Innovation Economics, Productivity, and Internationalization: Evidence from Emerging Market Firms

Innovation Economics, Productivity, and Internationalization Evidence from Emerging Market Firms Doctoral student: Jude Ndubuisi Edeh Director: Prof. Dr. Francisco José Acedo González ii iii To my parents: Nwatu and Ifeoma iv ACKNOWLEDGMENTS During my doctoral education, I have worked with a great number of people whose different contributions deserve special mention. It is a pleasure to express my heartfelt gratitude to them all in this acknowledgment. This dissertation would not have been possible without the help and support of supervisors: Professors Francisco José Acedo González and Encarnación Ramos Hidalgo. I am tremendously thankful to you for giving me the opportunity and guiding me to explore my area of interest with clarity and working on it with full flexibility. I sincerely appreciate your generosity with your time, energy, and support. Above all, thank you very much for your trust in my abilities. I owe, in addition, a significant debt of gratitude to the co-authors of one of the papers in this dissertation: Prof. Ramos and Divine Obodoechi. It is always a stimulating and productive experience working with you. I would never have made it this far without my family. I will always remain indebted to you for your undying care, love and encouragement and support. This is for you: Ezi n’ ulo Edeh, n'ezie e nweghị m ike i kele unu nke ọma màkà ị me m ihe m bụ taa màkà na ọ bụ agụkata agba awaa. Màkà na ọ na abụ e kelee nwaanyị akịdị ọ gwọta ọzọ; ma e too dike na nke o mere o mekwaa ọzọ. Ọfọ m bụ kà Chineke nye unu ihe unu ga-eri na ọ bụghị ihe ga-eri unu, ihe unu ga-achịrị ọchị na ọ bụghị ihe ga-achị unu ọchị. Unu ga-adị kà echi, màkà na echi a naghị agwụ agwụ. Ọ gaadịrịrị anyị mma n'Eke, Orie, Afọ na Nkwọ. v “An economist is an expert who will know tomorrow why the things he predicted yesterday didn't happen today.” L.J. Peter (1919-1988) vi CONTENT Chapter 1: Introduction 1 Chapter 2: Technological Progress, Innovation, and Economic Growth 20 Chapter 3: SMEs, Innovation, and Productivity in Developing countries 59 Chapter 4: Measuring Innovation Outputs 92 Chapter 5: Innovation Efforts, External Supports and Productivity 112 Chapter 6: Innovation Strategies and Export Growth 149 Chapter 7: Conclusions, Limitations and Future Research 175 vii CONTENT (Extended) List of Figures x List of Tables xi List of Abbreviations xii Chapter 1: Introduction 1.1. Introduction 1 1.2. Background of study 2 1.3. Research aims and objectives 8 1.4. Research methodology 11 1.5. Significance of the study 11 1.6. Structure of the thesis 14 References 16 Chapter 2: Technological Progress, Innovation, and Economic Growth 2.1. Introduction 20 2.2. Concept of innovation 21 2.3. Measurement of productivity 24 2.3.1. Total factor productivity 25 2.3.2. Partial factor productivity 26 2.4. Modern theories of economic growth 27 2.4.1. Schumpeter’ theory of economic growth 27 2.4.2. From Keynesian to Neoclassical economic growth theory 32 2.4.3. New growth theory 36 2.4.4. Evolutionary theory of economic growth 39 2.5. Implications of economic growth theories on developing countries 41 2.5.1. Productivity Growth at the Macro Level 42 2.5.2. Technology, Innovation, and Productivity in SSA 44 2.6. Conclusion 49 References 51 viii Chapter 3: SMEs, Innovation, and Productivity in Developing Countries 3.1. Introduction 59 3.2. Definition of SMEs in Nigeria 60 3.3. Contributions of SMEs to the Nigerian economy 63 3.4. Innovation as engine of productivity growth in SMEs 68 3.5. Country-specific determinants of innovation 69 3.5.1. Access to power supply 70 3.5.2. Legal system 72 3.5.3. Financial system 74 3.6. Firm-level determinants of innovation 75 3.6.1. Firm size 76 3.6.2. Firm age 77 3.6.3. Employee training programme 79 3.6.4. Cooperation 80 3.6.5. International trade 81 3.7. Conclusion 83 References 84 Chapter 4: Measuring Innovation Outputs 4.1. Introduction 92 4.2. Innovation data collection methods 93 4.2.1. Object-based method 93 4.2.2. Subject-based method 95 4.3. Innovation surveys in developing countries 96 4.4. Innovation surveys in Nigeria 99 4.5. Choice of Innovation Indicators 102 4.6. Definitions of Innovation Output Indicators 104 4.7. Conclusion 106 References 107 ix Chapter 5: Innovation Efforts, External Supports and Productivity 5.1. Introduction 112 5.2. Literature review 114 5.3. Government Intervention and Innovation 120 5.4. Empirical strategy: Innovation-Productivity Analysis 123 5.5. Descriptive Statistics 126 5.6. Empirical findings 128 References 141 Chapter 6: Innovation Strategies and Export Growth 6.1. Introduction 149 6.2. Theoretical background 150 6.3. Hypothesis development 157 6.4. Model specification 160 6.5. Empirical findings 161 References 166 Chapter 7: Conclusion, Limitations and Future Research 7.1. Overview of the study 175 7.2. Summaries of the key Findings 176 7.2.1. Study I: Discussion and Contributions 176 7.2.2. Study II: Discussion and Contributions 181 7.3. Limitations and Future research directions 184 7.4. Conclusion 186 References 187 Appendix 4 developing countries such as in the Sub-Saharan African (SSA) region, there are overwhelming challenges that hinder their growth such as inadequate infrastructure, insufficient provision of basic services, inequality, high unemployment and level of illiteracy, among others. The SSA region is well known for its abundant resources; however, the low economic growth in the region clearly reveals the inability of most countries of the region to transform them into outputs. Akisik, Gal, and Mangaliso (2020:1) argue that the coexistence of abundant natural resources with large-scale poverty, squalor, and human deprivation has led to describing the continent as the "paradox of Africa's natural resource wealth" (Panford, 2017). However, given the rich evidence supporting the linkage between technological change and productivity growth, it is believed that the countries of the region can achieve superior economic growth if they invest more in technologies, knowledge, and human capital through high-quality education (Das and Drine, 2020; Shenkoya and Kim, 2020). In other words, the SSA region can solve its economic growth paradox by fully embracing technological change. The interconnectedness between macroeconomics and microeconomics shows that various factors at the national level hugely influence the behaviour of firms. There are many factors that influence the speed of productivity growth of firms, such as national structures, institutions, and policies. The report of the Growth Competitiveness framework, which is used by the World Economic Forum’s Global Competitiveness Report (GCR) to assess the capacity of the world's economies to achieve sustained economic growth, shows that the SSA region is the least competitive region, with 25 of the 34 countries assessed in 2019 scoring below 50 (Schwab, 2019). In its analysis, the GCR focuses on 12 pillars, which centres on enabling environment, human capital, markets, and innovation ecosystem. As 5 shown in Table 1, the SSA region is at the lowest performance in all the pillars, except in the labour market, where it slightly outperformed South Asia. Table 1: Regional Performance by Twelve Pillars of Economic Growth Source: World Economic Forum analysis, 2019 In terms of enabling environment, which includes institutions, infrastructure, information, and communication technology (ICT) adoption, and macroeconomic stability, Europe and North America have the overall highest performance with an aggregate of 307. 4 points, followed by East Asia and the Pacific with 296.3 points. When compared to Latin America and the Caribbean (227 points), the survey shows that the SSA region (195.3 points) not only lags behind the developed economies but also other developing countries. The results are similar also in the areas of financial systems, business dynamism, and innovation capability. In other words, these results clearly reveal the SSA region is still underperforming in the core determinants of economic growth; namely technology, high-quality public institutions, and a stable macro-economic environment. Research shows that these factors to a large extent influence the entrepreneurial activities and innovation performance of firms, especially SMEs (McArthur and Sachs, 2002). Firms supported by well-functioning and efficient 6 institutional systems not only achieve superior performance but also contribute immensely to national economic growth. More precisely, productivity at the national level depends on the underlying productivity of all firms in the economy. This implies that the countries in the SSA region can increase their aggregate productivity by improving the institutional systems, business environment, and promoting policies that improve the productivity of firms, especially SMEs. The contributions of SMEs to the economic growth of many countries are undisputed. These firms account for most of the enterprises globally and are major contributors to job creation and economic growth. According to the Annual Report on European SMEs, there are more than 25 million SMEs in 28 EU-member states, accounting for 99.8% of all enterprises and contributing 56.4% of value added and 66.6% of employment in the non-financial business sector. It is predicted that these firms will grow by 4.1% in 2019 and 4.2% in 2020, while the employment rate is expected to grow by 1.6% in 2019 and 1.4% in 2020. These firms are responsible for a significant share of economic growth and new-job generation in developed economies (Bianchi, and Wickramasekera, 2016). In the Latin American and Caribbean region, the SME sector makes up over 99.5 percent of all firms and generate 60 percent of formal productive employment (OECD/CAF (2019). Across sub-Saharan Africa (SSA), SMEs are an important driver of growth, accounting for over 95% of all enterprises and 60% of total employment (ITC, 2018). They contribute, for example, 49 percent to GDP in Ghana, and 49.78 percent in Nigeria. While the SME sector has been experiencing growth over the past three decades, they still considerably lag behind their peers 7 not only in the developed countries but also in other developing countries (Quartey et al., 2017). There are numerous factors linked to the low productivity of the SME sector in the SSA region, ranging from lack of managerial skills, poor infrastructure, and access to the capital market to corruption among others. It is argued that improvements in macro-economic and public institutional policies will enable a conducive business environment and consequently enhance the competitive capabilities of SMEs. OECD Policy Note states that "governments can support innovation in SMEs by fostering a sound business environment, helping SMEs to develop and use their internal strategic resources effectively, and building an innovation system that is effective in the commercialisation of research and inclusive of a large range of SMEs" (OECD, 2018:3). While a significant percentage of SMEs engage in various innovation strategies, especially firms from higherincome countries, most of the countries in the SSA region are yet to invest enough resources in technological change to stimulate innovation and enhance the productivity of SMEs. In other words, efficient public policies and investment in R&D at the national level can enable SMEs to build their internal strategic resources, innovation capabilities, and competitiveness, thereby closing the productivity gap between them and their peers in other parts of the world. 8 1.3. Research aims and objectives Even though these firms in general experience a high degree of challenges than their peers in developed countries, evidence shows that they are evolving and engaging in various types of innovation activities (Tekin and Hancioğlu, 2018). Thus, the main objective of this current study is to examine the innovation efforts of these firms in relation to their labour productivity and export growth. Nigeria is an interesting setting given its place in the African economy. Nigeria is not only the most populous country, but also considerably the largest economy in Africa as it overtook South Africa with 2.3% GDP growth in 2019. Besides, Nigeria has the highest number of SMEs in Africa. According to the Nigerian Bureau of Statistics (NBS), SMEs account for 48% of national GDP and account for 96% of all enterprises. They contribute about 50% of industrial jobs and approximately 90% of the manufacturing sector. The SMEs in the manufacturing sector is selected for empirical investigations in this study. The choice is made for the following two reasons. First, the SMEs in the manufacturing sector represents a high proportion of all SMEs in Nigeria. In terms of employment rate, out of about 2, 889, 714 million persons employed by the SMEs in 2017, the SME in the manufacturing sector employed 606,839.94 thousand persons, resulting in 21 percent of the workforce. Second, manufacturing has traditionally played a key role in economic growth, especially through export intensity. Nigerian SMEs in the manufacturing sector reported the highest number of entities with exportable product entities compared to other sectors. Research has identified export shares as a source of growth for developing 9 countries. Export growth contributes hugely to capital inflows, employment, expansion of industry, and widening the production base. The motivation for innovation among Nigerian SMEs in the manufacturing sector can be explained as follows. First, studies suggest that firms from a weak business environment have a higher tendency of entering foreign markets where efficient institutions allow for increased learning opportunities and technology sourcing (Dunning, 1998). This supports the claim that international expansion enables SMEs to explore new resources and capabilities (Fu, Mohnen, and Zanello, 2018). Technology spillover in the context of international expansion allows SMEs from developing countries to make up for the lack of resources (e.g. technology knowledge, human capital) required for innovation activity (Buckley, 1997; Del Giudice et al., 2019). The effect of knowledge spillover enabled by internationalization is both supported theoretically (Romer, 1987; Grossmann and Helpman, 1991) and empirically (Keller, 2004; Buturac, Mikulić, and Palić, 2019). Thus, the motivation to enhance their technological capabilities in the international markets reflects the critical role of innovation on firm growth (Radicic and Djalilov, 2018). Second, the manufacturing sector consists of a large number of firms producing and marketing a variety of products. The dynamics of the sector is such that there is a high degree of obsolescence and the ensuing situation of a short product life cycle (Koren, 2010). In other words, product life cycles are getting shorter and customers' demands are becoming more diverse, thus spurring SMEs in the manufacturing sector to innovate efficiently and more frequently (Sommer et al., 2015; Nafisi et al., 2019). In recent years, a growing number of empirical studies suggest that the success and failure of SMEs depend largely on their ability 10 to introduce both new or significantly improve products as well as bring them to the marketplace faster (O'Regan et al., 2006; Mishra, 2016). As manufacturing SMEs in developing countries such as Nigeria are latecomers, they are pressurized to invest in various innovation types to secure competitive parity. The main objectives of the study are: 1) To examine the determinants of innovations in Nigerian SMEs in the manufacturing sector. 2) To investigate the linkages among external financial supports, innovation efforts, and productivity growth of these firms. 3) To explore the innovation types used by these firms in pursuit of growth strategies. To achieve these aims and objectives, I will address the following issues: 1) Estimate the impacts of firm-level characteristics as well as exogenous factors determining the innovation capabilities of Nigerian SMEs in the manufacturing sector. 2) Evaluate the effect of financial supports acquired from external sources on the various innovation stages of these firms using a structural modelling approach. 3) Empirically analyse the significance of technological and non-technological innovations to ascertain the innovation types contributing most to the productivity and export growths of these firms. 11 1.4. Research methodology This work follows a quantitative deductive method; and uses Nigeria Innovation Survey (NIS) collected by the National Centre for Technology Management (NACETEM). The survey covers firm-level data on innovation activities from wave 1 (2005-2007) and wave 2 (2008-2010). I considered different econometric issues such as heterogeneity, multicollinearity, endogeneity, simultaneity, and selection bias, etc. Furthermore, I develop conceptual frameworks based on the literature review to implement the two empirical strategies used in this study, namely econometric model based on CDM approach and Dynamic Least Square (DOLS) model. 1.5. Significance of the study This study makes various contributions. First, in contrast to previous studies that relied on innovation indicators such as R&D investment or patents as proxies for innovation output (Aw, Roberts and Winston, 2007), this study uses innovation output variables to better understand the competitive capacity of SMEs in Nigeria. Scholars suggest that productivity and export growth to a large extent depends on the firm's ability to introduce new or improved products and production methods, rather than mere R&D investments (Ganotakis and Love, 2011). Moreover, relying solely on R&D investment as a measure of innovation has a disadvantage of 12 underreporting the innovative behaviour of firms that do not have a separate R&D department, which nonetheless innovates (Wakelin, 1998). This is particularly true for developing countries, which are dominated by SMEs. Thus, by using innovation output variables as measures of technological innovations, this study contributes to this stream of research by offering new evidence from developing country SMEs. As most of the existing evidence focused on large manufacturing firms in industrialized economies (Higón and Driffield, 2010), this study enhances our understanding of the dimension of technological innovation that affects productivity and export growth of SMEs in developing economies. Second, the traditional view of innovation is increasingly criticized for ignoring other types of innovations (Grimpe et al., 2017). Technological innovation does not fully explain the innovation activities of firms (Geldes et al., 2016). Mothe and Nguyen (2010) called for more studies explaining the impact of other types of innovation on firm performance. Radicic and Djalilov (2018) note that the lack of empirical evidence on the effect of non-technological innovation is even more prominent in the context of SMEs. To fill this research gap, this study integrates the significance of marketing innovation on the growth performance of SMEs in Nigeria. Thus, by focusing on both technological and non-technological innovations, this study offers a more comprehensive analysis of what innovation types have more significance on the firm's performance. As there are relatively few studies adopting a broad perspective of innovation in the context of developing economies, this study is among the first in SSA to provide empirical evidence on both the individual and joint effects of innovation types on SME growth performance. 13 Third, there is a substantial body of empirical literature on the relationship between innovation and productivity using the CDM modelling approach. However, the majority of them either focus mainly on developing economies or large firms. Thus, there are still few studies in the context of SSA (e.g. Goedhuys, Janz and Mohnen, 2008; Cirera, Lage and Sabetti, 2016; Fu, Mohnen and Zanello, 2018; Morsy and Amira El-Shal, 2020). Despite the valuable contributions of these studies, surprisingly, there is a dearth of empirical studies explicitly exploring the relationship between innovation efforts and productivity of SMEs notwithstanding their immense contributions to the SSA economy. By filling this research gap, this study makes the following contributions: (1) even though there are prior studies that empirically analysed the impact of innovation on firms using the NIS (e.g. Oluwatope et al., 2016; Sanni, 2018; Medase and Barasa, 2019), to date, there are no studies estimating the linkage between innovation efforts and productivity in Nigeria using the CDM modelling approach. (2) Research shows that the effect of financial constraints on SMEs is more pronounced in developing countries such as Nigeria. It is argued that financial supports from external sources and donors enable these firms to overcome these constraints. However, we know very little about the influence of these supports on the innovation activities and performance of SMEs in SSA regions. To this end, following Griffith et al. (2006) and Aldieri et al. (2019), this study adjusted the original version of the CDM model to accommodate the impacts of external financial supports in the regressions. However, unlike prior studies (Raffo et al., 2008), this study included external financial supports from the state, federal, and foreign governments in the four equations of the CDM model to understand their impacts on the knowledge input stage all the way to productivity. 20 Chapter 2 Technological Progress, Innovation and Productivity Growth Productivity isn't everything, but in the long run, it is almost everything. A country’s ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker. Paul Krugman (1994) 2.1. Introduction Productivity is important because it helps us understand how efficiently a country or firm transforms its available resources into economic values in the long run. In this regard, productivity growth is probably one of the most crucial ingredients of economic prosperity. It improves a country's standards of living and quality of life in several ways. For example, productivity growth can enhance the situations of a country facing development challenges such as inadequate infrastructure, poor access to basic services, widening inequality, rising unemployment, etc. While there is no single path per se, technological change has been widely identified as a major source of sustained economic growth (Mokyr, 2005; Bogliacino and Pianta, 2011; Zhou, Song, and Cui, 2020). Generally, technological change refers to the rate at which new ideas are generated, new products and methods of production are introduced and adopted by nations as well as firms. This definition highlights the three main elements of technological change, namely invention, innovation, and diffusion (Jaffe, Newell and Stavins, 2003). In other words, it is the interplay of these elements that improve the real output, social conditions, and wellbeing of society. Similarly, a faster rate of growth of technological change leads to a faster 21 rate of productivity growth, all other things being equal. In addition, technological change confers competitive advantages and superior economic performance in both nations and firms (Grossman and Helpman, 1991). Wurth (1993: 230) argues that "growth in productivity growth has become a measure, if not a definition, of technological progress and economic advance". The persistent and wide difference in productivity over time and between countries largely explains why this subjectmatter continues to attract the attention of both scholars and policymakers (Palma and Reis, 2019). Given the evidence from developed countries, it is argued that the productivity gap in developing countries is largely due to insufficient investments in technology through innovations (Cirera, Lage and Sabetti, 2016; Niebel, 2018). Thus, the purpose of this chapter is to review the relevant literature supporting the linkages between technological change and productivity growth. The chapter is structured as follows: In Section 2.2, I will offer some preliminary remarks on the concepts of innovation and productivity measurements. In Section 2.3, I will present the modern economic growth theories, highlighting the role of technological change in productivity growth. Finally, in Section 2.4, I will explore the implications of technological change in the economic growth of the SSA region. 2.2. Concept of Innovation Scholars and practitioners across a wide range of disciples have focused much attention on the concept of innovation (Damanpour and Schneider, 2006; Baregheh, Rowley, and Sambrook, 2009). The term innovation is from a Latin word ‘innovare’, which literally means 'to renew' or 'to make something new'. Even though innovation in itself is not a new phenomenon, it is only in the 20th century 22 that it received systematic documentation in the literature. More precisely, the opus of an Austrian economist, Joseph Alois Schumpeter is widely regarded as a seminal contribution to modern innovation research (Michaelides and Milios, 2009; Eggink, 2013; Cantner and Dopfer, 2015). In 'Theory of Economic Development' (originally published in 1911 under the title Theorie der wirtschaftlichen Entwicklung) Schumpeter (1934) defined innovation as follows: (1) The introduction of a new good – that is one with which consumers are not yet familiar – or a new quality of a good. (2) The introduction of a new method of production, that is one not yet tested by experience in the branch of manufacture concerned, which need by no means be founded upon a discovery scientifically new, and can also exist in a new way of handling a commodity commercially. (3) The opening of a new market, that is, a market into which the particular branch of manufacture of the country in question has not previously entered, whether or not this market has existed before. (4) The conquest of a new source of supply of raw materials or halfmanufactured goods, again irrespective of whether this source already exists or whether it has first to be created. (5) The carrying out of the new organization of any industry, like the creation of a monopoly position (for example through trustification) or the breaking up of a monopoly position. Schumpeter stresses the role of new combinations of existing elements and forces in the innovation process, especially within the industrial context (Malerba and McKelvey 2020). Kurz (2012: 883) suggests that new combinations are “the systematic production of new, economically useful knowledge out of existing knowledge”. Schumpeter highlights the multifaceted nature of innovation by distinguishing the different types of innovations, ranging from technological innovation to non-technological innovation. Over the years, innovation has been 23 conceptualized variously by scholars. Thompson (1965: 2) defines innovation as "the generation, acceptance, and implementation of new ideas, processes products or services". Plessis (2007) notes that: "innovation as the creation of new knowledge and ideas to facilitate new business outcomes, aimed at improving internal business processes and structures and to create market-driven products and services." According to Damanpour (1996, p. 694): Innovation is conceived as a means of changing an organization, either as a response to changes in the external environment or as a pre-emptive action to influence the environment. Hence, innovation is here broadly defined to encompass a range of types, including new product or service, new process technology, new organization structure or administrative systems, or new plans or program pertaining to organization members. Furthermore, the Oslo Manual refers to it as “the implementation (commercialization) of a new or significantly improved product (good or service), or process, or a new marketing method, or a new organizational method in business practices, workplace organization or external relations" (OECD, 2005: 31). This definition, which is rooted in Schumpeter's ideas, clearly distinguishes between four types of innovation, namely, product, process, marketing, and organizational innovations. The Oslo Manual's definition shifts the emphasis of innovation from being merely an in-house activity to include external relations. In other words, it emphasizes the importance of collaborations with other external agents in the introduction and commercialization of innovations (Hartley, Sørensen, and Torfing, 2013). 24 While there are some differences in the various definitions, they largely agree on the following basic features of innovation. First, novelty – innovation emerges from "the doing of new things or the doing of things that are already done, in a new way" (Schumpeter, 1947: 151). However, it is good to note that the notion of novelty includes both ‘new-to-the-world’, ‘new-to-the-market’, and 'new-to-firm' (OECD, 2009). Against a restrictive notion of novelty, Van du Ven et al. (1986) argue that "as long as the idea is perceived as new to the people involved, it is an 'innovation' even though it may appear to others to be an 'imitation' of something that exists elsewhere". This has important implications for developing countries, where incrementalism and imitation dominate innovation activities (Li and Huang, 2019; Wang and Chen, 2020). Nonetheless, the Oslo Manual does not recognize minor and insignificant changes or those with insufficient level of novelty as innovation (OECD, 2005: 37). The second feature is the distinction between innovation and invention. Innovation is not a mere invention, that is, the initial generation of new ideas. For example, a firm may generate and develop new ideas; however, they must be realized and commercialized to be regarded as innovations. In other words, innovation is the realization and the extraction of (economic) values from generated ideas. 2.3. Measurement of Productivity Productivity is the ability to convert inputs (e.g. labour, capital, land, raw materials, and information) into output. It is an increase in the value of outputs produced for a given level of inputs, typically over a specific period of time. Thus, the productivity 25 of a firm or country grows when they produce more outputs per unit of input. Moss (1979: 276) writes that "efficient production of goods and services is a primary goal of economic effort, and statistical measurement of productivity is an important tool for monitoring and promoting its advances". The measurement of productivity is not always straightforward given that scholars consider productivity growth both as exogenous and endogenous factors. However, it has become common in the literature to measure it in two main ways. 2.3.1. Total factor productivity Total factor productivity (TFP) is regarded as a part of output growth that cannot be explained by input growth (Comin, 2008). The unexplained portion is often referred to as the Solow residual, which captures the impact of technological change on productivity when the variations in capital and labour inputs are subtracted from the output growth per head (Solow, 1957). TFP is the ratio of total output to total production inputs. Thus, growth in TFP implies that more output is being produced from a constant amount of resources used in the production process. We shall return to the assumptions of this productivity measurement later in this chapter when we discuss the Solow's model of economic growth. Suffice to say that one of the challenges of estimating TFP is the difficulty of capturing all the factors of productivity in a single model. Besides, scholars suggest that it is not fitting for measuring technological change, especially improved technologies 26 embedded in new machinery and equipment (Jorgenson and Stiroh, 1999; Lipsey and Carlaw, 2004). 2.3.2. Partial Factor Productivity Partial factor productivity (PFP) refers to the measure of produced output per unit of each input used. Unlike the TFP, this measurement calculates each input (i.e. factor of productivity such as capital productivity, labour productivity, or energy productivity) separately. For example, capital productivity measures how efficiently physical capital is used in producing goods and services. It is expected that an improvement in physical capital will lead to an increase in output. For the purpose of this work, I will focus on labour productivity at the firm level, which is the total volume of output produced per unit labour. Labour productivity is assumed to grow, when the output grows faster than the inputs, which, in turn, makes the existing inputs more productively efficient. We shall return to this measurement in the subsequent section. Taken together, the choice of either TFT or PFP depends on the study's objective, availability, as well as the nature of data in question. In our case, PFP is more fitting in ascertaining the impact of innovation activities on the growth performance of firms. In addition, the availability of microdata from the innovation survey allows us to estimate these relationships. 27 2.4. Modern Theories of Economic Growth The relationship between economic structure and productivity growth has received a sustained attention. Due to the significance of productivity growth to the wellbeing of a country or a firm, economists have attempted to explain why some countries grow fast and enjoy long-term economic prosperity, while others lag behind. For example, these scholars are interested in understanding whether economic growth is a static or dynamic process; whether technological change determines productivity growth exogenously or endogenously? Thus, in this section, I will discuss four main modern economic theories dealing with the linkage between technological progress and productivity growth. 2.4.1. Schumpeter’ Theory of Economic Growth As pointed out above, Schumpeter’s work is of great interest to technological change in the process of economic growth. By moving away from the notion of market equilibrium, Schumpeter explains why and how a disruptive disequilibrium force, which enables growth in the economy, is created. According to him, new combinations of forces and materials creates a continuous market disequilibrium. He writes "the development is a spontaneous and discontinuous change in the channels of the circular flow, disturbance of equilibrium which forever displaces the equilibrium state previously existing" (Schumpeter, 1934: 64). Unlike the 28 concept of circular flow based on perfect competitive equilibrium, he argues that a change in the existing production system, embodied in new and radical innovations, determines productivity growth. Schumpeter’s thoughts have been categorized into two main patterns of innovation activities, namely, Schumpeter Mark I and Schumpeter Mark II (Nelson and Winter, 1982; Kamien and Schwartz, 1982). In the former, Schumpeter argues that the entrepreneur is the main agent of disequilibrium. That is, agents (e.g. entrepreneurs and new small firms) creatively destroy existing systems in search of profits. Hérbert and Link (1989:39) note that entrepreneurship "pertains to the actions of a risk-taker, a creative venturer into a new business or the one who revives an existing business". More precisely, equipped with new products, new processes, or new sources of supply, the entrepreneurs can launch new enterprises that challenge the established firms. In so doing, they open up markets, defeat monopoly, and thus, win the price competition. Thus, unlike a competitive equilibrium condition, where the price of each product equals its cost of production, Schumpeter argues that dynamic changes from innovations create profits for entrepreneurs in an industry. He writes: It is not ...[price] competition which counts but the competition from new commodity, the new technology, the new source of supply, the new types of organisation ... competition which commands a decisive cost or quality advantage and which strikes not at the margins of the profits... of the existing firms but at their foundations and their very lives (Schumpeter, 1943:84). 29 In short, innovation is essentially a competitive process that shapes the market structure through creative destruction. On Schumpeter Mark I, economic growth is driven by the demand for innovative goods and services, and the profit-seeking entrepreneurial firms lead this process by replacing old technologies with ones (Acemoglu, 2009; Batabyal and Yoo, 2018). Malerba, (2007: 353) writes that the Schumpeter Mark I is characterized by "high technological opportunities, low appropriability, and low cumulativeness (at the firm level) conditions and a limited role of generic knowledge are more likely to lead to low degrees of concentration of innovative activities with a relatively large number of innovators, high rates of entry and high instability in the hierarchy of innovators". Figure 2.1: Schumpeter Mark I Model Furthermore, in the second pattern of innovation activities (Schumpeter Mark II), Schumpeter shifts his emphasis from small firms/entrepreneurs to the roles of inhouse R&D and large firms in technological innovation activities. 1 According to 1 In contrast to Mark I, Malerba (2007: 353) notes that Schumpeter Mark II is characterised by "high appropriability and cumulativeness (at the firm level) conditions and a generic knowledge are more generally associated with high degrees of concentration of innovation activities, low rates of 36 The main drivers of the Solow's growth model are the notions of pure and perfect competition in product as well as in capital, constant returns to scale, perfect substitutability between capital and labour, and diminishing marginal productivity of labour and capital. In this view, there is no need for investing in the capital beyond the constant return to scale point due to the impact of the law of diminishing returns. In contrast to this position, the labour productivity theory assumes that economic growth is a function of not only investments in physical capital, but also human capital and technology. The neoclassical model of growth continues to influence economic analysis (Li and Tanna, 2019; Neto, Claeyssen, and Júnior, 2020). Acemoglou (2009: 37) writes that ‘this model has shaped the way we approach not only economic growth but the entire field of macroeconomics”. 2.4.3. New Growth Theory The Solow-Swan model has been criticised, especially for its inability to fully explain the role of technological change in economic growth (Ruttan, 1998). More precisely, by treating technological change and knowledge as exogenous factors, Solow-Swan failed to explain the process underlying the long-term growth with the Keynesian tools (Cavusoglu and Tebaldi, 2006). Regarding this, Barro and Sala-i- Martin (1995: 11) writes "we end up with a model of growth that explains everything K q A k w q A k =+ 37 but long-run growth, and obviously unsatisfactory situation". Thus, the main motivation of the new growth theorists is to propose models that ensure that the long-run economic growth depends on endogenous forces (Romer, 1986, 1991, Lucas, 1988). 3 According to Romer (1986: 1003), what is required is “an equilibrium model of endogenous technical change in which long-run growth is driven primarily by the accumulation of knowledge by forward-looking, profitmaximizing agents". This assumption brings back the image of the Schumpeterian entrepreneurs who in the hunt for profit causes changes through her innovations. Here, knowledge is not only an input to the production of new goods, but also production creates further knowledge. 4 Even though the creation of new knowledge may display diminishing returns at the firm level, it has positive spillover effects on the technology production of other firms (Romer, 1986). This implies that knowledge is a public good, which can be exploited by others repeatedly and indefinitely (Grossman and Helpman, 1991). Barro and Sala-i- Martin (1995: 12) argue that the "spillovers of knowledge across producers and external benefits from human capital are parts of this process, but only because 3 It is worth noting that the new growth theorists departed from the neoclassical model by abandoning the idea of perfect competition and homogeneous products and constant returns in favour of imperfect competition and increasing returns. Ruttan (1998:5) states that "an important implication of the model is that the market equilibrium is suboptimal since the external effects of the accumulation of knowledge is not considered by the firm in making production decisions". 4 Unlike in the neoclassical theory, where new production factors are exogenous and assumed to appear from time to time, the new growth theorists, similar to Schumpeter's thought, conceive production as a dynamic process that is determined by innovation embedded in the model. 38 they help avoid the tendency for diminishing returns of the accumulation of capital". Another factor that plays a vital role in the new growth model is human capital. Lucas (1988) endogenizes human capital in the development of new knowledge and innovation. In this view, the level of human capital accumulation explains the variations in per capita output across countries. More precisely, due to the presence of the scale-effect of human and physical capitals, "economies that are initially poor will remain poor, relatively, though their long-run rate of income growth will be the same as that of initially (and permanently) wealthier economies" (Lucas 1988: 39). Similarly, Romer (1990: S99) argues that "an economy with a large total stock of human capital will experience faster growth” Therefore, unlike the neo-classical assumption, the endogenous model predicts the idea of divergence. Moreover, scale-effect plays a vital role in the development of technology. As innovation is costly, the success of generating profit depends largely on the size of the market. For example, large markets enable profit-making firms to produce a large number of intermediate goods, increase production possibilities, and productivity growth. In other words, large economies investing in knowledge can generate technological progress and grow faster than small economies (Aghion and Howitt 1998; Peretto and Smulders 2002). This implies that policy intervention can have an impact on the long-run growth rate of the economy. Evidence shows that policy intervention subsidies for R&D initiatives or grants stimulate innovations (Shaw, 1992). 39 2.4.4. Evolutionary theory of economic growth The new growth theory is limited in its explanation of the institutional complexity and economic growth at the macro-level. However, this limitation led to the development of alternative approaches, of which the evolutionary theory of economic growth has been found to be very influential. The most notable expression of this theory is found in the work of Richard Nelson and Sidney Winter (1982), who adopted and extended several aspects of Schumpeter and Thorstein Veblen’s thoughts (Hodgson, 1996; Eggink, 2013). In ‘An Evolutionary Theory of Economic Change’ these authors regarded technological change and dynamic competition as the main drivers of economic growth in capitalist economies. They employed biological concepts and metaphors to study the capabilities and behaviour of firms. Just as animal species compete for survival and growth in the natural environment, the market environment, governed by the mechanism of selection and variety, determines the success of firms (Castellacci, 2008). In addition, the notion of bounded rationality is pivotal to Nelson-Winter's evolutionary theory (Simon, 1959). Given the complexity of the environment and cognitive limitations, it is not possible for firms to possess perfect information in their predictions and strategic decisions. As a result, firms strive to adapt to changes in the market environment through the process of routine, search, and selection. On one hand, routines refer to the capabilities and decision rules that change over time due to either deliberate problem-solving efforts or external events. Nelson and Winter suggest that routines determine firm behaviour roughly in the same way that genes determine animal and human behaviour (Castellacci, 2008). They outlined three kinds of routines: (1) operating characteristics 40 regarding the firm's short-term activity, (2) the rules of investment, and (3) higherlevel procedures that influence lower-level procedures (Buensdorf, 2007). Unlike genes, however, firms can change their routines through a deliberate search (Nelson and Winter, 1977, 1984). On the other hand, search mechanism kicks in when there are changes in the market environment, and firms are faced with the challenges of survival and growth. Nelson and Winter suggest that firms can survive either through the acquisition of new routines and techniques (innovation) or adoption of their rivals' existing superior routines (imitation). In other words, a firm can continually renew itself (especially through investments in knowledge) in line with the changes in the market environment and outperform their counterparts. Given the localized pattern of search, new knowledge and superior routines will at some point diffuse, benefiting firms in the same environment. However, it becomes obsolete, thereby spurring firms to engage in a new search. This circularity – never-ending and everchanging process – explains how technological change drives economic growth in the evolutionary theory. Contrary to the neo-classical assumption, the economy is characterised by endogenous innovation activities. That is, the continuous creation of new varieties (enabled by innovation) in the economy serves as the engine oil of economic growth. In other words, the development of new variety and innovation occurs when firms actively attempt to discover routines that depart from established practice. The new variety manifests, on one hand, in forms of new product, process, organizational and market innovation. On the other hand, it shows up in form of radical and incremental innovations. Thus, it is the 'neverending' introduction of innovation, varieties, search-for-information, and searchcapacity of the firms that drive economic growth. 41 Nonetheless, the evolutionary theory is restrictive in its idea of localised search. This has implications in that it narrows the ability of firms to search beyond their boundaries. Studies show that external knowledge is critical to developing innovations (Lee et al., 2016; Asimakopoulos et al., 2020). Firms can complement their innovation capabilities by collaborating with other agents outside their environment. This is particularly true in today’s knowledge-driven global business environment, where firms with more access to a variety of knowledge sources tend to perform better than their counterparts. In the context of developing country SMEs, searching beyond their immediate business environment is very critical to their survival and growth as it enables them to exploit and complement their resources (Hou and Mohnen, 2013; Medase and Abdul-Basit, 2020). 2.5. Implications of Economic Growth Theories on Developing Countries Productivity growth differs considerably not only over time but also between countries. Research reveals a ubiquitous, large, and persistent productivity gap between developed and developing economies (Maddison, 1983; Sanfilippo, 2015). As a consequence, it is important to understand the role of technological change and innovation on economic growth in the context of developing country economies. Thus, this section provides a systematic overview of productivity trajectories in the SSA region vis-à-vis a number of other regions of the world. This exercise is important to understanding how country/regional-specific factors influence the innovation activities of firms. 42 2.5.1. Productivity Growth at the Macro Level In the previous section, I referred to the concept of partial factor productivity as the ratio of output to an input. Although no single measure provides a comprehensive picture of productivity performance, labour productivity is the most commonly used indicator due to its robust link to economic growth, competitiveness, and standards of living (Hall and Jones 1999). Labour productivity is the total output produced per unit of labour during a specific reference period. In statistics, it is measured in three main different ways: per person employed, per full-time equivalent, and per hour worked. Traditionally, labour productivity is measured as real GDP produced per hour of work. As shown in figure 2.3, SSA region has been experiencing a consistent positive economic growth. In 2014, the region's GDP per capita growth rate amounted to around 3, 848.57 US dollars, and by 2019 it increased to 4,195,40 US dollars. It is forecasted to increase to 4, 934.00 US dollars in 2024. This notable economic performance is attributed to several factors such as capital intensity, high domestic demand, external debt reliefs, and fairly stable political conditions in many countries in the region (Heshmati and Rashidghalam, 2018). 43 Figure 2.3: GDP per capita in Sub-Saharan Africa from 2014 to 2024(in US Dollars) Sources: IMF, Statista, 2019 Nonetheless, the productivity growth rate in SSA still substantially lags behind those of other regions of the world as shown in figure 2.4, North American and European regions clearly outperform other regions of the world. The aggregate GDP per capita of Latin American and Caribbean region amounted to 8,847.43 US dollars, while that of the Arab world is 6, 580. 06 US dollar in 2019. Concretely, the statistics reveal that the SSA region not only lags behind developed economies but also other developing economies. In other words, SSA represents an interesting case for our study as it calls for investigation on the extent to which technological change affect the persistent productivity gap in the region. 4,934 4,764.56 4,603.91 4,455.24 4,319 4,195.4 4,097.85 3,977.91 3,893.86 3,905.31 3,848.57 0 1,000 2,000 3,000 4,000 5,000 6,000 2024* 2023* 2022* 2021* 2020* 2019* 2018 2017 2016 2015 2014 44 Figure 2.4: GDP per capita in selected global regions at current prices in 2019 (in U.S. dollars) Sources: IMF, Statista, 2019 2.5.2. Technology, Innovation, and Economic Growth in SSA According to the economic theories reviewed above, technological change is a vital agent of long-run productivity growth (Barro and Sala-i-Martin, 1992; Perez- Trujillo and Lacalle-Calderon, 2020). In developed economies, there is abundant literature on the relationship between technological progress and economic growth (e.g. Mokyr, 2005; Pradhan et al., 2020). For example, Aiginger and Falk (2005) find a statistically significant positive impact of business R&D intensity on GDP per capita in OECD countries. Huňady and Orviska (2014) examine the effect of R&D expenditures on economic growth using panel data of 26 EU countries. They find a positive impact of R&D expenditures on economic growth. In a recent study, Kurniawati (2020) finds strong endogenous relationships among innovation, ICT, globalization, and economic growth in both short and long run in OECD countries. In emerging economies, evidence shows that countries rapidly investing in technologies are achieving productivity gains (Seck, 2012; Acikgoz and Ali, 2019). For example, Yanrui (2010) finds that R&D activities and innovation are positively 1,585.44 1,959.92 6,580.06 8,847.43 11,526.74 24,696.34 34,843.3 38,925.67 63,327.46 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 Africa Sub-Sahara South Asia Arab World Latin America and Caribbean East Asia and Pacific Europe and Central Asia EU Euro area North America 45 related to economic growth in China. Likewise, Shen, Lin, and Wu (2019) find that R&D promotes growth in regional TFP by helping to absorb new technologies embodied in FDI and foreign trade. Based on this evidence, there is a reason to believe that the SSA’s technological gap is one of the main sources of its inability to catch up to the levels of per capita similar to advanced countries. SSA countries are behind other regions in terms of share of GDP spending on R&D as shown in Figure 2.5. Figure 2.5. Share of GDP spent on research and development (R&D) in 2017, by region Sources: IMF, Statista, 2019 R&D expenditure is a key indicator of innovative activities for countries. According to statistics, northern America, eastern and south-eastern Asia, and Europe are top R&D performers. The economic growth trajectories in these regions confirm the impact of the technological change on the medium- and long-term economic growth as proposed by the endogenous growth theorists (Romer, 1986; 1990; Aghion and Howitt, 1992; 1997). Even if an absolute convergence between SSA and developed economies is difficult to achieve, evidence shows that investment in technology can enable the SSA countries leapfrog and thus achieve a higher level of growth that can enhance the standard of living and quality of life and its citizens 2.69% 2.13% 1.86% 1.78% 0.81% 0.67% 0.54% 0.38% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% Northern America Eastern and South-Eastern Asia Europe Oceania Middle East and Northern Africa Latin American and the Caribbean Central and Southern Asia Sub-Saharan Africa 52 Bogliacino, F., and Pianta, M. (2011). Engines of growth. Innovation and productivity in industry groups, Structural Change and Economic Dynamics, 22,1,41-53. Cantner, U., and Dopfer, K. (2015). Schumpeter and his contemporaries—précis and road marks. Journal of Evolutionary Economics 25, 1–19. Castellacci, F. (2008). Technological paradigms, regimes, and trajectories: Manufacturing and service industries in a new taxonomy of sectoral patterns of innovation, Research Policy, 37,6-7,978-994. Cavusoglu, N., and Tebaldi, E. (2006). Evaluating growth theories and their empirical support: An assessment of the convergence hypothesis, Journal of Economic Methodology, 13:1, 49-75. Cirera, X., Lage, F., and Sabetti, L. (2016). ICT Use, Innovation, and Productivity: Evidence from Sub-Saharan Africa. Policy Research Working Paper; No. 7868. World Bank, Washington, DC. Cohen, D., and Soto, M. (2007). Growth and human capital: Good data, good results, Journal of Economic Growth, 12 (1) 51-76. Comin D. (2008) Total Factor Productivity. In: Palgrave Macmillan (eds) The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95121-5_1681-2. Damanpour, F. (1996). Organizational Complexity and Innovation: Developing and Testing Multiple Contingency Models. Management Science, 42(5), 693-716 Damanpour, F., and Schneider, M. (2006). Phases of the Adoption of Innovation in Organizations: Effects of Environment, Organization, and Top Managers1. British Journal of Management, 17: 215-236. Danquah, M., and Amankwah-Amoah, J. (2017). Assessing the relationships between human capital, innovation, and technology adoption: Evidence from sub-Saharan Africa, Technological Forecasting and Social Change, 122, issue C, p. 24-33. Domar, E. D. (1946). Capital Expansion, Rate of Growth, and Employment, Econometrica, Vol.14,2,137-147. Domjahn, T. (2016). Economic Growth and Development: Theories, Criticism, and an Alternative Growth Model, The European Journal of Development Research, 28,2,352-354. Eggink, M.E. (2013). The Components of an Innovation System: A Conceptual Innovation System Framework, Journal of Innovation and Business Best Practices, DOI: 10.5171/2013. 768378. 53 Easterlin, R. A. (1981). Why Isn't the Whole World Developed? The Journal of Economic History, 41, issue 1, p. 1-17. Fagerberg, J. (2003). Schumpeter and the Revival of Evolutionary Economics: An appraisal of the Literature, Journal of Evolutionary Economics 13: 125–59. Fassio, C., Kalantaryan, S., and Venturini, A. (2020). Foreign Human Capital and Total Factor Productivity: A Sectoral Approach. Review of Income and Wealth, 66: 613-646. Galbraith, J.K. 1952. American Capitalism: The Concept of Countervailing Power. Boston: Houghton Mifflin. Garcia, A., and Mohnen, P. (2010). Impact of government support on R&D and innovation, MERIT Working Papers 2010-034, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT). Glewwe, P., Maïga, E., and Zheng, H. (2014). The Contribution of Education to Economic Growth: A Review of the Evidence, with Special Attention and an Application to Sub-Saharan Africa, World Development, 59, 379-393. Griffith, R., Harrison, R., and Simpson, H. (2006). The link between product market reform, innovation and EU macroeconomic performance, European Economy - Economic Papers 2008 - 2015 243, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission. Grossman, G., and Helpman, E. (1991). Trade, knowledge spillovers, and growth, European Economic Review, 35,2-3,517-526. Guo, Y., Wang, L., and Chen, Y. (2020). Green Entrepreneurial Orientation and Green Innovation: The Mediating Effect of Supply Chain Learning. SAGE Open. https://doi.org/10.1177/2158244019898798. Hall, R. E., and Jones, C. (1999). Why do Some Countries Produce So Much More Output Per Worker than Others? The Quarterly Journal of Economics, 114, issue 1, p. 83-116. Harrod, R. F. (1939). An essay in Dynamic Theory. The Economic Journal, Vol.49, No.193,14-33. Hartley, J., Sørensen, E., and Torfing, J. (2013). Collaborative Innovation: A Viable Alternative to Market Competition and Organizational Entrepreneurship. Public Administration Review, 73(6), 821-830. Hérbert, R., and Link, A. (1989). Search for the meaning of entrepreneurship. Small Business Economics, 1(1), 39–49. 54 Heshmati, A., and Rashidghalam, M. (2016). Labour Productivity in Kenyan Manufacturing and Service Industries, IZA Discussion Papers, No. 9923, Institute for the Study of Labor (IZA), Bonn. Hodgson, G. (1996). An Evolutionary Theory of Long-Term Economic Growth. International Studies Quarterly, 40(3), 391-410. Hou, J., and Mohnen, P. (2013). Complementarity between internal knowledge creation and external knowledge sourcing in developing countries, No. 2013-010, MERIT Working Papers, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT). Huňady, J., and Orviská, M. (2014). The Impact of Research and Development Expenditures on Innovation Performance and Economic Growth of The Country—The Empirical Evidence, CBU International Conference Proceedings, ISE Research Institute, 2(0), 119-125. Jaffe, A. Newell, R., and Stavins, R. (2003). Technological change and the environment, in Mäler, K. G. and Vincent, J. R. eds., Handbook of Environmental Economics, 1, Elsevier. Jorgenson, D. W., and Stiroh. K.J. (1999). Information Technology and Growth. American Economic Review, 89 (2): 109-115. Kamien, M.I., and N.L. Schwartz, 1982, Market structure and innovation, Cambridge University Press, Cambridge. Keynes, J.M. (1936). The General Theory of Employment Interest and Money. New York: Harcourt Brace. Kurniawati, M.A. (2020). The role of ICT infrastructure, innovation, and globalization on economic growth in OECD countries, 1996-2017, Journal of Science and Technology Policy Management, Vol. 11 No. 2, pp. 193-215. Krueger, A., and Lindahl, M. (2001). Education for growth: Why and for whom? Journal of Economic Literature, 39 (4), 1101-1136. Krugman, P. (1997). The Age of Diminished Expectations, 3rd Edition: U.S. Economic Policy in the 1990s, MIT Press Books, The MIT Press. Kurz, H. (2012). Schumpeter’s new combinations: revisiting his Theorie der wirtschaftlichen Entwicklung on the occasion of its centenary. Journal of Evolutionary Economics, 22(5), 871–899. Lee, K., Yoo, J., Choi, M., Zo, H., and Ciganek, A.P. (2016). Does External Knowledge Sourcing Enhance Market Performance? Evidence from the Korean Manufacturing Industry. PLOS ONE 11(12): e0168676. https://doi.org/10.1371/journal.pone.0168676. 55 Li, B., and Huang, L. (2019). The Effect of Incremental Innovation and Disruptive Innovation on the Sustainable Development of Manufacturing in China. SAGE Open. https://doi.org/10.1177/2158244019832700. Li, C., and Tanna, S. (2019). The impact of foreign direct investment on productivity: New evidence for developing countries, Economic Modelling,80,453-466. Lipsey, R.G. and Carlaw, K.I. (2004). Total factor productivity and the measurement of technological change. Canadian Journal of Economics/Revue canadienne d'économique, 37 : 1118-1150. Lucas, R. E., Jr. (1988). On the Mechanics of Economic Development. Journal of Monetary Economics, Vol. 22, No.4, pp.3-4. Maddison, A. (1983). A Comparison of Levels of GDP Per Capita in Developed and Developing Countries, 1700-1980. The Journal of Economic History, 43(1), 27-41 Malerba, F. (2007) Schumpeterian patterns of innovation and technological regimes in The Elgar Companion to Neo-Schumpeterian Economics, Edward Elgar Publishing. Malerba, F., and Orsenigo, L. (1995). Schumpeterian Patterns of Innovation, Cambridge Journal of Economics, 19,1,47-65. Malerba, F., and McKelvey, M. (2020). Knowledge-intensive innovative entrepreneurship integrating Schumpeter, evolutionary economics, and innovation systems, Small Business Economics, Springer, 54(2) 503-522. Mankiw, G.N. Romer, D., and Weil, D.N. (1992). A Contribution to the Empirics of Economic Growth, The Quarterly Journal of Economics, Volume 107, Issue 2, 407–437. Medase, S.K., and Abdul-Basit, S. (2020). External knowledge modes and firmlevel innovation performance: Empirical evidence from sub-Saharan Africa, Journal of Innovation & Knowledge,5, 2, 81-95. Michaelides, P., and Milios, J. (2009). Joseph Schumpeter and the German Historical School. Cambridge Journal of Economics, 33(3), 495-516. Mokyr, J. (2005). The Intellectual Origins of Modern Economic Growth, The Journal of Economic History, 65,2,285-351. Moss, M. (1979). Welfare Dimensions of Productivity Measurement, in Measurement and Interpretation of Productivity, Washington, DC: National Academy of Sciences. 56 Muller, E. (2000). Innovation Interactions Between Knowledge-Intensive Business Services and Small and Medium-Sized Enterprises, Springer-Verlag Berlin Heidelberg. Neto, J.P.J., Claeyssen, J.C.R., and Júnior, S.S. P. (2019). Returns to scale in a spatial Solow–Swan economic growth model, Physica A: Statistical Mechanics and its Applications,533, https://doi.org/10.1016/j.physa.2019.122055. Nelson, R. R., and Winter, S. (1982). An evolutionary theory of economic change. Cambridge: The Belknap Press of Harvard University Press. Niebel, T. (2018). ICT and economic growth – Comparing developing, emerging and developed countries, World Development, Elsevier,104, 197-211. Odia, L.O., and Omofonmwan, S.I. (2007). Educational System in Nigeria Problems and Prospects. Journal of Social Science, 14, 81-86. Palma, N., and Reis, J. (2019). From Convergence to Divergence: Portuguese Economic Growth, 1527–1850. The Journal of Economic History, 79(2), 477-506. Pavitt, K., Robson, M., and Townsend, J. (1987). The Size Distribution of Innovating Firms in the UK: 1945-1983. The Journal of Industrial Economics, 35(3), 297-316. Peretto, P., and Smulders, S. (2002). Technological distance, growth, and scale effect, The Economic Journal, 112: 603-624. Perez-Trujillo, M. and Lacalle-Calderon, M., (2020). The impact of knowledge diffusion on economic growth across countries, World Development, Elsevier, vol. 132(C). DOI: https://doi.org/10.1016/j.worlddev.2020.104995. Plessis, M. D. (2007). The role of knowledge management in innovation. Journal of Knowledge Management 11: 20–29. Pradhan, R.P., Arvin, M.B., Nair, M., and Bennett, S.E. (2020). Sustainable economic growth in the European Union: The role of ICT, venture capital, and innovation. Review of Financial Economics. 38: 34– 62. Reati, A. (2012). Total factor productivity - a misleading concept. PSL Quarterly Review, 54, 218,2037-3643. Romer, P. M. (1986). Increasing Returns and Long-Run Growth. Journal of Political Economy,94: 1002-1037. Romer, P. M. (1990). Endogenous Technological Change''. Journal of Political Economy, Vol. 98, No.5, S71-S102. Ruttan, V. W. (1998). The new growth theory and development economics: A survey, The Journal of Development Studies, 35:2, 1-26. 57 Sanfilippo, M. (2015). FDI from emerging markets and the productivity gap—An analysis on affiliates of BRICS EMNEs in Europe, International Business Review, 24,4, 665-676. Saunila, M. (2019). Innovation capability in SMEs: A systematic review of the literature, Journal of Innovation & Knowledge. DOI: https://doi.org/10.1016/j.jik.2019.11.002. Schultz, T. (1961). Investment in Human Capital. The American Economic Review, 51(1), 1-17. Seck, A. (2012). International technology diffusion and economic growth: Explaining the spillover benefits to developing countries, Structural Change and Economic Dynamics, 23, issue 4, p. 437-451. Shen, X., Lin, B, and Wu, W. (2019). R&D Efforts, Total Factor Productivity, and the Energy Intensity in China, Emerging Markets Finance and Trade, 55:11, 2566- 2588. Shu, P., and Steinwender, C. (2019). The Impact of Trade Liberalization on Firm Productivity and Innovation, Innovation Policy, and the Economy 19: 39-68. Simon, H.A. (1959). Theories of decision making in economics and behavioral science. Am. Econ. Rev. 49 (3), 253–283. Solow, Robert M. (1956). A Contribution to the Theory of Economic Growth. Quarterly Journal of Economics 70: 65-94. Solow, R. M. (1957). Technical Change and the Aggregate Production Function. Review of Economics and Statistics, August 1957,39(3)312-20. Shaw, G. K. (1992). Policy Implications of Endogenous Growth Theory, Economic Journal, 102, 412,611-21. Thompson, Victor A. (1965). Bureaucracy and innovation. Administrative Science Quarterly 10: 1–20. Van de Ven, A. H. 1986. Central problems in the management of innovation. Management Science, 32(5): 590-607. Ulrich Witt (2002) How Evolutionary is Schumpeter's theory of Economic Development? Industry and Innovation, 9:1-2, 7-22. Wei, J., and Liu, Y. (2015). Government support and firm innovation performance: Empirical analysis of 343 innovative enterprises in China, Chinese Management Studies,9,1,38-55. Wojan, T. R., Crown, D., and Rupasingha, A. (2018). Varieties of innovation and business survival: Does pursuit or incremental or far-ranging innovation make 58 manufacturing establishments more Resilient? Research Policy, 47(9), 1801– 1810. Woschke, T., Haase, H., and Kratzer, J. (2017). Resource scarcity in SMEs: effects on incremental and radical innovations, Management Research Review, 40(2),195-217. Wurth, A. H. (1993). Productivity growth or factor substitution? identifying technological change. Technology in Society, 15(2), 229–241. Yanrui W. (2010). Innovation and Economic Growth in China, Economics Discussion / Working Papers 10-10, The University of Western Australia, Department of Economics. Zhou, X. Song, M., and Cui, L. (2020). Driving force for China's economic development under Industry 4.0 and circular economy: Technological innovation or structural change? Journal of Cleaner Production,271. DOI: /doi.org/10.1016/j.jclepro.2020.122680. Zhu, T.T., Peng, R.H., and J.Y. Zhang (2018). The influence of higher education development on economic growth: evidence from central China High Educ. Pol., 31 (2018), 139-157. 59 Chapter 3 SMEs, Innovation and Productivity Growth in Developing Countries SMEs that grow have a considerable positive impact on employment creation, innovation, productivity growth and competitiveness. (OECD, 2018) 3.1. Introduction In recent years, the linkage between SMEs and economic growth has attracted increasing attention both from researches and policy-makers. SMEs play a vital role in most economies, accounting for 90% of businesses and more than 50% of employment worldwide. As discussed in the previous chapter, small entrepreneurial firms are both agents of innovation and economic growth in the Schumpeter Mark I (Aghion and Howitt, 1992; Lazonick, 2005). In a later work, Solow (2007: 11) recognizes the role of the entrepreneurial activity in bridging “the gap between specific pieces of technological knowledge and innovations in actual production, often through the creation of new firms”. According to him, if the impact of small entrepreneurial firms on economic growth can be empirically accounted for, it would add immensely to the explanatory power of growth theory. In a seminal study, Beck, Demirguc-Kunt, and Levine (2005) estimated an adjusted standard growth model of relative size of the SME sector for a cross-section of countries. They find a positive and significant relationship between SMEs and economic growth, measured in terms of GDP per capita. Likewise, in an extended neoclassical model, Audretsch and Keilbach (2004) reveal that entrepreneurship 60 capital is a significant and important factor that shapes the output and productivity of German start-ups. Despite the contributions of the extant studies, there is still paucity of studies exploring the significance of innovative SMEs on the economic growth in developing economies (Cravo, Gourlay and Becker, 2012), especially in the SSA region. This research gap is surprising given that the SME sector is an account for over 90% of all enterprises, employs up to 95% of the enterprise workforce, and generates more than 49% of domestic output of developing countries (World Bank Group, 2020). In the SSA region, the SME sector is an important driver of growth, accounting for over 90% of all enterprises and 60% of total employment (ITC, 2018). Thus, the goal of this chapter is to discuss the role of SMEs on the developing country economies; and how innovations can help them to be more competitive; and contribute to the economic growth of the SSA region. To achieve the objective of this chapter, I will focus on Nigeria because it offers an interesting representative case study. SMEs account for a greater share of all businesses in Nigeria. Besides, these firms share similar characteristics and challenges with their peers from other lower-middle-income countries. The remainder of this chapter is organised as follows: In Section 3.2, I will present the definition of SMEs in Nigeria, and the contributions of the sector to the Nigerian economy. In Section 3.3, I will discuss the country-specific factors mitigating against the innovation and productivity performance of SMEs; while in Section 3.4, I will examine the firm-level determinants of innovation activities of Nigeria SMEs. 61 3.2. Definition of SMEs in Nigeria While SMEs are the engine of growth, there is no universal definition of SMEs as it varies across countries, regions, and organisations (Storey, 1994). According to OECD (2005), "SMEs are non-subsidiary, independent firms which employ less than a given number of employees”. Table 3.1. Definitions of SMEs in OECD, China, South Africa, and Nigeria Countries Source Micro Small Medium OECD OECD (2005) Employee < 10 AND Turnover ≤ EUR 2 million OR balance sheet total ≤ EUR 2 million Employee < 50 AND Turnover ≤ EUR 10 million OR balance sheet total ≤ EUR 10 million Employee < 250 AND Turnover ≤ EUR 50 million OR balance sheet total ≤ EUR 43 million China (Manufacturing industry as reference) Ministry of Finance, China (2011) Employee < 20 OR Revenue ≤ RMB 3 million (~ EUR 377,315) Employee < 300 AND Turnover ≤ RMB 20 million (~ EUR 2,516,219 million) > Revenue ≥ RMB 3 million (~ EUR 377,446.98) Employee < 1000 AND Turnover ≤ RMB 400 million (~ EUR 50,308,700 million) > Revenue ≥ RMB 20 million (~ EUR 2,516,219 million) South Africa (Manufacturing industry as reference) National Small Business Amendment Act (2003) Employee < 5 AND Turnover ≤ ZAR 0.20 million (~ EUR 10,184) OR balance sheet total ≤ ZAR 0.10 million (~ EUR 5,072) Employee < 50 AND Turnover ≤ ZAR 13 million (~ EUR 659,360) OR balance sheet total ≤ ZAR 5 million (~ EUR 253,600) Employee < 200 AND Turnover ≤ ZAR 51 million (~ EUR 2,586,723 million) OR balance sheet total ≤ ZAR 19 million (~ EUR 963,681) Nigeria Small and Medium Enterprises Development Agency of Nigeria (2013) Employee < 10 AND Total Asset ≤ NGN 5 million (~ EUR 11,263.74) Employee < 50 AND Total Asset ≤ NGN 50 million (~ EUR 112,714.) Employee < 200 AND Total Asset ≤ NGN 500 million (~ EUR 1,127,145 million) Note: The exchange rate between Euro and RMB (1 EUR = 7.95 RMB), Euro and Naira (1 EUR = 443.60 NGN), and Euro and ZAR (1 EUR = 19.74 ZAR) are based on the figures as of September 25, 2020. 68 3.4. Innovation as Engine of Productivity Growth in SMEs Even though the SME sector plays a significant role in the Nigerian economy, its growth potential is yet to be fully realised due to some factors hindering the prospects for the development of innovation capabilities. As we saw above, the export propensity of SMEs in Nigeria is still very low. Despite the export development and promotion programmes in Nigeria, they have not succeeded in enhancing the exporting capacity of SMEs (Leigh and Blakely, 2016). One of the most plausible explanations is the insufficient attention of given to the role of innovation on export growth and productivity of SMEs. As we saw in the previous chapter, countries in the SSA region are not investing enough in R&D activities. In contrast, countries with sufficient level of R&D investment can achieve higher economic growth by promoting innovations activities of SMEs. Substantial evidence shows that innovation is a key driver of competition and dynamic market efficiency (Plessis, 2007). Innovation can create sustainable growth that confers competitive advantages on SMEs operating both in domestic and international markets. On the other hand, SMEs are the engines of technological change, innovation, and economic growth (Acs and Audretsch 1988, 1990). Rosenbusch et al. (2011) argue that their smaller, nimbler structures and entrepreneurial orientation enable them to engage in successful innovation activity. In today's global market, SMEs possessing a strong source of competitive advantages are more likely to survive and achieve superior performance. On this basis, one would expect innovative SMEs from developing countries such as Nigeria to grow faster and become more efficient than non-innovative counterparts (Ratten, 2015). 69 However, there are series of both internal and external factors mitigating against the innovation activities and productivity of SMEs in Nigeria. Srholec (2011) argues that the characteristics of a firm and the institutional settings within which it operates do matter for innovation activities. Thus, in this section, I will focus on country-specific barriers and firm-specific determinants of SMEs' innovation in Nigeria. 3.5. Country-Specific Determinants of Innovation The institution plays an important role in the innovation structure and performance of firms (Jackson and Deeg, 2008). Pindado et al. (2015) reveal that country-level institutions have a significant impact on the market valuation of the firm R&D investment. This is in line with the institutional theory, which suggests that the institutional settings of a country enable investment by providing incentives and supports, creating a stable environment, reducing transaction costs, risk, and uncertainty. Firms operate and innovate within an environment, which can either promote or impede innovation and productivity (Alam, Uddin, and Yazdifar, 2019). Research shows that country-specific factors to a large extent determine the innovation and entrepreneurial activities of firms (Waarden, 2001). Allred and Steensman (2005) argue that a country's level of development has a direct effect on firms’ innovation investments. Countries differ in several aspects such as economic size, infrastructure, level of development, etc. As a result, the innovation activities of firm can be affected by institutions through laws, regulations, and policies on one hand; and by structures through infrastructure, education system, on the other hand (Wang et 70 al., 2015). For example, in developing economies, SMEs face numerous factors mitigating against their innovation capabilities. It is no surprise that their SMEs operating in an environment supported by advanced legal systems, low levels of bureaucracy, and ease of access to financial services achieve higher performance. Alam, Uddin, and Yazdifar (2019) find that institutional quality has a strong influence on R&D investment. More precisely, these authors observe that government effectiveness, rule of law, and regularity quality have a positive impact, whereas corruption and political instability have a negative impact on R&D investment. The majority of developing countries, especially the SSA countries, have underdeveloped or less-efficient institutions to support the activities of SMEs (Lee et al. 2015; Medase and Barasa, 2019). In what follows, I will consider how lack of access to reliable power supply, inefficient legal and financial systems hinder the innovation capacity and productivity of SMEs in Nigeria. 3.5.1. Access to Power Supply In recent years, the government in Nigeria both at the national and state levels is increasingly providing infrastructure as a way of enabling a better business environment and economic growth. Electricity is an important factor in economic activities. Research shows that firms with access to stable power supply can achieve rapid and sustained growth, technological progress and in turn, create more employment opportunities. Conversely, lack of access to electricity leads to low productivity (Fakira, 1994; Alby, Dethier, and Straub, 2010). Goedhuys and Sleuwaegen (2010) find that frequent electricity cut is the main barrier to the growth of entrepreneurial firms in 10 manufacturing sectors of 11 Sub-Saharan 71 African countries. Grimma, Hartwig, and Lay (2013) suggest that a stable power supply can exert a positive influence on performance, with electricity contributing to the uptake of modern machinery and business operation in the SSA region. Lack of electricity has been a chronic challenge bedeviling the business environment and productivity in Nigeria. Olayemi, (2012) find that electricity generation and supply have a significant and negative effect on the productivity growth of manufacturing sector in Nigeria. The IMF survey shows that lack of access to reliable electricity costs the Nigerian economy an estimated USD29 billion every year (IMF, 2019). This is consistent with the recent World Bank Enterprise Surveys (WBES), which shows that the time and cost of accessing stable electricity connection is very high in Nigeria (World Bank Group, 2019). The current situation is so bad that firms have to heavily rely on power generators. However, given the high cost linked to the installation and maintenance of power generators, only firms with stable financial resources can afford it. This situation has intensified the operation and productivity problems of the SME sector. Even though there are several policies and projects both in Nigeria and across the SSA region to accelerate access to electricity through alternative energy supply, access to reliable energy remains a key barrier to the productivity of firms, especially SMEs. Innovation and technology development largely depend on sustainable energy supply and as such, it does not come as surprising that the lack of it is one of the main factors mitigating against the innovative activities and productivity growth of SMEs in Nigeria. Research attributed the poor performance of the power sector to government spending on unproductive sectors (Olayemi, 2012). Thus, it is without a doubt that efficient policies and capital investment in the energy sector 72 will enhance electricity supply and consequently improve the productivity of SMEs in Nigeria. 3.5.2. Legal System Prior studies show that a strong legal system promotes R&D investment (Seitz and Watzinger, 2017). The legal systems in most developing countries such as Nigeria have a negative impact on the business environment and innovation investment. Research shows that efficient legal systems promote technological progress through the protection of creativity and innovation of firms (Hall and Sena, 2017). More precisely, the intellectual property system fosters economic growth, innovation, and international competitiveness (Gould and Gruben, 1997). Furukawa (2007) find that a well-functioning legal system provides strong protection of patent rights and investment incentives that motivate the investors in R&D activity. Similarly, Jiao, Koo, and Cui (2015) find that local legal system has a significant and positive effect on firms' product innovation, technological innovation, process innovation, and management innovation in China. A recent survey shows that SMEs in European countries investing in intellectual property rights are more likely to achieve higher growth (EUIPO, 2019). Intellectual property rights and protection in Nigeria include patents, industrial designs, trademarks, and copyright. Table 3.5. shows some of the laws governing intellectual property rights and protection in Nigeria. 73 Table 3.4: Some Legal Frameworks of IPRs in Nigeria • Copyright Act (as amended), Cap. C28, Laws of the Federation of Nigeria 2004 • Patents and Designs Act, Cap. P2, Laws of the Federation of Nigeria 2004 • Trade Marks Act, Cap. T13, Laws of the Federation of Nigeria 2004 • Merchandise Marks Act, Cap. M10, Laws of the Federation of Nigeria 2004 • Trade Malpractices (Miscellaneous Offences) Act, Cap. T12, Laws of the Federation of Nigeria 2004 Source: Banwo and Ighodalo, 2019 However, for these tools to be effective, a country needs a well-functioning legal system. As in most of the SSA countries, intellectual property rights and protection are yet to be fully integrated into the institutional arrangements. Edosomwan (2019:6) suggests that “corruption and lack of coordination among the various agencies of government involved in the development and protection of IP rights are also a big challenge”. The author also observed that intellectual property theft is high in Nigeria, with the manufacturing, consumer goods, technology, software, biotechnology, and pharmaceuticals as the most hit sectors. More so, Banwo and Ighodalo (2019) highlight the negative impacts of administrative holdups, unqualified personnel, and inadequate penalties for infringements, outdated laws, and inventory systems. Rapp and Rozek (1990:75) argue that: If property rights to potential innovation do not reside with an innovator, the incentive to devote resources to innovation is reduced, and society can expect fewer new products and processes. Proper assignment of these property rights brings forth innovations that provide increased productivity and, thus, economic growth and development. 74 Certainly, more efforts from the government in areas of effective mechanism, awareness, and enforcement of intellectual property rights will boost the innovation activities and productivity of entrepreneurial firms. 3.5.3. Financial System The recognition of a relationship between financial system, innovation, and productivity growth of SMEs is widely documented in the literature (e.g. Levine, 1997; Robb and Robinson, 2014; Fraser et al., 2015). The financial system of a country plays a pivotal role in economic growth because it facilitates the transfer of financial resources from surplus sectors to deficit sectors (Choe and Moosa, 1999). The quality of financial institution can stimulate the R&D investment by enabling firms to get access to financial resources. For example, a well-organized financial system can promote successful innovation and productivity by providing support such as R&D subsidies, tax reduction on innovation activities to the entrepreneurial firms. However, the financial markets are underdeveloped and highly imperfect in the SSA region when compared to developed economies (Sacerdoti, 2005; Fowowe, 2017; Quartey et al., 2017). The difficulty of accessing finance stands out as the main factor underlying low productivity of SMEs in the region (Page and Soderbom, 2015). Previous studies reveal that financial challenges faced by firms in the SSA are twice as higher as other developing countries; and only about twenty-three (23) percent of firms use bank loans (Otchere et al., 2017; Ayalew and Xianzhi, 2018). Despite the low percentage of bank loans for entrepreneurial projects, small firms are less likely to secure loans when compared to large firms. 75 In most cases, traditional providers hesitate to finance the activities of SMEs due to information asymmetry (Greenwald and Stiglitz, 1986). In addition to intangibility and uncertain returns characterizing innovation activities (Silva and Carreira, 2010), the lack of detailed firm information as well as weak institution make it difficult for financial providers to assess the potentials of innovation projects of these firms (Kerr and Nanda, 2015). Hence, in the face of high degree of information asymmetry, the traditional financial providers are either reluctant to finance innovation projects of SMEs or charge high-interest rate to scare them away. What's more, banks face greater liquidity constraints than other lenders; thus, as a result of weak investors' protection and institutional qualities (Winton and Yerramilli, 2008), they find it hard to finance innovation projects of SMEs in this region. Given the high level of financial constraints faced by SMEs in Nigeria, in chapter 5, I shall empirically ascertain whether access to external financial supports enhances both the innovation capabilities and productivity growth of these firms. 3.6. Firm-Level Determinants of Innovation Having considered the impact of country-specific factors on SMEs' innovation activities and productivity, in section, I will focus on the main firm-level determinants of innovation and productivity. Research shows that firms are heterogeneous in their internal resources (Penrose, 1956). As a result, the amount of resources devoted to innovation by a firm largely depends on some firm-specific characteristics (Klette and Kortum, 2004). 76 3.6.1. Firm Size Recall Schumpeter’s two patterns of innovation activities presented in chapter 2. Schumpeter Mark (I) posits that entrepreneurs or small firms are the engine of innovation and economic growth and innovations. Whereas Schumpeter Mark (II) suggests that large firms in a concentrated market are the drivers of technological progress. Empirical studies are mixed on the role of firm size on innovationdecision and productivity (Ayalew et al. 2019). For example, Gault (2013) finds that large firms have a higher propensity of conducting R&D activities and innovate than smaller firms. Shefer and Frenkel (2005) suggest that larger firms in Israel tend to undertake more share of industrial R&D than smaller ones. Similarly, Roper and Hewitt-Dundas (2008) reveal find that larger-sized firms are more likely to introduce product and process innovation than smaller firms. Research suggests this trend is linked to several advantages that larger firms have over smaller ones. For example, they have more access to finance, scale economies, advertising power, and scope of economies for R&D (Scherer,1965; Cohen and Klepper, 1996). Despite the advantages, burgeoning evidence shows that SMEs are not less innovative than large firms (Acs and Audretsch, 1988; Saunila, 2019). For example, Hall, Lotti, and Mairesse (2009) find that both product and process innovation have a positive impact on the productivity of Italian SMEs. Besides, the effect of large-size can hamper innovation activities through the loss of managerial control or excessive bureaucratic power. As well, rigid hierarchies that characterise most of the large firms can disincentivize R&D personnel and, in turn, affect innovation 77 performance (Cohen 2010). Unlike large firms, SMEs enjoy the advantages of adaptability, less rigid management structures, and entrepreneurial orientation, which allow them to implement innovations (Hewitt-Dundas, 2006; Alshanty, and Emeagwali, 2019). Furthermore, it is argued that the impact of firm size on innovation activities depends on the type of innovation in question. For example, SMEs are more likely to introduce radical innovations; whereas large firms may have an edge regarding innovation types that require huge capital investments (Morck and Yeung 2001). Evidence from emerging markets shows that small firms are rapidly engaging in innovation activities given that they have to survive and compete with foreign companies (Aksoy, 2017; Afriyie, Du, and Ibn Musah, 2019). For example, Acquaah and Agyapong (2015) find that innovation has a significant positive effect on the performance of micro and small family businesses in Ghana. Similarly, in a study of 1058 manufacturing SMEs from Sub-Saharan LDCs, Abubakar et al. (2019) find that firm size is positively related to innovation. These authors show that R&D activities have a strong impact on both product and process innovation performances of SMEs in Djibouti, Tanzania, Uganda, Zambia, and the Democratic Republic of Congo. Based on these empirical results, I expect SMEs in Nigeria to engage in innovations as it improves competition efficiency and productivity trajectories. 3.6.2. Firm Age In recent years, research shows that age affects the innovation capabilities and performance of firms (Rothaermel, Hitt, and Jobe, 2006). Generally, it is assumed 84 References Abdu, M, and Jibir, A. (2017). Determinants of firms’ innovation in Nigeria. Kasetsart Journal of Social Sciences, 1–9. Abubakar, Y.A., Hand, C. Smallbone, D., and Saridakis, G. (2019). What specific modes of internationalization influence SME innovation in Sub-Saharan least developed countries (LDCs)?Technovation,79,56-70, Acharya, R. C., and Keller, W. (2007). Technology Transfer through Imports, no 13086, NBER Working Papers, National Bureau of Economic Research, Inc. Acs, Z., and Audretsch, D. (1988). Innovation in Large and Small Firms: An Empirical Analysis. The American Economic Review, 78(4), 678-690. Acs, Z.J. and Audretsch, D.B. (1990) Innovation and Small Firms. The MIT Press, Cambridge. Afriyie, S., Du, J., and Ibn Musah, A. (2019). Innovation and marketing performance of SME in an emerging economy: the moderating effect of transformational leadership. J Glob Entrepr Res 9, 40. Aghion, P., and Howitt, P. (1992). A Model of Growth through Creative Destruction, Econometrica, 60, issue 2,323-51. Aksoy, H. (2017). How do innovation culture, marketing innovation, and product innovation affect the market performance of small and medium-sized enterprises (SMEs)? Technology in Society, 51, 133–141. Alam, A., Uddin, M., and Yazdifar, H. (2019). Institutional determinants of R&D investment: Evidence from emerging markets, Technological Forecasting, and Social Change,34-44. Alby, P., J.-J. Dethier, and S. Straub (2010). Firms operating under Infrastructure and Credit Constraints in Developing Countries: The Case of Power Generators. Policy Research Working Paper 5497. World Bank. Allred, B.B., and Steensma, Kevin. (2005). The influence of industry and home country characteristics on firms' pursuit of innovation. Management International Review. 45. 383-412. Alshanty, A. M., and Emeagwali, O.L. (2019). Market-sensing capability, knowledge creation, and innovation: The moderating role of entrepreneurialorientation, Journal of Innovation & Knowledge, 4, Issue 3, 171-178. 85 Andersson, M., Johansson, S., and Lööf, H. (2008). Productivity and international trade: Firm-level evidence from a small open economy. Review of World Economics/Weltwirtschaftliches Archiv, 144(4), 774–801. Asheim, B.T., and A. Isaksen, (1997). Localisation, Agglomeration, and Innovation: Towards Regional Innovation Systems in Norway? European Planning Studies 5 (3), 299-330. Audretsch, D. and Keilbach, M. (2004). Entrepreneurship Capital and Economic Performance, Regional Studies, 38:8, 949-959. Ayalew, M.M., Xianzhi, Z., and Hailu, D.H. (2019). The finance of innovation in Africa, European Journal of Innovation Management, Vol. 23 No. 3,348-382. Ayalew, M. M., Xianzhi, Z., Dinberu, Y. D., and Hailu, D. H. (2019). The Determinants of Firm's Innovation in Africa. Journal of Industry, Competition, and Trade. doi:10.1007/s10842-019-00313-4 Banwo-Ighodalo (2016). Strengthening Intellectual Property Rights and Protection in Nigeria, http://www.banwo-ighodalo.com/greymatter/strengthening-intellectual-property-rights-and-protection-in-nigeria Bauernschuster, S., Falck, O. and Heblich, S. (2009), Training and Innovation, Journal of Human Capital, 3, issue 4, 323-353. Beck, T., Demirguc-Kunt, A., and Levine, R. (2005). SMEs, Growth, and Poverty: Cross-Country Evidence. J Econ Growth 10, 199–229. Cheng, C.C.J.and Sheu, C. (2018). Enhancing radical innovation: the interplays of open innovation activities, firm capabilities, and environmental dynamism, Asian Journal of Technology Innovation, 26:3, 369-397. Chesbrough, H. (2003). Open Innovation: The New Imperative for Creating and Profiting, from Technology. Harvard Business School Press, Boston. Chesbrough, H. and Crowther, A.K. (2006), Beyond high tech: early adopters of open innovation in other industries. R&D Management, 36: 229-236. Choe, C. and Moosa, I. A., (1999). Financial System and Economic Growth: The Korean Experience, World Development, 27, 6, 1069-1082. Coad, A., Segarra, A., and Teruel, M. (2016). Innovation and firm growth: Does firm age play a role? Research Policy, 45, Issue 2,387-400. Cohen, W. M. (2010). Fifty Years of Empirical Studies of Innovative Activity and Performance, Handbook of the Economics of Innovation, in: Bronwyn H. Hall and Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 1, chapter 0, pages 129-213, Elsevier. 86 Cohen, W.M., and Klepper, S. (1996). Firm Size and the Nature of Innovation within Industries: The Case of Process and Product R&D. The Review of Economics and Statistics, 78(2), 232-243. Cravo, T., Gourlay, A., and Becker, B. (2012). SMEs and regional economic growth in Brazil, Small Business Economics, Springer, vol. 38(2) 217-230. Aifuwa Edosomwan, A. (2019). Protecting intellectual property rights in Nigeria: A review of the activities of the Nigerian Copyright Commission, World Patent Information,58. DOI: https://doi.org/10.1016/j.wpi.2019.101908. Evenson, R. E., and Westphal, L. E., (1995). Technological change and technology strategy, ch. 37, p. 2209-2299 in Chenery, Hollis and Srinivasan, T.N. eds., Handbook of Development Economics, vol. 3, Part 1, Elsevier. Fakira, H. (1994). Energy for micro-enterprises. Energy Policy Research and Training Project. Energy and Development Research Centre. University of Cape Town. Paper No. 12: 16-25. Fowowe, B. (2017). Access to finance and firm performance: Evidence from African countries, Review of Development Finance,7, Issue 1,6-17. Fraser, S., Bhaumik, S. K., and Wright, M. (2015). What do we know about entrepreneurial finance and its relationship with growth? International Small Business Journal, 33(1), 70–88. Gallié, E-P., and Legros, D. (2012). Firms' human capital, R&D, and innovation: a study on French firms, Empirical Economics, 43, issue 2,581-596. Gault, F. (2013). Handbook of Innovation Indicators and Measurement, volume 19, Innovation indicators, and measurement: challenges, pages 441–464. Edward Elgar Publishing. Goedhuys, M., and L. Sleuwaegen (2010) High-growth entrepreneurial firms in Africa: a quantile regression approach. Small Business Economics 34:31-51. González, X., Miles-Touya, D. and Pazó, C. (2016) R&D, worker training and innovation: firm-level evidence, Industry and Innovation, 23:8, 694-712 Gossling, T., and Rutten, R. (2007). Innovation in regions European Planning Studies, 15, 253-270. Greenwald, B. C., and Stiglitz, J. E. (1986). Externalities in Economies with Imperfect Information and Incomplete Markets, The Quarterly Journal of Economics, Volume 101, Issue 2,229–264 Grimma, M., Hartwiga, R., and Layd, J. (2013). Electricity Access and the Performance of Micro and Small Enterprises: Evidence from West Africa, European Journal of Development Research (2013) 25, 815–829. 87 Grossman, G. M., and Helpman, E. (1993). Innovation and Growth in the Global Economy. Scottish Journal of Political Economy, 40: 231-232 Hall, B., and Sena, V. (2017). Appropriability mechanisms, innovation, and productivity: evidence from the UK, Economics of Innovation and New Technology, 26, issue 1-2,42-62. Hall, B.H., Lotti, F., and Mairesse, J. (2009). Innovation and productivity in SMEs: empirical evidence for Italy. Small Bus Econ 33, 13–33 Hewitt-Dundas, N. (2006). Resource and Capability Constraints to Innovation in Small and Large Plants, Small Business Economics, 26, issue 3, 257-277. Huergo, E. and Jaumandreu, J. (2004), Firms' age, process innovation and productivity growth, International Journal of Industrial Organization, 22, issue 4, p. 541-559. ITC (2018). Business Ecosystems for the Digital Age, the International Trade Centre, Geneva, Switzerland. Jabbour, L. S., Beatriz, A. Ndubisi, N. O., and Seles, R. P. (2020). Sustainable development in Asian manufacturing SMEs: Progress and directions, International Journal of Production Economics, 225, https://doi.org/10.1016/j.ijpe.2019.107567 Jackson, G., and Deeg, R. (2008). Comparing capitalisms: understanding institutional diversity and its implications for international business. J Int Bus Stud 39, 540–561. Jiao, H. Koo, C.K., and Cui, Y. (2015). Legal environment, government effectiveness, and firm's innovation in China: examining the moderating influence of government ownership Tech. Forecasting Soc. Chang., 96, 15-24 Klette, T., and Kortum, S. (2004). Innovating Firms and Aggregate Innovation. Journal of Political Economy, 112(5), 986-1018. Kraus, S., Kailer, N., Dorfer, J., and Jones, P. (2020). Open innovation in (young) SMEs. The International Journal of Entrepreneurship and Innovation, 21(1), 47– 59. Laursen, K., and Salter, A. (2006). Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms. Strat. Mgmt. J., 27: 131-150. Lazonick, W. (2005). The Innovative Firm in Jan Fagerberg, David Mowery, and Richard Nelson, eds., The Oxford Handbook of Innovation, Oxford University Press: 29-55. 88 Lee, R. P., Özsomer, A., and Zhou, K. Z. (2015). Introduction to the special issue on Innovation in and from Emerging Economies. Industrial Marketing Management 50, 16-17. Leigh, N. G., and Blakely, E. J. 2016. Planning local economic development: Theory and practice, SAGE publications. Levine, R. (1997). Financial Development and Economic Growth: Views and Agenda, Journal of Economic Literature, Vol. 35, No. 2, pp. 688–726. Levitt, B., and March, J. G. (1988). Organizational Learning, Annual Review of Sociology, Vol. 14, 319-340. Lin, B., and Benjamin, I. N. (2018). Causal relationships between energy consumption, foreign direct investment, and economic growth for MINT: Evidence from panel dynamic ordinary least square models. Journal of Cleaner Production, 197, 708–720. Lorenz, E. (2014). Do Credit Constrained Firms in Africa Innovate Less? A Study Based on Nine African Nations’, GREDEG Working Paper Series No. 2014‐29. Medase, S.K., and Abdul-Basit, S. (2020). External knowledge modes and firmlevel innovation performance: Empirical evidence from sub-Saharan Africa, Journal of Innovation & Knowledge,5, Issue 2, 81-95. McGuirk, H., Lenihan, H., and Hart, M. (2015). Measuring the impact of innovative human capital on small firms' propensity to innovate Res. Policy, 44 (4) (2015), 965-976. Medase, K., and Barasa, L. (2019). Absorptive capacity, marketing capabilities, and innovation commercialisation in Nigeria, European Journal of Innovation Management,22, 5, 790-820. Mincer, J. (1996). Economic development, growth of human capital, and the dynamics of the wage structure. J Econ Growth 1, 29–48. Morck, R., and Yeung, B. (2004). Family Control and the Rent-Seeking Society. Entrepreneurship Theory and Practice, 28(4), 391–409. Muscio, A. (2007). The Impact of Absorptive capacity on SME's Collaboration, Economics of Innovation and New Technology, 16:8, 653-668. OECD (2018). Strengthening SMEs and entrepreneurship for productivity and inclusive growth, Key Issues paper, SME Ministerial Conference, Mexico. Olayemi, S-O. O. (2012). Electricity Crisis and Manufacturing Productivity in Nigeria (1980-2008), Developing Country Studies, Vol 2, No.4, 16 - 21. 89 Olayungbo, D.O. (2019). Effects of oil export revenue on economic growth in Nigeria: A time-varying analysis of resource curse, Resources Policy,64, https://doi.org/10.1016/j.resourpol.2019.101469. Ortega-Argilés, R., Moreno, R., and Caralt, J.S. (2005). Ownership structure and innovation: is there a real link?. Ann Reg Sci 39, 637–662. Otchere, I., Senbet, L., and Simbanegavi, W. (2017). Financial sector development in Africa–an overview, Review of Development Finance,7, Issue 1, 1-5. Page, J., and Söderbom, M. (2015). Is Small Beautiful? Small Enterprise, Aid, and Employment in Africa. African Development Review, 27: 44-55. Penrose, E.T. (1956), Towards a Theory of Industrial Concentration, Economic Record, 32: 64-77 Plessis, M. D. (2007). The role of knowledge management in innovation. Journal of Knowledge Management 11: 20–29. Quartey, P., Turkson, E., Abor, J., and Iddrisu, A. (2017). Financing the growth of SMEs in Africa: What are the constraints to SME financing within ECOWAS? Review of Development Finance. 7. 10.1016/j.rdf.2017.03.001. Rapp, R.T., and Rozek, R.P. (1990). Benefits and Costs of Intellectual Property Protection in Developing Countries. Journal of World Trade, 24, 75-102. Robb, A. M. and D. T. Robinson (2014), ‘The Capital Structure Decisions of New Firms’, Review of Financial Studies, Vol. 27, No. 1, pp. 153–79. Robertson, C., and Chetty, S.K. (2000). A contingency-based approach to understanding export performance, International Business Review, Vol. 9 No. 2, pp. 211-235. Roper, S., and Hewitt-Dundas, N. (2008). Innovation persistence: Survey and case-study evidence, Research Policy, 37, issue 1, 149-162. Rosenbusch, N., Brinckmann, J., and Bausch, A. (2011). Is innovation always beneficial? A meta-analysis of the relationship between innovation and performance in SMEs, Journal of Business Venturing, 26,441-457. Rothaermel, F.T., Hitt, M.A., and Jobe, L.A. (2006). Balancing vertical integration and strategic outsourcing: effects on product portfolio, product success, and firm performance. Strat. Mgmt. J., 27: 1033-1056. Sacerdoti, E. (2005). Access to Bank Credit in Sub-Saharan Africa; Key Issues and Reform Strategies, IMF Working Papers 05/166, International Monetary Fund. Safari, A. and Saleh, A.S. (2020). Key determinants of SMEs’ export performance: a resource-based view and contingency theory approach using potential 90 mediators, Journal of Business and Industrial Marketing, Vol. 35 No. 4, pp. 635- 654. Scherer, F. (1965). Firm Size, Market Structure, Opportunity, and the Output of Patented Inventions. The American Economic Review, 55(5), 1097-1125 Schumpeter, Joseph A., 1934, The Theory of Economic Development, Oxford University Press. Seitz, M., and Watzinger, M. (2017). Contract enforcement and R&D investment Res. Policy, 46 (1), 182-195. Shefer, D., and Frenkel, A. (2005). R&D, firm size, and innovation: an empirical analysis, Technovation, 25, Issue 1,25-32. Silva, F., and Carreira, C. (2010). Measuring firms’ financial constraints: Evidence for Portugal through different approaches, no 2010-15, GEMF Working Papers, GEMF, Faculty of Economics, University of Coimbra. Solow, R. M. (2007). The last 50 years in growth theory and the next 10, Oxford Review of Economic Policy, Oxford University Press,23(1), 3-14. Srholec, M. (2011). A multilevel analysis of innovation in developing countries Ind. Corp. Chang., 20 (6), 1539-1569 Storey. D.J. (1994). Understanding the Small Business Sector. London: Routledge/ITP. Storper, M., and Scott, A. (2009). Rethinking human capital, creativity and urban growth, Journal of Economic Geography, 9, 2,147-167. Tidd, J., and Bessant, J. (2009). Managing innovation: Integrating technological, market, and organizational change. Wiley. Tripsas, M. and Gavetti, G. (2000), Capabilities, cognition, and inertia: evidence from digital imaging. Strat. Mgmt. J., 21: 1147-1161. van Uden, A., Knoben, J., and Vermeulen, P. (2017). Human capital and innovation in Sub-Saharan countries: a firm-level study, Innovation, 19:2, 103- 124. Waarden, F.V. (2001). Institutions and innovation: the legal environment of innovating firm Org. Stud., 22 (5) (2001), pp. 765-795. Wang, C. Yi, J., Kafouros, M. and Yan, Y. (2015). Under what institutional conditions do business groups enhance innovation performance? J. Bus. Res., 68, 694-702. Winton, A., and Yerramilli, V. (2008). Entrepreneurial finance: Banks versus venture capital, Journal of Financial Economics, 88, issue 1, 51-79. 91 Wu, J., Wang, C., Hong, J., Piperopoulos, P., and Zhuo, S. (2016).Internationalization and innovation performance of emerging market enterprises: the role of host-country institutional development, J. World Bus., 51, 251-263 Yunusa, L. A. (2020). Exchange rate volatility and Nigeria crude oil export market, Scientific African,9. DOI://doi.org/10.1016/j.sciaf.2020.e00538. Yeoh, P.L., and Jeong, I. (1995). Contingency relationships between entrepreneurship, export channel structure, and environment: a proposed conceptual model of export performance, European Journal of Marketing, Vol. 29 No. 8, pp. 95-115. 92 Chapter Four Measuring Innovation Outputs Fundamental to the evaluation of any research program is an ability to measure the output(s) from the research. Albert N. Link, 1995 4.1. Introduction The nature and availability of data influence our understanding of the pattern of innovation, innovation output, and productivity of firms. The measurement of innovation activities is not always an easy task. For example, researchers have to decide whether to analyse the innovation performance using the measures of innovation input, intermediate or output indicators. The task is even more daunting in the context of developing country SMEs, where firm-level data are not readily available and comprehensive compared to the developed countries. Besides, the majority of these firms operate in the informal sector, thereby increasing the task of collecting reliable innovation data. Research suggests that collecting appropriate innovation information about SMEs is very important as it not only reveals the innovation pattern of these firms but also helps in evaluation processes and policy decisions (Link, 1995). The objective of this chapter is to present the data used in the empirical section of this research. The chapter is organised as follows: In Section 4.1, I will outline the two main approaches to collect innovation data. In Section 4.2, I will 93 discuss the features and challenges of collecting innovation data in developing countries. In Section, 4.3., I will present the innovation survey database used in this study. Finally, in Section 4.4., I will describe the three innovation output variables used in the empirical studies. 4.2. Approaches in the Collection of Innovation Data 4.2.1. Object-based Approach There are several ways of measuring innovation in the literature (Arundel and Hollanders, 2005; Bogliacino et al., 2012; Hoskens, 2015). These attempts highlight both multidimensional nature of innovation activities as well as the challenge of mapping innovation efforts, especially at the firm level (Young, 1996; Smith, 2005). However, there are two generally accepted methods used in the literature, namely: the object approach and subject approach. The former is "a single, focal, most important innovation, facilitating information retrieval about enablers, features, and outcomes of business innovations" (OECD, 2019). The emphasis of this approach is on the phenomenon itself, that is, individual innovation activities. The object-based innovation data are generally collected from publications such as magazines, technical and trade journals. In the object-based approach, the process consists of identifying the most significant technological innovations and then examining the features of the firms as well as new products and processes in question (Pianta and Sirilli, 1997). This approach goes back to the work of Edwards and Gordon (1984), who prepared an innovation database for the U.S Small Business Administration (SBA). The database includes 8,074 innovations introduced to the US market in 1982, 100 The questionnaire contains 12 sections. The survey contains information both on firm characteristics and innovation activities. More precisely, in collecting the information, firms were asked whether they have engaged in innovation activities during the periods of the survey. It covers different types of innovations, including information on a variety of indicators on inputs, outputs, innovation sources, cooperation, and hampering factors, etc. Table 4.1. shows a summary of the aspects covered in the survey. Table 4.1: Breakdown of the NIS Sections • General information: firm location, industry branch, geographical scope of the market, number of employees, personnel qualifications, total turnover, etc. • Indicators of innovation outputs • Ongoing and abandoned innovation activities • Innovation activities and expenditures: R&D expenditures, acquisition of machineries, software, patents and licenses, outsourced R&D, and personnel training. • Source of information and cooperation for innovation activities • Effects and objectives of innovation activities • Factors hampering innovation activities • Intellectual property rights • Government supports for innovation The sampling frame used was based on a directory of enterprises from both the Nigerian Stock Exchange (NSE) and the National Bureau of Statistics (NBS). It is good to mention that NSE’s list includes only firms in the formal sector. On the other hand, the list of NBS contains firms in both formal and informal sectors. Besides, a proportional probability sampling tool was used to select firms with a minimum of 10 employees. Consequently, the NSE and NBS lists were crossreferenced and any firm that appeared in both of them was automatically included in the sample. 101 The survey used a multistage systematic random sampling technique. First, the firms were stratified into manufacturing and service sectors using the Industrial Classification of all Economic Activities (ISIC revision 3.1). Second, the firms were stratified according to geographical locations, covering the six geopolitical zones of Nigeria, namely north-east, north-west, north-central, southwest, south-east, and south-south. Thus, the survey is a fair representation of the innovation activities of firms in Nigeria. Third, the firms were stratified according to employee size. The survey instruments were hand-delivered by field officers. To increase the response rate, the physical addresses of the firms were confirmed. In the case of firms that are out of business operation or with no traceable addresses, they were replaced with similar firms from the same sector and geographical location. Other actions taken to increase the response rate were telephone calls, follow-up visits, etc. The final sample of the surveys is shown in table 4.2. Table 4.2: Final sample of the NIS (Wave 1: 2005 – 2007; Wave 2: 2008 – 2010) Sector Year of survey (2008) Year of survey (2011) Total Manufacturing 519 371 890 Service 209 260 469 Total 728 631 1,359 Even though the survey questionnaires were not identical in the two surveys, they were very similar in the sections used in this study. The final pooled sample includes 1359 firms, an overall response rate of 54.3 percent. For the purpose of this work, I included a sample of firms in both sectors and eliminated firms with missing information, which reduced the sample to 162 firms (service sector) and 255 firms (manufacturing sector). 102 4.5. Choice of Innovation Indicators The choice of innovation output indicators is one of the main challenges of mapping innovation activities and performance of firms. For example, technological innovations have been traditionally measured by patents (Schmookler, 1950, 1953). Scholars suggest that patents are a good proxy for innovation both at the regional level (Acs, Anselin and Varga, 2002) and firm level (Griliches, 1990). Patents as a proxy allow for a cross-country comparison of innovation activities (Connolly, 1997, 1998; Smith, 2005). However, its use as a measurement of innovation has been criticised on several grounds (Mansfield, 1986). First, it is a fraction of innovation, which is more an indirect or intermediate measure, instead of measure of innovation output (Comanor and Scherer, 1969). What's more? Kleinknecht (2016:2) argues that: The 'propensity' to patent an invention may differ between industry branches, depending on the relative costs of innovation and imitation: if innovation costs are high and imitation costs are low, the propensity to patent is high; the opposite may hold if imitation costs are relatively high. Moreover, little is known about what firms do with their patents: what is the share of patents that is actually translated into commercially viable products? One of the main limitations of patents is that it does not capture all aspects of innovation activities. Recall that generated ideas have to be commercialized to be 103 considered as innovation. However, in reality, not patented inventions are commercialized; and not innovations are patented (Griliches, 1990; Kleinknecht, Poot, and Reijnen, 1991). The latter case is more predominant in the developing countries, where legal systems and patent offices are either not fully developed or inefficient as mentioned above. As a result of these limitations, scholars argue that “the actual economic significance of new technology can be better understood by means of a direct measure of innovation output” (Santarelli and Piergiovanni, 1996: 690). Both Schumpeter and Oslo Manual’s definitions show that innovation is a multifaceted construct, cutting across the technological and non-technological spheres. Technological innovation refers to the use of new technology to produce changes in products or services, and also to how products and services are produced (Damanpour, 1987). As global competition is driving technological innovations, SMEs are commercializing disruptive products at the expense of large firms (Carayannopoulos, 2009). Radicic and Djalilov (2018) show that SMEs investing in technological innovation perform better in the export markets. Similarly, Becker and Egger (2013) find that technological innovation positively affects the performance of German firms. Since product and process innovations are related to the development or application of new technologies, this study understands them as technological innovation (OECD, 2005). However, although technological innovation drives competitive advantage, it is not sufficient for managing contingency factors in today's global market. Research shows that firms can create and sustain a competitive advantage via nontechnological innovation (Mothe and Nguyen, 2012). Gupta and Foroudi (2016) 104 suggest that the adoption of marketing innovation can lead to changes in product, pricing, and promotion strategy, and consequently, improve firm performance. 4.6. Definition of Innovation Output Indicators a) Product Innovation Product innovation is the introduction of goods or services that are new or have had their features and intended uses significantly improved. It includes significant improvements in technical specifications, components, and materials, software in the product, user-friendliness, or other functional characteristics (Oslo Manual, 2005). In the Schumpeterian model of creative destruction, product innovation is pivotal to firm competitiveness and productivity as we saw in the previous chapter. For example, SMEs can create a competitive advantage by introducing technologically advanced products with novel and unique features that meet the market's demands. Through horizontal and vertical product differentiations, SMEs can successfully enter new foreign markets as well as increase shares in existing markets and, in turn, productivity gains (Becker and Egger, 2013). The NIS defined product innovation as "the introduction to market of a new or significantly improved good or service with respect to its capabilities, such as improved userfriendliness, components, software or sub-system". 105 b) Process Innovation Process innovation refers to the implementation of a new or significantly improved production or delivery method. It includes a significant change in techniques, equipment, and/or software (Oslo Manual, 2005). The objective of firm pursuing process innovation is to decrease unit costs of production or delivery, to increase quality, or to produce or deliver new or significantly improved products (Ganotakis and Love, 2010). SMEs producing new products at a lower price can increase their efficiency and consequently, perform better in the export market than noninnovating firms (Becker and Egger, 2013). The NIS defined process innovation as “the use or implementation of new or significantly improved process or method for the production or distribution of goods or services or supporting activity”. In the cases of product and process innovation, NIS specified that “the innovation (new or improved) must be new to your enterprise, but it does not need to be new to your industry sector or market”. c) Marketing Innovation Marketing innovation is the implementation of a new marketing method involving significant changes in product design or packaging, product placement, product promotion, or pricing (Oslo Manual, 2005). It represents ways through which a firm addresses customers' demands, opens up new markets, and positions products on markets to increase its sales (Gunday et al., 2011). Grimpe et al. (2017) argue that marketing innovation is neither subordinate to nor a mere ‘mechanism for exploiting technologically novel products commercially’. This highlights the 106 fundamental role of marketing innovation as a source of competitive advantage. In other words, the competitiveness of a firm goes beyond mere ownership of technology (Patterson et al., 2003). It also includes a firm’s ability to respond to the market environment through the ‘knowledge gathered from customers and competitors in the process of market research’ (Grimpe et al., 2017: 362). Such knowledge embodied in a new marketing strategy results in superior performance in the export market. This re-echoes Drucker’s claim, “that any business enterprise has two–and only two–basic functions: marketing and innovation” (Drucker, 1954: 40). The NIS defined marketing innovation as “significant changes to the aesthetic design or packaging of a good or service (exclude changes that alter the product’s functional or user characteristics –these are product innovations)”. 4.7. Conclusion While the significant role of innovation in survival and growth is largely uncontested, its measurement is a challenging task. This has led to several attempts to capture various aspects of innovation activities, especially innovation output at the firm level. In this chapter, I presented the two main approaches used in the collection of innovation data, namely, object-based approach and subject-based approach. I discussed the characteristics and challenges linked to the collection of innovation data in developing countries. Finally, I presented the NIS database as well as described the three innovation output variables used in the empirical analyses in the next two chapters. 107 References Acs, Z.J. and Audretsch, D.B. (1990) Innovation and Small Firms. The MIT Press, Cambridge. Acs, Z., Anselin, L., and Varga, A. (2002). Patents and innovation count as measures of regional production of new knowledge, Research Policy, 31, 7, 1069- 1085. Anlló, G. (2006). An overview of Latin America innovation surveys. In Blankley, W., Scerre, M., Molotja, N., & Saloojee, I. (Eds.). Measuring innovation in OECD and non-OECD countries (Chap. 10, pp. 141–162). Cape Town, South Africa: HSRC Press. Arundel, A., and Hollanders, H. (2005). EXIS: An exploratory approach to innovation scoreboards. Brussels: European Commission, DG Enterprise. AU—NEPAD. (2010) African Innovation Outlook 2010. AU—NEPAD: Pretoria, South Africa. AU—NEPAD. (2019) African Innovation Outlook 2010. AU—NEPAD: Pretoria, South Africa. Audretsch, D. B., and Belitski, M. (2020), The role of R&D and knowledge spillovers in innovation and productivity, European Economic Review, 123, DOI: https://doi.org/10.1016/j.euroecorev.2020.103391 Becker, S.O., and Egger, P.H. (2013). Endogenous product versus process innovation and a firm's propensity to export, Empirical Economics, Vol. 44 No. 1, pp. 329-354. Bogliacino, F., Perani, G., Pianta, M., and Supino, S.(2012).Innovation and development. The evidence from innovation surveys, Latin Am. Bus. Rev., 13 (3)219-261. Brouwer, E., and Kleinknecht, A. (1999). Innovative output, and a firm's propensity to patent.: An exploration of CIS micro data, Research Policy,28, 6, 615-624. Carayannopoulos, S. (2009). How Technology‐Based New Firms Leverage Newness and Smallness to Commercialize Disruptive Technologies. Entrepreneurship Theory and Practice. 33. 419 - 438. Castellacci, F., and Natera, J. (2013). The dynamics of national innovation systems: A panel cointegration analysis of the coevolution between innovative capability and absorptive capacity, Research Policy, 42,3,579-594. Cirera, X. and Muzi, S. (2020). Measuring innovation using firm-level surveys: Evidence from developing countries, Research Policy, 49, Issue 3. DOI: https://doi.org/10.1016/j.respol.2019.103912. 108 Comanor, W. S., and Scherer, F. M. (1969). Patent Statistics as a Measure of Technical Change, Journal of Political Economy, 77,3,392-98. Connolly, M. (1997). Technology, Trade, and Growth: Some empirical Findings, Federal Reserve Bank of New York: Research paper No. 9727. Connolly, M. (1998). The Dual Nature of Trade: Measuring its Impact on Imitation and Growth”, Duke University Cozzens S. and Sutz J. (2014). Innovation in informal settings: Reflections and proposals for a research agenda. Innovation and Development; 4 (1): 5–31. Crespi, G., and F. Peirano. (2007). Measuring Innovation in Latin America: What We Did, Where We Are and What We Want to Do. Paper presented at the Conference on Micro Evidence on Innovation in Developing Countries UNUMERIT. Maastricht. Damanpour, F. (1987). The Adoption of Technological, Administrative, and Ancillary Innovations: Impact of Organizational Factors. Journal of Management, 13(4), 675–688. Edwards, K.L. and T.J. Gordon, 1984, Characterization of Innovations Introduced on the US Market in 1982, prepared for the US Small Business Administration by The Futures Group under contract #SBA-6050-OA-82, March 1984 (The Futures Group). Feldman, M., and Florida, R. (1994). The Geographic Sources of Innovation: Technological Infrastructure and Product Innovation in the United States. Annals of the Association of American Geographers, 84(2), 210-229. Ganotakis, P. and Love, J.H. (2011), R&D, product innovation, and exporting: evidence from UK new technology-based firms, Oxford Economic Papers, Vol. 63 No. 2, pp. 279-306. Gault, F. (2011). Social impacts of the development of science, technology, and innovation indicators, UNU-MERIT Working Paper 2011-008, Maastricht: UNUMERIT, pp. 15. Geroski, P. (1990). Innovation, Technological Opportunity, and Market Structure. Oxford Economic Papers, 42(3), new series, 586-602. Griliches, Z. (1990). Patent Statistics as Economic Indicators: A Survey. Journal of Economic Literature, 28(4), 1661-1707. Grimpe, C., Sofka, W., Bhargava, M., and Chatterjee, R. (2017). R&D, Marketing Innovation, and New Product Performance: A Mixed Methods Study. J Prod Innov Manag, 34: 360-383. 109 Gunday, G., Ulusoy, G., Kılıç, K., and Alpkan, L. (2011). Effects of innovation types on firm performance, International Journal of Production Economics, 133(2), 662-676. Gupta, S., Malhotra, N., Czinkota, M., and Foroudi, P. (2016). Marketing innovation: A consequence of competitiveness. Journal of Business Research. 69. 10.1016/j.jbusres.2016.02.042. Hoskens, M. (2015). Short vs. Long Form: Method Effects in Measuring Innovation. Paper presented at the OECD NESTI/EUROSTAT scoping workshop on the third revision of the Oslo Manual. Oslo, Norway. Horbach, J., and Rennings, K. (2013). Environmental innovation and employment dynamics in different technology fields – an analysis based on the German Community Innovation Survey 2009, Journal of Cleaner Production,57, 158-165. Ibidunni, A. S., Kolawole, A.I., Olokundun, M. A., and Ogbari, M. E. (2020). Knowledge transfer and innovation performance of small and medium enterprises (SMEs): An informal economy analysis, Heliyon,6, Issue 8, https://doi.org/10.1016/j.heliyon.2020.e04740. Kleinknect, A. (1993). Why do we need new innovation output indicators? An Introduction, in Kleinknecht, A. and Donald B. (eds.), (1993). New Concepts in Innovation Output Measurement, New York: St. Martin's Press. Kleinknecht, A. and Donald B. (eds.), (1993). New Concepts in Innovation Output Measurement, New York: St. Martin's Press. Kleinknecht, A., T. P. Poot, and J. O. N. Reijnen (1991). Formal and Informal R&D and Firm Size: Survey Results from The Netherlands, in Z. J. Acs and D. B. Audretsch, Innovation and Technological Change. An International Comparison. (London: Harvester Wheatsheaf),84–108. Link, A. N. (1995). The Use of Literature-based Innovation Output Indicators for Research Evaluation, Small Business Economics 7: 451-455. Lugones, G. (2006). The Bogotá Manual: standardising innovation indicators for Latin America and the Caribbean. In Blankley, W., Scerri, M., Molotja, N., & Saloojee, I. (Eds.). Measuring innovation in OECD and NON-OECD countries selected seminar papers (pp. 163–182). South Africa: HSRC Press. Mansfield, E. (1986). Patents and Innovation: An Empirical Study. Management Science, 32(2), 173-181. NEPAD. (2005) African Science, Technology and Innovation Indicators (ASTII), Towards African Indicator Manuals. A Discussion Document. http://www. nepadst.org/doclibrary/pdfs/iastii_jun2006.pdf, accessed March 2009. 116 al. (2018) for Spain; Peters et al. (2018) for Germany, Ireland, and the UK; and Frick, Jantke and Sauer (2018) for Germany, Spain, France, and Italy, show that investment in R&D is correlated with innovation output; and innovation success positively affects productivity, albeit with variations in the size of the coefficients. b) Innovation Efforts and Productivity in developing economies The majority of the extant empirical studies focus on large firms from developed or newly industrialized economies. The effects of innovation are very heterogeneous, thereby making it difficult to generalize the results obtained by the developed economies for developing economies. Besides, the extant studies focus on R&D activities as the main determinant of innovation. However, substantial innovation activities in developing countries are based on improvements in existing processes and product designs via the absorption of foreign technologies (Howell, 2017). Firms in these economies innovate far from the technological frontier and often face extremely resource constraints as we saw in the previous chapters (Hobday, 2005). Thus, to understand the drivers of productivity in developing economies, it is important to consider innovation efforts of firms beyond the traditional R&D activities (Raffo et al., 2008; Arza and López, 2010). Previous studies in developing countries show that innovation determinants of firms are heterogeneous, thus, deviating from the evidence from developed economies (e.g. Chudnovsky, López and Pupato, 2006; Wadho and Chaudhry, 2018; Barros, 2021). For example, in a study of six Latin American countries, Crespi and Zuniga (2012) find that firms investing in knowledge activities, not just R&D, are likely to introduce new technological advances and those that engage in 117 innovation have greater productivity. These authors show that the acquisition of embodied and disembodied technologies, industrial engineering, tooling-up, and technical training are main drivers of innovations in Argentina, Chile, Colombia, Costa Rica, Panama, and Uruguay. Besides, their findings differ from the OECD countries as only technological innovation, but not technological innovation is positively related to productivity in Argentina and Colombia. Benavente (2006) find that innovation activities are related to firm size, sector, and market power. However, their results reveal that the productivity of Chilean firms is not affected by R&D expenditure in the short run. For Mexican manufacturing firms, Brown and Guzmán (2014) show that innovation efforts, along with export intensity, foreign direct investment (FDI), access to technology, are the main determinants of innovation, particularly in local firms as compared to foreign firms. More so, Busom and Vélez-Ospina (2017) find that increase in human capital improves productivity in manufacturing and service industries in Colombia. However, innovation output increases productivity below the median of the productivity distribution. For Chilean firms, Álvarez, Bravo- Ortega, and Zahler (2015) find that manufacturing and service industries have similar determinants of technological innovations. Besides, they find a positive effect of technological and nontechnological innovations on labour productivity for both sectors. Wadho and Chaudhry (2018) find that product innovation leads to increased labour productivity in Pakistan. Above all, their results reveal that vertical knowledge flows from foreign clients and suppliers are major determinants of innovation decisions. c) Innovation Activities and Productivity in Sub-Saharan Africa 118 Despite a recent surge of studies in the tradition of CDM modelling in developing countries, they overly concentrate on Latin America and Asia (Morsy and Amira El-Shal, 2020). Moreover, empirical evidence from these countries is highly mixed (Crespi, Tacsir, and Vargas, 2016; Aboal, Ezequiel Tacsir, 2018; Shi et al., 2020). There is a paucity of studies empirically investigating innovation efforts and productivity gains in Sub-Saharan Africa. To date, there is a handful of evidence such as Goedhuys, Janz, and Mohnen (2008), who find that technological variables, R&D, product innovation and process innovation, license of technology, and training, negatively affected productivity. They show that only foreign ownership, ISO certification seem to affect productivity. Cirera, Lage, and Sabetti (2016) estimated the impact of the adoption of information and communications technology (ICT) and innovation on productivity using firm-level data for a sample of six Sub-Saharan African countries. Their findings suggest that ICT is an important determinant of product, process, and organization innovations in the Democratic Republic of Congo, Ghana, Kenya, Tanzania, Uganda, and Zambia. Nevertheless, it is only the degree of novelty that positively affected productivity. For Ghanaian manufacturing firms, Fu, Mohnen, and Zanello (2018) find that acumen and skills of entrepreneurs, instead of R&D activities, as the drivers of innovation activities. These authors reveal that technological innovations have more effect on labour productivity of firms than managerial innovations. In addition, the role of innovation in productivity tends to be greater for formal firms than informal firms. Finally, Morsy and Amira El-Shal (2020) show that access to external knowledge, largely through ICT, R&D, and skills development are determinants of innovation efforts in African firms. 119 However, it is only product innovation that strongly affects the productivity of these firms. The results obtained from these studies significantly differ not only from the OECD countries but also from other developing countries. This highlights the importance of reconsidering the factors underlying innovation efforts and productivity in SSA van Uden, Knoben, and Vermeulen, 2017; Abdu and Jibir, 2018). In this region, R&D intensity is generally very low (WIPO, 2019), and firms lag considerably behind in knowledge acquisition, absorptive capacity, managerial and production skills (Goedhuys, 2007; Goedhuys and Sleuwaegen, 2010). Despite these contributions both in the developed and developing countries, there are generally very few empirical studies explicitly exploring the innovationproductivity link in SMEs. One of the first attempts was from Hall et al. (2009), who find that international competition stimulates R&D intensity, especially for high-tech firms in Italy. Also, firm size, R&D intensity, and investment in equipment increased the probability of implementing process and product innovations. In turn, innovation outputs, especially process innovation, have a positive impact on productivity. Furthermore, for German micro firms, Baumann and Kritikos (2016) find that the R&D intensity is positively correlated with the likelihood of reporting innovation, with a larger effect size for product than for process innovations. Moreover, innovation success of these firms enhanced labour productivity in a comparable way as larger firms. Finally, Audretsch, Kritikos, and Schiersch (2020) observe that highly skilled employees, together with R&D activity, drive the innovation output of micro-firms in the knowledge-intensive service sector. 120 5.3. Government intervention and innovation The current study acknowledges the contributions of previous scholarship on SMEs, while also exploring its implications in SSA for the following reasons. SMEs in Nigeria differ from their peers from developed economies, especially in terms of capital market structure. The high cost of innovation hampers the productivity of small entrepreneurial firms lacking internal financial resources (Wellalage and Fernandez, 2019). Under normal circumstances, firms can overcome their internal financial constraints through traditional external providers such as banks, venture capitalists, etc. However, the situation in Nigeria is different as we saw in the previous chapter. Public support is a useful instrument for mitigating the negative effects of financial constraints on private innovative activities (Hyytinen and Toivanen 2005). Previous studies conducted in several developed economies show that firms receiving government financial supports invest more in innovative activities and grow faster (e.g. Görg and Strobl, 2007; Czarnitzki and Lopes-Bento, 2013). For example, Aerts and Schmidt (2008) find that government R&D subsidies significantly improved the innovation activities of Flemish and German firms. Similarly, Bérubé and Mohnen (2009) show that Canadian plants that benefited from receiving R&D grants and R&D tax credits have more world‐first product innovations and were more successful in commercializing their innovations. Government invention in innovation is justified in several ways. First, in face of market failures, government intervention can relax the tension between social returns to R&D and private rate of returns across industries and sectors (Nelson, 1959). The problem of appropriability implies that the private rate of return to R&D 121 is likely to be below the social return, leading firms to under-invest in R&D activities (Arrow, 1962). Second, successful innovations involve high costs that are often beyond the financial capacity of most private firms, especially SMEs. Thus, firms require assistance from government or external donors due to the difficulties linked to accessing financing from the traditional financiers. Hall (2008:410) argues that […] it may still be difficult or costly to finance R&D using capital from sources external to the firm because there is often a wedge between the rate of return required by an entrepreneur investing his own funds and that required by external investors. By this argument, unless an investor is already wealthy, or firms already profitable, some innovations will fail to be provided purely because the cost of external capital is too high, even when they would pass the private returns hurdle if funds were available at a ‘normal’ interest rate. As a result of pervasive market imperfections in Nigeria, it is reasonable to assume that policy instruments can create positive incentives for private-sector innovations (Oyelaran-Oyeyinka, 2014). In other words, efficient governments can stimulate innovation and productivity in SMEs and consequently, enhance longterm national economic growth (Abdu and Jibir, 2017). Due to the persistent financial challenges facing firms in Nigeria, the government both at the national and state levels are implementing various forms of financial resources, ranging from R&D grants, taxes to subsidies, to stimulate innovation activities in growth-oriented firms (AUC, 2014; OECD, 2019). If these instruments work, it is expected that they will offset the financial constraints and 122 consequently improve R&D intensity of firms. While previous studies have advanced our understanding of the impacts of government support on private business innovations, the evidence is still inconclusive. In fact, it is still unclear whether such interventions stimulate innovation activities. In Nigerian context, studies examining the impacts of external supports for innovation activities on the productivity of firms are still scarce. The nascent research on the innovative activities of firms in Nigeria is yet to provide sufficient empirical evidence on the following critical questions: RQ1: To what extent do different sources of external financial supports affect innovation intensity, innovation success, and productivity of Nigerian SMEs? RQ2: In addition to R&D investments, what other factors determine innovation efforts and productivity of these firms? RQ3: What types of innovation have more significant impact on their productivity? 5.4. Empirical strategy: Innovation-Productivity Analysis I rely on the CDM model to analyse the relationship between innovation efforts and productivity. The standard version of CDM model is an empirical structural model consisting of four equations (Hall and Mairesse 2006; Lööf and Heshmati 2006). In the first stage, firms decide whether (or not) to invest in R&D activity (Selection equation); and upon choosing to engage in innovation, firms decide the amount of resources to invest in R&D activities (Intensity equation). The second stage 123 estimates the knowledge production function in which innovation inputs lead to innovation outputs (Innovation equation). In the final stage, the innovation outputs are linked to economic performance, generally expressed by firm's labour productivity (Production equation). The model accounts for issues arising from innovation surveys as well as corrects for potential simultaneity and endogeneity problems in the various stages. First, to account for the various sources of external financial supports, I categorize them into state government support, federal government support, and foreign government support. In this study, unlike previous studies, I added these sources of external financial supports in the four equations of the CDM model. The approach allows us to understand their impacts on the different stages of innovation process as well as on productivity of firms. Second, instead of focusing on only technological innovations (Morris, 2018), I consider three types of innovations: product innovation, process innovation, and marketing innovations. Recent literature shows that firms investing in different types of innovations have higher productivity than their counterparts focusing on a single-innovation strategy (Hashi, and Stojčić, 2013; Wadho and Chaudhry, 2018). Third, I included a set of firm internal characteristics (age, size, and 2-digit sector) and other exogenous factors (technical training, cooperation, patent, export orientation, and competition) to capture their impacts, alongside R&D activities, on innovation efforts and productivity of Nigerian SMEs. 124 a) R&D equations The first stage of the model includes two equations, namely the selection equation and intensity equation. Even though all firms in our sample engage in some sorts of innovation efforts, not all of them report it (Griffith et al.2006). The CDM model addresses the selectivity biases that may arise in this stage of the estimation. Thus, Eqn. (1) is the selection equation, which captures the decision to invest and report R&D, expressed as a standard sample model: 𝑅&𝐷 𝐼𝑁𝑉𝑖={1 𝑖𝑓 𝑅𝐷 𝐼𝑁𝑉𝑖∗=𝛽𝑥𝑖+𝜀𝑖>𝑧 0 𝑖𝑓 𝑅𝐷 𝐼𝑁𝑉𝑖∗=𝛽𝑥𝑖+𝜀𝑖≤𝑧 (1) where 𝑅&𝐷 𝐼𝑁𝑉𝑖 is an (observable) indicator function that takes 1 if a firm 𝑖 reports a positive innovation expenditure, and 0 otherwise. R&D 𝐼𝑁𝑉𝑖∗ a latent innovation variable that takes 1 if firm 𝑖 decides to engage and report innovation activities when the investment is above a given threshold 𝑧. 𝑥𝑖 is a vector of explanatory variables (external financial supports, along internal firm-level characteristics and external factors influencing R&D decision), and finally, 𝜀𝑖 is an error term. Conditional on a firm’s decision to invest in innovation, the second equation estimates the R&D intensity as follows: 𝑅&𝐷 𝐼𝑁𝑇𝑖={ 𝑅𝐷 𝐼𝑁𝑇𝑖∗=𝛾𝜅𝑖+𝜖𝑖 𝑖𝑓 𝑅𝐷 𝐼𝑁𝑉𝑖=1 0 𝑖𝑓 𝑅𝐷 𝐼𝑁𝑉𝑖=0 (2) 125 where R&D 𝐼𝑁𝑇𝑖∗ is the unobserved latent variable reflecting a firm 𝑖 investment intensity; and 𝜅𝑖 is a vector of determinants of R&D expenditures. Following prior studies (Baumann and Kritikos, 2016; Aldieri et al.,2019), I estimate selection equation (1) and intensity equation (2) using the Heckman selection model on the assumption that the error terms 𝜀𝑖 and 𝜖𝑖 are bivariate normal with zero mean and variance equal to unity. b) Innovation Function In the second stage, I consider the determinants of product, process, and marketing innovations using a knowledge production function in Eqn. (3), expressed thus: 𝐼𝑁𝑁𝑂𝑖𝑗=𝛿𝑅&𝐷 𝐼𝑁𝑇𝑖∗+𝜃𝑤𝑖+𝑢𝑖 𝑗=1,…,3 (3) Where 𝑅&𝐷 𝐼𝑁𝑇𝑖∗ is the latent R&D effort proxied by the predicted value of the R&D intensity from Eqn. (2); 𝐼𝑁𝑁𝑂𝑖𝑗 i is a dummy variable taking the value of 1 if firm 𝑖 has done product innovation, process innovation, or marketing innovation and 0 otherwise; w𝑖 is a set of covariates influencing the innovation outcomes of firms; and finally, 𝑢𝑖 is the error term. I introduce a set of interaction term to examine whether the effect of introducing various types of innovations is greater for firms receiving more external financial supports. I estimated Eqn. (3) as a binary probit model. 132 Moreover, there is the possibility that some international firms that could not handle high degree of competition, partly due to resource constraints, exited the foreign markets and instead pursued their innovation strategies in the national markets. Taken together, cooperation, foreign government support, physical and human capitals play a crucial role in the innovation-decision and R&D intensity of SMEs in Nigeria. These findings highlight the roles of absorptive capacity, physical capital, and external partners in the innovation activities of SMEs (Cantabene and Grassi, 2020). More precisely, at the initial stage of innovation, firms in developing economies require intangible resources (human capital) to identify, absorb and assimilate technologies (physical capital) created elsewhere (Capozza and Divella, 2019). Besides, they require external financial supports and innovation cooperation to complement their internal knowledge capacity (Xie and Wang, 2020). b) Innovation Equations The predicted value of R&D intensity (Eqn. 2) is used to estimate the knowledge production functions (Eqn.3). I used the predicated value as an instrumental variable, to control potential simultaneity issues between innovation effort and the expectation of innovation outcome (Hall, Lotti and Mairesse, 2009). For the dependent variables, I used binary variables for three types of innovation outputs: product innovation, process innovation, and marketing innovation. Table 5.4 shows the estimates of Eqn. (3) with main effects (columns 1, 3, and 6) and interaction effects (columns 2, 4, and 6) of the variables of interest on the three types of innovation output respectively. 133 First, the result shows that predicated R&D intensity has a positive and significant effect on the likelihood of introducing product innovation and marketing innovation. These findings are corroborated in several empirical studies both in developed and developing economies (e.g. Aldieri et al., 2019; Morsy and El-Shal, 2020). The positive and significant coefficient for marketing innovation is interesting as only technological innovations are traditionally linked to scientific discovery and R&D investments. One possible explanation for this result is the R&D-spillover effect present in firms investing in multiple innovation strategies. This claim is supported by recent studies on the interplay between R&D and novel marketing strategies (Grimpe et al., 2017). The results get even more interesting as the predicted R&D investment is not statistically significant for process innovation. Acosta et al. (2015) have similar results for Spanish firms. This finding is not surprising given the positive and strong significant coefficient for external R&D investment. In other words, it shows that when introducing process innovation, firms do not invest in internal and external R&D simultaneously. Besides, investment in human capital is positively associated with the successful introduction of process innovation. Second, the external financial support variables, as expected, are positively related to the innovation output, albeit revealing heterogeneous effects. In the main effect (column 1), state government funds and firm age have a high statistical significance on product innovation. This can be interpreted as a confirmation that larger SMEs are more likely to invest funds received from external sources in product innovation. In the interaction effect (column 2), the significant coefficient of Pred. R&D*Foreign government suggests that the probability of investing external financial support in product innovation is high for larger (size = 0.1389 at 134 99%) and older (age = 0.1200 at 95%) SMEs receiving foreign government fund, but not state and federal government financial supports. In other words, the positive impact of foreign government supports on predicted R&D intensity increases the probability of these firms having product innovation. However, as a standalone variable (column 1), foreign government supports negatively affects the probability of implementing product innovation. Possibly, this result indicates that firms not receiving greater amount of foreign government supports hesitate (or fail) to invest it in product innovation. Concerning process innovation, the three sources of external supports have positive and significant coefficients, albeit at different statistical levels. Unlike in the case of product innovation, the results reveal that access to external financial supports is not exclusively tied to older firms. In fact, younger firms are more likely to receive state, federal, and foreign government funds for process innovation. 135 Table 5.4: Results of Probit regression analysis (Step 2) Variables Product innovation Process innovation Marketing innovation Coef. /se (1) Coef. /se (2) Coef. /se (3) Coef. /se (4) Coef. /se (5) Coef. /se (6) Predicted R & D 2.062*** (0.510) 2.037*** (0.511) 0.378 (0.500) 0.369 (0.501) 1.123** (0.501) 1.147*** (0.501) Size 0.107*** (0.032) 0.1389*** (0.038) -0.022 (0.032) -0.012 (0.037) -0.000 (0.031) 0.022 (0.037) Age 0.072 (0.058) 0.120* (0.065) -0.130** (0.058) -0.116* (0.064) -0.162*** (0.057) -0.131** (0.064) Sector 0.001 (0.080) 0.080 (0.094) -0.052 (0.079) -0.029 (0.092) -0.226*** (0.079) -0.173* (0.092) Patent 0.054 (0.065) 0.059 (0.065) 0.015 (0.064) 0.018 (0.064) -0.012 (0.064) -0.008 (0.064) Cooperation -0.134* (0.077) -0.189** (0.085) -0.097 (0.076) -0.114* (0.083) -0.064 (0.077) -0.101 (0.083) ln (Human capital) -0.262*** (0.064) -0.303*** (0.069) 0.085* (0.063) 0.073 (0.068) 0.073 (0.063) 0.044 (0.068) ln (Physical capital) -0.438*** (0.166) -0.503*** (0.171) 0.058 (0.163) 0.039 (0.167) 0.538*** (0.163) 0.498*** (0.168) Training -0.139** (0.057) -0.1349** (0.057) -0.120** (0.057) -0.118** (0.056) 0.037 (0.056) 0.042 (0.056) Competition 0.024 (0.052) 0.057 (0.052) 0.112** (0.051) 0.113** (0.051) 0.037*** (0.056) 0.217*** (0.051) External RD 0.097* (0.054) 0.098* (0.053) 0.147*** (0.053) 0.146*** (0.053) -0.039 (0.053) -0.038 (0.053) Export orientation 0.133** (0.055) 0.154*** (0.056) -0.035 (0.054) -0.029 (0.055) -0.085* (0.053) -0.079 (0.055) State government 0.078* (0.054) -0.082 (0.251) 0.126** (0.053) 0.099 (0.246) -0.083* (0.053) -0.618** (0.246) Federal government 0.043 (0.055) -0.036 (0.243) 0.094* (0.055) -0.095 (-0.095) -0.084* (0.054) 0.047 (0.237) Foreign government -0.308*** (0.063) -0.775*** (0.265) 0.168*** (0.062) 0.183 (0.259) 0.056 (0.062) -0.009 (0.259) Pred.R&D*State government 0.208 (0.324) 0.031 (0.318) 0.707** (0.317) Pred.R&D*Federal government 0.142 (0.325) 0.259 (0.316) -0.149 (0.315) Pred.R&D*Foreign government 0.594* (0.333) -0.032 (0.324) 0.066 (0.3243) *** p < 0.001; ** p < 0.01; * p < 0.05 136 However, the insignificant results for the interaction effect (column 4) show that a greater amount of these funds does not lead to process innovation. Moreover, firms investing in external R&D and human capital are more like to implement process innovation. Finally, only firms receiving more state government funds have a greater probability of introducing marketing innovation (column 6). Finally, other explanatory variables in equation (3) influencing the relationship between innovation inputs and innovation success include export orientation and size, with positive and strong significance for product innovation. These findings are consistent with the co-evolution of innovation and internationalization in SMEs (Vuorio, Torkkeli, and Sainio, 2020). Studies show that SMEs with international growth strategy is more likely to introduce product innovation that will open up new market niches (Love and Roper, 2015). Second, competition has a positive and significant effect on process innovation. In the last decade, global competition has intensified, imposing new competitive pressures on SMEs, especially from developing economies. The results are consistent with evidence suggesting that firms operating in more fierce competitive markets are more likely to engage in cost-reducing innovations than their peers in less intense competitive markets (Delbono and Denicolo, 1990). What’s more, the positive and significant coefficient for external R&D activities indicates that the firms under study adopted new process technologies as a means of enhancing their competitive advantage (Buffa, 1985). Besides, research increasingly suggests that marketing innovation is key to SMEs’ survival and growth (Naidoo, 2010). Thus, the results confirm that competition increases the probability of implementing marketing innovation. Finally, another variable of interest is cooperation, which has a negative impact on product and process innovations. These results call for a further 137 investigation on the impacts of various types of cooperation on innovation performance. We shall undertake this exercise in the subsequent chapter. c) Productivity equations Finally, in Eqn. (4), I estimated the link between innovation outputs on labour productivity, where log of sales per employee is the dependent variable. To control for endogeneity problem, I use the predicated probabilities of product innovation, process innovation, and marketing innovation from Eqn. (3). Table 5.5 reports the main effects (columns 1, 3, and 5). I introduced interaction term to ascertain whether the impact of innovation success on productivity is greater for firms receiving more external financial supports from the state government, the federal government, and foreign government (columns 2, 4, 6). In general terms, the results show that innovation outputs have positive and significant effects on labour productivity in all the regressions. Thus, consistent with prior evidence for both developed and developing. However, when the impact of the various innovation types is considered, firms that implemented marketing innovation have the greatest productivity gains, followed by process innovation and product innovation respectively. Prior evidence suggests that process innovations have more impact on productivity of firms more than product innovation (Waheed, 2011). This finding deviates from the evidence that considered product innovation the main driver of productivity gains (Mairesse et al., 2009). Furthermore, it is worth highlighting the importance of pursuing a diversified innovation strategy when pursuing growth. The size of the coefficient for marketing innovation (13.752) shows that firms implementing novel marketing strategies are 138 productive than firms investing in product innovation (2.572) and process innovation (6.951) combined. This finding makes a case for devoting more effort to understanding the dynamics of marketing innovation and its impact on firm performance, especially in developing countries (Quaye and Mensah, 2019). Thus, I will further explore this issue by considering the separate and joint effects of technological innovation and marketing innovation on export growth. Moreover, external financial supports have varied effects on labour productivity as seen in the models. In the case of product innovation, productivity increases for firms that received supports from federal and foreign governments, but decrease with state government as shown in column (1). However, the impact of product innovation on productivity is insignificant for firms with more access to external funds columns (2). For process innovation, access to external funds from state, federal, and foreign government reduced the productivity of firms (columns 3 and 4). However, the positive and significant coefficients in the case of marketing innovation indicate that the three sources of external funds increase productivity gains only in the main effect. On average, we observe that access to more external funds has heterogeneous effects on innovation outputs and productivity gains (Carvalho and Avellar, 2017). These findings call for efficient policy geared towards stimulating innovation activities and productivity in SMEs (Mohnen and Hall, 2013). As for the other explanatory variable in equation (4), firm age has positive and significant coefficients in all the regressions, suggesting that older SMEs are more productive than their younger counterparts. However, firm size is significant across the models but negatively related to labour productivity. 139 Table 5.5: Results of Production Function regression analysis (Step 3) Variables Product innovation ln(Productivity) Process innovation Marketing innovation Coef. /se Coef. /se Coef. /se Coef. /se Coef./se Coef./se Inno  2.572*** (0.057) 2.522*** (0.074) 6.951*** (0.155) 6.936*** (0.155) 14.310*** (0.344) 13.752*** (0.395) Size -0.083*** (0.0056) -0.083*** 0.006 -0.091*** (0.005) -0.091*** (0.005) -0.443*** (0.012) -0.420*** (0.014) Age 0.153*** (0.007) 0.156*** (0.007) 0.306*** (0.008) 0.299*** (0.009) 0.392*** (0.010) 0.369*** (0.013) Sector 0.270*** (0.009) 0.277*** (0.011) 0.002 (0.009) -0.002 (0.009) 0.405*** (0.012) 0.379*** (0.015) Patent -0.063*** (0.011) -0.064*** (0.0105) -0.083*** (0.010) -0.081*** (0.010) -0.024** (0.010) -0.023** (0.012) Cooperation -0.102*** (0.009) -0.104*** (0.009) 0.337*** (0.011) 0.328*** (0.013) 0.971*** (0.024) 0.907*** (0.033) ln (Human capital) 0.112*** (0.005) 0.115*** (0.006) -0.148*** (0.008) -0.145*** (0.008) 0.069*** (0.006) 0.066*** (0.006) ln (Physical capital) 0.252*** (0.014) 0.256*** (0.015) 0.058*** (0.017) 0.059*** (0.017) -2.099*** (0.067) -1.958*** (0.083) Training 0.148*** (0.009) 0.152*** (0.010) 0.338*** (0.011) 0.330*** (0.013) -0.201*** (0.011) -0.187*** (0.011) Competition -0.005 (0.008) -0.006 (0.008) -0.286*** (0.010) -0.278*** (0.012) -1.194*** (0.030) -1.117*** (0.040) External RD -0.095*** (0.009) -0.097*** (0.009) -0.401*** (0.012) -0.390*** (0.014) 0.219*** (0.010) 0.206*** (0.011) Export orientation -0.051** (0.008) -0.053*** (0.008) 0.061*** (0.008) 0.060*** (0.008) 0.257*** (0.010) 0.241*** (0.012) State government -0.110*** (0.009) -0.174*** (0.063) -0.325*** (0.011) -0.329*** (0.064) 0.582*** (0.016) 0.578*** (0.057) Federal government 0.052*** (0.008) -0.002 (0.059) -0.313*** (0.011) -0.340*** (0.063) 0.160*** (0.009) 0.255*** (0.058) Foreign government 0.177*** (0.008) 0.191*** (0.060) -0.397*** (0.013) -0.196*** (0.066) 0.058*** (0.008) 0.203*** (0.058) Inno  *State government 0.104 (0.103) 0.025 (0.129) -0.067 (0.126) Inno  *Federal goverment 0.093 (0.098) 0.077 (0.128) -0.215* (0.121) Inno  *Foreign government -0.016 (0.102) -0.405*** (0.131) -0.306** (0.120) *** p < 0.001; ** p < 0.01; * p < 0.05 140 These findings are in line with prior evidence in Latin American countries (Crespi and Zuniga, 2011). Although patenting can mitigate the imitation by rivals, and consequently, increase turnover from innovation (Shapiro, 2001), it involves substantial costs, which the majority of SMEs in developing countries can hardly afford. In other words, unlike prior studies, patent negatively affects the productivity of Nigerian SMEs (Andries and Faems, 2013). Besides, the results show that cooperation, physical capital, training, and export orientation improved the productivity of firms investing in process innovation strategy. Similarly, human capital, physical capital, training, and sector increased the productivity gains of firms investing in product innovation. Finally, sector, cooperation, human capital, and export orientation enhanced the productivity gains of firms pursuing marketing innovation. 141 References Aboal, D., and Tacsir, E.(2018). Innovation and productivity in services and manufacturing: the role of ICT, Industrial and Corporate Change, Volume 27, Issue 2, 221–241. Acosta, M., Coronado, D. & Romero, C. (2015). Linking public support, R&D, innovation, and productivity: New evidence from the Spanish food industry, Food Policy, Elsevier, vol. 57(C) 50-61. Adegboye, A.C., and Iweriebor, S. (2018). Does Access to Finance Enhance SME Innovation and Productivity in Nigeria? Evidence from the World Bank Enterprise Survey. African Development Review, 30: 449-461. Aerts, K., and Schmidt, T. (2008). Two for the price of one?: Additionality effects of R&D subsidies: A comparison between Flanders and Germany, Research Policy, 37, 5,806-822. Aldieri, L., Barra, C., Vinci, C.P. and Zotti, R. (2019): The joint impact of different types of innovation on firm's productivity: evidence from Italy, Economics of Innovation and New Technology, DOI: 10.1080/10438599.2019.1685211. Álvarez, R., Bravo-Ortega, C., and Zahler, A. (2015). Innovation and Productivity in Services: Evidence from Chile, Emerging Markets Finance, and Trade. 51(3),593-611. Andries, P., and Faems, D. (2013). Patenting Activities and Firm Performance. J Prod Innov Manag, 30: 1089-1098. Arza, V., and Lopez, A. (2010). Innovation and Productivity in the Argentine Manufacturing Sector, IDB Working Paper Series, No. IDB-WP-187, Inter- American Development Bank (IDB), Washington, DC. Arrow, K. (1962). Economic Welfare and the Allocation of Resources for Invention, p. 609-626 in the Rate and Direction of Inventive Activity: Economic and Social Factors, National Bureau of Economic Research, Inc. Audretsch, D. B., and Belitski, M. (2020), The role of R&D and knowledge spillovers in innovation and productivity, European Economic Review, 123, DOI: https://doi.org/10.1016/j.euroecorev.2020.103391. Audretsch, D.B., Kritikos, A.S. and Schiersch, A. (2020). Microfirms and innovation in the service sector. Small Bus Econ 55, 997–1018. Ayalew M.M. and Xianzhi, Z. (2018). Bank competition and access to finance: evidence from African countries. J Ind Compet Trade 19:155–184.