Complex organizational knowledge structures for new product development teams

Knowledge-Based Systems 24 (2011) 652–661

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Complex organizational knowledge structures for new product development teams Han-Chao Chang a,b,⇑, Ming-Ten Tsai c, Chung-Lin Tsai d a

Instrument Technology Research Center (ITRC), National Applied Research Laboratories (NARL), Taiwan, ROC Biomedical Technology and Product Research Center (BTPRC), National Applied Research Laboratories (NARL), Taiwan, ROC c Department of Business Administration, National Cheng Kung University, Taiwan, ROC d Department of Finance, Chang Jung Christian University, Taiwan, ROC b

a r t i c l e

i n f o

Article history: Received 10 March 2010 Received in revised form 15 November 2010 Accepted 5 February 2011 Available online 17 February 2011 Keywords: Organizational knowledge structure New product development team Knowledge conversion Taiwanese Complex

a b s t r a c t When a new product development team faces challenges, such as the cross-functional knowledge conversion task, both simple and existing organizational structures are comprised of various management methods and knowledge characteristics, analogous to a fully armed military force. However, these features are not arranged in order of priority. Each step within the knowledge conversion process of new product development may not require such a full depot of management methods and keynotes. Therefore, this study adopted Blackler’s perspective to examine the suitable organizational knowledge structure for cross-functional knowledge conversion within new product development teams. This study found that the continual steps of socialization, externalization, combination, and internalization in the new product development team’s knowledge conversion process were positively related to various organizational knowledge structures based on a survey of 107 Taiwanese high-technology small and medium-sized enterprises’ new product development teams – not merely a simple structure for all steps. Thus, these results confirm that complex structures are required to perform knowledge conversion. Socialization requires knowledge characteristics from both the communication-intensive organization and the symbolic-analyst-dependent organization. Externalization requires elements of knowledge from the communication-intensive organization, the symbolic-analyst-dependent organization, and knowledge-routinized organization. Combination requires features from the knowledge-routinized organization, and internalization requires characteristics from the expert-dependent organization. In addition, this study attempted to integrate the knowledge features from communication-intensive organization and symbolic-analyst-dependent organization structures for the socialization and externalization stages. This effort concentrated on solving novel and irregular problems through a simplification of the complex organizational structures which make the new product development’s knowledge conversion run smoother. Crown Copyright Ó 2011 Published by Elsevier B.V. All rights reserved.

1. Introduction The term organization in the context of this study refers to a firm’s formal rules, mechanisms, and authority that control and coordinate employees’ work in order to achieve targets. Organizational structure offers options for administrators when choosing their team members or resources [28]. Under different structures, administrators may employ divergent management methods and resources in an attempt to reach organizational goals. The employees must also have the ability to obtain knowledge from their experiences and communicate and distribute it throughout the organization [17]. Therefore Nagata et al. [21] described an organization as not only an information-processing machine, but an entity that creates knowledge through action and interaction. Also, ⇑ Corresponding author. Address: Instrument Technology Research Center (ITRC), National Applied Research Laboratories (NARL), Taiwan, ROC. E-mail address: [email protected] (H.-C. Chang).

the knowledge-creation by an organization is an interactive process between tacit knowledge and explicit knowledge [21–23]. These scholars labeled the interaction between these two types as knowledge conversion or knowledge spiral.

2. Research target Small and medium-sized Taiwanese high-technology new product development (NPD) teams are the leading global providers of high-technology components; however, little literature has focused on the knowledge conversion process within these teams, with the exception of Henderson et al. [12] and Huang and his colleagues [13]. These previous studies examined issues of knowledge conversion between the R&D and marketing departments during the NPD period. Results showed that transfer barriers between R&D and marketing did exist. However, an organizational knowledge structure and management method to assist the cross-functional

0950-7051/$ - see front matter Crown Copyright Ó 2011 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.knosys.2011.02.003

H.-C. Chang et al. / Knowledge-Based Systems 24 (2011) 652–661

knowledge was not presented. Therefore, the current research addresses this gap by examining what kind of organizational structure is suitable for this type of knowledge conversion. This study also examined the characteristics of the organizational knowledge structure needed to increase new product output.

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combination steps. Knowledge and experience becomes personal tacit knowledge (internalization). This means employees can upgrade their own working capabilities and expertise through the NPD experience, thus building a better base for future NPD projects [5]. 4. Organizational knowledge structure for an NPD team

3. Knowledge conversion in the NPD period Nonaka and Takeuchi [23] and Huang [14] emphasized this knowledge conversion model by describing it as a dynamic replacing/transferring process between tacit knowledge and explicit knowledge. The four steps within the conversion model include socialization (from tacit to tacit), externalization (from tacit to explicit), combination (from explicit to explicit), and internalization (from explicit to tacit). These occur continually and in sequence [23,25]. Furthermore, none of the steps can be ignored or skipped [13]. More recently, some scholars have purported that the NPD team’s knowledge transfer between R&D and marketing should follow this knowledge conversion model [6,13,14,16]: during stage 1 (planning), knowledge related to NPD exists independently within the R&D group and marketing group. Tacit knowledge transfer and creation occurs between inner members through social interaction (socialization). Overall, organizational knowledge about the product is low and slow to develop as both R&D and marketing attempt to formalize their ideas and plan future development of the new product. In stage 2 (development), more formalized concepts are developed into prototypes by R&D. The organization’s product knowledge will rapidly develop to overcome problems in both R&D and marketing [12,13]. Tacit knowledge, such as experimental steps and market analysis, will be made explicit through transcription (externalization). The greatest proportion of product knowledge will be created at this time. In stage 3 (marketing), R&D members offer the patent context and technology bluebook, while marketing members offer a marketing analysis and plan [12,13]. Knowledge about the new product is shared completely within the whole team (combination). Prototypes are completed and tested, and pre-launch marketing information is collected based upon these prototypes. Small modifications to the prototypes may be made based upon combined knowledge; for example, from pre-launch trials conducted by the marketing department. After the products have been released in stage 4 (commercial), the NPD staff have experienced the socialization, externalization, and

Emphasis on collective endeavor

Emphasis on contribution of key individuals

Departmental-stage [27], functional structure [3], and matrix organizations [9,11] are the main organizational theories underpinning the functions of the NPD team. Saren [27] collected various organizational models that stimulate and facilitate information flow across functional groups during the NPD period. These models are: departmental-stage models, activity-stage models, decisionstage models, and conversion process and response models. Clark and Wheelwright [3] described four types of team structure: functional, lightweight, heavyweight, and autonomous. They classified these structures based on duty assignment and resources allocation, using NPD teams as an experimental sample to discuss the advantages and disadvantages of the four structures. Later, Griffin and Hauser [11] also identified three organizational structures, based on NPD project responsibilities, such as coordinating groups, matrix organizations, and project teams, and designation of a coordinator within these three groups. These theories focused on the duties of each style; however, they did not describe the knowledge characteristics of each structure, nor did they detail the management methods of the NPD’s team activities. On the other hand, Blackler [2] undertook a comprehensive meta analysis of the term knowledge management, outlining the key points required to manage each organizational structure. He found that different tasks and characteristics of organizational knowledge were accompanied by different organizational structures. He proposed two factors of organizational structure: (1) similarity of problems faced by the organization, and (2) reliance on personal or collective endeavors (Fig. 1). This second factor was further divided into four types of organizations: knowledge-routinized organization (KRO), communication-intensive organization (CIO), expert-dependent organization (EDO), and symbolic-analyst-dependent organization (SADO). 4.1. What is a complex knowledge structure? Lyles and Schwenk [18] pointed out the importance of sensitivity to internal and external changes through the existing knowledge structure. Such changes threaten to impair company competitiveness if unnoticed. Some scholars later emphasized that

(1)

(2)

Knowledge-Routinized

Communication-Intensive

organisation (KRO)

organisation (CIO)

(3)

(4)

Expert-Dependent organisation

Symbolic-Analyst-Dependent

(EDO)

organisation (SADO)

Focus on familiar problems

Focus on novel problems

Fig. 1. Organisation and knowledge types.

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knowledge structure complexity (amount of information or the number of elements) should elevate organizational members’ ability to respond to environmental changes and new situations [14,15]. A more complex knowledge structure can encompass a greater number of new situations and problems and can help a firm overcome higher levels of uncertainty, which then encourages cooperation across departments, such as in the case of NPD teams, where more diverse information can be able to recognized and processed. However, a simple structure may cause managers to ignore or reject unrecognized environmental signals [30]. When an NPD team faces challenges, such as the crossfunctional knowledge conversion task, both simple and existing organizational structures are comprised of various management methods and knowledge characteristics, similar to a fully equipped military force. Although well armed, development teams are unable to prioritize the use of their resources, each step within the NPD’s knowledge conversion process may not require deployment of all management methods and keynotes. Thus, this study has assumed that a complex knowledge structure can be treated as a separate body from a simple or the existing organizational structure. A firm simply chooses the required management methods and keynotes (weapons) that assist the organization to efficiently acquire and use knowledge from internal employees and external resources. Probably, a complex knowledge structure, such as Blackler’s four organizational structures, better explains cross-functional knowledge conversion during the NPD period than existing or simple knowledge structure models.

5. Hypotheses of this study Nonaka and Takeuchi [23] defined (knowledge creation) socialization’’ (abbreviated as KCS) as using social interaction and experience sharing to increase tacit knowledge between members. An individual does not use the language, but gains the tacit knowledge from others (mentoring). Mentoring is a form of lower-level formalization only for those who are in the same department and who have common expertise, unless a common expertise exists between departments. According to the classification of Blackler’s organizational knowledge structure and its properties [2], the CIO is an advocacy structure (Table 1) that possesses characteristics of lower-level formalization, highly specialized division, and coordination [20]. The CIO emphasizes integrating and sharing of knowledge as key processes in strengthening the team’s abilities. This point is consistent with the process of KCS, since the cross-functional R&D and marketing groups offer a platform to carry out continuous dialogue and interaction between members and the team [24]. Hence, this study infers that: H1-1. The higher the level of communication-intensive organization (CIO), the higher the level of socialization (KCS) within the NPD team’s knowledge-conversion process.

Knowledge management of symbolic-analyst dependent organizations (SADO) [2] places emphasis on concepts, ideas, and

Table 1 Comprehensive survey on Blackler’s organisational knowledge structures. Source: Collected by this study.

CIO

Blackler (1995)

Mintzberg (1983)

Collins (1993)

Emphasizing the integrating and sharing of knowledge as the key processes in strengthening the team’s abilities:

Advocacy structure:

Encodified knowledge: Emphasizing the cultures, common views and values that exist within the organization, and that employee cannot take them away

1. Communication and cooperation are the primary works, the strength of adaptation and elasticity are monitored in this group 2. Knowledge sharing will reinforce the team’s endeavors by integrated the individual teams 3. It will improve the movement of expertise within organization fluidly without any barriers 4. Encourage the knowledge-creating interaction 5. Establish the IT internet for communication between groups KRO

This kind of knowledge is learned, improved continually by the organization then formed into a collective knowledge: 1. Reinforce the working procedures, the cooperation endeavors and efficiency 2. Place emphasis on an integrated information system in order to reduce the costs and raise the competitive advantages 3. Emphasize the learning benchmark to other staff

EDO

SADO

1. The skills of experts and their working ability are the core competitive resources for an organization 2. The knowledge of experts is the source of authority. 3. Training the junior staff to become the expert and experienced staff. 4. Highlighting the employee’s hiring, on-the-job training, the catching speed on knowledge SADO is a knowledge intensive firm, for example, a software consulting company. The employee faces non-routine and unique problems: 1. The critical ability of staff in this section lies in solving the new problems 2. Emphasising the outstanding concepts, ideas and abilities of individual employees 3. Empowering staff in order to develop the new ideas 4. Supporting the equipment available to staff for assessing the useful knowledge rapidly

1. 2. 3. 4.

Lower level of formalization Decentralized decision-making Highly specialty division Horizontal coordination between group

Machine bureaucracy: 1. This structure is suitable for the mature and larger companies 2. Highly level of formalization 3. Centralized decision-making 4. Highly specialized division of labor 5. High complexity Professional bureaucracy: 1. Lower level of formalization 2. Decentralised decision-making 3. Highly specialized division of labor

Embedded knowledge: This image emphasized the relationship to the knowledge of division, coordination and cooperation within routine works in the organization such as effective IT structure, services and manufacturing lines

Embodied knowledge: Relies on people’s physical presence, and sentient and sensory information which are acquired by ‘doing’ (learning by doing)

Embrained knowledge: This image of knowledge highlights the conscious ability and conceptual knowledge that is held in employee’s mind such as the employee observe or smile, the company cannot survive in changeable environments, it is a kind of sense making

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abilities from individual employees (what Collins [4] refers to as embrained knowledge). This ability is dependent on conceptual knowledge being retained within the minds of staff. This kind of ability can only be acquired through broad education and logical inferences, namely by learning through analysis; such ability cannot be acquired by technical copying or training. During the development stage of the NPD period, the organization’s knowledge of the product will rapidly develop and problems will be identified and overcome by both R&D and marketing. The personal and inner group’s tacit knowledge, such as experimental steps and market analysis, will be transcribed into explicit knowledge. These concepts are similar to the characteristics of externalization (abbreviated as KCE). This is the process of making tacit knowledge tangible by converting it into words (explicit knowledge). Therefore, the symbolic-analyst-dependent organization (SADO) will be positively associated with Nonaka’s externalization within the NPD team’s knowledge-conversion process. Hence this study infers that:

employees have the opportunity to learn new skills and knowledge when they are working. The knowledge management within this type of organization focuses on training the staff to become experts and emphasizes hiring employees, on-the-job training, and knowledge resources. These meanings are similar to internalization as defined by Becerra-Fernandez and Sabherwal’s paper [1]. They highlighted the importance of factors such as company information systems, employee utilization of training, review of the blue guidebook, discussions with other staff to transfer explicit knowledge into personal experience, and tacit knowledge. Hence this study infers that:

H1-2. The higher the level of symbolic-analyst-dependent organization (SADO), the higher the level of externalization (KCE) within the NPD team’s knowledge-conversion process.

Based on the above, the following research framework is proposed and the causal relationship between organizational knowledge structure and knowledge conversion is hypothesized. The organization theory of Blackler [2], which includes such categories as CIO, EDO, KRO, and SADO, are employed in the current study as independent variables. The four steps of the knowledge conversion [23], including KCS, KCE, KCC and KCI, are used as dependent variables. A cross-sectional, descriptive, and inferential research study design was used in the current study. Purposive sampling techniques were employed, focusing on Taiwanese SME high-technology firms. The contacts (including telephone numbers and email addresses) were obtained from respective firms’ websites, as well as through personal relationships. Following initial contact and agreement, the questionnaire was sent via email. Frequent follow-up calls were made after two weeks if the questionnaire was not returned. Inclusion criteria included: (1) fewer than 250 employees; (2) an R&D department; (3) one or more successful cases of new product development, and (4) one or more intellectual property assets, such as an issued patent or a copyright registration. Each participant supplied written consent after being fully informed of the study, and completed the self-administered questionnaire at his or her convenience. Survey items were adopted from the theoretical constructs of Blackler [2] and Nonaka and Takeuchi [23].

The knowledge-routinized organization (KRO) tends toward machine bureaucracy (Table 1), which includes the properties of high formalization, highly centralized decision-making, and meticulously specialized divisions [20]. Such an organization faces routine tasks and a more static environment, with internal operations having highly vertical divisions, high formalization, and low skill requirements. Such an organization emphasizes the knowledge of division, coordination, and cooperation, as seen in effective IT structure, service, and manufacturing lines (what Collins [4] refers to as embedded knowledge). This kind of knowledge is learned, and improved continually by the organization, thus forming group knowledge. In addition, the knowledge management of such an organization focuses on coordination ability and efficiency during routine processes, emphasizing the integrated information system in order to reduce communication costs. In addition, both the embedded knowledge in KROs and the KCC of the knowledge conversion emphasize continual learning and improvement to form group knowledge and the passing on of such knowledge. Fernandez and Sabherwal [7] point out that, ‘‘the company has established the expertise and knowledge data base to assist the employees acquiring the required knowledge in combination stage’’. This point is similar to the knowledge management approach of KRO in Blackler [2] where, ‘‘the company takes account of the integrated information system to reduce the communication costs’’. Hence this study infers that: H1-3. The higher the level of knowledge-routinized organization (KRO) the higher the level of combination (KCC) within the NPD team’s knowledge-conversion process. According to the classification of organizational knowledge structure and its properties [2], the expert-dependent organization (EDO) tends toward professional bureaucracy (Table 1), which includes properties of lower formalization, decentralized decisionmaking, and highly specialized divisions [20]. Knowledge management of EDO emphasizes physical presence, and sentient and sensory information, and is acquired by doing, learning by doing (what Collins [4] refers to as embodied knowledge). These properties are similar to the KCI of the knowledgecreating process advocated by Nonaka and Takeuchi [23,24], where

H1-4. The higher the level of expert-dependent organization (EDO), the higher the level of internalization (KCI) within the NPD team’s knowledge-conversion process.

6. Research design and methodology

6.1. Tools and methods of statistical analysis This study contributes to the knowledge management literature by showing, for the first time, the organizational knowledge structure required to support each step of knowledge conversion. Additionally, this is the first quantitative research study on knowledge conversion since the concept was developed in 1995. To measure the effectiveness of the question sets (Appendix A), surface validity was used in this study. Two professors specializing in knowledge management, two business Ph.D. candidates, and three hightechnology R&D engineers with graduate degrees were asked to score the questionnaire. A Likert five-point scale was used (1 = strongly unacceptable – delete, 2 = unacceptable – seriously amend, 3 = acceptable – partial amendment needed, 4 = acceptable – detail needs amending, 5 = acceptable, no amendment). After expert confirmation, the questionnaire was sent to numerous hi-technology industries, including integrated circuit (IC), photo electrical, communication, biotechnology, fine-mechanics, computers, information service, semi-conductor, food products, chemical and materials, and general manufacturing companies.

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Category analysis was used to generate values for both independent and dependent variables. Statistical methods included reliability analysis and validity analysis, Pearson correlation analysis, and multiple linear regression analysis (all using SPSS 12.0). This study examined trends with Pearson correlation analysis and then identified and quantified the relationships between variables. Furthermore, multiple linear regression analysis was employed to judge the ability of the independent variables in predicting the dependent variables. Multiple linear regression models the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed data [8]. Multiple linear regression is employed to account for (predict) the relationship between each independent variable and their relationship to dependent variables. The method can establish that a set of independent variables explains a proportion of the variance in a dependent variable (using an R2 value to test statistical significance). Such an approach can establish the relative predictive importance of independent variables by comparing beta weights for further analysis [8]. 7. Results A total of 250 surveys were distributed with 132 returned (52.8%). Twenty five returned surveys contained non-valid responses and were removed, leaving a total of 107 for analysis. 7.1. Reliability analysis Cronbach’s a value was used to measure reliability and consistency of the questions. According to Nunnally [26] and McKinley et al. [19], an a value between 0.70 and 0.98 indicates high reliability, while a value above 0.6 is acceptable for exploratory research. Scores ranged from 0.62 to 0.89 (Table 2) indicating an acceptable internal consistency for all dimensions, and indicating that the questions captured the properties of the variables under study. 7.2. Agreement of questions Gravetter and Wallnau [10] pointed out that in quantitative research, the standard error value provides a way to measure the average or standard distance between a sample mean and the population – sampling error. The lower the standard error (closer to zero), the better the data represent the sample population. Table 3 shows that most of the standard error values were lower than 0.082, indicating that the 107 samples could well represent Taiwanese high-technology SMEs’ NPD teams. The agreement and mean of each question (n = 107) is shown in Table 4. 7.3. Correlation analysis Pearson correlation analysis was employed to examine trends in the data. The following key variables were included: CIO, SADO, KRO, EDO, KCS, KCE, KCC, and KCI. Results showed (see Table 5) Table 2 Reliability of each variable (n = 107). Variables

Quantity of questions

Cronbach’s a

Communication-intensive organization Symbolic-analyst- dependent organization Knowledge-routinized organization Expert-dependent organization Socialization Externalization Combination Internalization

4 4 4 5 5 5 5 5

0.75 0.78 0.87 0.89 0.62 0.73 0.86 0.88

Table 3 The means of questionnaires (including standard deviation and standard error). Variables

Mean

Standard deviation

Standard error

CIO SADO KRO EDO KCS KCE KCC KCI

4.08 3.92 3.82 4.06 4.00 3.27 3.36 3.43

0.718 0.687 0.852 0.823 0.522 0.602 0.770 0.836

0.069 0.066 0.082 0.080 0.050 0.058 0.074 0.080

that KCS was positively associated with the variables CIO (p < 0.001), SADO (p < 0.001), KRO (p < 0.01), and EDO (p < 0.01), supporting hypothesis 1-1. The KCE variable was positively associated with CIO (p < 0.001), SADO (p < 0.001), KRO (p < 0.001), and EDO (p < 0.001). This was consistent with hypothesis 1-2. Additionally, the KCC variable was positively associated with KRO (p < 0.001) and EDO (p < 0.05), supporting hypothesis 1-3. Furthermore, KCI was positively associated with CIO (p < 0.05) and EDO (p < 0.01), consistent with hypothesis 1-4.

7.4. Multiple linear regression analysis Next, multiple linear regression analysis was employed to test the capability of independent variables to predict dependent variables, and to describe the relationship among independent variables. First, the CIO, SADO, KRO, and EDO were employed as independent variables while KCS was set as a dependent variable. Results (see Table 6) indicated that the SADO and CIO accounted for 48.4% of the variance in KCS (R2 = 0.484, F = 23.933, p < 0.001). Evaluation of the regression coefficients indicated that CIO (b = 0.260, p = 0.006) and SADO (b = 0.460, p < 0.001) were the statistically significant components of the model. Second, the CIO, SADO, KRO, and EDO were set as independent variables and KCE as dependent. Results (see Table 6) indicated that CIO, SADO, and KRO accounted for 56.6% of the variance in KCE (R2 = 0.566, F = 33.211, p < 0.001). Evaluation of the regression coefficients showed that CIO (b = 0.308, p < 0.001), SADO (b = 0.396, p < 0.001), and KRO (b = 0.189, p = 0.008), were statistically significant components of the model. Third, when the CIO, SADO, KRO, and EDO were used as independent variables, with KCC as dependent, results (see Table 6) indicated KRO accounted for 15.1% of the variance in KCC (R2 = 0.151, F = 4.521, p = 0.002). Evaluation of the regression coefficients indicated that KRO (b = 0.308, p = 0.002) was a statistically significant component in the model. Finally, using CIO, SADO, KRO, and EDO as independent variables, with KCI as the dependent one, results (see Table 6) indicated that EDO accounted for 10.2% of the variance in KCI (R2 = 0.102, F = 2.909, p = 0.025). Evaluation of the regression coefficients indicated that EDO (i = 0.262, p = 0.015) was a statistically significant component of the model. Results from the Pearson correlation analysis and multiple linear regression analysis both supported hypotheses 1-1, 2-1, 3-1, and 41. These results confirmed that the steps of socialization, externalization, combination, and internalization, within the NPD team’s knowledge conversion process, are positively related in a complex structure. Socialization (KCS) needed knowledge characteristics from CIO and SADO structures; and externalization (KCE) needed knowledge characteristics from CIO, SADO, and KRO structures. Furthermore, a combination (KCC) only needed features from KRO. Finally, internalization (KCI) only needed features from EDO. On the other hand, if the NPD team’s knowledge conversion merely requires a simple organizational structure to transfer

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H.-C. Chang et al. / Knowledge-Based Systems 24 (2011) 652–661 Table 4 The agreement and mean of each question (n = 107). No. of questionnaire

Agreement (low ? high) 1

CIO-01 CIO-02 CIO-03 CIO-04 SADO-01 SADO-02 SADO-03 SADO-04 KRO-01 KRO-02 KRO-03 KRO-04 EDO-01 EDO-02 EDO-03 EDO-04 EDO-05 KCS-01 KCS-02 KCS-03 KCS-04 KCS-05 KCE-01 KCE-02 KCE-03 KCE-04 KCE-05 KCC-01 KCC-02 KCC-03 KCC-04 KCC-05 KCI-01 KCI-02 KCI-03 KCI-04 KCI-05

2 2 0 0 0 0 0 0 2 2 1 2 2 0 1 1 0 0 0 0 0 0 0 3 0 3 6 2 7 0 7 0 2 8 1 3 6

n (%) (1.9) (1.9) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (1.9) (1.9) (0.9) (1.9) (1.9) (0.0) (0.9) (0.9) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (2.8) (0.0) (2.8) (5.6) (1.9) (6.5) (0.0) (6.5) (0.0) (1.9) (7.5) (0.9) (2.8) (5.6)

2

Mean ± SD n (%)

3

11 (10.3) 8 (7.5) 9 (8.4) 0 (0.0) 12 (11.2) 6 (5.6) 6 (5.6) 6 (5.6) 3 (2.8) 12 (11.2) 10 (9.3) 10 (9.3) 10 (9.3) 8 (7.5) 5 (4.7) 9 (8.4) 10 (9.3) 0 (0.0) 0 (0.0) 39 (36.4) 0 (0.0) 0 (0.0) 3 (2.8) 24 (22.4) 6 (5.6) 18 (16.8) 43 (40.2) 9 (8.4) 33 (30.8) 14 (13.1) 21 (19.6) 7 (6.5) 14 (13.1) 21 (19.6) 7 (6.5) 11 (10.3) 36 (33.6)

n (%)

24 (22.4) 0 (0.0) 15 (14.0) 33 (30.8) 34 (31.8) 30 (28.0) 19 (17.8) 21 (19.6) 20 (18.7) 38 (35.5) 32 (29/9) 27 (25.2) 16 (15.0) 14 (13.1) 30 (28.0) 11 (10.3) 22 (20.6) 18 (16.8) 25 (23.4) 27 (25.2) 9 (8.4) 33 (30.8) 30 (28.0) 41 (38.3) 68 (63.6) 56 (52.3) 43 (40.2) 40 (37.4) 40 (37.4) 46 (43.0) 51 (47.7) 35 (32.7) 21 (19.6) 48 (44.9) 22 (20.6) 39 (36.4) 35 (32.7)

4

n (%)

27 36 45 40 47 24 37 57 42 23 34 38 36 50 30 36 23 39 49 19 40 45 39 20 27 24 12 30 19 29 19 40 42 20 54 28 21

(25.2) (33.6) (42.1) (37.4) (43.9) (22.4) (34.6) (53.3) (39.3) (21.5) (31.8) (35.5) (33.6) (46.7) (28.0) (33.6) (21.5) (36.4) (45.8) (17.8) (37.4) (42.1) (36.4) (18.7) (25.2) (22.4) (11.2) (28.0) (17.8) (27.1) (17.8) (37.4) (39.3) (18.7) (50.5) (26.2) (19.6)

5

n (%)

43 (40.2) 61 (57.0) 38 (35.5) 34 (31.8) 14 (13.1) 47 (43.9) 45 (42.1) 23 (21.5) 40 (37.4) 32 (29.9) 30 (28.0) 30 (28.0) 43 (40.2) 35 (32.7) 41 (38.4) 50 (46.7) 52 (48.6) 50 (46.7) 33 (30.8) 22 (20.6) 58 (54.2) 29 (27.1) 35 (32.7) 19 (17.8) 6 (5.6) 6 (5.6) 3 (2.8) 26 (24.3) 8 (7.5) 18 (16.8) 9 (8.4) 25 (23.4) 28 (26.2) 10 (9.3) 23 (21.5) 26 (24.3) 9 (8.4)

3.92 ± 1.100 4.36 ± 0.956 4.05 ± 0.915 4.01 ± 0.795 3.59 ± 0.857 4.05 ± 0.975 4.13 ± 0.902 3.91 ± 0.795 4.07 ± 0.918 3.66 ± 1.081 3.77 ± .996 3.79 ± 1.019 4.01 ± 1.051 4.05 ± 0.873 3.98 ± 0.971 4.17 ± 0.986 4.09 ± 1.033 4.30 ± 0.742 4.07 ± 0.736 3.22 ± 1.152 4.46 ± 0.648 3.96 ± 0.764 3.99 ± 0.852 3.26 ± 1.085 3.31 ± 0.665 3.11 ± 0.850 2.65 ± 0.859 3.64 ± 1.002 2.89 ± 1.022 3.48 ± 0.925 3.02 ± 0.990 3.78 ± 0.883 3.75 ± 1.047 3.03 ± 1.032 3.85 ± 0.867 3.59 ± 1.055 2.92 ± 1.047

Table 5 Pearson correlation between main study variables.

CIO SADO KRO EDO KCS KCE KCC KCI

CIO

SADO

KRO

EDO

KCS

KCE

KCC

KCI

1 0.597*** 0.152 0.398*** 0.567*** 0.617*** 0.104 0.209*

0.597*** 1 0.251** 0.297** 0.651*** 0.660*** 0.180 0.181

0.152 0.251** 1 0.313** 0.255** 0.370*** 0.362*** 0.064

0.398*** 0.297** 0.313** 1 0.316** 0.408*** 0.225* 0.297**

0.567*** 0.651*** 0.255** 0.316** 1 0.542*** 0.134 0.194*

0.617*** 0.660*** 0.370*** 0.408*** 0.542*** 1 0.347*** 0.038

0.104 0.180 0.362*** 0.225* 0.134 0.347*** 1 0.106

0.209* 0.181 0.064 0.297** 0.194* 0.038 0.106 1

Note: N = 107. * p < 0.05. ** p < 0.01. *** p < 0.001.

knowledge between R&D and marketing groups, the results from both the Pearson correlation and the multiple linear regression analyses show that only one structure from Blackler’s organizations had a positive relationship with each of the four steps of knowledge conversion. For example, only EDO individually relates to KCS, KCE, KCC, and KCI. In sum, reliability analysis results showed high internal consistency for most dimensions. Thus, the survey items used in this study are valid in quantifying the theories about the four types of organizational knowledge structures [2] and the four steps of knowledge conversion [23]. From the planning to the commercialization stage, each step within the NPD team’s knowledge

conversion needed one or more knowledge features from various organizational knowledge structures – not merely a simple structure for all steps. Thus, these results confirm that complex structures are required to perform knowledge conversion. Socialization requires knowledge characteristics from communication-intensive organizations and symbolic-analyst-dependent organizations. Externalization requires features from communication-intensive organizations, symbolic-analyst-dependent organizations, and knowledge-routinized organizations. Combination requires features from knowledge-routinized organizations. Internalization requires characteristics from expert-dependent organizations.

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Table 6 Results of multiple linear regressions. Dependent variable

Independent variable

KCS

CIO SADO KRO EDO

KCE

R2

F

0.260** 0.460 *** 0.084 0.050

0.484

23.933***

0.071 0.073 0.049 0.054

0.308*** 0.396*** 0.189** 0.109

0.566

33.211***

0.510 0.107 0.278 0.112

0.128 0.130 0.088 0.097

0.047 0.096 0.308** 0.119

0.151

4.521**

0.079 0.090 0.046 0.266

0.143 0.145 0.099 0.108

0.068 0.074 0.047 0.262**

0.102

2.909**

Unstandardized coefficients

Standardized coefficients

B

Std. Error

b

0.189 0.350 0.052 0.032

0.067 0.069 0.047 0.051

CIO SADO KRO EDO

0.259 0.347 0.134 0.080

KCC

CIO SADO KRO EDO

KCI

CIO SADO KRO EDO

Note: N = 107. ⁄ p < 0.05. ** p < 0.01. *** p < 0.001.

8. Discussion This study found that the continual steps of socialization, externalization, combination, and internalization in the NPD team’s knowledge conversion process were positively related to various organizational knowledge structures, in Taiwanese high-technology SME NPD teams. Based on multiple linear regression analysis, knowledge characteristics from both CIO and SADO structures are required to completely transfer tacit knowledge between R&D and marketing groups, within the NPD socialization stage. In addition, contact, coordination, and flexible structure between R&D and marketing groups have been identified as important (Table 4); however, there is little evidence of the existence of collective knowledge at the organizational level between cross-functional NPD members (Table 4). Hence, IT equipment and integrated information systems are required for an NPD team to gain desired information rapidly. Finally, the communication costs between R&D and marketing groups could be reduced (Table 4). In addition, senior NPD members need learning by analysis and learning by doing methods, which are the main features from CIO and SADO, to acquire both experience and knowledge related to new product development (Table 4). The commonality between CIO and SADO is a focus on irregular and novel problems (Fig. 1). Nevertheless, these two structures differ in their knowledge management features. The CIO structure emphasizes the company should retain flexibility in the organizational structure [2]. Contact and coordination between R&D and marketing staff during the NPD period will increase, facilitating the flow of professional knowledge within the NPD team. Furthermore, encultured knowledge [4], such as the organizational culture, common consensus, and values, can be formed by team members during this stage. Meanwhile, the SADO structure emphasizes availability of equipment that helps staff rapidly gain desired knowledge and solve novel problems [2]. As staff acquire the embrained knowledge [4], through the method of learning by analysis, there is relatively little for them to gain from general technical educational training. When the NPD team possesses the knowledge features of these two organizational structures, during the planning stage, it will be easier for both the R&D and marketing groups to transfer tacit knowledge and acquire past NPD experiences through a mentoring

approach [23]. The current study shows that one of the main features of socialization, the mentoring learning approach, is not helpful in transferring internal tacit knowledge to other groups (Table 4). Marketing staff can only obtain knowledge from R&D through documented materials. The R&D staff members also face the same problem. Statistical results show that the R&D and marketing staff, within the NPD team, possessed less common knowledge, less understanding of related terminology, and less common thinking (Table 4). Thus, the team leader or manager must carefully apply the knowledge characteristics of both CIO and SADO at the socialization stage, such as building up the organizational culture and encouraging common perspectives. This can subsequently help knowledge conversion proceed smoothly. Similar to the KCS stage, knowledge characteristics from CIO, SADO, and KRO structures are needed during the KCE stage to carry out the externalization of knowledge between groups. When knowledge features and keynotes of these three organizational structures involve the externalization process, during the development stage, team members will naturally represent their knowledge and concepts in concealed and metaphoric ways (Table 4). The R&D staff can clearly transfer the R&D concepts into readable steps and processes in documents, while the marketing staff can do the same (Table 4). Meanwhile, team leaders must establish collective knowledge at the organizational level between the cross-functional NPD team members. A rigorous verification method needs to be implemented to judge the correctness of the documented knowledge (Table 4). If this is done, the next step within knowledge conversion can run smoothly. Results also indicated that during the socialization stage, NPD teams need both CIO and SADO structures to solve novel problems (Fig. 1). Next, in the KCE stage, not only do they need to solve novel problems, but they also need to address familiar problems (Fig. 1; [2]). In addition, in collective endeavor (teamwork), the KRO, acts as the starting point. Thus, the demands on the organizational knowledge structure change with NPD knowledge conversion, from focusing on novel problems to familiar problems. This phenomenon aligns well with the four knowledge creation steps. Next, based on multiple linear regression results, as well as survey responses (Table 4), during the NPD combination stage, the company must emphasize organizational knowledge structures

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that in turn focus on coordination ability, along with knowledge transfer between R&D and marketing team members. The firm must establish a knowledge database to assist employees in acquiring knowledge, often in the form of an integrated information system, to reduce communication costs between the two functional groups. At this stage, the features of embedded knowledge [4] can be encouraged. Group knowledge can form and become organizational knowledge. With such a result, future NPD teams can combine marketing analysis, product specification, and patent contexts before a new product launch, which will improve product quality, better match market needs, and decrease lead times. In addition, features of knowledge management, such as a highly specialized division [20] between R&D and marketing, and highlighting personal abilities, are required within the internalization stage. The skills and knowledge of senior R&D engineers and senior marketing experts are core resources for the NPD team (Table 4). However, the company must select staff who have better potential to absorb new knowledge once they join NPD teams. The company must also offer training courses to junior staff regarding new product development activities (Table 4), including such practices as learning by doing [4]. Finally, internalizing this knowledge becomes a personal skill, which is helpful in future projects. In addition, results show that during the socialization and internalization stages of NPD, respondents accept that an NPD member can upgrade his or her working ability by experience and knowledge related to previous NPD projects (Table 4). This is in agreement with Souder’s [29] theory that, ‘‘the experience from past NPD can assist the works of next new product developing’’. By

superseding the NPD projects, knowledge and experience can be gained from linkages between past internalization and new socialization, if the NPD knowledge conversion continues to progress. 9. Harmonizing the CIO and SADO structures for the KCS and KCE stages Based on the NPD multiple linear regression analysis findings, and knowledge characteristics from CIO, SADO structures are needed to completely transfer tacit knowledge between R&D and marketing groups on NPD’s KCS stage and to carry out knowledge externalization between groups during the KCE stage. Even the KCE stage requires the KRO to address familiar problems (Fig. 1; [2]). This study attempts to combine the knowledge features from CIO and SADO structures, concentrating on solving novel and irregular problems (Fig. 1) through a simplification of the complex organizational structures required for NPD activities within Taiwanese high-technology SMEs, which make the NPD’s knowledge conversion run smoother. Summarizing agreements from 4 (agree) to 5 (strongly agree), in Table 4, we can see common relationships between CIO and SADO, with 70% agreement. The harmonized and conflicting relationships between CIO and SADO are shown in Table 7. The cross interactions in box CI02–SD03 indicate that senior staff may link experiences and communication skills, then spend more time in developing a common language at the KCS stage for the interface between R&D and marketing. Consequently, the collectivity knowledge at the organizational level will be established, making coordination between R&D and marketing staff more fluent. This may serve as

Table 7 Questions designed for CIO and SADO.

CI01. Contact between R&D and marketing occurs very frequently during the new product development (NPD) period (65.4%) CI02. Connection and coordination between R&D and marketing staff is necessary during the NPD period (90.6%)

CI03. The company highlights the flexibility of organisational structure and the adaptability of NPD staff during the NPD period (77.6%) CI04. The collective knowledge at the organisational level does not exist between the cross-functional NPD members (69.2%)

SD03. People who possess more expertise and more experiences have a higher position in the NPD team (76.7%)

SD04. Both of the experiences and knowledge of senior NPD members do not gain from the general education and trainings, they acquire from the sense-making of mind, namely the ‘‘Learning by analysis’’ (74.8%) Senior NPD staff can have more sense on people’s thinking ways, then encourage the ‘‘Learning by analysis’’, if more contacts occur between R&D and marketing

SD01. The company offers the equipment for staff to rapidly gain the desired knowledge (57.0%)

SD02. The company gives full authority to staff to support their originality (66.3%)

No relationship

Contact + full authority

The IT equipment can assist with the connection between departments. However how the hardware facilitates the obtaining of the ‘‘desired knowledge’’ from opposing group may be the basis for a research topic SD01. The company offers the equipment for staff to rapidly gain the desired knowledge (57.0%)

No relationship

Senior staff may link between experience and communication skills, and spend more time in developing a common language. This may provide a role model for junior staff

Communication focused upon specific need tasks, rather than common sense making activity

SD02. The company gives full authority to staff to support their originality (66.3%)

SD03. People who possess more expertise and more experiences have a higher position in the NPD team (76.7%)

No relationship

Originality of staff is not the only source of NPD; the firm needs to be flexible and open to all opportunities

A flexible structure may not lead to NPD teams structured around experienced

SD04. Both of the experiences and knowledge of senior NPD members do not gain from the general education and trainings, they acquire from the sense-making of mind, namely the ‘‘Learning by analysis’’ (74.8%) Flexible structures may generate a greater number of learning opportunities for senior NPD team members

Hardware aspects of KM may not be important across the R&D/ marketing interface

No relationship

Experienced staff may have learned to overcome interface barriers in order to be successful at new product developing

1. Higher position can give order. 2. However the hierarchical problem may occur with communication with between staff of widely different levels

Learning the common and specialized elements may be a prerequisite to seniority

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a role model to junior staff on an NPD team in this CIO and SADO assembly. Furthermore, box CI02–SD04 indicates communication is focused on specifically required tasks, rather than common sense activities. Hence, senior staff can concentrate on solving new problems in KCS and KCE stages reducing time spent on unnecessary dialogue. 10. Conclusion This study found that the complex organizational structure is more likely to be effective in managing the NPD’s knowledge conversion. This outcome illustrates that a team leader or project manager cannot exclusively follow the culture and knowledge management characteristics of existing structures. Nor can a manager incorporate knowledge management characteristics from a single communication-intensive organization, or an expertdependent organization, into the existing structure; instead, he must possess enough working experience within this complex organizational structure and the product-development process to have the skills to accomplish cross-functional knowledge conversion. Meanwhile, the distinction between a flexible structure and a complex structure presents a range of difficulties. Results verified that the complex structure can assist NPD’s cross-functional knowledge conversion. On the other hand, the flexibility structure might change over time, but not support more difficult tasks. Additionally, the main knowledge feature of CIO, flexible organizational structure, may bring some benefits to a CIO and SADO assembly. Flexible structures may provide greater learning opportunities for senior NPD team members. Learning common specialized elements may be a pre-requisite to seniority. Experienced staff may have learned to overcome interface barriers in order to be successful at new product development. A flexible structure may not lead to NPD teams structured around experience; however, this study still supports this feature in terms of assembly. Finally, the current study suggests knowledge characteristics of a CIO and SADO assembly should include: 1. Lower level of formalized communication and highly specialized division within the R&D and marketing groups in the form of an advocacy style [20], within the NPD team. 2. Sharing conceptual knowledge and experiences that are retained in the minds of senior staff, in the form of embodied knowledge [4] in order to merge with the team’s culture, common shared views, and values in the form of encodified knowledge [4], within the NPD team. In addition, keynotes on KM should be separated into primary and secondary categories relating to a CIO and SADO assembly. The primary components include: 1. Reinforcing the connection and coordination between R&D and marketing staff. 2. Ensuring senior staff maintain a higher position within the NPD team. 3. Bringing the benefits of a flexible organizational structure (Table 7) to experienced and senior staff. 4. Establishing a common language and collective knowledge at the organizational level. 5. Secondary factors include: IT equipment and empowering staff. This is the first study to combine the theories of Nonaka and Takeuchi’s [23] knowledge conversion with Blackler’s [2] organizational knowledge structure. This study employed expert validity (surface validity) analysis to confirm the effectiveness of the employed measurement instruments. While expert validity and reli-

ability analysis showed high scores, scholars may dispute the effectiveness of such an approach. Thus, in future studies, construct validity can be further assessed through the use of confirmatory factor analysis. Such an approach can test if the survey questions are consistent with each variable and whether they improve the measurement for each theory. Meanwhile, it is hoped that other scholars can extend the current findings. The knowledge characteristics of the current findings supplement Blackler’s [2] organizational structure theories and assist NPD team knowledge conversion. It is also hoped that costs related to knowledge transfer can be reduced and the time devoted to new product development shortened. In addition, this study found that NPD teams need a complex organizational structure to process cross-functional knowledge conversion. Thus, in the future, it would be useful to conduct an empirical study bringing complex organizational structures into NPD teams, and then contrast the complex and existing organizational structures within the context of cross-functional knowledge transfers. Appendix A Section I. Organisational Structure 1. Contact between R&D and marketing occurs very frequently during the new product development (NPD) period. 2. Connection and coordination between R&D and marketing is necessary during the NPD period. 3. The company highlights the flexibility of organisational structure and the adaptatility of NPD staff during the NPD period. 4. The company highlights the organisational-level knowledge of the cross-functional NPD team. 5. The collectivity knowledge in organisational-level do not exist between the cross-functional NPD members. 6. The company offers the equipment for staff to rapidly gain the desired knowledge. 7. The company gives full authority to staff to support their originality. 8. People who possess more expertise and more experiences have a higher position in the NPD team. 9. Both of the experiences and knowledge of senior NPD members do not gain from the general education and training both from the sense-making of mind, namely the ‘‘learning by analysis’’. 10. The company emphasises coordination ability and efficiency during the productive developing process. 11. The company utilises an integrated information system to reduce the communication cost of the two functional groups of R&D and marketing. 12. The company highlights the performance of knowledge transferring between R&D and marketing personnel. 13. NPD members can offer paradigms on different expertise, and become the learning benchmark to other inner staff and the opposing functional department staff. 14. The jobs in the R&D and marketing groups are clearly demarcated, and there is suitable possibility to attain promotion according to ability and qualification. 15. Employees have the chance to learn new skills and knowledge when they are working (Learning by Doing). 16. The skills and knowledge of senior R&D engineers and senior Marketing experts are the core knowledge-resource of the NPD team. 17. Training up the junior staff to become senior is also an intention for the company in the new product developing process.

H.-C. Chang et al. / Knowledge-Based Systems 24 (2011) 652–661

18. When the company selects the staff to become NPD team members, importance is placed on their working quality and the rate at which they can absorb new knowledge. Section II. Knowledge Creating Process 1. The learning way of master and mentoring (senior guides junior) is utilised inside the department to transfer knowledge. 2. The RD staff can not transfer the RD knowledge to market staff through an apprentice approach. 3. Marketing staff can only obtain the RD knowledge by the documental papers. 4. NPD team members are willing to share past NPD experiences with the current team. 5. The R&D and marketing staff in the NPD team possess less common knowledge and terminology. 6. NPD members can represent their knowledge and concepts in implicit and metaphoric ways. 7. R&D staff can precisely transfer the R&D concepts into readable steps and processes. 8. Marketing staff can precisely transfer the knowledge gained from market research and marketing analysis into readable documents. 9. The marketing staff can support more documental knowledge in NPD problems solving than RD staff. 10. The company possesses a rigorous verification mechanism to judge the correctness of documental knowledge. 11. NPD staff collect information of their own accord and actively join the training courses on product developing. 12. The company has established a knowledge database to assist employees to acquire the required Knowledge. 13. RD members can combine the patent context with regulation issues to form the knowledge of the product’s competitive situation. 14. Marketing members can combine marketing analysis, product specification and patent context and then offer an integrated marketing plan to the consumer. 15. The NPD team applies information learned from new product post-launch meetings and knowledge accumulated from the prototype to match clients’ needs. 16. The NPD team can trust each other when they are working. 17. The company offers a training course to increase members’efficiency. 18. Based on the experience and knowledge of past NPD projects, a member can upgrade his/her working ability. 19. NPD members can acquire expert knowledge through documents and transfer it to his/her mind. 20. The patent context is in documental format, and marketing staff can fully comprehend the documents and understand the competitive advantage and patent claims of our company.

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