Journal of Human Resource and Sustainability Studies, 2013, 1, 60-67
Published Online December 2013 (http://www.scirp.org/journal/jhrss)
http://dx.doi.org/10.4236/jhrss.2013.14009
Open Access JHRSS
Engaging in Social Action at Work: Demographic
Differences in Participation
Aimee Dars Ellis
Ithaca College, Ithaca, USA
Received August 7, 2013; revised September 8, 2013; accepted September 16, 2013
Copyright © 2013 Aimee Dars Ellis. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accor-
dance of the Creative Commons Attribution License all Copyrights © 2013 are reserved for SCIRP and the owner of the intellectual
property Aimee Dars Ellis. All Copyright © 2013 are guarded by law and by SCIRP as a guardian.
ABSTRACT
Many organizations are utilizing corporate social responsibility initiatives that require employee participation. These
initiatives, which involve social action at work (SAW), can be a source of reputational gains, benefit the community,
and increase employee organizational identification [1]. Although research has been conducted on employee volunteer
programs (EVP), one aspect of SAW, those studies have not identified the characteristics of employees who are most
likely to participate in EVP nor have they considered the wide range of SAW programs. In the field of Sociology, ex-
tensive research has been conducted to identify characteristics of volunteers, but these volunteer programs are outside
the context of CSR initiatives. This research addresses this gap by identifying the characteristics of enployees who en-
gage in SAW across a wide range of activities. The results of the study can help hone future research questions and aid
practitioners in develop ing and marketing SAW programs that resonate with employees and maximize participation for
the good of the employees, organization, and community as a whole.
Keywords: Corporate Social Responsibility; Employee Engagement; Employee Volunteerism
1. Introduction
As part of their corporate social responsibility programs,
many organizations utilize employee efforts to reach out
to their communities. They conduct United Way cam-
paigns, sponsor blood drives, and organize volunteer
programs, to name a few examples. Previous papers [1,2]
have provided a typology of these CSR initiatives that
require employee involvement and have discussed their
impact on employee attitudes and behaviors, finding that
participation in these activities can provide benefits be-
yond the financial bottom line, including increased or-
ganizational identification on the part of employees.
These papers did not, however, discuss which employees
may be most likely to p articipate in social action at work
(SAW). While others have investigated particular aspects
of SAW, such as employee volunteer programs (e.g.,
[3-6]), knowing the characteristics of the employees who
engage in diverse SAW programs will help managers
target these programs effectively as well as identify other
SAW opportunities that may be more appealing to the
groups that do not currently engage in companies’ CSR
initiatives. The results of this research will also help
scholars of future studies hone future research questions.
2. Theory and Hypotheses Development
2.1. Social Ties
A social tie is an association between two individuals, A
and B, the strength of which depends on the time,
intensity, intimacy, and reciprocation which characterizes
the tie [7]. Podolny and Baron [8] created a typology of
social ties in the workplace, distinguishing between
position-to-position and person-to-person ties, the former
based on job interdependence and the latter based on
interpersonal attraction, or friendship. However, they
stress that the distinction is “a matter of d egree not kind”
and should not be overstated [7, p. 677]. Consequently, I
will use the term “social tie” to refer to both formal and
informal ties between two coworkers. To illustrate the
impact of social ties on SAW, I will discuss social ties in
the context of social movements, activism, and volun-
teering.
Research on social activism has found that having a

A. D. ELLIS 61
social tie is related to participation in social movements
[9-13]. Schussman and Soule [14], in a study investi-
gating reasons why p eople participate in protest activ ities
(defined as a protest, march, or demonstration in res-
ponse to a local or national prob lem) found that being as-
ked to take part is the strongest predictor of particip ation.
Social capital, as measured by embeddedness in social
networks (e.g., community leadership) and social norms
(i.e., social and interracial trust), is also related to
volunteerism and charitable giving [15]. Brady and col-
leagues [16] found that recruiters for political activism
were more successful in gaining pos itive responses when
they knew their target. Close ties predisposed targets to
assent to requests; furthermore, recruiters were able to
leverage personal information to appeal to the targets’
personal interests, values, and goals. A case study of a la-
bor strike at a large university campus revealed that em-
ployees were more likely to strike if others in their unit
were also participating [9].
Volunteering is more likely when social ties exist, and,
conversely, volunteering strengthens social ties [17].
Furthermore, volunteering is strengthened through social
interactions [18]. Social ties also affect responses to
volunteerism: Kulik [19] demonstrated that volunteers
who enjoyed fa mily su pport enjoyed their volunteer work
more and suffered less burnout th an those without family
support. Wilson and Musick [18] observed that volun-
teers with more frequent attendance at meetings of reli-
gious or charitab le groups were less likely to drop out of
volunteer activities.
Non-profit groups have utilized this knowledge of so-
cial ties. In its literature for workplace blood drive co-
ordinators, the United Blood Services stresses the impor-
tance of peer-to-peer recruiting for a successful event
[20]. Because of the impact social ties have on a variety
of categories of social action, I offer t he followin g:
Hypothesis 1: Employees with social ties to others en-
gaged in SAW will be more likely to p articipate in SAW
than employees without social ties.
2.2. Past Volunteering/Donating Experiences
Employees who have volunteered or donated in the past
likely will be more likely to repeat their behavior. In a
study of blood donors, Lee, Piliavin, and Call [21] found
that past behavior was predictive of the giving of time,
money, and blood, suggesting th at these activities helped
form a role identity, and consistency in action helped
establish and maintain the identity. Using the Theory of
Planned Behavior to analyze charitable giving, Smith and
McSweeney [22] uncovered a relationship between past
and current donations. Given these results in a general
context, I expect the same drive for role identity
consistency will be present in the workplace, leading to
the following:
Hypothesis 2: Past experiences volunteering or donat-
ing to charities will b e related to cu rren t volu n teering and
donating at work.
2.3. Demographic Differences
Individual differences variables also likely play a role in
an employee’s decision to participate in SAW. Gender,
for example, is related to the likelihood to volunteer with
women volunteering at a higher percentage than men
[23]. Differences in hope, gratitude, and altruism are also
likely related to SAW. To illustrate, a study of 308 white
collar employees by Andersson, Giacalone, and Jurkie-
wicz [24] demonstrated a relationship between hope and
gratitude with concern for CSR. Worldview, particularly
the ethics of care (a concern for others based on empathy
and need) versus the ethics of justice (a universal
perspective), is another individual variable expected to
influence SAW. Evidence shows that women are more
likely to have an ethics of care wh ich is related to higher
levels of volunteerism [25,26]. Together, these findings
suggest:
Hypothesis 3: Women will be more likely to partici-
pate in SAW than men.
Some studies indicate that in general, older adults
volunteer more often than younger adults [27-30]. These
results might be explained by the amount of time older
adults, often retired, have available to devote to volunteer
activities. Within the contex t of SAW, however, age may
not show the sa me relationship with participation. Age is
often viewed as a proxy for tenure, and,usually, the lon-
ger the tenure at a job, the more responsibilities one has.
At the same time, employees with shorter tenure may
feel more pressure to perform and therefore devote more
time and energy to on-the-job concerns. Despite the mi-
xed findings, because of the strong relationship with age
and social action, I propose:
Hypothesis 4: Older employees will be more likely to
participate in SAW than younger employees.
Racial differences also emerge when considering
participation in volunteerism [31,32]. According to Sun-
deen, Garcia, and Raskoff [31], Caucasians volunteer at
the highest rate. Wilson [32 ] suggests this may be du e to
other racial groups’ access to human capital or because
they are not as embedded in social networks and there-
fore are not asked to volunteer. As a result, I suggest:
Hypothesis 5: Ethnic and racial groups will show dif-
ferent levels of participation in SAW.
3. Methods
3.1. Sample
This research was performed as part of a larger study
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A. D. ELLIS 62
conducted at a Southwest location of a major semicon-
ductor manufacturer which I will call ChipMaker to pro-
tect the identity of the sample site. Worldwide, the com-
pany has almost 100,000 employees according to their
2006 annual report with over 10,000 of those employees
at the sample site. ChipMaker produces microprocessors,
motherboards, flash memory, products for network stor-
age, and wireless products. For my study, 1000 employ-
ees from the selected site were randomly chosen to re-
ceive invitations to participate. Of these participan ts, 314,
or 31.4%, completed the survey.
3.2. Procedure
A top-ranking member of ChipMakers’s management
team provided a letter of endorsement for th e study, send
via email, that highlighted the benefits of the survey to
the firm, asks employees to fill out the survey, which was
available through the commercial web-based program
Survey Monkey, and stressed that employee responses
will remain confidential. To allay some concerns about
socially desirable responding, the instructions also re-
minded participants that responses would remain con-
fidential and indicated that there were no right or wrong
answers; but that we were interested in participants’ ho-
nest opinions [33]. A week after th e initial invitation was
sent, a representative from ChipMaker emailed a second
notice to all participants thanking them for participating
and asking them to complete the survey if they had not
done so. Participants were also asked to complete a
follow-up survey two weeks after the initial survey. Of
those respondents, 210 (21% of entire sample, 66.8% of
Part 1 respondents) completed Part 2. Hence, I had
complete data for 210 respondents and used only this
matched data in my analyses. A contact from ChipMaker
reasoned that the low response rate could be due to a
number of factors: low morale due to staff reductions, a
sense that the survey didn’t relate to a core business and
therefore respondents’ day-to-day activities, and lack of
an incentive for completing the survey. Additionally,
though the invitation to participate was sent from the
Corporate Vice President for Corporate Affairs, that VP
may not have been familiar to recipients since he is not in
their direct line of command. A few participants res-
ponded to the invitation to complete part 2 with messages
like, “too long” or “not interested”. Finally, as discussed
earlier, the survey was administered during December
and January, a time of year when other obligations may
take precedence over a voluntary survey. Given the cons-
traints with our research design, and the inevitable at-
trition in multi-part surveys, a 21% response rate with
210 usable cases seems justifiable.
Though collecting data from a single source, i.e. a
self-reported survey, can be a source of common method
variance [34], in this study the construc ts all reflect indi-
vidual perceptions and cognitions; therefore, no reason-
able alternative sources of information exist. To the ex-
tent possible, I controlled for common method bias
through control variables and study design.
3.3. Sample Characteristics
Two hundred nine participants provided their job
category: 165 (78.6%) individual contributors, 28 (13.3%)
managers, 12 (5.7%) administrators, and 4 (1.9%)
executive managers. A range of tenure categories was
present in my sample. Out of the 185 participants who
respondents to this question, 7 (3.3%) had worked in
their current position at ChipMaker less than 1 year, 13
(6.2%) for 1 year, 12 (5.7%) for 2 years, 19 (9%) for 3,
13 (6.2%) for 4, 22 (10.5%) for 5, 36 (17.1%) for 6, 11
(5.2%) for 7, 18 (8.6%) for 8, 12 (5.7%) for 9, 7 (3.3%)
for 10, and 15 (7.1%) for 11 years. Two (1%) of the
sample has a high school degree, 42 (20%) some college,
81 (38.6%) a college degree, 59 (28.1%) a master’s
degree, 10 (4.8%) a Ph.D. or J.D., and 2 (1%) are current
students.
Of the 193 participants providing information about
gender, 59 (28.1%) were female and 134 (63.8%) were
male. One hundred eighty-f ive particip an ts provid ed their
age range: 2 (1.0%) 18 - 24 years old, 51 (24.3%) 25 - 34,
72 (34.3%) 35 - 44, 43 (20.5%) 45 - 54, 10 (4.8%) 55 -
64, 5 (2.4%) 65 - 74, and 2 (1%) over 75. One hundred
eighty-two respondents offered their race or ethnic
background: 12 (5.7%) Black or African-American, 14
(6.7%) Asian, 129 (61.4%) Caucasian, and 18 (8.6%)
Hispanic. Six (2.9%) respondents provided multiple
categories while 3 (1.4%) specified “other”. One hundred
ninety-one participants provided their marital status: 27
(12.9%) single, 142 (67.6%) married, 2 (1%) domestic
partnership, and 20 (9.5%) divorced.
3.4. Measures
Social Action at Work. Social action at work (SAW) is
directly related to opportunities provided by the partici-
pating site, so items were written to reflect the types of
charitable and philanthropic opportunities the company
provides. A preliminary list based on analysis of
ChipMaker’s published material was provided to our
focus group who reviewed the items for language and
relevance. Participants were asked nine items, “How
often do you participate in the following activities?”
Activities included items such as “I recycle at work” and
“I donate to a charity of choice through my work”. Res-
ponses were assessed using a five-item Likert scale with
the anchors infrequently and frequently.
Charitable Giving Outside the Workplace. Based on
the Social Capital Community Benchmark Survey (as
cited by Brady et al., [16]), items to assess participants’
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A. D. ELLIS 63
charitable giving and volunteerism were included: 1) “I
have donated money, property or other assets for cha-
ritable purposes in the past 12 months” and 2) “I have
performed unpaid work to help people besides my family,
friends, or coworkers in the past 12 months.” These were
measured on a 5-point Likert scale ranging from strongly
agree to strongly disagree. In addition, respondents were
asked how important donating and volunteering are to
them.
Social Ties. Social ties were measured through three
items developed expressly for this study: 1) I am more
likely to participate in CSR activities when my cowork-
ers attend, 2) I normally volunteer for CSR activities on
my own, and 3) I normally volunteer for CSR activities
with my coworkers. I had considered asking these ques-
tions for each SAW delineated on the survey, but due to
the possibility of surv ey fatigue and on the recommenda-
tion of my representatives at ChipMaker, I opted to use
fewer items. While this sacrifices some fine-grained data,
the possibility of losing a participant at Time 2 was a
greater cost.
3.5. Data Analysis
SPSS version 12.0.0 was used for the data analysis. Re-
gression analysis was used to test hypotheses involving
social ties and past volunteering and charitable donations.
ANOVA analyses were utilized to test the hypotheses
relating to demographic differences and conduct post hoc
tests to probe significant differences among groups. To
test the hypotheses, I used an average of the different
SAW activities as well as analyzed differences among
groups in individual SAW activities.
4. Results
Descriptive statistics for the variables are presented in
Table 1 and a summary of the results of the d ata analysis
are presented in Tables 1 through 4. Hypothesis 1 was
supported. After controlling for gender, age, and race,
employees with more social ties were more likely to
participate in SAW, explaining 42.6% of the variance
(R2 = 0.426, F(4,241) = 46.501, p < 0.001). Hypothesis 2
was also supported. Employees exhibiting a pattern of
participation in volunteering or donating were likely to
continue to do so. After controlling for gender, race, and
age, this explained 21.5% of variance, R2 = 0.215,
F(5,167) = 10.397, p < 0.001; however, only past vol-
unteering was significant (β = 0.308, p < 0.001). Hypo-
thesis 3, that women will have higher leve ls of SAW than
men, was supported (see Table 2 for means). On average,
women were more likely than men to participate in SAW
(F(1,269)=18.487, p < 0.001). Women were more likely
to donate to a charity of choice, F(1,269) = 6.315, p = 013,
Table 1. Descriptive Statistics.
N Mean Std. Deviation
Donate to United Way 314 3.37 1.770
Donate to Charity of Choice 314 2.99 1.734
Recycle at Work 314 4.57 0.892
Local School Volunteer 314 2.83 1.584
Employee Sustainability Network 314 1.68 1.051
Company Sponsored Volunteer 314 2.89 1.495
Employee Group Volunteer 314 2.59 1.446
Blood Drive Participant 314 1.86 1.301
Donate Expertise 314 2.44 1.413
SOCTIE1 314 4.74 1.664
SOCTIE2 314 3.45 1.081
SOCTIE3 314 2.88 1.170
Past Donations 210 3.90 1.217
Past Volunteering 210 2.97 1.546
Table 2. SAW by Gender.
Mean—Males N = 190 Mean—Females N = 81
Donate to United Way 3.39 3.67
Donate to Charity of Choice 2.83 3.41
Recycle at Work 4.63 4.58
Local School Volunteer 2.64 3.31
Employee Sustainability Network 2.45 3.09
Company Sponsored Vo l un t ee r 2.66 3.65
Employee Group Volunteer 2.45 3.09
Blood Drive Particip a nt 1.78 1.93
Donate Expertise/Skill 2.23 2.93
SAW Average 2.69 3.16
volunteer at a local school, F(1,269) = 10 .291, p = 0.001,
participate in a sustainability group, F(1,269) = 5.819, p
= 0.017, join the ChipMaker sponsored EVP, F(1,269) =
28.452, p < 0.001, volunteer with their employee group,
F(1,269) = 11.401, p < 0.001, or donate their skills or
expertise to community organizations, F(1,269) = 14.989,
p < 0.001. There were no significant differences between
men and women concerning donating to the United Way,
F(1,269) = 1.365, p = n.s., recycling at work F(1,269) =
0.219, p = n.s., or donating blood at work F(1,269) =
0.756, p = n.s. Hypothesis 4, that older employees would
be more likely to participate in SAW than employees in
ounger age groups was not supported (F(7,255) = 1.630, y
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A. D. ELLIS
Open Access JHRSS
64
Table 3. SAW by Age.
18 - 24
N = 3 25 - 34 N =74 35 - 44 N=99 45 - 54 N = 63 55 - 64 N = 14 65 - 74 N = 7 75 - 84 N = 2 85+ N = 1
Donate to United 1.00 2.95 3.37 4.17 4.07 2.71 4.0 5.0
Way
Donate to Charity 1.00 2.54 3.18 3.38 3.43 2 .14 4.5 1.0
Recycle at Work 4.67 4.50 4.67 4.68 4.57 4.57 5.0 5.0
Local School Volunteer 3.00 2.55 3.01 2.87 2.79 3.71 2.50 1.00
Sustainability 2.00 1.65 1.64 1.71 2.07 1.57 1.00 1.00
Group
Company EVP 2.67 2.74 3.04 3.08 2.93 3.00 2.50 3.00
Group EVP 2.67 2.47 2.64 2.97 2.43 2.29 2.00 3.00
Blood Drive 1.33 1.81 1.75 2.11 2.71 1.00 1. 00 2.00
Participant
Donate 2.67 2.34 2.54 2.57 2.21 2.43 3.00 1.00
Skills/Expertise
SAW Average 2.4444 2.6171 2.8698 3.0617 3.0238 2.6032 2.8333 2.4444
Table 4. SAW by Race.
Black/African-American N = 14 Asian
N = 24 Caucasian N = 179 Hispanic N = 26 Multiple Races N = 7 Other N = 6
Donate to United Way 3.79 3.75 3.37 3.50 2.29 3.33
Donate to Charity 3.00 3.63 2.90 2.69 2. 86 2.00
Recycle at Work 4.36 4.25 4.69 4.35 5.00 4.67
Local School Volunteer 3.29 2.54 2.75 3.08 2.86 3 .50
Sustainability Group 2.21 1.75 1.61 1.69 2.00 1.50
Company EVP 3.00 2.71 2.94 3.04 3.43 2.67
Group EVP 3.0 2.92 2.60 2.46 3.00 2.67
Blood Drive Participant 1.21 1.88 1.93 1.85 1.57 2.17
Donate Skills/Expertise 3.14 2.75 2.28 2.19 3.29 2.67
SAW Average 3.00 2.90 2.79 2.76 2.91 2.81
p = n.s), nor was Hypothesis 5, that racial and ethnic
groups will show different patterns of participation
(F(5,250) = 0.268, p = n.s); see Table 4. I did test for
differences for each unique SAW initiative. Because the
highest age groups had small numbers, I collapsed them
into a single category. ANOVA tests showed differences
in participation among age groups in United Way dona-
tions, F(5,257) = 5.353, p < 0.001, dona tions to the char-
ity of choice, F(5,257) = 3.090, p = 0.01, and giving
blood at work F(5,257) = 2.659, p = 0.023. Post-hoc tests,
including Tukey, Bonferroni, Scheffe, and LSD reveal
that the youngest age groups (18 to 24) are the least
likely to en gage in SAW, while employees 45 and older
are the most likely to participate. Even after investigating
each SAW individually, no differences among race or
ethnic groups in parti ci pat i o n l evel s were ob served.
5. Discussion
Knowing the characteristics of employees likely to
participate in SAW can help researchers advance relevant
and appropriate studies and managers develop and
market CSR initiatives that resonate with employees. As
expected, employees who had social ties with others
participating in SAW programs were more likely to
participate themselves, while those who had participated

A. D. ELLIS 65
in the past were also more likely to join SAW initiatives.
In terms of demographic differences, women were more
likely than men to participate in SAW. In past research,
women have exhibited higher tendencies to donate to
charitable organizations and to volunteer as well as to
display higher levels of ethics of care, which is related to
these activities. However, it is important to note that this
engenders a crucial question: are women burdened by the
expectation to care for others at the expense of activities
that might give them more visibility at work or to help
promote their career internally. Historically, women have
been assigned to “busy work” which can be detrimental
to long-term career progression [35]. Additionally, this
type of work may involve more emotional labor, and
emotional labor requirements of women who have been
in positions are more intens e [36].
Differences in SAW participation among ethic/racial
groups or in age groups were not observed in this study.
It is possible due to the small numbers in the population
of some of the groups; for example, the two age groups
representing the oldest employees contained only four
participants. More variance in the sample could produce
more robust results. However, it is possible that the cul-
tural norms at the sample site overpower the influences
that have led to differences among these groups in vol-
unteering and do nat i n g out side of the work context.
5.1. Limitations
The current stud y w as co ndu cted in the United States and
may not be generalizable to other countries. Lee and
Chang [37] for example, found different patterns in Ta-
wainese citizens’ donating and volunteering behavior
from those observed in Western countries. It is reaso-
nable to guess that in the context of SAW, national and
cultural differences would also emerge.
Due to study constraints at the sample site, it was not
possible to collect data regarding some of the dimensions
underlying the hypotheses generated here. For example,
it would be useful to have been able to measure ethics of
care directly rather than using gender as a proxy.
Additionally, some demographic groups had very small
populations. Having more evenly distributed group mem-
bership would provide more assurance in the pattern of
results. Finally, while I attempted to minimize the im-
pact of common method bias, it remains a concern.
5.2. Future Research
A number of other characteristics can be identified to
investigate as antecedents of SAW. As mentioned, the
direct mechanisms such as ethics of care, could be stud-
ied to better understand the characteristics of employees
who participate in SAW. Future research may be able to
incorporate direct, rather than self-reported, measures of
participation in SAW. Additionally, organizational cul-
ture likely influences employees in this process. Since
the current study was conducted at a single site, it was
not possible to investigate this avenue, but I encourage
others to compare a single organization at multiple sites
as well as multiple organizations to see how the culture
affects SAW involvement. In open-ended questions, a
handful of participants mentioned they participated in
social action outside of work. It would be interesting to
see the relationships among the motivations and invol-
vement in social action at work and outside of work.
Given the study design, I could not investigate the moti-
vations to engage in SAW, which is a critical step in the
research stream in this area.
5.3. Managerial Implications
Managers can make their SAW programs more suc-
cessful if they are able to get more male employees in-
volved. Across the board, participation in SAW is low
(see Table 1). The most popular SAW programs are
recycling at work and donatin g to the United Way. These
programs likely see the highest levels of participation
because they are well-established, well-publicized, and
easy to use. These programs can serve as a model to im-
prove the participation in other SAW initiatives.
Managers should also investigate reasons why women
are more likely to participate in these programs and de-
velop programs that would gain equal participation
among genders. Knowing that social ties aid SAW, ma-
nagers can utilize friendship and social networks to pro-
mote and carryout CSR initiatives involving employees.
They can utilize the information from this paper to help
more of their employees participate in enriching SAW
activities that provide benefits to the employee him or
herself, the company, and the community as a whole.
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