Paper to be presented at the DRUID Academy Conference 2018 at University of Southern Denmark, Odense, Denmark on January 17-19, 2018
The Impacts of Cross Functional Team on Innovation Performance and Openness
Kangmin Lee Sogang University
Management of Technology(MOT)[email protected]
Joon Mo Ahn Sogang University
Management of Technology(MOT)[email protected]
AbstractRecent years have witnessed the increasing complexity of technology development and the shortening periodproduct development. To survive in this fierce completion, firms have attempted to enhance their innovationprocess in various ways, and one of those solution would be the formation and utilisation of Cross Functional
Team(CFT). To date, the literature has investigated what are critical determinants for successful CFT formationand operation, but little attention has paid to whether the formation of CFT which would enhance internal
communication and organisational flexibility can also stimulate open innovation. To address this research gap,this study analysed the effect of CFT on firms’ innovation performance and innovation collaboration using the
productivity survey data on 599 Korean manufacturing firms. The results from Propensity Score Matching(PSM)analysis show that the CFT formation is positively associated with not only innovation performance but alsofirms’ open innovation activity. Structural equation modelling (SEM) analysis results also suggest that the
efficient operation of CFT can promote cooperation within the firm as well as other externalities, whichinfluence innovation performance. This study provides managerial implications for the formation and efficient
operation of CFT.
Key words: Cross Functional Team, Product Development, Open Innovation
Jelcodes: O32,C31
The Impacts of Cross Functional Team on Innovation Performance and Openness
Kang Min Lee1 and Joon Mo Ahn2
Graduate School of MOT (Management of Technology), Sogang University
35 Baekbeom-ro, Mapo-gu, Seoul, 121-742, South Korea
TEL: +82-2-705-7986, FAX: +82-2-3274-4808
Recent years have witnessed the increasing complexity of technology development and the shortening
period product development. To survive in this fierce completion, firms have attempted to enhance
their innovation process in various ways, and one of those solution would be the formation and
utilisation of Cross Functional Team (CFT). To date, the literature has investigated what are critical
determinants for successful CFT formation and operation, but little attention has paid to whether the
formation of CFT which would enhance internal communication and organisational flexibility can
also stimulate open innovation. To address this research gap, this study analysed the effect of CFT on
firms’ innovation performance and innovation collaboration using the productivity survey data on 599
Korean manufacturing firms. The results from Propensity Score Matching (PSM) analysis show that
the CFT formation is positively associated with not only innovation performance but also firms’ open
innovation activity. Structural equation modelling (SEM) analysis results also suggest that the
efficient operation of CFT can promote cooperation within the firm as well as other externalities,
which influence innovation performance. This study provides managerial implications for the
formation and efficient operation of CFT.
Key words: Cross Functional Team, Product Development, Open Innovation.
1
1. Introduction
Due to the globalization of capital and labor and the shortening technology development cycle, firms
have developed new competence to survive in the fierce market. Merger & acquisition (M&A), strategic
alliances would be such efforts, but there is also an attempt to maximize innovation competence by
reconfiguring internal resources, i.e., cross-functional team (CFT). CFT is a kind of temporary or
project-based internal division which is consisted of various employees from other divisions, such as
research and development (R&D), production and marketing (Pinto et al., 1993). There are many
benefits CFT can bring in. For instance, firms can reduce the time necessary for decision making of
new products or services, or they can reduce market response time by accelerating innovation process
(Lopes Pimenta et al., 2014). These benefits of CFT are linked to the advantages of small organizations.
Rothwell and Dodgson (1994) claimed that small organizations have behavioral while large firms have
material advantages. They suggested that small organizations’ strengths lie in entrepreneurial
characteristics, such as flexibility, and quick responses to environmental changes. Therefore, to benefit
from these entrepreneurial characteristics, some successful firms, such as 3M, have attempted to
develop behavioral advantages by incorporating small teams which continue to operate ‘independently’
like small organization (Rothwell and Dodgson, 1994, Mortara et al., 2011). The virtue of CFT also lies
in this flexible and independent characteristic. As CFT can behave like a small firm, its low hierarchy
and quick decision-making process will help the firm to accelerate its new product or service
development. The literature has paid attention to this by looking what factors influence the successful
implementation of CFT. So, the success factors of CFT, such as functional diversity (Clark et al., 2002,
Jassawalla and Sashittal, 1999), organizational flexibility (Liu et al., 2009), empowering (McDonough,
2000), motivation (Webber, 2002), communication (Ghobadian and Gallear, 1997). However, the
literature has paid little attention towards the linkage between CFT and the organizational level of
openness. The above-mentioned factors are, in fact, organizational facilitators which contribute to the
development entrepreneurial orientation and simple and flexible hierarchy. We argue that a CFT can
behave like an aggressive start-up, in the sense that it has its clear own purpose, i.e., the development
of new product/service and it implement its innovation with efficient decision-makings. To investigate
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this, the current research explores the effects of CFT on innovation performance and openness. By
analyzing Korean firm data with and without CFT we compare the influence of CFT using propensity
score matching (PSM) and investigate structural relationships between CFT efficiency, internal and
external collaborating using structural equation modeling (SEM). The reminder of the paper comprises
three sections. Next section develops research hypotheses and the following section illustrates the data
and method. Final section concludes with discussion.
2. Theoretical background and research hypotheses
2.1. Characteristics of cross functional team
CFT (Cross Functional Team) is defined as a temporal or project-based special group with different
functional expertise working toward a common goal (Krajewski and Ritzman, 2005). The literature has
found that functional diversity (Clark et al., 2002, Jassawalla and Sashittal, 1999), organizational
flexibility (Liu et al., 2009), empowering (McDonough, 2000), motivation (Webber, 2002) and
communication (Ghobadian and Gallear, 1997) as the success factor of CFT. As shown in Table 1, there
are many pros and cons of CFT. Benefits of CFT are from the greater scope and depth of information
utilization (Lee and Chen, 2007). Because CFT is organized by various fields of people from finance
and human resource to operation and production department, a new combination of knowledge can be
smoothly achieved from the functional diversity of CFT. By sharing their own expertise, CFT can
increase its problem-solving capability while reducing the problem-solving time (Clark et al., 2002).
Also, as CFT is organized as a small team, its simple hierarchy enables the CFT establish more
horizontal relationships. Complex hierarchy triggers internal competitions between divisions, thus may
result in functional separation, which will impede smooth information delivery and quick decision-
making (Mohamed et al., 2004). However, there are also disadvantages of CFT if it is not successfully
implemented. Too much functionality may hinder smooth agreements in innovation process (Ancona
and Caldwell, 1992). Yet, if the functions of the members are too similar, the benefits of CFT will
decrease (Patrashkova and McComb, 2004). If the appropriate leadership is not guaranteed to the CFT
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leader or the role of the CFT is not distinguished from incumbent divisions, conflict management will
rise as an important issue to be addressed (Denis, 1986).
Table 1. Pros and Cons of CFT
Pros and Cons of CFT
Pros literature
- Increased problem-solving capability
- Enhanced receptiveness
- Quick decision-making process
- Decreased problem-solving time
- Engaging employee participation
- Increased knowledge sharing
Clark et al., 2002
Liu & Wei, 2009
McDonough, 2000
Cons literature
- Difficulties of agreements
- Conflict management
Ancona & Caldwell, 1992
Denis, 1986
Patrashkova & Mccomb, 2004
2.2. Cross functional team and openness
The virtue of CFT is high flexibility and independent characteristics which enable internal innovation
process, such as new product development. However, we pay attention to the fact that these
characteristics are also important antecedents of openness. As noted by Van de Vrande et al. (2009), the
level of openness in large established firms are not relatively higher than that of small and medium-
sized enterprises (SMEs). This suggests that the characteristics of small organizations, such as
organizational flexibility and simple hierarchy are important determinants for openness. As opening its
boundary demands the firm to take the necessary risks and uncertainty from heterogeneous new
knowledge and different work protocols, the firm with open propensity will have a high level of
receptivity for newness. In this regard, key factors of CFT, such as flexibility and simple hierarchy, will
play a vital role not only in enhancing innovation efficiency but also in establishing open climate for
the embracement of new knowledge. Empowerment, strong motivation for innovation and
communication will encourage the CFT behave a highly entrepreneurially oriented organization, and
these factors are also important determinants for open innovation. High flexibility of CFT will enable
the firm to easily assimilate newness and empowered leadership and good communication for CFT will
4
enable the firm to develop the necessary collaboration skills with external partners. Thus, we propose
the following hypotheses:
(Hypothesis 1) CFT will positively influence innovation performance.
(Hypothesis 2) CFT will positively influence innovation collaboration with external partners.
3. Data and method
3.1 Data
The purpose of this study is investigating the effect of CFT on innovation performance (i.e., new product
development, manufacturing process efficiency) and innovation collaboration. For this, we used the
manufacturing productivity survey (MPS) data conducted by the Korean Productivity Center in 2012,
entrusted by the Korean Ministry of Trade, Industry and Energy. For analysis, Propensity Score
Matching Analysis (PSM) and structural equation (SEM) were employed to compare the effects of CFT
and explore the structural relationships. The survey’s data sampling is composed of 2,218 firms in the
first stage which contained firms more than 50 employees based on KIS-LINE database of NICE
Information Service. Finally, 599 firms (27%) responded to the survey. The original MPS survey
questionnaire was divided into seven categories: financial management, personnel management,
planning management, sales planning, production management, purchasing management, and research
and development, responses from each division responded to the questionnaire, and for this study,
questionnaires of planning management and research and development departments were used. The
managers of the development department responded to the questionnaire about the CFT related items,
the cooperation between the internal departments, the new product development, the process efficiency,
and the flexibility of development, and the person who is in charge of planning and management
answered the questionnaire on external cooperation and R&D intensity.
Table 2 shows the classification by firm size and technology level. As for the number of large
enterprises account for 6.68%, mid-size firms, 55.1%, and small firms 84.14%. According to the
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distribution of corporations according to the technology level of the enterprises, the high-technology
industry account for 21.2%, the number of companies in high tech is 127 including the aerospace
industry, pharmaceutical industry, office and computer machinery, radio TV communication equipment,
have. The medium-high-technology level accounts for more than half of the total (351, 58.6%),
including the electrical, automotive, chemical, rail transport and other machinery industries. In the
medium-low technology industry, 99 firms accounted for 16.53%, shipbuilding and repair, rubber and
plastic products, petroleum refining industry, other non-metal and metal industries, 22 low-technology
industries, 3.67% including recycling industry, Wood pulp and paper manufacturing, and the textile
industry
Table 2 Descriptive statistics
Firm Size Technological level
Division # of firms Ratio (%) Division # of firms Ratio (%)
Large 40 6.68 High Tech. 127 21.2
Mid-size 55 9.18 Medium High Tech. 351 58.6
Small 504 84.14 Medium Low Tech. 99 16.53
Low Tech 22 3.67
Total 599 100 Total 599 100
3.2 Method of Analysis
3.2.1 Propensity Score matching
In this study, we analyzed the effect of CFT composition on innovation performance and innovation
collaboration. Simply comparing firms with and without CFT can generally lead to biased estimates
because the distribution of the two groups of observational variables can be different. Therefore, it is
necessary for us to verify the effect of dependent variables depending on whether CFT is constructed
through PSM (Propensity Score Matching). Cochran (1968) found that the more subclasses, the less
deflection, which leads to more accurate results by eliminating distorted values. In his study, he found
that the relationship between smoking rates and age and mortality was found to be biased by sub-
classification. Rosen Baum and Rubin (1983) devised a PSM method based on Cochran's sub-
6
classification method. The purpose of the propensity score was to group the individuals with similar
propensity scores by grouping the covariate information into a single score to make it easier to perform
stratification in the observational study and to classify the propensity scores into the stratified group. It
is possible to estimate the bias of the parameter by layer because the covariance effect that can confuse
the relationship can be eliminated (Rosen Baum & Rubin, 1983; Dehejia & Wahba, 2002). Propensity
score matching was analyzed based on STATA 13 “pscore” command. “pscore” estimates propensity
scores and then Average Treatment Effect on Treated (ATET) using various matching techniques. PSM
is to classify the observed subjects into treatment group and control group, and to compare the influence
of dependent variables by comparing variables in similar situation with control variables (Caliendo &
Kopeinig, 2008). In order to estimate the effect of the CFT configuration, the propensity score should
be estimated first. When the covariance is given, the binary variable is constituted as a dependent
variable is used to predict the propensity score through the Probit regression analysis to classify the
data.
3.2.2 Structural equation modeling
PSM method helps us to eliminate bias by comparing treatment and control group with similar sample
characteristics. However, the score is given based on probit regression, so we can only compare binary
groups, i.e., whether CFT is organized in the firm or not. Therefore, to investigate more complex
structural relationships, SEM analysis was conducted to explore the research model shown in Figure 1.
Figure 1 SEM Research model
7
In this model, instead of binary variable, i.e. whether CFT is organized or not, latent variable “CFT
efficiency” comprising five indicators, such as empowerment, functional diversity, coordination,
participation in key personnel, and motivation were used. Also, the mediation effects of two types of
collaboration, internal and external collaboration were analyzed in the relationship between CFT
efficiency and innovation performance. This is to analyze the relationship between factors such as CFT
efficiency, internal collaboration, external collaboration, and innovation performance among 280 firms
with CFT experience using SEM. First, we calculated the overall model fit and the reliability of each
latent variable (Cronbach's alpha, Composite Reliability (CR), and Averaged Variance Extracted (AVE))
to assess whether the structural equation model was properly constructed. The overall model fit index
indicates that the model is well represented (CFI > 0.950, TLI >= 0.950, RMSEA < 0.10) (Baumgartner
& Homburg, 1996), Cronbach's alpha > 0.6, CR value should be 0.7 or higher and AVE value should
be 0.5 or higher (Bagozzi & Yi, 1988). Table 3 shows the results of the reliability and validity analysis
of variables between latent variables and measurement variables. In this research, the model fit index is
satisfied with CFI = 0.958, TLI = 0.950, and RMSEA = 0.067. The CFT efficiency is a combination of
five items asking whether the CFT was operating well, and satisfied the model fitness criterion as α =
0.88, CR = 0.873, AVE = 0.581. The Development-Operations variable satisfies the model fitness
criterion as α = 0.94, CR = 0.935, and AVE = 0.959, which is a total of four items on the cooperation
status of development department and production adverb. The Development-Marketing variable is
modeled as α = 0.95, CR = 0.949, and AVE = 0.962, which is a composite of the four items evaluated
for cooperation between the development department and the marketing department. The innovation
performance variable consists of six items evaluated by comparing the development performance with
the peer average, α = 0.89, CR = 0.880, and AVE = 0.553. The R&D collaboration variable is an average
of two items of collaborative R&D and field development.
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Table 3 Results of the reliability and validity analysis of variables
4. Results and discussion
In this study, the propensity score matching method was used to analyze the effect of cross-functional
team formation on development performance and organizational openness. We divided the observed
values into two groups (CFT structured (D = 1) and non-structured (D = 0)) and compared the mean
effects on development performance.
y = {y1 ⅈf D = 1y0 ⅈf D = 0
Latent variable Measurement variable Factor
loadings α CR AVE
CFT Efficiency
Functional diversity 0.713
0.88 0.873 0.581
Empowerment 0.686
Participation in key personnel 0.749
Coordination 0.859
Motivation 0.791
Interdepartmental
Collaboration
(Development-
Operation)
Common goal 0.914
0.94 0.935 0.959
Design for manufacturing 0.889
Cooperation each other 0.893
Information sharing 0.855
Interdepartmental
Collaboration
(Development-
Marketing)
Common goal 0.908
0.95 0.949 0.962
Cooperation each other for new product development 0.930
Sharing of new technology information 0.893
Sharing of market/customer information 0.898
Innovation
Performance
Number of NPD 0.650
0.89 0.88 0.553
New product sales proportion 0.605
Quality of new product 0.833
Development schedule 0.773
Development cost 0.817
Flexibility of development 0.755
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The propensity score model constructs a probit regression model with D as a dependent variable and X
as an independent variable.
p(x) = prob(D = 1|x) = E(D|x)
In this model, the CFT composition was used as a dependent variable and the Probit regression analysis
was performed using the level of technology, firm size, R&D investment performance, industrial
environment dynamics and uncertainty as independent variables. Probit regression analysis results are
shown in Table 4, except that the probit estimates of all other variables are statistically significant at the
10% significance level except for the industrial environment dynamics(commodity) and uncertainty
(domestic market) variables.
Table 4 results of probit regression
CFT Composition Coefficient Std. Err. z
Level of technology 0.162 0.076 2.14**
Firm size 0.527 0.104 5.05***
R&D investment 0.112 0.04 2.76***
Dynamics(commodity) -0.032 0.053 -0.61
Dynamics(Process) 0.132 0.059 2.23**
Uncertainty(Domestic) -0.04 0.056 -0.72
Uncertainty(Overseas) 0.107 0.056 1.91*
Constant -2.377 0.395 -6.02***
N. Observation=599, Pseudo R2 = 0.0765
R chi2(7) = 63.51 / Prob > chi2 = 0.0000
The propensity score is the conditional probability of being treated. There are several ways to match
the results of the treatment group and the control group according to the propensity score, which are
analyzed using Nearest neighbor matching. Nearest-neighbor matching is a method of selecting an
Figure 2 Nearest neighbor matching
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observation value j with the closest x for each observed value i processed. Figure 1 briefly describes
nearest neighbor matching.
mⅈn‖pi − pj‖
∑ 𝕀{‖𝑝𝑗 − 𝑝𝑖‖ ≤ ‖𝑝ℓ − 𝑝𝑖‖} = 𝑚
j:Wj≠Wi
Table 5 shows the results of 595 companies divided by the score of propensity and classified into 5
groups. There were 291 companies in the CFT group and 304 in the non-CFT group.
Table 5 classification of propensity score
The Average Treatment Effect can be used to analyze whether CFT operations affect development
performance. ATE is the difference between the treatment group and the control group.
Δ = y1 − y0
ATE = E(Δ) = E(y1|x, D = 1) − E(yo|x, D = 0)
The ATE can be used to simply test the difference between the treatment group and the control group
with t-test, but there is a possibility that the treatment group and the control group are not properly
controlled and can be biased. In the treatment group and the control group, if the propensity score is
matched among the members, it is judged as the same effect as the random assignment, but if the closest
propensity constant is selected for the accurate matching, the region where the propensity scores
between the treatment group and the control group are overlapped may be incomplete , There is a
disadvantage in that, even if there is a difference in the propensity score values in order to maximize
the number of samples in the treatment group, matching may result in inaccurate results.
Inferior of block of pscore CFT Composition
Number of firms Composition X Composition O
0.1523256 8 1 9
0.2 112 57 169
0.4 143 137 280
0.6 36 67 103
0.8 5 29 34
Total 304 291 595
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ATET = E(Δ|D = 1) = E(y1|x, D = 1) − E(y0|x, D = 1)
ATET = E(Δ|𝑝(𝑥), D = 1) = E(y1|𝑝(𝑥), D = 1) − E(y0|𝑝(𝑥), D = 0)
The average treatment effect on the Treated (ATET) also can compare the results of the CFT with the
actual treatment group when the treatment group did not constitute the CFT. This can effectively verify
the effect of the CFT configuration by comparing the actual effect with the posterior hypothesis. The
ATET can be estimated by subtracting the average treatment effect of the control group from the ATET
on a particular disposition score. In this study, we analyzed the effect of CFT configuration on corporate
innovation performance and organizational openness by estimating ATET value.
Table 6 before and after propensity score matching
Table 6 demonstrates the average of the characteristics of firms that experienced CFT and those who
did not experience CFT before performing matching analysis by propensity score of 599 firms surveyed
Variable
Propensity score before matching Propensity score after matching
Comp X Comp O t-value Comp X Comp O t-value
Level of technology 2.896 3.055 2.7*** 3.055 3.055 0.00
Firm size 1.107 1.351 5.49*** 1.347 1.351 0.06
R&D investment 3.802 4.32 4.53*** 4.323 4.32 -0.03
Dynamics(commodity) 4.001 4.206 2.01** 4.247 4.206 -0.40
Dynamics(Process) 4.094 4.426 3.67*** 4.402 4.426 0.28
Uncertainty(Domestic) 4.919 5.069 1.60* 5.217 5.069 -1.58
Uncertainty(Overseas) 4.825 5.144 3.42*** 5.244 5.144 -1.11
Figure 3 before and after propensity score matching
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in the survey of productivity enterprises. 291 firms (48.58%) experienced CFT and 308 firms (51.42%)
did not. This shows the difference in the characteristics of firms that have experienced CFT and those
who have not. There is a difference in the mean level within the significance level among the technology
level, firm size, R&D investment, uncertainty of market structure, and industry dynamics before
matching scores. However, the tendency score matching analysis shows that the average value of firms
that experienced CFT and those that did not differ within the significance level. The inclination score
matching analysis is a comparative analysis of the treatment group and the control group of similar
characteristics. Therefore, the characteristics of the control variables must show almost the same values
to verify the effect on the dependent variable. Figure 3 is a graph after the propensity score matching
and propensity score matching. It can directly identify that the treatment group and the control group
are grouped among those having the same characteristics after the propensity score matching analysis.
Table 7 explains the result of propensity score matching and it is verified whether the effect is
statistically significant at 5% level using the bias-corrected interval calculated by the bootstrapping
method. The results of the analysis show that the firms constructing the CFT have positive effects on
both the new product development performance, development efficiency, and openness. In the case of
the new product development performance, the highest estimate was obtained when the number of new
product development was the dependent variable. In addition, the propensity to match the new product
sales and the quality goal attainment as the dependent variable were tested.
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Table 7 result of propensity score matching
3.3.2 Structural Equation Modeling
The results of propensity score matching analysis proved that firms have a positive effect on
innovation performance and openness when they operate CFT.
Table 8 results of structural equation modeling
Variable Effect Std. Err z Bias-corrected
95% interval
NPD
Performance
Number of NPD 0.608 0.133 4.56*** 0.557 ~ 0.667
New Product Sales proportion 0.451 0.138 3.28*** 0.104 ~ 0.576
Quality of New Product 0.393 0.116 3.40*** 0.285 ~ 0.55
Development
Efficiency
Development Schedule 0.473 0.133 3.56*** 0.299 ~ 0.79
Development Cost 0.464 0.130 3.56*** 0.269 ~ 0.642
Openness
Interdepartmental Collaboration
(Development- Operation) 0.293 0.125 2.34** 0.101 ~ 0.382
Interdepartmental Collaboration
(Development- Marketing) 0.324 0.110 2.94*** 0.323 ~ 0.552
External Collaboration 0.380 0.170 2.24** 0.262 ~ 0.461
Path
Standardized
Estimate
Std. Err C.R.
Development-Production CFT Efficiency 0.875*** 0.074 13.851
Development-Marketing CFT Efficiency 0.846*** 0.079 13.314
R&D Collaboration CFT Efficiency 0.104* 0.101 1.666
Performance CFT Efficiency 0.203 0.162 1.258
Performance Development- Production 0.470*** 0.103 3.904
Performance Development - Marketing 0.175* 0.081 1.757
Performance R&D Collaboration 0.078* 0.027 1.803
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Table 8 shows the results of the structural equation modeling. The results of this study were statistically
significant except for the path from CFT Efficiency to Performance. The better CFT operation, the more
positive impact it had on internal collaboration. In the path estimates, the CFT efficiency was
statistically significant at 0.875 for development-production department collaboration and 0.846 for
development-marketing department. As the CFT operation became better, the collaboration with the
outside was 0.104, which was statistically significant.
15
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