Functional Diversity, Absorptive Capability and ...

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CREATIVITY AND INNOVATION MANAGEMENT

Functional Diversity, Absorptive Capability and Product Success: The Moderating Role of Project Complexity in New Product Development Teams Atif Açıkgöz, Ayşe Günsel, Cemil Kuzey and Gökhan Seçgin Absorptive capability appears to be an appealing concept in the technology and innovation management literature. Though absorptive capability attracts researchers from a variety of disciplines, team-level empirical research on it is scant. In this study, we operationalized team absorptive capability as a multidimensional construct involving knowledge acquisition, assimilation and exploitation. This study also explores the moderating effect of project complexity between team absorptive capability and new product success. In studying the data from 239 new product development projects using partial least squares structural equation modelling, we found that team functional diversity is a significant determinant of team absorptive capability. Moreover, regarding the relationships between team absorptive capability and new product success, we uncovered that (i) new product success is dependent on the ability to understand the acquired knowledge, and (ii) the teams appear to be more cautious in putting the assimilated knowledge into practice to the extent that project complexity increases.

Introduction

T

oday’s business environment witnesses dramatic changes as well as increasing turbulence. In such a turbulent environment, existing knowledge might quite easily lose its potential to contribute to competition amongst high-technology firms due to rapid changes in customer preferences and technological developments (Günsel & Açıkgöz, 2013). These types of firms use new product development (NPD) teams intensively in order to maximize the absorption of external knowledge, take full advantage of sophisticated technologies, and reach higher levels of organizational learning (Açıkgöz et al., 2014). Drach-Zahavy (2004) stresses that NPD teams have become the common units for managing hyper-competition, especially in knowledge-intensive industries. New knowledge creation, therefore, becomes

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key in terms of developing successful new products (García-Morales, Ruiz-Moreno & Llorens-Montes, 2007), producing innovations (Murovec & Prodan, 2009) and obtaining competitive advantage (Jansen, van den Bosch & Volberda, 2005). The analysis of knowledge development processes for continuous innovation and value generation requires attention to both internal knowledge creation and external knowledge absorption (Camisón & Forés, 2011). Nevertheless, NPD teams are increasingly abandoning the idea that new knowledge creation is an in-house activity (Escribano, Fosfuri & Tribó, 2009). Instead, the ability to absorb new external knowledge is more critical to NPD processes (e.g., Cohen & Levinthal, 1990; Kostopoulos et al., 2011) because innovation is dependent on an NPD team’s ability to access external knowledge as open access overcomes

© 2015 John Wiley & Sons Ltd

FUNCTIONAL DIVERSITY, ABSORPTIVE CAPABILITY AND PRODUCT SUCCESS

competency traps, particularly in dynamic technological environments (Bouncken & Kraus, 2013). In order for the NPD process to ensure a desired output, NPD teams need to recognize, assimilate and apply the new external knowledge to commercial ends (Jansen, van den Bosch & Volberda, 2005). Accordingly, team absorptive capability and the NPD processes are intertwined based on the fact that innovation is closely related with the successful utilization of new external knowledge (Murovec & Prodan, 2009). Following the seminal work of Cohen and Levinthal (1990), this study conceptualizes team absorptive capability as the collective ability of NPD team members to value, assimilate and apply new external knowledge for commercial ends. In reality, NPD teams are cross-functional, comprising members from the core disciplines of engineering, marketing, industrial design and manufacturing, and members from the social sciences, such as sociology and anthropology (Squires & Byrne, 2002). These teams are continuously engaged in knowledgeprocessing and knowledge-producing activities. By assembling team members with diverse functional backgrounds, the process of valuing, assimilating and applying new knowledge can be potentially enhanced (Cohen & Levinthal, 1990). Nevertheless, the role of team functional diversity construct on team absorptive capability and resulting project outcome (i.e., new product success) is relatively unexplored; a systematic framework for such a relationship was not yet been developed in the technology and innovation management (TIM) literature. In this context, focusing on new product success is critical for NPD projects (Plaza & Turetken, 2009), where 23–40 per cent of projects are cancelled before completion and 33–53 per cent of projects become operational, but

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over-budget, past deadline and/or with fewer features than initially specified (Açıkgöz et al., 2014). Finally, although team functional diversity impacts team absorptive capability, this relationship is subject to the complexity level of NPD projects, i.e., project complexity, which is yet to be empirically investigated in the TIM literature. Project complexity indicates the extent to which the NPD projects are sophisticated in terms of their communication, development and commercialization processes (Açıkgöz et al., 2014). Here, we assert that, as a moderating variable, project complexity systematically modifies the strength of the relationship between team absorptive capability and new product success. The aim of this study is, therefore, to conceptualize and operationalize team absorptive capability, and to empirically test the role of team functional diversity on team absorptive capability, resulting in new product success, with a view to enhancing the TIM literature. Specifically, as shown in Figure 1, this study is guided by the following questions: 1. How does team functional diversity affect team absorptive capability? 2. How does team absorptive capability affect new product success? 3. To what extent does NPD project complexity affect team absorptive capability on new product success? Theoretically, this study makes an incremental contribution to organizational learning theory – the process of improving a firm’s functions and activities as a result of external knowledge absorption. Firms may be determined to attain new external knowledge from the business environment, but they still may fail to draw advantage from it. Even though the source of knowledge exists in the business

Figure 1. Research Model


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environment, firms may fail to catch the external knowledge because of low-level absorptive capability (Tsai, 2001). By vivifying absorptive capability, NPD teams also enhance firms’ learning abilities, such as the learning rate potential and the level of external learning. The logic behind this assertion is that current literature widely emphasizes organizational learning dependency on both individual and team learning (e.g., Senge, 1990; Huang & Li, 2012). NPD team members integrate their functionally diverse knowledge in order to develop a team knowledge base, which is institutionalized by the firm (Yang & Chen, 2007). Team learning is therefore an interface between individual and organizational learning (Yang & Chen, 2005). In a sense, team learning is a way of mutually absorbing external knowledge, which allows NPD teams to modify their behaviours by reflecting new insights acquired (Nonaka, 1994; Açıkgöz et al., 2014). The study’s contribution to organizational learning theory is built by regarding team absorptive capability as a learning tool arising in a community of interaction, where NPD team members can explore and experiment with different interpretations of the same external knowledge. On the other hand, practically, project management needs to encourage an NPD team to use their absorptive capability in order to gather task-related external knowledge as a response to information, knowledge and capability asymmetries and, in turn, take full advantage of technology-, market-, and project-related opportunities. By enhancing absorptive capability, NPD teams become capable of utilizing external knowledge through the consecutive processes of exploratory (i.e., knowledge acquisition), transformative (i.e., knowledge assimilation) and exploitative learning (i.e., knowledge exploitation), leading to successful resolution of project-related technical problems (Lane, Koka & Pathak, 2006). Furthermore, learning enables organizations to introduce innovations into markets via pioneer and follower strategies in uncertain situations (Bouncken, Pesch & Kraus, 2015).

Literature Review and Hypotheses Development Absorptive capability appears to be one of the leading determinants of an NPD team’s ability to absorb new external knowledge from today’s hypercompetitive business environment (Chen, Lin & Chang, 2009). Absorptive capability is first defined by Cohen and Levinthal (1990, p. 128) as ‘the ability of a firm to recognize the value of new, external information, assimilate it, and apply it to commercial ends’. Then, Zahra

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and George (2002, p. 185) redefine it as ‘a set of organizational routines and processes by which organizations acquire, assimilate, transform, and exploit knowledge to produce a dynamic organizational capability’. In a sense, Zahra and George (2002) substitute the capability of ‘recognizing the value’ with ‘acquisition’ and add the capability of transformation. Following that definition, some researchers argue that there is a linear relationship between acquisition, assimilation, transformation and exploitation of external knowledge to create innovation (e.g., Jansen, van den Bosch & Volberda, 2005). Nonetheless, Todorova and Durisin (2007) note that assimilation and transformation are two parallel sub-capabilities of absorptive capability. Regarding the majority of the literature on absorptive capability, this study operationally defines it as the set of abilities that allow NPD teams to acquire, assimilate and exploit new external knowledge to achieve commercial ends (e.g., Lane, Koka & Pathak, 2006; García-Morales, Ruiz-Moreno & Llorens-Montes, 2007; Todorova & Durisin, 2007; Vega-Jurado, Gutiérrez-Gracia & Fernándezde-Lucio, 2008). Knowledge acquisition refers to the ability to identify, value and acquire external knowledge (Zahra & George, 2002). On the other hand, knowledge assimilation refers to the ability to analyse, interpret and understand the acquired knowledge (Cohen & Levinthal, 1990); knowledge exploitation refers to the ability to use new assimilated knowledge for commercial ends (Camisón & Forés, 2011). Without absorptive capability, teams are unable to learn from or transfer technology-, market-, and project-related emerging knowledge from their industrial environment. They fail because of their embedded knowledge, rigid capabilities and path-dependent cognitive representations of the business environment (Tsai, 2001; Todorova & Durisin, 2007). Teams that are able to develop absorptive capability are more likely to be reactive in searching for new alternatives, aiming to turn challenges into opportunities (Cohen & Levinthal, 1990). Indeed, teams should strengthen their operational capabilities, such as the ability to manage technology- and market-related change by vivifying absorptive capability and then transferring external knowledge to update these capabilities (García-Morales, Ruiz-Moreno & Llorens-Montes, 2007). External knowledge stimulates learning, improves the understanding of the environment and decreases failure (Bouncken, Pesch & Kraus, 2015). When new knowledge is successfully integrated into the cognitive maps of teams, absorptive capability becomes team-specific (i.e., the capability cannot be imitated by other teams) and



path-dependent (i.e., accumulated knowledge in the team knowledge base will permit more efficient accumulation in the next project) (Roberts et al., 2012). By incorporating new knowledge into ongoing projects, NPD teams possibly become capable of increasing the market life cycle of developed products (García-Morales, Ruiz-Moreno & Llorens-Montes, 2007). NPD teams successfully vivifying absorptive capability will probably become able to build inimitable routines that gather, process and create new knowledge to consolidate existing NPD projects or to encourage new ones to emerge within a firm (Zahra & George, 2002). Cohen and Levinthal (1990) argued that the ability to evaluate and utilize external knowledge is largely a function of prior related knowledge. Additionally, the absorptive capacity of members will determine the absorptive capability of the firm. Therefore, team absorptive capability is composed of an overlap in the knowledge, skills and experience of each NPD team member (Roberts et al., 2012). Different experiences of team members provide multiple mental maps that enable the team to make accurate cognitive connections between existing and newly acquired knowledge. This is because NPD team members share different perspectives, backgrounds and knowledge in developing new products (Zoogah et al., 2011). According to Auh and Menguc (2005), uniform cognitive maps might work against teams by confining the ability to scan the business environment for operational knowledge assets. It is the functional diversity that enables teams to understand what knowledge is valuable to NPD projects and how the team will use this knowledge (Dahlin, Weingart & Hinds, 2005). This study operationalizes team functional diversity as the degree to which team members differ with respect to their operational backgrounds (Qian, Cao & Takeuchi, 2013). In the TIM literature, the effect of team functional diversity on team absorptive capability is not clear, especially in the context of NPD projects. To address this ambiguity, we assert that team functional diversity might be an important determinant for vivifying team absorptive capability in terms of knowledge acquisition, knowledge assimilation and knowledge exploitation. Functional diversity makes NPD teams capable of calling into play a wide variety of project-related tacit knowledge embedded in the know-how of its members, increasing understanding of the implications of each method that can be taken (Dahlin, Weingart & Hinds, 2005). As functional diversity increases, it is generally expected that the mental maps of the teams expand and vary, thus facilitating acquisition of technology-, market-, and project-related knowledge to generate more


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creative ideas and to develop innovative products (Auh & Menguc, 2005). In a hypercompetitive environment, functional diversity has the potential that it enhances teams’ abilities to catalyse the exploitation of external knowledge because diversity gives rise to creative abilities and a superior capacity to envision a broad range of knowledge (Gotteland & Haon, 2010). In such environments, the difficulty of predicting transformations in technology and the market imposes important demands on NPD teams to sense, collect and analyse emerging knowledge (Qian, Cao & Takeuchi, 2013). Accordingly, it is plausible to hypothesize as follows: Hypothesis 1a: Team functional diversity will be positively related to knowledge acquisition in the NPD projects. Hypothesis 1b: Team functional diversity will be positively related to knowledge assimilation in the NPD projects Hypothesis 1c: Team functional diversity will be positively related to knowledge exploitation in the NPD projects. An NPD team can be seen as a knowledge processing unit, and their projects can be thought of as a scheduled innovation activity in which a team creates new products by applying assimilated knowledge to a set of resources. NPD projects are important driving powers for the firms to achieve competitive advantage. In order to determine NPD projects’ performance, various potential factors are used, such as new product success (Verona, 1999) – the major NPD outcome in terms of sales, market share, and profitability (Carbonell & Rodriguez Escudero, 2010). According to Cooper and Kleinschmidt (1986), the developers of successful new products need to have a deep understanding of customer’s needs and they need to perform a detailed market analysis. Absorptive capability gives an NPD team an opportunity to assimilate acquired knowledge and to exploit newly created knowledge, leading to the generation of market intelligence. Such intelligence is expected to have a positive effect on new product success because the absorption of new external knowledge enables the team to swiftly identify the commercialization and marketing practices for favourable positioning in the market (Carbonell & Rodriguez Escudero, 2010). In the TIM literature, there is a gap linking the effect of team absorptive capability to new product success. By revealing this effect, it becomes apparent that while NPD teams become capable of absorbing new external knowledge, they could possibly learn more, resulting in conveying external knowledge into the new products. Similarly, if an NPD

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team vivifies absorptive capability, it will probably be able to create new knowledge and, in turn, achieve superior NPD performance. Accordingly, it is plausible to hypothesize as follows: Hypothesis 2a: Knowledge acquisition will be positively related to new product success in the NPD projects. Hypothesis 2b: Knowledge assimilation will be positively related to new product success in the NPD projects. Hypothesis 2c: Knowledge exploitation will be positively related to new product success in the NPD projects. Developing a new product is a highly complex process as it involves the interaction of a large number of parties in unconventional ways (Açıkgöz et al., 2014). The term ‘complex’ originated in the Latin word complexus, meaning twisted together (Boushaala, 2010). Complexity is something undesirable that can make an NPD project challenging to perform (Geraldi, 2009). This study operationalizes project complexity as the extent to which the development process is sophisticated. In other words, the core source of complexity in NPD projects is process oriented (Açıkgöz et al., 2014). Although project complexity consists of a great deal of different interconnecting parties, the interpretation of these parties is subjective, comprising anything characterized by difficulty (Chronéer & Bergquist, 2012). In this sense, the degree of the complexity is closely related with the resemblance of the communication, development and commercialization processes used during an NPD project as compared to the process the team traditionally had been using (Lynn & Akgün, 1998; Açıkgöz et al., 2014). For example, as knowledge processing requirements increase due to project complexity, communication frequency among team members can be expected to differ between ongoing or completed projects (Kennedy, McComb & Vozdolska, 2011). Such complexity has the potential to systematically influence the relationship between team absorptive capability and new product success (Boushaala, 2010). Accordingly, it is plausible to hypothesize as follows: Hypothesis 3a: Project complexity positively moderates the effect of knowledge acquisition on new product success in the NPD projects. Hypothesis 3b: Project complexity positively moderates the effect of knowledge assimilation on new product success in the NPD projects. Hypothesis 3c: Project complexity positively moderates the effect of knowledge exploitation on new product success in the NPD projects.

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Research Design The design for this study included a large-scale, cross-sectional survey involving survey questions based on the constructs contained in the review. Academic experts familiar with the TIM literature were consulted and they reviewed the survey questions to provide further improvements, resulting in a draft questionnaire that was translated into English (see Table 1). A parallel translation method was employed (Brislin, 1980) to ensure that the variables creating the model were measured and that the meaning of the research questions was clear. This involved having one person who was highly proficient in English to Turkish translation work with the resultant questionnaire, while a second highly proficient language expert translated that translation back into English. The object of this exercise was to ensure that the questions were properly stated. Following this exercise, the two translators went through the results and brought the results of the translations into harmony, and the draft survey questionnaire was approved as correct. At the same time, two independent researchers, also familiar with the TIM literature, were consulted to assess the Turkish form of the draft questionnaire and to make appropriate suggestions as to how it could be further improved (i.e., content validity). Then, three project leaders who work in the field of developing new products were randomly selected and asked to assess any variables in terms of their readability and their significance. These leaders reported that the variables as stated were clear and easily understood, which confirmed that the variables in the data collection forms were accurate and complete (i.e., face validity). Following this exercise, we distributed and collected the questionnaire using the personally administered questionnaire method.

Measures Using previous studies, multi-item scales were developed to test the developed hypotheses with the object of measuring the latent constructs using a seven-point Likert-type scale, which ranged from ‘strongly disagree’ (1) to ‘strongly agree’ (7). For the purpose of this study, a first-order reflective model was employed over a formative model. Baxter (2009) stated that correct specification of reflective or formative scales relies very much on correct conceptualization. The difference between reflective and formative is very distinctive. In the reflective case, questionnaire items are indicators of the domain of the measured construct; they are caused by the construct,



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Table 1. Discriminant Validity of Construct Measures Factor Rotation LV

Manifest Variables

SL

E

VE (%)

UV (%)

FD

The members of the project team were from different areas of expertise The members of the project team had skills that complemented each other The members of the project team had a variety of different experiences The members of the project team varied in functional backgrounds Acquiring knowledge from customers Acquiring knowledge about competitors’ activities Acquiring knowledge about relevant publics other than customers and competitors Acquiring knowledge from external experts, such as consultants Summarize acquired knowledge, reducing its complexity Organize acquired knowledge in meaningful ways Rely heavily upon new knowledge to make decisions relating to the project Use new knowledge to solve specific problems encountered in the project Provide new knowledge to effectively implement the project By using new knowledge, the team …: transfers customer needs to product design specs takes corrective action immediately, when they found out the changes in customers’ preferences, wants and needs responds to significant changes in the business environment generates different market and technology scenarios The development process used in this team was similar to the process the company traditionally uses The commercialization process used in this team was similar to the process the company traditionally uses The processes used on this project to communicate across functional disciplines, with suppliers and customers were similar to the communication process the company traditionally uses Met or exceeded volume expectations Met or exceeded the first year number expected to be produced and commercialized Overall, met or exceeded sales expectations Met or exceeded profit expectations Met or exceeded return on investment (ROI) expectations Met or exceeded overall senior management’s expectations Met or exceeded market share expectations Met or exceeded customer expectations

0.80

2.41

8.93

6.74

2.34

8.68

4.55

2.93

10.84

11.07

2.32

8.58

4.41

2.57

9.53

8.49

6.02

22.30

33.57

AC

AS

EX

PC

NPS

0.58 0.79 0.74 0.59 0.80 0.77 0.67 0.81 0.84 0.59 — 0.60 0.57 0.62 0.73 0.65 0.86 0.89 0.85

0.75 0.84 0.89 0.86 0.86 0.80 0.81 0.79

Notes: FD = Team Functional Diversity, AC = Knowledge Acquisition, AS = Knowledge Assimilation, EX = Knowledge Exploitation, PC = Project Complexity, NPS = New Product Success, LV = Latent Variable, SL = Standardized Loading, E = Eigenvalue, VE = Variance Explained, UV = Unrotated Variance.


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and overlap in meaning so that they correlate moderately strongly. However, in the formative case, the indicators are independent causes of the construct being measured, with little correlation between them, and all need to be present in order to adequately specify the measured construct (Baxter, 2009). The measures used in this study were specifically denoted as indicators of the measured construct. This was not an easy decision, as it is not always clear which model (reflective or formative) would best suit the purposes of this project. To assist in making this decision, Jarvis, MacKenzie and Podsakoff (2003) discussed distinctions between measurement models, indicating that reflective models can be identified by the following means: the direction of causality is from construct to measure, measures are expected to be correlated, dropping an indicator from the model does not affect the construct, and measurement error is taken into account at the item level rather than at the construct level. It was, therefore, deemed appropriate to employ reflective measures for the proposed research model, taking all of these factors into consideration, and especially given that all the indicators were expected to be highly correlated with the latent variable score in this research model, as well as with the measures of construct cause. Table 1 lists the question items in this study. Further information about each of the observed and latent variables that were used are as follows. Measurements regarding team functional diversity were derived from Campion, Medsker and Higgs (1993). Since this is a unidimensional variable, product developers from the target audience were consulted through four items on the questionnaire. Additionally, various scales were incorporated in the questionnaire so that team absorptive capability could be measured and could fully represent knowledge acquisition, knowledge assimilation and knowledge exploitation. Three questions adapted from Moorman and Miner (1997) were selected to measure knowledge acquisition. Five questions, adapted from Akgün, Dayan and Di Benedetto (2008), were used to measure knowledge assimilation, and a further three questions, adapted from Akgün, Lynn and Yılmaz (2006), were included to assess knowledge exploitation. Eight questions were used directly from Cooper and Kleinschmidt’s (1986) study to help measure new product success, and finally, we used the three-item list, developed by Lynn and Akgün (1998), to address project complexity.

Sampling The unit of analysis in this study is the team, the target population being the NPD teams that are

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specifically working on product development projects in İstanbul, Turkey. The accessible population included the NPD teams in the İstanbul district, the largest city in the Marmara region and in Turkey. In addition, the Marmara region is considered to be the hub of Turkish business. The majority of the NDP projects have been designed and implemented in this region as here are located large numbers of software, information and communication technology, automotive, electrical and electronic, and chemical and pharmaceutical sectors. There are 235 firms that operate in the high-tech industry, as indicated through records supplied by the Istanbul Chamber of Commerce. These were contacted with a request to supply data for the study. The first contact was a telephone conversation with the managers of the firms in order to solicit their co-operation, with a request that they confirm their position as the product developers who most understood their NPD projects. Of the 235 contacted firms, 34 per cent (79 firms) indicated that they were willing to participate in the study. From this group, 15 firms indicated they were working with one NPD project, 17 firms were working with two, 13 firms were working with three, 19 were working with four, and 15 firms were working with five NPD projects. In the end, there were 239 NPD projects as several firms participated with more than one project, and there were a total of 460 individual participants. The distribution of the industries that the sample includes are provided in Table 2. Moreover, all of the respondents were assured that their responses would remain completely anonymous (Huber & Power, 1985), and they were told that there were no predetermined right or wrong answers in the hope that they would answer the questions honestly and directly (Podsakoff et al., 2003). It was expected that the product developers would provide the best information when considering their own work experience (Phillips & Bagozzi, 1986) and would easily understand the aim of the questionnaire, based upon their work experience on their projects. The teams were composed as follows: 35 per cent included 2–5 persons, 25 per cent included 6–9 persons, 19 per cent had 10–15 persons, 8 per cent had 16–19 persons, and 13 per cent of the teams had 20 or more members. Additional descriptive statistics are provide in Table 2.

Measure Validity and Reliability In accordance with Fornell and Larcker’s (1981) recommendation, it was necessary to first subject the data to a purification process in order



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Table 2. Descriptive Statistics Variables Gender Age

Education Level

Experience

Industries

Male Female Total Before 1959 1960–1969 1970–1979 1980–1989 After 1990 Total High School Associate Undergraduate MSc PhD Total 0–5 years 6–10 years 11–15 years 16–19 years 20+ years Total Software Information and Communication Technology Automotive Electrical and Electronic Chemical and Pharmaceutical Total

to assess their reliability, discriminant validity, convergent validity and unidimensionality. For robustness of all the constructs (i.e., team functional diversity, team absorptive capability, project complexity and new product success), an exploratory factor analysis (EFA) was initially conducted into 28 measured items; the construct has six variables. The main reason behind this procedure was to refine the measurements of the constructs by controlling certain results from the EFA, such as factor loadings, item-to-total correlation and Cronbach’s alpha (α), as recommended by Nunnally (1978). To that end, a principal component with a varimax rotation was employed with an eigenvalue of 1 selected as the cut-off point. Due to the low levels of factor loadings, one item was dropped from the analysis from knowledge assimilation. An examination of these items revealed that dropping that item would not compromise the content validity of the related construct. A single factor was extracted for each multipleitem scale in this analysis. The Kaiser-MeyerOlkin (KMO) measure of sampling adequacy was 0.89, which was higher than the proposed threshold value of 0.70, and also the Bartlett test


Frequency

Percent

344 116 460 1 36 122 295 6 460 10 16 314 110 10 460 189 139 65 39 28 460 269 101

74.78 25.22 100.00 0.22 7.83 26.52 64.13 1.30 100.00 2.17 3.48 68.26 23.91 2.17 100.00 41.09 30.22 14.13 8.48 6.09 100.00 58.48 21.96

43 32 15 460

9.35 6.96 3.26 100.00

of sphericity was significant at p < 0.000 (χ 2 (351) = 7826.081). In addition, the extent of common method bias with Harman’s one-factor test was measured. The test enters all constructs into an unrotated principal components factor analysis and analyses the resultant variance (Harman, 1960). The threat of common method bias is high if a single factor accounts for more than 50 per cent of the variance (e.g., Açıkgöz et al., 2014). The results showed that none of the factors significantly dominated the variance (see the last column of Table 1); therefore, it is apparent that common method bias was unlikely. These results indicate the appropriateness of the data for the EFA procedure. The items (including the dropped items) and their factor loadings after EFA, eigenvalue and percentage of variance explained, are shown in Table 1. Following the initial analysis, a confirmatory factor analysis (CFA) was conducted because EFA by itself does not provide an explicit test of unidimensionality (Segars, 1997). A series of two-factor models were instituted, in which individual factor correlations were restricted to unity, in accordance with Bagozzi, Yi and

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Phillips’s (1991) suggestion. The constrained and unconstrained models were compared with regard to fit, with 30 models being calculated using AMOS (Arbuckle & Wothke, 1999). As shown in Table 3, the chi-square change (Δχ 2) in each model, constrained and unconstrained, was significant (Δχ 2 > 3.84), which indicates that the constructs prove discriminant validity (Anderson & Gerbing, 1988). Further, the measures were subjected to one model CFA employing AMOS. As shown in Table 4, the resulting measurement model was found to fit the data reasonably well: χ 2 (358) = 985.997, comparative fit index (CFI) = 0.93, incremental fit index (IFI) = 0.93, Tucker-Lewis Index (TLI) = 0.91, χ 2/d.f. = 2.75, and root mean square error of approximation (RMSEA) = 0.06. Additionally, all items loaded significantly on their respective constructs (with the lowest t-value being 2.50), providing support for convergent validity. Table 5 shows correlations among all six variables as well as various quality scores, such as average variance extracted (AVE), Cronbach’s alpha, and composite reliability (CR) in order to establish convergent, discriminant validity and reliability (Hair et al., 2010). The relatively low-to-moderate correlations provide further evidence of discriminant validity. Further, as suggested by Fornell and Larcker (1981), the squared root of AVE for each construct was greater than the latent factor correlations between the pairs of constructs, suggesting discriminant validity. All the reliability estimates, including the coefficient alphas, the AVE for each construct and the AMOS-based composite reliability are well beyond the threshold levels suggested by Nunnally (1978) and

Fornell and Larcker (1981). For instance, the CR values ranged between 0.85 and 0.96, which exceeded the recommended 0.70 threshold value (Bagozzi & Yi, 1988). The AVE scores, ranging between 0.60 and 0.81, were all bigger than the threshold value of 0.50 (Fornell & Larcker, 1981). All of the CR scores were larger than the AVE values (Byrne, 2010). In conclusion, the evidence provided by the validity and reliability measures suggests that the measures were unidimensional, with adequate reliability and discriminant validity.

Hypothesis Testing The analysis was based on a team level, aggregating the individual responses from the questionnaires, and using data collected from a minimum of two individuals working in NPD teams. In this study, the unit of analysis was the project team. Therefore, the team scores of individual question items were aggregated (Akgün et al., 2011). To obtain the data aggregation, the mean of the responses was used, which reflected team-level information. The inter-rater agreement was calculated using the rwg(j) index (James, Demaree & Wolf, 1984). According to Kozlowski and Hattrup (1992) the rwg(j) index should be employed when respondents essentially have the same ratings at team-level measures, and it also refers to interchangeability among the respondents. The obtained index results of rwg were between 0.85 and 0.98, which are significantly higher than the threshold value of 0.60 (Hurley & Hult, 1998). In accordance with this, the results

Table 3. Discriminant Analysis of the Construct Measures Constructs

Functional Diversity Functional Diversity Functional Diversity Functional Diversity Functional Diversity Knowledge Acquisition Knowledge Acquisition Knowledge Acquisition Knowledge Acquisition Knowledge Assimilation Knowledge Assimilation Knowledge Assimilation Knowledge Exploitation Knowledge Exploitation Project Complexity

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Unconstrained (χ2/d.f.) ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔

Knowledge Acquisition Knowledge Assimilation Knowledge Exploitation Project Complexity New Product Success Knowledge Assimilation Knowledge Exploitation Project Complexity New Product Success Knowledge Exploitation Project Complexity New Product Success Project Complexity New Product Success New Product Success

19.8/12 78.8/23 15.1/10 16.8/12 70.1/35 41.8/17 5.8/6 16.1/8 72.2/26 66.1/15 58.8/17 124.8/45 9.5/6 49.8/24 53.7/26

Constrained (χ2/d.f.) 35.6/13 123.8/24 71.6/11 285.7/13 279.3/36 48.0/18 105.2/7 96.7/9 149.7/27 85.8/16 93.4/18 308.4/46 296.7/7 263.5/25 127.3/27

Δχ2

15.80 45.00 56.50 268.90 209.20 6.20 99.40 80.60 77.50 19.70 34.60 183.60 287.20 213.70 73.60



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Table 4. Measurement Models and Confirmatory Factor Analysis Construct

Parametera

Standardized Coefficient

t-Valueb

λFD1 λFD2 λFD3 λFD4 λAC1 λAC2 λAC3 λAS1 λAS2 λAS3 λAS4 λAS5 λEX1 λEX2 λEX3 λPC1 λPC2 λPC3 λNPS1 λNPS2 λNPS3 λNPS4 λNPS5 λNPS6 λNPS7 λNPS8

0.75 0.67 0.68 0.63 0.78 0.71 0.64 0.68 0.80 0.71 0.70 0.77 0.55 0.71 0.75 0.93 0.83 0.84 0.88 0.91 0.89 0.86 0.82 0.75 0.75 0.81

Scaling 12.74 11.75 10.91 Scaling 12.38 11.62 Scaling 18.36 13.02 13.05 13.98 Scaling 9.67 9.29 Scaling 24.07 24.37 Scaling 27.37 25.45 24.36 29.09 19.89 19.90 20.20

Functional Diversity

Knowledge Acquisition Knowledge Assimilation

Knowledge Exploitation Project Complexity New Product Success

Notes: χ 2 (358) = 985.997, CFI = 0.93, IFI = 0.93, TLI = 0.91, RMSEA = 0.06. FD = Functional Diversity, AC = Knowledge Acquisition, AS = Knowledge Assimilation, EX = Knowledge Exploitation, PC = Project Complexity, NPS = New Product Success. a λ parameters indicate paths from measurement items to first-order constructs. b Scaling denotes λ value of indicator set to 1 to enable latent factor identification.

Table 5. Correlation Coefficients, Descriptive Statistics, and Reliability Analysis Results No.

Variables

1 2 3 4 5 6

Functional Diversity Knowledge Acquisition Knowledge Assimilation Knowledge Exploitation Project Complexity New Product Success Mean Standard deviation Composite reliability Average variance extracted Cronbach’s alpha Inter-rater agreement Intra-class correlation (1) Intra-class correlation (2)

1

2

3

4

5

6

(0.77) 0.29** 0.37** 0.42** 0.28** 0.32** 5.40 1.02 0.85 0.60 0.77 0.98 0.45 0.76

(0.77) 0.42** 0.36** 0.41** 0.32** 4.68 1.30 0.86 0.59 0.78 0.92 0.55 0.78

(0.85) 0.56** 0.30** 0.47** 5.58 0.85 0.92 0.72 0.87 0.91 0.58 0.87

(0.76) 0.29** 0.51** 5.82 0.84 0.85 0.58 0.73 0.93 0.47 0.73

(0.90) 0.37** 4.28 1.38 0.93 0.81 0.76 0.95 0.71 0.88

― 5.07 1.10 0.96 0.74 0.95 0.85 0.70 0.95

Note: * p < 0.05, ** p < 0.01. Diagonals show the square root of AVEs.


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demonstrated a satisfactory consistency among the respondents as well as a level of inter-rater agreement for each aggregate measure in an individual team. The intra-class correlations, ICC(1) and ICC(2), were calculated for each construct. While ICC(1) estimates the proportion of variance between participants that could be accounted for by differences in team membership, ICC(2) estimates the reliability of the aggregate scores for each construct at the team level (James, 1982). The results shown in Table 5 illustrate that the ICC(1) values for each construct range between 0.45 and 0.71, which are above the threshold value of 0.10 (Bliese, 2000). In addition, the values of ICC(2) range between 0.73 and 0.95, which are above the proposed threshold value of 0.70. The PLS approach (Ringle, Wende & Will, 2005) and the bootstrapping re-sampling method (Chin, 1998) were employed by SmartPLS 2.0, a computing software program to estimate the main and the interaction effects, and for testing the hypothesis and predictive power of the proposed model (see Figure 1). This procedure entailed generating 5,000 sub-samples of cases (Hair et al., 2013) that were randomly selected, with replacement obtained from the original data, after which path coefficients were generated for each sub-sample. t-Statistics were calculated for all coefficients based on their stability across the sub-samples in order to determine the links that were statistically significant. The path coefficients and their associated t-values demonstrated the direction and impact of each hypothesized relationship. Following the suggestion of Chin, Marcolin and Newsted (2003), a hierarchical approach for testing the hypotheses was employed: a model with main effects (and covariates) only was assessed, after which the interaction effects were added. Table 6 shows hypotheses, including paths, betas, significance levels and results. The findings demonstrated that functional diversity has a significantly positive impact on each of the sub-dimensions of team absorptive capability: knowledge acquisition (β = 0.36, p < 0.01), knowledge assimilation (β = 0.32, p < 0.01) and knowledge exploitation (β = 0.43, p < 0.01). The results prove that H1 is fully supported. The two subdimensions of team absorptive capability are positively associated with new product success. In detail, knowledge assimilation (β = 0.17, p < 0.01) and knowledge exploitation (β = 0.34, p < 0.01) had a significant positive effect on new product success. Nevertheless, it was impossible to show a significant statistical association between knowledge acquisition and new product success. Therefore, H2 was only partially supported. A two-step construction procedure was employed to address the hypotheses pertaining

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to the moderating effects of project complexity (namely, H3a, H3b and H3c) (Chin, Marcolin & Newsted, 2003), which allows for explicit estimation of the standardized latent variable scores after saving the obtained results (Tenenhaus et al., 2005). To eliminate collinearity problems, the interaction terms were established using the product-indicator approach (Chin, Marcolin & Newsted, 2003), which entails standardizing the items of constructs and computing the interaction term by multiplying each item of one construct with all the items of the moderator. Here, each item of knowledge acquisition, knowledge assimilation, knowledge exploitation and project complexity were standardized. Following this procedure, the standardized question items were multiplied. The overall multiplied results indicated that ‘knowledge acquisition and project complexity’, ‘knowledge assimilation and project complexity’, and ‘knowledge exploitation and project complexity’ had 9, 15 and 9 product indicators, respectively. The inclusion of product indicators (33 in total) for the latent variables that represent the moderators does not create a serious issue, as the partial least squares method is hardly affected by a large number of product indicators, as proved by Chin, Marcolin and Newsted (2003). The results demonstrated a positive interaction effect between knowledge assimilation and new product success (β = 0.26, p < 0.1), as well as a negative interaction effect between knowledge exploitation and new product success (β = 0.29, p < 0.05). However the results provided no empirical evidence in support of a statistically significant interaction effect of knowledge acquisition and new product success. Accordingly, H3 was marginally supported.

Structural Model In order to validate the PLS-SEM approach, various quality scores, such as coefficient of determination (R2) (Cohen, 1988), effect sizes (ƒ2) (Cohen, 1988), and the goodness-of-fit index (GoF) (Tenenhaus et al., 2005) were considered. The R2 values of the endogenous constructs were used to evaluate the model fit, indicating how well data points fit a line or curve (Chin, 1998; Tenenhaus et al., 2005). As suggested by Chin (1998), the categorization of R2 values is small (0.02 ≤ R2 < 0.13), medium (0.13 ≤ R2 < 0.26) and large (0.26 ≤ R2). Then, the comparison of explained variance amounts when a predictor is either included or excluded in the path model is utilized to evaluate ƒ2 (Chin, Marcolin & Newsted, 2003). The recommended threshold values of ƒ2 are small



Knowledge Acquisition Knowledge Assimilation Knowledge Exploitation New Product Success New Product Success New Product Success New Product Success New Product Success New Product Success

➔ ➔ ➔ ➔ ➔ ➔ ➔

➔

➔

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

Functional Diversity Functional Diversity Functional Diversity Knowledge Acquisition Knowledge Assimilation Knowledge Exploitation Project Complexity × Knowledge Acquisition Project Complexity × Knowledge Assimilation Project Complexity × Knowledge Exploitation

Relationships

Table 6. Results of Hypotheses

0.29**

0.26*

0.36*** 0.32*** 0.43*** 0.06 0.17*** 0.34*** 0.18

Path Coefficient

H3c

H3b

H1a H1b H1c H2a H2b H2c H3a

Sub-hypotheses

Not supported

Supported

Supported Supported Supported Not supported Supported Supported Not supported

Sub-results

Fully supported Partially supported Marginally supported

H2 H3

Results

H1

Hypotheses

FUNCTIONAL DIVERSITY, ABSORPTIVE CAPABILITY AND PRODUCT SUCCESS 101

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(0.02 ≤ ƒ2 < 0.15), medium (0.15 ≤ ƒ2 < 0.35) and large (0.35 ≤ ƒ2) (Cohen, 1988). Finally, GoF was employed to globally evaluate the overall fit of the model, seeking a concordance between the performance of the measurement and the structural model, as well as being consistent with the geometric mean of the average communality and the average R2 of endogenous latent variables. GoF ranges between 0 and 1, showing that a higher value represents a better path model estimation. In line with the effect sizes for R2, using 0.5 as a cut-off value for communality (Fornell & Larcker, 1981), threshold values for the GoF criteria were categorized as small effect sizes (0.1 ≤ GoF < 0.25), medium effect sizes (0.25 ≤ GoF < 0.36) and large effect sizes (0.36 ≤ GoF). The R2 statistic values of the endogenous constructs were used to assess model fit (Chin, 1998; Tenenhaus et al., 2005). Table 7 shows R2 and GoF values as the fit measures of the structural model. According to the outcomes of the final model, new product success (R2 = 0.45) had large effect sizes while knowledge exploitation (R2 = 0.18) and knowledge acquisition (R2 = 0.13) had medium effect sizes, respectively, and knowledge assimilation (R2 = 0.10) had a small effect size. Because of the effect of project complexity on interaction, the R2 for the value of new product success in the final model was 0.45, while the main effects model yielded only 0.32. Using an incremental F-test (Chin, Marcolin & Newsted, 2003), the results asserted that the R2i R2e of 0.13 was statistically significant at α = 0.01 (F3.231 = 18.2; p = 0.0). Then, ƒ2 was estimated to assess the effect size of the interaction terms in the final model, so the results suggested a medium-to-large effect size (ƒ2 = 0.24). According to another fit measure, the result of GoF was 0.38 for the final model while it was 0.34 for the main effects model. The obtained GoF results show a good fit (see Table 7).

Discussion Innovation is increasingly regarded as one of the crucial drivers of organizational success in hypercompetitive markets (Bouncken & Kraus, 2013; Açıkgöz et al., 2014). According to this study, innovation is closely related with the recognition, assimilation and application of new external knowledge. Here, the concept of team absorptive capability has gained attention from both scholars and practitioners. Accordingly, this study tries to expand the literature on TIM by presenting a model to understand interrelationships among team functional diversity, team absorptive capability and product effectiveness within the context of NPD projects (see Figure 2). In particular, this study aims to incrementally contribute to organizational learning theory by investigating team absorptive capability as a tool for team learning, which is an interface between individual learning and organizational learning. This study, specifically, makes three contributions to the relevant literature. First of all, this study empirically demonstrates the role of team functional diversity as an antecedent to address how team absorptive capability evolves within the context of NPD projects. The findings show that team functional diversity is significantly and positively related to the development and utilization of team absorptive capability in NPD projects. The greater the diversity in perspectives and competences resulting from operational backgrounds, the greater the understanding of each method’s implications. Specifically, NPD teams with superior functional diversity are better at (i) identifying, valuing and acquiring external knowledge that is relevant to the product development projects (e.g., knowledge acquisition), (ii) analysing, interpreting and understanding the acquired knowledge as well as retaining and reactivating such knowledge over time (e.g., knowledge assimilation), and (iii) exploiting new assimilated knowledge

Table 7. Structural Model ƒ2

Fit Measures Endogenous Constructs Main Effect Model Final Model Probability (α) R2

GoF

Knowledge Acquisition Knowledge Assimilation Knowledge Exploitation New Product Success

0.13 0.10 0.18 0.32 0.34

0.13 0.10 0.18 0.45 0.38

0.002

0.06

Notes: GoF = √ Average Communality × Average R2 ƒ2 = (R2i – R2e)/(1 – R2i), where R2i indicates R2 of final model and R2e represents R2 of main effect model.

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Figure 2. Research Model with Results

for commercial ends (e.g., knowledge exploitation). This finding justifies the widely held belief that it is such diversity that enables teams to understand which knowledge is valuable to NPD projects and helps them to use a wide variety of project-related knowledge embedded in the know-how of its members, that determines how the team will use this knowledge and that increases the understanding of the implications of each method that can be taken. Secondly, this study researches the contingency of team absorptive capability on product effectiveness in terms of new product success. The results show that team absorptive capability is positively and directly related with new product success in the context of NPD projects. This means that team absorptive capability, which indeed refers to the ability of a team to value, assimilate and apply external knowledge for commercial ends, enables NPD teams to foresee changes ahead of time in the business environment. When these teams respond effectively to environmental changes, they are more likely to successfully complete NPD projects. Particularly, when NPD teams (i) can easily analyse, interpret and understand the acquired knowledge as well as retain and reactivate such knowledge over time (e.g., knowledge assimilation), and (ii) are prominent at exploiting new assimilated knowledge for commercial ends (e.g., knowledge exploitation), new products have superior success in the market. By vivifying absorptive capability, NPD teams become able to understand the customers’ needs and the market trends, competence in marketing, good implementation of


the NPD processes, effective coordination of resources, and the first-mover advantage via early launch of the new product into the market, leading to the product’s success. To our surprise, in this study, we could not find any direct statistical association between knowledge acquisition and new product success. However, this does not mean that knowledge acquisition has no relationship with new product success; rather, it influences new product success via other team absorptive capability dimensions due to the significant covariance among them. Specifically, knowledge acquisition has potentially partial effects on new product success, when other team absorptive capability dimensions are controlled for. In a sense, the influence of one team absorptive capability dimension is not fully independent from the team context created by the other team absorptive capability dimensions, e.g., one team absorptive capability dimension triggers another. Third, our claim is that when project complexity, as the moderator variable, increases, the relationship between team absorptive capability and new product success gets stronger. In the absence of the interaction effects in the model (i.e., main effect model), the results showed a significant relationship between the two dimensions of team absorptive capability – knowledge assimilation and knowledge exploitation – and new product success. In the next stage, with the incorporation of the interaction effects into the model (i.e., final model), the results show that there is a stronger relationship between knowledge assimilation and new product success, while there is a weaker

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relationship between knowledge exploitation and new product success. Moreover, the relationship between knowledge acquisition and new product success is not found to be significant. This result primarily confirms that when sudden and unwanted events make an NPD project more complicated and challenging to perform, the NPD team members exert greater effort to learn and internalize the knowledge within the shared mental models of the team. Thus, knowledge assimilation enables the NPD teams to quickly analyse, interpret and understand the acquired knowledge to achieve project objectives and to be successful, particularly in dynamic environments. Accordingly, those teams create products of superior quality. Surprisingly, when the NPD project is more complicated and challenging to perform, the NPD team members are more likely to be cautious in putting the assimilated knowledge into practice. A plausible explanation for this may be that the team members do not feel confident about what is really going on in the technological and market environment when the project complexity is higher. Fear of making mistakes constrains them in converting the new knowledge and novel ideas into innovative products. Contrary to our expectations, we could not find any significant relationship between knowledge acquisition and new product success by employing project complexity as a moderator variable. This conflicting result may be attributed to the nature of the knowledge acquisition, which is mainly related to product effectiveness rather than process efficiency of NPD projects.

Theoretical Contributions This study hypothesized that (i) team absorptive capability is enhanced by team functional diversity, and (ii) team absorptive capability increases new product success. There are insightful implications in these results for the TIM literature and organizational learning theory. First, the results of this study highlight a significant implication for the TIM literature to account for teams’ diversity and absorptive capability. Indeed, developing and sustaining absorptive capability in an NPD team context in particular is still a matter of concern. The findings suggest that team functional diversity– cross-functional team design determines the level of knowledge and absorptive capacity in NPD projects. The combined knowledge and skills of team members from different functional backgrounds, and thus team members with a variety of individual thought worlds,

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views and experiences, serve as an important factor contributing to importing knowledge from sources outside an organization, facilitating the assimilation and usage of the imported knowledge. Even the TIM literature puts forward emerging terms called cocreation or co-development with a particular focus on the involvement of lead customers or suppliers from a knowledge processing perspective. Second, knowledge processing is involved in organizational learning theory, and one suggestion in the relevant literature is that teams will accomplish knowledge processing more easily via absorption of external knowledge and internalizing it within team routines, procedures and operations. Based on organizational learning theory, the findings provide deeper insights by addressing the external (i.e., knowledge acquisition) and internal learning mechanisms (i.e., knowledge assimilation, knowledge exploitation) in NPD and how they can be leveraged to foster new product success. Through absorptive capability, NPD teams: (i) engage in the acquisition of external knowledge to capitalize on emerging market opportunities; (ii) analyse, interpret and understand the acquired knowledge so they become capable of retaining and reactivating such knowledge over time; and (iii) convert this assimilated knowledge into innovative products. As a consequence, NPD teams internalize, transmute and apply the acquired external knowledge commercially to achieve NPD project objectives. In line with the mainstream assumptions, which claim that new knowledge creation can also be dependent on absorbing new external knowledge instead of just an in-house process, team absorptive capability provides a comprehensive and integrative learning mechanism for NPD projects. This integrative learning mechanism allows NPD teams to effectively and successfully complete NPD projects accurately and on time. These findings indicate the importance of the combined use of external and internal learning by addressing knowledge absorption for new product success of the project teams, an issue that has been scarcely researched in the related literatures. Finally, the findings suggest that team combination is associated with external knowledge absorption capability, which ultimately leads to higher levels of new product success. This result contributes to organizational learning theory by addressing the cross-functionality and diversity of the teams as an important indicator of team learning. The richer the team variety, the more the team learns, particularly from the outside sources, and the greater the new product’s success.



Managerial Implications This research illustrates that team functional diversity contributes significantly to the development and utilization of team absorptive capability. Teams having a proficiency in absorptive capability effectively and successfully complete NPD projects accurately and in a timely manner. What we can learn from these results is that through taking team functional diversity into consideration as a determinant for team absorptive capability, NPD projects can be effectively achieved. Based on this awareness, project leaders and managers should develop and exploit team absorptive capability as a way of reaching excellence in launching new products to the marketplace. To reach this aim, project leaders and managers should create and nurture the outward communication channels to acquire updated external knowledge from a variety of resources, such as stakeholders, rivals, universities, etc. In particular, they can follow their competitor’s actions as well as build collaborations and partnership relationships with the other research and development (R&D) institutions, such as universities. Moreover, they should support and encourage internal interaction mechanisms that enable NPD team members to collectively integrate this new external knowledge to team routines, procedures and operations. With the support of interaction mechanisms, NPD teams are more likely to convert this assimilated knowledge into innovative products. On the other hand, project leaders and managers should build teams with members from a variety of specialized areas, background cultures and perspectives. Furthermore, managers and project leaders should establish a psychologically safe climate in which team members are encouraged to use their different experiences and proficiencies to interact and collaborate freely with each other to discuss ideas, without fear of reprisal. Finally, project leaders and managers should provide training programmes to support team functional diversity by means of providing new knowledge, insights and capabilities.

Limitations and Future Research There are some methodological limitations to this study. First, the sample size is relatively small (n = 239 NPD projects). Therefore, readers should be cautious in generalizing the results. A larger sample may provide a better representation of the population of NPD teams. Moreover, Turkey, as a developing country, lacks NPD experience within the labour market,


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which makes the NPD approach more challenging. In addition, the study is conducted in a specific national context of Turkey. Instead of conducting a nationwide survey, data is collected from İstanbul, which is the biggest city in Turkey in terms of economic development and urban population. İstanbul generates 27.5 per cent of total Turkish value added, and its gross domestic product per capita is $10,352 (in 2012), nearly 50 per cent higher than the corresponding value for the country as a whole (Çetindamar & Günsel, 2012). İstanbul creates 3.3 million jobs, 15 per cent of the total employment of Turkey. İstanbul is a city region that gathers together a science and engineering workforce, universities, R&D centres and industry (Çetindamar & Günsel, 2012). So we assume that the results of İstanbul as representative of the Turkish national context is based on the fact that the heart of Turkish industry beats in this city. However, a crosscultural analysis that contributes to the generalizability of this study to other national contexts would be of significant interest for future studies. Specifically, given that the same respondents answered both the dependent variable and the independent variable in a cross-sectional manner, this study is likely to be prone to common method bias. This problem emerges as a result of the difficulty in accessing the NPD teams and, further, the developers and users. We checked this potential problem using Harman’s one-factor test. According to the unrotated principal component analyses, none of the factors significantly dominated the variance (see Table 4). Indeed, the literature contains many examples of empirical studies in which all the measures were assessed retrospectively, and even the dependent variables, including NPD outputs, are measured by the same respondent (e.g., Açıkgöz et al., 2014). Further, a number of recent studies have addressed that NPD team members are able to judge project outputs better than consumers. Consequently, Harman’s one-factor test indicates that common method bias is not a major problem in this study. Given that NPD projects are dynamic, utilizing a cross-sectional questionnaire might not provide objective results even if there is a growing area of research regarding NPD projects in a natural environment (Graziano & Raulin, 1997). Nevertheless, the researcher should also declare that this research provides some evidence of associations as a crosssectional field study. In this sense, Podsakoff and Organ (1986, p. 539) stated that ‘because correlational field studies often provide useful information about relationships among important

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variables in actual organizational settings, only a few would claim that they be totally discarded’. In order to go beyond this limitation, longitudinal studies in which one can follow the perceptions of the developers over time can be employed as a future research strategy. Moreover, the current study focused on only one important determinant of team absorptive capability: team functional diversity. Team functional diversity, as a basis, is significantly important, but there may be additional antecedents that deserve further investigation. Furthermore, although the survey is a powerful method to generate perception information about individuals, other research methods, including experiments, field research and focus groups, should be explored to investigate team absorptive capability and new product success to generate rich data on this timely issue facing researchers. In terms of suggestions for future research, we would like to see large cross-sectional surveys conducted on teams working on different types of NPD projects or in different national contexts. As noted above, longitudinal or experimental studies that follow how these variables change in relationship to each other would be fruitful for developing a causal model of the inter-relationships among these variables. Finally, extending the model with different project outputs in addition to market success, or with different antecedents of team absorptive capability in addition to team functional diversity, such as team memory, is also recommended.

Conclusion In today’s dynamic and ever-changing business (or industrial) environments, ‘to innovate or die’ is the obvious reality that organizations have to face. The ability to absorb new external knowledge is more critical to product innovation than creating new knowledge as an in-house process. Accordingly, the question of how to achieve absorption of the external updated knowledge and how to transmute such knowledge to innovative products has attracted many researchers and practitioners from a variety of fields or schools, such as organizational learning theory. The construct of team absorptive capability has risen as a possible answer. In this study, the potential antecedents and consequences of team absorptive capability is examined within the NPD context. The findings of the study demonstrate that (i) team functional diversity is an important antecedent of team absorptive capability, and (ii) team absorptive capability is positively associated with successful products. Finally, we tested the moderating effect of project complexity and

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found that both knowledge assimilation and knowledge exploitation were positively associated with new product success, particularly when projects were highly complex. In conclusion, this study has enlarged our understanding of this important, but understudied topic. Future researchers will find the area of absorptive capability in NPD teams rich and fruitful for the TIM literature.

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Podsakoff, P.M., MacKenzie, S.B., Podsakoff, N.P. and Lee, J.Y. (2003) The Mismeasure of Man (agement) and Its Implications for Leadership Research. The Leadership Quarterly, 14, 615–56. Qian, C., Cao, Q. and Takeuchi, R. (2013) Top Management Team Functional Diversity and Organizational Innovation in China: The Moderating Effects of Environment. Strategic Management Journal, 34, 110–20. Ringle, C.M., Wende, S. and Will, A. (2005) SmartPLS – Version 2.0. Universität Hamburg, Hamburg. Roberts, N., Galluch, P.S., Dinger, M. and Grover, V. (2012) Absorptive Capacity and Information Systems Research: Review, Synthesis, and Directions for Future Research. MIS Quarterly, 36, 625–48. Segars, A.H. (1997) Assessing the Unidimensionality of Measurement: A Paradigm and Illustration within the Context of Information Systems Research. Omega, 25, 107–12. Senge, P.M. (1990) The Fifth Discipline: The Art and Practice of the Learning Organization. CurrencyDoubleday, New York. Squires, S. and Byrne, B. (2002) Creating Breakthrough Ideas: The Collaboration of Anthropologists and Designers in the Product Development Industry. Greenwood, Westport, CT. Tenenhaus, M., Vinzi, V.E., Chatelin, Y.M. and Lauro, C. (2005) PLS Path Modeling. Computational Statistics & Data Analysis, 48, 159–205. Todorova, G. and Durisin, B. (2007) Absorptive Capacity: Valuing a Reconceptualization. Academy of Management Review, 32, 774–86. Tsai, W. (2001) Knowledge Transfer in Intraorganizational Networks: Effects of Network Position and Absorptive Capacity on Business Unit Innovation and Performance. Academy of Management Journal, 44, 996–1004. Vega-Jurado, J., Gutiérrez-Gracia, A. and Fernándezde-Lucio, I. (2008) Analyzing the Determinants of Firm’s Absorptive Capacity: Beyond R&D. R&D Management, 38, 392–405. Verona, G. (1999) A Resource-Based View of Product Development. Academy of Management Review, 24, 132–42. Yang, J.-S. and Chen, C.-Y. (2005) Systemic Design for Improving Team Learning Climate and Capability: A Case Study. Total Quality Management, 16, 727–40. Yang, C. and Chen, L.-C. (2007) Can Organizational Knowledge Capabilities Affect Knowledge Sharing Behavior? Journal of Information Science, 33, 95–109. Zahra, S.A. and George, G. (2002) Absorptive Capacity: Review, Reconceptualization, and Extension. Academy of Management Review, 27, 185–203. Zoogah, D.B., Vora, D., Richard, O. and Peng, M.W. (2011) Strategic Alliance Team Diversity, Coordination, and Effectiveness. International Journal of Human Resource Management, 22, 510–29.



Atif Açıkgöz ([email protected]) is an assistant professor of Management and Organization in the School of Business Administration at Fatih University, Turkey. He received his MS in Science of Strategy (2010) from Gebze Institute of Technology, and his PhD in Technology and Innovation Management (2013) from Fatih University. His work has appeared in numerous journals including Group Decision and Negotiation, Creativity and Innovation Management, and Journal of Cleaner Production, among other journals. His research areas are organizational behavior, human/social psychology, and strategic management in technology and innovation management. Ayşe Günsel ([email protected]) is an associate professor at management department in Kocaeli University. She attended her PhD at Gebze Institute of Technology specialized in technology and innovation management. She has worked a postdoctoral researcher at Sabancı University and she has been a visiting scholar at University of Hertfordshire. She


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has published papers in journals such as Journal of Product Innovation Management, Group Decision and Negotiation, European Planning Studies, among other journals. Cemil Kuzey ([email protected]) is an associate professor at the Department of Management at Fatih University in Istanbul, Turkey, teaching Operation Research and Statistics for Social Sciences. He acquired his Ph.D. degree in Business Administration through the Department of Quantitative Analysis, Istanbul University, Turkey. Among his academic pursuits, he took several graduate courses at the Ontario Institute for Studies in Education, University of Toronto. His research interests are related to Operation Research, Data Mining, and Business Intelligence. Gökhan Seçgin ([email protected]) is a PhD student at the Faculty of Business Administration at Fatih University, Turkey. His research focuses on leadership, entrepreneurship etc. He joined Fatih University in 2009 as a research assistant.

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