1. Introduction
The global business environment tends to be more challenging and competitive [1]. In the face of increasing competitiveness with high volatility and uncertainty levels, firms must develop strategic capabilities to sustain growth in international markets [2]. A competitive advantage is often achieved through strategic resource allocation, market diversification, and internationalization [3]. The development of strategic capabilities is critical for firms seeking to navigate volatile economic environments [4]. Firms operating in volatile environments with export-driven economies must continuously refine their strategic approaches to maintain competitiveness.
The case of China Taipei presents a unique example, as its firms operate within an export-oriented landscape. As a Newly Industrialized Country (NIC), China Taipei relies heavily on exports and maintains a significant trade integration with China and other global markets. However, this dependency poses critical challenges, particularly in times of global economic disruptions and geopolitical tensions. China Taipei firms face a vulnerable environment due to their high dependency on China, making them susceptible to economic shocks, trade tensions, and political uncertainties. Furthermore, sentimental policies and political influences add to the complexity of their business operations [5].
Despite these challenges, many China Taipei firms have demonstrated resilience and an ability to sustain their competitiveness. This paradox presents a compelling research gap: understanding how China Taipei firms strategically navigate these vulnerabilities while maintaining their competitive advantages remains largely unexplored [6]. According to the current existing research on the global business environment, there is still a notable gap in understanding the specific performance of China Taipei firms [7]. The existing studies have broadly addressed the effects of various economic and industrial factors, but there is limited empirical evidence on how firm-level factors, such as dependency on China, the non-labor intensity, and supply chain management influence the performance of China Taipei firms [8]. Additionally, the moderating roles of internationalization and product diversification in this context remain underexplored [9,10]. This gap is critical given China Taipei’s significant economic ties with China and its reliance on exports, which make its firms particularly vulnerable to global disruptions.
This study aims to address these business challenges, to understand and explore how China Taipei firms’ strategies enhance their firms’ performance and make their competitiveness sustainable. It highlights the importance of diversifying supply chains and reducing dependence on single markets like China. A firm’s ability to internationalize and diversify its operations has been extensively studied as a key driver of export performance and competitive advantage [6]. International expansion allows firms to access new customer bases, optimize resource utilization, and mitigate the risks associated with over-reliance on a single market [2].
This study also underscores the need for supportive policies that encourage international expansion, enhance resilience, and increase global competitiveness. This investigation will analyze the reaction of the Taiwan stock market (TWSE) to the global position by calculating the Cumulative Abnormal Return (CAR). The CAR will reveal whether this external competition shock affects both the China Taipei economy and the performance of China Taipei companies. This study will examine the impact of firm-resource factors, such as the non-labor intensity, supply chain management, and dependency on China, on firm performance. Furthermore, this study will explore how the degree of internationalization and product diversification moderates the relationship between firm-resource elements and firm performance in the current global business environment. Therefore, interesting questions arise, as follows:
(1). Do firm-level factors, including the non-labor intensity, supply chain management, and China dependency, contribute to enhancing firm performance in the current global business environment?
(2). Does the degree of internationalization and product diversification play a moderating role in the relationship between firm-level factors and firm performance in the current global business environment?
2. Literature Review
2.1. Non-Labor Intensive
Labor intensive is defined as a production process where the primary component is labor costs, suggesting that the capital (machines/factories) constitutes a modest percentage of the final cost. According to Islam and Shazali [11], a labor-intensive approach involves using manpower for production with minimal technological support. The agriculture industry serves as a typical example of high labor intensity. So, a firm can be “capital-intensive”, indicating a production process that requires a significant investment in fixed assets, such as machines, capital, and plants [12,13]. Capital-intensive processes entail a comparatively high level of capital investment in comparison with labor costs. Wang et al. [14] argued that the capital intensity often reflects a firm’s operating leverage, and different industries exhibit varying levels of capital intensity, including railroads, cruise lines, airlines, mining, utilities, hotels, and restaurants.
A firm may be R&D-intensive, implying a substantial expenditure on research and development to expand product knowledge, manufacturing, and technology, and to foster innovation through basic and applied research [15]. These factors are crucial to consider, especially as we investigate how non-labor intensity affects firm performance in this research. A firm classified as non-labor intensive might be capital-intensive, R&D-intensive, or both. Highly capital-intensive firms, according to some researchers, can allocate significant cash for fixed assets, leading to cost savings through subsequent operations, thereby reducing business risk [16]. Lee [17] asserted that risk reduction may be more pronounced within uncertain economic contexts or during economic downturns, contributing to elevated firm performance.
The relationship between R&D activities and performance has been extensively studied. Firms engaging in R&D activities show significant differences in revenue growth rates compared to those that do not [18]. According to the resource-based view advocated by Barney [19] and Wernerfelt [20], R&D activities contribute to the development of unique, rare, immobile, and difficult-to-imitate resources, leading to improved firm performance. During financial crises, R&D becomes more crucial, with Teece [21] highlighting the importance of a firm’s dynamic capabilities for adapting to changing environments. Innovation, a key element of dynamic capabilities, enhances a firm’s ability to combine, restructure, renew, and recreate its resources [22], making it more adept at handling economic shocks [23].
In the context of the current global business environment, government measures like social distancing and halting socio-economic activities led many employee-intensive companies to freeze operations, causing financial losses, layoffs, and exposing their vulnerability to external shocks. In contrast, non-labor-intensive companies that leveraged digital technologies and automation adapted quickly, maintaining operations and even growing. This contrast highlights the need for robust contingency plans and investment in technologies that reduce reliance on physical labor. However, the balance between public health measures and economic stability remains debated, with the long-term impacts on industries and labor markets still unfolding. The following hypothesis is proposed:
There is a positive relationship between non-labor intensity and firms’ performance.
2.2. Supply Chain Management
Several methods have been proposed to enhance both operational and financial performance, with a particular focus on effective supply chain management. The emphasis lies in reducing the lead times and material costs while improving product quality and responsiveness [24]. A supply chain is a network comprising a company and its suppliers, aiming to produce and distribute a specific product to the final buyer. This network involves various activities, people, entities, information, and resources. While there are numerous definitions of supply chain management [25], they all involve approaches and practices to connect manufacturers, suppliers, distributors, and customers, fostering the long-term performance of a firm [26]. The fundamental concept is integrating an organization’s internal processes with its suppliers and customers.
Tsai [27] noted that, with the development of the internet and web-based systems, firms can achieve a strong integration with their customers and suppliers to manage inventory, forecast demand, and maintain relationships. In recent decades, firm leaders have recognized the strategic significance of supply chain management and the clear competitive advantages it can bring [28]. They have sought not only to understand whether supply chain management positively contributes to financial performance, but also how to steer their investments to enhance their competitive advantages and maximize their financial outcomes. Previous studies have demonstrated the impact of supply chain management on financial performance. For instance, Ou et al. [29] found positive benefits derived from well-managed supply chain management, improving operational performance, customer satisfaction, and financial performance. Lakner et al.’s [30] study involving supply chain and materials managers showed the positive influence on a firm’s performance of effective supply chain management. Xu and Smith [28] found positive links between supply chain management strategies and both marketing and financial performance.
In uncertain environments, establishing stronger external partnerships through supplier integration becomes crucial. This facilitates faster delivery, improves production flexibility, and enables quick reactions to market changes [31]. Firms with superior supply chain performance are better positioned to financially outperform. The following hypothesis is proposed:
There is a positive relationship between supply chain management and firms’ performance.
2.3. China Dependency
Since the mid-1990s, China Taipei firms have significantly increased their foreign investments to leverage their assets, such as patents, technological assets, reputation, production skills, marketing, and advertising [32]. China Taipei’s modest market size and shortage of natural resources have compelled China Taipei firms to heavily rely on international trade. Recently, rising production costs, environmental protection regulations, and constraints on international trade have driven many China Taipei firms to expand abroad in search of an efficient production base [14]. These expansions have been observed in mainland China, Vietnam, Thailand, Indonesia, and other locations to gain competitive advantages and establish a more effective trading platform for global customers [22].
Despite the introduction of the New Southbound policy by President Tsai Ing-wen in 2016, aimed at reducing the island’s dependency on China’s economy, China remains China Taipei’s primary import source, accounting for 19.3% in 2017 (worth USD 50 billion), followed by 18.6% worth USD 39.7 billion in 2018. The majority of these imports were machinery products, steel and iron, miscellaneous chemical products, and optical apparatus.
Chen’s [33] study on China Taipei’s investments in cities, like Guangzhou and Shenzhen (in Guangdong province) and Xiamen and Fuzhou (in Fujian), during the period of industrialization indicated that in 1991, China Taipei’s investments in China were concentrated in Guangdong and Fujian, making up 45% of the total number of investment cases and 44% of the total contracted capital. Subsequently, in 1993, China Taipei’s investments shifted to China’s northern and western parts, including an increased investment percentage in Shanghai, from 11.5% in 1991 to 12.1% in 1994 of China Taipei’s total investments in China [34]. China’s role as China Taipei’s industrial powerhouse over the past decade can be observed at the provincial or specific city level.
The familiarity of China Taipei’s investors with Chinese markets due to their shared language, culture, and geographic proximity enables them to effectively monitor their firms and respond to economic changes. Conversely, China Taipei’s investors exhibit less confidence in operating in other regions due to differences in the language and legal systems [14]. Most China Taipei firms have facilitated their international expansion by leveraging their previous experience in China, reducing the amount of information needed when entering new markets [35]. Therefore, while the high level of dependency on China has typically stimulated the performance of China Taipei companies, the current global political and business environment has turned this previous advantage into a challenge for China Taipei firms’ performance. The following hypothesis is proposed:
There is a negative relationship between China dependency and firms’ performance.
2.4. Internationalization, Product Diversification, and Non-Labor Intensive
The internationalization of firms has long been recognized as a pivotal issue in international business. The growth resulting from internationalization, often termed “geographical diversification”, represents a significant strategic option for both small and large companies [36]. This strategy allows firms to achieve economies of scale and scope [37], enhance market power [38], reduce costs [39], and serves as a tool to mitigate political and financial risks [40]. Companies with subsidiaries in different countries have the opportunity to gain specific advantages in the host country, broaden their knowledge base, enhance their capabilities, and improve their competitiveness through experiential learning [41,42].
Various theories have been proposed to understand the drivers of international expansion, including a transaction cost analysis, resource-based view (RBV), and the eclectic paradigm (EP). Transaction cost analysis suggests that transaction costs arise from asset specificity and market imperfections [43]. The RBV posits that a firm’s competitive advantages stem from the different resources it controls [44], while the EP proposes that firms make foreign direct investments based on a thorough analysis of the attractiveness of foreign markets [45]. However, international expansion comes with costs, with geographical diversification potentially increasing the costs related to information collection, processing, and dissemination, and exposing firms to financial and political risks [46,47].
The relationship between performance and the degree of internationalization (DOI) remains a debatable issue among academics and company strategists. Some have argued for a positive relationship, citing studies that have shown internationalization leading to improved company performance and rising profitability. Conversely, others have claimed a negative relationship, suggesting that under certain controls, the DOI has a negative effect on the rate of firm growth. Additionally, some scholars have posited an indeterminate relationship between internationalization and performance, while others have proposed a U-shaped curvilinear relationship. The evidence supports the idea that performance may decrease initially due to the diseconomies of scale associated with international diversification, but begins to rise to higher levels due to the benefits of economies of scope [48,49].
Product diversification plays a crucial role in the strategic behavior of large-scale companies [39]. This strategy involves expanding the existing product range with new products or modifying existing ones, providing firms with opportunities for growth by entering new markets or meeting the needs of existing customers. Researchers have offered various definitions of product diversification, with industry relatedness being the most common method of measurement [50].
Theoretical arguments have suggested that diversification can have both value-boosting and value-lowering effects [51]. Some have argued that firms benefit from advantages, such as market power, risk reduction, expanded debt capacity, and tax advantages, through product diversification [51]. However, diversification may also incur costs, including logistical, distribution, and managerial training costs [52], as well as information asymmetry costs between central management and divisional managers [36]. Utilizing common inputs across different industries poses challenges, requiring modifications to production and process technologies, leading to various degrees of costs [53]. Beyond a certain degree, diversification can increase the governance costs and negatively impact performance [38].
Recent scholarly perspectives have proposed a curvilinear relationship between diversification and firm performance, following an inverted U-shaped pattern. Positive effects on performance are observed when single-segment firms become relatedly diversified, while negative effects are seen when firms expand with unrelated diversification [39,50]. Our inquiry is grounded in a crucial insight regarding the signaling value of two moderators, namely the degree of internationalization (DOI) and product diversification (PD), on China Taipei firms’ performance during economic shocks. Firstly, scholars have embraced a contingency view of the intricate relationship between non-labor intensity and firm performance by exploring the role of moderators. However, limited studies have delved into the relationship between non-labor intensity and firm performance under the moderation of the DOI, and the results remain inconsistent.
In a study on the relationship between multinationality and performance by Shin et al. [54], it was suggested that capital-intensive firms exhibit negative performance when multinationality levels are low, and positive performance as they further internationalize. Zhou, Van, and Zhang [15] demonstrated that firms in industries with higher R&D intensity are more inclined to acquire foreign firms, while those in labor-intensive industries are more likely to remain domestic for better performance. This could be attributed to the fact that less labor-intensive firms, facing fewer constraints, show greater flexibility in expanding their business coverage [55].
Subsequently, shedding light on the role of PD in moderating the relationship between non-labor-intensive factors and firms’ financial performance is essential. Fukui et al. [56], in a study on the relationship between corporate diversification and labor productivity, asserted that significantly lower labor productivity is observed in firms that have diversified into multiple product segments. This loss in productivity is linked to a decrease in annual revenues, amounting to millions of dollars. The explanation lies in the fact that as a firm’s size and complexity increase through diversification, the management system loses control over efficiently utilizing employees. According to [23], the ratio of administrative employment to total employment rises with increased diversification, leading to an overall reduction in labor productivity and a decline in performance. The following hypotheses are proposed:
The degree of internationalization plays a moderating role on the relationship between non-labor intensity and firms’ performance.
Product diversification plays a moderating role on the relationship between non-labor intensity and China Taipei firms’ performance.
2.5. Supply Chain Management and China Taipei Firms’ Performance
We also propose that the relationship between supply chain management and a firm’s performance is influenced by two moderators. The first relationship is how the degree of internationalization (DOI) moderates supply chain management and companies’ profitability. Recent studies support the idea that strong links with suppliers and distributors can enhance the competitiveness of the entire supply network, fostering firms’ development and facilitating innovative solutions [57,58]. Particularly from an international perspective, internationalization poses challenges and requires improved innovation performance [59]. Giovannetti, Marvasi, and Sanfilippo [60] claimed that well-managed supply chains can promote firms’ engagement in cross-border markets, opening new niches and enabling enterprises to overcome obstacles, like information costs, contract incompleteness, and other structural barriers to the internationalization process, ultimately leading to long-term profits. Thus, firms with well-organized supply chain networks are likely to expand their business beyond their borders to enhance their performance.
Secondly, we also examine the relationship between supply chain performance and a firm’s financial performance under the influence of product diversification (PD). A firm can diversify its products, even into related or unrelated businesses [61], but it is noteworthy that once a firm has diversified, it cannot solely focus on its business unit strategy. Rather, the firm must pursue diversification in its activities as it faces greater competition [30]. Thus, a coordination capability to manage the internal diversity and complexity arising from diversification is required for firms to diversify successfully. To achieve such coordination, remarkable interactions among the organizational units, which can be geographically scattered, and the external transactions with suppliers and customers are necessary. Firms that pursue a diversification strategy are recommended to proactively seek efficient linkage or integration among their internal functions and with their suppliers and customers [25]. A firm’s achievement of internal integration across its supply chain is expected to lead the firm to new organizational forms and operating relationships. This is be achieved based on the higher level of information integration and better use of the internal value chain. In turn, this may boost the firm’s strategic capabilities and extend the scope of its economies, assisting the firm in its process of diversification. The following hypothesis is proposed:
The degree of internationalization plays a moderating role on the relationship between supply chain management and firms’ performance.
Product diversification plays a moderating role on the relationship between supply chain management and firms’ performance.
2.6. China Dependency and China Taipei Firms’ Performance
In this final section, it is crucial to elucidate the link between China dependency and a firm’s performance. Additionally, it is undeniably necessary to determine whether the two moderators have impacts on that relationship. In fact, there is no prior study on the relationship between a China Taipei firm’s degree of dependency on China and its performance, moderated by the DOI and PD. Consequently, in this section, we predict that the link between the dependent variable and the independent variable is strongly negative, as mentioned above. For that reason, we also assume that the DOI and PD have a moderating impact on this relationship. As an explanation, if a China Taipei firm has a strong investment in China, the firm has experience in expanding its business internationally. In particular, Wang et al. [14] stated that, regardless of geographical location, general knowledge, including of behaviors in terms of sales and marketing strategies and the common features of foreign markets, is of great help. With such knowledge, firms have less information that needs to be gathered and evaluated almost simultaneously, because such knowledge can be transferred from one country to another [32,35,62]. Enterprises with international experience accumulated in the Chinese market can use that experience as leverage to smoothen their international operations, in order to enhance their performance.
Furthermore, we also suppose that PD moderates the association between China Taipei enterprises’ degree of China dependency and their performance. In fact, when dealing with the governance costs and risks of organizing complex operations in different business lines, firms are constrained by the costly and less profitable process of diversification and are unable to maintain their competitive position. Therefore, highly China-dependent and highly diversified firms will not perform well, especially during an economic shock. Meanwhile, firms pursuing a product diversification strategy are able to hedge their external risks thanks to their lesser dependency on only one business item. The following hypotheses are proposed:
The degree of internationalization plays a moderating role on the relationship between China dependency and firms’ performance.
Product diversification plays a moderating role on the relationship between China dependency and firms’ performance.
Based on the literature discussed, the proposed conceptual framework is presented in Figure 1.
3. Materials and Methods
3.1. Materials
Our dataset originates from the TEJ database by Taiwan Economic Journal Co., Ltd. (Tapei, Taiwan), a highly reliable source with extensive coverage of China Taipei and surrounding regions, like mainland China, Hong Kong, and Southeast Asia. In our initial assessment, we identified a population of 997 companies listed on the Taiwan Stock Exchange (TWSE). We employed a purposive sampling method with specific criteria [23,63]. The selection criteria included companies operating in eight industries that are directly impacted by global business competition, namely Electronic Components, Automobile, Tourism, Shipping and Transportation, Machine Tool, Retailers, E-commerce, and Paper Making.
This study only considered firms with a consistent dataset across the observation period, from 2019 to 2024, for analysis. Firms with inconsistent reporting structures or changes in industry classification over time were excluded to maintain a stable industry composition. This process helped maintain the robustness and reliability of the dataset by removing any incomplete or unreliable information before analysis began. After excluding disqualified and omitted samples with missing or inconsistent data, our final dataset comprised 360 firms across eight industries directly impacted by global business competition. Furthermore, this study performed analysis of 2160 observation samples in the panel analysis, based on a sample of 360 firms across eight industries.
Analyzing cross-sectional data from 2019, we explored whether independent variables allowed China Taipei firms to hedge external risks, how they influenced China Taipei firms’ performance, and whether diversification strategies adjusted the relationship between independent and dependent variables [63]. To collect CAR data for China Taipei firms, we used the Taiwan Stock Market Exchange (TWSE) as the index classification and market return as the index code. Specifically, we used TWSE data to investigate the existence of CAR during the BRICS global tension as the global business event. The estimation period spanned from −300 to −91 days before the event, and the observation time covered −90 to 60 days after the event. We select 12 February 2024 as the event day, marking the official beginning of BRICS global tension in the global business environment.
The CAR is a measure used in event studies to assess the impact of an event on stock prices. It is calculated by summing the Abnormal Returns (ARs) during a specified event [64]. The formula for CAR is given as
(1)
where CARt1, t2 represents the Cumulative Abnormal Return from time t1 to t2 and ARt is the abnormal return on day t (1). The BRIC global tension duration, defined by t1 (start date) and t2 (end date), helps assess the total market reaction to the event. The abnormal return (ARt) at a given time t is determined by the difference between the actual stock return and its expected return, which is estimated using a chosen model. The formula for AR isARt = Rt − E(Rt)(2)
where Rt is the actual return of the stock at time t, and E(Rt) is the expected return (2), especially estimated using the Market Model. A common approach to estimating expected returns is the Market Model, which expresses expected return asE(Rt)= α + βRm, t(3)
where α is the intercept obtained from historical regression, β is the sensitivity (beta coefficient) of the stock relative to market movements, and Rm,t is the market return at time t (3).3.2. Methods
To assess a firm’s performance in various scenarios, we utilized the event study approach, focusing on the BRICS global tension as the global business event. Specifically, we employed the Cumulative Abnormal Return (CAR) within the OLS risk adjustment model to gauge the market response post-event. This method, consistent with the work of Hair et al. [63], allows for a comprehensive examination of event study techniques. Additionally, the TEJ+ event study tool was applied for accurate calculations, considering factors like data input sufficiency, estimated period, time window setup, and statistical tests to ensure data normality. This methodology offers a robust analysis of firm performance amidst significant events, whether at the firm level or broader macro events, such as international trade disputes.
In this study, we define “non-labor intensive” as the characteristic indicating that a firm is not reliant on labor for its operations, encompassing R&D or capital intensity rather than being labor-intensive. To quantify the non-labor-intensive variable, we employ the natural logarithm of the ratio of the number of employees to total assets (4). A higher ratio signifies that a firm is non-labor intensive, while a lower ratio indicates the opposite [23].
(4)
This ratio is employed to assess the effectiveness of supply chain performance, encompassing dimensions, such as delivery precision, lead time, customer satisfaction, and inventory turnover. Amabile and Gryskiewicz [65] highlighted the prevalence of inventory turnover as a key indicator in their research on supply chain performance measurement, indicating that 50% of surveyed companies favored this metric. In this study, we employ the inventory turnover ratio to gauge the adeptness of a firm’s supply chain management, defined as the number of times inventory cycles or turns over per year. The inventory turnover is calculated by dividing the Cost of Goods Sold (COGS) by the average inventory (5), where the average inventory is determined by taking the sum of the beginning and ending inventory and dividing it by two (6). Inventory turnover provides insights into the efficiency and effectiveness of a firm’s supply chain practices [29,60].
(5)
China Taipei companies derive advantages from their manufacturing operations in mainland China, benefiting from cost efficiency and competitive production factors. This research contends that the extent of a China Taipei firm’s reliance on China significantly affects its performance. To elucidate the influence of China Taipei’s investments in mainland China on firm performance, we quantified China dependency by computing the ratio of a firm’s capital in mainland China to its total capital (7). The formula for China dependency is given as
(6)
The degree of internationalization (DOI) can be conceptualized and measured in various ways. Existing studies have utilized measures, such as the ratios of foreign sales to total sales (FSTS), foreign assets over total assets (FATA), the number of foreign offices to the total number of offices (FOTO), and the number of employees in foreign subsidiaries over total employees (FETE). One of the most commonly used and measurable indexes is the performance element represented by the ratio of foreign sales over total sales (FSTS), which is used in this study [7]. The formula for FSTS is given as (8)
(7)
Product diversification implies that firms have created and operated in more than one industry, one product line, or market. The prevalent methods to measure the degree of diversification in economic studies are Entropy and Herfindahl indices (9), which do not notably correlate but provide comparable outcomes [36]. We decided to use the Berry–Herfindahl index [66], which is defined by the following formula:
(8)
where mij is the proportion of the j classified group to the ith firm’s total sales, and j indicates the number of classified groups in which a firm operates. The values of the Herfindahl index (HHI) range from zero, when a firm operates in a single product market, to one, when a firm’s total sales are divided equally among any of the classified groups. More importantly, in this study, after calculating the HHI, we used a dummy variable to indicate the product diversification, written as “1” if a firm has a low degree of diversification (meaning a high HHI) and “0” if a firm has a high degree of diversification (meaning a low HHI).4. Results
In this section, we introduce the statistical analysis procedures and techniques employed in this study. We used STATA v.14 to present a summary of the statistical data, perform a generalized least squares (GLS) model calculation, and create a graphical representation. Performing a generalized least squares (GLS) analysis involves collecting and cleaning the data to ensure accuracy, specifying the model with the dependent and independent variables, and addressing assumptions like error covariance. A GLS adjusts for heteroscedasticity and autocorrelation, which is crucial for an accurate estimation. The validation includes checking the residuals and for multicollinearity. A GLS estimation calculates the coefficients while adjusting for covariance structures, with its interpretation involving assessing the significance, model fit, and using visual aids like scatter plots for validation. This method provides robust insights into data relationships [63].
The descriptive statistics and correlation matrix are presented in Table 1 and Table 2. Table 1 presents the descriptive statistics for a total of 360 observation samples, representing 360 China Taipei companies across eight industries. The dependent variable, CAR (Cumulative Abnormal Return), is considered for three different values: CAR060 (from the 0th day of the event to the 60th day), CAR030 (from the 0th day to the 30th day), and CAR015 (from the 0th day to the 15th day). The mean values for CAR060, CAR030, and CAR015 are −10.30156, −13.87839, and −4.390568, respectively.
The firm age has a mean value of 31.76667, with a maximum value of 73 and a minimum value of 2. The firm size has a mean value of 15.45619, while the mean value for the debt ratio is 1.69841. The non-labor intensity, represented by the natural logarithm of the ratio of employees to total assets, has a mean value of 9.045768. The mean values of supply chain management and China dependency are 28.77931 and 0.0856666, respectively. The moderator DOI, represented by FSTS, has a mean value of 0.7251911. The product diversification (nKHHI), using a dummy variable model, has a maximum value of 1 and a minimum value of 0.
4.1. Average of Abnormal Returns and Cumulative Abnormal Returns
Figure 2 depicts the trends in the average Abnormal Returns (ARs) and Cumulative Abnormal Returns (CARs) during the observation period of the coronavirus event for the firms in the eight key China Taipei industries. The red line, representing the average CARs of the firms, exhibits a significant downward trend from −90 days to +90 days. Notably, there is a pronounced decline, from −5% to −12%, in the average CARs after the event on February 12, 2024. Subsequently, the average CARs of the China Taipei firms experienced a steep drop to approximately −23% at the end of February, following a stable period from the 2nd day to the 28th day. Afterward, there was a recovery of the CARs of the China Taipei firms, reaching up to 14% from the 29th day to the 60th day. However, overall, the BRICS global tension had a negative impact on China Taipei firms’ CARs, as the average CARs showed a rebound but did not turn positive until the end of the observation period.
4.2. Results of Regression of Generalized Least Squares (GLS)
Model 1 served as the baseline model. Notably, its coefficients consistently indicated a positive effect and statistical significance (p < 0.001). Moving on to Model 2, we tested Hypotheses H1, H2, and H3 to scrutinize the impact of the independent variables on firms’ performance. Model 3 introduced square terms to capture a curved relationship between them. Model 4 expanded on Model 3 by including the two moderator variables (DOI and PD) to assess their moderating effects and the interaction of each independent variable. This aimed to examine if there was an interaction effect of diversification and firm-level factors on a firm’s performance. Model 5 incorporated the square terms of the interaction effect.
The statistical results allowed us to comprehend their intrinsic nature through visualized graphs. Specifically, according to Table 2, Model 3 reveals a significant positive relationship with a statistically significant value (p < 0.05) between the non-labor-intensive variable and the independent variable CAR060. This supports H1, there is a positive relationship between non-labor intensity and firms’ performance. Therefore, Figure 3 implies that a non-labor-intensive firm is likely to perform better.
Hypothesis 2 posits a positive relationship between a well-managed supply chain and the performance of the listed China Taipei firms. In fact, the statistical results exhibit a significant positive coefficient (p < 0.05) in Model 2. Therefore, as depicted in Figure 4, it is evident that the performance of supply chain management, represented by inventory turnovers, is positively associated with the performance of China Taipei firms.
To examine Hypothesis 3, we analyzed the regression results presented in Table 3, with CAR015 as the representative dependent variable. The outcome observed for Model 2 in Table 3 reveals a linear and negative relationship between China dependency and the dependent variable CAR015, supporting our Hypothesis 3: a negative relationship exists between China dependency and the performance of the listed China Taipei firms. It implies that the firms that are highly dependent on the Chinese market tended to perform worse during the BRICS global tension period. The visual representation of Hypothesis 5 is illustrated in Figure 5.
The results for Model 5 in Table 2 help confirm Hypotheses 4a and 4b. However, only product diversification is found to be significant in the relationship between non-labor intensive and China Taipei firms’ performance, supporting Hypothesis 4b. In other words, the coefficient of the product diversification interaction effect is significant. As shown in Figure 6, a high degree of product diversification moderates the association between non-labor intensity and performance, with a greater change than a firm with low product diversification. It can be understood that, when two firms have the same ratio of non-labor intensity, the highly product-diversified firm performs worse than the other in an economic shock context.
In the next section, based on Model 4 and Model 5 in Table 2, we can test Hypotheses 5a and 5b. The results support both Hypotheses 5a and 5b, indicating that both the DOI and PD have moderating effects on the relationship between supply chain management and China Taipei firms’ performance. As shown in Figure 7, the interaction terms between supply chain management and the DOI have a noticeable and positive effect on firms’ performance. The graph illustrates that a firm that performs well in supply chain management has a better overall performance with a higher DOI. However, the interaction terms between supply chain management and PD have a statistically significant impact on the performance of China Taipei firms, and it differs with low and high levels of product diversification. Firms with well-managed supply chains find it easier to achieve a good performance with a low degree of PD. Figure 7 shows the moderating effect of PD on the relationship between supply chain management and China Taipei’s financial performance.
Finally, based on Table 2 and its Model 5, Hypothesis 6b is supported, illustrating that the level of product diversification has a significant impact on the relationship between China dependency and the performance of the China Taipei companies. It is shown in Figure 8 that a firm that is highly dependent on the China market is likely to perform better with a high degree of PD rather than a lower degree of PD.
This data analysis section is a pivotal segment within the research, elucidating the systematic processes employed to interpret, organize, and derive meaningful insights from the collected data. It outlines the methodologies, techniques, and tools utilized to analyze the gathered information, aiming to uncover the patterns, trends, relationships, and associations relevant to the research objectives. This section not only expounds upon the specific analytical approaches, but also elucidates the reasoning behind their selection and their alignment with the research questions and hypotheses. Through a transparent documentation of the analytical procedures, this section contributes to the rigor and credibility of this study’s findings, offering a clear understanding of how the data were processed and interpreted to draw conclusions.
5. Discussion
In this study, our aim is to assess and analyze the influence of firm-level factors on the performance of China Taipei firms amid economic uncertainty, employing an event study approach to gauge the stock market responses to the BRICS global tension. The examined firm-level factors encompass non-labor intensity, supply chain management, and China dependency. Additionally, we explore how the moderating variables related to diversification strategies, specifically internationalization and product diversification, impact the relationship between these key predictors and the performance of China Taipei firms. The findings are presented in following Table 4.
This study examines how non-labor-intensive structures, supply chain management, and China dependency affected the performance of the China Taipei listed firms during the BRICS global tension. The findings indicate that non-labor-intensive firms, such as capital- or R&D-intensive companies, performed better by reducing their fixed costs or enhancing their dynamic capabilities, especially in times of economic uncertainty [12,15,67]. Effective supply chain management also positively correlated with firm performance by fostering stronger supplier relationships and production flexibility [25]. The firms with high dependency on the Chinese market saw poorer performance due to the BRICS global tension’s severe impact on China’s economy, as it disrupted operations and supply chains [44]. Notably, the firms with greater product diversification performed better despite their China dependency, as diversification helped them hedge their risks by reducing reliance on a single market [51]. The firms with higher product diversification experienced lower labor productivity, but diversification enabled them to maintain their performance by spreading the risks across different product segments [7]. The firms with strong supply chain networks achieved better performance with lower product diversification, as complex strategies become harder to manage when diversification increases.
This aligns with prior studies indicating that firms with higher capital intensity often optimize their internal efficiencies rather than seeking external market expansion [3,5,6,15]. From an institutional perspective, China Taipei firms face geopolitical uncertainties, trade regulations, and cultural barriers that may hinder the expected benefits of internationalization, making it less effective as a moderator. These findings contrast with the research suggesting that internationalization enhances firm performance by providing access to larger markets and diversified revenue streams [38,47,54].
Internationalization had no significant moderating impact on the relationships between non-labor intensity or China dependency and firm performance [34], but product diversification significantly moderated these relationships. The non-significant moderating impact of internationalization on the relationships between non-labor intensity, China dependency, and firm performance can be attributed to several underlying factors. The strategic benefits of internationalization may take longer to materialize, and firms operating in capital-intensive industries often require significant time and investment to establish a competitive presence in foreign markets. This delay could limit the immediate impact of internationalization as a moderator. Moreover, many China Taipei firms have deeply integrated supply chains and trade dependencies with China, making it difficult for internationalization to offset these entrenched business structures. The geopolitical and economic complexities involved in expanding into new markets may also reduce the expected benefits of internationalization, particularly in industries where competitive advantages are strongly tied to domestic capabilities.
Industry-specific characteristics likely influenced the effectiveness of internationalization as a moderating factor. Firms in technology-driven sectors may benefit more from international expansion due to access to global innovation networks, whereas traditional manufacturing firms may face higher operational costs and regulatory barriers that limit their international growth potential. Institutional differences in foreign markets may impose challenges on firms trying to diversify, leading to inefficiencies that diminish the potential performance benefits. The results highlight that firms in China Taipei continue to navigate geopolitical risks and economic uncertainties while maintaining performance stability. This finding supports the previous literature on adaptive strategies in volatile business environments [3,6,68].
6. Conclusions
This study provides valuable insights into the strategic factors that influence the performance of China Taipei firms in a highly competitive global business environment. By examining the effects of non-labor intensity, supply chain management, and dependency on China, the findings underscore the importance of internationalization and product diversification for mitigating risks and enhancing firm resilience. The results indicate that firms with strong supply chain networks and diversified product offerings tend to perform better, particularly in volatile economic conditions. Furthermore, this study highlights that an over-reliance on China presents significant vulnerabilities, reinforcing the need for firms to explore alternative markets and operational strategies.
Even though this study was successfully completed, we realize that there are still limitations that should be improved. First, this study faced data availability constraints, as some firms lacked complete financial records, leading to the exclusion of certain cases from the final sample. Second, during the observation period, there were unexpected events that happened, including geopolitical developments, such as trade restrictions and policy shifts, external economic shocks, the COVID-19 pandemic, and supply chain disruptions, which introduced unforeseen variables that may have influenced firm performance in ways that were not initially anticipated.
This study provides practical recommendations for China Taipei firms, emphasizing innovation, R&D investment, and strengthening supply chain partnerships to enhance resilience. Firms are encouraged to employ product diversification strategically to mitigate external risks while considering industry-specific characteristics. Future research should expand the sample, incorporate additional internationalization variables, and explore long-term changes through longitudinal studies.
Conceptualization, A.A.A.; Methodology, A.A.A. and Y.P.A.; Formal analysis, Y.P.A. and R.O.D.; Resources, Y.P.A.; Data curation, Y.P.A.; Writing—original draft, A.A.A.; Writing—review & editing, A.Y.K.; Visualization, A.A.A. and A.Y.K.; Project administration, Y.P.A., R.O.D. and I.N.F. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The data supporting the findings of this study are available from the corresponding author, Yudhistira Pradhipta Aryoko (Email address: [email protected]), upon reasonable request.
The authors would like to thank to Universitas Muhammadiyah Purwokerto and the National Yunlin University of Science and Technology, which supported and collaborated on this research.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 2. Averages of Abnormal Returns and Cumulative Abnormal Returns. Source: Secondary data from TEJ database, processed in 2024.
Enlarge this image.Figure 3. Plot test of the relationship between non-labor intensive and firms’ performance.
Enlarge this image.Figure 6. The relationship between non-labor intensive and firms’ performance moderated by product diversification.
Enlarge this image.Figure 7. The moderating effect of the DOI on the relationship between supply chain management and CAR060.
Enlarge this image.Figure 8. The relationship between China dependency and firms’ performance moderated by product diversification.
Descriptive statistics.
| Variables | Obs | Mean | Std. Dev. | Min | Max |
|---|---|---|---|---|---|
| CAR060 | 360 | −10.30156 | 17.51424 | −93.1136 | 65.5854 |
| CAR030 | 360 | −13.87839 | 13.53278 | −80.1993 | 31.1005 |
| CAR015 | 360 | −4.390568 | 7.763321 | −30.3504 | 28.5337 |
| Non_lain | 360 | 9.045768 | 1.347787 | 6.641456 | 13.86264 |
| supchain | 360 | 28.77931 | 202.7722 | 0 | 230 |
| ChinaD | 360 | 0.0856666 | 0.1112107 | 0 | 1.186713 |
| DOI | 360 | 0.7251911 | 0.4320225 | 0 | 1.8866 |
| nKHHI | 360 | 0.5277778 | 0.4999226 | 0 | 1 |
Results of the study with CAR060.
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|
| CAR060 | CAR060 | CAR060 | CAR060 | CAR060 | |
| Non_lain | −0.491 | 15.02 ** | 18.67 ** | 4.291 | |
| supchain | 0.0203 ** | 0.0292 *** | −0.0166 | −0.0329 | |
| ChinaD | −1.151 | 11.79 | 5.323 | −28.50 | |
| Non_lain2 | −0.799 ** | −0.874 ** | −0.148 | ||
| supchain2 | −0.0000305 ** | −0.00000528 | 0.00000518 | ||
| ChinaD2 | −26.38 | −22.62 | 64.10 | ||
| DOI | 10.99 | −1.866 | |||
| nKHHI | 9.784 | −123.5 * | |||
| DOI × Non_lain | −1.700 | 1.464 | |||
| DOI × supchain | −0.0501 | −0.0675 * | |||
| DOI × ChinaD | 25.58 | 7.318 | |||
| nKHHI × Non_lain | −1.138 | 26.25 * | |||
| nKHHI × supchain | 0.0550 ** | 0.0776 | |||
| nKHHI × ChinaD | −16.74 | 40.78 | |||
| DOI × Non_lain2 | −0.147 | ||||
| DOI × supchain2 | 0.000222 * | ||||
| DOI × ChinaD2 | 47.50 | ||||
| nKHHI × Non_lain2 | −1.418 * | ||||
| nKHHI × supchain2 | −0.0000909 | ||||
| nKHHI × ChinaD2 | −136.5 * | ||||
| _cons | −30.11 ** | −35.22 ** | −114.8 ** | −129.9 ** | −64.68 |
| (−2.99) | (−3.15) | (−3.12) | (−3.16) | (−0.80) | |
| N | 360 | 360 | 360 | 360 | 360 |
t statistics in parentheses. * p < 0.10; ** p < 0.05; *** p < 0.001.
Results of the study with CAR015.
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|
| CAR015 | CAR015 | CAR015 | CAR015 | CAR015 | |
| Non_lain | 0.114 | 10.46 ** | 10.33 ** | 1.590 | |
| supchain | 0.00308 | 0.00553 * | −0.00505 | −0.0115 | |
| ChinaD | −7.233 * | −12.00 * | 2.060 | 5.275 | |
| Non_lain2 | −0.542 ** | −0.517 ** | −0.0680 | ||
| supchain2 | −0.00000419 | 0.00000210 | 0.00000845 | ||
| ChinaD2 | 4.823 | 9.015 | −2.393 | ||
| DOI | 4.636 | −42.50 | |||
| nKHHI | −0.0721 | −19.71 | |||
| DOI × Non_lain | −0.547 | 9.649 | |||
| DOI × supchain | 0.00139 | −0.000547 | |||
| DOI × ChinaD | −8.712 | −26.42 | |||
| nKHHI × Non_lain | 0.280 | 4.172 | |||
| nKHHI × supchain | 0.0104 | 0.0165 | |||
| nKHHI × ChinaD | −12.31 | 2.644 | |||
| DOI × Non_lain2 | −0.523 | ||||
| DOI × supchain2 | 0.0000293 | ||||
| DOI × ChinaD2 | 49.23 | ||||
| nKHHI × Non_lain2 | −0.205 | ||||
| nKHHI × supchain2 | −0.0000166 | ||||
| nKHHI × ChinaD2 | −31.12 | ||||
| _cons | −7.173 | −15.47 ** | −68.59 *** | −67.30 *** | −25.47 |
| (−1.59) | (−3.09) | (−4.21) | (−3.68) | (−0.70) | |
| N | 360 | 360 | 360 | 360 | 360 |
t statistics in parentheses. * p < 0.10; ** p < 0.05; *** p < 0.001.
Hypothesis summary.
| Hypothesis | Results |
|---|---|
| H1: A positive relationship exists between non-labor intensive and firms’ performance. | Supported |
| H2: A positive relationship exists between supply chain management and firms’ performance. | Supported |
| H3: A negative relationship exists between China dependency and firms’ performance. | Supported |
| H4a: Internationalization moderates the relationship between non-labor intensive and firms’ performance. | Not supported |
| H4b: Product diversification moderates the relationship between non-labor intensive and firms’ performance. | Supported |
| H5a: Internationalization moderates the relationship between supply chain management and firms’ performance. | Supported |
| H5b: Product diversification moderates the relationship between supply chain management and firms’ performance. | Supported |
| H6a: Internationalization moderates the relationship between China dependency and firms’ performance. | Not supported |
| H6b: Product diversification moderates the relationship between China dependency and firms’ performance. | Supported |
References
1. Fernandes, N. Economic Effects of Coronavirus Outbreak (COVID-19) on the World Economy. SSRN Electron. J.; 2020; [DOI: https://dx.doi.org/10.2139/ssrn.3557504]
2. Silva, G.M.; Dias, Á.L.; Lisboa, A.C.; Silva, F.P. Drivers and Outcomes of Sustainable Export Marketing Strategies in International Environments. Rev. Int. Bus. Strategy; 2023; 33, pp. 627-648. [DOI: https://dx.doi.org/10.1108/RIBS-05-2022-0056]
3. Viktora-Jones, M.; Parente, R.; Drori, N.; Zhao, Y. Firm Performance Drivers within a Dynamic Emerging Market Ecosystem. J. Int. Manag.; 2024; 30, 101119. [DOI: https://dx.doi.org/10.1016/j.intman.2023.101119]
4. Malhotra, G.; Dandotiya, G.; Shaiwalini, S.; Khan, A.; Homechaudhuri, S. Benchmarking for Organisational Competitiveness: A Resource-Based View Perspective. Benchmarking; 2024; ahead of print [DOI: https://dx.doi.org/10.1108/BIJ-09-2023-0668]
5. Gao, Z.; Li, L.; Hao, Y. Resource Industry Dependence and High-Quality Economic Development of Chinese Style: Reexamining the Effect of the “Resource Curse”. Struct. Change Econ. Dyn.; 2024; 68, pp. 1-16. [DOI: https://dx.doi.org/10.1016/j.strueco.2023.09.013]
6. Canh, N.P.; Thanh, S.D. The Dynamics of Export Diversification, Economic Complexity and Economic Growth Cycles: Global Evidence. Foreign Trade Rev.; 2022; 57, pp. 234-260. [DOI: https://dx.doi.org/10.1177/0015732520970441]
7. Lee, C.-Y.; Wen, C.-R.; Thi-Thanh-Nguyen, B. Board Expertise Background and Firm Performance. Int. J. Financ. Stud.; 2024; 12, 17. [DOI: https://dx.doi.org/10.3390/ijfs12010017]
8. Ding, C.; Ma, Q. Does Social Trust Promote the Size of Enterprises? Evidence from China. J. Asian Econ.; 2024; 91, 101705. [DOI: https://dx.doi.org/10.1016/j.asieco.2023.101705]
9. Saunila, M.; Ukko, J.; Kinnunen, J. Sustainability Partnership as a Moderator in the Relationship between Business Sustainability and Firm Competitiveness. Bus. Strategy Environ.; 2024; 33, pp. 123-133. [DOI: https://dx.doi.org/10.1002/bse.3493]
10. Montes Ninaquispe, J.C.; Vasquez Huatay, K.C.; Ludeña Jugo, D.A.; Pantaleón Santa María, A.L.; Farías Rodríguez, J.C.; Suárez Santa Cruz, F.; Escalona Aguilar, E.O.; Arbulú-Ballesteros, M.A. Market Diversification and Competitiveness of Fresh Grape Exports in Peru. Sustainability; 2024; 16, 2528. [DOI: https://dx.doi.org/10.3390/su16062528]
11. Islam, S.; Syed Shazali, S.T. Determinants of Manufacturing Productivity: Pilot Study on Labor-intensive Industries. Int. J. Product. Perform. Manag.; 2011; 60, pp. 567-582. [DOI: https://dx.doi.org/10.1108/17410401111150751]
12. Huang, Y.; Ma, Y.; Yang, Z.; Zhang, Y. A Fire Sale without Fire: An Explanation of Labor-Intensive FDI in China. J. Comp. Econ.; 2016; 44, pp. 884-901. [DOI: https://dx.doi.org/10.1016/j.jce.2016.04.007]
13. Huang, J.-W.; Li, Y.-H. Slack Resources in Team Learning and Project Performance. J. Bus. Res.; 2012; 65, pp. 381-388. [DOI: https://dx.doi.org/10.1016/j.jbusres.2011.06.037]
14. Wang, L.-H.; Lin, C.-H.; Fung, H.-G.; Kao, T.-C. Foreign Direct Investment and Downside Risk: Evidence from Taiwan. Pac.-Basin Financ. J.; 2019; 57, 101114. [DOI: https://dx.doi.org/10.1016/j.pacfin.2019.01.010]
15. Zhou, C.; Witteloostuijn, A.; Zhang, J. The Internationalization of Chinese Industries: Overseas Acquisition Activity in Chinese Mining and Manufacturing Industries. Asian Bus. Manag.; 2014; 13, pp. 89-116. [DOI: https://dx.doi.org/10.1057/abm.2014.1]
16. Lubatkin, M.; Chatterjee, S. Extending Modern Portfolio Theory into the Domain of Corporate Diversification: Does It Apply?. Acad. Manag. J.; 1994; 37, pp. 109-136. [DOI: https://dx.doi.org/10.2307/256772]
17. Lee, S. Effects of Capital Intensity on Firm Performance: The U.S. Restaurant Industry. J. Hosp. Financ. Manag.; 2010; 18, pp. 1-13. [DOI: https://dx.doi.org/10.1080/10913211.2010.10653882]
18. Del Monte, A.; Papagni, E. R & D and the Growth of Firms: Empirical Analysis of a Panel of Italian Firms. Res. Policy; 2003; 32, pp. 1003-1014. [DOI: https://dx.doi.org/10.1016/S0048-7333(02)00107-5]
19. Barney, J.B. Resource-Based Theories of Competitive Advantage: A Ten-Year Retrospective on the Resource-Based View. J. Manag.; 2001; 27, pp. 643-650. [DOI: https://dx.doi.org/10.1177/014920630102700602]
20. Wernerfelt, B. A Resource-based View of the Firm. Strateg. Manag. J.; 1984; 5, pp. 171-180. [DOI: https://dx.doi.org/10.1002/smj.4250050207]
21. Teece, D.J. Business Models and Dynamic Capabilities. Long Range Plan.; 2018; 51, pp. 40-49. [DOI: https://dx.doi.org/10.1016/j.lrp.2017.06.007]
22. Wang, C.L.; Ahmed, P.K. Dynamic Capabilities: A Review and Research Agenda. Int. J. Manag. Rev.; 2007; 9, pp. 31-51. [DOI: https://dx.doi.org/10.1111/j.1468-2370.2007.00201.x]
23. Lome, O.; Heggeseth, A.G.; Moen, Ø. The Effect of R & D on Performance: Do R & D-Intensive Firms Handle a Financial Crisis Better?. J. High Technol. Manag. Res.; 2016; 27, pp. 65-77. [DOI: https://dx.doi.org/10.1016/j.hitech.2016.04.006]
24. Kannan, V. Just in Time, Total Quality Management, and Supply Chain Management: Understanding Their Linkages and Impact on Business Performance. Omega; 2005; 33, pp. 153-162. [DOI: https://dx.doi.org/10.1016/j.omega.2004.03.012]
25. Tripathi, S.; Talukder, B.; Rangarajan, K. Do Supply Chain Performance Influence Firm Profitability? A Predictive Approach in the Context of the Indian Pharmaceutical Industry. IIM Kozhikode Soc. Manag. Rev.; 2024; 13, pp. 166-183. [DOI: https://dx.doi.org/10.1177/22779752211003453]
26. Viergutz, K.; Schmidt, C. Demand Responsive-vs. Conventional Public Transportation: A MATSim Study about the Rural Town of Colditz, Germany. Procedia Comput. Sci.; 2019; 151, pp. 69-76. [DOI: https://dx.doi.org/10.1016/j.procs.2019.04.013]
27. Tsai, C.-Y. The Impact of Cost Structure on Supply Chain Cash Flow Risk. Int. J. Prod. Res.; 2017; 55, pp. 6624-6637. [DOI: https://dx.doi.org/10.1080/00207543.2017.1330568]
28. Xu, L.; Smith, D.L. Combining Traditional Accounting and Value-Added Activity Measures to Maximize Supply Chain Performance. J. Account. Financ.; 2018; 18, pp. 80-93. [DOI: https://dx.doi.org/10.33423/jaf.v18i3.414]
29. Ou, C.S.; Liu, F.C.; Hung, Y.C.; Yen, D.C. A Structural Model of Supply Chain Management on Firm Performance. Int. J. Oper. Prod. Manag.; 2010; 30, pp. 526-545. [DOI: https://dx.doi.org/10.1108/01443571011039614]
30. Lakner, Z.; Kiss, A.; Popp, J.; Zéman, Z.; Máté, D.; Oláh, J. From Basic Research to Competitiveness: An Econometric Analysis of the Global Pharmaceutical Sector. Sustainability; 2019; 11, 3125. [DOI: https://dx.doi.org/10.3390/su11113125]
31. Seidel, T.; Vakkuri, J. Institutional Change of Quasi-Market Arrangements in Local Public Transportation–Comparative Observations from Germany and Finland. Hrvat. Komparat. Javna Uprava Čas. Teor. Praksu Javne Uprave; 2015; 15, pp. 595-615.
32. Hsu, C.-M.; Liu, W.-C. The Role of Taiwanese Foreign Direct Investment in China: Economic Integration or Hollowing-Out?. Economic Integration and Multinational Investment Behaviour; Edward Elgar Publishing: Cheltenham, UK, 2004; pp. 403-421. [DOI: https://dx.doi.org/10.4337/9781035305117.00026]
33. Chen, X. Taiwan Investments in China and Southeast Asia: “Go West, but Also Go South. Asian Surv.; 1996; 36, pp. 447-467. [DOI: https://dx.doi.org/10.2307/2645493]
34. Wang, H.; Chen, Y. The Impact of Population Aging on Capital Structure Decisions and Capital Market Efficiency: Evidence from China. Int. Rev. Financ. Anal.; 2024; 95, 103408. [DOI: https://dx.doi.org/10.1016/j.irfa.2024.103408]
35. Liou, J.-L.; Hsu, P.-C.; Wu, P.-I. The Effect of China’s Open-Door Tourism Policy on Taiwan: Promoting or Suppressing Tourism from Other Countries to Taiwan?. Tour. Manag.; 2020; 78, 104055. [DOI: https://dx.doi.org/10.1016/j.tourman.2019.104055]
36. Schommer, M.; Richter, A.; Karna, A. Does the Diversification–Firm Performance Relationship Change Over Time? A Meta-Analytical Review. J. Manag. Stud.; 2019; 56, pp. 270-298. [DOI: https://dx.doi.org/10.1111/joms.12393]
37. Chen, S.-S. The Economic Impact of Corporate Capital Expenditures: Focused Firms versus Diversified Firms. J. Financ. Quant. Anal.; 2006; 41, pp. 341-355. [DOI: https://dx.doi.org/10.1017/S002210900000209X]
38. Volkov, N.I.; Smith, G.C. Corporate Diversification and Firm Value during Economic Downturns. Q. Rev. Econ. Financ.; 2015; 55, pp. 160-175. [DOI: https://dx.doi.org/10.1016/j.qref.2014.08.002]
39. Le, D.-N.; Nguyen, V.-H. Does Quality Certification or Product Diversification Improve the Performance of Small and Medium Enterprises?. Sustainability; 2024; 16, 2023. [DOI: https://dx.doi.org/10.3390/su16052023]
40. Hughes, J.S.; Logue, D.E.; Sweeney, R.J. Corporate International Diversification and Market Assigned Measures of Risk and Diversification. J. Financ. Quant. Anal.; 1975; 10, 627. [DOI: https://dx.doi.org/10.2307/2330611]
41. Sulistiyani, R.; Harwiki, W. How SMEs Build Innovation Capability Based on Knowledge Sharing Behavior?. Phenomenol. Approach. Procedia Soc. Behav. Sci.; 2016; 219, pp. 741-747. [DOI: https://dx.doi.org/10.1016/j.sbspro.2016.05.070]
42. Sunarjo, A.I.W.A.; Nurhayati, S. SMEs as a Driver of Integrative Dynamic Capability towards the Three Pillars of Sustainability Performance. Hongkong J. Soc. Sci.; 2022; 58, pp. 656-665.
43. Brouthers, K.D.; Brouthers, L.E.; Werner, S. Transaction Cost-enhanced Entry Mode Choices and Firm Performance. Strateg. Manag. J.; 2003; 24, pp. 1239-1248. [DOI: https://dx.doi.org/10.1002/smj.362]
44. Brouthers, K.D.; Geisser, K.D.; Rothlauf, F. Explaining the internationalization of ibusiness firms. J. Int. Bus. Stud.; 2016; 47, pp. 513-534. [DOI: https://dx.doi.org/10.1057/jibs.2015.20]
45. Dunning, J.H. The Eclectic (OLI) Paradigm of International Production: Past, Present and Future. Int. J. Econ. Bus.; 2001; 8, pp. 173-190. [DOI: https://dx.doi.org/10.1080/13571510110051441]
46. Lee, K.C.; Kwok, C.C.Y. Multinational Corporations vs. Domestic Corporations: International Environmental Factors and Determinants of Capital Structure. J. Int. Bus. Stud.; 1988; 19, pp. 195-217. [DOI: https://dx.doi.org/10.1057/palgrave.jibs.8490381]
47. Vithessonthi, C. Capital Investment and Internationalization. J. Econ. Bus.; 2017; 90, pp. 31-48. [DOI: https://dx.doi.org/10.1016/j.jeconbus.2016.12.003]
48. Knight, G.A.; Liesch, P.W. Internationalization: From Incremental to Born Global. J. World Bus.; 2016; 51, pp. 93-102. [DOI: https://dx.doi.org/10.1016/j.jwb.2015.08.011]
49. Kumar, V.; Singh, N. Internationalization and Performance of Indian Pharmaceutical Firms. Thunderbird Int. Bus. Rev.; 2008; 50, pp. 321-330. [DOI: https://dx.doi.org/10.1002/tie.20217]
50. Lin, Y.; Zhang, S.; Shi, Y. The Impact of Operational Stickiness on Product Quality: Product Diversification Moderation. J. Manuf. Technol. Manag.; 2020; 32, pp. 423-447. [DOI: https://dx.doi.org/10.1108/JMTM-09-2020-0360]
51. Doukas, J.A.; Lang, L.H.P. Foreign Direct Investment, Diversification and Firm Performance. J. Int. Bus. Stud.; 2023; 34, pp. 153-172. [DOI: https://dx.doi.org/10.1057/palgrave.jibs.8400014]
52. Penrose, J. The Theory of the Growth of the Firm; Oxford University Press: Oxford, UK, 2010; Volume 1, [DOI: https://dx.doi.org/10.1093/oso/9780198753940.003.0011]
53. Qian, G. Multinationality, Product Diversification, and Profitability of Emerging US Small- and Medium-Sized Enterprises. J. Bus. Ventur.; 2002; 17, pp. 611-633. [DOI: https://dx.doi.org/10.1016/S0883-9026(01)00080-5]
54. Shin, J.; Mendoza, X.; Hawkins, M.A.; Choi, C. The Relationship between Multinationality and Performance: Knowledge-Intensive vs. Capital-Intensive Service Micro-Multinational Enterprises. Int. Bus. Rev.; 2017; 26, pp. 867-880. [DOI: https://dx.doi.org/10.1016/j.ibusrev.2017.02.005]
55. Untoro, W.; Rahardian, R. Firm Size and Diversification Strategies: Does Labor Intensity Matter?. Corp. Ownersh. Control; 2015; 12, pp. 327-331. [DOI: https://dx.doi.org/10.22495/cocv12i4c2p8]
56. Fukui, T.; Mitton, T.; Schonlau, R.J. Determinants of Capital Structure: An Expanded Assessment. J. Financ. Quant. Anal.; 2023; 58, pp. 2446-2488. [DOI: https://dx.doi.org/10.1017/S0022109022001405]
57. Vachon, S.; Klassen, R.D. Environmental Management and Manufacturing Performance: The Role of Collaboration in the Supply Chain. Int. J. Prod. Econ.; 2008; 111, pp. 299-315. [DOI: https://dx.doi.org/10.1016/j.ijpe.2006.11.030]
58. Nishat Faisal, M. Sustainable Supply Chains: A Study of Interaction among the Enablers. Bus. Process Manag. J.; 2010; 16, pp. 508-529. [DOI: https://dx.doi.org/10.1108/14637151011049476]
59. Tan, E.N.; Smith, G.; Saad, M. Managing the Global Supply Chain: A SME Perspective. Prod. Plan. Control; 2006; 17, pp. 238-246. [DOI: https://dx.doi.org/10.1080/09537280500285417]
60. Giovannetti, G.; Marvasi, E.; Sanfilippo, M. Supply Chains and the Internationalization of Small Firms. Small Bus. Econ.; 2015; 44, pp. 845-865. [DOI: https://dx.doi.org/10.1007/s11187-014-9625-x]
61. Narasimhan, R.; Kim, S.W. Effect of Supply Chain Integration on the Relationship between Diversification and Performance: Evidence from Japanese and Korean Firms. J. Oper. Manag.; 2002; 20, pp. 303-323. [DOI: https://dx.doi.org/10.1016/S0272-6963(02)00008-6]
62. Luo, L.P.; Zhang, J. Public Transportation OD Calculation Based on Trip Chain. Appl. Mech. Mater.; 2013; 401–403, pp. 2151-2154.
63. Hair, J.; Black, W.; Babin, B.; Anderson, R. Multivariate Data Analysis; 7th ed. Pearson Education: Upper Saddle River, NJ, USA, 2014.
64. Sun, P.W.; Wen, Z. Stock Return Predictability of the Cumulative Abnormal Returns around the Earnings Announcement Date: Evidence from China. Int. Rev. Financ.; 2023; 23, pp. 58-86. [DOI: https://dx.doi.org/10.1111/irfi.12380]
65. Amabile, T.M.; Gryskiewicz, N.D. The Creative Environment Scales: Work Environment Inventory. Creat. Res. J.; 1989; 2, pp. 231-253. [DOI: https://dx.doi.org/10.1080/10400418909534321]
66. Jacquemin, A.P.; Berry, C.H. Entropy Measure of Diversification and Corporate Growth. J. Ind. Econ.; 1979; 27, 359. [DOI: https://dx.doi.org/10.2307/2097958]
67. Wang, Q.; Zhang, F. The Effects of Trade Openness on Decoupling Carbon Emissions from Economic Growth—Evidence from 182 Countries. J. Clean. Prod.; 2021; 279, 123838. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.123838] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32863606]
68. Ericson, R.; Pakes, A. Markov-Perfect Industry Dynamics: A Framework for Empirical Work. Rev. Econ. Stud.; 1995; 62, 53. [DOI: https://dx.doi.org/10.2307/2297841]
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; Aryoko, Yudhistira Pradhipta 2
; Rhis Ogie Dewandaru 3
; Kharismasyah, Alfato Yusnar 1
; Fatchan, Ilham Nuryana 3 1 Department of Management, Faculty of Economics and Business, Universitas Muhammadiyah Purwokerto, Purwokerto 53182, Indonesia;
2 Department of Management, Faculty of Economics and Business, Universitas Muhammadiyah Purwokerto, Purwokerto 53182, Indonesia;
3 Department of Accounting, Faculty of Economics and Business, Universitas Muhammadiyah Purwokerto, Purwokerto 53182, Indonesia;