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Article

Antecedent and Consequence of Innovation Output: Evidence from Thailand

by
Muttanachai Suttipun
and
Krittiga Insee
*
Faculty of Management Sciences, Prince of Songkla University, Hat Yai 90112, Thailand
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(22), 9758; https://doi.org/10.3390/su16229758
Submission received: 23 September 2024 / Revised: 22 October 2024 / Accepted: 4 November 2024 / Published: 8 November 2024
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Abstract

:
The study aims to examine the impact of research and development (R&D) investment (the antecedent) on competitive advantage (the consequence) for the innovation output of private firms in Thailand. The population of this study includes all private Thai firms which have registered with the Office of National Higher Education Science Research and Innovation Policy Council. Path analysis and factor confirmation, including correlation matrix, are used to test the mediator effect of innovation output on the relationship between R&D investment and firm performance competitive advantage. The research data are tested and analyzed by the structure equation model. The results reveal that R&D investment was not found to directly influence competitive advantage, while R&D investment indirectly affects competitive advantage through innovation output. The findings of this study demonstrate that innovative output plays a mediating role between R&D investment and competitive advantage. In addition, dynamic capability theory can be used to explain the influences of R&D investment and innovation output on competitive advantage for private firms in Thailand.

1. Introduction

Innovation is essential for a business’s survival and growth, especially innovative output (Lee and O’Neill, 2003; Zahra and Covin, 1994) [1,2]. Innovation output is crucial to the business’s long-term viability and competitiveness (Geroski, 1993) [3]. In today’s world, business is highly competitive and demand is rapidly expanding. Furthermore, e-commerce systems enable consumers to quickly access products from around the world rather than being limited to consumption within their own country. Therefore, innovative output plays an important role in creating competitive advantage, product innovation focuses on creating markets and reaching customers, while the innovation process focuses on the business’s internal processes to increase efficiency (Turulja and Bajgoric, 2019) [4].
Although Thailand was upgraded from a lower-middle-income country to an upper-middle-income country in 2011 (The World Bank, 2011) [5], the country has not been able to progress to being a high-income economy country yet. Past studies in Thailand explain that the development of innovative products and services is a crucial factor in enhancing competitiveness (Yodudom, 2020) [6]. This focus on innovation helps businesses adapt to market changes, meet consumer needs, and ultimately drive economic growth. As a result, fostering a culture of creativity and research is essential for sustainable development in the country. The World Bank (2023) [7] reported that innovation in East Asia plays an important role in driving sustainable economic recovery. However, many countries in the region, including Thailand, are still lagging behind the innovation leaders. Thailand is in the second group together with Malaysia, Vietnam, and Mongolia, but only Malaysia is close to the top group (The World Bank, 2023) [7]. According to data from the World Intellectual Property Organization, Thailand ranked 43rd out of 132 nations in 2023. Based on statistics between 2020 and 2023, the ranking of Thailand in the GII 2023 was between 43 and 46 (WIPO, 2023) [8].
Information on research and development (R&D) investment in Southeast Asia and Central Asia reveals that R&D investment in developed countries is significantly greater than in emerging economies. While developed countries, such as Singapore, South Korea, and Japan, have the largest levels of R&D investment, emerging economic countries have much lower levels of R&D investment. In Thailand, an emerging economy, although there has been an increase in R&D investment during the last five years, it remains somewhat behind Singapore, South Korea, and Japan (Huda et al., 2020) [9]. Thailand has increased R&D budgets as a percentage of GDP as follows: 0.62 percent in 2015, 0.78 percent in 2016, 1.00 percent in 2017, 1.11 percent in 2018, 1.14 percent in 2019, and 1.33 percent in 2020 (UNESCO Institute for Statistics, 2023) [10]. A study on the factors contributing to Thailand’s potential for innovation revealed that Thailand is currently dependent on foreign innovation and technology (Yodudom et al., 2020) [6]. For Thailand to grow sustainably and independently, it is crucial that new ideas are generated to increase the nation’s level of competitiveness (Yodudom et al., 2020) [6].
Previous research on the relationship between R&D investment, innovation output, and competitive advantage has yielded varied results. In general, R&D investment tends to increase knowledge, which helps in the development of innovative products for the organization (Artz et al., 2010; Hall and Bagchi-Sen, 2002; Medda, 2020; Parthasarthy and Hammond, 2002; Sudirjo, 2023; Zhu et al., 2020) [11,12,13,14,15,16] These positive associations are most commonly found in studies in the United States and Europe (Artz et al., 2010; Medda, 2020; Parthasarthy and Hammond, 2002) [11,13,15], while negative associations have been found in Asia (Greve, 2003; Wang et al., 2010; Lin Xie, 2023) [17,18,19]. Some studies show a U-shaped relationship, meaning that R&D has a positive effect on innovation initially but decreases at a certain point (Artz et al., 2010; Lin and Xie, 2023) [11,18]. The opposite findings from other studies have shown no relationship at all or negative relationships (Greve, 2003; Wang et al., 2010; Un et al., 2010) [17,19,20]. Previous studies indicate that innovation output is a significant factor influencing competitive advantage in many industries (Aziz and Samad, 2016; Salim, Sulaiman, and Kuala, 2011; Turulja and Bajgoric, 2019; Kuncoro and Suriani, 2018; Udriyah et al., 2019) [4,21,22,23,24], including studies in Thailand (Chamsuk et al., 2017; Distanont and Khongmalai, 2020) [25,26]. This is because a business with innovation can grow by launching new products or services or having various patents registered (Kroll and Kou, 2019; Kammerlander and van Essen, 2017) [27,28]. The relationship between innovation output and competitive advantage can be explained by dynamic capability theory, which highlights how businesses develop new products and processes through innovation. It emphasizes the ability to harness resources and expertise from both internal and external sources (Helfat, 1997; Teece, 2007) [29,30]. Most studies indicate a positive relationship between innovation output and competitive advantage, particularly in Asia (Aziz and Samad, 2016; Salim et al., 2011; Turulja and Bajgoric, 2019; Kuncoro and Suriani, 2018; Udriyah et al., 2019) [4,21,22,23,24]. In contrast, research from non-Asian contexts often shows a negative impact of innovation output on firm performance and growth (Freel, 2000; Forsman and Temel, 2011) [31,32]. Research on the relationship between R&D investment and competitive advantage is limited, with results varying by context (Chamsuk et al., 2017; Guo et al., 2018; Liao and Tow, 2002) [25,33,34]. Liao and Tow (2002) [34] found that R&D can enhance competitiveness by developing unique products. However, high R&D costs can contribute to business failures, and some studies report a negative correlation between R&D investment and competitive advantage (Guo et al., 2018) [33]. Additionally, a non-linear relationship exists between R&D investment and operating results (Chen and Ibhagui, 2019; Siripong et al., 2019) [35,36].
From the research problems presented above, this study aims to determine whether R&D investment has a direct positive impact on competitiveness. This is because, despite an increase in investment, Thailand’s ranking in knowledge and technology output has declined (WIPO, 2023; UNESCO Institute for Statistics, 2023) [8,20]. Furthermore, the findings of this study indicate that R&D investment does not directly impact competitive advantage in Thailand, but rather has an indirect effect through innovation output. This suggests that innovation output plays a key role as a mediating variable in the relationship between R&D investment and competitive advantage.
This study will help to fill the research gaps and expand the results of studies on the relationship between R&D investment, innovation output, and competitive advantage in a Thai context. Secondly, the study aims to examine the role of innovation output because it is believed to have an important role in mediating R&D investment and competitive advantage. For example, if a company is unable to innovate its products or services to distinguish them from competitors, R&D investment will have no positive influence on competitive advantage because the investment in research and development is considered a cost to the business, but does not yield a competitive advantage (Guo et al., 2018) [33]. Past studies both in Thailand and abroad indicate that innovation output has a positive impact on competitive advantage. This study will help to increase knowledge and allow us to better understand the mediating role of innovation output.
This study makes both practical and theoretical contributions. For example, the study will contribute to a better understanding of the relationship between innovation output, R&D investment, and competitive advantage in Thailand and other emerging economic countries. Additionally, it provides the strategic direction for business research investment to increase competitiveness and transform Thailand’s economy into a sustainable innovation economy. In terms of theoretical implications, dynamic capability theory may be able to explain the influence of innovation output as a mediating variable between R&D investment and competitive advantage. In addition, the study results will be beneficial to companies who may employ R&D investment as a part of their strategy.
The following section reviews the theoretical perspective, the literature, and hypothesis development. Next, the research design is explained, including the population and sample, data collection, variable measurement, and data analysis. The findings and discussion and, finally, the study conclusion will be presented, including a summary, the practical and theoretical contributions, the limitations of the study, and suggestions for further study.

2. Literature Review

2.1. Theoretical Perspective

Dynamic capability theory is used to explain the process of corporate resource management to increase capabilities in order to adapt to environmental changes (Eisenhardt and Martin, 2000) [37]. Dynamic capability theory also refers to the corporate capability to generate and combine knowledge and resources from both internal and external sources (Teece, 2007) [30]. There are three characteristics used to characterize the nature of dynamic capabilities (Wang and Ahmed, 2007) [38]: adaptive capability, absorptive capability, and innovation output capability. Adaptive capability is the capability to identify and take advantage of emerging or new market opportunities, including the capability to alter the company’s product line in response to demands from customers and market prospects. Absorptive capability refers to the ability to evaluate and combine knowledge within the organization with information from outside the organization and to put it to good use, including learning new things from partners and integrating information. Finally, innovation output capability refers to a company’s ability to develop new products or markets through strategic innovation management with innovation output behaviors and processes. This is an important factor for the evolution and survival of the company in terms of external competition.
The relationship between R&D investment, innovation output, and competitive advantage can be explained by the dynamic capability theory because product demand is constantly shifting, and businesses are looking for the acquisition of a competitive advantage by changing their clients’ expectations. The study of bringing new goods or services to market in response to quickly shifting consumer demands is characterized by the link between investment in research and development and innovation output (Sudirjo, 2023) [14]. The process of responding to these challenges provides companies with an advantage over their competitors (Weerawardena and Mavondo, 2011) [39]. Therefore, dynamic capability theory is the most common theory used to explain the relationship between innovation output and the performance of companies (Turulja and Bajgoric, 2019) [4]. In addition, research in Thailand studied dynamic capabilities, whereby the competitive advantage and success of Thailand’s processed food business revealed the importance of the business’s dynamic ability to adapt to the competitive environment (Sakhonkaruhatdej et al., 2016) [40].

2.2. Innovation Output

Innovation output is defined as scientific and technological activities intended to improve products or services (OECD/Eurostat, 1997) [41]. Innovation output can be summarized as innovation which involves the creation of something new or the improvement of something old (Thornhill, 2006) [42]. Innovation output is often driven by R&D factors and human capital, which leads to knowledge creation. Innovation output can be divided into two main types: product innovation and process innovation (cost reduction) (Griliches, 1979) [43]. In Thailand, innovation is described by the Ministry of Finance as the integration of knowledge from science and technology with regard to the development of new products or creative procedures. In 2018, the Thai government enhanced awareness of innovation output under the banner “the year of innovation diplomacy” (NIA, 2015) [44]. Previous research on innovation output counted the number of patents and the volume of new product launches (Kroll and Kou, 2019; Kammerlander and van Essen, 2017) [27,28]. In addition, the study of Brouwer and Kleinknecht (1999) [45] determined the indicators of differences in innovation output and divided them into three types: (1) the main product is unchanged, (2) the main product depends on changes in technology, and (3) the main products have changed greatly.

2.3. R&D Investment

R&D investment is widely regarded as investments in business research and development activities. There are several ways in which investing in R&D differs from common investing. The salaries and compensation of researchers make up more than half of the investments used for research and development. The rest of the amount is investment in labs, equipment, test samples, and other research preparations. Research and development is an important part of knowledge development, technological advancement, and the creation of new products for the benefit of the business (Hall and Lerner, 2010) [46]. R&D investment can be classified into three dimensions: (1) business R&D support, (2) business knowledge developments, and (3) new high-risk business projects (Brown, 1972) [47]. In terms of R&D support, R&D investment is used to maintain or improve profitability by improving product quality or by reducing production costs. In terms of business knowledge developments, R&D investment will search for new business projects that involve a new product or new process. Finally, in terms of new high-risk business projects, R&D investment is focused on developing a new product, process, or market by using new technology-related projects. Compared to the other investment categories, R&D investment is more complicated. This is because it takes time for an investment project to yield the expected results, and the firm will not initially profit from the investment. These qualities could be detrimental to the company’s financial standing (Bremser and Barsky, 2004) [48]. In addition, most R&D investment is intangible in the form of knowledge and technology development, production process development, and high levels of compensation for specialists. These factors may make it difficult to assess the value of the business (Hall and Lerner, 2010) [46]. In Thailand, the businesses with R&D investment receive tax exemptions from the Ministry of Finance. The company or juristic partnership must undergo an assessment from the technology and innovation research and development management system and be registered by the National Science and Technology Development Agency or other agencies as specified by the Minister (Ministry of Finance, 2016) [49]. There are various methods to calculate R&D investment. The most common method of assessing R&D investment is to divide the total assets by the R&D investment (Kim et al., 2018; Vithessonthi and Racela, 2016) [50,51]. A proxy of R&D investment can be measured by R&D investment divided by operating income (Guo et al., 2018) [33], or R&D investment divided by total revenue (Lome et al., 2016) [52]. However, there is no study on R&D investment measured by the efficiency of R&D investment in the context of groups of companies with R&D activities using primary data collection methods.
R&D investment is a key component in the advancement of technological knowledge. A company’s level of R&D activities rises with higher R&D investment, and it also develops technological abilities that will help the business to develop new innovative outputs (Parthasarthy and Hammond, 2002) [15]. Therefore, the organization’s R&D investment is the cause of innovation output. Most of the R&D investment is allocated to knowledge creation and the creation of new, innovative products for the benefit of the business. Dynamic capability theory can be used to explain the relationship between R&D investment and innovation output (Helfat, 1997) [29] because the amount of R&D investment has a positive impact on the capability to create new products and services (innovation output). It also affects how many innovative outputs are patentable. For example, Parthasarthy and Hammond (2002) [15] found that a higher percentage of R&D investment has positive impacts on technology development and leads to a rise in the number of new products. Hall and Bagchi-Sen (2002) [12] found a positive relationship between R&D investment and a company’s innovation output patenting. These results are similar to Medda (2020) [13], who conducted research in seven European countries, including France, Germany, Italy, Spain, the United Kingdom, Austria, and Hungary, by analyzing 5500 manufacturing companies. The study revealed that R&D investment had a significant positive effect on product and process innovation output. Zhu et al. (2020) [16]’s recent study in China revealed that R&D investment generally has a significant positive effect on innovation performance. According to Sudirjo (2023) [14]’s recent research, product innovation is influenced by R&D investment in the Indonesian wood industry. Innovation empowers the industry to anticipate and address customer needs more efficiently by concentrating on research and making modifications to the external environment. In Thailand, Chamsuk et al. (2017) [25] investigated 220 firms in the automotive industry and found that R&D investment had a positive effect on innovation output. Nonetheless, some research results are different. For example, Wang et al. (2010) [19] found a negative relationship between R&D investment and innovation output for 210 private manufacturing companies in China. This is because innovation output may not result from increased R&D investment, but may come from the creativity of employees in the organization. A study by Un et al. (2010) [20] examined the impact of the relationship between R&D cooperation and product innovation among 781 Spanish manufacturing companies. The results of the study revealed that R&D cooperation had a negative impact on product innovation. In addition, Lin and Xie (2023) [18]’s analysis showed an inverted U-shaped relationship when studying the relationship between research and development and green technology innovation in the renewable energy industry. The research results revealed that there was an inverted U-shaped relationship between R&D investment and innovation, meaning that when research intensity is too high, it can have a negative impact on innovation. On the other hand, Greve (2003) [17], found no relationship between R&D investment and the innovation output of companies in the Japanese shipbuilding industry. This may be because these companies import innovation output from abroad rather than investing in R&D themselves. The research hypothesis is based on the previously described arguments.
H1. 
There is a positive influence of R&D investment on innovation output in Thailand.

2.4. Competitive Advantage

Competitive advantage can be viewed from many perspectives (Barney, 1991) [53]. To develop a competitive advantage, companies need to offer something beyond that of their competitors and be a market leader in the same industry as their competitors (Miles et al., 1978; Healy et al., 2014) [54,55]. Additionally, a corporate competitive advantage is important to increasing the business’s performance (Porter, 1997) [56]. Liao and Greenfield (2000) [57] defined competitive advantage in terms of six perspectives: overall higher value-added (OHVA), focus-segment higher value-added (FHVA), overall cost advantage (OCA), focus-segment cost advantage (FCA), overall differentiation (ODF), and focus-segment differentiation (FDF). In more detail, OHVA indicates that the business aims to create high-tech items with higher perceived value. FHVA shows that the corporation works hard to provide high-tech, expensive products that cater to markets. OCA indicates that the business wants to fill different market segments by offering items at the lowest feasible cost. FCA indicates that the business aims to supply goods at the lowest feasible cost to cater to a small market or clientele. ODF shows that the corporation tries to differentiate its products. Finally, FDF indicates that a business tries to set itself apart from the competition to cater to a particular consumer base or small geographic market. Moreover, Healy et al. (2014) [54] classified corporate competitive advantage into four dimensions: (1) differentiation (developing styles and guidelines for creating products that are different from other competitors in the same industry), (2) cost leadership (occurring when customers shop around for the best deal, and companies can provide high-quality products at competitive costs), (3) niche markets (the main goal to gain a competitive edge in target markets, by focusing on market sectors), and (4) quick response (giving prompt attention to customer needs). In addition, in prior related studies in Thailand, Healy et al. (2014) [54] has been applied to study competitive advantage for corporations and entrepreneurs (Sukglun et al., 2018) [58]. For this study, therefore, competitive advantage comprises four sections, following Healy et al. (2014) [54].
Prior related studies indicated that businesses spend money on R&D because they are impacted by competition that influences investment and strategic choices (McGrath and Nerkar, 2004) [59]. Corporate decisions to invest in R&D must be in line with their competitive advantage. Based on the previous related studies on the relationship between R&D investment and competitive advantage, most literature has revealed a positive impact of R&D investment on competitive advantage (Liao and Tow, 2002; Guo et al., 2018; Chamsuk et al., 2017) [25,33,34]. For example, Liao and Tow (2002) [34] found that R&D investment could increase and develop the competitive advantage of 220 high-tech firms in Japan. In China, Guo et al. (2018) [33] found a positive impact of R&D investment on product difference competitive advantage. Chamsuk et al. (2017) [25] found that R&D investment had a direct positive impact on the competitive advantage of companies in the automotive industry in Thailand. The findings of this research can be understood with reference to dynamic capability theory, which states that R&D affects businesses’ future directions by supporting the creation of new goods and markets. For the business to survive, adaptation is especially necessary to be able to sustain and improve its competitive advantage by being able to quickly create more sophisticated products to fulfill client needs. However, Guo et al. (2018) [33] found that R&D investments had a negative impact on cost leadership competitive advantage. This is because R&D investment is expensive for corporations, which can impact both performance and competitive advantage. In addition, Kim and Gu (2015) [60] found a negative impact of R&D investment on competitive advantage, which may explain why there is more competition in large economies, but less product differentiation; R&D investment might not always be beneficial. Although mixed results are noted above, most research shows a positive relationship. Therefore, the assumptions of this study can be formulated as follows.
H2. 
There is a positive direct influence of R&D investment on competitive advantage in Thailand.
It is essential for corporations to encourage innovation output to obtain a competitive advantage and long-term sustainable performance (Geroski, 1993) [3]. Making innovation output can result in a corporate competitive advantage. This is consistent with dynamic capability theory, which discusses the importance of adaptation for organizational survival. The innovative goods that result from utilizing new ideas demonstrate the organization’s flexibility and will give it an advantage in satisfying clients with regard to the quality of the goods or services (Salim, Sulaiman, and Kuala, 2011) [23]. Furthermore, enterprises must adjust and prepare for impending changes in the economy. Innovation is a tactical weapon in this crucial race to develop, launch, and grow enterprises to accomplish sustainable development and competitive advantage (Distanont and Khongmalai, 2020) [26]. Most prior related studies have found a positive relationship between innovation output and competitive advantage (Chamsuk et al., 2017; Distanont and Khongmalai, 2020; Kuncoro and Suriani, 2018; Salim et al., 2011; Udriyah et al., 2019; Zhu et al., 2020) [16,22,23,24,25,26]. For example, Udriyah et al. (2019) [24] found a positive impact of innovation output on competitive advantage for SMEs in the textile industry. Kuncoro and Suriani (2018) [22] studied the relationship between product innovation and sustainable competitive advantage in Indonesia and found that product innovation had a huge impact on sustainable competitive advantage. Aziz and Samad (2016) [21] studied the influence of innovation on competitive advantage in food-producing SMEs in Malaysia and found that innovation had a strong positive impact on competitive advantage. Innovation was found to contribute 73.5 percent of the competitive advantage. In Thailand, Chamsuk et al. (2017) and Distanont and Khongmalai (2020) [25,26] found that innovation output had a positive effect on the competitive advantage of firms. However, there are also studies that show opposite results. For example, a study by Freel (2000) [32] of 228 small manufacturing companies found that organizational innovation was strongly negatively related to profitability because creating something new can come at a high cost. Similarly, a study by Forsman and Temel (2011) [31] explained that innovative organizations do not have a positive effect on revenue or return on investment. Based on prior results, the hypothesis can be formulated as follows:
H3. 
There is a positive influence of innovation output on competitive advantage in Thailand.
When considering innovation output as a mediating variable in the relationship between R&D investment and competitive advantage, the results of most previous related studies have revealed a positive relationship between variables (Chamsuk et al., 2017; Guo et al., 2018) [25,33]. Dynamic capability theory can be used to explain how corporations can produce or acquire information and resources from both internal and external sources, and this results in the capacity to adapt to shifting environmental circumstances (Teece, 2007) [30]. This process can create new products and operations that are ready to deal with changing market situations and environmental changes (Helfat, 1997) [29]. Chamsuk et al. (2017) [25], for example, found that innovation output can be the mediating variable in the positive relationship between R&D investment and the competitive advantage of Thai companies in the automotive industry. The following formulation of the study hypothesis is made based on the previously described arguments.
H4. 
Innovation output mediates the positive influence between R&D investment and competitive advantage in Thailand.

3. Methodology

To examine the mediator effect of innovation output on the relationship between R&D investment and firm performance competitive advantage in Thailand, the analysis encompasses three industrial sectors: 1. manufacturing, 2. services, and 3. wholesale/retail. According to the database from the Office of National Higher Education Science Research and Innovation Policy Council, there are currently 9000 private companies in Thailand identified as being engaged in research and development activities. This total includes 3552 companies in the manufacturing sector, 1335 in the service sector, and 4113 in wholesale/retail. For data collection, we opted for a simple random sampling method for convenience. We distributed 735 surveys via email using Google Forms. Finally, 151 responses were complete and usable for data analysis, resulting in a response rate of 20.54%.
To determine the sample size, if considering general criteria, the minimum number of samples suitable for analysis using the parameter estimation method and Maximum Likelihood Estimation (ML) is between 100–150 samples (Ding et al., 1995) [61]. Furthermore, Structural equation modeling should have a minimum sample size of at least 100 samples to be appropriate (Hair et al., 2010) [62].
This study is a quantitative study using an email questionnaire. The mail questionnaire used in this study was adapted from the previous studies of Turulja and Bajgoric (2019), Parnell et al. (2012), and NXPO (2019) [4,63,64]. The email questionnaire has four main parts: (1) company demographics such as age of business, number of employees, and industry type; (2) innovation output, divided into two components: product innovation and service innovation (8 questions); (3) R&D investment (7 questions); and (4) competitive advantage, divided into four elements: differentiation, cost leadership, niche market, and quick response (20 questions). In the second to fourth part of the email questionnaire, a Likert scale with five levels (1 = lowest, 2 = low, 3 = medium, 4 = high, and 5 = highest) was used to indicate the level of innovation output and investment in research and development, which influence the competitive advantages of companies in Thailand. A summary of the data is shown in Table 1.
Structural equation modeling analysis is an examination of the consistency of the structural equation model developed by researchers from concepts, theories, literature, and research related to empirical data, including the calculation of the magnitude of both direct and indirect influences of the structural equation model. This study aims to examine both the direct influence of R&D investment on competitive advantage and the direct influence of innovation output on competitive advantage, and to examine the indirect influence of innovation output as a mediator in the relationship between R&D investment and competitive advantage. The structural equation model analysis has the following criteria for checking consistency:
This research organized the questions using Exploratory Factor Analysis (EFA), specifically employing Principal Component Analysis (PCA) with a single component without rotating the axes. The results indicated that the cumulative variance extracted was 32.72%, suggesting that this study did not encounter a Common Method Bias problem, as the values obtained from the questionnaire were below 50% (Eichhorn, 2014) [65]. This implies that the survey, which targeted a single respondent, did not exhibit bias stemming from the data collection method. Thus, the data can be considered valid for analysis according to the conceptual framework, and the responses from the questionnaire will be specified accordingly.
To check the validity of the measurement model, covariance-based criteria such as composite reliability and average variance were assessed. The standardized factor loadings were all above 0.45 (J. Hair et al., 1998) [66]. Cronbach’s alpha coefficients exceeded the cut-off point level of 0.70, and the average variance extracted (AVE) for all constructs was satisfactory (>0.50) (Bagozzi and Yi, 1988) [67]. In addition, the statistics indicate that the composite reliability (CR) of all scales was greater than 0.70 (J. F. Hair et al., 2010; Healy et al., 2014) [54,62]. The examination of the relationship between individual items and the overall set of instruments (Item–Total Correlation) should be greater than 0.30 (Hair Jr., Black, Babin, and Anderson, 2010) [62].
As shown in Table 2, the standardized factor loadings were all above 0.450 (J. Hair et al., 1998) [66], ranging from 0.521 to 0.880. The size of the average variance extracted (AVE) for each variable was also acceptable at the recommended value of 0.500. Composite reliabilities (CR) of constructs also ranged from 0.947 to 0.948, exceeding the recommended value of 0.600 (Bagozzi and Yi, 1988) [67]. Furthermore, Cronbach’s alphas showed satisfactory levels of reliability of internal consistency, ranging from 0.803 to 0.833 (Hulland, 1999) [68]. The discriminant validity of the constructs was assessed by using the square roots of the average variance extracted (AVE) (Fornell and Larcker, 1981) [69]. The analysis revealed that the items demonstrated discriminative power ranging from 0.310 to 0.822.

4. Findings and Discussion

Based on the 151 mail questionnaire responses, Table 3 presents the results for the demographic information. According to the results, the most common type of industry in this study was the manufacturing industry, with 115 respondents (76.16 percent), followed by the wholesale and retail industry, with 20 respondents (13.25 percent), and the service industry, with 16 respondents (10.59 percent). The most common number of employees was less than 100 staff (58 respondents, 38.41 percent), followed by between 100 and 499 staff (57 respondents, 37.75 percent), between 500– and 999 staff (19 respondents, 12.58 percent), and more than 1000 staff (17 respondents, and 11.26 percent). Finally, the average corporate age was 31.07 years (SD = 17.54).
Table 4 presents the descriptive statistics of the variables used in the framework of the study (mean, standard deviation, skewness, and kurtosis), as well as a correlation matrix (Pearson correlation). The means and standard deviations of the variables used consisted of INNO (mean = 3.440, SD = 0.687), RD (mean = 2.727, SD = 0.775), and CA (mean = 3.789, SD = 0.458), which are at high levels. Before the path analysis, a correlation matrix was used to find multicollinearity problems between the variables used in this study. An individual correlation should not exceed 0.800 according to Field (2015) [70], and the variables satisfied this requirement, with the highest Pearson correlation of 0.738 between INNO and CA. From the correlation coefficients between the three variables used in this study, there were significant positive correlations between INNO, RD, and CA at the 0.01 level.
Table 5 presents the findings of the structural equation model analyzed by the M-Plus Statistics Software Program version 7.4, which indicate that the model was a good fit with the data used in this study (chi-square/df = 117.287, df = 61, p < 0.000, CFI = 0.950; TLI = 0.936; RMSEA = 0.078; SRMR = 0.057). Moreover, all paths were significant at the 0.05 level (p < 0.05). All predictor variables, namely R&D and INNO, were able to explain 54.30 percent of the total variance in CA (see Figure 1).
The model was assessed in relation to H1 to H4 in Table 6. The path analysis from RD to INNO in H1 was supported (b = 0.584, sig = 0.001 ***), RD to CA in H2 was rejected (b = −0.026, sig = 0.768), INNO to CA in H3 was supported (b = 0.938, sig = 0.000 ***), and RD-INNO-CA in H4 was supported (b = 0.548, sig = 0.000 ***). Therefore, the hypotheses H1, H3, and H4 were supported and H2 was rejected in this study.
The finding of the positive influence of R&D investment on innovation output in Thailand in this study was similar to the prior related studies of Artz et al. (2010) [11], Chamsuk et al. (2017) [25], Hall and Bagchi-Sen (2002) [12], Medda (2020) [13], and Parthasarthy and Hammond (2002) [15]. This is because corporate R&D investment can boost the technological know-how necessary to produce unique and creative products and services (Parthasarthy and Hammond, 2002) [15]. Based on dynamic capability theory, innovation output is the process of developing new products and services from corporate R&D investment and activities (Helfat, 1997) [29]. Thus, the amount of money spent on corporate R&D has a positive impact on the capability to create and develop new products and services.
The results of this study do not indicate any influence of R&D investment on competitive advantage. This finding is different from prior related studies (Liao and Tow, 2002; Guo et al., 2018: Chamsuk et al., 2017) [25,33,34]. The reason that this relationship was not found may be due to the limitations of the study sample in Thailand. It revealed that companies in Thailand still have a low ratio of research and development expenses to the country’s GDP compared to developed countries, such as Sweden at 3.53 percent, the United States at 3.45 percent, and Germany at 3.14 percent (The World Bank, 2022) [73]. The study sample revealed a low level of R&D investment. This may result in not being able to clearly reflect the differences in research and development investments of the companies in the sample.
The result of the positive influence of innovation output on competitive advantage is similar to the studies of Aziz and Samad (2016) [21], Salim et al. (2011) [23], Udriyah et al. (2019) [24], Chamsuk et al. (2017) [25], Distanont and Khongmalai (2020) [26], and Kuncoro and Suriani (2018) [22]. It is essential for corporations to have innovation output to obtain a competitive advantage and long-term sustainable performance (Geroski, 1993) [3]. Creating innovation output can result in a corporate competitive advantage. This is in line with the dynamic capability concept, which highlights how important adaptation is to an organization’s ability to survive. Innovative products that come from applying fresh concepts show how adaptable the company is and will offer it a competitive advantage when satisfying customer expectations based on the standard of products and services provided (Salim, Sulaiman, and Kuala, 2011) [23].
Finally, the results of this study reveal that innovation output mediates the positive influence of R&D investment on competitive advantage in Thailand. This study is partially consistent with Chamsuk et al. (2017) [25], as innovation plays a partial mediation role in the positive relationship between R&D investment and competitive advantage. It illustrates why R&D capabilities are a crucial part of the process of innovation in a quickly shifting modern economic environment. Thailand’s businesses and entrepreneurs must conduct internal and external research and development to provide unique and imaginative products. In addition, the process of developing new operations and products allows businesses to obtain priceless and difficult-to-replicate assets that may provide them a competitive advantage (Barney, 1991) [53] and lead to an ability to cope with changing environmental conditions (Teece, 2007) [30] (Figure 2).

5. Summary and Suggestions for Future Study

The influence of R&D investment and innovation output on competitive advantage in Thailand is the primary research problem. The results indicate that R&D investment does not directly influence competitive advantage, but R&D investment has an indirect impact on competitive advantage through innovation output. Therefore, from the results of this study, innovation output plays the role of a full mediating variable in the relationship between R&D investment and competitive advantage.
This study’s findings have several theoretical and practical implications. The first theoretical contribution is that, through innovation output factors, the favorable impact of R&D investment indirectly influences competitive advantage and assists in determining the role that innovation output plays in shaping Thailand’s competitive advantage. The study findings indicate how utilizing the resources of an organization can improve its ability to adapt to a changing environment (Eisenhardt and Martin, 2000) [37]. The results can be used as a basis to explain the influence of R&D investment and innovation output in Thailand and other countries with similar environments to Thailand. Lastly, this study contributes to the body of knowledge about innovation output as a mediating factor in the relationship between R&D investment and competitive advantage.
Regarding the practical contribution, businesses that fund R&D investment have a significant impact on the output of innovation, according to studies. Businesses that make significant investments in R&D are more likely to produce more innovative products. Companies should therefore think about encouraging investment in R&D, whether it takes the form of recruiting outsiders, developing software for R&D investments in equipment, or hiring individuals specifically to increase the production of innovative ideas. Following that, a company should focus on building the organization’s policy for innovation, including both process and product innovation to provide goods and services that are separate from rivals in the same industry, and to concentrate on developing new items and refining those that already exist. Lastly, the findings of this study can be used to inform policy decisions or provide direction for promoting R&D in Thai private enterprises, including working with government departments to coordinate different policies.
However, this study has some limitations. First, there are only two independent variables used in this study: R&D investment and innovation output. There may be other variables that influence competitive advantage, such as the organization’s leadership style, the external environment, society and culture, and resource planning systems. Second, the study focuses only on Thailand. Finally, this research used a questionnaire method to collect data and required only one respondent, but the data in the questionnaire consisted of many parts. In addition, some of the information in the study was confidential to the business, resulting in a small number of responses to the questionnaire. In future research, it is advisable to include study variables such as organizational leadership characteristics and systems for resource planning to improve data gathering by asking multiple respondents to fill out only the questions that pertain to their roles and responsibilities.

Author Contributions

Conceptualization, M.S. and K.I.; Methodology, M.S. and K.I.; Formal analysis, K.I.; Investigation, M.S. and K.I.; Data curation, K.I.; Writing—original draft, K.I.; Writing—review & editing, M.S. and K.I.; Visualization, K.I.; Supervision, M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Thailand Science Research and Innovation (TSRI) has announced guidelines for conducting research involving human subjects in the fields of behavioral sciences and humanities, Section 3 states that research projects in human behavioral sciences, social sciences, and humanities that do not require approval from the institutional ethics review board include: (3) Research projects that involve designing surveys, interviews, or observations of research subjects that do not affect or involve the body, mind, cells, cellular components, genetic material, samples, tissues, bodily secretions, health, or behavior, and where individuals cannot be identified either directly or indirectly. Consequently, this research does not require the Human Research Ethics Committee approval based on this announcement. Announcement dated 18 March 2021.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Number of companies registered with the Office of National Higher Education Science Research and Innovation Policy Council.
Figure 1. Number of companies registered with the Office of National Higher Education Science Research and Innovation Policy Council.
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Figure 2. Results of the structural equation model.
Figure 2. Results of the structural equation model.
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Table 1. Variable measurement.
Table 1. Variable measurement.
VariablesSymbolMeasurement Scale
Innovation outputINNOFive-level Likert scale
R&D spendingRDFive-level Likert scale
Competitive advantageCAFive-level Likert scale
Table 2. Scale items and latent variable evaluation/construct validity of formative constructs.
Table 2. Scale items and latent variable evaluation/construct validity of formative constructs.
VariableItemsFactor LoadingCronbach’ AlphaCRAVEItem–Total Correlation
Innovation outputProduct innovation
Process innovation 		0.773
0.880		0.8030.9480.7310.635–0.739
0.430–0.747
R&D spendingThe firm has a policy of investing in R&D on a regular basis.0.7610.8860.9470.6940.302–0.822
The firm has expenses associated with R&D.0.821
The firm has expenses in R&D personnel.0.912
The firm has outsourced its R&D.0.521
The firm has expenses on land, buildings, and structures for R&D.0.668
The firm spends on software used for R&D.0.640
The firm has expenses on machinery and equipment for R&D.0.763
Competitive Advantage Differentiation0.7750.8330.9470.7500.310–0.682
Cost leadership0.787 0.361–0.601
Niche Market0.688 0.465–0.658
Quick Response0.731 0.357–0.605
Table 3. Demographical information (n = 151).
Table 3. Demographical information (n = 151).
VariablesFrequencyPercent
Industries
Manufacturing11576.16
Service1610.59
Wholesale and retail2013.25
Employees
<1005838.41
100–4995737.75
500–9991912.58
1000>1711.26
VariablesMeanSDMax.Min.
Business age31.0717.54100.002.00
Table 4. Means, standard deviations, bivariate correlations, and average variance extracted.
Table 4. Means, standard deviations, bivariate correlations, and average variance extracted.
Variables (n = 151)INNOR&DCA
INNO10.501 **0.738 **
RD-10.427 **
CA--1
MEAN.3.4402.7273.789
MIN.1.1251.002.40
MAX.5.004.5744.85
SD0.6870.7750.458
SKEWNESS−0.415−0.007−0.226
KURTOSIS0.357−0.4070.136
Note: ** is significant at the 0.01 level.
Table 5. Descriptive measures of overall model fit.
Table 5. Descriptive measures of overall model fit.
Fit MeasureModel FitAcceptable Fit *
1. χ 2 /df117.287/61/Sig 0.001 < χ2/df < 3
2. CFI0.9500.95 ≤ CFI < 0.97
3. TLI0.9360.95 ≤ TLI < 0.97
4. RMSEA0.0780.05 < RMSEA ≤ 0.08
5. SRMR0.0570.05 < SRMR ≤ 0.10
Notes: * The conceptual measurement model is consistent with empirical data. Hu and Bentler (1999) [71], Schermelleh-Engel et al. (2003) [72].
Table 6. Direct effect, indirect effect, and total effect, as well as hypothesis testing results.
Table 6. Direct effect, indirect effect, and total effect, as well as hypothesis testing results.
Hypothesized PathStd.Coef.SEp-ValueResults
Direct effect
H1RD → INNO0.584 ***0.0680.000Supported
H2RD → CA−0.0260.0880.768Rejected
H3INNO → CA0.938 ***0.0710.000Supported
Indirect effect
H4RD → INNO → CA0.548 ***0.0880.000Supported
Total effect 0.5220.0720.000
Notes: Significant *** p < 0.001.
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Suttipun, M.; Insee, K. Antecedent and Consequence of Innovation Output: Evidence from Thailand. Sustainability 2024, 16, 9758. https://doi.org/10.3390/su16229758

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Suttipun, Muttanachai, and Krittiga Insee. 2024. "Antecedent and Consequence of Innovation Output: Evidence from Thailand" Sustainability 16, no. 22: 9758. https://doi.org/10.3390/su16229758

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Suttipun, M., & Insee, K. (2024). Antecedent and Consequence of Innovation Output: Evidence from Thailand. Sustainability, 16(22), 9758. https://doi.org/10.3390/su16229758

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