The importance of research on the role of knowledge spillovers on sectoral innovation and firms’ TFP in the manufacturing industries in Vietnam .... KNOWLEDGE SPILLOVER, SECTORAL INNOVAT
INTRODUCTION
PROBLEM STATEMENT
1.1.1 The significance of the research’s topic
1.1.1.1 The importance of the research the role of knowledge spillovers on innovation at sector level
Innovation and knowledge spillover are essential drivers of economic development, particularly in developing countries Both endogenous growth and new growth theories highlight the critical role of innovation and technological change in sustaining long-term economic growth (Romer) Emphasizing the importance of these factors, fostering innovation can lead to increased productivity, competitive advantage, and overall economic progress.
Innovation often stems from investments in knowledge and knowledge spillovers, which play a crucial role in economic growth According to Griliches (1992), investments in knowledge tend to spill over, allowing third-party firms to commercialize ideas without bearing their full costs Pioneering economists like Romer (1986), Lucas (1988, 1993), and Grossman and Helpman (1991) have demonstrated that knowledge spillovers are vital mechanisms driving endogenous growth and long-term economic development.
The success of four Asian NICs in industrialization underscores the crucial role of innovation in economic development According to the 2016 Global Innovation Index (GII) by WIPO, Vietnam scored only 35.4 out of 100, ranking 59th among 128 countries, and lagged behind ASEAN peers such as Singapore (7th), Malaysia (33rd), and Thailand (48th) This highlights the urgent need for Vietnamese policymakers to focus on boosting innovation capacity to drive sustainable economic growth Understanding the mechanisms of knowledge spillovers is essential for fostering innovation and achieving broader economic development.
Understanding the innovation capacity at the sector level and the channels of knowledge spillovers is crucial for developing effective innovation-driven growth policies Sector-level analysis offers valuable insights into the relationship between knowledge accumulation and diffusion, which can inform targeted strategies for enhancing innovation Mehrizi and Ve (2008) highlight that sector analysis enables researchers to connect firm-level determinants with broader macroeconomic conditions, facilitating a comprehensive understanding of innovation dynamics Malerba (2002) emphasizes the importance of examining innovative and production activities within sectors, while Padoan (1999) advocates for a sectoral perspective to effectively explore knowledge accumulation and diffusion processes.
Innovation capacity in a firm or sector depends not only on their own knowledge and technology but also significantly benefits from knowledge spillovers from other firms and industries, as highlighted by Aghion and Jaravel.
Innovations within one firm or sector often build upon knowledge generated by others, leading to spillovers that can enhance collective progress While traditionally viewed as reducing firms’ incentives to invest in R&D, recent research by Cohen and Levinthal (1989) and Onodera (2009) suggests that R&D investments can actually increase a firm's absorptive capacity This capacity enables firms to effectively assimilate external knowledge, thereby contributing to sectoral upgrading through internal spillovers and fostering innovation across related industries via external spillovers.
Current research on the roles of knowledge spillover channels in sector innovation capacity is limited, with most studies focusing on how sector characteristics influence innovation (Castellacci, 2008; Yurtseven & Tandoğan, 2012; Hage et al., 2013; Piqueres et al., 2015) Recent studies have begun to explore the impact of R&D spillovers on innovation (Autant-Bernard and Lesage, 2011; Ang and Madsen, 2013; Malerba et al., 2013), but there remains a gap in understanding both sectoral and regional factors related to knowledge spillovers beyond R&D, such as those examined by Buerger and Cantner (2011) and Aiello and Ricotta To comprehensively understand sector innovation capacity, further research is needed on diverse knowledge spillover channels, including regional influences and non-R&D related knowledge flows.
Vietnam, there are few studies on innovation and most of these studies focused on firm level (Jordan, 2015; Doan Thi Hong Van and Bui Le Nhat Uyen, 2017;
This study investigates the impact of knowledge spillovers on sectoral innovation through three key channels: R&D activities, foreign direct investment (FDI), and international trade Using spatial regression models, the research aims to understand how these channels facilitate the transfer of knowledge and stimulate innovation across different sectors, building on previous findings by Le Thi Ngoc Bich et al (2017) and Tran Hoai Nam et al (2017).
1.1.1.2 The importance of the research on heterogeneity of firms’
TFP in considering both firms’ characteristics and spillover effects from sectors and regions
Relying solely on capital and labor limits the potential for sustainable economic development, as total factor productivity (TFP) represents the residual output not explained by these inputs According to the Solow model (1956), TFP embodies technological progress, which is crucial for long-term growth Countries like Korea and Singapore have experienced substantial TFP growth, significantly contributing to their development For instance, Korea's TFP growth was only 8.3% from 1970-1980 but surged to 31.5% between 1980-1990, highlighting the importance of enhancing TFP in development strategies Therefore, boosting TFP remains a central focus of effective economic policy to achieve sustainable growth.
Understanding the determinants of total factor productivity (TFP) is crucial for formulating effective development policies Technological progress is widely recognized as the primary driver of sustained growth, as highlighted by Solow (1956) However, Acemoglu (2009) emphasizes that TFP heterogeneity often stems from factors beyond mere technology adoption, such as differences in how firms utilize technologies efficiently Additionally, Capeda and Ramos (2015) identify various components like economies of scale and resource combination methods that influence TFP, noting that even firms using similar technologies can exhibit significant productivity differences due to sector-specific characteristics or geographic location.
Firm heterogeneity in total factor productivity (TFP) primarily stems from differences in firm characteristics such as size, production technology, and human capital These factors influence overall firm performance, which is further affected by sector-specific external economies of scale According to Krugman and Obstfeld (2009), sector concentration within regions creates positive externalities like specialized suppliers, labor market pooling, and knowledge spillovers Moreover, regional environment and endowments significantly impact firm performance, as regions with diverse and high-quality resources benefit firms differently External economies of scale tend to promote industrial localization, encouraging firms to cluster together to capitalize on these benefits, exemplified by regions like California’s Silicon Valley and New York’s financial sectors.
Understanding the determinants of firms' Total Factor Productivity (TFP) requires a multilevel cross-classified modeling approach that captures influences across firm, sector, regional, and provincial levels This advanced model isolates the impacts of various factors at different hierarchical dimensions, providing a more accurate analysis of TFP drivers Multilevel analysis effectively addresses error correlation among firms operating within the same levels, which traditional single-level models often overlook Unlike single-condition models that tend to underestimate variance by ignoring hierarchical structures, the multilevel cross-classified model precisely assesses variance at each level, leading to more reliable and comprehensive insights into TFP determinants.
However, most of studies on firms’ TFP focused on the determinants as firms’ characteristics (Sjoholm 1999; Blalock and Veloso, 2006; Waldkirch and Ofosu, 2010; Lopez, 2008; Baptist and Teal, 2014; Fernandes, 2008; Seker and Saliola,
2018) In Vietnam, studies on TFP are still very limited (CIEM, 2010) although
Total Factor Productivity (TFP) is increasingly recognized as a crucial indicator of development quality This study introduces a novel approach by applying a multileveled cross-classified model to investigate TFP in Vietnam, providing deeper insights into its determinants The findings have significant policy implications, offering guidance not only for individual firms but also for sectoral and regional development strategies.
1.1.1.3 The importance of research on the role of knowledge spillovers on sectoral innovation and firms’ TFP in the manufacturing industries in Vietnam
The manufacturing industry is a vital pillar of Vietnam’s economy, contributing 18.35% to the country's GDP in 2016, making it the leading economic sector (APO, 2016) Additionally, it consistently ranks as the second-largest employer after agriculture, forestry, and fishing industries from 2010 to 2016, underscoring its significant role in job creation and economic development (APO).
2016) In comparison to other countries in the region, the output growth of manufacturing sector in Vietnam was in the top five at 9.3% in the period from
2000 to 2014 Moreover, the manufacturing in Vietnam was also the leading sector with the biggest contribution share to output growth at 26,98%.
Despite its significant role in Vietnam’s economy, this sector experienced low labor productivity growth compared to other countries, with a growth rate of only 2.7% from 2000 to 2014, which ranked it just above Brunei and Indonesia (APO, 2016) Vietnam’s manufacturing productivity growth was substantially lower than China’s impressive 7.6% during the same period, highlighting a significant gap Additionally, the percentage of enterprises engaging in technological modifications was very low at just 0.87% between 2010 and 2014, indicating limited technological updates within the sector Regarding innovation activities, including patent acquisition and technological development beyond mere modifications, only 9.02% of enterprises participated in such initiatives, reflecting relatively modest engagement in innovation (VES data).
RESEARCH OBJECTIVES AND QUESTIONS
This study explores the critical role of knowledge spillovers in driving sectoral innovation It emphasizes that a firm’s or sector’s innovative capacity depends not only on its own knowledge and technology but also significantly on external knowledge flows from other firms and sectors Understanding these knowledge spillovers is essential for explaining how innovation diffuses across industries, ultimately fostering broader economic growth (Aghion and Jaravel).
2015) The first general objective is to investigate channels of knowledge spillovers on sectoral innovation in manufacturing industries in Vietnam, the study focuses on the following research questions:
1.1 Is sectoral innovation directly affected by R&D activities of that sector in manufacturing industries in Vietnam?
1.2 Is sectoral innovation indirectly affected by R&D activities of other sectors in manufacturing industries in Vietnam?
1.3 Is sectoral innovation directly affected by transactions with FDI enterprises in that sector in manufacturing industries in Vietnam?
1.4 Is sectoral innovation indirectly affected by transactions with FDI enterprises in other sectors in manufacturing industries in Vietnam?
1.5 Is sectoral innovation directly affected by trade activities in that sector in manufacturing industries in Vietnam?
1.6 Is sectoral innovation indirectly affected by trade activities in other sectors in manufacturing industries in Vietnam?
This study aims to examine how firm-specific, regional, and sectoral characteristics influence firms' total factor productivity (TFP) The research explores the impact of these various factors to better understand the determinants of productivity across different contexts By analyzing firm-level attributes alongside regional and sectoral dynamics, the study provides comprehensive insights into the key drivers of TFP The findings will help identify critical areas for policy intervention and strategic development to enhance overall productivity.
2.1 How much heterogeneity in firms’ total factor productivity is explained by firm-level, sector-level and province-level determinants?
2.2 Does firms’ size have impact on firms’ TFP in manufacturing industries in Vietnam?
2.3 Does the capital intensity in firms have impact on their TFP?
2.4 Is there difference in TFP of exported firms and non-exported firms?
2.5 Is firms’ TFP affected by their sectoral innovation in manufacturing industries in Vietnam?
2.6 Does the human resource in a province have impact on the TFP of firms in that province?
RESEARCH METHODOLOGY and RESEARCH SCOPE
This study investigates three channels of knowledge spillovers influencing sector innovation capacity using Spatial Regression analysis To examine heterogeneity in firm productivity, a cross-classified model is applied across three determinant groups: sector, regional, and firm levels The research utilizes data from the Vietnam Enterprises Survey (VES) and the Vietnam Technology and Competitiveness Survey (TCS), complemented by Input-Output (I/O) analysis to provide comprehensive insights into the factors affecting innovation and productivity in Vietnam's economy.
Vietnam in 2012 Besides, the study also uses the annually surveyed data on province of General Statistics Office (GSO).
This study analyzes the impact of R&D, FDI, and trade on sectoral innovation, with the sector serving as the primary analysis unit The data is aggregated from Vietnamese manufacturing firms spanning from 2010 to 2014 Sectoral relationships are established based on intermediary transactions identified within Vietnam's Input-Output tables, providing insights into how these variables influence innovation across different industrial sectors.
2012 By spatial regression model, the study finds the direct as well as indirect impact of R&D, FDI and trade on sectoral innovation.
This study analyzes the impact of firm-level, regional, and sectoral characteristics on total factor productivity (TFP) by focusing on manufacturing firms in Vietnam from 2011 to 2014 Utilizing data from TCS and VES, the research assesses firm-specific features and sectoral attributes, providing a comprehensive understanding of factors influencing TFP Additionally, the provincial data on the Province Competitive Index (PCI) is incorporated to evaluate the role of human resources at the regional level, enabling a multidimensional analysis of productivity determinants across different regions.
RESEARCH CONTRIBUTION
This study advances both theoretical understanding and policy implications by developing and testing a sector-level knowledge spillover framework using a novel Spatial Regression Model, providing new insights into sectoral innovation dynamics It explores the impact of contextual factors, such as innovation activities and human resources, on firms' Total Factor Productivity (TFP) through the application of the Cross-classified Model, highlighting how sector and province-level characteristics influence firm performance The research offers valuable findings for Vietnam, identifying core spillover factors that enhance sectoral innovation capacity and informing policymakers on the significance of firm, sector, and regional attributes in boosting productivity.
This study develops a framework for understanding knowledge spillovers within and among sectors, grounded in the intra-sector and inter-sector concepts from Cohen and Levinthal (1989) and Griliches (1992) It introduces the innovative use of Spatial Regression Models to analyze these spillovers, allowing for the examination of both direct and indirect effects of R&D, FDI, and trade on sectoral innovation This approach provides valuable insights for identifying the most effective channels to boost innovation activities across sectors.
This study examined the heterogeneity in firms’ TFP in Vietnam using a novel approach, the Cross-Classified Multilevel Model, which evaluates the impact of firm, sector, and provincial levels It also highlighted the significance of sectoral innovation spillover effects, based on Griliches (1992), and provincial human resource spillovers, from Morretti (2004b) The findings suggest that understanding these spillover effects can inform effective policy measures to enhance productivity across sectors and regions.
STRUCTURE OF THIS STUDY
This article is structured into five chapters, beginning with an Introduction that sets the context for the study The second chapter presents a comprehensive Literature Review, covering both the Theoretical Framework and Empirical Studies related to the research objectives The methodology chapter details the application of advanced modeling techniques, including the Spatial Regression Model and the Cross-Classified Model, to ensure rigorous analysis Overall, this study integrates theoretical insights and empirical data through sophisticated spatial analysis methods to achieve its research objectives efficiently.
This article covers the Model Specification, Variable Measurement, and Data collection, providing a foundation for understanding the research framework The subsequent chapters focus on Results and Discussion, with one chapter analyzing Sectoral Innovation and Spillover Effects to highlight their impact on firm performance Another chapter examines Heterogeneity in Total Factor Productivity (TFP) among Vietnamese manufacturing firms, offering insights into industry variations The final chapter presents Conclusions and Policy Implications, emphasizing actionable strategies for fostering innovation and productivity growth in Vietnam’s manufacturing sector.
LITERATURE REVIEW
DEFINITION AND CONCEPTS
Knowledge spillovers occur when benefits from others' investments in knowledge are gained without full payment, due to knowledge’s property as a public good that is non-rival and non-excludable Arrow (1962) highlighted that these characteristics distinguish knowledge from traditional economic resources, with non-rivalry meaning knowledge is not depleted by use and non-excludability indicating it is difficult to prevent others from benefiting Kaiser (1960) pointed out that knowledge spillovers can arise from failures in protecting generated knowledge within innovative firms Griliches (1992) further emphasized that investments in knowledge tend to spill over, facilitating commercialization by third parties who do not bear the full costs of access and implementation.
Knowledge spillovers can occur either voluntarily or involuntarily, depending on the type of knowledge involved (Romer, 1990) Knowledge is generally classified into codified knowledge, which can be protected through mechanisms like patents, and tacit knowledge, which resides in employees' skills and is harder to safeguard Because tacit knowledge is embedded in routines and experience, it is a primary source of involuntary knowledge spillovers, facilitating the dissemination of vital information across agents.
Firms can access external knowledge through various channels, including market transactions such as purchasing knowledge assets from other organizations or forming formal collaborations Additionally, due to the non-rival and non-exclusive nature of knowledge, firms can gain external insights without formal transactions, tapping into sources like customers, suppliers, and competitors For example, understanding customer needs and expectations helps identify market opportunities and emerging trends (Hippel, 1976), while knowledge embedded in goods from suppliers or competitors also serves as a valuable external knowledge source.
Most of studies on knowledge spillovers made distinguish between
Knowledge spillovers are categorized as ‘horizontal’ or ‘vertical’ based on the relationship between the firms involved ‘Horizontal’ spillovers occur when knowledge sharing happens within the same industry, while ‘vertical’ spillovers happen across different industries, involving firms from different business fields Importantly, firms require a certain level of absorptive capacity—defined as their ability to recognize, absorb, and utilize external knowledge—to effectively access and implement external knowledge (Cohen and Levinthal, 1989) Developing strong absorptive capacity is essential for firms seeking to benefit from both intra- and inter-industry knowledge spillovers.
Knowledge spillover is a crucial concept in both economics literature and public policy, highlighting how externalities and social increasing returns drive economic growth According to Griliches (1992), the "New" growth economics emphasizes that sustained, undiminished economic growth is unlikely without significant externalities or spillovers These knowledge spillovers serve as vital catalysts for innovation and productivity, underscoring their importance in fostering long-term economic development and informing effective policy strategies.
Recent research confirms that the spillover effect, in conjunction with other externalities, plays a crucial role in determining whether an economy underinvests or overinvests in knowledge Strong spillover effects have led developed countries to heavily subsidize knowledge investments, fostering innovation and growth For developing countries, leveraging knowledge spillovers can significantly boost economic growth and development by enhancing their innovation capacity and technological progress.
Innovation is a multifaceted concept that varies based on the degree of novelty, the area of change, and measurement criteria It encompasses introducing entirely new products, such as goods unfamiliar to consumers, new production methods not previously tested in industry, or opening entirely new markets where specific domestic industries have not operated before Additionally, innovation involves acquiring new raw materials or semi-finished products and introducing new organizational practices within a industry According to the OECD (2005), innovation is defined as the implementation of a new or significantly improved product or process, a new marketing method, or a new organizational method in business practices, workplace organization, or external relations.
Innovation is distinguished from invention by its degree of novelty; while invention is often defined as a “global first” or “new to the world” and holds little value unless applied, innovation encompasses “new to the firm” or “new to the market” (Onodera, 2009) This distinction leads to two primary types of innovation: radical and incremental According to Barbieri & Álvares (2016), radical innovations often stem from inventions, models, proposals, or plans rooted in intellectual creation, whereas incremental innovations typically result from specific activities implemented without formal processes Continuous improvements represent ongoing incremental innovation, where terms like “improvement” and “incremental innovation” are often used interchangeably.
Innovation primarily refers to introducing new ideas or methods, with many definitions rooted in Schumpeter's (1943) approach According to Martin (2016), during the 1960s, innovation was predominantly understood as technology-based, especially in manufacturing sectors involving R&D and patenting, reflecting the economic dominance of manufacturing in developed countries Early definitions of innovation focused heavily on product and process improvements, highlighting the importance of technological advancements in driving economic growth and competitiveness.
Recent studies highlight the importance of organizational development and marketing strategies driven by innovation, as emphasized in OECD (2005) According to this manual, innovation involves introducing significantly improved products, processes, marketing methods, or organizational practices that can be new to the firm and enhance productivity and employment While innovation can take various forms, its core purpose is to drive business growth by implementing improvements that positively impact efficiency and market competitiveness.
Innovation measurement varies based on research objectives, chosen methodologies, and interpretations of what constitutes novelty According to Martin (2016), in the era of technology-driven innovation, particularly involving patenting, researchers have developed tools that utilize indicators such as R&D funding, the number of researchers, and patent counts Patent counts serve as a key metric in assessing regional innovation (Ponds et al., 2010; Capello & Lenzi, 2016; Wang et al., 2016) as well as firm-level innovation (Guan et al., 2015; Lin, 2015; Blazsek & Escribano, 2016; Li et al., 2017; Qiu et al., 2017).
Guan and Pang, 2017) and sectoral innovation (Buerger and Cantner, 2011). However, Martin (2016) argued that such indicators may be ‘missing’ much innovative activities that is incremental or is not patented.
2.1.3 Knowledge production function and the determination of innovation in this study
This study utilizes the knowledge production function (KPF), originally proposed by Pakes and Griliches (1984), to analyze the drivers of innovation and its determinants The KPF model helps to understand how various inputs contribute to the generation of new knowledge and technological advancements Pakes and Griliches (1984) provided a simplified diagram of the knowledge production process, highlighting the relationship between research inputs and innovative outputs By applying the KPF framework, this research aims to identify key factors that influence innovation performance across different sectors The findings contribute to a better understanding of how knowledge production impacts overall economic growth and technological progress.
Figure 2.1 The framework of knowledge production function
This diagram illustrates that the rate of knowledge creation (˙) is generated by a knowledge production function (KPF), which converts previous research investments (R) and a disturbance term (U) into new inventions The disturbance term captures the combined influence of additional nonformal R&D activities and the inherent randomness involved in the invention production process.
Pakes and Griliches (1984) explored the transformation function from input r to output, employing the Knowledge Production Function (KPF) model They assumed a Cobb-Douglas form for the KPF, allowing for firm-specific constants and incorporating a time trend to account for technological progress.
The model includes an independent and identically distributed disturbance term, denoted as ε_i, which is uncorrelated with r and captures the randomness inherent in the Knowledge Production Function (KPF) Firm-specific factors, represented by the coefficients a_i, account for variations in private productivity of research efforts, arising from differences in appropriability conditions, temporal opportunities, and managerial abilities These elements collectively influence the efficiency and outcomes of firm-level research activities.
Next, the relationship between p and �˙ was presented as follows:
Where � is the elasticity of patents with respect to knowledge increments, and d is a measure of the trend in factors determining the propensity to patent.
THEORETICAL FRAMEWORK
2.2.1 Developing model on Knowledge Spillovers at sector-level
Given the limited prior empirical and theoretical research on the relationship between R&D, FDI, trade activities, and innovation at the sector level, we focused on developing a functional form that effectively links these variables within our dataset to better understand their interconnections.
Cohen and Levinthal (1989) constructed a model of firm’s stock knowledge as follow:
A firm's innovative capability is primarily determined by its stock of technological and scientific knowledge, which is enhanced through investments in R&D The firm's ability to absorb and utilize external knowledge—its absorptive capacity—is represented by the fraction of publicly available knowledge that it can assimilate and exploit Additionally, intra-industry spillovers and the level of knowledge generated from outside the industry further contribute to innovation, with intra-industry spillovers indicated by the parameter ther, and extra-industry knowledge represented by T Overall, continuous investments in R&D and effective assimilation of external knowledge are crucial for maintaining a competitive edge in the industry.
� j for j≠i also contribute to �i This model implies the intra as well as inter sectoral knowledge spillover among sectors.
We defined Zs to be the total output of knowledge in the sector s: Zs
Similarly, Ms is the total input of knowledge in the sector s: �� =∑ � �i. (2.21)
In the industry, N represents the total number of firms operating within the market Among these, n denotes the subset of firms actively engaged in innovation activities, while m refers to those involved in R&D efforts Understanding these distinctions helps analyze the level of innovation and research investment across the industry, providing insights into firms' commitment to technological advancement and competitive dynamics.
Then the average knowledge stock in the industry is �̅ = Z�
The average R&D investment in the industry is � ̅ = ��
We assume that � i = �̅ + ði and �i = �̅+ ∂i
Besides intra-industry spillover, Griliches (1992) suggested the more complicated spillover when a whole array of industries which “borrow” different i=
The ith industry acquires knowledge from diverse sources based on their economic and technological proximity, ensuring relevant and applicable insights Griliches (1992) quantified this process by representing the total amount of aggregate knowledge borrowed by the ith industry from all available sources, highlighting the importance of knowledge exchange in industry development and innovation.
Where Kj measures the levels available sources of knowledge while wij is the
“weighting” function that can be interpreted bas the effective fraction of knowledge in sector j by industry i One of earlier suggestions was based on
The "vertical" borrowing approach (Griliches, 1992) utilizes input-output tables to measure the "closeness" of industries, which is proportional to their mutual purchases This study aims to examine rent knowledge spillovers both within and outside a sector, distinguishing them from pure knowledge spillovers by focusing on those embodied in transaction goods Consequently, employing the transaction matrix derived from input-output tables is the most appropriate method for capturing these spillovers.
According to Griliches (1992), the amount of aggregate knowledge borrowed by the ith industry from all available sources is represented by T in equation (2.39), which corresponds to T in equation (2.38) T can be understood as the total technological and scientific knowledge accumulated across all sectors, highlighting the interconnected nature of industry knowledge transfer.
Based on equation (2.40), we derive the relationships illustrating the direct and indirect impacts of knowledge spillovers resulting from R&D, FDI, and trade activities within our model These insights highlight how innovation and international economic activities influence knowledge diffusion and economic growth, emphasizing the importance of fostering collaborative R&D efforts, attracting foreign direct investment, and promoting trade to maximize positive spillover effects Understanding these mechanisms enables policymakers to design strategies that enhance technological progress and economic development through effective knowledge transfer channels.
In the equation (2.41), the left-hand side express the percentage of firms in the industry having innovation activities This percentage may depend on the R&D
Investment in the industry is reflected by the percentage of firms engaged in R&D activities, highlighting the industry's reliance on research and development This dependence underscores the direct impact of R&D efforts on fostering innovation within the sector, as detailed in the methodology chapter Incorporating R&D investment metrics is crucial for understanding the driving forces behind industry innovation and competitiveness.
2.2.2 Channels of knowledge spillovers and the research hypothesis of the first objective
The percentage of firms engaged in innovation activities within an industry is influenced not only by internal factors but also by technological and scientific knowledge spillovers from other industries, as outlined in equation (2.41) This knowledge transfer often stems from R&D investments across sectors, meaning that the innovation capacity of a sector is indirectly affected by the R&D activities of other industries Griliches (1979) emphasizes that industry knowledge is derived from both internal R&D and knowledge borrowed from external sectors, highlighting that the productivity of a sector depends on the R&D investments of other industries Consequently, the study hypothesizes that R&D activities have a direct impact on innovation within a sector and an indirect impact through inter-industry knowledge spillovers.
H11: The research and development (R&D) in the sector i may have positive impact on its sectoral innovation.
H12: The sectoral innovation in the sector i may be positively affected by the R&D from other related sectors.
Technological and scientific knowledge within an industry is derived not only from R&D investments but also significantly influenced by vertical linkages of FDI transactions and the embodied knowledge gained through trade activities Prior research highlights that technological progress results from a synthesis of internal R&D efforts and external knowledge flows, including foreign direct investment linkages and international trade, which facilitate the transfer and diffusion of innovative capabilities across industries.
According to Engelbrecht (1997), international trade and foreign direct investment (FDI) are the most effective channels for transferring external knowledge and foreign technologies across countries Activities like importing, exporting, and FDI facilitate the establishment and maintenance of communication channels that promote cross-border learning This process enhances understanding of production methods, product design, organizational practices, consumer preferences, and market conditions, thereby driving innovation and economic growth.
Foreign direct investment (FDI) significantly enhances knowledge spillover effects, as supported by studies from Jaffe (1986), Jaffe et al (1993), and Keller (2002) Due to the critical role of geographical proximity in facilitating knowledge transfer, FDI often serves as a more vital source of externalities compared to international trade Hofmann and Wan (2013) identified various channels through which FDI promotes knowledge spillovers, including horizontal (intra-industry), vertical downstream (inter-industry), vertical upstream (inter-industry), and labor market-based spillovers, highlighting the multifaceted nature of FDI-driven knowledge externalities.
Hofmann and Wan (2013) highlight that horizontal externalities from FDI impact domestic firms through four channels: competition, imitation and adoption, labor turnover, and second-round effects via input suppliers Multinational enterprises (MNEs) can exert competitive pressure, forcing local firms to innovate and potentially lose market share MNEs also possess advantages in technology, management, R&D, and human capital, which can lead to knowledge spillovers through imitation and adoption of products, production methods, and market strategies, exemplifying the demonstration or learning-by-watching effect described by Wang and Blomström (1992) Additionally, FDI facilitates labor turnover effects, where domestic firms benefit from hiring former MNE employees, and information exchange occurs through social contacts The second-round effects involve domestic firms gaining from improved inputs and intermediate goods suppliers, often driven by the requirements set by MNEs, ultimately stimulating local industry development.
Vertical externalities from FDI can significantly impact domestic firms across various industries through both backward and forward linkages Backward vertical externalities occur when multinational enterprises (MNEs) as suppliers influence domestic customer firms, potentially leading to technology transfer and increased demand for high-quality inputs Conversely, forward vertical externalities arise when MNEs, as customers, affect domestic suppliers by demanding better products, providing technological assistance, or offering managerial training The rising import demand from MNEs can create economies of scale for local suppliers, fostering positive externalities through knowledge sharing and improved input quality, ultimately boosting domestic industry competitiveness.
Markusen and Venables (1997) developed an analytical framework to assess the effects of industrial linkages, highlighting how foreign direct investment (FDI) can influence supply and demand across related industries They found that while FDI may increase competition and potentially harm local industries, such competition can also be advantageous to firms in other sectors by encouraging innovation and efficiency For example, FDI can lead to price reductions, benefiting consumers and stimulating further industry growth.
EMPIRICAL STUDIES
2.3.1 Empirical Studies on determinants of sectoral innovation
There are three main unit levels in investigating determinants of innovation.
Sectoral-level studies are fewer compared to national and firm-level research (Buerger & Cantner, 2011; Chamberlin, Doutriaux, & Hector, 2010; Piqueres et al., 2015; Malerba et al., 2013) The determinants of sectoral innovation are explained through various theories, including R&D mainstream theory, industry lifecycle, evolution theory, and sectoral innovation systems Malerba et al (2013) investigated national and international intersectoral R&D spillovers and their impact on innovative activity in six large industrialized countries from 1980 onwards, emphasizing the role of R&D spillovers in driving sectoral innovation.
Piqueres et al (2015) confirmed the importance of all determinants influencing sectoral innovation capacity, highlighting the role of industry lifecycles, evolution theory, and sectoral innovation systems They developed a comprehensive sector innovation capacity determinants model that includes factors such as previous innovation experience, innovation infrastructure, and sectoral structure, emphasizing their combined impact on fostering innovation within industries.
Due to the lack of sector-specific data, most studies on sectoral innovation rely on firm-level variables to generate sectoral indicators Common approaches include measuring sectoral innovation through firm patent counts (Capello and Lenzi, 2016; Piqueres et al., 2015; Malerba et al., 2013; Li et al., 2017; Lin, 2015), innovation counts (Castellacci, 2008), or the value of product sales (Hashi & Stojcic, 2013) A straightforward method involves calculating the number or proportion of firms within a sector that engage in innovation activities, such as holding at least one type of innovation (Aiello and Ricotta, 2015) or adopting innovation practices outlined by the Oslo Manual (Piqueres et al., 2015).
To assess the robustness of sectoral innovation proxies, Piqueres et al (2015) employed factor analysis to construct a comprehensive sectoral innovation variable Additionally, other studies have examined sectoral innovation by utilizing weights based on the significance level of innovation within each sector (Capello and Lenzi, 2016) These methodological approaches enhance the accuracy and reliability of measuring sectoral innovation across different contexts.
Constructing spillover variables is a complex process that includes various types such as R&D spillovers, innovation spillovers, and FDI spillovers (Autant-Bernard and Lesage, 2011; Moralles & do Nascimento Rebelatto, 2016; Malerba et al., 2013; Chen & Guan, 2012; Chen et al., 2015; Kováč & Žigić, 2016; Tian, 2016; Wang & Wu, 2016) A straightforward method involves summing spillovers without weighting; for example, Tian (2016) calculated intra-sector FDI spillovers based on foreign presence within a sector and inter-sector spillovers considering foreign presence outside that sector To capture the nuanced interactions, researchers often weight spillovers by linkages, similarities, or geographical proximity among sectors or firms Goya et al (2016) distinguished between intra-industry spillover innovation, measured by total R&D stock within a sector, and inter-industry spillovers, which are weighted by intermediate purchases between sectors derived from input-output relationships.
Various methods are used to investigate determinants of sectoral innovation, ranging from OLS regression to multi-level analysis Multiple regression, often combined with two- or three-stage least squares (2SLS or 3SLS), is commonly employed in this research (Chen et al., 2015; Kováč & Žigić, 2016; Lee, Kim, & Lee, 2017; Luo, Guo, & Jia, 2017) When the dependent variable is a count, such as patent filings, studies frequently utilize Tobit or negative binomial regression models (Lọpple et al., 2016; Buerger & Cantner, 2011; Castellacci, 2008) Additionally, research focusing on the geographical characteristics of sectors often applies spatial regression techniques to account for spatial dependencies (Autant-bernard & Lesage).
Several studies have examined the combined impact of sectoral and regional factors on firm innovation, often utilizing multi-level regression analysis to capture the complex interactions between these levels (Guan & Pang, 2017; Aiello & Ricotta, 2015).
Research on sectoral innovation highlights that certain determinants are not related to spillover effects, as confirmed by Piqueres et al (2015), Chamberlin, Doutriaux, & Hector (2010) Multiple studies demonstrate a positive relationship between R&D and sectoral innovation, with Autant-Bernard & Lesage (2011) emphasizing that both private and public R&D investments significantly boost innovation, while Kaygalak & Reid (2016) find that innovation processes in Turkey are more geographically concentrated than organizational proximity suggests Additionally, Malerba et al (2013) reveal that intersectoral R&D spillovers play a crucial role in innovation activities, with domestic R&D exerting a stronger influence than international R&D.
Most sectoral innovation research has predominantly focused on developed countries within Europe, particularly in the European Union (EU) Several studies examine specific EU countries, such as France (Autant-Bernard & Lesage, 2011), Germany (Bade et al., 2015; Buerger & Cantner, 2011), and Spain (Piqueres et al., 2015), while others analyze groups of EU countries (Castellacci, 2008; Hage et al., 2013; Malerba et al., 2013) Additionally, research has extended to non-European nations, including Turkey and Canada (Chamberlin et al., 2010), as well as Japan and Korea (Jung & Lee, 2010) Some studies investigate broader groups of countries, such as six developed nations (Barcenilla-Visús et al., 2014) or ten Asian countries, highlighting the geographical scope of sectoral innovation analysis.
Research on R&D and knowledge spillovers primarily focuses on developed countries such as the United States (Blazsek & Escribano, 2016; Capello & Lenzi, 2016; Hashi & Stojcic, 2013; Lin, 2015) and France (Autant-Bernard & Lesage, 2011) Studies also explore innovation spillovers across Asian countries, including ten nations (Chen & Yang, 2011), as well as in Spain (Stare & Damijan, 2015) Additionally, a significant body of research examines FDI spillovers in China, highlighting the country's dominant role in this area (Li et al., 2017; Liu et al., 2010; Qiu et al., 2017; Tian, 2016; Wang & Wu, 2016; Zhang, 2017).
Research on sectoral innovation, innovation spillovers, and R&D spillovers in Vietnam is limited, with most studies focusing on firm-level analysis (Jordan, 2015; Doan Thi Hong Van & Bui) These focused investigations highlight the need for broader research into the systemic impacts of innovation across industries within the Vietnamese economy Understanding the dynamics of innovation spillovers is crucial for fostering sustainable growth and improving national competitiveness Further studies are essential to explore how sectoral innovation influences economic development and to inform policy decisions that promote R&D activities across sectors.
Le Nhat Uyen, 2017; Tran Thi Bich et al.,2017; Srholec, 2011; Nham Tuan et al, 2016; Nguyen Ngoc Anh et al., 2008) By questionnaire survey from November
Between 2015 and 2016, studies involving 500 firms across Hanoi, Danang, and Ho Chi Minh City using a 5-point Likert scale identified six key determinants of innovation: awareness of innovation, innovation strategy and policy, organizational support, human resources for innovation, and absorptive capacity (Tran Hoai Nam et al., 2017) Research on 380 enterprises in sectors such as electronics, microelectronics, ICT, telecommunications, precision engineering, automation, biotechnology, and nanotechnology revealed that innovation capacity is positively influenced by total quality management, internal human resources, absorptive capacity, government support, and collaboration networks (Doan Thi Hong Van and Bui Le Nhat Uyen, 2017) Moreover, regional analysis by Jordan (2015) highlighted that firms in the Red River Delta, including Hanoi, are more likely to engage in product innovation or improvements compared to firms in other Vietnamese regions Further, Nham Tuan et al (2016) demonstrated that process, marketing, and organizational factors positively impact the performance of supporting industries in Hanoi Lastly, Nguyen Ngoc Anh et al (2008) found that innovation—measured by new products and new production processes—significantly affects export performance in Vietnamese SMEs.
Enhancing existing products is a crucial factor driving exports among Vietnamese SMEs Additionally, the study reveals evidence of endogeneity in export activities, which may have caused biased estimates of the impact of innovation in previous research.
2.3.2 Empirical Studies on channels of knowledge spillover and applications of Spatial Regression Model
Numerous studies have explored the impact of regional knowledge spillovers on innovation and economic growth, highlighting that advancements in communication technology have diminished the influence of spatial distance on knowledge diffusion (Huggins and Thompson, 2015; Lōpple et al., 2016; Ponds et al., 2010; Shang et al., 2012) A primary source of knowledge transfer is through research and development (R&D) activities, which have been shown to significantly boost firm innovation and productivity (Raymond and St-Pierre, 2010; Wieser, 2005) The effects of R&D spillovers among firms have garnered increasing scholarly attention, with foundational research by Griliches (1979) on R&D returns paving the way for numerous subsequent studies (Bloom and Reenen, 2007; Kovac and Zigic, 2016; Khazabi and Quyen, 2017; Savin and Egbetokun, 2016; Yang and Maskus, 2001) Additionally, research has examined R&D spillover impacts at regional (Rodriguez-Pose and Villarreal, 2015) and national levels (Ang and Madsen, 2013; Tientao et al., 20xx), emphasizing the broad relevance of knowledge diffusion across geographic boundaries.
Malberba et al (2013) examined the impact of R&D spillovers on innovation activity across various levels, including national, international, intra-sectoral, and inter-sectoral scales Their study focused on six industrialized countries over a specific period, highlighting how knowledge flows influence technological advancements within and between industries globally Understanding these spillover effects is crucial for fostering innovation and enhancing competitiveness in advanced economies.
1980-2000 Moralles et al (2016) found the positive R&D spillover effects among industries in Brazil.