1351----100
ΚΙLKIS-563112223--7-----41
CHALKIDIKI33574173121-7-----53
A’ THESS/NIKI114113798911142765261143
Β’ THESS/NIKI243444610810142237911-1176
SERRES2323345432227-----78
TOTAL729
Table 2. Retaining outer loadings after their reliability check.
Table 2. Retaining outer loadings after their reliability check.
CONSTRUCTRETAINED OUTER LOADINGS AFTER DELETION
Emotional intelligence (EI)17, 20, 21, 24, 30
Self—efficacy (SE)1, 2, 3, 4, 8
Resilience (R)3, 4, 13, 16
Formative Assessment (FA)1, 2, 3, 4, 6, 10
Table 3. Construct reliability and validity.
Table 3. Construct reliability and validity.
Cronbach’s AlphaComposite Reliability (rho_a)Composite Reliability (rho_c)Average Variance Extracted (AVE)
EI0.7690.7730.8440.519
FA0.8170.8410.8630.513
R0.7250.7250.8300.550
SE0.7830.7940.8510.534
Table 4. Discriminant validity of the measurement model based on the Fornell–Larcker criterion.
Table 4. Discriminant validity of the measurement model based on the Fornell–Larcker criterion.
EMOTIONAL INTELLIGENCEFORMATIVE ASSESSMENTRESILIENCESELF-EFFICACY
EI0.721
FA0.2730.716
R0.5690.4620.741
SE0.4920.2140.5410.731
Table 5. Cross-loadings matrix (higher loadings across lines are written in bold).
Table 5. Cross-loadings matrix (higher loadings across lines are written in bold).
EMOTIONAL INTELLIGENCEFORMATIVE ASSESSMENTRESILIENCESELF-EFFICACY
assessment_10.2160.7730.4580.301
assessment_100.1460.6580.2880.043
assessment_20.0980.7130.2620.161
assessment_30.1340.7210.1750.103
assessment_40.3050.7270.4100.157
assessment_60.1860.6990.1920.027
efficacy_10.3530.1100.3790.739
efficacy_20.3630.0990.3790.725
efficacy_30.3320.1330.3440.691
efficacy_40.3100.1530.3570.711
efficacy_80.4220.2570.4920.784
emotional_170.6900.2010.3910.353
emotional_200.7140.1340.4180.334
emotional_210.7340.1940.3470.284
emotional_240.7570.2220.5270.370
emotional_300.7070.2270.3400.417
resilience_130.4350.2890.6950.441
resilience_160.4290.3240.7200.414
resilience_30.4260.3920.7470.398
resilience_40.3930.3570.7990.346
Table 6. Heterotrait–monotrait ratio (HTMT).
Table 6. Heterotrait–monotrait ratio (HTMT).
Heterotrait–Monotrait Ratio (HTMT)
FA <-> EI0.493
R <-> EI0.855
R <-> FA0.678
S <-> EI0.746
SE <-> FA0.380
SE <-> R0.828
Table 7. R2 values of the model’s endogenous variables.
Table 7. R2 values of the model’s endogenous variables.
Original Sample (O)Sample Mean (M)Standard Deviation (STDEV)T Statistics (|O/STDEV|)p Values
FA0.2150.2310.0583.7370.000
R0.4140.4210.0616.8100.000
SE0.2420.2520.0494.9720.000
Table 8. Collinearity statistics of the model (VIF).
Table 8. Collinearity statistics of the model (VIF).
VIF
EI -> FA1.592
EI -> R1.319
EI -> SE1.000
R -> FA1.706
SE -> FA1.521
SE -> R1.319
Table 9. Direct, indirect, and total effects of the research model.
Table 9. Direct, indirect, and total effects of the research model.
DirectpIndirectpConfidence Interval 97.5%Total Directp
EI -> SE -> FA −0.0290.4450.047
EI -> SE -> R -> FA 0.0810.0000.126
EI -> R -> FA 0.1900.0010.307
EI -> SE -> R 0.1690.0000.246
SE -> R-> FA 0.1640.0000.247
EI -> FA0.0320.680 0.2730.001
EI -> R0.4000.000 0.5690.000
EI -> SE0.4920.000 0.4920.000
R -> FA0.4750.000 0.4750.000
SE -> FA−0.0590.424 0.1060.000
SE -> R0.3440.000 0.3440.000
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MDPI and ACS Style

Panagiotidou, A.; Chatzigeorgiou, C.; Christou, E.; Roussakis, I. Human Resource Management in Industry 4.0 Era: The Influence of Resilience and Self-Efficacy on the Relationship Between Emotional Intelligence and Formative Assessment: A Study of Public Primary Educational Organizations. Societies 2025, 15, 138. https://doi.org/10.3390/soc15050138

AMA Style

Panagiotidou A, Chatzigeorgiou C, Christou E, Roussakis I. Human Resource Management in Industry 4.0 Era: The Influence of Resilience and Self-Efficacy on the Relationship Between Emotional Intelligence and Formative Assessment: A Study of Public Primary Educational Organizations. Societies. 2025; 15(5):138. https://doi.org/10.3390/soc15050138

Chicago/Turabian Style

Panagiotidou, Athanasia, Chryssoula Chatzigeorgiou, Evangelos Christou, and Ioannis Roussakis. 2025. "Human Resource Management in Industry 4.0 Era: The Influence of Resilience and Self-Efficacy on the Relationship Between Emotional Intelligence and Formative Assessment: A Study of Public Primary Educational Organizations" Societies 15, no. 5: 138. https://doi.org/10.3390/soc15050138

APA Style

Panagiotidou, A., Chatzigeorgiou, C., Christou, E., & Roussakis, I. (2025). Human Resource Management in Industry 4.0 Era: The Influence of Resilience and Self-Efficacy on the Relationship Between Emotional Intelligence and Formative Assessment: A Study of Public Primary Educational Organizations. Societies, 15(5), 138. https://doi.org/10.3390/soc15050138

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