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University of Wollongong
Research Online
Faculty of Commerce - Papers Faculty of Commerce
2009
Not just any volunteers: segmenting the market to
attract the high contributors
Melanie J. Randle
University of Wollongong, mrandle@uow.edu.au
Sara Dolnicar
University of Wollongong, sarad@uow.edu.au
Research Online is the open access institutional repository for the
University of Wollongong. For further information contact Manager
Repository Services: morgan@uow.edu.au.
Publication Details
Randle, M. J. & Dolnicar, S. (2009). Not just any volunteers: segmenting the market to attract the high contributors. Journal of
Nonprofit and Public Sector Marketing, 21 (3), 271-282.
Not just any volunteers: segmenting the market to attract the high
contributors
Abstract
Growing competition in the third sector has resulted in nonprofit organizations making more sophisticated
use of marketing techniques to attract volunteers. Not only are organizations attempting to attract more
volunteers but increasingly the focus is shifting to attracting the right type of volunteers, or those who will
contribute the most hours. This study segments the volunteering market by number of hours contributed in
the past 12 months, and identifies significant differences between the characteristics of high-contribution and
low-contribution volunteers. High-contribution volunteers are found to (a) exhibit distinctive
sociodemographic characteristics; (b) have a greater number, and broader range, of motivations for
volunteering; and (c) initially get involved in volunteering in different ways to low-contribution volunteers.
Findings are important because they provide practitioners with a description of those individuals most likely
to contribute more hours, which can be used to increasingly attract these types of people and subsequently
reduce the amount spent continually attracting new volunteers.
Keywords
any, high, contributors, not, just, segmenting, market, attract, volunteers
Publication Details
Randle, M. J. & Dolnicar, S. (2009). Not just any volunteers: segmenting the market to attract the high
contributors. Journal of Nonprofit and Public Sector Marketing, 21 (3), 271-282.
This journal article is available at Research Online: http://ro.uow.edu.au/commpapers/612
Not Just Any Volunteers: Segmenting the
Market to Attract the High Contributors
Melanie Randle &Sara Dolnicar
a School of Management and Marketing, Market Research Innovation Centre, University of
Wollongong, Wollongong, Australia
Abstract
Growing competition in the third sector has resulted in nonprofit organizations making more
sophisticated use of marketing techniques to attract volunteers. Not only are organizations
attempting to attract more volunteers but increasingly the focus is shifting to attracting the
right type of volunteers, or those who will contribute the most hours. This study segments the
volunteering market by number of hours contributed in the past 12 months, and identifies
significant differences between the characteristics of high-contribution and low-contribution
volunteers. High-contribution volunteers are found to (a) exhibit distinctive
sociodemographic characteristics; (b) have a greater number, and broader range, of
motivations for volunteering; and (c) initially get involved in volunteering in different ways
to low-contribution volunteers. Findings are important because they provide practitioners
with a description of those individuals most likely to contribute more hours, which can be
used to increasingly attract these types of people and subsequently reduce the amount spent
continually attracting new volunteers.
* This research was funded by the Australian Research Council through the Linkage Grant
Scheme (LP0453682) and our industry partner, Bushcare Wollongong, a division of
Wollongong City Council. We would particularly like to thank Paul Formosa, Natural Areas
Coordinator for Wollongong City Council, for his support and assistance with this project.
Keywords:
segmentation; volunteering; nonprofit marketing
INTRODUCTION AND BACKGROUND
In recent decades, many countries around the world have experienced a dramatic expansion
of the third sector. The increase in nonprofit organizations has been attributed to a number of
factors, including the shift of responsibility for services previously provided by government
to the third sector (Kingfisher, 2002). The growing number of social issues requiring support
has also contributed, for example the growth in mortgage debt and resultant financial
pressures on families, as well as environmental-related problems such climate change, which
have not yet emerged as high priorities on the agendas of many government bodies.
However, the increase in the number of third sector organizations has not been accompanied
by an equal growth in the availability of human and financial resources. Therefore, in an
effort to continue achieving their goals, nonprofit organizations are taking a more pragmatic
approach to their operation and are using techniques and processes which have, until now,
been more commonly seen in the for-profit sector.
This has also been the case in academia, with marketing researchers applying marketing
techniques to gain greater insight into volunteers and volunteering organizations, with the
aim of providing practitioners with the information and tools that allow them to compete
more effectively. For example, the traditionally “commercial” concepts such of branding,
positioning, and targeting have been demonstrated as not only relevant but important to the
nonprofit sector (see for example Wray, 1994; Venable, Rose, Bush, & Gilbert, 2005;
Chiagouris, 2005; Bennett & Kottasz, 2000; Ewing, Govekar, Govekar, & Rishi, 2002; Yavas
& Riecken, 1997).
One marketing technique which has for years been widely adopted and commonly used in the
commercial sector, but still less so in the nonprofit sector, is market segmentation. Market
segmentation is the process of grouping customers within an heterogeneous market into
different segments, within which individuals have similar requirements which can be fulfilled
by a specific marketing mix (McDonald & Dunbar, 1998). Although not yet commonly used
by volunteering organizations, there is growing acknowledgement of the heterogeneous
nature of the market and the value of segmentation as a way of more effectively targeting
individuals likely to volunteer. As a result, there have been some notable applications of
segmentation techniques to the volunteering sector which warrant mention.
Wymer has conducted a number of a priori studies which segment the market (Mazanec,
2000) using a number of criteria, including sociodemographic characteristics and
volunteering behavior (Wymer, 1997, 2003; Wymer & Starnes, 1999; Wymer, 1999). In each
case, results show that the segments displayed distinctive profiles indicating that customized
marketing mixes could be designed to more effectively attract them.
There have also been limited attempts to segment the volunteer market a posteriori (Mazanec,
2000), using data driven methods (Dolnicar, 2004) to derive the segments. Studies by
Dolnicar and Randle have found that that there are groups of volunteering organizations
which compete with each other for the same individuals (Dolnicar & Randle, 2005), and that
groups of volunteers exist with particular combinations of motivations for involvement
(Dolnicar & Randle, 2007). In both cases, the value of segmenting the volunteer market was
demonstrated and the potential for further applications of the concept for the nonprofit sector
highlighted.
Increased competition has also resulted in many volunteering organizations considering not
only the number of volunteers they attract but the type of volunteers they attract. Recruitment
costs can be dramatically reduced if organizations can recruit volunteers who are prepared to
contribute more time to the cause and stay involved longer (Wymer & Starnes, 2001a). These
individuals are valuable because they become experienced in the particular role they are
performing and reduce the advertising and training costs associated with attracting and
inducting new volunteers. Wymer and Starnes (2001b) provide a detailed analysis and
discussion of the various factors which contribute to volunteer retention and the importance
of ensuring a positive volunteer experience. This includes, for instance, making sure
volunteers feel needed, gain a sense of job satisfaction and accomplishment, and have the
opportunity to meet people and develop a network of friends.
Currently, however, there is little information regarding who these high-contribution
volunteers are, and how or if they are different from individuals who volunteer less
frequently. The purpose of this study is to identify whether high-contribution volunteers
differ significantly from low-contribution volunteers in their personal characteristics and
motivations, and whether this information can be used to more effectively target high-
contribution volunteers.
PRIOR RESEARCH
Many studies have attempted to identify and describe those individuals most likely to
volunteer. Some have taken a sample of people who have performed a particular type of
volunteer work, for example volunteering for the Girl Guides (Nichols & King, 1999) or the
Red Cross (Frisch & Gerrard, 1981), and simply described their characteristics. Others have
compared a sample of volunteers with a sample of nonvolunteers to test for, and describe,
significant differences (Dolnicar & Randle, 2006). However a limitation of most studies is
that to form the segment of “volunteers” they group together everyone who has performed
any volunteer work within a defined timeframe, for example the past 12 months. This means
that individuals who have volunteered only once for a few hours during the past year are
grouped with those who have regularly contributed multiple hours every week during the past
year. These studies fail to consider that there may be very different types of people who
engage in low- versus high-frequency volunteering activities, and that understanding these
differences may make it easier for organizations to attract people who are likely to contribute
more hours. This study addresses this gap by segmenting the volunteering market a priori
based on the number of hours contributed by the individual in the past 12 months.
In terms of who is most likely to volunteer, the results of previous studies are conflicting. One
factor which consistently emerges as a strong predictor of volunteering behavior is education
(McPherson & Rotolo, 1996; Reed & Selbee, 2000). However, for other sociodemographic
characteristics the profile of volunteers varies significantly from study to study. For example,
Curtis, Grabb, and Baer (1992) found volunteers most likely to be employed full-time,
whereas Smith (1999) found volunteers more likely to work part-time. A study of Red Cross
volunteers found women more likely to volunteer (Frisch & Gerrard, 1981), while studies of
political volunteers find men more likely to be involved (Riecken, Babakus, & Yavas, 1994).
One organization that reported on the sociodemographic characteristics of its longer serving
members was the Girl Scouts, which found that they were older (at least 40), had attended at
least four years of college, and were employed full-time or part-time (Girl Scout U.S.A.
Profile of Adult Volunteers,1998, cited in Wymer & Starnes, 2001b). While this information
is useful in terms of knowing the volunteers they have already successfully attracted, it is less
helpful in identifying potential new markets. The question remains: when an organization is
looking to expand its base of high-contribution volunteers, is there a distinctive segment of
the market which should be targeted? Considering that most profiling studies of volunteers
have found that different segments of the volunteering market do display distinct
characteristics, it is expected that when segmented by volunteering contribution the resultant
groups will also display distinctive profiles. Therefore, it is hypothesised that:
H1: High-contribution volunteers will be characterized by a distinct sociodemographic profile
when compared to low-contribution volunteers.
In terms of reasons why people volunteer, most researchers acknowledge that motivations are
multifaceted (Bendapudi, Singh, & Bendapudi, 1996; Omoto & Snyder, 1995). Wymer,
Riecken, & Yavas (1996) discuss the generally agreed upon opinion that altruistic
motivations (e.g. to help those less fortunate) and egoistic motivations (e.g. enjoyment or to
develop skills to help their career) are not necessarily mutually exclusive, and that most
individuals who volunteer because of a genuine desire to help others still want to have a
rewarding experience. However, what is not known is whether the number of reasons for
involvement is different for different segments of the market. It seems likely that individuals
who volunteer regularly and consistently over a long period are motivated by a greater
number of reasons for involvement. For these people, if one of their reasons for volunteering
reduces in importance the other reasons for involvement are enough to keep them actively
volunteering. On the other hand, if low-contribution volunteers are motivated by fewer
factors and one of these reduces in importance (e.g. if they volunteer for their child's school
but the child leaves that school) they would have no other reason to continue and therefore
stop volunteering for that cause. Therefore it is hypothesized that:
H2: High-contribution volunteers cite a greater number of motivations for volunteering than
low-contribution volunteers.
H3: High-contribution volunteers exhibit a broader range of motivations for volunteering
than low-contribution volunteers.
Numerous studies have investigated the attitudes or mindset that characterize volunteers.
Findings include that volunteers display prosocial attitudes (Wymer, 1997), a strong sense of
civic duty (Florin, Jones, & Wandersman, 1986), and feel a personal responsibility to support
the common good (Reed & Selbee, 2000). Accepting that volunteers do have distinctive
attitudes which intrinsically motivate them to perform some form of social service, it is
hypothesized that this would also influence the way they come to be involved in volunteering
activities. That is, the more involved, higher-contribution, volunteers are more likely to have
initiated involvement themselves (intrinsic motivation) rather than have been asked or
persuaded by others (extrinsic motivation) to become involved. Therefore, it is hypothesized
that:
H4: High-contribution volunteers are more likely to actively seek out volunteering
opportunities, whereas low-contribution volunteers are more likely to become involved
because they were encouraged to do so by someone else.
METHODOLOGY
Fieldwork Administration
The data for this study was collected during September and October of 2006 in Australia
using an online internet panel, which was set up and maintained such that it was
representative of the population. The online panel was recruited not only online but also face-
to-face and over the phone to minimize the sample bias which can occur if one recruitment
method is used exclusively. An invitation to participate in the survey was sent to a subset of
the panel which was also representative of the Australian population.
An online research design was chosen because it allowed a national Australian sample to be
collected within the cost and time constraints of the project. Due to the questionnaire being
completed online the data was automatically entered into an SPSS file. This eliminated the
possibility of errors in data entry which can be a problematic aspect of survey research. In
addition, the online survey was programmed such that respondents were not able to proceed
to the next question until they had provided a valid answer for the current question. This
eliminated any possibility of missing data.
Measures
The data used for this study were collected as part of a larger 30 minute online survey which
asked detailed questions relating mostly to giving unpaid help. To allow for hypothesis
testing and profiling of segments in this study, individuals answered questions relating to
their past volunteering behavior, reasons for volunteering, and personal characteristics.
Level of contribution
To split the sample according to level of volunteering contribution, respondents were asked to
indicate the number of hours of unpaid help they had contributed to organizations or groups
in the past 12 months. Respondents could select one of seven answer alternatives: 1-9 hours,
10-19 hours, 20-39 hours, 40-79 hours, 80-139 hours, 140-299 hours, or 300 hours or more.
Reasons for volunteering
Following a review of relevant literature and an extensive qualitative phase which contributed
to the design of this study, respondents were given a list of 19 possible reasons for
volunteering, for example to help those less fortunate or to maintain services they may use
one day. Respondents indicated which of the reasons applied to them by either checking or
leaving blank the box next to each reason. Participants could select as many reasons as
applied to them.
How became involved in volunteering
Respondents were asked to indicate how it was that they first came to give unpaid help to the
organization or group. Six answer alternatives were given and participants were allowed to
select only one option as the main reason. The reasons were because they knew someone
involved, someone asked them to become involved, previous involvement with the
organization, they saw an ad/report in the media, they found out about it themselves, and
other. Respondents who selected the “other” alternative were asked to describe how they
became involved.
Socio-demographic characteristics
To enable profiling of segments and testing for significant differences, respondents were
asked questions relating to sex, age, education, income, employment status, relationship
status, and family status.
Sample Characteristics
Because of the highly multicultural nature of Australian society, the sample for this study was
deliberately structured to ensure that a wide range of cultural groups were represented,
including individuals from Anglo-Celtic, Asian, Middle Eastern, European, and American
ancestries.
A total of 848 individuals who completed the survey indicated they had volunteered in the
past 12 months. Segments were constructed according to the total number of hours
volunteered in the past year. An extreme group design was employed (Alf & Abrahams,
1975), whereby high and low subgroups were isolated and the remaining midlevel group
eliminated from analysis. Rather than simply take a median split to derive the groups, experts
in the field of volunteering were consulted as to how they would define typical high-
contribution volunteers and low-contribution volunteers. Following these interviews it was
determined that low-contribution volunteers would be defined as individuals who contributed
between 1-19 hours in the past 12 months (n = 449) and high-contribution volunteers would
be defined as those who contributed 40 or more hours in the past 12 months (n = 271).
Those in the midrange, who had volunteered for between 20-39 hours (n = 128), were
excluded. Therefore, the total sample size used for this analysis was 720.
Data Analysis
Data analysis for this study was conducted using SPSS statistical software. The variables
used to test hypotheses H1 and H4 are categorically or ordinal scaled. Therefore, chi-squared
tests were performed to check for significant differences between groups, and the p-values
Bonferroni corrected to account for multiple testing on the same data. For hypothesis H2, the
average number of motivations was calculated for each group, and ANOVA used to test for
significant differences. For hypothesis, H3 frequency counts were performed to identify
which motivations were selected by over half of the total sample. Unless otherwise specified,
all statistics reported in this paper have significance levels of 95% or higher.
RESULTS
H1: High-contribution volunteers are characterized by a distinct sociodemographic profile
when compared to low-contribution volunteers.
No significant differences were found between the groups in relation to sex or income.
Surprisingly, and inconsistent with other studies of volunteers, education also failed to
discriminate between segments. Despite this, a number of significant differences were found
between the groups. High-contribution volunteers had a significantly higher number of part-
time employees (27%) and non-working individuals (17%) than the low-contribution group
(14% and 12% respectively), while low-contribution volunteers had the highest proportion of
full-time employees (60%, compared to 50%), χ2(4) = 23.3, p = .000. Intuitively this seems
logical because nonworking and part-time employees would have more time to regularly
devote hours to another cause, while the time restraints placed on full-time employees would
be a barrier to frequent involvement. High-contribution volunteers were more likely to be
older, with 33% aged 36-45 (compared to 18% for low-contribution volunteers), and 27%
aged 46 or above (compared to 13% for the low-contributors). Conversely, low-contribution
volunteers had a high proportion of younger individuals aged 18-25 (31% compared to 16%)
and 26-35 (37% compared to 25%), χ 2(5) = 55.0, p = .000. High-contribution volunteers
were also more likely to have children (51% compared to 38%) while low-contribution
volunteers were likely to be without children (62% compared to 49%), χ 2(1) = 6.6, p =
.049. That a number of significant sociodemographic differences were found between the
high- and low-contribution volunteers means hypothesis H1cannot be rejected.
H2: High-contribution volunteers cite a greater number of motivations for volunteering than
low-contribution volunteers.
The average number of reasons ticked by each group was calculated and ANOVA performed
to assess the significance of differences. The average number of motivations applying to the
low-contribution group was 6.7 and the average for the high-contribution group was 9.0. This
was significant at the 99.9% level (F = 53.6, df = 1), therefore hypothesis H2 cannot be
rejected.
H3: High-contribution volunteers exhibit a broader range of motivations for volunteering
than low-contribution volunteers.
To test hypothesis H3 not only were the average number of reasons for volunteering
considered but the types of reasons were investigated. Those reasons which applied to the
majority of each segment (that is, the reasons which were selected by over 50% of the
segment) were analyzed. In the low-contribution group, only four motivations applied to the
majority. These were, “it gives me the chance to help others”; “I can give something back to
society”; “it will improve my community”; and “I can support an important cause.” These
four reasons also applied to the majority of the high-contribution group, but an additional five
reasons also applied to the majority of the high-contributors. These were, “I can meet
different types of people”; “it sets a good example for others”; “I can socialize with people
who are like me”; “it keeps me active”; and “I feel like I am doing a good job.” Interestingly,
while the four reasons applying to the low-contribution group are all altruistic in nature, the
high-contribution group nominated not only altruistic motivations for involvement, but a
number of egoistic reasons that essentially provide benefits not only to others but also to
themselves, such as being able to meet different types of people and keep active. This balance
of helping others plus benefiting oneself is likely to be a key factor in the high-contribution
group being willing to keep donating more hours for a longer period, because they feel as
though they also have benefited personally in some way from their involvement. This is the
fundamental concept of relationship marketing, that the relationship must be mutually
beneficially for it to be healthy and lasting in the long term (Arnett, German, & Hunt, 2003).
That the majority of the high-contribution group indicated a wider range of altruistic and
egoistic reasons for involvement means that hypothesis H3 cannot be rejected.
H4: High-contribution volunteers are more likely to actively seek out volunteering
opportunities, whereas low-contribution volunteers are more likely to become involved
because they were encouraged to do so by someone else.
Participants were asked to indicate how it was that they first became involved in volunteering
by selecting one of five answer options. As expected, high-contribution volunteers had the
highest proportion indicating they became involved after finding out about volunteering
opportunities themselves (21%), while low-contribution volunteers were more likely to
indicate they became involved because someone asked them to be involved (27%), X 2(5) =
19.8, p = .001. Therefore, hypothesis H4 cannot be rejected.
DISCUSSION AND FUTURE RESEARCH
The significant differences that have been found between high- and low-contribution
volunteers allow managers of volunteering organizations to design marketing strategies
which more effectively target these particular types of individuals. The distinctive socio-
demographic characteristics of high-contribution volunteers provide insight as to who the
people are that contribute the most hours to volunteering. They are likely to be married or
living with a partner, not working or in part-time employment, have children, and be over 35
years old.
That high-contributors are motivated by a range of altruistic and egoistic motivations means
that a package of benefits not only to others and the community, but also to themselves, is the
message that will be most meaningful and motivating for them. The fact that they are the
group most likely to seek out information on volunteering opportunities themselves means
that organizations need to make sure that information on their particular cause is readily
available, clear, and concise.
The present study has provided evidence to suggest that high-contribution volunteers are
significantly different from low-contribution volunteers in terms of their socio-demographic
characteristics, motivations for involvement and how they initially became involved in
volunteering. Results are important for managers of volunteering organizations because they
enable them not only to identify those individuals who are likely to become involved in
volunteering, but those who are likely to contribute the highest number of hours. By doing
this they can minimize the costs of advertising, recruitment, and training which are associated
with new volunteers. In the current environment of reduced funding and increased
competition, it is fundamental that organizations make the most efficient use of their limited
resources. Only by doing this can they continue to meet their organizational goals and
provide important public services which would otherwise not be provided by government.
This study provides a platform from which further research could be launched, for example
investigations of additional characteristics of the high-contribution group. Questions that
remain include: are there particular roles that high-contribution volunteers are more likely to
perform or certain organizations or types of causes that they are attracted to? Do the profiles
of high-contribution volunteers differ depending on the cause? For example is the profile of a
high contributor to environmental causes different from high contributors for humanitarian
causes?
Further insight into the mindset of this group and the ways in which they differ from other
groups of volunteers, or indeed non-volunteers, would only add to the tools available to
volunteering managers in trying to target the types of people who are most likely to be
interested in their volunteering offering and contribute the most hours doing that specific type
of work.
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Notes
This research was funded by the Australian Research Council through the Linkage Grant
Scheme (LP0453682) and our industry partner, Bushcare Wollongong, a division of
Wollongong City Council. We would particularly like to thank Paul Formosa, Natural Areas
Coordinator for Wollongong City Council, for his support and assistance with this project.