The Influence of R&D Intensity on Financial Performance: The Mediating Role of Human Capital in the Semiconductor Industry in Taiwan

Abstract

Knowledge transfer is a strategy used by high-tech companies to acquire new knowledge and skills. Knowledge can be internally generated or externally sourced. The access to external knowledge is a quick fix, but the risks associated with reliance on external sources are often overlooked. However, not acquiring such knowledge is even riskier. There have been a slew of litigations in the semiconductor industry in recent years. The acquisition and assurance of intangible assets is an important issue. This paper posits that internal R&D should take into consideration the knowledge intensity and capital investment in the industry. This study focuses on the relationship between intangible assets and financial performance. It sourced the 2004 to 2016 financial data of semiconductor companies in Taiwan for panel data modeling and examined case studies for empirical validation. This study found that the higher the R&D intensity (RDI) in the value-added component of human capital, the better the financial performance of the company. RDI has a positive influence on the accumulation of human capital and financial performance metrics, and such influence is deferred. Meanwhile, human capital is a mediating factor in the relationship between RDI and financial performance. RDI is integral to the semiconductor industry’s pursuit of business sustainability.

1. Introduction

Knowledge is power. In the age of the knowledge economy, companies seek to quickly acquire new knowledge and competences via acquisitions, strategic alliances, patent licensing or technology transfers. However, this approach to pursuing innovations comes at the cost of control by others. The frequent occurrence of litigations associated with intellectual property infringements in top industries speaks of the importance of intangible assets. For example, MediaTek (a semiconductor company from Taiwan) was sued by ESS and Oak from the U.S. in 2003 and 2004, respectively. Another semiconductor company from Taiwan, United Microelectronics Corporation (UMC), sued Silicon Integrated Systems (SiS) for the infringement of its advanced processes. It eventually acquired SiS. In 2019, GlobalFoundries, headquartered in the U.S., sued Taiwan Semiconductor Manufacturing (TSMC) in the U.S. and Germany for the infringement of a few dozen patents, demanding that TSMC stop using the infringed technology in their manufacturing processes and refrain from selling the produced products. These litigations are strong indicators of the cost of failing to develop intangible assets internally. Paying for patents or taking a cut in profitability due to infringement expenses may still be manageable, but reputation may be at stake, the worst result of acquisition. In brief, it is risky to operate solely on externally sourced knowledge.

Jordão and Novas (2017) argued that the operating process of corporate entities is subject to the influence of two factors, i.e., knowledge management (KM) and intellectual capital (IC). In fact, these two factors are closely related. They are the catalysts of the innovation, competitiveness, value creation, financial performance, and sustainability pursued by companies [1]. IC is a new type of capital that provides new skills and competences for innovation [2]. Human capital (HC), as a component of IC, is the precondition and guarantee of a firm’s technological innovation [3]. R&D in the high-tech industry is an innovation activity. RDI is integral to the development of corporate sustainability. Its influence is one of the key factors because tech companies are in a constant state of transformation driven by technology and are at the forefront of innovation. These changes urge companies to continuously adjust their business structure and capital assets in response to competitors [4]. This is the case with the semiconductor and other tech sectors when it comes to the pursuit of new knowledge and technology. The global semiconductor market was valued at USD 412.221 billion in 2017, up 21.6% year-on-year. This growth was driven by the demand for smart electronics and artificial intelligence products [5]. The semiconductor industry in Taiwan is strongly connected with the supply chain in China and the U.S. The industry grew by just 3.56% in 2018 due to various macroeconomic factors [6]. According to a 2019 survey on the global semiconductor industry conducted jointly by KPMG and Global Semiconductor Alliance (GSA), innovations and R&D expansions remain the most important strategy for companies. However, the increasing cost of R&D is one of the biggest obstacles to further development. Talent risks are considered the greatest threat to growth [7]. The semiconductor industry is a multi-disciplinary technology domain, specifically a knowledge-intensive industry [8]. The construction of an organization is based on creativity and innovation. Given the rapid development of science and technology, large companies should gear their R&D management toward the internal circulation of knowledge. R&D projects in prominent industries require a cross-disciplinary approach and rapid development of new products and workflows. The creation of new knowledge and the resolution of complex problems in a fast-paced environment is challenging [9]. The economic policy in the European Union for the development of tech industries is closely related to the emphasis on R&D and human capital, which are the two key drivers of technological advancements, and human capital has a direct (not indirect) effect on innovation, making it important for regional growth [10].

In the field of economics, knowledge accumulation is the most important element of innovation [11]. Knowledge has become the most important strategic resource [12]. The accumulation of knowledge required by a company stems from the dynamic interaction between internal competences and external knowledge. External knowledge is one of the important sources for R&D and innovation activities [13]. It is necessary to effectively utilize the existing knowledge base and skillsets of management and employees to continue operations or enhance competitiveness. Under this circumstance, a detailed analysis of the functioning of human capital is in order. This is because human capital is one of the most important internal resources in a company and it is pertinent to its capability in innovation, profitability and competitiveness. R&D and human capital are key factors in the continued growth of a macro-economy [14]. R&D education and competences should serve as an internal mechanism to create value from an open sourcing strategy of new knowledge (information) [15]. Managers should enhance R&D investment and capacity, integrate/transcend the established external knowledge, lower the industry boundaries and enhance the absorption and transfer of knowledge going forward [16]. Corporate RDI drives knowledge transfer activities within a company and eventually orients it toward commercialization and profitability. In sum, companies, as the entity of innovations, need to absorb, extract, and apply new knowledge and technology, whether internally generated or externally sourced to bring such knowledge and technology in line with the operational status. Therefore, companies should emphasize the value adding options of intangible assets with internal RDI and human capital in the pursuit of innovation performance and corporate sustainability.

Hence, this paper seeks to explore the relationship between internal R&D activities, human capital, and financial performance in organizations from the perspective of knowledge transfer. A panel data model is constructed to analyze financial data, and a case study is conducted to verify the empirical findings with financial analysis. The research takes into consideration the following: (1) the deferred effects of R&D are defined as the knowledge transfer in a semiconductor company and the waiting period for the outcomes to be reflected in financial performance; (2) RDI is one of the major activities in knowledge transfer in semiconductor companies. It enhances overall knowledge and builds up technical momentum in an organization. Eventually, the benefit of knowledge transfer is translated into financial performance. This paper intends to explore the role of human capital (as an intangible asset) in the organization.

2. Literature Review and Research Hypotheses

2.1. Deferred Effects of R&D and the Influence of Knowledge Transfer on Financial Performance

R&D activities are a strategy of knowledge acquisition and learning [17]. The effects are widespread and they are seen at the corporate level, as well as in the economic performance of a country or a region. R&D investment is key to the increase in toplines and the establishment of a competitive edge. R&D activities represent a learning process [18]. This requires planning and purpose. Learning helps an organization to absorb knowledge, creating the competence to identify external knowledge. This allows for the combination of prior knowledge and skills and thus the application of integrated knowledge and skills. Knowledge absorption is the ability to internalize external knowledge [19]. This concept is relevant to the working and use of intangible assets, defined as knowledge materialized from knowledge assets. The latter is considered the key driver of corporate performance. Absorption capability is measured along with the company’s R&D costs.

Technology knowledge is implicit [20]. RDI is the investment required in the in-house knowledge generation required to mitigate the risks associated with the loss of technological competitiveness. Research indicates that R&D investments enhance a company’s learning ability [21]. The relationship between RDI and corporate performance develops in stages. R&D spending during the current period will have a negative impact on the organization’s financial performance in that period. However, R&D spending during the prior period has a positive effect on the sales during the current period. This is because R&D investment ignites knowledge transfer in a company. The allocation of budgets and personnel training creates a knowledge spiral. The benefits of use cases, manufacturing process optimization and new product launches take some time to reflect on financial performance. This is the deferred effect of R&D, also known as the deferred effect of knowledge transfer [22]. R&D costs are expended during the current period. How long it takes to reflect on financial performance will differ according to industry characteristics and knowledge thresholds. Given the high capital input, knowledge intensity and the requirement for multi-disciplinary expertise in the industry, the larger the semiconductor company, the more resources it has. This paper expects that the higher the RDI, the greater the benefit to production effectiveness or operating performance. R&D enhances the operating performance via labor productivity and yields [23]. The higher the level of R&D spending, the higher the production benefits in high tech domains. However, the influence on sectors which are not as technologically driven is limited.

Based on the above, R&D is a way to drive knowledge transfer within a company. It improves existing technology and accelerates the learning of new tasks. However, there is a time lag between R&D and innovation activities and the financial performance that materializes with knowledge transfer. Thus, this paper proposes H1:

Hypothesis 1 (H1).

Corporate RDI has a positive influence on the financial performance materialized with knowledge transfer and such influence is deferred in nature.

2.2. The Influence of RDI on Human Capital in an Organization

There is extensive literature on human capital. Market value can be divided into the tangible element of financial capital and the intangible element of intellectual capital, which is mainly categorized into human capital and structural capital [24]. Human capital is the aggregate of the knowledge owned by individuals, while the skillsets and competences of organizational human resources are the necessary knowledge base for entrepreneurship, innovation, and quality improvement [25]. Human capital is the collection of knowledge, skills, professional expertise, and experience held by employees and managers [26]. It is the resources owned by internal members to resolve problems and add value. Human capital consists of the knowledge, skillsets and experience required to offer customers products and services, establish core competitiveness and engineer solutions [27]. In brief, human capital is a production factor and the sum of knowledge, experience, skills, stamina, and capabilities. Manpower is comprised of knowledge and skills. It is the outcome of investments. Knowledge and skills constitute human capital.

Internal R&D and training activities serve as the catalyst for the accumulation of human capital. Internal training encourages employees to work extra hard, enhancing their commitment to the company [28]. For example, the accumulation of human capital via prevalent training programs leads to lower staff turnover, particularly with entry-level employees. If workflows are complex, companies are advised to embark on specialized training schemes (according to their operational scenarios) or step up RDI to develop professional human capital. This empowers employees to fully utilize their implicit knowledge. In fact, specialized human capital serves a greater purpose. By offering continued learning, companies fill the knowledge and skillset gaps in the workforce. This allows the ongoing accumulation and renewal of human capital and improves work motivation and attitudes [29]. Corporate training enhances the knowledge, competences, and attitudes of employees. It is an important investment in human capital. The value creation of knowledge enterprises is driven by human capital [30]. This knowledge creation not only encourages innovation, but also commercializes internal knowledge. It helps to improve overall costs and productivity. It is the most common and frequently used strategy in the accumulation of human capital. R&D activities create an avenue to a rich experience in development and innovation. This strengthens the organizational capacity to absorb internal and external knowledge. In short, the buildup of human capital is a growing treasure of corporate knowledge.

Organizational learning is the cornerstone of human capital upgrades in an organization. The increasing changes in external environments leads to greater uncertainty in the battle for corporate survival and development [31]. To enhance their ability to adapt to the external environment, companies must constantly improve organizational agility. Organizational learning has a positive influence on such agility because it can eliminate internal factors which conflict with external environments. Organizational learning is important to the stimulus and outputs of innovation. It helps to lay down the foundation necessary to the acquisition or generation of new knowledge and the momentum of human capital improvement. Knowledge transfer between organizations or among employees is beneficial to human capital enhancement. R&D programs improve human capital and accelerate the buildup of existing knowledge and technical competences, to create new knowledge [32]. The rollout and intensity of corporate R&D activities may be seen as a way of promoting learning and knowledge transfer among employees, as well as the level of emphasis on such learning and knowledge transfer. Xu et al., (2019) [2] propose that there is a close relationship between the capability and caliber of the employees in a company and the structure of its human capital. Employees can rapidly transform organizational knowledge via constant learning into business value, which helps the technological innovation of the company as a whole. Meanwhile, new knowledge and technology are continuously acquired via the pursuit of technological innovation. This is how HC is accumulated. It has a certain degree of positive influence on the sustainable development of a company as a whole [2]. Based on the above this paper infers that RDI has a certain level of influence on the enhancement of human capital and the development of corporate sustainability. Thus, this paper proposes H2:

Hypothesis 2 (H2).

Corporate RDI has a positive influence on the accumulation of human capital.

2.3. The Influence of Human Capital on Financial Performance

Human resource management, in the context of knowledge-based human resource management, can affect the intellectual capital within a company [33]. Human capital can mediate the dynamics between knowledge-based human resource management, relational capital and structure, and can create greater innovation performance. Human capital in an organization can be divided into two elements: investment and the result of human capital. The result of investment in human capital is knowledge and skills [34]. Companies may integrate existing or fragmented knowledge and skills by investing in human capital or by carrying out a new project. These two methods share the same purpose of increasing the knowledge inventory and building human capital that are proprietary to the company. The acquired new knowledge and skills are then applied to daily operations, products/services, and workflow innovations. This not only translates the employees’ knowledge and skills into actual output, but also transforms human capital into business strengths and competitive advantages. All these factors will eventually be reflected in financial performance [35]. Thus, this paper proposes H3:

Hypothesis 3 (H3).

The accumulation of human capital has positive influence on financial performance materialized with knowledge transfer.

2.4. The Mediating Effects of Human Capital in the Relationship between RDI and Financial Performance Materialized with Knowledge Transfer

In the context of knowledge transfer, R&D activities serve as the starting point for generating new knowledge and skills. The emergence of new knowledge leads to changes in the knowledge structure (knowledge needs and gaps). In this case, companies need to create a new process accordingly. R&D activities often serve as a means to enhance knowledge absorption or organizational learning. It is a learning process and method [36]. The process of organizational learning is to acquire and transfer new knowledge to promote continued change and the improvement of existing practices, knowledge, and skills. This is particularly noteworthy in the manufacturing industry. R&D enhances a company’s ability to absorb knowledge [37]. The impact on technological participation and business results depends on the caliber of technological involvement and human capital. R&D intensity and operating performance have a certain influence on the process of knowledge transfer. Organizational learning significantly benefits the employees’ creativity [38]. Alternatively, it is possible to establish a knowledge platform for the team to share or acquire practical knowledge within the organization. The creation and transmission of knowledge promotes learning via knowledge spillovers and dynamic information externality. The collection and the spillover effects of new knowledge benefits the accumulation of human capital [39]. In this way, employees can utilize knowledge to create new solutions, enhance efficiency and resolve problems in the organization. This will undoubtedly enhance the creativity of employees and R&D personnel. Therefore, R&D activities and operations should be oriented toward commercialization of knowledge. This means that there is a relationship between the requirements for new knowledge and skills, motivation, and organizational performance. There are different routes which may be taken, i.e., internal R&D spending, cooperation with external R&D parties and technology transfer, to obtain valuable new knowledge and technology to drive the accumulation of human capital in the organization.

Based on the above, this paper argues that R&D activities help to enhance financial performance materialized with knowledge transfer. In fact, this performance may result from R&D activities. In the process of knowledge transfer, the investment in R&D manpower and budgets will integrate fragmented knowledge and skills, accumulate the human capital in the organization and eventually enhance financial performance materialized with knowledge transfer. Thus, this paper proposes H4:

Hypothesis 4 (H4).

Human capital has a mediating effect on the influence of RDI on financial performance materialized with knowledge transfer.

3. Research Design and Empirical Analysis

3.1. Research Design

3.1.1. Data Sources

This paper samples the semiconductor companies listed on the Taiwan Stock Exchange and the Taipei Exchange and refers to the listed companies in the textile industry as the control group. Financial data from 2004 to 2016 (a total of 13 years) were sourced from the Taiwan Economic Journal (TEJ). Based on the variable definitions and the performance of individual companies in the research model, this paper eliminated companies whose data are incomplete or whose history is less than six years. A total of 120 semiconductor and 52 textile companies were sampled (172 in aggregate).

3.1.2. Model Variables

Before the model design, this paper defines the following variables.

• Dependent variable
  Financial performance materialized with knowledge transfer: return on assets (ROA) measures financial performance, innovation performance [40]. Equation: EBITDA/total assets.
• Independent variable
  RDI (R&D Intensity): R&D expenses and RDI are often used as the measurement of a company’s emphasis on R&D activities (knowledge absorption capability). RDI is a means to the exploration and acquisition of knowledge [40]. Equation: RDI_i,t during the current period (RDI = R&D expenses during the year/sales during the year). The symbol RDI_i,t-k denotes the deferred effect of RDI on performance. k represents the number of deferred periods.
• Mediation
  Human capital: This paper refers to the Value Added Human Capital Coefficient (VAHU^TM) [41] as the proxy variable for human capital. One example is a study in Thailand on the effect of R&D expenses on intellectual and human capital and the influence on financial performance in the manufacturing industry in 2006–2009 [42]. Value added is defined as net earnings plus wage expenses, interest expenses and income taxes [43]. The term VAIC^TM (Value Added Intellectual Coefficient) [41] was coined based on [24], which studied Skandia’s market value as driven by intellectual capital. The efficiency in added value creation by utilizing capital is calculated as VACA as expressed in Equation (1), and VAHU as expressed in Equation (2).

  Value Added Capital Employed Coefficient (VACA) = Value Added (VA)/Capital Employed (CE)

  (1)

  where CE = tangible assets + financial assets = total assets - intangible assets.

  Value Added Human Capital Coefficient (VAHU) = Value Added (VA)/Human capital (HU)

  (2)

  where human capital (HU) = wage costs = direct labor + indirect labor + wage expenses.
• Control variable

  (1)

  Firm sizes (SIZE):

  Large companies have more resources. This affects their operational model and financial performance. On the basis of return to scale, the benefit of R&D investments is contingent on the size of the firm [44]. This paper measures firm size with the natural logarithm of net sales.

  (2)

  Leverage Ratio (LEV):

  This is an important factor in the evaluation of firm performance and operational risk. Leverage ratio measures the effect of the capital structure on financial performance [45].

  Equation: (total debt/total assets) * 100%.

  (3)

  Gross Profit Margin (GPM):

  A high gross margin indicates strong competitiveness or product exclusivity. This creates higher earnings so that the company can spend on new knowledge development, training and education and product R&D. Equation: (Gross profits/net sales) * 100%.

  (4)

  Staff seniority (SS):

  In the context of human resource management, employees who have served long tenures are more willing to participate in R&D activities [46]. They reported higher knowledge application rates and demonstrated stronger knowledge absorption capability. In other words, they are more able to create higher profits for the company. The senior staff’s productivity curve gradually decreases over time [47].

  (5)

  Company’s History (CH):

  The history of a company affects how its professional knowledge has been built. Companies with a long history tend to be more conservative and standardized procedures restrict R&D activities. Their long history also presents more opportunities to accumulate resources. Equation: natural logarithm of (current year–inception).

  (6)

  Employee Fluidity (EF):

  Employees are one of a company’s key resources. Staff turnover is often used to evaluate personnel stability. Staff turnover affects firm performance, innovation, and other internal governance issues.

3.1.3. Methodology and Model Building

The first step is to conduct the Levin Lin Chu (LLC) [48], Im Pesaran Shin (IPS) [49], and Phillips Perron (PP) [50] unit root tests to validate whether the data are stationary (stable). Panel data techniques are used for quantitative analysis. Panel data are also known as longitudinal data. This paper combines panel data methods with cross-sectional and time series data techniques. Panel data analysis is the continued observation of a sampled company over time. Differing from multiple regression that only handles cross-sectional data or time series analysis that processes only time series data, a panel data model can conduct a dynamic analysis of a time series and accommodate the characteristics of different companies to avoid estimate bias. Panel data models can be divided into fixed effect and random effect models. A Hausman test is conducted first. If the Chow test rejects the null hypothesis, a fixed effect model is established. Otherwise, a random effect model will be established. The Chow test is a statistical and econometric test. It is often used to verify the structural change of a model in an empirical analysis based on a time series. According to the test results, this paper establishes a fixed effect model. A dummy variable is added to measure the influence of the unobserved variable on the model as an assessment of the differences between sampled companies. The fixed effect model is also known as the least-square dummy variable model. This paper incorporates, therefore, different intercepts for different sampled companies to control the constant quality latent that is not easily measurable. For example, management capability or other human resource management techniques serve as the dummy variable for different years in the model to control the effects across the years [51]. The symbol ${\mathsf{\epsilon }}_{\mathrm{t}}$ denotes the residuals during the time period t to validate whether there is autocorrelation in the residuals. Durbin–Watson (DW) statistics are used to determine whether the errors in the regression analysis are mutually independent. A value of between 1.5 and 2.5 implies that error terms are mutually independent and not auto-correlated [52]. If a delayed effect or a lag period is assumed, the unit root technique, ADF-Fisher [53], Chi-square and vector autoregression (VAR) lag order selection criteria are used to verify the lagged period. Finally, case studies are performed and analyzed to compare against the empirical findings.

Model Establishment: R&D investment (in terms of budgets and manpower) enhances the accumulation of human capital and affects firm competitiveness and performance. There are many difficult to quantify, fuzzy results in the process of knowledge transfer in high-tech companies. This involves the basic capabilities and knowledge absorption ability of employees, the level of knowledge thresholds and the scale of corporate resources. From the generation of new knowledge to the commercialization of products, phased investments are required to train employees, develop competences, try-and-test and integrate new and old knowledge and technology, and refine knowledge and skills. All these take time and cannot immediately be translated into performance. The result is deferred performance.

Deferred performance: as a factor is incorporated in the empirical model, where G_i denotes ROA, as the proxy variable of financial performance materials with knowledge transfer, of the i-th company during the t year; SIZE_i,t is the net sales of the i-th company during the t year; LEV_i,t denotes the leverage ratio of the i-th company during the t year; RDI_i,t-k denotes the R&D intensity of the i-th company during the t-k year; k denotes the deferred period; VAHU_i,t-1t represents the human capital of the i-th company during the t-1 year; GPM_i,t is the gross profit margin of the i-th company during the t year; SS_i,t denotes the employee seniority of the i-th company during the t year; CH_i,t denotes the operating history of the i-th company during the t year; EF_i,t is the staff turnover of the i-th company during the t year; β represents the coefficients to be estimated; α_i is the intercept of the i-th company during the t year, indicating the constant performance of individual company over time; and D_t is the dummy variable for the t year.

To validate whether RDI and financial performance materialized with knowledge transfer are positively correlated, Model 1 is established as expressed in Equation (3):

$${\mathrm{G}}_{\mathrm{i},\mathrm{t}}={ \mathsf{\beta }}_{1,\mathrm{t}}{\mathrm{SIZE}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{2,\mathrm{t}}{\mathrm{LEV}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{3,\mathrm{t}}{\mathrm{GPM}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{4,\mathrm{t}}{\mathrm{SS}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{5,\mathrm{t}}{\mathrm{EF}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{6,\mathrm{t}}{\mathrm{CY}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{7,\mathrm{t}}{\mathrm{RDI}}_{\mathrm{i},\mathrm{t}-\mathrm{k}}+{\mathsf{\alpha }}_{\mathrm{i}}+{\mathsf{\gamma }}_{\mathrm{t}}{\mathrm{D}}_{\mathrm{t}}+{ \mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(3)

To validate whether RDI and human capital are positively correlated, Model 2 is established as expressed in Equation (4):

$${\mathrm{VAHU}}_{\mathrm{i},\mathrm{t}-1}={ \mathsf{\beta }}_{1,\mathrm{t}}{\mathrm{SIZE}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{2,\mathrm{t}}{\mathrm{LEV}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{3,\mathrm{t}}{\mathrm{GPM}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{4,\mathrm{t}}{\mathrm{SS}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{5,\mathrm{t}}{\mathrm{EF}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{6,\mathrm{t}}{\mathrm{CY}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{7,\mathrm{t}}{\mathrm{RDI}}_{\mathrm{i},\mathrm{t}-\mathrm{k}}+{\mathsf{\alpha }}_{\mathrm{i}}+{ \mathsf{\gamma }}_{\mathrm{t}}{\mathrm{D}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(4)

To validate whether human capital and financial performance materialized with knowledge transfer are positively correlated, Model 3 is established as expressed in Equation (5):

$${\mathrm{G}}_{\mathrm{i},\mathrm{t}}={ \mathsf{\beta }}_{1,\mathrm{t}}{\mathrm{SIZE}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{2,\mathrm{t}}{\mathrm{LEV}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{3,\mathrm{t}}{\mathrm{GPM}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{4,\mathrm{t}}{\mathrm{SS}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{5,\mathrm{t}}{\mathrm{EF}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{6,\mathrm{t}}{\mathrm{CY}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{7,\mathrm{t}}{\mathrm{VAHU}}_{\mathrm{i},\mathrm{t}-1}+{\mathsf{\alpha }}_{\mathrm{i}}+{\mathsf{\gamma }}_{\mathrm{t}}{\mathrm{D}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(5)

Finally, to validate whether human capital serves as a mediator between RDI and financial performance materialized with knowledge transfer, Model 4 is established as expressed in Equation (6):

$${\mathrm{G}}_{\mathrm{i},\mathrm{t}}={ \mathsf{\beta }}_{1,\mathrm{t}}{\mathrm{SIZE}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{2,\mathrm{t}}{\mathrm{LEV}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{3,\mathrm{t}}{\mathrm{GPM}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{4,\mathrm{t}}{\mathrm{SS}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{5,\mathrm{t}}{\mathrm{EF}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{6,\mathrm{t}}{\mathrm{CY}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_{7,\mathrm{t}}{\mathrm{RDI}}_{\mathrm{i},\mathrm{t}-\mathrm{k}}+{ \mathsf{\beta }}_{8,\mathrm{t}}{\mathrm{VAHU}}_{\mathrm{i},\mathrm{t}-1}+{\mathsf{\alpha }}_{\mathrm{i}}+{\mathsf{\gamma }}_{\mathrm{t}}{\mathrm{D}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

(6)

3.2. Empirical Analysis

3.2.1. Descriptive Statistics and Correlation Analysis

The sampling pool of semiconductor companies is divided into two groups according to their emphasis on knowledge transfer, expressed by mean R&D expenses. The financial performance materialized with knowledge transfer of these two groups is measured for the same time period. This avoids the distortion of research findings with regard to the influence of human capital as a result of a sudden change in the macroeconomy. The results show that semiconductor companies that are highly focused on knowledge transfer report better overall knowledge, skills (human capital) and financial performance than those that are not as focused on knowledge transfer. This finding is persistent throughout the years, even in 2008, when the global financial crisis hit. The results are summarized in Figure 1 and Figure 2 as follows:

3.2.2. The Panel Data Model and Empirical Analysis

• Unit root tests
  Unit root tests determine whether the variables are stationary.

  Table 1. Unit-root test results.

  Method	ROA	RDI	VAHU	SIZE	LEV	GPM	SS	EF	CH
  LLC	−21.861 ***	−298.34 ***	−36.523 ***	−14.773 ***	−16.815 ***	−14.52 ***	−20.152 ***	−34.294 ***	−61.553 ***
  IPS	−14.674 ***	−29.13 ***	−17.262 ***	−5.822 ***	−9.431 ***	−8.214 ***	−6.148 ***	−21.438 ***	−447.29 ***
  PP	932.888 ***	490.74 ***	922.948 ***	666.838 ***	644.046 ***	720.407 ***	698.119 ***	1333.9 ***	3076.25 ***

  Note: use Akaike information criterion (AIC); *** p < 0.01.

  shows all the variables in the model with p < 0.1); hence, all the variables are stationary and there is no need to conduct differentials on the variables.
• Determination of the R&D lag period
  The lag period indicates the deferred effect of R&D (i.e., deferred performance of knowledge transfer). Based on theoretical inferences and presumptions, this paper defines corporate R&D as the action of knowledge transfer (RDI_{i, t-k}), and argues that the performance is deferred. This begs the question on how long (measured in years) the lag period (k) is. This paper deploys an auto-correlation regression model (

  Table 2. Selection of lag period lengths in vector autoregression model.

  Lag (k)	AIC	SIC	HQ
  1	−2.385	−2.379	−2.383
  2	−2.399	−2.389	−2.4
  3	−2.453 *	−2.44 *	−2.448 *

  Note: * p < 0.1

  ) and conducts ADF––Fisher unit root tests (

  Table 3. ADF-Fisher unit root tests on selection of lag period lengths.

  	AIC				SIC
  Lag (k)	k = 1	k = 2	k = 3	k = 4	k = 1	k = 2	k = 3	k = 4
  Chi square	521.15 ***	436.237 ***	509.605 ***	432.967 ***	516.736 ***	441.635 ***	532.081 ***	439.486 ***
  Choi Z	−4.058 ***	−2.253 ***	−3.562 **	0.012	−3.848 ***	−2.343 ***	−3.98 ***	−0.143

  Note: ** p < 0.05, *** p < 0.01.

  ) before determining that k = 3 is the optimal choice for the deferred effect of R&D.
• Hausman tests
  Hausman tests are conducted to decide whether a fixed effect model or random effect model is most appropriate for the panel data analysis. As shown in

  Table 4. Relationships between RDI, human capital and knowledge transfer performances.

  Variable	Tech Companies (Semiconductor)				Traditional Companies (Textile)
  	Model 1-1	Model 2-1	Model 3-1	Model 4-1	Model 1-2	Model 2-2	Model 3-2	Model 4-2
  	ROA	VAHU(−1)	ROA	ROA	ROA	VAHU(−1)	ROA	ROA
  SIZE	0.494 *** (5.977)	0.003 (0.045)	0.485 *** (7.804)	0.488 *** (5.92)	0.406 *** (3.872)	0.255 (0.148)	0.165 * (1.77)	0.398 *** (3.842)
  LEV	−0.034 (−0.774)	−0.074 * (−1.869)	−0.017 (−0.417)	−0.027 (−0.605)	−0.159 ** (−2.404)	−0.24 *** (−2.83)	−0.162 *** (−2.727)	−0.188 *** (−2.853)
  GPM	0.728 *** (17.994)	0.506 *** (14.018)	0.7327 *** (20.367)	0.705 *** (17.134)	0.592 *** (10.88)	0.109 *** (1.583)	0.503 *** (12.547)	0.606 *** (11.252)
  EF	−0.025 (−0.999)	0.012 (0.551)	−0.041 * (−1.793)	−0.024 (−0.954)	−0.011 (−0.299)	−0.126 *** (−2.788)	−0.024 (−0.726)	−0.027 (−0.769)
  SS	0.042 (0.748)	0.119 ** (2.386)	0.001 (0.001)	0.036 (0.644)	0.023 (0.435)	−0.087 *** (−1.278)	−0.031 (−0.612)	0.009 (0.165)
  CH	−0.392 *** (−4.541)	−0.304 *** (−3.953)	−0.486 *** (−7.463)	−0.35 *** (−4.005)	0.275 ** (2.455)	0.184 (1.299)	0.309 *** (3.619)	0.302 *** (2.722)
  RDI(−3)	0.101 *** (3.127)	0.056 * (1.95)		0.095 *** (3.075) p = 0.002	0.186 *** (3.023)	0.088 (1.134)		0.198 *** (3.267) p = 0.001
  VAHU(−1)			0.049 ** (2.355)	0.078 *** (2.731) p = 0.006			−0.048 (−1.528)	−0.13 *** (−3.577) p = 0.001
  Adj-R2	0.623	0.473	0.624	0.625	0.61	0.289	0.594	0.620
  D-W	1.654	1.539	1.545	1.74	2.053	2.419	1.697	1.842
  F	15.964 ***	9.135 ***	19.24 ***	15.998 ***	14.965 ***	4.621 ***	16.637 ***	15.306 ***
  Hausman	168.794 ***	62.14 ***	85.921 ***	173.8 ***	22.695 ***	17.848 **	25.171 ***	33.095 ***
  Fixed Effect Model

  Note: ***, p < 0.01, **, p < 0.05, *, p < 0.1. Coefficient estimates are standardized. Inside the bracket are the t stats of the coefficient estimates.

  , the test stats for Model 1 are 168.794 and 22.695, respectively, with p smaller than 0.05 in both cases. The test stats for Model 2 are 62.143 and 17.848, respectively, with p smaller than 0.05 in both. The test stats for Model 3 are 85.921 and 25.171, respectively, with p smaller than 0.05. The test stats for Model 4 are 173.8 and 33.095, respectively, with p smaller than 0.05. As all the test stats fall in the rejection region, a fixed effect model is applicable.
• Panel data analysis

  (1)

  RDI and financial performance materialized with knowledge transfer

  According to Table 4, the adjusted R² is 0.623 for semiconductor companies in Model 1-1. The DW test statistics result on the error term in the regression model stands at 1.654, between 1.5 and 2.5, indicating the mutual independence of error terms and no auto correlation in the model. The F stats result is 15.964 (p < 0.01). When k = 3 for RDI, its influence on the correlation with financial performance materialized with knowledge transfer (measured with ROA) is β = 0.101, t = 3.127, p < 0.01. The RDI in the semiconductor companies sampled shows a significant influence on ROA, and the effect on R&D is deferred. The adjusted R² is 0.623 for semiconductor companies in Model 1-1. The results of the sampled textile companies, shown in Model 1-2, show that the adjusted R² is 0.61 and the DW test statistics result is 2.053, indicating the mutual independence of error terms. The F stats result is 14.965 (p < 0.01). When k = 3 for RDI, its influence on the correlation on ROA is β = 0.186, t = 3.127, p < 0.01. The RDI in the textile companies sampled is significantly and positively correlated with ROA, and the effect on R&D is deferred.

  (2)

  RDI and human capital

  As shown in Table 4, the adjusted R² is 0.473 in Model 2-1, and the result of the DW test statistics is 1.539, which is between 1.5 and 2.5, indicating the mutual independence of error terms and no auto correlation. The F stats result is 9.135 (p < 0.01). When k = 3 for RDI in the sampled semiconductor companies, its correlation with human capital (VAHU^TM) is β = 0.056, t = 1.95, p < 0.1. The RDI shows significant and positive influence on VAHU, and the effect is deferred. The adjusted R² in Model 2-2 is 0.289 and the result of the DW test statistics is 2.419, evidencing the mutual independence of error terms. The F stats result is 4.621 (p < 0.01). The influence of RDI on VAHU is insignificant, given p is greater than 0.1.

  (3)

  Human capital and financial performance materialized with knowledge transfer

  The adjusted R² is 0.624 in Model 3-1, and the result of the DW test statistics is 1.545, falling between 1.5 and 2.5, which indicates the mutual independence of error terms and no auto correlation in the model. The F stats result is 19.24 (p < 0.01). When k = 1 in the sampled semiconductor companies, the correlation between VAHU and ROA is β = 0.049, t = 2.355, p < 0.05. The VAHU exhibits a significant and positive influence on ROA, and the effect is deferred. The adjusted R² in Model 3-2 is 0.594, the DW test statistics result is 1.698, evidencing the mutual independence of error terms. The F stats result is 16.637 (p < 0.01). The influence of VAHU on ROA in the textile industry with k at 1 is insignificant, given p greater than 0.1.

  (4)

  The mediating effect of human capital on the relationship between RDI and financial performance materialized with knowledge transfer.

  Model 4 aims to verify whether human capital (VAHU^TM) serves as a mediator in the relationship between RDI and financial performance (measured by ROA). This paper uses Model 1 as the basis, imports the variable VAHU into Model 4, and conducts panel data regression analysis on all the indicators. The test on the mediating effects requires three conditions:

Condition (1) is the correlation between independent variables and dependent variables; Condition (2) is correlation between independent variables and the mediating variable; Condition (3) is the correlation between the mediating variable and dependent variables with control of the independent variable. If all three conditions are met, there exists a mediating effect. Whether the mediating effect is in part or in full depends on whether the influence of the independent variables on the dependent variables remains significant after the incorporation of the mediating variable. If the influence becomes insignificant, the mediating effect is complete. If the independent variable remains statistically significant and the coefficient is reduced, the mediating effect is partial. The presence of mediating effects does not have to hinge on the correlation between independent variables and dependent variables, provided both Condition (2) and Condition (3) are met [54].

According to the results of Model 1-1 shown in Table 4, Model 4-1 is constructed by adding the human capital coefficient (VAHU^TM) into Model 1 as a variable. The adjusted R² is 0.625, and the DW test statistics result is 1.74, falling between 1.5 and 2.5, which indicates the mutual independence of error terms and no auto correlation in the model. The F stats result is 15.998 (p < 0.01). The correlation with VAHU is β = 0.078, t = 2.731, p < 0.01, and the variable is statistically significant. The correlation with RDI is β = 0.095, t = 3.075, p < 0.01 and the variable is also statistically significant. However, RDI’s β coefficient is 0.006 lower (0.101–0.095), and the t value 0.052 is lower (3.127–3.075). According to the above stated theory on mediating effects, this paper infers that human capital, as a variable, has a partial mediating effect. As the adjusted R² in Model 4-1 is higher than that in Model 1-1, Model 4-1 has stronger explanatory power. The statistics for the sampled textile companies do not meet Condition 2 or Condition 3 in the theory; hence, there is no mediating effect.

3.3. Case Studies—Semiconductor Companies in Taiwan

In the context of the theories on knowledge transfer and human capital, this study examines three leading semiconductor companies in Taiwan, i.e., MediaTek (MTK), Taiwan Semiconductor Manufacturing Company (TSMC) and Advanced Semiconductor Engineering (ASE), to understand the influencing factors of knowledge transfer. The purpose is to examine how factors such as R&D, training and absorption capability affect human capital, knowledge transfer and financial performance. This is followed by a comparison between the semiconductor and textile industry.

(1) Value adding capacity of human capital in an organization

MTK places a heavy emphasis on R&D and sales activities. The company has put in place a series of procedures to ensure a strategic approach to recruitment. A high standard is set for academic background, and a comprehensive internal training and education program has been established. R&D budgets are allocated and the R&D personnel spans three continents from Asia to Europe and the U.S. The company has been building R&D momentum via the division of labor and integration across countries. MTK’s value-added coefficient for its human capital is 51.97, higher than the foundry heavyweight TSMC’s 19.905 and higher than the mean of 2.69 in the semiconductor industry and the mean of 0.828 in the textile industry (

Table 5. Comparison of knowledge transfer and human capital of semiconductor companies and textile companies in Taiwan in 2004–2016.

Supply Chain	Company	Business	RDI	Staff Turnover	VAHU	ROA	No. of Patents (2018/12)	Global Market Share (2017 Ranking)
Upper stream	MTK	IC design	0.079	15.1%	51.973	21.292	21,630	7.83% (W4)
Mid stream	TSMC	Foundry	0.066	7.2%	16.415	19.905	27,146	55.9% [55] (W1)
Down stream	ASE	IC testing and packaging	0.043	18.7%	2.154	8.082	7421	19.2% [56] (W1)
	Semiconductor		0.117	14.9%	2.69	4.517
	Textile		0.0079	15.04%	0.828	1.411

Source: TEJ; non-consolidated financial statements; RDI (R&D expenses/net sales); human capital as the value-added coefficient of human capital; ROA based on EBIAT; no. of patents based on public information from the Global Patent Search System and company disclosures as of the end of 2018.

).

TSMC has established a series of training, education, R&D, and incentive programs. Its R&D initiative the “Nighthawk Project” runs around the clock. The company’s value-added coefficient for its human capital stands at 19.905, higher than the average in the semiconductor industry or the textile industry.

Based on existing technologies and targeting advanced products, ASE allocates between 3% and 5% of its annual sales to R&D. Over recent years, the company has focused on M&A activities with other companies involved in testing and packaging. As a result of its R&D budget and internal training and education, ASE reports a value-added coefficient for its human capital at 2.154, higher than the mean of 0.827 in the textile industry but lower than the mean of 2.69 in the semiconductor industry.

(2) Knowledge transfer achievements

MTK places heavy emphasis on R&D activities. As of 2018, it had a total of 2163 patents. Its return on capital employed, based on EBIAT, was 21.292% in 2004–2016, higher than the foundry company TSMC’s 19.905%. It is also higher than the average of 4.517% in the semiconductor industry and 1.411% in the textile industry.

TSMC has more than 40,000 patent applications and nearly 30,000 patents approved worldwide. In 2017, the company had over 1100 patents approved in China and Taiwan, and 2428 in the U.S. [57]. In terms of patent quality, 98% of the applications in the U.S. were approved. This demonstrates the company’s strong focus on innovation and the positive effect of such focus. In 2017, the gross profit margin stood at 50.6%. The net income ratio came in at 35.3%, 0.2% lower due to a higher R&D expense ratio. TSMC’s return on capital employed, based on EBIAT, was 19.905%, higher than the average in the semiconductor industry and in the textile industry.

As of 2018, ASE had 7421 patents. In 2017, the company’s return on assets was 7.76%, with a net margin of 22.9%. Its return on capital employed, based on EBIAT, was 8.082%, higher than the average of 4.517% in the semiconductor industry and 1.411% in the textile industry. However, ASE’s return on capital came in much lower than that of the upstream player MTK or the midstream player TSMC.

Summary: Semiconductor companies should seek to create a capable environment through R&D investments, education, and training to guide and instruct senior managers and entry-level employees. This helps to retain talent and human capital. R&D investment and training enhance the knowledge base, accumulate skills, strengthen absorption capability, and increase the value added by human capital. Finally, the upgrade of overall competences will manifest in the launch or enhancement of products, the growth of patent portfolios and financial performance. In fact, there is a connection between the value added by human capital and financial performance. This is consistent with the empirical conclusion drawn in the previous section. However, the knowledge transfer status and effectiveness, patent outputs and financial performance in the semiconductor industry differ from one company to the next, due to the difference in supply chain activities, internal/external environments, competitors, and knowledge thresholds.

4. Results and Discussion

In reference to relevant theories and practical contexts, this paper sources panel data from 2006–2016 to examine the relationship between the RDI, human capital, and financial performance of semiconductor companies in Taiwan. The conclusions and arguments described below are based on the empirical findings.

This paper finds the positive influence of RDI on financial performance, which is in line with H1. In other words, companies use RDI as a means of promoting internal knowledge transfer and translating the results into financial performance. However, the R&D effects are deferred in the knowledge transfer process. This is consistent with the results of Chen et al., (2019) [22]. Continued knowledge transfer helps to boost the likelihood of continuous financial performance growth for companies [58].

RDI and human capital show a positive influence, thus supporting H2. This result implies that RDI is used by companies as a method of encouraging learning by employees and placing emphasis on knowledge transfer. Moreover, the result proves that employees learn and transfer knowledge in this way to enhance their own capabilities and build up skills. Link and Swann (2016) [59] posited that companies should acquire knowledge by investing in R&D and seek to boost human capital as part of their operational policy [59], which is consistent with the findings of this paper.

Human capital exhibits a positive and significant influence on financial performance, thus supporting H3. This result is the same as [60], and indicates that the enhancement of employee caliber, as well as their overall knowledge and skills, helps to better financial performance and achieve sustainable development. This conclusion is consistent with [61].

Human capital has a mediating effect on the relationship between RDI and organizational performance. This result supports H4. Song et al. [62] held that R&D investment has a positive influence on company performance in the management of firm-wide R&D investments and human capital structures. Meanwhile, the increase in the percentage of highly-skilled workers is also a positive contributor to investments in company performance. This finding is consistent with the conclusion of this paper. However, the argument that human capital provides a mediating effect on R&D due to its influence on financial performance is slightly different from the argument in this paper. The ultimate goal of human capital improvement is the same; what is different is the research perspective. A case in point is the emphasis on the importance of the structure and caliber of human capital to the process of R&D’s influence on financial performance. This paper focuses on how the knowledge transfer and organizational learning resulting from R&D investments affects the accumulation of human capital and the influence on financial performance. It is necessary, throughout the process, to boost the new skills and competences of the whole organization. That said, the influence of the caliber and structure of human capital on the process of knowledge transfer cannot be denied. This is the reason why staff seniority and employee fluidity are defined as control variables.

According to Farnese et al. (2019) [63], the knowledge transfer model described in knowledge creation theory may prompt companies to adopt measures that encourage the formation of new knowledge in order to achieve knowledge synergy in the workplace. This facilitates the management of knowledge resources and eventually improves performance by applying practical knowledge and skills [63]. In this regard, it is possible to use DI as a measurement of the resources required for the promotion of organizational learning and knowledge transfer, in order to create knowledge value and establish one of the cornerstones for business sustainability. The prerequisite during this period is the enhancement and accumulation of a company’s proprietary human capital.

(1)

Knowledge transfer starts from the allocation of R&D budgets and the mobilization of personnel. However, it takes time for the benefits of knowledge transfer to translate into performance. The length of this waiting period depends on whether knowledge transfer enhances efficiency or contributes to operations. This is different from the new knowledge and skills acquired via technology transfer and patent purchases. Knowledge transfer begins with knowledge requirements and gap analysis, with externalization, internalization, and application as it outputs. Everything takes time, be it product improvement, new product introductions, or enhancement and innovation of manufacturing processes. This is the reason why R&D’s performance benefits are deferred, and the benefit of knowledge transfer is also deferred. It is the same for all industries.

(2)

R&D intensity (RDI) in the semiconductor industry has a positive influence on human capital and financial performance materialized in knowledge transfer, and such influence is deferred in nature, with a higher coefficient than that in the textile industry. Meanwhile, human capital and financial performance are positively correlated, and the performance is deferred in the semiconductor industry. However, this is not obvious in the textile industry. This suggests a higher knowledge intensity in the semiconductor industry than in the textile industry. There is a deferred effect from the requirement for new knowledge to the application of such knowledge. This involves the workforce’s capability, knowledge absorption ability, industry knowledge threshold and corporate resources. From the commencement of R&D activities, budget allocations for employee training, the consolidation, integration and enhancement of old and new knowledge and technology, and the resulting accumulation of human capital also take time, i.e., in the form of deferred effects.

(3)

Human capital has mediating effects on the relationship between RDI and knowledge transfer/financial performance. However, such mediating effects are only evident in the semiconductor industry. This implies that R&D investments by semiconductor companies help to enhance operating performance but only through the enhancement of overall human capital. R&D activities are, in fact, the starting point of knowledge transfer. The process of knowledge transfer enriches the knowledge base of the whole organization, develops new skillsets, improves manufacturing processes, or launches new products. This is then reflected in financial performance materialized through knowledge transfer.

Finally, as described in the previous section, the influence of RDI on HC and that of HC on financial performance is present in the semiconductor companies in the high-tech industry (the experimental group) but not in the textile companies (the control group), due to the knowledge intensity and capital intensity of the industry. As mentioned by Xu et al., (2019) [2], knowledge-intensive industries have higher HC and intellectual capital (IC) than non-knowledge-intensive industries. Meanwhile, the influence of HC on financial performance and profitability is greater in high-tech industries than in non-high-tech industries [64].

5. Conclusions

In sum, the premise of this paper is that knowledge transfer in a company is subject to the characteristics associated with knowledge transfer among individuals and organizations. Organizations are advised to seek to create an environment conducive to knowledge transfer to nurture human capital as a proprietary intangible asset and achieve the requisite transfer of knowledge. The lessons learned are as follows:

(1)

R&D is the cornerstone of knowledge absorption capability, human capital accumulation and transfer performance. R&D investment creates momentum for the transfer of knowledge. It is an organizational learning method and is often used to measure the level of absorption capability. R&D spending benefits are deferred with respect to performance. It enhances the knowledge and competence levels of the whole organization. The accumulation of human capital provides a meaningful and positive enhancement of knowledge transfer performance (overall innovation capability) and thus improves the financial performance of a company. The absorption capability at the organizational level is the foundation of the knowledge transfer. The greater the absorption capability, the better the knowledge base and competence levels. The accumulation of human capital will benefit the overall innovation capability and operating profits of the organization.

(2)

R&D activities offer the best and most feasible option for knowledge transfer and operations. The semiconductor companies in Taiwan may pursue high value-added products, enhance quality and production efficiency, and optimize workflows, or seek to migrate production sites to regions with lower labor and production costs. The latter may be a quick fix and may result in production cost reductions. However, should this be repeated once the local costs increase again? This is worthy of thought. This paper posits that R&D investments are the best solution to improving financial performance. R&D activities as a means to transfer knowledge can enhance innovation capability and develop intellectual properties. This is critical in the semiconductor industry, as it avoids any damages associated with patent litigations or increased bargaining power in negotiations. At the end of 2019, TMSC and GlobalFoundries reached a settlement by cross licensing global patents. That said, R&D activities affect financial performance through profit margins. They also influence operational methods. Companies should focus on RDI to improve commercial viability going forward. In the long run, RDI creates a competitive edge, enhances profitability and generates intangible assets through innovation, creating goodwill and notions not subject to perception from stakeholders or social efficiency.

Innovation is a means to pursue sustainable operations, and R&D is an integral part of corporate innovation. Therefore, R&D investments should aim for commercialization, which is an investment activity that drives new knowledge, skillsets, and high-caliber workforces. Such actions are necessary to the appreciation and accumulation of human capital, and will eventually be reflected in a company’s financial performance. The purpose of R&D is thus to establish a core competitive advantage and achieve corporate sustainability.

6. Suggestions for Future Research

(1)

This paper refers to the value-added coefficient of human capital, as intellectual capital, and a measure of human capital in an organization. Without digging into the external sources of knowledge and the strength of the macroeconomy, this paper seeks to focus on the internal operation, management, and resource measures. However, there is a long list of influencers, effects and financial performance metrics associated with R&D. Follow-up studies may incorporate issues such as structural capital or external knowledge sources, or compare and contrast the similarities and differences in structural capital or relational capital between high-tech firms and traditional companies.

(2)

This paper examines the impact of R&D investments on financial performance only, without exploring the effects on the number of patents. Future studies may refer to the number of patents as a dependent variable to evaluate the efficiency with which R&D investments are translated into patent outputs. It is worth noting that patent outputs are not necessarily the operational indicator most emphasized by companies. This paper focuses on semiconductor companies, albeit with no classification of supply chain activities. Subsequent studies may look at different industry characteristics as a result of external environments, the internal/external scenarios, or different lag periods to yield new insights.