Characteristics of engagement in volunteer and citizen science projects

In the last decade, the study of engagement has gradually emerged in both volunteering and citizen science research literature. The subject of engagement itself has been studied widely across other disciplines and includes user (Chapman 1997), work (Kahn 1990; Maslach 2011), and student engagement (Newmann 1992; Garrett 2011).

The term “engagement” has no generally agreed upon definition or conceptual framework. Instead, it is broadly defined and oriented towards the research field that is being studied. For instance, in studies on user engagement, the term has been defined loosely as “a process and product of interaction” in which its degree of intensity can alter with time depending on a host of factors, including attention, aesthetics, challenges, feedback, and motivation (O’Brien and Toms 2008). By contrast, work engagement centres on an employee’s cognitive connection to their work and is said to be persistent in the event of challenges encountered. Within each of these study fields, there are two types of engagement: Attentional (intensity or degree of involvement and duration expenditure) and emotional (perception, motivation, cognitive, and experience) (Maslach 2011). In this study, we focus on attentional engagement, referred to here as the interaction or behaviour presented by a volunteer in relation to the project in which they engage. These behaviours can be used to understand a volunteers’ level of engagement and are an area of limited yet valuable research amongst the volunteering sector with regards to volunteer recruitment and project management (Reed and Selbee 2001; Mohan and Bulloch 2012).

Often employed in user engagement research for website design, attentional engagement can be derived from a person’s engagement characteristics (e.g., click-through rates, page views, and time spent on a website). These can be measured through a series of metrics (e.g., length, frequency, and geographical location of activity), with those characteristics being evaluated according to what information is obtainable or required. Researchers have begun using this approach to evaluate the attentional engagement of volunteers who engage in online and ICT dependent citizen science projects, using information collected about volunteers (e.g., identification codes and tasks contributed) via computerised databases and online platforms (Ponciano and Brasileiro 2014). A similar type of data about volunteers’ attentional engagement is also being collected by some volunteering projects (e.g., TCV); these data have the potential to be used to characterise the nature of volunteer engagement within the volunteering sector.

This study used the following engagement characteristics: Longevity, frequency, and distance travelled. These characteristics were previously evaluated in online and ICT dependent citizen science projects (e.g., Tulloch and Szabo 2012; Ponciano and Brasileiro 2014; Boakes et al. 2016) and were selected for the current research owing to their importance to the sustainable management and design of environmental volunteering projects.

Longevity is the length of time that a person continues to be involved in volunteering and citizen science activities. Research has long shown much variation in the length of time that a person commits to engaging in volunteering and citizen science activities relating to a number of internal and external factors including intrinsic motivation, time availability, and type of volunteer project (Macduff 2005; Nov et al. 2011; Dunn et al. 2016). Understanding volunteers’ longevity behaviours is important for overall project management in relation to the sustainability and continuity of their practices, adequacy of recruitment and retention, and the allocation of resources (e.g., equipment and training) (Nov et al. 2011; Chu et al. 2012).

Frequency refers to the regularity of a person’s involvement in volunteering and citizen science activities. Online user engagement research commonly measures a person’s level of involvement in an activity. The higher the involvement, the more engaged they are considered to be. This measurement is important from a project management perspective, particularly when planning for regular activities (e.g., weekly or monthly) or single events (Ponciano and Brasileiro 2014).

Finally, distance travelled measures the distance that a person travels to a volunteering or sample site from a starting location. In the context of this study, we refer to distance travelled as the distance that a volunteer travels to a volunteering project from their place of residence. Unlike most citizen science projects where volunteers can choose to do an activity either from home or in close proximity (depending on the project), environmental volunteering projects are organised group sessions that often occur for an entire day in set locations which require volunteers to travel. To date, no studies in volunteering research have looked into patterns of distances travelled; instead, they have identified the importance of distances travelled to a person’s willingness to engage in environmental volunteering activities (Pope 2005; O’Brien et al. 2008; Tulloch and Szabo 2012). Potential barriers include insufficient public transport, car accessibility, and reimbursement of transport costs (Pope 2005; Tulloch and Szabo 2012). Nonetheless, such knowledge is of particular importance to project managers in both environmental volunteering and citizen science projects to assist in identifying funds for volunteers’ travel reimbursement, selecting locations to hold volunteering sessions or data collection surveys, and to organise travel arrangements if required (e.g., minibus or car sharing).

Related work and existing gaps in knowledge

Characterising volunteer’s motivations that can underlie their engagement has a wide literature base in volunteering and citizen science research, examining both qualitative (e.g., questionnaires) and quantitative dimensions (e.g., measurement indexes). Previous research has touched on many areas including motivations to volunteer (Clary and Snyder 1999); the linkage between volunteer role and self-identity (Stryker 1980); comparing the relationship between volunteer motivation and project contributions (Nov et al. 2011; Balestra et al. 2016); individual trait-based characteristics of those who choose to volunteer (Davis Smith 2005); and task or activity preferences (Willems and Walk 2013). Studies have revealed how factors of personality characteristics, team affiliation, social enjoyment, types of volunteering projects, current life situations, and personal motivations can predict a person’s initial and ongoing involvement in volunteering activities. Yet, whilst these findings provide useful insights into volunteers’ underlying motivations for project engagement, more research is needed which focuses on project management and adaptability to levels of volunteer retention in order to help strengthen their practices. This is particularly important in volunteering fields where empirical research has been limited.

Studies in volunteering research which have focused on the engagement behaviours of volunteers include exploring the total amounts of charitable giving, volunteering and participation in civic engagement, and analysing associations between civic engagement and religiosity (Reed and Selbee 2001; Mohan and Bulloch 2012). Quantitative methods used in each of these volunteering studies were descriptive (e.g., percentage proportions and total counts) and derived from governmental surveys based on subsamples of the national population. In each of these studies researchers found that few volunteers contributed the majority of UK volunteering activities, findings that resonate closely with patterns of participatory inequalities identified amongst online and ICT dependent citizen science projects (Haklay 2016). Further, the study of Mohan and Bulloch (2012) which explored the socio-demographic backgrounds of highly contributing volunteers found that they were largely from prosperous, middle-aged, and highly educated sections of the UK population. However, findings from volunteering studies remain largely generalised in scope and have not yet fully explored the different types of volunteer engagement characteristics or volunteering projects, nor have they cross-examined these patterns amongst urban-rural population density distributions. Such knowledge would help to identify variability in volunteers’ engagement patterns as well as any relating factors (e.g., level of engagement, type of volunteering project, and transport mobility).

Research on engagement behaviours of volunteers also has emerged in the field of human computation, in particular Volunteer Thinking projects (e.g., Zooniverse, SETI@home, and The Milky Way Project). Existing studies in this area include analysing the contributing activity of volunteers (Neis and Zipf 2012), understanding volunteers’ recording behaviours in species monitoring (Boakes et al. 2016), characterising volunteer engagement (Tulloch and Szabo 2012; Ponciano et al. 2014), and volunteer engagement profiling (Ponciano and Brasileiro 2014). The studies use various quantitative analytical approaches (e.g., descriptive statistics and cluster analytical approaches) to evaluate the activity logs of volunteers who engage in online and ICT dependent citizen science projects. These studies reveal how these analytical approaches can be used to identify volunteers’ contributing behaviours and engagement patterns in these projects. For instance, Ponciano and Brasileiro (2014) used a clustering analytical approach to identify engagement profiles of volunteers, grouping volunteers according to similarities and dissimilarities between their engagement characteristics. This inductive analytical inquiry is a commonly used technique for classifying information data into categories or groups that share similar characteristics and is used where no previous knowledge of these categories or groupings exists. Whilst the analytical approaches in these studies have been used to explore citizen science projects specifically, there is scope for these methods to be applied in other related or nearby fields (e.g., volunteering) that wish to explore people’s engagement for project management purposes.

Data Source

Described as one of the UK’s largest environmental volunteering charities, TCV has engaged people from diverse backgrounds and abilities who have taken part in various practical conservation projects since it was established in 1959. In this study, we focus on the charity’s larger and more well-known projects, the Action Teams. This project was established when the charity was initially set up and engages environmental volunteers in practical conservation work, including pond weeding, dry-stone walling, and coppicing trees. Despite differences in types of activities, environmental volunteering projects share goals with many environmental citizen science projects, as each engage members of the public in activities that help contribute to the conservation and restoration of natural environments (Roy et al. 2012). TCV was selected as a case study project to identify whether participatory patterns in environmental citizen science projects exist in environmental volunteering projects. Findings observed in this study may be applicable to both environmental volunteering and citizen science projects in areas of project design and management.

In 2001, the charity was one of the first in its field to develop an online database initially as a simple volunteering tracking system in Northern Ireland. Its information functionality and requirements have been shaped by TCV’s management approach and work context. Since then, the database has evolved into a complex national volunteer project management tool, storing a total of 222,605 records of volunteers who collectively have undertaken 241,990 conservation tasks over 12 UK geographical regions. The information in the system was collected by TCV staff using both paper and online forms to record volunteer’s information (e.g., socio-demographics, TCV sites, regions, and projects attended) as well as their activities (e.g., number of days and conservation tasks contributed) usually after the activity ended. Much of the data collected about volunteers and their activities are similar to the types of volunteer data collected in online and ICT dependent citizen science projects.

To calculate the characteristics (longevity, frequency, and distance travelled) previously identified as being important for measuring engagement patterns in environmental volunteering projects, we selected the following data items: Volunteer identification code, region identification, site identification, total active days, start and end dates of volunteering, and postcodes of volunteer’s places of residence and TCV sites attended. This enabled us to explore engagement characteristics and contributing behaviours of volunteers who engaged in TCV projects. Further, due to the scale and volume of volunteer records, the study was based on all volunteers registered from January 2010 to December 2013.

The analysis focused on the following volunteering regions: Greater London (n = 6690), Greater Manchester (n = 810), and Yorkshire (n = 2871). We analysed these three regions to identify any influences of population density distributions in rural (e.g., Yorkshire) to urban areas, differences in mobility potential (e.g., public transport services), and as cross-examination between relatively similar urban regions (e.g., Greater London and Manchester). All volunteer information was both confidential and anonymised in line with ethical practices and a data sharing agreement with TCV. No personal or sensitive information was used in the analysis, including gender, ethnicity, and economic status.

Data analysis

Data analysis consisted of two main parts; first, engagement characteristics and contributor activity, and second, volunteer engagement profiles. All statistical analysis was performed using R Version 3.1.1 (R Core Team 2014) and all spatial analysis was conducted using ArcGIS (Version 10.1).

Engagement characteristics and contributor activity of volunteers was identified and assessed as follows. First, exploratory data analysis, both graphical (bar graphs) and non-graphical (descriptive tables, ratio proportions, and percentages) was used to reveal and visually represent the underlying features of the dataset in relation to the following: Numbers of volunteers, net annual changes in volunteer numbers, length of time volunteering, number of volunteering sessions attended, number of days between sessions, proportion of “One-Session” to “Multi-Session” types of volunteers, and their individual contributions to overall volunteering activities. Second, confirmatory data analysis was used to assess relationships and differences among geographical regions, year of analysis, and TCV sites. This was carried out using a series of 2-Way ANOVA (with replication) statistical tests on the following data information (Dytham 2011); volunteering numbers and number of volunteering sessions (also referred to as activities).

Volunteer engagement profiles were identified using engagement metrics and clustering algorithms described by Ponciano and Brasileiro (2014), which analysed engagement patterns of those who participated in Volunteer Thinking projects that are part of the Zooniverse. Unlike Ponciano and Brasileiro’s (2014) analysis, the current study includes volunteers who attended only one volunteer session. This was to ensure both a full analysis of all volunteers who “engage” in volunteering activities and that their engagement characteristics could be made and understood (Rochester 2006).

Three of the four engagement characteristics defined by Ponciano and Brasileiro (2014) were used: Activity Ratio, Relative Activity Duration, and Variation in Periodicity. Such characteristics are particularly useful for understanding a volunteer’s level of productivity, attendance frequencies, and length of time spent volunteering. Activity Ratio is the number of days that a volunteer was active divided by the total days they are linked to the volunteering project, i.e., all of the days between the first and last days of engaging in volunteering activities. A volunteer was considered to be active if they attended at least one session. Relative Activity Duration is the ratio between the numbers of days that a volunteer was active divided by the overall study observation period in days. Finally, Variation in Periodicity is the average number of days elapsed between two sequential days that an individual was active divided by the total average number of days elapsed between active days of all individuals. Engagement metrics were then normalised to span 0 to 1 using the following normalisation scaling formula where x is the engagement metric and i is the volunteer:

In addition to the three engagement characteristics outlined above, distances travelled by volunteers was included in the engagement profiling method. This was calculated using ArcGIS. Volunteer’s place of residence and TCV sites were first geocoded using Code-Point® Open dataset and ESRI’s World Street base map data. As 2,896 (27.9%) of volunteer postcodes for their places of residence were either missing or incorrect, the analysis was based on a total of 7,475 volunteers for the three regions, Greater London (n = 4887), Greater Manchester (n = 624), and Yorkshire (n = 1964). Using OS Master Map® Integrated Transport Network Layer dataset (Version 09/2015), we calculated the shortest distance (in miles) between each volunteer’s places of residence and TCV volunteering sites, approximating the distance travelled by volunteers using the shortest route between locations inclusive of roads (Zhu et al. 2013). This ensured a higher degree of accuracy than the Euclidean (straight line) distance, taking account for actual street network structures (e.g., bridges, rivers, and road networks) that can affect volunteer’s distances travelled and time investment (Nicoară and Haidu 2014). This is particularly important in London, where a short distance across the river can be translated to a much longer travel time on the road, due to the limited number of bridges.

Following this, we identified behavioural profiles of volunteers who exhibited similar engagement characteristics using a clustering analytical approach (Ponciano and Brasileiro 2014). This inductive analytical inquiry is a commonly used technique for classifying information data into categories or groups that share similar characteristics, and is used where no previous knowledge of these categories or groupings is available. First, the hierarchical cluster analysis using the R package “cluster” (Maechler et al. 2015) was applied to understand the grouping of volunteers using Ward’s Minimum Variance method to estimate the observed similarities and dissimilarities between volunteer’s engagement metrics. This enabled the datasets to be organised into distinguishable grouped clusters where no predefined number had been selected (Fielding 2007). Drawing on this, the within-group sum of squares by the number of clusters for each dataset region was then plotted to identify the number of grouped clusters. K-Means clustering approach was then applied to partition data points into the k number of groupings selected, which sorted data values according to the nearest mean at each cluster’s centre (Wagstaff et al. 2001). An Averaged Silhouette Width (Hennig 2015) was then used to validate the numbers of clusters selected and evaluate each cluster’s degree of tightness and separation (Rousseeuw 1987). Those scores equal to or larger than 0.51 were used as a reference to indicate sufficient partitioning. In addition, Spearman’s rank correlation coefficients were used to observe whether relationships could be identified between each of the engagement metrics that would further explain volunteer engagement profiles.

Volunteer characteristics: Total volunteering numbers and retention levels

We calculated the total numbers and retention levels of TCV volunteers from January 2010 to December 2013 (See Table 1). First, the total number of volunteers from each of the three geographical regions varied widely. Greater London presented the highest overall volunteer population (2539 to 724) by comparison to Greater Manchester (237 to 178) and Yorkshire (907 to 592). From this, the percentage of each region’s volunteering population size relative to the TCV’s overall volunteering population (6989 to 3693) was calculated as follows; Greater London (19.6–36.3%), Greater Manchester (2.7–4.8%) and Yorkshire (10.8–16%). Further, there were no stable trends in volunteering numbers across sites, instead they were quite variable. Similarly, there were no differences in volunteering numbers between months throughout the year.

Volunteers’ yearly retention levels differed across each region presenting no clear trends. In general, volunteering numbers increased from 2010 to 2012, with decreases occurring across all regions between 2012 and 2013. The Greater London region accounted for most of these yearly changes in volunteering numbers, having the highest retention levels. By comparison, Greater Manchester and Yorkshire have lower retention levels.

In summary, there were no clear trends in volunteer numbers across all three regions, both yearly and between individual volunteering sties. Greater London exhibited the highest volunteer population and retention levels, compared to the other two regions. Further, seasonal variability shows no bearing on volunteer numbers, with the volunteering site having more of an impact.

Volunteer characteristics: Longevity, frequency, and distance travelled

We calculated the total length of time that volunteers were actively engaged (longevity) in volunteering activities as well as the number of volunteering sessions they attended (frequency) for each region. The total length of time that volunteers were actively engaged varied from 1 day to up to 3 years. We also observed differences in the number of sessions that volunteers attended and the amount of time that lapsed between these sessions. Overall, findings showed similar trends in longevity and frequency characteristics across all geographic regions (See Figures 1 and 2), with a higher number of volunteers engaging in one volunteering session.

Ratio proportions of “One-Session” to “Multi-Session” volunteers varied. The overall value for the UK was 18:7, which differed from Greater London (5:1), Greater Manchester (3:2), and Yorkshire (1:1). Overall, participatory patterns across all regions revealed that a small percentage of “Multi-Session” volunteers contributed the most to volunteering activities, with a larger proportion of “One-Session” volunteers contributing the least. For instance, of the 207,671 total volunteering activities for all UK regions (Table 2), 89.1% were conducted by “Multi-Session” volunteers, who make up 27.6% of the total volunteers. This participatory pattern of “Multi-Session” volunteers is similar across each of the regions: in Greater London 11.1% of volunteers contributed 95.6% of activity, while in Greater Manchester 39.5% contributed 80.4% of activity, and in Yorkshire 33% contributed 96.7% of activity.

Findings showed that the numbers of volunteers decreased with increasing distances travelled. These trends were replicable between both “One-Session” and “Multi-Session” volunteers, with the majority living within a 20-mile distance of the TCV site they attended. Overall estimated distances travelled by volunteers for each region were as follows: Greater London about 32 miles, Greater Manchester about 36.5 miles, and Yorkshire about 42.3 miles. Findings also showed that in the two urban regions most volunteers (Greater London 79.9%, Greater Manchester 85.56%) lived within a 5 to 20-mile distance from the TCV site they attended. By contrast, most volunteers (62.05%) in Yorkshire lived within close proximity of up to 5 miles from the TCV sites they attended. These findings were consistent whether a volunteer attended one or multiple sessions, as illustrated in Table 3.

To summarise, similar trends were observed in longevity and frequency characteristics across all geographic regions, with a large proportion of volunteers engaging in one volunteering session. Additionally, participatory patterns across all regions revealed that a small percentage of volunteers contributed the most to volunteering activities, with a larger proportion contributing the least. Finally, most volunteers lived within a commutable distance of the TCV site they attended, with few travelling from farther distances.

Volunteer engagement profiles

A clustering analytical approach identified engagement profiles of volunteers, grouping volunteers according to similarities and dissimilarities between their engagement characteristics. This analytical approach revealed three distinguishable profiles of volunteers termed as One-Session, Short-Term, and Long-Term. These were shown to be the number that best optimised the trade-offs between the number of groups and the within-group sum of squares. This was validated in Averaged Silhouette widths, with scores above 0.51 indicating sufficient partitioning of the clustered groupings (Greater London 0.58; Greater Manchester 0.55; Yorkshire 0.57). The general descriptives of the three regions’ engagement metrics subdivided into profile types as defined by Ponciano and Brasileiro (2014) are shown in Table 4.

In general, the average number of days that volunteers were active during the time they volunteered (Activity Ratio) were moderate to high across all regions, as indicated in means, medians, and quartiles. The length of time that volunteers actively engaged in environmental volunteering activities (Relative Activity Duration) tended to be longer in Greater Manchester, with a higher proportion volunteering for a short duration. Further, the number of days elapsed between each volunteering session (Variation in Periodicity) shows a slight variability between regions, as indicated in the range. For instance, volunteers in Yorkshire attended sessions less frequently than did volunteers in urban regions (e.g., Greater Manchester). In addition, the mean average distance travelled is relatively similar across each region, with most volunteers living within a proximity of up to 10 miles to the TCV sites they attend.

Volunteer’s engagement profiles (see Table 5) are as follows:

• One-Session volunteer: These volunteers account for the largest proportion of the volunteer population, travelling the least distance and committing the shortest amount of time.
• Short-Term volunteer: These volunteers had a profile type mid-way between the other two.
• Long-Term volunteer: These volunteers represented the smallest proportion of the volunteering population, travelling the farthest distance and committing the longest amount of time to the organisation.

Regions differed in engagement characteristics. In general, activity ratios were relatively similar in Greater London and Manchester, with volunteers being active for shorter periods than those in Yorkshire. Marked differences were also observed in the time elapsed between the number of volunteering sessions attended (periodicity) by volunteers as well as in their distances travelled. This suggests that each of the profile types have a variable range with noticeable distinctions between those more urbanised regions (e.g., Greater London and Manchester) to those semi-urban and rural (e.g., Yorkshire). Further, there is a noticeable gradient in engagement metrics across each engagement profile. This is supported by a series of Spearman’s rank correlation coefficient (as data presented a non-normal distribution) where a statistical significance was observed between each of the four engagement metrics (p < 0.001).

Numbers and percentages of volunteers classified in each engagement profile grouping are shown in Figures 3 and 4. One-Session volunteers make up the largest group of volunteers, with Long-Term volunteers being the smallest, and this pattern is present across all regions.

In summary, there were clear distinctions between those more urbanised regions to those semi-urban and rural, with volunteers from urban regions (e.g., Greater London) being more actively engaged in volunteering activities for longer periods than those in semi-urban and rural areas (e.g., Yorkshire). Further, we identified three types of volunteer engagement profiles that are similar in scale across all regions. Volunteer engagement profiles presented a distinct gradient in their engagement characteristics, with “One-Session” and “Long-Term” being on opposite ends of the spectrum.

Study limitations and future research

We highlight the following issues for future research. First, though this study is able to characterise the nature of engagement in environmental volunteering projects, such quantitative analytical methodology fails to provide further contextual explanation behind these volunteers’ patterns of engagement (e.g., motivations to volunteer). There is need to use complementary forms of qualitative methodological approaches (e.g., interviews and observations) in conjunction with quantitative approaches to provide a more in-depth understanding of volunteer engagement. Such mixed methods would allow for multiple perspectives, identifying not only the magnitude and frequency of volunteers’ engagement patterns, but examining the meaning and understanding behind their occurrence (Bryman 2012).

Second, whilst figures calculated for volunteer’s shortest distance travelled served as a good estimate of distances travelled, the study does not include the mode of transport. However, because findings suggest that distance travelled is associated with a volunteer’s level of engagement, we recommend that future research implement these factors to ensure a more accurate assessment of volunteer’s travelling distance.

Third, whilst engagement profiles were case specific to those that attended TCV, similar, more generalised findings on differentiated patterns of contribution also have been identified in existing volunteering research (Reed and Selbee 2001; Mohan and Bulloch 2012). This suggests that these findings could be extendable. Further work is needed to understand whether the profile types, scales (e.g., individual, regional, and national) as well as their proportional structures over time are attributable to other volunteer organisations. Further research also would provide a more in-depth understanding of volunteering typologies to help organisations adapt and accommodate their volunteering opportunities to existing trends in recruitment and retention.