This descriptive-correlational study used a census of Ohio State University Extension county directors and a random sample of county staff throughout the State of Ohio. Data were collected utilizing Bar-On’s Emotional Intelligence Quotient instrument (county directors) and Warner’s job satisfaction instrument (county staff).
The study examined the relationships between emotional intelligence of county directors, job satisfaction of county staff and several demographic characteristics. Stepwise linear regression analysis was used to measure the proportion of variance in county staff’s job satisfaction that could be explained by county directors’ emotional intelligence and demographic characteristics.
The findings suggested there is not a significant relationship between emotional intelligence of unit directors and job satisfaction of staff. The researchers concluded the level of job satisfaction of staff was not influenced by the level of emotional intelligence of unit directors. Some correlations existed between job
satisfaction and selected demographic characteristics. These findings may be of interest to individuals who serve in a variety of leadership roles within organizations.
Unit directors provide leadership to foster harmonious relationships and effective performance among colleagues and members of their unit’s program and support staff (Buford, Bedeian and Lindner, 1995). The largest numbers of managers within OSU Extension assume leadership/management roles on the county level. These individuals are responsible for administrative and economic responsibilities, plus most also assume programmatic responsibilities.
Unit directors may not possess any previous management experience or course/degree work in management techniques. Budget constraints and political pressures place additional time constraints on the educational opportunities for staff to expand their management and leadership knowledge base (Meyers and Pigg, 1990). Their cognitive skills in management may stem from leadership responsibilities, membership in community organizations and/or past participation in leadership roles.
Goleman (1998) defined emotional intelligence as a construction of two kinds of competencies: a) threshold competencies, including intellect and expertise, people need to get the job done and b) distinguishing competencies, often referred to as the emotional competencies, including persuasion and influence, which set the star performers apart from average performers.
This study and other research exist to support that emotional competencies play a far larger role in superior job performance than do cognitive abilities and technical expertise. Therefore, unit directors should study emotional intelligence to provide stability and direction that inspires commitment to the organization, motivation for others to accomplish workplace goals and give impact to decisions, questions, dreams and concerns of employees. These personal and people skills are crucial ingredients to effective organizational leadership. Emotional intelligence is part of who and what unit directors are and help to “incorporate the message of the emotions in a balance way so that they support our reasoning” (Feldman, 1999).
Statement of the Problem
Extension county directors are continuously working with a diverse and complex set of personalities and backgrounds among the staff of the local county Extension office. These administrators must be able to develop and apply their emotional and social skills effectively to influence constructive endeavors in their staff members (Feldman, 1999).
Although county directors with high levels of either or both cognitive and emotional intelligence skills can greatly benefit the local operations, high levels of these skills alone do not guarantee success. The leadership and management skills are only a part of the many critical ingredients that would enable the county director to meet the individual goals of the staff members as well as the goals of high productivity in the local operating unit. For these leaders to be successful, they must keep their professional and personal abilities and the ability to work and stimulate productivity in others in alignment with one another.
Purpose and Objectives of the Study
The primary objective was to determine if a correlation existed between the level of emotional intelligence of county directors and the job satisfaction of county staff. The researchers planned to use the findings of the study as the basis for suggesting and recommending leadership development and training materials for county directors and ways to improve relationships in the local Extension units. Listed below were the specific objectives of the study:
To describe the professional and personal characteristics of county directors and county staff.
To describe the emotional intelligence levels of county directors.
To describe the level of job satisfaction of county staff.
To examine the relationship between the level of emotional competence of the county director and the level of job satisfaction of county staff.
Emotional Intelligence Defined
Based on real-life examples of keeping emotions under control, Mayer, Salovey and Caruso (2000) defined emotional intelligence as “the ability to perceive and express emotion, assimilate emotion in thought, understand and reason with emotion, and regulate emotion in self and others” (p. 396). Peter Salovey of Yale University and Jack Mayer of the University of New Hampshire (1990) first used the term emotional intelligence while researching factors important to functioning
well in society. Their belief was that emotional intelligence was composed of: 1) verbal and non-verbal expression, 2) identification and appraisal of emotions within self and others, and 3) use of emotions to strive toward action. Later in 1997, Mayer and Salovey included the potential for intellectual and emotional growth as components in their revised model. The new model included the four categories of: 1) perception, appraisal, and expression of emotion; 2) emotional facilitation of thinking; 3) understanding, analyzing and implementing emotional knowledge; and 4) reflective regulation of emotions to potential emotional and intellectual growth.
Understanding one’s emotions can offer sensible meaning to how an individual approaches problem solving, and helps develop the process by which an individual thinks through as a part of their decision-making. Emotions give impact to one’s decisions, questions, dreams and concerns. They are “part and parcel of who and what we are” (Feldman, 1999, p. 10). When an individual can effectively manage their emotions, the individual becomes better equipped to determine the most effective course of action to resolve problems (Weisinger, 1998). Feldman (1999) believed leaders using emotional intelligence “provide the direction and stability that inspires the commitment and motivation crucial to organizational success” (p. 7).
Core Components of Emotional Intelligence
General intelligence is the combination of cognitive intelligence and emotional intelligence. IQ measures cognitive intelligence. Emotional intelligence is a measure of well being and measured by an emotional quotient (EQ). Bar-On (2002) defined emotional intelligence as “an array of non-cognitive capabilities, competencies, and skills that influence one’s ability to succeed in coping with environmental demands and pressures” (p. 14). Intelligence “describes the aggregate of abilities, skills and abilities” as a mass of knowledge used to cope with life effectively (Wechsler, 1940, p. 15). Emotional refers to a type of intelligence that is distinctively different from cognitive intelligence. Salovey, Mayer and Caruso (2002) stated emotional intelligence involves the ability to process emotion-laden information competently. This information is used to guide cognitive activities like problem solving and to focus energy on required behaviors
Bar-On developed the EQ-i by a logical clustering of variables and underlying factors thought to contribute to individual success and well-bring. Bar-On’s (2000a) measure of emotional intelligence included an array of emotional and social knowledge and abilities: 1) the ability to be aware of, to understand, and to
express oneself, 2) the ability to be aware of, to understand, and to relate to others, 3) the ability to deal with strong emotions and control one’s impulses, and
4) the ability to adapt to change and to solve problems of a personal or social manner. The five domains of Bar-On’s model are interpersonal skills, intrapersonal skills, adaptability, stress management, and general mood (Bar-On, 1997a). As in Table 1, the emotional skills (multifactorial components) that lead to an individual’s potential for performance through emotional intelligence include: 1) self-regard, 2) emotional self-awareness, 3) assertiveness, 4) independence, 5) self-actualization, 6) empathy, 7) social responsibility, 8) interpersonal relationship, 9) reality testing, 10) flexibility, 11) problem solving,
12) stress tolerance, 13) impulse control, 14) optimism and 15) happiness. Grouped in Table 1 are the EQ-i composite scales and sub-scale components.
Stress Management * Stress Tolerance, Impulse Control
Adaptability * Reality Testing, Flexibility, Problem Solving
General Mood * Happiness, Optimism
Emotional Intelligence and Organizational Leadership
Research studies have communicated a conceptual or theoretical link between emotional intelligence and organizational behavior. (Abraham, 1999; Massey, 1999; Sosik & Megerian, 1999). These studies hypothesized that administrators with higher levels of emotional intelligence will be more successful in problem solving and managing conflict, coping with items that may become stressors in the workplace, interacting with political factors in the workplace and with increased interpersonal skill (Buford, 2001).
A national survey focusing on what employers are looking for in entry level workers found that the most desirable skills in employees were the ability to learn on the job, listening and oral communication, adaptability and resilience when facing obstacles, interpersonal effectiveness and leadership potential (Goleman, 1998). Emotional intelligence has also been connected to the transformational institution (Munaker, 1997), effective writing skills (Holbrook, 1998), leadership style and organization impact (Goleman, 2000), constructive thinking (Epstein, 1998) and accomplishing life goals and general life satisfaction (Martinez-Pons, 1997).
As a construct, job satisfaction is extremely complex with no single conceptual model completely and accurately describing the construct (Hagedorn, 2000).
Locke (1976) defined job satisfaction as “a pleasurable or positive emotional state resulting from the appraisal of one’s job or job experiences” (p. 1300). Vroom (1964) defined job satisfaction as the attitude an individual carries about work roles and the corresponding relationship to worker motivation. Job satisfaction refers to a dedicated evaluation of the job as a whole, but also refers to components such as financial rewards, resources to get the job completed, interest, challenge, use of valued skills, variety, occupational prestige, autonomy, relations to co-workers and supervisors, involvement in decision making, and comfort factors such as hours, physical surroundings and travel time. The essence of job satisfaction is the fit of congruence of the worker and the job (Mortimer, 1979). Job satisfaction is concerned with the attitudes people have about work rather than efforts to fill a need, or the past tense involving outcomes already experienced.
Job Satisfaction in Extension
County directors constantly battle with demands from clientele, stakeholders, funders and subordinates. These demands include delivering faster and more prominent educational services within a lower budget. The result is increased pressure, turmoil, conflict, ambiguity and change for the local Extension unit. While once “concerned with doing things right rather than doing the right things” (Licke, 1987, p. 1), Extension administrators, at all levels, must “shift their focus from command and control to creating a culture of productivity – one which challenges, rather than reinforces, established practices” (Padde, 1993, p. 43).
Age, years of experience, gender, types of agents, job title of personnel, job commitment, education level, salary, urban and rural agents, organizational justice, decision making, work area changes and attendance have been the focus
of numerous job satisfaction studies conducted nationally within the Cooperative Extension system (Schmiesing, 2002; Miller, 1997; Boltes, Lippke and Gregory, 1995; Bowen, Radhakrishna and Keyser, 1994; Riggs and Beus, 1993; Mallilo, 1990; Keffer, 1976).
Leventhal, Karuza and Fry (1980) suggested tenure in the organization, gender and highest level of formal education as related to organizational performance. Gender has been investigated to have a relationship to commitment and satisfaction to stay with a job (Sweeney & McFarlin, 1997). Additional education, including previous management training, has been studied with some relationship to work performance and satisfaction (Daily and Delaney, 1992). An individual’s position has been studied in relation to organizational issues and found to have little significance to satisfaction within Extension staff (Schmiesing, 2002). No study was found that focused on emotional intelligence levels of administrators related to job satisfaction.
The study was descriptive-correlational in nature and designed to determine the relationships between the level of emotional intelligence of county directors in Ohio and the job satisfaction of county staff (program and support staff). Data were examined to determine the nature and strength of the relationship between variables. The researchers utilized the Emotional Intelligence Inventory (Bar-On, 2002) and a mailed questionnaire to collect data to accomplish the study objectives. Trained and certified professionals administered the Emotional Intelligence Inventory (EQ-i) via the Internet.
The independent variable in this study was the emotional intelligence scores (EQ-
of county directors. The dependent variable in this study was the level (scores) of job satisfaction of county staff. The research questions asked whether the emotional intelligence level of county directors and the intervening variables of gender, tenure, educational degree, program area and previous administrative experience or management training would affect the dependent variable.
The researchers used two different instruments in this study. One instrument was used to determine the level of emotional intelligence in county directors. The second instrument was a job satisfaction index to collect data from county staff. A questionnaire was also given to both groups to acquire information about eight demographic variables.
The self-reporting Bar-On EQ-i was used to provide a comprehensive measure and assessment of the level of emotional intelligence for county directors in the Ohio Extension system. The EQ-i is comprised of 133 brief items in a five-point response set (ranging from “Not True of Me” to “True of Me”) and combines existing observations, theories, methodological strategies, research findings and multi-factorial comprehensive scores (Bar-On, 2002). The EQ-i assessment provides four validity scale scores, a total emotional quotient (EQ) score, five composite scores and 15 EQ subscale scores.
The researchers used a job satisfaction index to collect data from county staff. The index was developed by Brayfield-Rothe (1951) and then modified by Warner (1973). The original Brayfield-Rothe index was modified through feedback from a field test, resulting in four items that reflected low scores on a correlated split- half correlation being eliminated. The revised index was a 14-item version.
The researchers sent an e-mail invitation to all county directors in Ohio with personnel responsibilities to complete the emotional intelligence instrument on- line. The final county director response rate was 61 percent (52 individuals). For job satisfaction, data were collected according to the mailed survey procedure outlined by Dillman (1978). The job satisfaction survey was mailed to 251 county staff with a cover letter. The final study response rate was 86 percent and included 222 county staff.
Measures of association were used to determine the linear relationship between job satisfaction (county staff), emotional intelligence (county directors) and selected individual characteristics. Job satisfaction data from county staff and the EQ-i scores of county directors were merged by matching the corresponding job satisfaction scores of county staff to the county director EQ-i scores. The resulting number of county staff’s job satisfaction scores reviewed in this section of correlational analysis was 130. Stepwise linear regression analysis was used to determine the best predictor(s) of the dependent variable – job satisfaction of county Extension staff.
The dependent variable for this study’s regression model was the total job satisfaction score for Extension county staff. The independent and extraneous variables entered into the regression model included: emotional intelligence scores, including the total EQ-i and the five composite scales of the EQ-i, gender, race/ethnicity, appointment, main program area, location (by district), length of employment with Extension (all locations), level of highest education completed and previous management training. The total R2 was computed to determine the amount of variance accounted for by the linear combination of the independent and extraneous variables. The researchers calculated the descriptive statistics to meet study objectives. Davis’ (1971) conventions were used to describe measures of association.
County Directors Level of Emotional Intelligence
The self-reported emotional intelligence scores of county directors are reported in Table 2 by a total emotional intelligence inventory score and five composite scale scores that included competencies in intrapersonal skills, interpersonal skills, stress management, adaptability and general mood. For the 52 county directors completing the EQ-i, total EQ-i scores ranged from 75 to 118. The highest individual composite scale score mean was stress management with intrapersonal and adaptability the next highest.
Table 2. Emotional Intelligence Scores and Ranges of Scores of County Directors
Note: a: More than one mode; smallest mode reported
Scores for the Bar-On EQ-i (2002) will generally fall (99.9% of the time) between 55 and 145 (+/- 3 standard deviations from the mean). Extreme scores on the EQ-i are generally uncommon and most respondents achieve EQ scores near 100.
Generally, high EQ-i scores indicate skills measured as being strong, well developed and functioning effectively. On the contrary, low scores suggest a deficiency may exist in a particular area and a need to improve particular competencies and skills to adhere to environmental demands. Scores are
evaluated by how far they fall from up to one standard deviation (+/- 15 points) from the mean.
EQ-i scores can be interpreted from using Table 3, which provides interpretive guidelines for Bar-On’s EQ-i scale scores (Bar-On, 2002). Individuals with average or above scores are generally expected to possess the competencies of emotional intelligence to a greater degree for each of the inventory’s five factors. The total EQ-i score gives a snapshot of emotional well being, or general indication, of how emotionally intelligent the respondent is and how successful the individual is in coping with environmental demands. The total EQ-i score provides general information about the respondent because it covers a broad range of skills and competencies. Greater emphasis is placed on the five composite scores because a high total EQ-i score can hide a low score on one or more of the underlying subscales. High EQ-i scores describe individuals who feel good about themselves, are in touch with their feelings, feel fairly successful in realizing their potential, understand the way others feel (and respond accordingly), are realistic, assertive and successful in problem solving, are happy and carry a positive outlook on life.
County directors generally have strong emotional intelligence capacity. Nearly 81 percent of the responding county directors have a total EQ-i score in the average or above level (average score 90 or above). The strongest EQ-i composite scale score was in adaptability (84.6% average or above emotional capacity). Other composite scores percentages where county directors scored at the average or above level of emotional capacity were intrapersonal (80.8%), interpersonal (73.1%), stress management (82.7%), and general mood (82.7%).
Table 3: Interpretive Guidelines and EQ-i Scores for County Directors
OSU Extension County Director Scores (% of Total)
Standar d Score
Job Satisfaction of County Staff
Table 4 portrays county staff’s overall perceptions of job satisfaction. For the 222 county staff completing the job satisfaction instrument, individual scores ranged from 1.50 to 4.36. With 3.00 being a neutral score of job satisfaction, county staff were slightly negative in their level of job satisfaction.
Table 4. Perceptions of Job Satisfaction of OSU County Extension Staff Relationships between Job Satisfaction and Selected Characteristics of County Staff
The overall relationship between selected characteristics with the dependent variable, job satisfaction, was assessed using the Kendall’s tau-b and Pearson’s R correlation coefficient measures of association. The Kendall’s tau-b and Pearson’s R correlations are shown in Table 5. These measures of association identified four characteristics with a significant relationship to job satisfaction at the .05 alpha level: highest level of education completed, appointment, previous management training and main program area. The characteristic with the highest level of correlation was highest level of education completed. Although the highest correlation, the relationship between the dependent variable and highest level of education completed was still considered a low association with a .23 correlation.
Other relationships identified with low correlations were appointment (.18), previous management training (.16) and main program area (.15). The correlations between the dependent variable and years in Extension (.05), gender (.10), and race/ethnicity (-.08) were identified as negligible associations.
Table 5. Summary Data of Intercorrelations between Selected Characteristics and Job Satisfaction of County Staff
Job Satisfaction (X ) Appointment (X )
Years in Extension (X ) Highest Education (X ) Gender (X ) Race/Ethnicity (X )
Previous Training (x ) Main Program Area (X )
Note: Coefficients reported as Kendall’s tau-b and Pearson’s R correlations x2: 1 = Program staff, 0 = Support staff
x3: 1 = Less than 14 years, 0 = 15 or more years x4: 1 = Advanced degrees, 0 = Bachelor’s or less x5: 1 = Male, 0 = Female
x6: 1 = All other than white, 0 = White
x7: 1 = Previous training, 0 = No previous training
Stepwise Multiple Regression Model for County Staff
Stepwise multiple regression analyses were performed to determine the proportion of variance in job satisfaction that was explained by the linear combination of selected characteristics for county staff. Since all assumptions for multiple regression were met, stepwise multiple linear regression was performed to determine the best predictor of the dependent variable – job satisfaction. The regression model was run with the selected characteristics in the study, including appointment, main program area, length of employment, level of highest education completed, gender, race/ethnicity and previous management training.
The regression model in Table 6 depicts the one characteristic that entered into the regression model for job satisfaction. The model indicated that 4.8 percent of the variance in job satisfaction could be explained by highest level of education completed. Since highest level of education completed was the only variable to enter the regression equation, it is the only characteristic to explain a change in variance for job satisfaction for county staff.
Table 6. Regression of Job Satisfaction with Selected Variables for County Staff – Stepwise Entry (n = 212)
Variables R2 R2Change
Highest Level .048 .048
Std. Error = .036 Adjusted R2 = .043 For model: F = 10.584
Relationships between EQ-i and Selected Characteristics
The EQ-i composite scale scores were used rather than the total EQ-i score in the investigation of relationships between emotional intelligence levels of county directors and the job satisfaction of county staff. The total EQ-i score gives general information on the county directors’ emotional well being while the composite scale scores provide a deeper level of information concerning emotional capacity for each respondent. The overall relationships between EQ-i composite scales and selected characteristics were assessed using the Kendal’s tau-b and Pearson’s R correlation coefficients measures of association. The Kendall’s tau-b and Pearson’s R correlations are shown in Table 7.
The Kendall’s tau-b and Pearson’s R measures of association identified five characteristics with a significant relationship to job satisfaction at the .05 alpha level: appointment, main program area, highest level of education completed, gender and previous management training. The characteristic with the highest level of correlation was level of education completed (.241). This characteristic held the highest level of correlation, but was still a low correlation explaining the relationship between the dependent variable, job satisfaction and highest level of education completed. Other relationships with low intercorrelations to job satisfaction, included appointment (.198), main program area (.150), gender (.186), and previous management training (.165). The correlations between the dependent variable and race/ethnicity (-.081), years with Extension (.013) and the five composite scales of the EQ-i resulted in negligible associations. The EQ-i
correlations included intrapersonal (-.067); interpersonal (-.027); stress management (-.083); adaptability (-.011); and general mood (-.061).
Stepwise Multiple Regression Model
Stepwise multiple regression analysis was performed to determine the proportion of variance in job satisfaction of county staff that was explained by the linear combination of the EQ-i five composite scale scores of county directors. Since all assumptions for multiple regression were met, stepwise multiple linear regression was performed to determine the best predictor of the dependent variable – job satisfaction. Since the job satisfaction scores had to be matched with county director EQ-i scores to run the regression model, in cases where the county director did not participate in the study, those county staff scores were eliminated. Therefore, the n for the multiple regression model was lower due to the unavailability of information from certain county directors matched to county staff data.
The five EQ-i composite scale scores were run in a regression model with job satisfaction. The multiple regression analyses showed no relationship to job satisfaction and thus, no explanation of the variance related to job satisfaction. The regression model in Table 8 depicts the one characteristic that entered into the regression model for job satisfaction. The model indicated that 5.8 percent of the variance in job satisfaction could be explained by gender. Since gender was the only variable to enter the regression equation, it was the only characteristic to explain a change in variance for job satisfaction. The mean value of the dichotomous variable coded 1 (male) was .168 points higher than the mean value of the variable coded 0 (female); therefore, whatever part of the variance was explained by females, the variance explained by males was .168 points higher.
Table 7. Regression of Job Satisfaction with EQ-i and Selected Variables – Stepwise Entry (n = 124)
Std. Error = .061 Adjusted R2 = .05 For model: F = 7.561
Based upon the review of literature and the findings related to the research objectives, the following conclusions, applicable to the populations of this study, were reached:
A large percentage of the county directors responding to this study had an average or higher than average total and composite scales score of emotional intelligence.
No association existed between the level of emotional intelligence of county directors and the level of job satisfaction of county staff.
Only four of the county staff’s characteristics explored in this study had any degree of significance to job satisfaction. Of those four characteristics (appointment, highest level of education completed, previous management training and main program area) only low associations were observed.
The area needing the most training for county directors and their level of emotional intelligence is in interpersonal skills.
County directors responding to the EQ-i, on the average as a group, scored higher than previous respondents did from other studies (Bar-On, 2002).
The level of job satisfaction of county staff has dropped significantly (from 4.13 to 2.79) since a study conducted in 2002 with similar staff (Schmiesing, 2002).
When looking at emotional intelligence scores for OSU Extension county directors, the majority of the scores indicated county directors have an adequate or higher level of emotional intelligence. This supports the findings by Endler and Parker (1994) where administrators need coping patterns and strategies to be effective leaders. Administrators with higher levels of emotional intelligence will be more successful in problem solving and managing conflict, coping with items that may become stressors in the workplace, interacting with political factors in the workplace and with increased interpersonal skill (Buford, 2001).
The study by Ayers and Stone (1999) indicated that most of the Extension core competencies of Extension educators were related to emotional intelligence. The researchers found that emotional intelligence was a better predictor of job success and that competency curriculum development, including emotional intelligence in skill training for supervisors, is practical and advisable.
The value of unit directors knowing their EQ-i scores is to help identify their areas of relative strength in their current environment. Administrators can utilize the information to assist in tasks associated with human resources, organizational development and general office unit operations. The average mean near 100 (and/or included within 15 points of this mean score) for most of the composite scores of the EQ-i indicated that county directors have an average ability and typical healthy functioning within their responsibilities within OSU Extension.
For those county directors responding to the EQ-i, approximately 80.8 percent were considered to have adequate or well-developed emotional intelligence skills. The areas of greatest strength for county directors were in stress management and adaptability. Extension staff must have strengths in to survive in their programming roles and with clientele. The weakest area was in interpersonal skills where three county directors scored in the markedly low level of emotional capacity. Individuals with low scores can experience a variety of challenges in working with their co-workers. The researchers suggest that the competencies evaluated by the EQ-i, or the people management skills, are a strong element in the job function of unit directors.
The composite scale scores of intrapersonal, interpersonal, stress management, adaptability and general mood give a general indication of coping abilities and present functions of county directors. Strengths and weaknesses can be identified in the five composite scale areas. Strengths can be enhanced and weaker areas can be focused upon for improvement by additional training and performance appraisal.
A review of the five composite scores indicated nearly 81 percent of the county directors in this study have average or higher emotional capacity with intrapersonal skills. The interpersonal composite scores had the widest range of scores, from 61 (markedly low) to 122 (very high) and the highest percentage of county directors needing improvement in this area of emotional capacity. While these low scores existed within this respondent group, the overall percentage of county directors with average or above emotional interpersonal capacity was still 72 percent of the total respondents. Nearly 83 percent of the county directors responding carry average or higher stress management competencies, while almost 85 percent responding were at the average or higher level of emotional capacity in adaptability. The large number of respondents with high scores in this composite scale offers a substantial contribution to OSU Extension because of their flexibility, effectiveness in understanding problematic situations and realism. The researchers suggest that adaptability is an area of strength for unit directors with the current changes placed upon staff in regards to budgets, reduction in force, organizational restructuring and day-to-day frustrations from staff trying to make adjustments in the midst of change. Nearly 83 percent of the county director respondents carried a composite scale score indicating an adequate or higher level of emotional capacity in general mood.
The research question that emotional intelligence of county directors would have an impact on the job satisfaction of county staff was not supported with the findings in this study. Other factors may have more direct impact on job
satisfaction than the ones used in this study. OSU Extension was in the midst of a large amount of change during this study’s timeline and other factors than the emotional intelligence levels of county directors may have affected job satisfaction of county staff.
Job satisfaction is an extremely complex construct with no single conceptual model completely and accurately describing the construct (Hagedorn, 2000). This study substantiated this trend by not identifying a substantial association with emotional intelligence or selected characteristics for the respondents in this study. A low, significant correlation existed between county staff’s previous management training and the dependent variable, job satisfaction. The findings do not provide strong evidence that participation in management training has a major impact on a county staff member’s job satisfaction.
The review of literature, the findings of this study, and the resulting conclusions and implications have led these researchers to several recommendations for individuals in a variety of leadership roles within organizations. Following are these recommendations:
Organizational administration should incorporate training and update sessions on emotional intelligence into employee development curriculum for unit directors and staff. Specific attention should be given to developing strengths in interpersonal skills.
Unit directors should have their emotional intelligence scores evaluated periodically throughout their administrative tenure to determine if a person’s scores are malleable and change over time. Improvement plans should be established, implemented and supported for each individual unit director. The qualitative aspect of emotional intelligence (i.e., personal feedback from unit directors by case study analysis, individual interviews and/or direct observation) should also be investigated for implementation.
To assess the understanding and reaction of incorporating emotional intelligence into the workplace structure, a web-based survey could be used as a method to involve all staff in providing input to this operational change. Staff could express questions about areas of less understanding and provide suggestions for potential training and use in the workplace.
Encourage staff to operate as a proactive team where a safe environment for exploring, embracing and relying on emotions can be utilized for empowerment of all staff members and administrators.
The unit offices should operate as a team in understanding and incorporating emotionally intelligent attitudes in the workplace. Organizational administration should develop a set plan for evaluation of the cognitive and emotional intelligence competencies of unit directors. The information should be shared with unit directors to improve their skills and abilities to lead and work with staff.
Human resources staff considering individuals as potential candidates for unit director positions should evaluate the competencies needed to become successful unit directors before positions are filled. New hires should be introduced to the construct of emotional intelligence and its viability to the success of the workplace.
Organizational administrators should support emotional intelligence analysis for all staff as a required job enhancement responsibility. Individuals that make strong efforts in improving their emotional intelligence and success in office leadership should be praised from supervisors and rewarded appropriately within the organization.
Encourage and support staff to dedicate more time for participation in various types of personal or professional development learning experiences, including enhancement of emotional intelligence skills.
Organizational administration regularly review and strive to understand the current levels of job satisfaction/dissatisfaction among staff and focus efforts to support staff through periods of change and transition.
Recommendations for Further Study
The following recommendations are offered:
Replicate this study using other variables that might effect job satisfaction other than those used in this study.
Investigate the correlation between the emotional intelligence levels of unit directors and their own level of job satisfaction. The willingness of respondents to receive personal feedback could be eliminated if seen as a barrier to participation in the study.
Use alternative testing mediums to measure emotional intelligence for comparison to the results of this study, including study of how supervisors of unit directors perceive the competencies and abilities of the unit director and how staff members perceive the competencies and capabilities of the unit director.
Evaluate whether other responsibilities that unit directors assume within their work assignments affect the EQ-i scores of unit directors.
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Dr. Judy Villard is currently the Extension Educator, 4-H Youth Development and County Extension Director for Ohio State University Extension in Richland County, Ohio. She provides state-wide programmatic leadership for OSU Extension in the areas of communication development/presentation and safety education.
Dr. Garee Earnest provides programmatic leadership for the Ohio State University Leadership Center where he delivers high-energy, content-driven, experiential workshops that enhance person, professional and organizational development.