Leadership and Satisfaction in Tennis: Examination of Congruence, Gender, and Ability

By Riemer, Harold A.; Toon, Kathy | Research Quarterly for Exercise and Sport, September 2001 | Go to article overview

Leadership and Satisfaction in Tennis: Examination of Congruence, Gender, and Ability


Riemer, Harold A., Toon, Kathy, Research Quarterly for Exercise and Sport


The study investigated: (a) the congruency hypothesis, and (b) the member characteristics hypotheses relating to ability and gender, of Chelladurai's (1978) Multidimensional Model of Leadership. One hundred forty-eight tennis players (77 women) competing at the NCAA Division land II Tennis Championship level participated in the study. Results indicated athlete satisfaction (Athlete Satisfaction Scale; Riemer & Chelladurai, 1998) was not dependent on the congruence between preferred and perceived leadership behavior. Other results indicated that an Athlete's level of ability did affect preferences for leadership behavior. Further, while athlete gender was responsible for some variance in preferences for autocratic behavior and positive feedback behavior, the gender of the athlete's coach had a significant effect on the athlete's preferences for social support behavior.

Key words: social support, regression

Barrow (1977) defined leadership as "the behavioral process of influencing individuals and groups toward set goals" (p. 232). In athletics and sport, leadership has been assigned great value. Coaches are often elevated to hero status and credited for the role they played in a victory(s) (e.g., George, 1993). In stark contrast to the importance assigned to athletic leadership have been the efforts to understand it (Chelladurai & Riemer, 1997; Riemer & Chelladurai, 1995). One of the few theoretical frameworks advanced for studying leadership in the athletic context has been Chelladurai's Multidimensional Model of Leadership (MML; Chelladurai, 1993). Building on the works of Fiedler (1967), 1-louse (1971), Osborne and Hunt (1975), and Yukl (1971), the MML placed equal importance on three critical dimensions of leadership (e.g., the leader, the subordinate, the context). The latest version of the model (Chelladurai, 1993) is presented in Figure 1.

The focus of this paper is on two primary propositions made by the model. First, the MML hypothesizes that team member satisfaction (Box 7) is a function of the congruence between the leadership behavior preferred by the members (Box 6) and the actual leadership behavior exhibited (Box 5). Second, member preferences for specific leader behaviors are hypothesized to be a function of member difference factors (e.g., gender, ability; Box 3 to Box 6).

Congruence Hypothesis

A central thesis of the MML is that congruence between preferred and actual leadership behavior enhances member satisfaction. Previous findings related to this central thesis have been inconsistent (e.g., Chelladurai, 1984; Home & Carron, 1985; Schliesman, 1987). While some indicated a significant curvilinear relationship between discrepancy scores of leadership behavior and satisfaction with leadership (i.e., satisfaction was highest when discrepancy was zero), others reported only significant linear relationships (i.e., satisfaction was greatest when perceptions were greater than preferences), or no relationship at all.

Chelladurai (1990; 1993; Riemer & Chelladurai, 1995) suggested the inconsistencies in the direction and pattern of the reported significant relationships may stem from the problems associated with the use of difference or discrepancy scores. Although intuitively appealing, many scholars have called attention to the problems associated with difference-discrepancy scores (e.g., Berger-Gross & Kraut, 1984; Peter, Churchill, & Brown, 1993). A promising solution is the application of regression techniques (Berger-Gross, 1982; Berger-Gross & Kraut, 1984). Essentially, the base scores (i.e., preferences and perceptions) are entered first, followed by their interaction term. The congruence hypothesis is accepted if the interaction significantly increases the amount of variance explained; relative size of the interaction to other equation terms is not important. The interaction is then interpreted by plotting the resulting regression equations. …

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