Introduction
The effect of business performance feedback on innovation has prompted numerous studies (e.g., Chen, Reference Chen2008; Chen & Miller, Reference Chen and Miller2007; Greve, Reference Greve2003; He, Huang, & Yang, Reference He, Huang and Yang2021; Jirásek, Reference Jirásek2019; Yu, Minniti, & Nason, Reference Yu, Minniti and Nason2019). The behavioral theory of the firm offers a solid foundation for the literature on performance feedback, which proposes that when performance falls below aspirations, firms conduct problemistic search and prefer risk-taking (Cyert & March, Reference Cyert and March1963). Therefore, these firms may increase R&D investment (Chen, Reference Chen2008; He et al., Reference He, Huang and Yang2021). The threat-rigidity thesis posits that organizations react to underperformance by conserving resources (Staw, Scandelands, & Dutton, Reference Staw, Scandelands and Dutton1981). Firms may tighten available budgets and reduce expenses, such as by decreasing R&D investment. These contradictory arguments are reconciled by the shifting focus of attention model, which suggests that firms focus on the different reference points between aspirations and survival depending on how far firms are performing below their aspirations (March & Shapira, Reference March and Shapira1992). This model is one of the dominant frameworks for understanding firms’ responses to performance shortfalls (e.g., March & Shapira, Reference March and Shapira1992; Miller & Chen, Reference Miller and Chen2004; Iyer & Miller, Reference Iyer and Miller2008).
Most previous studies have focused only on the intensity of performance shortfalls (i.e., the distance between performance and aspirations) but have overlooked the impact of duration (i.e., the time length of performance shortfall) and the interaction between intensity and duration (e.g., Chen, Reference Chen2008; Chen & Miller, Reference Chen and Miller2007; Greve, Reference Greve2003; He et al., Reference He, Huang and Yang2021; Jirásek, Reference Jirásek2019). Psychological studies have already revealed the impact of performance persistence on decision-making (Neef, Shades, & Miller, Reference Neef, Shades and Miller1994; Yu et al., Reference Yu, Minniti and Nason2019). An extended period of underperformance may change an organization's perception of and response to the same intensity of performance shortfall (Lehman & Hahn, Reference Lehman and Hahn2013). In addition, Haleblian and Rajagopalan (Reference Haleblian and Rajagopalan2005) proposed that the failure to consider duration and intensity together is a plausible reason for the mixed and inconsistent findings of previous studies on performance feedback. The consideration of only underperformance intensity cannot completely reflect the performance feedback and accurately describe firms’ behavioral responses. Therefore, it is necessary to conceptualize performance feedback more completely by highlighting the time dimension of performance shortfall and considering underperformance intensity and duration together. We extend the research stream of performance feedback by integrating the shifting focus of attention model and temporal effect to explore how the intensity and duration of underperformance jointly affect firms’ R&D investment.
Using a sample of Chinese A-share listed manufacturing firms from 2010 to 2019, we find an inverted U-shaped relationship between underperformance intensity and firms’ R&D investment. When performance falls just below aspirations, firms focus on aspirations and conduct the problemistic search to improve performance (Cyert & March, Reference Cyert and March1963). Thus, firms may increase their R&D investment. As performance falls well below aspirations, firms shift their attention toward survival and enact risk-averse threat rigidity responses (Staw et al., Reference Staw, Scandelands and Dutton1981). Therefore, these firms may decrease R&D investment. Then, we explore the effect of the interaction between underperformance intensity and duration on the performance–innovation relationship. Our findings reveal that underperformance duration weakens the inverted U-shaped relationship when it lasts for a short period. Interestingly, the inverted U-shape flips to a U-shape when the performance shortfall persists for a long time.
By introducing the time dimension to explore the moderating effect of underperformance duration between underperformance intensity and R&D investment, we make several contributions. First, we contribute to the literature on performance feedback by considering both underperformance intensity and duration. This study conceptualizes the interaction of intensity and duration, which also reconciles the conflicting findings in extant studies from a new perspective. Second, we add new insights into innovation research by theorizing and empirically examining an overlooked boundary condition for the curvilinear relationship between performance feedback and innovation. We reveal that this curvilinear relationship depends on the duration of performance shortfall; that is, an inverted U-shaped relationship exists when the performance shortfall lasts for a short period, and a U-shaped relationship emerges when such underperformance persists for a long time. This finding calls for future research to explore the dynamics of the relationship and account for temporal effects.
Hypotheses Development
Underperformance Intensity and R&D Investment
Most of the current literature on performance feedback is rooted in the behavioral theory of the firm, which models organizations as goal-directed systems and explores the processes of organizational performance evaluation, search, and decision-making (Cyert & March, Reference Cyert and March1963). In general, organizations evaluate their current performance relative to aspirations, which are based on historical performance and comparable peer performance (Cyert & March, Reference Cyert and March1963). When performance falls below aspirations, firms initiate problemistic search to find a solution and engage in risk-taking (Cyert & March, Reference Cyert and March1963; Gavetti, Greve, Levinthal, & Ocasio, Reference Gavetti, Greve, Levinthal and Ocasio2012). Innovation is an important strategy that enables firms to enhance their core competence and competitiveness to improve performance (He et al., Reference He, Huang and Yang2021). The search for solutions through R&D is an important form of organizational search, which is the organizational process most directly related to innovation (Greve, Reference Greve2003). Thus, underperforming firms may increase R&D investment with high risk and high returns to mend their performance shortfall (Chen, Reference Chen2008; He et al., Reference He, Huang and Yang2021).
The threat-rigidity thesis offers another perspective to understand organizations’ responses to underperformance, especially when performance falls well below aspirations. This thesis suggests that organizations show rigidity or inability to act and do something new because of information restriction, control constriction, and resource conservation in the face of threats (Staw et al., Reference Staw, Scandelands and Dutton1981). When performance losses loom large enough to be perceived as existential threats, organizations may reduce their system-response capabilities or narrow their behavioral repertoires (Kotiloglu, Chen, & Lechler, Reference Kotiloglu, Chen and Lechler2021; Shimizu, Reference Shimizu2007; Staw et al., Reference Staw, Scandelands and Dutton1981). Organizations may tighten their available budgets and reduce costs (Liu, Chen, & Kittilaksanawong, Reference Liu, Chen and Kittilaksanawong2013; Staw et al., Reference Staw, Scandelands and Dutton1981). In addition, Sitkin and Pablo (Reference Sitkin and Pablo1992) suggested that negative situations encourage risk aversion through threat-rigidity responses. Several studies have used firms’ proximity to bankruptcy to examine the threat-rigidity effect and have shown that firms performing far below aspirations may develop conservative, risk-averse tendencies (Chen & Miller, Reference Chen and Miller2007; Liu et al., Reference Liu, Chen and Kittilaksanawong2013). Therefore, firms may decrease their R&D investment when they are greatly underperforming.
These contradictory arguments about the relationship between underperformance intensity and R&D may be partially reconciled by the shifting focus of attention model. This model suggests that decision-makers shift their attention between aspirations and survival according to the perceived threat from performance shortfalls, which influences risk-taking and risk aversion in response to underperformance (March & Shapira, Reference March and Shapira1992; Posen, Keil, Kim, & Meissner, Reference Posen, Keil, Kim and Meissner2018). An extended stream of literature predicts a complex relationship between performance feedback and risk preference based on the shifting focus of attention model (e.g., Hu, Blettner, & Bettis, Reference Hu, Blettner and Bettis2011; March & Shapira, Reference March and Shapira1992; Miller & Chen, Reference Miller and Chen2004). Firms focus more on the reference point closest to their current position (March & Shapira, Reference March and Shapira1992; Ref & Shapira, Reference Ref and Shapira2017). Underperforming firms focus on aspirations when their performance is in the neighborhood of aspirations; furthermore, they initiate problemistic search and prefer to take risks, such as increasing R&D investment, to improve their performance (Chen, Reference Chen2008; Chen & Miller, Reference Chen and Miller2007). When their performance falls well below aspirations, firms tend to focus more on survival, avoid risk and choose conservative strategies, in which the threat-rigidity thesis plays a leading role (Iyer & Miller, Reference Iyer and Miller2008; March & Shapira, Reference March and Shapira1992). Therefore, these firms would prefer to decrease R&D investment (Chen & Miller, Reference Chen and Miller2007).
To summarize, we propose that if performance falls just below aspirations, firms may increase R&D investment. As performance falls far below aspirations, firms may decrease R&D investment. Thus, our first hypothesis is as follows:
Hypothesis 1 (H1): Firms’ R&D investment first increases and then decreases with underperformance intensity, showing an inverted U-shaped relationship.
The Moderating Effect of Underperformance Duration
Previous studies have focused mainly on the intensity of underperformance (e.g., Chen, Reference Chen2008; Chen & Miller, Reference Chen and Miller2007; Greve, Reference Greve2003; He et al., Reference He, Huang and Yang2021; Iyer & Miller, Reference Iyer and Miller2008; Jirásek, Reference Jirásek2019). More recently, the temporal effect of performance feedback has been identified as potentially important (Haleblian & Rajagopalan, Reference Haleblian and Rajagopalan2005). A few studies have revealed that the duration of underperformance affects an organization's decision-making (Yu et al., Reference Yu, Minniti and Nason2019; Zhong, Ren, & Song, Reference Zhong, Ren and Song2023). However, these studies have not fully explored the joint effect of underperformance duration and intensity. Haleblian and Rajagopalan (Reference Haleblian and Rajagopalan2005) proposed that assessing the duration and intensity of performance together is necessary to predict strategic change. Even if firms have the same intensity of underperformance, their search activities and the degree of risk-taking may differ because they have different perceptions of performance shortfalls when they experience different durations of underperformance (Iyer, Baù, Chirico, Patel, & Brush, Reference Iyer, Baù, Chirico, Patel and Brush2019). Considering underperformance intensity and duration together provides a more comprehensive representation of performance feedback, which may help develop a more precise understanding of the behavioral response of firms. Therefore, we incorporate the temporal effect into the shifting focus of attention model by examining how underperformance duration may moderate the relationship between underperformance intensity and R&D investment. We expect that underperformance duration flattens the inverted U-shaped relationship by attenuating the problemistic search and threat-rigidity mechanisms.
When performance falls just below aspirations, firms focus on aspirations and initiate problemistic search as mentioned in hypothesis 1 (Chen, Reference Chen2008; Desai, Reference Desai2008). These firms are optimistic about their future performance and believe that they can repair their performance shortfalls through proactive problemistic search, such as by increasing R&D investment. However, a sustaining poor performance trajectory over time reduces the optimism about future performance. When an organization experiences a performance shortfall for a long duration, its expectations for the future appear bleak (Lehman & Hahn, Reference Lehman and Hahn2013). In such a case, members of such an organization believe that the firm's future performance is expected to decline even further; thus, managers are likely to think and act in a distressed and negative manner (Lehman & Hahn, Reference Lehman and Hahn2013). Hence, they have less motivation to proactively repair performance shortfalls than do those with low underperformance intensity and short duration. Such firms have been increasing and repurposing R&D investment for a long time but have failed to improve performance to meet aspirations. They may attenuate previous problemistic search, expand the search boundary and try alternative strategies to address the persistent performance shortfalls. Therefore, a long underperformance duration weakens the problemistic search mechanism.
As performance falls well below aspirations, firms focus on survival and enact the threat rigidity responses as mentioned in hypothesis 1. These firms may tighten available budgets and reduce costs (Staw et al., Reference Staw, Scandelands and Dutton1981), such as by decreasing R&D investment. However, when these high-intensity underperforming firms have a long underperformance duration, it implies that the threat of survival is intensified. It is most likely caused by radical changes in the market (Staw et al., Reference Staw, Scandelands and Dutton1981) while the underperforming firms are not able to catch up. Alternatively, with the market environment having undergone a series of incremental changes, firms are not able to respond in time and are thus progressively left behind due to their weak environmental awareness. Firms that have experienced failure in the past have difficulty scanning the environment and capturing changes, whether radical or incremental (Lant, Milliken, & Batra, Reference Lant, Milliken and Batra1992). Thus, it is difficult for them to find solutions in the changed environment, resulting in a much more unknown and unclear coping mechanism to address performance problems. Staw et al. (Reference Staw, Scandelands and Dutton1981) propose that organizational general threat-rigidity reactions are functional only when the environment is stable and the coping mechanisms are clear. So, the threat-rigidity effect may be dysfunctional when underperformance duration is long. Overall, underperformance duration weakens the threat rigidity mechanism.
All in all, we propose that underperformance duration moderates the effect of performance shortfall on R&D investment by weakening the problemistic search and threat-rigidity mechanisms. Thus, we make the following prediction:
Hypothesis 2 (H2): Underperformance duration weakens the inverted U-shaped relationship between underperformance intensity and R&D investment; that is, a longer underperformance duration results in a less sharp inverted U-shaped relationship between underperformance intensity and R&D investment.
Methods
Data and Sample
The manufacturing industry is a suitable context in which to test our predictions because it devotes a large portion of its budget to R&D expenses and is particularly suited to an investigation into how a firm's innovative behavior may change in response to underperformance (Yu et al., Reference Yu, Minniti and Nason2019). To avoid the impact of the financial crisis from 2007 to 2009, we selected the data period from 2010 to 2019 for Chinese A-share listed manufacturing companies. We lagged all explanatory variables by one year. We obtained the initial sample of firms from the Wind Economic Database and the China Stock Market & Accounting Research (CSMAR) database, which are authoritative databases offering information on all listed companies in China.
We cleaned the initial sample in several ways. First, we excluded the specially treated companies labeled ST, SST, and *ST, which are at risk of delisting due to the financial or other abnormal conditions, to ensure that the firms in our sample are in normal operation. Second, since the calculation of underperformance duration requires continuity in a firm's data, we kept only the data for firms that have uninterrupted data over the covered period. That is, if a firm's data have a gap due to the missing values, then we retain the data only for the longer time span. For instance, for a firm that has data from two time periods, e.g., 2010–2012 and 2014–2019, but not for 2013, we kept the data for only the six years from 2014 to 2019 instead of for the three years from 2010 to 2012Footnote 1. We dropped 163 firm-year observations as a result. Third, we excluded industries with fewer than five firms in a year to avoid possible biases in the estimates of social aspirations (SA) and industry R&D intensity (Chen & Miller, Reference Chen and Miller2007; Ye, Yu, & Nason, Reference Ye, Yu and Nason2021). We therefore further dropped an additional 133 firm-year observations. The final sample with complete information includes 1,296 firms and 7,002 unbalanced panel data.
Measures
Dependent variable
R&D investment at time t + 1 (RDi,t + 1). R&D investment is measured as R&D intensity, which is calculated as expenditures divided by sales (Chen & Miller, Reference Chen and Miller2007; Greve, Reference Greve2003).
Independent variables
Performance at time t (Pi,t). Following prior studies (e.g., Bromiley & Washburn, Reference Bromiley and Washburn2011; Chen & Miller, Reference Chen and Miller2007; He et al., Reference He, Huang and Yang2021), we use the generally representative return on assets (ROA) as the performance measure.
Aspirations at time t (Ai,t). According to the behavioral theory of the firm (Cyert & March, Reference Cyert and March1963; Greve, Reference Greve2003), aspiration is formed by historical aspiration (HA) and social aspiration (SA), which assumes that organizations have a single aspiration for a performance dimension. This concept is very much in line with corporate practice: companies typically retain only a set of stated goals for a given set of activities at a given time (Bromiley & Harris, Reference Bromiley and Harris2014). In addition, previous studies have shown that both HA and SA impact organizational R&D investment (Chen & Miller, Reference Chen and Miller2007; Yu et al., Reference Yu, Minniti and Nason2019). Therefore, we use a composite aspiration by combining HA and SA. We estimate weights by searching all parameter values in increments of 0.1 from 0.1 to 0.9 and choosing the highest ‘log-likelihood’ model (Greve, Reference Greve2003). This process produces a value of 0.2 for α. The specific formula is as follows:
Historical aspirations at time t (HAi,t). HAi,t is the HA level of firm i at time t, which is a mixture of actual performance and the HA level of firm i at time t−1 (Bromiley & Harris, Reference Bromiley and Harris2014; Greve, Reference Greve2003). Firm i's HA level at time 0 (HAi,0) is represented by the actual performance at time 0. We also estimate the weight of β by choosing the highest ‘log-likelihood’ model, producing a value of 0.7:
Social aspirations at time t (SAi,t). The SA level of firm i at time t (SAi,t) is the mean of performance at time t of other firms except the focal firm in the same industry. We calculate SA values according to 26 segments of the manufacturing industry from the Guidelines on Industry Classification of Listed Companies, revised by the China Securities Regulatory Commission in 2012:
Underperformance intensity at time t (Gap_inti,t). Drawing on previous studies, we use Ii,t × (Pi,t – Ai,t) to measure underperformance intensity; Ii,t is a dummy variable that equals 1 when performance falls below aspiration and 0 otherwise.
Moderator
Underperformance duration at time t (Gap_duri,t). According to Yu et al. (Reference Yu, Minniti and Nason2019), underperformance duration is the number of periods in which underperformance persists. We code this variable as 0 if the performance is above or equal to aspirations and as the length of the underperforming period for firms. For example, we code this variable as 1 if a firm has been underperforming for 1 year, 2 if it has been underperforming for 2 consecutive years, and so on. At time 0, if the firm is underperforming, the underperformance duration is 1 and 0 otherwise.
Control variables
We control for industry- and firm- level variables.
Industry-level variables. Industry R&D intensity at time t: We control for the R&D investment of each sector of the manufacturing industry that affects firms’ R&D decisions (Chen & Miller, Reference Chen and Miller2007; Yu et al., Reference Yu, Minniti and Nason2019). We measure this variable by excluding the focal firm, summing R&D expenditures in the same sector, and dividing that number by total industry sales. Industry sales growth at time t: Because industry prospects influence firms’ investments (Chen & Miller, Reference Chen and Miller2007), we control for the growth in industry sales from t−1 to t. Environmental dynamism at time t: Firms will adopt different strategies when faced with environmental dynamism (Ghosh & Olsen, Reference Ghosh and Olsen2009). We control the abnormal fluctuations of firms’ sales caused by environmental dynamism, that is, the coefficient of variation in abnormal sales for the past five years adjusted by industry (Ghosh & Olsen, Reference Ghosh and Olsen2009; Shen, Yu, & Wu, Reference Shen, Yu and Wu2012). First, we use the past five years of sales for each firm to estimate abnormal sales separately using the ordinary least squares (OLS) method with the following model:
That is, sales in year t/t−1/t−2/t−3/t−4 are used as the dependent variable, and the corresponding values for the variable Year are 5, 4, 3, 2, 1 respectively. The residuals (ɛ) are abnormal sales. Second, we calculate the unadjusted environmental dynamism, i.e., the coefficient of variation in the firm's abnormal sales over the past five years. Then, we divide it by the industry environmental dynamism, which is the median unadjusted environmental dynamism of all firms in the same industry and year. A greater value indicates higher environmental dynamism.
Firm-level variables. Size at time t: Because a firm's size can influence its innovation (Audia & Greve, Reference Audia and Greve2006), we use the natural logarithm of the final total assets to measure it (Kuusela, Keil, & Maula, Reference Kuusela, Keil and Maula2017). Age at time t: Aging processes also affect firms’ R&D investments and responses to underperformance (Desai, Reference Desai2008). We control for a firm's age using the natural logarithm of years of establishment plus 1. State-owned enterprise (SOE) at time t: We assign a value of 1 to SOEs and 0 to otherwise (He et al., Reference He, Huang and Yang2021), which affects firms’ risk preference and risky strategies, such as R&D investment. Board independence at time t: The independence of boards affects organizational strategic decisions through supervision; thus, we control for board independence using the ratio of independent directors to the total number of directors (He et al., Reference He, Huang and Yang2021). Institutional shareholdings at time t: Because institutional investors can participate effectively in the governance and supervision of firms, they play critical roles in strategic decisions. We use the total number of shares of institutional investors divided by the total share capital to measure it. Slack at time t: According to the behavioral theory of the firm, slack search also affects firms’ strategic decisions (Greve, Reference Greve2003). Following previous research (Bourgeois, Reference Bourgeois1981; Bourgeois & Singh, Reference Bourgeois and Singh1983; Chen, Reference Chen2008), we use the average value of available slack (current assets/current liabilities), recoverable slack (sales and administrative expenses/sales), and potential slack (owners’ equity/total liabilities) to measure slack. Outperformance intensity at time t: In line with the extant literature (Kuusela et al., Reference Kuusela, Keil and Maula2017; Yu et al., Reference Yu, Minniti and Nason2019), we also control for outperformance when performance is higher than aspirations, which is measured by (1 – Ii,t) × (Pi,t – Ai,t), where Ii,t is a dummy measuring whether the firm is underperforming. Outperformance duration at time t: In contrast to underperformance duration, we code this variable as 0 if performance is below aspirations and as the length of the outperforming periods. For example, we code this variable as 1 if the firm has been outperforming for 1 year, 2 if it has been outperforming for 2 consecutive years, and so on.
Firm, year and industry fixed effects. We include dummy variables for all firms and years to control for heterogeneity. Because 65 firms have changed industries in our sample, we also control for industry fixed effects.
Analytical Approach
We use Stata15 to analyze the panel data and explore the relationship between performance shortfall and R&D investment. Our full model is as follows:
where i represents the firm, and t indicates the year. μt represents time fixed effects, ωj is industry fixed effects, di is firm fixed effects, and ɛi,t is the random error term.
Results
Table 1 provides the descriptive statistics, and Table 2 presents the correlation matrix. We perform a cross-sectional dependence test, Breusch and Pagan Lagrange multiplier (LM) test for random effects, and Hausman specification test to determine the model. Based on the results of these tests, we finally use a panel data fixed effects model for the estimations.
Table 1. Descriptive statistics
Note: Number of observations = 7002.
Table 2. Correlation matrix
Note: ***p < 0.01, **p < 0.05, *p < 0.1.
Table 3 shows the results of the fixed effects panel regressions for H1 and H2. Model 1 is the basic model that contains only control variables. Model 2 adds underperformance intensity, the square of intensity and duration to Model 1 to test H1. The square term of underperformance intensity is negative and significant (β = −0.075, p = 0.00). We next explore the nature of this inverted U-shaped relationship (Haans, Pieters, & He, Reference Haans, Pieters and He2016). First, the confidence interval of the extreme point must fall inside the bounds of the independent variable. We find the extreme point to be −0.187 and the 95% confidence interval to be [−0.289; −0.084], which are within the range of the independent variable (−1.054, 0). Second, opposite signs should exist between the slopes at the lower and upper ends of this distribution. When underperformance intensity is at the lowest value of −1.054, the slope is significant at 0.131 (p = 0.00). When such intensity is at the highest value of 0, the slope is significant at −0.028 (p = 0.02). These results support H1, which predicts that R&D investment first increases and then decreases with underperformance intensity. Figure 1 shows this inverted U-shaped relationship.
Table 3. Results of fixed-effect regression analysis
Notes: Industry fixed-effects are included because a small number of firms changed their industries during the sample period. Bold text indicates the empirical results of hypotheses.
Figure 1. Estimated relationship between underperformance intensity and R&D investment
Model 3 examines the moderating effect of underperformance duration between underperformance intensity and R&D investment. We add the interactions between underperformance duration and underperformance intensity and between underperformance duration and the square of underperformance intensity based on Model 2. We test for a flattening effect by determining whether the coefficient of the interaction between underperformance duration and the square of underperformance intensity is significant (Haans et al., Reference Haans, Pieters and He2016). Model 3 in Table 3 shows that the interaction between underperformance duration and the square of underperformance intensity is positive and significant (β = 0.101, p = 0.00), which is the opposite of the negative coefficient of the square of underperformance intensity (β = −0.361, p = 0.00). These findings illustrate that underperformance duration may flatten the inverted U-shaped relationship between underperformance intensity and R&D investment. Figure 2 depicts the R&D investment value when underperformance duration is plotted at short (1 year), moderate (2 years), long (4 years), and very long (6 years) durations. The magnitude of the curvilinearity between underperformance intensity and R&D investment is −0.260 (short duration = 1), −0.160 (moderate duration = 2), 0.042 (long duration = 4), and 0.243 (very long duration = 6). Interestingly, the inverted U-shaped relationship flips to a U-shape when underperformance duration is more than 4 years. Following Haans et al. (Reference Haans, Pieters and He2016), we further calculate the value of underperformance duration at which the shape flip occurs. When duration = −β2/β5 = 3.584, where β2 is the coefficient of the square of underperformance intensity and β5 is the coefficient of the interaction between underperformance duration and the square of intensity shown in Model 3 in Table 3, the underperformance intensity–R&D investment relationship is linear, and no turning point exists. Notably, when the underperformance duration is more than 3.584 years, the inverted U-shaped relationship is reversed such that R&D investment first decreases and then increases with underperformance intensity. When performance is just below aspirations with a long underperformance duration, firms may reduce R&D expenses to temporarily prop up their performance. They lose the incentive to conduct problemistic search by increasing R&D. When performance is significantly below aspirations with a long underperformance duration, it means that the firm's early strategic decisions have been completely ineffective. Even if the firm reduces R&D investment, its performance improvement is negligible for rectifying the large performance shortfall with long-term duration. Managers may be more prone to take risks as a ‘last-ditch attempt’ at survival and respond to a changed market by increasing investment in R&D and upgrading products and services. This finding complements our identification of the moderating effect of duration in theory development.
Figure 2. Moderating effect diagram of underperformance duration
The results of several control variables also have some implications. The coefficients of outperformance intensity and outperformance duration are very small and not significant in all models, indicating that a performance that rises above aspirations does not affect a firm's R&D decision. This finding is consistent with the behavioral theory of the firm, which proposes that organizations may be satisfied with the present and thus do not change when performance is above aspirations (Cyert & March, Reference Cyert and March1963). The coefficients of slack are positive and significant in all models, showing that firms may increase R&D investment with slack. This result verifies the slack search proposed by the behavioral theory of the firm (Cyert & March, Reference Cyert and March1963). Organizational slack provides not only the resources needed for R&D but also an opportunity for trial and error (Greve, Reference Greve2003).
Robustness Tests
We conduct several robustness tests. First, we separate SA and HA (Table 4). We respectively calculate the intensity and duration of underperformance based on SA and HA to test the hypotheses. Second, we redetermine the weights of the HA and composite aspirations (A) (Table 5). Considering that different weights affect the results of underperformance intensity and duration, we perform a test by analyzing different values, which range from 0.1 to 0.9 in increments of 0.1, for the weights of the HA and A to exclude the effect of the weights on the results. We find no significant differences in all tests with different weights. Third, we redefine HA and SA such that we measure HA using the ROA of the previous year (Chen, Reference Chen2008; Ye et al., Reference Ye, Yu and Nason2021) and SA using the average value of a company's segmented industry in manufacturing without excluding the focal firm (Audia & Greve, Reference Audia and Greve2006) (Table 5). Fourth, we conduct a robustness check with only firms without gaps to eliminate sample selection bias (Table 6). Fifth, we winsorize the dependent variable and continuous control variables (except for outperformance intensity) at the 1st and 99th percentiles to minimize the effect of outliers (Yu et al., Reference Yu, Minniti and Nason2019) (Table 6). We avoid outliers for independent variables as much as possible during the sample processing by eliminating from the data each year specially treated firms and industries with fewer than five firms.
Table 4. Results of robustness test 1 by separating historical and social aspirations
Note: Industry fixed-effects are included because a small number of firms changed their industries during the sample period. Bold text indicates the results of robustness tests for hypotheses.
Table 5. Results of robustness tests 2 and 3
Note: Bold text indicates the results of robustness tests for hypotheses. In Robustness Test 2, we perform a test by analyzing different weights of HA and A and report only the results at α = 0.5 and β = 0.4.
Table 6. Results of robustness tests 4 and 5
Note: Bold text indicates the results of robustness tests for hypotheses.
In the results of all robustness tests, we find that the coefficients of underperformance intensity squared are significantly negative, indicating an inverted U-shaped relationship between underperformance intensity and R&D investment. The interactions between underperformance duration and the square term of underperformance intensity relate significantly and positively to R&D investment, which means that underperformance duration flattens the inverted U-shape. Thus, all results are consistent with our findings and further support H1 and H2.
Discussion
This study explores how performance shortfalls shape Chinese listed manufacturing firms’ decisions regarding R&D investment. According to the shifting focus of attention model that combines the behavioral theory of the firm and the threat-rigidity thesis, we examine the curvilinear relationship between underperformance intensity and R&D investment and explore the moderating effect of underperformance duration. These results carry significant implications for theory and practice.
Theoretical Implications
First, we extend the performance feedback literature by exploring the interaction between underperformance intensity and duration. A few but growing number of studies have examined how underperformance duration directly affects firms’ strategic decisions (Yu et al., Reference Yu, Minniti and Nason2019; Zhong et al., Reference Zhong, Ren and Song2023); however, they have ignored how underperformance duration affects firms’ perceptions of underperformance intensity and their interactions. Firms’ responses to performance feedback are simultaneously influenced by the intensity and length of time of their underperformance (Haleblian & Rajagopalan, Reference Haleblian and Rajagopalan2005). Different from previous studies that have considered only the single influence of intensity or duration (e.g., Chen & Miller, Reference Chen and Miller2007; Greve, Reference Greve2003; Yu et al., Reference Yu, Minniti and Nason2019), we consider intensity and duration of underperformance together, which not only conceptualizes performance feedback more completely by exploring the interaction of intensity and duration but also reconciles the contradictory findings regarding performance feedback in the literature from a new perspective (Haleblian & Rajagopalan, Reference Haleblian and Rajagopalan2005). We argue that firms adjust their responses to underperformance intensity based on the duration of performance shortfall. Our study improves the boundary conditions of the performance feedback model and provides a new perspective on the influence of underperformance duration, all of which enhance and improve performance feedback theory.
Second, we add new insights into innovation research by integrating the shifting focus of attention model and underperformance duration. Although recent studies have considered the curvilinear relationship between performance shortfall and innovation (Jirásek, Reference Jirásek2019; Yu et al., Reference Yu, Minniti and Nason2019), they have not studied the change in the relationship taking into account the temporal effect. We theorize and empirically examine an overlooked boundary condition for the curvilinear relationship between performance feedback and innovation. We reveal that the curvilinear relationship depends on the duration of performance shortfall. When performance falls below aspirations for a short period, underperformance intensity and R&D investment show an inverted U-shaped relationship, which is consistent with the results of recent studies (e.g., Chen & Miller, Reference Chen and Miller2007; Jirásek, Reference Jirásek2019). Interestingly, the inverted U-shaped relationship between underperformance intensity and R&D investment flips to a U-shaped relationship when the duration of underperformance is too long. This finding calls for future research to explore the dynamics of the relationship between performance feedback and R&D investment taking into account the temporal effect.
Figure 3 graphically summarizes the main conclusions of our article. This figure shows that when underperformance is low in intensity and short in duration, firms act positively by conducting problemistic search and taking risks to repair performance shortfalls, such as increasing R&D investment (e.g., Chen, Reference Chen2008; Cyert & March, Reference Cyert and March1963), which is in line with the behavioral theory of the firm. According to the threat-rigidity thesis, when underperformance is high in intensity and short in duration, firms may reduce costs to conserve resources and become risk averse in response to threats to survival (Liu et al., Reference Liu, Chen and Kittilaksanawong2013; Staw et al., Reference Staw, Scandelands and Dutton1981), thereby decreasing R&D investment (Chen & Miller, Reference Chen and Miller2007). When underperformance is low in intensity but long in duration, firms lose the incentive to conduct problemistic search and may decrease R&D investment to temporarily prop up their performance. When underperformance is high in intensity and long in duration, it is very difficult for these firms to reduce R&D expenses to solve the consistent failure and intensified threat. Instead, they may take the plunge by increasing R&D investment to upgrade their products and services in response to the changing environment. They hope to solve problems by adopting high-risk, high-return strategies as ‘last-ditch attempts’ at survival. Greve (Reference Greve2010) proposed that high risk-taking offers the opportunity to win back past losses, which is highly valued in loss situations.
Figure 3. Diagram of conclusion
Practical Implications
Our study also has some practical implications for the development of manufacturing firms in an innovation-driven economy. The transformation and upgrading of manufacturing firms must adhere to innovation-driven development to react to the complex and changing environment. Performance feedback is one of the most important factors that can be used by managers to evaluate the effectiveness of their current strategy and decide on the need for future strategic changes (He et al., Reference He, Huang and Yang2021), such as increasing R&D investment in innovation. Therefore, firms must objectively assess their performance in terms of both underperformance intensity and duration. When a performance shortfall persists, firms should adjust their strategies according to their previous noneffective responses to performance shortfalls. When underperformance intensity is high with a long duration, taking the appropriate risk may solve the persistent and severe problem of performance shortfall. However, firms should be aware of the gambler's fallacy. The result of taking risks related to high underperformance intensity with long duration can be a sequence of ‘double-or-nothing’ risk escalations to win back losses (Greve, Reference Greve2010). Firms should rationally evaluate opportunities and available resources to decide whether to take the risk of increasing R&D expenses to solve performance problems. In addition, boards of directors should strengthen their management of performance feedback and enhance the supervision of managers to prevent decision makers from making unreasonable decisions, such as blindly increasing R&D expenses in a manner related to the gambler's fallacy.
Limitations and Future Research Implications
Our work has limitations due to deficiencies in the research sample and dimensions of underperformance. First, we restrict our sample to A-share listed manufacturing firms and do not consider other industries and nonlisted companies that may have different preferences for using R&D investments to solve performance shortfalls. Future studies could expand the study sample and explore the impact of performance feedback in different industries to improve the generalizability of the conclusions and to enrich the findings related to performance feedback. Second, the dimension of underperformance is still limited in our study. We do not study how the scope of underperformance based on multiple indicators and the consistency between them affect firms’ strategies. In addition, the existing research has focused mostly on measuring firms’ performance using financial indicators while ignoring nonfinancial indicators, such as consumer satisfaction and brand; however, these indicators are becoming increasingly important factors for performance and may thus have a direct impact on R&D investments. Future research may assess firms’ performance by using nonfinancial indicators and exploring the influence of the scope and consistency of underperformance on firms’ decision making.
Conclusion
Our study reveals that the interaction between the intensity and duration of underperformance shapes organizational search patterns. Specifically, underperformance duration weakens the relationship between underperformance intensity and R&D investment. Notably, we identify a shape flip when the underperformance duration is very long. We hope that our study inspires additional behavioral research that goes beyond the intensity of performance shortfalls, expands the study of other dimensions, and explores the interactions among these dimensions.
Data Availability Statement
The data supporting the findings of this study are openly available in the Open Science Framework (OSF) at osf.io/bvf56/?view_only=a6d2d953e2694ef388f17bb50935d571
Acknowledgments
We are grateful for the insightful comments from Senior Editor Zilin He, and three anonymous reviewers. We also greatly appreciate Dr. Kathleen Reid (Shepherd University) and Professor Xinming He (Durham University) for their comments and suggestions on this article. This work was supported by the Major Projects of National Social Science Fund of China (Grant No. 22ZDA047) ‘Research on the strategic support of Scientific and technological self-reliance as national development’.
Xuefeng Liu (liuxuefeng@xmu.edu.cn) is Full Professor in the School of Management, Xiamen University, China, and a member of the Academy of Management. He received his PhD degree in Management from Zhejiang University, China. His recent research interests focus on global manufacturing, strategic management, and innovation management. He has published several research papers about innovation in emerging economies in international journals in English and has participated in several research programs supported by National Natural Science Foundation of China.
Lanlan Song (lanlan_song@outlook.com) is a PhD candidate of business administration at School of Management of Xiamen University in China. Her research interests include business strategy and innovation management.
Guowei Lai (gwlai@xmu.edu.cn) is an Assistant Professor in the School of Management of Xiamen University, China, and his main research field is business model innovation and technological innovation.
Yuying Xie (xyuying@gmail.com) is a Full Professor in the Department of Economics and Finance at Shepherd University, West Virginia. He received his Bachelor's degree in economics from Southwestern University of Finance and Economics, China, and PhD degree in Economics from Southern Illinois University Carbondale. His main research interests are technology spillover, product differentiation, and industrial organization. He has published multiple journal articles with regards to product quality, technology spillover, and technological development in various international journals.
