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Emotional Intelligence as a Predictor of Employee 

Engagement: A Study of U.S. Manufacturing Workers 

 

Trina Lang, PhD, MBA | Capella University, Minneapolis, Minnesota, USA 

Susan Saurage-Altenloh, PhD, MBA, MS | Capella University, Minneapolis, Minnesota, USA 

Contact: trina.lang@comcast.net 

Abstract 

According to the 2016 Gallup Report, manufacturing workers are the least engaged workers in the U.S. 

(Gallup, 2017). Studies on emotional intelligence (EI) indicate that EI has a significant, positive rela-

tionship with employee engagement (EE). Internationally, recent studies have operationalized the EI and 

EE constructs as a mix of unidimensional and multidimensional constructs, while domestically studies 

on EI and EE have been sparse, with more focus on EI and EE operationalized as unidimensional con-

structs examined along with other constructs. This study addressed these gaps by using the self-determi-

nation theory (SDT) to examine the extent to which EI and its dimensions of self-emotion appraisal 

(SEA), other’s emotion appraisal (OEA), use of emotions (UOE), and regulation of emotion (ROE) pre-

dict EE and its facets of vigor (VI), dedication (DE), and absorption (AB) in 167 manufacturing workers 

in the continental U.S. The research study used a quantitative methodology incorporating nonexperi-

mental reasoning in a predictive, cross-sectional survey using linear and hierarchical multiple regression 

analyses to analyze the data. The results go beyond the strength of the relationship between the con-

structs to demonstrate how each dimension of EI explains unique variance in EE and each of its facets. 

Data was collected using an online survey that included the WLEIS and UWES-9 instruments. The re-

sults of the study demonstrated that (a) EI and its dimensions have significant predictive relationships 

with EE and VI, (b) EI and its dimensions of SEA and UOE have significant predictive relationships 

with DE, but OEA and ROE do not, and (c) EI has a significant predictive relationship with AB, but 

SEA, OEA, UOE, and ROE do not. These outcomes provide information about the drivers of the rela-

tionship between EI and EE that leaders in manufacturing organizations may find influential when con-

sidering hiring practices, EI initiatives, and engagement efforts in the workplace. 

Keywords: emotional intelligence, employee engagement, self-determination theory, WLEIS, UWES-9 



 

78 January 2023 | Volume 2, Number 1 

Introduction 

Gallup Inc.’s most recent meta-analysis covering over 112,000 teams, over 2.7 million employees, in 

276 organizations across 54 industries in 96 countries found that employees in the top quartile on em-

ployee engagement significantly outperformed the employees in the bottom quartile in many perfor-

mance outcomes: (a) 81% lower absenteeism, (b) 58% fewer patient safety incidents, (c) 18% less turno-

ver in high-turnover organizations and 43% less turnover in low-turnover organization, (d) 28% less 

shrinkage, (e) 64% fewer quality issues, (f) 10% higher loyalty, (g) 18% higher productivity, and (h) 

23% higher profitability (Gallup, 2020). The study results suggest that employee engagement plays a 

critical role in many aspects of organizational performance, presenting a major challenge for U.S. manu-

facturing organizations that represent the job category with the lowest level of engagement at 25% (Gal-

lup, 2017). With only one out of four manufacturing workers engaged in the workplace, that leaves 75% 

of the manufacturing workers either not engaged or actively disengaged.  

The research literature on emotional intelligence indicates that employees’ emotional intelligence corre-

lates with their workplace engagement level (Karamustafa & Kunday, 2018; Zhoc et al., 2020). Recent 

studies demonstrate that the individual dimensions of emotional intelligence can predict employee en-

gagement (AlMazrouei et al., 2015; Barreiro & Treglown, 2020). Quantitative studies conducted in the 

education, health care, and business sectors of international countries find a positive relationship be-

tween the dimensions of emotional intelligence and employee engagement (Pérez-Fuentes et al., 2018; 

Sarangi & Vats, 2015; Zhoc et al., 2020). While a plethora of research on the EI and EE concepts has 

been conducted in the United States over the years, recent domestic studies have shown less focus on the 

specific topic of EI and EE, typically examining them along with other constructs (Boyatzis et al., 2017; 

Schutte & Loi, 2014).  

 

There is considerable research regarding EI and EE, yet there is a lack of research regarding how EI and 

its dimensions predict EE and its facets in U.S. manufacturing workers. This research incorporates the 

self-determination theory as the theoretical underpinning that supports the investigation of the variables 

included in the study’s conceptual framework. The research literature on EI indicates that EI has a sig-

nificant, positive relationship with employee engagement (Barreiro & Treglown, 2020; Pérez-Fuentes et 

al., 2018; Sarangi & Vats, 2015). SDT states that intrinsic and extrinsic motivations drive an individ-

ual’s behavior, and EI forms an intrinsic structure that motivates an employee to be engaged in the 

workplace. 

 

EI 

 

The emotional intelligence (EI) construct relates to measuring emotional intelligence. Salovey and 

Mayer (1990) defined emotional intelligence as “a form of social intelligence that involves the ability to 

monitor one’s own and others’ feelings and emotions, to discriminate among them and to use this infor-

mation to guide one’s thinking and actions” (p.189). Salovey and Mayer (1990) conceptualized EI using 

four sub-constructs: (a) self-emotional appraisal (SEA), defined as the appraisal and expression of emo-

tion in the self; (b) others’ emotional appraisal (OEA), defined as the appraisal and recognition of emo-

tion in others; (c) use of emotion (UOE), defined as the use of emotion to facilitate performance; and (d) 

regulation of emotion (ROE), defined as the regulation of emotion in self. EI and its dimensions are 

measured using the Wong and Law Emotional Intelligence Scale (WLEIS) (Wong & Law, 2002). The 



 

 

79   Business Management Research & Applications: A Cross-Disciplinary Journal 

scale comprises 16 items on a 7-point Likert-type scale that includes the four dimensions of SEA, OEA, 

UOE, and ROE to measure EI.  

 

EE 

 

The employee engagement (EE) construct relates to measuring employee engagement. Schaufeli et al. 

(2006) defined engagement as “a positive, fulfilling, work-related state of mind that is characterized by 

vigor, dedication, and absorption” (p. 1). Schaufeli et al. (2006) conceptualized EE using three sub-con-

structs: (a) vigor, described as “high levels of energy and mental resilience while working, the willing-

ness to invest effort in one’s work, and persistence even in the face of difficulties”; (b) dedication, de-

scribed as “being strongly involved in one’s work and experiencing a sense of significance, enthusiasm, 

inspiration, pride, and challenge”; and (c) absorption, described as “being fully concentrated and happily 

engrossed in one’s work, whereby time passes quickly, and one has difficulties with detaching oneself 

from work” (p. 702). Employee engagement and its facets are measured using the Utrecht Work En-

gagement Scale (UWES-9) (Schaufeli et al., 2006). The scale comprises nine items on a 7-point fre-

quency rating scale that includes the three facets of vigor, dedication, and absorption used to measure 

employee engagement. 

 

SDT 

 

Self-determination theory is “a theory of human personality and motivation that focuses on the individ-

ual’s psychological needs and the extent to which the individual’s behavior is driven by intrinsic or ex-

trinsic motivations” (Self-Determination Theory, 2015, p. 1). SDT evolved from research on intrinsic 

motivation, which is “the inherent tendency to seek out novelty and challenges, to extend and exercise 

one’s capacities, to explore, and to learn” (Ryan & Deci, 2000, p. 70). In formulating a theoretical per-

spective for studying the emotional intelligence and engagement of workers, self-determination theory 

provides a useful framework. SDT postulates that when the three basic needs of competence, autonomy, 

and relatedness are satisfied, it leads to enhanced self-motivation and well-being, and when thwarted, 

leads to diminished motivation and well-being (Ryan & Deci, 2000). When applied to the study, SDT 

holds that one would expect emotional intelligence and its dimensions to influence or explain employee 

engagement and its facets because EI acts as an intrinsic motivator, and more autonomous forms of mo-

tivation will enhance employees’ engagement at work. 

 

EI and SDT 

 

This study proposes that EI forms an intrinsic structure that motivates employees to be engaged in the 

workplace. Vallerand et al. (2014) posited that with the ability to understand and manage one’s own 

emotions and others’ emotions at the interpersonal and intrapersonal levels, workers with high emotional 

intelligence should act more autonomously. In a study of service employees in Seoul, Yang et al. (2015) 

examined how multi-dimensional emotional intelligence impacts intrinsic and extrinsic motivation and 

job satisfaction using labor perceived organizational support. The results of the study revealed that three 

dimensions of EI (appraisal of emotion, utilization of emotion, and expression of emotion) positively 

influence intrinsic motivation and that one dimension of EI (appraisal of emotion) positively influences 

extrinsic motivation (Yang et al., 2015). Lumpkin and Achen (2018) further illustrated the synergies be-

tween SDT and EI with the following shared characteristics: (a) EI: self-awareness and self-regulation 

→ SDT: autonomy, (b) EI: motivation → SDT: competence, and (c) EI: empathy and social skill → 



 

80 January 2023 | Volume 2, Number 1 

SDT: relatedness. These findings suggest that higher levels of EI should increase an employee’s self-

determination and, in turn, enhance their engagement in the workplace. 

 

EE and SDT 

 

Mueller (2019) suggested there is great merit in applying SDT as a theoretical framework in employee 

engagement research. SDT provides the theoretical lens that explains why employees exhibit a range of 

engagement levels in the workplace. SDT argues that supporting the three basic psychological needs in-

creases intrinsic motivation and internalization, leading to higher quality motivation and performance, 

while trying to control motivation through extrinsic rewards and sanctions generally fails, leading to 

lower quality motivation and performance (Ryan & Deci, 2020). Rich et al. (2010, p. 618) posited that  

intrinsic motivation is promoted by both work contexts and individual differences that foster 

feelings of competence, autonomy, and relatedness (Gagné & Deci, 2005), and it is argued to 

influence performance because opportunities to satisfy these three intrinsic needs facilitate self-

motivation and effective regulatory functioning through internalization of organizationally 

valued goals (Baard et al., 2004). 

SDT exemplifies the logic that explains employee engagement. 

 

Figure 1 presents the theoretical framework for the study. 

Figure 1 

Theoretical Framework 

 

The purpose of this quantitative nonexperimental predictive survey research is to apply the self-determi-

nation theory that relates the emotional intelligence construct and its dimensions of SEA, OEA, UOE, 

and ROE as measured by the Wong and Law EI Survey (WLEIS) to the employee engagement construct 

and its facets of vigor, absorption, and dedication as measured by the Utrecht Work Engagement Scale 



 

 

81   Business Management Research & Applications: A Cross-Disciplinary Journal 

(UWES-9) for U.S. workers from manufacturing companies. The study contributes to the existing litera-

ture on the significance of EI in predicting employee engagement. The study also affirms the link be-

tween EI and EE of manufacturing workers by assessing their EI (and its dimensions) and its relation-

ship to their EE (and its facets) via an online survey questionnaire.  

 

Methods 

Participants 

A total of 167 continental U.S. manufacturing workers who were 21-70 years of age, able to read and 

understand English, had been employed full-time in a manufacturing setting for more than one year, and 

currently worked for a manufacturing organization with at least 250 employees were recruited through 

the Alchemer panel system. The participants completed a web-based survey that included four screening 

questions, the informed consent form, and the two instruments which were used to measure emotional 

intelligence and employee engagement. 

Measures 

Emotional Intelligence. Emotional intelligence was measured using the WLEIS developed by Wong 

and Law (2002), consisting of 16 items on a 7-point Likert-type scale to measure the four dimensions of 

self-emotion appraisal, others’ emotion appraisal, use of emotion, and regulation of emotion. The 

WLEIS instrument was normed using samples of students and workers. The WLEIS survey instrument 

shows convergent, discriminant, and incremental validity of the 16-item EI scale (Wong & Law, 2002). 

The WLEIS also shows good convergence with other EI measures such as the Trait Meta-Mood and the 

EQ-i. The internal consistency of the WLEIS scale was evaluated by computing Cronbach’s coefficient 

alpha for each dimension: SEA = .83, OEA = .75, UOE = .79, and ROE = .87. The overall scale had 

high reliability of .90.  

Employee Engagement. Employee engagement was measured using the UWES-9 developed by Schau-

feli et al. (2006), which consists of nine items on a 7-point frequency rating scale to measure the three 

facets of vigor, dedication, and absorption. The UWES-9 instrument was normed using various occupa-

tional groups from 27 different studies. The factorial validity of the instrument is demonstrated using 

confirmatory factor analyses, and the three scale scores have good internal consistency and test-retest 

reliability (Schaufeli et al., 2006). The internal consistency of the UWES-9 was evaluated by computing 

Cronbach’s coefficients alpha for each facet: VI = .81, AB = .70, and DE = .83. The overall scale had 

high reliability of .90. The EI and EE scales demonstrated good internal consistency reliability with all 

Cronbach coefficient alphas at or above the minimum desirable value of .70 (Pyrczak & Tcherni-

Buzzeo, 2019).   

 

  



 

82 January 2023 | Volume 2, Number 1 

Results 
 

Preliminary Analyses 

The initial step in analyzing the data was to calculate the participant scores for EI and SEA, OEA, UOE, 

and ROE dimensions. When complete, each participant had a mean score for EI, SEA, OEA, UOE, and 

ROE. The WLEIS scale includes 16 questions on EI: four for SEA, four for OEA, four for UOE, and 

four for ROE. The mean score for emotional intelligence was calculated in SPSS by taking all 16 

questions and dividing the total by 16. The mean score of the four subscales was calculated in SPSS by 

adding the four scores on each subscale and dividing the total by four. 

Similarly, the scores for EE and its facets of VI, DE, and AB were calculated. The UWES-9 scale 

includes nine questions on EE: three questions for VI, three questions for DE, and three questions for 

AB. The mean score for employee engagement was calculated in SPSS by taking all nine questions and 

dividing the total by nine. The mean score of the three subscales was calculated in SPSS by adding the 

three scores on each subscale and dividing the total by three. Each participant had a mean score for EE, 

VI, DE, and AB when complete.  

The minimum sample size for the study was calculated using G*Power. The minimum sample size 

required for the study was 92 manufacturing workers (using an alpha of .05, a power of .80, a medium 

effect size of .15, and the multiple linear regression statistical test with five predictors). The study 

provided an actual sample size of 167 manufacturing workers, which was sufficient to conduct statistical 

analyses to test the hypotheses of the research questions. Outliers were identified by reviewing Casewise 

Diagnostics in a preliminary linear regression model that included all the data collected (EI Total vs. EE 

Total). The extreme outliers identified in the Casewise Diagnostics of the preliminary model were case 

#’s 69, 73, 91, 147. The four cases identified in the Casewise Diagnostics table were removed. The case 

processing summary was rerun to validate 163 valid responses (cases) with no missing responses (cases) 

for each study variable after removing the four extreme outliers. 

At this point, the data were ready to run descriptive statistics. Descriptive statistics were run on the 163 

cases to include minimum, maximum, mean, standard deviation, skewness, and kurtosis. Table 1 reflects 

the descriptive statistics for the nine variables used in the study. 

Table 1 

Descriptive Statistics 

Variable  N Min Max Mean 
Std. 

Deviation 
Skewness Kurtosis 

SEA 163 1.00 7.00 5.8819 .91300 -1.927 6.983 

OEA 163 3.00 7.00 5.6902 .83741 -.653 .302 

UOE 163 1.75 7.00 5.9969 .80746 -1.434 4.202 

ROE 163 1.00 7.00 5.6012 1.05798 -1.271 2.129 

EI Total  163 2.69 6.94 5.7926 .68185 -.769 1.878 
        



 

 

83   Business Management Research & Applications: A Cross-Disciplinary Journal 

VI 163 .33 6.00 4.0061 1.16503 -.482 -.167 

AB 163 .67 6.00 3.9550 1.12065 -.488 -.002 

DE 163 .33 6.00 4.3497 1.14324 -.653 .245 

EE Total 163 .44 6.00 4.1036 1.01476 -.556 .259 

                

 

All nine variables were within the range for skewness (±2) and kurtosis (±7) (Testing of Assumptions, 

2021). Skewness looks at the symmetry of the distribution, while kurtosis looks at the pointiness of the 

distribution (Field, 2018). 

 

Correlation analysis was also used to analyze the data. Table 2 presents the correlations between the  

variables in the study. 

Table 2 

Correlation Between Variables 

 Variables Mean 

Standard 

Deviation 1 2 3 4 5 6 7 8 9 

1. SEA 5.882 0.913 1 .482** .503** .395** .785** .546** .298** .527** .517** 

2. OEA  5.690 0.837  1 .368** .311** .698** .456** .329** .367** .433** 

3. UOE  5.997 0.807   1 .493** .769** .514** .347** .583** .543** 

4. ROE  5.601 1.058    1 .762** .484** .298** .451** .465** 

5. EI Total  5.793 0.682     1 .663** .419** .637** .647** 

6. VI 4.006 1.165      1 .629** .797** .913** 

7. AB  3.955 1.121       1 .618** .841** 

8. DE  4.350 1.143        1 .908** 

9. EE Total  4.104 1.015         1 

                          

**Correlation is significant at the 0.01 level (2-tailed), N = 163.  

 

All dimensions of EI are positively correlated with facets of EE. SEA has the strongest correlation with 

VI (.546), UOE has the strongest correlations with AB (.347) and DE (.583). OEA had the weakest cor-

relations with VI (.456) and DE (.367). SEA and ROE have the weakest correlation with AB (.298). 

Overall, the correlation results provide preliminary support for the hypotheses. 

 

The data were also tested to verify that the linear and hierarchical multiple regression assumptions were 

met. The assumptions for linear regression include (a) level of measurement, (b) independence of scores, 

(c) linearity, (d) homoscedasticity, (e) outliers, and (f) normality. One additional assumption was tested 

for hierarchical multiple regression, multicollinearity (Field, 2018). The level of measurement was veri-

fied using descriptive statistics. Independence of scores was tested using the Durbin-Watson test statis-

tic. Linearity for the linear regression model was inspected by visually inspecting a scatterplot of the DV 

plotted against the IV. For HMR, linearity was tested in two parts. Step one determined if a linear rela-

tionship exists between the dependent and independent variables collectively, which was achieved by 

plotting a scatterplot. Step two determined if a linear relationship exists between the dependent variable 



 

84 January 2023 | Volume 2, Number 1 

and each independent variable, which was achieved using partial regression plots. Homoscedasticity was 

checked using the scatterplot created to check for linearity. Outliers, high leverage points, and highly 

influential points were detected by checking Casewise Diagnostics and studentized deleted residuals 

(SDR), leverage points (LEV), and Cook’s Distance (COO). Normality was determined by looking at 

the distribution of residuals using a histogram with superimposed normal curve and P-P Plots or normal 

Q-Q Plots of the studentized residuals. Last, multicollinearity was checked by inspecting correlation co-

efficients and Tolerance/VIF values. All assumptions for linear and HMR were met except for some 

possible outliers; bootstrapping was applied to address any potential outliers in the data (Field, 2018). 

Main Analyses 

The study aimed to answer the following research questions: 

RQ1: To what extent does emotional intelligence (EI) predict employee engagement (EE) in 

U.S. manufacturing workers? 

RQ1a: To what extent do EI and its dimensions of SEA, OEA, UOE, ROE predict the vigor 

facet of EE? 

RQ1b:  To what extent do EI and its dimensions of SEA, OEA, UOE, ROE predict the 

dedication facet of EE? 

RQ1c:  To what extent do EI and its dimensions of SEA, OEA, UOE, ROE predict the 

absorption facet of EE? 

RQ1d: To what extent do SEA, OEA, UOE, and ROE predict EE? 

Once the data was screened by reviewing key descriptive statistics, testing the data to ensure the as-

sumptions were met, and adjusting for outliers, SPSS was used to conduct hypothesis testing and answer 

the research questions. Regression analysis was used in this study to conduct complex analyses with dif-

ferent types and quantities of predictor and outcome variables. Linear regression allows the prediction of 

a continuous dependent variable (DV) based on one continuous independent variable (IV). Multiple re-

gression allows the prediction of a continuous dependent variable based on multiple continuous or nomi-

nal independent variables (Hierarchical Multiple Regression, 2013). Regression analysis was appropri-

ate for this study because the independent and dependent variables are continuous and treated as ordinal 

data in the analysis (Field, 2018).  

 

Linear regression was run to understand the effect of emotional intelligence (predictor variable) on em-

ployee engagement and its facets of vigor, dedication, and absorption (outcome variables). The regres-

sion results for EI Total to EE Total demonstrate that R2 = .419 and emotional intelligence explains 

41.9% of the variability of employee engagement. H01 is rejected and concludes that emotional intelli-

gence is a statistically significant predictor of employee engagement. The regression results for EI Total 

to VI demonstrate that R2 = .439 and emotional intelligence explains 43.9% of the variability of vigor. 

H01a1 is rejected and concludes that emotional intelligence is a statistically significant predictor of 

vigor. The regression results for EI Total to DE demonstrate that R2 = .406 and emotional intelligence 



 

 

85   Business Management Research & Applications: A Cross-Disciplinary Journal 

explains 40.6% of the variability of dedication. H01b1 is rejected and concludes that emotional intelli-

gence is a statistically significant predictor of dedication. The regression results for EI Total to AB 

demonstrate that R2 = .176 and emotional intelligence explains 17.6% of the variability of absorption. 

H01c1 is rejected and concludes that emotional intelligence is a statistically significant predictor of ab-

sorption. 

 

Table 3 summarizes the linear regression results for EI Total. 

Table 3 

Linear Regression Results for EI Total 

Variable B SE t p 95% CI   p BCa 95% CI 

EE Total (DV) 

EI Total (IV) 0.963 0.089 10.774 **.000 [.787, 1.140]  N/A  

N = 163, R2 = .419, adj. R² = .415, p < .001       

VI (DV) 

EI Total (IV) 1.133 0.101 11.235 **.000 [.934, 1.332]  N/A  

N = 163, R2 = .439, adj. R² = .436, p < .001       

DE (DV) 

EI Total (IV) 1.068 0.102 10.482 **.000 [.867, 1.269]  0.001 [.882, 1.244] 

N = 163, R2 = .406, adj. R² = .402, p < .001; bootstrap = no significant changes    

AB (DV) 

EI Total (IV) 0.689 0.118 5.862 **.000 [.457, .922]  N/A  

N = 163, R2 = .176, adj. R² = .171, p < .001       

**p < .001 

  

Hierarchical multiple regression (HMR) allows you to add blocks of variables to a regression equation 

and determine how much each block of variables uniquely adds to the prediction of the dependent varia-

ble (Hierarchical Multiple Regression, 2013). HMR was used for hypothesis testing, in which each of 

the four dependent or outcome variables (VI, DE, AB, and Total EE) was analyzed against all five inde-

pendent or predictor variables (SEA, OEA, UOE, ROE, and Total EI). Similar statistical analyses were 

used in past empirical research on EI and EE (Sarangi & Vats, 2015).  

 

Hierarchical multiple regression was run to understand the effect of the emotional intelligence dimen-

sions of self-emotion appraisal, others’ emotion appraisal, use of emotion, and regulation of emotion on 

employee engagement and its facets of vigor, dedication, and absorption. The HMR results for SEA, 

OEA, UOE, and ROE to EE Total demonstrate that R2 = .425 and SEA, OEA, UOE, and ROE ex-

plain 42.5% of the variability of employee engagement. As Table 4 indicates, SEA with β = .248, t = 

2.937, p = .004 significantly predicted EE Total. Therefore, H01d1 is rejected and concludes that SEA is 

a statistically significant predictor of EE. OEA with β = .200, t = 2.358, p = .020 significantly predicted 



 

86 January 2023 | Volume 2, Number 1 

EE Total. Therefore, H01d2 is rejected and concludes that OEA is a statistically significant predictor of 

EE. UOE with β = .349, t = 3.667, p < .001 significantly predicted EE Total. Therefore, H01d3 is re-

jected and concludes that UOE is a statistically significant predictor of EE. ROE with β = .181, t = 

2.653, p = .009 significantly predicted EE Total. Therefore, H01d4 is rejected and concludes that ROE 

is a statistically significant predictor of EE.  

 

The HMR results for SEA, OEA, UOE, and ROE to VI demonstrate that R2 = .442 and SEA, OEA, 

UOE, and ROE explain 44.2% of the variability of vigor. As Table 4 indicates, SEA Total with β = 

.343, t = 3.587, p < .001 significantly predicted VI Total. Therefore, H01a2 is rejected and concludes 

that SEA is a statistically significant predictor of VI. OEA with β = .255, t = 2.651, p = .009 signifi-

cantly predicted VI. Therefore, H01a3 is rejected and concludes that OEA is a statistically significant 

predictor of VI. UOE with β = .292, t = 2.711, p = .007 significantly predicted VI Total. Therefore, 

H01a4 is rejected and concludes that UOE is a statistically significant predictor of VI. ROE with β = 

.244, t = 3.167, p = .002 significantly predicted VI. Therefore, H01a5 is rejected and concludes that ROE 

is a statistically significant predictor of VI.  

 

The HMR results for SEA, OEA, UOE, and ROE to DE demonstrate that R2 = .436 and SEA, OEA, 

UOE, and ROE explain 43.6% of the variability of dedication. As Table 4 indicates, SEA with β = .321, 

t = 3.406, p = .001 significantly predicted DE. Therefore, H01b2 is rejected and concludes that SEA is a 

statistically significant predictor of DE. OEA with β = .089, t = .935, p = .350 did not significantly pre-

dicted DE (to be significant p < .05). Therefore, H01b3 fails to reject and concludes that OEA is not a 

statistically significant predictor of DE. UOE with β = .501, t = 4.721, p = .001 significantly predicted 

DE. Therefore, H01b4 is rejected and concludes that UOE is a statistically significant predictor of DE. 

ROE with β = .168, t = 2.206, p = .050 did not significantly predicted DE. Therefore, H01b5 fails to re-

ject and concludes that ROE is not a statistically significant predictor of DE.  

 

The HMR results for SEA, OEA, UOE, and ROE to AB demonstrate that the four dimensions of EI are 

not statistically significant predictors of absorption, R2 = .183, p = .149. Therefore, H01c2 fails to reject 

and concludes that SEA is not a statistically significant predictor of AB; H01c3 fails to reject and con-

cludes that OEA is not a statistically significant predictor of AB; H01c4 fails to reject and concludes that 

UOE is not a statistically significant predictor of AB, and H01c5 fails to reject and concludes that ROE 

is not a statistically significant predictor of AB. 

 

See Table 4 for a summary of the HMR results. 

Table 4 

Hierarchical Multiple Regression Results 

                
Variable B SE  b t p 95% CI for B   p BCa 95% CI 

            LL UL     LL UL 

EE Total (DV) 

SEA (IV) 0.248 0.084 0.223 2.937 .004 .081 .415  .005 .068 .413 

OEA (IV) 0.200 0.085 0.165 2.358 .020 .033 .368  .020 .032 .384 

UOE (IV) 0.349 0.095 0.278 3.667 **.000 .161 .537  .003 .146 .523 



 

 

87   Business Management Research & Applications: A Cross-Disciplinary Journal 

ROE (IV) 0.181 0.068 0.188 2.653 .009 .046 .315  .031 .009 .350 

N = 163, R2 = .425, adj. R² = .411, p = .009; bootstrap = no significant changes      

VI (DV) 

SEA (IV) 0.343 0.096 0.269 3.587 **.000 .154 .531  .002 .080 .538 

OEA (IV) 0.255 0.096 0.183 2.651 .009 .065 .445  .014 .065 .444 

UOE (IV) 0.292 0.108 0.202 2.711 .007 .079 .504  .006 .081 .492 

ROE (IV) 0.244 0.077 0.222 3.167 .002 .092 .396  .008 .043 .445 

N = 163, R2 = .442, adj. R² = .427, p = .002; bootstrap = no significant changes 

DE (DV) 

SEA (IV) 0.321 0.094 0.256 3.406 .001 .135 .507  .001 .140 .476 

OEA (IV) 0.089 0.095 0.065 0.935 .351 -.099 .276  .350 -.071 .280 

UOE (IV) 0.501 0.106 0.354 4.721 **.000 .292 .711  .001 .268 .753 

ROE (IV) 0.168 0.076 0.155 2.206 .029 .018 .318  .050 -.016 .339 

N = 163, R2 = .436, adj. R² = .421, p = .029; bootstrap = significant changes      

AB (DV) 

SEA (IV) 0.080 0.111 0.065 0.722 .472 -.139 .300  .383 -.113 .321 

OEA (IV) 0.258 0.112 0.192 2.302 .023 .037 .479  .028 .008 .504 

UOE (IV) 0.253 0.125 0.182 2.021 .045 .006 .501  .055 -.009 .455 

ROE (IV) 0.130 0.090 0.123 1.451 .149 -.047 .307  .255 -.083 .366 

N = 163, R2 = .183, adj. R² = .162, p = .149; bootstrap = significant changes           

 

The study demonstrated that emotional intelligence statistically significantly predicts employee engage-

ment and its facets of vigor, dedication, and absorption. The emotional intelligence dimension of self-

emotion appraisal statistically significantly predicted employee engagement and its facets of vigor and 

dedication, but not absorption. The emotional intelligence dimension of others’ emotion appraisal statis-

tically significantly predicted employee engagement and its facet of vigor, but not dedication and ab-

sorption. The emotional intelligence dimension of use of emotion statistically significantly predicted 

employee engagement and its facets of vigor and dedication, but not absorption. The emotional intelli-

gence dimension of regulation of emotion statistically significantly predicted employee engagement and 

its facet of vigor, but not dedication and absorption. 

 

The results of the hypotheses tests are summarized in Table 5. 

Table 5 

Summary of Hypotheses Tests Results 

RQ1 

H01 reject  EI is a statistically significant predictor of EE 

   
RQ1a 

H01a1 reject EI is a statistically significant predictor of VI 

H01a2 reject SEA is a statistically significant predictor of VI 

H01a3 reject OEA is a statistically significant predictor of VI 

H01a4 reject UOE is a statistically significant predictor of VI 

H01a5 reject ROE is a statistically significant predictor of VI 

   



 

88 January 2023 | Volume 2, Number 1 

RQ1b 

H01b1 reject EI is a statistically significant predictor of DE 

H01b2 reject SEA is a statistically significant predictor of DE 

H01b3 fail to reject OEA is NOT a statistically significant predictor of DE 

H01b4 reject UOE is a statistically significant predictor of DE 

H01b5 fail to reject ROE is NOT a statistically significant predictor of DE 

   
RQ1c 

H01c1 reject EI is a statistically significant predictor of AB 

H01c2 fail to reject SEA is NOT a statistically significant predictor of AB 

H01c3 fail to reject OEA is NOT a statistically significant predictor of AB 

H01c4 fail to reject UOE is NOT a statistically significant predictor of AB 

H01c5 fail to reject ROE is NOT a statistically significant predictor of AB 

   
RQ1d 

H01d1 reject SEA is a statistically significant predictor of EE 

H01d2 reject OEA is a statistically significant predictor of EE 

H01d3 reject UOE is a statistically significant predictor of EE 

H01d4 reject ROE is a statistically significant predictor of EE 

 

Discussion 

The study examined one overarching research question further divided into four sub-questions. To what 

extent does emotional intelligence, defined as “a form of social intelligence that involves the ability to 

monitor one’s own and others’ feelings and emotions, to discriminate among them and to use this infor-

mation to guide one’s thinking and actions” (Salovey & Mayer, 1990, p.189) predict employee engage-

ment, defined as “a positive, fulfilling, work-related state of mind that is characterized by vigor, dedica-

tion, and absorption” (Schaufeli et al., 2006, p. 702) in U.S. manufacturing workers? 

 

EI and EE 

 

The first research question (RQ1) asked: To what extent does emotional intelligence (EI) predict em-

ployee engagement (EE) in U.S. manufacturing workers? The study results directly answered RQ1 

which focused on how EI predicts EE in U.S. manufacturing workers. The results demonstrated that 

emotional intelligence is a statistically significant predictor of employee engagement in U.S. manufac-

turing workers. Manufacturing workers with higher emotional intelligence demonstrate higher engage-

ment in the workplace. The results signified a statistically significant positive relationship between EI 

and EE.  

 

This finding aligns with other studies on the predictive relationship between EI and EE. For example, 

Zhoc et al. (2020) found that EI significantly predicted student engagement and promoted key learning 

outcomes in a study of first-year university students in Hong Kong. Similarly, in a study of business pro-

fessionals in Istanbul, Karamustafa and Kunday (2018) found a significant positive relationship between 

emotional intelligence and employee engagement.  

 



 

 

89   Business Management Research & Applications: A Cross-Disciplinary Journal 

EI and Its Dimensions and Vigor 

 

The second research question (RQ1a) asked: To what extent do EI and its dimensions of SEA, OEA, 

UOE, and ROE predict the vigor facet of EE? The study results directly answered RQ1a which focused 

on how EI and its dimensions predict the vigor facet of EE. The results demonstrated that EI, SEA, 

OEA, UOE, and ROE are all statistically significant predictors of vigor. Manufacturing workers with 

higher emotional intelligence, self-emotion appraisal, others’ emotion appraisal, use of emotion, and 

regulation of emotion demonstrate higher vigor defined as “high levels of energy and mental resilience 

while working, willingness to invest effort in one’s work, and persistence even in the face of difficul-

ties” (Schaufeli et al., 2006, p. 702). The results signified a statistically significant positive relationship 

between EI and VI, SEA and VI, OEA and VI, UOE and VI, and ROE and VI.  

 

The only finding in previous studies that aligns with the current study is the statistically significant posi-

tive relationship between EI and VI. Merida-López et al. (2017) found that EI was a significant predictor 

of vigor when studying teaching professionals in Spain. Extremera et al. (2018) found similar results in a 

study of Spanish professionals.  

 

The previous studies on EI and EE that measured and reported the variables as multidimensional con-

structs used a combination of instruments that defined and measured the sub-constructs differently from 

the current study. Therefore, the relationship between SEA and VI, OEA and VI, UOE and VI, and ROE 

and VI were not compared with previous findings. 

 

EI and Its Dimensions and Dedication 

 

The third research question (RQ1b) asked: To what extent do EI and its dimensions of SEA, OEA, 

UOE, ROE predict the dedication facet of EE? The study results directly answered RQ1b which focused 

on how EI and its dimensions predict the dedication facet of EE. The results demonstrated that EI and its 

dimensions of SEA and UOE are statistically significant predictors of dedication, but OEA and ROE are 

not statistically significant predictors of dedication. Manufacturing workers with higher emotional intel-

ligence, self-emotion appraisal, and use of emotion demonstrate higher dedication, defined as “being 

strongly involved in one’s work and experiencing a sense of significance, enthusiasm, inspiration, pride, 

and challenge” (Schaufeli et al., 2006, p. 702). The results signified a statistically significant positive 

relationship between EI and DE, SEA and DE, UOE and DE, but not OEA and DE, and ROE and DE.  

 

The only finding in previous studies that aligns with the current study is the statistically significant posi-

tive relationship between EI and DE. Merida-López et al. (2017) found that EI was a significant predic-

tor of dedication when studying teaching professionals in Spain. Extremera et al. (2018) found similar 

results in a study of Spanish professionals.  

 

The previous studies on EI and EE that measured and reported the variables as multidimensional con-

structs used a combination of instruments that defined and measured the sub-constructs differently from 

the current study. Therefore, the relationship between SEA and DE, OEA and DE, UOE and DE, and 

ROE and DE were not compared with previous findings. 

 

 

 



 

90 January 2023 | Volume 2, Number 1 

EI and Its Dimensions and Absorption 

 

The fourth research question (RQ1c) asked: To what extent do EI and its dimensions of SEA, OEA, 

UOE, ROE predict the absorption facet of EE? The study results directly answered RQ1c which focused 

on how EI and its dimensions predict the absorption facet of EE. The results demonstrated that EI is a 

statistically significant predictor of absorption, but SEA, OEA, UOE, and ROE are not statistically sig-

nificant predictors of absorption. Manufacturing workers with higher emotional intelligence demonstrate 

higher absorption, defined as “being fully concentrated and happily engrossed in one’s work, whereby 

time passes quickly, and one has difficulties detaching oneself from work” (Schaufeli et al., 2006, p. 

702). The results signified a statistically significant positive relationship between EI and AB, but not 

SEA and AB, OEA and AB, UOE and AB, and ROE and AB.  

 

The only finding in previous studies that aligns with the current study is the statistically significant posi-

tive relationship between EI and AB. Merida-López et al. (2017) found that EI was a significant predic-

tor of absorption when studying teaching professionals in Spain. Extremera et al. (2018) found similar 

results in a study of Spanish professionals.  

 

The previous studies on EI and EE that measured and reported the variables as multidimensional con-

structs used a combination of instruments that defined and measured the sub-constructs differently from 

the current study. Therefore, the relationship between SEA and AB, OEA and AB, UOE and AB, and 

ROE and AB were not compared with previous findings. 

 

EI Dimensions and EE 

 

The fifth research question (RQ1d) asked: To what extent do SEA, OEA, UOE, and ROE predict EE? 

The study results directly answered RQ1d which focused on the extent to which SEA, OEA, UOE, and 

ROE predict EE in U.S. manufacturing workers. The results demonstrated that all four EI dimensions of 

SEA, OEA, UOE, and ROE are statistically significant predictors of EE. Manufacturing workers with 

higher self-emotion appraisal, others’ emotion appraisal, use of emotion, and regulation of emotion 

demonstrate higher engagement in the workplace. The results signified a statistically significant positive 

relationship between SEA and EE, OEA and EE, UOE and EE, and ROE and EE. The UOE dimension 

had the strongest correlation with EE. 

These findings align with and contradict previous studies that examine the extent to which the EI 

dimensions of SEA, OEA, UOE, and ROE predict employee engagement. In a study that examined the 

impact of EI on work engagement of registered nurses in China, Zhu et al. (2015) found that all four 

sub-dimensions of EI positively correlated with work engagement, and the UOE dimension had the 

strongest correlation with EE. These results align completely with the results of the current study. 

However, two additional studies did not completely align with the current study. First, in a study of 

business employees in Oman, AlMazrouei et al. (2015) found that EI significantly predicted EE and that 

the dimensions of SEA, UOE, and ROE significantly predicted EE, but OEA did not. The UOE 

dimension had the strongest correlation with EE. AlMazrouei et al. explained that their study sample 

may have influenced the insignificant relationship between OEA and EE. The Oman culture does not 

allow women to interact openly with anyone except relatives, and more than half of their participants 

were women. D’Amico et al. (2020) conducted a study to assess if teachers who perceive themselves as 

emotionally competent experience higher levels of work engagement and job satisfaction and lower 



 

 

91   Business Management Research & Applications: A Cross-Disciplinary Journal 

levels of burnout. The study results demonstrated that EI significantly predicted EE and that the EI 

dimensions of UOE and OEA significantly predicted engagement, but SEA and ROE did not. Once 

again, the UOE dimension was found to have the strongest correlation with EE. 

Limitations 

There were four inherent limitations acknowledged in the study. First, the use of an online platform re-

stricted the researcher’s ability to (a) control the process, (b) clarify participant questions, and (c) ensure 

survey completion at the time it is administered (Theofanidis & Fountouki, 2018). Second, self-reported 

data introduced the possibility of participant bias when assessing themselves or reporting on their own 

experiences. Participants may also consciously or unconsciously misrepresent their actual, true behav-

iors or actions to appear socially acceptable, leading to responses that do not accurately reflect reality. 

Third, rating scales can be limiting when asking questions concerning attitudes or behaviors. There is 

also the possibility of participants being heavily concentrated on one response side or avoiding the ex-

treme options on the scale, resulting in inaccurate survey results. Last, quantitative statistical analyses 

examined the relationship between variables, but they could not determine or establish causality (Field, 

2018).   

Conclusions 

The study used SDT as a framework for examining EI and its dimensions as predictors of EE and its fac-

ets. Vallerand et al. (2014) posited that with the ability to understand and manage one’s own emotions 

and others’ emotions at the interpersonal and intrapersonal levels, workers with high emotional intelli-

gence should act more autonomously. Lumpkin and Achen (2018) illustrated the synergies between EI 

and SDT that suggested higher levels of EI should increase an employee’s self-determination and, in 

turn, enhance their engagement in the workplace. The study found that EI is a significant predictor of 

employee engagement and its facets of vigor, dedication, and absorption. The study also demonstrated 

that all four dimensions of EI, SEA, OEA, UOE, and ROE are significant predictors of employee en-

gagement.  

 

These findings support previous research on the alignment of the EI and EE constructs with SDT as the 

study’s theoretical framework. Ashforth and Humphrey (1995) posited that strong motivation and cogni-

tive engagement are not possible without an emotional connection to the work or work context. Emo-

tional intelligence forms the intrinsic structure that motivates an employee to be engaged in the work-

place. The study provides further understanding of the self-determination theory and the role of intrinsic 

motivation by demonstrating that EI can be measured as an intrinsic motivator to frame and explain how 

EI and its dimensions work to improve employee engagement in the manufacturing workforce. 

 

The manufacturing sector of the U.S. workforce employs the least engaged workers in the U.S. The 

meta-analysis conducted by Gallup Inc. (2020) highlighted the significance of employee engagement to 

many individual performance outcomes that directly or indirectly impact overall organizational perfor-

mance. Self-determination theory provides the theoretical lens that explains why employees exhibit a 

range of engagement levels in the workplace.  

 



 

92 January 2023 | Volume 2, Number 1 

The study examined the extent to which EI and its dimensions predict EE and its facets in U.S. manufac-

turing workers. This study proposed that EI forms an intrinsic structure that motivates employees to be 

engaged in the workplace. Findings from the study demonstrate a strong positive relationship between 

EI and its dimensions and EE and EI and its dimensions and vigor. However, EI and its dimensions of 

SEA and UOE exhibited a significant relationship with dedication, but OEA and ROE did not; EI 

showed a significant relationship with absorption, but SEA, OEA, UOE, and ROE did not. Previous 

studies were found that used WLEIS to measure EI and UWES-9 to measure EE, but they did not opera-

tionalize both EI and EE as multidimensional constructs. Therefore, the findings that examined the ex-

tent which SEA, OEA, UOE, and ROE predict vigor, dedication, and absorption could not be confirmed 

or refuted by previous research. A recommendation for a future study is to repeat this study adding de-

mographic information to validate or refute the current study’s findings and further examine the signifi-

cance of investigating EI and EE at a more granular level. 

  



 

 

93   Business Management Research & Applications: A Cross-Disciplinary Journal 

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