A CONFIRMATORY FACTOR ANALYSIS OF THE EMOTIONAL QUOTIENT MANAGEMENT MODEL
Presented at International Journal of Arts & Sciences Conference
Harvard Medical School, Harvard University, Boston
May 26-30, 2015
It was submitted to The International Journal of Arts & Sciences
on July 31, 2015
it was accepted to publish in the International Journal of Arts & Sciences, 8(4), 2015
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Charisma University, Turks & Caicos, UK
Burapha University, Chonburi, Thailand
University of Atlanta, Atlanta, Georgia, USA
Charisma University, Turks & Caicos, UK
Xi’an Jiaotong-Liverpool University, Suzhou, China
University of Atlanta, Atlanta, Georgia, USA
This is an empirical research. Population and Convenience Sampling sizes were selected from small entrepreneurs who worked in the USA, China, Turks and Caicos, and Thailand. Data collection was conducted through online survey method. There was testing reliability of the “tryout” groups. The Cronbach’s alpha coefficient value was .851. There were two main objectives of the research : 1) formulating the theoretical causal relationship model through the analysis of the related literature, and 2) examining the goodness of fit model with the empirical data. Data were analyzed by basic statistics and advanced statistics constructed by the LISREL 9.2 program. Advanced Statistics included both Structural equation modeling (SEM) and Confirmatory Factor Analysis (CFA) was designed to test a theoretical model. Research results revealed that both SEM analysis and CFA analysis strongly supported all research hypotheses. The second modified CFA analysis confirmed that the goodness of the EQ management model was fit with the empirical data: the Chi-Square = 17.40, df = 19, P – value = 0.56256, and RMSEA = 0.000 respectively.
Emotional Quotient (EQ), Emotional Intelligence (EI), Structural Equation Modeling (SEM), Confirmatory Factor Analysis (CFA), EQ Management Model
Today, worldwide organizational leaders in both public and private sectors are seeking various business models and methods to increase both personal and organizational competitiveness and achieve sustained success. In reality, emotion management has been seen as a prerequisite for effective leadership. Leaders with high emotional competencies are perceived as more efficient by their followers. Emotional Quotient (EQ) or Emotional Intelligence (EI) is a recent development in the area of intelligence as well as in affective science, both of which have given birth to overlapping perspective on human nature. The concept of EQ implies that humans are both rational and emotional beings (Thingujam, 2004). Malik et al. (2011) argued that global organizations spend huge amounts every year on employee satisfaction, motivation and training procedures. However, only a few global organizations focus on leader’s emotional quotient and involve their management in EQ activities. In this age of rapidly changing business environment, EQ management is more important than ever. The current organizational focus on revitalizing and transforming organizations to meet competitive challenges ahead has been accompanied by increasing interest among researchers in studying EQ Management. The concept of EQ Management is explored and offered as a means to predict both individual and organizational success. It can be used to solve any customer satisfaction information used to evaluate organizational success. According to Tischler et al. (2002), emotion is often seen as “black box”. It seems increasingly clear from the emotional quotient (EQ) literature that those with higher EQ management seem to be smarter, healthier, happier, and better at work. EQ management is important for leaders, as it is a key for leaders’ success in work life. Emotional intelligence is claimed as being one of the most important determining components of success in work and life (Goleman, 1995; Bar-On, 1995). Sucaromana (2010) stated that EQ management can be conceptualized according to two different types of models of the construct: the ability model and the mixed model. A lot of discussion and debate exist regarding what comprises emotional intelligence, inconsistency in measurement approaches, and terminology used to describe the construct (Amelang & Steinmayr, 2006; Conte, 2005; Dulewicz, Higgs, & Slaski, 2003; Landy, 2005; Locke, 2005; Matthews, Emo, Roberts, & Zeidner, 2006). There are different theories of emotional intelligence and also different approaches to measure and describe the construct. Interestingly, only a few empirical studies have been conducted on the East-West mixed model. Therefore, we conduct empirical research and our main purpose is to formulate the theoretical causal relationship model through the analysis of the EQ management literature and examine the goodness of fit model with the empirical data.
In most literature reviews and scholar researchers’ works, Emotional Quotient (EQ) often referred as Emotional Intelligence (EI). Popular interest in EI has, at times, tended to obscure definitional clarity (Matthews, Zeidner, & Roberts, 2002). The emerging literature on EI contains disparate terminology, including not only emotional intelligence (Goleman, 1995; Salovey & Mayer, 1990), but also emotional literacy (Cooper & Sawaf, 1997), emotional quotient (Cooper, 1997), and personal intelligences (Gardner, 1983). To further complicate the situation, the sub-components of EI are variously referred to as “branches” (Mayer, Caruso, & Salovey, 2000), “factors” (Bar-On, 1997), or “competencies” (Boyatzis, 1982).
Emotional intelligence was born out of the concepts of intelligence and emotion. As one of four classes of mental operations (motivation, cognition, emotion, and consciousness),
emotions are organized responses to events, internal or external which have a positive or
negative meaning for the individual. Emotional triggers in the brain
alert the brain’s cognitive mental function of a need for response (fight or flight). Thus,
emotions signal and allow one to respond to changes in relationships between the individual and the environment. Emotional intelligence was also described as “the subset of social intelligence that involves the ability to monitor one’s own and others’ emotions, to discriminate among them and to use this information to guide one’s thinking and actions” (Mayer et al., 2008). Social intelligence deals with the ways in which people actively seek to engage in their social environment and pursue desired outcomes in the important domains of their lives (Zirkel, 2000).
According to the current literature, emotional intelligence is generally categorized either
as an ability model or a mixed model (Palmer et al., 2008). The
ability model emphasizes the relationship between emotion and intelligence, whereas the
mixed model focuses on talents, mental abilities, and traits (Mayer et al.,
2004). In the ability model, emotional intelligence is defined as a type of intelligence that
focuses on aptitude for effectively processing emotional intelligence (Al Hajj & Dagher,
2010). In mixed models, emotional intelligence is conceptualized as a diverse construct,
which includes aspects of personality, as well as the ability to perceive and understand
emotions (Al Hajj & Dagher, 2010).
Cherniss (2010) added a component to standing definitions of emotional intelligence which included a focus on traits. The trait model is considered the second generation since it incorporates personal qualities that relate to affect. It is composed of four components: well-being (which incorporates self-confidence, happiness, and optimism), sociability (which incorporates social competence, assertiveness, and emotional management of others), stress control (which incorporates stress management, emotional regulation, and low impulsiveness), and emotionality (which incorporates emotional perception of self and others, emotional expression, and empathy).
The study of emotional intelligence has produced various debates. There is a
debate over whether the female participants or male participants exhibit higher levels of
this type of intelligence. According to some studies, the females have higher emotional
intelligence than the males (Brackett et al., 2004). Other debates centered
on the nature of emotional intelligence and the best way emotional intelligence can be
measured (Roberts et al., 2001). Brackett et al. (2004) maintained that
the best way to measure emotional intelligence is through ability models. According to
them, emotional intelligence is a capability of an individual that has to do with his or her
level of cognitive processing of affective information.
On the other hand, many researchers like Bar-On (1997) posited that emotional intelligence can be best measured through mixed models. For them, emotional intelligence is having personality, motivation, and affective dispositions aside from having the ability to gauge emotions. Moreover, for those that view emotional intelligence as an ability, emotional intelligence has been measured more by using objective, maximal performance measures that illustrated emotional intelligence to be more of a type of intelligence than simply some concept of personality (Brackett & Mayer, 2003; Lopes, Salovey, & Strauss, 2003; O’Connor & Little, 2003). Moreover, those who view and measure emotional intelligence using the mixed models measure emotional intelligence using self-report means, which illustrate that emotional intelligence is more of a personality trait, rather than a form of intelligence (Saklofske et al., 2003).
Additionally, culture plays a significant role in individuals’ EQ management. According to Wong et al. (2004), they argued that an individual’s answer to emotional intelligence questionnaires might be affected by cultural norms, which differ especially between people from eastern and western countries. Therefore, we need a proper EQ management test that is suitable for both eastern and western people.
EQ Management Test
Sucaromano (2010) argued that since Mayer and Salovey’s initial (1990) study of the construct of emotional intelligence, most descriptions of emotional intelligence have included one or more of the following key components: (a) the ability to be aware of, understand, and express one’s emotions, (b) the ability to understand others’ emotions and relate with people, (c) the ability to manage and control emotion, (d) the ability to manage change, adapt,
and solve problems of a personal and interpersonal nature, and (e) the ability to
generate positive mood and be self-motivated (Bar-On, 2004). We selected the model of
emotional intelligence management provided by the department of Mental Health, Thailand,
since it offers the theoretical basis for the EQ Management Measure. According to this model, emotional intelligence is the ability to live with others happily (Department of Mental Health, 2000). The competencies included in this concept comprise the five key
components above; further, each of these components comprises a number of
closely related aspects of emotional intelligence.
In response to the perception that a separate measure of emotional intelligence was
needed for the people of Thailand, the EQ Management test was developed in 2000 by the Department of Mental Health in the Ministry of Public Health, Thailand. The purpose of the tool was to measure Thai individuals’ beliefs about their own emotional abilities, so that, when provided with the scores, they can recognize their own levels of emotional intelligence and be able to improve themselves on what competencies they still lack. This measure
was intended for individuals aged between 12 and 60 years. It had been
normed in Thailand on a population of 6812 individuals. This self-report measure
can be grouped into three subscales known as Virtue, Competence, and Happiness.
The first subscale, virtue, consists of the first 18 items, which deal with self
awareness and social awareness. The second of these subscales, competence,
consists of the next 18 items, dealing with self-regulatory behavior and
relationship management. The third subscale, happiness, consists of the final 16
items, and measures one’s sense of self-efficacy and self-acceptance, along with
the ability to manage one’s emotions. A brief description of the model of
emotional intelligence competencies measured by the nine subscales displayed in
three factors (Department of Mental Health, 2000).
the first subscale, Virtue, included (a) emotional self control (comprising knowing one’s own emotions, controlling emotions, and expressing emotions appropriately), (b) empathy (comprising empathy, understanding and acceptance of others’ emotion, and appropriate expression of emotions, and (c) responsibility (comprising knowing how to give and take, the
ability to accept one’s own mistakes and forgive others for theirs, and social responsibility). The second subscale, Competence, included (a) self-motivation (comprising knowing one’s own competence, the ability to motivate oneself, and perseverance to achieve goals), (b) problem-solving (comprising knowing and understanding problems, knowing which steps are required to solve problems, and being flexible), and (c) interpersonal relationships (comprising building relationships with others, appropriate expression of emotions, and the ability to argue creatively when disagreement occurs). The third subscale, Happiness,
included (a) self-regard (comprising self-value and self-confidence), (b) life
satisfaction (comprising optimism, a sense of humor, and happiness with one’s
situation), and (c) peace (comprising having activities making one happy, being
relaxed, and having peaceful mind). It comprises 52 items, and is suitable for individuals 12 years of age and older. Completion of this measure takes approximately 15-20 minutes.
Elfenbein and Ambady (2003) point out that, instead of continuing the debate, both perspectives (the view that emotions are universal, and the view that emotions are culturally specific) should be integrated. Western measures are not appropriate for use in other cultural contexts and that culturally specific measures of emotional intelligence are required.
This is an empirical research. Population and Sampling sizes were selected from small entrepreneurs who worked in the USA, China, Turks and Caicos, and Thailand. Convenience Sampling was selected as appropriate method for our study. Data collection made through the online survey. There was testing reliability of the 30 “tryout” groups. The questionnaire’s Cronbach’s alpha value was .851. There were two main steps of the research : 1) formulating the theoretical causal relationship model through the analysis of the related literature, and 2) examining the goodness of fit model with the empirical data. Data were analyzed by basic statistics, and advanced statistics which constructed by the LISREL 9.2 program.
EQ Management Model Descriptive Statistics
Table 1 shows each item’s mean, standard deviation, skewness, and kurtosis. In terms of standard deviation, there was a range from .44 to .64. Skewness results indicated that the items were near 0 and ranged between 0.083 and 0.525. Only RES item and INT item consisted of negative value, therefore, the distribution was moderately skewed. Lastly, Kurtosis values revealed that the items were negative since they ranged from -0.058 to -0.640. However, EMO, RES, PRO, SELR, and LIF items’ values ranged from 0.238 to 0.506. Skewness and Kurtosis value results revealed that observed variables were normal curve distribution which were suitable for the structural equation modeling (SEM).
Table 1. The EQ Management Model Descriptive Statistics
Pearson’s Product Moment Correlation of the observed variables
Table 2 indicates that among 9 observed variables, there are 36 coefficients of correlation in the correlation matrix, and most of them have the same positive relationship and value from 0.030 to 0.338 with significant level at .01 and .05 respectively.
The statistic analysis of Bartlett’s test of sphericity has the value of Chi-Square = 199.894, df = 36, and p = 0.000. It means that the Covariance matrix is significantly different from the identity matrix. It correlates with the index analysis by Kaiser–Mayer–Olkin which has the value of 0.583 and closed to 1. Therefore, the observed variables was suitable for the further analysis of LISREL model.
Table 2. Pearson’s Product Moment Correlation of the observed variables (n = 400)
|Bartlett’s Test of Sphericity Approx. Chi-Square = 199.894 df = 36 P = 0.000|
|Kaiser-Meyer-Olkin Measure of Sampling Adequacy = 0.583|
* P< .05, ** P< .01
In this study, emotional quotient and management are theoretically distinct construct. Questionnaires of 52-items emotional quotient and management were tested. The Structural equation modeling (SEM) was carried out to determine the Confirmatory Factor Analysis (CFA). Bartlett’s test of sphericity (BT) with and Kaiser-Meyer-Olkin’s Measure of sampling adequacy was also considered. Based on these preliminary analyses, it was found that SEM analysis of the EQ management model consisted of the three factors: the virtue factor, the competence factor, and the happiness factor respectively.
Confirmatory Factor Analysis of the Virtue Factor
Hypothesis 1: The virtue factor is high and consistent with the empirical data
Table 3 indicates that the virtue factor consists of the three variables: Emotional control (EMO), Empathy (EMP), and Responsibility (RES). Result shows that the overall fit of the model was good. The Bartlett’s test of sphericity was found that Chi-Square = 18.231, df = 3, p = 0.000. Very small values of significance (below 0.05) indicate a high probability that there are significant relationships among these variables. The Kaiser-Mayer-Olkin (KMO) = 0.553. This indicates that that these variables were adequate for the further CFA analysis.
Table 3. The Correlation matrix of Mean and Standard Deviation of observed variables of the Virtue Factor
|Bartlett’s test of Sphericity Chi-Square = 18.231, df = 3, p = 0.000, KMO = 0.553|
* P< .05, ** P< .01
The CFA analysis results are shown in Figure 1, supporting research hypothesis 1. The CFA model was fitted to the empirical data (Chi-Square value = 0.07, df = 1, p = 0.78547, RMSEA = 0.000, GFI = 1.00, AGFI = 1.00). These analysis results confirm that the virtue factor was consistent with the empirical data. It can be inferred from the CFA analysis diagram that the virtue factor included Emotional Control (EMO), Empathy (EMP), and Responsibility (RES), which have Coefficient values were 0.44, 0.36 and 0.26 respectively. For the virtue factor indicator’s variance, this model accounted for 19%, 13%, and 7% respectively. Table 4 indicates results of the virtue factor’s validity measurement.
Figure 1. Confirmatory Factor Analysis of the Virtue Factor
Table 4. Results of the virtue factor’s validity measurement (validity factor loading of observed variables and factor value coefficient)
|Observed Variables||Factor Loading|
|Chi-Square = 0.07, df = 1, p = 0.78547, RMSEA = 0.000, GFI =1.00, AGFI = 1.00|
Confirmatory Factor Analysis of the Competence Factor
Hypothesis 2: The competence factor is high and consistent with the empirical data
Table 5 indicates that the competence factor consisted of the three variables: Self-motivation (SELM), Problem-solving (PRO), and Interpersonal relationship (INT). We analyzed the Correlation matrix among these three factors and found that no significance among them. The Correlation matrix results were at low level and ranged from -0.067 to 0.080. The Bartlett’s test result revealed that the overall fit of the model was good, Chi-Square = 4.619, df = 3, p = 0.022. The Kaiser-Mayer-Olkin (KMO) = 0.483. This indicated that these three variables were adequate for the further CFA analysis.
Table 5. The Correlation matrix of Mean and Standard Deviation of observed variables of the Competence Factor
|Bartlett’s test of Sphericity Chi-Square = 4.619, df = 3, p = 0.022, KMO = 0.483|
* p < .05 ** p < .01
The CFA analysis results are shown in Figure 2, supporting research hypothesis 2. The CFA model was fitted to the empirical data (Chi-Square value = 0.21, df = 1, p = 0.64963, RMSEA = 0.000, GFI = 1.00, AGFI = 1.00). These analysis results confirmed that the competence factor was consistent with empirical data. It can be inferred from the CFA analysis diagram that the competence factor has only Self-motivation (SELM) which Coefficient valued 2.42 and variance accounted 5.84. Table 6 indicates results of the competence factor’s validity measurement.
Figure 2. Confirmatory Factor Analysis of the Competence Factor
Table 6. Results of the competence factor’s validity measurement (validity factor loading of observed variables and factor value coefficient)
|Observed Variables||Factor Loading|
|Chi-Square = 0.21, df = 1, p = 0.64963, RMSEA = 0.000, GFI =1.00, AGFI = 1.00|
Confirmatory Factor Analysis of the Happiness Factor
Hypothesis 3: The Happiness factor is high and consistent with the empirical data
Table 7 indicates that the happiness factor consisted of the three variables: Self regard (SELR), Life-satisfaction (LIF), and Peace (PEA). We verified the correlation matrix of these three factors and found that the correlation matrix results differed from zero at .01 and .05 significant level. Correlation values were at low level and ranged from -0.0007 to 0.338. The Bartlett’s test results indicated that Chi-Square = 55.541, df = 3, p = 0.000 which differed from zero at .01 significant level. The Kaiser-Mayer-Olkin (KMO) = 0.485. This indicated that these three variables were adequate for the further CFA analysis.
Table 7. The Correlation matrix of Mean and Standard Deviation of observed variables of the Happiness Factor
|Bartlett’s test of Sphericity Chi-Square = 55.541, df = 3, p = 0.000, KMO = 0.485|
* p < .05 ** p < .01
The CFA analysis results are shown in Figure 3, supporting research hypothesis 3. The CFA model was fitted to the empirical data (Chi-Square = 0.19, df = 1, p = 0.66526, RMSEA = 0.000, GFI =1.00, AGFI = 1.00). These analysis results confirmed that the happiness factor was consistent with empirical data. It can be inferred from the CFA analysis diagram that the happiness factor included Life-satisfaction (LIF) Peace (PEA), and Self regard (SELR) which their Coefficient values were 2.83, 0.12, and 0.05 respectively. For the indicator’s variance, this model accounted for 8%, 1%, and 0% respectively. Table 8 indicates results of the happiness factor’s validity measurement.
Figure 3. Confirmatory Factor Analysis of the Happiness Factor
Table 8. Results of the happiness factor’s validity measurement (validity factor loading of observed variables and factor value coefficient)
|Observed Variables||Factor Loading|
|Chi-Square = 0.19, df = 1, p = 0.66526, RMSEA = 0.000, GFI =1.00, AGFI = 1.00|
Second Confirmatory Factor Analysis of the EQ Management Model
Hypothesis 4: The EQ management has the good fit with the empirical data
The Maximum likelihood method of LISREL 9.2 Program for Windows was used to compare formulated model fit with the empirical data. Model Indices included Chi-square, df, CFI, GFI, AGFI, RMSEA, and SRMR. Figure 4 revealed that the first draft of second developed CFA was not good fit with the empirical data. The good fit between the data and model indices were as follows: Chi-Square = 73.56, df = 24, p-value = 0.000, CFI = 0.072, GFI = 0.96, AGFI = 0.93, RMSEA = 0.072 and SRMR= 0.061. Table 9 indicates that only GFI value passed standardized criteria. Most of indices did not meet the criteria as defined by Jöreskog and Sörbom (1996). Therefore, we adjust the parameters using model modification indices by consent to relax preliminary agreement until the model indices were consistent with the empirical data. Thereafter, we adjust the structural equation modeling (SEM) which will be consistent with the empirical data.
Figure 4. Second Confirmatory Factor Analysis of the EQ Management Model
Table 9. Good Fit Model Indices with empirical data before modification of the EQ Management Model
|Indices of Model Fit||Criteria||Index||Result|
|/df||< 2.00||3.065||No Pass|
|CFI||≥ 0.95||0.72||No Pass|
|AGFI||≥ 0.90||0.93||No Pass|
|RMSEA||< 0.05||0.072||No Pass|
|SRMR||< 0.05||0.061||No Pass|
Figure 5 shows that fit indices for the proposed model based on a maximum likelihood CFA analysis. The CFA analysis results are shown in Figure 5, supporting research hypothesis 4. Table 10 indicates that the Chi-Square/Degree of Freedom (/df) was less than the standardized value of 3 (Iacobucci, 2010). The CFI, GFI, and AGFI exceeded the recommended value of 0.9 (Hair et al., 2006). The RMSEA and SRMR were lower than 0.05 (Browne and Cudeck, 1993). It can be stated that, the modified EQ management model was fit with the empirical data.
Figure 5. Second Confirmatory Factor Analysis of
the modified EQ Management Model
Table 10. Good Fit Model Indices with the empirical data after modification the EQ Management Model
|Indices of Model Fit||Criteria||Index||Result|
Table 11 indicates that Factor loadings of quality indicators were positive. Their values ranged from -0.15 to 0.80. The highest factor loading sub-factors were Self regard (SELR) in the happiness factor (b=0.80).The lowest factor loading sub-factors were Interpersonal Relationship (INT) in the competence factor (b= -0.15). The high factor loading values were happiness, virtue, and competence which have factor loading valued 2.04, 0.97, and 0.30 respectively. The highest variance in the virtue factor was empathy (EMP) (R2 = 19%). In the competence factor, the highest variance was self motivation (SELM) (R2 = 14%). For the happiness factor, the highest variance was self regard (SELR) (R2 = 19%) respectively.
Table 11. Result of Second Confirmatory Factor Analysis of the modified EQ Management Model
|Observed Variables||Factor Loading||Factor Score Coefficient|
|The EQ Management in summary|
|= 17.40||df = 19||p = 0.56|
|GFI = 0.99||AGFI = 0.98||SRMR = 0.029|
|CFI = 1.00||RMSEA = 0.000|
|Correlation Matrix of Latent Variables|
* p < .05 ** p < .01
The findings revealed that the EQ management model can be measured with the scientific method. In the past, most young workers’ work values heavily relied on “more money” or “work for organization” work-related values. Therefore, only the IQ management was a proper scientific tool to measure their skill and achievement since it was used to measure individual self, not others. However, due to contemporary workers’ work values changed, whether GenY or GenZ graduated from universities or not, most of them love “work for myself” or “freedom” work value. Therefore, the EQ management was a proper scientific tool to measure their achievement since entrepreneurial spirit is the ability to understand others. These findings supported and were consistent with the previous EQ management works (Boyatzis, 1982; Salovey & Mayer, 1990; Goleman, 1995; Bar-On, 1997; Mayer, Caruso, & Salovey, 2000; Matthews, Zeidner, & Roberts, 2002; Dulewicz, Higgs, & Slaski, 2003; Conte, 2005; Landy, 2005; Locke, 2005; Amelang & Steinmayr, 2006; Matthews, Emo, Roberts, & Zeidner, 2006; Sucaromano, 2010; Malik et al., 2011)
In these findings, it was found that the EQ management can help entrepreneurs to act with faster, better, and cheaper strategies since real understanding others will guarantee real success. Research revealed that both understanding others and happiness to serve others, as found in table 1, are consistent with previous study of Wong et, al., 2004.
Research results in table 11, supporting previous studies of Sucaromano, 2010, Bar-On, 2004, and Department of Mental Health, 2000, showed that the highest values are happiness.
One point to discuss here that is current work values are not the same as in the past. Work happiness value in the past means only making more money, however, work happiness in the present and real working life means self satisfaction, accomplishment, and enjoyment which translate into more fun, more friends, more connection, more freedom or relaxation, more love, more help, and more happiness. These “more” work values are related to the EQ Management. Why? at the moment, no single and clear answer yet, questions remain unsolved with future empirical studies.
Research findings suggest that leaders in public, private, foundation, and educational institutions should revise their vision, shape their mission, and change their policy from maximize profit to maximize others’ values. New work-related values such as more fun, more friends, more connection, more freedom, more love, more help, and more happiness, should be replace unexpected work-related values such as stress, pressure, difficulty, and money.
For researchers and entrepreneurs, the EQ management are the key factors for both theory and practice integration. Adaptability the EQ management from theory to practice and practice to theory remains ‘challenge status’ for global academic and practical groups. References
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