17.pdf

 

R&D driven innovation in emerging markets The moderating role of institutional Voids

Tara Goverts Student number: 2559281 MSc BA – Specialization Strategy & Organization VU University Amsterdam Application date: 01/12/2015 Submission date: 01/05/2016 Thesis supervisor: Second supervisor:

Dr. R.O Mihalache Dr. A.S. Alexiev

       

Master  Thesis  -­‐  T.  Goverts  

   

Table of content Abstract 1. Introduction

3 4

2. Theoretical Background 2.1. Innovation in emerging markets 2.2. Research and development 2.3. Institutional voids 2.4. Hypotheses development

7 7 8 9 10

3. Methodology 3.1. Research design and data collection 3.2. Measurement of variables 3.3. Data analysis

15 15 17 20

4. Empirical Results

21

5. Discussion 5.1. Managerial implications 5.2. Limitations and future research

25 25 27

6. Conclusion

29

7. References

30

8. Appendix Appendix A: conceptual model Appendix B: country sample Appendix C: operationalization of the model variables  

35 35 36 39

 

2  

       

Master  Thesis  -­‐  T.  Goverts  

   

Abstract As a response to the rising internationalization of R&D operations, this study attempts to increase understanding of the effectiveness of firm’s R&D in enabling innovation in emerging markets. It is often observed that the underdeveloped institutions of foreign markets (i.e., the institutional voids) challenge the operations of multinationals. Using cross-country panel data, this paper empirically investigates if and how underdeveloped institutional conditions moderate the relation between R&D and innovation. By performing the generalized estimating equations (GEE) technique, we find that different levels of institutional conditions significantly affect the strength of R&D as a driver for innovation output. Specifically, it is observed that the lack in training institutions in emerging markets weaken the relation between R&D and innovation, while the underdevelopment in financial market and local supplier conditions seem to strengthen the relation.                        

 

3  

       

Master  Thesis  -­‐  T.  Goverts  

   

1. Introduction In the past decades, a common understanding has been achieved about the importance of innovation. Economists have acknowledged that in an era where global competition and economic prosperity is rising, innovation is key to business success (Hult, Hurley & Knight, 2004; Back, Parboteeah & Nam, 2014). While innovation in developed markets has obtained extensive attention in academic research, considerably less attention has been paid to innovation in emerging markets (Hult et al., 2004; Ernst, Kahle, Dubiel, Prabhu & Subramaniam, 2015). This is striking as the need for innovation in emerging markets has increased significantly in the last few years (Global Innovation Index, 2015). Ever since the late 20th century, emerging economies have experienced rapid economic growth. Due to increase in foreign direct investments, the level of gross domestic product and income grew abundantly (Alfaro, Chanda, Kalemli-Ozcan & Sayek, 2004; World Bank, 2015). The rising wealth has brought millions of people out of abject poverty and lifted them into the low-middle income segment1. As this segment accounts for nearly 4 billion people and is expected to cover half of global consumption by 2025, it is undeniable that the demand for products and services is growing in this segment (London & Hart, 2004, Ernst et al., 2015). Consequently, innovation and new product development is growing in importance in emerging markets (Atsmon, Child, Dobbs & Narasimhan; 2012; Ernst et al., 2015). Hence, the locus of innovation is shifting (Govindarajan & Ramamurti, 2011 Li & Kozhikode 2009). While multinationals primarily rushed into emerging markets for cheap labor and low-cost production, recent economic growth has encouraged firms to explore the opportunities presented by the demand side and triggered the introduction of innovations strategies (Govindarajan, & Ramamurti, 2011; Li & Kozhikode 2009). By intensifying local research and development (R&D), multinationals strive to take the lead in new product development in emerging markets (Zhang, Li, Hitt, & Cui, 2007; Li & Kozhikode 2009) For years, scholars                                                                                                                

1  Ernst  &  Young,  Hitting  the  sweet  spot;  the  growth  in  the  middle  class  of  emerging  markets  (2013),  p.  5.  

 

 

4  

      Master  Thesis  -­‐  T.  Goverts         have acknowledged R&D as a strong driver of innovation (Wu 2007; Mairesse & Mohnen 2005). Therefore, it seems valid that multinationals rely on R&D to provoke overseas product development. Yet statistics show that despite the intensification of local R&D, multinationals are struggling to augment new product development in emerging markets (Khanna, Palepu & Sinha, 2005; Dawar & Chattopadhyay, 2002, Govindarajan, & Ramamurti, 2011). As of this, prior studies are questioning the role of R&D when striving for innovation output in foreign markets (Goñi & Maloney, 2014 Schneider 2005). A reason that is often suggested for the disappointing results is the lack of welldeveloped institutional conditions (Khanna et al., 2005; Goñi & Maloney, 2014). Institutions are argued to be of great importance in R&D processes as they provide complimentary resources and knowledge that is needed to efficiently transform R&D input into innovation output (Allen & Cohen, 1969). As emerging markets lack well-developed political, training or financial institutions, commonly referred to as institutional voids, it is expected that multinationals face difficulties in execution business models (Khanna & Palepu 1997, Dawar & Chattopadhyay, 2002; Porter & Stern, 2001). Consequently, literature questions the value of R&D in shaping innovation when operating in markets that lack well developed institutions (Porter & Stern, 2001; Khanna et al., 2005). Given the relatively new and unexplored phenomenon of innovation in emerging markets, little research has empirically investigated the affect of the lacking institutions on the productivity of R&D (London & Hart, 2004; Hult et al., 2004; Brem & Wolfram; 2014). Moreover, the little existing research that has addressed innovation in emerging markets has mostly been qualitative or single-country based (Ernst et al., 2015). As innovation in emerging markets is obtaining a more prominent role for future business growth, it is vital that firms understand how lacking institutions affect their models in shaping innovation (Khanna et al., 2005; Mair & Marti, 2009; Porter & Stern, 2001). This paper sets a first step in increasing this understanding by examining the influence of institutions, or the lack of them, on the R&D when striving innovation output in emerging markets.

 

5  

      Master  Thesis  -­‐  T.  Goverts         Drawing on a cross-country data, this study empirically investigates the following research question: “How do institutional voids influence the effectiveness of R&D in developing innovation output?” By providing insight in the moderating role of institutions on the relation between R&D and innovation this paper contributes to literature in several ways. Firstly, it amplifies the little existing literature on innovation in emerging markets by questioning the often taken-for-granted relationship between R&D and innovation (Wu 2007; Mairesse & Mohnen 2005). Secondly, it identifies the challenges set by institutional voids when striving new product development in foreign markets (Khanna et al., 2005; Khanna, & Palepu, 2013). Thirdly, it provides practical contributions by empirically mapping the moderating effect of these voids on the effectiveness of R&D models, hereby increasing managers’ insights in the value of R&D operations in foreign markets (Khanna et al., 2005; London & Hart, 2004). The rest of the paper is structured as follows. First, some theoretical background on the growing importance of innovation in emerging markets is given. Next we provide deeper insight in the role of institutional conditions on innovation activities and construct the hypotheses that are used to answer the research question. Subsequently, we discuss the research methods and present the empirical results. Lastly, some implications, limitations and suggestions for future research are given.

 

6  

       

Master  Thesis  -­‐  T.  Goverts  

   

2. Theoretical background In order to build a common understanding of why it is important to increase understanding of innovation in emerging markets some fundamental observations are introduced. Firstly, theoretical background on emerging markets and their need for innovation is given. Thereafter we discuss the essence of R&D and its role in new product establishment. The conflicting views that arise from current literature on the importance of R&D for innovation output in emerging markets will form the basis for our hypotheses development.   2.1 Innovation in emerging markets When talking about ‘emerging markets’, literature refers to markets in transition from planned to free-market economies that lack history of foreign investment, show high growth rates, huge potential and high levels of risk (Mody, 2004; Burgess & Steenkamp, 2006). In the past decades, scholars paid abundant attention to the movement of western multinationals to emerging markets (Dawar & Frost, 1999; Khanna, & Palepu 2013). As emerging economies loosened their tariff barriers and opened up to foreign trade, western multinationals turned to developing economies to access cheap supplies such as raw materials, labor and logistics (Garelli, 2008). As of the start of the 21st century, the world has entered a new wave of globalization (Garelli, 2008; Hitt, Keats & DeMarie, 1998). While at first multinationals turned to emerging markets for production reasons, the growing prosperity of emerging markets has shifted their intention (Li & Kozhikode, 2009; Garelli, 2008). In this next wave of globalization, the prior aim is to get access to the domestic markets of emerging economies (London & Hart, 2004, Garelli 2008). Due to the growing economic wealth, absolute poverty is gradually eradicating and countless people have been lifted into the world of consumption (Khanna and Palepu, 2004, World Bank, 2015). This growing demand for products and services from the lower segment creates tremendous opportunities for western firms longing to growth their business (London & Hart, 2004).

 

7  

      Master  Thesis  -­‐  T.  Goverts         However, it is often observed that firms are struggling to compete in the domestic markets of emerging nations (Khanna and Palepu, 1997; Khanna et al., 2005). Relevant studies state that multinationals merely target the top market segment of emerging markets (Khanna & Palepu, 2004). By exporting high-end, western products to the top market class, firms are neglecting the enormous potential presented by the lower consumption classes, hereby decreasing their chances of future business growth (Prahalad & Hart, 2002; London & Hart, 2004). The oftenexecuted strategy in which firms modify western products to local needs, known as global localization, restrains them from capitalizing on the opportunities of the lower and middle income segments (Hart & Christensen, 2002; Govindarajan & Ramamurti 2011). Fortunately it is observed that the need to adjust strategies when targeting the lower segments of foreign markets is slowly becoming apparent to both scholars and managers (Tiwari and Herstatt, 2014; Agnihotri, 2015) Academics are criticizing the earlier global localization models and are encouraging new strategies to establish affordable, customized products that are desired by the lower consumption class (Govindarajan & Ramamurti 2011). Moreover several multinationals, such as General Electrics, PepsiCo and Unilever, have acknowledged the need for low-cost innovation strategies (Govindarajan & Ramamurti 2011; Prahalad, 2012;). Overall it has become widely apparent that in order to successfully compete in the consumption class of emerging markets, in-depth market knowledge and innovative, customized products are required (London & Hart, 2004). 2.2. Research and development To establish innovation activity and increase new product development in foreign markets, most firms rely on research and development (R&D) (Mairesse & Mohnen 2005; Zhang et al., 2007). In the last decade, the world has experienced an enormous rise in international R&D spending’s of multinationals (Li & Kozhikode, 2009; Zhang et al., 2005). By increasing international R&D spending’s, multinationals aim to develop their technological capabilities, acquire vast knowledge of local conditions and source innovation activities overseas (Zhang et al., 2007). Extensive literature that has investigated the relation between R&D and

 

8  

      Master  Thesis  -­‐  T.  Goverts         innovation activity argues that high R&D investments correspond with high levels of new product development, hence it seems rational that firms turn to R&D to augment their overseas new product development (Mairesse & Mohnen 2005; Paul, Mytelka, Dunwiddie, Persinger, Munos, Lindborg & Schacht, 2010, Hagedoorn & Cloodt, 2003). The OECD Frascati Manual provides a universally accepted definition of R&D. According to this manual “research and development (R&D) comprises work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications and products” (OECD Frascati Manual). Scholars that have extensively researched the relation between R&D and innovation suggest that if R&D works productively, it is able to translate R&D input, such as ideas, knowledge or technology, into scalable innovative output, such as patents or new products (Paul et al., 2010; Penner‐Hahn & Shaver, 2005). Although the above arguments demonstrate support for the investment in R&D when striving new product development, more recent literature is less enthusiastic. Goñi & Maloney (2014) advocate that R&D expenditure alone is not likely to generate new products when operating in markets that lack the well-developed conditions of western economies. Schneider (2005) supports this view by concluding that even though R&D expenditures are in general relevant for both developed and developing countries, it seems to play a much bigger role in explaining innovation in developed countries. The significant level of market obstacles that are present in emerging markets are assumed to be effecting the translation of R&D input into innovation output (Allen & Cohen, 1969; Goñi & Maloney, 2014; Radjou & Prabhu, 2012). As of these arguments, the effectiveness of R&D in shaping innovation is being questioned when shying away from the developed frontier (Radjou & Prabhu 2012; Schneider 2005). 2.3. Institutional voids While emerging markets have been praised for their economic growth, the development of market institutions has stayed behind (Hoskisson, Eden, Lau & Wright, 2000; Khanna et al., 2005). This is concerning as well-developed institutions, such as legal frameworks and training institutions, are key for economic

 

9  

      Master  Thesis  -­‐  T.  Goverts         success (Hoskisson et al., 2000; North 1990). Therefore, if these institutions are either weak or lacking, huge challenges are imposed on the operations of businesses (Porter & Stern, 2001). Inadequate intermediaries and underdeveloped infrastructure make it hard to obtain reliable information on customer preferences or contractual agreements (Khanna & Palepu, 2004; Khanna et al., 2005). Moreover, the lack of qualified suppliers and trained laborers make it tough to attract or transform resources into valuable output (Allen & Cohen, 1969). Especially in costly and resource intensive processes as R&D, it is expected that the lacking institutions obstruct productivity (Bilbao‐Osorio & Rodríguez‐Pose, 2004; Falk, 2006). Literature that researched the lacking institutions in foreign markets has christened them as ‘the institutional voids’ (Khanna and Palepu 1997). The studies emphasize the need to identify the challenges presented by institutional voids and understand their affect on firm’s business models when entering emerging markets (Khanna and Palepu 1997; Khanna and Palepu 2013; Goñi & Maloney 2014). Thus, literature suggest that to effectively implement business models in emerging markets, firms need to recognize the influences of the lacking institutions on their operations (Khanna et al., 2005). As innovation is a rather new phenomenon in emerging markets, little literature has empirically investigated this influence on the R&D operation of multinationals (Brem & Wolfram, 2014; Hult et al., 2004; Ernst et al., 2015). However, the conflicting findings on the importance of R&D for shaping new product development in emerging markets emphasizes the need to improve understanding on this topic (Radjou & Prabhu 2012; Schneider 2005; Mairesse & Mohnen 2005; Zhang et al., 2007). 2.4. Hypotheses development As mentioned, firms are increasingly internationalizing their R&D activities to augment new product development in emerging markets. However the productivity of R&D is being disputed in markets that lack institutions development (Goñi & Maloney 2014; Schneider 2005). Yet, empirical, cross-country research on this topic is lacking (Ernst et al., 2015; Brem & Wolfram 2014). As innovation is emerging markets is getting more important for business growth, this paper aims to extend prior literature by mapping the affect of institutional conditions on R&D productivity, hence increasing insights in the effectiveness of R&D overseas.  

10  

      Master  Thesis  -­‐  T.  Goverts         In the next section, we identify some key institutional conditions that form the basis for our hypotheses. Appendix A provides the conceptual model that visualizes the proposed hypotheses. The availability of high-quality training institutions The first institutional condition that is integrated in the model is the availability of training institutions. While some markets possess extensive training services capable of educating well-qualified talent, others lack any sort of training institutions (Khanna et al., 2005). However, as it is often observed that firms are dependent on the skills of the labor pool to execute their business models, a lack in training services challenges the operations of firms (Khanna et al., 2005; Khanna & Palepu, 1999). An unqualified labor pools especially challenges the R&D activities of firms as those require complex processes for which skilled and technical talent is required (Howells, 2008). Prior literature supports the idea of the necessity of trained talent in firm’s R&D activities (Ballot, FakhFakh & Taymaz 2001; Falk, 2006). A first argument given for this statement is that the absorptive capacity of R&D activities depends on the availability and quality of trained employees (Falk, 2006). With absorptive capacity, we refer to the ability of a firm to recognize the value of new information, process it and most importantly, apply it to commercial ends (Cohen & Levinthal, 1990). Within R&D, the absorptive capacity of firms defines the capability of transforming R&D input into innovation output (Cohen & Levinthal, 1990). As well trained talent is a key assets of the absorptive capacity of firms, it is thus essential that training services are available to provide this talent (Cohen & Levinthal, 1990l Falk, 2006). Secondly, well-trained workers augment R&D processes, as they are capable of transferring new technologies, information and know-how into the firm (Falk, 2004; Allen & Cohen, 1969). R&D processes are dependent on the access to technological and market information as it allows them to produce customized products and services for local market needs (Hoskisson er al., 2000). Hence the incoming knowledge flow of trained talent augments the effectiveness of R&D in shaping innovation output since it enlarges insights in market needs, technological knowhow and local resources (Falk, 2004; Allen & Cohen, 1969).

 

11  

      Master  Thesis  -­‐  T.  Goverts         Considering the previous studies that expresses the importance of trained talent in transforming R&D in innovation output, it is assumed that a lack of high-quality training institutions, as often encountered in emerging markets, can be disadvantageous to the productivity of R&D processes. To empirically test this statement, we construct the following hypothesis: H1: A lack in high-quality training services negatively moderates the relation between corporate R&D and innovation output The government efficiency Secondly, we turn to the public institutions of countries to analyse the affect of institutional conditions on R&D productivity. We specifically focus on the efficiency of governmental regulations. Extensive literature has pointed to the positive influence of efficient regulatory policies, such as legal frameworks, government spending’s and intellectual property rights, on the level of corporate R&D (Falk 2004; Lin, Lin & Song, 2010). Effective legal rights allow firms to protect their technological inventions by contractual agreements hence increasing the benefits of their R&D efforts (Lin, Lin, & Song,, 2010; Falk, 2004). However, while the positive influence of legal policies on corporate R&D investment is well discussed, literature paid less attention to the influence of government efficiency in further stages of R&D processes; i.e. the productivity of R&D. The few literature that did researched the governmental influence on R&D productivity often suggests that governments indirectly affect corporate R&D. Especially, the role of governmental spending’s has been addressed. Corporate R&D can profit from efficient governmental spending’s on R&D labs, universities or research centres as it enhances technological advancements and human capital (Atkinson & Wial, 2008; Cohen & Levinthal, 1990). Since technology and human capital are vital for the capability of transforming R&D activity into new inventions, firms can benefit form the spill-overs derived from efficient R&D spending’s (Whelan, Collings & Donnellan, 2010; Atkinson & Wial, 2008; Cohen & Levinthal, 1990). This view is in line with the study of Rodríguez-Pose, Tijmstra & Bwire (2009) who state that firms that are operating in areas with low government efficiency struggle to fulfil their innovation potential due to the fact that knowledge  

12  

      Master  Thesis  -­‐  T.  Goverts         spill-overs and technological advancements and declined by poor and inadequate governmental policies. Thus prior research suggest that the efficient governmental institutions firstly encourage firms to increase R&D efforts and secondly (indirectly) strengthen R&D effectiveness in developing innovation. These arguments lead to the following hypothesis: H2: The lack of efficient governmental policies negatively moderates the relation between corporate R&D and innovation output The Financial Market institutions Another institutional condition that is often perceived to influence business operations, is the quality of the financial institutions (Mody, 2004; Burgess & Steenkamp, 2006). When talking about financial institutions of countries we refer to the banking-system, securities exchanges, local equity market and other financial agencies that provide capital or financial information to businesses (Global Competiveness Index, 2015). The sophistication of financial markets varies widely across countries. Whereas western markets show highly developed financial system, developing countries are known for their lack of financial market sophistication (Khanna at el., 2005). Financial systems are found to be of primary concern to the execution of corporate R&D processes. The most important argument for this suggestion is the fact that R&D activities are costly processes and require substantial financial investments before being capable of transforming knowledge into new product development (Bilbao‐Osorio & Rodríguez‐Pose, 2004; Becker, 2013). Thus, a vast amount of capital is needed to support the technological and resource intensive activities of R&D processes (Becker, 2013, Falk, 2004). Whenever the incoming stream of external capital is obstructed, it is thus expected to hamper innovation progress (Becker, 2013). The study of Müller & Zimmermann (2009) confirms this view by researching the importance of equity finance of R&D activity.

 

13  

      Master  Thesis  -­‐  T.  Goverts         Their study concludes that companies that focus on R&D need more equity capital and are therefore more reliant on a functioning market for external capital. Given the fact that R&D activities require substantial financial investments in order to effectively augment innovation, it is expected that a lack in financial market development hinders the relation between R&D input and innovation output. These arguments lead to the following hypothesis: H3: The lack of well developed financial markets negatively moderates the relation between corporate R&D and innovation output The quality of local suppliers The last institutional condition that is examined in order to map the effect of institutions, or the lack of them, on the relation between R&D and innovation, is the local suppliers quality.

It is often observed that there is a great variance in

development of product markets across countries (Khanna et al., 2015). While some countries show highly developed product markets, others lack end-to-end logistic providers, distribution channels or quality suppliers to efficiently transport resources from and to businesses (Khanna et al., 2005; Global Competiveness Index, 2015). In this study, we specifically focus on the variance in local supplier quality as it is often expressed that suppliers are of high importance for the productivity of R&D (Chung & Kim 2003, Arranz & Arroyabe, 2008). Different arguments are provided for the positive influence of local suppliers on R&D effectiveness (Rosell, Lakemond, Dabhilkar & Bengtsson 2011; Chung & Kim 2003; Arranz & Arroyabe, 2008). One of the most profound arguments relies on the fact that firms do not innovate in isolation but interact with external sources, such as customers or suppliers, to augment their internal processes for innovation development (Malerba, 2002). Local suppliers are capable of transferring complementary resources and valuable market knowledge into the firm, hence expanding the resource base on which R&D processes draw (Arranz & Arroyabe, 2008). Moreover does it allows for better insight in customer needs, hence increasing the ability to align R&D processes with local market demand (Chung &

 

14  

      Master  Thesis  -­‐  T.  Goverts         Kim 2003; Arranz & Arroyabe, 2008). Next to the provision of market knowledge and external resources, prior research points to the cost advantages derived from quality suppliers. It is often observed that the integration of supplier in business processes reduces the production costs and lead-time of new product development (Arranz & de Arroyabe 2008; Chung & Kim 2003). Moreover, Sobrero & Roberts (2002) revealed that high levels of supplier involvement are associated with both lower production costs and lower lead-time (time between the initiation and execution of a process). Consequently, R&D processes benefit from these cost-efficiencies as it speeds the transformation from R&D input into output while reducing operation costs (Sobrero & Roberts, 2002). As the above arguments suggest that local suppliers are of value to R&D processes, we hypothesize that a lack in local quality supply negatively influences the effect of R&D in shaping innovation. H4: The lack of quality suppliers negatively moderates the relation between corporate R&D and innovation output

3. Methodology In order to understand if the relation between R&D and innovation is affected by the above-mentioned institutional conditions, we analyse the constructed hypothesis. The next section discusses the research setting, data and analyses techniques that are uses to analyse the hypotheses. 3.1 Research setting and data collection The aim of this study is to gain insight in how institutional voids moderate the relation between R&D input and innovation output. In order to empirically examine this, we conduct longitudinal, cross-county data analysis over a time-span of 7 years. A cross-country analysis allows us to examine the relationship of R&D and innovation within different institutional context, hereby improving understanding of the role of institutional voids. Data from 124 countries over the period of 2008 up to and including 2014 is used in the analysis. The 124 countries represent a wide  

15  

      Master  Thesis  -­‐  T.  Goverts         variety of economies including both advanced and emerging economies. The diversity in country statuses ensures that different levels of institutional contexs are included in our research. Appendix B presents an overview of the economies that are included in the dataset. The data used to measure the level of institutions, R&D efforts and innovation output of countries is collected from the World Economic Forum Global Competiveness Index and the Global Innovation Index. The Global Competiveness Index is an annually published report provided by the World Economic Forum and describes the competitive landscape of different countries. It delivers valuable data on a country’s productivity and its ability to achieve sustained levels of growth (Global Competiveness Index, 2015). The World Executive Opinion Survey, capturing opinions of over more than 14,0000 business leaders around the world on a wide range of topics, provides the data that is depicted in the Global Competiveness Index. The second database from which this study extracts data is the Global Innovation Index. The Global Innovation Index depicts a ranking of the innovation capabilities and output of a large group of countries and is the result of a collaboration between the World Intellectual Property Organization (WIPO), Cornell University and INSEAD. It provides a rich database of detailed metrics regarding innovation input and output. The GII relies on two sub-indices: the Innovation Input Sub-Index and the Innovation Output Sub-Index. As this paper is interested in the innovation output, the latter index is used in our research. Since both indexes cover a large amount of economies, we are able to collect all necessary data on the 124 countries from these two indexes. Moreover, as both are published annually, they provide data on all 7 years that are included in our panel. Consequently, the dataset used for our analyses included 868 observations, equally distributed over the 124 economies and years 2008 to 2014. As mentioned, this paper uses panel data with a time-span of 7 years. Accordingly, our dataset includes two dimensions; a cross-sectional dimension represented by the 124 economies and a time-series dimension represented by the 7-year time-span

 

16  

      Master  Thesis  -­‐  T.  Goverts         (Hsiao, 2007). A panel dataset is used as it offers certain advantages to our study. Firstly, it provides more extensive numbers of observations, hereby increasing the reliability of the output (Hsiao, 2007). Secondly, more accurate interpretation of model parameters can be made when using time-series data (Frees, 2004; Hsiao 2014). As this study is interested in the moderating effect of (the lack of) institutions, it is vital that the parameters are comprehensible. Longitudinal data helps to increase the efficiency of the parameters as it lowers colliniearity among the independent variables used in the model (Hsiao, 2007). The data used to measure the level of institutions, R&D and innovation output of the countries is collected from the World Economic Forum Global Competiveness Index and the Global Innovation Index. The Global Competiveness Index is an annually published report provided by the World Economic Forum and describes the competitive landscape of different countries. It delivers valuable data on a country’s productivity and its ability to achieve sustained levels of growth (Global Competiveness Index, 2015). The World Executive Opinion Survey gathers the data that is depicted in the Global Competiveness Index. This survey captures the opinions of more than 14.000 business leaders around the world on a broad range of topics. 3.2 Measurements of variables The hypotheses that are employed in our research are operationalized using existing measures of the Global Competiveness Index and the Global Innovation Index. The next section provides an overview of the specific data that is used to depict our variables. Appendix C provides an overview of the total variables used in our model. Dependent variable: Innovation Output The independent variable in the constructs is innovation output. In academic research, numerous measures have been used to map innovation output, ranging from patents, invention disclosers, new products to sales ratio’s (Makri & Scandura, 2010; Back et al., 2014). As of today, a universal measurement of innovation is still lacking. In this study, innovation output is operationalized by data reported by the GII’s Innovation Output Sub-Index. This sub-index maps the total innovation output  

17  

      Master  Thesis  -­‐  T.  Goverts         of economies by measuring knowledge, technology and creative outputs of a country on a scale from 1 to 100 (Global Innovation Index, 2015). Measures that are included in the index are patents applications, utility model applications, royal and license fees, trademarks and creative goods. Independent Variable: Research and Development The independent variable in our model is R&D input. Literature often refers to R&D spending’s as a solid indicator of the effort that firms put in establishing R&D (Hagedoorn & Cloodt, 2003; Falk., 2004). Therefore this paper uses R&D spending’s to measure the R&D input of companies. We take the existing data of the Global Competiveness Index that depicts the company’s R&D spending’s per country to represent R&D input. The Global Competiveness Index has derived this data from the Executive Opinion Survey which asks its respondents to map the level of company’s R&D spending’s in their country on a scale from 1 to 7.

Moderating variables: Institutional Conditions The institutional conditions, represented by government efficiency, availability of training services, financial market development and local suppliers quality, are the moderating variables of our model. To operationalize these institutional structures, we take data from the Global Competiveness Index. All the moderating variables are depicted on a scale of 1 to 7, with 1 being either weak or low and 7 being high of strong. The first moderating variable included in the model is the availability of high quality training services. The Executive Opinion Survey asked business executives to rank the extent to which specialized training services are available in their country. The data that has been collected by the survey is used in our study to operationalize the variable on availability of training services in countries. Government efficiency, our second moderating variable, is operationalized by data taken from the 1st pillar of the Global Innovation Index that maps the public institutions of countries. The Executive Opinion Survey has collected data on the government’s efficiency by asking respondents to scale the level of legal frameworks, governmental regulations and efficient governmental spending’s in

 

18  

      Master  Thesis  -­‐  T.  Goverts         their country. Consequently, the Global Competiveness Index computed the arithmetic mean of the individual indicators into a single measurement depicting the government’s efficiency. Third, we derive data on the development of financial markets from the 8th pillar of the Global Competiveness Index. This pillar portrays data on the level and quality financial regulations, intermediaries and local equity of countries. To construct a single measurement for the overall financial market development, the Global Competiveness Index has bundled financial data on these indicators from the World Economic Forum, World Bank and International Finance Cooperation into one single measurement. Lastly, we take data from the 11th pillar of the Global Competiveness Index to measure our fourth moderating variable; the level of local quality suppliers of economies. By asking business leaders to rank the quality of local suppliers in their country, the World Economic Forum has constructed a reliable dataset on the supplier quality for each country. All data on the moderating variables are accurately copied and integrated in our own dataset. Control Variables To increase the reliability of the research, we need to account for exogenous factors that influence the level of innovation output in countries. In order to do so, this study adds several control variables to the model. The first factor that is controlled for is the level of competition. Numerous studies have discussed the influence of competition on innovation activity. While some studies suggest a positive (linear) effect between competition and innovation output (Becker; 2013, Blundell, Gri¢th and van Reenen, 1999; Geroski 1994), other studies refer to an inverted non-linear U relationship or a negative relation (Aghion, Bloom, Bluncell, Griffith and Howitt, 2002; Becker; 2013).

Although the moderating direction is being disputed,

literature agrees that competition is a vast influencer of innovation. As of this, we control for competition in our model to ensure validity of the results. The level of competition is measured by data taken from the 6th pillar of the Global Competiveness Index. This pillar provides computable data on the competition intensity of countries by merging data on domestic and foreign competition of the

 

19  

      Master  Thesis  -­‐  T.  Goverts         World Economic Forum, World Bank and International Finance Cooperation.

Next, we add market size as a control variable in our research. Both theoretical and empirical studies found that market size positively influences innovation output (Acemoglu and Linn, 2003, GCI, 2015). Porter and Stern (2001) state that even a single change in market size can explain a substantial variation in patents among economies. As the economies in our dataset show a wide variety in market size, it is essential to control for this variable. The data used to operationalize the market size of economies has been taken from the Global Competiveness Index that depicts the market size of countries a scale from 1 to 7. Lastly, we control for the level of education in our model. Numerous studies have proven that a good national educational system positively influences the capacity to create innovative activity (Nelson and Phelps, 1966; Ballot, FakhFakh, & Taymaz 2001). The Executive Opinion Survey gathers data on the level of the education system per economy and translated this in a measurable data by ranking it on a scale from 1 to 7. Consequently, we integrate this measurement in our model. 3.3. Data analysis After having collected data to operationalize the variables, we are able to test our proposed hypotheses. Our aim is to understand the moderating effect of institutions, or the lack of them, on the relationship between R&D and innovation output. In order to do so, we include interaction effects in our model. ‘An interaction is to be observed when the nature or strength of the relation between two variables (R&D and innovation) changes as function of the third variable (institutional voids)’ (González & Cox 2007). If the interaction effect shows statistical significance (pvalue <0,05), the moderation is supported and we can conclude that the variables are significantly influencing the relationship between R&D and innovation output (Jaccard & Turrisi, 2003; González & Cox 2007). The interaction effects are constructed by taking the product of the moderating variables and the independent variable (Jaccard & Turrisi, 2003). To ensure that the regression coefficients of the interaction effect have meaningful interpretations, we mean-centered the variables before constructing the interaction effect (Afshartous & Preston, 2011). By doing

 

20  

      Master  Thesis  -­‐  T.  Goverts         so, we reduce the potential multicollinearity of the interaction terms hence increasing interpretability (Aiken and West, 1991). We empirically analyze the data by performing the statistical technique of generalized estimating equations (GEE). This technique extends the generalized linear model (GLM) since it enables the analysis of longitudinal datasets using all available data (Zeger, Liang & Albert, 1988). The GEE facilitates panel analyses as it allows for more efficient and unbiased regression estimates when including repeated measurements over time (Ballinger, 2004; Zeger et al., 1988). Moreover, the GEE technique is recommended when studying estimation effects as well as the estimation of the coefficients (Hubbard, Ahern, Fleischer, Van der Laan, Lippman, Jewell & Satarian 2010). As this study is interested in the interaction effects of the moderating variables, the GEE is thus a proper technique to analyse our data. A reason why the GEE is recommended over other regression methods, is because it violates the independence assumption made in traditional regression methods (Hubbard et al., 2010; Ballinger, 2004; Zeger et al., 1988). In timemeasurements, it can be expected that the observations are dependent on earlier observations, such as that we expect that the level of government efficiency of an economy is dependent on the strength of the efficiency in previous years. The GEE allows for this correction by assuming a certain ‘working’ correlation structure for the repeated measurements of the outcome variable (Ballinger, 2004; Zeger et al., 1988). By controlling for independence, we improve the reliability of the data analysis. In the next section we depict the output and discuss the results of the GEE analysis hence allowing us to evaluate the proposed hypotheses.

4. Empirical Results Table 1 reports the descriptive statistics and the correlations coefficients of the variables used in the different empirical models of the study. Table 2 presents the results of the linear regressions performed by the generalized estimating equations (GEE). The first model, Model 1, includes the three control variables of our study. Model 2 enlarges the equations by adding the four moderating variables depicting  

21  

      Master  Thesis  -­‐  T.  Goverts         the institutional condition. Model 3 adds the main effects of R&D spending’s and lastly Model 4 integrates the interaction effects between the main effect and the moderating variables. As presented in table 1, we investigated the correlations of our key variables by means of the Pearson product-moment correlation coefficient technique before analysing the hypotheses. For all variables we found significant correlation levels with a P<0.001. Moreover, all correlations are positive (r>0), indicating that high (low) levels of one variable is associated with high (low) levels of the other. An additional test conducted before analysing the hypotheses is the reliability test of Cronbach Alpha. This test allows us to measure the scale reliability of the independent variables (Pallant & Manual, 2007). As most independent variables are measured on a Likert scale from 1 to 7, we need to check for internal consistency among the variables. An alpha (α) of 0,70 or above is considered to be acceptable (Santos, 1999; Pallant & Manual, 2007). As the alpha coefficient for the variables in our model is α=.901, we can conclude that there is internal consistency among the scaled variables used in the model.

Table 1. Descriptive statistics and correlation Mean s.d. 1 2 3 4 5 6 7 8 1. Innovation 23, 938 16,518 output 2. R&D Spending’s -0,001 0,877 0,37 3. Government 0,002 0,755 0,24 0,63 Efficiency 4. Training services 0,000 0,847 0,45 0,65 0,61 5. Financial Market -0,002 0,713 0,20 0,55 0,73 0,68 Development 6. Local Suppliers 0,000 0,580 0,23 0,63 0,41 0,73 0,57 Quality 7. Competition -0,002 0,565 0,34 0,63 0,79 0,68 0,77 0,53 8. Market Size 0,002 1,148 0,25 0,53 0,10 0,56 0,35 0,62 0,22 9. Education 0.003 0,917 0,33 0,71 0,74 0,73 0,63 0,48 0,67 0,20 System Notes: N: 868. Pearson product-moment correlation coefficients are reported. All correlations are significant at the P<0,01 (2-tailed).

 

9

-

22  

      Master  Thesis  -­‐  T.  Goverts         In order to analyze our hypotheses, we turn to table 2. This table reports the regression coefficients (b) that are taken from the parameter estimates output of the GEE. Before analyzing the interaction effects of the institutional conditions, it is important to stress that there is a significant relation between R&D and innovation. The significant coefficients of the main effect depicted in Model 3 and 4 reveal that R&D is positively related to innovation output. However, as the coefficients show different quantities in Model 3 and 4, it is argued that the relationship changes as of the integration of the interaction effects in Model 4. To evaluate our hypotheses and examine the effect of the moderating variables, we discuss the results of the full model, Model 4. The regression coefficients (b) that are reported in the table represent the changes in the relationship between the main effect and dependent variable as of a one-unit change in the moderating variables (Tabachnick, Fidell & Osterlind, 2001; González & Cox 2007). If the coefficients are statistically significant, we can conclude that the moderating variable is significantly influencing the relation (González & Cox 2007). A positive regression coefficient indicates that an increase in the moderating variable corresponds with a stronger relation between R&D and innovation. Additionally, a negative significant coefficient indicates that an increase in the moderating variable negatively effects the relation between R&D and innovation output. We find strong support for Hypothesis 1, which describes the moderating role of the availability of high-quality training services on the relation between R&D and innovation. The interaction term between the availability of training services and R&D spending’s’ is statistically significant (P<0,05) and shows a positive regression coefficients (β = 2,128). This indicates that a high (low) availability of training services in an economy corresponds with a stronger (weaker) relation between R&D spending’s and innovation output of countries. This is in line with the proposed hypotheses. However, the empirical results do not support a moderating role of government efficiency, hypothesis 2, as the interaction term between R&D spending’s and government efficiency is not statistically significant. Consequently we cannot argue about the validity of hypothesis 2 which suggests that lower levels of government efficiency are associated with lower R&D effectiveness.

 

23  

      Master  Thesis  -­‐  T.  Goverts         For both hypothesis 3 and 4, we do find statistical significance. The significance of the interaction term between R&D spending’s and financial market development (p< 0.05) provides support for the idea that financial markets moderate the relationship between R&D spending’s and innovation output. In addition, the interaction term of R&D spending’s and local quality suppliers shows an even stronger significance level with a P value of <0,001. However, both hypotheses are only partially supported as the regression coefficient reports a different direction than is suggested in the hypotheses. While it was expected that lower levels of financial market development and lower quality of suppliers would weaken the effectiveness of R&D in shaping innovation output, the empirical results report a negative regression coefficient (β = -1, 442 and β -2,293). Meaning that low levels of the moderating variable positively moderate the relationship. Consequently, hypotheses 3 and 4 are only partly supported by the empirical results.

Table 2. Results of the regressions by the generalized estimating equations for innovation output Control Variables Competition Market Size Education System Moderating Variables Training Services Government Efficiency Financial Market Development Local Quality Suppliers Main effect R&D Spending’s

Model 1

Model 2

Model 3

Model 4

3,025*** 1,356*** 2,397***

2,806*** 0,520 1,802***

1,502*** 0,391 2,731***

3,527*** 0,427 1,353***

3,162* -1,882** 1,147 -1,246

2,526*** -1,952** 1,026 -1,042

3,283*** -2,448*** 0,412 -1,466**

1,2177**

1,288*

Interaction Effects R&D spending’s X Training Services 2,128** R&D spending’s X Government Efficiency 0,703 R&D spending’s X Financial Market Development -1,442** R&D spending’s X Local Quality Suppliers -2,293*** Notes: N: 868. Standardized Coefficients are reported. * p < 0.1, ** p < 0,05, ***p < 0,01 (2-tailed)

 

24  

       

5. Discussion

Master  Thesis  -­‐  T.  Goverts  

   

 

  While firms are increasingly internationalizing R&D activities to respond to the rising product demand of emerging markets, modern literature disputes the effectiveness of R&D in shaping innovation in markets that are overshadowed by institutional voids. By conducting cross-country analysis this study builds on the need to clarify the disputed relevance of R&D in enhancing innovation (Goñi & Maloney 2014; Schneider 2005) and responses to the need to amplify innovation literature in foreign markets (Hult et al., 2004; Ernst et al., 2015). Drawing on a sample of 124 economies, we aim to answer the question of how institutional voids influence the effectiveness of R&D in enhancing innovation. 5.1. Theoretical and managerial implications This study finds empirical support for the idea that institutions are key influencers of R&D effectiveness in shaping innovation (Furman, Porter & Stern, 2000, North, 1990). By testing the moderating role of four key institutional conditions, we were able to identify a change in the relation between R&D and innovation as of a change in the institutional conditions. Specifically, we found support for the moderating role of training services, financial markets and supplier quality (hypothesis 1, 3 and 4). Identifying a significant interaction of the institutional conditions allows us to believe that firms might encounter varying results on their R&D efforts when expanding R&D activities across borders. This finding is in line with the ideas of institutional theory stating that institutional conditions are key influencers of firms operations (North, 1990) and supports the view of Porter & Stern (2001) who state that the productivity of innovation models is dependent on the location in which it is situated. As statistical support for the influence of institutional conditions is found, we continue our study by interpreting how the conditions affect R&D across countries. Specifically, we are interested in the affect of the (lacking) institutional conditions of emerging markets. As firms are increasingly targeting the consumption markets of emerging countries, it is vital to provide better understanding of firm’s innovation capacity in those countries (Li & Kozhikode, 2009; Zhang et al., 2007).

 

25  

      Master  Thesis  -­‐  T.  Goverts         Firstly, we tested how the lack of training institutions influences the effectiveness of R&D. Building on prior views that stress the importance of trained talent in R&D activity (Ballot et al., 2001; Falk, 2006; Cohen & Levinthal, 1990), we hypothesized that low levels of training institutions in emerging markets negatively moderate the relation between R&D and innovation. Empirical support for this statement was found. Consequently, it is argued that the ability of firms to transform R&D efforts into innovation output in emerging markets is weakened as of the lacking training institutions. Since firms are steadily expanding their R&D activities to emerging markets (London & Hart, 2004; Li & Kozhikode, 2009), it is incited that managers find ways to overcome the challenges presented by the lack in training services. Next it was argued that inefficient governmental policies hamper the relation between R&D and innovation (Atkinson & Wial, 2008; Cohen & Levinthal, 1990). However, when testing this assumption, we did not find significant support confirming this assumption. As of this, we cannot make a solid suggestion about the moderating role of the inefficient government in emerging markets on R&D’s effectiveness. Additionally, we investigated the moderating role of financial markets conditions on the effectiveness of R&D. As R&D require large capital investments, it was hypothesized that R&D effectiveness is weakened when executed in markets that lack financial market sophistication (Burgess & Steenkamp, 2006; Khanna et al., 2005). Although our study supports the view that financial institutions influence the effectiveness of R&D in shaping innovation, it contradicts the thought that underdeveloped financial markets reduce R&D effectiveness (Becker, 2013). Contradictory results were found, suggesting that R&D is a stronger influencer of innovation when operating in markets that lack well-developed financial institutions. This challenges the thoughts of prior research that argues that the effectiveness of R&D activities is reliant on external capital provided by financial institutions (Bilbao‐Osorio & Rodríguez‐Pose, 2004; Becker, 2013). Lastly, we found striking results for the affect of local suppliers on R&D effectiveness. Drawing on theory that reported the advantages of incoming knowledge spillovers (Arranz & Arroyabe, 2008; Sobrero & Roberts, 2002), it was

 

26  

      Master  Thesis  -­‐  T.  Goverts         argued that a lack of quality suppliers weakens the relation between R&D and innovation. Yet clashing results were found. Although the moderating role of suppliers in R&D productivity was supported, the findings report that low levels of supplier quality correspond with stronger R&D effectiveness. This finding contradicts prior views stating that supplier quality is vital for effective R&D activity (Arranz & Arroyabe, 2008). A plausible argument that might explain why a lack in both financial development and supplier quality does not seem to weaken the effectiveness in R&D is provided by Miott & Sachwald (2003). They argue that the lack of institutions can encourage firms to interact with other external sources that help them execute their operations. Firms might for example partner with universities or competitors, in the form of strategic alliances or partnerships, in order to obtain the complementary resources or capital necessary to effectively execute R&D activity (Miott & Sachwald, 2003; Arranz & Arroyabe, 2008; Khanna et al., 2005). However, to further support this thought, a better understanding of the role of external partnerships in R&D operations is required. 5.2. Limitations and future research Although this research was carefully prepared, there are still some limitations and shortcomings to the study. A first limitation of this study is the limited number of institutional conditions integrated in the model. We measure the affect of institutions on R&D effectiveness on the basis of four institutional conditions. Yet, institutional theory reports numerous other conditions that vary in development across nations, such as commercial and industrial infrastructure (North, 1990; Global Competiveness Index 2015). Analyzing the moderating roles of other types of institutional conditions would provide a more complete understanding of how institutions influence the effectiveness of R&D in shaping innovation. Moreover, this study uses country level data of the World Economic Forum to map the effort that firms put into R&D activities. Even though the World Economic Forum provides reliable and adequate data, it does provide a somewhat broad and generalized view of the efforts that firms put into R&D. If the study had included

 

27  

      Master  Thesis  -­‐  T.  Goverts         firm-level data to depict the R&D input, a more detailed and precise view of the both R&D effort and results would have been achieved. Lastly, prior research has indicated that in order to assess a firm’s capability in enhancing innovation, studies should rely on multiple indicators (Damanpour, 1991). It is adviced that innovation literature uses a composite measurement consisting all determinants of innovation input (Damanpour, 1991; Carayannis & Provance 2008). While this study takes a significant step in mapping a firm’s capability of R&D in determining innovation output, it neglect all other determinants of innovation. Including other indicators of firm innovativeness would provide a more complete understanding of a firm’s capacity to innovate hence augmenting understanding of how to innovate in emerging markets. Based on the results of this study, some opportunities for future research arises. While it was hypothesized that institutional voids would negatively influence R&D effectiveness, contradictory results were found for the lack in financial market and local quality suppliers. Consequently, this asks for further investigation. As mentioned, research argues that whenever institutions are lacking, firms are encouraged to focus on other informants that are capable of providing the necessary external resources (Miott & Sachwald, 2003; Khanna et al., 2005). Future research could explore which external sources substitute for the role of institutions in providing external resources. Furthermore, future research could build on the fact that this study provides insights in the influences of institutional voids on firms operations, hence neglects further suggestions on how to respond to these influences. Prior studies state that firms must modify their business models to the unique market conditions of countries in order to successfully implement their business models (Khanna et al. 2005; Khanna and Palepu 1997; Khanna and Palepu 2013). While some multinationals have managed to recognize the influences of institutional voids and successfully adapted their business models2, it is often observed that firms are struggling to effectively adjust their models to the underdeveloped institutions of foreign markets. Hence,                                                                                                                  To  illustrate  this  point:  McDonalds  is  often  referred  to  as  a  firm  that  manages  to  respond  successfully  to  the   differences  in  institutional  conditions.  E.g.  when  they  recognized  the  lack  in  training  institutions  in  China,  it  opened  a   local  Hamburger  University.  Within  4  years,  they  trained  more  than  4000  employees  and  increased  their  operations   immensely.  (McKinsey  &  Company,  Perspectives  on  global  organizations  (2012)  p.17)   2

 

28  

      Master  Thesis  -­‐  T.  Goverts         future research could consider providing insight in how firms should adapt their innovation models in order to capitalize on the unique market conditions of emerging markets.  

6. Conclusion This study contributes to existing literature by advancing the understanding of how corporate R&D is influenced by the institutional conditions of emerging countries. As firms are increasingly internationalizing R&D operations in order to respond to the growing market demand, it is vital that it is well understood if and how R&D operations are being affected by the local conditions of foreign markets. While prior research has addressed the issue of institutional voids, little research has tried to empirically map the affect of voids on the innovation models of firms. As innovation has become a primary source of future business growth, this study aims to increase insights in how R&D operations are affected by the institutional voids when striving for innovation development overseas. By investigating the moderating affect of four different types of institution, this study found that the effectiveness of R&D in shaping innovation changes as of a change in institutional context. Thus, whenever R&D activities are transferred across borders, firms can expect to experience a change in the results derived from their R&D efforts. As emerging markets differ extensively in institutional context from advanced markets, it is of high importance that firms acknowledge this finding. However no single answer can be given to the question of how institutional voids affect R&D effectiveness, as it appears that some voids are positively affecting the strength of R&D, while others show a negative influence. When zooming in on the specific institutional voids, we can conclude that the lack of training institutions hamper the relation between R&D and innovation, while a lack in financial markets and local suppliers strengthen the relationship. Consequently, it is suggested that future research extends this study by investigating how firms can respond to these varying influences in order to maximize results on R&D operations.

 

29  

       

Master  Thesis  -­‐  T.  Goverts  

   

7. References Acemoglu, D., & Linn, J. (2003). Market size in innovation: theory and evidence from the pharmaceutical industry (No. w10038). National Bureau of Economic Research. Afshartous, D., & Preston, R. A. (2011). Key results of interaction models with centering. Journal of Statistics Education, 19(3), 1-24. Agnihotri, A. (2015). Low-cost innovation in emerging markets. Journal of Strategic Marketing, 23(5), 399-411. Aghion, P., Bloom, N., Blundell, R., Griffith, R., & Howitt, P. (2002). Competition and innovation: An inverted U relationship (No. w9269). National Bureau of Economic Research. Aiken, LS., & West SG. 1991. Multiple Regression: Testing and Interpreting Interactions. Sage: Thousand Oaks, CA. Alfaro, L., Chanda, A., Kalemli-Ozcan, S., & Sayek, S. (2004). FDI and economic growth: the role of local financial markets. Journal of international economics, 64(1), 89-112. Allen, T. J., & Cohen, S. I. (1969). Information flow in research and development laboratories. Administrative Science Quarterly, 12-19. Arranz, N., & de Arroyabe, J. C. F. (2008). The choice of partners in R&D cooperation: An empirical analysis of Spanish firms. Technovation, 28(1), 88-100. Atkinson, R., & Wial, H. (2008). Boosting productivity, innovation, and growth through a National Innovation Foundation. Metropolitan Policy Program, Brookings Institution. Atsmon, Y., Child, P., Dobbs, R., & Narasimhan, L. (2012). Winning the $30 trillion decathlon: going for gold in emerging markets. McKinsey Quarterly, 4, 20-35. Back, Y., Parboteeah, K. P., & Nam, D. I. (2014). Innovation in emerging markets: The role of management consulting firms. Journal of International Management, 20(4), 390-405. Ballinger, G. A. (2004). Using generalized estimating equations for longitudinal data analysis. Organizational research methods, 7(2), 127-150. Ballot, G., FakhFakh, F., & Taymaz, E. (2001). Firms' human capital, R&D and performance: a study on French and Swedish firms. Labour economics, 8(4), 443-462 Becker, B. (2013). The determinants of R&D investment: a survey of the empirical research. Loughborough Universtiy Economics Discussion Paper Series, 9. Bilbao‐Osorio, B., & Rodríguez‐Pose, A. (2004). From R&D to innovation and economic growth in the EU. Growth and Change, 35(4), 434-455. Blundell, R. Gri th, R., and Van Reenen, J.(1999). Market Share, Market Value and Innovation in a Panel of British Manufacturing Firms. The Review of Economic Studies, Vol. 66, No. 3. (Jul., 1999), pp. 529-554.

 

30  

       

Master  Thesis  -­‐  T.  Goverts  

   

Burgess, S. M., & Steenkamp, J. B. E. (2006). Marketing renaissance: How research in emerging markets advances marketing science and practice. International Journal of Research in Marketing, 23(4), 337-356. Brem, A., & Wolfram, P. (2014). Research and development from the bottom up-introduction of terminologies for new product development in emerging markets. Journal of Innovation and Entrepreneurship, 3(1), 1-22. Carayannis, E. G., & Provance, M. (2008). Measuring firm innovativeness: towards a composite innovation index built on firm innovative posture, propensity and performance attributes. International Journal of Innovation and Regional Development, 1(1), 90-107. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative science quarterly, 128-152. Chung, S. A., & Kim, G. M. (2003). Performance effects of partnership between manufacturers and suppliers for new product development: the supplier’s standpoint. Research Policy, 32(4), 587-603 Damanpour, F. (1991). Organizational innovation: A meta-analysis of effects of determinants and moderators. Academy of management journal, 34(3), 555-590. Dawar, N. D. N., & Chattopadhyay, A. (2002). Rethinking marketing programs for emerging markets. Long Range Planning, 35(5), 457-474. Dawar, N., & Frost, T. (1999). Competing with giants: Survival strategies for local companies in emerging markets. Harvard business review, 77, 119-13nt2. Economist Intelligence Unit. (2012). Coming of age: Asia’s evolving R&D landscape. Ernst, H., Kahle, H. N., Dubiel, A., Prabhu, J., & Subramaniam, M. (2015). The antecedents and consequences of affordable value innovations for emerging markets. Journal of Product Innovation Management, 32(1), 65-79. Falk, M. (2004). What drives business Research and Development (R&D) intensity across OECD countries? Applied Economics, 38(5): 533-547. Garelli, S. (2008). New waves in globalization and competitiveness. IMD World Competitiveness Yearbook 2008. Goñi, E., & Maloney, W. F. (2014). Why don't poor countries do R&D?. Documento CEDE, (201423). González, A. B., & Cox, D. R. (2007). Interpretation of interaction: A review. The Annals of Applied Statistics, 1(2), 371-385. Govindarajan, V., & Ramamurti, R. (2011). Reverse innovation, emerging markets, and global strategy. Global Strategy Journal, 1(3‐4), 191-205. Hagedoorn, J., & Cloodt, M. (2003). Measuring innovative performance: is there an advantage in using multiple indicators?. Research policy, 32(8), 1365-1379.

 

31  

       

Master  Thesis  -­‐  T.  Goverts  

   

Hart, S. L., & Christensen, C. M. (2002). The great leap: Driving innovation from the base of the pyramid. MIT Sloan management review, 44(1), 51. Hitt, M. A., Keats, B. W., & DeMarie, S. M. (1998). Navigating in the new competitive landscape: Building strategic flexibility and competitive advantage in the 21st century. The Academy of Management Executive, 12(4), 22-42. Hoskisson, R. E., Eden, L., Lau, C. M., & Wright, M. (2000). Strategy in emerging economies. Academy of management journal, 43(3), 249-267. Hsiao, C. (2014). Analysis of panel data (No. 54). Cambridge university press. Hubbard, A. E., Ahern, J., Fleischer, N. L., Van der Laan, M., Lippman, S. A., Jewell, N., Bruckner, T., & Satariano, W. A. (2010). To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. Epidemiology, 21(4), 467-474. Hult, G. T. M., Hurley, R. F., & Knight, G. A. (2004). Innovativeness: Its antecedents and impact on business performance. Industrial marketing management, 33(5), 429-438. Jaccard, J., & Turrisi, R. (2003). Interaction effects in multiple regression (No. 72). Sage. Khanna, T., & Palepu, K. (1997) Why focused strategies may be wong for emerging markets. Havard business review, Khanna, T., & Palepu, K. (2004). Emerging giants: building world class companies from emerging markets. Harvard Business School Khanna, T., & Palepu, K. (2013). Winning in emerging markets: A road map for strategy and execution. Harvard Business Press. Khanna, T., Palepu, K. G., & Sinha, J. (2005). Strategies that fit emerging markets. Harvard business review, 83(6), 4-19. Li, J., & Kozhikode, R. K. (2009). Developing new innovation models: Shifts in the innovation landscapes in emerging economies and implications for global R&D management. Journal of International Management, 15(3), 328-339. Lin, C., Lin, P., & Song, F. (2010). Property rights protection and corporate R&D: Evidence from China. Journal of Development Economics, 93(1), 49-62. London, T., & Hart, S. L. (2004). Reinventing strategies for emerging markets: beyond the transnational model. Journal of international business studies, 350-370. Malerba, F. (2002). Sectoral systems of innovation and production. Research policy, 31(2), 247-264. Mair, J., & Marti, I. (2009). Entrepreneurship in and around institutional voids: A case study from Bangladesh. Journal of business venturing, 24(5), 419-435. Mairesse, J., & Mohnen, P. (2005). The importance of R&D for innovation: a reassessment using French survey data (pp. 129-143). Springer US.

 

32  

       

Master  Thesis  -­‐  T.  Goverts  

   

Makri, M., & Scandura, T. A. (2010). Exploring the effects of creative CEO leadership on innovation in high-technology firms. The Leadership Quarterly, 21(1), 75-88. Miotti, L., & Sachwald, F. (2003). Co-operative R&D: why and with whom?: An integrated framework of analysis. Research policy, 32(8), 1481-1499. Mody, M. A. (2004). What is an emerging market? (No. 4-177). International Monetary Fund. Müller, E., & Zimmermann, V. (2009). The importance of equity finance for R&D activity. Small Business Economics, 33(3), 303-318. North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge University press. Nelson, R. R., & Phelps, E. S. (1966). Investment in humans, technological diffusion, and economic growth. The American economic review, 56(1/2), 69-75. Pallant, J., & Manual, S. S. (2007). A step-by-step guide to data analysis using SPSS for windows version 15. Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010). How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature reviews Drug discovery, 9(3), 203-214. Penner‐Hahn, J., & Shaver, J. M. (2005). Does international research and development increase patent output? An analysis of Japanese pharmaceutical firms. Strategic M11anagement Journal, 26(2), 121-140. Porter, M. E., & Stern, S. (2001). Innovation: location matters. MIT Sloan management review, 42(4), 28. Furman, J. L., Porter, M. E., & Stern, S. (2002). The determinants of national innovative capacity. Research policy, 31(6), 899-933. Frees, E. W. (2004). Longitudinal and panel data: analysis and applications in the social sciences. Cambridge University Press. Prahalad, C. K. (2012). Bottom of the Pyramid as a Source of Breakthrough Innovations. Journal of Product Innovation Management, 29(1), 6-12. Prahalad, C.K. and Hart, S.L. (2002) 'The fortune at the bottom of the pyramid', Strategy+Business 26(First Quarter): 2-14. Radjou, N., Prabhu, J., & Ahuja, S. (2012). Jugaad innovation: Think frugal, be flexible, generate breakthrough growth. John Wiley & Sons. Rodríguez-Pose, A., Tijmstra, S. A., & Bwire, A. (2009). Fiscal decentralisation, efficiency, and growth. Environment and Planning A, 41(9), 2041-2062. Rosell, D. T., Lakemond, N., Dabhilkar, M., & Bengtsson, L. (2011). Purchasing Capabilities for Supplier Innovation in New Product Development. In 18th International Product Development Management Conference (IPDMC), Delft, the Netherlands, 5-7 June, 2011.

 

33  

       

Master  Thesis  -­‐  T.  Goverts  

   

Santos, J. R. A. (1999). Cronbach’s alpha: A tool for assessing the reliability of scales. Journal of extension, 37(2), 1-5. Schneider, P. H. (2005). International trade, economic growth and intellectual property rights: A panel data study of developed and developing countries. Journal of Development Economics, 78(2), 529-547. Sobrero, M., & Roberts, E. B. (2002). Strategic management of supplier–manufacturer relations in new product development. Research policy, 31(1), 159-182 Tabachnick, B. G., Fidell, L. S., & Osterlind, S. J. (2001). Using multivariate statistics. California State University, Northridge. Whelan, E., Collings, D. G., & Donnellan, B. (2010). Managing talent in knowledge-intensive settings. Journal of Knowledge Management, 14(3), 486-504. World Development Indicators (2015). GDP growth (annual %) and (Income growth annual %). Washington, DC: World Bank. Wu, L. Y. (2007). Entrepreneurial resources, dynamic capabilities and start-up performance of Taiwan's high-tech firms. Journal of Business research, 60(5), 549-555. Zeger, S. L., Liang, K. Y., & Albert, P. S. (1988). Models for longitudinal data: a generalized estimating equation approach. Biometrics, 1049-1060. Zhang, Y., Li, H., Hitt, M. A., & Cui, G. (2007). R&D intensity and international joint venture performance in an emerging market: moderating effects of market focus and ownership structure. Journal of International Business Studies, 38(6), 944

                               

34  

       

Master  Thesis  -­‐  T.  Goverts  

   

Appendix     Appendix  A:   Conceptual  model    

 

Control  Variables  

Research  &   Development  

Innovation  Output  

Level  of  Institutional  Voids  

Training   Services  

Government   Efficiency  

Financial  Market   Development  

Local  Quality   Suppliers    

Figure 1. Conceptual  model.

 

35  

        Appendix B: Country sample

Master  Thesis  -­‐  T.  Goverts  

   

Table: Country sample (N=124) Economy Albania Algeria Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Barbados Belgium Benin Bolivia Bosnia and Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Chile China Colombia Costa Rica Cote d’Ivoire Croatia Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt El Salvador Estonia Ethiopia Finland France Gambia Georgia Germany Ghana Greece Guatemala Guyana Honduras Hong Kong Hungary Iceland India Indonesia Ireland Israel Italy

 

Region (IMF, April, 2015) Emerging and Developing Europe Middle East, North Africa, and Pakistan Latin America and the Caribbean Commonwealth of Independent States Advanced economies Advanced economies Commonwealth of Independent States Middle East, North Africa, and Pakistan Emerging and Developing Asia Latin America and the Caribbean Advanced economies Sub-Saharan Africa Latin America and the Caribbean Emerging and Developing Europe Sub-Saharan Africa Latin America and the Caribbean Emerging and Developing Europe Sub-Saharan Africa Sub-Saharan Africa Emerging and Developing Asia Sub-Saharan Africa Advanced economies Latin American and the Caribbean Emerging and Developing Asia Latin American and the Caribbean Latin American and the Caribbean Sub-Saharan Africa Emerging and Developing Europe Advanced Economies Advanced Economies Advanced Economies Latin American and the Caribbean Latin American and the Caribbean Middle East, North Africa, and Pakistan Latin American and the Caribbean Advanced Economies Sub-Saharan Africa Advanced Economies Advanced Economies Sub-Saharan Africa Commonwealth of Independent States Advanced Economies Sub-Saharan Africa Advanced Economies Latin America and the Caribbean Latin America and the Caribbean Latin America and the Caribbean Advanced Economies Emerging and Developing Europe Advanced economies Emerging and Developing Asia Emerging and Developing Asia Advanced economies Advanced economies Advanced economies

36  

        Jamaica Japan Jordan Kazakhstan Kenya Korea Kuwait Kyrgyz Republic Latvia Lesotho Lithuania Luxembourg Macedonia Madagascar Malawi Malaysia Mali Malta Mauritius Mexico Mongolia Montenegro Morocco Mozambique Namibia Nepal Netherlands New Zealand Nicaragua Nigeria Norway Oman Pakistan Panama Paraguay Peru Philippines Poland Portugal Qatar Romania Russian Federation Saudi Arabia Senegal Serbia Singapore Slovak Republic Slovenia South Africa Spain Sri Lanka Sweden Switzerland Tajikistan Tanzania Thailand Trinidad and Tobago Tunisia Turkey Uganda

 

Master  Thesis  -­‐  T.  Goverts  

   

Latin America and the Caribbean Advanced economies Middle East, North Africa, and Pakistan Commonwealth of Independent States Sub-Saharan Africa Advanced economies Middle East, North Africa, and Pakistan Commonwealth of Independent States Advanced economies Sub-Saharan Africa Advanced economies Advanced economies Emerging and Developing Europe Sub-Saharan Africa Sub-Saharan Africa Emerging and Developing Asia Sub-Saharan Africa Advanced economies Sub-Saharan Africa Latin America and the Caribbean Emerging and Developing Asia Emerging and Developing Europe Middle East, North Africa, and Pakistan Sub-Saharan Africa Sub-Saharan Africa Emerging and Developing Asia Advanced economies Advanced economies Latin America and the Caribbean Sub-Saharan Africa Advanced economies Middle East, North Africa, and Pakistan Middle East, North Africa, and Pakistan Latin America and the Caribbean Latin America and the Caribbean Latin America and the Caribbean Emerging and Developing Asia Emerging and Developing Europe Advanced economies Middle East, North Africa, and Pakistan Emerging and Developing Europe Commonwealth of Independent States Middle East, North Africa, and Pakistan Sub-Saharan Africa Emerging and Developing Europe Advanced economies Advanced economies Advanced economies Sub-Saharan Africa Advanced economies Emerging and Developing Asia Advanced economies Advanced economies Commonwealth of Independent States Sub-Saharan Africa Emerging and Developing Asia Latin America and the Caribbean Middle East, North Africa, and Pakistan Emerging and Developing Europe Sub-Saharan Africa

37  

        Ukraine United Arab Emirates United Kingdom United States Uruguay Venezuela Vietnam Zambia Zimbabwe

 

Master  Thesis  -­‐  T.  Goverts  

   

Commonwealth of Independent States Middle East, North Africa, and Pakistan Advanced economies Advanced economies Latin America and the Caribbean Latin America and the Caribbean Emerging and Developing Asia Sub-Saharan Africa Sub-Saharan Africa

38  

      Master  Thesis  -­‐  T.  Goverts         Appendix C: Operationalization of the model variables

Variable

Source

Operationalization

Dependent Variable

Innovation output

Global Innovation Index

Data taken from the innovation output Sub-Index Pillar [1 = low output level; 100 = high output level]

Independent Variable

R&D Spending’s

Global Competiveness Index*

Data taken from the Executive Opinion Survey: In your country, to what extent do companies spend on research and development (R&D)? [1 = do not spend on R&D; 7 = spend heavily on R&D]

Moderating Variables

Availability of highquality Training Services

Global Competiveness Index*

Data taken from the Executive Opinion Survey: In your country, to what extents are high-quality, specialized training services available? [1 = not available at all; 7 = widely available]

Government Efficiency

Global Competiveness Index*

Data on the efficiency of government spending, government regulation and legal frameworks gathered by Executive Opinion Survey is aggregated in a single measurement World Economic Forum [1 = low efficiency; 7 = high efficiency]

Financial Market Development

Global Competiveness Index**

Data on the availability of financial services, local equity markets, regulations, banking systems, venture capitalist and legal rights gathered by the Executive Opinion Survey is aggregated in a single measurement by the World Economic Forum [1 = weakly developed; 7 = highly developed]

Level of Local Suppliers Quality

Global Competiveness Index*

Data taken from Executive Opinion Survey: In your country, how would you assess the quality of local suppliers? [1 = extremely poor quality; 7 = extremely high quality]

Competition

Global Competiveness Index*

Data on foreign competition and domestic competition is aggregated in a single measurement in the 6th pillar by the World Economic Forum. [1 = low levels of competition; 7 = high levels]

Market Size

Global Competiveness Index**

Data on the domestic market size index, Exports as a percentage of GDP and GDP (PPP$ billions) is aggregated into a single measurement by the World Economic Forum [1 = small market size; 7 = large market size]

Education System

Global Competiveness Index*

Data taken from Executive Opinion Survey: How well does the education system in your country meet the needs of a competitive economy? [1 = not well at all; 7 = extremely well]

Control Variables

Notes: * Data in the index is collected from the World Economic Forum. ** Data in the index is collected from the World Economic Forum, World Bank and International Finance Cooperation.

 

39