INTRODUCTION
Problem statement
International economic integration fosters the establishment of new firms and the entry of foreign companies into domestic markets, intensifying competition To gain a competitive edge, firms must innovate continuously, responding to the limited life cycle of products and the increasing demands of consumers Research by M Banbury and Mitchell (1995) indicates that frequent product innovation correlates with improved performance and a higher likelihood of long-term survival for both small and large firms (Vermeulena, De Jong).
Innovation is crucial for the survival of small firms, as emphasized by Freeman and Soete (1997), who stated that "not to innovate is to die." This significance has led to extensive research aimed at identifying the factors that influence product innovation within these companies (Martinez-Ros, 1999; Jong, 2006; Fritz, 1989; Vega-Jurado et al., 2008; Hadjimanolis, 2000; Freel, 2000).
Achieving improved or new products involves more than just investing in R&D; various factors significantly influence innovation activities Key elements include firm size, production technology (measured by the ratio of sales to fixed assets), ownership origin, and investment in market insights (Avermaetea et al., 2004) The impact of these factors on innovation varies across different economic sectors, highlighting the need for a model to simulate their effects.
Despite numerous international studies on this topic, there is a scarcity of research focused on Vietnam As a developing nation, Vietnam can experience significant increases in sales and market share through moderate technological advancements (Hadjimanolis, 2000) Therefore, it is crucial to develop a model tailored to the Vietnamese context This paper aims to fulfill that objective.
Vietnamese enterprises and policymakers can benefit from the findings of this paper Enterprises can use the proposed model to enhance product innovation by focusing on specific elements and their required control levels Similarly, policymakers can leverage this model to understand the key factors influencing each industry, enabling them to make informed decisions that support innovation within those sectors.
Research objectives
The study is expected to figure out determinants with significant impact on product innovation activity of Vietnamese SMEs and influential level of each factors.
To achieve optimal performance in product innovation, business leaders must comprehend the interplay between internal and external factors affecting their products By aligning their strategies with the unique characteristics of their environment, they can effectively adapt and enhance their innovation efforts.
Research questions
This study explores two key questions: first, it identifies the significant indicators that influence the product innovation behavior of firms; second, it examines the extent to which these factors affect product innovation.
Scope of the study
The study examines Small and Medium enterprises across Vietnam with data extracted from SMEs survey 2011.
Structure of the study
This paper consists of five chapters, beginning with an Introduction that outlines the issue and its background, followed by the research objectives Chapter II, the Literature Review, establishes a theoretical and empirical foundation for the topic, defining innovation and product innovation while exploring the determinants of product innovation activities and measurement methods Chapter III details the Research Methodology, including the methods used, data description, and analytical models employed In Chapter IV, Findings and Discussions, the paper analyzes regression results and the marginal effects of various factors on the probability of product innovation Finally, Chapter V concludes with recommendations for policymakers and firm management on strategies to enhance product innovation rates.
LITERATURE REVIEW
Innovation
Innovation activities, as defined by Hyvarinen (1990), encompass both internal and external efforts within firms aimed at developing new products and improving existing ones, including processes, governance, and marketing strategies These activities primarily stem from research and development (R&D), although firms can also acquire innovations through licensing, seminars, consultants, and customer or supplier input When technology is viewed broadly, beyond just products, it significantly contributes to the growth and development of small and medium-sized enterprises (SMEs) (OECD, 1982).
II.1.2Popular indicators of innovation activity in SMEs:
Three key proxies are commonly used to measure innovation: input, output, and impact These indicators can be analyzed both generally and specifically, with measurements often expressed in numerical or rate formats Inputs, such as time, capital, and labor, are preferred for assessing innovation effectiveness due to their ease of collection and accuracy The output indicator focuses on the direct results of innovation, including creation, expertise, and technology absorption, with patents serving as a typical proxy, although they may not apply universally across all industries Lastly, impact indicators assess the broader qualitative outcomes of innovation, examining the relationship between innovation and changes in sales, capital, productivity, and development within a firm.
( Harrison& Hart, 1987;Kamien & Schwartz, 1975; Meyer-Krahmer,1984; Walsh, 1984 and Scholz ,1988)
The table below describe three indicators of innovation when they are examined based on four dimensions including technology, individual, enterprise, market/ environment.
Table 1: The innovativeness indicators for SMEs in different dimentions
* Adoption and/or development of ideas
Growth Profit Better strategies Innovations Improved know-how Skills
Success Improved value added Equity increases Enlarged-market Better image Rewards Development of enterprise Internationalization
Development of branches New enterprises
Know-how Economic growths Diffusion of innovation
Innovation can be classified based on four key criteria: time, influencer, market, and technology Firstly, innovations are ranked by their newness, ranging from basic to applied and incremental innovations In the context of small- and medium-sized enterprises (SMEs), this classification highlights aspects such as the degree of newness, response delays to environmental changes, and the time-dependent nature of the innovation process Secondly, innovations can be categorized by their influencer, distinguishing between those driven by entrepreneurs and those initiated by firms Additionally, within organizations, there are five types of innovation: product, process, marketing, organizational, and social innovations Finally, an innovation is considered new to the market if it represents a significant amendment or advancement.
II.1.4 Characteristics of innovation activities in developing country
Typical features of developing countries related to innovation activities are listed as below:
There is a significant lack of effective policies that promote innovation and support organizations responsible for managing technology While some institutions, such as high-tech industrial zones, investment firms, and suppliers of technological materials, may exist, their capabilities are often underdeveloped This inadequate innovation system at the national level hinders the technological advancement of the entire economy (Fontes and Coombs, 1997).
Consumers often lack trust in the quality of innovative products made by domestic manufacturers Additionally, the limited size of the local market results in low demand for these innovative products, further diminishing their significance (Fontes and Coombs, 1997).
The economy is primarily composed of small firms, with only a limited presence of medium and large enterprises This industrial structure, as suggested by the theory linking firm size to innovation, does not foster an environment conducive to innovation Unlike developed countries, there is a lack of complementary relationships between small and large firms, which further hinders innovative activity.
• The cooperation between firms and research institutions, universities is not very tight There’s not much new techniques transferred from these organizations to manufacturing firms (Jones & D., 1996)
Innovation often stems from adapting techniques originally developed in advanced economies Therefore, fostering an open economy that facilitates connections with foreign nations is essential for driving innovation.
II.1.5 Comparison between large enterprises and SMEs based on indicator of product innovation
Innovation activity of firms is differed due to scale which is presented in Table 2 below.
Table 2: Comparison between large enterprises and SMEs based on indicator of product innovation
Resource: Staff, capital, market awareness, experience
Complex structure, application of innovation require complicated assessment
Simple structure, easy to apply innovation like new invention or improved process
Planning Well –prepared and inflexible Easy to adapt to market fluctuation
Flexibility Require formal project to apply innovation
Flexible and can react rapidly toward market fluctuation
Governance Final decision is not decided by one person
Difference in authorization between levels of staff is not much
Source of information Well aware of government‘s documents
Unofficial information plays a key role in some case.
Adaptation Disapprove of new idea, just create basic innovation
Easy to approve to use small, new unique idea
Level of innovation Moderate, not radical innovation
II.2.1 Definition of product innovation
Product innovation encompasses various activities aimed at creating and introducing new or significantly improved products to the market This process can involve inventing entirely new products, enhancing existing product quality, upgrading technical features, or incorporating new components and functionalities into established products.
1988) From another point of view, as long as the product is new to market, it will be considered as an innovation, even when alike products are already exist.
II.2.2 Classification of product innovation
Product innovation can be categorized into two main types: radical innovation and incremental innovation Radical innovation entails the development of entirely new products, involving a two-stage process that includes idea generation, product design, and technical preparation, followed by market analysis In contrast, incremental innovation focuses on improving existing products by enhancing their functions, technical value, and user-friendliness, typically employed during the decline phase of a product's lifecycle to extend its market viability (White, Braczyk, Ghobadian, & Niebuhr, 1988).
While many associate innovation primarily with groundbreaking changes, only a limited number of companies achieve this level of success Research by Wong (2014) reveals that over 80% of new products fail, and not all successful launches sustain consistent growth However, this is not a significant issue, as regular and incremental innovation on existing products has been shown to be more crucial than drastic changes In fact, since the 1970s, numerous industries globally have preferred product enhancements over the introduction of entirely new offerings.
Reviews of Related Theories
Neoclassical Economics was the foundational approach for economists and business managers to assess innovation activity, primarily viewing innovation as a basic production function without considering the unique characteristics of firms Schumpeter (1934) significantly advanced this field by analyzing the profound effects of technology on economic growth and exploring the relationship between firm characteristics and innovation, particularly focusing on firm size and market competition His work sparked considerable interest and subsequent research into technological advancements, highlighting the influence of external factors across various industries However, Schumpeter's study lacked a detailed exploration of other firm characteristics and the innovation creation process, indicating the need for further research to achieve a more comprehensive understanding.
Several theories have been proposed to clarify this concept, including transaction cost economics (Williamson, 1989), the positive theory of agency (Jensen & Meckling, 1976), evolutionary theory (Nelson & Winter, 1982), and the resource-based view of the firm (Wernerfelt, 1984).
Transaction cost economics examines the expenses associated with transferring products from sellers to buyers, encompassing three key phases: contact, contract, and control (Noote Boom, 1999) Contact costs involve the expenses related to searching for suitable products by buyers and the costs incurred by sellers in promoting their offerings to potential customers The contract phase entails the costs of negotiating specific terms to finalize the transaction Finally, control costs are associated with ensuring that both parties adhere to the agreed-upon terms and conditions outlined in the contract.
Transaction costs arise from three main factors: uncertainty regarding the behavior of both parties and the environment, specific investments needed for the operation of a product post-transfer, and information asymmetries These costs hinder the flow of technology between firms and researchers Therefore, it is advisable to internalize innovation rather than relying on third-party organizations for transactions.
Agency theory examines the conflicts arising between principals and agents, such as managers and shareholders, highlighting how information asymmetry can lead to inefficiencies (Jensen & Meckling, 1979) To mitigate these issues, the theory recommends limiting open corporations and controlling knowledge and technology transfers, as these can create divergent interests that undermine a firm's effectiveness Although networks are essential for firms to adopt new technologies (Tidd & Bessant, 2009), managing information asymmetry during the integration of external knowledge can be challenging Ultimately, agency theory suggests that firms may restrict technology investments to prevent potential information-related problems.
The evolutionary method examines the innovation development process within firms, positing that technological change is both regular and constant This approach highlights that technological advancements are cumulative, relying on historical progress (Pavitt, 1987) A key contribution of this theory is its focus on the varying technical capabilities of firms, which is essential for categorizing innovation processes (Dosi, Freeman, Nelson, Silverberg, & Soete, 1988).
The resource-based view theory emphasizes the importance of a firm's key resources in developing a competitive advantage, with innovation being a crucial asset that can be continuously accumulated and expanded (Wernerfelt, 1984) Innovation serves as a significant source of advantage, enabling firms to outperform competitors and address weaknesses It is both valuable and scarce, as it does not depreciate with use and requires substantial effort for successful transfer Due to constraints related to time and economies of scale, innovation is challenging to imitate or substitute However, the level of innovation within a firm is influenced by its specific characteristics, including capital, human resources, and governance structure, highlighting the need for a comprehensive analysis of these features to understand a firm's innovation activities.
The industrial organization model posits a linear relationship between technology and information, viewing technology as the bridge between science and innovation This perspective highlights that innovation is significantly influenced by external factors, with firms being shaped by their historical actions.
Reviews of Empirical Studies
II.4.1 Determinants of product innovation
Product innovation can be analyzed through two primary categories of determinants: internal and external factors Internal factors include aspects related to the characteristics of the enterprise, such as its size, technical and professional staff, investment in research and development, and marketing expenditures, as well as the entrepreneurs' traits, including age and prior experience with the product Conversely, external factors encompass elements that influence firms from the outside, such as the intensity of competition, the strength of relationships, and the role of outsourced consultants.
In another study of Damapour (1991), different factors having influence on innovation activities are divided into three groups as follows:
• Enterprises’ human resource which include owner, manager, technical staff…
• External factors having interaction or impact on enterprise, such as competition level, network…
In 1990, King expanded the theory of innovation by introducing two key antecedents: national innovation policies and inter-firm linkages Yoshihara, in his 1976 study, highlighted the role of national economies, noting that environmental changes can create both threats and opportunities for innovation within firms He categorized the business environment into two distinct areas: the direct environment, which encompasses market demand, supply, customer preferences, and anti-business sentiments, and macro factors, including national and international economic conditions, political climates, education, technology, and population dynamics.
In sum up, determinants of innovation activities consisting the argument and measurement in relevant references are summarized as below:
II.4.1.1 Internal factor a.Characteristics of owner/ manager
Numerous studies have demonstrated that the characteristics of owner/managers significantly influence the innovation rates of firms Schumpeter (1934, 1942) was among the first to highlight the impact of entrepreneurs on innovation, a view supported by more recent research (Mascitelli, 2000) Owner/managers are tasked with analyzing market conditions and identifying the optimal timing for investments in advanced technologies (Fontes & Coombs, 1996) Additionally, their direct relationships with key stakeholders enable them to decide whether to implement innovative ideas from potential sources (Lipparini, 1994) In small businesses, the owner/manager's role in strategic decision-making is particularly crucial, unlike in larger firms where decisions undergo multiple levels of evaluation According to Drucker (1985) and Urban & Hauser (1980), the innovative behavior of small firms is more heavily influenced by individual characteristics, whereas larger firms' innovation is shaped by organizational attributes such as product type, capital, and investment Ultimately, individual innovation activities play a vital role in the overall innovation resources and organizational behavior.
Research suggests that the age of an entrepreneur may negatively impact innovation, as younger managers and owners are often more motivated to innovate due to their longer potential tenure with the firm (Diederen, van Meijl, & Wolters, 2000) However, Avermaetea et al (2014) present a contrasting viewpoint, arguing that possessing a degree in science or technology and having extensive experience within the firm are key indicators of innovative capabilities.
Research indicates that the accumulation of knowledge and experience is valuable, yet the relationship between a manager's experience and their innovative capacity remains inconclusive (Romijn and Albaladejo, 2002) The educational background and skills of entrepreneurs are crucial factors in this analysis, with some studies suggesting that postgraduate managers may be more inclined to innovate due to enhanced communication, social connections, and technical fluency acquired during their studies (Cohen & Levinthal, 1999) However, recent literature has not robustly supported this claim (Romijn & Albaladejo, 2002) Additionally, an entrepreneur's attitude towards innovation plays a significant role, as firms are more likely to explore innovative opportunities when their leaders are interested in advanced techniques.
The debate over the impact of firm size on innovation presents two contrasting viewpoints Schumpeter (1942) and Frits (1989) argue that larger firms foster innovation due to their substantial budgets, which enable them to invest in innovative activities and acquire new technologies, leading to the development of more innovative products Conversely, Fritz (1989) and Martinez-Ros (1999) highlight the advantages of small firms, noting that their less hierarchical structure allows for quicker responses to market changes While large firms may pursue options like mergers and acquisitions to enhance their competitive edge, small firms often focus on innovation as their primary strategy Additionally, the dominance of large firms in the market can result in a tendency to undervalue innovation efforts.
According to Symeonidis (1996), the size of a firm significantly influences its capacity for innovation Larger firms can absorb the substantial initial costs of research and development due to their higher sales volumes They also benefit from economies of scale in the production of innovative products and can leverage their diverse business interests to capitalize on sudden innovations Additionally, large corporations can mitigate the risks associated with investing in new products by diversifying across various industries Their size facilitates easier access to external capital, and their market power enhances their ability to effectively utilize innovations, making them more inclined to pursue innovative endeavors.
Acs and Audretsch (1988) demonstrated that the size of a firm significantly impacts its innovation activities, influencing both inputs and outputs Their findings reveal that the relationship between size and innovation is not linear; as a firm's size grows, opportunities for innovation increase However, beyond a certain point, further increases in size may lead to a decline in the rate of innovation (Kamien & Schwartz, 1982).
The relationship between firm size and innovation varies significantly across different industries A study by Kamien and Schwartz in 1982 found a notable connection only within the chemical sector Meanwhile, Mansfield's 1981 research indicated that while large firms conduct the majority of fundamental studies, smaller firms tend to invest more heavily in product innovation.
Empirical studies often examine the relationship between firm size and innovation activity across various industries, typically treating firm size as an exogenous factor Research indicates that innovation directly impacts firm growth, subsequently influencing the size of the enterprise Therefore, the size of a firm in a given year is assumed to be connected to its innovation activities in the previous year (Scherer, 1992).
Innovation activities may be linked to uncertain, serially correlated factors, suggesting that a firm's size is expected to correlate with these factors in a given year Consequently, regressing innovation on firm size could yield biased results Conversely, some researchers argue that innovation impacts firms over several years, indicating that the effect of innovation on firm size is lagged Therefore, endogeneity concerns may be less significant in this context.
When analyzing the impact of firm size on innovation, it is crucial to consider the potential correlation between firm size and industry-level factors, such as technological opportunities, which can positively influence innovation This correlation may lead to biased regression results when samples include firms from diverse industries To mitigate this issue, incorporating control variables for industry effects is recommended (Cohen and Levin, 1989) However, this approach may not fully address the problem, as many large firms operate across multiple industries A more effective solution is to categorize industries at a more granular level, such as the 2-digit code, although this introduces challenges since sectors within the same industry may exhibit significant differences in certain characteristics.
Research consistently shows that a firm's size significantly impacts its innovative activities However, the direction, characteristics, and analytical approaches to this relationship differ among studies Additionally, human capital and investments in training and marketing play crucial roles in enhancing a firm's innovation potential.
In addition to capital, the labor force is a crucial element of a firm's production function, highlighting the significant impact of human capital on business performance According to Avermaetea et al (2004), the skills of the workforce and the firm's investment in these skills play a vital role in driving product and process innovation, particularly in small food firms Investing in regular training and coaching for employees is a strategic decision that can enhance innovation and competitiveness in the future (Freel, 2000).
Traditionally, innovation was thought to be the domain of specialists and engineers (Tidd, 1997) However, recent studies have shown that it is actually the collective effort of the entire workforce that drives innovation (Clark & Fujimoto, 1991) While individual contributions may seem modest, their cumulative effect is significant Employees can engage in the innovation process through various stages, including opportunity discovery, idea generation, development, testing, and market introduction (Kleysen & Street, 2001) Overall, the involvement of all staff members in new product development is crucial and undeniable.
RESEARCH METHODOLOGY AND DATA
Data and sample
This study analyzes data from a 2011 SME survey conducted in Vietnam, involving 2,552 firms The survey was executed by the Vietnamese Institute for Labour and Social Affairs (ILSSA) in collaboration with the Ministry of Labor, Invalids and Social Affairs (MOLISA) and the University of Copenhagen Data can be accessed through the official University of Copenhagen website The survey collected responses from managers and owners of SME firms across ten provinces, including Ha Noi, Hai Phong, Ha Tay, Phu Tho, Quang Nam, Nghe An, Khanh Hoa, Lan Dong, Ho Chi Minh City, and Long An Out of the total 2,552 observations, 1,418 had no missing values, which were used for the regression analysis.
This study focus on four major industries: food processing and producing, Textile
The footwear, mechanical, textile, and wood industries are key sectors in Vietnam, collectively representing 70% of the country's total export value A total of 1,418 observations were recorded, including 302 from the food sector, 102 from textiles, 240 from mechanics, 133 from wood, and 641 from other industries.
Variables and Measurement
The regression analysis incorporates various variables, categorized into independent and dependent groups Independent variables are divided into two categories: internal factors, which pertain to the characteristics of the firm, and external factors, which involve market features and outside sources of information.
Product innovation can be categorized into two distinct options: enhancing an existing product or developing a new one Each option is evaluated using dummy variables, which take on values of 0 or 1, indicating whether a product has been improved or created These two options are independent of each other, allowing a firm to pursue both radical and moderate innovations simultaneously, have no product innovation, or focus on just one type of innovation The details of this measurement are outlined in Table 3.
Description Measurement Acronym in stata
Firms have at least one product improved
Dummy:1, having improved product, 0 otherwise
Firms have at least one new product created
Dummy:1, having new product, 0 otherwise
III.2.2.1 Internal factor a Size: is expected to positively affect product innovation as large firms will have sufficient financial resource to invest on expensive R&D activity as well as on advanced technique and equipments There are several ways to measure this variable In the study of Avermaetea, et al.( 2004), size was measured by natural log of total workforce. Galende & Fuente (2003) used sales ( % mean sales of the sample) to quantify size of firms Fritz (1989) also used sales, but the total sales of firms in the last period to measure size In this study, size will be proxied by number of employee. b Experience with product: Romijn & Albaladejo (2002) stated that owner/ manager who has experience with product tend to come up with more innovation In the SMEs survey, owner/manager is questioned of whether establishing or working in another company which produces the same product before However, there are only 30 respondents answer this question so this variable is removed from the model. c Age: Age of entrepreneur is expected to have negative correlation with innovation In this study, age is actual age of manager/ owner in year. d Human resources: is represented by three variables : qualified technical staff, managerial and professional staff and managerial staff This factor is proxied by quantity of scientist and engineers employed in a study of Hadjimanolis(2000) and technical personnel in research of Meeus, Oerlemans& Hage (1999) and Avermaetea, et al (2004) As skill of staff and investment of enterprises for such skill is foundation for innovation activity, these variables are expected to be positively correlated with product innovation. e Training cost: as certified by Meeus, et al.(1999) and Avermaetea, et al (2004), training cost is anticipated to positively affect innovation rate As other studies, this element is measured by actual number of expenditure on training(Freel, 2000) f Origin of ownership: it is argued by many studies to increase innovative capability when firm's owner is foreigner and vice versa(Caves, 1982; Baldwin and Sabourin, 1999) However, data for this factor is not available in SME data Thus, it is not included in the empirical model. g Intensity of capital: this factor is usually proxied by investment spending on technology which is expected to be positively correlated with innovation However, data for such spending is not available in SME survey, thus factor is not present in the model. h R&D activity: In the research of Baldwin and Sabourin (1999), this determinant is represented by a dummy variable which take value of one when firm has R&D department and zero when firm doesn't have As measured by many other studies, R&D activity here is measured by expenditure on R&D This selection is partly due to availability of data and partly due to correction of continuous variable versus dummy variable (Huiban& Bouhsina, 1998; Grunert, Hartvig Larsen, Madsen, & Baadsgaard, 1996; Crepon, Duguet, & Mairesse, 1998). i Export orientation: In the study of Matinez (1999),export is proxied by revenue from export activity In this study, binary variable is utilized This variable takes value of 1 when firm export and zero when not.
III.2.2 External factors a) Competition level: in the paper of Baldwin and Sabourin (2000), competition level is measured by categorical variable representing number of firms in the industry with three value : 0 when there are under 5 firms, 1 when there are 6-20 firms and 2 when there are over 21 firms In another study of Matinez-Ros (1999),level of market concentration is measure by firm's profit over gross profit of the industry According to availability of data, in this study, competition is measured by scale variable In SME survey, competition results from 5 rivals, each has four levels (from 1-4) To simplify, competition here is the average of five rivals in the survey Thus, competition take numerical value from 1-4. b) Outsourcing: Although the finding of Martinez-Ros (1999) and Rothwell & Dodson
In 1991, contrasting methods for measuring outsourcing through expenditure on outsourced services were established, and my research adopts this measurement approach Additionally, Freel (2000) identifies external assistance and networking as crucial factors, encompassing a firm's relationships with suppliers, subcontractors, customers, competitors, and academic or governmental institutions The strength of these relationships is quantified by the average number of contacts a firm maintains monthly.
This study investigates the relationship between external assistance and innovation, using the number of technical consulting firms as a proxy for external support and the total number of supplier, partner, and competitor firms as a measure of network engagement Additionally, market knowledge is assessed through marketing expenditure as a percentage of the firm's turnover, although a more precise measure would focus on market analysis spending Due to data limitations, total marketing costs are utilized instead While Whitley (2000) examined innovation across various countries, this study is confined to Vietnam and does not analyze the country environment Furthermore, to account for industry-specific variations in innovation, four binary variables representing Food, Textile, Mechanics, and Wood industries are included, with the reference category being 'other industries' when all four variables equal zero.
Table 4: Determinants of Product Innovation
Description Measurement Unit Expected sign Acronym in stata
1 Age Age of the owner/ manager of the firms who answer the survey
Exact year of age Year - Age
2 Size Number of fulltime employee
Fulltime employee firms hired each year
3 Qualified technical staff The number of technical staff Qualified technical staff as absolute number
The percentage of managerial and professional staff over total staff
Management and professional staff as absolute number
Managerial staff Management staff as absolute number
6 Training cost Expenditure of firms on training Expenditures on training activities as absolute number
7 Export Whether firms' products are exported or not
Dummy 1: having export activity 0: otherwise
8 R&D Cost of research and development of firms Absolute number of research and development cost
(supplier, buyer, owner, debtor) firms currently have regular contact with (at least once every 3 months)
Absolute number of people firms currently have regular contact with (at least once every 3 months)
10 Technical consultants The probability to render technical consultant Dummy: 1, relied on technical consultants; 0, otherwise
11 Competition Level of industry competition according to firm owner/ manager
In the survey, competition results from 5 rivals, each has four levels ( from 1-4).
To simplify, competition here is the avarage of five
40 rivals in the survey Thus, competition take numerical value from 1-4
12 Marketing cost Expenditure of firms on marketing over total sale Expenditures on marketing activities as absolute number
Expenditure of firms on outsource
Expenditures on services outsourced as absolute number
15 Textile Dummy:1, Textile industry, 0 otherwise
16 Mechanics Dummy:1, Mechanic industry, 0 otherwise
Analytical approach
III.3.1 Empirical model, estimation method
This study examines the dependent variable "innovation activity," which can either involve "improving existing products" or "creating new products." Since a firm may simultaneously enhance its products and develop new ones, or may not engage in any product innovation, the dependent variable has two levels: yes or no To analyze these nominal variables effectively, a bivariate probit model is utilized.
In this study, the bivariate probit regression model is preferred over the binomial logit model for measuring product innovation, as it effectively accounts for the relationship between two dependent variables While different types of innovation may exhibit distinct behaviors in response to various determinants, the improvement of existing products and the development of new products are expected to be interconnected Therefore, the bivariate probit model is utilized to analyze how both innovative options respond to explanatory variables, considering their interactions.
The underlying latent propensity variable Y*h represents the product innovation activity, which includes both improving existing products and creating new ones These propensities are closely linked to the observable characteristics of the firm and various other factors.
45 market related variables Xh, with unknown weights βh, and other unobserved characteristics εh Assuming a linear relationship, the population regression function is:
The h index measures two distinct forms of product innovation In its most basic representation, this concept can be expressed as a binary probit equation for each type of innovation, mapping the latent variable to its observable outcomes.
Assume that εh (h=I,N) jointly follow a bivariate normal disribution with mean zero and covariance matrix Σ That is (εI, εN)' ˷ BVN(0, Σ), where
As exact value of error term cannot be measured, we assumed Var(εN) ≡ 1 The model with such a feature is called bivariate-probit model.
Most of previous author applied single-equation techniques which assume that error term follows univariate normal distribution, with ρ hk =0 ( h,k = I,N ; h≠k)
The simultaneous improvement and invention of products can lead to correlated error terms in the equations governing innovation decisions The general specification provided accommodates the correlation of disturbances within the same firm across various innovation choices.
There are four possible outcomes:
Firm having both new and improved product: β N X '
Firm having new product and no improve product: β N X '
P = Pr[ New=1, Improve=1] P = Pr[ New=1, Improve=0] ∫
Firm having improved product, no new product:
Firm having no new or improved product:
Experience of entrepreneur with products
* Knowledge of market/ Marketing cost
3.1 3.1 Empirical model, estimation method
IV.1 Innovation activity of SMEs in Vietnam
According to the SME survey report by CIEM (2010), one-third of manufacturing SMEs are concentrated in ten provinces: Ha Noi, Phu Tho, Ha Tay, Hai Phong, Nghe An, Quang Nam, Khanh Hoa, Lam Dong, Ho Chi Minh City, and Long.
Small and medium-sized enterprises (SMEs) are divided into 18 sectors, with approximately 70% engaged in trade and services, 31.2% in industry and construction, and only 1% in agriculture, forestry, and fishing According to the General Statistics Office (GSO), SMEs in manufacturing and construction are crucial for the national budget, as they achieve high investment returns, utilize advanced technology, and employ a significant workforce.
Innovation index could be figured from data of SMEs survey The calculation reveals that innovation index in 2005 are quite high for all categories, compared with
The introduction of the Enterprise Law in 2005 significantly boosted the establishment of Vietnamese firms, particularly in the technology sector, by simplifying investment requests and providing equal market access This legislative change initially led to increased innovation; however, from 2007 to 2009, innovation activities among firms declined sharply due to budget constraints, a lack of capital, and diminishing market demand for technology Consequently, while the innovation index peaked in 2005, it fell in subsequent surveys, with diversification also hitting a low point during this period.
2007 and gets the peak in 2009.
Table 5 : Innovation Rates in Manufacturing SMEs
EMPIRICAL RESULT
Innovation activity of SMEs in Vietnam
According to the SME survey report by CIEM (2010), one-third of small and medium-sized enterprises (SMEs) in the manufacturing sector are concentrated in ten provinces: Ha Noi, Phu Tho, Ha Tay, Hai Phong, Nghe An, Quang Nam, Khanh Hoa, Lam Dong, Ho Chi Minh City, and Long.
Small and medium-sized enterprises (SMEs) are categorized into 18 sectors, with approximately 70% focused on trade and services, 31.2% in industry and construction, and only 1% engaged in agriculture, forestry, and fishing According to the General Statistics Office (GSO), SMEs in manufacturing and construction are crucial for the national budget, as they achieve high investment returns, utilize advanced technology, and attract a significant labor force.
Innovation index could be figured from data of SMEs survey The calculation reveals that innovation index in 2005 are quite high for all categories, compared with
The introduction of the Enterprise Law in 2005 significantly contributed to the growth of Vietnamese firms, particularly in the technology sector, by simplifying investment requests and providing equal market opportunities This legal framework initially spurred innovation; however, firms often chose to repair existing products instead of developing new ones due to budget constraints The innovation index peaked in 2005 but declined in subsequent surveys in 2007 and 2009, reflecting a notable decrease in innovation activities during this period This decline can be attributed to a lack of capital, limited market access, and a reduced demand for technology (CIEM, 2007) Additionally, diversification among firms reached its lowest point during this time.
2007 and gets the peak in 2009.
Table 5 : Innovation Rates in Manufacturing SMEs
Data description
Among 1418 observations, only 71 firms create new product, while number of firms improve their product is 602, which is accounting for 5% and 42.45% of total sample respectively The summary is as table 6.
Table 6: Number of observations with improved and new product
The interaction between two types of product innovation reveals four distinct cases among firms: 784 firms have neither new nor improved products, 39 firms engage in both types of innovation, 563 firms focus solely on improving existing products without creating new ones, and 32 firms develop new products while making no improvements Notably, the number of firms participating in both innovation activities surpasses those that only create new products, indicating that product improvement is generally easier than new product development Additionally, once firms successfully launch a new product, they typically also engage in product improvements Table 7 below illustrates the figures and rates for each of these four cases.
Table 7: Four cases of combination between improving existing product and creating new product
Descriptive statistics
Descriptive statistics of 1418 observations are summarized in Table 8 below:
Variable Mean Standard deviation Min Max
2 Dummy variable Frequency of 0 Frequency of 1 Min Max
In a study of 1,418 observations regarding the "Improve" variable, 602 instances indicated a value of 1, while 816 instances showed a value of 0 This results in an improvement rate of 42.45%, reflecting a positive outcome for small and medium-sized enterprises (SMEs) in developing countries like Vietnam.
In a study of 1,418 observations concerning the "New product" variable, only 71 instances indicated a value of 1, while 1,347 showed a value of 0, resulting in a mere 5% rate of new product development This modest outcome aligns with expectations, particularly in Vietnam, where a significant portion of the population is engaged in agriculture The findings suggest that substantial investment is necessary to foster innovation and the creation of new products in such a low-tech environment.
Regarding "Both" variable, among 1418 observations, there are 39 observation has
Only 2.8% of Vietnamese firms engage in both product improvement and innovation, indicating a significant lack of comprehensive innovation activities With an improvement rate of just 2.6%, it appears that many companies face challenges stemming from insufficient physical and human resources, hindering their ability to innovate effectively.
The average firm in the survey employs 22 workers, with the overall sample averaging 16 employees The employee count ranges from a minimum of 1 to a maximum of 496 Additionally, the average age of entrepreneurs is 43.5 years.
The average cost of outsourcing stands at 5.17 million dong, with a range from 0 to 75 million dong, indicating significant interaction among firms Notably, only 0.084% of firms engage in exports, which is understandable given that not all entrepreneurs can meet the necessary quality and quantity standards for international trade.
In terms of human resources, the maximum number of technical staff is limited to 25, while managerial staff is capped at 22 The average representation of technicians and managers among the total workforce is approximately 2% However, the total number of managerial and professional staff is slightly higher, with a maximum of 40 and an average rate of 13%.
Companies allocate an average of $1.16 million for training, $6.5 million for research and development (R&D), and $8.67 million for marketing Notably, spending on training and R&D accounts for 13% and 75% of marketing expenditures, respectively These statistics indicate that businesses prioritize marketing efforts over innovation activities.
Despite the average firm maintaining connections with 34 individuals, only 0.8% of those surveyed provide technical consulting services Additionally, the average competition level among these firms stands at 2.6 out of a possible 4.
The findings reveal a low positive correlation of 0.0579 between product improvement and innovation A bivariate-probit model is more appropriate for this analysis than a restrictive single equation approach However, due to the weak correlation, a multinomial model may also be utilized if the study does not aim to explore the interaction between these two variables This suggests that companies that enhance existing products do not necessarily need to develop new ones, and vice versa.
Table 9 : Correlation between some independent variables
Size Outsource Export Age Techsnic al staff
A significant correlation of 0.6% exists among three variables representing human resources and firm size Notably, a strong relationship of 0.9% is observed between Technical staff and MPstaff, indicating multicollinearity To address this issue, MPstaff will be excluded from the model, as Technical staff plays a crucial role in technology and directly influences innovation activities.
The bivariate-probit model was initially analyzed using all variables, including size, technical staff, and managerial staff However, the findings revealed that none of these three variables were significant at the 10% level This outcome raises concerns, as it suggests that at least one of these determinants should have a measurable impact on innovation.
The bivariate-probit regression is re-run with Technical staff removed and
The analysis indicates that managerial staff has a minimal impact on innovation compared to company size In the regression results, neither size nor managerial staff showed significance at the 10% level Upon re-running the bivariate-probit analysis with managerial staff excluded and technical staff included, the findings remained consistent, with both size and managerial staff still not significant at the 10% confidence level.
Technical staff, including engineers and technicians, possess specialized skills for operating production chains and addressing technical issues, yet they often prioritize machine maintenance over product development Similarly, managerial staff may not have a direct impact on innovation Consequently, the biprobit model was analyzed without the inclusion of either technical or managerial personnel The findings reveal that size is statistically significant at the 5% level, supporting its relevance in understanding the factors influencing product innovation.
Regression Results
Out of eleven determinants analyzed, only five—size, export, age, competition level, and network—significantly influence product activity at a 10% significance level, while four industry control variables were excluded from this analysis The regression results are detailed in Table 10 below.
Table 10 : Summary of results from bivariate - probit regression
Independent variable Coefficient Standard Error
Note: *, **, *** denotes statistical significance at 10%, 5%, and 1% respectively.
The Likelihood-ratio test indicates a significant correlation between two error terms (χ2=4.3), leading to the rejection of the null hypothesis of zero correlation The estimated correlation coefficient, rho (ρ), is 0.15, which is statistically different from zero, with a Prob > chi2 value of 0.038 This suggests that the error terms of the two equations are correlated, warranting the use of a bivariate-probit model rather than separate probit models for analyzing product innovation behaviors The subsequent sections will discuss the effects and significance of the independent variables on innovation.
Larger firms often experience greater innovation and product improvement due to their increased workforce, as suggested by Schumpeter (1942) This indicates a positive correlation between company size and the rate of innovation, with larger organizations benefiting from more resources and capabilities compared to their smaller counterparts.
Access to 56 financial resources enables firms to invest significantly in product improvements, leveraging their scale to reach more customers and gather valuable feedback Fritz (1989) supports this view, highlighting that larger firms benefit from favorable conditions for innovation However, this perspective contrasts with Martinez-Ros (1999), who argues that reduced competition may lead large firms to undervalue product innovation Additionally, Fritz (1989) notes that smaller firms often rely on innovation for competitive advantage, while larger firms may pursue alternative strategies like mergers and acquisitions Ultimately, the findings suggest that financial resources are crucial for Vietnamese firms, playing a vital role in driving their innovation activities.
Export activity is found out to positively affect product improvement This is in line with studies of Meisel & Lin (1983), Lunn & Martin (1986), Braga & Willmore (1991).
Age of entrepreneur: This finding is in accordance with opinions of many authors.
In general, owner/manager is supposed to figure out technological chances and manage processes of analyzing or applying such an innovation activity (Mascitelli, 2000), (Fontes
Entrepreneurs who directly manage their firms often face more challenges, leading to a greater focus on problem-solving (Coombs, 1996; Lipparini, 1994) In small businesses, the owner or manager plays a crucial role in strategic decision-making, unlike in larger firms where decisions undergo multiple evaluations Research indicates that younger entrepreneurs tend to be more innovative due to their longer commitment to their businesses and greater enthusiasm for new ideas, compared to their older counterparts (Diederen, van Meijl, & Wolters, 2000) While older managers may possess more experience and knowledge, the drive and commitment of younger entrepreneurs are often more significant factors in fostering innovation within firms.
Competition levels significantly enhance product improvement, as supported by various studies Researchers like Fritz (1989) and Arrow (1962) found that in highly competitive environments, companies are more motivated to innovate, as differentiation through unique and valuable products is essential for outperforming rivals Conversely, Abernathy (1982) argued that for smaller firms, focusing on product enhancement may not be the most effective strategy for gaining a competitive edge; instead, these firms might consider alternative approaches, such as price reduction.
Networking plays a crucial role in enhancing product innovation, particularly for small firms that often lack the resources to innovate independently These companies must seek external information and capital to drive their development (De Propris, 2002; Freel, 2000) In contrast, larger firms typically have the means to establish in-house R&D departments and hire top scientists and academics They can also afford costly experiments that may only be justified by large-scale production Conversely, small and medium-sized enterprises must collaborate with partners, suppliers, specialists, and customers to identify innovative pathways for their products (Hadjimanolis, 2000; Kim, Kwangsun, & Jinjoo, 1993).
Marketing serves as a crucial tool for understanding customer needs and gathering feedback, enabling companies to enhance existing products or develop new ones that align with customer requirements However, research shows that there is no significant correlation between marketing expenses and innovation activities This may be attributed to Vietnamese SMEs primarily allocating their marketing budgets towards product promotion and advertising, rather than conducting in-depth market analysis.
Research and development (R&D) costs are anticipated to significantly influence innovation within firms; however, findings indicate that the impact of R&D expenditure on innovation is statistically insignificant at the 10% level This outcome is understandable, given that out of 1,418 responses, only 24 firms reported R&D investments, with an average spending of merely 6.5 million VND Such a limited budget is inadequate for effectively driving innovation activities.
The analysis reveals significant indicators across all four industry sectors, highlighting a clear relationship between these sectors and product innovation activities Notably, all sectors, except for food manufacturing and processing, demonstrate higher innovation rates compared to uncategorized sectors This finding is surprising given Vietnam's rich food culture; however, it may stem from the underdeveloped food processing techniques among Vietnamese SMEs, which often sell agricultural products unprocessed to foreign markets In contrast, the mechanics, textile and footwear, and wood sectors, which require substantial capital and specialized skills, exhibit higher innovation rates due to intense competition that drives firms to continuously innovate their products.
The determinants of new product development show that no significant variables impact the creation of new products, indicating that innovation is not necessarily linked to a firm's conditions or external factors Instead, new products may emerge unexpectedly or through specific firm strategies unrelated to the identified variables A survey by Vermeulena, De Jongb, & O'Shaughnessyc (2005) highlights three primary reasons why firms may not introduce new products: customers often demand only standard products, firms struggle to find suitable innovative options, and financial constraints hinder their ability to invest in new product development These factors may contribute to unexplained error terms in the model, suggesting that there are no identifiable variables that account for the new product behavior of SMEs in 2011.
The calculation of marginal effects differs between categorical and continuous variables For dummy variables, the marginal effect indicates the discrete change in the probability of an outcome occurring, transitioning from zero to one with a one-unit increase in the explanatory variable, while keeping all other variables constant at their means In contrast, for continuous variables, the marginal effect measures the instantaneous rate of change, reflecting the absolute change in the independent variable resulting from a one-unit increase in the dependent variable.
To address the marginal effects arising from the bivariate probit model, we can compute both unconditional and conditional marginal effects The unconditional marginal effect, represented as Pr(New product = 1) and Pr(Improving = 1), assesses how the probability of each type of innovation changes independently of the other In contrast, the conditional marginal effects involve four specific scenarios: Pr(New product = 1, Improving = 1), Pr(New product = 0, Improving = 1), Pr(New product = 1, Improving = 0), and Pr(New product = 0, Improving = 0) These calculations reveal how the occurrence probability of each innovation is influenced when the other innovation is held at a specific value, either 1 or 0.
The regression analysis indicates a correlation between the error terms of the two equations Additionally, the conditional marginal effects are provided to demonstrate the interaction between the two options of the dependent variable.
All conditional marginal effects which is significant at level of 10% are listed in Table 11 below:
When a company adds an employee without producing new products, product improvement increases by 0.085% while keeping other variables constant For every 100 additional employees, the likelihood of product improvement rises to 8.5% However, for small and medium-sized enterprises with fewer than 500 employees, expanding the workforce to drive product innovation is often impractical due to associated costs and challenges Instead of focusing solely on employee numbers, firms can enhance their innovation potential by increasing their capital budget through various means, such as loans, issuing bonds, or stocks.
Marginal 5 effects
The study identifies five key factors—firm size, entrepreneur age, export activity, competition level, and networking—that significantly influence product improvement Firms engaged in international trade can enhance the likelihood of upgrading existing products by 13%, while increasing staff or network size by 100 can boost this probability by 8.5% and 14%, respectively A 1-point rise in competition pressure correlates with a 2.5% increase in product innovation The findings highlight the strong positive effects of export orientation and networking on firms' innovative behaviors, encouraging them to explore foreign markets and expand partnerships Although firm size has a modest impact, it remains a secondary option for enhancing innovation when other strategies are unavailable Interestingly, heightened competition fosters innovation, suggesting that firms facing more competitors may develop major revenue-generating products Additionally, empowering younger employees can drive innovation due to their commitment and fresh ideas However, the optimal extent of rejuvenating staff needs clarification Overall, the study offers valuable insights for firms seeking to enhance their current products.
The creation of new products is not significantly influenced by any identified factors, as key elements like customer demand and firms' risk tolerance have not been included in the current model due to data limitations Future research aims to address these gaps to better understand the dynamics of product innovation.
CONCLUSION AND RECOMMENDATION
Recommendation
This study not only offers insights for firms to boost their innovation performance but also recommends policies for governments and authorities to enhance the overall innovation capabilities of SMEs.
The Ministry of Industry and Trade (MIT) can enhance networking opportunities for firms by supporting the establishment and operation of industry-specific associations These associations will facilitate connections among producers and suppliers, enabling them to share valuable information on new techniques, experiences, and know-how to enhance product quality By fostering these connections, firms can explore cooperative ventures, including co-investment in capital-intensive technologies for advanced product development Additionally, MIT can organize conventions, fairs, and workshops, providing firms with further opportunities to collaborate, discuss, and innovate their products.
International trade fosters innovation among firms, and the Vietnam Trade Promotion Agency plays a crucial role in enhancing export opportunities for Vietnamese businesses By organizing trade fairs across various provinces, the agency promotes local producers and their products Additionally, its international offices provide timely updates on foreign market demands and facilitate connections between international buyers and Vietnamese sellers Continuing and expanding these initiatives will enable more local products to reach foreign consumers effectively.
Research indicates that firm size significantly enhances product innovation, with larger companies more effectively utilizing modern production methods to create advanced products In contrast, small and medium-sized enterprises (SMEs) often face challenges in accessing capital To address this, central banks should implement policies that facilitate capital borrowing for SMEs across diverse regions Additionally, more efficient microfinance regulations should be established to ensure that these firms can easily and effectively access the necessary funding.
To foster innovation among firms, it is crucial to maintain a competitive market, as monopolies can hinder product development by restricting market entry When monopolistic practices and government support are eliminated, companies are incentivized to innovate and improve their products to capture a larger share of the market.
This study recommends the implementation and continuation of policies aimed at fostering innovation within SME firms These initiatives are expected to improve firm performance while simultaneously boosting government revenue and enhancing overall societal welfare.
Limitation and Future Research
The current research is constrained by limited data, omitting key factors that theoretically impact product innovation, such as customer demand, firm ownership origin, and technical capital Future studies should conduct comprehensive surveys to incorporate these important determinants for a more thorough analysis.
Dependant variables are measured by binary variable which take two value of 1 or
0 To deeply analyze innovation activity, these variable should be measure by scale which separate innovation into different levels In such case, order probit model will be applied.
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REGRESSION RESULT OF BIVARIATE PROBIT MODEL
Seemingly unrelated bivariate probit Number of obs = 1418
Coef Std Err z P>|z| [95% Conf Interval]
Likelihood-ratio test of rho=0: chi2(1) = 4.30469 Prob > chi2 = 0.0380
MARGINAL EFFECTS IN CASE: FIRM HAS IMPROVED PRODUCT AND NO NEW PRODUCT
Marginal effects after biprobit y = Pr(Improve=1,Newproduct=0) (predict, p10)
= 39900699 variable dy/dx Std Err z P>|z| [ 95% C.I ] X
MARGINAL EFFECTS IN CASE: FIRM HAS NEW PRODUCT AND NO
Marginal effects after biprobit y = Pr(Improve=0,Newproduct=1) (predict, p01)
= 02109573 variable dy/dx Std Err z P>|z| [ 95% C.I ] X
MARGINAL EFFECTS IN CASE: FIRM HAS NEITHER NEW PRODUCT NOR IMPROVED PRODUCT
Marginal effects after biprobit y = Pr(Improve=0,Newproduct=0) (predict, p00)
= 55407808 variable dy/dx Std Err z P>|z| [ 95% C.I ] X
MARGINAL EFFECTS IN CASE: FIRM HAS BOTH NEW PRODUCT AND
Marginal effects after biprobit y = Pr(Improve=1,Newproduct=1) (predict, p11)
= 02581919 variable dy/dx Std Err z P>|z| [ 95% C.I ] X
21.8484 5.17509 083921 43.5987 1168.05 8.67489 008463 2.60273 6508.46 34.4154 212976 071932 169252 093794 (*) dy/dx is for discrete change of dummy variable from 0 to 1
MP staff 0.0427 0.1309 0.6969 0.0343 0.3551 -0.0882 0.9030 Training -0.0046 0.0436 0.0971 0.0390 0.1261 0.0141 0.1129 Marketing cost -0.0077 0.0259 0.3656 0.1532 0.1969 0.0199 0.2041 Techconsultant 0.0141 0.0297 0.0030 -0.0039 -0.0002 0.0065 0.0196 Competition level -0.0093 0.0357
Managerial staff MP staff Training Marketing cost Techconsultant Competition level