Managing product innovation in low- and high-technology industries towards higher innovation performance: The mediating role of learning orientation, and the importance of organizational
structure.
By Rien Hendriks
1st supervisor: Dr. K.J. McCarthy 2nd supervisor: Prof. dr. Wilfred Dolfsma
Master Thesis: Msc BA, specialization: Strategic Innovation Management University of Groningen, Faculty of Economics and Business
Words: 9565
Abstract: Product innovation can be seen as a complex but critical process to the success of most
manufacturing enterprises (Cooper, 1999). Despite that with the introduction of a Best Practice Model (BPM) by Cormican and O’Sullivan (2004) practitioners can appeal to a complete framework for managing their product innovation process, there remains a scarcity to how applying best practices actually improves the innovation performance of manufacturing firms. Following the study of Wang (2008) where learning orientation was proven to be a mediator for the entrepreneurial orientation-performance relationship, this study will introduce, and empirically demonstrate that learning orientation also functions as a mediator in the best practices - innovation performance relationship and enables firms to maximize the impact of applying best practices on their innovation performance. Since the concept of learning orientation is intrinsically linked to a firm’s behavior (Garvin, 1993), and firm behavior is shaped and outlined by organizational structures (Covin and Slevin, 2002), this study also researched the role of organizational structures on the learning orientation – innovation performance relationship, yet found no significant contribution. Using data from 58 low-tech firms and 11 high-tech firms in the north of the Netherlands, this study also addressed the lack of comparative studies between high- and low-tech industries in terms of their innovation practices (Santamaría et al., 2009). The results support the extensive product innovation model by Cormican and O’Sullivan (2004) and shows that through learning orientation firms are able to improve their innovation performance.
1. Introduction
To survive in the global and competitive markets of today it has become increasingly important for firms to develop and implement innovations (Santamaría, Nieto, Barge-Gil, 2009). Therefore, research widely tried to understand how firms can improve their innovation performance: Viewing it from a creativity starting point (Amabile, Conti, Coon, Lazenby, and Herron, 1996), researching specific organizational structures that stimulate innovation (Brown and Eisenhardt, 1997; Schoonhoven and Jelinek, 1990), and from internal capabilities that constitute learning and help firms build sustainable competitive advantages (Zahra and George, 2002; Cohen and Levinthal, 1990). Effectively managing the innovation process of product has been widely recognized as critical to the success of most manufacturing enterprises (Cormican and O’Sullivan, 2004; Cooper 1999). The study by Cormican and O’Sullivan (2004) developed a best practices model to provide managers and practitioners in the field with a complete framework for improving their product innovation management process. This best practice model for product innovation management, indicates that managers should purposefully construct strategies and structures to enhance best practice for successful product innovation (Cormican and O’Sullivan, 2004).
Latest research has had more interest in the innovative practices of high-technology industries than of low-technology industries (Santamaría et al., 2009), while major differences exist between low- and high-tech industries regarding their competitive environment (Porter, 1980), and organizational realities (Kirner, Kinkel, and Jaeger, 2009; McCarthy and Kearney, 2013). Furthermore, as indicated by Von Tunzelmann and Acha (2005), generally high-tech industries only account for 3 percent of value-added to a countries GDP. It is clear that all these differences stress the importance of innovation research in the low-tech sectors. Despite these differences, the BPM model by Cormican and O’Sullivan (2004) has only been based on case-study research in high-tech industries.
Next to product innovation as critical factor to innovation performance, organizational structures are also regarded as factors that affect the innovative performance of firms (e.g. Brown and Eisenhardt, 1997). The ability of an organization to implement creative ideas into innovative products is dependent on the hardware side of organizations (Covin and Slevin, 2002; Jung, Wu, and Chow, 2008). Two classic attributes of an organizational structure, centralization and formalization, shape the framework in which employees operate (Jung et al., 2008). Research studied the effects of both centralization and formalization and its role in innovation (e.g. Jansen, Van Den Bosch, and Volberda, 2006), and the resulting effects of centralization and formalization in impeding innovation since applying these structures will give less control and freedom to workers in the organization (e.g. Amabile et al., 1996). What remains clear is that organizational structures shape and affect the framework of a firm’s behavior (Covin and Slevin, 2002).
The previous considerations indicate several important issues which this paper will try to address. Firstly, since research is somewhat underdeveloped in the low-tech industries, while these industries have a major contribution to a country’s GDP, there remains an importance in analyzing the innovation management practices of both low- and high-tech firms. Secondly, despite that research found important factors that could influence the innovation performance of firms, there remains a scarcity to how applying best practices and organizational structures actually affects the innovation performance of manufacturing firms. Following the study of Wang (2008), this paper will argue that through learning orientation (LO) firms are able to maximize the impact of applying best practices on firm performance.
Therefore, this study will try to extend our understanding about the differences and similarities between low- and high-tech firms in applying product innovation management practices, and how applying product innovation practices can impact their respective innovation performance. This paper will build upon Cormican and O’Sullivan’s (2004) product innovation management research, and Wang’s (2008) study on the mediating role of LO. Consequently, this study will provide, in addition to addressing the concern that existing research is too focussed on high-tech industries, new insights in the product innovation - innovation performance relationship through empirically testing LO orientation as mediating construct. Using a sample of 58 low-tech and 11 high-tech firms in the province Groningen, north of the Netherlands, this paper examined both the differences and similarities between low- and high-tech firms in their product innovation practices toward innovation performance.
This paper will proceed as follows. First, it will present a theoretical foundation followed by the development of research hypotheses. Then the methodological approach for testing these hypotheses is outlined. Thirdly, statistical analyses and results will be summarized. The discussion section of this paper will present its findings, their academic and managerial implications and limitations, and will conclude with suggestions for future research. Lastly, this paper will conclude with the main conclusions of this research.
2. Literature
The following will outline a theoretical foundation resulting in the development of hypotheses. First, the importance of product innovation and its relation toward innovation performance will be clarified. Second, the concept of learning orientation will be introduced as the expected mediator to the product innovation - innovation performance relationship. Third, since learning orientation will be linked to innovation performance as the mediator, the hypotheses will be focussed on the relation of the best practices and organizational structures on learning orientation. Last, the theory will be presented in a simplified framework, fig. 1.
2.1 Innovation performance through product innovation
uncover effective product innovation management practices, their literature has pre-dominantly been based on high-tech industries. As Santamaría et al., (2009) indicated, research has had more interest in the innovative practices of high-technology industries. The working paper of McCarthy and Kearney (2013), already tried to address this gap by simultaneously researching differences in the application of BPM in low- and high-tech industries. This research by McCarthy and Kearney (2013), showed that applying the BPM has a positive impact on innovation performance of both low- and high-tech firms, however, important differences exist.
Despite the contribution of these studies, developing best practices for product innovation (Cormican and O’Sullivan, 2004) and addressing the differences between low- and high-tech firms in relation to product innovation management (McCarthy and Kearney, 2013), the actual effect of best practices on innovation performance might still be underdeveloped. This underdevelopment stems from the fact that applying best practices by Cormican and O’Sullivan (2004) will, according to the authors, help firms with providing a synthesis of practices that contribute to an environment for effective product innovation management, and helps to continuously improve the process of product innovation. However, the literature on entrepreneurial orientation (EO) shows that providing an environment for effective innovation does not necessarily lead to a better innovation performance. With words of Wang (2008, p. 636): “entrepreneurial firms are innovative and risk-tolerant, and therefore provide the internal environment in which learning through exploration and experimentation is most likely to take place” (Wang, 2008, p. 636). Furthermore, Miller (1983) suggested that a firm’s entrepreneurial degree is the extent to which it innovates. Still as Wiklund and Shepherd (2005) indicate, simply examining a direct relation between the entrepreneurial orientation of a firm and its performance will provide an incomplete picture. This raises the question whether best practices in product innovation management directly influence the innovative performance of organizations, as the latter has been the reasoning of Cormican and O’Sullivan (2004) and McCarthy and Kearney (2013).
Building upon the article of Wang (2008) who showed that entrepreneurial orientation has a relation towards performance mediated by a firm’s learning orientation, the purpose of this paper will be to address the product innovation - LO - performance relationship, and simultaneously investigate if differences or similarities exist in product innovation and how this relates to the innovation performance of low- and high-tech industries.
2.2 Learning Orientation and its effect on innovation performance
Since the gap between the product innovation - innovation performance relationship, and the solution of LO as mediator have been introduced. The concept of LO will be further explained after which the paper will indicate the relation of the best practices and the organizational structures to LO.
vision. These three values contribute to both incremental and breakthrough learning that help organizations deal with changing environments (Wang, 2008). Consequently, the studies of Baker and Sinkula (1999) and Wang (2008) show that LO has a positive impact on the performance of firms since it emphasizes both the intensity and direction of learning.
2.3 Product innovation and its effect on Learning Orientation
The study of Wang (2008; p. 640), indicated the following: “EO opens up a scope for learning and particularly favors divergent learning, while LO emphasizes both intensity and a common direction of learning”. Since this paper wishes to determine if the BPM is mediated by LO instead of having an direct impact on innovation performance (IP). The following will show how the five best practices by Cormican and O’Sullivan (2004) relate to LO, since without this relation LO could not function as a mediator.
The first factor of the BPM by Corsican and O’Sullivan (2004), strategy and leadership, states that strategy should give focus, define aims and objectives, and integrate efforts of organizational activities. Furthermore, leadership should create a vision and clearly communicate this vision by setting objectives (Cormican and O’Sullivan, 2004). Therefore, both strategy and leadership will contribute to the LO of firms, since they support the direction of learning that LO provides.
Regarding the second factor, culture and climate, research reached a consensus that “culture can be described in terms of values, norms and beliefs while climate can be considered in terms of policies, practices and procedures” (Cormican and O’Sullivan, 2004, p. 822). Since Sinkula et al. (1997) described organizational values as the fundamental basis to a firm’s LO, the second factor of the BPM clearly contributes to the LO of an organization.
Thirdly, planning and selection, researchers such as Brown and Eisenhardt (1995) have indicated the importance of planning in product innovation, so that firms are able to anticipate problems in advance, and facilitate a faster integration of new technologies. Moreover, Cormican and O’Sullivan (2004) stipulate the importance of planning in regard to the integration of customers in the development process, and selecting the projects that will contribute to a right balance in a firm’s portfolio. Referring to the contribution of EO in relation to LO, EO would provide the scope for learning (Wang, 2008), considering the characteristics about planning and selection it seems that this third factor of the BPM helps firms to broaden their scope of learning towards customers, and finding the right balance in a firm’s portfolio. Therefore, planning and selection will probably contribute to the LO of a firm.
Fourthly, structure and performance, show a high contribution to LO. The literature concerning the BPM by Cormican and O’Sullivan focuses on the flexible structures that stimulate knowledge sharing, and on motivation and reward systems that should align the focus of workers to the focus of the organization (Cormican and O’Sullivan, 2004). Both the flexibility and motivational factors show consensus in stimulating the direction and intensity of LO.
Following the previous considerations, it seems fair to expect that application of best practices in product innovation management will help a firm to provide the scope for learning by stimulating planning and selection just as entrepreneurial oriented firms. Furthermore, the BPM also seems to contribute to LO since it gives direction in learning by setting clear strategies, structures, climate, and required communication. Therefore, the following hypothesis is developed:
H1: Product Innovation has a positive impact on LO that in turn has a positive impact on Innovation Performance.
2.4 Organizational structure and its effect on Learning Orientation
As previously introduced, organizational structures are also regarded as factors that affect the innovative performance of firms (e.g. Brown and Eisenhardt, 1997). Moreover, the ability of an organization to implement creative ideas into innovative products is also dependent on the hardware side of organizations (Covin and Slevin, 2002; Jung et al., 2008). How organizations are manifested has a great impact on the exhibition and success of innovative behaviors and initiatives (Goodale, Kuratko, Hornsby, and Covin, 2011). Two classic attributes of an organizational structure, centralization and formalization, shape the framework in which employees operate (Jung et al., 2008). As mentioned before, the study of Sinkula et al. (1997) described LO not simply as a model but rather as a function of its core values that are manifested in a firm’s behavior and processes (Wang, 2008). Since organizational structures shape the framework of possible behavior (Covin and Slevin, 2002), they also seem to shape or affect the values that constitute LO.
Centralization refers to the extent management delegates decision making, and to how it is concentrated in an organization (Damanpour, 1991). Referring to Jansen et al., (2006), research shows that centralization negatively influences both explorative and exploitative innovation. Moreover, centralization narrows communication (Cardinal, 2001), and decreases the likelihood that organizational members seek innovative and new solutions (Damanpour, 1991). While the value open-mindedness, which is part of LO, refers to the extent organizations are questioning assumptions and current practices (Sinkula et al., 1997), centralization will decrease the likelihood that organizational members will try to seek and question current practices (Damanpour, 1991). Furthermore, a narrow communication due to centralization (Cardinal, 2001), and reduce of knowledge sharing (Tsai, 2002) hampers the extensive communication that is needed for innovation (Cormican and O’Sullivan, 2004). Consequently, centralization, that shapes the framework of possible behavior for members (Covin and Slevin, 2002), seems to negatively affect the learning orientation of a firm in its core values. Consequently the following hypothesis is developed:
H2: Centralization negatively affects LO that in turn has a negative impact on Innovation Performance.
make existing knowledge and skills explicit, which will accelerate the diffusion of best practices throughout the organization (Zander and Kogut, 1995). Moreover, as argued by Zollo and Winter (2002), formalization facilitates the generation of continuous improvement to existing routines. Therefore, formalization provides direction through rules and procedures, which support the important shared vision value of LO. Furthermore, formalization supports a commitment to learning and open-mindedness as it stimulates continuous improvement to existing operations and routines (Zollo and Winter, 2002). Accordingly the following hypothesis is developed:
H3: Formalization positively affects LO that in turn has a positive impact on Innovation Performance.
2.4 Product Innovation differences following the Best Practice Model
Building upon the article of McCarthy and Kearney (2013), it is known that different organizational realities between high- and low-tech companies cause important differences in the application of the BPM and its effect on their respective product innovation performance. All five factors of the BPM were found to have a higher effect for high-tech firms compared to the low-tech organizations. Moreover, both ‘structure and performance’ and ‘leadership and strategy’ did not predict innovation performance of low-tech firms at all (McCarthy and Kearney, 2013). As the current study expects the BPM to have an effect on LO instead of directly on IP, it seemed important to further investigate the differences between high- and low-tech companies in their best practices impact on LO. It would be interesting to see if the differences remain, or if they are overcome by the mediating concept of LO and all have an equal effect on IP via LO.
Leadership and Strategy
As Cooper and Kleinschmidt (2007, p. 60) stated: “Top performers possess a product innovation strategy, driven by the leadership team and its strategic vision for the business”. Product strategy and leadership have been identified through research as critical success factors for product innovation, and are well established factors (Cormican and O’Sullivan, 2004). Whereas product strategy should define aims and objectives of the innovations efforts, and place them in line with the organizational strategy (Cormican and O’Sullivan, 2004). Leadership is seen as a behavior, which has a major impact on the innovative environment that could stimulate creativity and guide others toward creative processes (Amabile, 1998; Jung et al., 2008). In order for organizations to successfully manage their product innovation it is important that they develop consistent strategies across the organization (Cormican and O’Sullivan, 2004). Still as indicated by McCarthy and Kearney (2013), these product strategy and leadership concepts have originally been tested in the high-tech industries, while fundamental differences in the nature of low-tech firms could ask for other approaches. Moreover, the results of the paper of McCarthy and Kearney (2013) show that leadership and strategy, on its own, do not significantly predict the innovation performance of low-tech firms. Since this paper has argued that the best practices have an influence on LO, that in turn influences the innovation performance of firms, the following hypothesis is developed:
Culture and Climate
As mentioned before, culture is often described in terms of values, norms and beliefs, while climate refers to policies, practices and procedures (Cormican and O’Sullivan, 2004). Following Reichers and Schneider (1990), climate is regarded as a manifestation of culture. While climate is referred to as surface level actions of an underlying culture, culture is a deeper less consciously held set of meanings (Reichers and Schneider, 1990). As explained by Baer and Frese (2003), culture holds deeper within firms, and is formed from its assumptions, values, and artefacts. Consequently, culture can be both an enabler and a barrier to innovation as it also resides in bad habits and incompetence (Cormican and O’Sullivan, 2004). Organizational culture and climate prosper the innovative performance of firms (Cooper and Kleinschmidt, 2007). Recent research towards contextual ambidexterity indicated that it is especially important for high-tech firms, which are in quickly evolving environments, to simultaneously pursue exploration and exploitation (Wang and Rafiq, 2014). Wang and Rafiq (2014) found that the type of organizational culture that promotes both creativity and discipline is the basis for successful ambidexterity. Therefore, one could expect that high-tech firms have a higher level of importance placed on culture and climate to successfully support their ambidextrous roles. Following the previous considerations, the following hypothesis is developed:
H5: Culture and climate has a higher influence on the LO of high-tech firms than firms in low-tech industries.
Planning and Selection
Previous research has agreed on the importance of planning and selection of the right projects as key factors of innovation performance (Cooper and Kleinschmidt, 2007). As indicated by Cormican and O’Sullivan (2004), planning with regard to the integration of customers in the development process, and selection of projects that contribute to a right balance of a firm’s portfolio, can effectively support innovation performance. By indicating problems in advance due to secure planning (Brown and Eisenhardt, 1995), and focussing op projects that create synergies within a portfolio (Cormican and O’Sullivan, 2004; Cooper, 1999), it is expected that planning and selection have a positive effect on product innovation. Referring to the study of McCarthy and Kearney (2013), where they argued based on Quinn’s (1979) research that firms in high-tech industries have a greater emphasize on new product development, this resulted in the expectation that high-tech firms have a greater emphasize on planning and selection of innovative projects than low-tech firms. Since McCarthy and Kearney found support for this hypothesis, this paper developed the following hypothesis:
H6: Planning and selection has a higher influence on the LO of high-tech firms than firms in low-tech industries.
Structure and Performance
structures and performance measures. Innovation is easier when firms follow a organic organizational structure (Damanpour, 1991). Organic design is a structure, which is capable of promoting the development of a firm’s innovation capabilities (Volberda, 1996) and can provide flexibility for successful innovation (Camison and Villar-Lopez, 2012). Organic structures permit rapid organizational response to changing unpredictable environments, while mechanistic structures are more suitable for predictable environments (Covin and Slevin, 1989). Still organic structures are more common in high-tech industries compared to their lower technology counterparts (Covin et al., 1990).
According to Cormican and O’Sullivan (2004) motivation and rewards systems can positively motivate people. Still many companies use traditional measures of performance and inadequately try to motivate idea generation and knowledge sharing. Referring to the findings of McCarthy and Kearney (2013), who found that structure and performance do not significantly predict the innovation performance of low-tech firms, this paper expects the following hypothesis to be true:
H7: Structure and performance has a higher influence on the LO of high-tech firms than firms in low-tech industries.
Communication and Collaboration
As Cormican and O’Sullivan (2004) indicate, product innovation is a knowledge intensive process. This intensive process often requires communication and collaboration between functional departments to gather the needed information (Griffin and Hauser, 1996). Furthermore, product innovation can be regarded as an information transformation process where information is gathered, processed and transferred in a creative way (Cormican and O’Sullivan, 2004).
Literature has been elaborate on external collaboration, and the reasons why a more open innovation process can be positive for innovation performance of firms. As Teece (1986) indicated collaboration might supply firms with required complementary assets. Moreover, collaboration and networking is seen as an important ways to overcome knowledge fragmentation in innovation (Santamaría, Nieto, Barge-Gil, 2009). Furthermore, firms that engage in a variety of different inter-organizational collaborations are more likely to produce improved products and have higher innovation performance (Faems, Van Looy, and Debackere, 2005). Apart from collaboration, firms definitely need internal communication, which can be regarded a fuel to support internal collaboration in harmony (Griffin and Hauser, 1996). Quite logically, communication can be seen as an antecedent of collaboration (Henneman, Lee, and Cohen, 1995), which stresses the importance of properly managing both in harmony. Despite that communication and collaboration are considered as the strongest influencers of successful product innovation, the impact is still more significant from high-tech firms compared to the low-tech industries (McCarthy and Kearney, 2013). Therefore, it is hypothesized that:
Considering the previous hypothesis this paper suggest the following simplified framework (fig. 1):
3. Methodology
3.1 Sample
In this paper, Dutch manufacturing companies situated in the north of the Netherlands undertook the empirical study. The contact information was obtained by the KvK database, which offers identification of firms in classifications based on EUROSTAT’s definitions of high-technology and low-technology. Low-tech firms involved organizations active in the food, beverages, tobacco, textiles, clothing, leather, wood, paper, printing, and furniture industries. High-tech firms involved organizations active in the aerospace, pharmaceutical, computers, electronic, and optical products industries. Next to the EUROSTAT definitions the following prerequisites were included in the study: First, the organizations had to be situated in the province of Groningen, which is the north of the Netherlands. In this way the paper could secure its results from regional differences. Second, only manufacturing organizations that were classified into either the low-tech or high-low-tech sector were included. Third, a telephone number of every firm had to be available to facilitate contact for participation requests.
The total number of organizations meeting these conditions in the KvK database at the end of September of 2014 was 483. To gather the data, telephone calls to request firms’ participation were made to all firms, followed by distribution of the electronic survey by email. Still after the initiating telephone call with a direct follow-up email withholding the survey when participation was offered, plus an average of 3 reminders by email, only a total of 88 respondents accessed the survey. Furthermore, as only a number of 69 respondents indicated their background in either high- or low-tech, the study had to work with a response rate of 14%. This left the sample with 59 low-tech firms, and 11 high-tech firms.
3.2 Measurement of constructs
3.2.1 Innovation performance
The dependent variable, innovation performance, in this research was measured following the Likert-type scale from Goodale et al. (2011). Based on eight innovation performance criteria this scale has proven to measure innovation performance. The original scale asks respondents to scale their manager’s degree of importance attached, and satisfaction towards the innovation performance criteria (Goodale et al., 2011). Since the survey in our study was addressed to managers themselves the measurement scale has been reduced to only the degree of importance attached to each of the eight innovation performance criteria. Furthermore, to create clear consistency across all scales in our survey, the Likert-scale was reduced from a 7-point scale to a 5-point scale.
3.2.2 Product innovation
This paper used the Cormican and O’Sullivan’s (2004) BPM in order to measure the product innovation management of firms. Using the existing 5-point Likert-scale, which has proven to be reliable (Comican and O’Sullivan, 2004; McCarthy and Kearney, 2013), the 50 included questions ask the manager to scale the firm’s focus on each of the five factors that constitute the BPM. Ranging from 1 = ‘Strongly agree’ to 5 = ‘Strongly disagree’ the respondents could indicate the degree to which they believe the statements represent their organization.
3.2.3 Centralization and Formalization
The measures for centralization and formalization were adapted from the survey used in the study of Jung et al. (2008). Referring to Jung et al (2008, p. 587) the measures are “extensively used in prior research with adequate levels or reliability”. Originally the question could be answered with a 7-point scale, however, since this study chose to create consistency across its constructs to avoid wrong perception, and smoothen the quite extensive survey of 80 questions, the scale was reduced to a 5-point scale. The respondents were asked to answer questions to which degree authority has been delegated, and to identify to which level their company has written manuals and rules.
3.2.4 Learning orientation
The measures for the mediating independent variable, LO, were adapted from Sinkula et al. (1997) who developed the scale. Moreover, the LO scale has been retested and used in other research who found further support for its validity and reliability (Wang, 2008; Baker and Sinkula, 1999). Instead of applying the 7-point scale this study used 5-point scales and therefore, slightly adapted the original scale.
3.2.5 Control variables
have a negative influence, since the entrepreneurial orientation of firms might be negatively affected (Antoncic and Hisrich, 2001).
3.3 Data description, Reliability and Validity
In order to test the constructs used in this study, a factor analysis was performed including all survey questions. Unfortunately, probably due to a small sample size the results of this component analysis where inconclusive. Many questions loaded on unidentifiable items or had too many high cross-loadings. Therefore, this study chose to perform a second-best option, namely: construct by construct testing which questions had to be excluded in order to result in clear constructs. Following this approach, principal component analyses were performed with criteria eigenvalue above 1, KMO test and Varimax rotation. Eventually this resulted in the exclusion of 9 questions, after which the KMO of all constructs was above 0.6 (Allen and Bennett, 2010). With the remaining questions constructs were configured and subsequently tested for their reliability. Since all variables reported a Cronbach’s alpha above 0.7 (table 1), the included variables in this study are found to be reliable (Nunnaly, 1978; Camison and Vilar-Lopez, 2014).
4. Results
4.1 The mediating role of learning orientation on the BPM and Organizational Structures
Mediation has been tested based on an approach described by Baron and Kenny (1986). Following the practical guide by Newsom (2014) of the approach of Baron and Kenny (1986), the mediating role of learning orientation has been tested in four separate steps (Appendix 2).
Since mediation is expected for both, the best practices model and organizational structures, the mediation analysis included all items of table 3. Furthermore, since the preliminary regression results in table 3 have already indicated important differences between the BPM as one item, and the best practices as separate variables, the mediation analysis will test both options in two separate tests. Moreover, the first test will analyze mediation for each variable separately, while the second test will use the most complete models available and test for mediation of the BPM and the organizational structures simultaneously.
4.1.1 Mediation analysis: BPM as separate variables
Step 1: This step should determine if a variable has a direct effect on the dependent variable. This would
account for a direct effect of: The BPM variables (SL, CC, PS, SP, CCo), and the organizational structure variables (Cen, For) on the dependent variable IP. Referring to the results in table 3 it has already been determined that the all the variables on themselves have a significant impact on the innovation performance.
Step 2: This step will determine if the variables under discussion also have a direct effect on the expected
mediating variable. Consequently this meant the relationship of SL, CC, PS, SP, CCo, Cen, and For on the dependent LO as expected mediator. Table 3a see appendix 3 reports on these findings, and indicates that all the variables have a significant impact on LO as the dependent.
Step 3: Simply investigates if the expected mediating variable has a significant impact on the dependent
variable. Table 3 already reported on this relation since the single effect of LO on IP was found to be significant with 0.471***.
Step 4: If steps 1 to 3 are found to be significant one can proceed to step 4 (Newsom, 2014). In this step the
impact of the expected mediator will be analyzed by controlling for the impact of the other variables. If the mediator remains significant after controlling for X (X = other variable under discussion) mediation is found. Again table 3a (appendix 3) reports on these findings. Step 4 contains two models which are basically part of a hierarchical multiple regression in order to control for X. Full mediation is found for centralization, since LO remains significant when controlled for centralization while the direct effect of centralization on IP becomes insignificant (Newsom, 2014). Since LO also remains significant when controlling for PS, SP, and For it indicates mediation for these variables. However, since the single paths of SP, PS, and For also remain significant this indicates partial mediation (Newsom, 2014). In sum, performing the mediation analysis for each variable separately, mediation can only be partially found.
4.1.2 Mediation analysis: BPM as one item
separate variables of the BPM were combined together into one score for each firm, which also allowed for testing of mediation on the BPM as a whole. Secondly, to further control the analysis: A clustering criteria for high- and low-tech was added. Furthermore, since the controls, age and size, were widely scattered and showed a more curvilinear nature, the controls were now used as lg_age and lg_size in order to create more linear patterns. The results still indicate mediation support for the best practices, since the BPM has a significant effect on LO table 5, and remains to have a direct significant effect on IP in step 4 table 7. Furthermore, the results indicate the insignificant role of formalization and centralization beside the BPM, as they do not significantly impact either IP or LO. The findings of test 1 and 2 will now be further discussed in detail.
4.1.1 LO as a mediator for the BPM
The first analysis could only find partial mediation for two items of the BPM. Despite that it might be questioned how reliable a separate test of each variable represents the mediation of the best practices, found in test 1 table 3a, the second analysis also found support for the BPM. Again the results support the general reliability of the model by Cormican and O’Sullivan (2004). Since the second test provides the most complete model the focus will be on this test. The results found in table 7 indicate both a significant impact of the BPM and LO on the dependent IP, which could indicate partial mediation. Going over the results of step 1 to 3, it can be concluded that the BPM has a significant impact on IP (table 4), and a major but just significant impact of 0.715* (p<0.10*) on LO see table 5. Furthermore, as said the BPM remains to have a significant impact on IP of 0.449*** in step 4 (table 7), which indicates that the direct effect has not completely been taken over by LO as a mediator. However, since LO remains significant it is possible to conclude for partial mediation (Newsom, 2014). Therefore, H1 is supported because the BPM has a positive effect on LO, which in turn, as a partial mediator, has a positive effect on IP.
4.1.2 LO as a mediator for Centralization
Cen on LO, even in the individual analysis of the variables, and consequently cannot find a negative effect of LO on IP when controlled for Cen, H2 is rejected.
4.1.3 LO as mediator for Formalization
Also for formalization (For), the first analysis found support for partial mediation. Furthermore, the results of the second analysis found in table 7 (step 4) indicate that when LO is controlled for formalization, LO remains significant while formalization becomes insignificant. Despite that this could indicate full mediation, it is important to report on step 1 to 3 as with centralization. Unfortunately, the results of the second test in relation to the effects of For become less clear. First, the direct impact of For on IP is positive but rather insignificant in step 1 (table 4). Second, the relation between For explaining the dependent LO is suddenly negative in nature, and still insignificant step 2 (table 5). Therefore, despite that table 7 could indicate full mediation as LO remains significant while controlled for For, the other steps of the second test make it impossible to determine a consistent relation between formalization and LO. Consequently, H3 is rejected, since the results of For remain unclear and especially insignificant in the second and most complete test. 4.2 Industry comparison
Table 8 summarizes the findings of the interaction effects between low- and high-tech firms. The interaction-products are all quite negative and therefore, indicate that for the highest value of high/low there exists no significant difference in the impact of the best practice. Since the high/low variable consists of: 1=High and 2=Low, the beta of -0,745 for SL*High/Low indicates that the impact of strategy and leadership for low-tech firms is lower compared to high-tech firms. Moreover, since all the interaction products are negative it appears that the results from table 8 propose that all the best practice variables for the low-tech industry have a lower impact on LO compared to the high-tech firms, which has been hypothesized. However, since all the interaction products are also insignificant, the impact cannot be statistically supported. Unfortunately the insignificance does not allow concluding upon these scores, as any conclusion would rather be an assumption with low statistical ground. Therefore, the results of the industry comparison show that there does not exist an additional effect for either of the industries. Based on the previous considerations the
hypotheses 4 to 8 are rejected. 5. Discussion
The objective of this study was to shed new insights in the product innovation - innovation performance relationship of high- and low-tech organizations. Furthermore, the study tried to explore the similarities and differences between high- and low-tech firms considering their innovation practices. Lastly, this research tried to test the influences of organizational structures on learning orientation and the innovative performance of firms. Although previous research by Cormican and O’Sullivan (2004) has provided practitioners with a complete framework to facilitate the understanding of how firms can improve their product innovation process, the actual link towards innovation performance remained underdeveloped. Furthermore, the provided best practices of Cormican and O’Sullivan (2004) had only been based on case-based research in high-tech industries. Lastly, despite that previous insights has provided the literature with best practices for product innovation and the role of organizational structures in innovation, these two have not been elaborately tested simultaneously. This empirical study assessed all the previous considerations and tried to explore the possible mediating role of learning orientation in the product innovation - innovation performance relationship.
5.1 Key findings and implications
The findings of this study reveal the possible important role of learning orientation as a mediator between product innovation management - innovation performance of firms, and therefore, by introducing LO sheds new insights in the rather vague direct link between innovation management and performance (Wang, 2008). First, the results proved, that a general applicability of the BPM in the sample of high- and low-tech firms could be found. Following the results of table 3, this study found a significant impact of the BPM, either individually as separate variables but especially as one BPM item. Therefore, the BPM by Cormican and O’Sullivan (2004) was found to be reliable.
(2008) and Wiklund and Shepherd (2005) place their remarks of a direct link between a certain improved environment for innovation and respectively innovation performance, this study could only partially disregard the direct relation between a better innovation environment from best practices application and innovation performance. This might indicate the existence of multiple mediators or, to the contrary than the reviewed literature, the existence of a direct link.
Third, the results of this study did not find a significant role for the organizational structure variables in explaining innovation performance (table 4). Despite that the literature of Jung et al. (2008) indicate important roles for organizational structures on the innovation within firms, this paper could not find an added value of these organizational structures in explaining innovation performance beside the BPM and LO. As expected, centralization reported a negative impact on LO (table 5) and also on the innovation performance of firms (table 7). However, the results were not significant and therefore, did not provide enough statistical support to conclude upon the relation of the beta coefficient. Furthermore, formalization was expected to have a positive impact on both LO and innovation performance due to the facilitation of continuous improvement (Zollo and Winter, 2002). Referring to table 4 and 5, the results about formalization were inconsistent. While table 4 showed a positive effect of formalization directly to innovation performance, table 5 indicated a negative effect on LO while it was expected that the positive effect of formalization would find its way through LO toward the innovation performance. Comparable with centralization the results of formalization are also all insignificant, and do not provide statistical ground to make conclusions about the positive or negative characteristic of the impact. Specifically analyzing the significances of both formalization and centralization it can be observed that both are far from being significant, which could suggest the misplaced position of the organizational structures in this model (see tables 4 and 7). Furthermore, one could also argue the representation of organizational structures in the BPM by Cormican and O’Sullivan (2004). As for centralization, centralization refers to the extent management delegates decision making (Damanpour, 1991), which could be accounted for by questions such as: “team members are empowered to make decisions” (Cormican and O’Sullivan, 2004, p. 825). However, it remains rather speculation, and with the reliability problems of the sample one could also argue that a better sample could indicate otherwise.
5.2 Limitations and future research
There are several limitations to this study that are noteworthy. First, despite the extensive effort to increase the sample size, this study remained with a rather small one and only 14% response rate. Some preliminary testing proved the reliability of the sample, by testing for non-respondence and Cronbach’s alpha scores. However, these tests were only performed after the items were formed based on a second-best factor analysis, where each item was formed and tested separately. Future research could try to retest these findings by a larger sample in order to prove reliability of the BPM of Cormican and O’Sullivan (2004) with the combinative concept of LO by Wang (2008).
Second, this research has been based on a translated survey. Due to time constraints I only performed the translation process by myself and no specific method was applied. As McKay et al. (1996) explained in their article, there exist major difficulties in translation and the choice of method. Therefore, future research could investigate if the used surveys are also inter-cultural applicable, and what type of method suits the translation process.
Third, this study used a sample that is comprised of only Dutch firms in the north of the Netherlands. While this allowed for control of cultural influences, it also counts as a limitation since successful innovation might be country specific. Therefore, also referring to McCarthy and Kearney (2013), future research should extent this study to other countries that differ from cultural perspective.
Fourth, the EUROSTAT classification of low- and high-tech production firms possesses several different areas of expertise, like a bakery and a wood refinery. This study did not control for these separate areas. Therefore, it could not explore possible important differences in product innovation practices within the high- and low-tech clusters. Future research could make an effort to control for differences within the high- and low-tech classifications in order to explore and compare the innovation practices in these areas as well. Lastly, this study tried to do an analysis of innovation management practices and innovation performance between low- and high-tech industries based on surveys that have been primarily tested in high-tech case studies. Therefore, there is a possibility that specific low-tech innovation factors are overlooked. This might be an important angle for future research, to find and include factors that are specifically important for low-tech innovation management.
6. Conclusion
organizational structures on the learning orientation – innovation performance relationship yet found no significant contribution. Lastly, the concern about the lack of comparative studies between high- and low-tech industries in terms of innovation practices (Santamaría et al., 2009), was also tried to be considered. Extending upon the article of McCarthy and Kearney (2013), this study tried to re-examine the differences between high- and low-tech firms in their innovation practices. Focussing on the possible differences of impact on learning orientation as mediator, the study did not find any significant differences.
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Appendix 1
Appendix 2
Visual representation of the practical steps by Newsom (2014) to test for mediation. (Newsom, 2014, p. 1)
Citation:
“The purpose of Steps 1-3 is to establish that zero-order relationships among the variables exist. If one or more of these relationships are nonsignificant, researchers usually conclude that mediation is not possible or likely (although this is not always true; see MacKinnon, Fairchild, and Fritz, 2007).
Appendix 3
Appendix 4
Mediation test 2
Appendix 5 - Scales and Items
a. All items were measured on a five-point scale on which 1 was “strongly agree” and 5 was “strongly disagree”. b. Since we dealt with low- and high-tech firms in the Netherlands the survey was translated to Dutch.
Product innovation Management (Best Practice Model)
Adopted from Cormican and O’Sullivan (2004)
Strategy and Leadership
The product strategic plan is effective and used
Product strategy is clearly defined and communicated to all employees The product innovation programme has a long term thrust and focus Product strategy is used to align priorities with other functions Strategies are flexible enough to respond to changes in the environment Senior management is accountable for new product results
Leaders visibly drive innovation
Leaders adopt a consensus and shared approach to decision making Leaders adopt a participative decision making style
Senior management actively encourages the submission of new product ideas
Culture and Climate
The organization permits the emergence of intrapreneurs or product champions The organization provides support in terms of autonomy, time and reward Money is made available for internal projects
Adequate resources are available and committed to achieve project goals All employees participate in generating ideas
Senior management is committed to risk taking in product innovation Failures and mistakes are tolerated and not punished
Knowledge sharing is encouraged and rewarded All operations are driven by customer needs There is a formal idea generation process in place
Planning and Selection
An effective product innovation process is consistently implemented A formal process is used to determine and update project priorities Concepts are selected using pre-defined, multiple and explicit criteria Pre-development market and feasibility studies are rigorously undertaken Projects are terminated if and when necessary
Project proposals are tested for alignment with organizational goals
The project and the spending breakdown mirrors the organizations goals and measures There is a good balance of projects which maximizes the value of the portfolio The product portfolio is matched to the firm’s competencies and capabilities The voice of the customer is built into all product innovations
Structure and Performance
Projects are developed using effective cross-functional teams Project teams are organic, flexible and agile
All team operations are driven by customer needs
Team leaders are involved in setting the product performance objectives All team members are mutually accountable
Virtual team members are equipped with effective ICT tools Team members’ rewards are equitable
Performance indicators are aligned with the organizations goals Performance indicators encourage desired behavior
Communication and Collaboration
Gatekeepers are in place to continuously span the external environment Customers and suppliers are involved in the product innovation process Alliances are often formed with other organizations for mutual benefit Communications among team members is efficient and effective Communications between project teams is efficient and effective
Information on ideas generated, problems raised and project status is accessible User needs analyses are undertaken and communicated to all
Product strategy and performance measures are clearly communicated to all Individual skills are effectively leveraged within and between project teams Virtual team members seamlessly communicate with each other
Learning Orientation
Adapted from Wang (2008)
Managers basically agree that our organization’s ability to learn is the key to our competitive advantage. The basic values of this organization include learning as a key to improvement.
The sense around here is that employee learning is an investment, not an expense.
Learning in my organization is seen as a key commodity necessary to guarantee organizational survival. There is a commonality of purpose in my organization.
There is total agreement on our organizational vision across all levels, functions, and divisions. All employees are committed to the goals of this organization.
Employees view themselves as partners in charting the direction of the organization.
We are not afraid to reflect critically on the shared assumptions we have made about our customers.
Personnel in this organization realize that the very way they perceive the marketplace must be continually questioned. We rarely collectively, question our own business about the way we interpret customer information.
Formalization and Centralization
Adapted from Jung, Wu, and Chow (2008)
Any major decision that I make needs company’s approval
In my experience with this company, even quite small matters have to be referred to someone higher up for final answer
My experiences with this company have included a lot of rules and procedures stating how various aspects of my job are to be done I have to ask senior management before I do almost anything in my business
I can take very little action on my own until senior management approves of it
If a written rule does not cover some situation, we make up informal rules for doing things as we go along There are many activities in my company that are not covered by some formal procedure
Usually, my experience with my company involves doing things by the ‘rule book’ Contacts with my company are on a formal or preplanned basis
I ignore the rules and reach informal agreements to handle some situations When rules and procedures exist in my company, they are typically written
Innovation Performance
Speed with which new products or services are developed Speed with which new products or services are brought to market Ability to respond quickly to market or technological developments
Ability to pre-empt competitors in responding to market or technological developments Incorporation of technological innovations into product/ service offerings
Incorporation of technological innovations into internal operations
Controls
Adapted from Antoncic and Hisrich (2001)
