Employee Involvement (EI) and Workforce Agility (WA): The Mediating Role of Psychological Empowerment (PE)

Objective – This study has two goals: to investigate the impact of employee participation on WA, and to analyse how PE affects EI and WA at Universitas Katolik De La Salle Manado. Methodology – The sample consists of 110 lecturers from Universitas Katolik De La Salle Manado, with the study using EI, WA, and PE as the main variables for generating hypotheses to be tested via moderating regression analysis. Results – This study's results demonstrate that EI positively influences WA and is moderated by PE at Universitas Katolik De La Salle Manado. Research limitations/implications – This study suggests that Indonesian higher education institutions should prioritize EI and PA practices. This can be achieved through decision-making opportunities, training programs, autonomy, and employee recognition. The interaction between these factors does not significantly explain WA variance. Organizations should focus on individual implementations to maximize their impact. Further research is needed to explore other moderating variables and alternative models. Novelty/Originality – The study examines the relationship between EI, PE, and WA in Indonesian higher education institutions, expanding on previous research on these dimensions.


Introduction
The strategic significance of higher education in the growth of societies and nations has been the subject of extensive and in-depth research.Not only is its role in realizing sustainable living (Cortese, 2003), and its function in creating and disseminating knowledge (Shurville & Browne, 2007) related to its contribution to developing the future workforce (MENON, 2020).Globally, changes in the external environment, such as globalization, the use of modern technology, and VUCA (Volatility, Uncertainty, Complexity, and ambiguity), which depicts a chaotic, turbulent, and quickly changing educational environment, are currently affecting higher education.Higher education institutions must adjust fast to these changes, anticipate them collectively, and do so using efficient techniques and procedures (Waller et al., 2019).However, when it comes to implementing new information or technologies in reaction to risks and contingencies in its ecosystem, higher education is thought to be less strategic and less adaptable than the rest of the industrialized world (Shurville & Browne, 2007).In reaction to higher education's delay and resistance to change, Milter (2015) supports the proverb that "universities are the only institutions that are resistant to change compared to government agencies." According to Sherehiy and Karwowski (2014), the concept of an agile organization is the most prevalent and well-liked among the different suggestions for how to deal with uncertain and unexpected settings.However, according to Muduli must change drastically (Dove & Wills, 1996) by adopting and implementing the concept of agility.Just as businesses strive to establish an agile workforce to improve their ability to respond to quick changes in the marketplace, university educators must also become agile for the same reason (Goldman, Nagel, andPreis, 1995 in Dove &Wills, 1996).Agility is not only important for the survival of the institution, but it is also important for lecturers, who may find that the research on which their reputation is based becomes obsolete.Lecturers should also be able to model agility for their pupils, who will be entering a business sector that requires agility (Dove & Wills, 1996).
This study explores the concept of an agile workforce in private higher education institutions in Indonesia and examines how PE influences the link between EI and WA.Although this concept has been popularly studied in the industrial world, according to Menon (2020), the study of the concept of WA has never been conducted in Higher Education.Based on searches on Open Knowledge Maps, Publish or Perish, and VOS viewer analysis (figure 1 and Figure 2), studies of the concept of agility concerning higher education are still very limited.Thus, this research is expected to contribute to filling this gap.The study's originality stems from its assessment of the relationship between EI, PE, and WA in the context of Indonesian higher education institutions.Previous research has generally focused on these dimensions singly or within other industry contexts; however, this study adds to the literature by exploring their interplay within the unique environment of higher education.The purpose of this paper is to examine the significant role of PE in bridging the connection between EI and WA.It begins by conducting a thorough review of existing literature on EI, PE, and WA.The research methodology used in the study is also discussed, along with the analysis results presented.Finally, the conclusion section interprets the research findings and provides recommendations for future research and practical applications.

Employee Involvement
The term "EI" refers to management's role in developing and implementing programs aimed at increasing employee contributions and improving communication.While some forms of EI may offer opportunities for employees to enhance their influence, it does not necessarily mean sharing authority or control (Marchington et al., 1991).Based on the work of Marchington et al. ((1991), Dundon et al. (2022) argue that EI and Participation are essential in modern HRM.It integrates pluralism, practical insights, fair work practices discussions, and context-sensitive methods at all levels of development.Busch-Casler et al. (Palumbo et al., 2023) describe EI as a human resource management approach that promotes EI in managerial decisions.Lawler et al. (Sumukadas & Sawhney, 2004) define EI as the extent to which employees feel they have control over their work, receive performance feedback, and are rewarded based on organizational performance.Sumukadas & Sawhney (2004) distinguish between higher-order practices such as job enrichment and self-managed teams, and lowerorder practices focused on information, training, and incentives for individual performance improvement.Both approaches emphasize skill variety, autonomy, task importance, and performance feedback.
Job redesign, also known as job enrichment or expansion, has been discussed in management literature, but its benefits for employees and employers remain unclear.Hackman & Oldham's (1976) research shows that individuals with strong growth needs respond positively to complex jobs, even when their growth needs measure falls within the bottom quartile.Job design should consider psychological factors alongside organizational goals.However, this classification may not encompass all forms of employee engagement, including decision-making participation, voice, and empowerment.EI plays a critical role in organizational decision-making beyond job design and compensation.HRM research and policy should prioritize employee wellbeing, as it is ethically important and often overlooked (Guest, 2017).HRM practices should include investing in employees, providing engaging work, fostering a positive environment, promoting voice, and offering organizational support.

Workforce Agility
The concept of agility has evolved from its origins in fighter aircraft to become a global concept of an organization's ability to react swiftly to market changes and adapt to unforeseen developments (Breu et al., 2002).WA is the human component of overall agility within an organization, and it is largely related to HR activities (Asari et al., 2014;Dyer & Shafer, 2003, 2012).Franco et al. (2022) define WA as the capacity of an organization to effectively and efficiently redeploy/redirect its workforce to value creating activities, especially innovation.WA is characterized by speed, flexibility, and the ability to cope with change and deal with uncertain scenarios.It is also proactive, takes initiative, and solves problems foresightedly (Alavi & Wahab, 2013;Sherehiy & Karwowski, 2014;Tessarini Junior & Saltorato, 2021).There are different dimensions of WA, including intellect, competences, cooperation, culture, and information systems, as well as proactive, adaptive, and generative.Proactive and adaptive WA involves initiating opportunities to advance organizational success, improvisation, and the Employee Involvement (EI) and Workforce Agility (WA): The Mediating Role of Psychological Empowerment (PE) ability to play many roles (Dyer & Shafer, 2003).Generative WA involves the desire to actively engage in knowledge sharing within the organization and continuously pursue the attainment of proficiency in a variety of competency areas while avoiding overspecialization and conformity (Tessarini Junior & Saltorato, 2021).
Despite the various viewpoints, researchers agree that WA is crucial for organizations to thrive in the face of extraordinary dangers in the business environment.Agile manufacturing lacks a clear roadmap and model due to a lack of definitions and concept development (Alavi & Wahab, 2013).However, some organizations have successfully implemented agile practices by adopting a flexible and adaptable mindset, promoting collaboration and communication, and embracing change.By doing so, they have been able to respond quickly to market changes and customer needs, increase innovation and creativity, and ultimately achieve sustainable growth in the long run.There are various means of incorporating WA into a production system, such as cross-training employees, empowering them to make decisions, and creating a culture of continuous improvement (Oyen et al., 2001).However, it is important to note that implementing WA requires careful planning and management to avoid potential risks and challenges (Muduli, 2013).A worker's potential to become agile depends on a variety of factors, including their capacity for learning and selfdevelopment, their problem-solving skills, and how well they handle change, new concepts and developing technologies (Plonka, 1997).Finally, Dyer and Shafer (2003) argue that agility-oriented workers improve organizational financial position and marketplace influence.Moreover, Alavi & Wahab (2013) built an algorithm to help organisational leaders develop an agile workforce, as follows: Source: (Alavi & Wahab, 2013) The algorithm can be explained as follow: The first step is to assess the condition of the organization's environment.If the environment is steady, having an agile staff is not required, and adhering to human resource policies is sufficient.However, if the environment is uncertain, then an agile workforce is needed.The second stage is to assess whether the workforce can respond quickly to environmental events and changes.The responses in question are active in the form of initiative behavior, creative problem-solving, forecasting difficulties, predicting consumer needs, and other forms of generative methods.Proactive responses to swiftly adjust to changing events, such as changes in product manufacturing, new work roles, using new machines, and other sorts of adaptive behavior, are also considered.If the workforce is found to be capable of responding, then the need is to continue and develop human resource programs to retain current agile people and create new agile people.If, on the other hand, the workforce is not able to respond, then the organization needs to have a program to create an agile workforce.Programs that can be implemented include any kind of activity that makes workers multi-skilled and trained, such as organizational learning, knowledge management, and skill-based rewards.Then there are all kinds of flexible (active and reactive) activities like developing organizational structures, empowerment, authority, and other activities that help individuals to adapt well to and take advantage of their surroundings.

2.3
The Relationship between EI and WA As indicated in the previous explanation (2.1.),EI adoption is more likely in competitive product marketplaces among firms that adapt quickly and flexibly to market demands and have strategies that prioritize quality and innovation over low cost.EI entails creating work environments, systems, and procedures that stimulate employee input and feedback, leading in successful value creation.Implementing EI necessitates a fundamental cultural shift and involves involving employees in problemsolving and decision-making, giving them a say in decisions and actions affecting their job.Furthermore, EI can contribute to increased work satisfaction and retention.It can also instill in employees a sense of ownership and devotion to the organization's aims and objectives.
The adoption of EI practices, therefore, can support WA by promoting a culture of openness, transparency, and collaboration (Alavi & Wahab, 2013;Sherehiy & Karwowski, 2014).Employees who are involved in problem-solving and decisionmaking are more likely to be interested, motivated, and devoted to the aims of the business (Pun et al., 2001).This, in turn, can lead to higher job satisfaction, retention rates, and reduced turnover costs (Plonka, 1997).By involving employees in the decision-making process, organizations can also benefit from their diverse perspectives and insights, which can help identify opportunities and threats, and develop innovative solutions to complex problems (Breu et al., 2002).As a result, in today's fast-paced business climate, EI is a critical aspect in reaching WA and retaining a competitive edge.Muduli (2017) analysed the relationship between organizational practices consisting of organizational learning and training, reward system, EI, teamwork, information sharing and WA.The results showed that worker agility was not significantly impacted by information sharing, either directly or indirectly.EI and other factors have a big impact on WA.The level of employee participation in decision-making and autonomy at work is referred to as EI.To increase WA, organizations may want to give EI initiatives top priority.The study also emphasizes the significance of a welldesigned reward system that rewards and encourages staff members for their contributions to organizational learning and teamwork.

Psychological Empowerment
There are two perspectives on workplace empowerment that have been extensively discussed in the literature: structural empowerment and PE.As organizations increasingly recognize the value of empowerment as a management tool to enhance employee engagement and performance, both psychological and structural empowerment emerge as crucial factors (Boamah & Laschinger in Monje Amor et al., 2021).Structural empowerment refers to the presence of social structures within the workplace that facilitate individuals' ability to achieve their professional goals by providing access to opportunities, relevant knowledge, support, and resources (Kanter in Monje Amor et al., 2021) an organization's capability to offer employees access to knowledge, resources, opportunities, and support (Dedahanov et al., 2019).
PE is conceptualized as the feeling of being empowered (Muduli & Pandya, 2018).Dedahanov et al. (2019) define PE as "the process of increasing feelings of selfefficacy among members of an organization through the identification and elimination of conditions that foster powerlessness."Thomas and Velthouse defined empowerment as intrinsic motivation reflected in four cognitions that demonstrate an individual's orientation towards their work role (Monje Amor et al., 2021;Muduli & Pandya, 2018;Spreitzer, 1996).These four cognitions include meaning, competence, selfdetermination, and impact.Meaning refers to the alignment between job requirements and an individual's beliefs, values, and behaviors.It signifies the extent to which an individual perceives their work as meaningful and significant.Competence, closely related to self-efficacy, represents one's belief in their ability to perform job duties skillfully.Self-determination refers to an individual's capacity to make decisions about their work and exert control over their own work processes, closely linked to autonomy.Lastly, impact refers to an individual's ability to influence strategic, administrative, or operational outcomes within their workplace, closely tied to power, which involves the capacity to control or influence others (Spreitzer, 1996).By understanding and fostering both structural and PE, organizations can cultivate an engaged workforce that is motivated, capable, autonomous, and influential in achieving desired outcomes.
The four cognitions mentioned above indicate a proactive approach to one's actions in their professional role, rather than a passive attitude.This proactive mindset is often referred to as enthusiastic behavior or innovative positive work attitudes.It consists of adaptability, resilience, and persistence (Seibert, Wang, & Courtright, 2011;Thomas & Velthouse, 1990in Ahl, 2021).Research by Muduli and Pandya (2018) suggests that personal empowerment (PE), specifically through intrinsic motivation and self-efficacy, can lead to proactive, adaptive, and resilient workforce behavior.Additionally, Seibert et al. (2011) propose a set of characteristics that practitioners can consider when implementing PE in their organizations.At the organizational level, high-performance managerial techniques such as intensive training, open information sharing, decentralization, participatory decision-making, and contingent remuneration can be utilized to encourage greater PE among staff.Employee empowerment should also prioritize promoting strong co-worker relationships and effective leadership within the workplace.Job design itself can be used to foster PE, aligning with job characteristics theory (Hackman & Oldham, 1980in Seibert et al., 2011).According to Seibert et al. (2011), this approach effectively encourages empowerment both for individual employees and teams.
Empowerment has been widely discussed in the literature, with some researchers suggesting that it may be more effective in the service sector due to the greater opportunities for discretionary behavior.For instance, Batt (in Seibert et al., 2011) argues that service workers have more chances to engage in discretionary behavior, such as interacting with customers, compared to those in manufacturing environments where standardized procedures and bureaucratic structures are dominant.However, Argyris (1998) cautions against the idea that empowerment may go against human nature and advises management to be realistic in its implementation.While empowerment has its limits, it is crucial to understand what can be achieved and avoid over-generating it.Once established, it is essential not to abuse empowerment and clearly define who has the authority to make changes and set boundaries for permissible change.

Relationship between PE and WA
As discussed in part 2.4., empowerment leads to proactive and innovative work attitudes, including adaptability, resilience, and persistence.PE through intrinsic motivation and self-efficacy results in proactive and resilient workforce behaviour.High-performance managerial techniques and job design can encourage empowerment, as can strong co-worker relationships and effective leadership.Empowerment may be more effective in the service sector, but it must be implemented realistically without over-generating it and with clear boundaries.
This summary shows that empowerment is highly related to WA as it helps employees to become more proactive, innovative, and adaptable.When employees feel empowered, they often exhibit traits that are crucial for WA, such as adaptability, resilience, and persistence.The PE idea states that empowered employees will take greater pride in their work.This reflects a mentality in which workers desire to feel secure enough to define their roles and work environments and then actually accomplish it (Spreitzer, 1995).As a result, having confidence in oneself and being personally motivated can enhance productivity (Zhou & Chen, 2021).Moreover, empowering employees through intrinsic motivation, self-efficacy, and highperformance managerial techniques can help create a more agile workforce that is better equipped to respond to changing business conditions.Additionally, effective leadership and strong co-worker relationships can foster a culture of empowerment and increase WA.Therefore, businesses that strive for WA must prioritize empowerment as a key strategy to develop a workforce that is agile and resilient to change.
Numerous studies have explored the relationship between worker agility and physical education (PE).This research has identified two key factors that contribute to the development of work agility: organizational practices and PE activities (Munteanu et al., 2020).Additionally, PE not only benefits individuals but also acts as a mediator between agile practices such as team autonomy, agile communication, and innovative behavior (Malik et al., 2021).The significance of PE in fostering an agile workforce and ultimately an agile organization has been highlighted by Ahl (2021).Muduli (2017) further demonstrated that PE serves as a crucial moderating variable between organizational practices and worker agility.

Research Design
This study was conducted at the Universitas Katolik De La Salle Manado, involving all permanent lecturers employed by the De La Salle University Foundation Manado.The university was chosen for this research due to its convenience and accessibility, allowing direct access to participants and efficient data collection.Its diverse sample from different backgrounds made the findings applicable to various contexts in Indonesia.Additionally, the university's commitment to EI and PE provided an ideal setting to study their impact on WA through implemented policies and practices.Convenience sampling was used as the sampling method, with the researchers choosing to survey all available and willing permanent lecturers.The research aimed to investigate the relationships between Emotional Intelligence (EI), Personal Engagement (PE), Work Attitude (WA), and their impact on employee performance.Primary and secondary data were used in this study.Primary data was collected through questionnaires distributed to the research sample, while secondary data was obtained from books, journals, research papers, notes, and websites.The questionnaire consisted of 23 items, measuring EI (4 items), PE (12 items), and WA (7 items).The questionnaire items were adapted from Muduli's work in 2017 (Muduli, 2017).Respondents were asked to rate each statement using a 5-point Likert scale, ranging from strongly agree (1) to strongly disagree (5).The questionnaire was distributed online using Google Forms.

Operational Variable
This study employed three categories of variables: independent variable (X), dependent variable (Y), and moderating variable (M).EI was the independent variable, WA was the dependent variable (Y), and PE was the moderating variable.Simple linear regression was used to examine the relationship between the independent variable (X) and dependent variable (Y).Moderation regression analysis was used to examine the Employee Involvement (EI) and Workforce Agility (WA): The Mediating Role of Psychological Empowerment (PE) moderating impact of variable (M) on the relationship between EI and WA.Given the modest sample size, straightforward research topic, and absence of complex modelling requirements, the use of simple linear regression and moderation regression analysis is justified in this study.Statistical Product and Service Solution (IBM SPSS Statistics) version 26 was used for all analyses.
There are three hypotheses examined in this study, as follows: H1.EI positively and significantly affects WA.H2.PE positively and significantly affects WA.H3.PE mediates the effect of EI on WA.

Characteristics of Respondents
Out of a total of 123 lecturers, 110 (89%) lecturers completed the questionnaire distributed online.Regarding the number of research samples, Gay, L.R. & Diehl (1992) explained that a large sample size makes the research results not only representative but also generalizable.However, it is also explained that the number of acceptable samples depends on the type of research used.For correlational research, it is determined that the minimum sample is 30 subjects.Table 1 demonstrates that more women (50.5%) than men (49.5%) responded to the survey.The age group of responders with the highest proportion was 36-46 years old (42.3%), followed by greater than 46 years old (29.7%), while the age group of 25-35 years (27.9%) had the lowest percentage.The majority of respondents have a master's education background (84.7%).33 lecturers or 15.3% have a doctoral education background.Respondents with a tenure of 6-10 years totalled 36 people, then tenure above 16 years totalled 34 people, 11-15 years totalled 26 people, and the smallest number was lecturers with a tenure of 1-5 years, namely 15 people.

Factor Analysis
Hardjodipuro explains that factor analysis is one of the multivariate methods for examining the nature of a relationship between variables in a given set that essentially exhibits a certain pattern of association.Determine whether a set of variables can be explained by fewer "dimensions" or "factors" than the number of variables by using factor analysis (Purwanto, 2018).To verify theories regarding the fundamental makeup of factors, confirmatory factor analysis is used.Although they have not been pursued, factors do exist (Crocker and Algina in Purwanto, 2018).Its existence, independence, and the components that make up its content are tested using factor analysis (Kleinbaum andKupper, 1978 in Purwanto, 2018).Analyzing construct validity tests is known as factor analysis.Testing highly condensed elements or variables to determine their impact on fewer, easier-to-understand components is how the study is conducted.

KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
. .000 The KMO measure of sampling adequacy assesses the extent to which the variables in a dataset are correlated with each other.A value of 0.50 or greater is generally considered to be acceptable for factor analysis.The Bartlett's test of sphericity tests the null hypothesis that the variables in a dataset are uncorrelated.A statistically significant result (p < 0.05) indicates that the variables are correlated and therefore suitable for factor analysis.
In the output (Figure 5), the KMO MSA value is 0.863, which is greater than 0.50.The Bartlett's Test of Sphericity (Sig.)value is also 0.000, which is less than 0.05.Therefore, the factor analysis technique can be continued in this study.Anti-image matrices are a type of correlation matrix that can be used to assess the suitability of variables for factor analysis.In factor analysis, the MSA (mean squared multiple correlation) value is used to determine whether a variable is suitable for inclusion in a factor.A value of MSA>0.50 is generally considered to be acceptable for factor analysis.

Anti
Table 3 shows that the MSA value for all variables studied is > 0.50, indicating that all variables are suitable for inclusion in factor analysis.If the factor loading> 0.55 (sample size 110) and clustered in one factor, it can be concluded that the indicator used is valid.In the first test, although factor loading >0.55, it does not cluster in one factor.Therefore, indicators PE 7, PE 8, PE 9, PE 10, PE 11, PE 12, WA 5 and WA 7 have been excluded in subsequent tests.In the second test, the factor loading value of all indicators >0.55 and clustered in one factor.

4.3
Validity and Reliability Tests Pawirosumarto et al. (2017) explain that the validity test is used to evaluate the validity of the questionnaire.When r-count exceeds r-table and r is positive, the model is said to be valid.Because they have an r-count value greater than the r-table value (0.195), all queries or statements about the independent, mediating, and dependent variables are valid based on the analysis's findings.The indicators utilized in this study can therefore be deemed to be valid and useful as measurement variables.
Carmines and Zeller (Hamed Taherdoost & Lumpur, 2016) explain that "reliability is concerned with the extent to which the measurement of a phenomenon provides stable and consistent results."Reliability also includes repeatability.According to Moser and Kalton (in Taherdoost, 2016), a scale or test is regarded as dependable if it consistently yields the same results when measurements are taken repeatedly.The Cronbach Alpha coefficient is the most often used indicator of internal consistency, according to Hamed Taherdoost & Lumpur (2016).Whitley (2002), Robinson (2009) (in Hamed Taherdoost & Lumpur, 2016) asserts it is seen as "the most appropriate measure of reliability when using Likert scales."For exploratory or pilot studies according to Straub et al (inHamed Taherdoost & Lumpur, 2016), it is recommended that the reliability is equal to or above 0.60.Based on the analysis results, as shown in table 4.3.2, the α values of the three variables in this study are greater than 0.60.

Reliability Statistics
Cronbach's Alpha N of Items .88815

Hypotheses Testing 4.4.1 Partial Test (t Test)
The partial or individual effect between the independent variable EI (X) on the dependent variable WA (Y) is shown in the following  The results of linear regression analysis (Table 4.4.1.1.)show that the t-count of EI (X) is 4.180, which is greater than the critical t-table value of 1.98238.This indicates that the relationship between EI and WA is statistically significant at the 0.05 level.The regression coefficient of 0.294 indicates that a one-unit increase in EI is associated with a 0.294-unit increase in WA.This means that EI has a positive and significant influence on WA.

Simultaneous Test (F Test)
The simultaneous test is used to determine the effect of the independent variables together on the dependent variable (WA).The following table presents  The results of the simultaneous test (F test) show that the calculated f-value is 35,584.The significance value of 0.000 is less than the 5% significance value (0.00 < 0.05).This means that there is less than a 0.5% chance that the results of the study could have occurred by chance.Therefore, it can be concluded that simultaneously the EI variable (X) and PE (M) have a significant effect on the WA variable (Y).4.4.3.1., the t-count value for the EI variable is 1.287, with a significance value of 0.201.This indicates that the relationship between EI and WA is not statistically significant at the 0.05 level.The t-count value for the PE variable is 2.324, with a significance value of 0.022.This indicates that the relationship between PE and WA is statistically significant at the 0.05 level.The interaction variable (XxM) has a tcount of 0.843, with a significance value of 0.512.This indicates that the relationship between the interaction variable and WA is not statistically significant at the 0.05 level.

Model
Based on these results, it can be concluded that the moderator variable (PE) has a significant effect on the dependent variable, while the interaction variable (XxM) has no significant effect.Therefore, it is concluded that the moderator variable PE only plays a role as a moderation predictor (Predictor Moderation Variable), meaning that it only plays a role as a predictor variable (independent) in the relationship model formed.The coefficient of determination (R2) test is employed to assess the extent to which independent variables can account for variations observed in the dependent variable.This analysis includes three different tests of the coefficient of determination: (1) testing the relationship between EI and WA variables using linear regression models, (2) examining the relationship among EI, PE, and WA variables employing multiple linear regression models, and (3) assessing the MRA model consisting of EI as independent variables, PE as moderating variables, interaction variables, and WA as the dependent variable.

Coefficient of Determination
The results of regression testing with WA (Y) as the dependent variable and EI (X) as the independent variable are presented in the following table.According to the results of the SPSS model summary, the Adjusted R Square value is determined to be 0.385.This finding suggests that the independent variables (EI, PE, and interaction variables) hold the capability to account for approximately 38.5% of the variation observed in the dependent variable (WA).It should be noted that the residual 61.5% can be attributed to factors beyond the scope of this particular research study.

Conclusion, Implication and Limitation
EI (X1) and PE (M) have significant and positive influences on WA (Y), according to the findings of this study.This conclusion is supported by the results of both the partial test (t-test) and the simultaneous test (F-test) analyses.However, the moderated regression analysis (MRA) reveals no significant influence of the interaction between EI and PE on WA.According to the Adjusted R Square values, the independent factors explain between 13.1% and 39.9% of the variation in WA.Furthermore, the MRA model reveals that the independent variables and their interaction can explain approximately 38.5% of the variance in WA.
Based on the findings of this study, it is recommended that both private and public higher education institutions in Indonesia actively prioritize practices that promote EI and PE to enhance WA.These practices can include creating opportunities for employees to participate in decision-making processes, providing training and development programs, offering autonomy and control over work tasks, and recognizing and valuing employee contributions.The significant and positive influences of EI and PE on WA indicate their importance in navigating a Volatility, Uncertainty, Complexity, and Ambiguity (VUCA) environment.By fostering EI, organizations can tap into the diverse skills and perspectives of their workforce, enabling them to adapt and respond effectively to unpredictable situations.Likewise, promoting PE can empower employees to take ownership of their work, make autonomous decisions, and navigate complex challenges, contributing to enhanced agility within the organization.
However, it is important to note that the interaction between EI and PE does not significantly contribute to explaining the variance in WA.Thus, organizations should not solely rely on the combination of these factors to enhance agility.Instead, they should emphasize the individual implementation of each factor to maximize its impact.In line with Thomas and Velthouse's four cognition concept (Seibert et al., 2011), an organization can promote empowerment by implementing various strategies.These include engaging employees in decision-making, investing in their training and development, granting autonomy and self-determination, and acknowledging and rewarding their contributions.By employing these approaches, organizations can enhance decision-making processes, elevate employee morale, optimize job performance, augment productivity, and cultivate a culture of excellence.Moreover, based on Oyen et al.'s (2001) concepts on cross-functional training and, based on the observation of the organizational practices at Universitas Katolik De La Salle Manado as well as the Kampus Merdeka programs, there are several strategies suggested to optimize EI practices.These include hosting interdepartmental workshops, facilitating job rotation programs, encouraging collaborative projects, implementing cross-training initiatives, offering mentoring programs, providing access to professional development courses, and offering leadership training programs.Additionally, future research should explore other potential moderating variables or investigate alternative models to comprehensively understand the complex relationship between these variables and WA.This will help deepen our understanding of how different factors interact and influence agility in the higher education sector.
Figure 1.Open Knowledge Maps search.Kw: WA and higher education Figure 3. WA Algorithm . Another definition of structural empowerment focuses on

Table 1 .
Based on the Decree of the Minister of Research, Technology and Higher Education of the Republic of Indonesia Number 164/M/KPT/2019 concerning the Academic Titles of Lecturers in English. *

Table 4 .
SPSS output after Varimax rotation

Table 6 .
Reliability Test the results of the simultaneous test conducted in this study.

Table 8 .
Simultaneous Test Results Square value, depicted in table 4.5.1 of the SPSS output, represents the coefficient of determination and portrays the extent to which variance is accounted for by independent variables in relation to the dependent variable.Specifically, the observed Adjusted R Square value is 0.131, indicating that the independent variable (EI) can account for 13.1% of the variation in the dependent variable.The remaining 86.9% of the variability is attributed to other variables not included in this study.Square value, a measure of the coefficient of determination, illustrates the extent to which the variance in the dependent variable can be attributed to the independent variable.Analyzing Table4.5.2 reveals that the adjusted R square value increases by 25.7% from the first regression model to the second regression model (adjusted R square in the initial model is 13.1%).The adjusted R square value of 38.8% suggests that variable Y may be explained by variable X under moderation by M. The remaining 61.2% can be accounted for by other factors.