EXPERTS, NOVICES OR A COMBINATION: WHO IS GIVING ME THE BEST RESULTS FOR INNOVATION? A RESEARCH ON THE DIVERSITY IN INNOVATION KNOWLEDGE AND ITS IMPACT ON INNOVATION OUTCOMES Word count: 14.219 Isabeau Van Strydonck Student number: 01403696 Promotor: Prof. dr. Frederik Anseel Supervisor: Saar Van Lysebetten A dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of Master of Psychology Academic year: 2018 – 2019
Acknowledgements
This master thesis is the final chapter of my five-year psychology education. I would not
have been able to write this dissertation without the help and guidance of a few people. I
would therefore like to use this moment to express my gratitude to some who have
supported and helped me during this process.
First of all, I would like to thank my promotor Prof. dr. Frederik Anseel for giving
me the opportunity to immerse myself for two years in the fascinating world of
innovation. I am also extremely grateful to my supervisor, Saar Van Lysebetten. Saar,
thank you for your support and the lovely collaboration during the past two years. You
always assisted me with good advice and gave me that little ‘push’ in the right direction
when I needed it.
Henri, if someone deserves my gratitude, it certainly is you. You have been an
incredible support for the past two years. You have listened, encouraged, loved, proofread
and so much more. Your hugs and 'everything will be fine' statements always reassured
me so that I could continue my thesis full of enthusiasm.
My dear family and friends, finishing my master’s degree would not have been
possible without the moral support you gave me. Mama, papa, thank you for giving me
the opportunity to start this study and assisting me with advice and support during these
past five years. It was always a pleasure to come home every Friday night and to spend
the weekend with you. Thank you for always believing in me and for supporting me with
your visits during the exams. Moeke, vake, also a big thank to you for taking such good
care of me during these five years. Thank you for providing me with a weekly supply of
food (“so that I would certainly not starve”) and vitamins (“a weekly banana and two
pears will ensure that you can study well”). I can confirm that it has helped indeed!
And last, but certainly not least, this study could not be conducted without the help
and participation of various organizations. Thank you.
Isabeau Van Strydonck
Puurs, May 4, 2019
Abstract Currently, organizations become more dependent on team- and group-based networks to
foster their innovation process (Anderson, De Dreu & Nijstad, 2004). In order to guide
organizations in how to compile a team to attain innovation success, this study aims to
investigate the role of team composition in obtaining effective team innovation outcomes.
In this way, we tried to identify an "innovation dream team", that is most efficient in
achieving team innovation results. Where previous research tends to focus on team
composition in terms of functional diversity (education, job tasks, etc.) and background
diversity (gender, ethnicity, etc.), this study focuses on the diversity in procedural
knowledge among team members. In particular, we investigated the effect of diversity in
innovation knowledge on team innovation outcomes. To do so, we conducted a field study
and examined the innovation profiles of 82 employees from 19 R&D teams, using a
situational judgment test. Based on these profiles, we classified the teams into novice,
expert or balanced teams and investigated which of these three compositions provided the
best team innovation outcomes. Our results showed that expert teams, compared with
balanced teams, had significantly lower team innovation outcomes. This study, however,
found no significant difference between novice and expert teams, neither between
balanced and novice teams. Since past research showed that both knowledge sharing
(Taylor & Greve, 2006; Yu, Yu-Fang and Yu-Cheh, 2013) and voice behavior (LePine &
Van Dyne, 1998; Bashshur & Oc, 2015) lead to more innovative behavior and better team
performance, we wanted to investigate whether these two variables would also strengthen
the relationship between individual innovation and team innovation results. However, our
findings did not show any significant moderation effect. Possible explanations for our
results are presented in the discussion, followed by practical implications of the study and
suggestions for further research.
Abstract Vandaag de dag worden organisaties steeds meer afhankelijk van team- en groep-
gebaseerde netwerken om hun innovatieproces te bevorderen (Anderson, De Dreu &
Nijstad, 2004). Om organisaties een beeld te kunnen geven over hoe men een team moet
samenstellen om innovatiesucces te bekomen, beoogt deze studie de rol van
teamsamenstelling voor het verkrijgen van effectieve teaminnovatieresultaten te
onderzoeken. Dit met als doel een “innovation dream team” te identificeren, dat het meest
efficiënt is in het behalen van goede resultaten op het gebied van teaminnovatie. Waar
eerder onderzoek zich voornamelijk toespitst op teamsamenstelling in termen van
functionele diversiteit (opleiding, job taken, enz.) en diversiteit in achtergrond (geslacht,
etniciteit, enz.), legt deze studie de focus op de diversiteit in praktische kennis tussen
teamleden. In het bijzonder hebben we het effect van diversiteit in innovatiekennis inzake
teaminnovatieresultaten onderzocht. Om dit te doen, voerden we een veldstudie uit,
waarin we de innovatieprofielen van 82 medewerkers uit 19 R&D-teams onderzochten,
aan de hand van een situationele beoordelingstest. Op basis van deze profielen,
verdeelden we de teams in “novice”, “expert” of gebalanceerde teams en onderzochten
we welke van deze drie composities de beste resultaten voor teaminnovatie opleverden.
Onze resultaten toonden aan dat “expert” teams, in vergelijking met gebalanceerde teams,
significant lagere scores behaalden op vlak van teaminnovatie uitkomsten. Deze studie
vond echter geen significant verschil tussen “novice” teams en “expert” teams, noch
tussen gebalanceerde teams en “novice” teams. Vermits eerder onderzoek aantoonde dat
zowel “knowledge sharing” (Taylor & Greve, 2006; Yu, Yu-Fang en Yu-Cheh, 2013),
als “voice behavior” (LePine & Van Dyne, 1998; Bashshur & Oc, 2015) tot innovatiever
gedrag en betere teamprestaties leiden, wilden we in deze studie nagaan of deze twee
variabelen ook de relatie tussen individuele innovatie en teaminnovatie zouden
versterken. Onze resultaten toonden echter geen significante moderatie-effecten aan.
Mogelijke verklaringen voor onze resultaten worden weergeven in de discussie, gevolgd
door praktische implicaties van de studie en suggesties voor verder onderzoek.
Table of content
Introduction ................................................................................................................ 1
Literature review ........................................................................................................ 3
Innovation ................................................................................................................. 3 Idea generation. ...................................................................................................... 3 Idea championing. .................................................................................................. 4 Idea implementation ............................................................................................... 4
Teams ........................................................................................................................ 6 Team composition and diversity ............................................................................. 6
Diversity in innovation knowledge ............................................................................. 8 Prototype theory ..................................................................................................... 9 Cognitive transformation theory ........................................................................... 10
Knowledge sharing .................................................................................................. 12
Voice behavior......................................................................................................... 14
Method ...................................................................................................................... 16
Procedure & Design ................................................................................................ 16
Sample ..................................................................................................................... 16
Measures ................................................................................................................. 17
Analysis & Results .................................................................................................... 19
Discussion .................................................................................................................. 24
Limitations .............................................................................................................. 28
Practical implications .............................................................................................. 29
Further research...................................................................................................... 30
Conclusion ................................................................................................................. 31
References ................................................................................................................. 32
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“Innovation is fostered by information gathered from new connections; from insights
gained by journeys into other disciplines or places; from active, collegial networks and
fluid, open boundaries. Innovation arises from ongoing circles of exchange, where
information is not just accumulated or stored, but created. Knowledge is generated anew
from connections that weren't there before.” - Margaret J. Wheatley
Currently, innovation is one of the most important aspects for organizations. It
provides a competitive advantage and leads to identifiable benefits such as organizational
performance and success (Anderson, Potočnik & Zhou, 2014). Due to innovation, a firm
can deal with discontinuous environmental change (Levinthal & March, 1993) and can
sustain long-term survival (Anderson et al., 2014). Like Steve Jobs said: “Innovation is
the only way to win.” Therefore, organizations should rather focus the question “How do
we need to innovate?” instead of “Do we innovate?”
Team-level predictors have an important effect on innovation and are widely
investigated in the scientific literature (Burningham & West, 1995; Hülsheger, Anderson
& Salgado, 2009), especially since team- and group-based networks are now more than
ever an essential part of a company (Anderson et al., 2004). One team-level predictor is
team interaction, which has a very important influence on innovation (Isaksen, 1990;
Puccio & Cabra, 2010), and is crucially influenced by team composition (Carpenter,
Geletkancz & Sanders, 2004). For that reason, it can be useful for organizations to know
which team composition in innovation knowledge acts as a kind of ‘dream team’ and is
the most efficient in achieving team innovation results.
To predict team interaction and innovation, previous research has been especially
interested in the effect of job-related (e.g., education, job experiences) (Jackson, 1992)
and non-job-related diversity (e.g., gender, ethnicity) (Hülsheger et al., 2009) in team
composition. Those studies (Jackson, 1992; Hülsheger et al., 2009) found that functional
or task-relevant diversity can positively influence team performance. However, those
studies are based on diversity in declarative knowledge (i.e., knowledge gained from
school or textbooks) between members of the same team. For that reason, we will not
examine the effect of diversity in declarative knowledge. Instead, our study will
investigate the effect of the diversity in team members’ procedural knowledge, which is
the knowledge acquired through experience (Hoffman, 1998).
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More specific, this study will focus on the diversity in innovation knowledge
among team members and its effect on team innovation outcomes. There is a lack of
research on the effect of diversity in team members’ innovation knowledge and how this
knowledge influences team innovation outcomes. There are some studies that investigate
the effect of being an expert versus being a novice in the generation of ideas (Baron &
Ensley, 2006; Dew, Read, Sarasvathy & Wiltbank, 2009). However, the effect of
someone’s level of expertise in innovation knowledge on championing and implementing
ideas has been less investigated. To our knowledge, no research has been done on the
effect of diversity in team composition in terms of levels of expertise in innovation
knowledge through all the phases of the innovation process. Therefore, this study will
explore the importance of expertise through all phases of the innovation process to predict
which team composition leads to the best team-related innovation outcomes.
In this paper, we will first review the literature within the domain of innovation
and teams. Subsequently, we will declare how innovation is influenced by team
characteristics, with a focus on the level of expertise in innovation knowledge. Next, we
explain which method is used in this study, followed by an analysis and results report.
These results will be interpreted in the discussion section. To end, a conclusion is
presented.
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Literature review
Innovation
Innovation can be defined as the creation and application of new ideas, processes
or products (West & Farr, 1989), with the intention to improve the performance of the
individual, group or organization (Janssen, 2000) and to assure an organization’s
capability to stay competitive (Chen, Farh, Campbell-Bush, Wu & Wu, 2013). Creativity
is a necessary condition to achieve innovation (Anderson et al., 2014). Yet, it is important
to make a clear distinction between creativity and innovation, as the meaning of these
concepts are different. Anderson et al. (2014, p.1298) provide a clear definition of the
difference between innovation and creativity: “Creativity and innovation at work are the
process, outcomes, and products of attempts to develop and introduce new and improved
ways of doing things. The creativity stage of this process refers to idea generation, and
innovation refers to the subsequent stage of implementing ideas toward better procedures,
practices, or products. Creativity and innovation can occur at the level of the individual,
work team, organization, or at more than one of these levels combined but will invariably
result in identifiable benefits at one or more of these levels of analysis.” Innovation
distinguishes itself from creativity because it includes more than just the generation of
ideas (Anderson et al., 2004). Whereas creativity can be compared with the generation
process of novel and useful ideas itself (Mumford & Gustafson, 1988), innovation is a
multistage process (Kanter, 1988), which not only refers to the generation but also to the
application of those new and useful ideas (West et al., 1989). Innovation generally
contains three stages: idea generation, idea championing and idea implementation (West
et al., 1989).
Idea generation. The innovation process usually starts with idea generation
(West, 2002), which is the way in which people try to reinvent existing products to
accomplish potential opportunities for the organization (Alexiev, Jansen, Van den Bosh
& Volberda, 2010). Furthermore, it can lead to advancements in work processes or to
better problem solving (Minh, Badira, Quangb & Afsar, 2017).
Moreover, idea generation embraces three different activities namely idea
exploration, idea generation and idea selection (Kornish & Hutchison-Krupat, 2017). Idea
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generation mostly starts with idea exploration, which is the identification or recognition
of a problem (Scott & Bruce, 1994) and ends with a discussion on which types of ideas
have to be produced related to the recognized problem (Kornisch et al., 2017). After the
problem recognition has occurred, new or adopted ideas or solutions can arise (Scott et
al., 1994). Or defined in the words of Terwiesch and Ulrich (2009, p.8): “Idea generation
is finding a new match between a need and a solution.” At the end, the team selects the
best generated ideas that will receive further attention in the next phases of the innovation
process (Kornisch et al., 2017). The generation of new ideas is a crucial activity in the
innovation process (Kornisch et al., 2017) and especially depends on individual
characteristics such as individual creativity, self-confidence and knowledge (Axtell,
Holman, Unsworth, Wall, Waterson & Harrington, 2000).
Idea championing. Once the new ideas are generated and selected, the second
innovation phase occurs in which a member of the innovation team, called the idea
champion (Howell & Higgins, 1990), generates support for the idea within the
organization (Baer, 2012). Most of the time, the proposed ideas are not in line with the
current ideas in the work group or organization (Kleysen & Street, 2001). For that reason,
the champion needs to persuade and influence other people or stakeholders in the
organization (Kleysen et al., 2001) to collect resources to bring the creative ideas to life
(Howell et al., 1990). For that reason, a champion needs to be self-confident and
enthusiastic while promoting innovation. The champion has to overcome resistance, but
above all: it must be someone who dares to take risks (Howell et al., 1990).
Idea implementation. The final innovation phase is idea implementation (Minh
et al., 2017). During this phase the innovation idea becomes a part of the working process,
where “new ideas are converted into new and improved products, services or ways of
doing things” (Baer, 2012, p.1102). The result of the implementation process is a
prototype which can be touched or experienced by employees and, when it is successful,
can lead to institutionalization or production (Kanter, 1988). A lot of research has been
done on the generation of ideas in contrast to their implementation (Axtell et al., 2010).
While the generation of ideas can be an individual process, we see that idea
implementation is more an interpersonal process because it depends on the acceptance
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and support of others (Van de Ven, Angle & Poole, 2000). For that reason, group and
organizational factors such as team and management support and team participation,
rather than individual factors, are more associated with idea implementation (Axtell et
al., 2010). A successful implementation covers a commitment of the entire team when the
changes are realized and accepted (Thenhaus, 2014).
When the innovation process is successfully implemented, it can lead to
identifiable benefits such as organizational performance and success (Anderson et al.,
2014). Totterdell, Leach, Birdi, Clegg & Wall (2002) investigated possible types and
consequences of innovation in five industrial sectors in the UK. They distinguished
between four types of innovation: technology innovation (i.e., which can be the use of
new machineries or systems), HRM innovation (e.g., changes in rewards, training, etc.),
organizational restructuring innovation (e.g., acquisitions) and product and service
innovations (e.g., developing new products). Furthermore, they looked at three possible
outcomes of such innovations: financial outcomes (e.g., cost effectiveness, competitive
advantage), employee relations outcomes (e.g., trust, commitment) and customer-based
outcomes (e.g., customer satisfaction). The results showed that when the innovation
process had been successful, it positively benefited all of the three outcomes. In addition,
the organizations that had been through an innovation failure, reported negative financial,
employee relations and customer-based outcomes. These results are in line with a study
of Laforet (2011), which concluded that successful innovation leads to better business
performance, better communication and increased satisfaction (West & Anderson, 1996),
but failed innovation can lead to reputation loss and financial risks. However, due to the
multiple benefits successful innovation brings to an organization, believing it is just a
one-time process, will make organizations less competitive (Thenhaus, 2014). Therefore,
in order to respond to dynamic marketplaces and to sustain a competitive position
(Baregheh, Rowley & Sambrook, 2009), organizations should invest in innovation on a
continuous base (Thenhaus, 2014).
Chen et al. (2013) investigated potential individual antecedents of innovative
behavior. They concluded that an employee’s motivation to change their work
environment, combined with a proactive personality to take new actions, would lead to
more innovative behavior. Besides personality and motivational drivers, cognitive factors
such as the employee’s knowledge (Jackson, 1992) and expertise (Ross, Philips, Klein &
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Cohn, 2005) can also positively benefit team performance outcomes such as innovation
(Jackson, 1992). For the reason that these cognitive factors have mainly been overlooked
in scientific literature, this study will focus on the diversity of employees’ expertise in
innovation knowledge and how this diversity influences team innovation outcomes.
Teams
To respond to dynamic environments (Van Offenbeek & Koopman, 1996), teams
have become the common work units in organizations (Jackson, 1992). For that reason,
teamwork is a crucial aspect regarding the innovation processes (e.g., a new product
development process) (Souder, 1981).
“Teams are units of two or more individuals who interact inter-dependently to
achieve a common objective” (Bell, 2007, p.1). Contrary to the past, current organizations
are more dependent of group-based networks and collaborative interdisciplinary teams
(Anderson et al., 2004), due to the fact that teams are very efficient in responding to
changing environments (Burke, Stagl, Salas, Pierce & Kendall, 2006). Working in a team
increases collaboration and allows team members to brainstorm together. As a result of
this interaction, communication between team members will be encouraged and more
ideas will be developed (Boyer, 2017). Research of team innovation and their underlying
team processes are therefore important to investigate (Drach-Zahavy & Somech, 2001).
The composition of a team plays a significant role in accomplishing good team
interaction (Carpenter et al., 2004), which in turn leads to better innovation (Puccio et al.,
2010). For that reason, we will describe the effect of team composition and diversity on
innovation.
Team composition and diversity. Today, organizations make use of diverse
teams more than ever before (Shin, Kim, Lee & Bian, 2012). Generally, there are two
main levels of diversity: deep-level diversity and surface-level diversity (Drach-Zahavy
& Somech, 2010). Differences in deep-level composition variables such as personality,
attitudes and values show small but negative effects on team performance (Bell, 2007).
Surface-level diversity can be divided in job-relevant diversity, also known as functional
diversity (Tang & Naumann, 2016), which refers to heterogeneity in function, education
or knowledge (Hülsheger et al., 2009) and background diversity (i.e., non-task related
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diversity such as age, gender, ethnicity) (Hülsheger et al., 2009). Background diversity
has a negative relation with team performance (Hülsheger et al., 2009). A possible
explanation for this result may be found in the similarity/attraction theory (Byrne, 1971),
which assumes that people are more attracted to people who they perceive as similar to
themselves. In contrast to the negative effect of background diversity, job-relevant
diversity brings cognitive resources within the team and has for that reason a positive
relation with team performance (Jackson, 1992). For example, informational diversity,
which is the difference in educational background between team members (Hobman,
Bordia & Gallois, 2004), may lead to different perspectives (Milliken, Bartel &
Kurtzberg, 2003) and is for that reason the most relevant heterogeneity variable within a
team (Williams & O’Reilly, 1998). Furthermore, there is a positive relationship between
job-related diversity and both quality and quantity of team performance (Horwitz &
Horwitz, 2007).
Team composition has a powerful influence on team processes and outcomes
(Drach-Zahavy et al., 2001). The study of Drach-Zahavy et al. (2001) is therefore relevant
research to discuss in this study. These authors investigated the role of team interaction
processes such as information exchange and learning on team innovation. They found
that the exchange of information leads to more knowledge in the team which in turn
fostered team innovation. Furthermore, the authors investigated the role of functional
heterogeneity on team innovation. In line with research from Jackson (1992), they
concluded that team heterogeneity was positively related to team interaction processes
such as knowledge sharing and increased learning processes. For instance, due to the
different perspectives, skills and expertise, the team will consider a broad range of
information resources, which results in an increase of knowledge sharing and team
learning. Those team interaction processes have led to better team innovation results.
Furthermore, the authors emphasized the importance of functional team heterogeneity
during all the stages of the innovation process: the more heterogeneous the team, the more
information that is shared among the team members, the more a learning process is
developed, the more team innovation is accomplished. Therefore, in order to foster
innovation within the team, organizations must see innovation as an interactive process
(Agrell & Gustafson, 1996), where participation and interaction (Puccio et al., 2010) of
all team members is essential (Van Offenbeek et al., 1996).
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In the literature we see that some studies (Jackson, 1992; Miliken et al., 2003;
Horwitz et al., 2007) investigated the role of theoretical knowledge (i.e., declarative
knowledge; knowledge gained from school or textbooks). For example, researchers found
that team composition in terms of task-relevant and informational diversity can lead to
positive benefits for team performance (Jackson, 1992; Miliken et al., 2003; Horwitz et
al., 2007). However, besides theoretical knowledge, we can also look at the diversity in
procedural knowledge about the innovation process (i.e., innovation knowledge, which is
knowledge acquired through innovation experiences) (Hoffman, 1998) and its effect on
team performance such as innovation results. In the next section, we will give a broader
explanation of this type of diversity. Subsequently, we will try to explain the diversity in
innovation knowledge based on two scientific theories, followed by possible outcomes of
team diversity in innovation knowledge. To end, we will posit our first three hypotheses.
Diversity in innovation knowledge
Diversity in innovation knowledge is in this study defined as the level of expertise
someone has through all three innovation phases. Following Hoffman (1998), we define
a novice as someone with little procedural knowledge about a certain field or domain. A
novice may have acquired a great amount of theoretical knowledge in a certain domain
but has no actual lived experience yet (Ross et al., 2005). For example, a newly graduated
student who will start his first work experience in a certain company normally has a great
amount of textbook and classroom knowledge (e.g., theoretical or declarative knowledge)
but a lack of procedural knowledge. Experts on the other hand, have a great amount of
procedural knowledge, which is the knowledge of knowing what kind of steps are needed
to reach a certain goal (Rittle-Johnson & Schneider, 2014) and which is acquired through
experience (Hoffman, 1998). Novices have a lack of procedural knowledge which makes
it harder for them to get access to appropriate actions (Ross et al., 2005).
The aim of our study is to examine which team composition in terms of diversity
in innovation knowledge between members of the same team will lead to the highest
innovation team results. As mentioned above, functional, task-relevant or informational
diversity can have a positive influence on team performance (Jackson, 1992; Miliken et
al., 2003; Horwitz et al., 2007). The difference with prior studies in the literature is that,
in this study, diversity in innovation knowledge or, in other words, the distinction between
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an expert or a novice is not based on declarative knowledge but made on procedural
knowledge. Therefore, instead of examining informational diversity in terms of
theoretical or declarative knowledge (Hobman et al., 2004), this study focuses on the
effect of team diversity in procedural knowledge on innovation outcomes.
There are multiple theories in the literature such as the prototype theory
(Whittlesea, 1997) and the cognitive transformation theory (Klein & Baxter, 2009), which
explain how the knowledge of an expert is created, developed and can have a sufficient
advantage over novices.
Prototype theory. According to the prototype theory (Whittlesea, 1997), experts
acquire patterns and cognitive frameworks (e.g., prototypes) through experience (Baron
et al., 2006). With help from these cognitive frameworks, experts can easier identify
possible connections between unrelated concepts, which can lead to awareness of
opportunities for growth (e.g., advances in technology, changes in markets, etc.) (Baron
et al., 2006). This pattern recognition can arise from previous experiences and expertise
in a certain field or industry (McKelvie & Wiklund, 2004), which ensures that experts
can better apply their knowledge (Dew et al., 2009). During the process of pattern
recognition, experts have better access to their knowledge than novices (Dew et al., 2009),
which makes it easier for them to compare new events or objects with existing prototypes
these experts have gained through experience (Baron et al., 2006). More specifically,
experts compare new, innovative ideas with their current thought of “business
opportunity” (Shane, 2003).
Baron et al. (2006) investigated the differences in cognitive frameworks between
novices and experts for the identification of business opportunities. They concluded that
experienced entrepreneurs (e.g., experts) acquired more refined cognitive frameworks
that made their expertise a meaningful benefit over novices in terms of generating new
and successful ideas. Furthermore, experts were more appropriate to turn those new ideas
into realized financial gains (Baron et al., 2006). Consistent with these result, a study of
Chi (2006) also concluded that experts rather than novices know better which strategies
are appropriate to implement, because some level of experience or know-how is necessary
to create valuable products or services (Hargadon & Fanelli, 2002).
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Cognitive transformation theory. Complementary with the prototype theory,
Klein et al. (2009) suggest, with their cognitive transformation theory, that when people
are learning new things or gain new experience in a certain field, they are redefining their
existing patterns and prototypes. Furthermore, the authors declare that experts see the
world through their mental models, which are beliefs and experiences about how the
world works that are organized in knowledge structures and schemata, which are
equivalent with the patterns and prototypes discussed above. Experts, rather than novices,
have, through their experience and procedural knowledge, better refined mental models
in the domain they are active in (Klein et al., 2009). These mental models can be applied
in several situations (Klein et al., 2009). Due to their more accurate mental models,
experts have a higher change of immediately recognizing the relevance of a certain idea
for which they can handle a problem quickly and efficiently (Ross et al., 2005).
Based on the above evidence, working with experts is highly appreciated in the
professional field. This can cause organizations to believe that recruiting experts, due to
all the beneficial knowledge they have, is absolutely necessary to improve the
organization’s performance. However, only having experts in a team is not always
pertinent. Some studies claim that when there is knowledge of all levels (i.e., including
both experts and novices) in the team, the team members link the available knowledge to
combine ideas for new products or services (Dew et al., 2009). Furthermore, expertise is
domain-limited (Chi, 2006) which makes it very hard to find someone whose expertise is
applicable for the generation, championing and implementation of ideas. Next to this, the
war for talent, which is the competition among organizations for top employees
(Porschitz, Smircich, Calas, 2016), makes it simply impossible for a lot of organizations
to attract and retain those employees, due to the high costs this incurs. This problem may
be solved by also hiring novices in the team and invest in training to develop their skills.
Structured on-the-job training, which is the process whereby experienced employees train
novices in an actual work setting, can be an appropriate and effective training approach
for novices (Jacobs & Bu-Rahmah, 2012). Research by Ross et al. (2015) shows for
example that, at the novice level, the best training to build new mental models to achieve
a better performance, requires dialogues with an instructor or mentor who helps the
novice with developing new, experiential knowledge. The expert in the team can act as
the mentor of the novices and, as such, shares his knowledge and experiences within the
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team. This interaction might allow novices to develop accurate mental models, similar to
those of experts. We also have to consider that a team with only experts in all of the three
phases of the innovation process might cause conflict due to their different experiences
and approaches in the past. Furthermore, research of Cropley and Kaufman (2012)
concludes that non-experts are capable to identify widely accepted aspects such as
effectiveness and novelty of creative products. This result may suggest that even when an
employee is not an expert in generating an idea, this person can still give his opinion
about the usefulness of the idea.
Although these prior studies investigated the advantages or disadvantages of
having novices or experts in the team, none of the research examined the effect of
diversity in innovation knowledge through all the phases of the innovation process in
R&D teams. Due to the importance of innovative R&D teams for organizations
(Caldbeck, 2018), it can be useful to investigate which team composition in innovation
knowledge is the most efficient and leads to the best team innovation outcomes. We agree
with previous studies that R&D teams have to consist of at least one innovation expert.
Due to the expert’s experiences in a certain domain or field, an expert will know what
action to take to solve a certain problem or situation (Ross et al., 2005). Therefore, the
expert can act as an on-the-job trainer and share relevant experiences and knowledge
within the team so that the novices can gain new experiences and refine their mental
models (Jacobs et al., 2012).
Hypothesis 1: A balanced team consisting of both innovation experts and
innovation novices will obtain higher innovation team results than a team only consisting
of innovation novices.
Even though an expert can handle a situation immediately and efficiently, his
decision-making process can often be intuitive (i.e., without effort or conscious
awareness) (Ross et al., 2005). Experts might be very confident about their skills and
expertise, due to the extent of their past experiences, such that they will rely more on their
intuition. A novice, on the other hand, will use a more analytical strategy to determine a
solution (Hogarth, 2002). According to Kahneman & Klein (2009), intuitive decision
making is only allowed in high-validity environments (i.e., environments where there are
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stable relations between actions and possible outcomes). Due to dynamic and sometimes
unpredicted marketplaces organizations face (Baregheh et al., 2009), we believe R&D
teams do not always work in high validity environments. For that reason, we believe it is
better to have a balanced mix of experts and novices in order to combine both intuitive
and analytical approaches.
Hypothesis 2: A balanced team consisting of both innovation experts and
innovation novices will obtain higher innovation team results than a team primarily
consisting of innovation experts.
Although experts tend to use a more intuitive decision-making process than
novices, a study of Chi (2006) concluded that, in order to effectively implement products
or services, some level of experience or know-how is needed. Baron et al. (2006) showed
that experts acquire more refined cognitive frameworks over time and are therefore more
successful not only in generating, but also in implementing new and successful ideas.
Experts’ actual lived experience allows them to turn those new ideas into realized
financial gains. For the reason that novices have a shortage of actual lived experience in
the field (Ross et al., 2005) and did not yet acquire patterns or cognitive frameworks
through experiences (Whittlesea, 1997), we believe that a team primarily consisting of
experts will lead to better team innovation results than a team full of novices.
Hypothesis 3: A team primarily consisting of innovation experts will obtain higher
innovation team results than a team only consisting of innovation novices.
Knowledge sharing
Team interaction processes such as team communication and social cohesion,
which is the degree to which team members are committed to each other and share mutual
trust (Forsyth, 1990), can predict team performance (Afolabi & Ehigie, 2005).
Furthermore, team interaction can reduce work place issues because effective team
communication leads to a better team collaboration and a maximized job satisfaction
(Duggan, 2018).
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To achieve those team interaction processes, knowledge sharing is an important
factor (Mesmer-Magnus & DeChurch, 2009). Knowledge sharing is the process whereby
individuals exchange their knowledge of information through communication and
interaction (Mittal & Dhar, 2015). De Dreu, Nijstad and Van Knippenberg (2008)
suggest, based on the motivated information processing in group (MIP-G) theory, that
individuals share information among team members due to their epistemic and social
motivation. Epistemic motivation (e.g., cognitive or knowledge motivation) can be
activated by functional diversity because team members seek to obtain new knowledge
when they hear different ideas and perspectives (De Dreu, Nijstad, Bechtoldt & Baas,
2011). Research and development (R&D) teams often comprise diverse functional
experts (West, 2002). This diverse knowledge can be a benefit to achieve team innovation
in R&D projects (Taylor et al., 2006). The different perspectives within a team ensure
that members seek internal advice from other team colleagues which leads to more
exploratory innovation (idea generation) (Alexiev et al., 2010). Besides, knowledge
sharing can also lead to individual growth. For example, cooperating within a team is an
occasion to learn from other team members in order to acquire new skills and knowledge
(Kumar, 2015).
On the other hand, some studies also show that functional diversity can have a
negative influence on knowledge sharing, due to concerns about potential risks associated
with knowledge sharing (Edmondson, 1999; Bunderson & Sutcliffe, 2002; Rosendaal &
Bijlsma-Frankema, 2015). For example, experts in a certain field may worry that other
members of the team (with expertise in other domains) do not understand their knowledge
(Bunderson et al., 2002). They might concern that sharing their knowledge could lead to
criticism (Edmondson, 1999). Furthermore, they may also worry that they can share their
knowledge, but do not receive any knowledge in return (Rosendaal et al., 2015).
To realize idea generation, trust within teams is very important (Cheung, Gong,
Wang, Zhou & Shi, 2016). This trust can facilitate idea generation by improving
bidirectional interactions (Phelps, 2010). Besides idea generation, knowledge sharing
would also be useful for implementing the ideas (Taylor et al., 2006). According to Yu et
al. (2013), involvement of employees in sharing their knowledge among team members
would lead to a greater amount of knowledge in the team, which only benefits innovative
behavior. In line with Taylor et al. (2006) and Yu et al. (2013), we believe that when
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individuals are motivated to share their knowledge through the team, better innovation
outcomes will be obtained. When there is a lack of trust within teams, it may be possible
that team members do not want to share their knowledge and, for that reason, effective
innovative behavior will not occur. We also believe that teams who do not share their
knowledge among team members will perform less well than those who do share their
knowledge.
Hypothesis 4: Knowledge sharing moderates the relationship between innovative
behavior and innovation team results such that innovative behavior will lead to higher
innovation team results if knowledge sharing in the team is high.
Voice behavior Besides team-level predictors, there are also some individual variables that can
have an important influence on innovative behavior (Burningham et al., 1995). Therefore,
in this study, we will not only examine team-level predictors such as knowledge sharing
in teams, but we will also discuss the effect of an employee’s individual voice behavior
on the effectiveness of the team innovation process.
Voice behavior is defined as the “speaking out and challenging the status quo with
the intent of improving the situation” (LePine et al., 1998, p.853). Voice behavior can
lead to multiple positive outcomes for organizations (McClean, Burris & Detert, 2013).
For example, voice behavior can be effective when a team member dares to express one’s
thoughts in order to suggest alternative approaches or to change an existing procedure
(LePine et al., 1998), which in turn can boost the functioning of all the stakeholders in
the organization (Bashshur et al., 2015). Due to the fact organizations come up against
changing environments (Carnevale, Huang, Crede, Harms & Uhl-Bien, 2017), they need
employees who are proactive (Parker, Bindl & Strauss, 2010) and engage in the business
process (Van de Ven, 1999) to remain competitive and innovative (Whiting, Podsakoff
& Pierce, 2008). Therefore, voice behavior can be seen as an important factor in teams
(LePine et al., 1998).
A review of Bashshur et al. (2015) looked a little bit closer at the possible
individual, team and organizational outcomes of voice behavior. At the individual level,
voice expression is related to better job attitudes and lower levels of organizational
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turnover, which is the rate at which employees leave the organization (Ongori, 2007). At
the team level, voice behavior will lead to better performance and more innovative
behavior, which also benefits the organizational performance. Important to keep in mind
is that these positive outcomes can turn negative when voice behavior of an employee is
ignored (Bashshur et al., 2015). A positive climate for voice behavior is therefore needed
(Bashshur et al., 2015).
Given the ongoing need for innovation in organizations (Anderson et al., 2014),
voice behavior is crucial to make effective innovation take place (LePine et al., 1998): an
individual may be good at generating new ideas, but if the employee is unwilling to share
or communicate it with other team members, it will never reach the champion or
implementation phase (Rank, Pace & Frese, 2004). Voice behavior can therefore
positively influence the idea generation process because it fosters creativity among
employees, which leads to new ways of thinking (Chen & Hou, 2016). If someone dares
to speak up in a group, the other team members may see him as a role model, which
encourages them to give their opinion as well (MacKenzie, Podsakoff & Podsakoff, 2011)
and which translates itself into higher levels of creativity of all team members (Bashshur
et al., 2015).
We agree with previous research (LePine et al., 1998; Rank et al., 2004) that
individual voice behavior is crucial to start the innovation process and, furthermore, to
benefit the innovation team results, because it fosters creativity and idea generation (Chen
et al., 2016). Therefore, we believe that teams existing of employees who do not engage
in voice behavior will perform less well than teams with members who do engage in voice
behavior. Therefore, we posit our last hypothesis:
Hypothesis 5: An employee’s voice behavior moderates the relationship between
innovative behavior and innovation team results such that innovative behavior will lead
to higher innovation team results if employee voice behavior in the team is high.
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Method
Procedure & Design
To conduct this experiment, we contacted approximately 150 companies with an
R&D department in Belgium via e-mail. The e-mail included general information about
the study and the question to participate in our study. If the organization was interested
in participating, an initial phone conversation with the HR department or the head of the
R&D department followed. After agreement of all members of the R&D team(s), we
received a confirmation e-mail including the names, professional e-mail addresses and
preference to fill out the surveys in Dutch or English for all team members.
After agreement, in the first step of the study, all participants received an email
with a personal link to the online questionnaire in which demographic information, the
dependent variable (team innovation outcomes), an independent variable (individual
innovation) and moderators (knowledge sharing and voice behavior) were questioned.
The survey started with an informational introduction where participants were informed
about the purpose and length of the survey. Respondents were also guaranteed that the
collected data would be anonymized and used confidentially. This entire survey took
approximately 15 minutes.
When the participants had completed this questionnaire, they received a second
email with a personal invite from Innduce.me, where they could complete the situational
judgement test. Completing this situational judgement test took approximately one hour.
When the participants had completed the test, they received an online report regarding
their innovation profile.
Sample
In this study, 87 participants completed the first online survey. Of these, 86 people
also filled out the situational judgement test. After data cleaning, which we describe in
the analysis and results section, 19 teams, consisting of 82 participants, remained.
With regards to gender, there were 58 male and 24 female respondents, this
corresponds respectively to 70,7 % and 29,3% of the sample. Most of the participants
(40,2%) were in the age category of 30 – 39 years old. Besides gender and age, level of
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education was also measured. Most respondents (42,7%) obtained a master, followed by
those who obtained a PhD (26,8%).
Measures
Demographic variables. Three demographic variables were included in the
study, namely gender, age and level of education.
Level of innovation knowledge. To determine whether a participant was an
innovation expert or an innovation novice, we used a situational judgement test from
Innduce.me. This test consisted of 15 scenarios, which were real-life cases, related to the
innovation process. There were 5 scenarios for the generation phase, 5 for the
championing phase and 5 for the implementation phase. Each of the 15 scenarios is
repeated 7 to 13 times, where each time a different pair of responses is given to the
participant. Participants’ answers to this situational judgement test were compared with
the answers of 26 innovation experts, in order to define to which level of expertise they
belonged. Previous research (Van Lysebetten, Anseel & Velghe, 2018) found that the
empirical reliability of this situational judgement test was .73, which can be considered
as a good internal consistency. After completing the situational judgement test,
participants received an overview of their separate scores in each of the innovation phases
(ideation, championing and implementation phase), as well as their total innovation score
across the three phases. There were six possible types of innovation profiles to which
participants could be assigned. This included three general profiles: (1) an innovation
contributor, (2) an innovation partner and (3) an innovation master. In the case an
innovation master had an especially high score in one of the three innovation phases, he
was assigned a more specific innovation profile, namely (4) an ideator, (5) a champion or
(6) an implementor.
Team composition. To investigate which team composition in innovation
knowledge leads to the best team innovation results, we categorized the teams in novice
teams, balanced teams and expert teams. Based on the answers participants gave in the
situational judgment test, they were assigned an innovation profile, that reflected their
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expertise level in innovation knowledge. We decided to classify participants with a profile
of an innovation contributor or an innovation partner as a novice.1 Participants with a
profile of an innovation master, ideator, champion or implementor were classified as
experts. In a second step, we examined the composition of team members innovation
profiles within each team. Teams that only consisted of ‘innovation contributors’ and
‘innovation partners’ were categorized as novice teams. We classified teams of which
more than half of the team members were identified as experts as an expert team.2 When
half or less than half of the team consisted of people with an expert innovation profile,
we classified the team as a balanced team.
Individual innovation. Individual innovation was measured using Janssen's
(2000) nine-item scale of individual innovation in the workplace, which made use of
Kanter's (1988) work on the three stages of innovation (generation, championing and
implementation). A sample item was: ‘I create new ideas for improvement.’ We used
Cronbach’s test of reliability to assess whether the internal consistency between items
was sufficient. Analysis revealed that the internal consistency of this scale was good with
a Cronbach’s α of .85. Participants scored all items in the survey on a Likert-scale ranging
from 1 (Strongly disagree) to 5 (Strongly agree).
Team innovation. Team innovation was measured with a scale that consisted of
three items selected from the team innovation scale from West and Wallace (1991).
Participants were asked to think of the level of teamwork of their team in the last 6 months
and to review the following performance items: ‘The team initiated new procedures and
methods.’, ‘The team developed innovative ways of accomplishing work
targets/objectives.’, ‘The team developed new skills in order to foster innovations.’ We
added one extra item: ‘The team initiated improved products and/or processes.’
Participants scored all four items on a Likert-scale ranging from 1 (Strongly disagree) to
5 (Strongly agree). Cronbach’s α of this measure was .66.
1 In this study, we worked with real R&D teams from different organizations, which makes it hard to find teams of which all team members are innovation novices. When analyzing the innovation profiles within the 19 teams, we observed that no team consisted solely of innovation contributors. 2 In this study, we worked with real R&D teams from different organizations, which makes it hard to find teams of which all team members are innovation experts. When analyzing the innovation profiles within the 19 teams, we observed that no team consisted solely of innovation experts.
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Knowledge sharing. We operationalized knowledge sharing by using three items
from Wah et al. (2007). A sample item was: ‘Ideas and best practices are shared routinely
within the team.’ Participants scored the three items on a Likert-scale ranging from 1
(Strongly disagree) to 5 (Strongly agree). Cronbach’s α was .64.
Voice behavior. We measured voice behavior by using a 6-item scale from
LePine et al. (1998). An item example was: ‘I develop and make recommendations
concerning issues that affect this work group.’ Participants scored all items on a Likert-
scale ranging from 1 (Strongly disagree) to 5 (Strongly agree). Cronbach’s α was .72,
which indicated an acceptable internal consistency.
Analysis & Results
Before we analyzed the participants’ innovation profiles to classify them as
novice, balanced or expert teams, we first checked the data for outliers. Of the 86
participants who completed the situational judgement test, 4 people had a total innovation
score lower than 5/100. Since this can be considered as an extremely low score, we
assumed that these participants completed the test randomly. For the reason that retaining
these participants in the sample could give an incorrect representation when analyzing
team compositions and its effect on team innovation outcomes, we removed these
participants from the sample before analyzing our data. In the end, the sample included
19 teams, consisting of 6 novice teams, 9 balanced teams and 4 expert teams.
Table 1 shows the correlation between all study variables. We see a significant
positive relation between gender and level of education (r = .22, p < .05) and a significant
negative relation between gender and individual innovation (r = -.23, p < .05).
Furthermore, there are four variables that correlate positively with individual innovation,
namely age (r = .33, p < .01), knowledge sharing (r = .23, p < .05), team innovation (r =
.29, p < .01) and voice behavior (r = .47, p < .01). Regarding the moderators, we see that
knowledge sharing has a positive significant relation with level of expertise (r = .27, p <
.05), voice behavior (r = .27, p < .05) and team innovation (r = .32, p < .01).
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Table 1 Means, Standard Deviations, Correlations and Internal Consistencies for the Study Variables
Note. M = mean; SD = standard deviation; internal consistency reliabilities (alpha estimates) are presented along the diagonals; N = 82 persons ** p < .01, * p < .05.
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Our second moderator, namely voice behavior, correlates positively with team innovation
(r = .29, p < .01).
To test the first three hypotheses, a Kruskal-Wallis test was conducted to
determine if there were differences in team innovation scores between teams that differed
in their level of innovation knowledge, namely the novice (n = 24), balanced (n = 41) and
expert (n = 17) groups. Distributions of team innovation scores were similar for all
groups, as assessed by visual inspection of a boxplot. Differences in median team
innovation scores were statistically significant between the three groups (χ2 (2) = 7,411,
p = .03). Subsequently, pairwise comparisons were performed using Dunn’s (1964)
procedure with a Bonferroni correction for multiple comparisons. Adjusted p-values were
presented. This post-hoc analysis revealed statistically significant differences in median
team innovation scores between the balanced (4.00) and the expert teams (3.75) (p = .04),
but not between the novice (3.50) and the expert teams (p = 1.00) nor between the novice
and balanced teams (p = 0.22). We therefore found statistical evidence that a balanced
team consisting of both innovation experts and innovation novices obtains higher
innovation team results than a team primarily consisting of innovation experts. However,
we did not find statistical evidence that a balanced team consisting of both innovation
experts and innovation novices obtain higher innovation team results than a team only
consisting of innovation novices, neither that a team primarily consisting of innovation
experts obtains higher innovation team results than a team only consisting of innovation
novices.
To test hypothesis 4, we conducted a linear regression analysis. The results of this
linear regression analysis are presented in Table 2. First, the three control variables (age,
gender and level of education) were regressed on the dependent variable (team
innovation). In the second step, we added the standardized independent variable
(individual innovation) and the standardized independent moderator (knowledge
sharing), and regressed them on the dependent variable. Finally, we added the interaction
term between the standardized moderator and the standardized independent variable and
regressed it on the dependent variable.
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Table 2 Results of Linear Regression Analyses
a
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Table 3 Results of Linear Regression Analyses
a
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Our results showed that the relationship between individual innovation and team
innovation was not moderated by knowledge sharing (b = .17, p = .32). This means we
did not find scientific support for our fourth hypothesis, which expected that knowledge
sharing would positively moderate the relationship between innovative behavior and team
innovation results. We found a significant main effect of knowledge sharing on team
innovation (b = .26, p = .01). However, the main effect of individual innovation on team
innovation was not statistically significant.
To test hypothesis 5, we also conducted a linear regression analysis. Results of the
analysis are presented in Table 3. The same three control variables (age, gender and level
of education) were regressed on the dependent variable (team innovation). Subsequently,
we added the standardized independent variable (individual innovation) and the
standardized independent moderator (voice behavior), and regressed them on the
dependent variable. Lastly, we added the interaction term between the standardized
moderator and the standardized independent variable and regressed it on the dependent
variable. We could not find a significant interaction effect (b = .21, p = .34) of voice
behavior in the relationship between individual innovation and team innovation. This
means we have to reject hypotheses 5. We also did not find significant main effects for
these variables.
Discussion
To cope with continuously changing environments and to be able to compete with
other organizations, being innovative is essential for organizations (Levinthal et al., 1993;
Anderson et al., 2014). The way in which an organization develops effective innovative
strategies rapidly becomes, or already is, a key question for most organizations. As
organizations tend to move to more team-based structures, this study attempted to
investigate the role of team composition in achieving team innovation outcomes. A great
amount of studies has been performed on investigating innovation outcomes within teams
and/or organizations. However, we see that previous research mostly focused on
professional (e.g., education) and non-professional (e.g., gender, ethnicity) diversity and
its effect on innovation outcomes. Although there are some studies (Dew et al, 2009, Ross
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et al, 2015, Cropley et al. 2012) that investigated the advantages or disadvantages of
having novices or experts in the team, these studies primarily focused on the effect of
being an expert versus a novice in the generation of ideas. None of this research examined
the effect of diversity in innovation knowledge through all the phases (generation,
championing and implementation) of the innovation process and its effect on team-related
innovation outcomes. To our knowledge, this study was one of the first studies that is
conducted to compare different team compositions in terms of innovation knowledge. We
have measured the level of innovation knowledge among different R&D teams and, based
on their innovation profiles, we have classified them as novice, expert or balanced teams.
Subsequently, we analyzed which of the three team compositions leads to the best
innovation outcomes. In this way, we tried to identify an "innovation dream team" that is
most efficient in achieving team innovation results.
In order to find out which team composition would lead to the best team
innovation results, we posited three hypotheses. Our first hypothesis assumed that
balanced teams would achieve better team innovation results than novice teams. For the
second hypothesis, we investigated whether balanced teams would score better than
expert teams. The third hypothesis examined whether a team of experts achieved higher
innovation team results than a team of novices. Our findings solely supported our second
hypothesis, which indicated that having a balanced team consisting of both innovation
experts and innovation novices results in better team innovation outcomes than having a
team that primarily consists of innovation experts.
We did not find evidence for hypothesis 1 and hypothesis 3. The data indicated
that in terms of team composition, balanced teams do not score better on team innovation
results than teams that only consist of novices. A possible explanation for not finding a
significant difference in team innovation scores between novice teams and balanced
teams, may be due to the way in which we classified our teams in either novice or
balanced groups. We have to take into account the fact that we conducted a field study
with real R&D teams from diverse organizations. Although a field study gives a more
realistic representation of the research, it also has a number of disadvantages. One of the
disadvantages is that we cannot control the composition of the teams since the teams are
already formed by the organizations their selves. This makes us dependent on how the
teams are put together in practice and makes it extremely difficult for us to find teams
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that only consisted of novices or experts. After examining the innovation profiles of all
team members, we did not have teams that solely consisted of innovation contributors
(i.e., people with little or no innovation knowledge) in our data sample. Therefore, we
decided to classify both innovation contributors and innovation partners (i.e., people with
a certain amount of innovation knowledge) as novices. This decision allowed us to
compare novice teams with balanced teams, but we might have risked giving an incorrect
representation from our results since innovation partners already have a certain amount
of innovation knowledge and therefore can strictly theoretically not be seen as a complete
novice.
Our results also indicated that teams that primarily consist of experts do not have
higher team innovation results than teams that fully consist of novices. In contrast to
previous research (Whittlesea, 1997; Ross et al., 2005), which showed that novices,
because of their lack of actual lived experience in the field and a lack of cognitive
frameworks and mental models, normally would score lower on performance outcomes
than experts do, we did not find this relationship regarding team innovation outcomes.
Shared mental models may provide an explanation for the lack of a statistically significant
difference between expert and novice teams. Shared mental models allow team members
to have a common understanding of the tasks and the ways to tackle them (Kozlowski &
Klein, 2000). Several studies showed that having shared mental models among team
members plays an important role in establishing team performance, such as team
innovation (Harrison, Price, Gavin, & Florey, 2001; Reagans & Zuckerman, 2001; Olson,
Parayitam, & Twigg, 2006). A research of Reuveni & Vashdi (2015), for example,
showed that having shared mental models among team members would influence team
innovation for both the generation as the implementation of ideas. Former research
(Smith-Jentsch, Campbell, Milanovich, & Reynolds, 2001) demonstrated that it is not
only relevant to investigate the extent to which mental models are shared, but also how
accurate they are. They believe that similar mental models among team members do not
always guarantee optimal team performance if their mental models are inaccurate,
especially when only a limited number of effective strategies is available (Edwards, Day,
Arthur Jr. & Bell, 2006).
Considering that there are multiple approaches and strategies to achieve
innovative solutions, there are also multiple accurate mental models that are possible to
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tackle innovation issues. For the reason that experts might be more confident about their
own skills, knowledge and previous innovation experiences, there can be a risk that they
have built up their own mental model over the years and do not want to accept that there
are other possible ways to take into account when trying to deal with innovative questions.
As a result of their previous experiences, they believe more in their own abilities and own
mental models, which can prevent them from listening to others and from taking
approaches and opinions of other experts into account. In such situations, there is no
common understanding within the team, so that mental models will not be similar nor
shared. Novices, on the other hand, do not have actual lived experiences and have
therefore little intuition about possible accurate approaches. They need to communicate
with each other from the start and brainstorm about what would be a good method of
handling, which, in turn, facilitates the creation of shared mental models within the team.
The fact that research shows that having shared mental models among team members
ensures better team innovation results (Reuveni et al., 2015), may partly explain why we
found no difference in terms of team innovation outcomes between novices and experts.
Although experts generally have more accurate mental models than novices, they are
often blinded by their own mental model and own approaches, such that they are not
open-minded for other strategies or ideas from other expert-colleagues. As a result, each
expert may have a different opinion on how to approach a certain innovative question,
such that effective team innovation outcomes will not occur. Novices, on the other hand,
are more likely to communicate with each other from the start, since they have little
inspiration about what a good or accurate approach is. They have a bigger chance to create
a common understanding of what should be done and how it should be done, and as a
result, they feel they are doing well as a team and tend to rate their team higher on team
innovation outcomes. Furthermore, this study aimed to invest a moderation effect of knowledge sharing
and voice behavior between individual innovation and team innovation outcomes. Our
results indicated that there is no significant interaction effect of both knowledge sharing
and voice behavior, in the relationship between individual innovation and team
innovation outcomes. Hypothesis 4 and hypothesis 5 could therefore not be confirmed. A
possible explanation is that moderation effects are more difficult to find in field studies
(Chaplin, 1991; McClelland & Judd, 1993). Our results, on the other hand, did show a
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main effect of knowledge sharing on team innovation outcomes. These results are in line
with previous research from Taylor et al. (2006), Phelps (2010) and Yu et al. (2013) which
showed that the involvement of employees in sharing their knowledge among team
members would lead to a greater amount of knowledge in the team, which only benefits
team innovative behavior. For voice behavior we saw no significant interaction effect as
well as no significant main effects. In contrast to the results of LePine et al. (1998) and
Rank et al., (2004), we did not find a significant main effect of voice behavior on the
innovation process (e.g., team innovation outcomes).
Limitations
While we found support for hypothesis 2, which claimed that having a balanced
team consisting of both novices and experts would lead to better team innovation
outcomes, we did not find empirical evidence for our other four hypotheses. Below, we
will discuss some limitations of this study, which can partly explain why we found no
support for our other four hypotheses.
A first limitation of the current study is that we only had a small sample size of 19
teams (with a total of 82 participants). To conduct our research, we aimed to do a field
study with real R&D teams from different organizations, to make our research more
reliable by giving a realistic, real-world representation about how team composition could
influence team innovation results. A disadvantage of using this study design is that it was
therefore not easy to find R&D teams who wanted to take the time to complete both the
questionnaire and the situational judgement test, since it took each team member
approximately one hour and a half to complete both. A second limitation, that is in line
with the first one, is that we had no teams available in our data sample that entirely
consisted of innovation contributors. This entails that, from a strictly theoretical point of
view, we did not analyze entirely novice teams. Thirdly, at the start of the research, our
goal was to investigate different R&D teams within the same sector. Given the fact that
it was very difficult to even find teams that wanted to participate in the study, we decided
to work with R&D teams, spread across different sectors. This can be a limitation, because
we work less standardized, which in turn has an effect on the interpretation of our
outcome variable. We asked participants to think of their teamwork in the last 6 months
EXPERTS, NOVICES OR A COMBINATION: WHO IS GIVING ME THE BEST RESULTS FOR INNOVATION?
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and to review some innovative performance data. It is possible that in some sectors it is
easier to implement innovative strategies within 6 months, while in other sectors, it might
take a longer period (e.g., 1 – 1,5 year) to accomplish innovative strategies. This may give
an incorrect representation when interpreting our outcomes variable. A last limitation is
that we used self-reporting questionnaires to measure team innovation outcomes,
knowledge sharing and voice behavior. This is especially important for team innovation
outcomes, because team members had to subjectively estimate how their team scores on
a number of innovation outcomes. This implies that individuals can give their team a high
score on achieving innovation outcomes, while in reality it is possible that this is not the
case. In order to make sure that every team member would consider the same period over
time, when rating the team innovation outcomes, we asked explicitly to assess the team
innovation results of the past 6 months.
Practical implications
Being innovative is a widely accepted ambition of most organizations.
Organizations are therefore looking for ways to improve their innovativeness. As teams
and their interaction play a crucial role in this process (Puccio et al., 2010) and
organizations become more dependent on R&D teams (Caldbeck, 2018), we wanted to
investigate which team composition in innovation knowledge acts as a kind of ‘dream
team’ and is the most efficient in achieving team innovation results. We demonstrated
that a team of experts does not always lead to the best team innovation results. With regard
to the war for talent, finding experts is usually a very challenging task for organizations
and does not only require a lot of time, but also a lot of financial resources. Our results
can help organizations to further shape the composition of their teams and to focus on a
more balanced team composition, which implies that they should not only focus on
attracting and recruiting expert profiles and, therefore, do not have to invest a great
amount of time and money in only attracting expert profiles. We also found a significant
main effect of knowledge sharing on team innovation outcomes. To achieve good
innovation team outcomes, it is therefore of great importance that organizations do invest
in creating a knowledge sharing culture that facilitates and stimulates employees to
connect with each other and share novel and valuable knowledge and information. When
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30
employees believe they can share and suggest new ideas and insights in a safe, supportive
way without being criticized (Cabrera & Cabrera, 2005), they will feel comfortable and
encouraged (Fu, Yu, Cheng & Chou, 2007), which facilitates their innovative behaviour
(Yu, et al., 2013). Organizations should therefore create opportunities, both formal (e.g.,
meetings, etc.) and informal (e.g., knowledge sharing lunches, team events, etc.), where
people can meet and share their ideas and thoughts in an open and trustful way.
Further research
Further research could improve the design of the current study. First, it may be
interesting to use a more objective measure for team innovation outcomes. This makes it
possible to counteract the self-reporting bias (Jahedi & Mendez, 2014). For example,
further research can find out whether there is an effect of team composition in innovation
knowledge on some financial innovation indicators such as market share, gross profit,
ROI, etc. Additionally, it is recommended that further research also uses more teams to
participate in the study. This to increase the reliability of the study. In the current study,
the participated R&D teams mainly come from smaller organizations or start-ups, who
are employed in various sectors. This makes it difficult to compare these teams for
innovation outcomes, as this can vary from sector to sector. Further research could
standardize this more by, for example, comparing teams from larger, more well-known
organizations within the same sector (e.g., healthcare).
As we discussed the role of shared mental models within teams as a possible
explanation for not finding support for some of our hypotheses, further research could
also examine the role of shared team mental models on team innovation outcomes.
Scientific research (Klimoski & Mohammed, 1994; Kraiger & Wenzel, 1997; Marks,
Mathieu & Zaccaro, 2001) has already shown that shared mental models influence both
team processes (e.g., communication, knowledge sharing) and team outputs (e.g., team
performance). Research by Reuveni et al. (2015) has shown that shared mental models
can be seen as an important mediator between professional heterogeneity and team
innovation outcomes. Further research can determine whether shared team mental models
are also a mediator in the relationship between diversity in innovation knowledge and
team innovation outcomes. In this way, further research can examine whether a certain
EXPERTS, NOVICES OR A COMBINATION: WHO IS GIVING ME THE BEST RESULTS FOR INNOVATION?
31
team composition in innovation knowledge (a team of novices, experts or a balanced mix)
is more likely to build up shared mental models and thus achieve better team innovation
outcomes.
Conclusion
In times when organizations seek for innovativeness and are more dependent on
team- and group-based networks, more insight is sought in what could be the right team
composition to accomplish effective team innovation. Therefore, this study aimed to
investigate which team composition in innovation knowledge acts as a kind of ‘dream
team’ and is most efficient in achieving team innovation results. We classified 19 R&D
teams from diverse organizations into novice, expert or balanced teams and investigated
which of these 3 compositions provided the best team innovation outcomes. Our results
showed that a balanced team scored significantly better than a team of experts. We found
no significant differences in team innovation outcomes between the other groups. The
second goal of the study was to determine whether variables such as knowledge sharing
and voice behavior positively moderate the relationship between individual innovation
and team innovation. Our results found no significant interaction effect of both
knowledge sharing and voice behavior, but we did confirm previous research (Taylor et
al., 2006; Phelps, 2010 and Yu et al., 2013), which showed that knowledge sharing within
the team positively benefits team innovation outcomes. In terms of implications for
practice, we demonstrated that a team primarily consisting of experts does not always
lead to effective team innovation outcomes. This conclusion is, from a financial point of
view, very interesting for organizations, since attracting and recruiting experts is usually
accompanied with greater hiring costs for organizations. This study revealed that
balanced teams consisting of both novices and experts lead to better innovation results
and organizations therefore do not necessary have to spend a lot of time and money in
only attracting and recruiting expert profiles.
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References Afolabi A. O., Benjamin 0.E. (2005). Psychological diversity and team interaction
processes: A study of oil-drilling work teams in Nigeria. Team Performance Management: An International Journal, 11(7/8), 280-301. https://doi.org/10.1108/13527590510635161
Agrell, A., & Gustafson, R. (1996). Innovation and creativity in work groups. In M.A. West (Ed.), The handbook of work group psychology (pp. 317–344). Chichester: John Wiley.
Alexiev, A. S., Jansen, J. J. P., Van den Bosch, F. A. J., & Volberda, H. W. (2010). Top management team advice seeking and exploratory innovation: The moderating role of TMT heterogeneity. Journal of Management Studies, 47(7), 1343–1364. https://doi.org/10.1111/j.1467-6486.2010.00919.x
Anderson, N., de Dreu, C. K. W., & Nijstad, B. A. (2004). The routinization of innovation research: a constructively critical review of the state-of-the-science. Journal of Organizational Behavior, 25, 147–173. https://doi.org/10.1002/job.236
Anderson, N., Potočnik, K., & Zhou, J. (2014). Innovation and Creativity in Organizations. Journal of Management, 40(5), 1297–1333. https://doi.org/10.1177/0149206314527128
Axtell, C. M., Holman, D. J., Unsworth, K. L., Wall, T. D., Waterson, P. E., & Harrington, E. (2010). Shopfloor innovation: Facilitating the suggestion and implementation of ideas. Journal of Occupational and Organizational Psychology, 73(3), 265–285. https://doi.org/10.1348/096317900167029
Baer, M. (2012). Putting Creativity To Work: the Implementation of Creative Ideas in Organizations. Academy of Management Journal, 55(5), 1102–1119. https://doi.org/10.5465/amj.2009.0470
Baregheh, A., Rowley, J., & Sambrook, S. (2009). Towards a multidisciplinary definition of innovation. Management Decision, 47(8), 1323–1339. https://doi.org/10.1108/00251740910984578
Baron, R. A., & Ensley, M. D. (2006). Opportunity Recognition as the Detection of Meaningful Patterns: Evidence from Comparisons of Novice and Experienced Entrepreneurs. Management Science, 52(9), 1331–1344. https://doi.org/10.1287/mnsc.1060.0538
Bashshur, M. R., & Oc, B. (2015). When Voice Matters: A Multilevel Review of the Impact of Voice in Organizations. Journal of Management, 41(5), 1530–1554. https://doi.org/10.1177/0149206314558302
Bell, S. T. (2007). Deep-level composition variables as predictors of team performance: a meta-analysis. Journal of Applied Psychology, 92(3), 595–615. https://doi.org/10.1037/0021-9010.92.3.595
Boyer, S. (2017). The Importance of collaboration in the workplace. Retrieved from http://www.nutcache.com/blog/the-importance-of-collaboration-in-the-workplace/
Bunderson, J. S., & Sutcliffe, K. M. (2002). Comparing Alternative Conceptualizations of Functional Diversity in Management Teams: Process and Performance Effects. The Academy of Management Journal, 45(5), 875–893. Retrieved from https://journals.aom.org/doi/abs/10.5465/3069319
Burke, C. S., Stagl, K. C., Salas, E., Peirce, L., & Kendall, D. (2006). Understanding team adaptation: A conceptual analysis and model. Journal of Applied Psychology, 91, 1189–1207. http://dx.doi.org/10.1037/0021-9010.91.6.1189
EXPERTS, NOVICES OR A COMBINATION: WHO IS GIVING ME THE BEST RESULTS FOR INNOVATION?
33
Burningham, C., & West, M. A. (1995). Individual, Climate, and Group Interaction Processes as Predictors of Work Team Innovation. Small Group Research, 26(1), 106–117. https://doi.org/10.1177/1046496495261006
Byrne, D. (1971) The Attraction Paradigm. Academic Press, New York. Cabrera, E. F., & Cabrera, A. (2005). Fostering knowledge sharing through people
management practices. The International Journal of Human Resource Management, 16(5), 720–735. https://doi.org/10.1080/09585190500083020
Caldbeck, R. (2018). Innovation can help old consumer brands win customers and influence people. Retrieved from https://techcrunch.com/2018/04/23/innovation-can-help-old-consumer-brands-win-customers-and-influence-people/
Carnevale, J. B., Huang, L., Crede, M., Harms, P., & Uhl-Bien, M. (2017). Leading to Stimulate Employees’ Ideas: A Quantitative Review of Leader–Member Exchange, Employee Voice, Creativity, and Innovative Behavior. Applied Psychology, 66(4), 517–552. https://doi.org/10.1111/apps.12102
Carpenter, M. A., Geletkancz, M. A., & Sanders, W. G. (2004). Upper echelons research revisited: Antecedents, elements, and consequences of top management team composition. Journal of Management, 30(6), 749–778. https://doi.org/10.1016/j.jm.2004.06.001
Chaplin, W. F. (1991). The next generation of moderator research in personality psychology. Journal of personality, 59(2), 143-178. https://doi.org/10.1111/j.1467-6494.1991.tb00772.x
Chen, A. S. Y., & Hou, Y. H. (2016). The effects of ethical leadership, voice behavior and climates for innovation on creativity: A moderated mediation examination. Leadership Quarterly, 27(1), 1–13. https://doi.org/10.1016/j.leaqua.2015.10.007
Chen, G., Farh, J. L., Campbell-Bush, E. M., Wu, Z., & Wu, X. (2013). Teams as innovative systems: Multilevel motivational antecedents of innovation in R&D teams. Journal of Applied Psychology, 98(6), 1018–1027. https://doi.org/10.1037/a0032663
Cheung, S. Y., Gong, Y., Wang, M., Zhou, L., & Shi, J. (2016). When and how does functional diversity influence team innovation? The mediating role of knowledge sharing and the moderation role of affect-based trust in a team. Human Relations, 69(7), 1507–1531. https://doi.org/10.1177/0018726715615684
Chi, M. T. H. (2006). Two Approaches to the Study of Experts’ Characteristics. The Cambridge Handbook of Expertise and Expert Performance (pp. 21–30).
Cropley, D. H., & Kaufman, J. C. (2012). Measuring functional creativity: Non-expert raters and the creative solution diagnosis scale. Journal of Creative Behavior, 46(2), 119–137. https://doi.org/10.1002/jocb.9
De Dreu, C. K. W., Nijstad, B. A., & Van Knippenberg, D. (2008). Motivated information processing in group judgment and decision making. Personality and Social Psychology Review, 12(1), 22–49. https://doi.org/10.1177/1088868307304092
De Dreu, C. K. W., Nijstad, B. A., Bechtoldt, M. N., & Baas, M. (2011). Group creativity and innovation: A motivated information processing perspective. Psychology of Aesthetics, Creativity, and the Arts, 5(1), 81–89. https://doi.org/10.1037/a0017986
Dew, N., Read, S., Sarasvathy, S. D., & Wiltbank, R. (2009). Effectual versus predictive logics in entrepreneurial decision-making: Differences between experts and novices. Journal of Business Venturing, 24(4), 287–309. https://doi.org/10.1016/j.jbusvent.2008.02.002
EXPERTS, NOVICES OR A COMBINATION: WHO IS GIVING ME THE BEST RESULTS FOR INNOVATION?
34
Drach-Zahavy, A., & Somech, A. (2001). Understanding team innovation: The role of team processes and structures. Group Dynamics: Theory, Research, and Practice, 5(2), 111–123. https://doi.org/10.1037/1089-2699.5.2.111
Drach-Zahavy, A., & Somech, A. (2010). From an intrateam to an interteam perspective of effectiveness: The role of interdependence and boundary activities. Small Group Research, 41, 143-174. http://dx.doi.org/10.1177/1046496409356479
Duggan, T. (2018). The importance of interaction in workplace issues. Retrieved from http://smallbusiness.chron.com/importance-interaction-workplace-issues-11429.html
Edmondson, A. (1999). Psychological Safety and Learning Behavior in Work Teams. Administrative Science Quarterly, 44(2), 350-383. https://doi.org/10.2307/2666999
Edwards, B. D., Day, E. A., Arthur Jr, W., & Bell, S. T. (2006). Relationships among team ability composition, team mental models, and team performance. Journal of applied psychology, 91(3), 727-736. http://dx.doi.org/10.1037/0021-9010.91.3.727
Forsyth, D.R. (1990). Group Dynamics. Michigan: Brooks/Cole Publishing Company Fu, H.-Y., Yu, K.-D., Cheng, Y.-P., & Chou, C.-H. (2007). The study on the relationship
among organisational culture, knowledge sharing and organizational innovation knowledge types as moderator in the shipping industry. Chinese Maritime Research Institute, 16, 1-16.
Hargadon, A., & Fanelli, A. (2002). Action and Possibility: Reconciling Dual Perspectives of Knowledge in Organizations. Organization Science, 13(3), 290–302. https://doi.org/10.1287/orsc.13.3.290.2772
Harrison, D. A., Price, K. H., & Bell, M.P. (1998). Beyond relational demography: time and the effects of surface- and deep-level diversity on work group cohesion. Acadamy of Management, 45(5), 1029–1045. https://doi.org/10.5465/256901
Hobman, E. V., Bordia, P., & Gallois, C. (2004). Perceived Dissimilarity and Work Group Involvement. Group & Organization Management, 29(5), 560–587. https://doi.org/10.1177/1059601103254269
Hoffman, R. R. (1998). How can expertise be defined? Implications of research from cognitive psychology. Exploring Expertise, 81–100. https://doi.org/10.1007/978-1-349-13693-3_4
Hogarth, R.M. (2002). Deciding analytically or trusting your intuition? The advantages and disadvantages of analytic and intuitive thought. Economics and Business Working Paper, 654. http://dx.doi.org/10.2139/ssrn.394920
Horwitz, S. K., & Horwitz, I. B. (2007). The Effects of Team Diversity on Team Outcomes: A Meta-Analytic Review of Team Demography. Journal of Management, 33(6), 987–1015. https://doi.org/10.1177/0149206307308587
Howell, J. M., & Higgins, C. A. (1990). Champions of Technological Innovation. Administrative Science Quarterly, 35(2), 317–341. http://dx.doi.org/10.2307/2393393
Hülsheger, U. R., Anderson, N., & Salgado, J. F. (2009). Team-level predictors of innovation at work: A comprehensive meta-analysis spanning three decades of research. Journal of Applied Psychology, 94(5), 1128–1145. https://doi.org/10.1037/a0015978
Isaksen, S. G. (1990). Educational implications of creativity research: an updated rationale for creative learning. In K. Gronhaug & G. Kaufmann (Eds.). Innovation: a cross disciplinary perspective (pp. 167-203). Oslo: Norwegian University Press.
EXPERTS, NOVICES OR A COMBINATION: WHO IS GIVING ME THE BEST RESULTS FOR INNOVATION?
35
Jackson, S. E. (1992). Consequences of group composition for the interpersonal dynamics of strategic issue processing. Advances in Strategic Management, 8, 345–382. Retrieved from https://www.researchgate.net/profile/Susan_Jackson14/publication/237714178_Consequences_of_Group_Composition_for_the_Interpersonal_Dynamics_of_Strategic_Issue_Processing/links/565db76808ae1ef929835247/Consequences-of-Group-Composition-for-the-Interpersonal-Dynamics-of-Strategic-Issue-Processing.pdf
Jacobs, R. L., & Bu-Rahmah, J.M. (2012). Developing employee expertise through structured on-the-job training (S-OJT): an introduction to this training approach and the KNPC experience. Industrial and Commercial Training, 44(2), 75–84. https://doi.org/10.1108/00197851211202902
Jahedi, S., & Méndez, F. (2014). On the advantages and disadvantages of subjective measures. Journal of Economic Behavior & Organization, 98, 97-114. https://doi.org/10.1016/j.jebo.2013.12.016
Janssen, O. (2000). Job demands, perceptions of effort-reward fairness and innovative work behaviour. Journal of Occupational and organizational psychology, 73(3), 287-302. https://doi.org/10.1348/096317900167038
Kahneman, D., & Klein, G. (2009). Conditions for Intuitive Expertise: A Failure to Disagree. American Psychologist, 64(6), 515–526. https://doi.org/10.1037/a0016755
Kanter, R. M. (1988). Three tiers for innovation research. Communication Research, 15(5), 509-523. http://dx.doi.org/10.1177/009365088015005001
Klein, G. A., Baxter, H. C. (2009). Cognitive transformation theory: Contrasting cognitive and behavioral learning. Education: Learning, requirements and metrics (pp. 50–65). Santa Barbara, CA: Praeger Security
Kleysen, R. F., & Street, C. T. (2001). Toward a multi-dimensional measure of individual innovative behavior. Journal of Intellectual Capital, 2(3), 284–296. https://doi.org/10.1108/EUM0000000005660
Klimoski, R., & Mohammed, S. (1994). Team mental model: Construct or metaphor? Journal of management, 20(2), 403-437. https://doi.org/10.1177/014920639402000206
Kornish, L. J., & Hutchison-Krupat, J. (2017). Research on Idea Generation and Selection: Implications for Management of Technology. Production and Operations Management, 26(4), 633–651. https://doi.org/10.1111/poms.12664
Kozlowski, S. W., & Klein, K. J. (2000). A multilevel approach to theory and research in organizations: Contextual, temporal, and emergent processes. Retrieved on https://www.researchgate.net/publication/232580888_A_multilevel_approach_to_theory_and_research_in_organizations_Contextual_temporal_and_emergent_processes
Kraiger, K., & Wenzel, L. H. (1997). Conceptual development and empirical evaluation of measures of shared mental models as indicators of team effectiveness (pp. 75-96). Psychology Press.
Kumar, S. (2015, April 20). Importance of teamwork in organisations. Retrieved from https://www.linkedin.com/pulse/importance-teamwork-organizations-surendra-kumar-sahu/
Laforet, S. (2011). A framework of organisational innovation and outcomes in SMEs. International Journal of Entrepreneurial Behavior & Research, 17(4), 380–408. https://doi.org/10.1108/13552551111139638
EXPERTS, NOVICES OR A COMBINATION: WHO IS GIVING ME THE BEST RESULTS FOR INNOVATION?
36
LePine, J. A., & Van Dyne, L. (1998). Predicting voice behavior in work groups. Journal of Applied Psychology, 83(6), 853–868. https://doi.org/10.1037/0021-9010.83.6.853
Levinthal, D. A. & March, J. G. (1993). The myopia of learning. Strategic Management Journal, 14, 95–112. https://doi.org/10.1002/smj.4250141009
MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Challenge-oriented organizational citizenship behaviors and organizational effectiveness: Do challenge-oriented behaviors really have an impact on the organization’s bottom line? Personnel Psychology, 64, 559-592. http://dx.doi.org/10.1111/j.1744-6570.2011.01219.x
Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. (2001). A temporally based framework and taxonomy of team processes. Academy of management review, 26(3), 356-376. https://doi.org/10.5465/amr.2001.4845785
McClean, E. J., Burris, E. R., & Detert, J. R. (2013).When does voice to exit? It depends on leadership. Academy of Management Journal, 56, 525–548. http://dx.doi.org/10.5465/amj.2011.0041
McClelland, G. H., & Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological bulletin, 114(2), 376-390. http://dx.doi.org/10.1037/0033-2909.114.2.376
McKelvie, A., & Wiklund, J. (2004). How Knowledge Affects Opportunity Discovery and Exploitation Among New Ventures in Dynamic Markets. Research in Entrepreneurship and Management (pp. 219-240). Greenwich, CT: Information Age Publishing.
Mesmer-Magnus, J. R., & DeChurch, L. A. (2009). Information Sharing and Team Performance: A Meta-Analysis. Journal of Applied Psychology, 94(2), 535–546. https://doi.org/10.1037/a0013773
Milliken, F. J., Bartel, C. A., & Kurtzberg, T. R. (2003). Diversity and creativity in work groups: A dynamic perspective on the affective and cognitive processes that link diversity and performance. In P. B. Paulus & B. A. Nijstad (Eds.), Group creativity: Innovation through collaboration (pp. 32-62). New York, NY, US: Oxford University
Minh, N. Van, Badir, Y. F., Quang, N. N., & Afsar, B. (2017). The impact of leaders’ technical competence on employees’ innovation and learning. Journal of Engineering and Technology Management, 44, 44–57. https://doi.org/10.1016/j.jengtecman.2017.03.003
Mittal, S., & Dhar, R. L. (2015). Transformational leadership and employee creativity. Management Decision, 53(5), 894–910. https://doi.org/10.1108/MD-07-2014-0464
Mumford, M. D., & Gustafson, S. B. (1988). Creativity syndrome: Integration, application, and innovation. Psychological Bulletin, 103(1), 27–43. http://dx.doi.org/10.1037/0033-2909.103.1.27
Olson, B. J., Parayitam, S., & Twigg, N. W. (2006). Mediating role of strategic choice between top management team diversity and firm performance: Upper echelons theory revisited. Journal of Business and Management, 12(2), 111-126. Retrieved from https://search.proquest.com/docview/211507808?pq-origsite=gscholar
Ongori, H. (2007). A brief review of the literature. African Journal of Business Management, 49–54. https://doi.org/10.1177/036354657700500601
Parker, S. K., Bindl, U. K., & Strauss, K. (2010). Making things happen: A model of proactive motivation. Journal of Management, 36, 827-856. http://dx.doi.org/10.1177/0149206310363732
EXPERTS, NOVICES OR A COMBINATION: WHO IS GIVING ME THE BEST RESULTS FOR INNOVATION?
37
Phelps, C. C. (2010). A longitudinal study of the influence of alliance network structure and composition on firm exploratory innovation. Academy of Management Journal, 53(4), 890–913. https://doi.org/10.5465/AMJ.2010.52814627
Porschitz, E.T., Smircich, L., Calas, M.B. (2016). Drafting “foot soldiers”: the social organisation of the war for talent. Management learning, 47(3), 343-360. https://doi.org/10.1177/1350507615598906
Puccio, G. J., & Cabra, J. F. (2010). Organizational creativity: A systems approach. Cambridge handbook of creativity (pp. 145–173). New York: Cambridge University Press.
Rank, J., Pace, V.L., & Frese, M. (2004). Three avenues for future research on creativity, innovation, and initiative. Applied Psychology: An International Review, 53(4), 518–528. http://dx.doi.org/10.1111/j.1464-0597.2004.00185.x
Reagans, R., & Zuckerman, E. W. (2001). Networks, Diversity, and Productivity: The Social Capital of Corporate R&D Teams. Organization Science, 12(4), 502-517. https://doi.org/10.1287/orsc.12.4.502.10637
Reuveni, Y., & Vashdi, D. R. (2015). Innovation in multidisciplinary teams: The moderating role of transformational leadership in the relationship between professional heterogeneity and shared mental models. European Journal of Work and Organizational Psychology, 24(5), 678-692. https://doi.org/10.1080/1359432X.2014.1001377
Rittle-Johnson, B., & Schneider, M. (2014). Developing conceptual and procedural knowledge of mathematics. In R. C. Kadosh & A. Dowker (Eds.), Oxford handbook of numerical cognition (pp. 1102–1118). Oxford: Oxford University Press.
Rosendaal, B., & Bijlsma-Frankema, K. (2015). Knowledge sharing within teams: Enabling and constraining factors. Knowledge Management Research and Practice, 13(3), 235–247. https://doi.org/10.1057/kmrp.2013.45
Ross, K. G., Phillips, J. K., Klein, G., & Cohn, J. (2005). Creating expertise: A framework to guide technology-based training. Retrieved from https://issuu.com/cognition/docs/ross__k.g.__phillips__j.k._2005_creating_expertise
Scott, S. G., & Bruce, R. A. (1994). Determinants of Innovative Behavior : A Path Model of Individual Innovation in the Workplace. The Academy of Management Journal, 37(3), 580–607. http://dx.doi.org/10.2307/256701
Shane S. 2003. A General Theory of Entrepreneurship. Edward Elgar: Northampton, MA. Shin, S. J., Kim, T.-Y., Lee, J.-Y., & Bian, L. I. N. (2012). Cognitive Team Diversity and
Individual Team Member Creativity: a Cross-Level Interaction. Academy of Management Journal, 55(1), 197–212. http://dx.doi.org/10.5465/amj.2010.0270
Smith-Jentsch, K. A., Campbell, G. E., Milanovich, D. M., & Reynolds, A. M. (2001). Measuring teamwork mental models to support training needs assessment, development, and evaluation: Two empirical studies. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 22(2), 179-194. https://doi.org/10.1002/job.88
Souder, W. (1981). Encouraging entrepreneurship in large corporations. Research Management, 24, 18-22. http://dx.doi.org/10.1080/00345334.1981.11756665
Tang, C., & Naumann, S. E. (2016). Team diversity, mood, and team creativity: The role of team knowledge sharing in Chinese R&D teams. Journal of Management and Organization, 22(3), 420–434. https://doi.org/10.1017/jmo.2015.43
EXPERTS, NOVICES OR A COMBINATION: WHO IS GIVING ME THE BEST RESULTS FOR INNOVATION?
38
Taylor, A., & Greve, H. R. (2006). Superman or the fantastic four? Knowledge combination and experience in innovative teams. Acadamy of Management Journal, 49(4), 723-740. http://dx.doi.org/10.5465/AMJ.2006.22083029
Terwiesch, C., & Ulrich K. T. (2009). Innovation tournaments: Creating and selecting exceptional opportunities. Cambridge, MA: Harvard Business School Press
Thenhaus, R. (2014). Adapt or die? The importance of continuous innovation management. Retrieved from: https://www.innovators.org/blog/adapt-or-die-the-importance-of-continuous-innovation-management/
Totterdell, P., Leach, D., Birdi, K., Clegg, C., & Wall, T. (2002). An Investigation of the Contents and Consequences of Major Organizational Innovations. International Journal of Innovation Management, 6(4), 343–368. https://doi.org/10.1142/S1363919602000641
Van de Ven, A. H., Angle, H. L., & Poole, M. S. (Eds.). (2000). Research on the management of innovation: The Minnesota studies. Oxford University Press on Demand.
Van de Ven, A.H. (1999). The innovation journey. New York: Oxford University Press Van Lysebetten, S., Anseel, F. & Velghe, C. (2018). How to measure a person’s
innovation insights? The development and validation of a situational judgement test of innovation skills. Paper presented at the fourth Israel Organizational Behavior Conference, Tel Aviv, Israel.
Van Offenbeek, M. A. G., & Koopman, P. (1996). Interaction and decision-making in project teams. In M. A. West (Ed.), Handbook of Work Group Psychology (pp. 159 - 187). Chichester, New York, etc.: Wiley.
West, M. A. (2002). Response: Ideas are ten a penny - It’s team implementation not idea generation that counts. Applied Psychology, 51(3), 411–424. https://doi.org/10.1111/1464-0597.01006
West, M. A., & Farr, J. L. (1989). Innovation at work: Psychological perspectives. Social Behaviour, 4(1), 15-30. Retrieved from http://psycnet.apa.org/record/1989-31447-001
West, M. A., & Wallace, M. (1991). Innovation in health care teams. European Journal of social psychology, 21(4), 303-315. https://doi.org/10.1002/ejsp.2420210404
West, M.A. and Anderson, N.R. (1996), Innovation in top management teams. Journal of Applied Psychology, 81, 680-693. https://doi.org/10.1037/0021-9010.81.6.680
Whiting, S. W., Podsakoff, P. M., & Pierce, J. R. (2008). Effects of task performance, helping, voice, and organizational loyalty on performance appraisal ratings. Journal of Applied Psychology, 93, 125-139. http://dx.doi.org/10.1037/0021-9010.93.1.125
Whittlesea, B. W. A. (1997). Production, evaluation, and preservation of experiences: Constructive processing in remembering and performance tasks. In D. L. Medin (Eds.), The psychology of learning and motivation: Advances in research and theory (pp. 211-264). San Diego, CA, US: Academic Press.
Williams, K. Y., & O'Reilly III, C. A. (1998). Demography and Diversity in Organisations: A review of 40 years of research in BM Staw and LL Cummings (eds) Research in Organisational Behaviour. Jai Pres, Connecticut.
Yu, C., Yu-Fang, T., & Yu-Cheh, C. (2013). Knowledge Sharing, Organizational Climate, and Innovative Behavior: A Cross-Level Analysis of Effects. Social Behavior and Personality: An International Journal, 41(1), 143–156. https://doi.org/10.2224/sbp.2013.41.1.143