Does Innovation Sustainability Attract Retail Investors? The Clientele Effect in China

Abstract

Innovation sustainability is essential for businesses to maintain their competitive edge and ensure long-term growth. This not only benefits individual companies but also entire industries. Despite its importance, research on retail investors’ preferences for innovation sustainability remains limited. To address this gap, we analyzed unique data on shareholder numbers in listed Chinese companies from 2007 to 2020. We differentiate between institutional and retail investors to analyze the latter’s preferences. This finding indicates that retail investors prefer to invest in companies with higher innovation sustainability. This preference stems from their limitations in capabilities of information collection, analytical skills, and risk diversification. The clientele effect is more pronounced when companies face a poor innovation environment, an opaque information environment, and a weak political connection. This study contributes to the existing literature by providing empirical support for the clientele effect and shedding light on retail investors’ preferences and investment behavior. By focusing on company fundamentals, our study extends the examination of the clientele effect to the corporate governance level. These insights have significant implications for promoting sustainable development, impacting both companies and the capital market.

1. Introduction

Sustainability is a crucial consideration for humanity, with the United Nations aiming to “end poverty, protect the planet, and ensure that all people enjoy peace and prosperity by 2030” [1]. In recent years, the emphasis on sustainable development has increased substantially [2]. As a result, sustainability has become a driver of innovation, prompting companies to develop novel competencies and sustainable solutions [3]. A critical facet of sustainability is innovation sustainability, known as the sustainability of a firm’s innovation activity. Innovation sustainability is not merely a trend but a strategy for guaranteeing operational effectiveness [4]. It is essential for bolstering corporate competitiveness, as a company’s long-term prosperity increasingly hinges on its capacity to innovate sustainably [5]. Therefore, innovation sustainability is foundational for establishing competitive advantages. Through persistent innovation activity, companies could enhance their capabilities and gain advantages that underpin sustainable development.

While existing research on innovation sustainability has focused mainly on influencing factors [6,7] and the smoothness of innovation activity, the economic consequences of innovation sustainability remain understudied. However, a significant research gap exists in understanding how capital markets evaluate and respond to innovation sustainability. This gap warrants further investigation, as innovation sustainability reflects a firm’s capacity to maintain sustainable competitiveness. Further research is necessary to examine whether and how capital markets recognize and reward firms’ investments in innovation sustainability, providing valuable insights for both academics and practitioners.

Retail investors often have weaker capabilities of information collection, information analysis, and risk diversification compared with institutional investors. This study empirically investigates whether retail investors exhibit a clientele effect, preferring firms with higher innovation sustainability. On one hand, innovation sustainability’s consistent and predictable approach may appeal to retail investors. On the other hand, the inherent risk and uncertainty of innovation may temper retail investors’ enthusiasm for firms with high innovation sustainability. Our research aims to determine whether a clientele effect exists, shedding light on retail investors’ investment behavior in relation to innovation sustainability.

In emerging markets, retail investors constitute a significant portion of market participants. Their investment behavior has a significant impact on the efficiency of resource allocation. However, data on retail investors’ behavior is limited in many countries [8]. Fortunately, China’s distinctive disclosure regulations mandate that companies publicly disclose the total number of their shareholders, providing a valuable tool for identifying retail investors and their investment behaviors. Notably, retail investors dominate China’s capital market. Therefore, Chinese data are chosen for this study, leveraging the country’s unique disclosure regulations and the dominant role of retail investors.

By using a sample of A-share listed companies from 2007 to 2020, this research finds that retail investors exhibit a stronger preference for firms with higher innovation sustainability. This indicates the presence of a clientele effect for innovation sustainability among retail investors. The results remain robust after conducting a series of robustness tests. Further analyses show the clientele effect is more pronounced among firms facing unfavorable conditions, such as a poor innovation environment, low information transparency, and weak political connections. Specifically, this research finds that retail investors’ preference for firms with higher innovation sustainability intensifies when firms exist in these less favorable environments.

Our study makes several contributions to the literature. First, it expands the existing literature on innovation sustainability by examining its economic consequences, particularly its impact on retail investors’ behavior. Previous research has mainly focused on the smoothness [9] and the influencing factors of innovation sustainability [6,7,10]. This study reveals that innovation sustainability significantly influences retail investors’ preferences and investment behavior.

Second, our study provides further evidence for the clientele effect in investment behavior. Previous studies have primarily focused on the differences in investment preferences among different investors, such as cash dividends [11,12,13,14,15] and liquidity [16,17]. However, there is a lack of research examining the clientele effect related to corporate governance and fundamental performance. Our study fills this gap by shifting the focus to preferences for corporate business behavior and fundamentals and extends the clientele effect to the level of corporate governance. Moreover, our findings suggest that this preference is more pronounced under disadvantaged firm conditions, indicating that retail investors recognize the long-term benefits of innovation sustainability despite information disadvantages.

Third, this study also contributes to the literature on investor behavior. Prior studies have shown that different types of investors exhibit varying preferences, such as retail investors favoring high dividend yields [14]. Notably, we provide evidence that retail investors recognize the long-term value of innovation sustainability despite information disadvantages. Furthermore, our findings indicate that the clientele effect is stronger when firms face disadvantaged conditions in innovation environments, information environments and political connections. This challenges the notion that retail investors are less sophisticated, as our results suggest they prioritize fundamentals over short-term gains.

The remainder of this paper proceeds as follows: Section 2 develops research hypotheses and provides our empirical predictions. Section 3 describes the research design and data. Section 4 presents our main results and robustness checks. Section 5 conducts further cross-sectional analyses. Section 6 concludes and discusses the findings.

2. Literature Review and Research Hypothesis

2.1. Innovation Sustainability

The growing emphasis on sustainable development has elevated the importance of innovation sustainability. Innovation, despite its substantial rewards, is accompanied by considerable risks and uncertainties. Innovation sustainability suggests a consistent and predictable approach to innovation over time [18]. Achieving innovation sustainability is essential for bolstering corporate competitiveness and establishing a sustainable impact [5].

Research on innovation sustainability mainly focuses on the smoothness and influencing factors of innovation sustainability [19]. Several studies have demonstrated that the savings level, operating costs, and government subsidies affect the smoothness of innovation sustainability [9]. Furthermore, related studies indicate that a company’s innovation experience, corporate strategy, and government subsidy play crucial roles in determining innovation sustainability [20]. Other research emphasizes that companies adopting R&D-driven or technology-driven strategies are more likely to achieve higher innovation sustainability [6].

Previous research has not adequately addressed the economic consequences of innovation sustainability, particularly the impact on investors’ investment behavior. Given the substantial financial requirements of innovation activity, it is of practical importance to understand how investors respond to innovation sustainability. However, there is a noticeable lack of scholarly research in this area. This research gap presents an opportunity to investigate the relationship between innovation sustainability and investor behavior.

2.2. Clientele Effect

The clientele effect theory emerged from the tax differential theory, which examines the influence of marginal income tax rates on shareholders’ dividend preferences [21]. This theory posits that investors’ tax rates significantly shape their choices for cash dividends. The clientele effect describes that various types of investors are drawn to specific stocks, influenced by unique tax situations, risk preferences, and investment strategies [22]. Research finds that retail investors prefer cash dividends due to the behavioral biases of self-control and regret aversion [11,12]. Additionally, other studies show retail investors exhibit significant disparities in information capabilities and behaviors compared to other investor types [13,15,17].

Initially, academic research on the clientele effect centered on dividend policy but has since expanded to encompass areas such as information disclosure and investment markets. Objectively, investors possess varying abilities to utilize information, while news dissemination mitigates information asymmetry between informed and uninformed investors. The clientele effect of information disclosure is reflected in studies examining how changes in the readability of corporate annual reports affect investors [23,24]. Furthermore, the preference of retail investors tends to increase with age, suggesting that demographic characteristics shape investment preferences [14].

Given the significant differences between retail investors and other investors, this paper argues that retail investors and other investors may also exhibit different preferences for a company’s operating decisions, such as innovation activity. However, there is currently a research gap. Therefore, this paper attempts to explore whether retail investors exhibit a clientele effect on a firm’s innovation sustainability.

2.3. Hypotheses Development

Innovation carries inherent risks and uncertainties which may affect investors’ return. Compared to institutional investors, retail investors are more sensitive to uncertainty due to their relatively limited capabilities in information collection, information analysis, and risk diversification [14]. Considering the significant differences, retail investors may have distinct preferences regarding a company’s innovation activity. Do retail investors exhibit a clientele effect towards the innovation sustainability of listed companies? Does innovation sustainability attract retail investors? Two possible outcomes exist; it may either attract them or it may not.

Companies with high innovation sustainability may have lower investment uncertainty. A key characteristic of innovation is the unpredictability of its outcomes, which leads investors to expect higher returns when investing in technology-driven enterprises. However, innovation sustainability can reduce the uncertainty by implying that a company’s innovation activity is persistent and reliable [6,7]. High innovation sustainability suggests that future investments in innovation are predictable. The company will maintain a stable and continuous level of innovation investment, such as product technology, organizational management, and market exploration. Conversely, low innovation sustainability implies irregular and uncertain investment in innovation, heightening the uncertainty in investors’ valuation of these companies. Retail investors are more greatly influenced by the uncertainty and tend to choose investments with lower uncertainty.

In terms of information collection, retail investors have weaker access to information compared to institutional investors and major shareholders. In emerging markets such as China, where information is relatively opaque and costly to obtain, these challenges are more pronounced. Retail investors face greater restrictions in gathering and analyzing information and typically lack the incentive to actively collect it due to the cost–benefit principle. Even when information channels are available, retail investors often lack the time and resources to utilize them effectively. They primarily rely on search engines, but the reliability and completeness of the information are not guaranteed [25].

In contrast, institutional investors and major shareholders have access to a wide range of information sources and can leverage economies of scale in information collection [26]. Moreover, institutional investors and major shareholders can directly influence corporate governance by attending shareholder meetings, submitting proposals, collecting proxy votes, seeking board seats, and negotiating with management. This level of involvement allows them to access critical internal information which is often unavailable to retail investors. Additionally, the information tends to be more reliable and is more readily accepted by investors [27]. As a result, institutional investors are better positioned to acquire first-hand direct information from management.

In terms of information analysis, retail investors have significantly weaker information discrimination abilities compared to institutional investors and major shareholders. Institutional investors typically employ professional analysts who possess greater expertise in analyzing and interpreting information [23], whereas retail investors largely rely on their own judgment when making investment decisions. Additionally, stock investment is the primary focus for institutional investors, who have extensive experience with numerous companies. This experience gives them a deeper understanding of industry best practices, fundamental principles, and industry-specific knowledge. Over time, the accumulation of such expertise creates a strong advantage in professional analysis for institutional investors.

In contrast, retail investors are limited by their lack of professional knowledge and the costs associated with acquiring it. Retail investors may lack sufficient financial expertise and struggle to interpret key financial metrics, such as those found in financial reports [24]. Therefore, they often have significant weaknesses in information analysis and are more easily influenced by other investors [28]. Due to these limitations, retail investors also tend to chase after social hot topics and concepts rather than making decisions based on thorough analysis.

In terms of risk diversification, retail investors face significant limitations compared to institutional investors. Institutional investors, with their larger capital reserves, can construct diversified portfolios that effectively mitigate specific risks. In contrast, retail investors typically have smaller amounts of capital, making it difficult for them to build sufficiently diversified portfolios to reduce idiosyncratic risks associated with stocks [29]. Due to this limitation, retail investors are less likely to engage in high-risk investments and often avoid aggressive investment strategies. Innovation activity involves a high level of information asymmetry and potential moral hazards. Due to the non-excludability of knowledge, innovation activity is generally not disclosed as business secrets. External investors often find it difficult to obtain genuine relevant information about R&D. Retail investors, lacking access to private information and the industry expertise needed to assess innovation sustainability, are at an even greater disadvantage. Without the ability to diversify these risks through portfolio construction, retail investors may struggle to accurately assess the risks and potential returns of investing in innovation-driven companies. Consequently, they are more likely to avoid investments in companies with uncertain innovation activity.

In summary, retail investors, with more limited information collection capabilities than institutional investors and major shareholders, may struggle to access timely, reliable, and privileged information about innovation activity and future plans. Their weaker analytical skills may also hinder their ability to interpret innovation-related information and interpret its implications. Additionally, their insufficient capital constrains their ability to diversify risk, making them more vulnerable to the inherent volatility of innovation. Consequently, retail investors may be more inclined to invest in companies that demonstrate high innovation sustainability, as it signals a more stable and predictable innovation environment with lower uncertainty. This study suggests that retail investors, given their weaker information collection, information analysis, and risk diversification capabilities, may prefer companies with higher innovation sustainability and lower uncertainty. To test the prediction, the hypothesis is stated as follows:

Hypothesis 1.

Retail investors will demonstrate a preference for investing in firms with high innovation sustainability.

However, retail investors may struggle to capture the concept of innovation sustainability, which may lead to a lack of preference for firms prioritizing it. Innovation sustainability is essential for companies to maintain long-term competitive advantages in complex and uncertain environments. Although it positively influences the innovation output, innovation sustainability remains relatively unfamiliar to the public. Its effects on corporate performance are not immediately visible, especially in emerging economies, making it more likely to be overlooked by retail investors.

The inherent risk and uncertainty of innovation may deter risk-averse investors who prioritize stability. Additionally, retail investors often focus on short-term gains, which may not align with the long-term potential of innovative companies [11,12,30]. The difficulty in evaluating the true value and future impact of innovation combined with a preference for the tangible value offered by established companies further discourages investment in such ventures. Moreover, information asymmetry between institutional and retail investors places the latter at a disadvantage, leading to hesitancy in investing in companies perceived as complex. Furthermore, the fear of missing out can lead to impulsive and emotionally driven investment behavior that may not align with risk tolerance or financial goals. Therefore, while innovation sustainability is recognized, it is not a universal driver of investment behavior among retail investors, who often favor more established and predictable companies. In summary, an alternative hypothesis is proposed as follows:

Hypothesis 2.

Retail investors may not exhibit a clear preference for firms with high innovation sustainability.

3. Methodology and Sample

3.1. Empirical Model and Variable Definition

To test the clientele effect of retail investors on innovation sustainability, this paper constructs the following multiple regression model:

INNOV_t+1 = a₀ + a₁ INNOV + a₂ SHSHR + a₃ INNOV × SHSHR + a₄ INSSHR + a₅ INNOV × INSSHR + ∑Control_Var + ε

(1)

3.1.1. Dependent Variable

INNOV_t+1 denotes the firm’s innovation level in period t + 1. Following Kramer et al. [31] and Fan et al. [32], INNOV_t+1 is the ratio of the change in firms’ intangible assets at the end of period t + 1 compared to the end of period t to their total assets at the end of period t. Intangible assets, including patents, copyrights, and trademarks, encompass a broader range of innovation inputs and serve as a direct outcome of innovation investments. Innovation sustainability is measured by the regression coefficient a₁ of the innovation level in period t + 1 (INNOV_t+1) on the innovation level in period t (INNOV) in Model (1). A positive a1 indicates a positive correlation between the firm’s innovation level in period t + 1 (INNOV_t+1) and its innovation level in period t (INNOV), implying persistent innovation by the firm over time.

3.1.2. Independent Variable

SHSHR represents a firm’s retail investor base, measured by the average retail investor shareholding ratio at year-end, as referenced by Berger [29] and consistent with INSSHR. This is calculated as the ratio of total shareholding of a firm’s retail investors to the number of retail investors, expanded and multiplied by 10⁸. A lower average shareholding ratio indicates a larger retail investor shareholder base, serving as a reverse indicator. The average retail shareholding ratio has a significant negative correlation of −0.0152 with the number of retail investors, at a 1% level. The total retail shareholding ratio is computed as (1 − shareholding ratio of the top ten non-institutional shareholders at year-end-total institutional shareholding ratio at year-end). The number of retail investors is (total number of shareholders disclosed at year-end − number of the top ten non-institutional shareholders at year-end − number of disclosed institutional investors at year-end).

This paper chooses the average retail shareholding ratio to measure the investor base for several reasons. First, compared to institutional investors and major shareholders, retail investors are far larger in number, differing by orders of magnitude, hence a direct comparison is unfeasible. Second, the difference in their shareholding ratios captures the influence on corporate decisions like investments and financing. Finally, share price and market capitalization are excluded as potential measures due to concerns about endogeneity, as they can directly impact the investor base. To enhance robustness, the number of retail investors is employed as an alternative measure of the retail investor base, aligning with the existing literature and providing a complementary perspective.

In this paper, the coefficient a₃ of the cross-multiplier term INNOV × SHSHR is used to examine the potential existence of a clientele effect of retail investors on innovation sustainability. When the coefficient a₃ exhibits a negative significance, it indicates a significant negative correlation between the average shareholding ratio of retail investors and innovation sustainability. This suggests that firms with a larger retail investor base have greater innovation sustainability and that the clientele effect is confirmed, thus supporting Hypothesis 1.

The following control variables are included: INSSHR denotes the average institutional investor shareholding ratio, which is calculated as the proportion of the total shareholding ratio held by institutional investors of a firm divided by the number of institutional shareholders of the firm. INNOV × INSSHR denotes the cross-multiplication between the firm’s level of innovation in the current year and the average shareholding ratio of the institutional investor as opposed to the preference of retail investors. The nature of ownership, specifically the presence of a state-owned enterprise (SOE), is represented by a dummy variable. This variable takes a value of 1 if the firm’s ultimate controller is state-owned, and 0 otherwise.

3.1.3. Control Variables

To mitigate the effects of confounding factors, several control variables are constructed. Firm size (SIZE) is defined as the natural logarithm of the total assets of the firm. Capital intensity (TANG) refers to the ratio of a firm’s fixed assets to its total assets. Profitability, specifically return on assets (ROA), is calculated by dividing the net profit of a firm by its total assets. Operating cash flow (CFO) refers to the net cash flow generated from a firm’s operating activities, which is then divided by the total assets of the company. The leverage (LEV) refers to the ratio of a firm’s total liabilities to its total assets. The market-to-book ratio (MB) is calculated by dividing a firm’s market value by its book value. Growth (GROWTH) refers to the rate at which a company’s operating revenues increase over time. Listed years (AGE) is equal to number of years that a firm has been listed on a stock exchange. Equity liquidity (ILLIQ), referenced by Amihud [33], is calculated by multiplying the annual measure of a firm’s stock illiquidity by 10⁸. A higher value indicates lower liquidity of the stock. Analyst following (ANST) is a metric that represents the natural logarithm of the sum of the number of analysts following a firm, increased by one. Top shareholder ownership (TOP1) refers to the shareholding ratio of the largest shareholder in a firm. The paper also controls for industry and year fixed effects.

Table 1. Definitions of variables.

	Variable	Definition
Dependent Variable	INNOV	Change in intangible assets deflated by total assets at the beginning of period t.
Independent Variable	SHSHR	Total shareholding ratio of a firm’s retail investors deflated by number of retail investors multiplied by 108 at the end of period t.
Control Variables	INSSHR	Total shareholding ratio of a firm’s institutional investors divided by number of institutional shareholders at the end of period t.
	SOE	Dummy variable, equal to 1 if the firm’s ultimate controller is state-owned at the end of period t, 0 otherwise.
	SIZE	The natural logarithm of the total assets of the firm at the end of period t.
	TANG	Fixed assets deflated by total assets at the end of period t.
	ROA	Net profit deflated by total assets at the end of period t.
	CFO	Net cash flow from operating activities deflated by total assets at the end of period t.
	LEV	Total liabilities deflated by total assets at the end of period t.
	MB	Market value deflated by book value at the end of period t.
	GROWTH	Growth rate of the firm’s operating revenues at the end of period t.
	AGE	Number of years the firm has been listed at the end of period t.
	ILLIQ	Referring to Amihud [33], equal to the annual indicator of the firm’s stock illiquidity multiplied by 108 at the end of period t.
	ANST	Natural logarithm of the sum of the number of analysts following the firm plus one at the end of period t.
	TOP1	Shareholding ratio of the firm’s largest shareholder at the end of period t.

presents the variable definitions in detail.

3.2. Sample and Data

In the stock markets of emerging economies, retail investors constitute a substantial portion of market participants. By utilizing the unique data of shareholder numbers in Chinese listed companies, we are able to identify retail investors and their behaviors. Chinese distinctive disclosure regulations provide a valuable means to analyze retail investors’ investment behavior, which has not been extensively explored in comparison to institutional investors.

Our initial sample consists of all A-share listed firms in China between 2007 and 2020. In 2006, China issued new accounting standards. Therefore, the data used in this study begin in 2007. Considering the pandemic, the data period covered in this paper is from 2007 to 2020.

We exclude financial industry firms, firm-year observations under financial distress, and firm-year observations with insufficient data to calculate our independent, dependent, or control variables. After this selection process, we have a sample of 26,247 firm-year observations.

The main data are retrieved from the CSMAR, RESSET. or WIND database. We winsorize our continuous variables at the upper and the lower 1% to avoid potential impacts of outliers on our empirical results.

4. Empirical Results and Analyses

4.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variable	Obs	Mean	Sd	Min	P50	Max
INNOV	26,247	0.009	0.030	−0.031	0.000	0.202
SHSHR	26,247	20.473	39.221	0.022	5.598	244.777
INSSHR	26,247	0.309	0.504	0.000	0.106	2.845
GROWTH	26,247	0.415	1.168	−0.662	0.133	8.592
SOE	26,247	0.418	0.493	0.000	0.000	1.000
SIZE	26,247	22.128	1.279	19.832	21.942	26.100
TANG	26,247	0.046	0.051	0.000	0.033	0.318
ROA	26,247	0.041	0.052	−0.172	0.037	0.194
CFO	26,247	0.048	0.071	−0.160	0.046	0.246
LEV	26,247	0.433	0.203	0.054	0.430	0.867
MB	26,247	0.619	0.242	0.125	0.619	1.135
AGE	26,247	9.775	6.751	0.000	9.000	25.000
ILLIQ	26,247	0.065	0.087	0.003	0.039	0.610
ANST	26,247	1.529	1.165	0.000	1.609	3.761
TOP1	26,247	35.266	14.950	8.800	33.360	74.960

Note: This table shows the summary statistics of firm-year observations of variables for the period from 2007 to 2020.

reports the descriptive statistics of the variables. Among listed companies in China, the mean value of innovation level (INNOV) is 0.009 with a standard deviation of 0.030. This finding is consistent with another study, suggesting that there is a notable disparity in the innovation investment among these companies. Furthermore, the level of innovation investment among firms is generally low. The average retail investor shareholding ratio (SHSHR) has a mean value of 20.437 and a standard deviation of 39.221. The findings provide evidence that the average shareholding ratio of retail investors in China is relatively low and that there are significant variations in average retail investor shareholding ratios across different firms. The data pertaining to the main control variables, namely the mean value of firm size (SIZE) at 22.1276, the mean value of leverage (LEV) at 0.4327, the mean value of nature of ownership (SOE) at 0.4178, and the mean value of the top shareholder ownership (TOP1) at 35.266, align with the findings reported in the existing literature.

4.2. Baseline Results

Table 3. Baseline results.

	(1)	(2)
	INNOVt+1
INNOV	0.0512 ***	0.0683 ***
	(4.7114)	(5.1857)
SHSHR	0.0000	0.0000 **
	(1.1040)	(2.1111)
INNOV × SHSHR		−0.0004 ***
		(2.5775)
INSSHR	0.0013 ***	0.0015 ***
	(3.1459)	(3.2296)
INNOV × INSSHR		−0.0127
		(−0.8582)
GROWTH	−0.0000	−0.0000
	(−0.2885)	(−0.2005)
SOE	−0.0001	−0.0001
	(−0.3181)	(−0.3386)
SIZE	−0.0003	−0.0003
	(−0.9475)	(−0.8780)
TANG	0.0141 *	0.0136 *
	(1.8787)	(1.8443)
ROA	0.0214 ***	0.0210 ***
	(5.1509)	(5.0562)
CFO	−0.0028	−0.0027
	(−1.0231)	(−0.9930)
LEV	0.0022 *	0.0021 *
	(1.8375)	(1.7736)
MB	−0.0077 ***	−0.0076 ***
	(−6.6401)	(−6.6384)
AGE	−0.0001 ***	−0.0001 ***
	(−3.6162)	(−3.5079)
ILLIQ	0.0155 ***	0.0156 ***
	(4.4903)	(4.5087)
ANST	0.0014 ***	0.0014 ***
	(6.4985)	(6.4505)
TOP1	0.0000	0.0000
	(0.8012)	(0.8229)
CONS	0.0123 **	0.0118 **
	(2.1486)	(2.0486)
Year	Control	Control
Industry	Control	Control
N	26247	26247
F	15.12 ***	14.53 ***
R2	0.0408	0.0415

Note: This table reports the baseline regression results of INNOV_t+1 on INNOV × SHSHR. INNOV_t+1 is the ratio of the change in firms’ intangible assets at the end of period t + 1 compared to the end of period t to their total assets at the end of period t. SHSHR represents a firm’s retail investor base, measured by the average retail investor shareholding ratio at year-end. The coefficient of INNOV × SHSHR is interesting. Industry and year fixed effects are included. Robust t-statistics are reported in parentheses. *, **, and *** indicate the 10%, 5%, and 1% significance levels, respectively.

reports our baseline regression results. Column (1) presents the coefficient of firms’ innovation level in period t + 1 (INNOV_t+1) on the innovation level in period t (INNOV) as 0.0512, which is significant at the 1% level. The coefficient exhibits a positive and statistically significant relationship, indicating that the level of innovation within firms is enduring. The coefficient of the average institutional investor shareholding ratio (INSSHR) is 0.0013, which is statistically significant at the 1% level. This suggests institutional investors’ shareholding will increase the innovation level of firms in the second year. The coefficient of the average retail investor shareholding ratio (SHSHR) is 0.0000 with a t-value of 1.1040, which is insignificant. This implies that retail investor shareholding does not impact the innovation level of firms in the second year. Comparing the two coefficients reveals that retail investors and individual investors have different preferences for innovation sustainability.

Column (2) of Table 3 includes the cross-multiplier of firms’ innovation level in period t with the average retail investor shareholding ratio (INNOV × SHSHR) and the cross-multiplier of firms’ innovation level in period t with the average institutional investor shareholding ratio (INNOV × INSSHR). The examination of preferences for firms’ innovation sustainability can be conducted by utilizing the regression coefficients of the cross-multiplier terms, which provide insights into the preferences of both retail investors and institutional investors.

The results show that the coefficient of INNOV × SHSHR is negative and significant at the 1% level. This finding suggests that the lower the average retail investor shareholding ratio, the larger the base of retail investors and the higher the innovation sustainability. This implies the existence of a clientele effect, where firms with higher innovation sustainability tend to attract more retail investors. This result confirms Hypothesis 1. At the same time, the coefficient of INNOV × INSSHR is negative but not significant. This indicates institutional investors do not have the same preference in terms of the innovation sustainability of a firm as retail investors do.

4.3. Robustness Tests

4.3.1. Alternative Measurement of Innovation Sustainability

In the main empirical test, this paper employs the ratio of intangible asset changes to initial total assets as a proxy for a firm’s innovation level. However, this indicator cannot fully reflect the cumulative and dynamic nature of a firm’s innovation level. Following Tang and Liu [34], we alternatively proxy the innovation level of firms based on R&D inputs and utilizes the method of chain growth rate and total value over the past two years (INNOV2). Compared to the R&D inputs, patent count is used to quantify the output of R&D activities. The calculation method for this variable is specified as follows:

$$INNOV2={\displaystyle \frac{R{D}_{i,t}+R{D}_{i,t-1}}{R{D}_{i,t-1}+R{D}_{i,t-2}}}\times \left(R{D}_{i,t}+R{D}_{i,t-1}\right)$$

(2)

Table 4. Alternative proxy for innovation sustainability.

	(1)	(2)
	INNOV2t+1
INNOV2	0.8942 ***	0.9235 ***
	(49.1406)	(56.3266)
SHSHR	0.0040 ***	0.0062 ***
	(4.4643)	(3.5603)
INNOV2 × SHSHR		−0.6662 ***
		(−4.6249)
INSSHR	0.2001 **	0.9283 ***
	(2.1023)	(4.9683)
INNOV2 × INSSHR		−0.0033 **
		(−2.2786)
CONTROLS	Control	Control
Year	Control	Control
Industry	Control	Control
N	14029	14029
F	1482.83	1457.76
R2	0.8262	0.8300

Note: This table reports the results of the alternative measure INNOV2_t+1 on INNOV2 × SHSHR. INNOV2_t+1 is defined as R&D inputs in the next two years multiplied by its average growth in the current two years. SHSHR represents a firm’s retail investor base, measured by the average retail investor shareholding ratio at year-end. The coefficient of INNOV2 × SHSHR is interesting. Industry and year fixed effects are included. Robust t-statistics are reported in parentheses. ** and *** indicate the 5%, and 1% significance levels, respectively.

presents the robustness check results of alternative innovation sustainability measures. Column (1) shows that the coefficient of INNOV2 is 0.8942 and significant at the 1% level. Column (2) incorporates the cross-multipliers INNOV2 × SHSHR and INNOV2 × INSSHR; the coefficients of INNOV2 × SHSHR and INNOV2 × INSSHR are negative and significant at the 1% level. Our inferences are unchanged.

4.3.2. Alternative Measurement of Investor Base

The average shareholding ratio of retail investors is influenced more by the number than by the total shares held by them. This is because the denominator exhibits greater magnitude and fluctuation than the numerator. For a robustness check, we use the natural logarithm of the number of retail investors (SHNUM) as a measure of the retail investor base and the number of institutional investors (INSNUM) as a measure of the institutional investor base. The regression results are shown in

Table 5. SHNUM serves as an alternative proxy for the retail investor base.

	(1)	(2)
	INNOVt+1
INNOV	0.0524 ***	−0.2806 **
	(4.8302)	(−2.2997)
SHNUM	−0.0002	−0.0005
	(−0.7777)	(−1.5610)
INNOV × SHNUM		0.0258 **
		(2.0742)
INSNUM	0.0007 ***	0.0005 *
	(2.6109)	(1.8350)
INNOV × INSNUM		0.0251 ***
		(3.3315)
CONTROLS	Control	Control
Year	Control	Control
Industry	Control	Control
N	26247	26247
F	14.87 ***	14.74 ***
R2	0.0405	0.0433

Note: This table reports the baseline regression results of INNOV_t+1 on INNOV × SHNUM. INNOV_t+1 is the ratio of the change in firms’ intangible assets at the end of period t + 1 compared to the end of period t to their total assets at the end of period t. SHNUM represents a firm’s retail investor base, measured by the natural logarithm of the number of retail investors at year-end. Industry and year fixed effects are included. Robust t-statistics are reported in parentheses. *, **, and *** indicate the 10%, 5%, and 1% significance levels, respectively.

. The findings are similar to the baseline results, as shown in Table 3. In Column (2), the regression coefficient of INNOV × SHNUM is positive and significant at the 5% level and the regression coefficient of INNOV × INSNUM is positive and significant at the 1% level. The findings of this paper are robust.

Following Larkin et al. [35], we use the natural logarithm of the total number of shareholders as a proxy variable to measure the retail investor base. The number of shareholders of a listed company decreases as institutional investors and major shareholders hold a larger share of the company’s stocks, while retail investors make up more than 99% of the remaining shareholder base after excluding substantial and institutional shareholders. Therefore, we instead use the total number of shareholders (GDZS) as a metric for the retail investor base. As shown in

Table 6. GDZS serves as an alternative proxy for the retail investor base.

	(1)	(2)
	INNOVt+1
INNOV	0.0530 ***	0.0304 **
	(4.9166)	(2.3304)
GDZS	0.0000	−0.0000
	(0.3272)	(−0.7728)
INNOV × GDZS		0.0056 ***
		(2.7092)
CONTROLS	Control	Control
Year	Control	Control
Industry	Control	Control
N	26245	26245
F	15.16 ***	15.26 ***
R2	0.0402	0.0413

Note: This table reports the baseline regression results of INNOV_t+1 on INNOV × GDZS. INNOV_t+1 is the ratio of the change in firms’ intangible assets at the end of period t + 1 compared to the end of period t to their total assets at the end of period t. GDZS denotes a firm’s retail investor base, measured by the natural logarithm of the total number of shareholders at year-end. Industry and year fixed effects are included. Robust t-statistics are reported in parentheses. ** and *** indicate the 5% and 1% significance levels, respectively.

, the regression coefficient of INNOV × GDZS is positive and significant at the 1% level. This indicates that a higher shareholder base of listed firms is associated with higher innovation sustainability. The findings of this paper remain robust.

5. Further Analysis

5.1. Cross-Sectional Analyses: Innovation Environment

Retail investors’ assessments of firms’ innovation activity and investment behavior are intricately linked to the prevailing innovation environment within which these firms operate. A conducive innovation environment has the potential to stimulate innovation investment, foster innovation collaboration, and provide a spillover effect. Consequently, this may lead to a significant improvement in innovation performance. Factors that contribute to the innovation environment, including technological opportunities, competitive market pressures, tacit knowledge, firm size, industry attributes, R&D intensity, and the firm’s property rights structure, can significantly impact innovation sustainability [6]. The innovation environment of a firm is expected to have an impact on the clientele effect of retail investors on innovation sustainability. This is evident in the observation that a firm with a more favorable innovation environment is likely to have higher innovation efficiency and lower levels of uncertainty for investors. Conversely, in cases where the innovation environment of a firm is less favorable, retail investors can only mitigate the risk of uncertainty associated with corporate innovation by investing in firms with higher innovation sustainability.

This paper measures a firm’s innovation environment by using R&D investment, market competition, and firm size. R&D investment utilizes R&D intensity, market competition uses the HHI market concentration index, and firm size employs total assets. Based on these variables, subgroup regressions are performed. Generally, firms facing more severe innovation environments have lower R&D intensities, lack monopoly advantages, and are smaller. Investing in such firms brings higher uncertainty for investors. Retail investors, weaker in information collection, analysis, and risk diversification, may prefer firms with high innovation sustainability and relatively lower uncertainty. This implies the clientele effect could be stronger when firms face a worse innovation environment.

Table 7. Heterogeneous effects of innovation environment.

	(1)	(2)	(3)	(4)	(5)	(6)
	INNOVt+1
	Low R&D	High R&D	Low HHI	High HHI	Small Size	Large Size
INNOV	0.0533 ***	0.0790 ***	0.0290 ***	0.1166 ***	0.0508 ***	0.0798 ***
	(3.6056)	(4.1261)	(3.0140)	(4.6038)	(3.2911)	(4.2954)
SHSHR	0.0000	0.0000	0.0000 *	0.0000	0.0000	0.0000
	(0.8999)	(1.5819)	(1.6468)	(0.1151)	(0.2728)	(1.6164)
INNOV × SHSHR	−0.0004 **	−0.0006	−0.0001	−0.0005 *	−0.0004 **	−0.0003
	(−2.0313)	(−1.6246)	(−0.6396)	(−1.6575)	(−2.2221)	(−1.0254)
INSSHR	0.0016 **	0.0012 *	0.0015 **	0.0018 **	0.0012 **	0.0015 *
	(2.4427)	(1.7022)	(2.5584)	(2.2907)	(2.1519)	(1.6610)
INNOV × INSSHR	0.0012	−0.0194	−0.0083	−0.0281	0.0218	−0.0644 ***
	(0.0480)	(−0.9077)	(−0.6338)	(−0.9782)	(1.2297)	(−3.1978)
CONTROLS	Control	Control	Control	Control	Control	Control
Year	Control	Control	Control	Control	Control	Control
Industry	Control	Control	Control	Control	Control	Control
N	14891	11356	16689	9558	14382	11865
F	10.69 ***	7.38 ***	16.08 ***	6.33 ***	9.66 ***	8.00 ***
R2	0.0495	0.0381	0.0341	0.0659	0.0321	0.0787

Note: This table presents the cross-sectional analysis with respect to the innovation environment. We proxy the innovation environment with three metrics. For columns (1) and (2), we divide the sample based on intensity of R&D investment (R&D). For columns (3) and (4), we divide the sample based on market competition measured by the HHI market concentration index (HHI). For columns (5) and (6), we divide the sample based on firm size proxied by total assets. The dependent, independent and control variables are the same as those in column (2) of Table 2. Industry and year fixed effects are included. Robust t-statistics are reported in parentheses. *, **, and *** indicate the 10%, 5%, and 1% significance levels, respectively.

presents subgroup test results based on the innovation environment. Columns (1) and (2) show high and low R&D intensity subgroups, columns (3) and (4) show high and low market concentration subgroups, and columns (5) and (6) show firm size subgroups. The coefficient of the interaction term (INNOV × SHSHR) is negative and significant in subgroups with low R&D intensity, high market concentration, and small firm size, and are significant at the 5%, 10%, and 5% levels, respectively. This confirms the clientele effect more likely occurs when firms face a poorer innovation environment.

5.2. Cross-Sectional Analyses: Information Environment

Firms’ information environment has a significant impact on the evaluation of firms’ innovative behavior by retail investors. Increased uncertainty in the information environment leads to greater information asymmetry between firms and investors, resulting in a decrease in investors’ willingness to invest. Conversely, a more transparent information environment not only enhances investors’ willingness to invest but also provides firms with more opportunities for growth [36]. Hence, in cases where a firm’s information environment is inadequate, there is a higher probability of eliciting the clientele effect of retail investors on innovation sustainability. In this study, we employ indicators such as the level of accrual manipulation and institutional investors’ shareholding to proxy for the firm’s information environment. Among these factors, a firm’s information environment is negatively affected by a larger absolute value of discretional accruals and a lower institutional ownership.

Table 8. Heterogeneous effects of information environment.

	(1)	(2)	(3)	(4)
	INNOVt+1
	Low absDA	High absDA	Low INSSHR	High INSSHR
INNOV	0.0877 ***	0.0458 ***	0.0522 ***	0.0963 ***
	(4.7422)	(3.0070)	(3.4840)	(4.0101)
SHSHR	0.0000 ***	−0.0000	0.0000 **	0.0000
	(3.1810)	(−0.7538)	(2.0622)	(0.8488)
INNOV × SHSHR	−0.0003	−0.0004 **	−0.0005 **	−0.0003
	(−1.3868)	(−2.0094)	(−1.9965)	(−1.1724)
INSSHR	0.0019 ***	0.0009	0.0019 ***	0.0006
	(3.0959)	(1.3017)	(2.8979)	(0.6379)
INNOV × INSSHR	−0.0522 ***	0.0291	−0.0060	−0.0404 **
	(−2.9100)	(1.4083)	(−0.2399)	(−1.9819)
CONTROLS	Control	Control	Control	Control
Year	Control	Control	Control	Control
Industry	Control	Control	Control	Control
N	15426	10821	17856	8391
F	8.42 ***	8.45 ***	9.68 ***	6.52 ***
R2	0.0414	0.0448	0.0321	0.0549

Note: This table presents the cross-sectional analysis with respect to the information environment. We proxy the information environment with two metrics. For columns (1) and (2), we divide the sample based on the absolute value of discretional accruals (absDA) derived from the modified Jones model estimated by each year and each industry. For columns (3) and (4), we divide the sample based on institutional ownership (INSSHR). Industry and year fixed effects are included. Robust t-statistics are reported in parentheses. ** and *** indicate the 5%, and 1% significance levels, respectively.

presents subgroup test results based on the absolute value of discretional accruals and institutional ownership. Columns (1) and (2) show absolute value of discretional accruals subgroup results, while columns (3) and (4) present institutional ownership subgroups. The interaction term (INNOV × SHSHR) coefficient is negative and significant at the 5% level in subgroups with a high absolute value of discretional accruals and low institutional ownership. This verifies the clientele effect more likely occurs when the firm information environment is poorer.

5.3. Cross-Sectional Analyses: Political Connections

Firms’ political connections can influence innovation activity and thus shape retail investors’ judgments of innovation investment. Connected firms can “rent-seek” additional resources and concessions. As Ding et al. [37] note, political ties provide access to key information, scarce resources, and closer executive–official relationships, potentially influencing policymaking. Specifically, connections can bring loans, tax incentives, and other resources. Yu et al. [38] find executives’ government backgrounds help Chinese firms obtain more bank financing, longer debt maturities, and tax incentives, especially when local tax burdens are heavier. Given China’s transitional economy, imperfect formal institutions lead firms to build connections for benefits. Since innovation faces uncertainties like resource constraints, weaker political connections amplify innovation uncertainty. Thus, this paper argues the clientele effect more likely occurs when connections are weaker.

This paper measures political connections using ownership type and affiliations. State-owned firms inherently have close government ties and advantages; thus, firms are considered connected if state-owned. Connections also arise when executives with political experience are hired to navigate uncertainties. Here, affiliation is measured by executives having served as National People’s Congress deputies or Chinese People’s Political Consultative Conference members.

Table 9. Heterogeneous effects of political connections.

	(1)	(2)	(3)	(4)
	INNOVt+1
	Non-SOE	SOE	Non-PA	PA
INNOV	0.0624 ***	0.0766 ***	0.0634 ***	0.0744 ***
	(3.6373)	(3.8900)	(2.7434)	(4.7530)
SHSHR	0.0000	−0.0000	0.0000	0.0000
	(1.1859)	(−0.4408)	(1.2493)	(1.3218)
INNOV × SHSHR	−0.0005 **	0.0000	−0.0007 **	−0.0002
	(−2.2316)	(0.0125)	(−2.2955)	(−0.8336)
INSSHR	0.0015 **	0.0015 *	0.0006	0.0019 ***
	(2.3510)	(1.9515)	(0.8838)	(3.1434)
INNOV × INSSHR	0.0201	−0.0523 ***	0.0495 **	−0.0496 ***
	(1.0160)	(−2.8341)	(2.0713)	(−3.1520)
CONTROLS	Control	Control	Control	Control
Year	Control	Control	Control	Control
Industry	Control	Control	Control	Control
N	15280	10967	9743	16504
F	10.81 ***	7.15 ***	6.50 ***	11.18 ***
R2	0.0467	0.0427	0.0426	0.0459

Note: This table presents the cross-sectional analysis with respect to political connections. We proxy political connections with two metrics. For columns (1) and (2), we divide the sample based on whether the firm is a state-owned enterprise (SOE). For columns (3) and (4), we divide the sample based on whether the firm has a political connection (PA). Industry and year fixed effects are included. Robust t-statistics are reported in parentheses. *, **, and *** indicate the 10%, 5%, and 1% significance levels, respectively.

presents regression results, with columns (1) and (2) showing ownership subgroups and columns (3) and (4) showing affiliation subgroups. The interaction term (INNOV × SHSHR) coefficient is negative and significant at the 5% level for non-state-owned and unaffiliated subgroups. This confirms the clientele effect more likely occurs in firms with weaker political connections.

6. Discussion and Conclusions

The growing emphasis on sustainable development has elevated the importance of innovation sustainability as a critical facet of this broader agenda. This paper finds clear evidence for a clientele effect, where retail investors demonstrate preference for firms with higher innovation sustainability. This effect remains robust across various measures of innovation sustainability and the retail investor base. Furthermore, we demonstrate that this clientele effect manifests predominantly when firms face disadvantaged conditions in their innovation environment, information environment, and political connections. Specifically, the preference for high innovation sustainability intensifies in these less favorable environments. Our findings highlight that even with absent strong external conditions and resources, retail investors recognize and value corporate efforts to build innovation sustainability.

Our study makes several important empirical and practical contributions by elucidating the economic impact of innovation sustainability on investment behavior. First, it provides empirical evidence shedding light on investor preferences and investment behavior. In addition, by suggesting that there is a notable disparity in the innovation investment among companies, our finding is consistent with other studies. Our study extends the examination of the clientele effect to the corporate governance level. Second, this finding offers new insights into investor behavior and expands the understanding of corporate innovation strategies. By linking innovation sustainability to capital allocation outcomes, we underscore the practical importance of innovation sustainability as a means to attract retail investors. This paper enhances our understanding of how corporate policies and contexts intersect with specific investor preference and behavior. Ultimately, we find a clientele effect in which retail investors prefer firms with higher innovation sustainability, providing new insights into retail investors’ investment behavior. By uncovering this specific clientele effect related to a firm’s investment decisions and corporate governance, our findings hold practical significance for promoting the health and sustainable development of firms and the capital market.

The findings of this paper offer valuable practical insights for both firms and policymakers aiming to attract investment through strategic innovation management, especially in challenging environments. First, the clientele effect highlights retail investors’ recognition of innovative firms. To encourage more capable innovative companies to go public, policymakers should develop a multi-tiered capital market system that caters to the diverse financing needs of these firms and enhances resource allocation efficiency. Second, external challenges increase innovation uncertainty, leading retail investors to prefer firms with higher innovation sustainability. Policymakers should focus on systematically improving the innovation ecosystem and information transparency through policies that strengthen intellectual property protection, foster talent development, and enhance corporate transparency. Firms should pioneer best practices and participate in collaborative platforms. These combined efforts will enhance innovation quality and efficiency, unlocking the significant value of innovation sustainability for long-term economic growth.

Three aspects of our study require some caution when interpreting the results. First, the analysis is confined to the period from 2007 to 2020 due to database constraints. Extending the research timeline beyond 2021 could offer valuable insights into retail investors’ behavior, although their behavior may be extremely disturbed during the pandemic. Future studies with access to more recent data may enhance the applicability of findings. Second, the research design of SHSHR possibly weakens the research results. However, there are some reasons to measure SHSHR in two methods, such as the shareholding ratio and the number of retail investors. Additionally, the measurement of innovation sustainability (INNOV) could benefit from exploring alternative research designs. Future investigations may refine these methodologies to strengthen the robustness of the findings. Lastly, we chose only one country as the research scenario, which limits our global applicability. Practically, data of retail investors’ behavior are limited in many countries, given Chinese distinct disclosure regulations. However, the work of collecting and processing shareholding data of other countries is worth doing, since it is essential to explore the clientele effect across different markets [39].

Future research could be expanded in several directions. First, further investigation could focus on the underlying mechanisms, aiming to uncover the underlying decisions. Analyzing these decisions through the perspectives of psychology and behavioral economics may offer deeper insights into retail investors’ investment behavior. Second, it would be valuable to explore different types of retail investors to determine whether investment behaviors for innovation sustainability vary across investor types. By comparing these groups, more targeted policy recommendations could be provided to address the needs of different investors. Third, conducting international comparative studies would help assess whether the clientele effect varies across countries, revealing potential similarities or differences in different regions. This would help expand our findings to other regions. Finally, research could explore ways to enhance retail investors’ investment capabilities, focusing on how to improve the investment skills and risk awareness of retail investors.