Analysis of EAC Using Multiple Regression and Conditional Process: A Statistical Approach

Muskan Singh^1,*, Sachin Ghai¹, Amar Kumar Mishra² and Nupur Goyal³

¹Department of Management Studies, Graphic Era Deemed to be University, Dehradun, India
²Department of Management Studies, Brainware University, Kolkata, India
³Department of Mathematics, Graphic Era Deemed to be University, Dehradun, India
E-mail: muskansinghofficial1998@gmail.com; professorsachinghai@gmail.com; amrs2310@gmail.com; nupurgoyalgeu@gmail.com
*Corresponding Author

Received 27 December 2023; Accepted 08 May 2024

Abstract

In the modern world of increased competition, evolving technologies and volatile environment every institution must poise themselves to innovate and change, not only to prosper but merely to survive. The situation is more acute in institutions of higher education in India where things are changing with unprecedented speed. The survival of the institutions will depend upon the adaptability of their employees to embrace the change. However, employees’ acceptance to change cannot be developed overnight and also in isolation. It requires intensity and persistency of efforts in the right direction from the management. In the present paper, the researchers have identified the role of emotional intelligence and locus of control in enhancing employees’ acceptance to change in HEIs, Uttarakhand using multiple regression and conditional process analysis. The data for the present study were collected using stratified sampling and structured questionnaires from 432 employees from various HEIs in Uttarakhand. The finding revealed that EI enhances the EAC in HEI. Further, it also revealed that Job Satisfaction acts as a partial mediator in the relation between EI and EAC.

Keywords: Employee acceptance to change (EAC), emotional intelligence (EI), higher educational institution (HEI), locus of control (LOC), multiple regression, conditional process.

1 Introduction

Organizations throughout the world have witnessed the prominence of an era. From the formula 60s, sensitive 70s, strategy 80s, and excellence 90s, it would not be an exaggeration to say that this is the century of creativity and performance. Organizations that can’t perform are bound to perish. The situation is more prominent in the foundation of Higher Education (HEI) in India as their survival, growth and prosperity depend primarily on the creative performance of their employees. India witnessed a mushrooming growth of HEIs post-liberalization, privatization and globalization resulting from the new industrial policy. However, most of them failed to survive the onslaught of the demand of the modern time characterized by instability, insecurity, complication and uncertainty, and are struggling at the brink of closure. Amidst the plethora of reasons, one of the most prominent ones is the failure to respond to change and the scarcity of creative performance [1]. The field of education operates as an advisory enterprise with a high volume of interpersonal and intellectual exchanges, and knowledge, particularly between educators and learners that brings creativity. HEIs are under pressure to offer services that go above and beyond the specifications of a demanding academic model [2].

Changes can guide to the introduction of novel and innovative teaching methods, technology, and curricula. These changes can improve the learning experience for students and help them acquire the skills necessary to excel in their future Careers. Changes in higher educational institutions can also lead to the development of new programs and courses that are relevant to the needs of society. Moreover, changes in higher educational institutions can foster a culture of innovation and creativity. The higher education landscape has seen substantial alterations lately, including rise of distance education, the proliferation of new technologies, and the growing demands for accountability and transparency. These changes have had a significant impact on faculty in higher education institutions, both in terms of their teaching and research roles, as well as their overall job satisfaction and well-being. One of the most significant impacts of these changes is the need for faculty to adapt their teaching methods and approaches to accommodate new technologies and online learning platforms. While these changes have generated new possibilities for innovation and collaboration, they have also presented new challenges for faculty members, requiring them to adapt and evolve to meet the demands of a rapidly changing upgrading studies landscape.

Having a population of 25.9 million children registered in more than 45,000 degree-granting entities, India boasts a few of the biggest systems of higher learning in the globe. Enrollments in schools and students have rapidly increased during the last ten years [3]. Retaining brilliant teachers becomes essential, especially in light of admission to international universities and tempting job offers from the business world. Recent research has shown that elements such as increased job amusement, firm dedication, organizational culture, and the perception of backing from managers have a beneficial effect on a staff member’s health and desire to continue working through company [4–6]. Commercial learning establishments largely count on their employees to deliver high-standard facility and results because they have marketed most of their services. This makes staff retention even more crucial. To the best of our comprehension, no research project has yet looked into how educators’ constructions are related to one another. By examining the connections between faith at work, workplace health, and the desire to remain with the institution, across instructors in institutes provides a non-theoretical advanced study in India.

Not only is organizational restructuring necessary for staff in higher education, but it is also essential for the industry’s the continuation of life, expansion, and profitability [7]. The majority of the time, institutional breakthroughs is what bring about change. Innovative thinking improves the business environment and forges close ties between the company and its stakeholders. A major overhaul of the higher schools is urgently needed at this time, when the business sector and higher education are experiencing a severe split. This racial windfall will cause higher education to more than double in size to over 500 million learners over the following ten years, including both elite and mass students. In order to make this shift meaningful, which will require extensive transformation; reconstruction, and creativity, higher education-related institutions will need to take certain precautions [8]. However, without the triggering effect of human resources, no organizational reform can endure.

The literature on research at higher education institutions is scarce, despite the tremendous expansion Studies regarding institutions transformation. The majority of study has concentrated on outcomes like administration, technological advancements, managerial roles, psychological competence, and emotional intelligence (EI) towards particular modification to change behaviors [9]. Researcher found that organizations with data collection power will have a better understanding of market vitality and can make imaginative, distinct products in accordance of market demand. Despite the fact that resistance to change is an instinctive response for a variety of individual and organizational reasons, executives should support all employees by educating them on the benefits it will have on their continued existence, development, and happiness in the organization, thereby assisting it in coming to view change as helpful and enticing [10]. Over the past 25 years, the phrase “emotional intelligence” (EI) has acquire popularity as a form of intelligence for both individual and organizational success. Despite being a hot issue, there are still disagreements over how to conceptualize, measure, and apply it. According to several studies [11–14], those with elevated EI values tend to have happier lives, improved interpersonal and individual connections, and successful careers. Employees’ EI is linked to effects for the corporation, such as contentment with work, productivity, and dedication to the organization from ancorporation perspective [15], according to study.

A person’s perceptions about how much control they have over events can be conveyed through a notion in psychology called locus of control [16]. In the context of the social learning theory, [17] outlined the notion of LOC. People who have a strong view that their life’s events and the change they value depend more on randomness, fate, and other circumstances are typically under external control. On the other hand, people are referred to as persons if they think that fate, change, and other circumstances have a significant influence on the things that transpire between them with more external locus of control [18].

2 Literature Review

2.1 EI and EAC

Emotional intelligence (EI) and Employee acceptance to change (EAC) are two crucial constructs in the workplace. EI is the ability to identify, express, and regulate feeling of oneself, as well as being able to recognize and react to the thoughts of others. EAC is the inclination of individuals to contribute and participate in institutional changes. The present analysis of the existing research seeks to give a summary of the most recent studies on the connection among EI and EAC. We now better understand the importance of the emotional aspect in this change management thanks to prior research. According to [19], it is crucial to stress that change must be lasting and that employees are obligated to being a part of the change. The administration needs to be aware that their employees need to see the benefits of making changes at various organizationallevels in the proper light [20]. [21] Researcher investigated how emotion and emotional intelligence (EI) affected how amenable employees were to change. According to [22], it takes a lot of employee emotional energy to mobilize a dramatic shift that is sustainable. [23] Additionally determined the following four EAC members: executive management, susceptibility to shift, and psychological characteristics of individuals. According to [24–27], it is also seen as identification of conscience-perceived emotional control uncertainty frequently comes with change, which can lead to upsetting circumstances over which people have little control and not sufficient assets to cope. According to certain researches, there is a strong correlation among EI and corporate dedication [28], which in turn helps employee change. Additionally, studies have revealed a strong link between institutional Achievement in development and adaptation when EI is higher [29]; as well as a link between both favorable and adverse sentiments, opposition, dedication, and support for change. According to [30], it is particularly crucial to concentrate on the emotional components of change implementation in the context of universities. This includes both perceptions before changes occur and feelings that follow them.

Several investigations have revealed a strong connection between EI and EAC. For instance, completed research to look into the outcome of EI on EAC among nurses and indicated that nurses who had top levels of EI were more likely to be accepting of change in the workplace. Similarly, it was found that workers with superior. EIwere additional successful in implementing organizational changes. [31] Discovered a favorable correlation between EI and adaptability, an important aspect of ATC. Workers with greater EI scores were better able to adapt to new situations and were more open to change. It is explored the connection between EI, EAC, and work productivity of personnel in the Moroccan banking sector. The study states that employees with upper levels of EI were more accepting of change, which had a positive impact on their emotional intelligence. Furthermore, it is examined the relationship between EI, ATC among nurses. The findings states that nursewithincreased concentrations of EI more inclined to be accepting of change, which had a positive impact on their job amusement. [6] Investigated the links between EI and EAC among employees in the Malaysian public sector. The outcome states that employees with upper levels of EI were more accepting of change in the organization.

Hypothesis 1(H1): In HEI, EI and EAC have a favorable correlation in Uttarakhand.

2.2 EI and LOC

The literature on the connection between EI and LOC is compound and inconclusive. Although numerous investigations suggest a positive connection between EI and an internal locus of control, others report no significant relationship or even a negative relationship. Further study is required in order to comprehend the underlying mechanisms and contextual factors that may explain these mixed findings and provide a clearer understanding of the relationamong EI and LOC. Numerous researches have looked into the connection between EI and LOC. The recent generation has experienced a plethora of study regarding the connection involving EI and LOC. For instance, a study by [32] investigated the relation between EI and Locus of Control among Iranian high school students. The findings of their research promote a positive correlation between EI and LOC. Another study by [33] explored the connection between Emotional Intelligence and Locus of Control in a sample of Spanish adolescents. This suggests that people with greater emotional intelligence tend to have a greater feeling of control over the circumstances influencing how they live. Similarly, a study explored the relation between EI and LOC in a sample of Pakistani university students. In contrast, a study by [34] found no link between Emotional Intelligence and Locus of Control in Iranian nurses. The authors suggest that this may be due to society disparities and the specific context of the study. According to [35], LOC and EI are both essential factors.

Numerous studies have examined the link between EI and Locus of Control, and the findings have been mixed. Some studies have suggested that individuals with upper levels of EI which havean locus of control, while others have found no significant relationship or even a negative relation with EI and LOC. One perspective in the literature this indicates those who have higher EI more inclined to seek have andomestic locus of control. These individuals may possess better emotional awareness, regulation, and interpersonal skills, which can enable them to perceive themselves as having more control over their lives. They may be more proactive in managing their emotions and taking charge of their circumstances, leading to a locus of control. For example, a study by [14] found that emotional intelligence was positively correlated with locus of control among illustration of college students. On the contrary, some investigation has discovered no significant relationship between EI and Locus of Control, indicating that other considerations may be at play. For instance, a study by [36] found no significant association between EI and locus of control in a sample of adult participants. Similarly, a study found no significant correlation between EI and LOC among a trial of high school students.

Interestingly, there are also studies that have reported a negative relation among EI and locus of control. These studies suggest that individuals with higher EI may have an external locus of control, which may ascribe their Achievement or defeat due to outside influences rather than their own efforts or abilities. For example, a study by [31] found that higher levels of EI were linked up with a more external locus of control among a sample of undergraduate students. It is significant to remember that there are numerous circumstances that could be mixed findings in the literature, including differences in measurement tools, sample characteristics, and theoretical frameworks. Additionally, the directionality among the EI and locus of control remains unclear, as it is possible that the relationship may be bidirectional, with each construct influencing the other over time.

Hypothesis 2(H2): In HEI, EI and LOC are strongly and favorably correlated in Uttarakhand.

2.3 LOC and EAC

According to a study by [37], a variety of factors, including LOC and employee acceptance, and the extent of the transformation itself, influences how well individuals accept change. It refers to an individual’s willingness to adapt to new circumstances, processes, or systems within an organization. The study found that individuals who perceived the change as being compatible with their values and beliefs were more likely to accept it, while those who felt that the change was imposed upon them were more likely to resist it. Another study examined the role of LOC in acceptance to change. The study found that Locus of Control, including providing clear and consistent messages, actively listening to concerns and feedback, and involving employees in the change process, was crucial for promoting acceptance to change. In an analysis of the research on acceptance to change, [38] identified several key factors that contribute to acceptance, consisting of apparent benefits of the change, the level of trust in employees, and the employee’s personal characteristics, like the willingness to try something novel. Similarly, a study by [24] says that individuals with advanced levels of openness to experience were more possible to accept changes in the workplace. The study also found that individuals who were more committed to their organization were more likely to accept changes, as they saw the changes as beneficial for the organization as a whole.

As a result, research has begun to pay significant attention to the study of control reaction, which explores how people react to change and looks at how individual differences affect these reactions. While observer such as [39] have examined how people react to Locus of Control in terms of their outlook on change, level of receptivity to change, ability to deal with change, etc. have examined the impact of personality traits like LOC. The majority of studies, including [13] have focused their research on LOC regardless of other factors. The majority of studies have shown that LOC is directly related to acceptance of change, particularly for internals who believe they have control over the surroundings. As a result, they are more inclined to engage in to a change than newcomers in order diminish dissonance by maintaining logical coherence [40].

Hypothesis 3(H3): The relationship between LOC and EAC is both significant and favorable in HEI in Uttarakhand.

2.4 LOC as a Mediator

Investigators have examined the mediated effects of LOC on EI and OC [41], the mediated effects of LOC on the connection between EI, managerial civic follows (OCBs), and mental health and the relationship among LOC and EI [42]. Alteration is unavoidable, especially in today’s chaotic world where it pervades every aspect of life. Every organization now needs to understand and anticipate how people will respond Locus of Control [23]. One study examined the relation between EI and EACamongemployees. The results showed that LOC mediated the connection between EAC and EI. Specifically, individuals with an internal locus of control were more likely to appraise EAC in a positive way that in turn was related to higher emotional intelligence and better employee change. In another study by [43] the researchers explored the relationship between EI, EAC and location of control’s mediating effect. The findings indicated that locus of control partially mediated the relation between EI and employee acceptance. Specifically, individuals with an internal LOC were more likely to perceive greater command, which was associated with higher creativity in response to EI.

A study examined the relationship between EI and EAC among healthcare professionals, and the mediating role of locus of control. The results indicated that LOC partially mediated the relationship between emotional intelligence and employee acceptance to change. Specifically, persons with elevated emotional intelligence were more likely to have an internal LOC, which in turn was related to employee intelligence. In a study [44], the researchers explored the relationship between EI, and EAC nurses, and the mediating role of locus of control. Overall, these studies highlight the important role of LOC as a mediator in various relationships between variables. Understanding the mediating role of LOC can help individuals and organizations to develop strategies to promote an internal LOC and improve outcomes such as emotional intelligence, employee acceptance to change.

Hypothesis 4(H4): LOC in HEI in Uttarakhand favorably mediates the beneficial link between EI and EAC.

3 Conceptual Framework and Hypothesis Formulation

A framework of psychology called the Theory of Planned Behavior (TPB) describes how individuals act in terms of views, personal standards, and the impression of control over behavior. According to the TPB, there are three key elements that influence human behaviour:

• Attitudes: A person’s opinion or judgment of a specific behavior which includes an individual’s belief about the pros and cons related to the behavior, and the overall evaluation of those outcomes.

• Subjective norms: the perceived social pressure or influence from others to engage in a particular behavior that includes individual values about what significant others (e.g., family, friends, society) think about the behavior, and the drive to proceed in synchronization with those convictions.

• Capability: do a specific behavior as perceived by the subject is known as perceived behavioral control that includes the individual’s beliefs about the simplicity or complexity of executing an action, and the perceived barriers or facilitators to engaging in the behavior.

According to the TPB, Beneficial behavior-related to thoughts are more probable to manifest in an intent to engage in that behavior, a stronger perceived social norm in favor of behavior is more likely to result in the intention to engage in that behavior and a higher perceived behavioral control is prone to cause a reaction to outcome engage in the behavior. The best predictor of conduct is intention to act in a particular way. In other words, if an individual has a strong intention to perform behavior, they are more likely to actually perform that behavior. However, the TPB also acknowledges that there may be external factors that could influence behavior, such as environmental constraints or situational factors. Conferring by the conceptual model, LOC of employees significantly and favorably mediates the association between Emotional Intelligence and EAC in HEI in India (Figure 1).

Figure 1 Proposed conceptual model.

4 Research Methodology

4.1 Sample Collection

700 staff members of several HEIs in Uttarakhand, India, were given access to an internet-based poll. The sample size was calculated using Yamane’s formula as –

n = \frac{N}{1 + N e^{2}}

Where N is the population in the study and e is the margin error. Further model fitness was checked to confirm the optimality of the sample size. The samples were decided using stratified sampling method where statisticswereused to include representatives across age, gender and academic experience [61, 62]. The confidentiality of each of the respondents’ answers was guaranteed. To help with the response time, reminder emails were issued on a frequent basis [45]. A response rate of 61.7% was achieved when 432 valid and full samples were returned. There were 51.46 percent male and 48.54 percent female in the 25 to 60 year age range. Outline of the participants are listed in Table 1.

4.2 Constructs Used

All of the studied variables’ information was gathered by self-reported measures.

• EI: Itwas assessed using 21 item DLOQ questionnaires [46]. This instrument gauges the efficacy of EI in the collaborating institutions.

• EAC: It was assessed using a [47] 18-item questionnaire. This instrument gauges the EAC’s three dimensions.

• LOC: Itwas assessed using 24 items, [48] questionnaire.

5 Analysis and Results

5.1 Demographic Profile of Participants

The participants for current research states academic along with employees aged 25 to 60, who worked in HEIs all over India. In terms of gender, 51% of respondents were men and 49% were women. In Table 1, a thorough profile of the participants is provided.

Table 1 Demographic profile of participants

Demography	Particular	Frequencies
Gender	Men	206
	Women	226
Age	26 to 40 years	182
	41 to 55 years	173
	56 to 70 years	77
Academic Qualification	Post Graduate	208
	Doctorate	192
	Postdoctoral	32
Total		432

5.2 Results Analysis

5.2.1 Descriptive statistics

Table 2 displays descriptive statistics and Table 3 shows correlation for every research component. The distribution can be presumed to be normal because the value of skewness is positive while its kurtosis is negative but falls within the range of ( $-, /, +$ 1) [49]. The study reveals modest correlations between variables that are significant at the 1% level. As long as correlation value is less than 0.7, there is no problem with multiple linearity [50]. The variability of the expansion value ( $<$ 5) for all variables further confirms this. Last but not least, all Cronbach alpha coefficients are above 0.8, showing excellent accuracy for the measures [51].

Table 2 Descriptive analysis

	Descriptive Statistics
	Mean	Standard Deviation	Skewness	Kurtosis
	Statistic	Statistic	Statistic	St.d Error
LOC	3.07	0.96	0.091	0.136
EAC	3.15	1.01	0.077	0.136
EI	2.76	0.8	0.071	0.136

Table 3 Correlation matrix

	Pearson Correlation
	Coefficient (r)
	Statstics	LOC	EAC	EI	VIF	ALPHA
LOC	$-$ 0.74	0.977			1.996	0.838
EAC	$-$ 0.583	0.541***	0.975		2.025	0.827
EI	$-$ 0.629	0.444***	0.247***	0.947	NA	0.863
Sign*p $<$ 0.01, p $<$ 0.05 Source: compiled by authors.

Figure 2 Measurement model.

5.2.2 Analytical approach

Confirmatory Factor Analysis (CFA) is a multivariate statistical technique that is used to confirm hypothesis about the study. It is a special case of structural equation modeling (SEM). Aim of both CFA and SEM are to identify latent variables using a set of observed variables. CFA requires unambiguous provisions of all appearances of hypothesized measurement model. It focuses on modeling the relationship between observed variables and latent variables. As per SEM –

• Latent Variables: In this study, emotional intelligence, employee acceptance to change, locus of control are latent variables.

• Observed Variables: Mean, std. deviation, skewness, kurtosis, correlation coefficient, variance etc. are observed variables.

• Casual Connections: How the different aspects of study connect in modelling are described by the flow diagram as depicted in Figure 1. CFA was utilized to assess the hypothesized measurement model using IBM. SPSS, Amos.v21. For the statistics employed in this investigation, the cutoff values were as follows: 2/df $>$ 1 [52]; CFI $\geq$ 0.95; SRMR $=$ 0.08; RMSEA $=$ 0.06; and PClose $>$ 0.05 [53]. The measurement model’s values are listed below.

The study offered a great fit for the information provided $χ$ 2 $=$ (2411) 2716.625, p $<$ 0.001, $χ$ 2/df $=$ 1.127, CFI $=$ 0.990, RMSEA $=$ 0.017, SRMR $=$ 0.033, RMSEA $=$ 0.017, PClose $=$ 1.000. The composite reliabilities of construct were 0.796(LOC), 0.804(EAC) and 0.773(EI). Using the AMOS 22.0 programmed, the construct validity was evaluated. The convergent validity requirement (average variance extraction values (AVEs) $>$ 0.5 and CR $>$ AVE; Table 4) and the discriminate validity requirement (MSV AVE and square root of the AVE $>$ correlation between other structures) were also satisfied.

Table 4 Construct validity

	CR	AVE	MSV	MaxR(H)	LOC	EI	EAC
LOC	0.984	0.667	0.261	0.984	0.817
EI	0.977	0.674	0.274	0.978	0.511***	0.821
EAC	0.975	0.665	0.274	0.976	0.497***	0.523***	0.815

It was important to examine the unfairness because only one approach was used to obtain the data. According to [54], the common method bias has been evaluated using the common latent factor technique. There were two sets of generalized regression weights: one with CLF and one without CLF. The very little ( $<$ 0.200) variation in weights proved that there was no biasness [54]. Table 5 lists the factor loading for every parameter with respect to its constructions. It should be noted that all loadings are bigger than 0.7 indicating that the variables have been adequately loaded with their constructs.

Table 5 Total variance explained

	Initial Eigenvalues	Extraction Sums of Squared Loadings
Component	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	32.636	45.966	45.966	32.636	45.966	45.966
Extraction Method: Principal Component Analysis.

5.2.3 Hypothesis analysis (applications and methods)

CFA is widely used in various fields. In this study, authors use this for hypothesis testing and results validation. In this study, author employ covariance-based SEM (CB-SEM). This method focuses on covariance and correlations among variables.

The output obtained using Process Macro is given below in Tables 6, 7, 8:

The analysis shows that EI has a significant impact on LOC (r 0.5203, standardized beta 0.5141, p $<$ 0.05, both LLCI and ULCI being positive); EI has animportant impact on EAC (r 0.5354, standardized beta 0.5340, p $<$ 0.05, both LLCI and ULCI being positive); and LOC has a significant impact on EAC (r 0.5986, standardized beta 0.3166, p $<$ 0.05, both LLCI and ULCI being positive). Thus, first three hypotheses are validated.

Table 6 Outcome variable of LOC

Model Summary
R	R Sq.	MSE	F	dF1	dF2	p
0.5203	0.2702	0.9583	159.6376	1.0000	430.0000	0.0000
	Coeff.	SE	t	p	LLCI	ULCI
constant	1.4201	0.1313	10.8173	0.0000	1.1621	1.6782
EI	0.5142	0.0407	12.6348	0.0000	0.4342	0.5942

Table 7 Outcome Variable of EAC

Model Summary
R	R Sq.	MSE	F	dF1	dF2	p
0.5354	0.2866	0.9548	172.7532	1.0000	430.0000	0.0000
	Coeff.	SE	t	p	LLCI	ULCI
constant	1.3105	0.1310	10.0008	0.0000	1.0530	1.5681
EI	0.5340	0.0406	13.1436	0.0000	0.4541	0.6138

Table 8 Impact of EAC with EI and LOC

Model Summary
R	R Sq.	MSE	F	dF1	dF2	p
0.5986	0.3584	0.8608	119.7991	2.0000	429.0000	0.0000
Model
	Coeff.	SE	t	p	LLCI	ULCI
constant	0.8610	0.1403	6.1352	0.0000	0.5851	1.1368
EI	0.3712	0.0452	8.2174	0.0000	0.2824	0.4599
LOC	0.3166	0.0457	6.9263	0.0000	0.2267	0.4064

Further, the mediating role of LOC was assessed using Hayes approach that postulates that the presence of significant indirect effect implies the presence of mediator. Table 9 shows that indirect effect (IE) is significant (standardized b $=$ 0.1628 having LLCI and ULCI both positive 0.03 and 0.09 respectively). This confirms the fourth hypothesis that assumed the mediating role of LOC in the relation between EI and EAC. This IE of EI on EAC through LOC signifies that two cases that differ by one unit on EC are estimated to differ by 0.1628 units on EC as a result of the effect of EI on LOC which, in turn, affects EAC.

Table 9 Total, direct, and indirect effects of X on Y

Total effect of X on Y
Effect	se	t	p	LLCI	ULCI
0.5340	0.0406	13.1436	0.0000	0.4541	0.6138
Direct effect of X on Y
Effect	se	t	p	LLCI	ULCI
0.3712	0.0452	8.2174	0.0000	0.2824	0.4599
Indirect effect of X on Y
	Effect	Boot SE	Boot LLCI	Boot ULCI
LOC	0.1628	0.0337	0.0979	0.2313

All the findings can be summarized as follows (Table 10):

Table 10 Validation of hypotheses

Hypothesis	Regression Weight	Result	Conclusion
H1	0.5340***	Accepted	EI positively impacts EAC.
H2	0.5142***	Accepted	EI positively impacts LOC.
H3	0.3666***	Accepted	LOC positively impacts EAC.
H4	0.1628***(Indirect Effect)	Accepted	LOC in part mediates the
			correlation between EAC and EI.

Figure 3 Structural model.

6 Discussion

This study aimed to determine the degree of correlation between EI and EAC as well as investigate the role of LOC in elucidating the process by which this link is maintained. The results of the AMOS study demonstrated that EI had a positive effect on EAC and was positively correlated with LOC. Moreover, it was discovered that LOC had a significant effect on EAC [55]. Method for mediation analysis, it was further demonstrated that LOC is the process by which the association between EI and EAC is maintained. It also demonstrated that LOC’s function as an ineffective mediator. Nature of the relation between these variables can be presented as follows:

$EAC$	$= 1.3105 + 0.5340 EI + error$	(1)
$LOC$	$= 1.4201 + 0.5142 EI + error$	(2)
$EAC$	$= 0.8610 + 0.3166 LOC + error$	(3)
$EAC$	$= 0.8610 + 0.3712 EI + 0.3166 LOC + error$	(4)

The direct effect i.e., 0.3712 quantifies the estimated difference in EAC between two people that are equal on LOC but differ by one unit on EI; Whereas the indirect effect i.e., 0.1628 quantifies that two people that differ by one unit of EI are estimated to differ on EAC by 0.1628 unit as a result of EI’s influence on LOC, which in turn influences EAC.

A multiple regression was run to predict EAC from EI, and LOC. This resulted in a significant model, F(2, 429) $=$ 119.799, p $<$ 0.01, R2 $=$ 0.65. The individual predictors were examined further and indicated that EI (t $=$ 8.21, p $<$ 0.01) and LOC (t $=$ 6.92, p $<$ 0.001) were significant predictors.

Hypothetically, first hypothesis assumed EI to be positively related to EAC. Table 7 shows the standardized regression weight of 0.341 (t $=$ 10.716, p-value $<$ 0.001) quantifying the significant strength of the EI on EAC. This supports Hypothesis 1. The findings regarding positive impact of EI on EAC is consistent with the findings [46].

Second Hypothesis posited a posited a positive relation between EI and LOC. Table 6 shows the standardized regression weight of 0.347 (t $=$ 6, 18 p-value $<$ 0.001) quantifying the significant strength of the EI on LOC. This supports Hypothesis 2. The study, thus, finds EI as positive predictor of LOC supporting the findings [47].

Similarly, Hypothesis 3 posited a positive relation between LOC and EAC. Table 8 shows the standardized regression weight of 0.444 (t $=$ 8.553 p-value $<$ 0.001) quantifying the significant strength of the LOC on EAC. This supports Hypothesis 3. The study, thus, finds LOC as positive predictor of EAC process supporting the findings [48].

Finally, Hypothesis 4 assumed LOC as the mediator in the relationship between EI and EAC. Table 9 exhibits the results to test Hypothesis 4 which assumed that LOC mediates the relationship between EI and EAC. It is germane to first consider the conditions of Mediation as suggested [55]. [56] Approach, this suggest that the significant indirect effect is the only condition for mediation. Further, a significant direct effect followed by significant indirect effect indicated partial mediation whereas an insignificant direct effect followed by significant indirect effect indicated partial mediation.

Table 7 shows a significant indirect effect i.e. relation between EI and EAC through LOC (path ab $=$ 0.237***) indicating presence of a mediator. Further, the direct relation between EI and EAC (path c $^{'}$ = 0.01) is non-significant. This proves the condition for partial mediation; supporting Hypothesis 4.

Hence, all four hypotheses were validated as shown in Table 10. These findings support the extant theoretical frameworks posited by researchers integrating the field of LOC, EAC and EI.

Assumed in terms of First hypotheses, Table 6 states the standardized regression weight of 0.514 (t $=$ 12.649, p-value $<$ 0.001) measuring the substantial capability of the LOC on EI. This promotes Hypothesis 1. The results indicate a beneficial outcome of LOC on EI is in accordance with the conclusions [46].

Individuals with higher emotional intelligence tended to believe that they had control over their lives and were able to take steps to achieve their goals. The fact finder builds that individuals with upgraded EI tended to believe that they had control over their lives, while those with lower emotional intelligence tended to believe that external factors were responsible for the outcomes in their lives. These findings suggest that emotional intelligence may be a key factor in determining an individual’s sense of control over their life.

Second Hypothesis has a positive correlation among LOC and EAC. Table 8 predicts standardized regression weight of 0.317 (t $=$ 6.942, p-value $<$ 0.001) quantifying the significant strength of the LOC on EAC. This promotes Hypothesis2. In light of these findings, the study identifies LOC as a promising EAC process predictor [57]. In order to create LOC, it is crucial to acknowledge the significance of EAC. LOC gives staff direction and fosters an understanding of unity with EAC operations. This fosters innovation and the emergence of new ideas. Organizational performance relies on reciprocity. LOC fosters a custom of cooperation, group education, and target-oriented.

The third hypothesis postulated a similar advantageous connection between EAC and EI. Table 7 shows the standardized regression weight of 0.371 (t $=$ 8.237 p-value $<$ 0.001) measuring the substantial capability of the EAC on EI. This empowers Hypothesis 3. As a result, the study confirms the findings by identifying EAC as a favorable determinant of the EI process [58].

The finding suggests believe there is a good correlation between EI and EAC. Further EI score individuals have a greater probability to be accepting of change in the workplace, which could be attributed to their ability to regulate how they feel as well as comprehend what others are feeling and how to react to it. Thus, organizations should promote the development of EI among their employees to facilitate a positive attitude towards change.

At the very foremost, Hypothesis 4 presupposed LOC to act as the mediator allying connection with Emotional Intelligence and EAC. The relationship between LOC and EI through EAC (path ab $=$ 0.163***) in Table 9 indicates the presence of a mediator and is a substantial indirect impact. Additionally, there is no significance in the direct relation with EI and EAC (path c $^{'}$ $=$ 0.371). This supports Hypothesis 4 and establishes the prerequisite for partial mediation [48].

7 Conclusion

The successful implementation of organizational changes is contingent upon the voluntary acceptance of employees. Whereaschange of the initial and following degree is readily acknowledged, third-order changes (gamma) require a certain level of readiness and willingness from employees. Resistance to change may arise for various reasons, and organizations may utilize various strategies such as instruction, announcement, involvement, cooperation, treatment, or coercion to overcome such resistance. Sustainable and efficient implementation of change requires educating and communicating with employees to instill a feeling of commitment, particularly with those who have a high internal locus of control (LOC) and are emotionally connected to the organization.

The current research aimed to examine the connection between emotional intelligence (EI), employees’ acceptance of change, and the mediating role of LOC in Indian higher education institutions. The findings showed a statistically significant relationship between EI and organizational readiness for change, with EI having the strongest impact on explaining such readiness. The learning has realistic implications for supervisor, leaders, and firm that face managerialtransform, particularly in higher learning institutions that are experiencing significant changes in their environments. To enhance successful implementation, executive could classifystaff with higher EI and internal LOC, utilizing them as change agents to educate others. This is especially crucial in the modern complex, diverse, and uncertain higher education environment.

8 Implications

It has applications for executives, supervisors, and enterprises that face organizational change, particularly in higher learning institutions that are experiencing significant changes in their environments. To enhance successful implementation, Personnel with greater EI and internal LOC could be identified by administration, utilizing them as change agents to educate others. As transition often results in profound feelings [60], Emotional Intelligence is an essential factor to research for improved change effectiveness. People must develop their EI skills if they are to tolerate change [59]. Employee mindsets and actions must be developed in order to execute change effectively [37].

References

[1] Upadhyay, R. K., and Mishra, A. K. (2021). Emotional intelligence and employee acceptance to change with mediating role of locus of control. International Journal of Business Innovation and Research, 26(1), 126–2.

[2] Shafait, Z., Khan, M. A., Sahibzada, U. F., Dacko-Pikiewicz, Z., and Popp, J. (2021). An assessment of students’ emotional intelligence, learning outcomes, and academic efficacy: A correlational study in higher education. Plos one, 16(8), e0255428.

[3] Deshpande, S. (2016). Higher education: An uncertain policy process. Economic and Political Weekly, 37–2.

[4] Sarooghi, H., Libaers, D., and Burkemper, A. (2015). Examining the relationship between creativity and innovation: A meta-analysis of organizational, cultural, and environmental factors. Journal of business venturing, 30(5), 714–2.

[5] Chew, J., and Chan, C. C. (2008). Human resource practices, organizational commitment and intention to stay. International journal of manpower, 29(6), 503–2.

[6] AbuAlRub, R. F., and Nasrallah, M. A. (2017). Leadership behaviours, organizational culture and intention to stay amongst Jordanian nurses. International Nursing Review, 64(4), 520–2.

[7] Heckmann, N., Steger, T., and Dowling, M. (2016). Organizational capacity for change, change experience, and change project performance. Journal of business research, 69(2), 777–2.

[8] Ugwuanyi, C. S., Okeke, C. I., and Ageda, T. A. (2020). Motivation and self-efficacy as predictors of learners’ academic achievement. Journal of Sociology and Social Anthropology, 11(3–2), 215–2.

[9] Tayal, R., Kumar Upadhya, R., Yadav, M., Rangnekar, S., and Singh, R. (2018). The impact of transformational leadership on employees’ acceptance to change: Mediating effects of innovative behaviour and moderating effect of the use of information technology. VINE Journal of Information and Knowledge Management Systems, 48(4), 559–2.

[10] Johannsdottir, L., Olafsson, S., &Davidsdottir, B. (2015). Leadership role and employee acceptance of change: Implementing environmental sustainability strategies within Nordic insurance companies. Journal of Organizational Change Management, 28(1), 72–2.

[11] Coetzee, M., and Harry, N. (2014). Emotional intelligence as a predictor of employees’ career adaptability. Journal of Vocational Behavior, 84(1), 90–2.

[12] Fitness, J. (2001). Emotional intelligence and intimate relationships. Emotional intelligence in everyday life: A scientific inquiry, 98–2.

[13] Law, K. S., Wong, C. S., Huang, G. H., and Li, X. (2008). The effects of emotional intelligence on job performance and life satisfaction for the research and development scientists in China. Asia Pacific Journal of Management, 25(1), 51–2.

[14] Schutte, N. S., Malouff, J. M., Bobik, C., Coston, T. D., Greeson, C., Jedlicka, C., …and Wendorf, G. (2001). Emotional intelligence and interpersonal relations. The Journal of social psychology, 141(4), 523–2.

[15] Zampetakis, L. A., Kafetsios, K., Bouranta, N., Dewett, T., and Moustakis, V. S. (2009). On the relationship between emotional intelligence and entrepreneurial attitudes and intentions. International Journal of Entrepreneurial Behavior and Research, 15(6), 595–2.

[16] Grimes, P. W., Millea, M. J., and Woodruff, T. W. (2004). Grades–Who’s to blame? Student evaluation of teaching and locus of control. The Journal of Economic Education, 35(2), 129–2.

[17] Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological monographs: General and applied, 80(1), 1.

[18] Wurdinger, S. D., and Carlson, J. A. (2009). Teaching for experiential learning: Five approaches that work. R&L Education.

[19] Buchanan, D., Fitzgerald, L., Ketley, D., Gollop, R., Jones, J. L., Lamont, S. S., …and Whitby, E. (2005). No going back: A review of the literature on sustaining organizational change. International Journal of Management Reviews, 7(3), 189–2.

[20] Sofat, K., Kiran, R., and Kaushik, S. (2015). Management of organizational change and its impact on commitment: A study of select Indian IT companies. Global Business and Management Research, 7(3).

[21] Huy, Q. N. (1999). Emotional capability, emotional intelligence, and radical change. Academy of Management review, 24(2), 325–2.

[22] Fiol, C. M., and O’Connor, E. J. (2002). When hot and cold collide in radical change processes: Lessons from community development. Organization Science, 13(5), 532–2.

[23] Judge, T. A., Thoresen, C. J., Pucik, V., and Welbourne, T. M. (1999). Managerial coping with organizational change: A dispositional perspective. Journal of applied psychology, 84(1), 107.

[24] Mayer, J. D., and Salovey, P. (1993). The intelligence of emotional intelligence. intelligence, 17(4), 433–2.

[25] Bar-On, R. (2010). A broad definition of emotional-social intelligence according to the Bar-On model. [online] http:/www.reuvenbaron.org/bar-onmodel/essay.php.

[26] Goleman, D. (1998). Working with emotional intelligence. Bantam.

[27] Intelligence, E. E., and Goleman, D. (1998). What makes a leader? Harvard Business Review.

[28] Letam, D. B. (2017). Emotional intelligence and organizational commitment in three industrial sub-sectors in Rivers State. International Journal of Advanced Academic Research, 3(9), 1–2.

[29] Pradhan, R. K., Jena, L. K., and Singh, S. K. (2017). Examining the role of emotional intelligence between organizational learning and adaptive performance in Indian manufacturing industries. Journal of Workplace Learning, 29(3), 235–2.

[30] Dasborough, M., Lamb, P., and Suseno, Y. (2015). Understanding emotions in higher education change management. Journal of Organizational Change Management, 28(4), 579–2.

[31] Ciarrochi, J. V., Chan, A. Y., and Caputi, P. (2000). A critical evaluation of the emotional intelligence construct. Personality and Individual differences, 28(3), 539–2.

[32] Ghazanfarpour, H., Karimi, S., Khabbazi, M., and Pourkhosravani, M. (2021). Analysis of the Educational Spaces of School in District 2 of Kerman. The Journal of Geographical Research on Desert Areas, 9(1), 65–2.

[33] García-Sancho, E., Salguero, J. M., and Fernández-Berrocal, P. (2014). Relationship between emotional intelligence and aggression: A systematic review. Aggression and violent behavior, 19(5), 584–2.

[34] Naghneh, M. H. K., Tafreshi, M. Z., Naderi, M., Shakeri, N., Bolourchifard, F., &Goyaghaj, N. S. (2017). The relationship between organizational commitment and nursing care behavior. Electronic physician, 9(7), 4835.

[35] Miners, C. T., Côté, S., and Lievens, F. (2018). Assessing the validity of emotional intelligence measures. Emotion Review, 10(1), 87–2.

[36] Petrides, K. V., Pita, R., and Kokkinaki, F. (2007). The location of trait emotional intelligence in personality factor space. British journal of psychology, 98(2), 273–2.

[37] Armenakis, A. A., Harris, S. G., and Mossholder, K. W. (1993). Creating readiness for organizational change. Human relations, 46(6), 681–2.

[38] Oreg, S., Vakola, M., and Armenakis, A. (2011). Change recipients’ reactions to organizational change: A 60-year review of quantitative studies. The Journal of applied behavioral science, 47(4), 461–2.

[39] Dunham-Presenter, R. B., Grube, J. A., Gardner, D. G., Cummıngs, L. L., and Pıerce, L. (1989). The Development of an Attitude toward Change Instrument. In Annual Academy of Management Conference Madison.

[40] Lau, C. M., and Woodman, R. W. (1995). Understanding organizational change: A schematic perspective. Academy of management journal, 38(2), 537–2.

[41] Turnipseed, D. L. (2018). Emotional intelligence and OCB: The moderating role of work locus of control. The Journal of social psychology, 158(3), 322–2.

[42] Klein, K. J., and Sorra, J. S. (1996). The challenge of innovation implementation. Academy of management review, 21(4), 1055–2.

[43] Farh, C. I., Seo, M. G., and Tesluk, P. E. (2012). Emotional intelligence, teamwork effectiveness, and job performance: The moderating role of job context. Journal of applied psychology, 97(4), 890.

[44] Jukiæ Matiæ, L., and Glasnoviæ Gracin, D. (2016). The use of the textbook as an artefact in the classroom: A case study in the light of a socio-didactical tetrahedron. Journal für Mathematik-Didaktik, 37(2), 349–2.

[45] Ponto, J. (2015). Understanding and evaluating survey research. Journal of the advanced practitioner in oncology, 6(2), 168.

[46] Yang, R. T. (2003). Adsorbents: fundamentals and applications. John Wiley and Sons.

[47] Marsick, V. J., and Watkins, K. E. (2003). Demonstrating the value of an organization’s learning culture: the dimensions of the learning organization questionnaire. Advances in developing human resources, 5(2), 132–2.

[48] Scheuner, M. T., Wang, S. J., Raffel, L. J., Larabell, S. K., and Rotter, J. I. (1997). Family history: a comprehensive genetic risk assessment method for the chronic conditions of adulthood. American journal of medical genetics, 71(3), 315–2.

[49] Schutz, R. W., Gessaroli, M. E., Singer, R., Murphey, M., and Tennant, L. (1993). Use, misuse, and disuse of psychometrics in sport psychology research. Handbook of research on sport psychology, 901–2.

[50] Hooper, D., Coughlan, J., and Mullen, M. (2008, September). Evaluating model fit: a synthesis of the structural equation modelling literature. In 7th European Conference on research methodology for business and management studies (Vol. 2008, pp. 195–2).

[51] Nunally, J. C., and Bernstein, I. H. (1978). Psychometric theory. New York McGraw-Hill.

[52] Bollen, K. A. (1986). Sample size and Bentler and Bonett’s nonnormed fit index. Psychometrika, 51, 375–2.

[53] Levenson, H. (1973). Multidimensional locus of control in psychiatric patients. Journal of consulting and clinical psychology, 41(3), 397.

[53] Arnett, M. J., Thorpe, R. J., Gaskin, D. J., Bowie, J. V., and LaVeist, T. A. (2016). Race, medical mistrust, and segregation in primary care as usual source of care: findings from the exploring health disparities in integrated communities study. Journal of Urban Health, 93, 456–2.

[54] Hayes, S. C., Luoma, J. B., Bond, F. W., Masuda, A., and Lillis, J. (2006). Acceptance and commitment therapy: Model, processes and outcomes. Behaviour research and therapy, 44(1), 1–2.

[55] Baron, R. M., and Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173.

[56] O’Dell, C., and Hubert, C. (2011). The new edge in knowledge: How knowledge management is changing the way we do business. John Wiley and Sons.

[57] Vveinhardt, J., and Sedziuviene, N. (2022). Acceptance of change by reducing employee resistance and strengthening organizational commitment. Polish Journal of Management Studies, 26.

[58] Coetzee, M., and Harry, N. (2014). Emotional intelligence as a predictor of employees’ career adaptability. Journal of Vocational Behavior, 84(1), 90–2.

[59] Daus, C. S., Dasborough, M. T., Jordan, P. J., and Ashkanasy, N. M. (2012). We are all mad in wonderland: An organizational culture framework for emotions and emotional intelligence research. In Experiencing and managing emotions in the workplace (Vol. 8, pp. 375–2). Emerald Group Publishing Limited.

[60] Roy, S., Sharma, P., Singh, K. K., and Srivastava, R. (2023). On a Statistical Model Useful for Demographics: Estimating the Mean Number of Children Ever Born Through the Distribution of Male Births with an Application to Data from India. Journal of Reliability and Statistical Studies, 16(1), 57–2.

[61] Malik, S., Kanika and Atul (2024). An improvement in regression estimator through exponential estimator using two auxiliary variables. Journal of Reliability and Statistical Studies, 16(2), 329–2.

Biographies

Muskan Singh received the bachelor’s degree in commerce in 2019 from Gorakhpur University, Uttar Pradesh, India. She received master’s degree in Management in 2021 from H.N.B Garhwal University, Srinagar, Uttarakhand, India. Currently she is pursuing PhD in Management from Graphic Era Deemed to be University, Dehradun, India. Her research interests are in human resource management and organization behaviour.

Sachin Ghai is an Academician, Trainer, Startup mentor, and a Business and Life coach with over 2 decades of experience and a proven track-record in teaching and training at various levels.He has a Doctorate in Economics in the area of Urban Development Planning and Management. He holds a Master’s degree in Economics and Bachelor’s degree in Management & Information Technology. He is presently working in the capacity of Head of the Department – Management Studies and Director – Technology Business Incubator at Graphic Era Deemed to be University, Dehradun. He has been the Program Convener of the first MBA program in Innovation, Entrepreneurship and Venture Development in India offered at Graphic Era Deemed to be University.

Amar Kumar Mishra has done PhD in HR, MBA (OB & HR), M. Com from the University of Burdwan (West Bengal). Besides he has qualified UGC NET in Management, Commerce and HR. He has got experience of 20 years in academics. At present he is rendering his services to Brainware University, Kolkata as Professor in the Department of Management.

Nupur Goyal received the Bachelor’s degree in Computer science in 2009 from Kurukshetra University, Kurukshetra, Haryana, India. She received the master’s degree in mathematics in 2011 from H.N.B. Garhwal University, Srinagar, Uttarakhand, India and Ph.D. degree from Graphic Era University, Dehradun, Uttarakhand, India in November 2016. Her research interests are in the area of reliability theory and operation research. she is an Assistant Professor in department of Mathematics, Graphic Era Deemed to be University, Dehradun, India. She is a reviewer of various international journals.

Journal of Reliability and Statistical Studies, Vol. 17, Issue 1 (2024), 109–136.
doi: 10.13052/jrss0974-8024.1715
© 2024 River Publishers