Within the last several decades there has been tremendous growth in citizen science (Cooper et al. 2007) as indicated by both the increase in peer-reviewed publications (Stepenuck and Green 2015, McKinley et al. 2017) and exponential growth in programs and participant numbers (Theobald et al. 2015, Parrish et al. 2019). This is not a new phenomenon; public participation in science traces its lineage in the West to the traditions of the naturalist, the almanac keeper, and the amateur collector (Miller-Rushing et al. 2012). Growth from these historic roots is evident in the current diversity of approaches to nonprofessional involvement in environmental science, defined herein as out of doors, nature focused, and grounded in issues or questions, from ecotourism (Caissie and Halpenny 2003) and hobbyist activities (Jones et al. 2017, 2018) to agency- and academic-sponsored environmental monitoring (Dickinson et al. 2010) to place-based, community-sponsored social justice programs (Ballard et al. 2008). Although some public participation programs focus on youths (National Research Council 2009, Ballard et al. 2017), many programs actively recruit adults (Burgess et al. 2017, National Academies of Sciences, Engineering, and Medicine 2018). Unlike students within the formal education system, these “free-choice” learners (Falk et al. 2007) may join, continue, or withdraw from any given program at any time.
Understanding the motivations that adults have to participate in free-choice activities is essential for successful and sustainable program design within citizen science (Nov et al. 2014, Wright et al., 2015, West and Pateman 2016) and can also shed light on questions of motivation and engagement more broadly. Katz (1960) suggested that participation in many different types of activities is broadly dependent on whether a given activity serves a positive function for the individual, satisfying specific motivational needs. Clary and Snyder (1999) used this approach to create the Volunteer Functions Inventory, a general survey instrument assessing six basic categories fundamental to volunteering (Table 1). Subsequent work on environment-oriented volunteer motivations (e.g., Bruyere and Rappe 2007, Asah and Blahna 2013, Carballo-Cárdenas and Tobi 2016, Domroese and Johnson 2017) has adapted these basic categories (Table 1), while simultaneously uncovering domain-specific motivators for participation including science broadly, as well as nature or the environment (Ryan et al. 2001, Frensley et al. 2017, Ganzevoort et al. 2017, Jones et al. 2017). Finally, studies of environmental volunteerism have highlighted situation-specific motivators, or motivations attached to the specifics of the activity at hand (Table 1). These include getting outside, as well as aspects of each specific program (Bruyere and Rappe 2007, Wright et al. 2015). In sum, this work collectively suggests basic motivations broadly applicable across many types of volunteerism; domain-specific motivations attached to broad disciplines (e.g., discovery science vs. medicine) and/or categories of activity (e.g., scientific research vs. social services) relevant to the work; and situational motivations describing specific aspects of program or place. We refer to these categorical levels as structured functionalism.
Notwithstanding the functionalist approach, many researchers have recognized motivation as an elusive, ill-defined concept and have thus attempted to operationalize motivation as a special form of interest, which itself has a long and varied history as an explanatory concept in psychology and social psychology (Deci and Ryan 1986, Krapp et al. 1992, Hidi and Renninger 2006). Krapp (1993, 1999, 2002), in particular, proposed a model of interest incorporating the psychology of individuals within their social and cultural contexts that we have adopted. Krapp and colleagues (Schiefele et al. 1983, Krapp 2002) conceptualized interest in terms of three interacting and interdependent components: the object(s) of interest, e.g., science; the action(s) of interest, e.g., help or contribute to science; and features of the persons themselves, e.g., identifies as a citizen scientist. “Person-object theory of interest” (hereafter POI; Krapp 2005) may allow for a more nuanced examination of the relationships between the “what” (i.e., object), “how” (i.e., action), and “who” (i.e., sense of self) shaping motivation in adult citizen science activity. Because objects and actions are often paired, they can also be nested within the categories of structural functionalism listed previously.
Individuals join volunteer activities for different, and often multiple, reasons (Katz 1960, Clary and Snyder 1999, Krapp 2005, Bruyere and Rappe 2007). However, only those whose interests are satisfied by the activity remain (Clary and Snyder 1999), effectively narrowing the range of interests of the persisting population. One consequence of this winnowing process is that the persisting population may come to more closely match the values and tenets of the organization and each other (Clary and Snyder 1999, Carballo-Cárdenas and Tobi 2016). A cultural-historical approach explains this matching by suggesting that persisting individuals develop situational identities tied to the program (Wenger 1998, Engström 2009) and begin to adopt the goals or values of the program as their own (Stetsenko and Arievitch 2004, Stetsenko 2005). Within this paradigm, the connections between sense of self, development of interest, and the volunteered activity are reframed such that identities are embedded within, and emerge from, involvement with the work and with other participants (Penuel and Wertsch 1995). Finally, Rotman et al. (2012) suggest that individuals joining citizen science programs tend to start out with a higher degree of self-interest (egoism), shifting their interests with deepened engagement toward attention to benefits for others (altruism), including nonhuman others, e.g., animals, nature, and the environment, in line with the values of the particular program for which they volunteer.
Collectively, these approaches to the study of volunteer motivation suggest that participants joining a hands-on, environmental citizen science program should initially be motivated by self-interest regarding objects of specific interest to them (Krapp 2002, Rotman et al. 2012), including seeking opportunities to gain knowledge and understanding and to develop skill sets connected to the environment and nature (Ryan et al. 2001, Bruyere and Rappe 2007, Domroese and Johnson 2017). These individuals should also seek a degree of social interaction within the activity (Stetsenko 2005), perhaps defined by friends, family, and like-minded others (Asah and Blahna 2013). By contrast, individuals who have participated in a program for some period of time may espouse relatively stronger altruistic motivations, including a desire to give back to the community, help the environment, and contribute through collective action to science, for instance (Busser and Norwalk 2001, Caissie and Halpenny 2003, Rotman et al. 2012, Land-Zandstra et al. 2016), and/or adopt new interests realized as a consequence of participation (Carballo-Cárdenas and Tobi 2016). Underlying this evolution, continuing participants should increasingly match the mission and goals of the program (Clary and Snyder 1999, Stetsenko and Arievitch 2004), coming to identify themselves relative to their roles in the activity (Stetsenko 2005). Finally, individuals realizing that their values do not coincide with the work or goals of the program, or feeling that they are not being sufficiently recognized, should withdraw their participation (Katz 1960, Rotman et al. 2012, Frensley et al. 2017).
We explore the motivations to recruit to, and be retained in, an outdoor, hands-on citizen science program, the Coastal Observation and Seabird Survey Team (COASST; Parrish et al. 2017), as a function of level of participant engagement, measured as time in program. We frame our study in POI, specifically qualitative coding for objects of interest, actions related to those objects, and senses of self, and then map those findings back onto the more generalized categories within structured functionalism. Given existing work, we predicted the following across the participating populations, i.e., newly engaged versus those engaged for more than 1 year:
Our study is one of only a handful of longitudinal studies examining motivations of adult citizen science participants engaged in hands-on, outdoor, environmental activities (see also Domroese and Johnson 2017, Pagès et al. 2018, Phillips et al. 2019), and it is one of the first to concomitantly explore situated sense of self. Such studies are increasingly needed given the explosive growth in biodiversity citizen science (Theobald et al. 2015), where a trained corps of continuing participants can become an informed voice for conservation (Haywood et al. 2016).
COASST is a 19-year-old citizen science program principally focused on beach-cast marine birds as environmental indicators of nearshore marine health. As of 2018, the program had serially recruited ∼4500 participants throughout coastal northern California, Oregon, Washington, and Alaska, with ∼800 currently active in the beached bird program. After a 5-hour, expert-led training, attendees may elect to sign up for the program by agreeing to survey “their beach” on a monthly basis, collecting data on the abundance, condition, and identification of bird carcasses. Species identifications are verified by experts, and data are used in a wide range of scientific and natural resource management outlets (Parrish et al. 2017). Stories of data usage are also conveyed to participants through extensive web-based and in-person communications.
We used answers to two free-write questions (“Why did you join COASST?”/“Why do you continue to be involved in COASST?”) from assessment questionnaires delivered to both “new” and “seasoned” COASST participants, respectively, during the course of two separate research programs initiated in 2012 and 2016 and conducted under University of Washington Institutional Review Board protocols 37516 and 47963 (see Appendix 1 for complete question text; see Haywood et al.  for details on assessment design). New participants were defined as attendees to an introductory training who filled out the assessment questionnaire. Seasoned participants were defined as individuals who had been actively collecting program data for at least a year at the time of our study.
Over 41 months and 60 trainings, 310 new participants elected to complete assessment questionnaires (71% return rate). Seasoned participants received questionnaires via mail resulting in 623 completed questionnaires (68% return rate). However, 104 seasoned participants answered both 2012 and 2016 versions, respectively, because they were continuously active in the program over that entire period of time. For this specific seasoned participant subpopulation, we only used 2012 data, creating a final data set of 519 “unique seasoned participants.” Because all questions were optional, final sample sizes for each question were lower than the responding population in total (new: N = 299; seasoned: N = 462).
Across new and unique seasoned participant populations that answered either of the relevant questions on the assessment questionnaire, and that chose to provide demographic information, female was the dominant gender (new: 69%, N = 271; seasoned: 63%, N = 431). Average age at training was 52 years (SD = 16.4; N = 240) for new participants, slightly younger than that of seasoned participants (57 ± 19.9; N = 365). At the time of the assessment, the average length of participation for seasoned participants was 4.5 years (SD = 2.7; N = 411). Self-assessed level of bird experience revealed that most participants did not consider themselves as birders (advanced + expert: 12%, N = 250 [new]; 19%, N = 367 [seasoned]).
We developed a codebook for analysis of the free-write answers based on existing literature on the interests and motivations of adult participants in environmental monitoring programs (e.g., Bruyere and Rappe, 2007, Asah and Blahna 2013, Land-Zandstra et al. 2016, Ganzevoort et al. 2017) and previous studies of COASST participants (Haywood 2014, Haywood et al. 2016), using POI (Krapp 2005) as a framework (see Appendix 2 for codes and examples), as follows:
A diagrammatic representation of our POI approach to free-write coding is shown in Figure 1. Note that within any one respondent, single to multiple objects and/or actions and/or senses or self could be apparent; co-occurrence across object-action pairs was possible but not a prerequisite; and across an entire respondent population, i.e., new and seasoned, a frequency distribution of objects, actions, and senses of self, as well as object-action co-occurrences, emerged. This approach allowed us to map object-action interests back onto functions (sensu Clary and Snyder 1999) structured as basic, domain specific, and situation specific (i.e., Table 1), as well as explore the situational identity (Vryan 2007), or “role” within the socio-cultural activity system (Engström 2000, Roth et al. 2009) that is COASST, e.g., that of data collector.
Two authors (He and Parrish) independently conducted 5 rounds of pilot coding (145 respondent answers in total). Discrepancies were discussed to reach consensus and associated refinement of the coding scheme. At 78% agreement and no further refinements to the codebook, pilot coding stopped, and one author (He) coded all 761 responses (Dedoose Version 7.6.6). For all codes, we calculated the frequency of occurrence within the respondent population. For the 3 most frequent object and action codes, respectively, we calculated the percent of co-occurrence with all action (object) codes, i.e., object-action pairs. Co-occurrence was also used to explore the degree to which participants assigned importance to objects or actions. To simplify presentation, we created minimum thresholds at the population level, i.e., new and seasoned, for inclusion in our graphics (objects and actions: 3%; sense of self: 1%).
To independently assess situational identity, we also used a question only found on the 2016 assessment explicitly exploring roles and tasks within the program (see Appendix 1 for complete question text). Note that this data set adds back “duplicate” seasoned participants between 2012 and 2016 and who elected to answer the question (N = 104). This question asked participants to decide whether they thought they would be (new), or were or wanted to be (seasoned), engaged in a series of 17 named tasks that collectively frame and define the science process within the COASST program. New participants (N = 166) were asked to select tasks they imagined they might be doing in COASST based on their knowledge/experience of other citizen science or science programs. Seasoned participants (N = 305) selected all tasks they did perform, as well as tasks they were not performing but wished to perform. In both questionnaires, participants were told that tasks could be assigned to other roles, e.g., COASST staff or partner scientists, or left unassigned.
We used Z-tests at the code level to determine whether differences as a function of engagement discernible by year (i.e., 2012 vs. 2016) were conserved in the combined data set (i.e., 2012 + 2016). Out of 39 codes tested, only 2, i.e., “greater good + worldview” and “citizen scientist,” lost their significance in the pooled data set (see Table A3.1 in Appendix 3). As these 2 codes were also infrequent (< 10% of the responding population), we opted to combine our data across years, which improved sample size and allowed us to focus on the effect of engagement.
For the combined data set, we used chi-square contingency tables to compare code occurrence between new and seasoned respondent populations. To assess whether there was a narrowing of object, action, or sense-of-self code diversity between new and seasoned populations, we used the Simpson index of diversity (D), which accounts for both code occurrence and the relative frequency (or evenness) of codes. The Simpson index is relatively sensitive to changes in the dominance of a particular type, i.e., frequent response codes, and comparatively less sensitive to rarity (Buckland et al. 2005). Within each interest category, and for each code, denoted by i, we calculated the frequency of occurrence, pi , according to:
where Ni is the number of times code i was recorded and ncode is the number of unique codes. The Simpson index, D, was then calculated as follows:
To quantify variability, we used a bootstrap resampling approach (1000 permutations), standardized for sample size differences, whereby responses were resampled at random with replacement to generate a distribution for and subsequently processed to extract a 95% confidence interval (CI). Note that combining data across years improved the absolute number of rare codes.
Among the 9 object categories named by greater than 3% of any respondent population, birds and beach were the most frequently mentioned (Fig. 2). New participants focused on the organism more often (birds: χ² = 11.08, df = 1, p = 0.0009), whereas seasoned participants tended to focus more on place (beach: χ² = 8.94, df = 1, p = 0.0028). Among less frequent responses, new participants referred more often to their interests in nature and the environment (χ² = 7.26, df = 1, p = 0.0070) and to citizen science (χ² = 5.99, df = 1, p = 0.0144), whereas seasoned participants referred more to COASST itself (χ² = 11.03, df = 1, p = 0.0009). Because the code “COASST” covered a wide range of interests, we subdivided this object into 3 more specific codes: the COASST “program” including its structure, organization, personnel, and materials; the value and usage of “data and results” produced by the program as a whole versus the data an individual participant collected; and the physical “practice” of the COASST protocol on the beach. Although seasoned participants displayed increased interests relative to new participants across almost all comparisons, this trend was most dramatic with respect to COASST data and results (χ² = 16.82, df = 1, p < 0.0001).
Similar to the distribution of object codes, 2 of the 7 action codes were paramount: being outdoors, referred to more often by seasoned participants (χ² = 8.72, df = 1, p = 0.0031); and helping/contributing, a code equally referred to by both populations. Within less frequent, but still relatively common actions, new participants mentioned learning much more frequently than did seasoned participants (χ² = 8.95, df = 1, p = 0.0028), and the opposite was true of monitoring/observing (χ² = 14.97, df = 1, p = 0.0001). Of infrequent responses, seasoned participants referred more frequently to having fun/enjoyment (χ² = 6.44, df = 1, p = 0.0112) and preserving health (χ² = 5.06, df = 1, p = 0.0244).
To more readily investigate POI, we explored co-occurrence among the 3 most frequently mentioned objects, i.e., beach, birds, and COASST, and actions, i.e., being outdoors, help/contribute, and learn, respectively, and all other nontrivial action (object) codes. Co-occurrence reveals the relative strength of association between any given object-action pair, regardless of whether there was a change in the overall response rate of the underlying object or action (i.e., Fig. 2). The strongest connection for both new and seasoned participants was between “beach” and “being outdoors” (Tables 2 and 3), with all other beach*action, and being outdoors*object, pairings accounting for less than 20% of the relevant responding population, with most less than 10%. “Birds” was most strongly associated with the desire to learn, and vice versa, in both new and seasoned participants (Tables 2 and 3). This association was constant despite the drop in “learn” as a function of engagement (i.e., Fig. 2). There was a lesser association between “learn” and “environment/nature,” which weakened as a function of engagement (learn*environment/nature: χ² = 4.13, df = 1, p = 0.0421; Table 3). Relative to new participants, seasoned COASST members associated “beach” and “birds” with “monitoring/observing” much more frequently (beach*monitor: χ² = 14.13, df = 1, p = 0.0002; birds*monitor: χ² = 4.95, df = 1, p = 0.0261; Table 2). The action “help/contribute” was principally focused on “science,” and this association was intensified for seasoned participants (help*science: χ² = 7.84, df = 1, p = 0.0051; Table 3), despite the equality of the underlying action response (i.e., Fig. 2).
Although “COASST” was not usually mentioned with a specific action, it was more frequently connected with “importance” than any other object of interest (Fig. 3). In fact, frequent objects and actions (i.e., > 30% of respondents) were rarely labeled as important (i.e., upper right quadrats of Fig. 3 panels are empty). Instead, importance was most often assigned to relatively infrequent objects (i.e., < 30% of respondents) and especially to COASST and its data and results. As a group, seasoned participants tended to elevate all codes referred to as important, with COASST data/results reaching well above the 30% threshold (Fig. 3).
Based on the literature, we predicted that interests would narrow and become increasingly aligned with the values and goals of the program as a function of engagement, i.e., from the new to the seasoned population, and that there would be a concomitant shift from self-interested motivations to altruistic ones. We used the Simpson diversity index to investigate the effect of engagement on the range of respondent interests, where higher numbers indicate more codes at a given population size. There was no significant difference in object code diversity as a function of engagement (new: D = 0.84, 95% CI 0.83-0.85; seasoned: D = 0.83, 95% CI 0.82-0.84). For actions, there was actually significant increase in diversity, the opposite of our prediction (new: D = 0.82, 95% CI 0.80-0.83; seasoned: D = 0.85, 95% CI 0.84-0.86).
To investigate the population tendency to match the goals and values of the program, we tracked changes in the frequency of science-based codes, including the objects “science” and “COASST,” the action “monitor/observe,” and the co-occurrence of importance with these codes. Seasoned participants mentioned the object “COASST” and the action “monitoring/observing” significantly more than new participants (Fig. 2). They also intensified science-oriented action*object pairs: monitor/observe with both beach and bird (∼3-5 times over new participants; Table 2) and help/contribute with science (∼2 times over new participants; Table 3). Finally, they increased their tendency to label COASST data/results as important (Fig. 3). Collectively, this suggests that seasoned participants may be adopting program products and practices into their motivations to participate.
Finally, to explore shifts from self-interest to altruism, we labeled the actions “learn,” “enjoyment/fun,” and “be healthy” as those representing self-interest, and “help/contribute” and “conserve/protect” most representative of altruism (Fig. 2). Across these five codes there was no coherence, as shifts in both directions, that is, away from self-interest, i.e., decline in learning, and toward self-interest, i.e., increase in enjoyment/fun and being healthy and decline in help/contribute, were both apparent.
The open-ended assessment questions did not ask participants to describe themselves; however, about half (42%-49%) of the participants volunteered information about themselves within the context of COASST. We coded these statements as expressions of a COASST “sense of self” and categorized them a posteriori into 3 broadly overlapping identities: social identity, or participants‛ relationships with other people in the COASST program; science identity, or the tasks, roles, and formal credentials directly related to science, including within COASST; and individual attributes, or descriptions of personal accomplishments or states of being (Fig. 4). Note that at the code level, elements of identity overlap; e.g., “survey team member” is both social identity and science identity. We coded self-esteem separately.
Participants often described COASST as a social activity, or something to do with friends and family and survey team partner(s) and/or as a part of the community (Fig. 4). Seasoned participants tended to talk more about their family (χ² = 4.76, df = 1, p = 0.0292) and less about their friends (χ² = 5.56, df = 1, p = 0.0175). Within science identity, the roles of data collector and science team member were the most prevalent codes, among the most prevalent sense-of-self codes overall, and statistically invariant between new and seasoned populations (Fig. 4). However, within science identity, several codes did change significantly among participant populations. Seasoned participants referred more often to being part of a larger effort composed of many individuals all engaged in the same thing (collective: χ² = 4.90, df = 1, p = 0.0269). By contrast, new participants talked more about themselves, including holding scientific jobs (χ² = 4.72, df = 1, p = 0.0298) and possessing degrees (χ² = 5.79, df = 1, p = 0.0161). In fact, except for “citizen scientist” and “learner,” all other forms of individual attribute description, including those less directly associated with science, decreased markedly as a function of engagement (birder: χ² = 10.98, df = 1, p = 0.0009; retiree: χ² = 6.62, df = 1, p = 0.0101; Fig. 4). Finally, seasoned participants registered a significant increase in self-esteem relative to new participants (from 7% to 16%, χ² = 13.44, df = 1, p = 0.0002), and this was largely tied to their actions in, and for, the program.
To more explicitly explore situational identity, we included a question in our 2016 assessment asking respondents to select all of the scientific tasks they imagined they would be doing (new) or were doing or wanted to do (seasoned). Relative to answers given by seasoned participants, the new participant population overestimated their future involvement in almost all tasks, excepting collect data, make measurements, and enter data (χ² = various, df = 1, p < 0.0005; Fig. 5; see Table A3.2 in Appendix 3). However, when tasks were ranked by frequency of response (e.g., Fig. 5, light gray), the top 4 tasks were coincident across new and seasoned populations. A fifth task, enter data, was equally frequent across new versus seasoned (∼50% of respondents) but was ranked slightly lower (eighth) by new participants.
Seasoned participants did not seem to miss these “lost” opportunities, because the percentage of respondents checking “don’t now do but want to do” was frequently under 5% (Fig. 5, dark gray). Only 2 tasks, interacting with scientists and interacting with resource managers, might be interpreted as something participants remained desirous of, because these were initially highly ranked by new participants (fifth and seventh) and received the largest frequency of “want to do” responses by seasoned participants (> 10%; Fig. 5, dark gray).
Within sense of self, we found strong support for both predictions that situational identity should narrow and become more aligned with the goals and values of the program. Across all identity codes, the Simpson diversity index dropped significantly from new to seasoned participants (new: D = 0.92, 95% CI 0.91-0.92; seasoned: D = 0.89, 95% CI 0.88-0.91), reflecting the move away from individual attributes and toward science and social identities. Narrowing toward science was also apparent in the task responses (Fig. 5), albeit also mixed with a strong sense of social interaction. In sum, these responses suggest that seasoned participants interpreted their role as both fulfilling basic scientific tasks necessary in COASST, i.e., data collector (taking measurements and collecting and entering data), and social tasks helping to sustain the program, i.e., participating with family, communicating results, and recruiting others.
As one of the few longitudinal studies within environmental citizen science (and see Domroese and Johnson 2017, Pagès et al. 2018, Phillips et al. 2019), our study begins to uncover how the motivations of adult free-choice learners can change as a function of their engagement level, where motivation captures both the interests, i.e., objects, actions, and object:action pairs (sensu Krapp 2002), and senses of self or situational identity (Stetsenko 2005, Vryan 2007) of the participants.
We have proposed a structured “environmental approach” to functionalism (sensu Katz 1960, Clary and Snyder 1999), which both interprets functions basic to volunteerism and adds domain- and situation-specific functions (i.e., Table 1). Among all COASST participants in our study, actions largely mapped to basic functions, whereas objects almost exclusively mapped to domain- or situation-specific functions (Fig. 2; and see subsequent quotes). This suggests that basic, domain-specific, and situation-specific interests are not necessarily exclusive, but intertwined and complementary (see also Pagés et al. 2018).
Situational only: It‛s a great excuse to spend time on the beach! (Full answer, seasoned)
Basic, domain, and situational: It is important data/information necessary for baseline information as to what is “normal” in the event of natural or human caused disaster or changes in climate—such as ocean temps—food supply or others. I like knowing I am making a small contribution to this effort, the oceans are our “life support” systems, birds are indicators of its health. (Full answer, seasoned)
Among basic functions, the function of “values” was well represented in the COASST participant corps, expressed as a desire to help with or contribute to science specifically, as well as to be involved in a program focused on conserving or protecting environmental and natural resources (Fig. 2, Table 3):
I have an interest in seabird conservation and research. I want to help contribute to these fields through citizen science. (Full answer, new)
It‛s an easy way for me to scientifically contribute, albeit in a small way, to the stewardship of the environment and thus, humanity. (Full answer, seasoned)
These findings align with other studies that have also found a strong values theme, including conservation or environmental concern (Jones et al. 2017), contributing to science (Land-Zandstra et al. 2016, Domroese and Johnson 2017), or simply doing something for the greater good (Miles et al. 2000).
The basic function “understanding,” mostly expressed as interest in learning, was also well represented in our study, as participants espoused an interest in learning about birds, learning new skills, and learning about their beach and the environment more broadly (Fig. 2, Table 3):
I have learned, and continue to learn, so much about seabirds (their anatomy, etc.) and beach changes over time on the WA coast. (Excerpt, seasoned)
Although some form of understanding of the work, system, or science at hand is represented in most studies of environmental volunteerism, relatively few have reported learning as the dominant function (but see Domroese and Johnson 2017; ranked as second via Likert scoring: Ganzevoort et al. 2017).
Both “ego enhancement” and “social” functions were present in our coding, albeit not as prevalently as either values or understanding. Ego enhancement, expressed as self-worth associated with citizen science efforts, was nontrivial (to 16% of the responding population):
COASST is one of several types of bird-related citizen science that I do. Citizen science projects are among the most worthwhile, rewarding activities for me in retirement. (Excerpt, seasoned)
Whether self-esteem is a widely subscribed function is unclear, because many studies do not appear to separate feelings of self-worth from the value-based functions of contributing to the greater good, as in the work being a “meaningful action” (Miles et al. 2000). Other forms of ego enhancement, including acknowledgement or public recognition, were undetected in our coding. By contrast, Phillips et al. (2019) found aspects of public recognition were widespread (40% of respondents) among participants in 6 different environmental citizen science programs. Social functions of “volunteerism” and specifically conducting the work with like-minded others appear across a wide range of studies (see especially Asah and Blahna 2013, Pagés et al. 2018, Phillips et al. 2019). We found evidence of social interactions within our sense-of-self coding (Fig. 4), most specifically focused on those individuals that participants wanted to support, i.e., friends and family (see second quote in Fig. 1), and work with, i.e., survey team members:
I LOVE working as a volunteer and working with other volunteers even more. (Excerpt, new)
The remaining basic functions, i.e., “career opportunities” and “protective interests,” were present in our data, but only in a minor way. Given that the participating population in most biodiversity citizen science programs is largely adult and retired (Burgess et al. 2017), the lack of a career focus is understandable. Protective functions, including enjoyment, relaxation, and/or exercise, have only rarely occurred as a top function in other studies of environmental volunteerism (but see Wright et al. 2015). Within the COASST participant population, both enjoyment and health-related benefits (including both physical and mental health) were rarely mentioned (Fig. 2). However, both codes increased significantly in frequency in the seasoned participant population, suggesting that although these functions may not be primary motivators, they may be “unintended” benefits of continued activity.
Based on the empirical literature within environmental volunteerism, including citizen science, and the theoretical literature on activity theory (Stetsenko and Arievitch 2004), we constructed a series of three predictions regarding the influence of engagement, i.e., time in the program: Compared to the newly participating population, COASST participants with at least 1 year of experience in the program, i.e., seasoned participants, should have a narrower range of expressed motivations, which align more closely with, or match, those of the program, and which have shifted to a stronger sense of altruism relative to expressions of self-interest.
Both new and seasoned participants expressed the same suite of interests, i.e., actions and objects, which remained largely invariant in their relative ranking (Fig. 2). Narrowing of objects or actions was not substantiated. One interpretation of this finding is that incoming participants already had a good sense of the program, and their interest functions (sensu Clary and Snyder 1999) were largely accommodated. However, both senses of self (Fig. 4) and task self-assignment (Fig. 5) narrowed significantly from new to seasoned participant populations. Krapp (2002) posited that as the objects and actions of interest change, so should the sense of self. That is, a situational identity tied to the activity at hand should develop (Stetsenko and Arievitch 2004). Individuals new to COASST sought to define themselves with personal attributes culturally acknowledged as relevant to the activity at hand: birding expertise (“I am a birder”), scientific credentials (“I am a biologist by training”), and experience in informal science practice (“I am a citizen scientist”). Seasoned participants obviously remained in these roles in their lives but may have felt less need to be defined by them within COASST. Instead, they favored program-specific roles including member of a science team, member of the collective (sensu Halpenny and Caissie 2003, Haywood et al. 2016), and most especially data collector (Fig. 4). Phillips et al. (2019) suggest that participants in environmental citizen science become strongly attached to the role of hands-on data collector over other science tasks (see also Weston et al. 2003, Frensley et al. 2017).
We maintain that continued engagement did create a participant corps with expressed motivations that more closely matched the program, i.e., our second prediction. The program itself as a source of interest increased (Fig. 2), as did the fraction of the respondent population assigning importance to COASST data/results (Fig. 3). Expressions of the work of science increased as well, including the desire to help or contribute to science (Table 3) and the use of specific scientific phrasing such as “monitor” and “observe” (Fig. 2, Tables 2 and 3). These findings echo Domroese and Johnson (2017) who found a doubling in the motivation “contributions to scientific research” from new to seasoned (assessed at end of their first year) participants in the Great Pollinator Program.
Our final prediction concerned the range of motivations indicative of a shift from self-interest to those more aligned with altruism (Rotman et al. 2012). Several studies of the motivations driving hands-on environmental volunteers have found strong self-interest themes, most prevalently acquiring knowledge and/or sharpening skill sets (Caissie and Halpenny 2003, Domroese and Johnson 2017, Ganzevoort et al. 2017, Jones et al. 2017), but also including increasing career opportunities, having fun, and receiving recognition and other egoist accolades (Clary and Snyder 1999, Bruyere and Rappe 2007, Asah and Blahna 2013). Strong altruistic themes, prominently including helping/contributing and conservation, are also apparent across the environmental volunteerism literature (Land-Zandstra et al. 2016, Frensley et al. 2017). Although participants new to COASST emphasized their desire to learn relative to seasoned participants (Fig. 2), they also espoused a range of actions more aligned with altruism, including most prominently their desire to help or contribute to science and to the environment (Fig. 2, Table 3). Seasoned participants were similarly multidimensional in their functional interests:
I find the surveys enjoyable and interesting. I love being outside, hiking the beaches, learning something new each time, exploring, and, hopefully, supporting the work that COASST is doing. (Full answer, seasoned)
These results suggest that participant motivations, at least at the population level, are more complex than a simple shift from self-interest to altruism.
Within an activity theory construct, Stetsenko and Arievitch (2004) and Stetsenko (2005) point to the “ineluctably social” nature of communities collectively accomplishing work (for our study, the “activity” COASST science). Pagés et al. (2018) refer to these interactions as conviviality. Our findings that social identity factors including family and friends and, more generally, survey team members were motivators for joining and/or remaining active in the program (Fig. 4) support this socio-cultural construct. Whether the shift from friend-dominant to family-dominant between new and seasoned participant populations in COASST holds social significance is unknown but does suggest that friends may instigate joining, whereas participants who recruit as, or subsequently recruit, family members are more likely to persist. Regardless, COASST participants clearly understood the program to be social. The third most common task response in both new and seasoned populations was communicating results, and the fourth most common was recruiting others (Fig. 5):
Over the years we’ve gathered a crew of people who often come with us so it has become a social event. (Excerpt, seasoned)
Asah and Blahna (2013) also found that interactions with friends, family, and like-minded people were overwhelmingly influential in determining volunteer commitment. Phillips et al. (2019) noted the importance of social interactions between participants and program staff as seminal to continued engagement, a finding echoed in COASST seasoned participants‛ desire to interact with scientists and resource managers (Fig. 5, dark gray). On the other hand, Ganzevoort et al. (2017) found that individuals engaged in biodiversity monitoring overwhelmingly worked alone (90% of their 2193 survey respondents). Loners are also present in COASST, as ∼9% of participants conduct their surveys alone (Parrish et al. 2019). Whether this reflective, loner-helper mentality is more attached to environmental hobbyists who develop a lifelong passion for a place or taxon (e.g., Wright et al. 2015, Jones et al. 2018) is an open question. In sum, divergent findings as to the degree to which social interactions play a role in determining participant motivation suggest that environmental citizen science is not a monolithic enterprise, but rather a collection of activity structures attracting and sustaining multiple possible identities and roles.
Environmental, hands-on programs allowing participants the chance to master skills and gain knowledge through repeated activity are one of the fastest expanding areas of citizen science (Parrish et al. 2019). Our study, together with other recent empirical and theoretical work, helps to set the stage for this expansion by collectively suggesting a set of four emergent principles that we believe are fundamental to successful recruitment and retention of participants, that is, successfully motivating people to become core members of the community of practice (Lave and Wenger 1991, Wenger 1998) that is both the citizen science and the science.
First, dominant motivations to participate appear to be situational or context oriented (Fig. 2), tied both to a strong sense of place (Haywood 2014, Haywood et al. 2016) and sense of biodiversity, including the subject of study (e.g., Jones et al. 2017) and outcomes for it (e.g., Carballo-Cárdenas and Tobi 2016). More generally, this suggests that hands-on citizen science with a focus on the environment can deepen the connection between people, place, and ecosystem via rigorous, bona fide science (National Academies of Sciences, Engineering, and Medicine 2018, Pagés et al. 2018).
Second, volunteers who remain engaged become committed to the activity and the organization (Vecina et al. 2012) and gradually adopt the primary essence and mode of the program as their own (Figs. 2-5). They increasingly adopt the mantle of science without becoming scientists. For environmental citizen science programs with a primary goal of generating rigorous science outcomes, participants strongly identify themselves as data collectors (Figs. 4 and 5) and may actively resist attempts to expand or change their role to incorporate additional science tasks (Frensley et al. 2017, Phillips et al. 2019). Allowing participants full “membership” in the science team with both respect and recognition according to their role (Phillips et al. 2019) will result in a committed and continuing data collection corps.
Third, both altruism and self-interest are powerful motivators that should be supported through initial and continued participation (Fig. 2). We suggest that the relative strength of each may be program dependent rather than related to the strength of engagement (sensu Rotman et al. 2012) and that both should be incorporated into program design.
Finally, participants clearly understand their role as having both scientific and social aspects (Figs. 4 and 5). They express both cognitive and affective engagement (Phillips et al. 2019) and also express that science is a social enterprise with positive emotional rewards (i.e., what Jaber and Hammer  call epistemic affect). These findings suggest that citizen science programs that can effectively combine rigorous science with social interaction may be most successful in recruitment and retention. At the same time, attention to the types of environmental citizen science identities that collectively define engaged publics, from loner hobbyists (e.g., birders; Jones et al. 2017) to social data collectors (e.g., this study) to activism designers (Extreme Citizen Science; Stevens et al. 2014), will facilitate a future where every person has multiple opportunities to engage in authentic science research and learning while making a difference in the world.
We thank the COASST beached bird participants who took part in the recruitment and retention surveys used herein and whose collective data have made the program what it is. Cindy Char, Jane Dolliver, and Erika Frost contributed to survey design, survey delivery and data collation. Hillary Burgess, Jackie Lindsey, Tina Phillips, Jennifer Preece, Andrea Wiggins, and two anonymous reviewers improved the work with critical reviews. J. K. Parrish acknowledges National Science Foundation DRL-1322820 and DRL-1114734 and Washington Sea Grant R/RCE-9 for supporting COASST.
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