factors affecting decisions to volunteer in nongovernmental ...

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10.1177/0013916503256642 ENVIRONMENT Martinez & McMullin AND / FBEHA ACTORS VIORAFFECTING / January 2004 DECISIONS TO V OLUNTEER

FACTORS AFFECTING DECISIONS TO VOLUNTEER IN NONGOVERNMENTAL ORGANIZATIONS TERESA A. MARTINEZ holds an MS from the Virginia Tech Department of Fisheries and Wildlife Sciences and is currently employed with the Appalachian Trail Conference in Blacksburg, Virginia. Her thesis focused on volunteerism in natural resource organizations. STEVE L. MCMULLIN is an associate professor in the Department of Fisheries and Wildlife Sciences at Virginia Tech in Blacksburg, Virginia. His research focuses on management effectiveness of state agencies and planning for conservation of natural resources.

ABSTRACT: In 1997, we surveyed members of the Appalachian Trail Conference to identify characteristics and assess motivations of the active and nonactive members in this organization. We investigated the effects of social networks, competing commitments, lifestyle changes, personal growth, and belief of the efficacy of one’s actions on decisions to become and remain active members. We found the determining factors in decisions regarding volunteer activity were competing commitments and efficacy. Active members indicated that the efficacy of their actions was most important in their decision to participate, whereas nonactive members cited the importance of competing commitments in their decision not to participate. Recruitment and retention of volunteers may be aided by increasing the awareness of volunteer programs, ensuring that programs provide results of which individuals are proud, requesting the participation of individuals on both local and national levels, and recognizing volunteers for their contributions. Keywords:

volunteerism; volunteer; nongovernmental organizations; efficacy

Volunteerism has a long tradition in natural resource and conservation efforts. From the contributions of national conservation organizations such as the National Audubon Society and the Sierra Club to local community groups fighting for the protection of resources in their own backyards, nongovernmental organizations (NGOs) and their members have played ENVIRONMENT AND BEHAVIOR, Vol. 36 No. 1, January 2004 112-126 DOI: 10.1177/0013916503256642 © 2004 Sage Publications

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major roles in natural resource management (Hendee & Pitstick, 1994). The first natural resource NGOs, formed in the late 19th century, profoundly influenced forest policy, wildlife habitat protection, conservation, preservation, and recreational policy issues (Dunlap & Mertig, 1991; Hendee & Pitstick, 1994). These first conservation-related NGOs and their members lobbied for the protection of public lands, for the conservation of wildlife species, and for the creation of the National Park Service and the United States Forest Service. Volunteers, or active members, are responsible for many of the programs and accomplishments associated with NGOs. Because of their broad appeal and effectiveness, NGOs and their volunteer programs have experienced nearly exponential growth both in the number of organizations and in membership (Snow, 1992). This proliferation of organizations resulted in redundancy and competition for members and, more importantly, for active members. Recruitment of volunteer resources requires the three most limited resources in NGOs (staff, time, and funding); therefore, identifying effective and efficient methods for recruitment and retention is imperative for continued success. In a study of political participation, Verba, Schlozman, & Brady (1995) noted that, to understand why some people become active volunteers, it is important to ask why people do not participate. They identified three reasons for nonparticipation: (a) individuals lacked the capacity to volunteer, (b) individuals lacked motivation, or (c) individuals had not been asked. This implied that both access to resources and capacity to take part, in conjunction with motivation to take part, are necessary for members to become active. It also suggested that requests for participation act as catalysts for participation among those with the resources and desire to become active (Verba et al., 1995). Manzo and Weinstein (1987) found that volunteers and nonactive members of the Sierra Club differed in their experience with environmental harm, their likelihood of belonging to and volunteering in other NGOs, their social ties to the Sierra Club, and their belief in the political efficacy of their actions. They found that social networks influenced a person’s decision to become and remain active. Volunteers were more likely than nonactive members to have known other members before they joined the Sierra Club, to have made friends within the group, and to have asked others to join the organization. Additionally, they found that most volunteers had been affected directly or knew people who had been affected by environmental issues thereby suggesting that volunteers came to the Sierra Club with a personal commitment to environmental efforts. They found that personal values, age, sex, social status, and employment were not predictors of volunteer involvement, but

114 ENVIRONMENT AND BEHAVIOR / January 2004

those factors helped to predict the likelihood of an individual joining an organization. Van Til (1988) described five characteristics of volunteerism: (a) People volunteer to satisfy personal and social goals and needs, (b) the individuals who volunteer typically do so after carefully weighing alternatives, (c) the realm of voluntary action is complex and multifaceted in which different organizational tasks appeal to different motivational forces, (d) concern for others, although not purely altruistic, remains an important force among volunteers, and (e) the motivation to give is shaped by broader social realities (e.g., to leave the world a better place). Successful volunteer programs engage volunteers with tasks that are meaningful and contribute to the effectiveness and success of the program. Babchuck and Booth (1969) identified the importance of structure and function of the association itself, as well as family status, age, work, family stage, and life stage, to the incidence of membership in voluntary programs and to the pattern of affiliation and membership turnover. Through a longitudinal analysis of voluntary associations, they identified the following characteristics of membership: (a) Memberships stay relatively stable over time, (b) most individuals will add and drop affiliations over time but maintain at least one continuous membership, and (c) the structure and function of association influence the occurrence of affiliation changes. They found that membership tenure is often greater and turnover lower in groups that have multiple objectives, large memberships, and long histories. Smith (1994) suggested that lifestyle characteristics might dominate a person’s choice to volunteer. He reported that parents with young children tended to volunteer in activities that benefited their children and that employment status may affect decisions to volunteer. Heidrich (1990) assessed the preference of individuals for four types of voluntary roles within organizations: direct service, leadership, general support, and members-at-large. She suggested that volunteer roles may appeal to people with certain lifestyles based on (a) one’s position in a job; (b) whether one is employed full-time, part-time, is retired, or is a homemaker; (c) age; and (d) the expectations and associated responsibilities of the role(s) one would fill. Furthermore, Manzo & Weinstein (1987) found that membership policies and organization structure influenced who became members and what proportion of them became active. Numerous studies have shown that volunteers tend to be middle-aged, with middle to high incomes, male, highly educated, and employed full-time (Heidrich, 1990; Manzo & Weinstein, 1987; Pearce, 1993; Smith, 1994). Despite a great deal of research conducted on the type of person who volunteers, it is difficult to predict who will participate in volunteer activities.


However, identifying contrasting characteristics of volunteers and nonactive members, as well as how active members justify their involvement, may suggest where NGOs can best apply resources in their recruiting efforts. The objective of this study was to better understand the motivations and characteristics of individuals who participate in volunteer activities in the Appalachian Trail Conference (ATC) in comparison to those who remain inactive. The three major questions addressed in this study were: (a) What are the motivations and characteristics of active members in NGOs, (b) are these motivations and characteristics different from nonactive members in these organizations, and (c) can this knowledge be used to better recruit and retain active members? Participants. We addressed our three research questions by surveying the membership of the ATC. The ATC is the national, nonprofit organization that oversees the management and protection of the Appalachian National Scenic Trail. Formed in 1925, the ATC has a long tradition of volunteerism. Volunteers constructed and maintained the trail for more than 45 years before the federal government became officially involved following the passage of the National Trail System Act of 1968 (Brewster, 1991). In addition to its general membership, ATC represents the interests of 31 local volunteer-maintenance clubs from Maine to Georgia. It is governed by a 25-member volunteer Board of Managers and has approximately 60 paid professional staff members. In 1996, the ATC reported a membership base of 24,000 members including 4,500 active members. In 1994, ATC’s volunteers contributed more than 147,000 hours of volunteer labor along the Appalachian Trail (ATC, 1994). For the purpose of this research, we defined an active member as one who donated time to the ATC and a nonactive member as a member who only paid membership fees.

STUDY DESIGN

Survey methodology. We randomly sampled 721 active and 900 nonactive members from ATC’s mailing lists. We sent questionnaires to each member of our sample following a modification of the total design method (Dillman, 1978). This included the initial mailing, a follow-up postcard, and two follow-up mailings to nonrespondents. Materials. We used one questionnaire to survey both active and nonactive members. We designed the questionnaire to address the motivations involved


in decisions regarding volunteer participation and developed multiple-item question lists to assess these motivations. We based the multiple-item list on an extensive literature review that identified six variables associated with volunteer participation. These variables included efficacy, personal motivation, request, social networks, lifestyle changes, and competing commitments. We assessed the importance of individual items and these factors in decisions to volunteer using an importance scale that ranged from very important and somewhat important to not very important, not at all important, and not applicable. Statistical analysis. Statistical tests were conducted at the α = .05 significance level. We compared responses of active and nonactive members to categorical questions using chi-squared (χ2) tests. To compare means of openended numerical responses, we used the standard t test for normally distributed data and the Wilcoxon rank-sum test for nonnormally distributed data. We collapsed a 24-item list of reasons why an individual might volunteer into indexes using factor analysis with varimax rotation and maximum likelihood extraction (Cronbach, 1951; Kim & Mueller, 1978; Manly, 1986). We only included items with a factor loading ≥ 0.5 and only considered indexes with eigenvalues ≥ 1.0 and Cronbach’s alpha ≥ .50 (Kim & Mueller, 1978; Nunnally, 1967). One-way analysis of variance determined which of the factors and two-way interactions differed significantly for volunteers and nonactive members. We then used the significant factors and interactions to predict the likelihood of participation using logistic regression (Kessler et al., 1994; Teachman, 1988). We conducted brief telephone interviews with 40 randomly selected nonrespondents to assess nonresponse bias because our response rates fell below 60% (Dolsen & Machlis, 1991).

RESULTS

Nonresponse bias. Completed questionnaires were received from 476 nonactive members (52%) and 392 active members (54%). We found no differences between nonrespondents and respondents. The high occurrence of outdated information suggested that nonresponses might have been largely because of outdated mailing addresses rather than sample population differences. Demographics. Active members belonged to the ATC almost twice as long as nonactive members and a greater percentage of active members


belonged to local trail clubs (see Table 1). Nearly one third of active members held higher order memberships (life-individual and trail maintainer memberships) compared to less than 4% of nonactive members. Seventy percent of nonactive members held individual and family memberships compared to 51% of active members. On average, both active and nonactive members belonged to 2 additional conservation organizations. Although active members donated significantly more volunteer hours during the previous 12 months to those organizations than nonactive members in a statistical sense, the difference did not appear to be significant in a practical sense. Additionally, active members had volunteered for an average of 6.6 years with the ATC, held memberships for 2.3 years before becoming active, and donated 60 hours during the previous 12 months to the ATC. Sixty-six percent of active members and 6% of nonactive members received requests to participate in volunteer activities (See Table 1). Both active and nonactive ATC members tended to be employed full-time or were retired, highly educated, and between the ages of 36 to 55. Both groups tended to be married and male; however, the percentage of people who volunteered did not differ significantly by gender (see Table 1). Whereas most active and nonactive members had middle-level incomes, a greater percentage of nonactive members fell into higher income ranges (> $60,000 per year). Factors that determine willingness to volunteer. The factor analysis produced five factors that help to explain decisions about participation in volunteer activities (Table 2). We labeled the first and most influential factor Efficacy. Items loading on the Efficacy factor included the ability of the individual to help in protecting the Appalachian Trail and to contribute to management of natural resources. The second factor, Competing Commitments, included items related to demands that volunteer activities would have on an individual’s time, finances, family, and job. The third factor, Social Networks, included items that dealt with knowing or meeting others involved in volunteer activities. The fourth factor, Lifestyle Changes, included changes in marital status or residence. The fifth factor, Personal Growth, included gaining experience for future employment and opportunities to grow as an individual. Four of the five factors differed significantly between active members (volunteers) and nonactive members (Table 3). Active members placed greater importance on Efficacy and Social Networks than nonactive members. Nonactive members placed greater importance on Competing Commitments than active members. Neither group considered Personal Growth to be very important, but it was less important to nonactive members than to active

118 ENVIRONMENT AND BEHAVIOR / January 2004 TABLE 1 Comparisons of Organizational and Demographic Characteristics of Active and Nonactive Members Belonging to the Appalachian Trail Conference

Active Organizational characteristics Membership length (years) Total NGO memberships Hours donated to other NGO’s in 1 year (hours) Membership type: Individual Life-individual Trail maintainer Local chapter membership Percentage of individuals receiving requests Demographic characteristics Age (years): Under 25 26-35 36-45 46-55 56-65 66-75 76 or older Gender: Men Women Income: Under $15,000 $15,001-$30,000 $30,001-$45,000 $45,001-$60,000 $60,001-$75,000 $75,001-$90,000 $90,001-$105,000 Above $ 105,001 Employed (full-time) Education (greater than high school)

Nonactive

10.2 2.1 91

5.7* 2.8 86**

50.7% 15.6% 15.0% 56.0% 66%

70.1%* 3.3% 0.5% 11.0%* 6%*

5.0% 9.5% 23.0% 25.5% 15.0% 13.5% 5.5%

5.0% 12.0% 27.0% 22.0% 16.5% 12.0% 3.8%

44.9% 51.0%

55.1% 49.0%

8.2% 10.0% 18.0% 14.5% 16.5% 6.0% 4.0% 9.0% 57.9% 89.1%

5.5% 11.0% 13.0%* 15.5% 10.5%* 11.0%* 8.0% 15.0% 61.7% 93.0%

NOTE: All percentages reflect responses to categorical questions. Significance levels (two-tailed) refer to t tests (continuous data) or chi-squared tests of association (percentages). NGO = nongovernmental organization. *p < .01. **p < .05.

members. Neither group considered Lifestyle Changes to be important. We observed significant two-way interactions between Competing Commitments and Lifestyle Changes ( p = .043), Competing Commitments and

Martinez & McMullin / FACTORS AFFECTING DECISIONS TO VOLUNTEER 119 TABLE 2 Results of Factor Analysis on 24 Items Affecting Decisions of Appalachian Trail Conference Members to Volunteer (Active) or Not (Nonactive); Factor Loadings for Each Item and Cronbach Alpha for Factors Are Also Presented

Factor Loading Factor 1 (Efficacy) I am able to help protect the Appalachian Trail I am able to ensure the existence of the trail for future generations I can ensure the future of the trail for my enjoyment To contribute to the management of natural resources Factor 2 (Competing Commitments) The demand activities would place on my time The demand activities would place on my financial resources The effect activities would have on my family commitments The effect activities would have on my employment commitments Factor 3 (Social Networks) I can meet others with similar interests I know others involved in activities I knew I could participate in volunteer activities Factor 4 (Lifestyle Changes) I changed my marital status I changed my place of residence Factor 5 (Personal Growth) I will gain experience for future employment I am able to grow as an individual

Mean Active

Mean Cronbach Nonactive Alpha

1.94

1.46

.81

2.05

1.49

.90

2.22

1.69

.75

1.77

1.42

.64

1.69 0.57

1.21 0.95

.70

1.24

1.66

.54

–0.03

0.20

.64

0.58

1.07

.57

0.49 0.31 0.76 –0.24

0.84 –0.04 0.39 –0.47

0.35 –0.33 –0.45 –0.23 –0.53

–0.05 –0.39 –0.51 –0.27 –0.77

–1.21 0.19

–1.49 –0.06

.66 .66 .51 .99 .59 .54 .56

.87

.69

.71

.74 .52

NOTE: Mean scores represent averages of importance scale items ranging from very important (3), somewhat important (1), not applicable (0), not very important (–1), and not at all important (–3).

Personal Growth (p = .002), and Social Networks and Lifestyle Changes (p = .027). Logistic regression of the binary variable, volunteerism (active vs. nonactive), versus all significant factors and two-way interactions plus a control for income yielded significant predictive capacity only for Efficacy, Competing Commitments, and income (Table 4). Efficacy was positively related

120 1.94 0.57 0.31 –0.33 –0.53 1.34 1.01 –0.66 –0.63 0.12 –0.01 –0.07 –0.07 0.32 0.52

Active Member Mean 1.46 0.95 –0.04 –0.39 –0.77 1.52 0.58 –0.34 –0.66 0.17 –0.33 –0.70 0.24 0.67 0.69

Nonactive Member Mean 24.049 (674) 13.585 (681) 11.823 (675) 0.438 (658) 4.574 (665) 0.578 (668) 3.826 (668) 2.632 (657) 0.013 (657) 0.065 (674) 4.118 (658) 9.686 (661) 4.940 (658) 3.527 (661) 0.802 (658)

F Statistic (Degrees of Freedom)

p < .001 p < .001 p = .001 p = .508 p = .033 p = .447 p = .051 p = .105 p = .910 p = .799 p = .043 p = .002 p = .027 p = .061 p = .371

Level of Significance

NOTE: Mean scores represent the averages of importance scale items and ranged from very important (3), somewhat important (1), not applicable (0), not very important (–1), and not at all important (–3).

Efficacy Competing Commitments Social Networks Lifestyle Changes Personal Growth Efficacy × Competing Commitments Efficacy × Social Networks Efficacy × Lifestyle Changes Efficacy × Personal Growth Competing Commitments × Social Networks Competing Commitments × Lifestyle Changes Competing Commitments × Personal Growth Social Networks × Lifestyle Changes Social Networks × Personal Growth Lifestyle Changes × Personal Growth

Factor

TABLE 3 Results of Analysis of Variance Comparing Mean Scores of Active and Nonactive Members of the Appalachian Trail Conference for Each of Five Factors and All Two-Way Interactions.

Martinez & McMullin / FACTORS AFFECTING DECISIONS TO VOLUNTEER 121 TABLE 4 Results of Logistic Regression of Active and Nonactive Members of the Appalachian Trail Conference Versus Factors and Interactions That Were Significant at the p .05 Level Plus Income

Regression Coefficient (b)

Factor Efficacy Competing Commitments Social Networks Personal Growth Competing Commitments × Lifestyle Changes Competing Commitments × Personal Growth Social networks × Lifestyle Changes Income

Standard Error

Level of Significance

0.401 –0.249 0.077 –0.059

0.085 0.079 0.082 0.077

p < .001 p = .002 p = .347 p = .444

0.039

0.054

p = .475

0.013 –0.056 –0.100

0.047 0.057 0.044

p = 775 p = .333 p = .023

to the likelihood of volunteering, whereas Competing Commitments and income were negatively related to volunteer activity. The model predicted the likelihood of members being nonactive (83% correct) more accurately than the likelihood of members being active (34% correct). Overall, the model explained very little regarding complex decisions to volunteer (Cox & Snell R2 = .09).

DISCUSSION

We designed this study to identify differences between active and nonactive members that could be used to determine the likelihood of participation in volunteer activities. We found that active and nonactive members of the ATC had similar demographic characteristics. Both groups tended to be middle-aged, employed full-time, and had similar gender compositions. Unlike Pearce (1993), who reported that those with higher incomes were more likely to volunteer, we found that nonactive members tended to have higher incomes. However, the two groups differed significantly with regard to four out of five factors affecting their decisions to participate in volunteer activities. Efficacy and Competing Commitments warrant further discussion. Efficacy weighed more heavily in decisions to volunteer than any other factor for both active and nonactive members. Although Competing Commitments also was moderately important to both groups, it had greater influence


on the decisions of nonactive members. On average, Efficacy was more than three times more important to active members than Competing Commitments (mean of 1.94 vs. 0.57, respectively). The difference for nonactive members was much less pronounced (1.46 vs. 0.95). These findings suggest that, although both groups felt that making a difference was important to their decisions to volunteer, nonactive members were more likely to let concerns for competing commitments prevent them from volunteering. In particular, nonactive members placed greater importance than active members on the effect of activities on family commitments (nonactive mean = 1.07 vs. 0.58 for active). Active and nonactive members of the ATC had similar profiles and, by inference, similar competing commitments. However, the previous experience of active members who knew how donating their time affected the organization and the trail resource overshadowed the importance of competing commitments in their decisions. Active members witnessed the effects of their actions, witnessed the success of the organization, and achieved a level of personal accomplishment. Active members believed that they could make a difference. For nonactive members, the potential benefits and outcomes may have been important but unknown. Nonactive members may have had the motivation to volunteer, but, lacking the confidence of active members that their participation could make a difference, competing commitments became the issue dictating their lack of participation. The ATC may be able to increase its volunteer resources through recruitment invitations that reinforce the efficacy of individuals’s actions. Manzo and Weinstein (1987) suggested that when individuals join an organization they are a “rather homogeneous group with similar, sincere concerns about the environment, but that knowing other members or meeting others during outings leads to involvement, to responsibility, and to commitment, and to belief in the efficacy of action” (p. 690). This may help explain why active and nonactive members differed in perceptions of efficacy and capacity that seem to be crucial in decisions regarding volunteer activity. Manzo and Weinstein also found (similar to our findings) that active and nonactive members belonging to the Sierra Club were similar in age, sex, and employment status. They suggested two possible explanations for active involvement of members: differences between the groups existing prior to their involvement and differences in experiences after becoming members of the Sierra Club. They settled on the second explanation: What influences behavioral commitment or volunteering occurs after individuals join and become active in organizations. Furthermore, Manzo and Weinstein suggested that “the feeling of accomplishment experienced when projects are successfully carried out offers further reinforcement for taking an active role.


Nonactive members, in contrast, are bystanders who gain neither friendships nor a sense of accomplishment” (p. 689). This suggests that volunteering with an organization is the factor that provides opportunities to build stronger ties and commitment to the organization. Volunteer activities in the Sierra Club and the ATC differ significantly. The Sierra Club is primarily a political action organization, whereas the volunteers in the ATC focus primarily on maintenance of the trail. Despite the differences, ATC volunteers appeared to share many of the same motivations as Sierra Club volunteers. Active members of the ATC more frequently belonged to local trail clubs and had belonged to the ATC for longer than nonactive members. Thus, ATC volunteers had more opportunities to experience the sense of accomplishment from successful completion of trail maintenance projects. The nature of activities. Heshka (1983) suggested that focusing only on an individual’s motivations in volunteer decisions provides only a partial explanation of the situation. He stated that “it neglects the fact that in certain settings or situations there is considerable uniformity of behavior across different persons, indicating that an explanation of the behavior should be sought in the characteristics of the situation, rather than the person” (p. 138). In the case of the ATC, situational and real physical barriers exist. Sixty-one nonactive members commented that the distance to activities was the main reason for not being active. Many others cited age and physical limitations that affected their ability to participate. Because of the physical nature of the activities associated with the trail management organization, physical barriers tied to age are real concerns for nonactive members. However, this further highlights that, for nonactive members, believing that they could not effectively contribute to the organization’s activities may be the deterrent to their participation. Thus, nonactive members may have wanted to participate but felt they could not effectively do so. We hypothesize that the physical nature of volunteer activities in the ATC may also explain, at least in part, why nonactive members had higher incomes than active members. Members with higher incomes have more freedom to contribute financially to the organization than those with lower incomes. Members with lower incomes are more likely to make contributions in the form of sweat equity. The role of social networks. Manzo and Weinstein (1987) found that behavioral commitment developed through participation in an organization, specifically through the nature of activities within an organization. The fewer the social networks available to participants, the less likely are members to develop commitment to the organization. Although we found a significant


difference between active and nonactive members with regard to the Social Networks factor, it did not contribute significantly to the predictive model. However, the difference between active and nonactive members in the frequency of requests and the indication that active members agreed to volunteer because someone made a request suggest that social networks may play a more functional role in decisions to volunteer. Pearce (1993) reported that most volunteers are recruited by friends, relatives, or acquaintances, and those with more extensive personal contacts are more likely to be recruited. Verba et al. (1995) found that requests served as the catalysts to participation. Requests to nonactive members may help to develop a personal connection to the organization, and, although this may not motivate the individual, it does provide an opportunity to ask important questions and receive important information about volunteer activities (Pearce, 1993). This information may be the key to removing perceived barriers to participation, especially perceived lack of efficacy. The importance of requests, whether they are personal interactions or broad general requests, is that they may appeal to an individual’s desire to feel needed enough to override the potential costs to their time, other resources, or perceptions of ability. Interaction of factors. Although the ANOVA yielded three significant two-way interactions, none of the three contributed significant predictive power to the logistic regression model. Mean interaction terms for active members were near zero for each of the three significant interactions. For nonactive members, mean interaction terms were slightly negative for Competing Commitments × Lifestyle Changes (–0.33), more strongly negative for Competing Commitments × Personal Growth (–0.70), and slightly positive for Social Networks × Lifestyle Changes (0.24; an artifact of multiplying two negative numbers). Although the Competing Commitments × Personal Growth interaction term had no effect in the regression model, it was the only one of the three significant terms (from the ANOVA) that made intuitive sense. Members who perceived competing commitments to be a substantial barrier to participation and who perceived little opportunity for personal growth might be expected to volunteer less often than those who perceived either condition separately. Statistical analysis did not support that intuitive relationship. Conclusions. In this study, we addressed three main questions: (a) What are the motivations and characteristics of active members in NGOs, (b) are these motivations and characteristics different from nonactive members in these organizations, and (c) can this knowledge be used to better recruit and retain active members? The answer to the first major question addressed in


this study was that active members of the ATC were motivated primarily by a perception of efficacy of their participation. In answer to the second question, we found that active and nonactive members were not substantially different demographically or in competing commitments for their time. However, efficacy far outweighed competing commitments in active members’ decisions to volunteer, whereas competing commitments had greater relative importance to nonactive members. The logistic regression analysis provides the answer to our third question: Fueling the belief in efficacy of one’s actions and providing adequate information about commitments necessary to volunteer is crucial to successful volunteer recruitment. Although requests to volunteer did not significantly increase the likelihood of volunteering, requests may provide the vehicle for overcoming concerns for competing commitments through appeals to the efficacy of one’s actions.

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