Motivations

The many parallels between volunteering and CS participation can advance understanding of CSV motivations (Geoghegan et al. 2016; West and Pateman 2016). This section explores Katz’s (1960) seminal functional approach to understanding volunteerism, the subsequent creation of the Volunteer Functions Inventory (VFI), and the progression of motives research alongside relevant environmental volunteerism literature. Finally, I present two models specific to understanding CSVs, as they are the foundation on which this study’s conceptual framework positions itself.

The functional approach to understanding volunteering was created initially by Katz (1960) and was later applied by Clary and Snyder (1999). The Approach contains three fundamental assumptions. First, it is a motivational perspective that directs inquiry into the personal and social processes that initiate, direct, and sustain action (Katz 1960). Second, volunteers perform the same actions because of various psychological functions (e.g., different people engage in the same volunteer activity but do so to fulfill different motives). Third, it suggests that important psychological events, such as embarking on a course of volunteer activities and then maintaining those activities over extended periods of time, depend on matching the motivational concerns of individuals with situations—i.e., volunteer opportunities—that can satisfy those concerns (Clary and Snyder 1999).

Research employing the functional approach discovered that volunteers may be motivated by several factors simultaneously, and that motives may intersect and interact in multifaceted, complex manners (Cnaan and Goldberg–Glen 1991; Clary and Snyder 1999; Yeung 2004; McDougle, Greenspan, and Handy 2011). That is to say, people may volunteer for the same activity for different reasons, and if they feel the activity does not satisfy their motivation (Shirk et al. 2012), they will cease participation (Bruyere and Rappe 2007). Using the functional approach, Clary et al. (1998) developed the Volunteer Functions Inventory (VFI) with six functions:

1. Values—acts to express an important value, such as helping the environment.
2. Understanding—desires to learn more about a subject or gain skills.
3. Enhancement—aims to improve psychologically through volunteering.
4. Career—wants to network or gain experience.
5. Social—participates to create and strengthen social relationships.
6. Protective—uses volunteer activities to reduce negative self-image.

This approach was applied (as well as modified) in several environmental volunteerism studies (Ryan et al. 2001; Martinez and McMullin 2004; Yeung 2004). As such, the VFI is “the most widely used approach for studying and understanding motivations for volunteerism” (Bruyere and Rappe 2007, p. 506). However, Ryan, Kaplan, and Grese (2001) and Bruyere and Rappe (2007) uncovered two additional motivators that were not addressed by the inventory: These are “user” (recreationists who use the spaces and feel the need to help maintain them through volunteerism) and “get outside.” Moreover, Bruyere and Rappe (2007) found the Protective and Enhancement Functions to be only “marginally represented” in their study. As such, a downfall of the VFI is that the functions are not exhaustive and subject to modification.

Because of the apparently evolving nature of the VFI and the fact that motives are often context-specific (Bennett et al. 2018), scholarship on volunteer motives tends to employ a broader approach with the motivational framework of intrinsic and extrinsic motivations, as developed by Finkelstien (2009) (for examples, see Cecere, Mancinelli, and Mazzanti 2014; Moskell, Broussard, and Ferenz 2010; Martinez and McMullin 2004; Measham and Barnett 2008). Intrinsic motivations manifest as action from an underlying ethic, value, or belief that forms one’s identity (Chan et al. 2016; Fernandes and Guiomar 2016; Leopold 1949; Robinson et al. 2012; Maslow 1943). Extrinsic motivators ensure a reward for action; these may be career-oriented, economic, social, or even legal (Bennett et al. 2018).

Intrinsic motivations are often the principal motivator driving CSVs, and these types of motivations are more durable and enduring than extrinsic motivators (Bennett et al. 2018, p. 603; Campbell and Smith 2005). Accordingly, previous studies conclude the intrinsic motivator “helping the environment” is often most frequently selected, and career advancement, which is an extrinsic motivator, to be the least selected (Grese et al. 2001; Bruyere and Rappe 2007; Alender 2016; Cetas and Yasué 2017). In CS, the principal motive varies. CSVs involved in Wright et al.’s (2015) bird atlas project named “contributing to science” as the top motivator, while Crall et al.’s (2013) project investigating invasive species and Domroese and Johnson’s (2017) bee-monitoring assessment found that learning was the strongest motivator for participation. By comparison, Alender (2016) conducted a study of water quality monitors; the top motivator was helping the environment, with scientific contribution ranking fourth. Additional studies report common reasons for participation to be an interest in (1) the project’s topic, (2) science in general, and (3) learning something new (Hobbs and White 2012; de Vries, Land-Zandstra, and Smeets 2019; Jennet et al. 2016; Alender 2016).

Research specifically on CSVs engaged with the two principal motives of intrinsic and extrinsic and found that (1) both motivational factors are important at several stages (West and Pateman 2016; Land-Zandstra et al. 2016; Mueller and Tippins 2011; Rotman et al. 2012), (2) multiple motives can drive participation simultaneously (Larson et al. 2020; Asah and Blahna 2012; Katz 1960), and (3) motivation(s) to participate may change over time (Ryan et al. 2001; Jacobs 2018). Two process models exist to explain CSV motivations and stages of participation.

In the first model, Rotman et al. (2012) applied the four motives for community involvement, developed by Batson, Ahmad, and Tsang (2002), wherein a person is motivated to act owing to one or more of the following: egoism (to increase one’s welfare), altruism (to increase the welfare of one or more individuals), collectivism (to increase the welfare of a group), and principlism (to uphold a moral principle or values).

Rotman et al. (2012) used the four motives to understand CSV involvement throughout a project’s timeframe with feedback from scientists. They determined that egoism—in the form of personal interest and a “thirst for knowledge”—was the most important motivational factor at the initial stages of participation. Without recognition from the scientists or program staff, CSVs were likely to end their participation (Carballo-Cárdenas and Tobi 2016). Alternatively, if recognition and additional training through program support were provided to the CSV, participation would likely continue owing to the extrinsic (reward-based) nature of the motivation. After sustained involvement, the motivations for participation shift from egoism to collectivism and altruism as a result of the inclusion in community involvement and advocacy, e.g., how the CS data are used and disseminated (Ward-Fear et al. 2020). However, if this level of involvement in the community is not reached, participation is likely to cease (Rotman et al. 2012, p. 224). Hence, CS project participation is an iterative process and should be assessed via inclusion of the (in)actions of the organization.

Accordingly, responding to the various motivational factors at different points of participation can help keep volunteers engaged (Land-Zandstra et al. 2016), and recognition/rewards may be important to certain CSVs for sustained participation (Alender 2016). It is important to note that some CS projects have a specific start and end date, with perhaps one or more time periods wherein data are collected. Alternatively, other projects, like the water quality monitoring program in the present study, are ongoing and aim to grow data-collection efforts by attracting new and sustaining current volunteers.

In the second explanatory model, West and Pateman (2016) detail the process with key factors of participation over time in three distinct stages: decision to participate, initial participation in the project, and sustained participation. West and Pateman (2016) expanded and modified Penner’s (2002) basic model on volunteering with two key variables—dispositional and organizational—to fit CS participation. Dispositional variables that influence participation are the attributes of an individual, such as motivation, personal circumstances, and sociodemographic characteristics. Organizational variables are the attributes of the organization leading the project, i.e., the program’s organization and communication (Geoghegan et al. 2016) and/or the program’s reputation, values, and practices (Penner 2002). Accordingly, the organizational variable plays a role in the decisions to participate, to initiate involvement/participation, and to sustain participation.

Applications of such process-modeling and changing motivations over time are few (Geoghegan et al. 2016). Ryan et al. (2001) found that new volunteers tend to be motivated by wanting to help the environment (principlism) and to learn new things (egoism), but that social factors (collectivism/altruism and community involvement) were more important for long-term retention. This is similar to Rotman et al.’s (2012) understanding of the initial motivator to be egoism (wanting to learn to improve the self) and then a shift to collectivism or altruism (wanting to continue to participate for the welfare of the group or others). Likewise, Fisher et al. (2011) and Measham and Barnett (2008) found that social contact and personal ties were key for participation. Volunteers are also more likely to continue to participate if their motivations are fulfilled and they are satisfied with their role (Welty Peachey et al. 2014; Wu, Li and Khoo 2016; de Vries et al. 2019; Haywood 2016). To that end, CSV motivations tend to play a role in maintaining a strong volunteer base, and motives are linked to organizational attributes. Therefore, there is a need to examine the two in concert (West and Pateman 2016).

Sample characteristics

There were 327 complete responses to the survey, with a response rate of 13%. Sociodemographic characteristics indicated that respondents were predominately female (60%), white (85%), affluent (42%), liberal (58%), not employed in the environment/water field (73%), retired (29%), and well educated by having obtained a 4-year degree or higher (84%) (Table 1). Similar to previous studies on field-based CS programs (rather than online/virtual), the demographics are mostly white and well educated (Herodotou et al. 2020). A major difference in this sample population is gender: Other studies have predominately male respondents (Ganzevoort et al. 2017; Isaacs 2017).

Hypothesis 1: sociodemographic characteristics and motivations

Results of the EFA recommended five factors that were well defined and retained for further analysis (Table 2). The five factors explained almost 60% of the variation, which is considered acceptable (Hair et al. 2013). On the basis of the EFA results, motivational variables were grouped into five categories representing underlying factors or desires driving CSV participation: social exchange, personal enhancement, values expression, career oriented, and knowledge attainment. Theoretically, these five are similar to the VFI (social, values, enhancement, career, and learn), which is notable as they are commonly thought to cover the basic functions of motives to volunteer and are reflected in this study. The social exchange motivational factor contained statements of wanting to share experiences and knowledge, learn from others with more experience, interact with like-minded people, and meet new people. Overall, this group is motivated by interpersonal interactions, termed here as social exchange.

The factors that loaded highly on personal enhancement were related to wanting to do something physically active, get outside and connect with nature, and have fun. This motivational group is interpreted as participating for personal entertainment, exercise, and enjoyment. The values expression group contains citizen scientists expressing their desire to help the community and environment and to contribute to scientific knowledge. Further, values expression also holds the highest-scored statement: “I want to help or enhance the environment” (m = 4.84) (Table 3). Next, career oriented consists of the statements: “I want to advance my career…”—which is the lowest-scored statement (m = 3.02)—and “I want to enhance my reputation…” and are interpreted as volunteering for career purposes or for professional growth. Lastly, knowledge attainment holds statements of wanting to learn new skills or gain hands-on knowledge and learn about water resources (Table 3).

The first aim of the DOIF is to assess sociodemographic characteristics and types of motivations. H1 aims to determine what, if any, differences exist among sociodemographic characteristics in the scoring of the five categories for motivation. Evidence supports H1. Table 4 describes results from nonparametric statistics wherein the category mean of the motivation factor category (Table 3) was used as the measurement variable against the sociodemographic characteristics (grouping variable), as shown in Table 1. Political views (a sociodemographic characteristic) and values expression (a motive) showed no significant differences and are not included in Table 4.

Between-group differences were found with social exchange as well as employment and environmental job. For employment, private and nongovernment organization (NGO)/nonprofit employees scored social exchange items highest (m = 4.00 and 4.10, respectively). Significant between-group differences (p < 0.05) existed among student (m = 3.94) and retired (m = 3.33) groups. Also, the retired group score was significantly different (p < 0.05) than government and public (m = 3.86), private, and NGO/nonprofit. Additionally, those employed in an environmental job scored social exchange differently than those not employed in the field (“yes,” m = 3.98; “no,” m = 3.64) (Table 4).

Personal enhancement had significant differences in scores for age and education. Significant differences (p < 0.001) in between-group scores for age were those 18–24 years of age (m = 4.68; highest score) and 65–74 (m = 4.10). CSVs with “some education” scored personal enhancement highest (m = 4.61) and significantly differently (p < 0.01) than those with “doctorate/professional degree” (m = 4.19).

Career oriented had significant between-group differences in each sociodemographic characteristic except for gender (Table 4). Regarding race, nonwhite CSVs scored this motive higher (m = 3.76) than white CSVs (m = 3.11). Employment pairwise comparisons revealed multiple between-group differences. Students, while providing the highest score (m = 4.36), significantly differed from retired, self-employed, private, and government/public respondents (p < 0.05). No difference exists between students and NGO/nonprofit respondents (m = 4.04). Further, retired CSVs provided the lowest score (m = 2.15), which was also significantly different (p < 0.01) from the score of private, government/public, and NGO/nonprofit CSVs (all groups except self-employed [m = 2.95]). Related to employment, environmental job values were significantly different between those who reportedly held these jobs (“yes” m = 4.03) and who did not (“no” m = 2.90). Pairwise comparisons show many significant between-group differences (p < 0.01) among the age-group levels. CSVs aged 18–24 scored career oriented highest (m = 4.32) and significantly different from the scoring of those 45 and above. Age level 25–34 (m = 4.12) was significantly different from age levels 55 and above. Finally, the age level 35–44 (m = 3.70) was significantly different from age levels 55 and above. No differences were found in age levels 18–24, 25–34, and 35–44.

The remaining between-group differences for career oriented are in education and household income. Those with “some college” (m = 3.80) scored items highest and significantly differently (p < 0.001) than those with a “doctorate/professional degree” (m = 2.84). CSVs with lower incomes ($10,000–$29,000) scored items higher (m = 4.19) and significantly differently (p < 0.01) than those reporting higher income brackets of $50,000 to $79,999 (m = 3.08) and $80,000 or more (m = 2.96).

The motive knowledge attainment had significant between-group differences with gender (women scored higher [m = 4.67] than men [m = 4.48]) and education (Table 4). Those with “some college” scored highest with a mean of 4.67, which was significantly different (p < 0.01) from “doctorate/professional degree” (m = 4.44).

To summarize, motivational factors showed many between-group differences as hypothesized. Social exchange was scored differently by employment type and environmental job. Personal enhancement was scored differently by age and education levels. Career oriented had between-group differences with each sociodemographic characteristic except gender. Finally, knowledge attainment was scored differently by groups in gender and education. Values expression did not have any between-group differences, which is noteworthy and will be explored in the discussion section.

Hypothesis 2: organizational variables and motivations

The remaining overarching variable in the DOIF relates to attributes of the organization. EFA revealed three distinct latent variables, which are interpreted as individual gains, tangible results, and program efficacy (Table 5). The three factors explained 37% of the variation, with factor 1 (individual gains) offering the highest proportional variation (~17%). Although this amount of explained variation is less than ideal (Hair et al. 2013), an exploratory study such as this may be subject to more uncertainty. Nonetheless, the factors appear to be a good fit for the data collected based on the measures of fit in Table 4, namely the root mean square error of approximation (RMSEA), which, at < 0.01, indicates a good fit (Hu and Bentler 1999).

Individual gains, factor 1, loaded highly statements of gaining recognition or advancement for the self: “I like to earn recognition,” “Opportunities for my role to grow … are important to me,” and “It is important to me to receive some form of recognition for my work.”

Factor 2, the tangible results an organization produces, are important for CSVs who want their data to be used in scientific publications and “like” when environmental problems are solved because of their data, and when data/results are shared with them.

Factor 3 is interpreted as program efficacy, as the organizational variables grouped are related to general satisfaction with program management and outcomes: “The data collected for this project are used appropriately,” “I think the data collected are not being used to their full potential,” and “I feel that staff would support me if I wanted to deepen my level of participation.” Note that “I think the data collected are not…” was coded in reverse to indicate level of agreement in a positive manner.

Among these three factors, which are descriptively presented in Table 6 above “form of recognition,” the highest scored statement was “I like when environmental problems are addressed because of our data” (m = 4.89). The lowest scored statement was “I think the data are not being used to their full potential” (m = 2.90). Further, the “forms of recognition” (lower portion of Table 6), were also scored moderately low. CSVs found the seven forms of recognition to be only slightly to moderately important. These forms of recognition are not used in analyses, but presented as descriptive information to CS practitioners to compare with future assessments of recognition types.

Table 7 provides the results of testing H2—that motivation factors would reflect in the organizational variables valued. Organizational factors (individual gains, tangible results, and program efficacy) were collapsed into categorical variables with three levels of agreement, as detailed in table notes, to gauge between-group differences in a nonparametric assessment. The purpose was to examine trends in agreement in how high-scoring motivational factors also reflect organizational factors CSVs valued or agreed were likable or important. Significant (p < 0.001) between-group differences existed in individual gains and all motivation factors, and between program efficacy and career oriented, and between program efficacy and values expression. No differences existed among motivational factors and agreement with tangible results.

The organizational factor individual gains differed in scoring among each motivational factor (Table 7). First, individual gains and social exchange showed a trend of agreement whereby those who agreed (m = 4.04) with valuing individual gains also scored items of social exchange highest and significantly differently (p < 0.01) than those who were neutral (m = 3.64) and those who disagreed (m = 3.22); disagree and neutral were also significantly different (p < 0.05). Next, those who scored personal enhancement highly (m = 4.46) also agree with individual gains significantly differently (p < 0.0001) than those who disagree (m = 4.0). Career oriented held significant between-group differences (p < 0.0001) between CSVs who agree (m = 3.72) and those who were neutral (m = 2.87), as well as between those who agree and those who disagree (m = 2.36).

Values expression and individual gains: although overall scores were high (m > 4.5), significant between-group differences (p < 0.01) were found between the groups that agreed (m = 4.89) and disagreed (m = 4.64) and between groups that were neutral (m = 4.79) and disagreed. Lastly, knowledge attainment had between-group differences (p < 0.001) with those who agree (m = 4.79) and those who were neutral (m = 4.41), and between those who agree and disagree (m = 4.36).

Finally, agreement with program efficacy showed differences with career oriented and values expression (Table 7). Between-group differences revealed CSVs who agree (m = 3.32) with program efficacy did so significantly differently (p < 0.05) than with those who were neutral (m = 2.75). CSVs who scored items of values expression highest (m = 4.90) were in the disagree group of program efficacy. Significant between-group differences (p < 0.05) exist between those who agree (m = 4.82) and those who are neutral (m = 4.73).

H2 is supported by the results, as differences in scoring of motivational factors were reflected in the valuing of organizational variables with a general trend of agreement. Some dispositional (motivational) factors held more substantial evidence for agreement with organizational variables. To explicate, in individual gains and social exchange, all three agreement-level groups are significantly different, and others trend in agreement with weaker evidence (e.g., program efficacy and career oriented). Differences existed only between those who agree and rhose who were neutral and program efficacy and values expression, in which the disagree group scored value expression higher but no different than the agree group.

Hypothesis 3: indicators of commitment and dispositional (motivational) variables

H3 was that indicators of commitment differ on the basis of motivations/dispositional variables. The study survey captured several indicators of commitment (Table 8), which the DOIF posits serve as a proxy for sustained participation. CSVs were asked how likely they were to maintain, decrease, and increase their rate of volunteerism. In this study, the community partner encourages CSVs to maintain a rate of volunteerism, which is typically one water-monitoring test per month. Participation may increase through attending advanced training sessions, testing more frequently, or volunteering in other capacities. As such, we explicitly asked if participants were likely to decrease their commitment, which indicates the CSV is becoming disengaged from the CS program.

In addition to assessing the rate of volunteerism, we asked how likely CSVs were to recommend volunteering to others (Table 8). The length of time as a volunteer and frequency of water testing were also captured. CSVs were overall likely to maintain their rate of volunteerism (m = 4.21) and recommend volunteering to others (m = 4.47). Furthermore, they were likely to maintain the amount of time they volunteered (m = 4.21). The predominant category time volunteered is 1 to 3 years (39%), and the majority of CSVs monitored water quality once a month (73%). These indicators were assessed for relationships among dispositional and organizational variables (Table 9).

Significant differences were found among increase and social exchange and knowledge attainment; decrease and social exchange and values expression; and time a volunteer and career oriented (Table 9). Maintain and frequency were no different among any motivational factor, and personal enhancement and program efficacy were no different among any indicator of commitment. Between-group differences (p < 0.01) for increase and social exchange were between “likely” to increase rate of volunteerism (with a mean score of 3.85 for social exchange) and “neutral” (m = 3.47). Pairwise comparisons for increase and knowledge attainment yield weak evidence for between-group differences (p < 0.10) with scores of knowledge attainment for those who are likely (m = 4.67) and neutral (m = 4.48).

Decrease was scored differently when compared with social exchange groups at a 90% confidence interval, with between-group differences with likely not to decrease (m = 3.38) and neutral (m = 3.54) (p < 0.10). Decrease and values expression had a significant global test and significant between-group differences (p < 0.05) with unlikely to not decrease (read: likely to decrease rate of volunteerism) (m = 4.63) and likely to not decrease (m = 4.82). Lastly, CSVs who volunteered for 1 to 3 years scored career oriented higher and significantly differently (p < 0.01) than those who had volunteered for 5 years or more (m = 2.84).

Indicators of commitment—excluding maintain, recommend, and frequency—had significant differences with social exchange (increase and decrease), values expression (decrease), knowledge attainment (increase), and career oriented (time a volunteer). H3 is largely accepted, with some caveats because maintain and frequency did not have any significant differences among the organizational variables and because of the weak evidence for a relationship between decrease and social exchange.

H4: indicators of commitment and organizational variables

The final hypothesis (H4) was that indicators of commitment differ based on valued organizational variables (Table 9). Indicators and organizational factors with significant differences are increase and individual gains and tangible results, and recommend and tangible results. Program efficacy was not different among any indicators.

Increase and individual gains had a significant global test, but pairwise comparisons showed no actual between-group differences. Not dissimilar, increase and tangible results had a significant global test, and pairwise comparisons revealed a weak difference (p < 0.10) between groups likely (m = –4.74) and unlikely (m = 4.56) to increase the rate of volunteerism. Lastly, recommend and tangible results showed significant between-group differences (p < 0.05) between likely (m = 4.71) and neutral (m = 4.33).

Because some differences do exist, H4 is supported. However, where differences occur in group scores, there is no strong evidence of between-group differences. The most reliable relationship exists between recommend and tangible results.

Social exchange

The social exchange motive was scored highly by college students, NGO/nonprofit employees, and those already employed in the environmental field. The NGO/nonprofit and environmental field employees may be the same respondents because these were two separate survey items. They valued individual gains from the organization, and are likely to increase participation, which indicates sustained participation.

Indeed, young adults in the US are becoming an important group of volunteers in environmental science initiatives (McDougle et al. 2011). Young adults volunteer for environmental causes at a higher rate than older adults (Bureau of Labor Statistics 2015), and US student enrollment in environment-related degree programs is increasing (Galbraith 2009), likely because of students’ awareness of climate change, of their place within the ecological landscape, and of other issues (McDougle et al. 2011). In light of this awareness and intrinsically held values for learned environmental protection, normative and social influences from peers are also presumed to play a role in a young adult’s choice to become involved in environmental science and environmental volunteerism (Kollsmuss and Agyeman 2002).

Studies show students and young adults do indeed volunteer for different motives that change over time (Rehberg 2005; Winniford, Carpenter, and Grider 1995). “Altruistic motivations were the most important factor for students to begin volunteering, self-interest motivations such as social interactions were what eventually caused them to stay committed to the organization” (McDougle et al. 2011, p. 329). Further, regardless of the CSV’s age, CS organizations that focus on social engagements, in addition to environmental objectives, are often more successful in their environmental objectives than organizations that forgo social interaction as an organizational goal (Garrah et al. 2019; Neves 2009; Sommer 2003; Foster 2018, Ahmed 2004). As posited by Shirk et al. (2012), CSVs who feel satisfied with their volunteer experience may also increase the quantity of time dedicated to volunteering; i.e., organizational variables affect sustained participation. Toward that end, value expression, social exchange, and individual gains are likely to contribute to sustained participation in the long term.

Personal enhancement

The personal enhancement motive seems to resonate with young adult (likely college student) CSVs. They value individual gains and offer no clear indication of commitment or sustained participation. Their CS participation is a means to “get outside and connect with nature,” “do something physically active,” and “have fun.” As the functional approach suggests, volunteers may be motivated by several factors simultaneously, and those motives may intersect and interact in multifaceted, complex manners (Clary and Snyder 1999; Yeung 2004; McDougle, Greenspan, and Handy 2011). Personal enhancement may be a secondary motive for young adults. This finding may change based on CS program type. In this specific program, water quality monitors are designated a surface water–testing site. No indoor work or data entry are required. Thus, conducting their (most likely monthly) water monitoring activity is a way to spend some time near a body of water, a place to which they may not otherwise have access.

Career oriented

CSVs with a career advancement motive were nonwhite, college students, lower-income earners, employed in the environmental field, and younger (< 45). They valued individual gains and program efficacy. Although no clear indicators of commitment were present, the length of time they were volunteers altered the scoring of the career-oriented motive. CSVs with the program 1–3 years were more motivated by career advancement than those who had volunteered for 5 years or more. Although, as here, career advancement is not usually a primary driver of participation (Alender 2016; Cetas and Yasué 2017), a subgroup in this sample likely volunteers for their career.

Indeed, because of their desire to “gain experience,” “network,” and “enhance [their] reputation,” these CSVs use volunteering to build their resume, gain experience, and establish networks (Herodotou et al. 2020; Larson et al. 2020). As suggested above in the sections “Personal enhancement” and “Social exchange,” this subpopulation of young adult volunteers is motivated by desire to increase their well-being (Rehberg 2005) through work experience and to develop social connections and relationships (Omoto, Snyder, and Martino 2000; Handy et al. 2010; Gage and Thapa 2011).

The results highlight issues of diversity and inclusion in environmental organizations and CS programs such as the community partner in this study. Recall, 85% of CSVs in this study were “white alone,” which is not reflective of the demographics of the state of Texas where these participants reside and work (Figure 2); Texas is only 41% “white alone” (U.S. Census Bureau 2019). In addition to the disparities in volunteer participation (Newman et al. 2012), nonwhite people typically hold fewer positions in environmental jobs than their white counterparts (Taylor 2018). Therefore, nonwhite CSVs may sense the need to gain (1) more experience, (2) enhance their reputation, and (3) add more network connections than their white counterparts to “get a foot in the door” to an environmental field.

Knowledge attainment

Knowledge attainment was scored highly by women and college students. They also valued individual gains, and indicated they are likely to increase participation. Interestingly, this was the only motive factor scored differently by gender. Women’s involvement with CS programs and confidence and enjoyment in data collection processes are shown to be on the rise (Lewandowski et al. 2017; Kimura 2019). Although it is beyond the present study’s scope to further examine the role of gender, this finding may be useful to future studies.

Application to citizen science programs

Overall, CSVs are more likely to continue to participate when their motivations are fulfilled, and they are satisfied with their role (Welty Peachey et al. 2014; Wu, Li and Khoo 2016; de Vries et al. 2019; Haywood 2016). To that end, CS organizations may wish to understand their CSVs more comprehensively, through approaches such as the community geography one used in this study. Alternatively, study findings can be quickly employed by administering a short questionnaire using the five major motives as simple constructs for assessment; these five motives may be reliable as they reflect the VFI and previous CS studies’ findings. For example, a CS organization could employ a five-item questionnaire asking incoming CSVs to rank their motivational desires, and/or the five-item inquiry could be posed to current volunteers in a newsletter or other form of communication. From these responses, CS programs can assess whether their volunteers are motivated by one or more of the five major motivators, and if these motives have changed over the course of an individual’s volunteerism. Having such insights provides direction for program resource use (e.g., should a program focus on teaching, frontloading volunteers, communicating findings, promoting social exchange among CSVs, and/or providing support for young environmental professionals). In sum, cultivating a general understanding of CSV motives would undoubtedly provide guidance to ensure a successful CS campaign; a community geography or PAR approach could facilitate the research if the CS program does not have the capacity.

A note on recognition

Previous studies of recognition show mixed results (Roggenbuck et al. 2001; Rotman et al. 2012), specifically when it comes to the age of the CSV (Alender 2016). Individual gains in the form of recognition played a role as an important organizational variable in this study, but the low-scored forms of recognition show a disconnect. Mean scores of individual gains were more varied than other statements among motivation factors (Table 6). Further, the types of recognition (e.g., personalized email, volunteer appreciation event) were generally labeled as “slightly” to “moderately important,” with high variation. This study shows variation is also based on motivation type and supports previous findings that those who are motivated by social factors would also like recognition (Bruyere and Rappe 2007; Butt et al. 2017). As such, it is not solely those who are motivated for career advancement who desire recognition. As the DOIF aimed to understand basic interactions, unpacking recognition by other sociodemographic characteristics is beyond the scope of this study; the findings presented may be useful in future studies.

Limitations

Several issues limited the study. First, the survey respondents are from one CS program that is managed at a central location, but subgroups are responsible for working with CSVs across the state (Figure 2). This is problematic because the program is not managed unilaterally and therefore each subgroup has its own strengths and weakness. However, this limitation may be beneficial because it demonstrates that although CSVs are being managed by subgroups, there are still clear patterns and relationships present. In turn, this makes the results more applicable to the general phenomenon of CS, as demonstrated by five motives that correspond to the VFI. Another limitation of the survey was that location was not asked; it is unknown if the respondents were clustered (mostly from a few subgroups) or distributed throughout the state.

In terms of the survey instrument, the survey was created in a community geography partnership to best serve the needs of the organization. As such, a variety of statements were included to address specific program needs, and were, at times, ranked on differing scales, and the motives categories were not constructed based on literature constructs (egoism, altruism, collectivism, and principlism, or the VFI). New, comparable categories with the VFI emerged organically from the EFA. Accordingly, the organizational variables were created along this same limitation.