Theoretical background

The RBV is an effective theoretical framework by which to explain how enterprises obtain and maintain competitive advantages (Cui & O'Connor, Reference Cui and O'Connor2012; Peteraf, Reference Peteraf1993; Sammarra & Biggiero, Reference Sammarra and Biggiero2008). This theory assumes that resources are distributed across different enterprises, and resource differences persist over time (Huang, Chen, & Liang, Reference Huang, Chen and Liang2018). Therefore, when enterprises own specific or unusual resources, they can obtain sustainable competitive advantages by implementing new strategies for creating value, and these strategies are difficult for competitors to copy (Conner & Prahalad, Reference Conner and Prahalad1996; Lopez-Vega et al., Reference Lopez-Vega, Tell and Vanhaverbeke2016). In addition, if the acquired resources are complementary to enterprises' related activity systems, the enterprises' potential to create lasting competitive advantages will increase (Cassiman & Veugelers, Reference Cassiman and Veugelers2006; Felipe, Monteiro, & Birkinshaw, Reference Felipe, Monteiro and Birkinshaw2016). To explain why and how some enterprises possess competitive advantages in the context of rapidly and irrevocably changing environments, scholars have begun to extend the RBV to dynamic markets (Jiang, Tao, & Santoro, Reference Jiang, Tao and Santoro2010; Teece, Pisano, & Shuen, Reference Teece, Pisano and Shuen1997). In dynamic markets with fast-changing competition patterns, dynamic capabilities become a source of sustainable competitive advantages – i.e., through managers' ability to rapidly integrate, build, and reallocate internal and external advantages to cope with changing environments (Jiang, Tao, & Santoro, Reference Jiang, Tao and Santoro2010; Nilssen, Bertheussen, & Dreyer, Reference Nilssen, Bertheussen and Dreyer2015). The resources that can be used to execute value-creation strategies, such as special equipment, scientific research personnel, and organizational structure, constitute the core foundation of enterprises' competitiveness (Mcevily & Marcus, Reference McEvily and Marcus2005). Therefore, there is a need to study the influence of subject heterogeneity of enterprises' available resources on innovation performance.

Many studies have indicated that the scale of knowledge acquisition significantly impacts the positive effect of technological M&As on innovation performance (Ahuja & Katila, Reference Ahuja and Katila2004; Datta & Roumani, Reference Datta and Roumani2015). Particularly in high-tech industries with high knowledge density, external knowledge can be effectively acquired through technological M&As in the face of rapid technological innovation and highly complex environments. However, some scholars feel not only that innovation should come from new knowledge, but that more importance should be attached to recombining different types of existing knowledge (Lee, Shin, & Kim, Reference Lee, Shin and Kim2019). In other words, even if acquiring firms can gain numerous knowledge resources conducive to innovation through technological M&As, they may not necessarily improve their innovation performance directly (Cefis & Triguero, Reference Cefis and Triguero2016; Valentini, Reference Valentini2016). If acquiring firms fail to add a suitable knowledge resource, or if this resource is insignificant, the innovative risk is very high. Datta and Roumani (Reference Datta and Roumani2015) discussed M&As based on the essence of the knowledge base, and argued that when the characteristics of the knowledge base from both sides are not the same, acquiring firms obtain a ‘mixed bag’ of useful and useless knowledge from target firms. In other words, when the heterogeneity of resources is too high, it is difficult for acquiring firms to integrate and apply these knowledge resources to its own knowledge base; on the contrary, many problems, such as storage costs or capacity risk, arise as a consequence. Despite the abovementioned research, few empirical studies have discussed the heterogeneous characteristics of technological M&As and innovation performance. According to the RBV, knowledge is a resource; thus discussing the heterogeneous characteristics of resources offers several theoretical and practical implications. This study focuses on three characteristics of acquired firms' resources to investigate the impact of heterogeneity on innovation performance: financial slack resources, governance structure, and equity structure.

Financial slack resources and innovation performance

Resources comprise an essential guarantee of, and are a necessary premise for, R&D and innovation within high-tech enterprises (Mazzarol, Reboud, & Soutar, Reference Mazzarol, Reboud and Soutar2009). In terms of the RBV, previous studies have broadly distinguished two kinds of organizational slack: absorbed slack and unabsorbed slack (Tan & Peng, Reference Tan and Peng2003). Liu, Chen, and Wang (Reference Liu, Chen and Wang2017) stated that absorbed slack can be regarded as existing fixed assets within the organization, which are difficult to apply strategically in a flexible way; although unabsorbed slack can be seen as logistic support for innovation at any time, which meets resource demands for high-tech enterprises when they conceive creative projects and new innovative strategies. Nevertheless, in technological M&As there are two kinds contained in the slack absorbed by acquired firms. It is clear that unabsorbed slack can improve innovation performance, but there exists a knowledge gap in the discussion about absorbed slack. Liu et al. (Reference Liu, Chen and Wang2017) and Liu, Ding, Guo, and Luo (Reference Liu, Ding, Guo and Luo2014) considered previous studies on the slack–innovation link, which also verified that absorbed slack has a positive effect on innovation. However, these studies were conducted in the non-M&A context, indicating that the discussion in the context of technological M&As remains underdeveloped. In the current study, financial slack resources are cited as one of the factors of heterogeneity, since, whether for acquiring firms or target firms, the attributes of unabsorbed slack are similar and highly compatible, and acquiring firms can be directly applied in existing or future innovation activities (Voss, Sirdeshmukh, & Giraud Voss, Reference Voss, Sirdeshmukh and Giraud Voss2008). However, some absorbed slack may be difficult to integrate due to differences in resource characteristics. For instance, the inventory or exclusive assets of target firms may give rise to extra handling costs for acquiring firms.

Financial slack resources consist of redundancy of cash and redundancy of liabilities, referring to the financial resources that are retained within enterprises and exceed the requirements of daily operations (Sorenson & Stuart, Reference Sorenson and Stuart2001). Existing literature has generally posited that financial redundancy can promote enterprises' R&D investment (Troilo, Reference Troilo2014; Voss, Sirdeshmukh, & Giraud Voss, Reference Voss, Sirdeshmukh and Giraud Voss2008), but agreement has not yet been reached on the impact of financial redundancy on enterprise innovation performance. In addition, some scholars hold the opposite opinion, believing that slack resources may make managers feel overly comfortable and lack motivation to promote innovation, thereby reducing investment in R&D (Nerkar & Atul, Reference Nerkar and Atul2003). In technological M&As, acquiring firms need unabsorbed financial slack resources that provide capital when they conduct new product development or innovative behaviors. Since the innovation of high-tech firms is focused on speed and quality, absorbed financial slack resources make the firms dedicate more time to integration and screening, thereby indirectly increasing time for innovation (Ma & Liu, Reference Ma and Liu2017). Moreover, Herold, Jayaraman, and Narayanaswamy (Reference Herold, Jayaraman and Narayanaswamy2006) suggested that financial redundancy is synonymous with ‘waste’ and ‘inefficiency,’ and the amount of slack resources held is negatively correlated with innovation efficiency. With respect to the RBV and technological M&As, although specific assets and investments of target firms can be regarded as financial slack resources, in the case of low compatibility these idle assets may make it difficult for acquiring firms to allocate them effectively (Chen, Reference Chen2020; Chen, Li, & Meng, Reference Chen, Huyghebaert, Lin and Wang2019). Thus, in seeking to utilize these financial slack resources managers may erroneously decide to change the existing innovation-oriented resource allocation, thereby failing to facilitate the approval of fund usage in the face of uncertainty and innovative stream (Ma & Liu, Reference Ma and Liu2017). Based on above arguments, this study proposes that financial slack resources in technological M&As may not merely enable managers to dispose of more resources and lead to intensified agency problems, but also mean that the controlling shareholders' decisions on strategic innovation behavior cannot be effectively supervised. Thus, Hypothesis 1 is proposed as follows.

Governance structure and innovation performance

Governance structure refers to the formal structure, informal structure, and processes within the company that entails regulatory roles and responsibilities (Hambrick, Werder, & Zajac, Reference Hambrick, Werder and Zajac2008). The board of directors is a key part of corporate governance (Sapra, Subramanian, & Subramanian, Reference Sapra, Subramanian and Subramanian2015). Existing research has shown that board size has a significant impact on the innovation ability of an enterprise (Felipe, Monteiro, & Birkinshaw, Reference Felipe, Monteiro and Birkinshaw2016; Gerard & Wood, Reference George, Robley Wood and Khan2001). An active board of directors can provide complementary scientific advice to aid technology innovation decisions by managers, improve the efficiency and accuracy of decisions, and promote R&D innovation activities of enterprises (Borch, Huse, & Senneseth, Reference Borch, Huse and Senneseth1999; Gabrielsson & Politis, Reference Gabrielsson and Politis2007). However, an excessively large board of directors will reduce the decision influence of individual directors and increase the cost of poor decisions made by individuals, thus giving rise to the ‘free-rider’ phenomenon (Lipton & Lorsch, Reference Lipton and Lorsch1992; Müller & Victor-Octavian, Reference Müller2014).

Prior to technological M&As, target firms' managers are likely to give up good investment opportunities and cut down R&D spending funds and personnel (Alzoubi, Reference Alzoubi2016; Khafid & Arief, Reference Khafid and Arief2017), thus harming the interests of shareholders who are at an informational disadvantage (Jensen & Meckling, Reference Jensen and Meckling2007). Through technological M&As, independent directors of acquiring firms can make public comments on the proposals of the board of directors from a third-party standpoint, significantly reducing target firms' opportunistic behaviors and meaningless decision-making (Hamidi & Gabrielsson, Reference Hamidi and Gabrielsson2017). For acquiring firms, technological M&As are purposeful strategic behaviors, and managers may cause free cash flow to be wasted and enact excessive investments at the expense of shareholders' interests (Hans & de Jong, Reference Hans and de Jong2006). However, the superior governance structure can effectively overcome the information asymmetry between outside shareholders and internal management echelons, play a role in supervision, reduce the plunder of profits that reduces the value of the firm, and increase investment in innovative activities. Cornett, Marcus, and Tehranian (Reference Cornett, Marcus and Tehranian2008) found that a higher proportion of independent directors can increase the relative independence of the board of directors, making innovation decisions of enterprises more scientific. On this basis, Hypothesis 2 is proposed as follows.

Equity structure and innovation performance

Most scholars agree that differences in equity structure have a significant influence on firm performance; however, there has been no consistent conclusion on the validity of such influence. Some scholars have argued that a centralized equity structure can improve the efficiency of decisions and reduce information asymmetry (Alzoubi, Reference Alzoubi2016; Partington, Reference Partington2010). In technological M&As, there may be cross-shareholding among the directors of two companies. Generally, the stock rights for acquired firms are higher than those of target firms. When a target firm has abundant resources (Ahuja & Katila, Reference Ahuja and Katila2004), it improves the ability of the top management of the acquiring firm to identify market opportunities (Joshi & Campbell, Reference Joshi and Campbell2003), and an acquiring firm with higher stock rights may pursue ground-breaking innovative development through the combined knowledge obtained (Srivastava & Gnyawal, Reference Srivastava and Gnyawal2017). However, on the contrary, as the rights of majority shareholders expand, majority shareholders tend to absorb the final decision-making power of the enterprise by virtue of their absolute controlling stake; thus, it can be inferred that majority shareholders seek private interests at the expense of minority shareholders and the enterprise, resulting in the ‘entrenchment effect.’ In addition, innovation activities such as technological M&As feature high risk, chronicity, and slow reaction (Aliasghar, Rose, & Chetty, Reference Aliasghar, Rose and Chetty2019). The board of directors of acquired firms applies a pyramid shareholding structure with layers of holding (Almeida & Wolfenzon, Reference Almeida and Wolfenzon2006; Morck, Wolfenzon, & Yeung, Reference Morck, Wolfenzon and Yeung2005) to ensure that the strategy direction of technological M&As is consistent with the original target. However, following technological M&As a board of directors with an excessive concentration of equity in minority shareholders needs to consider the possibility of profit or loss brought about by innovation activities, which may be inconsistent with the existing business team in making strategic decisions, and further leads to the principal agency problem of ‘principal–principal’ (Chen et al., Reference Chen, Lin, Lin and Hsiao2016; Masulis, Pham, & Zein, Reference Masulis, Pham and Zein2011). Since the innovation benefits brought about via technological M&As require time to manifest, in the process of short-term operation a board of directors with a high concentration of equity chooses steady returns in order to maximize stockholders' equity (Love & Nohria, Reference Love and Nohria2005), rather than making it conducive to the enhancement of long-term value for the enterprise, and thereby reducing innovation investment. Furthermore, limited by a centralized shareholding structure, enterprises are prone to form ideological shackles, which encourages managers' passive attitude and failure to promote innovation (Hussinger & Wastyn, Reference Hussinger and Wastyn2015), and leads to the unreasonable use of high-quality resources and the continuous use of inferior resources (Gotsi et al., Reference Heracleous, Papachroni, Andriopoulos and Gotsi2017), thus falling into the ‘competency trap’ (March, Reference March1991). Srivastava and Gnyawal (Reference Srivastava and Gnyawal2017) further examined the competency trap of firms and explained that the strained relations between value creation and protection reduce the firm's benefits from portfolio resources. Through empirical research, Lehmann, Warning, and Weigand (Reference Lehmann, Warning and Weigand2004) proved a negative correlation between high ownership concentration and M&A performance. Thus, this study argues that the more concentrated the acquiring firms' equity, the less constrained the self-interested majority shareholders following M&As will be (Partington, Reference Partington2010). This will ultimately damage the M&A performance of listed companies. As a result, Hypothesis 3 is proposed as follows.

Sample formation

A-share listed companies in the top five high-tech industries specified in the statistical classified catalog published by the State Statistical Bureau were taken as the sample enterprises for this study (Note: The official name of A-share is RMB common stock; that is, it is issued by China-registered companies and listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange, with the face value indicated in RMB, and can be traded and subscribed to in RMB by individuals and institutions in China [excluding Hong Kong, Macao, and Taiwan]). Such enterprises have greater demands for knowledge and technologies, and technology innovation and development are their primary strategies. Technological M&As by companies in the high-tech industry from 2012 to 2016 were selected as the initial research sample. Considering the lag of innovation results, patent application data from 2012 to 2018 were used. In this study, technological M&As are defined based on the criteria outlined by Ahuja and Katila (Reference Ahuja and Katila2004). An M&A announcement indicates that a target firm acquired the patents, technologies, or key R&D personnel of a target firm, or that a target firm obtained patents within 5 years prior to the M&A. On this basis, the initial research samples were screened to: (1) exclude failed M&As; (2) exclude M&As of listed companies with abnormal financial status and other abnormalities – i.e., companies subject to Special Treatment; (3) ensure acquiring firms acquired a controlling stake of not less than 20% of the target firm, and would have had a significant influence on the operating decisions of the target firm; (4) in the case of multiple M&As that occurred in the same year, include the one with the largest transaction amount in the sample (multiple M&As that occurred in different years are included in the sample); and (5) exclude samples with incomplete data. Subject to the above criteria, a total sample size of 169 was obtained. The M&A and financial data used in this study came from the China Stock Market Accounting Research Database, and the patent data were taken from the Patent Inquiry System of the State Intellectual Property Office. Because data were collected by hand, a more generalizable sample was obtained. This is expected to improve the external validity of the results and the construct validity of the M&A variable (Barkema & Schijven, Reference Barkema and Schijven2008; Reus, Reference Reus2012). Moreover, holding the acquiring firm's country constant alleviated home country effects. In addition, by including only Chinese acquirers in the sample, the information about the acquiring firm was collected from a single database, providing more reliable data. As a result, keeping the host country constant increased both the reliability and construct validity of the study (Cook & Campbell, Reference Cook and Campbell1979; Schwab, Reference Schwab1999). All data were subjected to manual sorting and comparison before use.

Operational definition of variables

Independent variables

Relevant literature has shown that several characteristics impact the innovation performance of enterprises. Net profit, net working capital, and intangible assets affect the level of R&D spending of high-tech enterprises, while input in intangible assets has a significantly positive correlation with operating profits (Basso, Reference Basso2012). Board size has a significantly positive effect on R&D spending; moreover, scholars have suggested that ownership concentration is significantly correlated with the performance of acquiring firms (Lehmann, Warning, & Weigand, Reference Lehmann, Warning and Weigand2004).

To avoid multi-collinearity of the original variables and to present the main characteristics of enterprises in all directions, seven indexes, including net profit, operating profit, net value of intangible assets, board size, number of independent directors, largest shareholder shareholding ratio, and equity balance degree, are used for factor analysis. The seven indexes are simplified into three principal component factor indexes using steps such as data standardization, feasibility test, principal component extraction, and maximum rotation factor analysis. Financial slack resources, equity structure, and governance structure of enterprises are used as the final measurement indexes, as shown in Table 1.

Table 1.

Index system of main characteristics

First, data are subject to the KMO test and Bartlett's test of sphericity to determine whether the selected indexes are suitable for factor analysis. It can be seen from Table 2 that the KMO value of the selected original variables is .523, while the p-value of Barlett's test of sphericity is less than .001 and there is a strong correlation among variables. Thus, the selected data are suitable for factor analysis. Second, the principal component factor variables are extracted. The cumulative contribution rate of variances of the first three common factors reaches 79.933%, indicating that the three common factors extracted explain 79.933% of the total information, and the information contained in the original variables is mostly retained. It can be seen from the rotated component matrix that the first common factor (F1) is mainly determined by NP, EBIT, and IA, of which load values for the principal factors are .939, .903, and .629, reflecting the financial slack resource index (Avail) of the acquiring firms. The second common factor (F2) has larger load values on BOD and ID, which are .917 and .925, respectively, reflecting the governance structure (Govern) of the acquiring firms. Similarly, the third common factor (F3) represents the equity structure (Owner) of enterprises.

Table 2.

Rotation component matrix

Control variables

In view of the influence of factors other than the independent variables on innovation performance, and based on existing research, this study uses the scale, total number of employees, age, cash flow, debt paying ability, profitability, growth, and nature as control variables (Table 3).

Table 3.

Definition and specific measurement of variables

Model estimation

Based on technology innovation theory, learning theory, and synergistic effect, a multiple linear regression model is used for data analysis in this study, and measurement models related to technological M&As are created as follows:

First, measurement model (1) is created to verify the influence of subject heterogeneity under technological M&As on innovation performance:

Then, the interactive items of financial slack resources, equity structure, and governance structure with absorptive capacity are added to the above basic model to verify the moderating effect exerted by the absorptive capacity between the main characteristics in technological M&As and innovation performance. Thus, the moderating effect is created in model (2) as follows:

(1)$$\eqalign{{\rm Patent} =\, & {\rm \alpha }_{\rm 1}+{\rm \beta }_{\rm 1}{\rm Size }+{\rm \beta }_{\rm 2}{\rm Num }+{\rm \beta }_{\rm 3}{\rm Age }+{\rm \beta }_{\rm 4}{\rm Cash }+{\rm \beta }_{\rm 5}{\rm Lev }+{\rm \beta }_{\rm 6}{\rm ROA } \cr & +{\rm \beta }_{\rm 7}{\rm Growth }+{\rm \beta }_{\rm 8}{\rm Nature }+{\rm \beta }_{\rm 8}{\rm Avail }+{\rm \beta }_{\rm 8}{\rm Govern }+{\rm \beta}_{\rm 9}{\rm Owner }+ \varepsilon}$$

(2)$$ \eqalign{{\rm Patent} &= {\rm\alpha}_1 + {\rm\beta}_1 {\rm Size} + {\rm\beta}_2 {\rm Num} + {\rm\beta}_3 {\rm Age} + {\rm\beta}_4 {\rm Cash} + {\rm\beta}_5 {\rm Lev} + {\rm\beta}_6 {\rm ROA} + {\rm\beta}_7 {\rm Growth} \cr & \quad+ {\rm\beta}_8 {\rm Nature} + {\rm\beta}_8 {\rm Avai} + {\rm\beta}_9 {\rm Absorb} + {\rm\beta}_{10} {\rm Avail} \times {\rm Absorb} + {\rm\varepsilon} {\noalign{\vskip10pt}}\cr {\rm Patent} & = {\rm\alpha}_1 + {\rm\beta}_1 {\rm Size} + {\rm\beta}_2 {\rm Num} + {\rm\beta}_3 {\rm Age} + {\rm\beta}_4 {\rm Cash} + {\rm\beta}_5 {\rm Lev} + {\rm\beta}_6 {\rm ROA} + {\rm\beta}_7 {\rm Growth} \cr & \quad+ {\rm\beta}_8 {\rm Nature} + {\rm\beta}_8 {\rm Govern} + {\rm\beta}_9 {\rm Absorb} + {\rm\beta}_10 {\rm Govern} \times {\rm Absorb} + {\rm\varepsilon} {\noalign{\vskip10pt}} \cr {\rm Patent} & = {\rm\alpha} 1 + {\rm\beta} 1{\rm Size} + {\rm\beta} 2{\rm Num} + {\rm\beta} 3{\rm Age} + {\rm\beta} 4{\rm Cash} + {\rm\beta} 5{\rm Lev} + {\rm\beta} 6{\rm ROA} + {\rm\beta} 7{\rm Growth} \cr & \quad+ {\rm\beta} 8{\rm Nature} + {\rm\beta} 8{\rm Owner} + {\rm\beta} 9{\rm Absorb} + {\rm\beta} 10{\rm Owner} \times {\rm Absorb} + {\rm\varepsilon} } $$

Descriptive statistics

Descriptive statistical analysis was first performed on each variable; the analysis results are shown in Table 4. Specifically, the mean value of innovation performance is 282.36, and the maximum and minimum values are 10,545 and 7, respectively. This indicates that the innovation performance of the sample enterprises following technological M&As varies quite significantly. Therefore, it is highly valuable to study what kinds of characteristics can improve the innovation performance of enterprises on the basis of acquiring external resources through technological M&As. The financial slack resources, governance structure, and equity structure are calculated from the results of the factor analysis, and all of them meet the standard normal distribution. Thus, the mean value is 0, and the standard deviation is 1.

Table 4.

Descriptive statistics results of variables

Correlation analysis

The Pearson correlation test is adopted in this study to initially verify the correlation between variables. Table 5 shows that there is a strong correlation between variables. Specifically, the independent variable – financial slack resources – is significantly correlated with innovation performance at a significance level of 5%; control variables such as scale, total number of employees, age, and cash flow are significantly correlated with innovation performance at a significance level of 5%. In order to test for possible multi-collinearity between the independent variables, variance inflation factors (VIFs) are calculated, and values of all the independent variables are found to be below the threshold. This shows that there is no multi-collinearity between the independent variables used in this study (Shin, Han, Marhold, & Kang, Reference Shin, Han, Marhold and Kang2017). The maximum VIF values for each regression model are indicated in Table 6.

Table 5.

Correlation analysis results of variables

Note: ** and * mean the significant correlation at the significance levels of 5% and 10%, respectively.

Table 6.

Regression analysis results of variables

Note: Data in the table are standardized regression coefficients, and the data in brackets are t-test values.

***^, **^, and * indicate significant correlation at the significance levels of 1%, 5%, and 10% levels, respectively.

Analysis of regression results

The moderating effect of absorptive capacity

Three groups of interactive items are generated to test the moderating effect of absorptive capacity, and hierarchical regression analysis is conducted with innovation performance following M&As as the dependent variable. As shown in Table 6, model 3 adds financial slack resources, absorptive capacity, and the interactive items of the two indexes on the basis of model 1. Compared with model 1, the degree of fit (R ²) of model 3 clearly increases, and the financial slack resources have a significantly negative correlation with the interactive items of absorptive capacity at a significance level of 5%. This indicates that absorptive capacity exerts a moderating effect between financial slack resources and innovation performance of the acquiring firms. If the acquiring firms have many financial slack resources, the increase in R&D spending following technological M&As will enhance the utilization of financial resources, thus improving the innovation performance of acquiring firms following M&As. Therefore, Hypothesis 4a is supported.

Likewise, models 4 and 5 add governance structure with absorptive capacity, equity structure with absorptive capacity, and interactive items of the two indexes on the basis of model 1. Both interactive items of governance structure with absorptive capacity and interactive items of equity structure with absorptive capacity are positively significant at a significance level of 10%. This shows that absorptive capacity will intensify the relationship between governance structure and equity structure with innovation performance of the acquiring firms. Hypothesis 4c is thus supported, and Hypothesis 4b is partially supported. In the same way, in model 6 the full model contains all variables to test all moderating effects, and the results are similar to those of models 3–5, thus verifying the moderating role of absorptive capacity between heterogeneity characteristics of the technological M&As and innovation performance.

Robustness test

To ensure the reliability of the regression results, the mean value of the number of patent applications in the current year of the technological M&A, and the first and second year following the technological M&A are used to replace the dependent variable as a robustness test. As shown in Table 7, the financial slack resources and highly centralized equity structure of enterprises are not conducive to improving their innovation performance following technological M&As. A regulation effect test shows that the increase in R&D investment intensity following a technological M&A helps to enhance the allocation efficiency of financial redundancy capital, and motivate enterprises to carry out R&D innovation, thus enhancing their innovation performance. This is in line with the preceding regression results, signifying the reliability of the results.

Table 7.

Regression analysis results of variables

Note: Data in the table are standardized regression coefficients, and the data in brackets are t-test values. ***^, **^, and * indicate significant correlation at the significance levels of 1%, 5%, and 10% levels, respectively.