Conversational Peers and Idea Generation:
Evidence from a Field Experiment ∗
Sharique Hasan
Stanford GSB
Rembrand Koning
Stanford GSB
September 29, 2015
Abstract
Social interaction is thought to affect individual and organizational innovation. We argue
that individuals and teams are better able to generate high quality ideas if they converse with
peers who provide them with new insight, perspective and information. Further, we argue
that not all individuals can equally capitalize on this new information. Specifically, extroverted
peers, because they are more willing to share and transmit their experiences facilitate idea
generation. Moreover, innovators who are open to experience absorb this new information
better and can more effectively convert it to better ideas. We test our claims using novel data
from a randomized field experiment at an entrepreneurship bootcamp where individuals were
randomized to both teams and conversational peers. We find that conversations with extroverted
peers help individuals generate higher quality ideas, more open individuals benefit more from
such peers, and teams with more cohesion can convert these raw ideas into better performance.
∗
Both authors contributed equally. Manuscripts from this project have alternating authorship sequence. Please
direct correspondence to sharique@stanford.edu and rkoning@stanford.edu. Special thanks to everyone who made
Innovate Delhi happen, especially Ponnurangam Kumaraguru and Randy Lubin. This research was made possible
by IIIT-Delhi and the Indian Software Product Roundtable. This work was funded by Stanford’s Graduate School
of Business, Institute for Research in the Social Sciences, and the Institute for Innovation in Developing Economies.
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Introduction
What role does social interaction play in the idea generation process? A growing literature in
sociology and management has begun to explore this question, with the aim of learning how
networks facilitate idea generation (Fleming, Mingo and Chen, 2007; Burt, 2004, 1992; Reagans
and Zuckerman, 2001; Reagans, Zuckerman and McEvily, 2004; Tortoriello and Krackhardt,
2010; Azoulay, Graff Zivin and Wang, 2010). One major strand of this literature has focused on
the importance of collaboration. Research unequivocally suggest that teams and co-authorships
dominate lone individuals in the development of new ideas by facilitating recombination of
diverse and specialized knowledge (Singh and Fleming, 2010; Wuchty, Jones and Uzzi, 2007;
Girotra, Terwiesch and Ulrich, 2010). However, co-creation and formal collaboration constitute
only one class of social interaction. Innovators also interact with, and benefit from, more informal interaction with people outside their team (Lee, Lee and Pennings, 2001; Menon and
Pfeffer, 2003). Such informal interactions include conversations with peers in other firms, customers, and other individuals not formally involved in their innovation process. Indeed, much of
modern entrepreneurship education and practice encourages conversations to enhance individual
creativity (Blank, 2013; Kelley and Kelley, 2013; Ries, 2011; Sutton and Hargadon, 1996) . As
a result, an increasingly important question is determining whether and why social interaction
affects ideation.
An important source of external knowledge for both innovators and teams are the conversations that they have with external peers (Shue, 2013; Uzzi, 1996, 1997). Peers possess many
important resources and traits that should benefit the innovator. Peers possess general and
specialized knowledge (Sacerdote, 2001; Hasan and Bagde, 2013), diverse contacts (Hasan and
Bagde, 2015), and financial resources (Shane and Cable, 2002). Peers also possess intangible
information accumulated from their own experience that may give the individual new perspective and insight (Lerner and Malmendier, 2013; Nanda and Sørensen, 2010). By conversing
with an external peer, the focal innovator can develop awareness about new problems, insight
into possible solutions, or develop novel hypotheses by recombining divergent experiences. The
ability of the innovator to generate new insight and perspective from her conversations, and thus
generate good ideas, should rest on the interaction dynamics with her peers (Carrell, Sacerdote
and West, 2013; Hasan and Bagde, 2013). Thus, a key question is understanding the social
factors that lead to more fruitful interaction with conversational peers.
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An important, though often overlooked, trait likely to enhance interaction dynamics between
the innovator and her peer is the peer’s willingness to share her experiences (Goh, 2002). A large
body of literature in psychology and management suggests that this willingness to talk, share,
and express one’s views can be succinctly captured by a person’s level of extraversion (De Vries,
Van den Hooff and de Ridder, 2006; Cheng-Hua et al., 2007; Matzler et al., 2008). Extraversion is
a relatively stable and independent personality trait (Rammstedt and John, 2007). Extroverts
enjoy and gain energy from social interactions. In conversations, they are talkative, express
positive emotion, and willingly disclosing information about themselves (John and Srivastava,
1999; McCrae and John, 1998). In contrast, introverts are more reserved and reflective, gaining
energy through time alone (Brebner and Cooper, 1978). Conversations, particularly short ones,
with extroverted peers should produce interactions with more informational volume, greater
affect, and are also more likely to expose the listener to the perspectives and experiences of her
peers (McCroskey and Richmond, 1995). Thus, relative to having a more introverted peer, an
innovator should accumulate a greater range of raw material for her idea generation process,
and thus develop better ideas, when she talks to an extrovert (Aral and Van Alstyne, 2011).
Recent research in sociology and economics, however, suggests that the mere presence of a
peer with beneficial traits does not invariably lead to good outcomes for the focal individual
(Carrell, Sacerdote and West, 2013; Hasan and Bagde, 2013). Specifically, research suggests
that a complementary “match” between the traits of the focal individual and peer moderate
the degree of social influence. Absent a match, resources, knowledge, and insight may be lost
in transmission (Beshears et al., 2015; Hasan and Bagde, 2013). In the context of peers, the
focal individual must both absorb the information produced by her peer and and make novel
associations between this information and her own experiences to develop good ideas (Zahra and
George, 2002; Cohen and Levinthal, 1990; Borgatti and Cross, 2003; Liao, Fei and Chen, 2007).
One individual level trait that captures this receptivity to external stimuli—such as information
gleaned from conversations with others—is a person’s openness to experience (hereon, openness)
(John and Srivastava, 1999). Individuals with greater openness possess intellectual curiosity,
openness to sensory experiences, active imaginations, a greater need for cognition, and are
more responsive to engaging stimuli (George and Zhou, 2001; McCrae and Sutin, 2009; McCrae,
1987). Consequently, if conversations with external peers are conceptualized as an information
transmission process, more open innovators should have greater sensitivity to information from
more extroverted peers. Thus, the pairing of an innovator open to experience and an extroverted
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peer should help the former develop better ideas.
The processes described above outlines the idea generation process of individuals. However,
this process should also hold at the level of the team (Reagans and Zuckerman, 2001; Reagans, Zuckerman and McEvily, 2004; Oh, Chung and Labianca, 2004; Oh, Labianca and Chung,
2006). If members of a team talk to many extroverts, then that team should also possess a
greater quantity of raw material for the idea generation process and also higher quality raw
ideas generated by its individual members. Furthermore, if the team’s members are open to
experience, the quality of their ideas in aggregate should also be superior to teams with less
open members. This simple process of aggregation should result in higher quality ideas for
teams with more open team members and more extroverted peers. Moreover, internal team
dynamics—particularly the extent to which the team members exhibit cohesive and collaborative behaviors—should moderate whether the team can capitalize on the knowledge generated
through external interaction (Reagans, Zuckerman and McEvily, 2004; Woolley et al., 2010).
Despite the theorized importance of external peer conversations for the innovation process,
testing whether peers help individuals and teams innovate is difficult for several reasons. Primary among these reasons is that while team-memberships, co-authorships, and other formal
collaborations are easier to observe by the researcher, short external conversations are often
unobserved. Thus, external peer conversations are generally outside the purview of empirical
analysis. Second, even if conversations were to be enumerated, capturing the psychological and
other characteristics of peers (e.g. extraversion or openness) is necessary to test our proposed
extraversion-openness match theory. Third, to test whether individuals and teams have better
ideas because of these conversations, researchers should have access to the wide set of ideas
generated at both the earlier and later stages of the process as well as ratings of such ideas in
terms of their quality. Fourth, even if the above data were available, the selection of conversational peers by the focal innovator in most contexts is endogenous—depending on the often
unobserved traits and preferences of both the focal individual and the peer (Sacerdote, 2001;
Manski, 1993, 2000). Thus, without accounting for self-selection in peer choice, the relationship
between peers and ideation outcomes may be spurious. Thus, the ideal test would be one where
there is random assignment of conversation partners. Finally, to assess the impact of conversations at the team level, the composition of the team based on the openness of its member should
not depend on the self-selection of teams premised on the value of openness (Hartmann et al.,
2008). Thus, teams ideally should also be randomly composed such that variance in a team’s
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openness and members’ cohesive orientations is not due to self-selection.
To overcome these empirical challenges we designed a novel field experiment embedded in
a bootcamp for young entrepreneurs held in New Delhi, India in June of 2014. During the
first week of the bootcamp the 112 participants were randomly assigned to work in 40 teams
of approximately three individuals to develop a mobile software product concept for the Indian
wedding industry. To overcome the four empirical hurdles described above we measured every
individual’s and team’s idea generation and development process during the week, ranging from
the generation of the original concept by individual team members to the team’s submission
of the final product concept and prototype for prizes at the week’s end. Furthermore, we
measured multidimensional evaluations of the ideas generated at each stage. To ensure that we
could get valid causal estimates of peers on idea quality, we randomly assigned individuals to
three external conversation peers, with each conversation lasting 20 minutes, on the second day
of this competition. We used pre-event measures of peers’ and individuals’ extraversion and
openness to test our hypotheses about the role of peers in the idea generation process.
Our results provide strong support for the hypothesis that conversations with peers improve
a focal individual and team’s ability to generate novel and high quality ideas. Specifically our
results indicate that individuals who have conversations with extroverted peers have higher
quality ideas in terms of business value, demand potential and novelty. Furthermore, we find
that the quality of an individual’s ideas improve even more when a match exists between a
high-openness focal innovator and an extroverted peer. Finally, we find that teams who, in
aggregate, had more extroverted conversation peers had better rated final projects and that this
effect is greater for teams with members high in openness to experience and who show greater
team cohesion.
The theory and empirical results provided in this article extend the sociological model of
innovation in several ways (Reagans and Zuckerman, 2001). We show that external peers influence the idea generation process for individuals and also aggregate up to the level of the team.
Second, our results dovetail with a growing body of literature that suggests that peer-influence is
not a homogenous process, but fundamentally depends on a match between peer traits and focal
and team level characteristics, in our case peer extraversion and individual and team openness
(Carrell, Sacerdote and West, 2013). Third, our model tests a key idea in the literature that
internal team processes affect the value of external knowledge. This component of our model
allows us to enrich the standard sociological account by embedding within it psychological and
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cognitive processes. We think many of the insights from our study should be useful for scholars
who study social processes beyond innovation, such as educational success, labor market search,
and the development of cultural tastes. Finally, a key contribution of this research is methodological. We provide a novel field-experimental framework for studying the dynamics of social
influence and innovation in a rigorous manner.
Theory and Hypotheses: Social Dimensions of Ideation
Social factors are central to how sociologists as well as practitioners conceptualize the production function of innovation (Fleming, Mingo and Chen, 2007; Burt, 2004, 1992; Reagans and
Zuckerman, 2001; Reagans, Zuckerman and McEvily, 2004; Tortoriello and Krackhardt, 2010;
Azoulay, Graff Zivin and Wang, 2010). In the development of new ideas, products, and ventures,
research has found that individuals and teams with a wide network of external contacts are advantaged over their equally capable, though less connected, competition (Fleming, Mingo and
Chen, 2007; Burt, 2004). Networks are thought to provide innovators with a host of benefits,
ranging from greater access to capital (Shane and Cable, 2002), an edge in hiring talent (Fernandez and Fernandez-Mateo, 2006), as well as status and reputation (Podolny, 2001, 1993).
Perhaps the most intriguing of the network-based mechanisms is the idea that external networks
lead to a more regular and higher fidelity stream of raw information (Burt, 2004; Reagans and
Zuckerman, 2001). Networks are thought to aid learning on a range of dimensions. Better
networked innovators can acquire more information about the problems of customers, new technologies, and accumulate diverse facts and insights that they can combine into novel hypotheses.
All these benefits give the networked individual an advantage in the innovation process (Burt,
2004).
Yet, this network effect rests on several critical assumptions about the concrete social interactions that individuals have with members of their network (Reinholt, Pedersen and Foss,
2011). First, the network effect assumes that the focal individual actually activates his or her
network (Smith, Menon and Thompson, 2012; Smith, 2005). For the network to be a source of
information, the focal innovator must engage and converse with her contacts or peers,. Through
informal chats, lunches, feedback sessions and the like she can learn about the experiences,
insights, and knowledge of her peers. Second, even if networks are activated and interaction
occurs, the ability of the focal individual to learn something through the interaction depends
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fundamentally on whether her peer possesses resources such as higher stocks of beneficial experiences, insight and knowledge (Sacerdote, 2001; Zimmerman, 2003). Resource-rich peers should
have a far greater ability to transmit important information to the innovator than resource-poor
ones. However, research in a variety of contexts suggests that even when concrete interaction
between an individual and her peer occurs and a peer possesses important resource stocks, the
magnitude of peer effects are often small and insignificant (Sacerdote, 2014). A fundamental
question arises: when does interaction lead to transmission?
In recent years, scholars have suggested that one approach to understanding when peers
will matter is to view interaction as a noisy medium for resource and information transfer
(Carrell, Sacerdote and West, 2013). Even if two individuals interact and the peer possesses
valuable information, characteristics of the dyad or interaction may either facilitate or hinder
transfer and thus affect the whether the focal individual benefits from the interaction (Diette
and Oyelere, 2014). On the one hand, the transfer of information from a peer depends on
that peer’s willingness and ability to share her experience, perspective, or knowledge (De Vries,
Van den Hooff and de Ridder, 2006). If the peer does not share (either by choice or by habit)
the resources she possesses, then the focal individual will not benefit. On the other hand, even if
the peer does share information, a beneficial transfer may not occur because the focal individual
is either unwilling or incapable of absorbing the shared knowledge (Tortoriello, 2015). Thus,
the extent to which peer interaction is beneficial to an individual depends on a peer’s ability to
share knowledge, the focal individuals ability to absorb it, and therefore a match between the
sender and receiver’s traits.
Peer Extraversion and Idea Generation
While peer interaction may provide a wide range of benefits to individuals, perhaps the most important interaction in the early days of the ideation process is informal conversation (Sosa, 2011;
Perry-Smith, 2006). Conversations—over lunch, coffee, or in other contexts—is commonplace.
Individuals, during the idea generation stage, meet with friends, co-workers, acquaintances and
even the acquaintances of their acquaintances to seek advice, share a raw idea, or just chat.
For example, an entrepreneur who is developing a human resource management product may
meet with a CEO of a rapidly growing firm, talk to a friend about her experience as an employee, or seek advice from an acquaintance who is a human resources professional. Through
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these interactions, the entrepreneur gains insight into important though overlooked problems,
potential solutions, and ultimately business opportunities. Indeed, given the potential for early
conversations to yield novel ideas, many modern approaches to innovation such as the “design
thinking” methodology of IDEO and the “lean startup” methodology rely on early conversations to guide product strategy (Kelley and Kelley, 2013; Ries, 2011; Blank, 2013; Brown et al.,
2008). The purpose of these conversations is not to acquire specialized knowledge about a technology. Rather, these early conversations help the entrepreneur to accumulate a wide base of
raw data that can be used to generate ideas and develop high potential business plans. A more
voluminous amount of data should result in greater pool of raw material.
What then should be the traits of an ideal conversation at this stage? More ideal conversation
should yield specific and authentic descriptions of experiences, detailed anecdotes, emotions, as
well as the peers’ opinions about problems and even solutions (Kelley and Kelley, 2013; Ries,
2011). Conversely, less ideal conversation at this stage would be slow, lack description of concrete
experiences, and would possess little of the emotional content that could enhance the likelihood
that the innovator experiences her conversational partners perspective (Kelley and Kelley, 2013).
Thus, after the latter conversation the innovator may leave with a few facts about her peer, but
no deep insight into someone else’s experiences and emotions with a given problem domain. The
former conversation, however, should result in the innovator having more raw information for
idea generation.
Consider an entrepreneur designing a product for parents traveling with young children. If
the entrepreneur is fortunate enough to talk to a parent willing to share his experiences in detail,
she could learn a range of information: the difficulty in installing and removing a car seat from
a car, the difficulty in finding a place to change a diaper, the difficulty of taking a stroller up a
tall flight of stairs, and the need to hold a baby close when she is crying. On the other hand, if
an entrepreneur talks to a parent less forthcoming with his experience, she may only learn that
traveling with children is hard, but few specific points of frustration or work-arounds developed
by parents. Thus, the former entrepreneur—the one who converses with a parent willing to
share the nuances of his experience—is better positioned to develop a higher quality and more
useful product. This is because she has access to detailed information about the process she is
trying to innovate upon.
One dimension of a conversational peer that can produce variance in the amount and richness of information is a peer’s level of extraversion (De Vries, Van den Hooff and de Ridder,
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2006; Cheng-Hua et al., 2007; Matzler et al., 2008). Extraversion is a stable and independent
personality trait and one of five traits that psychologists believe constitute important variance
in human personality (Rammstedt and John, 2007). Extroverted individuals exhibit a range of
outwardly focused behaviors. In conversation, they are warm, gregarious, talkative, expressive
and show comfort in large groups and around new people (John and Srivastava, 1999; McCrae
and John, 1998). At the opposite pole of this spectrum are introverts. Research finds that
introverted individuals are more likely to keep their emotions private and display quiet and
reserved behavior, especially in the company of new people (Brebner and Cooper, 1978). Interactions and conversations with extroverts are likely to information rich and emotional powerful,
whereas conversations with introverts are likely to possess fewer of these traits. Conversations
with extroverts should produce a greater amount of raw data such as experiences, emotions, and
anecdotes drawn from personal experience of the peer (De Vries, Van den Hooff and de Ridder,
2006). Conversations with introverts are likely to lack the raw volume of experience and emotion
useful at this stage of the ideation process. Thus, we hypothesize:
Hypothesis 1 Individuals who converse with extroverted peers outside their team will develop
higher quality ideas.
Openness, Receptivity and Asymmetry in Ideation
Incorporating a peer’s ability and willingness to share experiences resolves one important bottleneck in the transmission process. However, as with any transmission process the absorptive
ability of the receiver will affect whether transfer is successful (Cohen and Levinthal, 1990; Liao,
Fei and Chen, 2007; Tortoriello, 2015; Tsai, 2001; Zahra and George, 2002). Thus, the ability of
a receiver to absorb and then convert the information she receives from her peer into high quality
ideas may constitute another bottleneck in the innovation process. Two facets of an innovators
behavior are likely to modulate whether she can benefit from the information she receives from
her peer. The first set of behaviors that should affect whether an individual benefits from such
an interaction is whether the innovator is curious and attentive. The focal individual enacts
this behavior through a greater willingness to see others’ perspectives as well as an inquisitive
orientation (Kelley and Kelley, 2013; Csikszentmihalyi, 1997; Grant and Berry, 2011). A second
trait likely to increase the value of external information is a focal individuals ability to make
novel associations using the received stimuli. Combined, these two traits should increase the
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likelihood that raw information gleaned from conversation with extroverted peers translates into
better ideas.
An individual characteristic that captures such an ability is an innovator’s openness to
experience (George and Zhou, 2001; McCrae, 1987; McCrae and Sutin, 2009). Openness, like
extraversion is one of the core personality traits in the five-factor model (John and Srivastava,
1999). Individuals who have greater levels of openness exhibit many characteristics that allow
them to both better appreciate and absorb external stimuli—such as conversations—as well as
combine the information accumulated through this stimuli into novel ideas (George and Zhou,
2001). In one respect, more open individuals are more intellectually curious and willing to see
things from others’ perspectives. These traits are likely to pair well with extroverted peers
willing to share experiences and perspectives. Conversely, individuals more closed to experience
exhibit fewer of these traits and thus may not be able to capitalize as effectively in conversations
with peers who provide novel insight.
Given the potential variability in both a peer’s ability to transmit information and an innovators ability to absorb and use it, it is likely that only a subset of peer interactions are
beneficial in the innovation process. Thus, for effective transmission, a complementary match is
necessary between the sender and receiver. Given a peer willing to talk and share information,
that information is useful for the innovation process insofar as the innovator absorbs and uses
it. If the innovator does not have the faculty to take this information in, it is likely that the
information may only play a minor role in her idea generation process (Cohen and Levinthal,
1990). If, however, the innovator is open to the experiences of her peer, then the information
can be better incorporated into the associational process used to generate ideas. Thus, increasingly the likelihood that an individual generates a high-quality and novel idea. Conversely, even
if an innovator has all the traits that would allow her to absorb information and make novel
associations, if her conversational partner is unforthcoming, then relative to other more open
innovators, she will have less material to generate high-quality and novel ideas. The ideal match
therefore is a pairing between an extroverted peer—who willingly shares his experience—and
an innovator who shows behaviors related to greater openness. Thus, we hypothesize:
Hypothesis 2 High-openness individuals will develop higher quality ideas than those with less
openness after they talk to extroverted peers outside their team.
The prior theorized mechanisms are inherently directional in how they view the accumu-
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lated benefits of an extroverted–openness pairing. Extroverted individuals provide information,
open individuals absorb and use it to generate ideas. The converse may not hold: extroverted
individuals should not benefit as much from their conversations with open peers as as the open
peers benefit from talking to them. However, a key assumption underlying the directional
model is that the interaction is fundamentally a transfer and that little if anything “shared”
emerges through the interaction. An alternative perspective on the interaction dynamics in such
a pairing is that complementary matches affect not just the directional flow of information, but
qualitatively change a conversation’s character (McFarland, Jurafsky and Rawlings, 2013).
Consider a conversation between two innovators. Like before, an extroverted individual interacts with one open to experience. In conversation, the former shares more. The latter is
curious and asks questions. This complementary back and forth in conversation is public such
that both parties can learn from the conversation and its constituent information. Specifically,
though the extroverted individual shares more of his experiences, he also benefits from hearing
and learning from the questions posed by her more open conversational partner. Furthermore,
the social ease that may emerge from such a pairing may create a positive feedback loop where
both parties eventually share and inquire—resulting in a high volume of information and experience accessible to both parties. In this scenario, both individuals benefit because the value of
the interaction is emergent and not just a directional transfer. Thus, we would hypothesize:
Hypothesis 3 Extroverted individuals will develop higher quality ideas than those who are less
extroverted after they talk to high-openness peers.
Extraversion, Openness and Team Dynamics
The proposed mechanisms have implications at the level of the team as well. Teams, rather
than lone individuals, constitute the more natural level of innovation in both markets and
organizations today (Guimera et al., 2005). Small teams, such as startups or internal product
teams rely on conversations with external peers at the ideation stage. External peers can
potentially provide teams with insight, information, perspective, problems, possible solutions,
and a host of other data. When individual team members interact with external peers, they
accumulate raw data that they can bring back to their team. A greater volume of accumulated
information should lead to wider and more high quality set of ideas generated by the team
(Reagans and Zuckerman, 2001; Reagans, Zuckerman and McEvily, 2004). However, the ability
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of a team to capitalize on the raw information it has accumulated depends on whether the
team creates novel associations and generate creative hypotheses from the information they
accumulate (Woolley et al., 2010; Girotra, Terwiesch and Ulrich, 2010).
Thus, a key facet of a team’s innovative process its ability to incorporate external information
and recombine it into new ideas (Paulus and Yang, 2000). Teams whose members interact with
extroverts should have more information; teams whose members are more open should be better
positioned to effectively recombine. Specifically, a team whose members interact with many
extroverts should be able to accumulate a large volume of information. This raw information
enhances the team’s ability to innovate in two ways. First, if individuals use only the information
gleaned from their own external conversations to generate ideas, teams with extrovert-open
pairings should have a higher quality pool of ideas from which to select. Thus, even if no crosssharing of external information across team members occurs, a team with higher levels of peer
extraversion and within-team openness should have higher quality ideas (Girotra, Terwiesch
and Ulrich, 2010). Second it provides a pool of shared data that the team can draw upon in
jointly generating ideas (Woolley et al., 2010). For instance, if individual A brings back novel
information from a peer conversation to the team, another team member B now has access to
this information and can build upon it to generate an idea. In this scenario, the team engages
in in a joint innovation process where the externally accumulated data is used by all members of
a team to develop ideas jointly. Regardless of which mechanism is operating, one should expect
the following to hold:
Hypothesis 4 Teams who converse with, on average, more extroverted peers outside their team
will develop higher quality ideas.
Hypothesis 5 Teams with higher levels of average openness will develop higher quality ideas
than those with less openness after they talk to extroverted peers outside the team.
Finally, not all teams should be equally placed to capture the returns to greater quantities
of information and higher levels of openness (Reagans, Zuckerman and McEvily, 2004; Woolley
et al., 2010). Internal team dynamics, particularly a team’s ability to have positive collaborative
experiences and coordinate effectively should affect whether it can develop novel ideas, even if
it has extroverted peers and open members. Specifically, the joint-innovation mechanism rests
upon the premise that team members effectively share information, allow for discourse, and
maintain an environment where team members feel comfortable generating novel ideas even if
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they are drawn from the extremities of the quality distribution (Woolley et al., 2010). Thus, we
hypothesize:
Hypothesis 6 Teams with high-quality collaboration dynamics will be better able to convert the
benefits of extroverted peers and team openness into higher quality ideas.
Empirical Setting and Methods
We propose that extroverted peers provide a greater volume of information to individuals; that
individuals with greater openness to experience benefit more from conversations with extroverted
peers; that teams with an extrovert-openness match generate better ideas; and that internal team
dynamics moderate the extent to which the team can capitalize on this process. Testing such a
theory is difficult for four important reasons. First, unlike formal collaborations, conversations,
especially with external peers are difficult to observe by researchers. They are rarely recorded
by those participating in them. Furthermore, conversations vary dramatically in their structure,
purpose, and length and comparing across conversations to test the impact of peer characteristics
is often confounded by these variations. Second, researchers rarely observe the dynamics of the
innovation process. What is often observed is the final product developed by a team and
rarely the raw ideas generated by individuals and team. Third, while researchers often have
data about peers’ level of education, publication records, or patents, more nuanced measures
of peer characteristics such as personality are generally difficult to gather, especially for peers
with whom an individual only has a conversation. Finally, and perhaps just as importantly,
the choice of both team and external conversational partners is endogenous—often self-selected
based on peer and individual characteristics that are unobservable to the researcher (Manski,
1993). Thus, the empirical demands for testing our hypotheses are considerable.
Experimental Design: An Innovation Competition
We overcome many of these hurdles by conducting a novel field experiment embedded in an
entrepreneurship bootcamp held in New Delhi, India in July of 20141 . During the bootcamp,
112 aspiring entrepreneurs from across India participated in a three-week program intended to
1
The experimental nature of the bootcamp was reviewed by our university’s Institutional Review Board. All
participants signed two consent forms, one online at the time of application and the second paper-based on the first
day of the bootcamp.
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help them develop skills in idea generation, design thinking, prototype design, and business
model development. The age range of the 112 graduates ran from 18 to 36, with a mean age
of just over 22. Our program had 25 women and everyone had, or was enrolled in, college
with 60 of the participants enrolled in a college, masters, or phd program. Our program was
regionally diverse with 62 of the participants from the state of Delhi and the rest from across
India. The class was primarily engineering and computer science degree holders (78), followed
by 18 business degrees, and the rest from the arts and sciences. Finally, 8 people were enrolled
in, or, had graduated from advanced degree programs.
The bootcamp provided instruction from leading member’s of India’s startup ecosystem including successful entrepreneurs, designers, and venture capitalists. The program was structured
into three week-long modules. The first week, which was the most structured (and on which
we base this study) focused on the idea generation process. To incentivize participation and
effort, the three most highly rated proposals and prototypes from this week won prizes totaling
45,000 Indian Rupees (789.47 dollars). The major prizes were team based. The first prize was
20,000 INR, second 10,000 INR, and third 7,500 INR. The prize allocation was based on the
average rating a team’s proposal received during the peer review process, with the three highest
teams winning the top three prizes. The second week focused on business models; the final week
was the least structured and participants could select their own teams of three people from the
bootcamp to develop a business concept and prototype to receive up to 8,000 USD in funding
and support to implement their idea.
We use the activities from the first week and data collected before the bootcamp began to test
our hypotheses. Before the bootcamp began, we asked all participants to complete surveys, chief
among these was the 44-item Big Five Inventory (John and Srivastava, 1999). All participants
allowed us to collect pre-bootcamp (and thus, pretreatment) measures of Extraversion, Openness
to Experience, Neuroticism, Agreeableness and Conscientiousness. We discuss the construction
of our key variables using this inventory in the variables section below.
The first day (Monday) was dedicated to logistics, an introduction to the program, and a
short icebreaker in a randomized group at the end of the day. We did not collect any data during
this day, as it was not part of the experimental setup for the week. The second day (Tuesday)
began with individuals reporting to one of forty tables where they sat with their icebreaker
group and were asked to individually generate as many or as few ideas for innovative software
products for the Indian wedding industry. The text of the prompt read:
14
On November 27, 2011, over 60,000 weddings took place on this one single day in New
Delhi, just because the day was auspicious. Every wedding hall in Delhi was booked
for every shift and families paid large premiums of at least 1 to 2 lakhs, or more to
book even the smallest halls. Even on less auspicious days, Indian weddings are big,
fun, complex, loud, colourful, and most of all expensive. Today, the size of the Indian
wedding industry is estimated to be around 2.25 trillion Indian rupees or 38 billion
US dollars. The industry is also diverse—it includes such products and services as
marriage gardens, match-making, clothing, decorations, makeup, gifts, jewelry, and a
lot more. Startups in India have only scratched the surface of this industry—the most
prominent example is Shaadi.com which has revolutionized matchmaking and made
many aunties across India obsolete. Your task for this week is to develop a product
concept for a mobile and web app that will reinvent part of the wedding experience–
either before the wedding, during the wedding and after the wedding—in India. On
to reinventing!
We chose the Indian wedding industry as our prompt for three reasons. First, based on
conversations with Indian entrepreneurs and venture capitalists, the Indian wedding industry is
large and has significant market potential. Several venture capital firms are actively investing in
software products for this large market. The choice of the wedding industry was therefore based
in part on concerns of external validity. Second, unlike finance or biotechnology, the “Indian
wedding” was something that that the vast majority of bootcamp participants had experienced,
but was an industry where a subset of individuals would not have a systematic skill or knowledge
advantage. Third, we chose this industry because it was a relatively diverse domain, constituting
problems ranging from finding mates to buying wedding dresses to honey-moon selection and
even post-marital counseling. Thus, the Indian wedding context had the potential to produce
a differentiation in the types and quality of ideas generated by the participants. For one hour,
the participants entered each discrete idea into a software application as short texts. Figure
1 depicts the prompt and entry screen for a participant. Individuals generated 6.6 ideas with
each idea having a length of approximately 505 characters. We call these ideas “pre-treatment”
ideas.
[Figure 1 about here.]
Conversational Peer Randomization. To test our hypotheses, we randomized a set of three
15
empathy interviews that participants had with other members of the bootcamp. These interviews are a staple of the Design Thinking approach (Kelley and Kelley, 2013). Each empathy
interview lasted 14 minutes and consisted of a random pairing between two individuals at the
bootcamp. We put each pair in random and preassigned seats across from each other, with
participants assigned (randomly) to an “A” and a “B” position. The protocol of the interview
was semi-structured and participants were asked to learn about their conversational peer’s experience with an Indian wedding. We began with person “A” interviewing and listening to person
“B’s” perspective for four minutes; followed by person “B” interviewing and listening to person
“A’s” perspective for the same amount of time. Next, person “A” was asked to “dig deeper”
by asking person “B” more questions for 3 more minutes; followed by person “B” repeating this
process with person “A.” During and after the conversation, the participants could take notes
about their conversation and could record it in the sheet depicted in Figure 2. After the first
pairwise peer interaction, individuals were re-randomized to two more pairwise interactions following the same structure. After all three randomizations, individuals were instructed to return
to their original assigned table and generate new ideas for one hour. Participants generated an
average of 4.5 ideas with the average idea having 476 characters. We call these “post-treatment”
ideas.
[Figure 2 about here.]
Anonymous Peer Evaluations of Individual Ideas. The next morning from 9.30am to 11am
(Wednesday; Day 3), all participants anonymously evaluated a random subset of both the preand post-treatment ideas of other bootcamp participants. Our choice of double-blind anonymous
peer evaluations arises from three considerations. First, peer evaluation is perhaps the most
common evaluation in many contexts. In academia, research articles are evaluated anonymous
peers as are grants (Marsh, Jayasinghe and Bond, 2008); In organizations, many decisions
about products and design choices are evaluated by peers; In education, peer evaluations are
becoming increasingly common for evaluating classroom projects (Cooper and Sahami, 2013;
Reily, Finnerty and Terveen, 2009). Second, many prior studies of creativy have used peer
ratings as measures of the creative output of teams and individuals (Amabile et al., 2005, 2004).
Third, peer evaluation, particularly in this context is superior to evaluations by external or online
parties who may not have either the incentive or ability to effectively assess an idea’s worth.
Finally, research indicates that peer evaluations are more accurate when evaluators are blinded
16
to the identity of the subject. They are also harsher and more accurate when evaluating more
than three items (Marsh, Jayasinghe and Bond, 2008; Boudreau et al., 2012). Thus, we asked
individuals to rate approximately 50 ideas; on three dimensions on a 5-point likert scale strongly
disagree to strongly agree: whether the idea was novel; whether the product was something
that the rater would buy, and whether the idea had business potential. Each idea received
approximately 6.24 ratings. The average rating of business value was 2.45; buy likelihood of
2.59; and of novelty 2.43.
Idea development in teams. At the end of the evaluation session on day 3, individuals were
randomly assigned teams of approximately three individuals. It is within these teems that
individuals worked on day 3, day 4 and day 5 to develop a mock-up prototype and business
plan. The team was given the freedom to work on any idea that they jointly chose. The idea
could be one from the pre-treatment ideation session, the post-treatment session, a combination
of both, or neither. By midnight of day five (Friday) participants submitted a complete packet
of the prototype which included a “splash page” consisting of a graphic describing their product;
a presentation walk-through of their software prototype; a text description of their product and
the problem it was intended to solve; a one sentence description of their product; and a product
name.
Final submission evaluations and rating of team dynamics On day six (Saturday) we assigned
the 112 participants five random and anonymous project submissions to to evaluate (excluding
their own). Participants evaluated their assigned submissions using an online system where
students ranked projects from the best to the worse in the subset of five assigned submissions.We
recoded the rankings so that a ranking of 5 indicated the best submission and a ranking of 1
indicated the worst. Each submission therefore received approximately 14 evaluations on 12
dimensions including: product novelty, unique insight, display of empathy for customer needs,
feasibility, business potential, as well as the quality of the prototype walkthrough and splash page
(Girotra, Terwiesch and Ulrich, 2010). Furthermore, team members also rated their teammates
using the Society for Human Resource Management’s 24-item “Manager Effectiveness Scale”
360 degree evaluation. These questions concerned individual team members’ pro-collaborative
behaviors within the team during the first week.
Figure 3 summarizes the process of the experiment, the randomizations, and data collection.
[Figure 3 about here.]
17
Individual-level Hypotheses
Dependent variables. Our first set of hypotheses concerns the relationship between conversational peer extraversion on the quality of post-treatment ideas generated by individual innovators.
The key dependent variables for this analysis derive from the anonymous peer evaluations (day
three) of the raw ideas generated by individuals on day two. The first of these dependent variables is Idea Quality and it is the sum of the evaluations an idea receives from an anonymous
evaluator on the dimensions of business value, buy likelihood, and novelty.2 We also model
the relationship between our key independent variables on the three individual ratings as well:
Business, Buy, and Novelty.
Independent variables To examine the relationship between peer extraversion and idea quality, we create a variable for the average extraversion score of an individual’s three randomly
assigned conversational peers. Extraversion scores are calculated average of the positively coded
likert scores for the 8-item extraversion scale from the BFI. We call this variable Extraversion
(peer). We also create a variable called Openness (self ) measuring the average of an individual’s
positively coded responses from the 10-item openness scale. Finally, we created an interaction
variable called Extraversion (Peer) × Openness (Self ) measuring the product of conversational
peers’ extraversion scores and the focal individual’s own openness score. All independent variables are rescaled so that means are “0” and standard deviations are “1.”
Causality and control variables. The key strength of our empirical analysis is the experimental design, which consists of randomization and lagged independent variables. The lagged
covariates and randomization allow us to deal with two of the main inferential challenges in the
study of social influence: the reflection problem and the selection problem. Lagging our peer
extraversion measure ensures that a focal individuals’ post-treatment idea quality does not cause
increases in her peers’ level of extraversion. Further, the random assignment of conversational
peers reduces the likelihood that peer interaction emerges from self-selection and homophily
(Manski, 1993, 2000). However, scholars recommend controlling for other covariates even in
research designs with randomization (Imai, King and Stuart, 2008). Thus we include in several
of our models, controls for a focal individual’s own extraversion, as well as scores for both the
focal individual and peers’ level of agreeableness, neuroticism and conscientiousness. Furthermore, we include a variable called Ability for both the focal individual and peers’ measuring the
2
Some ideas received evaluations on all dimensions while others received evaluations on only one. For the construction of Idea Quality we code as missing this score if it does not receive evaluations on all three dimensions.
18
average rating of each person’s application received from four independent evaluators during the
admission process. The application scores were reverse coded so that the better applications
received high scores and worse applications received lower scores. Raters assessed ability based
on the participants grades in college, the prestige of their college, the quality of their application
essay, as well as their skills in business topics such as finance, marketing and sales as well as
technical skills such interaction design and programming. Table 1 presents summary statistics
for our key variables. Furthermore, we correlated measures of our key treatment Extraversion
(peer) with pretreatment characteristics of a focal individuals’ idea quality and traits and find,
as expected, that they are uncorrelated. Table 2 presents these bivariate regressions.
[Table 1 about here.]
[Table 2 about here.]
To test our key hypotheses we used ordered logistic regression models to regress all evaluations e of idea d by individual i on the her randomized conversational peers’ average level
of extraversion. Further, we include both the main effect of individual i’s openness and the
interaction between peer extraversion and individual openness to test the match hypotheses.
Since we have multiple evaluations and multiple ideas for individuals i, we correct our standard
errors by clustering them at the individual level, thus creating 112 clusters for the individuals
participating in the study3 .
Team-level Hypotheses
Dependent variables. Our second set of hypotheses concerns how conversations with extroverted
peers affects team performance and how individual openness at the level of the team moderates
this effect. To conduct this analysis we construct four dependent variables. As mentioned
earlier, at the week’s end (Saturday, day 6) individuals conducted double-blinded evaluations of
five projects randomly selected from the 39 other submissions (excluding one’s own submission).
Using the reverse coded ranking along the 12 dimensions (5 best, 1 worst) we created four indices.
The first is Total which measures the average of the rankings a project received across all 12
dimensions. Higher scores mean better rated projects. Furthermore, we created a variable called
Novelty and Insight which was the average of the Novelty, Insight and Empathy rankings. To
3
Some individuals did not generate ideas at this stage or their ideas were not evaluated on all dimensions by the
evaluators, thus in some models we have fewer than 112 clusters
19
measure a submission’s business potential we created a variable called Business that measures
the average of the proposed product’s feasibility and business potential. Finally, we created a
variable called Prototype measuring the average of the rankings a submission received for its
visual splash page and it’s prototype walk through slides.
Independent variables. To test our hypotheses regarding extroverted peers, average team
openness, and team dynamics we created five variables. The first variable Extroverted Peers
(team) that measures the average of all team members’ peer extraversion scores: we calculate
the average score within a team of every team member’s Extraversion (peer) score. The second
variable we construct is Openness (team) that measures the average level of a team’s openness
to experience. To create the interaction variable of the level of a team’s peers’ extraversion and a
team’s level of openness, we interact the latter two variables to create Extroverted Peers (team)
× Openness (team). Finally, we create a variable called Team Dynamics measures the mean
of each individual’s average score on the 360 feedback “Manager Effectiveness Scale.” Again,
we normalize all independent variables to have a mean of “0” and a standard deviation of “1.”
Table 3 presents summary statistics for our key variables. Furthermore, we correlate measures
of our key treatment Extraversion (peer) with pretreatment characteristics of a focal team and
find, as expected due to our randomization, that they are uncorrelated. Table 4 presents these
bivariate regressions.
To test our key hypotheses we used linear regression models to regress all evaluations e of
submission s by team i on the team’s randomized conversational peers’ average level of extraversion. Further, we include both the main effect of team i’s average openness and the interaction
between peer extraversion and team openness to test the match hypotheses. Further, to test
our hypotheses that within-team dynamics moderates whether information advantage arising
from the extraversion-openness match affects team performance, we interact our independent
variables with the Team Dynamics score. Since we have multiple evaluations for each team’s
ideas, we correct our standard errors by clustering them at the team level, thus creating 40
clusters, one for each team.
[Table 3 about here.]
[Table 4 about here.]
20
Results
Do conversations with extroverted peers increase idea quality?
We begin our analysis by examining whether individuals develop better rated ideas if they have
extroverted conversational peers (Hypothesis 1). We regress measures of idea quality on the
average extraversion of an individuals’ randomly assigned peers. Table 5 presents the first set
of estimations. All models cluster correct standard errors at the individual i level to account
for multiple evaluations of an individual’s ideas.
Column 1 presents estimates of peer extraversion on the aggregate post-treatment Idea Quality measure. The coefficient is both positive and statistically significant (p < .01), suggesting
that when individuals have conversations with extroverted peers, they generate better rated
ideas. The magnitude of this effect can be more easily interpreted as log-odds, by exponentiating the coefficient we find that the log odds for the peer extraversion variable are 1.165.
This suggests that for individuals who have extroverted peers one unit higher than average, are
about 16% more likely to receive ratings higher than those individuals with the mean level of
extraversion. To test the robustness of this result, in Column 2 we include a measure of a focal
individual’s own extraversion and find that it does not alter our coefficient estimate substantively. To ensure that our measure of extraversion is not related to some measure of a peer’s
ability or education, we include measures of a peers’ average Ability. While the coefficient is
positive, it is small and statistically indistinguishable from zero. Similarly, in column 4, we
include measures of the focal individual’s ability as well. Inclusion of this variable does not
alter our effect. In columns 5 and 6, we present a complete model and also include a measure
of the average quality of a focal individual’s pre-treatment ideas. We find a modest effect of
pre-treatment idea quality on the post-treatment idea quality. However, our main effect of peers’
extraversion remains stable. Thus, we find strong support for hypothesis 1.
[Table 5 about here.]
In table 6 we examine whether our results hold in the more disaggregated versions of the
post-treatment idea ratings. We find that indeed, peer extraversion increases the quality of
ideas on the dimensions of business value, buy likelihood, and novelty. Further, our results also
indicate that if an individual developed novel pre-treatment ideas, they also developed novel
post-treatment ideas. For comparison, the estimate of pre-treatment idea novelty is approxi-
21
mately the same size as that of peer extraversion. Suggesting the importance of greater volumes
of external information in the idea generation process.
[Table 6 about here.]
Does a focal actor’s openness moderate the effect of extroverted peers?
In tables 7 and 8 we examine the hypothesis that an individual’s openness to experience moderates the extent to which peer extraversion affect idea quality (Hypothesis 2). To do so, we
re-estimate the models above and a variable for an individual’s level of openness and an interaction of this variable with her peers’ average extraversion. Columns 1 through 3 in table
7 show that when the focal individual has a one unit higher level of openness and her peers
are extroverted, her ideas are rated higher than if she had extroverted peers alone. Regarding
magnitude, we find that the log-odds effects of such a match on an ideas business value increase
from1.25 to 1.36, buy likelihood increases from 1.25 to 1.47, and novelty increases from 1.18 to
1.24. Thus, we find a substantial benefit of openness for incorporating the information transmitted by extroverted peers. In table 8 we test for this effect with the aggregated score. In
column 1, we find that a one-unit increase in openness increases the log-odds of a higher rating
from 1.32 to 1.40. Thus, we find support for hypothesis 2 that openness increases the value of
interaction with extroverted peers.
[Table 7 about here.]
[Table 8 about here.]
To test hypothesis 3, that the benefits to an extraversion-openness match are shared by both
parties, we examine whether benefit exists for extroverted individuals when they are paired with
peers with greater openness to experience. Tables 7 and 8 present these results. Our results here
suggest that such a match does not benefit the focal individual. Their ideas are, if anything, less
good or no better when they talk to more open people. We find little support for hypothesis 3.
To ensure that our measures of peer extraversion and focal individual openness are not
picking up on other personality traits, we included controls for other peer traits and interaction
with own traits. Our results hold under these specifications, giving us greater confidence about
our posited mechanisms. These results are presented in table 9.
[Table 9 about here.]
22
We also wanted to ensure that our estimates were not biased by whether an evaluator had
knowledge of or interacted with the individual generating the idea they were evaluating anonymously. To do this, we conduct an analysis where we control for the presence of a relationship
prior to the treatment between an evaluator and the focal innovator. Our results, presented in
table 10 indicate that such a bias does not appear to exist nor does it affect our key results.
Further, our results hold if we drop all evaluations conducted by an evaluator who knew the
individual whose blinded idea they were evaluating. Again, our results remain robust.
[Table 10 about here.]
These results together provide strong evidence that peer interaction—namely interaction
with extroverted peers increases the quality of individual ideas. Further, we find evidence that
this effect is larger for individuals more open to experience.
Peers, Teams and Innovation
Our second set of hypotheses concern the relationship between peer extraversion, team openness
and the dynamics of the collective innovation within a team. To test these hypotheses we regress
measures of final submission quality on the team-level measures of peer extraversion, team
member openness, and team member collaborative skill. Table 11 presents the estimations for
this analysis. All models cluster correct standard errors at the team level to account for multiple
evaluations of a team’s submissions.
Column 1 regresses total final project score Total on team level measures of our key independent variables. Our results provide modest support for hypothesis 4 that the benefits of
extroverted peers also extend to the final ideas generated by teams. We find that the main
effect of peer extraversion is positive and statistical significant at the p = .057 level when we
look at total score and becomes large and more significant for the novelty model in Column 2.
Further, we find that the interaction of team openness and peer extraversion are also positive
and statistically significant at least at the p < .1 level for the total rating, novelty, and business
value models. This suggests that teams with more open members are better able to generate a
pool of ideas with which to generate one high quality product concept. These results also provide support for hypothesis 5, that an extraversion-openness match also increases idea quality
at the team level.
23
Finally, we test hypothesis 6 which argues that when internal team dynamics are cohesive—
that individuals seek input from others, show concern, seek diverse feedback, encourage diverse
perspectives, and other integrative and cohesive behaviors—the team is better able to incorporate external ideas in their innovation process. Column 5 tests this hypothesis by including the
main effect of team member cohesiveness as well as an interaction with the extraversion-openness
match variable. We find that although the main effect of the positive team dynamics variable is
not statistically significant, the interaction is significant and of substantial magnitude (β = .493,
p < .05). Thus, our results indicate strong support for the idea that more cohesive behaviors by
team members allow the team to better incorporate and capitalize on the acquired information
and generated raw ideas. Finally, it is worth noting that high ability teams developed higher
rated prototypes than teams with lower-ability team members.
[Table 11 about here.]
Discussion
Social networks and social interaction have become central components of the standard sociological model of innovation (Burt, 2004; Fleming, Mingo and Chen, 2007; Reagans, Zuckerman
and McEvily, 2004; Obstfeld, 2005). In this article, we incorporate the role of social psychological processes into a more structural model of peer interaction to show how individual traits,
interpersonal dynamics and team dynamics moderate how purely structural variance in peer
interaction affects the generation of high-quality and novel ideas. Our key finding is that individuals generate better ideas when they have conversations with more talkative conversational
partners willing to share their experiences—i.e. extroverted peers. The magnitude of this effect
is modest, suggesting that there a one unit increase in peer extraversion increases the log-odds
that an idea is rated higher by 16% or approximately .25 points on a 5 point scale. While this
effect will not alone make the lowest ability quality ideas the best ones, it can shift ideas at the
margins of “good.” to become “very good.” Further, our experimental treatment in this article
only varied peer interaction for three, 20-minute sessions. It is likely that the magnitude of this
effect will be greater if peer interaction were more frequent and had greater duration.
Our findings, however, also dovetail with a growing body of recent research on the contingent effects of social interaction on a host of outcomes. For extroverted peers to be most useful
during the innovation process, innovators should be open to experience to benefit from the high
24
volume of information provided by their extroverted conversational peers. The magnitude the
extraversion–openness match results indicate that individuals who have one unit more extroverted peers and are themselves one unit more open than the average match, have ideas rated
higher by 25% to 40%, depending on the metric. These results strongly indicate that social
interaction should not be viewed as a unilateral transfer of information, with receivers as pliant
acquirers of external resources. Indeed, our findings indicate that variance in individual traits
shapes the degree to which individuals can capitalize on their social interactions.
A key benefit of our study is our ability to trace how early peer conversations translate into
team performance. We argue that at the level of the team the aggregation of information from
extroverted peers combined with team members’ own openness affects the ability of a team to
develop an idea. Our findings support this claim. We find evidence for both the main effect
of peer extraversion at the aggregate level of the team and evidence that this effect increases
when an extraversion–openness match exists. This finding gives us greater confidence in the
individual level results and also suggests that the knowledge generated by individuals through
peer interaction is not just an individual resource, but rather a resource shared by the team.
Finally, we find that some teams are better able to use and recombine the information
acquired by their individual team members. Teams that have more collaborative members—
i.e. those that share ideas, listen to feedback, and allow for discourse—and also have high
extraversion-openness matches have better rated ideas. To provide a sense of magnitude, a oneunit increase on peer extraversion, team member openness and team member cohesion increases
the average rating of an idea by .488 points out of 5. While the size of these combined effects
indicates that that while such access to external information and good team dynamics alone
will not take a bad idea and make it great; it could take a good idea and make it very good or
make a very good idea great.
Conclusions
This study holds several implications for the study of innovation, particularly the role that social
interaction plays in the generation of novel ideas both at the level of the team and the individual.
The primary contribution of this article is the embedding of social-psychological processes in a
structural model of innovation. First, we build on the work of Reagans, Zuckerman and McEvily
(2004), as well as network and innovation scholars such as Burt (2004) and Fleming, Mingo and
25
Chen (2007) by incorporating the role that concrete conversations and social interaction have
in the information acquisition process from network ties. Our results support the idea that
external networks provide important information to individuals and teams. Furthermore, our
results indicate the criticality of matches in focal-individual and peer traits for information flow
to be most beneficial. These results provide a social psychological basis for individual level
differences in absorptive capacity. Third, and finally, our results highlight the importance of
intra-team processes—namely cohesion—in increasing the ability for teams to capitalize on this
flow.
Our results also have implications beyond the context of innovation. Scholars in a variety of
domains have been studying the role of external peers and social networks in creating divergences
in important outcomes. Perhaps one of the largest of these literatures is the study of peers and
peer groups in the production of human capital (Sacerdote, 2001). While research has found
mixed evidence for the existence of peer effects in this context (Sacerdote, 2014), recent work
is beginning to suggest that matching processes, like the one described here, moderate the
extent to which learning happens across peers in educational contexts (Carrell, Sacerdote and
West, 2013; Hasan and Bagde, 2013). We also think the general framework of conceptualizing
peer effects as matching processes should also apply to network processes within labor markets
(Fernandez and Fernandez-Mateo, 2006), organizational promotion contexts (Burt, 1992), and
other domains where social interaction affects the flow of knowledge and information. Further,
our results lend support to the idea that the design of social systems to improve individual and
group level outcomes is not merely a process of matching people with resources to those without
them. Interpersonal dynamics and other kinds matching must be incorporated into the design
of matches.
Our study contributes to the literature from a methodological perspective as well. In this
article, we use data a from a field setting—and entrepreneurship bootcamp—in which we embedded a randomized field experiment. By randomizing social interaction, namely external peer
conversations and team assignments, as well as measuring detailed data ideation and individual characteristics we could trace a nuanced and dynamic process from its earliest inception (a
conversation with an external peer) to the performance of teams five days afterwards. While
the specific context of our study is clearly not generalizable to all contexts, we imagine that
longer-duration field experiments with a subset of the innovations introduced in our study could
be used to, for instance study how external peer conversation affect the success of startups in
26
more naturalistic contexts. This can be accomplished, for example, by working with incubators to facilitate external conversations between startups and an external pool of entrepreneurs,
venture capitalists, and customers.
In conclusion, we note several limitations of the present study. As with many field experiments, our findings though benefiting from randomized peer interaction and detailed measurement relies on a very specific context—an entrepreneurship bootcamp held in New Delhi, India.
Thus, our findings may not have broad generalizability outside of the early entrepreneurial
context. However, we think that future research, by accumulating a greater body of findings
across a wide array of contexts has the potential to sharpen our understanding of these network
processes. Further, while our empirical tests are conducted in one specific context, our theoretical model is build from general assumptions derived from a large body of literature both in
sociology, psychology and management (Fleming, Mingo and Chen, 2007; Reagans, Zuckerman
and McEvily, 2004; Reagans and Zuckerman, 2001; Cummings, 2004; Paulus and Yang, 2000;
Woolley et al., 2010).
Moving forward, we think several opportunities exist for extending our results and adding
even further nuance. One key limitation we have in this study is that the conversations between
the focal and the peer are never observed. Thus, we must infer based on our understanding
of the structure of the interaction and psychological theory the nature and volume of information transmitted. However, we may learn much more about what the actual benefits are of
such extraversion-openness matches. Similarly, other types of measurement into interpersonal
interaction is likely to give greater depth to our theories. Another possibility for extending the
work in this paper is to take the idea of matching and formally incorporate it into our empirical
tests. In this article, we used randomization to create variance in peer and focal individual
characteristics within a set of pairings. Matches, thus, are a byproduct of the randomization
process and are not explicitly designed. Future designs, particularly for match effects decide in
randomized contexts should be designed to see whether they hold. Recent work by Carrell, Sacerdote and West (2013) suggests that implementing policy based on findings from randomized
studies may not necessarily yield expected results due to interpersonal dynamics. Finally, we
think an important future direction for this research is studying the implications of our findings
at a macro scale: more connections between individuals and organizations increase the overall
innovative capabilities of ecosystems and regions (Saxenian, 1996). The ideal situation would
be that network ties such as the ones created here increase performance for all members, and
27
not just reshuffle the outcome distribution.
28
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1
1.1
Appendix
Individual Idea Generation
To provide context for the nature of the ideas generated during the individual idea generation process,
we, present examples of raw ideas generated immediately after the three randomized interviews that
were rated highly as well as poorly on the three dimensions of business potential, buy likelihood and
novelty.
Examples of highly rated ideas include:
Feast on demand lets the wedding planners minimise food wastage during the feasts in the
events. Through this app, the wedding planners can generate a link and forward it to all
the guests. On opening that link, the guests are confronted with a set of choices of food
items/dishes they wish to consume during the event. After the guests give their preferences,
the wedding planner gets the data and can arrange the food according to these estimates.
Also, the dishes with low preference can be eliminated to the reduce wastage.
Behavioral analysis of bride and grooms online profiles on key social networks. This could
be done exclusively by a company which would give a detailed analysis by psychologists. This
would definitely aid the match-making process, making it more thorough.
Renting of Wedding Dresses. Most women don’t sell off jewelry bought, but dresses cannot
be re-worn. Since branding is all that matters when it comes to second hand, the dresses
could be dry washed and repacked in bags and delivered.
Examples of ideas that received low ratings include:
PERSONALISED CARDS. [my interviewee] said that it gets to be highly painful to write
names on cards and thus I propose that an agency that sends personalised cards and tracks
whether they have reached.
connectivity of app event and fb event is a nice way to spread info easily
Build an app that would give users a complete guide on personal grooming tips for weddings
(from deciding on what to wear to how to wear the make-up to how to carry yourself,etc)
customized according to the user’s built, complexion, and personality.
34
1.2
Team Submissions
To provide reference points for how evaluators rated the final team submissions, we provide examples
of submissions in the top, middle, and bottom quartiles of submissions in terms of total score.
An example of a submission in the top quartile was a prototype for mobile app called “Snappily
Wed.” The team’s description of the product is:
Your guests use smart phones to take photos at the wedding but don’t share them with you.
For you it’s a loss of precious memories. Our App solves the problem by allowing your
Guests to take pictures and directly saving them on the cloud. Don’t miss out on your
wedding. Capture and retain every photo taken by Everybody at your wedding (be it your
uncle playing with your nieces or your brother taking photos of the food served).The marrying
couple (you or the person maintaining your account) will have access to these pictures and
will retain and share the ones which are great, while discarding the rest, for your loved ones
to view.
Their splash page depicted in figure 4, is clear and visually appealing:
[Figure 4 about here.]
An example of a submission in near the 50th percentile is “Tender my Wedding.” The team describes
their idea as:
TenderMyWedding is a platform which turns the process of finding vendors for a wedding
upside down. Rather than the customer looking for vendors for their wedding needs, we
let Vendors look for them. All they do is simply post their requirements with budget and
within no time, top service providers from everywhere would be competing to get them as
their customer. It’s a win-win as you get multiple cost-effective quotes for the requirements
without stepping out of your home and Vendors get new business.
Their splash page submission, depicted in figure 5:
[Figure 5 about here.]
An example of a submission in the bottom quartile of the ratings is “Invite My Pals” which is
described as:
Invite My Pals makes inviting people a much easier task with superb efficiency! Be it wedding
or any other occasion, using this app you can send invitations to people that will not just
directly reach them but also would let you keep track of how many people are going to join
you on your day. With the video invites and e-cards best suiting to your taste you send
invitations in more personalised way than ever before!!
Their splash page submission, depicted in figure 6:
[Figure 6 about here.]
35
List of Figures
1
2
3
4
5
6
Photo of participant entering idea into system. . . . . . . . . . . . . . .
Note taking sheet for each empathy interview. . . . . . . . . . . . . . . .
Visual summary of experimental procedure and data collection.. . . . .
Splash page for submission in the top quartile—Snappily Wed. . . . . .
Splash page for submission in the middle quartile—Tender my Wedding.
Splash page for submission in the bottom quartile—Invite My Pals. . . .
36
37
38
39
40
41
42
Figure 1: Photo of participant entering idea into system.
37
Figure 2: Note taking sheet for each empathy interview.
38
Figure 3: Visual summary of experimental procedure and data collection..
39
Figure 4: Splash page for submission in the top quartile—Snappily Wed.
40
Figure 5: Splash page for submission in the middle quartile—Tender my Wedding.
41
Figure 6: Splash page for submission in the bottom quartile—Invite My Pals.
42
List of Tables
1
2
3
4
5
6
7
8
9
10
11
Summary statistics for ideation analysis . . . . . . . . . . . . . . . . . .
Balance test examining relationship between peers and pre-conversation
ideas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Summary statistics for feedback analysis . . . . . . . . . . . . . . . . . .
Balance test examining correlation between self and peer’s traits for feedback analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Do conversations with extroverted peers increase idea quality? . . . . . .
Do extroverted peers increase the quality of ideas all all dimensions? . .
Does a focal innovators openness moderate the effect of extroverted peers?
50
The moderating effect of focal innovator’s openness on idea quality. . . .
Robustness to controlling for other peer characteristics. . . . . . . . . .
Robustness to controlling for whether evaluator and focal innovator have
a relationship (pre-treatment). . . . . . . . . . . . . . . . . . . . . . . .
The moderating effect of team openness and internal dynamics on the
effect of extroverted peers. . . . . . . . . . . . . . . . . . . . . . . . . . .
43
44
45
46
47
48
49
51
52
53
54
Table 1: Summary statistics for ideation analysis
Idea Quality
business
buy
novelty
Extraversion (Peer)
Extraversion (Self)
Ability (Peer)
Ability (Self)
Pre-treatment Idea Quality
Agreeableness (Peer)
Conscientious (Peer)
Neuroticism (Peer)
Openness (Peer)
Observations
count
1150
1203
1352
1765
2071
2071
2071
2071
2052
2071
2071
2071
2071
2071
44
mean
7.548
2.476
2.578
2.405
-0.000
-0.000
-0.000
0.000
0.000
0.000
-0.000
-0.000
-0.000
sd
2.824
1.055
1.106
1.097
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
min
3.000
1.000
1.000
1.000
-3.299
-2.955
-2.322
-2.387
-2.296
-2.347
-2.548
-3.340
-2.054
max
15.000
5.000
5.000
5.000
3.071
2.399
2.244
1.622
4.438
2.602
3.376
2.315
2.529
Table 2: Balance test examining relationship between peers and pre-conversation ideas
(1)
(2)
(3)
(4)
Business (pre) Buy (pre) Novelty (pre) Openness (Self)
Extraversion (Peer)
-0.076
0.003
-0.041
0.008
(0.082)
(0.098)
(0.112)
(0.078)
Observations
107
107
107
108
ll
-146.566
-147.296
-154.197
-156.459
Standard errors in parentheses
All tests are two tailed. Standard errors clustered at the individual innovator level.
∗
p < .1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
45
(5)
Extraversion (Self)
0.048
(0.082)
108
-154.665
Table 3: Summary statistics for feedback analysis
Idea Quality
business
buy
novelty
Extraversion (Peer)
Extraversion (Self)
Ability (Peer)
Ability (Self)
Pre-treatment Idea Quality
Agreeableness (Peer)
Conscientious (Peer)
Neuroticism (Peer)
Openness (Peer)
Observations
count
1150
1203
1352
1765
2071
2071
2071
2071
2052
2071
2071
2071
2071
2071
46
mean
7.548
2.476
2.578
2.405
-0.000
-0.000
-0.000
0.000
0.000
0.000
-0.000
-0.000
-0.000
sd
2.824
1.055
1.106
1.097
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
min
3.000
1.000
1.000
1.000
-3.299
-2.955
-2.322
-2.387
-2.296
-2.347
-2.548
-3.340
-2.054
max
15.000
5.000
5.000
5.000
3.071
2.399
2.244
1.622
4.438
2.602
3.376
2.315
2.529
Table 4: Balance test examining correlation between self and peer’s traits for feedback analysis.
Empathy Peers’ Extraversion (Teamlevel)
(1)
Team’s Avg Extraversion
0.016
(0.181)
(2)
Team’s Avg Openness
0.042
(0.080)
-0.010
(0.162)
40
0.000
0.015
(0.091)
40
0.005
Constant
Observations
R2
Standard errors in parentheses
All tests are two tailed. Standard errors clustered at the individual innovator level.
∗
p < .1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
47
Table 5: Do conversations with extroverted peers increase idea quality?
Idea Quality
Extraversion (Peer)
Extraversion (Self)
(1)
(2)
0.153∗∗
(0.060)
(3)
(5)
(6)
0.170∗∗∗
(0.063)
0.169∗∗∗
(0.062)
0.177∗∗∗
(0.060)
-0.093
(0.064)
-0.091
(0.064)
-0.082
(0.067)
0.015
(0.061)
0.019
(0.057)
0.047
(0.056)
-0.042
(0.065)
-0.021
(0.062)
-0.018
(0.061)
Ability (Peer)
0.012
(0.062)
Ability (Self)
(4)
Pre-treatment Idea Quality
Observations
1150
1150
1150
1150
1150
Innovators
107
107
107
107
107
ll
-2735.939 -2734.451 -2740.497 -2740.161 -2734.307
All tests are two tailed. Standard errors clustered at the individual innovator level.
∗
p < .1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
48
0.122∗
(0.066)
1141
107
-2712.812
Table 6: Do extroverted peers increase the quality of ideas all all dimensions?
(1)
business
(2)
buy
(3)
novelty
0.141∗∗
(0.063)
0.165∗∗∗
(0.054)
0.140∗∗
(0.061)
Extraversion (Self)
-0.053
(0.063)
-0.060
(0.064)
-0.061
(0.066)
Ability (Peer)
-0.008
(0.055)
0.061
(0.058)
0.009
(0.058)
Ability (Self)
0.007
(0.063)
0.005
(0.056)
-0.035
(0.057)
Business (pre)
0.050
(0.058)
main
Extraversion (Peer)
Buy (pre)
0.106
(0.066)
0.160∗∗∗
(0.054)
Observations
1192
1342
1749
Innovators
107
107
107
ll
-1698.504 -1965.674
-2516.945
All tests are two tailed. Standard errors clustered at the individual innovator level.
∗
p < .1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Novelty (pre)
49
Table 7: Does a focal innovators openness moderate the effect of extroverted peers?
(1)
business
(2)
buy
(3)
novelty
(4)
business
(5)
buy
(6)
novelty
0.191∗∗∗
(0.055)
0.224∗∗∗
(0.049)
0.172∗∗∗
(0.060)
0.179∗∗∗
(0.058)
0.219∗∗∗
(0.058)
0.200∗∗∗
(0.062)
Extraversion (Self)
-0.057
(0.060)
-0.084
(0.058)
-0.057
(0.072)
-0.044
(0.064)
-0.067
(0.064)
-0.048
(0.069)
Ability (Peer)
0.042
(0.055)
0.088
(0.056)
0.036
(0.062)
0.031
(0.055)
0.083
(0.061)
0.042
(0.063)
Ability (Self)
0.022
(0.058)
0.011
(0.053)
-0.014
(0.057)
0.033
(0.064)
0.037
(0.058)
0.010
(0.055)
Pre-treatment Idea Quality
0.121∗∗
(0.052)
0.088∗
(0.052)
0.134∗∗
(0.066)
0.111∗∗
(0.055)
0.076
(0.053)
0.124∗
(0.064)
Openness (Self)
-0.078
(0.053)
-0.070
(0.055)
-0.077
(0.050)
Extraversion (Peer) × Openness (Self)
0.195∗∗∗
(0.072)
0.232∗∗∗
(0.056)
0.122∗∗
(0.059)
-0.092
(0.063)
-0.119∗∗
(0.060)
-0.164∗∗∗
(0.061)
-0.102∗
(0.060)
1342
107
-1963.205
-0.043
(0.062)
1749
107
-2513.369
main
Extraversion (Peer)
Openness (Peer)
Openness (Peer) × Extraversion (Self)
-0.061
(0.063)
Observations
1192
1342
1749
1192
Innovators
107
107
107
107
ll
-1691.823 -1959.315 -2515.840 -1695.209
All tests are two tailed. Standard errors clustered at the individual innovator level.
∗
p < .1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
50
Table 8: The moderating effect of focal innovator’s openness on idea quality.
(1)
Idea Quality
(2)
Idea Quality
0.281∗∗∗
(0.052)
0.250∗∗∗
(0.060)
Extraversion (Self)
-0.072
(0.061)
-0.055
(0.064)
Ability (Peer)
0.117∗
(0.061)
0.104∗
(0.060)
Ability (Self)
0.046
(0.066)
0.051
(0.067)
Pre-treatment Idea Quality
0.119∗∗
(0.054)
0.111∗
(0.057)
Openness (Self)
-0.126∗∗
(0.057)
-0.108∗
(0.062)
Openness (Peer)
-0.167∗∗
(0.068)
-0.180∗∗
(0.071)
Extraversion (Peer) × Openness (Self)
0.184∗∗∗
(0.065)
Idea Quality
Extraversion (Peer)
Openness (Peer) × Extraversion (Self)
-0.056
(0.063)
Observations
1141
1141
Innovators
107
107
ll
-2703.955
-2707.460
All tests are two tailed. Standard errors clustered at the individual innovator level.
∗
p < .1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
51
Table 9: Robustness to controlling for other peer characteristics.
(1)
Idea Quality
(2)
Idea Quality
(3)
Idea Quality
(4)
Idea Quality
(5)
Idea Quality
0.222∗∗∗
(0.051)
0.282∗∗∗
(0.052)
0.260∗∗∗
(0.050)
0.204∗∗∗
(0.052)
0.218∗∗∗
(0.053)
Extraversion (Self)
-0.092
(0.061)
-0.076
(0.062)
-0.096
(0.066)
-0.072
(0.060)
-0.078
(0.059)
Ability (Peer)
0.084
(0.058)
0.106∗
(0.059)
0.073
(0.063)
0.091
(0.060)
0.086
(0.054)
Ability (Self)
-0.004
(0.058)
0.043
(0.061)
-0.020
(0.057)
-0.017
(0.054)
-0.008
(0.057)
Pre-treatment Idea Quality
0.129∗∗
(0.062)
0.138∗∗
(0.061)
0.139∗∗
(0.058)
0.120∗∗
(0.061)
0.129∗∗
(0.062)
Openness (Self)
-0.089
(0.058)
-0.117∗∗
(0.056)
-0.084
(0.056)
-0.105∗
(0.057)
-0.065
(0.058)
Extraversion (Peer) × Openness (Self)
0.189∗∗∗
(0.069)
0.135∗
(0.073)
0.214∗∗
(0.090)
0.185∗∗∗
(0.070)
0.159∗∗
(0.070)
Idea Quality
Extraversion (Peer)
-0.157∗∗∗
(0.061)
Openness (Peer)
Openness (Peer) × Openness (Self)
0.122
(0.075)
Neuroticism (Peer)
0.121∗
(0.066)
Neuroticism (Peer) × Openness (Self)
0.023
(0.088)
Conscientious (Peer)
-0.103∗∗
(0.050)
Conscientious (Peer) × Openness (Self)
-0.112∗∗
(0.054)
Agreeableness (Peer)
-0.024
(0.051)
Agreeableness (Peer) × Openness (Self)
Observations
1141
1141
1141
Innovators
107
107
107
ll
-2707.965
-2702.455
-2705.436
All tests are two tailed. Standard errors clustered at the individual innovator level.
∗
p < .1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
52
1141
107
-2704.661
0.073∗
(0.037)
1141
107
-2706.432
Table 10: Robustness to controlling for whether evaluator and focal innovator have a relationship
(pre-treatment).
(1)
Idea Quality
(2)
Idea Quality
(3)
Idea Quality
(4)
Idea Quality
(5)
Idea Quality
0.229∗∗∗
(0.054)
0.228∗∗∗
(0.054)
0.229∗∗∗
(0.053)
0.230∗∗∗
(0.054)
0.233∗∗∗
(0.055)
Extraversion (Self)
-0.105
(0.065)
-0.109∗
(0.066)
-0.107
(0.065)
-0.106
(0.065)
-0.112∗
(0.064)
Ability (Peer)
0.083
(0.060)
0.084
(0.060)
0.085
(0.061)
0.085
(0.060)
0.068
(0.059)
Ability (Self)
-0.006
(0.059)
-0.006
(0.059)
-0.006
(0.059)
-0.006
(0.059)
-0.006
(0.062)
Pre-treatment Idea Quality
0.145∗∗
(0.062)
0.146∗∗
(0.062)
0.146∗∗
(0.062)
0.145∗∗
(0.062)
0.152∗∗
(0.061)
Extraversion (Peer) × Openness (Self)
0.186∗∗∗
(0.068)
0.186∗∗∗
(0.068)
0.186∗∗∗
(0.068)
0.186∗∗∗
(0.068)
0.173∗∗∗
(0.066)
Evaluator knows innovator
-0.035
(0.197)
0.228
(0.377)
1123
107
-2652.511
1094
107
-2588.157
Idea Quality
Extraversion (Peer)
Evaluator is friends with innovator
0.402
(0.500)
Innovator sought advice from evaluator
0.338
(0.394)
Same ice-breaker table
Observations
1123
1123
1123
Innovators
107
107
107
ll
-2652.670
-2652.443
-2652.479
All tests are two tailed. Standard errors clustered at the individual innovator level.
∗
p < .1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
53
Table 11: The moderating effect of team openness and internal dynamics on the effect of extroverted
peers.
(1)
Total
0.107∗∗
(0.053)
(2)
Novelty
0.163∗∗
(0.072)
(3)
Business
0.029
(0.046)
(4)
Prototype
0.097∗
(0.056)
(5)
Total
0.079
(0.055)
Team’s Avg Ability
0.101∗
(0.050)
0.072
(0.066)
0.087∗
(0.047)
0.181∗∗∗
(0.060)
0.080
(0.051)
Empathy Peers’ Ability (Teamlevel)
-0.030
(0.047)
-0.074
(0.057)
-0.063∗
(0.033)
0.050
(0.060)
-0.008
(0.049)
Team’s Avg Extraversion
-0.119∗∗
(0.051)
-0.073
(0.068)
-0.183∗∗∗
(0.043)
-0.022
(0.064)
-0.116∗∗
(0.051)
Team’s Avg Openness
0.107
(0.087)
0.073
(0.118)
0.040
(0.073)
0.097
(0.112)
0.114
(0.081)
(Teamlevel) Extraversion (Peer) * Openness (Team)
0.302∗∗∗
(0.093)
0.288∗
(0.147)
0.352∗∗∗
(0.072)
0.103
(0.089)
0.298∗∗∗
(0.090)
Empathy Peers’ Extraversion (Teamlevel)
Member Cohesiveness (Team)
-0.048
(0.033)
Member Cohesiveness (Team) x Open (Team) x Extrav (Peer)
0.488∗∗
(0.228)
2.988∗∗∗
(0.045)
Observations
577
Teams
40
R2
0.044
All tests are two tailed. Standard errors clustered at the team level.
∗
p < .1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Constant
54
2.989∗∗∗
(0.058)
577
40
0.032
2.986∗∗∗
(0.046)
577
40
0.039
3.000∗∗∗
(0.063)
577
40
0.032
2.957∗∗∗
(0.048)
577
40
0.053