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Relationship between Culture and Innovation
Outputs at the National Level
MASTER THESIS
School: University of Amsterdam
Program: MSc Business Studies
Track: Entrepreneurship & Innovation
Name: Ali Erdi Tuğmaner
Student Number: 10304983
Supervisor: Dr. Wietze van der Aa
Second Reader: Dr. Tsvi Vinig
Date: 27.06.2014
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ABSTRACT
The purpose of this study is to investigate the relationship between different cultural attributes and innovation outputs at the national level. Unlike most of the existing studies, which concentrate on specific regions, this thesis includes 96 countries all around the world. Hofstede’s Cultural Dimensions Theory and Global Innovation Index by WIPO and INSEAD are used as main data sources. Correlation and regression analysis have been performed in order to capture the influence and it was confirmed that culture is a very important determinant of innovation outcomes. At the end, individualism and pragmatism turned out to be positively related to innovation outputs and power distance showed a negative correlation. Masculinity index did not show any significant correlation and unlike the researches done before, uncertainty avoidance also did not reflect any correlation with innovation results and the reasons of this issue has been explored.
3 TABLE OF CONTENTS 1. INTRODUCTION ... 4 2. LITERATURE REVIEW... 7 2.1 CULTURE ... 7 2.1.1 CULTURAL DIMENSIONS ... 8 2.1.2 CRITICISM OF HOFSTEDE ... 10 2.2 INNOVATION ... 11 2.2.1 DIMENSIONS OF INNOVATION ... 12 2.2.2 DETERMINANTS OF INNOVATION ... 14
2.3 INNOVATION AND CULTURE ... 15
3. CONCEPTUAL FRAMEWORK ... 19
4. METHODOLOGY AND THE DATA ... 22
4.1 DATA COLLECTION ... 23 4.2 VARIABLES ... 23 4.2.1 INDEPENDENT VARIABLES ... 24 4.2.2 DEPENDENT VARIABLES ... 25 4.2.3 CONTROL VARIABLE ... 28 5. RESULTS ... 28 5.1 CORRELATION ANALYSIS ... 28 5.2 REGRESSION ANALYSIS ... 33 6. DISCUSSION ... 37 6.1 FINDINGS ... 37 6.2 MANAGERIAL IMPLICATIONS ... 41 6.3 LIMITATIONS ... 43 7. CONCLUSION ... 45 8. INDIVIDUAL REFLECTION ... 47 9. REFERENCES ... 49 10. APPENDIX ... 54
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1. INTRODUCTION
In the contemporary business world, it is generally accepted that innovation has a very important role for growth and economic development (Rosenberg, 2004). A recent PwC research within 1757 Chief Executive Officers all around the world, show that innovation is seen as the most important tool for growth, surpassing penetrating into new markets, mergers and acquisitions and other alliances. The same research found that 78% of the CEOs believe that, their innovation attempts will result in new revenue generation and cost reductions in the coming three years. As Bruce Hassall, CEO of PwC, says; "In today's fast-moving environment, companies must constantly improve and re-invent their products, services and even brands”. Innovation is a matter of survival as it gives companies a competitive advantage and creates growth.” As the world keeps changing, companies face with new problems and the ones who find new solutions to these problems will be successful. That is why innovation is becoming more important each day and it has been commonly accepted that innovation leads to wealth creation (van Otterloo, 2014). Another research conducted by Australian Bureau of Statistics concludes that 91% of the innovating businesses benefit from their innovative activities in forms of “improved customer service, increased revenue, reduction of costs and obtaining a competitive advantage”.
As the studies prove the importance of innovation as a critical source of competitive advantage (Crossan and Apaydin, 2004), various researches concentrated on analyzing the determinants of innovation. Several studies conducted by researchers (van Everdingen and Waarts, 2003 & Wong et al. 2008) show that one of these determinants is a nation’s culture, such as the societies’ shared values, beliefs and behaviors. The study of Kaasa (2013) claims that even though the countries in the EU have similar laws, governances and geographically close to each other, their innovation performances show great differences, which might be explained by cultural backgrounds. Studying in an international environment has been also very helpful to me in order to realize how different cultural backgrounds result in having different perspectives. Throughout all of the course projects, these differences had an impact on
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people’s innovative activities and their engagement with innovative ideas. For all of these reasons, I wanted to base my research on the effect of national culture on innovation; through using Cultural Dimensions Theory (Hofstede and Hofstede, 2010) and Innovation Outputs (Dutta and Lanvin, 2013). The objectives of this research are:
• To understand and analyse the relationship between national culture and innovation outputs. • To explore the potential effects of this relationship on management of innovation in
international firms.
Thus the research question will be:
What is the relationship between culture and innovation output at the national level?
The relation between culture and innovation had been a subject to many researches and this study adds something new to the literature. The prior researches done in this field (Daghfous et al. 1999 & Wong et al. 2008 & Laznjak, 2011 & Kaasa, 2013) were mainly based on a specific geographic areas, mostly Europe, rather than a global one; taking all of the 101 countries, that are available via Hofstede’s study, is another be a distinction point for this research. In addition to that, focusing on Innovation Outputs, rather than the general index or just the patent applications, makes a difference between this research and the ones done before. Finally, including the fifth dimension of Hofstede, long/short-term orientation, also brings a novel perspective as it is a fairly new dimension and that is why it has not took place in many researches.
The thesis starts with a literature review, where the terms culture and innovation are defined and the relevant literature that examines their relation are revealed. This is followed by the conceptual framework, where the hypotheses are explained. The data and methodology section gives information about the data, based on its characteristics and quality and the method of the research is defined. The
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results of the research are succeeded by discussion and conclusion, where the managerial implications and limitations of the research are disclosed as well as the analysis of the results.
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2. LITERATURE REVIEW 2.1 CULTURE
In order to explain the effect of national culture on innovation, first the term “culture” should be defined. There is no agreement about the definition of culture, even though it has been studied for more than a century (Apte, 1994) and different research fields, such as sociology, anthropology and humanities have different definitions of culture. In order to increase the focus and show the direction of this research, the description of culture should be clarified and the opinions of the relevant scholars should be understood well. For the purposes of this research, sociological point of view is used and Kluckhohn (1951), a social theorist, defined culture as a “blueprint for life” and claimed that it is the behaviors used by the society, to solve problems. According to Harris (1979), religion, history, proximity and education are important determinants of the establishment of a culture.
Trompenaars (1993) explained culture as “the shared ways in which groups of people understand and interpret the world”. Moreover, Hofstede (1980) claimed that culture is a set of shared values, beliefs and expected behaviors. He continues to refer culture as “the collective programming of the mind that distinguishes the members of one group or category of people from another” and compares it to a “software of the mind”. In addition to that, culture has the most comprehensive effect on the way human-beings think and behave (Soares et al. 2007). Even though, culture is not the only determinant of human behavior, it has a remarkable effect because people pursue their own interests based on their own character and within the standards of the society they belong (Fink et al. 2007). All of these definitions have a common ground as they all imply that individuals are affected by their cultural backgrounds in the way they think, behave and explain the things happening around them.
Individuals can belong to different culture groups based on their nationalities, genders and races; however, national culture is the most important factor regarding the economy; as it is explained by
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Hayton (2002), the cultural characteristics of a nation, have a big influence on entrepreneurship, which is an innovative activity, because it acts as a moderator between contextual factors and entrepreneurial outcomes. Hofstede (2001) added that national culture is the set of beliefs and values that differentiates a nationality from the other and it has a very stable nature. In addition to this, culture is considered as an explanation to many differences in market structures and economical actions of the nations, when these cannot be explained by concrete causes (Buzzell, 1968). That is why; this research concentrates on national culture, rather than any other aspect.
2.1.1 CULTURAL DIMENSIONS
Until today, many scholars have tried to characterize the cultures and to be able to do that, they derived various dimensions that classifies cultures. The dimensions measure the values of individuals, that shape their decisions and behaviours, and which ways they use to reach to an end (Fink et al. 2007). Fink also tells that cultural dimensions are widely used in the cross-cultural management studies and there is an ongoing discussion about which dimensions resemble the actual situation. Many scholars conducted countless researches in order to come up with the most accurate explanation related to culture. The dimensions of GLOBE are popular as they reviewed the prior literature and large sample studies and based their conclusions on these various researches (such as Hofstede, 1980) and they used both qualitative and quantitative methods in their academic efforts. The use of different methodologies gave GLOBE an advantage as it increased the diversity of their research and at the end; they identified nine culture competencies (Chhokar et al. 2008). In addition to GLOBE, management consultants Trompenaars and Turner spent ten years to study the cultural dimensions and came to conclusions that there are seven cultural dimensions. Even though, their dimensions are also being used, their theories are not as widely accepted as GLOBE’s or Hofstede’s (Tung and Verbeke, 2010).
The most common dimensions theory used in business circles is Hofstede’s; as it creates a general framework for cross-cultural analysis. His theory allows people to understand the fundamentals of
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cultures by eliminating complexities. He came up with four dimensions, which was later increased to six with the contribution of Bond and Minkov. Every country has a different score for each of these dimensions and the scores they receive explain the values and behaviours of a society that shapes the national culture. These dimensions are (Hofstede et al. 2010):
1. Power Distance: To what extent the less powerful members of institutions and organizations within a country expect and accept that power is distributed unequally. It is a measure of inequality.
2. Individualism vs. Collectivism: It measures to what extent the ties between the individuals are loose or tight. In an individualistic society, everyone is expected to take care of himself, whereas in a collectivistic one people protect their group members and are completely loyal to them. 3. Femininity vs. Masculinity: Feminine societies are more modest and concerned with the quality
of life, whereas the members of a masculine society are tough, assertive and focused on material success.
4. Uncertainty Avoidance: To what extent members of a society feel threatened by ambiguous situations. The stronger uncertainty avoidance implies that the members of the society or trying to control the future, rather than just letting it happen and they are more reluctant to change. 5. Pragmatic vs. Normative: In normative societies, people have a desire to attain the absolute truth
and have a respect for the traditions. They focus on achieving quick results; whereas pragmatic societies concentrate on living their lives and pursue long-term goals rather than quick wins. This dimension was called as Short-term vs Long-term Orientation until 2010.
6. Indulgence vs. Restraint: To what extent the members of the society try to control their desires. For the purposes of my research I will not refer to this dimension since this is a fairly new dimension, thus there is not sufficient information and data about the dimension and its scope.
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2.1.2 CRITICISM OF HOFSTEDE
Hofstede’s work has been the most cited cross-cultural study (Bond, 2002) and his theories made a very significant contribution to this field. However, even his groundbreaking theories could not escape from criticism as many scholars choose to take a critical look on his work. As there are academics, who criticize Hofstede, there are more who choose to take sides with him and support his studies.
Alilon (2008) is one of the academics, who criticized Hofstede by saying that survey cannot be the proper method to conduct such a research because of the sensitivity of the subject; Hofstede answered him by saying that surveys was just a part of the research but it was not the only method he used. Hofstede’s research has been considered as outdated, as the world is rapidly changing (Jones, 2007); however, Hofstede claims that the cultural traits have been established through centuries and a culture will not change overnight; also the updates they make to the scores of the countries’ dimensions show that they are constantly reviewing their work. Jones (2007) keeps criticizing his theories as the diversity of a nation is neglected in the research. On the contrary to Hofstede’s assumption, the nations are not homogenous as they have various ethnic groups within them. However, Hofstede neglects the effects of the minor groups in the society, leaving his work biased. Also a similar critique has been made; culture is not only decided by nations as it can go beyond the borders of a nation. So, it is not appropriate to restrain a culture to only one nation and a nation to only one culture (McSweeney, 2002); Hofstede’s response to this is very concise: he says that those national identities are the only tools we have, to measure the cultural differences. As Hofstede got a lot of praise for his studies being very simple, it is also a major cause for criticism (Papamarcos et al. 2007). However, another academic, Williamson (2002) claimed that the simplicity of his model is the quality that makes it special and that is the reason why it can be applied to many fields; furthermore the addition of two new dimensions to his theory proves that Hofstede and his theory are open to improvements. Lastly, some critics claim that his work is not representative because only one company, IBM, has been used to drive to conclusions (Williamson,
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2002). On the other hand, Hofstede sees this as one of the strengths of his studies; he thinks that through one company approach, he managed to eliminate the effects of corporate policy and management practices, leaving culture as the only changing variable.
As it can be seen from the reviews, Hofstede’s work has been a big topic of discussion among many scholars. Even though some of those critics have made important points, Hofstede’s reply to the critics and the work done by scholars after him proves that his dimensional theory is still reliable.
2.2 INNOVATION
The other important aspect of my research is innovation and just like culture, it is a broad term with multiple meanings (Crossan and Apaydin, 2010). One of the reasons for this complication is that innovation includes uncertainty and discontinuity, which cannot be easily explained and heterogeneity of the products and processes make it even harder (Baumol, 2005). The attempts to explain innovation and innovative behavior started with Schumpeter (1934), who made a great contribution to theory of entrepreneurship and innovation by making the first studies regarding this topic. He described innovation as the introduction of new goods, creating new methods of production, establishing new markets and building new supply sources. Some other scholars wanted to create a simplified definition and said that innovation is performing new activities or performing old activities in new ways (van de Ven, 1999). However, no matter how simplified the definitions can be; innovation requires a multiple level of analysis and has a lot of various determinants and dimensions and the cross-cultural study of Crossan and Apaydin have been very successful in this analysis.
In their analysis, firstly they reviewed the data about innovation from the highly-cited articles and studies and then synthesized the data they obtained, in order to create a “comprehensive multidimensional framework of innovation”. They integrated various dimensions of innovation and consolidated these dimensions in two main categories based on the literature: innovation as a process
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and as an outcome. After the dimensions; they separated the determinants of innovation into three meta-constructs: leadership, managerial levers and business processes and explained the measures for each determinants regarding to the literature they reviewed.
Source: Crossan, M.M., Apaydin, M. (2010). A multi---dimensional framework of organizational innovation. Journal of
Management Studies, 47, p. 1154---1191.
At the end of their analysis, they came up with this framework, which is very informative and easy to perceive. As they completed their level of analysis for the determinants of innovation at the firm level, they identified the determinants within the realm of organizational and individual, which is not very effective for the purposes of my research as I choose to have a macro perspective.
2.2.1 DIMENSIONS OF INNOVATION
Before analysing the determinants of innovation, in a macro level, the dimensions should be explained as they include both the processes and outcomes related to innovation. The authors make this distinction in
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these two concepts because the process dimension answers the question “How”, where outcomes answer the question “What”.
The dimensions, driver and source, can be both internal and external and that is their common ground. An internal driver of an innovation can be the resources already obtained and external driver would be an opportunity in the market or a change in the regulations. Internal source will be invention of an idea within, whereas external source is the adaptation of an existing innovation. The locus dimension explains the extent of the process, as firm only (closed process) or network (open process). The difference between top-down and bottom-up innovation makes the view dimension of the process and it explains how the process is initiated and developed. The level dimension is very simple as it makes the distinction between the impact of the innovation, as individual, group or firm.
As the authors say, the difference between processes and outcomes may not be crystal clear; however, the distinction can be made as the dimensions that relate to innovation as an outcome answer the questions “What” and “What kind”. One of these dimensions is referent, which explains for whom this innovation brings novelty; it can be for the firm, market or industry. Magnitude is very similar to this dimension as incremental innovations are usually new to the firm, whereas radical innovations make an impact on the market or the industry. Form is an important dimension that has been discussed by many scholars before; Product-Service innovation is the development of new products (services) and changes in the existing products (services); whereas process innovation is the implementation of new or improved ways of producing a product or creating new ways of delivery methods. (OECD, 2009) and this should not be mixed with innovation as a process, the other aspect of the dimensions. The type dimension makes a distinction between the reflection of the innovation, either administrative or technological. Lastly, the nature dimension, the explicit or tacit nature of innovation, can be an answer to both “how” and “what” questions and that is why it is included in both sides of the framework.
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As the researchers explain in their study, innovation as a process and outcome are not equally important. The outcome part is necessary and enough for the exploitation of an idea; on the other hand process is necessary but it is not sufficient alone. That is why the empirical studies related to innovation choose to concentrate on the innovation outputs, as I am doing in this research and that is why this framework has been very helpful for this thesis.
2.2.2 DETERMINANTS OF INNOVATION
Since innovation has become a crucial part of the businesses, there has been a lot of research done to understand the dynamics of innovation. Crespi (2004), made a study to represent the determinants of innovation, and there he mentioned 6 factors that he thinks are important; intellectual property rights, market structure, corporate governance systems, geography, human capital and demand. Among them, IPRs are seen as an important determinant as differences in the law create differences in innovation outputs of countries (Malerba, 2002). Arrow’s findings (2002) showed that businesses have more incentive to innovate if they are in a perfectly competitive market, rather than being the monopolists, meaning that market structure is a determinant of innovation. Moreover, different corporate governance systems have different impacts on the level of innovation and those differences are very useful to understand variations in countries patterns of innovation (Tylecote and Conesa, 1999). The existence of industry clusters, which was analyzed in detail by Porter (1990), and the competition between the local firms are accelerators for innovation, making geography and proximity relevant factors for innovation (Antonelli, 1995). Human capital, also took a lot of attention by scholars as a factor of innovation. The cross-country research showed that there is a strong correlation between the number/quality of the human capital and productivity (Hall and Jones, 1999). Besides the supply side factors, Gilpin (1975)’s research proved that there is another side, which is vital for innovation: demand side. The innovations appear more often in the times where there is a growing demand for them (Gerosky and Walters, 1995). In addition to all of these determinants, various studies, which are
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mentioned in the following part, point out that culture is another strong determinant of innovation and it has a remarkable effect on the innovative actions.
2.3 INNOVATION AND CULTURE
After defining the concepts, culture and innovation, separately, the relationship between those two should be examined to conclude the literature review. The analysis of the pieces regarding this relationship shows that this is a very hot topic since it has been discussed by many scholars. Shane (1993) is one of the first scholars, who wrote about this relationship; he chose to make a broad research and based his study on the national rates of innovation of 33 countries. He focused on the period between 1975 and 1980; targeting a course of 5 years enabled him to observe the differences in the relationship between culture and innovation over the course of a period. His research had an impact on the executives and policy-makers because it is considered as revolutionary with the way he explained this relationship. Since it is an old research, the validity of the findings for the contemporary business world is open to discussions. However, there is no doubt that this research very inspiring because it is one of the first ones written in this field and it is definitely stimulating for this thesis with his conclusions. His findings proved that countries cannot increase their innovativeness directly by increasing their research and developments spending or by implementing better infrastructure. So, his research showed that the national rates of innovation are affected by fundamental forces more than economic conditions; meaning that the values of the society has to be changed in order to create an innovative nation. Thus, in order to be more innovative, societal changes would be needed along with the adequate economical moves. He continued his research to point out the relative cultural dimensions and found that uncertainty avoidance has a negative correlation with risk-taking behaviour because innovation requires a tolerance for risks. Also, high individualism and low power distance have been found to have a remarkable influence on innovation.
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Another important and up-to-date study on the relationship between innovation and culture focuses on the differences in innovation culture in Europe (Didero et al. 2008). They define concepts of the national culture and innovation; then they briefly explain the relationship between those two before going on empirical research; which had been inspired for me to follow a similar pattern by explaining the concepts before moving to the research. Further, they make a cross-analysis of the research conducted in this field and analyse EU’s reports on innovation and entrepreneurial activities, with their possible relation to cultural dimensions. One of the studies mentioned in this paper belongs to Wennekers et al. (2007); they looked at the effects of uncertainty avoidance on innovative behaviour, such as new business ownership. To be able to explain their work, the terms push and pull factors should be well understood; a push factor is a negative situation that drives people to seek a new solution and a pull factor is an opportunity that gets people to go for a novelty. So, they started with two hypotheses, one that high uncertainty avoidance will act as a push factor in the society to increase innovative behaviour; and the second that low uncertainty avoidance will be a pull factor and increase innovative behaviour. At the end of their research, they found that both of these contradicting hypotheses could be equally valid as the economical dynamics change and evolve over time. Another study mentioned in Didero et al.’s study belongs to Menzel et al. (2006). They measured the effect of national culture on “intrapreneurship”, which they describe as an important source of developing radical innovation in companies. They took Hofstede’s five dimensions into consideration as well as another one called “open systems orientation”. Their findings were similar to Shane’s (1993) and different than Wennekers’ (2007). They found low uncertainty avoidance as a remarkable trigger of innovative behaviour, as well as low power distance. Their results showed that a long-term orientation is enhancing innovation and an average level of masculinity/femininity is desired for an ideal intrapreneurship-supportive culture.
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Source: Menzel, H.C. Krauss, R. Ulijn, J.M., Weggeman, M. (2006). Developing characteristics of an intrapreneurship-supportive
culture, Working Paper 06.10. Eindhoven: Eindhoven Centre for Innovation Studies, Department of Technology Management.
Another study by Herbig and Dunphy (1998) proves that there is a significant effect of culture on the adoption of innovative technologies. They stated that “existing cultural conditions determine whether, when, how and in what form, a new innovation will be adopted.” They concluded their research by claiming that only if cultural factors taken into consideration, the acceptance rates of innovation will be at the desired level and the innovation outputs will be satisfying; thus confirming the influence of culture on innovative behaviour.
The literature also has many articles written about the relationship between entrepreneurship and culture, which is related to this thesis. Hofstede claims that entrepreneurship and innovation are closely connected to each other and the cultural factors that affect one will also have an impact on the other. Also, innovation and entrepreneurial activities tend to happen simultaneously (Beguelsdijk, 2007) because innovation is a specific function of entrepreneurship (Drucker, 2002) and entrepreneurial activity depends on the process of innovation (Okpara, 2007). One of the successful articles about entrepreneurship and culture is written by Thurik and Dejardin (2012); where they investigate the relationship between culture and entrepreneurship. They analyse the different approaches taken by the scholars before them and make advices to the policy makers. They explain three different approaches
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regarding the relationship between culture and entrepreneurship: aggregate psychological traits approach and social legitimation approach; both see culture as a pull factor, as the culture of the society encourages people to engage in entrepreneurial activity and the work of Shane is a great example of this factor. On the other hand, dissatisfaction approach sees culture as a push factor and gives the exact opposite results that the two other options would give. Thurik and Dejardin take all of these approaches into consideration and conclude that culture has an undeniable effect on entrepreneurship, whatever approach is taken. However, in their advices to policy makers, they concentrate on pull factors, as increasing the dissatisfaction (push), is not a feasible option when creating policies to enhance entrepreneurial behaviour. In the establishment of my hypothesis, I also need to make a choice between considering culture as either pull or push element. The studies I have read so far and the approach these authors took drive me into acknowledging culture as a pull factor since it is more feasible to drive management implications.
As it can be seen from all of the studies, the impact of culture on innovation is undeniable. However, there is still a lot of controversy about the extent and determinants of this effect, as the literature has no consensus about this issue. Even though the literature I reviewed inspired me to create my own conceptual model and hypotheses, it also proved me that there is still a research gap in this field. The studies that are reviewed, concentrate on a specific region, rather than a global point of view, and none of the studies take the Global Innovation Index as a basis for innovation levels. That is why I will be able to have a novel research, while benefiting from the existing studies and their findings.
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3. CONCEPTUAL FRAMEWORK
Until this point, the concepts, culture and innovation, are explained and the link between those two have been made in order to answer the main research question. The proper definitions of culture and innovation have been made with the help of the literature and models created by the scholars and the focus has been put on the relationship between those two concepts. However, in order to further explore to what extent culture influences innovation, hypotheses must be formulated based on the learnings I acquired from the previous studies. In my hypotheses, I focused on the cultural dimensions of Hofstede separately and established a hypothesis for each of these dimensions.
As it can bee seen from the literature review (Shane 1993, Menzel et al. 2006, Wennekers et al. 2007), uncertainty avoidance has been the most discussed dimension in the studies regarding the relationship between culture and innovation. The main reason of this choice is that innovation creates uncertainty and challenges the old way of doing things, that is why it is very important to have a tolerance for uncertainty in order to engage in innovative actions. So, the countries with high uncertainty avoidance index tend to resist to innovations and innovations are constrained through rules and regulations (Hofstede, 2001). In the risk averse societies, innovative behavior is avoided and innovation can exist through the adaptation of existing and less risky innovations. Hence, I propose the following hypothesis:
H1: The higher a country’s uncertainty avoidance index gets, it is less likely that innovative actions will be seen in that country. So, there will be a negative correlation between UAI score and Innovation Index score.
Power distance is another dimension that is closely linked with innovative behavior. In the nations with a high power distance index, the decisions are taken centrally, hierarchy is very important, there are formal rules to be followed and subordinates expect to get commands from their superiors (Hofstede, 2001). All of these characteristics of a high PDI country clearly does not favor innovativeness, as people
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tend to take less initiatives to engage in innovative behavior and innovation can only happen top-down. Hence, I propose the following hypothesis:
H2: The higher a country’s power distance index gets, it is less likely that innovative actions will be seen in that country. So, there will be a negative correlation between PDI score and Innovation Index score.
Individualism vs. collectivism dimension have been subject to many researches from many fields and the study of the relationship of culture with innovation is no exception. The research that have been until today shows a correlation between this index and innovation, such as Shane and Kaasa, and they claim this correlation to be positive because collectivism is seen as a restriction for individual behaviour. According to Hofstede, the countries with high individual index, have people who take individual decisions, pursue their own interests and are more mobile when it comes to changing occupations. However, in collectivist countries, people seek to have collective decisions, are less mobile and acts according to the norms of the society. So, in individualistic countries people have a bigger motivation to engage in innovative activities, hence, I propose the following hypothesis:
H3: The higher a country’s individualism index gets, it is more likely that innovative actions will be seen in that country. So, there will be a positive correlation between IDV score and Innovation Index score.
The masculinity is another interesting dimension of Hofstede, as it makes a very striking distinction between what he calls feminine societies and masculine societies. There are very contradicting findings related to masculinity index and its effect on innovativeness, as Kaasa (2013) finds that masculinity is anti-innovative and Menzel et al. (2006) concludes that a balance between masculinity and femininiy is ideal for entrepreneurial behavior. Furthermore, Rogers (1995) suggested that desire for achievement and determination, qualities of masculinity, is related to innovation, so masculinity should enhance innovativeness. Since the literature has many contradicting opinions and it is hard to reach to a
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consensus based on the studies we have, the effect of masculinity on innovation is ambiguous. Hence, I propose the following hypothesis:
H4: The country’s masculinity index is irrelevant to innovative actions. So, there will not be a correlation between MAS score and Innovation Index score.
The last hypothesis concentrates on the relatively new dimension of Hofstede; pragmatic vs. normative or as it used to be called short-term orientation vs. long-term. The countries with a high pragmatic index, try to achieve quick results and people in normative countries pursue long-term goals rather than achieving quick wins. So, exploitation is related to being short-term oriented, where exploration is more important for long-term orientation. Hence, I propose the following hypothesis:
H5: The higher a country’s pragmaticm index gets, it is less likely that innovative actions will be seen in that country. So, there will be a negative correlation between PRA score and Innovation Index score.
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4. METHODOLOGY AND THE DATA
The research design will be “conclusive” and “descriptive”. The main characteristics of this design are that the information needed is clearly defined, the research process is structured, the sample is large and the data analysis will be quantitative. For all these reasons, my research design will be conclusive rather than exploratory.
Descriptive research is mainly used to describe the characteristics of relevant groups and this is exactly what I am looking for. I am trying to find the cultural characteristics of the nations who score high on the innovation outputs. This descriptive design will allow me to understand the relation between Hofstede’s cultural dimensions and innovation output; thus help me to drive correlations between those two. The main method will be used for the purposes of this research is: secondary data: quantitative analysis. Using secondary data has some strengths; such as the collection process (fast and easy), collection cost (cheap or nothing) and collection time (short). Since I only had 5 months to complete this thesis and I am taking a macro perspective, using secondary data was the most feasible option for this research. However, using secondary data may have some drawbacks if it doesn’t match the certain criteria such as the accuracy, currency, objective, nature, dependability and reliability of the data. That is why only the data from reliable, up-to-date and accurate researches are used in this research and this will prevents me from making misleading conclusions.
The data are analysed via using SPSS; through this program, correlation and regression analysis are performed in order to prove the relationship between culture and innovation. Even though, the first model I had in mind was to group the nations according to their Innovation Output rankings, dividing 96 nations into 10 groups and taking the average innovation index point of the group in subject and comparing these figures with the relevant cultural dimension index; I decided to eliminate this model. The reason is that grouping countries, which has no cultural or geographical connections, would lead to
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biases and negatively affect the accuracy and reliability of this research; that is why, every country is assessed separately. The only grouping has been performed to European countries, in order to compare the global results with this continent and see the effects of culture in a global and continental point of view.
4.1 DATA COLLECTION
The sample for this study consists of 96 countries, which are mentioned in both the Global Innovation Index by INSEAD and Cultural Dimensions theory by Geert Hofstede. This choice of a large sample is appropriate for my research question because I proposed to have a global analysis, rather than focusing on a region. The countries included in this index accounts for more than 90% of world’s population and more than 95% of the world’s Gross Domestic Product.
As secondary data is being used; all of the data is already available and easily accessible through the official websites of the sources. There, they also provide the information about their methodology and quality and reliability of their data and this helps to decrease the concerns about relying on someone else’s prior work.
4.2 VARIABLES
In this section, the different variables that are used in this thesis are introduced and explained. Firstly, the independent variables will be discussed; this will be followed by the introduction of the dependent and control variables. The lists of the variables and the indexes that are used in this research are available in the appendix and the table that summarizes the variables used in this research are as follows:
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4.2.1 INDEPENDENT VARIABLES
This study has five independent variables, all coming from one single source: Hofstede’s Cultural Dimensions Theory. Power distance (PDI), individualism vs. collectivism (IDV), masculinity vs. femininity (MAS), uncertainty avoidance (UAI) and pragmatic vs. normative (PRA) are the dimensions that are used as independent variables and the effects of these on the innovation are analysed. All of these variables constitute the corner stones for all of the hypotheses and their effect on innovation is examined both separately and together, using correlation and regression analysis.
The relevant data are available online and more insights about this ranking can be found in their book, written in 2010 and it contains 110 countries. As it was explained in the Literature Review part, the work of Hofstede and his methodology have been a big topic of discussion among many scholars. Hofstede based his research on questionnaires, which are conducted with the employees of IBM; by this single company approach he eliminated the effects of the corporate policy and found the results for national culture. Every country got a rank between 0 and 100 based on their stand against the relative dimension. Even though he made a great contribution to the literature with his cross-cultural study, he received a lot of criticism. However, as it was explained in the Literature Review part of this thesis, Hofstede’s reply to the critics and the work done by scholars after him proves that his theory is well-respected; hence it is used in this research.
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4.2.2 DEPENDENT VARIABLES
The dependent variables of this thesis are the innovation indexes and it has been drawn from two different sources. From these sources, the Global Innovation Index 2013 by INSEAD and WIPO is the main source to measure the Innovation Outputs. The data already exists, which contains 142 different countries, and it is accessible along with their report, the results are explained in detail. In order get a deep understanding of this variable, the conceptual framework of their research should be explained. The Global Innovation Index bases its framework on two main indices; Innovation Input Index and Innovation Output Index and they built these indexes around seven pillars. Institutions, human capital and research, infrastructure, market sophistication and business sophistication are the key pillars to measure Innovation Input. On the other hand, Innovation Output is based on two main pillars: knowledge and technology outputs (knowledge creation, impact and diffusion) and creative outputs (intangible assets, creative goods and services and online creativity). According to their report, these pillars are created based on these criteria:
Knowledge Creation: Number of patent and utility model applications filed by the residents at the
national patent office and WIPO; number of scientific publications and articles that received H publications.
Knowledge Impact: Growth of GDP per person engaged, number of new registered firms; total computer
software spending as % of GDP; total ISO 9001 certificates; high and mid-tech outputs as a % of total manufactures output.
Knowledge Diffusion: Royalties and license fee receipts as a % of total service imports; high-tech,
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Intangible Assets: Number of (national and international) trademark registrations at the national
trademark office and WIPO; survey about to what extent information and communication technologies create new business models, services, products and new organizational models.
Creative Goods and Services: Audio-visual and related services exports; national feature films produced;
daily newspaper circulation; creative goods exports; printing and publishing manufactures outputs as a % of total manufactures outputs.
Online Creativity: Number of generic and country code top level domains (gTLDs and ccTLDs); number of
Wikipedia monthly edits.
In order to measure these pillars, they use the data from various sources such as WIPO, World Trade Organization, UN, IMF and World Economic Forum.
The quality of the data is open to discussions; the index has been criticized since it includes many indirect parameters that may affect innovation and having more than 80 parameters causes weak indicators to block the stronger ones (The Economist, 2012). This blocking happens because the presence of indirect
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parameters decreases the weight of the direct parameters when the innovation index is created. By taking account only Innovation Outputs, I will be able to eliminate these indirect effects and have a better understanding of the relationship between national culture and innovation. Another limitation is the simplification of the index. As it was explained before, innovation is a very broad term and measuring it is not easy. That is why, WIPO and INSEAD choose some specific parameters to measure innovation and this leads the results to be oversimplified, thus creating a limitation.
On the other hand, this index has been prepared by INSEAD and World Intellectual Property Organization, which are very reputable organizations, and 2013 Index is the sixth version of their work. So, besides their reputation, their experience also allows them to have an index of high quality.
The second variable about innovation outputs belongs to another very reputable source, Bloomberg. They calculate the Bloomberg Innovation Quotient by examining the innovative activities of the countries based on a decade of their activity; however, even though their research consists of 81 countries, they only publish the top 50, which creates a limitation for this research. Also, even though they mainly concentrate on the innovation outputs, their research also includes the factors such as researcher concentration and R&D intensity, which is mostly related to innovation inputs. Another limitation of this source is that, they do not explain their methodology as detailed as WIPO and INSEAD does; which leads us to question whether this index is suitable for this research or not. Since there are some limitations regarding this source, this will not be the main independent variable that will confirm the hypotheses; it will help to double check the results. I will calculate the correlations between the independent variables and Bloomberg Innovation Quotient and compare these results with the correlations of Global Innovation Output Index. Using two dependent variables and comparing their results will help me to see if there are any inconsistencies between the results and in case of a dispute, I will look for the reasons that may explain the differences.
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4.2.3 CONTROL VARIABLE
There have been many researches regarding the relationship between education levels of the human beings and the innovations and the work of researchers (D’Acunto, 2014) show that there is a positive relationship between these two concepts. That is why, human capital and its education level will be the control variable of this research. The data for this variable comes from the report of Human Development Report 2013 by United Nations and the Education Index, which is used as the control variable in this thesis, is based on the citizens with at least secondary education.
5. RESULTS
In this section, the results of the correlation and regression analysis will be mentioned. The focus will be on the purpose of these analysis and the outcomes.
5.1 CORRELATION ANALYSIS
The correlation analysis is performed in order to observe and find the patterns of correlation between the variables in subject. The correlation levels can be between -1 and +1 and the closer the correlation coefficient gets to these extremes, the relationship between the variables gets. Also statistical significance is an important element of this analysis, as it is the determinant of whether these results are meaningful and they did not just occur because of chance. The results of the correlation analysis, both global and Europe, which is done through SPSS, can be seen in the Table 1 and Table 2.
First of all, the results provide me with very important insights about the relationships of the variables. It is very interesting to see that the correlation coefficients of the same variables show some very distinct results between the global and Europe focus.
The correlation level between the dependent variables, 0.631, can be considered as a high-correlation but at the same time it shows that there are some differences in the datasets that measures the same concept. The difference between these two variables can be attributed to the different methodologies
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and pillars used to explain innovation by these two sources. On the other hand, the high correlation level shows that even though the criteria used to mine the data was different; they do not have very different conclusions. That is why; the correlation levels are not very different, no matter which dependent variable is used to analyze the relationship with the independent variables.
However, since the methodology, strengths and limitations of GIOI is better known compared to Bloomberg Innovation Index and GIOI is more focused on innovation outputs, the main dependent variable will be the one that is prepared by WIPO and INSEAD.
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The most striking difference between the Global results and European ones can be seen in the correlation coefficient of uncertainty avoidance index (UAI) and innovation outputs. The study of the European countries proves the effect of UAI, as there is a negative correlation of -0.590 between the variables. However, the Global results are not even close to this, as its correlation is -0.041, which is far from being significant. The differences in these figures also make it difficult to accept or reject the hypothesis; since one result is proving it and the other is not supporting. However, since the main goal of this research is to reflect a Global analysis, since it fills a research gap in this area, it can be said that there is no strong relationship between uncertainty avoidance and innovation outputs in the global level; hence Hypothesis 1 is not confirmed.
In both the Global and European results, power distance (PDI) is clearly an important determinant of innovation outputs. There is a negative correlation between power distance and innovation output index; -0.616 for Global and -0.652 for European. Both of these correlations are significant at the 0.01 level and it shows that as power distance in a culture decreases, the innovation outputs prosper; hence Hypothesis 2 is confirmed.
Individualism Index proves to be a significant factor of innovation outputs, as there are high correlations between this index and innovation outcomes. The high positive correlation between individualism and innovation, 0.690 for Global and 0.652 for Europe, show that as the individualistic behavior in a country increases, the innovation outputs follow it. Hence, Hypothesis 3 is confirmed.
Masculinity Index does not seem to have a connection with the outputs as there is no correlation between those variables. The correlations are -0.033 for both Global and European and this figure is too low to extract any strong relationship. That is why we can conclude that masculinity and femininity is not an important determinant for innovation; hence Hypothesis 4 is confirmed.
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The last independent variable in this analysis is the pragmatic index (PRA) and the findings of Globe and Europe are contradicting each other once again. The Global results show a positive correlation between PRA and innovation outputs, 0.471; which gives the exact opposite result of the proposal in hypothesis 5. The European results show that there is no strong correlation, -0.006, which cannot prove any connection between the dependent and independent variables. Since the scope of this research is global, we can conclude that Hypothesis 5 is refuted.
5.2 REGRESSION ANALYSIS
Regression analysis is another important tool to test the hypothesis and see to what extent culture effects innovation. This analysis contains several independent variables (PDI, IDV, MAS, UAI and PRA), one control variable (EDU) and their affect on the dependant variable is measured (GIOI). The main idea of regression analysis is to find the best fitting straight line through the set of points determined by the relationship between the independent variables and the dependent one. This equation is being used to explain this relation:
Y = β0 + β1x1 + β2x2 + …. ei
And the regression equation of this research is:
GIOI = β0 + β1*PDI + β2*IDV + β3*UAI + β4*MAS + β5*PRA + β6*EDU + ei
The slope (β) can be interpreted as the amount increase in the dependent variable, with the one unit increase of the relevant independent variable. The error term accounts for the difference between the actual result and the estimated result. Also, this regression analysis helps us to calculate a coefficient, which is called R2; this coefficient of determination shows us to what extent the independent variable
accounts for the behavior of the dependent variable, so it is an important indicator of a relationship between the variables.
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The Table 3 shows the regression analysis, when each independent variable has been tested alone. The results are very similar to the ones I discussed in the correlation analysis part. The coefficients of determination for PDI, IDV and PRA give statistically significant results, proving the relationship between them and the dependent variable. However, UAI and MAS indexes have very low R2 values, hence a
relationship between them and GIOI cannot be extracted from this analysis.
Another model I have used in this part is conducting two multiple regression analysis, with and without the control variable, in order to capture the effect of the control variable and see the overall effect that culture might have on innovation. Table 4 and 5 show the outcomes of this analysis.
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These multiple regression analyses show that the variables PDI, IDV, PRA and EDU have statistically significant effects on the dependent variable. With and without the control variable, the multiple analysis gives a high coefficient of determination, proving that culture is really an important determinant for the level of innovation outputs. The R2 value of 0.627 show that 62.7% of the variation in the
dependent variable can be explained by the independent variables, which constitutes the cultural dimensions theory of Hofstede. All in all, both the correlation and the regression analysis show that culture is undeniably related to innovation outputs. The effects and the strenght of these relationships of the various dimensions can variate but culture has proven to be a significant determinant of innovation, which leads us to the conclusion part.
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6. DISCUSSION
This section focuses on linking the existing literature with the findings of the data analysis, which is followed by managerial implications of the results and the limitations of this research.
6.1 FINDINGS
In the earlier parts of this study, I established a conceptual framework in order to explain the relationship between cultural dimensions and innovation outputs. The framework tried to depict the ideal distribution of cultural attributes in order to get optimum innovation outputs and this was done based on the existing literature.
After the data analysis, I realized that some of the results were in line with the literature, thus confirming the hypothesis that was established. However, the findings about some of the dimensions were in contradiction with what was expected and here I will try to focus on the reasons of this.
First of all, the scholars, such as Shane, Menzel and Kaasa, all predicted power distance (PDI) to be low for an ideal innovation environment, which led me establish my hypothesis in that way. Furthermore, both the correlation and regression analysis, proved this relationship right. The high negative correlation between PDI and Innovation Outputs clearly indicates that as the power distance in a society disappears, that nation gets better results in terms of innovation outputs. The reason of this relationship is very simple to explain: In the horizontal organizations, people feel that they are equal with the others and this leads them to act and think more freely, which creates a suitable environment for innovations. Also, if people feel the equality, they feel more responsible and more accountable about what they are doing, which helps them to speak up about their ideas (Sweetman, 2012). Only in such an environment, creativity will be high, which is very important for innovation as it was explained before. Also, the sense of responsibility helps in the execution of the ideas, which is as important as the creation of novel ideas. Existence of authority and hierarchy will make people uncomfortable, as they will be afraid of being
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judged and receive harsh comments about their ideas. So, in such an environment, innovation will most likely happen top-down and it will not be embraced by the whole team, leading low levels of innovation outputs.
Individualism was another parameter that all of the researchers before me had a consensus on; the more a society shows individualistic behaviors, the more successful they will be in terms of innovating (Shane, 1993; Kaasa, 2013). My analysis also gave the same results, as this relationship showed a positive correlation with statistically significant results. Individualism stresses personal achievement and freedom, which are important for innovation. Individualistic societies encourage pursuing own interests, whereas collectivism prevents individuals from standing out in the society. Also, as Williams and McGuire (2005) said, innovation initiation is considered as an act of the individual; since the ideas are usually created by an individual and the research I have made for my prior courses showed me that even brainstorming is more effective when it is done in solitary (Diehl & Stroebe, 1987). So, in such an individualistic society people will have more possibilities to come up with new ideas and creating novelty is an essential step for innovation.
Another outcome that complied with the expectations was about the masculinity vs. femininity index. Previous research concentrated on finding the possible effects of this index on innovation and deciding what would be the most optimum level to maximize innovation. However, the findings of the scholars were contradicting each other and that is why I did not expect to find a correlation between this index and innovation outputs in my research and the results I got proved my assumption. Before me, Shane (1993) and Williams and McGuire (2005) also failed to find a direct relationship between this index and economic creativity, which leads to innovation. As masculine cultures emphasize values like achievement and independence, which are innovation enhancing values, one could conclude that a high level of masculinity is good for innovation. On the other hand, feminine values such as conflict avoidance and trust are also values that are needed for successful innovations; Sivakumar and Nakata (1996) claimed
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that feminine culture is more ideal when dealing with uncertainties that are resulted by innovation. So, one can conclude that masculinity or femininity alone is not ideal for innovation; only a combination of these values will enhance innovation outputs and take this into desired levels.
The most interesting result this research showed was about uncertainty avoidance index. This was one single relationship, where all of the researchers agreed: uncertainty avoidance affects innovation negatively. Herbig and Dunphy (1998) concluded that the society has to accept uncertainty in order to engage in riskier activities such as innovation and developing new products. Williams and McGuire (2005) also supported this claim; they said that the cultures, who try to minimize uncertainties, will have problems in developing new ideas and people will have a hard time proposing and accepting these new solutions. My conceptual framework was based on these statements; however, the results showed me that uncertainty avoidance did not have a significant correlation with innovation outputs. I decided to look into these results since it was unexpected and I tried to think of theories to explain the reasons of this outcome. Firstly, I decided to look into the European countries, where innovation outputs are significantly higher than the rest of the world, which is 44 for European countries and 35 globally. Also comparing the UAI levels helped me to have a possible explanation for these unexpected results; European countries, which are high on innovation outputs, had higher uncertainty avoidance than global average. This prevented a possible correlation that would happen between UAI and innovation and that is why I tried to understand why Europeans have higher uncertainty avoidance and how come they are more successful on innovation despite this fact.
First of all, I realized that there is a research gap, regarding the analysis of why Europeans might have higher uncertainty avoidance levels. That is why, I could not find a direct source, including Hofstede, to explain this issue; however, I noticed that, especially western European countries stress a lot of importance on planning before acting in order to decrease the chances of bad surprises. They want to be prepared for the contingencies that might occur and this is one of the reasons why they want to avoid
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ambiguity. As Schneider and DeMeyer (1991) mentioned, this leads “risk-averse societies” to be proactive and seek solutions to adapt into dynamic environments; these solutions can in turn enhance their innovative outcomes since they will be ready for the problems they may face along the way. Also in those cultures, law and regulations are highly developed, as they very important tools to avoid risks (Baker and Carson, 2011). The existence of these structured laws decreases the uncertainty and at the same time creates an environment to secure intellectual properties, which helps the innovative activities. Lastly, Yang-Ming (2008) claimed that people’s willing to take risks increases as their income increases; since European countries have higher GDPs compared to rest of the world, they are taking risks that makes them more successful in innovation, even though they score higher on UAI index. As it can be seen from all these reasons, Europeans are better innovators, on the contrary to their UAI results.
The last dimension that had a place in the research was pragmatic vs. normative and again, the results were contradicting my conceptual framework. Since this is a fairly new dimension compared to others, the literature did not have any research regarding the relationship between pragmatism and innovation. That is why, I had to base my hypothesis only on the description of Hofstede and I claimed that there will be a negative correlation between PRA score and innovation outputs, however, the results were opposite. After this result, I decided to dig deeper into the concept of being pragmatic and I realized that pragmatism is not necessarily anti-innovation. In their book, Vogel et al. (2005) defines the concept of pragmatic innovation. Here, they challenge the myth that all successful innovations are radical and bring sophisticated solutions and claim that usually the most successful innovations are very simple. In addition to this, innovation can also be driven by pragmatic reasons such as surviving through competition and that is why a pragmatic culture can foster innovation, as it was shown in the data analysis. This discussion about the effect of pragmatism on innovation shows that different definitions and measurements of the concept “innovation” will give different results, which is why the literature review focused on the distinctive perspectives on this matter.
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6.2 MANAGERIAL IMPLICATIONS
“Culture eats strategy for breakfast” says Peter Drucker, a well-known management consultant, and there is a lot behind this statement. All the efforts put in to make plans and create a strategy will not mean anything unless the employees are engaged and willing to take responsibility (Deloitte, 2006). That is why, creating an organizational culture that fits the mission and the vision of a company is very crucial. Innovation is no longer something nice-to-have but a necessity for the survival of the companies (Kogan, 2006). For this reason, there are a lot of researches happening in order to increase the efficiency of innovations and to get more successful outputs (Schein 2006, Lau & Ngo, 2004). Those researches mainly concentrate on underlining the importance of culture for innovation and determining the most suitable organizational culture in order to maximize innovation efficiency and my research also contributes to this field.
With its findings, this thesis aspires to add value to the management discipline from an academic point of view, but at the same time aims to be relevant for the contemporary business world. The challenge in this step is to derive managerial implications from a national level study. It is not possible to make a direct connection between national cultures and organizational cultures, since one takes a macro perspective and the other one micro. On the other hand, since Hofstede derived his findings from the survey’s he conducted at an organization, the results of this research can be adapted to management of innovation at the company level, even though one has to be careful when going from macro to micro analysis.
The first lesson to learn from this thesis is that cultural differences really matters. It is important for a manager to be aware of these differences when he is dealing with innovation and it is in his hands to turn culture into his advantage rather than a barrier. It is not in the manager’s hands to transform a national culture; however it is in his hands to transform its organizational culture. First of all, the
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manager should know precisely how different dimensions of culture affect innovation; only then he can take the necessary measures to transform the organization into an innovation-friendly one. Secondly, a manager should be able to lead and manage change in an organization; it is never easy to implement a change that is against the values of the people, however with a high-quality communication strategy, it is not impossible.
Power distance and collectivism has been found to have negative impacts on innovation and if a manager is working in such a culture, he should be aware of this fact. The employees should feel comfortable and be encouraged to take action without the fear of being ridiculed. Only if they do not feel the hierarchical pressure from their superiors, they can feel relaxed and contribute without limiting their creativity. Also since individualism is a positive actor for innovation, employees should be encouraged to be proactive by giving them individual targets and bonuses, along with the company goals. If the employees know that they will be rewarded if they come up with an idea or be active in successfully implementing one, they will feel more responsible and it will be for the benefit of both the individual and the company. This behavior can also be considered as pragmatic, which has been found as an enhancing factor for increasing the outcomes of an innovation.
Even though masculinity vs. femininity index did not give a correlation, there are very important lessons to take away from this finding. Even though, my research does not reveal the ideal character for innovation regarding this dimension, it shows that dominance of these styles does not imply an ideal situation. If this information is supplemented by the knowledge we got in the literature review (Rogers 1995; Sivakumar 1996; Menzel 2006), it can be concluded that a combination of both characters is needed for innovation. A manager should emphasize the importance of achievement and independence to his employees, which are the values of a masculine society, and at the same time he should create a relationship with them based on trust and be understanding. Lack of those attitudes will result in a decrease in the intended innovation outputs.
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Lastly, uncertainty avoidance is a very interesting dimension that should be dealt with well. Even though uncertainty avoidance has been considered to have a negative impact on innovation as the literature says, my research show that the societies with high UAI are still capable of being successful in innovation. The possible reasons of this are: being proactive, planning carefully, thinking a couple of steps ahead and having well-designed law and regulations. So, a manager in a low uncertainty avoidance culture, should try to implement the steps that are mentioned here and one in a high ambiguity avoidance should try to encourage his employees to be more courageous.
Even though the manager knows the optimum cultural environment for innovation and takes the necessary actions to transform the organization, he will face a very difficult challenge, which is the defense of people against change. Human beings, by nature, are inclined to support the status quo and resistant to change (Kahneman, Knetsch and Thaler, 1991) and it is not easy for a manager to imply changes that does not comply with the culture people grew up with. A lot of research has been done to get over those psychological barriers and Everse (2011) concluded her research by stating the importance of communication in overcoming those barriers. According to her, first of all the core reason for the change should be explained to the people. They should know the real meaning of the cultural change they are facing. Unless the “Why” is explained explicitly, the full commitment and acceptance of the individuals cannot be expected. In order to implement a cultural change successfully, mutual trust of both sides is needed, thus making the existence for internal communication is vital. Along with the “why”, individuals will want to learn the answers to how, what, when and by whom; and a successful communication should provide all of the individuals with the certain answers for those questions.
6.3 LIMITATIONS
This thesis focused on the influence of culture on innovation outputs at the national level and I experienced some inevitable limitations for the research.
