EXPERTS, NOVICES OR A COMBINATION: WHO IS ......In het bijzonder hebben we het effect van...

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

EXPERTS, NOVICES OR A COMBINATION: WHO IS GIVING ME THE BEST RESULTS FOR INNOVATION?

1

“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).


2

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.


3

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


4

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


5

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 &


6

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


7

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).


8

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


9

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).


10

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


11

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


12

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).


13

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


14

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


15

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.


16

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


17

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


18

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.


19

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).


20

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.


21

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.


22

Table 2 Results of Linear Regression Analyses

a


23

Table 3 Results of Linear Regression Analyses

a


24

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


25

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


26

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


27

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


28

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


29

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


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


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.


32

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


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


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.


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


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


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


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

Date post:	20-Sep-2020
Category:	Documents
Upload:	others
View:	1 times
Download:	0 times

EXPERTS, NOVICES OR A COMBINATION: WHO IS ......In het bijzonder hebben we het effect van...

Documents