Organizational Culture and Innovation: Examining the Relationship at the Country Level

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Master Thesis

Organizational Culture and Innovation: Examining the

Relationship at the Country Level

Amelia Sarinastiti –

S2039036 a.sarinastiti@student.rug.nl Supervisor: Mariko J. Klasing, Ph.D Co-Assessor: Dirk Akkermans, Ph.D

MSc in International Business and Management Faculty of Economics and Business

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Abstract

This study analyzes the impact of organizational culture on innovation at the country level. Different types of organizational culture, namely adhocracy, hierarchy, market and clan culture are considered in this study. The analysis is conducted using cross-country data, in a sample of 67 countries. The results show that different types of organizational culture have different effects on innovation at the country level. Hierarchy culture has an inverted U-shape relationship with innovation in poor countries, while in rich countries this culture is not related to innovation. Furthermore, market culture has a negative impact on innovation at the country level. Finally, clan culture fosters innovation in poor countries, while in rich countries this culture hinders innovation. Regarding clan culture, it can be concluded that the sense of family is more important for innovation, than cohesiveness, participation and teamwork.

Keywords: Innovation, Organizational Culture, Adhocracy Culture, Hierarchy Culture, Market

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1. Introduction

According to Evanschitzky, Eisend, Calantone and Jiang (2012), research on innovation has been widely conducted over the past three decades. Hauser, Tellis and Griffing (2005) explain that various disciplines have highlighted the topic of innovation, such as marketing, technology management, and organizational behavior. In the field of organizational behavior, an increasing amount of research focuses on the effects of organizational culture on innovation (Jaskyte and Dressler, 2005). Furthermore, researchers have found that some organizational cultures may enhance innovation, whilst other organizational cultures may hinder innovation.

Many studies have adopted the competing values framework (CVF), which is developed by Cameron and Quinn (1999), to examine the relationship between organizational culture and innovation at the firm level. The framework includes different types of organizational culture, namely adhocracy, hierarchy, market and clan culture. Using Spanish firms as their data sample, Valencia, Valle and Jimenez (2010) found that hierarchy culture has a negative impact on product innovation and adhocracy culture has a positive impact on development of new products or services. Yesil and Kaya (2012), using data from Turkish firms, found that adhocracy culture is positively related to innovation capability, while clan, hierarchy and market culture are not related to it. Moreover, Deshpande, Farley and Webster (1993) found that firms that implement market and adhocracy cultures outperform firms that implement clan and hierarchy cultures. Based on this, it can be said that studies on the relationship between organizational culture and innovation using the CVF at the firm level have shown different results.

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behavior. For example, the concept of national culture has been used to study organizational behavior across cultures and nations (e.g. Dastmalchian et al., 2002; Hofstede, 1981).

In fact, there is a lack of research about the relationship between organizational culture based on the competing values framework (CVF) and innovation in a broader context. Previous studies using the CVF have mainly focused on the effect of organizational culture on innovation at the firm level, but not at the country level (e.g. Valencia et al., 2010; Yesil and Kaya, 2012; Depshande et al., 1993). The study of this relationship at the country level remains somewhat limited. However, studies have found that organizations adopt national cultural attitudes and values. Moreover, these cultural aspects become embedded within organizations. Regarding the examination of organizational culture, national culture may reflect the attitudes and values existing among the members of the organization. Hence, national culture may be reflected in the organizational culture. Subsequently, national culture can be used as a proxy for organizational culture to indicate whether there is an impact of organizational culture on innovation at the country level.

The conceptual interrelationships above indicate that there is a need to fill the research gap by conducting research, which studies the effect of organizational culture based on the Competing Values Framework (CFV) on innovation at the country level. Based on this, the main research question of this study is:

“Does organizational culture affect innovation at the country level?”

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to examine the relationship between organizational culture and innovation at the country level. In order to measure innovation at the country level, this study uses data regarding the total royalty payments and receipts, patents applications granted by the US patent and trademark office, and scientific and technical journal articles.

The main contribution of this paper is to provide a deeper understanding of how organizational culture affects innovation at the country level. Prior research on the relationship between organizational cultures and innovation using the CVF has mainly focused on the firm level. Therefore this country level study demonstrates a unique sampling method by assessing national cultural attitudes and values, which can provide novel insights for academic researchers in organizational cultures and innovation. Another contribution worth mentioning is the data collection method used in this study. Many prior studies primarily used questionnaires as a method for data collection, whilst this study adopts a different method by using secondary data, which is subsequently assessed using factor analysis. Therefore, this can lead to interesting avenues for future research. Finally, this paper will provide a useful insight for business practitioners, since organizational culture may have an effect on a firm’s innovativeness. To conclude, the findings of this study will contribute to existing literature on organizational culture and innovation as well as being of use to business practitioners.

The study is structured in the following manner. First, the literature review in the field of innovation and organizational culture will be presented. Second, the methodology will be described. Third, the empirical findings will be reported. The last section will discuss the empirical findings, research limitations and suggestions for further research.

2. Theoretical framework

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2.1 Innovation

A broad definition of innovation is the successful implementation of a creation (Heunks and Ross, 1992; Heunks, 1998). Another definition of innovation is the development and/or implementation of products or production processes that require a substantial degree of learning (Bodewes and de Jong, 2003). In a study about organizational innovation, Damanpour (1991) explains that innovation can be referred to a new product, service, production process technology, administrative system, plan or program pertaining to the organization’s members.

Substantial practitioner-oriented literature argues that innovation is the only way for a firm to survive in a competitive market (e.g. Kim and Maubourgne, 2005; Rosenbusch, Brinckmann and Bausch, 2011). Many scholars have noted the importance of being innovative within an organization (Rogres, 1983; Deshpande et al., 1993). For example, Neely and Hii (1998) state that innovation may enhance business performance in two ways. First, product innovation can increase firm competitiveness. Second, process innovation can transform a firm’s adaptive capability. Moreover, some scholars argue that firms that implement a high innovation-supportive culture can foster innovation activities more effectively. In support of this, Jassawala and Sashittal (2000) argue that this culture promotes risk-taking, teamwork and creative action, which are positively related to innovation.

Prior studies about national innovation systems have indicated that in the present day innovation is not only focused on the firm level. Feinson (2003) states that innovation can be situated within a larger system, such as at the country level. The innovative process performance of a country is determined by the degree to which the actors support each other as elements of a collective system of knowledge creation and the use of technology (OECD, 1997). In the end, a country that actively promotes innovation may gain more advantages, such as a rise in economic development (Leger and Swaminathan, 2007) and country competitiveness (Fagerberg, 2003).

2.2 Organizational Culture

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organization’s members to have the same organizational perspective on achieving organizational goals (Robbins, 1996; Martins and Terblanche, 2003).

Buschgens, Bausch and Balkin (2013) argue that organizational culture is an important aspect in terms of innovation. Organizational culture is characterized by strategy, structure and support mechanisms within an organization, which in the end may affect perspectives and behaviors toward innovation among the organization’s members (Martins and Terblanche, 2003; Zdunczyk and Blenkinsopp, 2007). As aforementioned, an innovation-supportive culture is considered an effective organizational culture for developing innovation. This culture may cultivate innovative behavior among the organization’s members since it leads them to have the same perspective in achieving innovation and therefore their commitment towards innovation can increase (Hartmann, 2006; Valencia et al., 2010). In addition, an innovation–supportive culture encourages the organization’s members to have creative and innovative ways of representing problem and finding solutions (Lock and Kirkpatrick, 1995; Martins and Terblanche, 2003).

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the organizational culture through the attitudes and values existing among the members of organizations.

2.2.1 Types of Organizational Culture

Some researchers argue that different types of organizational culture may have different impacts on innovation. Cameron and Quinn (1999) argue that the theory of organizational form itself is relatively broad and to include every organizational culture factor is not easy. Therefore, in this study, the organizational culture model developed by Cameron and Quinn (1999), namely the Competing Values Framework (CVF), is used. The framework has been implemented in many studies to examine the relationship between organizational culture and innovation. Based on the CVF, organizational cultures are classified as adhocracy, hierarchy, market and clan culture. The following figure illustrates these four types of organizational culture.

Figure 1: The Competing Values Framework1

As illustrated in figure 1, Cameron and Quinn (1999) used two cultural dimensions. First, the y-axis shows the degree of flexibility and discretion versus stability and control. Second, the

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x-axis shows the degree of external focus and differentiation versus internal focus and integration. Using this framework, therefore, it can be seen which value drivers and which type(s) of organizational culture an organization adopts. In order to examine organizational effectiveness, the framework assumes that referring to only one criterion is not sufficient (Nazarian and Atkinson, 2012). The four organizational cultures are defined based on these dimensions as well as six characteristics of the organization. The six characteristics are dominant characteristics, organizational leadership, management of employees, organizational glue, strategic emphases and criteria of success.

The four quadrants above illustrate the four types of organizational culture. First, adhocracy culture is a type of organizational culture that focuses on creative and adaptive behavior (Desphande, et al., 1993). Suderman (2012) further explains that adhocracy culture promotes a dynamic and creative work environment, which is a favorable circumstance for innovation activities. Based on this, it can be said that adhocracy culture adopts an innovation-supportive culture. Second, the value drivers of hierarchy culture are order, rules and regulations and uniformity. Furthermore, this culture emphasizes predictability and smooth operation within a bureaucratic organization (Dephande et al., 1993). Therefore, an organization that adopts hierarchy culture has a formalized and structured work environment. Third, market culture has a long-term concern of competitive action and achieving goals and targets (Cameron and Quinn, 1999). However, Valencia et al. (2010) explain that market culture has a high degree of control and stability. Fourth, clan culture emphasizes cohesiveness, participation, teamwork and a sense of family. An organization that adopts clan culture has a humane work environment. Therefore, this culture has a relatively high degree of trust and cooperation among the organization’s members.

2.3 Hypotheses development: Organizational Culture and Innovation

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culture will be used to analyze the impact of organizational culture on innovation at the country level. Based on this, the hypotheses will be formulated.

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may lead to a lack of attention to market changes (Desphande et al., 1993). Based on this, clan culture is perceived as a culture that may inhibit innovation.

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Based on this, the following hypotheses are developed:

H1: Adhocracy culture has a positive impact on innovation at the country level. H2: Hierarchy culture has a negative impact on innovation at the country level. H3: Market culture has a positive impact on innovation at the country level. H4: Clan culture has a negative impact on innovation at the country level.

2.3.1 Conceptual Model

The following figure illustrates the conceptual model used in this research.

Figure 2. Conceptual model

3. Research Methodology

In this section, firstly, the data sample of this study will be described. Subsequently, the measurement as well as the data collection of the independent, dependent and control variables will be discussed. Finally, the analysis method will be explained.

3.1 Sample

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based on the data availability of the dependent, independent and control variables. The final sample of this study consists of 67 countries, which are consistently applied for all variables. It should be noted that United States is excluded in this study since national rates of innovation, which is used as dependent variable in this study, encompasses patents granted by the U.S. patent office. Therefore, to include United States in the analysis may lead to biased results, since the country may be overrepresented in terms of patent giving.

As aforementioned, this study uses the average over different time periods, in order to produce cross-sectional data. For dependent variable, the selection of time is based on the data availability. The database for innovation index provides the year 1995, 2000 and 2012. Independent variable includes data from the year 1981 to 2009. According to Hofstede (2001) and Taras et al. (2012), culture is effectively unchangeable and cultural change would be extremely slow. Therefore, in assessing culture, research mainly uses longer period of time frame. Applying a larger sample size may increase the generalizability and reliability of the data, and allow the assessment of more targeted populations across a wider range of conditions and at more precise degrees of time (Taras et al., 2012). In addition, the time frame for the control variables covers the same period as dependent variable, which is from 1995 to 2012.

3.2 Dependent Variable

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3.2.1 Patents

Prior studies have widely used patents as an indicator to measure national rates of innovation (e.g. Taylor and Wilson (2012) and Shane (1993). According to Taylor and Wilson (2012), using patents as a proxy for innovation can bring some weaknesses. For example, raw patent does not take into consideration the quality and impact of patented innovation itself (Taylor and Wilson, 2012). However, patents are the most appropriate measurement of invention because it can indicate which innovation ideas that eventually become viable products (Shane, 1993).

Based on this, this study uses the data of patent application granted by the US Patent and Trademark Office (USPTO). The data include utility patents as well as the other types of US documents, such as plant patents, design patents, defensive publications, reissues, and statutory inventions and registrations. Furthermore, to generate per capita index, the variable is weighted by million population.

3.2.2 Royalty and License Fees Payments and Receipts

Royalty payments and license fees can also be used as a measurement for innovation. For instance, this variable is classified as a part of innovation index on The World Bank’s Knowledge For Development Database. The database provides royalty and license fees data earned by each country. The amount of royalty and licenses fee payments and receipts that countries obtain may indicate their income generated from innovation. Therefore, it can be seen whether a country’s innovation is successful. In this study, the variable is the sum of royalty and license fees payments (per million population) and royalty and license fees receipts (per million population) (in US $ million).

3.2.3 Scientific and Technical Journal Articles

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they are affected by various incentives and produced by a different set of innovators and judged based on different institutional standards (Bourke and Butler, 1996; McMillan and Hamilton, 2000; Glanzel and Moed, 2000; Taylor and Wilson, 2000). The scientific and technical journal articles used in this study refer to scientific and engineering articles, which are published in the fields of physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology and earth and space sciences. The data are weighted by million population.

3.3 Independent Variable

The independent variable of this study is organizational culture, which encompasses adhocracy, hierarchy, market and clan culture. This study utilizes national culture data as a proxy to measure organizational culture at the country level. National culture data are gathered from the World Values Survey (WVS) Database. The database is aimed to help in understanding changes in cultures, including beliefs, values and motivations among people from all over the world. It is a well-known database, which is designed to facilitate cross-national comparison by employing random or quota sampling design in each of its participant nations (Freese, 2004). The WVS database currently provides six survey waves (1981-84, 1990-94, 1995-98, 1999-04, 2005-09, and 2010-14), containing a total of 400.000 observations. The last survey wave (i.e. 2010-14) is excluded in this study since the time frame of dependent variable only covers until the year 2012.

The WVS database provides various survey questions that are related to national cultural beliefs, values and motivations. Therefore, in order to select the questions that are relevant to organizational culture, this study uses several steps, which will be further explained in the next subsections. First, the WVS survey questions are rigorously selected based on the value drivers of four organizational cultures. Second, factor analysis is conducted to statistically confirm that the selected survey questions can be used to measure organizational culture. Finally, the selected survey questions are used to generate the average score in each country.

3.3.1 Organizational Culture Assessment

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1). The analysis begins with manually selecting the survey questions. The survey questions that have similar aspects to the value drivers of adhocracy, hierarchy, market and clan culture, are selected for further analysis (i.e. the principal component factor analysis).

As a result, there are 96 survey questions, which are appropriate to be used as a measurement of the four organizational cultures. For example, the questions such as “Important in life: Family; A001” and “Important in life: Friends; A002” can be referred to clan culture since one of the value drivers of clan culture is a sense of family. Another example, the questions such as “Future changes: Greater respect for Authority; E018“ and “Autonomy Index: Y003” can be used as a measurement of hierarchy culture.

3.3.2 The Principal Component Factor Analysis (PCFA)

The factor analysis is conducted to statistically verify the latent factors of four organizational cultures (i.e. adhocracy, hierarchy, market and clan culture) based on the selected survey questions. By doing this, it leads to a more accurate analysis of organizational culture as well as increases the validity of the results. The principal component factor analysis (PCFA) and varimax rotation are used in the factor analysis of this study.

The analysis begins with excluding some survey questions that have missing (country) data. In order to do it, the analysis is run with cut-off schemes of 65%, 75%, 80%, 95% and 100%. However, the requirement of PCFA is to include a sample size of more than 60 observations. As a result, the analysis is run on a cut-off at the 95% level (non-missing frequency of >= 95%), with 83 observations.

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Table 1 below presents the eigenvalue, which according to Acock (2013) is how much of the total variance over all the items is explained by certain factor. It can be see from table 1, that the first factor is very strong with an eigenvalue of 4.263. It means that 42.63% of the variance in the set of items is explained by the first factor. Factor 2, can also be considered as a strong factor, with an eigenvalue 2.231. In comparison to factor 1 and 2, factor 3 and 4 have a weaker eigenvalue. However, both factors still have an eigenvalue of more than 1.0. The factors with an eigenvalue of less than 1.0 are ignored in this study. Based on this, therefore it can be said that there are four latent factors in this study.

Table 1. The Principal Component Factor Analysis (PCFA) Results

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Table 2. Factor Loadings (After Varimax Rotation)

Variables that form a factor in each row, are indicated in bold.

The average score per country should be weighted with the factor loading score. The formula for measuring organizational culture is presented below. By executing this, the average response per country across all waves (See Appendix 2), which has been weighted with the factor loading score can be obtained. As a result, each country has an average score of each organizational culture.

OC

_inm

_

_(x

in m



.F

_nm

₎

………. (1) I = country n = variable m = factor

x = average score of variable n in country i

Fn = factor loading score of variable n

Variables Label Factor 1 Factor 2 Factor 3 Factor 4 Uniqueness

Y003 Autonomy Index 0.895 -0.032 -0.156 0.030 0.173

D054 One of main goals in life has been

to make my parents proud 0.871 0.157 -0.157 0.092 0.184

E018 Future changes: Greater respect for

authority 0.797 0.054 0.042 -0.177 0.328

A165 Most people can be trusted -0.674 -0.329 -0.086 0.255 0.366

A030 Important child qualities: hard work 0.142 0.791 -0.014 -0.124 0.317 A173 How much freedom of choice and

control 0.223 -0.734 0.297 -0.019 0.324

E016 Future changes: More emphasis on

technology 0.456 0.692 -0.012 -0.224 0.262

E039 Competition good or harmful 0.271 0.461 -0.213 0.329 0.560

A035 Important child qualities: tolerance and

respect for other people 0.020 -0.143 0.848 0.177 0.228

A032 Important child qualities: feeling of

responsibility -0.414 -0.202 0.673 -0.237 0.280

A034 Important child qualities: imagination -0.233 -0.252 0.445 0.440 0.490

A002 Important in life: Friends -0.191 -0.190 -0.004 0.734 0.389

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3.3.4 Control Variables

When testing the effect of organizational culture on innovation, some control variables are included in this study (i.e. institutional quality, education, GDP/Capita, R&D spending, trade openness and natural resources). These variables are assumed to have an influence on innovation at the country level. The data are gathered from different databases, which represent national estimates.

3.3.4.1 Institutional Quality

Tebaldi and Elmslie (2011) have stressed the importance of institutional quality on innovation at the country level. It is believed that institutional quality positively contributes to innovation. The data of institutional quality in this study are gathered from The World Bank’s Worldwide Governance Indicators (WGI) database. This variable includes different measures, such as aggregate governance indicators of voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law and control of corruption.

3.3.4.2 Education

Lundvall (2010) explains that national education and training system are important factors for innovation system. Many researchers have found that higher education results a positive effect on innovation. Also, higher education investment can lead to a bigger impact on a country’s ability to make leading-edge innovations (Aghion, 2008). In order to measure education, this study uses average years of total schooling from the Barro and Lee database.

3.3.4.3 GDP/Capita

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3.3.4.4 R&D Spending

According to Furman et al. (2002), R&D spending is an important determinant of innovation at the country level. They further argued that public R&D spending adds to the innovation process by reinforcing the common innovation infrastructure. The data for measuring R&D spending in this study is taken from The World Bank’s WDI database.

3.3.4.5 Trade Openness

It is argued that trade openness can provide competitive motivation for long-run innovation (Daniels, 1997; Grossman and Helpman; 1991; Taylor, 2012). Trade openness can be measured based on its percentage compared to GDP score. The World Bank’s WDI provides trade (% of GDP) index which is therefore used in this study.

3.3.4.6 Natural Resources

Natural resources are considered as an obstacle to innovation; otherwise, an innovative country has a tendency to be dependent on its exports of energy, metals, raw materials and agricultural products (Ross, 1999; Sachs and Warner, 1995; Gelb, 1998; Taylor, 2012). In this study, the measurement of natural resources is based on total natural resources rent (% of GDP) data, which is gathered from The World Bank’s WDI database.

3.3.5 Data Analysis

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The model for the linear regression is in the following:

……… (2)

= Innovation = Country = Intercept

= Coefficient of predictor variables

= Organizational culture

= Coefficient of control variables = Vector of control variables = Error term

The results of nonlinear regression test, which will be further discussed in the next section, show that only market and clan 2 culture that have significant effects on innovation. Based on this, non-linear regression test is conducted to see whether there is a curvilinear relationship between the other two organizational cultures (i.e. hierarchy and clan 1 culture) and innovation. Furthermore, prior studies about organizational culture indicate that it is possible to find a curvilinear effect of organizational culture on innovation (e.g. Khazanchi, Lewis and Boyer, 2007). Therefore, non-linear regression is also included in this study. The exact model of the nonlinear regression used in this study is written below:

……….…..(3)

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4. Results

The results will be further discussed in this section. This section is structured in the following way. First, the descriptive statistics and correlation matrix will be presented. Second, the results of the relationship between organizational culture and innovation will be shown in the linear and nonlinear regression tables.

4.1 Descriptive Statistics

Table 3 shows the descriptive statistics, which provide an outline of the numbers of observations, mean, standard deviation, as well as minimum and maximum value of all variables. It can be seen in the table that the sample size of all variables are 67 countries.

In addition, based on the results of the principal component factor analysis (PCFA) of the World Values Survey Database using cut off at 95%, there are four factors found, which are subsequently used as the independent variables in this study. The four factors are hierarchy, market, clan 1 and clan 2 culture.

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4.2 Pairwise Correlation Matrix

Table 4 below shows the results of pairwise correlation matrix between the variables, including the dependent, independent cultures and control variables. In this study, the dependent variable used is innovation, while the independent variable used is organizational culture. The control variables used in this study are institutional quality, education, GDP/Capita, R&D spending, trade openness and natural resources. It is shown that both hierarchy and market culture are negatively correlated with innovation. Clan 1 and clan 2 culture are positively correlated with innovation. In addition, some control variables (i.e. institutional quality, GDP/Capita, education and R&D spending) are highly correlated with innovation. For example, innovation and institutional quality are correlated with a value of 0.859, while innovation and GDP/Capita with a value of 0.852.

Table 4. Pairwise Correlation Matrix

Innovation Hierarchy Market Clan 1 Clan 2

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4.3 Regression Results

In this section, the results of the regression analysis are shown. The data are assessed using linear regression (i.e. an ordinary least squares (OLS) and nonlinear regression (i.e. squared term regression). Each organizational culture is assessed with innovation as the dependent variable. As has been noted, there are five regression models, which are run in this study.

4.3.1 Hierarchy Culture

The first independent variable tested in this study is hierarchy culture. Table 5 shows both linear and nonlinear regression results. The linear regression result table shows that hierarchy culture does not have a significant effect at all confidence levels of 99%, 95% and 90% on innovation when it is tested in all models. Column (1), column (2) and column (4) indicate that hierarchy culture has a positive relationship with innovation. Interestingly, in column (3), it shows that in rich countries, hierarchy culture has a negative correlation with innovation although there is no significant relationship. In addition, R-squared value shows that there is a relatively small percentage of dependent variable, which can be explained by the linear model used.

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squared term; it decreases innovation by 1.456 standard deviations. Therefore, it can be concluded that it is possible to partly reject hypothesis 2.

In addition, GDP/Capita and R&D Spending show a positive and significant influence on innovation when it is examined using both linear and nonlinear regression, in all models. Among all control variables, both variables have the strongest effect at the confidence level of 99%.

Table 5. Hierarchy Culture and Innovation

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.10. Marginal significance levels are indicated with †, p<0.15. Standardized beta coefficients are reported in italics.

No Controls (1) All Controls (2) Rich Countries (3) Poor Countries (4) No Controls (1) All Controls (2) Rich Countries (3) Poor Countries (4)

VARIABLES _Innovation _Innovation _Innovation _Innovation _Innovation _Innovation _Innovation _Innovation

Hierarchy 0.291 0.081 -0.069 0.124 5.188 3.334ᶧ 2.084 6.253ᶧ 0.043 0.012 -0.015 0.028 0.761 0.489 0.446 1,419 (0.844) (0.288) (0.233) (0.429) (8.730) (2.265) (2.252) (3.809) Hierarchy Squared -1.152 -0.770ᶧ -0.510 -1.498ᶧ -0.722 -0.483 -0.466 -1,456 (2.085) (0.535) (0.532) (0.925) Institutional Quality 0.611** 0.587*** 0.282 0.634** 0.579** 0.060 0.223 0.262 0.061 0.231 0.259 0.013 (0.259) (0.213) (0.533) (0.272) (0.222) (0.531) Education 0.250*** 0.176*** 0.141** 0.240*** 0.192*** 0.096 0.295 0.192 0.249 0.283 0.209 0.170 (0.046) (0.055) (0.063) (0.048) (0.066) (0.067) GDP/Capita 0.071*** 0.049*** 0.295*** 0.069*** 0.048*** 0.292*** 0.359 0.308 0.562 0.350 0.306 0.556 (0.019) (0.014) (0.053) (0.021) (0.014) (0.051) R&D Spending 0.435** 0.440*** 1.852*** 0.462*** 0.438*** 2.103*** 0.177 0.281 0.357 0.188 0.280 0.406 (0.164) (0.115) (0.486) (0.133) (0.099) (0.494) Trade Openness -0.002 -0.001 0.002 -0.002 -0.001 -0.000 -0.044 -0.036 0.034 -0.046 -0.037 -0.008 (0.002) (0.001) (0.005) (0.002) (0.001) (0.005) Natural Resources -0.029** -0.038*** -0.020 -0.029** -0.039*** -0.024 -0.122 -0.262 -0.099 0.479 -0.122 -0.267 -0.123 (0.013) (0.010) (0.022) (9.024) (0.014) (0.012) (0.022) Constant 5.546*** 2.486*** 4.124*** 0.770 -0.768 1.783 -4.964 (1.866) (0.758) (0.694) (1.169) (2.263) (2.612) (3.718) Observations 67 67 38 29 67 67 38 29 R-squared 0.00 0.91 0.94 0.86 0.01 0.91 0.94 0.87

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4.3.2 Market Culture

The next organizational culture tested in this study is market culture. The nonlinear regression results of market culture are not demonstrated since linear regression results are sufficient for the interpretation of the relationship between market culture and innovation. Column (1) shows that market culture has the coefficient value of -0.543, however it does not have a significant correlation with innovation. As soon as market culture is tested using control variables, it shows that there is a negative significant relationship at the 10% level, with the coefficient value of -0.281 and standardized beta coefficient value of -0.064. The standardized beta coefficient value of -0.064 can be explained as if an increase of one standard deviation decreases innovation by 0.064 standard deviation. In addition, R-squared increases by 0.89 percentages in comparison to model 1.

In rich countries, it can be noted that the effect of market culture on innovation is positive, with the coefficient value of 0.231. However, the culture is not significantly related to innovation in rich countries. In poor countries, the regression result is somewhat similar to the model in column (2). It shows that market culture has a negative relationship with innovation, with the coefficient value of -0.024 and standardized beta coefficient of -0.010. Yet, the significance level is not promising, neither at the confidence level of 99%, 95% or 90%. Based on this, it can be said that hypothesis 3 is rejected.

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Table 6. Market Culture and Innovation

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.10. Standardized beta coefficients are reported in italics. Squared term regression is not shown since the OLS regression result is sufficient for the interpretation and curvilinear effect is not identified.

4.3.3 Clan 1 Culture

Table 7 illustrates the relationship between clan 1 culture and innovation. In order to explain the relationship, both linear and nonlinear regression results are presented. First, it can be seen from the linear regression results that there is no significant relationship found between clan 1 culture and innovation in all models. Column (1) shows that clan 1 culture has a positive

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correlation with innovation, with the coefficient value of 3.012. Similarly, the result remains the same when clan 1 culture is tested using all control variables, with the coefficient value of 1.124. Column (2) demonstrates that clan 1 culture has a positive effect on innovation. In rich countries, clan 1 culture has a different result. The culture has a negative coefficient value, however it is not significantly related to innovation. In poor countries, clan 1 culture, has a positive correlation with innovation but does not show any significant relationship with innovation.

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Table 7. Clan 1 Culture and Innovation

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4.3.4 Clan 2 Culture

In comparison to the other three organizational cultures, clan 2 culture has the strongest impact on innovation. It can be seen in table 8 that clan 2 culture has a significant positive effect on innovation in all models. Column (1) indicates that clan 2 culture has the coefficient value of 1.128 and it is significant at the 1% level. When control variables are added to the regression test, the effect remains the same. Clan 2 culture is positively correlated with innovation at the 1% level. The coefficient value is 0.386 and standardized beta coefficient value is 0.070. In comparison to the regression result without any control variable, r-squared value increases by 0.87 in model 2.

Next, column (3) and (4) show different regression results in rich and poor countries. In rich countries, the effect of clan 2 culture on innovation is no longer positive, with the coefficient value of -0.908 and confidence level of 95%. In contrary, clan 2 culture has a significant positive effect on innovation in poor countries, with the beta coefficient value of 0.295 and it is significant at the 1% level on innovation in poor countries. As a result, hypothesis 4 is accepted in rich countries and rejected in poor countries.

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Table 8. Clan 2 Culture and Innovation

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4.3.5 Organizational Culture

After showing the individual effects of each organizational culture on innovation, all organizational cultures are simultaneously tested using linear and nonlinear regression. In table 9, the linear regression results show that only clan 2 culture that is significantly related to innovation in all regression models. Meanwhile, it can be noted that hierarchy, market and clan 1 culture do not have a significant effect on innovation.

Column (1) indicates that clan 2 culture has a significant positive correlation with innovation, with the coefficient value of 1.051 and it is significant at the 5% level. Based on the standardized beta coefficient value, one standard deviation changed in the variable gives an increase on innovation by 0.190 standard deviation. Column (2) shows that only clan 2 culture that has a significant positive effect on innovation, with the beta coefficient value of 0.335 and confidence at the 1% level.

The effect of clan 2 culture on innovation in poor countries is stronger than in rich countries. The coefficient value of clan 2 culture in rich countries is noted as -0.891 and it is significant at the 10% level. In poor countries, the coefficient value is 0.438 and it is significant at the 1% level. Therefore, it can be concluded clan 2 culture has a negative and significant effect on innovation in rich countries, whereas in poor countries the effect is positive and significant.

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Table 9. Organizational Cultures and Innovation

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5. Discussion

The results of the principal component factor analysis (PCFA) show that there are four latent factors among the selected survey questions from the World Values Survey (WVS) database. These factors are further indicated as hierarchy, market, clan 1 and clan 2 culture. Adhocracy culture cannot be included in this study since the PCFA results show that there is no latent factor regarding adhocracy culture in the data sample, which was previously tested using the PCFA with cut off at 95%. Furthermore, based on the PCFA results, this study uses two measures of clan culture (i.e. clan 1 and clan 2 culture). Clan 1 and clan 2 culture measure different aspects of the culture. Clan 1 culture measures the societal attitudes toward cohesiveness, participation and teamwork, while clan 2 culture measures the societal values toward a sense of family.

Based on the regression results, it can be concluded that different types of organizational culture have different impacts on innovation. Adhocracy culture, which may be expected to be positively related to innovation, cannot be statistically proven to be in this study. However, it is assumed that if hierarchy culture has a negative impact on innovation, adhocracy culture might have the opposing impact, i.e. a positive impact on innovation, since hierarchy culture contrasts with adhocracy culture. It is illustrated in the Competing Value Framework (See Figure 1); hierarchy and adhocracy culture seem to reflect opposing positions in the diagram. Hierarchy culture represents a high degree of stability, control, internal focus and integration, while adhocracy culture represents a low degree of stability, control, internal focus and integration. In addition, prior studies have shown the opposing impacts of both cultures on innovation by finding that hierarchy culture inhibits innovation and adhocracy culture enhances innovation (e.g. Valencia et al. (2010) and Lau and Ngo (2004)).

Interestingly, this study shows different results regarding hierarchy culture and its impact on innovation. Instead of showing a negative linear relationship with innovation, hierarchy culture has an inverted U-shape relationship with innovation in poor countries, whereas in rich countries hierarchy culture is not related to innovation. Based on this, it is difficult to confirm that adhocracy culture has the opposite impact to hierarchy culture in this study.

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innovation. It indicates that some degree of stability and control, and internal focus and integration, which are nested in hierarchy culture, may bring some advantages in terms of innovation in poor countries. This culture promotes consistency and systematic problem solving activities, which can ultimately increase efficiency within an organization. Therefore, innovation activities can also become more efficient. Finally, in this culture, the organization’s members follow the procedures, such that if the organization’s procedure promotes the importance of innovation, the members may become more aware of innovation activities. On the other hand, if the degree of stability and control, and internal focus and integration is too high, it can hinder an innovation within an organization. This circumstance can limit employees’ flexibility and creativity in creating innovation.

Prior research on power distance culture has also indicated an inverted U-Shape relationship between the culture and GDP/Capita (e.g. Tang and Koveos (2008), and Cox et al. (2011)). It is worth highlighting that although this finding cannot be generalized in the context of hierarchy culture and innovation, power distance culture is embedded in hierarchy culture and GDP/Capita is closely related to innovation. Therefore, it is likely that hierarchy culture has an inverted U-shape relationship with innovation, which indicates that this culture first increases and then decreases levels of innovation.

The results of this study show that market culture decreases innovation. It is contradictory to the hypothesis, which states that market culture is expected to increase innovation. As illustrated in the Competing Value Framework, market culture tends to focus more on stability and control, than flexibility and discretion. The culture makes an organization less adaptive in terms of dealing with market changes. The value driver of this culture is result-oriented, which is about getting the job done. Therefore, initiating an improvement and becoming creative and innovative are presumably not the major concerns of this culture. In addition to that, market culture stresses the external focus, e.g. focusing on external partnership or customers. Regardless of the fact that market culture has an external orientation, it is likely that flexibility and discretion are the most crucial elements for enhancing innovation.

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relationship with innovation in rich countries. Based on this, it can be concluded that the sense of family, which is illustrated in clan 2 culture, is possibly more important in creating innovation, than the other value drivers of clan culture illustrated in clan 1 culture (i.e. cohesiveness, participation and teamwork). Therefore, an organization in which the members have a sense of family can increase innovation more effectively. On the one hand, it can be assumed that as soon as the organization’s members have a sense of family, they may have a sense of cohesiveness, participation and teamwork, which eventually are beneficial in fostering innovation. On the other hand, if the organization’s members only have a sense of cohesiveness, participation and teamwork without a sense of family, the cooperation and knowledge spillover creation cannot be optimized. Presumably, the sense of family is related to the level of trust among the organization’s members. Therefore, a lack of trust may have an influence on cohesiveness, participation and teamwork creation. Based on this, an organization that implements clan culture is suggested to primarily focus on creating a sense of family among the members. Subsequently, this circumstance may motivate them to build cohesiveness, participation and teamwork, which are favorable in terms of developing innovation.

Based on the results of clan 2 culture, it can be concluded that clan culture has a positive and significant relationship with innovation in poor countries, while in poor countries the culture has a negative and significant relationship with innovation. Furthermore, it is likely that collectivism is mostly found in poor countries, whereas individualism is found in rich countries. Clan culture itself focuses on collectivism, instead of individualism. Vincent et al. (2004) explain that clan culture, which stresses employee participation, teamwork and cohesiveness, can create a flexible environment that fosters innovation. In addition to that, Jaskyte and Dressler (2005) state that innovation could be considered as a social process in which the implementation of ideas is dependent on the involvement of other members of the organization. Based on this, clan culture can foster innovation, especially in collectivist countries.

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6. Conclusion

In this final section, conclusion, limitations of the study and some suggestions for future research will be further discussed.

6.1 Conclusion

This study is designed to examine the relationship between organizational culture and innovation at the country level. The types of organizational culture used in this study are based on the Competing Values Framework (CVF), which is developed by Cameron and Quinn (1999). The framework distinguishes four types of organizational culture, which are adhocracy, hierarchy, market and clan culture.

In order to examine the relationship at the country level, the samples used in this study are national culture and national rates of innovation. National culture is used as a proxy to measure organizational culture, since based on prior studies and empirical test; there are some of national cultures, which are embedded in the four organizational cultures. In order to measure innovation at the country level, this study uses the total royalty payments and receipts, patents applications granted by the US patent and trademark office, and scientific and technical journal articles. By conducting this, therefore the main research question below can be answered.

“Does organizational culture affect innovation at the country level?”

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6.2 Limitations and Suggestions for Future Research

This study has several limitations. First, not all hypotheses are tested in this study. Hypothesis 1, which aimed to examine the impact of adhocracy culture on innovation, could not be statistically assessed. The factor analysis results show that the survey questions of the World Values Survey database, which are related to adhocracy culture, have many missing country data. Therefore, the survey questions regarding to adhocracy culture cannot be further analyzed in this study. To overcome this, future research could triangulate datasets by using data generated from Hofstede (2001), Schwartz (2006) and Globe (House et al, 2004). The three databases also provide national culture data, which can be used to measure organizational culture at the country level.

Second, according to Taylor and Wilson (2012), statistical analysis can show a correlation between variables but it cannot further explain causality. In this case, the individual interpretation of the author becomes crucial. In a cross-cultural field, in fact, it is required to understand why and how culture cultivates the behavior among an organization’s members, since in the end it can affect firm performance, including innovation activities. In order to directly observe causality, future research could examine organizational culture and innovation using other methodological approaches, e.g. case study. By conducting this research, it is expected that the results and interpretations would become more objective and valid.

Due to restricted data availability, this study uses only 67 countries as the final sample. The problem may occur when countries are classified into rich and poor countries. It can be seen from the regression result table that there are only 38 countries that are included in rich countries and only 29 countries in poor countries. The rule of thumb for research in the business field is to use a sample size of 30 to 500 (Roscoe, 1975; Sekaran and Bougie; 2010). Therefore, further research could include a larger sample size, which may increase the generalizability of the results.

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research, which aims to see the relationship between organizational culture and innovation at the firm level, is expected to test the relationship between organizational culture on innovation and its impact on firm performance.

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Appendix A. The WVS Survey Questions Important in life: Family

A001 Family is not important     Family is important

1 2 3 4

Important in life: Friends

A002 Friends are not important     Friends are important

1 2 3 4

Important child qualities: hard work A030 Being a hard worker is not

important

  Being a hard worker is

important

0 1

Important child qualities: feeling of responsibility A032 Being responsible is not

important

  Being responsible is

important

0 1

Important child qualities: imagination A034 Being imaginative is not

important

  Being imaginative is

important

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Important child qualities: tolerance and respect for other people A035 Being tolerant and respectful

is not important

  Being tolerant and respectful

is important

0 1

Most people can be trusted

A165 People cannot be trusted   People can be trusted

0 1

How much freedom of choice and control

A173 _{I do not feel free} _{          I feel free}

1 2 3 4 5 6 7 8 9 10

One of main goals in life has been to make my parents proud D054

Parents are not proud of me     Parents are proud of me

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Future changes: More emphasis on technology E016 _{More emphasis on}

technology is not good _ _ _

More emphasis on technology is good

1 2 3

Future changes: Greater respect for authority

E018 _{Authority is a bad thing}

  

Authority is a good thing

1 2 3

Competition: good or harmful

E039 _{Competition is harmful} _{          Competition is good}

1 2 3 4 5 6 7 8 9 10 Autonomy Index Y003 More emphasis on determination or perseverance (being independent)

     More emphasis on obedience

or religious faith

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Appendix B. Average Response Per Country

No. Country A001 A002 A030 A032 A034 A035 A165 A173 D054 E016 E018 E039 Y003

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Appendix 2. Average Response Per Country (Cont’d)

No. Country A001 A002 A030 A032 A034 A035 A165 A173 D054 E016 E018 E039 Y003

41 New Zealand 3,925 3,520 0,389 0,607 0,310 0,800 0,500 7,869 2,645 2,340 2,453 7,608 2,413 42 Nigeria 3,970 3,449 0,816 0,316 0,081 0,641 0,218 6,931 3,720 2,838 2,804 8,060 3,913 43 Norway 3,876 3,594 0,117 0,908 0,452 0,781 0,695 7,432 2,431 2,337 1,885 7,484 2,095 44 Pakistan 3,874 2,824 0,555 0,554 0,074 0,532 0,276 4,677 3,625 2,700 2,554 n.a. 3,813 45 Peru 3,822 2,761 0,585 0,764 0,180 0,676 0,075 7,086 3,299 2,725 2,761 7,577 3,517 46 Philippines 3,970 3,248 0,653 0,622 0,114 0,539 0,071 6,918 3,432 2,672 2,634 7,001 3,128 47 Poland 3,904 3,129 0,183 0,796 0,154 0,828 0,235 6,485 3,188 2,745 2,451 6,768 3,275 48 Romania 3,857 2,901 0,752 0,745 0,269 0,641 0,196 7,076 3,070 2,712 2,664 8,015 2,950 49 Russia 3,810 3,131 0,904 0,737 0,106 0,691 0,293 6,244 2,951 2,808 2,530 7,259 2,635

50 Saudi Arabia 3,945 3,436 0,419 0,569 0,311 0,564 0,530 6,604 3,727 2,713 2,695 n.a. 3,270

Organizational Culture and Innovation: Examining the Relationship at the Country Level

Master Thesis