Combinative Effects of Product Innovation and Management Innovation on
Firm Performance: A Firm-Level Empirical Analysis
By
August Teodor Haugen
Master’s Thesis
MSc Business Administration, Strategic Innovation Management
University of Groningen, the Netherlands Faculty of Economics & Business
Supervisor: dr. I. (Isabel) Estrada Vaquero Co-assessor: prof. dr. D.L.M. (Dries) Faems
20th of June 2016
A.T. Haugen, s2791579
Bugårdsåsen 8, 3214 Sandefjord – Norway a.t.haugen@student.rug.nl
Abstract
Innovation is highly stated to be of pivotal importance for firm performance and sustainable competitive advantage. However, whereas innovation-research commonly has examined the performance effect of singular innovation-typesdoes this study contribute to a new stream of research that examines the effects of jointly implementing different innovation types. In particular the present study examine the combinative effects on firm performance achieved by simultaneously introducing product innovation and management innovation. Using the resource-based view of the firm as a theoretical framework, it is proposed that firms that combine product innovation with management innovation (i.e. complex innovators) outperform firms that solely implement technological innovations (i.e. narrow innovators). In addition, the present study extend the literature by investigating the so far unexplored impact the novelty of product innovations has on these combinative effects, and it is proposed that the effects are stronger for a higher degree of novelty (i.e. new-to-the-market products). An empirical analysis of 109 innovation-active Dutch firms is conducted, including 82 firms with a complex innovation approach and 27 firms with a narrow approach. The first hypothesis is supported when measuring firm performance as innovative performance, however it is rejected when measuring for overall firm performance. Hypothesis 2 is rejected. The study contributes to the innovation-literature by adding knowledge to the complementary effects on firm performance achieved by following a complex innovation approach, moreover this study extends the current literature by shedding light on the so far unexplored impact the novelty of product innovation might have on these effects.
Keywords
Table of Contents
1. Introduction ... 4
2. Literature Review and Hypotheses ... 7
2.1 Innovation: Definition and Types ... 7
2.1.1 Product Innovation ... 8
2.1.2 Process Innovation ... 9
2.1.3 Management Innovation ... 9
2.1.4 Combinative Effects of Complex Innovations ... 10
2.1.5 Novelty of Product Innovations ... 11
2.2 Hypotheses Development ... 13
2.2.1 Combinative Effects of Product Innovation and Management Innovation ... 13
2.2.2 Different Effects of New-to-the-Market vs. New-to-the-firm Product Innovations ... 14
3. Methodology ... 15 3.1 Sample ... 15 3.2 Measures ... 18 3.2.1 Dependent Variables ... 19 3.2.2 Independent Variables ... 20 3.2.3 Control Variables ... 22 3.3 Methods of Analysis ... 23 4. Results ... 24 4.1 Descriptive Statistics ... 24 4.2 Regression Analysis ... 30 4.3 T-Test ... 34 4.4 Post-Hoc Analyses ... 35 5. Discussion of the Findings ... 37 6. Conclusion ... 42 6.1 Implications ... 43 6.1.1 Theoretical Implications ... 43 6.1.2 Managerial Implications ... 45
6.2 Limitations and Future Research ... 45
References ... 49
1. Introduction
There is wide consensus among scholars, policy makers and corporate executives that innovation is key for economic growth, firm performance, and sustainable competitive advantage (e.g. Damanpour, Walker, & Avellaneda, 2009; Camisón & Villar-López, 2011; Damanpour, 2014). Indeed, Crossan & Apaydin (2010) points out that management scholars view innovation capability as the most important determinant of firm performance, and both technological (i.e. product and process) and non-technological (i.e. management and marketing) innovations are important for firm performance (Damanpour, 2014). Historically, innovation-scholars have investigated the innovation-performance relationship with a singular approach, i.e. examining how one innovation type individually affects firm performance (Damanpour et al., 2009). However, recently a new stream of research (e.g. Battisti & Stoneman, 2010; Sapprasert & Clausen, 2012; Černe, Jaklič, & Škerlavaj, 2013; Damanpour, 2014) has shed light on the combinative effects of technological and non-technological innovations. The authors argue that technological and non-technological innovations should not be viewed as substitutes, but rather as complimentary to each other, suggesting that innovation types are related, interdependent sets, where implementation of one type can affect the other; hence the organizational outcome (i.e. firm performance) depends on the joint introduction of both innovation types, and one innovation type cannot have optimal effects without the other (Damanpour, 2014).
By the same token, previous research (e.g. Evangelista & Vezzani, 2010; Hervas-Oliver, Sempere-Ripoll, Boronat-Moll, & Rojas, 2015) have found that firms’ jointly introducing technological and technological innovations perform superior to non-innovating firms, as well as to firms with a narrow singular approach, i.e. implementing pure product innovations. Based on the terminology developed by Evangelista & Vezzani (2010),
Hence, building on the arguments of Camisón & Villar-López (2014) the purpose of this paper is to provide firm-level empirical evidence of the possible combinative effects of product and management innovation on firm performance. In addition, the present study seeks to fill the current literature gap in the complex innovation literature by investigating if the novelty of product innovations yields differential effects. The study targets the firm level to provide managers with insights so they can build and enable a successful innovation strategy. In particular, the paper aims to answer the following two research questions: 1) what are the combinative effects of product innovation and management innovation on firm performance? And, 2) how does the novelty of product innovation affect such combinative effects?
The remainder of this paper proceeds as follows: in the next section relevant literature will be discussed, followed by the development of the hypotheses. Section three will outline the methodology and the variables used. In section four the results will be presented, followed by a discussion of the results in section five. Section six will contain the conclusion, as well as managerial and theoretical implications. And lastly, the limitations and suggestions for future research will be discussed.
2. Literature Review and Hypotheses
In this chapter, the theoretical background and the hypotheses development of the research will be discussed.
2.1 Innovation: Definition and Types
practices, workplace organization or external relations”. Specifically, innovations are implemented with the aim of gaining a competitive advantage by either increasing the demand of the firm’s products (e.g. by offering new products, expanding to new markets, or increasing product quality) or to reduce the firm’s costs (e.g. by reducing unit costs of production or distribution) (Schmidt & Rammer, 2007). Innovation scholars commonly distinguish innovation into four categories: product, process, marketing and management innovations (OECD, 2005), where the former two are categorized as technological innovations, and the latter two are categorized as non-technological innovations (Cooper, 1998).
In specific, product innovation is defined by OECD (2005, p. 48) as “the introduction of a good or service that is new or significantly improved with respect to its characteristics or intended use.” While, process innovation in defined as “the implementation of new or significantly improved production or delivery methods” (OECD, 2005, p. 49). Further, OECD (2005, p. 49) defines marketing innovations as “the implementation of a new marketing method involving significant changes in product design or packaging, product placement, product promotion or pricing”. Lastly, management innovation is defined as “the implementation of a new organizational method in the firm’s business practices, workplace organization or external relations” (OECD, 2005, p. 51).
However, due to the scope of this present study, marketing innovation will not be studied, and process innovation will only be somewhat emphasized. Hence, the main research focus of this paper will be on product innovation and management innovation.
2.1.1 Product Innovation
market (Utterback & Abernathy, 1975; Crossan & Apaydin, 2010). Hence, as Cooper (1998) emphasize; product innovations are adoption of new ideas that directly impact the basic output processes of the firm. In particular, product innovations are found to positively influence firm performance through sales of new offerings to the market (Crossan & Apaydin, 2010), which might lead to sales and market growth (Wang & Ahmed, 2004).
2.1.2 Process Innovation
A process innovation is the introduction of a new production method that changes the firms’ production of a product (good or service) (Garcia & Calantone, 2002; Crossan & Apaydin, 2010). Scholars argue that process innovation positively impact firm performance via its effect on improved efficiency and productivity on the production of goods and services (Garcia & Calantone, 2002; Crossan & Apaydin, 2010; Hollen et al., 2013).
2.1.3 Management Innovation
Management innovations are recognized under various overlapping terms in the literature, including managerial, organizational, administrative, and organizational innovations (Damanpour, 2014). Management innovations are innovations that affect the policies, allocation of resources, and other factors associated with the social structures of the organization (Cooper, 1998). In other words, it is immaterial ways of changing business activities, such as the use of new business methods and new organizational concepts (Schmidt & Rammer, 2007). Furthermore, management innovation pertains to changes in management systems, structures, knowledge, and managerial skills that enable an organization to function effectively and efficiently (Hamel, 2006).
Cheng, & Kee-Hung (2006) found that management innovation could increase productivity through the implementation of more efficient business methods. Evangelista & Vezzani (2010) and Corbett, Montes-Sancho, & Kirsch, (2005) found that management innovations positively affect sales growth. However, the vast majority of scholars point out that management innovations have an indirect effect on firm performance through its facilitating effect on technological innovations (Damanpour et al., 2009; Černe et al., 2013; Camisón & Villar-López, 2014). Particularly, Damanpour (2014) and Wong (2013) argue that management innovations are key for the firm to create a culture of creativity, change, and learning, which are crucial for fostering and driving product innovations within the firm.
2.1.4 Combinative Effects of Complex Innovations
Historically, product, process and management innovations have mainly been researched with a singular approach (Damanpour et al., 2009). In other words, scholars have only investigated how one innovation type individually impact firm performance. However, a new stream of innovation research has started to investigate a broader innovation approach, namely the combinative effects of complex innovations (e.g. Schmidt & Rammer, 2007; Damanpour et al., 2009; Battisti & Stoneman, 2010; Evangelista & Vezzani, 2010; Polder et al., 2010; Camisón & Villar-López, 2014). Damanpour (2014) argue that management innovations and technological innovations should be regarded as complementary, and not as substitutes for each other. Furthermore, Sapprasert & Clausen (2012), Hollen et al. (2013), and Damanpour (2014) claim that one innovation type can impact the effect of another; hence combinative effects can arise when introducing complex innovations. This claim can be theoretically supported using the framework of the resource-based view (RBV) of the firm.
2014). Barney (1991) emphasise the importance of managing and combining different types of resources, and according to the main assumption of the RBV theory, firms need particular resources and capabilities with special characteristics to gain competitive advantage (Camisón & Villar-López, 2014). Hervas-Oliver et al. (2015) argue that firms with a lack of certain crucial resources and capabilities needed for firm performance can overcome this weakness by building complex systems of interconnected assets that mutually reinforce one another. Further, the authors argue that when product innovations are combined with management innovations, mutually reinforcing assets may arise. These arguments are also in line with Teece (1986), whom emphasize that product innovations often need to be combined with complementary assets. By complementary assets Teece (1986) referrers to those resources that must be jointly implemented with the product innovation in order to best exploit it. Also, Teece, Pisano, & Shuen (1997) and Eisenhardt & Martin (2000) emphasize that to facilitate organizational renewal and to best cope with the dynamic nature of environmental change often posed by product innovations, innovating across the organization’s functions and systems (i.e. management innovations) is crucial.
Hence, due to the creation of complex systems of interconnected assets (c.f. Hervas-Oliver et al., 2015), the need to combine product innovation with complementary assets (c.f. Teece, 1986), and the importance of facilitating for organizational renewal made necessary due to environmental change posed by product innovations (c.f. Teece et al., 1997; Eisenhardt & Martin, 2000), one might expect the joint implementation of product and management innovation to yield combinative effects on firm performance.
2.1.5 Novelty of Product Innovations
characteristics and requirements associated with different degrees of novelty (Danneels & Kleinschmidt, 2001; Garcia & Calantone, 2002). In the literature there are different typologies used to categorize product innovation novelty (Garcia & Calantone, 2002). For this research, I have adopted the most often used typology of new products (Danneels & Kleinschmidt, 2001), namely the Booz, Allen, and Hamilton (1982) typology, which categorize product innovations as either new to the developing firm or new to the market. This typology categorizes innovations either at the microperspective (i.e. new-to-the-firm) or macroperspective (i.e. new-to-the-market) (Song & Montoya-Weiss, 1998). However, the typology should be considered closely related to radical versus incremental innovations (Garcia & Calantone, 2002; Oerlemans et al., 2013).
Since new-to-the-market and new-to-the-firm product innovations both are defined as new to the developing firm, both types of innovations require the firm to make use of new knowledge and resources. Hence, the biggest differences in terms of characteristics and requirements for the two types is that new-to-the-firm innovations require change only at the micro (i.e. firm) level, while new-to-the-market innovations also often need to facilitate for change at a macro (i.e. market) level (Song & Montoya-Weiss, 1998; Garcia & Calantone, 2002; Oerlemans et al., 2013). In particular, for new-to-the-firm product innovations a dominant design is already set in the market, hence the innovating firm can be more certain about what the target market demands (Teece, 1986; Oerlemans et al., 2013). While new-to-the-market innovations often do not meet a recognized demand among customers, but rather creates a demand that was previously unrecognized (Garcia & Calantone, 2002; Oerlemans et al., 2013). Indeed, this leads to an overall higher uncertainty associated with new-to-the-market product innovations as compared to new-to-the-firm product innovations.
uncertainty, one might expect that the combinative effects of complex innovations differ depending on the novelty of the product innovation.
2.2 Hypotheses Development
2.2.1 Combinative Effects of Product Innovation and Management Innovation
Based on the arguments of the RBV theory; Damanpour et al. (2009), Damanpour (2014), and Hervas-Oliver et al. (2015), argue that adopting management innovations and product innovations jointly will result in the creation of interconnected assets that may lead to sustainable competitive advantage. In particular, Damanpour et al. (2009) and Hervas-Oliver et al. (2015) find empirical support that complex innovations create synergetic effects that outperform narrow innovations with regards to firm performance. The authors explain these synergetic effects by applying the framework of the RBV (as presented in section 2.1.4). Specifically, the joint adoption of management innovations and product innovations will create superior assets, as well as building complex and integrated innovation capabilities that enhance firm performance (Hervas-Oliver et al., 2015). Moreover, Damanpour et al. (2009) found that simply implementing narrow innovations will in the long run negatively affect firm performance.
firm performance. Polder et al. (2010) found that technological innovations needed to be combined with management innovations in order to yield positive effects. Lastly, Schmidt & Rammer (2007) found that with regards to sales growth and cost reduction, innovators that introduced both technological and management innovations performed better than innovators that solely relied on product innovations.
Based on the arguments of the RBV as well as with empirical support from e.g. Schmidt & Rammer (2007), Damanpour et al. (2009), Evangelista & Vezzani (2010), Battisti & Stoneman (2010), Polder et al. (2010) and Hervas-Oliver et al. (2015), I hypothesize:
H1: Firms with a complex innovation approach achieve a better firm performance as compared to firms with a narrow innovation approach, due to complementary effects. 2.2.2 Different Effects of New-to-the-Market vs. New-to-the-firm Product Innovations
Ruekert (1995) argue that for new-to-the-market product innovation, the firm need to allocate additional recourses to identify and educate the target market. Lastly, Damanpour et al. (2009) emphasize that new-to-the-market product innovations often require reliance and access to more external knowledge and recombination of more specialized information, which often require management innovation.
Based on the above arguments, I hypothesize:
H2: The combinative effects of complex innovations on firm performance are stronger for new-to-the-market product innovations as compared to new-to-the-firm product innovations.
3. Methodology
3.1 Samplestarted with some general questions regarding the respondent’s position within the company and the established year of the company. Secondly, a more specific innovation-part followed with questions asking if the company had introduced any significantly improved goods and/or services in the period 2012-2014, and if the company had introduced any process and/or management innovations between 2012-2014. A brief description and particular examples of the innovation types was presented together with the questions to increase the likelihood that respondents interpreted the concepts correctly. Further the survey asked questions aimed at examining the research and development (R&D) intensity of the company. Then some indicators of human resources were examined, with questions covering the total number of full time employees (FTE) of the firm, and the educational background of the employees. Lastly, insights regarding market conditions and environmental factors were examined with questions covering the profits and the turnover of the company in 2014, and to identify the industry the company operated in, the respondents were asked to state the SIC-code of the company (the SIC-code indicate the most important industry the company operated in). To avoid common method bias (Gunday, Ulusoy, Kilic, & Alpkan, 2011), the survey required different types of responses, such as yes/no answers, indications of percentage and answers requiring absolute numbers. The “Innovation Benchmark 2015”, in similarity to the CIS study, should be regarded as “subject-oriented” since it directly surveyed individual firms if they were able to produce different innovations (Laursen & Salter, 2006).
included innovation active companies (i.e. firms that introduced minimum one type of innovation during the three-year period), consequently 25 non-innovating firms were excluded. In addition, as much as 129 firms did not provide crucial answers with regards to the measurement of the important variables, hence these data had to be excluded. Especially a vast number of the respondents did not provide details with regards to the turnover of the company. After excluding the non-innovation active companies as well as the respondents with crucial missing values and inconsistencies in their answers, the final valid sample consisted of N = 109 firms.
Table 1
Industry Frequencies.
Industry Frequency Percent
Manufacturing 25 28.1
Specialized business services 23 25.8 Wholesale and retail trade 18 20.2 Information and communication 13 14.6
Construction 4 4.5
Culture and recreation 2 2.2
Other business services 2 2.2
Other services 2 2.2
Not stated 20
Notes: Total number of observations (N) = 109, whereas n = 89 stated the industry, and n = 20 did not state the industry they operated in. The companies that did not state their industry are excluded from the percentage calculations of the sample.
3.2 Measures Table 2
Description of the Variables.
Variable Type Description
Dependent Variables:
Innovative Performance Continuous (Ln) 1+ percentage of turnover in 2014 attributed to new-to-market products.
Overall Firm Performance Continuous (Ln) 1+ turnover in 2014 per full time employee. Independent Variables:
Complex Innovator Dummy Dummy variable: ‘1’ if the firm is a product and management innovator, ‘0’ otherwise.
New-to-the-market Dummy Dummy variable: ‘1’ if the complex innovator attributed the majority of turnover as new-to-market products relative to new-to-firm, ‘0’ otherwise. New-to-the-firm Dummy Dummy variable: ‘1’ if the complex innovator attributed the majority of
turnover as new-to-firm products relative to new-to-market, ‘0’ otherwise. Novelty of Prod. Innov. Continuous Ordinal variable with 3 values. Value ‘1’ if new-to-firm products are most
prevailing, value ‘2’ if equal spread of novelty, and value ‘3’ if new-to- market is more prevailing.
Control Variables:
Firm Size Continuous (Ln) total number of full time employees of the company in 2014. Firm Age Continuous Amount of years the company has been operated up to 2015. R&D Intensity Continuous Total number of R&D employees per overall number of employees. Higher Education Continuous Percentage of employees with a higher education degree.
Industry Dummy Multiple dummy variables created based on the SIC-code of the firms. Process Innovator Dummy Dummy variable: ‘1’ if the firm introduced process innovations, ‘0’
3.2.1 Dependent Variables
The dependent variables in this study are aimed at indicating firm performance. Camisón & Villar-López (2014) and Ballot, Fakhfakh, Galia, & Salter, (2015) argue that multiple performance measures should be included to enhance the validity of the findings; hence, in this research firm performance will be measured in terms of innovative performance and overall firm performance.
Innovative performance can be measured in many different ways, and some of the most common indicators are R&D inputs, patent counts and patent citations (Hsu, Lien, & Chen, 2013). However, in this research the measure of Faems et al. (2010) will be adopted, namely innovative performance measured as the proportion of turnover attributed to new-to-the-market goods and services. In the survey, respondents were asked to indicate how the turnover of the company in 2014 spread in percentage over: 1) goods and services introduced to the market between 2012-2014 that were new to the market, 2) goods and services introduced to the market in the same period that were new to the firm (and not to the market), or 3) goods and services that were unchanged or only marginally changed in the same period. The proportion of turnover attributed to the first category indicates the innovative performance of the company (Faems et al., 2010). However, to transform the variable into a normal distribution for non-zero values, the innovative performance is calculated as the natural logarithm of 1 + the proportion of turnover attributed to new-to-the-market goods and services (Faems et al., 2010).
Ballot et al., 2015), however the measure is labelled under various terms in literature such as labour productivity and turnover per employee (Evangelista & Vezzani, 2010).
3.2.2 Independent Variables
The independent variables of this study cover if the firm is a complex innovator, as well as the degree of novelty of product innovations introduced.
Complex innovator. As defined in the previous chapters, a complex innovator is a company that introduce both a product and management innovation. In the survey, respondents were asked if they had introduced a new or significantly improved good or service in the period 2012 - 2014. A dummy variable (prod_innov) was then created, showing the value of ‘1’ if the company had introduced a new or significantly improved good and/or service, and ‘0’ otherwise. When analysed it became apparent that the entire sample received a ‘1’ on this variable, in other words all firms in the sample had introduced a product innovation. Further, management innovation was in the questionnaire presented to the respondents with three items, namely; (1) the introduction of new business practises for organizing work or procedures, (2) new methods for organizing responsibilities and decision-making within the company, or (3) new methods of organizing external relations with other firms or public institutions. A dummy variable (manag_innov) was then created, giving the value of ‘1’ if the firm had introduced minimum one of the three items, and ‘0’ if none of the items were introduced. Finally, the dummy variable (complex_innov) was created. This variable received the value ‘1’ if prod_innov + manag_innov = 2, and the value of ‘0’ if prod_innov + manag_innov = 1. Taken into account that the entire sample comprised of product innovators, those companies that scored a ‘0’ on complex_innov were considered to be narrow innovators.
3.2.3 Control Variables
The perception of the independent variables on the dependent variable might depend on certain firm characteristics (Tourigny & Le, 2004), therefor several control variables was adopted to control for possible confounding effects (Faems et al., 2005).
Firm size and age. Both firm size and age is strongly associated with innovation success and firm performance (Cohen, 1995; Chen & Huang, 2009; Camisón & Villar-López, 2014). For example Teece (1986) argue that large firms are more able to get access to complementary assets that are crucial for exploiting the full value of the innovation, while on the other hand do Battisti & Stoneman (2010) and Mothe, Nguyen-Thi, & Nguyen-Van
(2015) emphasize that larger firms are less flexible in introducing innovations due to the complexity and nature of their organizational structures. Hence, firm size, measured as the natural logarithm of full time employees in 2014, was controlled for. This specific firm size measure is highly used in previous research (e.g. Lavie, 2007; Faems, de Visser, Andries & Looy, 2010; Lahiri & Narayanan, 2013). Furthermore, adopted from Camisón & Villar-López (2014) was firm age measured as the year the survey was distributed (2015) subtracting the founding year of the company.
R&D Intensity. Internal R&D activities are proposed to positively influence the effectiveness of innovative performance (Faems et al., 2005), this is especially associated to new-to-the-market product innovations (Barbosa et al., 2013). Thus, as a proxy of the firm’s internal R&D efforts, a measure commonly used in literature (e.g. Faems et al., 2005; Ballot et al., 2015; Estrada, Faems, & de Faria, 2016) was adopted, measuring R&D intensity as the ratio of the number of full time R&D employees in 2014 divided by the total number of FTE in the organization the same year.
Stoneman, 2010), this is especially salient for the relationship between management innovation and performance (Mol & Birkinshaw, 2009), therefor an education measure was calculated as the percentage of the employees with a higher education degree.
Industry. Environmental factors might impact the firms’ behaviour and performance, and the industry the firm operate in is often used as a proxy for environmental factors (Chen & Huang, 2009; Černe et al., 2013). Industries often differ in terms of technological opportunity, appropriability regimes, emergence of dominant designs, and competition intensity (Faems et al., 2005; Tsai, 2009), which can affect firm performance. Thus, based on the SIC-codes of the companies, eight different industries were identified, and in line with Černe et al. (2013), eight industry dummies were created, the variable received the value ‘1’ if the firm operated in the particular industry, ‘0’ otherwise.
Process Innovator. Lastly, previous research (e.g. Damanpour et al., 2009; Evangelista & Vezzani, 2010) that investigates the combinative effects of technological and non-technological innovations has often included process innovation in their concept of complex innovations. Therefor, in line with Schmidt & Rammer (2007) and Mol & Birkinshaw (2009), process innovation is controlled for by creating a dummy variable receiving the value of ‘1’ if the company had introduced process innovation, ‘0’ otherwise.
3.3 Methods of Analysis
variable ‘overall firm performance’ (Model 3 and 4). By conducting two individual analyses of the dependent variables it was possible to examine if the independent variable had different effects depending on the performance measures. An overview of the full sample is provided in Table 3, while the regression results for Hypothesis 1 are reported in Table 7.
However, for Hypothesis 2 a different approach was needed since this hypothesis examines differences between populations (i.e. the complex innovators that had a majority of product innovations as the-market vs. complex innovators with majority as new-to-the-firm). To test this hypothesis two different analyses were conducted. For both analyses the sample was reduced to only include the complex innovators (n = 82). With regards to the first analysis a linear regression analysis was conducted with the variable ‘novelty of product innovation’ as the independent variable. This variable indicates the degree of novelty of introduced goods and services. In addition an independent t-test that compared the firms where new-to-the-market was mot prevailing (n = 39) and the firms where new-to-the-firm (n = 27) was most prevailing, was conducted. An overview of the subsamples used in the analysis for Hypothesis 2 is provided in Table 4, while Table 8 and 9 reports the regression analysis and t-test analysis respectively.
4. Results
4.1 Descriptive Statistics
innovation), while n = 67 companies introduced all three types of innovations. Furthermore, among the narrow innovators did 10 companies solely introduce product innovations, while 17 companies introduced product and process innovations jointly. The fact that the vast majority of respondents were complex innovators already points to some vague indications of possible combinative effects of product and management innovation.
Table 3
Overview of full sample.
Variables N Percentage
Product Innovators 109 100
Process Innovators 84 77.1
Management Innovators 82 75.2
Narrow Innovators 27 24.8
Whereas solely product innovation 10 9.2 Product innov. combined with process innov. 17 15.6
Complex Innovators 82 75.2
Whereas solely product and management innov. 15 13.8 Product, management and process innov. 67 61.5
Full Sample 109
Table 4 gives an overview of the subsamples used to examine Hypothesis 2. For Hypothesis 2 only the complex innovators were included, this lead to a sample of N = 82 companies, whereas 39 (47.6%) had their majority of turnover in new-to-the-market product innovations, 27 (32.9%) had their majority of turnover in new-to-the-firm product innovations, and 16 (19.5%) companies had an equal spread of turnover between new-to-the-market and new-to-the-firm product innovations.
Table 4
Overview of subsample complex innovators (H2).
Variables N Percentage
New-to-the-market product innov. 39 47.6 New-to-the-firm product innov. 27 32.9 Equal novelty-spread
Total Subsample
16 82
Further, Table 6 reports a strong positive correlation between ‘product innovation performance’ and both ‘new-to-market product innovations’ (r = 0.548, p < 0.01), and ‘novelty of product innovation’ (r = 0.608, p < 0.01), as well as a strong negative correlation between ‘product innovation performance’ and ‘new-to-firm product innovations’ (r = - 0.563, p < 0.01). These significant correlations might suggest that, in line with Hypothesis 2, the joint effects of complex innovations are stronger for a higher degree of novelty of the product innovations.
Furthermore, as indicated by the Pearson Correlation Matrix in both Table 5 and 6, several of the variables are highly correlated. When there is a high correlation between the independent variables in a sample it could be a sign of multicollinearity (Grewal, Cote, & Baumgartner, 2005) hence to check for this a Variance Inflation Factors (VIF) test was conducted. With regards to the full sample (Table 5) the independent variable ‘complex innovator’ received a VIF = 1.089. Among the control variables was the highest VIF observed for ‘Firm Size’ with the value of 1.981. Further, regarding the subsample of complex innovators (Table 6), the independent variable ‘novelty of product innovation’ had a VIF = 1.481, while ‘Firm Size’ had the highest VIF among the control variables with a value of 1.8381. In literature there are no universal agreed upon cutoff value concerning acceptable
VIF-values, however scholars usually refer to a value between 5-10 (Camisón & Villar-López, 2014). Hence, since all of the variables have a VIF considerably below 5, it indicates that the extent of multicollinearity within the sample is within the acceptable range (Schleimer & Faems, 2016).
1 The independent variables New-to-Market and New-to-Firm are not included in regression analyses;
4.2 Regression Analysis
Table 7
Results of Regression Analysis full sample (N = 109).
Dependent variables
Product Innovation Performance (Ln)
Overall Firm Performance (Ln)
Model 1 Model 2 Model 3 Model 4
Control Variables: Firm Size (Ln) 0.039 (0.126) 0.012 (0.123) 0.185 (0.317) 0.172 (0.318) Firm Age -0.123 (0.006) -0.111 (0.006) -0.094 (0.016) -0.088 (0.016) R&D Intensity 0.276* (0.325) 0.271* (0.317) -0.070 (0.818) -0.073 (0.817) Higher Education 0.387** (0.005) 0.384** (0.005) -0.071 (0.013) -0.073 (0.013) Process Innovator 0.014 (0.322) -0.021 (0.318) -0.052 (0.812) -0.069 (0.821) Manufacturing Industry -0.046 (0.406) 0.008 (0.403) -0.049 (1.022) -0.024 (1.040) Spec. Business Services Industry -0.188 (0.442) -0.153 (0.434) -0.162 (1.113) -0.145 (1.120) Wholesale and Retail Trade Ind. 0.022 (0.447) 0.051 (0.438) 0.081 (1.125) 0.095 (1.131) Information & Communication Ind. -0.234* (0.499) -0.225* (0.486) 0.029 (1.256) 0.033 (1.256) Construction Industry 0.092 (0.758) 0.131 (0.750) -0.162 (1.910) -0.143 (1.935) Culture & Recreation Industry 0.036 (1.000) 0.028 (0.976) -0.151 (2.519) -0.155 (2.519)
Other Business Services Industry -0.056 (1.001) -0.058 (0.976) -0.150 (2.521) -0.151 (2.520) Other Services Industry -0.059 (1.003) -0.067 (0.979) 0.020 (2.526) 0.017 (2.526) Independent Variable: Complex Innovator 0.221* (0.302) 0.105 (0.781) Model parameters: Intercept 1.727* (0.665) 1.230† (0.680) 10.986*** (1.676) 10.427*** (1.756) R2 0.265 0.309 0.163 0.173 ΔR2 F-Value 2.638** 0.044 2.997*** 1.428 0.010 1.408 Notes: Standardized coefficients (β) are reported. Standard errors are in parentheses. Significant results are highlighted in bold.
Furthermore, Table 8 reports the regression analysis for the subsample of complex innovators used to examine Hypothesis 2. Model 5 and 7 presents the baseline model with regressions of the control variables and the dependent variables, while in Model 6 and 8 the independent variable ‘Novelty of Product Innovation’ is included. However, the findings reported in Model 6 should be interpreted in a highly cautious way, and in fact should only the findings regarding overall firm performance (as reported in Model 8) serve as reliable findings concerning the novelty of product innovations and firm performance. With this in mind does Model 6 illustrate a significant positive relationship between ‘novelty of product innovation’ and ‘product innovation performance’ (β = 0.518, p < 0.01). However, this relationship cannot be interpreted in a sufficiently reliable way due to the measurements used2. Furthermore, Model 8 indicates a negative relationship between ‘novelty of product innovation’ and ‘overall firm performance’, however the relationship is not significant. Moreover, also in this subsample (Model 6) it is observed a positive relationship between ‘product innovation performance’ and ‘R&D Intensity’ (β = 0.245, p < 0.05). With regards to ‘Higher Education’ and ‘product innovation performance’ it is observed a significant relationship, however the significance decreases when the independent variable is added (β = 0.223, p < 0.1. Further, in similar to Table 7 is the ‘information & communication industry’ negatively associated to ‘product innovation performance’ (β = - 0.212, p < 0.1), however the significance decreases when the independent variable is added, while the ‘Specialised Business Services’ is only significant with innovative performance when product innovation performance and the control variables are included in the model. Finally, Table 8 reports a negatively significant relationship for operating in the ‘other business services industry’ and ‘overall firm performance’ (β = - 0.195, p < 0.1), while the ‘culture & recreation industry’ is only significant before the independent variable is added (Model 7).
Table 8
Results of Regression Analysis Complex Innovators (N = 82).
Dependent variables
Product Innovation Performance (Ln)
Overall Firm Performance (Ln)
Model 5 Model 6 Model 7 Model 8 Control Variables: Firm Size (Ln) 0.155 (0.133) 0.083 (0.114) 0.227 (0.314) 0.230 (0.318) Firm Age -0.152 (0.007) -0.182† (0.006) -0.032 (0.016) -0.030 (0.017) R&D Intensity 0.311* (0.323) 0.245* (0.276) -0.100 (0.761) -0.097 (0.772) Higher Education Process Innovator 0.470*** (0.005) -0.116 (0.364) 0.223† (0.005) 0.019 (0.321) -0.098 (0.012) 0.005 (0.857) -0.087 (0.013) -0.001 (0.899) Manufacturing Industry -0.041 (0.427) 0.037 (0.365) 0.058 (1.007) 0.055 (1.024) Spec. Business Services Industry -0.231† (0.455) -0.156 (0.388) -0.062 (1.072) -0.065 (1.088)
Wholesale and Retail Trade Ind. 0.041 (0.459) 0.031 (0.389) 0.080 (1.082) 0.081 (1.090) Information & Communication Ind. -0.356** (0.494) -0.212† (0.432) 0.070 (1.163) 0.063 (1.211) Construction Industry 0.103 (0.937) 0.067 (0.796) 0.012 (2.207) 0.014 (2.229) Culture & Recreation Industry 0.044 (0.921) -0.034 (0.791) -0.191† (2.170) -0.188 (2.217)
Other Business Services Industry -0.106 (0.924) -0.074 (0.785) -0.193† (2.178) -0.195† (2.199) Other Services Industry -0.128 (0.929) -0.135 (0.787) 0.039 (2.190) 0.039 (2.206) Independent Variables:
Novelty of Prod. Innov. 0.518*** (0.152) -0.024 (0.425)
Model parameters: Intercept 1.963** (0.673) 0.488 (0.635) 10.171*** (1.585) 10.317*** (1.779) R2 0.356 0.545 0.223 0.223 ΔR2 F-Value 2.895** 0.189 5.728*** 1.499 0.000 1.375 Notes: Standardized coefficients (β) are reported. Standard errors are in parentheses. Significant results are highlighted in bold.
† Impact is significant at p ≤ 0.1 level.
4.3 T-Test
With regards to the empirical analysis for Hypothesis 2 an additional t-test was performed. The result of the t-test is reported in Table 9. The variance for the ‘Product Innovation Performance’ variable across ‘new-to-market’ and ‘new-to-firm’ was not equal (F = 10.886, p = .002), also the homogeneity of variance for ‘overall firm performance’ across ‘new-to-market’ and ‘new-to-firm’ was violated (F = 5.489, p = .022), hence the t-test for unequal variance was employed (Song & Montoya-Weiss, 1998). As reported in Table 9, there is found a significant difference in mean product innovation performance between ‘new-to-market’ and ‘new-to-firm’ (t = 6.210, p < 0.01). However, also for the t-test analyses can only the performance measure ‘overall firm performance’ serve as a reliable performance-measure. Table 9 indicate that the mean of overall firm performance for new-to-the-market product innovators is significantly weaker than for new-to-the-firm product innovators (t = -1.286, mean difference = -0.920, p < 0.05). Hence, the findings of Table 9 are opposing to Hypothesis 2. Indeed, the findings report that the combinative effects are stronger for lower degrees of novelty in terms of overall firm performance.
Table 9
Comparison of New-to-Market and New-to-Firm Product Innovations on the dependent variables. New-to-Market New-to-Firm t-test-results:
n = 39 n = 27 Mean
Variables Mean S.D. Mean S.D. Difference t Product Innov. Performance 3.488 0.963 1.606 1.355 1.881 6.210** Overall Firm Performance 10.113 3.502 11.033 2.309 -0.920 -1.286*
4.4 Post-Hoc Analyses
Furthermore, to check the robustness of the findings with regards to Hypothesis 2 (as reported in Table 8 and 9) another independent t-test was conducted. In the main t-test analysis (reported in Table 9) the firms were divided into subsamples depending on if their majority of turnover was attributed to new-to-the-market or new-to-the-firm product innovations, hence these subsamples contained some firms that had introduced both types of novelty. For the additional robustness check, the firms were divided in one subsample comprising of firms that only introduced new-to-the-market product innovations (n = 23), and the other subsample only introduced new-to-the-firm product innovations (n = 10). With regards to innovative product performance the new-to-the-market subsample had a significantly greater mean than the new-to-the-firm subsample (mean difference = 3.619, p<0.001), however this is quite obvious since innovative performance was measured as the percentage of turnover attributed to the-market products, and by definition the new-to-the-firm subsample did not introduce any new-to-the-market products. Hence, this finding cannot be interpreted too conclusive. However, contrary to Hypothesis 2, the new-to-the-firm subsample had a significantly greater mean than the new-to-the-market subsample in terms of overall firm performance (mean difference = -1.637, p < 0.1).
Nevertheless, the additional analyses3 seemed to indicate that the results of the main analyses (as reported in Table 7, 8 and 9) were robust.
5. Discussion of the Findings
The research was done to examine the combinative effects of product and management innovations. Although previous scholars (e.g. Damanpour et al., 2009; Evangelista & Vezzani, 2010; Hervas-Oliver et al., 2015) have found a positive relationship of combining technological and non-technological innovations, empirical results concerning the specific combination of product and management innovation is rather scattered (Camisón & Villar-López, 2014). In addition, this research extends the current literature by proposing that the novelty of product innovations affect these combinative effects differently. Overall, several interesting findings emerged from the research.
However, firstly, in Table 7 no significant implications were found with regards to the dependent variable overall firm performance, and in Table 8 did only two industry dummies yield significant results.Several issues can have caused these non-significant results. First, it could be the case that there are no significant relationships between the independent variables and overall firm performance. This is in line with Mothe & Nguyen-Thi (2010) that also did not found significant relationships between complex innovation approaches and financial performance. However, the non-significant results among all of the control variables (except the two industry dummies) are quite odd and might point to a measurement error. When examining the model parameters for Table 7 and 8 it becomes apparent that both the R2 and F-values are significantly lower for overall firm performance (Model 3, 4, 7 and 8) than product innovation performance (Model 1, 2, 5 and 6), for example in Model 6 the proportion of variance in the dependent variable explained by the independent variable (R2) is 54.5%,
3 Due to space limitations the full results of the additional analyses are not included. However, the results are
while in Model 8 the proportion is less than half (22.3%). Furthermore, as a general rule of thumb, the closer the F-value is to 1, then the differences observed are more likely due to chance and not caused by real effects (Chatfield, 1995). More important, the F-values are in all models only significant for product innovation performance. A non-significant F-value supports the null hypothesis that there are no statistically significant differences between overall firm performance and both the control variables and the independent variables (Chatfield, 1995), this further implies that the fit of Model 3, 4, 7 and 8 is rather low, and that the model as a whole does not have statistically significant predictive capability. Lastly, this is also underscored by the fact that the standard errors in both tables are notably higher for overall firm performance than for product innovation performance, indicating that the coefficients of overall firm performance are less precise estimates of the sample parameters than what is the case for product innovation performance (Cohen, 1988). Consequently, due to the insignificant results of Overall Firm Performance will Product Innovation Performance henceforth be regarded as the main performance measure of the regression results. Thus, the remainder of this paper will focus on the findings in Model 1, 2, 5 and 6, unless other is stated.
previous research that considers product and process innovation in one aggregated construct (e.g. Schmidt & Rammer, 2007; Černe et al., 2013; and Hervas-Oliver et al., 2015), as well as with previous research that make use of other performance measures than innovative performance (e.g. Battisti & Stoneman, 2010; Evangelista & Vezzani, 2010). Also, this study finds that the positive relationship on innovative performance is dependent on specific firm characteristics such as R&D intensity and the educational background of firm employees. Furthermore, when measuring performance as overall firm performance no significant results were found in terms of following a complex innovation approach. Hence, the overall findings with regards to Hypothesis 1 are in line with Mothe & Nguyen-Thi (2010), that found that product innovation and non-technological innovations have complimentary effects on technological innovation performance, however these effects are not significant in terms of financial performance. Nevertheless, the empirical findings of this study partially confirm Hypothesis 1. In terms of innovative performance is Hypothesis 1 confirmed, however in terms of overall firm performance is Hypothesis 1 rejected.
not depend on whether the innovation was new-to-the-market or new-to-the-firm, but whether the firm could exploit their current technological and non-technological competencies when developing and implementing the innovation. Nevertheless, this paper is the first study to explicitly examine if the novelty of product innovations affect the combinative effects caused by complex innovation approaches, and the empirical findings seems to reject Hypothesis 2 in terms of overall firm performance.
Moreover, interesting findings also appeared among the six control variables. First, surprisingly no significant implications were found with regards to firm size. This is highly contrary to previous research on the complementary effects of innovation types (e.g. Mol & Birkinshaw, 2008; Evangelista & Vezzani, 2010; Mothe et al., 2015). Indeed, Faems et al. (2005) points out that statistically significant size effects have been found in most econometric analyses on innovation performance. However, non-significant influencer effects of firm size has also been found in recent complex innovation research (e.g. Camisón & Villar-López, 2014; Ballot et al., 2015; Hervas-Oliver et al., 2015), this seems to suggest that firm size has no significant relationship with innovation output and firm performance (Klomp & van Leeuwen, 2001). However, when inspecting the size distribution of the sample (as described in chapter 3.1), large firms are highly underrepresented in the sample. Indeed only one out of 109 firms is characterized as a large firm (>250 employees). Hence, the lack of large firms in the sample may have caused the non-significant size effects.
Secondly, only in the regression analysis of the subsample of complex innovators (Table 8) do firm age yield significant effects. However, this (negative) relationship was only found when the independent variables were added (Model 6). This indicates that among the complex innovators, older firms achieve a lower innovative performance when they introduce product innovations with a certain degree of novelty.
relationship in all models. This is no surprise and in line with previous research (e.g. Lööf & Heshmati, 2002; Hashi & Stojčić, 2013; Ballot et al., 2015; Estrada et al., 2016). This finding indicates that innovation output depends largely on the innovation input efforts. Ballot et al. (2015) explains this relationship by pointing to the absorptive capacity (ACAP) effect of R&D. In particular, firms with greater R&D efforts often also achieve a greater ACAP, leading to both an enhancement of internal innovations, as well as facilitating for assimilation of external innovations, this lead to improved performance (Cohen & Levinthal, 1990).
Forth, Higher Education was positively significant in all models. This indicates that the share of high-skilled labour is an important determinant for innovative performance. This finding is in line with Schmidt & Rammer (2007) and Mol & Birkinshaw (2009) that also found the same relationship.
is worldwide and the number of competitors is high (i.e. it might be particularly hard to be the first to introduce an innovation to the market). Moreover, it is in Table 8 observed negative significant (at the 10% level) implications for: the ‘specialized business service industry’ and innovative performance (Model 5), the ‘culture & recreation industry’ and overall firm performance (Model 7), and lastly the ‘other business services industry’ and overall firm performance (Model 7 and 8). However, in particular the latter two implications should be interpreted with caution since they both only contain two observations, and the high standard errors of ≈ 2.2 imply that the findings lack validity. Anyhow, the industry findings do vaguely indicate that firms in these particular industries are associated with a negative overall firm performance. Nevertheless, in general the industry dummies show that differences in industries do not explain much of the variance in the dependent variables. This is in line with the findings of Mol & Birkinshaw (2009).
Finally, no significant implications were found with regards to process innovator. Hence, the adoption of process innovations in conjunction with product and management innovations seems to not affect the performance relationship. This is further underscored by the ad-hoc analyses, which found that for both narrow and complex innovators, whether process innovation was implemented or not in conjunction with product and management innovation did not seem to play a role on the performance effects.
6. Conclusion
innovation. The main analysis was conducted with a sample of 109 Dutch firms from a variety of industries, and the results indicate that complex innovators outperform narrow innovators in terms of innovative performance. Returning to the first research question: “What are the combinative effects of product innovation and management innovation on firm performance?”, it can hence be stated that the combination of product and management innovations lead to joint effects that are greater than the effect of singular product innovation approaches for firm (innovative) performance. In addition, to the best of my knowledge is this present study the first study to investigate if the degree of novelty of product innovations affect the combinative effects differently. In fact, the analysis of the complex innovators subsample (N=82) indicates that the combinative effects of complex innovations for overall firm performance are stronger for lower degrees of novelty, however this relationship is not significant in the linear regression analysis. By returning to the second proposed research question: “how does the novelty of product innovations affect such combinative effects?”, it can thus be stated that the combinative effects seems to be stronger when product innovations with lower levels of novelty (i.e. new-to-the-firm) are introduced.
6.1 Implications
Several theoretical and managerial implications arise based on the empirical findings of this study.
6.1.1 Theoretical Implications
This study contributes to the innovation literature, and in particular the literature regarding the implementation of several innovation types jointly. The positive effects of the simultaneous implementation of product and management innovation, as found in this study, support the notion proposed in recent innovation literature that innovation types should be regarded as complimentary and not as substitute to each other (Sapprasert & Clausen, 2012;
Damanpour, 2014). Indeed, this study emphasize that scholars should examine the combinative effects of different innovation types, rather than the stand-alone effects of singular types. In this sense, the premium effects on firm performance caused by implementing narrow innovations (e.g. product innovations) as found in previous research might not be fully due to the implementation of the product innovation itself, but could have been moderated by other types of innovations. Indeed, the findings of this study points to the moderating effect management innovation has on product innovation for firm (innovative) performance when they are implemented jointly. Hence, any study that examines the performance effects of implementing an innovation type should not be carried out in isolation from the implementation of other innovation types since this will neglect the potential for synergies and extra gains achieved from the joint implementation of complementary innovations (Battisti & Stoneman, 2010).
6.1.2 Managerial Implications
The empirical findings of this study suggest that managers should allocate their limited resources to implement management innovation practices in conjunction with product innovations to enhance their innovative performance; this is especially salient when the firm adopts new-to-the-market goods and services. However, the findings do not find support that this is the optimal strategy also to enhance overall firm performance.
Furthermore, the findings related to the control variables show that the firm specific factors R&D intensity and educational background of the employees positively influence innovative performance, while no effect is found for firm size and age. Hence, managers should attribute significant resources into R&D activities and employing a high-skilled labour force to enhance their innovative performance, while managers should be less worried about the size and age of the organization since this is less important. Also, the findings show that potential industry factors are rather limited, hence a complex innovation approach should be followed regardless of which industry the firm operate in (only exception is the information and communication industry where a negative implication was found). Lastly, the results with regards to process innovation implies that managers should rather use their limited resources to implement management innovation than process innovation in conjunction with their product innovations to best exploit the innovative performance of the firm.
6.2 Limitations and Future Research
This research has several limitations that should be considered when interpreting the findings. However the limitations also provide suggestions for future research. The first limitations regard the sample. The full sample of 109 firms, with a share of 75% complex innovators and 25% narrow innovators, is a rather small sample size with a highly skewed share of the populations, this lack the generalizability of the study as well as the validity of
the findings. To achieve higher generalizability and more valid findings, research with a larger sample size and a more equal spread of the share, should be conducted. Furthermore, due to the scope of the data collection process no measures were conducted to limit non-response biases (Song, Im, Van Der Bij, & Song, 2011). Hence, to achieve higher generalizability within the researched-population, future research should examine if there are significant differences in characteristics between the respondents and the non-respondent group. Additionally, since the database only included Dutch firms, the external validity is low and future research is needed to generalize the findings also to other countries and cultures. Lastly, since the entire sample comprised of product innovators the possible effects of being a narrow process or management innovator, or a complex innovator with solely process and management innovations, could not be investigated. Hence, future research should examine if the premium effects of being a complex innovator over a narrow innovator as found in this study, also apply for other compositions of the two groups.
could be significant in this relationship. However, the authors also points out that using data that span over a period of less than four years most likely do not show non-linear relationships due to the short time frame. However, it could be interesting for future research to examine if non-linear relationships (e.g. curvilinear) are present. Moreover, the industry dummies should be regarded as highly imperfect due to the considerable rate of non-responses for the industry variables. Hence the industry findings should not be interpreted too conclusive. Furthermore, previous research (e.g. Evangelista & Vezzani, 2010; Ballot et al., 2015) highlights that the measure of overall firm performance is highly imperfect as a measure of performance. Indeed Evangelista & Vezzani (2010) argue that this indicator is likely to be a too crude and biased proxy of performance. However, due to limitations in the dataset, as well as its anonymity, alternative firm performance measures derived from secondary sources (e.g. annual sales revenue derived from public records) could not be adopted. Hence, future research should adopt additional firm performance measures to investigate if the findings reported in this study are significant also for other measures of firm performance. Also, the same question in the survey was used to measure both innovative performance and to calculate the spread of product innovations attributed to new-to-the-market product innovations. This is far from ideal and can indeed cause serious correlation errors between the dependent variable (product innovation performance) and the independent variable (novelty of product innovation). Consequently are the findings reported in Model 6 (Table 8) considered to be unreliable to examine Hypothesis 2. Therefore is the performance-measure ‘innovative performance’ excluded from the interpretation of the findings regarding Hypothesis 2.
examined how introduction of innovations between 2012 - 2014 affected turnover in 2014, this might be a too short time frame to fully grasp the value-enhancing effects of the innovations. Hence, future research should use longitudinal data. In addition, due to unavailability of data, previous product innovation performance is not controlled for.
Also, the relationship between complex innovators and innovative performance might be moderated by other factors (Damanpour et al., 2009). Previous research points to a possible moderating effects of e.g. manager’s characteristics (e.g. pro-change attitude), innovation attributes (e.g. complexity of the product innovation) (Damanpour et al., 2009) or organizational structures (Brown & Eisenhardt, 1997). Possible moderating effects apart from process innovation is not examined in this research and should thus be explored in future research. For example, due to unavailability of data it was not possible to control for the introduction of marketing innovations. Future research should hence examine possible moderating effects of for example the manager’s characteristics and the structures of the organization, as well as the possible moderating effect and confounding effect marketing innovation may cause. Finally, the questionnaire used in this study did not include qualitative data. Hence, to enrich the understanding and to obtain a more complete picture of the relationships studied in this research, future research should include both quantitative and qualitative data.
Acknowledgement
I would like to gratefully acknowledge my supervisor, dr. Isabel Estrada Vaquero, for valuable feedback and inputs. I am sincerely thankful for her support during the entire process of this thesis.
References
Amabile, T. M. (1988). A model of creativity and innovation in organizations. Research in organizational behavior, 10(1), 123-167.
Ballot, G., Fakhfakh, F., Galia, F., & Salter, A. (2015). The fateful triangle: Complementarities in performance between product, process and organizational innovation in France and the UK. Research Policy, 44(1), 217-232.
Barbosa, N., Faria, A. P., & Eiriz, V. (2013). Industry-and firm-specific factors of innovation novelty. Industrial and Corporate Change, dtt029.
Baregheh, A., Rowley, J., & Sambrook, S. (2009). Towards a multidisciplinary definition of innovation. Management decision, 47(8), 1323-1339.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of management, 17(1), 99-120.
Battisti, G. & Stoneman, P. (2010). How Innovative are UK Firms? Evidence from the Fourth UK Community Innovation Survey on Synergies Between Technological and Organizational Innovations. British Journal of Management. 21(1): 187-206.
Birkinshaw, J., Hamel, G. & Mol, M.J. (2008). Management Innovation. In Academy of
Management Review. 33(4): 825-845.
Booz, Allen, Hamilton Inc. New Product Management for the 1980s. New York: Booz, Allen, and Hamilton Inc., 1982.
Bower, J. L., & Christensen, C. M. (1995). Disruptive technologies: catching the wave (pp. 506-20). Harvard Business Review Video.
Brettel, M., Heinemann, F., Engelen, A., & Neubauer, S. (2011). Cross‐functional integration of R&D, marketing, and manufacturing in radical and incremental product innovations and its effects on project effectiveness and efficiency. Journal of Product Innovation
Management, 28(2), 251-269.
Camisón, C., & Villar-López, A. (2011). Non-technical innovation: organizational memory and learning capabilities as antecedent factors with effects on sustained competitive
advantage. Industrial Marketing Management, 40(8), 1294-1304.
Camisón, C. & Villar-López, A. (2014). Organizational Innovation as an Enabler of Technological Innovation Capabilities and Firm Performance. Journal of Business
Research. 67: 2891-2902.
Černe, M., Jaklič, M., & Škerlavaj, M. (2013). Decoupling management and technological innovations: Resolving the individualism–collectivism controversy. Journal of International Management, 19(2), 103-117.
Chatfield, C. (1995). Problem solving: a statistician's guide. CRC Press.
Chen, C. J., & Huang, J. W. (2009). Strategic human resource practices and innovation performance—The mediating role of knowledge management capacity. Journal of business research, 62(1), 104-114.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York: NY: Psychology Press.
Cohen, W.M. (1995). Empirical Studies of Innovative Activity. In: Handbook of the Economics of Innovation and Technological Change, ed. P. Stoneman. Oxford: Blackwell, 182–264. Cooper, J. R. (1998). A multidimensional approach to the adoption of innovation. Management
decision, 36(8), 493-502.
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative science quarterly, 128-152.
Corbett, C. J., Montes-Sancho, M. J., & Kirsch, D. A. (2005). The financial impact of ISO 9000 certification in the United States: An empirical analysis. Management science, 51(7), 1046-1059.
Crépon, B., Duguet, E., & Mairessec, J. (1998). Research, Innovation And Productivity: An Econometric Analysis At The Firm Level. Economics of Innovation and new Technology, 7(2), 115-158.
Crossan, M. M., & Apaydin, M. (2010). A multi‐dimensional framework of organizational innovation: A systematic review of the literature. Journal of management studies, 47(6), 1154-1191.
