Posted: March 12th, 2023

Applying Social Psychology to Organizations.

Please answer one of the four questions.

1. Discuss the findings from the Murthy and Lewis article. How does or can social media help and hurt your workplace?
2. After reading the provided article, discuss the role that groupthink played in the ‘Challenger’ disaster.  What was learned from this experience, and what can be said about the decision making process in organizational settings? 
3. The reading in ISL discussed the concept of “emotional labor.” Describe what emotional labor is, and what type of jobs draw upon one’s emotional labor today. 
4. Consider the concept of cancel culture in today’s society. Do you think it will get better or worse in the future and why based on what you know of social psychology.

Chapter 7 Summary

Emotion Work and Feeling Rules

We all have to do emotion work during our lives. Hochschild sees emotion work as the active effort by an individual of managing their emotion in a particular social context. There are social norms for most social situations that an individual encounters which clearly define what emotion a person should feel. However, people do not always feel the emotion that they “should” feel in these situations. This is when emotion work is necessary.

Hochschild draws on Goffman’s work on emotional expressions of individuals to conform to social conventions. Goffman’s work identified the role of social norms in regulating the emotions that should or should not be felt and the proper expression of these emotions. Hochschild also draws on Freud’s work on emotional management as a means of dealing with unpleasant emotions and the role of an individual’s resources in emotion work. These two theorists are then built upon by the author to describe the emotion work that people do to consciously try to feel a certain emotion that is in line with social norms.

Feeling rules are an essential part of emotion work as a means of defining what an individual should feel. These rules are unspoken, but others may remind an individual if they feel that the individual is not showing the appropriate emotion. We can see how Goffman’s theory of the presentation of self is a part of the social regulations that a society uses to construct and guide individuals’ emotional work. Individuals must present the appropriate emotions to show others that they are conforming to social norms and others will sanction these emotions either positively or negatively.

American Behavioral Scientist
2015, Vol. 59(1) 149 –171

© 2014 SAGE Publications
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DOI: 10.1177/0002764214540504

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Article

Social Media,
Collaboration, and
Scientific Organizations

Dhiraj Murthy1 and Jeremiah P. Lewis2

Abstract
The use of social media by collaborative organizations has been studied in a variety
of contexts, including virtual teams, enterprise organizations, and social movements.
However, social media are not often examined within the context of scientific
organizations. This article explores how an organization of 122 life scientists and
science-related professionals—anonymized as Science City Network (SciCity)—
combine monthly symposia with social media, including Twitter, Facebook, and
blogs. Using an online survey, we found that younger SciCity members are more
interested in using social media to support a collaborative community, whereas
older members are more interested in social applications. Social media use was
not found to significantly differ by gender. Using social network analysis, we found
several individuals who act as hubs of information who keep the SciCity Twitter
network alive. However, the hierarchical structure of the network reveals that it is
better suited for information dissemination than innovation and collaboration. Our
examination of this scientific organization ultimately offers insight into how a coalition
of multiple social media technologies is used differentially by organizational members
and that there is ultimately no general consensus of the utility of social media to
scientific collaboration. This finding tempers some claims of the utility of social media
for scientific collaboration.

Keywords
collaboration, social media, virtual organizations, scientific organizations, meetups,
virtual science

1Goldsmiths College, University of London, UK
2Bowdoin College, Brunswick, ME, USA

Corresponding Author:
Dhiraj Murthy, Goldsmiths College, Department of Sociology, Lewisham Way, London, SE14 6NW, UK.
Email: d.murthy@gold.ac.uk

540504 ABSXXX10.1177/0002764214540504American Behavioral ScientistMurthy and Lewis
research-article2014

mailto:d.murthy@gold.ac.uk

http://crossmark.crossref.org/dialog/?doi=10.1177%2F0002764214540504&domain=pdf&date_stamp=2014-07-03

150 American Behavioral Scientist 59(1)

The use of social media by collaborative organizations has been studied in a variety of
contexts, including virtual teams (Muller et al., 2012; Murthy, Rodriguez, & Lewis,
2013), enterprise organizations (Brzozowski, 2009), and social movements (Juris,
2012). The notion that social media can foster trust and other preconditions for mean-
ingful collaboration in organizations makes intuitive sense as these technologies have
the potential to increase interactions between organizational members and ultimately
build social capital. Though social capital has been successfully used to study social
movements within virtual communities (Cogburn & Espinoza-Vasquez, 2011), a key
distinction of social capital online versus offline is that when it is acquired online, it
can lack an affect-based dimension. Erving Goffman’s (1967) studies of gestures and
communication argue that this affect is part of the richness of face-to-face interactions.
However, others have argued that social-media-driven technologies can successfully
(albeit, sometimes minimally) encourage the affective dimensions that foster the accu-
mulation of social capital (Valenzuela, Park, & Kee, 2009).

Emergent social media have forced us to expand our understanding of not only the
types of social capital that the Internet can foster (Sander, 2005), but also the speed of
social capital acquisition and new ways of using it. A particularly interesting case is
that of hybrid organizations that combine meaningful aspects of online and offline
social capital, potentially making them fruitful spaces for collaboration. Meetup.com,
a virtual community that comes together face-to-face via offline meetings, termed
“meetups,” is a well-known example. Sander (2005) argues that these organizational
hybrids can foster “alloy social capital,” a strong form of social capital that leverages
its ability to use the extensibility of the Internet and the low-friction context for inter-
action as well as stronger affect-based ties constructed through offline contact. This
follows Jurgenson’s (2012) argument that social media can “augment” offline interac-
tions and should not be thought of as oppositional to face-to-face interactions.

This article explores one such hybrid community, anonymized as Science City
Network (SciCity), a life science organization that combines regularly scheduled events
with social-media-based interactions. SciCity consists of two intersecting operational
modes: regularly scheduled meetings and an accompanying Twitter stream. In all, 122
people have attended SciCity events that generate significant Twitter activity during
these organized face-to-face events. A recent event generated more than 4,000 tweets.
SciCity’s monthly meetings cycle through diverse themes in the sciences ranging from
topical areas to science policy issues. Twitter feeds using the #SciCity hashtag support
the conversation and experience high traffic during monthly events and low traffic oth-
erwise. SciCity’s virtual participants include geographically remote members who use
the video streaming service Livestream to view the event and simultaneously use the
Twitter hashtag to interact with the group as a whole regardless of physical attendance.
Tweets are projected behind the conference speaker to unify the conversation.

Our case study is based around a survey administered during the summer of 2012.
Survey questions gathered data regarding demographics, social media usage and per-
ception, and the SciCity Twitter network. We were interested in explaining how Twitter
use complemented (or not) one’s offline interactions with SciCity as well as under-
standing the nature of interpersonal relationships between SciCity Twitter users. We

Murthy and Lewis 151

did not find evidence that respondents saw the use of SciCity-related social media as
fostering collaboration. Rather, the structure of the network, with several individuals
acting as hubs, served more as an information clearinghouse. This is a form of (weak)
collaboration, in which links and other knowledge are shared via information brokers.
However, stronger forms of collaboration, such as papers, grants, and so on, were not
found to be directly facilitated by Twitter or other social media.

Literature Review

Offline → Online

Exclusively virtual communities can and have been meaningfully collaborative. This
is particularly the case with virtual teams (Jarvenpaa & Leidner, 1999). However, even
these communities and groups have found a positive “effect of offline gatherings on
physically dispersed virtual communities” (Sessions, 2010, p. 375). In some cases, not
having meetups “risks loss of weak ties” (Sessions, 2010). This can be ameliorated by
videoconferencing technologies including Skype and Google Hangout. Knowledge-
based organizations have found that the lack of face-to-face interactions in distributed
virtual teams can lead to “individual profit . . . at the expense of the community” and
the loss of bridging social capital (Sessions, 2010). “Multiplex relationships” with
“media multiplexity” and meetups can be important for tie strength (Sessions, 2010).
The augmented strength (or alloy social capital) gained by multiplexity is seen in the
simple case where “meetup attendees strengthen their relationships with those they
meet offline” (Sessions, 2010, p. 391). This echoes other virtual community literature
(Rheingold, 1993; Sander, 2005). Face-to-face interactions in geographically distrib-
uted organizations can be an important way to bring team members closer together. In
addition, face-to-face interactions can tease out or create new relationships that would
not have organically formed online. For example, introverts can be more gregarious on
social media than in face-to-face interactions (Correa, Hinsley, & De Zuniga, 2010).
McCully et al. found that “online communities may benefit from face-to-face meet-
ings to fulfill a variety of needs and motivations for both the users and the site” and
these meetups can deter the “creation of sub-groups and a disconnection from the
broader community” (McCully, Lampe, Sarkar, Velasquez, & Sreevinasan, 2011). Of
importance, offline meetups can foster trust, which becomes manifested through sub-
stantive changes in online interactions, such as shifting some of their public posting to
private messaging with other users (McCully et al., 2011). These strengthened rela-
tionships can be better foundations for collaborative processes.

Online → Offline

The converse is also true and online interactions can and do shape face-to-face interac-
tions. In the United States, for example, incoming college students use Facebook to
interact with their soon-to-be roommates (Israel, 2006), mediated interactions that
deeply shape their first face-to-face encounter. The importance of online interactions to

152 American Behavioral Scientist 59(1)

offline interactions has been studied in a diverse range of organizational contexts (Lin,
2007; Subrahmanyam, Reich, Waechter, & Espinoza, 2008). Liu et al. (2012) found that
an event-based social network “does not only contain online social interactions as in
other conventional social networks, but also includes valuable offline social interactions
captured in offline activities.” They found that online social networks act as a “conven-
ing technology” where the online is an offline catalyst. Bode (2008) found that “various
types of Facebook behaviors have clear and significant effects on several types of posi-
tive offline political participation.” Cummings (2008) highlights that collaboration is
fostered by homophily, proximity, and familiarity. These can, of course, be fostered
through face-to-face and social-media-based interactions. For example, the regular
social familiarity bred by tweets and Facebook status updates can potentially strengthen
proximity and familiarity via “watercooler moments” (Zhao & Rosson, 2009) and even
test levels of homophily (e.g., do we share similar hobbies, interests, and friends?).

SciCity is a hybrid community that finds its offline and online interactions impor-
tant to its organizational goals. Both offline and online environments are an integral
part of building prosocial behaviors such as trust, mentorship, and collaboration. In
contrast to Anderson, Steinerte, and Russell’s (2010) reading of online communities
and trust in which they theorize that online collaboration is to some extent incompat-
ible with the development of interpersonal trust, Way and Austin (2012) argue it is not
a question of incompatibility, but rather the development of interpersonal trust online
can be qualitatively different than similar trust formed offline. Abfalter, Zaglia, and
Mueller (2012) suggest the notion of “sense of virtual community” (SVOC), the con-
cept that “feelings of membership, identity, and belonging, and attachment to a group
that interacts primarily through electronic communication,” an idea that helps us
understand how an organization can have SVOC despite having a significant and regu-
lar offline component. In other words, SVOC and offline meetings are not mutually
exclusive. Indeed, SVOC can be strong and compel or encourage offline interactions
that would have otherwise not been possible or likely (perhaps because of geographi-
cal or organizational differences that were bridged by SVOC).

Social Media and Scientific Collaboration

Scientific communities who actively use Twitter are growing, but still remain in the
minority. Twitter’s use during scientific conferences (Reinhardt, Ebner, Beham, &
Costa, 2009) and as a tool to disseminate scientific knowledge to broader audiences
(Letierce, Passant, Decker, & Breslin, 2010) has been explored, but the medium’s abil-
ity to promote scientific knowledge development remains underexplored. Scientific
organizations are not usually early adopters of social technologies and a certain level
of conservatism can grow. Kling and McKim (2000) argue that science lags technol-
ogy organizations because of institutional friction, different conventions, and different
predominant media. For example, programmers have a high regard for online collab-
orative knowledge producing spaces such as stackoverflow.com (Hanrahan,
Convertino, & Nelson, 2012). A notable exception is when the prominent publication
Scientific American blogged about its favorite Twitter accounts in 2009 (Wong, 2009),

Murthy and Lewis 153

when fewer than 2% of young Americans used the site on a typical day and well before
Twitter use grew to 8% in 2012 (Smith, 2012). Kling and McKim argue that the focus
on collaboration parallels trends in science toward international collaboration. For
example, Olson, Zimmerman, and Bos (2008) found that there has been far more sci-
entific collaboration using the Internet, which is directly reflected in an increase from
7% to 17% in international coauthorship in the sciences (from the 1980s to the 1990s).

Because social media are often used to share knowledge (e.g., links, grant solicita-
tions, etc.), a distinction needs to be made between sharing and collaboration. Hyde
et al. (2012) argue that “sharing of content alone does not directly lead to collabora-
tion” and that collaboration on social media needs “an additional layer of coordina-
tion.” This additional layer could be an instruction that compels you to tweet under a
particular hash tag (e.g., #occupywallst), to tweet photographs during disasters, and so
on. The important argument here is that aggregation can be a type of collaboration,
though it may be a weaker form of collaboration than an experimental project, paper,
or grant. This is not to say that weak collaboration is not important; rather, it is the
aggregated strength of weak contributions that is important to collaborative knowl-
edge production. A classic and well-studied example of this is Wikipedia (Ransbotham
& Kane, 2011).

Method

Data were collected during the summer of 2012 via an extensive online survey.
Respondents were recruited through a SciCity event in New York, Twitter, and tar-
geted emails. Snowball sampling (Biernacki & Waldorf, 1981) was used to achieve
high levels of coverage. Our survey response rate provided coverage of 19% of the
estimated SciCity population. Despite the drawbacks that a nonrandomized survey
incurs, it was necessary to use a snowball method to increase coverage rates given the
small size of the community, the diversity of groups involved, and the approximate
time it took to complete the survey (well over 10 minutes).

The survey included Likert-type questions (Maranell, 2007), questions regarding
members’ media consumption, and a network section that asked users to report on
their Twitter-based interactions with specific (self-identified) members of the SciCity
community. Likert-type questions were used to assess community satisfaction on
Twitter with the first asking whether the respondent felt part of a community on
SciCity and the second asking whether the SciCity community was a place where
users could seek guidance. Of importance, Likert-type questions asked respondents to
assess whether Twitter was a suitable place for scientific collaboration.

The survey was simultaneously advertised at a monthly face-to-face event and via
social media, consistent with the online/offline organizational structure of SciCity. The
survey link was retweeted by several influential SciCity members. Publicizing surveys
via Twitter has found some success in market research (Patino, Pitta, & Quinones,
2012). However, reach on Twitter is highly dependent on the following of the tweeting
account and the numbers of retweets (and their follower counts). Following Solomon’s
(2001) suggestion that personalized email appeals significantly increased response rate,

154 American Behavioral Scientist 59(1)

we used targeted emails. In our case, these efforts helped boost response rates. Social
network analysis (Knoke, Yang, & Knoke, 2008) was used to study the self-reported
Twitter network. Specifically, members were asked to report whom they interacted with
on Twitter, their level of trust of that user, their level of collaboration, as well as several
other measures. The network was studied for density, individual degree (the number of
inbound connections to an individual in the network), and clustering to understand the
structure and hierarchy of the SciCity Twitter network.

Results

The success of SciCity has been built on high levels of social media usage, including
Facebook, Twitter, blogs, and Livestream. The SciCity community uses social media
such as blogs to build critical topical discussion, archives tweet conversations into a
narrative using the Storify.com platform, and curates discussion on Facebook pages.
Livestream allows those unable to attend the event in person to participate and even
collaborate on the Twitter stream live (a process aided by the live video stream). In
addition to the Twitter hashtag, an official Twitter account coordinates Twitter follow-
ers and publicizes events. SciCity members are varied by occupation, race, gender,
education, and age. They represent a communal “coalition” whose cohesion is heavily
dependent on both social media and face-to-face events.

The community that participates in regular face-to-face events perceives knowl-
edge sharing as a significant benefit from interacting with SciCity’s social media
incarnations. Indeed, social media helps constitute the community itself, which would
otherwise be more geographically colocated. Age, race, gender, residence, education,
occupation, Twitter usage, and offline-event attendance were important variables for
understanding how this community maintains itself.

The SciCity Population

Our survey was launched simultaneously on Twitter and at a monthly offline meeting
of SciCity in the summer of 2012. The face-to-face “pitching” of the survey was
expected to increase response rates. Of interest, completion rates did not significantly
vary by event attendance. Given the small size of SciCity (122 members), the result of
34 users answering at least one question of our survey and 23 completing all 14
required questions was strong. One complete survey, by a self-described “survey-
enthusiast” was removed from analysis. Incomplete survey responses were used to
understand how the missing data biased our results. The survey attracted respondents
with varying degrees of interaction with SciCity. Of the 34 respondents answering at
least one question, 38% had attended 6 or more events, 26% had attended 3 to 5
events, and 35% had attended 2 or fewer events. SciCity event attendance was corre-
lated with survey completion. Of users who had attended more than 5 events, 85%
completed the survey, whereas for users who had attended 5 or fewer events the com-
pletion rate was 57%. The survey was not completed by any user who had never
attended an offline SciCity event. In other words, heavy attendees of offline SciCity

Murthy and Lewis 155

events were oversampled and data about attendees who exclusively participate virtu-
ally are completely lacking.

Survey completion of those who had attended 3 to 5 events was lower than that of
respondents who attended 1 to 2 events (56% and 77%, respectively). This may sug-
gest that beyond the core group of enthusiasts (the 6 or more event attendees) that
offline attendance does not indicate community commitment (as proxied by survey
completion). Respondents who failed to complete the survey were subset out of our
data, and all further results are based from fully completed surveys. A linear regression
comparing respondent participation with the number of survey questions answered
was inconclusive, though it had a negative slope (indicating that survey questions
answered decreased as SciCity participation decreased). Of attendees of 6 or more
events, 54% answered more than 20 survey questions, whereas 66% of attendees of 5
or fewer events answered no more than 20 questions.

SciCity users received information about SciCity most frequently through Twitter
than any other form (computer mediated or not). Of SciCity users, 87% use the social
media site and 70% of all respondents reported getting information about SciCity
through Twitter. Online blogs related to the community were negligibly cited as pro-
viding information about the community. Of respondents, 48% reported receiving
information about SciCity through the online mailing list. Respondents who received
information about the community through one medium were less likely to receive
information through the other.

Notable features of the SciCity membership include the high proportion of women
(64% of respondents), the geographical concentration of respondents in New York
City (78% of respondents), the mode educational status (graduate degree), and the
youthful skew in age (see Figure 1). It is important to note two archetypes of SciCity
members: young, racially diverse, female scientists who have obtained their PhDs, and
older, less diverse, graduate-degree-holding males in science-related fields. We

Figure 1. Characteristics of the SciCity membership.

156 American Behavioral Scientist 59(1)

hypothesized that early-career scientists would be interested in mentorship and col-
laboration and are knowledgeable about social media. We expected this to predict
heavier use of the medium, particularly for urbanites for whom Twitter has been found
to be especially popular (Fox, Zickuhr, & Smith, 2009).

Because age is highly correlated with social media use (Correa et al., 2010), it is an
important variable for studying collaboration and social media. Twenty-six percent of
American 18- to 29-year-olds are Internet users on Twitter, compared with only 14% of
those ages 30 to 49 (Smith, 2012). The higher likelihood of young people to use Twitter
suggests that the opportunities for virtual participation are skewed toward the very popu-
lation among which SciCity members are concentrated; SciCity’s population is gener-
ally young, with 48% of respondents 30 years old or younger (see Figure 2). The female
dominance of this group is important, as women have been found to be more active in
creating content on social networks (Hampton, Goulet, Marlow, & Rainie, 2012).

The most darkly shaded bars in Figure 2 illustrate SciCity participants with doc-
toral degrees by age group. The number of respondents with doctoral degrees peaks in
the 18 to 30 age group and declines in members older than 40 (with only one respon-
dent older than 40 who has obtained a doctorate). Not only does this fail to track with
overall rates of doctoral degrees when compared to educational attainment in the
United States as a whole (Bauman & Graf, 2003), it directly contradicts them. SciCity
is therefore formed from a small pool of highly educated Americans and draws a much
higher proportion of the young than it does of the old. This simultaneous success and

Figure 2. Education by age (highest degree completed).

Murthy and Lewis 157

failure—success at attracting young, educated participants and failure to attract estab-
lished, tenured senior scientists—suggests a strong interaction effect between age and
education. Respondent age was associated with being comfortable seeking guidance/
mentorship from SciCity (Fisher’s exact test, p < .05). This indicates a consensus opin-
ion among younger members (reporting agree to strongly agree) and a split, more
pessimistic result among older members.

Location

Location plays an important role in mediating SciCity’s online and offline interactions
(because much of the social media activity surges in the wake of offline events). The
combination of cognitive-based trust, such as ability, and affect-based trust, such as
personal trust, from both online and offline interactions supports a fully hybrid trust
model. Our survey was targeted to those who had attended at least one SciCity event.
The existence of an offline event that serves as a focal point of SciCity presents an
obvious geographical constraint that appears to circumscribe the limits of the com-
munity and is reflected in the respondent data (see Figure 1).

Of interest, one of the four “leaders” of SciCity is based overseas, which suggests
that geographical isolation is possible within SciCity. However, this is the exception
rather than the rule as proximity drives consolidated membership among New Yorkers.
What is also interesting was the fact that extreme rather than relative proximity was a
necessary pretext for the community. Living in the suburbs or adjacent states provides
perceived inadequate proximity (or that SciCity is unappealing to those not in New
York City) despite extensive virtual infrastructure supporting the offline exchange.
These location data (see Figure 1) are important given that urban individuals are more
likely to use Twitter than suburban or rural individuals (Smith, 2012). This suggests
that SciCity is doubly local—it has offline (inherently local) meetings and deploys a
technology popular (even native) to its locality. The overlapping coincidence of quasi-
local social media and local face-to-face meetings may drive the extreme concentra-
tion of SciCity’s users in physical space. Of importance, physical proximity has been
found to be an important factor for collaboration success (Tierney, 2000).

Despite the seamless integration of social media within SciCity, the organization
remained strongly anchored to the locality of its face-to-face events. There is a signifi-
cant difference between those who regularly attend SciCity and the likelihood they
attended the most recent event (Fisher’s exact test, p < .05) and the likelihood they feel
part of the SciCity community (Fisher’s exact test, p < .05). Members who attended
most SciCity events were found to be more positive about SciCity as a collaborative
community and the ways in which social media facilitated collaborative interactions.

Twitter

Eighty-seven percent of surveyed respondents are Twitter users and the mode response
for number of followers was between 100 and 500. The mode for followed was between
100 and 500. The number of followers each respondent had clustered around 100 to 500,

158 American Behavioral Scientist 59(1)

but the number of users each respondent followed varied widely, with the two most fre-
quent responses at 100 to 500 (30%) and 2,000 or more (20%). Very few respondents
reported “news gathering” accounts with low numbers of followers, but many accounts
followed (i.e., lurkers). Rather, there is a range of Twitter participants in SciCity includ-
ing “novices/disengaged” (low–low), “celebrity/thought leader” style accounts (low–
high), and heavy users with a reciprocal, social orientation (high–high).

Twitter users and nonusers alike were fairly evenly split between receiving infor-
mation through the mailing list and not. Unsurprisingly, respondents who answered
that they also were not Twitter users (13% of respondents) also reported that they did
not receive SciCity information through Twitter (providing an additional check on the
validity of our results). Twitter is actively used by the SciCity community for informa-
tion dissemination. Of respondents, 65% both used Twitter and received information
about SciCity through Twitter. Twitter users are evenly spread across different fre-
quencies of use, although the mode response was weekly (30% of respondents), with
monthly and daily the second and third most popular frequencies. Those who used
Twitter no more than once daily tended to also be the users with low numbers of fol-
lowers and low numbers of following, fitting the “novice” Twitter style. When follow-
ing and follower numbers are compared with Twitter usage frequency, an interesting
case arises. Low numbers of following and low frequency of Twitter use had frequent
overlap (this is characteristic of an unattended account). Of respondents following
large numbers of users, they were evenly split between high and low frequency of
Twitter use. This is compatible with a try it but quickly tire of it behavior. Similarly,
users were twice as likely to follow high numbers of users if they had a high frequency
of Twitter use.

These trends are in part substantiated and in part reversed by a comparison of fre-
quency of Twitter use and the number of followers. Low frequency Twitter use is
associated with a high numbers of followers (54% of low frequency users also had
high numbers of followers). And, of users who had high numbers of followers, 64%
used Twitter infrequently. It is clear that Twitter usage is more strongly predicted by
high numbers of followed rather than high followings. This does not speak strongly to
the social power of Twitter in scientific organizations (as one would expect that a
larger audience should drive more usage).

Of Twitter users, 90% reported using the SciCity hashtag. Ninety-five percent
reported that they retweet posts as a way of participating with SciCity on Twitter. For
those users who tweeted with high frequency (at least once per day), 83% interacted
with other SciCity members on Twitter and 66% posted links. Social uses such as
nonscience discussion and social communication are frequent uses for Twitter (65%
and 60% respectively). Fifty percent of respondents shared science news and 45%
shared blog posts.

Of interest, Twitter use was generally constant across age groups. Twitter frequency,
Twitter usage, the number of followers, and the number of following were not depen-
dent on the age of the respondent (see Table 1). There were no statistically significant
results as to whether one age group received SciCity information through Twitter more
often nor were members’ views of Twitter’s potential to enable collaboration

Murthy and Lewis 159

Table 1. Relationship Between Demographic Variables and Community Variables.

Age Race Education Gender Occupation STEM SciComm

Online community sentiment
I feel I am part of a community in

SciCity.
.172 .652 .926 1 1 1 .489

SciCity is a community where I can
seek guidance.

.012** .092* .685 .679 .593 .198 .85

Motivation for participating in online community
No. of motivations .042** .39 .494 .732 .252 .393 .375
Intellectual stimulation .59 .539 .771 .273 .161 .032** .229
Mentorship .59 1 .471 1 .677 1 1
Networking 1 .395 .308 .515 .435 .111 .486
Friendship .014** 1 .266 1 .095* .621 .339

Online media used to get info about community
Number social media used .684 .64 .298 .204 .326 .667 1
Facebook .005*** .621 .421 .343 .445 .182 1
Mailing list 1 .037** .209 1 .524 1 .68
Twitter 1 1 1 1 1 1 1

Twitter
Twitter user .59 .539 .771 .273 .578 1 1
Twitter frequency .141 .255 .695 .596 1 1 1
Twitter followers .37 1 .433 1 .647 .642 .406
Twitter following .17 .117 .72 .633 .522 .642 1
Use community hashtag 1 1 1 .505 .429 1 1
SciCity info from Twitter 1 1 1 1 .398 .345 .65
Twitter useful for SciCity

collaboration
.804 .753 .412 .139 .255 .146 .408

Ways respondent interacts with community on Twitter
Number of ways .005*** .587 .344 1 .778 .651 .642
Continue offline conv. .065* 1 1 .352 .659 1 .67
Share links .01*** .285 .159 .65 .449 .642 1
Retweet posts .45 1 .2 .421 .15 1 1
Discuss science news .07* .582 .133 1 .459 1 .37
Nonscience conversations .16 .249 .242 1 .22 1 .374

Blogs
Number of blogs read .092* .46 .223 .461 .042** .032** .761
Read local blog .667 .621 1 .649 .698 1 .685
Read general blog 1 .611 .055* .386 .007*** .007*** .417
Read practice oriented blog .069* .272 .11 1 .445 .182 1

Significance based on Fisher’s exact test. *p ≤ .1. **p ≤ .05. ***p ≤ .01.

significantly different (see Table 1). The rate of Twitter usage was not statistically
significant among age groups, which suggests that professional organizations differ in
their use of Twitter than the general population’s use of the medium (which has been
found to be age-related; Smith, 2012).

The results illustrated in Table 1 clearly reveal that age is the only variable that
shows a high correlation to social media (though, of note, this was not the case in many
of the Twitter questions). Overall, social media perception usage within SciCity was
heavily mediated by age. That being said, the perceived utility of Twitter for

160 American Behavioral Scientist 59(1)

collaboration on SciCity was low and was not significantly associated with any of the
survey’s core variables. SciCity members seek opportunities for mentorship and col-
laboration outside of traditional structures, which likely explains the high concentra-
tion of women in the organization. Gender is independent of all variables (p < .1),
which indicates that gender does not predict SciCity user behavior. This is important
given that social media use has been found to be influenced by gender (Correa et al.,
2010), with, for example, increases in social media usage among teenage girls (Lenhart,
Purcell, Smith, & Zickuhr, 2010). Gender is not similarly influential in the case of
SciCity as Twitter usage patterns, and the types of social media that respondents use to
interact with SciCity are not predicted by gender.

Both age and lower levels of education are significant deterrents to participation in
SciCity. This has important implications for two of SciCity’s core aims. First, SciCity’s
goal to extend membership beyond traditional academic boundaries is deterred by the
high educational level of members. Second, in failing to capture highly educated older
scientists (or even comparatively old in the case of the 31- to 40-year-olds), SciCity
risks being unable to offer a place for meaningful mentorship and collaboration
between partners of diverse experience levels. Science research can be fundamentally
transformed by the new perspectives of junior scientists (Rappa & Debackere, 1993),
and some senior scientists see a range of benefits to mentoring and collaborating with
junior scientists (Kahn & Greenblatt, 2009). As 68% of members are 18- to 40-year-
olds with at least a graduate degree, SciCity has the potential to be an effective col-
laborative space across ranks (contingent on recruitment of senior scientists).

Diverging Use of Technology

Social media usage in SciCity varied by age. Facebook was used more by older mem-
bers as a means to receive information about SciCity, but seldom used by younger
members. This suggests an aversion to Facebook use as a means of professional inter-
action by younger members of SciCity. Older respondents were also more likely not to
read SciCity’s science practice-oriented blog. This divergence in Facebook and blog
usage suggests a compartmentalizing of social and professional interactions for
younger members. This is substantiated by a much lower rate of younger SciCity
members seeking social interaction through social media when compared to older
SciCity users (p < .01). Furthermore, the number of ways SciCity users interact with
each other on Twitter varies strongly by age with older respondents using the medium
socially (e.g., for chatting or keeping in touch).

Older and younger members exhibit distinguishable patterns of Twitter usage. This
has implications not only for identifying younger or older Twitter “styles” but also for
the professional-oriented use of Twitter by younger SciCity members. This finding
supports Skeels and Grudin’s (2009) conclusion that Twitter is being deployed in pro-
fessional contexts. Older Twitter users were more likely to use Twitter in multiple
ways (e.g., direct messaging, retweeting, and social tweets; Fisher’s exact test, p <
.01), less likely to post links (Fisher’s exact test, p < .05), and more likely to continue
conversations initiated face-to-face at events (Fisher’s exact test, p < .1). This suggests

Murthy and Lewis 161

a greater fluency with Twitter by younger users, which simultaneously reduces their
investment in offline interaction (as demonstrated by their lower rates of continuing
offline conversations). The Twitter style of younger SciCity members also legitimates
the use of Twitter as a professional knowledge-sharing space for scientists.

However, this is not to say that social use by older members generated a higher
frequency of Twitter activity than younger members. Rather, the latter were more
likely to use Twitter to post and interact professionally. This suggests that while
younger SciCity members are less social, they are neither withdrawn nor disengaged
from the social media spaces of SciCity. We were interested in whether the social
aspects of the medium could foster increased levels of collaboration. However, we
found no statistically significant relationship between social interactions and col-
laboration on Twitter. This is an important finding as there is wide interest in evalu-
ating whether the social aspects of social media foster collaboration (Murthy, 2013)
by supporting affective-based trust development and, at a more limited level, cogni-
tive-based trust. What we did find is that older users tend to be community general-
ists, who seek many types of interaction through SciCity (and welcome social
interaction). This seems to pervade not only their community interactions, but also
their online interactions within SciCity (with younger users interested in divesting
their social interactions from SciCity, an organization they see as a more profes-
sional space).

The SciCity Twitter Network

The SciCity Twitter network is not particularly dense (density = 0.0383). This means
the Twitter network only has 3.83% of all possible ties present. It is also not highly
connected, as evidenced by an average degree of 1.84. In other words, the average user
has 1.84 inbound interactions, which is low in comparison to “small-world” networks,
with an average degree of 10 (Watts & Strogatz, 1998). This low degree means that
most members of the network are not interconnected and need to route information via
brokers much higher up in the network. This creates a deeply hierarchical network
with two SciCity members (Users 3 and 4) acting as the center of the network and most
other subgroups flowing through them (see Figure 3).

Collaboration levels are relatively weak on the #SciCity Twitter space, as reported
by respondents (see Figure 4; thicker lines represent stronger levels of reported col-
laboration). Only one interaction (between Users 19 and 25) has a collaboration level
of 3 out of 5. Most reported collaboration is between the levels of 1 and 2. The level
of collaboration is indicated by the thickness of line between two SciCity Twitter
users. As the network indicates, women are more central to the network (see Figure 9;
larger dots indicate a higher network degree).

The number of Twitter followers a SciCity member has does not significantly
change the lack of collaborative sentiment within the SciCity Twitter network (see
Figure 5). Because members generally have very high followings (evidenced by the
red, blue, and pink dots in Figure 5), the #SciCity Twitter network already has a high
audience reach and a high relative threshold in terms of follower counts. Also, the

162 American Behavioral Scientist 59(1)

Figure 3. Hierarchical clustering of #SciCity Twitter network.

strongest level of reported collaboration (between Users 19 and 25) involves interac-
tants with the lowest category of follower counts (colored in yellow in the online ver-
sion of this article).

The frequency by which SciCity members tweet within the #SciCity hashtag is also
not significant to engendering collaborative interactions. Central members such as
Users 4, 12, and 16 tweet within the hashtag daily (see Figure 6). However, User 4, for
example, does not elicit high levels of collaborative sentiment in return. This suggests
that activity within the hashtag can be kept to a minimum, but still obtain the average
levels of collaborative sentiment reported within the network.

Murthy and Lewis 163

Figure 4. Collaboration level by gender (blue = female, yellow = male, green = organization,
red = undisclosed, see colors in the online version of this article).

Figure 5. Collaboration level by number of followers (yellow = 0-100, green = 100-500,
red = 500-1,000, blue = 1,000-2,000, pink = >2,000 followers, see colors in the online
version of this article).

164 American Behavioral Scientist 59(1)

Figure 6. Collaboration by frequency of SciCity hashtag use (blue = weekly, white = not
disclosed, yellow = never, pink = monthly, green = several times daily, red = once daily, see
colors in the online version of this article).

A noteworthy finding is that SciCity members report high levels of trust developed
from their interactions on Twitter. As Figure 7 illustrates, this is not dependent on
gender (thicker lines indicate higher levels of trust). Rather, the Twitter network indi-
cates high levels of trust, which are the product of Twitter-mediated interactions.
Given overall survey data, these high levels of trust are most likely affect-based, rather
than cognitive-based. This is consistent with our finding of the lack of collaborative
sentiment fostered by Twitter. Specifically, cognitive-based trust is an important pre-
requisite for many forms of collaboration, both online and offline. This is to say not
that affect-based trust is unimportant, but that having high levels of affective but low
levels of cognitive-based trust is not as useful as the converse.

Communication frequency on Twitter is average (see Figure 7). More frequent
interactions seem to occur between users with larger Twitter followings (see Figure 7;
thickness of lines indicates communication frequency on Twitter). In addition, as pre-
viously discussed, central users of the network (e.g., Users 4, 3, 12, 16, and 11) all
have 1,000 or more followers on Twitter, which is well above Twitter’s average fol-
lower levels of 208 (Roberts, 2012).

Social network analysis indicates that there is a central leadership within the Twitter
network on SciCity (the larger dots in Figure 8 represent users with more reported
inbound interactions). Though there is a dearth of high levels of collaboration, there
are high levels of trust within the network (see Figure 6). The relatively high network

Murthy and Lewis 165

Figure 7. Trust by gender (blue = female, yellow = male, green = organization, red =
undisclosed, see colors in the online version of this article).

Figure 8. Communication frequency by number of followers (yellow = 0-100, green =
100-500, red = 500-1,000, blue = 1,000-2,000, pink = >2,000 followers, see colors in the
online version of this article).

166 American Behavioral Scientist 59(1)

degree for users within the center of the network (i.e., Users 3 and 4) reveals that there
are hubs/brokers of information who also act as leaders who maintain the fabric of the
#SciCity organization on Twitter. Those such as Users 3 and 4 who are central to the
network help maintain the cohesiveness of #SciCity. In this case, they also exhibit high
degrees of inbound trust sentiment (see Figure 6), average levels of collaboration sen-
timent (see Figure 3), and above-average frequency of contact (see Figure 7). The
SciCity network on Twitter is active and vibrant, though it does not foster collabora-
tion. Ultimately, it indicates that the types of trust developed on SciCity’s Twitter
network are more affect-based. This type of collaboration is much weaker and revolves
around collaborating at the level of information sharing and aggregation of informa-
tion, which Hyde et al. (2012) argue is a meaningful type of collaboration. Though the
levels of collaboration sentiment are not high overall, this should not be conflated with
a lack of a collaborative environment. First, information-sharing-based collaboration
does not require high levels of collaboration sentiment. Second, there is potential for
future, stronger collaborative networks to emerge (which do not exist now). In other
words, Twitter discourse may serve as a starting point to encourage and kick start
future collaborations that take place via other mediated communication or face-to-
face. Therefore, though the value of Twitter itself in terms of fostering meaningful
collaboration is not found to be significant directly, the knock-on effects in terms of
fostering collaboration on other media (though beyond the remit of our study) could
potentially be important to scientific work. Of importance, gender was not found to be
a significant variable in terms of the ways in which the SciCity Twitter network oper-
ates. As Figures 4-7 indicate, gender is not a determining factor influencing social
media perception or use in SciCity. That being said, SciCity users with higher degree

Figure 9. Collaboration by degree and gender (blue = female, yellow = male; larger vertex
size = higher degree, see colors in the online version of this article).

Murthy and Lewis 167

centrality are more likely to be women. Brokerage of information on the SciCity
Twitter network is therefore dependent on women, even if collaboration sentiment is
not tied to gender.

Conclusion

This article has sought to understand the role social media play in scientific organiza-
tions and whether they foster meaningful collaboration. Scientific organizations have
been conservative in adopting social media and have generally been pessimistic in
their view of the utility of social media to advancing scientific collaboration. Social
media literature has suggested that the social aspects of social media could help build
trust in virtual teams and that this trust could provide an important foundation for col-
laboration (Calefato, Lanubile, & Novielli, 2013). The case of SciCity highlights that
this is not generally the case. We found that social use of social media was more popu-
lar among older SciCity members and that these users were the same users least likely
to use social media within SciCity to foster collaborative interactions. In addition, the
community as a whole did not see Twitter as particularly useful to fostering scientific
collaboration. SciCity is an interesting case study as it is not a purely virtual commu-
nity, but a virtual/face-to-face hybrid. SciCity centers on two types of core interac-
tions: online social media and offline monthly “meetups.” The regularly scheduled
events of this scientific organization create and maintain an active membership and
social media interactions provide the cohesive glue between events. SciCity has
emerged out of an “augmented” (Jurgenson, 2012) communication style in which digi-
tal communication is simply another layer of an individual’s professional interactions.
SciCity members who used social media for social purposes were in the minority. In
SciCity, it appears that the interactions on social media do not merely extend the reach
(through time and space) of SciCity, but deepens organizational cohesion allowing
some members to even interact on a daily basis, sharing links to scientific news, or
grant opportunities for example. Collaboration is occurring on SciCity but at a weaker
level—information aggregation and knowledge sharing, rather than project-based col-
laboration. Though this is not a “traditional” mode of collaboration, it is a form of
aggregated collaboration based around collaborative knowledge sharing (akin to
Wikipedia edits).

The #SciCity Twitter network is an important part of the community and is led by
two well-followed Twitter users who act as central information brokers. Outside of
them, the number of connections a user in the #SciCity Twitter network averages less
than 2, making it a weakly connected network compared to “small-world” networks
for example. This, combined with a low density measure and a hierarchical cluster,
reveals the emergence of two dominant users. This structure is not ideal for using
social media to foster collaboration. That being said, Twitter and other social media,
including Facebook and blogs, have been important to SciCity and have helped foster
affect-based trust (in distinction to knowledge-oriented cognitive trust). This indicates
the possibility that social media can further trust within scientific organizations.
However, the types of trust being fostered may be more confined to weaker forms of

168 American Behavioral Scientist 59(1)

collaboration. Ultimately, the case of SciCity highlights the complexities of social
media and collaboration. However, for example, if senior scientists begin to have a
greater inclination to use social media professionally, there is real potential for social
media to advance scientific collaboration.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship,
and/or publication of this article: This material is based in part on work supported by the
National Science Foundation under Grant 1025428. Any opinions, findings, and conclusions or
recommendations expressed in this material are those of the authors and do not necessarily
reflect the views of the National Science Foundation.

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Murthy and Lewis 171

Author Biographies

Dhiraj Murthy is Senior Lecturer of Sociology at Goldsmiths College, University of London.
His current research explores social media, virtual organizations, and big data quantitative anal-
ysis. His work on social networking technologies in virtual breeding grounds was funded by the
National Science Foundation, Office of CyberInfrastructure. Dhiraj also recently published
Twitter: Social Communication in the Twitter Age, the first academic book about Twitter (pub-
lished by Polity Press).

Jeremiah P. Lewis was a research fellow at the Social Network Innovation Lab, Bowdoin
College and is currently a Congress-Bundestag Fellow.

Applying Social Psychology to Organizations

Overview of Chapter
Please listen to my brief introduction to this chapter on this slide by hitting the speaker button on the left of the slide.

Groupthink

Groupthink Theory
After reading the section in the book on groupthink theory, you can read it from this source as well:

https://en.wikipedia.org/wiki/Groupthink

Read from beginning up to the Prevention heading

Yes, sometimes I might refer to Wikipedia if I think it can help you

Schema
Schemas assist in this selective perception, our brain makes shortcuts to understand quickly unfolding information. As an example, I gave earlier in the semester, my brain knows to stop at a red light without having to reprocess the info each time.

This can be both helpful (as in driving)

But also harmful

Perceptual Biases
Errors that distort the perception process and lead to faulty judgement
Selective perception-a manager cannot notice everything around them, we all have too much information coming in, thus they selectively notice information or people

Halo effect: operates when we have a general impression of someone on one characteristic, and then assess other positive traits to the person

Horn or devil effect (not in book)-the opposite of halo effect. We have a general impression on one characteristic, then assess negative traits to them

Similar-to-me: people perceive others who are like themselves more favorably

Hiring
Note that many of these issues are especially important in the hiring and promotion process.

In her classic book: Men and Women of the Corporation, 1977, Rosabeth Moss Kanter discussed what she called “homosocial reproduction” which was how corporations ended up with white men in management, and white secretaries that moved with them as the “office wives.”
She is still a professor at the Harvard Business School

Job satisfaction
There are so many ways to measure job satisfaction
Earnings, growth potential, autonomy, safe environment, benefits etc. One must use an index if they want to measure this concept.

When you see lists of jobs with highest satisfaction, look at how they are measuring it, and see if they are only including white collar jobs.
Some that frequently come up are clergy, psychologists, firefighters, dental assistants, but they change depending on what is measured

Person-job fit
Job satisfaction is derived from personal disposition and environmental job characteristics

a bad Person-Environment fit leads to low job satisfaction
One commonly used model here is Karasek’s Job Demand model which has High Demand of the job and Low Control. It can also be when a person is in a skilled job, but has not enough education or training to handle the job.

There are huge literatures on these concepts!

Karasek, R, Theorell, T. Healthy Work: Stress, Productivity and the Reconstruction of Working Life. New York: Basic Books, 1990.

Summary
If you are interested in the concepts of this chapter, I encourage you to explore it in your final project

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