E conomic development and culture driven innovations

E

conomic development and culture driven innovations

A multi-level analysis

Thomas Mentink

S2788624 – 140674875

MSc. International Business and Management

University of Groningen – Faculty of Economics and Business

MSc. Advanced International Business Management and Marketing

Newcastle University Business School

Supervisors:

Dr. A.A.J. van Hoorn

Dr. J. Kimmitt

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ABSTRACT

Innovation serves for many companies as the foundation upon which to build new, or shape existing business. Innovation rates vary significantly across countries and cultures. This paper assesses the relationship between a country’s stage of economic development, and their ability to generate different types of innovations. Beside economic development, different types of innovation are explained by differences in culture. Uncertainty avoidance is a cultural dimension, affecting innovation because innovation requires firms to undergo uncertainty and take risks. The moderating role of culture on the relation between economic development and types of innovation is tested. This research has been conducted by doing a literature review on innovation and economic development. 14720 individual firms from 19 countries in the BEEPS database provided statistics to test the multi-level model in a logistic regression analysis. The results suggest that not stage of development on its own, but moreover the national income and institutions foster exploratory innovation. The results suggest that the relationship between stage of development and type of innovation is moderated by uncertainty avoidance cultural values, such that the relationship suggests more exploitative innovation for individuals with higher uncertainty avoiding cultural values. The results of this research contribute to existing literature as it increases our understanding of different types of innovation, and what factors influence them.

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ACKNOWLEDGEMENT

I would like to make use of this opportunity to thank everyone who helped me during the process of this master thesis and enabled me to conduct such a research. First of all, I would

like to thank my supervisors Dr. A.A.J. van Hoorn from the University of Groningen and Dr. J. Kimmitt from the Newcastle University Business School. They have provided me with

extensive feedback, guidance and support which was beyond guidelines and expectations. Of course, I would not have been able to write this thesis without the support of my friends and family. Therefore, I would like to thank in particular my parents, for their unconditional support for my decisions and their encouragement during my studies.

Thank you,

Thomas Mentink

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Table of content

4.1 Innovation in different types of economies ... 17

4.2 Cultural influence ... 24

4.2.1Uncertainty avoidance ... 25

5 Data and Method ... 29

5.1. Data sources ... 29

5.2 Sample ... 30

5.3 Variables ... 30

5.3.1 Dependent variable ... 30

5.3.2 Independent variables ... 30

5.4 Research design and analysis ... 33

6. Empirical results ... 34

6.1 Direct Effects of National Culture and Development Stage ... 34

6.2 Moderating effect of Uncertainty Avoidance ... 35

6.3 Implications for hypotheses ... 38

7. Discussion and Conclusion... 41

7.1 Main findings and theoretical implications ... 41

7.2 Limitations and further research ... 43

8. References... 45

9. Appendix ... 55

Appendix A – Frequency table of sample ... 55

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List of Figures

Table 5.1 Descriptive statistics of sample page 31

Table 6.1 Logistic regression results page 36

Table 6.2 Logistic regression results with moderator page 37

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1. Introduction

Innovation rates vary significantly across countries. The contribution of innovation to national economic growth has been well established in the economic literature, both theoretically (Solow, 1956; Romer, 1986) as well as empirically (Mansfield, 1972; Nadiri, 1993). However, that is not to say that it is well understood. “In the most fundamental sense, there are only two ways of increasing the output of an economy: you can increase the number of inputs that go into the productive process, or if you are clever, you can think of new ways in which you can get more output from the same number of inputs” (Rosenberg, 2004, page 1). Innovation brings new technologies and new products that “help address global challenges, new ways of producing goods and delivering services to boost productivity, create jobs and help improve citizens’ quality of life” (Ramadani et al, 2013, page 3).

Innovation is seen as a critical process as in the previous year, the average growth rate of 2500 leading companies’ R&D investments in 2013 was significantly higher than the growth rate of their sales revenues (EU R&D Scoreboard, 2014). For many entrepreneurs, innovation serves as the foundation upon which to either build new, or shape existing ventures (Westhead, Wright, & McElwee, 2011).

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In his study on culture, Hofstede (1980) found that differences in national culture vary substantially along four dimensions, known as: uncertainty avoidance, individualism, power distance and masculinity. Two of these cultural dimensions, Individualism and Power distance, are theoretically and empirically related to innovation and invention (Shane, 1992).

During the past three decades Hofstede’s work has been the foundation for most of the business and management research on national culture. Hofstede’s (1980) cultural values are unequally represented across countries and therefore might explain differences in national innovation rates (Shane, 1993).

There is agreement among scholars that culture explains differences in national entrepreneurship and innovation rates (Shane, 1993; Gorodnichenko & Roland, 2011; Grilo and Thurik, 2005; Mueller & Thomas, 2000; Hofstede 2004; Thurik & Dejardin, 2012).

Prior research, although limited, has shown that national rates of innovation are also associated with a country’s economic environment (Dkhli & De Clercq, 2003; Nelson & Winter, 1977; Vernon, 1966). Income levels and industrial structures on national level appear to facilitate innovation at the national level, with human capital and trade openness fostering innovation in a structural manner (Shane, 1993).

Regarding innovation, we know much about the cross-country pattern of SME innovation performance in Europe (Boter & Holmquist, 1996) but this is outdated and only little is known about patterns and differences in other regions (Terjesen, 2013).

Based on Hofstede and Bond’s (1988) finding that uncertainty avoidance provides inconsistent results among certain Asian cultures (which led to the introduction of the cultural value dimension of long-term orientation), further research in Asian cultures might provide additional insights (Reimann et al., 2008).

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Limited attention has been drawn on the differences among firms. Not all companies within a country have the same interest in research and development. Only limited research on innovation has been done on a firm-level differences. That is where this study picks up. This study provides a cross-sectional analyses of how national economic development influences firm-level innovation. It compares different stages of economic development with the type of innovation at 14720 observations on firm level in 19 different countries. It shows how national values of development represent characteristics related to types of innovation and invention at the organizational level, and so make some societies more inventive than others.

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2. Literature Review

The following chapter will systematically review the existing literature on innovation. The reader will be introduced to different types of innovation. The review shows important studies conducted in the field of innovation in general and the research gap and disagreements that currently exist among scholars will be presented.

2.1 General background

Product innovation remains a subject of extensive theorizing and empirical examination. Researchers define innovation alternately as “a discrete product or outcome” or as “a process” (Gopalakrishnan and Damanpour, 1994). In this research, we use the former approach and define innovation as a product, relatively new to the industry, developed and marketed by a firm; it may emerge from existing scientific/technological information (through extension or synthesis) or new information (Freeman, 1982).

Despite years of research, a question that keeps innovation scholars theorizing is: what should firms do to develop and market new products frequently? Early studies (e.g. Mansfield, 1968; Scherer, 1965; Schmookler, 1966) analysed innovation inputs (R&D spending, number of scientists employed) for answers. The belief was that higher inputs would enhance scientific and technological competence, engendering several proprietary technologies to emerge from it. New products could be successfully developed from these technologies through incremental refinement, testing, and verification (Clark and Fujomoto, 1991). Research examining R&D inputs in innovation frequency has generally found that input levels positively affect the number of important product technologies generated (Comanor and Scherer, 1969; Mansfield, 1968; Schmookler, 1966).

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As a result, an innovation process that increases the information exchange routine among domains should minimize product development time, thereby maximizing innovation frequency (Rosenau, 1990; Takeuchi and Nonaka, 1986). In essence, a higher R&D input and an innovation process that continuously integrates several product development activities (Thompson, 1967) are both essential for achieving innovation frequency (Clark and Fujomoto, 1991; Stalk and Hout, 1990; Takeuchi and Nonaka, 1986).

The traditional focus in product innovation research is on “the firm’s ability to generate new scientific or technological information” (Wheelwright and Clark, 1992, p. 118). A firm acquires this competency by regular allocation of resources to research and development. Higher R&D investment increases the level of research activity within a firm and enables it to engage in basic research, essential for generating proprietary scientific information (Nelson, 1959). Higher R&D spending, over the long-term, also supports a firm with an experience necessary to turn research projects into successes (Grabowski, 1968; Hambrick and Macmillan, 1985). In a nutshell, higher R&D spending heightens the level of research activity within a firm and builds specialized scientific or technological expertise as a result. The tangible outcome of it is the ability to develop several significant products or technologies (Parthasarthy and Hammond, 2002).

But, a problem with other studies according to some researchers (e.g. Levin, 1986) is in the use of patent count as proxy for new technologies developed. Not all firms patent their inventions otherwise this would signal competition about a firm’s new product activities; as a result, findings of these studies could be less than valid (Parthasarthy and Hammond, 2002). Current research does not take sufficient account of the institutional environment both formal (economic development) as informal (national culture).

This research therefore focuses on the more important purpose of this subject: whether R&D intense firms develop and market new products more frequently when the institutional environment of the country fosters innovation.

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R&D efforts can be measured by expenditures on R&D (as a percentage of a firm's total sales) or by the number of persons carrying out R&D (as a percentage of total employment in a firm) (Kleinknecht et al, 2002, page 110). The relative amount of expenditures on research and development has traditionally been used as an indicator of a firm’s innovative activity in many industries (Scherer, 1980). Governments even use R&D intensity as a target in their policy objectives to argue for more R&D resources (Voyer, 1999; Katz, 2005). While it was often complained that R&D data could not be split, in recent years, it is increasingly possible to subdivide R&D by product versus process efforts. This subdivision is very important for empirical analyses of the impact of innovation on firm performance since product innovation efforts seem to be essential for firm growth, employment, profits and survival (Brouwer, Kleinknecht and Reijnen 1993; Geroski et al. 1993; Kleinknecht et al, 2002).

2.2 Types of innovation

Following previous literature, innovations can be classified in two domains: (1) the proximity to existing technologies, products, and services, and (2) the proximity to existing customer or market segments (Abernathy and Clark 1985, Benner and Tushman 2003, Danneels 2002, Jansen et al 2006).

Schumpeter (1934) described innovations both by type (product, process, marketing, and organizational) as well as by degree of novelty – what Bradley (2012) calls differentiation-related or novelty-differentiation-related innovations. Differentiation-differentiation-related innovation is concerned primarily with how entrepreneurs position their products in relation to the competition. Entrepreneurs respond to shortages and surpluses in the market created by incomplete information through resource acquisition, resource recombination, and sales with the hope of making a profit (Kirzner, 1997; Loasby, 1982; Rosen, 1997). Entrepreneurs seek profit through arbitrage by matching existing supply with existing demand, thereby reallocating available resources from less to more efficient uses (Kirzner, 1997).

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(Reinert and Reinert, 2006). Creative destruction is the process of continuous innovation, with successful applications of new technologies making old technology obsolete. Novelty-related innovation is concerned primarily with new sources of demand or supply” (Bradley et al., 2012, page 691).

Exploratory innovations are radical innovations and are designed to meet the needs of emerging customers or markets (Benner and Tushman 2003, p. 243; Danneels 2002). They offer new designs, create new markets, and develop new channels of distribution (Abernathy and Clark 1985). Exploratory innovations require new knowledge or departure from existing knowledge (Benner and Tushman 2002, Levinthal and March 1993, McGrath 2001).

Conversely, exploitative innovations are incremental innovations and are designed to meet the needs of existing customers or markets (Benner and Tushman 2003, p. 243; Danneels 2002). They “broaden existing knowledge and skills, improve established designs or existing products and services, and increase the efficiency of existing distribution channels” (Abernathy and Clark 1985, p. 5). Hence, exploitative innovations build on existing knowledge and reinforce existing processes and structures (Abernathy and Clark 1985, Benner and Tushman 2002, Levinthal and March 1993, Lewin et al. 1999).

Now that we know the different types of innovation, but what are the drivers of innovation? Traditional thinking about innovation and its management focuses almost exclusively on the capabilities and processes within companies for generating and marketing technologies. Although the importance of these factors is “undeniable, the external environment for innovation is at least as important” (Porter and Stern, 2001, page 28).

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Regarding exploratory and exploitative innovations, previous literatures argued that environmental dynamism, culture and competitiveness are likely to moderate the impact of both types of innovations (Levinthal and March 1993, Lewin et al. 1999).

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3. Economic Development

The following chapter will review existing literature on economic development. The reader will be introduced to economic history distinguishing between stages of economic development. The review shows a modern view of economic development as suggested by Porter et al. (2002).

There are many concepts of economic development. A frequently used operational notion of economic development focuses on the interrelated processes of structural change, and is referred to as structural transformation (Syrquin, 1988). Accumulation of physical and human capital, and shifts in the composition of economic activities (production, employment, consumption) are regarded as the core components of this transformation (Shane, 1993). Related socio-economic changes are urbanization, demographic transitions, a rising level of education and changes in the distribution of income (Wennekers et al., 2005).

In economic history, one tradition distinguishes between ‘stages of economic development’, hereby emphasizing discontinuities in development (Wennekers et al., 2005). A well-known example is Rostow's theory (1960), which hypothesizes five stages of economic growth. Major criticisms of this theory has to do with the suggestion of one unique path of development that has to be followed. Afterwards, Syrquin (1988) identified three stages of transformation: primary production, industrialization and the developed economy. This distinction also takes into account the population size of countries and patterns of international specialization.

In a modern view of economic development, as suggested by Porter et al. (2002), economic development means increasing advanced ways of producing and competing, and implies the evolution from a resource-based to a knowledge-generating economy (Wennekers et al., 2005).

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For a successful transition to this stage with its related middle-income status, countries must subsequently get their labour and capital markets working more properly, attract foreign direct investment and educate their work-force to be able to adopt technologies developed elsewhere (Wennekers et al., 2005). International competitiveness is primarily based upon high rates of production efficiency in manufacturing. The essential processes in moving from the first to the second stage are capital accumulation and technological diffusion. These may enable countries to achieve a certain degree of 'catch-up growth' on other economies.

The third stage of Porter’s (2002) model is that of a technology generating economy, named the “innovation-driven stage”. According to Porter et al. (2002, p. 17), countries that have reached this stage “innovate at the global technological frontier in at least some sectors”. Perhaps the hardest transition is from technology-importing, efficiency-based development to innovation-based development says Porter et al (2002). “In most economies, the evolution from middle-income to high-income status involves the transition from a technology-importing economy to a technology-generating economy, one that innovates in at least some sectors at the global technological frontier” (Porter et al., 2002, page 2). This requires a direct government role in fostering a high rate of innovation, through public, as well as private investments in research and development, higher education, improved capital markets and regulatory systems that support the start-up and investments of high-technology enterprises (Porter, Sachs and McArthur, 2002).

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The level of economic development is said to influence the environmental opportunities of entrepreneurship, such as the shift from industrial structure to services industries creates opportunities for entrepreneurs, start-ups and smaller firms. This is because the scale of economies differs and barriers to enter are lower (Carree and Thurik, 2003). Other explanations of economic development for different rates in entrepreneurship are competition, female labour participation, and unemployment rates (Blanchflower, 2000, Verheul et al., 2002). Because of the effect of economic development on entrepreneurship opportunities, we expect this relation to be also applicable on innovation opportunities.

Throughout this chapter it became clear that already in economic history, scholars distinguished between stages of economic development. Throughout this research we will follow the modern view of economic development suggested in the framework of Porter et al. (2002). This framework groups countries together in Factor-driven, Investment-driven and Innovation-driven economies.

4. Theoretical Framework

In this chapter, the theoretical framework of this study is introduced. It links information and frameworks from the previous chapters and highlights connections. After theorizing, 4 hypothesis are constructed that guide this study on innovation.

4.1 Innovation in different types of economies

As suggested by Porter et al (2002) economic development countries at the lowest level, i.e. the factor-driven stage, base their production upon the mobilization of primary factors of production. These are land, commodities and unskilled labour. Competitiveness is primarily based upon low factor costs and the presence of minerals and other commodities.

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standard of living, but also by preventing the development of a market-place that views them as customers and seeks to serve their needs (Arndt, 1988; Karlan and Morduch, 2009; Prahalad, 2009).

Innovation in developing countries has often centred on changing institutions or infrastructure that addresses efficiency issues in the use of land, labour and other resources of production (Powell, 2008). Recently, more attention has been directed towards market innovation in the developing world as well (Auber, 2010; Mendoza and Thelen, 2008; World Bank, 2010). Although the capacity to innovate is partly determined by knowledge and motivation (McMullen and Shepherd, 2006), the poor in developing countries may face additional obstacles as well. These include inefficient formal institutions such as inadequate creation or enforcement of property rights grounded in the rule of law (Rodrik, 2000), low transaction governance capacity (Prahalad, 2009), or debilitating informal institutions that discourage or entirely prevent the belief that change is possible. Such ambivalence about the horizon of possibilities grows and can origin from historical conditioning or historical failed attempts to improve the conditions.

In addition, a country's legal, financial, fiscal and education systems, which Khanna and Palepu (1997) named “soft infrastructure”, have strong influence on how much value from an opportunity is appropriable for an entrepreneur. In terms of legal systems, matters such as degree of protection for property rights, patent protection and the legal rights are factors that vary widely across nations (La Porta et al., 1998). These factors play an important role in evaluating the attractiveness of an opportunity to an entrepreneur. Financial systems influence the evaluation process most directly through the cost and availability of capital. In contexts where market inefficiencies raise the cost of acquiring capital, possible benefits for entrepreneurs are diminished (Claessens et al., 1999). In national contexts where capital is unavailable, the benefits from a good business opportunity may not be available at all (Carney and Gedajlovic, 2002; Baker et al., 2005).

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different starting and running conditions for entrepreneurial ventures (Porter, 1990; Whitley, 1999; Baker et al., 2005).

Many entrepreneurs in developing economies face environments that provide neither specialized resources and institutional support nor good financial, educational, political or legal infrastructure (George and Prabhu, 2000).

Therefore, in developing economies, we would expect innovation to be important, though the types and degree of innovations may differ for several reasons. Innovation does not necessarily require extension of the knowledge frontier. An innovation may be new only to the focal society (McMullen, 2011). Prior research refers to these incremental innovations as exploitative innovations that “are designed to meet the needs of existing customers or

markets” (Benner and Tushman 2003, page 243). Bradley et al (2012) described, while

discussing Schumpeter (1934), that “differentiation-related innovation is concerned primarily

with how entrepreneurs position their products in relation to the competition” (Bradley et al.,

2012 page 691). The focus is on how entrepreneurs have innovated to differentiate their products from similar products. This implies that markets already exist and that entrepreneurial performance is a matter of outcompeting the competition using step-by-step improvements (Bradley et al, 2012).

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To summarize the above discussion, the following hypothesis can be formulated (H1):

Firms in factor-driven economies are less likely to introduce exploratory innovations (i.e. New products) and more likely to introduce exploitative innovations (i.e. Product upgrading).

Meanwhile, in the Innovation-driven stage of Porter’s (2002) model are countries grouped with a technology generating economy. Countries that have reached this third stage “innovate

at the global technological frontier in at least some sectors” (Wennekers et al., 2005, page

294).

The transition to this stage requires a country to develop its ability to generate as well as commercialize new knowledge (Porter et al., 2002). This entails intensive cooperation between universities, private businesses and governments. Once a critical mass of knowledge, technologies, skills and purchasing power has been built up, innovation may achieve increasing returns to scale (Sachs, 2000). For high-income economies at this innovation-driven stage of economic development, global competitiveness is critically linked to high rates of social learning, especially science-based learning, and the rapid ability to shift to new technologies (Porter, Sachs and McArthur, 2002). Once reaching high-income status, the basic challenge facing countries is typically to generate high rates of innovation and commercialization of new technologies. The critical institutions in a country will therefore differ from countries in lower stages of development (Porter et al., 2002).

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themselves together in teams or coalitions that are based on ‘swift trust’ networks to increase innovativeness (Laursen and Salter, 2006).

This is in line with what Porter (2002, page 7) has to say on the transition from investment to innovation-driven economies; “To reach the Innovation-driven stage, companies must

innovate at the world technology frontier, develop unique product designs, sell globally and create more decentralized and flexible organizational structures” (Porter et al., 2002, page 7).

Leading research institutions must emerge, along with strong research collaboration between universities and business, venture capital availability, truly sophisticated demand conditions and intense local competition all foster countries in development to an knowledge-creating economy (Porter, 2002).

Evidence suggests that innovation generally has a positive relationship with performance, whether through internal efforts at R&D (Lööf and Heshmati, 2006) or through more collective efforts with external partners of the firm (Garud and Karnoe, 2003). The challenge, however, becomes sustaining performance in innovative environments where knowledge spreads rapidly. When competitors are investing more heavily in search of technological opportunities, firms may have to increase the novelty of their innovations to maintain higher performance. Examinations of the robotics (Katila and Ahuja, 2002) and hard drive industries (Christensen, 1997), for example, suggest that search for more novel products leads to greater innovation and overall firm performance in developed nations. Thus, radical innovations provide a greater first-mover advantage and the opportunity to generate higher rents in the long term (Bradley et al., 2012).

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Therefore the following hypothesis can be formulated (H2):

Firms in innovation-driven economies are more likely to introduce exploratory innovation (New products) and less exploitative innovations (product upgrading).

As countries move to the second stage, the so called “industrialization stage”, economic growth becomes more capital intensive and thus more investment-driven (Porter et al., 2002). The investment-driven stage is associated with the manufacturing of intermediate and capital goods (heavy and chemical industrialization) and infrastructural building such as housing, transportation, communications and public works constructions (Ozawa, 1992).

For a successful transition to this stage and its related middle-income status, countries must eventually get their labour and capital markets working more properly than before, attract foreign direct investments for finance and educate their human resources to be able to adopt technologies developed elsewhere (Wennekers et al., 2005). Competitiveness is primarily based upon high rates of production efficiency in manufacturing. The key processes in moving from the first to the second stage are capital accumulation and technological diffusion. These may enable countries to achieve a certain degree of “catch-up growth” on competing countries. The investment-driven stage is related to scale-based advantages in large-scale, capital-intensive goods (Ozawa, 1992). As countries advance, the main challenge is to make connections with international production systems and attracting sufficient flows of FDI for upgrading productivity and efficiency (Porter et al., 2002).

In the Investment-driven stage, efficiency in producing standard products and services becomes a ruling source of global competitiveness. The products and services produced become more sophisticated, but technology and designs still largely come from abroad. Technology is often times accessed through licensing, joint ventures, foreign direct investment or imitations. Nations in this stage of development not only assimilate foreign technology, however, but they also develop their own capacity to improve on these technologies from abroad. The national business environment supports and requires investment in efficient infrastructure and modern production methods. Companies often produce “under contract to

foreign original equipment manufacturers (OEM), which control the design and marketing”

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These improvements in production methods and efficiencies are incremental innovations and are designed to meet the needs of existing customers or markets. These exploitative innovations build on existing knowledge and reinforce existing skills, processes and structures (Abernathy and Clark, 1985; Benner and Tushman, 2002; Levinthal and March, 1993; Lewin et al, 1999).

To summarize the above discussion the following hypothesis can be formulated (H3):

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4.2 Cultural influence

A country’s culture influences the rate of entrepreneurship through the cultural values of a society (Hofstede, 1980). The underlying value systems of individuals’ national culture motivates them to behave in a specific ways (Hofstede, 1980), such as participating in innovation. When studying existing literature it becomes clear that a precise definition of culture is hard to pin down, and it remains the subject of intense scholarly debate (Taylor and Wilson, 2012; Trompenaars and Hampden-Turner, 1998; Geertz, 1973). For the purpose of this paper, however, only a useful working definition is required. Hofstede (1980) defines culture as the "collective programming of the mind which distinguishes the members of one group from another". Hofstede’s definition is used throughout this research.

Hofstede (1980) initially found differences in national culture that vary substantially along four dimensions: power distance, individualism, masculinity and uncertainty avoidance. Hofstede (1980) found that these cultural values are unequally represented across countries. As such, differences in these values might explain differences in national rates of innovation (Shane, 1993). Hofstede (1991) stated that the power distance index dimension expresses the degree to which the less powerful members of a society accept and expect that power is distributed unequally. Characteristics of power distance affecting innovation include the presence and level of social or organizational hierarchy, centralized power, formal vertical communication flows, top down control, formal rules and procedures, and resistance to change (Jones and Davis, 2000; Shane, 1993; Thompson, 1967).

Hofstede (1991) defined the individualism dimension as a preference for having primarily a distant social framework in which individuals are expected to be responsible for themselves and their immediate families. Individualistic societies value freedom, which is viewed as necessary for creativity, more than collectivistic societies do (Shane, 1992). Innovation also requires an outward-looking view, through which individualistic societies are able to gather the information that is necessary for innovation (Shane, 1993). Collectivism and conformity are expected to impede innovation (Taylor and Wilson, 2012).

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continuous attention on training and the improvement of the individual. The emphasis on group integration as opposed to individual achievement, as found in more feminine societies, is typically believed to be less supportive of innovation (Jones and Davis, 2000).

4.2.1Uncertainty avoidance

This cultural dimension of Hofstede (1980) deals with to which extent national groups tolerate ambiguity. Cultural norms and practices are known to shape individuals’ entrepreneurial behaviours, such as international orientation, start-up attempts and innovative activities (Shane, 1993; Bowen and De Clercq, 2008; Wennberg, Pathak and Autio, 2013). A culture is characterized by high uncertainty avoidance when members feel jeopardized by ambiguous situations. Sully de Luque and Javidan (2004, page 618) suggest that “societies having lower

levels of uncertainty avoidance tend to be less calculating when taking risks and show less resistance to change”. Lower uncertainty-avoidant persons take higher risk options as they

perceive the option to be less risky than a person with higher uncertainty avoidance.

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According to the Green Paper on Entrepreneurship by the European Commission (2003), entrepreneurs in Europe must face a social stigma of failure which augments the risks associated with engaging in entrepreneurial activities and undergoing uncertainty. Apart from the formal legal and financial consequences implied by bankruptcy and entrepreneurial failure, the informal social repercussions often act as important obstacles to entrepreneurship and innovation, which include undergoing uncertainty (Vaillant and LaFuente, 2007).

Landier (2004) demonstrates how different personal attitudes of entrepreneurs explains differences in levels of entrepreneurship. The same author concludes that the stigma associated with failure is an important determinant of entrepreneurial activity, conditioning not only the decision to become an entrepreneur, but also the character of the venture to be launched, the intensity of research and development, and the decision to terminate an innovative project (Vaillant and LaFuente, 2007). In America the social norms are found to be more favourable to business failure, which is seen as a step within an entrepreneur’s personal development process (Saxenian, 1994).

Countries exhibiting strong uncertainty avoidance characteristics maintain fixed codes of belief and behaviour and are intolerant of unknown behaviour and ideas (Hofstede, 1991). The fundamental aspect of this cultural dimension is how a society deals with the fact that the future can never be known, given that some societies attempt to control the future while others just let it happen. Uncertainty accepting societies maintain a more flexible attitude whereby practice counts more than principles.

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cultural values. When a developed stage of development will suggest more exploratory innovations to be prevalent, this relationship will be moderated if a country faces high uncertainty avoidance, such that high uncertainty avoidance countries are more eager to introduce more exploitative innovations than their stage of development would suggest. Therefore, uncertainty avoiding countries tend to embrace the unknown world of exploratory innovation less than uncertainty accepting countries. This leads to formulating the following hypothesis (H4a):

The relationship between stage of development and type of innovation is moderated by the uncertainty avoidance cultural values such that the relationship suggests more exploitative innovation for individuals with high uncertainty avoidance cultural values.

This moderating effect would be the other way round in low uncertainty avoiding societies. Exploration implies firm behaviours characterized by search, discovery, experimentation, risk taking and innovation (Cheng and Van de Ven, 1996; March, 1991). In cultures where there exists a relatively greater tolerance and/or acceptance of entrepreneurial failure and acceptance of uncertainty, a far larger proportion of the adult population tend to engage and become involved in innovative activities (Landier 2004).

Radical innovations provide a greater first-mover advantage and the opportunity to generate higher rents (Bradley et al., 2012). Dynamic environments make current products and services obsolete and require that new ones be developed (Jansen et al. 2005, Sorensen and Stuart 2000). To minimize this threat of obsolescence, organizations need to introduce exploratory dinovations that depart from existing products, services, and markets (Janssen et al, 2006).

Therefore, I formulate the following hypothesis (H4b):

The relationship between stage of development and type of innovation is moderated by the uncertainty avoidance cultural values such that the relationship suggests more explorative innovation for individuals with low uncertainty avoidance cultural values.

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5 Data and Method

The following chapter illustrates the data and methods that are used for this research. The sources of data will be introduced first, together with the chosen sample. Afterwards, the dependent and independent are explained and descriptive statistics provided. The chapter ends with the research design and data analysis.

5.1. Data sources

The predicted hypotheses are tested using survey data from the BEEPS, supplemented with data from other sources. The data are taken from the BEEPS (Business Environment and Enterprise Performance Survey) from the years 2012-2015. The BEEPS is an initiative of the European Bank for Reconstruction and Development (EBRD) and the World Bank to investigate the extent to which government policies and practices facilitate or impede business activity and investment in Central and Eastern Europe and the former Soviet Union. The BEEPS survey is conducted uniformly in all the countries covered. The combined BEEPS V and MENA ES dataset which I use is prepared by the EBRD as a courtesy to the users. The harmonized variables of the two datasets follow the BEEPS V questionnaire guidelines.

The data for national levels of economic development are derived from the Global Entrepreneurship Monitor (GEM) data-set (2012). The Global Entrepreneurship Monitor is the world's foremost study of entrepreneurship. The GEM database includes various metrics of entrepreneurship, as well as a wide selection of explanatory variables from standardized national statistics (Wennekers et al., 2005). This data is used in combination with data on national cultural attributes collected by Hofstede, Hofstede and Minkov (2014). Together, the dataset contains 22,449 completed interviews in 39 countries or territories in Eastern Europe, Central Asia, the Middle East and North Africa. This data will be analysed trough IBM’s SPSS software version 22 (Statistical Package for the Social Sciences) and STATA Data Analysis and Statistical Software version 14.

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5.2 Sample

To be able to provide conclusions on the formulated hypothesis, decisions have to be made regarding which variables are most relevant for each hypothesis. The initial sample size contained 22449 firms in total. As not all necessary data is available for every firm and country, the sample needs to be narrowed down. After filtering out the above mentioned variables and countries, there is a final sample size of 14.720 firms covering 19 countries. The analysed countries include Albania, Bulgaria, Croatia, Czech Republic, Egypt, Estonia, Hungary, Israel, Jordan, Latvia, Lithuania, Morocco, Poland, Romania, Russia, Serbia, Slovenia, Turkey and Ukraine. The frequencies of respondents from each country is provided in Appendix A.

5.3 Variables

5.3.1 Dependent variable

The dependent variable in the analysis is probability of exploratory innovation. Question H.01 of the BEEPS survey asks the respondent if “the company has introduced new or significantly improved products or services recently?” Question H.02 that follows, asks if “any of the new or significantly improved products or services of this establishment are new to one of this establishment’s markets”? These questions fit perfectly to the distinction in types of innovation based on new markets, stated before and supported by Abernathy and Clark (1985), Benner and Tushman (2003), Danneels (2002) and Jansen et al (2006).

5.3.2 Independent variables

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and De Clercq 2008). A potential limitation of the GEM data-set for this research, however, is that it captures any kind of entrepreneurial activity, including necessity driven entrepreneurship. Table 5.1 shows the descriptive statistics of the used sample.

Table 5.11 – Descriptive statistics of sample

N Minimum Maximum Mean Std. Deviation

GDP Per Capita 22247 458,62 24019,76 5561,95 4622,79 Uncertainty Avoidance 15094 60 95 86,81 10,191 R&D Spending 22058 1,00 2,00 1,8911 ,31151 Firm Age 22018 ,00 190,00 17,44 13,46 Female Manager 22160 ,00 1,00 ,1532 ,36019 Population 22247 633,00 142703,00 50360,87 52784,746 Rule of Law 22247 -1,2669 1,1267 -,3355 ,5850

Valid N (list wise) 14702

The influence of national culture is checked with data from Hofstede et al., (2010). Hofstede, Hofstede and Minkov (2010) performed their study on cultural differences between countries and regions worldwide, this current data is updated and extended with more countries. The original authors developed an index to measure the extent to which cultural members feel threatened by insecure or unknown situations. The scale runs from 0 - 100 with 50 as a midlevel. The rule of thumb is that if a score is under 50 the culture scores relatively LOW on that scale and if any score is over 50 the culture scores HIGH on that scale. Higher scores reflect a preference for avoiding uncertainty (Hofstede, Hofstede and Minkov, 2010, page 197).

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5.4 Research design and analysis

Since culture is a collective construct theorizing about societal structures (Hofstede 1991), studies using individual-level perceptions of culture may suffer from ecological fallacy by assuming that collective-level attributes are directly reflected in individual behaviours (Peterson, Arregle, and Xavier 2012). Conversely, studies of entrepreneurship on the individual or firm levels of analyses often suffer from the individualistic fallacy of ignoring the broader context within which individuals are embedded (Stenholm, Acs, and Wuebker 2013). Multi-level designs help avoid these fallacies by allowing simultaneous consideration of country-level and individual-level factors. The data constitutes a cross-sectional panel grouped by country, combining observations at the individual and country levels. This implies more than one level of analysis. Such nested data necessitate multi-level techniques for analysis (Hofmann, Griffi, and Gavin, 2000). The units of analysis are individuals (at a lower level) who are nested within aggregate units at a higher level (Luke, 2004). The dependent variable, type of innovation, is at the lowest level of analysis, the individual firm level.

The models are based on random-effect logistic regression for which an individual firm’s probability of one type of innovation is the outcome, influenced by country-level and individual-level factors. Standard errors are clustered on country level.

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6. Empirical results

Chapter 6 presents the empirical results of the analysis. Different models will be tested to answer the hypotheses. Implications for the hypotheses are provided and discussed.

6.1 Direct Effects of National Culture and Development Stage

Table 6.1 illustrates the regression analysis for the determinants of exploratory innovations, both individually measured and combined. The p-values of the logistic regression model were significant (p < .01) for some of the independent variables, but not all. This indicates that the independent variables Uncertainty Avoidance, R&D Spending and Firm Age predict the probability of Exploratory Innovations for new markets within a firm. The relationship of, both individually and combined, the firm-level variables Firm Age and R&D Spending are significant, which suggests that the relationship of the regression coefficients are not attributed to chance. Country-level variable Uncertainty Avoidance is only statistically significant when the regression is done with all variables together.

Individually measured, the cultural influence of uncertainty avoidance has no significant result on the probability of explorative innovation. Uncertainty avoidance has a positive coefficient (.0260893) although not significant (p = 0.620) when measured individually. However, the relationship of uncertainty avoidance changes to an even more positive one, which implies that when other factors are taken into account, high uncertainty avoidance has a more positive impact on the probability of explorative innovation. The relationship is statistically significant, which implies the relationship is not attributed to chance (p = 0.006).

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in combination with other variables. The negative coefficients suggest that when a company starts spending on R&D, this lowers their probability of introducing explorative innovations.

6.2 Moderating effect of Uncertainty Avoidance

The second model that has been tested included the moderating effect of Uncertainty Avoidance. The results are provided in table 6.2. A moderating variable has been created out of the standardized values for Development Stage and Uncertainty Avoidance.

The inclusion of the moderator variable in the model has changed the outcome of the regression analysis. First of all, the negative coefficient of the moderator variable is statistically significant (P< 0.01). After introduction of the moderator variable, the

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Table 6.1 – Logistic regression results

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Table 6.2 – Logistic regression with moderator

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6.3 Implications for hypotheses

The above described results have thus consequences for the formulated hypotheses of this research.

Hypothesis 1 stated that firms in factor-driven economies are less likely to introduce exploratory innovations and more likely to introduce exploitative innovations (i.e. product upgrading). The coefficient for stage of development reports a negative relationship (Coef = -.353). This implies that when the firm is located in a lower development stage country, a firm is more likely to introduce exploratory innovations. The hypothesis suggested a positive relationship. Even though the result is significant, hypothesis 1 is rejected.

Hypothesis 2 was stated according to hypothesis 1. The second hypothesis stated that firms in innovation-driven economies are more likely to introduce exploratory innovation and less exploitative innovations. The hypothesis suggests a positive relationship. As mentioned above, the coefficient for stage of development reports a negative relationship (Coef = -.353). The results are statistically significant. This implies that when the firm is located in a higher development stage country, the firm is less likely to introduce exploratory innovations. Consequently, this means that no support is found for hypothesis 2.

Hypothesis 3 stated that in investment-driven economies, firms are likely to market fewer explorative innovations and more exploitative innovations. As mentioned above, the coefficient for stage of development reports a negative relationship (Coef = -.353). This hypothesis, however, suggests a negative relationship in contrast to hypothesis 1 and 2. When a firm is located in a more developed place, from factor- to investment-driven economy, it becomes more unlikely for the firm to market explorative innovations to new markets. The regression analysis confirms this hypothesis with statistical significance. Hypothesis 3 is supported.

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would suggest. The relationship is statistically significant with P< 0.000. Hypothesis 4A is confirmed.

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Table 7.1 – Hypothesis result summary

Hypothesis Result

H1 Firms in factor-driven economies are less likely to introduce exploratory innovations (i.e. new products) and more likely to introduce exploitative innovations (i.e. product upgrading).

Not supported

H2 Firms in innovation-driven economies are more likely to introduce exploratory innovation (new products) and less exploitative innovations (product upgrading).

Not supported

H3 In investment-driven economies, there will be fewer explorative innovations (i.e. new product innovations), and more upgrading of existing products i.e. more exploitative innovations.

Supported

H4a The relationship between stage of development and type of

innovation is moderated by the uncertainty avoidance cultural values such that the relationship suggests more exploitative innovation for individuals with high uncertainty avoidance cultural values.

Supported

H4b The relationship between stage of development and type of

innovation is moderated by the uncertainty avoidance cultural values such that the relationship suggests more explorative innovation for individuals with low uncertainty avoidance cultural values.

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7. Discussion and Conclusion

This chapter intends to discuss the findings of the analysis. Based on the theoretical framework implications are provided. This chapter wraps up this thesis. The contributions as well as limitations of this study are discussed, together with suggestions for future research.

7.1 Main findings and theoretical implications

The results of the clustered logistic regression analysis imply that when a firm is located in a country with a lower stage of development, the firm is more likely to introduce exploratory innovations. This is the opposite of what was expected in the literature review, however, it seems that development does not prevent the firms from being any more innovative than absolutely necessary to generate revenue for their business. It is important to note the big negative impact of R&D Spending on innovation. The results suggest that when firms start spending on R&D, the firm is less likely to introduce exploratory innovations. At the factor-driven stage of development, production is based upon the mobilization of factors of production. As countries move to the next stage, economic growth becomes more capital intensive and thus more spending- and investment-driven (Porter et al., 2002). Middle income countries need to attract foreign investment and technologies developed elsewhere. With this import of foreign technology, the level of innovativeness is lower, while spending on R&D becomes higher. This lower innovativeness is also reflected in the statistical analysis with hypothesis 3 confirmed. In the Investment-driven stage, efficiency in producing standard products and services becomes a ruling source of global competitiveness. The products and services produced become more sophisticated, but technology and designs still largely come from abroad. These countries should invest in a sound policies and strong rule of law, to be able to make the step forward to the knowledge-creating economy, the innovation-driven stage in the model.

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with a higher cultural values for avoidance of uncertainty, this result suggests a lower possibility of marketing exploratory innovations. The other way round, this suggests more exploitative innovations than their stage of development would suggest.

As noted in the literature review, it is already proven by scholars that cultural norms, values and practices shape individuals’ entrepreneurial behaviours (Shane, 1993; Bowen and De Clercq, 2008; Wennberg, Pathak and Autio, 2013). Stages of development group individual countries together on certain aspects of development. Some countries are just entering a development stage, while others are almost leaving the stage of development. Differences in avoidance of uncertainty occur on individual country level, moderating the effects of the complete group on innovation.

These differences within groups are also seen in the research after analysing levels of GDP. GDP initially explained a positive relationship with exploratory innovation. Higher levels of GDP per capita means higher stage of development in a country, which in turn increased the probability of exploratory innovation. However, after the introduction of the country-level variable Rule of Law, the relationship of GDP to innovation turned negative. This transition implies that not the GDP itself, but the state of development with existing regulations and institutions in a country explain the type of innovation. Countries with higher levels of GDP per capita, are more developed and have more developed regulatory systems. It has been mentioned before in the literature review, but innovation in developing countries has often centred on changing institutions and infrastructure (Powell, 2008). Inefficient formal institutions discourage or entirely prevent the belief that change is possible. Innovation requires firms and entrepreneurs to undergo uncertainty and undertake risky behaviour.

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7.2 Limitations and further research

In this thesis are some substantial and noteworthy limitations.

First, there is the concern of generalizability of the data and results. The BEEPS dataset that is used for analysis collected its data between 2012 and 2015, which means that at the time of writing, some data is three years old, some is from this moment. The business environments in some of the surveyed countries may have changed drastically, for example the developments of last year in Russia and Ukraine. Another entity that affects the generalizability is the fact that, despite the relatively sufficient amount of countries covered in the sample size, the covered countries in the research are all from Eastern Europe, with additions from Central-Asian and African countries close to Europe. Countries from other continents are not taken into account.

The second limitation is regarding the reliability and accuracy of the research. The sample size was narrowed down from 22499 observations in 39 countries, to 14720 in 19 countries. The main cause for this drop has been the addition of the Uncertainty Avoidance index by Hofstede (2010). The Hofstede Institute has not yet analysed all countries originally in the sample. After analysis of the GLOBE Survey as well, I decided to use Hofstede’s index because it provided the most observations in different countries. If more data on Uncertainty Avoidance for different countries would have been available, it would have provided more accurate results. Adding to this, most data is generated by conducting interviews. Although the quality of the research at BEEPS is generally high and professional, a cautious approach is recommended. Interviewers and interviewees could not always be telling the truth and could for example misinterpret questions or answers.

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Analysis of the data has shown some outliers in the sample, as 28.0% of the observations are from Russia. The results would be more reliable if the observations were more equally distributed based on their countries of origin.

As stated in the results, the R-squared scores for both models are low, indicating that the probability of explorative innovation can only limited be attributed to these firm- and country-level determinants of explorative innovation. The diversity between the observed companies on firm level makes it harder for a model to predict the values on country level. Additional or different variables could therefore lead to better results but these were not available in the dataset and due time constraints.

Although the available data and procedures both face limitations, the results lead to interesting avenues for future research. More extensive models could be tested, to include more independent variables which might explain and predict the probabilities of explorative innovation in more depth.

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