Motivating language in industry: its impact on job satisfaction and perceived supervisor...

This article reports on a study of the use of motivating language (ML) by employees of the southeast regional division of a Fortune 500 company. The relationship between the supervisory use of ML, communication competence, communication satisfaction (CS), employees' job satisfaction, and perceived

supervisory effectiveness was explored. The study was based on a sample of 136 participating employees surveyed via an interactive Internet survey of a 400-person organization. Suggestions for further research are presented. The identification of the specific relationship between the use of language and communication competence, CS, job satisfaction, and leaders' perceived effectiveness establishes a direct link between communication, leadership, and job satisfaction.

Keywords: motivation; language; job satisfaction; leadership; supervisor effectiveness

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The specific role of language in leaders' behavior has received little investigation from either leadership theorists or communication scholars. Although the importance of communication is implicit in much research on leadership, little work has been undertaken to define the impact language use may have on leadership in general and on leaders' effectiveness and job satisfaction in particular. The premises on which most leadership theory and research have been based include simplifying assumptions that the majority of leaders' language is an exchange of uncertainty-reducing information and that all leaders communicate equally well with their subordinates (Mayfield, Mayfield, & Kopf, 1995, 1998).

Recent research has begun to examine the role of language in leadership, specifically, as the means by which leaders express behavior to subordinates; and as a result, researchers have collectively begun questioning the previous simplifying assumptions (Conger, 1991; Fairhurst, 1993; Fairhurst & Chandler, 1989; Jablin, 1985; Jablin & Krone, 1994; Lamude, Daniels, & Grahm, 1988; Mayfield et al., 1995, 1998; Scandura & Graen, 1984; Sullivan, 1988; Waldron, 1991).

This study explored this role of language by extending the work of Mayfield et al. (1995, 1998), who developed and operationalized a scale for assessing the use of motivating language (ML) by leaders. This scale

   measures both a leader's general oral communication skills with
   subordinates and his/her strategic use of spoken language variance
   to motivate workers. [And as noted, it is] firmly grounded in
   current management and communication literature and [encompasses
   the] most relevant leader oral communication with workers. (Mayfield
   et al., 1995, p. 331)

In conjunction with the ML scale, Mayfield et al. also operationalized a communicator competence scale (Monge, Bachman, Dillard, & Eisenberg, 1982) to evaluate subordinates and a scale measuring the communication satisfaction (CS) of subordinates with their immediate superiors (a subset of International Communication Association's Organizational Communication Relationships questionnaire). We used the communicator competence scale to measure supervisors' communication competence, as originally used in Monge et al.'s (1982) study. This study also built on related work that studied the use of ML by cadet leaders serving in supervisory positions among other cadets at a military college and presented a scale for measuring perceived supervisory effectiveness (Sharbrough, 1998).

ML theory (MLT) "predicts that strategic applications of leader oral communication have positive measurable effects on subordinate performance and job satisfaction" (Mayfield et al., 1995, p. 332). This study further expands the Hoppock (1935) scale used to measure job satisfaction via correlation with ML use and with a scale measuring perceived supervisory effectiveness (Sharbrough, 1998). Recent research has investigated the relationship between ML and both work innovation and commitment (Mayfield & Mayfield, 2002, 2004). This study satisfied the dual criteria for relevant research in organizational communication by (a) relating current MLT to practical organizational issues and (b) responding to the needs of those in industry (Smeltzer, 1993).

In this study, we sought to further validate and generalize the application of MLT across diverse groups in industry by studying the use of ML by supervisors of employees taken from a representative group at a Fortune 500 company, spanning five levels of management (from vice president through direct supervisor). We assessed the effectiveness of all three types of ML communication in organizational outcomes, including accomplishing the objectives of the organization, the leader, and the organizational members. Additionally, we studied the interdependence between ML use and the level of subordinates' satisfaction with communication, supervisory effectiveness, and job satisfaction. The communicator competence questionnaire to measure supervisory communication was further validated and operationalized. Finally, the results and their future application in the form of leadership training and supervisor performance rating for business are discussed.

THEORY AND HYPOTHESES

ML Theory

The ML scale is based on MLT (Sullivan, 1988), which theorizes that the strategic use of language by leaders is an important motivational tool that has a positive and measurable impact on employees' performance and job satisfaction. MLT proposes that leaders' effectiveness in using three types of communication in accomplishing their tasks will have an impact on important organizational outcomes. These three types of communication are

* meaning making communication, which explains the rules, structures, and values of the culture of an organization;

* direction-giving or uncertainty-reducing communication, which clarifies instructions, clears up confusion, and so forth; and

* empathetic communication, which expresses the emotions of a leader through shared feelings, praise, criticisms, and so forth.

MLT includes four additional assumptions. First, language covers "most verbal expressions that can occur in leader-to-worker talk" (Mayfield et al., 1995, p. 332). Second, the effect of ML on workers' outcomes is moderated by leaders' behavior in the majority of cases. Third, if leaders' language and behavior are incongruent, the effect of leaders' behavior will dominate; that is, language "will only get a leader so far, and speech must be congruent with behavior to be taken seriously over time" (Mayfield et al., 1995, p. 330). The fourth assumption is that leaders are most effective through the "powerful use" of all types of ML (p. 331).

Relationship of Communication Competence and CS to MLT in This Study

Mayfield et al. (1995) validated the ML scale using convergent and divergent validity testing on the basis of ML being theorized to be a multivariate measure of leaders' communication. Bacharach (1998) stated that differentiation between two constructs can be demonstrated by a divergent relationship with a third construct. Subordinates' communication competence was used as the divergent variable, and subordinates' CS was used as the convergent variable. Convergent and divergent validity testing was used by Mayfield et al. (1995) to ensure that ML use was not the same as a subordinate merely being satisfied with how his or her supervisor communicates.

Mayfield et al. (1998) then operationalized the ML scale and correlated it to job satisfaction and subordinates' performance in a health care environment. The authors extended Mayfield et al.'s (1995) work by returning the communication competence scale used in their 1995 study to the format of Monge et al.'s (1982) original scale, a cross-situational measure of the communication competence of a supervisor directly related to the overall performance of the supervisor.

In this study, communication competence was operationalized as a supplemental, related measure of a supervisor's ability to communicate. The CS scale was also operationalized as a measure of a subordinate's satisfaction with his or her leader's communication, which supplements the assessment of a leader's ML use.

Immediate Supervisor's Communication Competence

An individual's communication competence can include general items such as having a clarity of expression, using language appropriate to a situation, providing a timely response, and being attentive. An employee's communication competence can also include job-specific or special communication skills; for example, customer service, sales, human resources, and so on, may all require special skills. An immediate supervisor's management or leadership efforts clearly require a broader range of communication competence than a nonsupervisory position.

Scudder and Guian (1989) stated that communication competence as measured by Monge et al.'s (1982) instrument is a good predictor of overall performance and that communication competence is easier to measure than some specialized performance factors, which strengthens the impact an individual's communication competence has on others. As McFall (1982) observed, "competence does not actually reside in the performance; it is an evaluation of the performance by someone" (Scudder & Guian, 1989, p. 224).

Communication Satisfaction

The CS of employees is a measure of how well the "available information fulfills the individual's requests for information pertaining to the task-role or for simply being informed about organizational activities" (Putti, Aryee, & Phua, 1990, p. 45). Thayer (1967) defined CS as the "personal satisfaction inherent in successfully communicating to someone or successfully being communicated with" (Putti et al., 1990, p. 45). Redding (1972) also defined CS as "the overall degree of satisfaction an employee perceives in his total communication environment" (Putti et al., 1990, p. 45). According to Thayer (1968), "communication satisfaction is occasionally mistaken for communication effectiveness ... as well as effectiveness for satisfaction" (p. 143). This study focused on the primary role of organizational communication in explaining task-role requirements and organizational policies and in providing feedback. These functions may make up an employee's communication environment (Putti et al., 1990).

Putti et al. (1990) demonstrated that organizational members' satisfaction with the amount of information available to them may enhance their commitment to an organization. They indicated that this commitment exists because satisfaction with information encourages a sense of belonging and identification with the values and objectives of the organization. Thus, CS is an important measure of the ability of an organization to use communication as a commitment-enhancing mechanism. According to Putti et al. (1990), there is a demonstrated link between commitment and such behavioral outcomes as performance, turnover, and absenteeism and therefore effectiveness. Hence, efforts to improve commitment through communication strategies should have a positive impact on organizational effectiveness. CS is an organizational outcome that should be associated with increases in the use of ML (Hughes, Ginnett, & Curphy, 1996; Mayfield et al., 1995).

Job Satisfaction of Subordinates

The use of consideration is reported in most research on leaders' behavior to have a direct impact on subordinates' satisfaction. However, subordinates are most satisfied when they perceive that their supervisors' behavioral approaches exhibit both consideration (relationship orientation) and the initiation of structure (task orientation) (Castaneda & Nahavandi, 1991). Pfeffer and Salancik (1975) suggested that leaders' task-related behaviors will be influenced by their bosses' expectations; however, leaders' social interactions will be influenced by the expectations of their subordinates. In North America, the cultural focus on tasks and the related emphasis on task efficiency are further heightened by an organizational link between effectiveness and task-related behaviors. However, if leaders ignore consideration, they may lose the opportunity to enhance employees' loyalty and teamwork that results from increased job satisfaction. If leaders are evaluated only by their supervisors using the typical task-based measures of effectiveness, organizations are likely to encourage unidimensional managers who do not meet the organizations' needs in a changing environment (Castaneda & Nahavandi, 1991).

Referring to MLT, the meaning-making type of communication correlates can be intuitively associated with consideration. Direction-giving or uncertainty-reducing communication is correlated with the initiation of structure from the behavioral theories of leadership. In addition, MLT adds the empathetic form of communication missing from behavioral theories.

Perceived Supervisory Effectiveness

Sharbrough (1998) evaluated perceived supervisory effectiveness on the basis of the use of ML. Perceived supervisory effectiveness was based on three measures of leaders' effectiveness adapted from Nahavandi (1991). Survey respondents were asked to what extent their leaders achieved (a) goals set for the leaders by the organization, (b) the leaders' own goals, and (c) the goals of the subordinates. These three variables included structuring behaviors as well as consideration behaviors, corresponding to early approaches to leadership (Nahavandi, 1991). Sharbrough found substantial support for the relationship between the use of ML and leaders' perceived effectiveness within an organization.

Hypotheses

As noted previously and as predicted by Mayfield et al. (1998), ML is important because it links leaders' strategic communication with the key outcome of employees' job satisfaction. ML use is directly linked with subordinates' CS and supervisory communication competence. Furthermore, ML use is linked to perceived supervisory effectiveness. These predictions led to the following hypotheses:

Hypothesis 1: There is a significant and positive relationship between a leader's use of ML and a subordinate's job satisfaction (Mayfield et al., 1998).

Hypothesis 2: There is a significant and positive relationship between a leader's use of ML and a subordinate's perception of a leader's effectiveness.

Hypothesis 3: There is a significant and positive relationship between a leader's use of ML and the leader's communication competence.

Hypothesis 4: There is a significant and positive relationship between a leader's use of ML and a subordinate's CS.

METHOD

Procedures and Sample

The sample for this test of ML use consisted of employees of the southeast regional division of a Fortune 500 company. The organization is a highly competitive company with a strong, ongoing emphasis on communication with customers, both internal and external, on training, and on leveraging technology. The company has a well-developed computer network, and most of the employees at all levels are connected to the Internet. Internet access is one of several key methods the company uses to communicate with its employees, and it uses many state-of-the-art applications to effectively use the Internet across its business, training, and administrative applications. A major focus of the company is its emphasis on building the skills and abilities of its people; its employees are its key resource for success. A link to a Web page containing the interactive survey for this project was disseminated through the company's e-mail system to take advantage of employees' electronic competence.

The study was based on a sample of 136 participating employees surveyed via an interactive Internet survey of the 400-person organization using a proprietary survey instrument. The organization spans five levels of supervision. Each employee rated his or her supervisor's use of ML, CS, and level of job satisfaction, and each employee rated his or her supervisor's communication competence and perceived effectiveness. The Internet survey had explanatory background information and written instructions embedded along with fail-safe programming to ensure trouble-free testing. The answers were sent to a secure central database, which did not record the identities of respondents. Great care was taken to explain and maintain strict response confidentiality.

The response rate of the survey was comparable with that of Mayfield et al.'s (1998) survey, approximately 34%. The survey was entirely voluntary, and there was no tracking of whether an employee had completed the survey. Of the 136 respondents, two surveys were discarded because of incomplete or insufficient information, leaving 134 usable surveys.

The sample's demographic makeup was comparable with that of similar industries. The majority of respondents (82%) were men; 18% were women. Individuals in supervisory roles made up 31% of the respondents, while 69% did not supervise anyone. Individuals with high school, trade school, or associate's degrees made up 57% of the sample, 34% had bachelor's degrees, and 9% had master's degrees or higher. Thirty percent of the participants were 35 years of age or younger, and 30% were older than 45 years. A majority of the sample, 65%, had 10 years or less of service with the company, while only 6% had more than 25 years of service.

Technical or engineering positions made up 59% of the sample, 15% of the positions were jobs labeled by the company as direct supervisory positions (some jobs were supervisory positions, but the supervisory roles were not reflected in their titles), 16% were sales-related positions, and 10% were administrative positions (on the basis of job title). The survey demographic data are summarized in the Table 1.

Model

The model specifications were based on the preceding hypotheses and ML, as conceptualized by Sullivan (1988) and further developed by Mayfield et al. (1998). As presented by Mayfield et al.,

   Sullivan hypothesized a single latent factor representing a
   superior's individualized use of ML. Sullivan also theorized that
   the latent ML factor could be wholly captured through the
   measurement of three observable factors; namely, the indicants of
   direction-giving, empathetic, and meaning-making language. (p. 238)

The research associated with ML documents that a leader's use of ML should affect a subordinate's job satisfaction, perceived supervisory effectiveness, the supervisor's communication competence, and the subordinate's CS. If these assumptions are true, the latent ML factor should be significantly and positively linked with measures of a subordinate's job satisfaction, perceived supervisory effectiveness, perceived supervisory communication competence, and the subordinate's CS.

Measures

After factor analysis extracts common factors for a given scale or set of scales, the common factors can be used as independent or predictor variables. It is prudent to run a reliability test for all the factors before using them in subsequent analyses. A reliability test confirms that the same set of scale questions would elicit the same responses if the questions were recast and readministered to the same respondents. Variables derived from the given scales are declared to be reliable only when they provide stable and reliable responses over a repeated administration of the test.

The internal consistency of each scale was measured for the sample using Cronbach's [alpha], which is a conservative estimate of reliability. The computation of [alpha] is based on the reliability of a scale relative to another scale with same number of items and measuring the same construct of interest (Hatcher, 1994).

Alpha coefficients range in value from 0 to 1 and may be used to describe the reliability of factors extracted from dichotomous (i.e., questions with two possible answers) and/or multipoint formatted questionnaires or scales (i.e., a rating scale of 1 = poor to 5 = excellent). The higher the score, the more reliable the generated scale is. Nunnaly (1978) indicated .70 to be an acceptable reliability coefficient, but lower thresholds are sometimes used (Santos, 1999).

All measures of ML showed high levels of reliability as measured by Cronbach's [alpha]. The ML and CS scales had very high reliabilities, 95% and 93%, respectively. Communication competence and leaders' perceived effectiveness had good reliabilities, 78% and 85%, respectively, while the job satisfaction scale had 74% reliability. Further information on the measures' reliabilities is presented in Table 2.

RESULTS

Confirmatory factor analysis indicated that the items of the ML scale loaded similarly to those used by Mayfield et al. (1998). Those authors reported three absolute factors: direction-giving, empathetic, and meaning-making language. The current study found that responses to the ML questionnaire loaded on three well-defined factors that represented the three factors presented in earlier research. This finding further confirmed that respondents were interpreting the ML questionnaire questions in a way comparable with respondents in previous ML research. The results are shown in Table 3.

Pearson's correlation coefficients were calculated to measure the relationships between each of the scales. ML had a statistically significant correlation with each of the factors represented by the respective scales of supervisory communication competence, supervisory CS, perceived leadership effectiveness, and job satisfaction. The results are shown in Table 4.

Hypothesis 1 investigated the relationship between a leader's use of ML and a subordinate's job satisfaction. Analysis revealed Pearson's correlation coefficient between these two concepts of .343, at a significance of p < .001. Thus, the relationship was both significant and in the predicted direction (positive), confirming this hypothesis.

Hypothesis 2 explored the relationship between a leader's use of ML and a subordinate's perceptions of the leader's effectiveness. The correlation between ML and leader's effectiveness was .672 (p < .001). Again, the relationship was both significant and positive, confirming Hypothesis 2.

Hypothesis 3 predicted a significant, positive relationship between ML and a leader's perceived communication competence. Analysis revealed a correlation of .592 (p < .001), confirming Hypothesis 3 as well.

Hypothesis 4 explored the relationship between ML and subordinates' CS. The correlation between the scales for the sample was .633 (p < .001). Analysis also confirmed this hypothesis.

DISCUSSION AND CONCLUSIONS

The results of data analysis support and validate the previous research on ML by Mayfield et al. (1995, 1998) and Sharbrough (1998) and their use of related scales. Continued development of the ML scale has both meaningful and practical applications in industry. As noted by Mayfield et al. (1998), ML shows potential as a diagnostic and remedial training tool; also, the instruments are cost-effective means of assessing both the use of ML and areas for improvement. The training potential for ML continues to increase with further research about applications of MLT.

This study incorporated ML with subordinates' CS and subordinates' job satisfaction, as did those of Mayfield et al. (1995, 1998), but changed the perceived subordinate communication competence scale to that of perceived supervisory communication competence and added the perceived leader's effectiveness scale. The results of data analysis provide support for the reliability and validity of the two scales and for adding scales measuring perceived supervisory effectiveness and perceived supervisory communication competence to the ML toolbox, providing additional tools for evaluating supervisory ML effectiveness.

This study also addressed the need for future research concerning the generalizability of ML and the application of leader-subordinate communications for group- and organization-level contexts. Specifically, the sample population was for an industry division that spans five levels of management, varied levels of education and lengths of service, both genders, and various career fields. The diverse sample population overcame some of the limitations of Mayfield et al.'s (1995, 1998) previous ML research based on a sample population that was predominately female nurses. The more diverse industrial workforce sample population expanded the application of previous research and increased the possibility of more widespread ML application across whole groups and entire organizations.

Finally, by using an Internet-based survey to gather data, a first step in the effective application of MLT in electronic communications was made.

Implications

The results indicate a clear and easily identifiable link between ML, subordinates' satisfaction with leaders' communication, perceived supervisory communication competence, perceived leader effectiveness, and employees' satisfaction. MLT could be used to improve management training, resulting in increased leader effectiveness. The shared variability (i.e., Pearson's correlation squared) of ML to the other factors predicts that a given amount of increased ML use by supervisors will result in corresponding percentage increases for the other factors. That is, the results showed that a given amount of increase in ML use by supervisors resulted in a corresponding 35% improvement in perceived supervisory communication competence, a 40% enhancement in subordinates' CS, a 45% boost in perceived leader effectiveness, and an increase of 12% in job satisfaction.

ML and the related scales provide management, particularly in an Internet or e-mail format, with a tool that can be used to quickly target leaders' communication deficiencies across an organization or in specific groups. The feedback from the scales along with MLT can form the basis for designing targeted supervisory training programs to address leaders' communication deficiencies unique to the organization or leaders in question.

Finally, the results show that ML is a communication strategy that can be used by leadership to build commitment to an organization. The increased use of ML should have a positive impact on organizational effectiveness, resulting in reduced costs related to employees' performance, turnover, and absenteeism.

Limitations and Future Research

Although this study has far-reaching implications for leadership and communication, it has limitations. These include the fact that the survey data were perceptual, not objective. Furthermore, there was an inherent overlap in the variables that may have led to inflated correlations among the scales.

Opportunities for future research include testing the scales using a public-sector, not-for-profit, government, or military population. Additionally, the relationship between ML and organizational commitment needs to be measured. Studying the relationship of the use of ML and absenteeism, productivity, and employee turnover should provide objective means of assessing the effectiveness of ML. Another avenue of research is the connection between ML and leadership theories such as transformational leadership theory (Bass, 1985) and leader member exchange theory (Graen & Cashman, 1975; Mayfield & Mayfield, 1998). A further step is the development of an ML training curriculum and then the evaluation of pre- and posttraining results as well as the retention of ML knowledge and application over time for given sample populations. Comparison of before and after measures of organizational effectiveness, such as employees' performance, turnover, and absenteeism results, can also be evaluated.

APPENDIX
Scales Used in the Study

A. Demographic Data Scale

Please answer the following questions:
1)  Your age is?
    a) 16-25
    b) 26-35
    c) 36-45
    d) 46-55
    e) 56-65+
2)  Your gender is?
    a) Male
    b) Female
3)  Your level of education is?
    a) High School
    b) Trade School
    c) Associate Degree
    d) Bachelor's Degree
    e) Master's Degree
    f) Doctorate
4)  Years of service with company?
    a) 0-5
    b) 6-10
    c) 11-15
    d) 16-20
    e) 21-25
    f) 26-30
    g) 31-35
    h) 36-40+
5)  Your job title category is
    a) administrative
    b) Direct Supervision
    c) Sales
    d) Technical/Engineering
6)  Do you supervise others?
    a) Yes
    b) No

B. Motivational Language Scale

Please choose the response that is most appropriate for you.
The examples below show different ways that your boss might
talk to you. Please choose the answer that best matches your
perceptions. Be sure to mark only one answer for each question.

Very Little    (VL)
Little          (L)
Some            (S)
A Lot           (A)
A Whole Lot    (WL)

DIRECTION-GIVING OR UNCERTAINTY-REDUCING LANGUAGE

1)  Gives me useful explanations of what needs
    to be done in my work.                                  VL L S AWL
2)  Offers me helpful directions on how to do
    my job.                                                 VL L S AWL
3)  Provides me with easily understandable
    instructions about my work.                             VL L S AWL
4)  Offers me helpful advice on how to improve
    my work.                                                VL L S AWL
5)  Gives me good definitions on what I must do in
    order to receive rewards.                               VL L S AWL
6)  Gives me clear instructions about solving
    job-related problems.                                   VL L S AWL
7)  Offers me specific information on how
    I am evaluated.                                         VL L S AWL
8)  Provides me with helpful information about
    forthcoming changes affecting my work.                  VL L S AWL
9)  Provides me with helpful information about
    past changes affecting my work.                         VL L S AWL
10) Shares news with me about organizational
    achievements and organizational financial status.       VL L S AWL

EMPATHETIC LANGUAGE

11) Gives me praise for my good work.                       VL L S AWL
12) Shows me encouragement for my work efforts.             VL L S AWL
13) Shows concern about my job satisfaction.                VL L S AWL
14) Expresses his/her support for my professional
    development.                                            VL L S AWL
15) Asks me about my professional well-being.               VL L S AWL
16) Shows trust in me.                                      VL L S AWL

MEANING-MAKING LANGUAGE

17) Tells me stories about key events in
    the organization's past.                                VL L S AWL
18) Gives me useful information that I couldn't
    get through official channels.                          VL L S AWL
19) Tells me stories about people who are admired
    in my organization.                                     VL L S AWL
20) Tells me stories about people who have worked
    hard in this organization.                              VL L S AWL
21) Offers me advice on how to behave at the
    organization's social gatherings.                       VL L S AWL
22) Offers me advice about how to "fit in" with
    other members of this organization.                     VL L S AWL
23) Tells me stories about people who have been
    rewarded by this organization.                          VL L S AWL
24) Tells me stories about people who have left
    this organization.                                      VL L S AWL

C. Communication Satisfaction Scale

Please choose the response that is most appropriate for you.
Be sure to mark only one answer for each question.

Strongly Disagree   (SD)
Disagree             (D)
Undecided            (U)
Agree                (A)
Strongly Agree      (SA)

25) I trust my immediate supervisor.                       SD D U A SA
26) My immediate superior is honest with me.               SD D U A SA
27) My immediate superior listens to me                    SD D U A SA
28) I am free to disagree with my immediate superior.      SD D U A SA
29) I can tell my immediate superior when
    things are wrong.                                      SD D U A SA
30) My immediate superior praises me for a good job.       SD D U A SA
31) My immediate superior is friendly with his/her
    subordinates.                                          SD D U A SA
32) My immediate superior understands my job needs.        SD D U A SA
33) My relationship with my immediate superior
    is satisfying.                                         SD D U A SA

D. Communication Competence Scale

Please choose the response that is most appropriate for you.
Be sure to mark only one answer for each question.

Strongly Disagree   (SD)
Disagree             (D)
Undecided            (U)
Agree                (A)
Strongly Agree      (SA)

34) My immediate supervisor has a good command
    of the language.                                       SD D U A SA
35) My immediate supervisor is sensitive to others'
    needs of the moment.                                   SD D U A SA
36) My immediate supervisor typically gets right
    to the point.                                          SD D U A SA
37) My immediate supervisor pays attention to what
    other people say to him or her.                        SD D U A SA
38) My immediate supervisor can deal with others
    effectively.                                           SD D U A SA
39) My immediate supervisor is a good listener.            SD D U A SA
40) My immediate supervisor's writing is difficult
    to understand.                                         SD D U A SA
41) My immediate supervisor expresses his or her
    ideas clearly.                                         SD D U A SA
42) My immediate supervisor is difficult to understand
    when he or she speaks.                                 SD D U A SA
43) My immediate supervisor generally says the right
    thing at the right time.                               SD D U A SA
44) My immediate supervisor is easy to talk to.            SD D U A SA
45) My immediate supervisor usually responds
    to messages (memos, phone calls, reports, etc.)
    quickly.                                               SD D U A SA

Questions 34, 36, 38, 40, 41, 42, and 43 measure the
encoding dimension of communication, while Questions 35, 37,
39, 44, and 45 measure the decoding dimension of
communication.

E. Leader Effectiveness Scale

In the following questions, we would like you to describe
how your immediate supervisor leads. Think about his/her
behavior in general, rather than about specific situations.
Be sure to mark only one answer for each question.

Strongly Disagree   (SD)
Disagree             (D)
Undecided            (U)
Agree                (A)
Strongly Agree      (SA)

46) He or she effectively achieves the goals set
    by our company.                                        SD D U A SA
47) He or she effectively achieves his or her own
    goals as a supervisor.                                 SD D U A SA
48) He or she aids me in achieving my goals as
    an employee.                                           SD D U A SA

E Job Satisfaction Scale

For each question, please check the response you feel is
most appropriate. Be sure to mark only one answer for each
question.

49) Choose ONE statement which best tells how well you like
    your job:
    a) I hate it
    b) I dislike it
    c) I don't like it
    d) I am indifferent to it
    e) I like it
    f) I am enthusiastic about it
    g) I love it

50) Check one of the following to show HOW MUCH OF THE TIME
    you feel satisfied with your job:
    a) All the time
    b) Most of the time
    c) A good deal of the time
    d) About half of the time
    e) Occasionally
    f) Seldom
    g) Never

51) Check the ONE statement which best tells how you feel
    about changing your job:
    a) I would quit this job at once if I could get anything
       else to do
    b) I would take almost any other job in which I could
       earn as much as I am earning now
    c) I would like to change both my job and my occupation
    d) I would like to exchange my present job for another
       job in the same line of work
    e) I am not eager to change my job, but I would do so
       if I could get a better job
    f) I cannot think of any jobs for which I would exchange
       mine
    g) I would not exchange my job for any other

52) Check one of the following statements to show how you
    think you compare with other people:
    a) No one likes their work better than I like mine
    b) I like my work much better than most people like
       theirs
    c) I like my work better than most people like theirs
    d) I like my work about as well as most people like
       theirs
    e) I dislike my work more than most people dislike
       theirs
    f) I dislike my work much more than most people dislike
       theirs
    g) No one dislikes his work more than I dislike mine

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William C. Sharbrough is an associate professor of business administration and head of the Management & Marketing Division of the School of Business Administration at The Citadel. Susan A. Simmons is a professor of business administration in the School of Business Administration at The Citadel. David A. Cantrill (MBA. The Citadel. 2001) is a professional engineer working as an installation project manager for the U.S. Navy in Charleston, South Carolina. Correspondence concerning this article should be addressed to William C. Sharbrough, The Citadel, School of Business Administration, 171 Moultrie Street, Charleston, SC 29409: e-mail: william.sharbrough@citadel.edu.

Table 1. Survey Demographics

Category                          n       %

Men                              112      82
Women                             24      18
Age (years)
  16 to 25                         4     2.9
  26 to 35                        36    26.5
  36 to 45                        54    39.7
  46 to 55                        32    23.5
  56 to 65                         8     5.9
  No response                      2     1.5
Education
  High school                     19    14.0
  Trade school                    28    20.6
  Associate's degree              30    22.1
  Bachelor's degree               46    33.8
  Master's degree or higher       12     8.9
  No response                      1     0.7
Length of service (years)
  0 to 5                          60    44.1
  6 to 10                         28    20.6
  11 to 15                        16    11.8
  16 to 20                        13     9.6
  21 to 25                        11     8.1
  26 to 30                         4     2.9
  31 to 35                         2     1.5
  36 to ([greater
    than or equal
    to]40                          2     1.5
Job type
  Technical/engineering           80    58.8
  Direct supervisor               21    15.4
  Sales                           22    16.2
  Administrative                  13     9.6
Supervisory
  Yes                             42    30.9
  No                              93    68.4
  No response                      1     0.7

Table 2. Scale Reliabilities

Survey Type                      Reliability
                                 (Cronbach's
                                   [alpha])

Motivating language                 .9558
Communication satisfaction          .9270
Communication competence            .7806
Perceived leader effectiveness      .8472
Job satisfaction                    .7417

Table 3. Rotated Factor Analysis Results

                             Direction                 Meaning
                               Giving     Empathetic    Making

Direction     1. USFL_EXP       .828         .269        .206
giving        2. HLP_DIRS       .831         .289        .179
              3. EZ_INSTR       .787         .153        .261
              4. WK+ADVIC       .697         .384        .324
              5. GD_DEFNS       .444         .341        .415
              6. CLR_INST       .796         .222        .254
              7. EVAL_INFO      .428         .309        .418
              8. CHG_INFO       .531         .541        .227
             9. PAST_INFO       .648         .271        .398
             10. SHAR_NWS       .096         .443        .487
Empathetic   11. GVS PRAIS      .182         .844        .156
             12. ENCOURGE       .310         .864        .141
             13. Encourage      .371         .805        .147
             14. PROF_DEV       .528         .599        .255
             15. WELL_BEI       .496         .621        .268
             16. SHW_TRST       .486         .442        .047
Meaning      17. EVNT_STR       .263         .110        .792
making       18. USFL_INF       .441         .176        .538
             19. ADMR_STR       .167         .222        .881
             20. WRK_STRS       .249         .214        .829
             21. ORGSCADV       .138         .168        .708
             22. FITN_ADV       .232         .263        .724
             23. RWRD_STR       .223         .184        .853
             24. LEFT_STR       .168        -.135        .762

Table 4. Pearson's Correlations

                   Motivating     Communication   Communication
                    Language       Competence     Satisfaction

Motivating
  language              1
Communication         .592              1
  competence       (p < .001)
Communication         .633            .791              1
  satisfaction     (p < .001)      (p < .001)
Leadership            .672            .675            .287
  effectiveness    (p < .001)      (p < .001)      (p < .001)
Job                   .343            .252            .615
  satisfaction     (p < .001)      (p < .004)      (p < .001)

                   Leadership          Job
                  Effectiveness   Satisfaction

Motivating
  language
Communication
  competence
Communication
  satisfaction
Leadership              1
  effectiveness
Job                   .167              1
  satisfaction     (p < .057)