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)lkm3471@gmail.com

Joon Mo Ahn Sogang University

Management of Technology(MOT)jmahn@sogang.ac.kr

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 lkm3471@sogang.ac.kr

2 jmahn@sogang.ac.kr

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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

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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.

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