The electric car industry, particularly in the context of China EV markets, has emerged as a pivotal force in driving green economic transformation and addressing climate change. As this sector experiences rapid expansion, it faces a critical shift from policy-driven growth, characterized by subsidies, to market-driven dynamics. This transition intensifies competition due to technological innovations and evolving consumer demands, making the effective utilization of internal resources, especially organizational slack, essential for sustainable development. Organizational slack refers to resources held by firms beyond immediate operational needs, serving as a strategic asset to enhance adaptability and flexibility. While prior research has explored its buffering role and impact on firm growth, the distinct effects of unabsorbed and absorbed slack in the electric car sector remain underexamined. Additionally, dynamic capabilities—defined as a firm’s ability to acquire, configure, and reconfigure resources—are crucial for maintaining competitive advantage. Although studies indicate that dynamic capabilities boost innovation, their moderating role in the relationship between organizational slack and growth warrants further investigation. Similarly, environmental uncertainty, stemming from market structure shifts and technological disruptions, may influence how slack resources affect firm growth. This study aims to systematically analyze these mechanisms through theoretical reasoning and empirical testing, offering valuable insights for electric car enterprises to leverage organizational slack for sustained growth and competitiveness.
In the China EV landscape, the reduction of policy subsidies has accelerated the need for firms to rely on internal resources. Organizational slack, as a form of strategic reserve, can be categorized into unabsorbed slack—highly liquid resources not yet integrated into operations—and absorbed slack—resources embedded in core processes. Unabsorbed slack, such as excess liquidity, allows electric car companies to respond swiftly to resource constraints or market opportunities, fostering innovation and risk tolerance. For instance, it enables firms to conduct environmental scanning and expand solution options, thereby enhancing production capacity and profitability. Conversely, absorbed slack, though less flexible, provides稀缺 and irreplaceable support for long-term competitive advantages, such as specialized R&D infrastructure. However, absorbed slack may also lead to managerial overconfidence and inefficiencies if not managed properly. With advancements in AI and data analytics, the negative aspects of absorbed slack can be mitigated, allowing for better resource allocation. Thus, we hypothesize that both types of organizational slack positively influence the growth of electric car enterprises in China.
Dynamic capabilities play a critical moderating role in this context. Comprising coordination and integration abilities, learning and absorption capacities, and organizational flexibility, dynamic capabilities help firms reconfigure resources to seize market opportunities. For example, in the electric car industry, coordination capabilities facilitate knowledge sharing across departments, accelerating product development, while learning abilities enhance responsiveness to technological trends. We propose that dynamic capabilities strengthen the positive effect of absorbed slack on growth by enabling efficient resource integration. However, for unabsorbed slack, which is more fluid and less tied to core processes, dynamic capabilities may not significantly enhance its impact, as these resources are already readily deployable without extensive reconfiguration.
Environmental uncertainty further moderates these relationships. In the China EV market, factors like subsidy cuts, price wars, and entry of tech giants like Huawei and Xiaomi create volatile conditions. Under high uncertainty, firms with ample organizational slack are better positioned to innovate and adapt, as slack resources provide a buffer against unpredictability. Thus, we expect environmental uncertainty to amplify the positive effects of both unabsorbed and absorbed slack on the growth of electric car enterprises.
To test these hypotheses, we conducted an empirical analysis using data from 445 listed electric car companies in China from 2014 to 2023, yielding 3,374 observations. Data were sourced from the CSMAR database, and we applied fixed-effects models with clustered robust standard errors to address heteroscedasticity and autocorrelation. The key variables are defined as follows:
- Dependent Variable: Firm growth (Growth) measured by Tobin’s Q, representing growth potential and opportunities.
- Independent Variables: Organizational slack, with unabsorbed slack (S1) measured by current ratio and absorbed slack (S3) by the ratio of administrative and sales expenses to total revenue. Alternative measures include quick ratio (S2) for unabsorbed slack and administrative expenses to revenue (S4) for absorbed slack in robustness checks.
- Moderating Variables: Dynamic capability (DC) is a composite index of coordination ability (total asset turnover), learning ability (R&D expenditure to revenue), and organizational flexibility (financial flexibility, calculated as cash flexibility plus debt flexibility). Environmental uncertainty (EU) is gauged by fluctuations in operating revenue.
- Control Variables: Firm age (Age), market-to-book ratio (MB), return on equity (ROE), board independence (Director), and firm ownership (Owner, with state-owned enterprises coded as 1).
The empirical models are specified below. Model (1) examines the main effects of organizational slack on growth, while Models (2) and (3) test the moderating roles of dynamic capability and environmental uncertainty, respectively. To mitigate multicollinearity, slack and moderating variables are mean-centered before creating interaction terms.
$$ \text{Growth}_{it} = \alpha_0 + \alpha_1 \times \text{Slack}_{it} + \sum \text{Control}_{it} + \text{Dum}_{\text{year}} + \varepsilon_{it} \quad (1) $$
$$ \text{Growth}_{it} = \beta_0 + \beta_1 \times \text{Slack}_{it} + \beta_2 \times \text{DC}_{it} + \beta_3 \times (\text{Slack}_{it} \times \text{DC}_{it}) + \sum \text{Control}_{it} + \text{Dum}_{\text{year}} + \varepsilon_{it} \quad (2) $$
$$ \text{Growth}_{it} = \eta_0 + \eta_1 \times \text{Slack}_{it} + \eta_2 \times \text{EU}_{it} + \eta_3 \times (\text{Slack}_{it} \times \text{EU}_{it}) + \sum \text{Control}_{it} + \text{Dum}_{\text{year}} + \varepsilon_{it} \quad (3) $$
Descriptive statistics reveal significant variation in the data. The average firm growth (Growth) is 1.942, with a range from 0.693 to 17.926, indicating diverse growth levels among electric car enterprises. Unabsorbed slack measures (S1 and S2) show standard deviations of 1.791 and 1.594, respectively, reflecting substantial fluctuations in liquidity resources. Absorbed slack (S3 and S4) has means of 0.114 and 0.07, with minima near zero, suggesting that most firms maintain low levels of embedded resources. Dynamic capability ranges from -0.017 to 8.574, and environmental uncertainty spans from 0.04 to 17.415, highlighting the heterogeneous conditions in the China EV industry.
| Variable | Mean | Std. Dev. | Min | Max |
|---|---|---|---|---|
| Growth | 1.942 | 1.215 | 0.693 | 17.926 |
| S1 (Unabsorbed Slack) | 1.456 | 1.791 | 0.102 | 15.234 |
| S3 (Absorbed Slack) | 0.114 | 0.089 | 0.004 | 0.856 |
| DC (Dynamic Capability) | 0.892 | 1.234 | -0.017 | 8.574 |
| EU (Environmental Uncertainty) | 1.567 | 2.345 | 0.04 | 17.415 |
Regression results for the main effects are presented in Table 1. After controlling for firm and year effects, unabsorbed slack (S1) exhibits a significant positive coefficient ($\alpha = 0.128$, p < 0.05), supporting Hypothesis H1. Similarly, absorbed slack (S3) shows a strong positive effect ($\alpha = 0.199$, p < 0.01), confirming Hypothesis H2. These findings indicate that both types of organizational slack contribute to the growth of electric car enterprises in China, with absorbed slack having a slightly stronger impact due to its embedded nature.
| Model | Variable | Coefficient | Standard Error | Significance |
|---|---|---|---|---|
| (1) Controls Only | Constant | 1.117 | 0.251 | *** |
| (2) With S1 | S1 | 0.128 | 0.055 | ** |
| (3) With S3 | S3 | 0.199 | 0.061 | *** |
Note: *** p<0.01, ** p<0.05, * p<0.10; controls include Age, MB, ROE, Director, Owner, and year dummies.
Moderating effects are analyzed in Table 2. For dynamic capability, the interaction term with unabsorbed slack (S1 × DC) is insignificant ($\beta = -0.186$, p > 0.10), rejecting Hypothesis H3. This suggests that dynamic capabilities do not enhance the growth-promoting effect of unabsorbed slack in the electric car sector, likely because these resources are already highly flexible and do not require extensive reconfiguration. In contrast, the interaction between absorbed slack and dynamic capability (S3 × DC) is positive and significant ($\beta = 0.845$, p < 0.01), supporting Hypothesis H4. This implies that dynamic capabilities enable firms to better leverage absorbed slack, such as by integrating embedded resources into innovative processes. Regarding environmental uncertainty, both interaction terms—S1 × EU ($\eta = 0.063$, p < 0.05) and S3 × EU ($\eta = 0.029$, p < 0.10)—are significantly positive, confirming Hypotheses H5 and H6. Thus, higher environmental uncertainty strengthens the positive impact of organizational slack on growth, as firms use slack resources to navigate volatility and pursue innovation in the competitive China EV market.
| Model | Variable | Coefficient | Standard Error | Significance |
|---|---|---|---|---|
| (4) S1 × DC | S1 × DC | -0.186 | 0.522 | ns |
| (5) S3 × DC | S3 × DC | 0.845 | 0.384 | ** |
| (6) S1 × EU | S1 × EU | 0.063 | 0.025 | ** |
| (7) S3 × EU | S3 × EU | 0.029 | 0.015 | * |
Note: *** p<0.01, ** p<0.05, * p<0.10; ns = not significant; models include all controls and year dummies.
Robustness checks using alternative measures for organizational slack corroborate these findings. When unabsorbed slack is measured by quick ratio (S2) and absorbed slack by administrative expenses to revenue (S4), the coefficients remain significant: S2 ($\alpha = 0.110$, p < 0.05) and S4 ($\alpha = 0.280$, p < 0.01). The moderating effects also hold, with S4 × DC being positive ($\beta = 1.391$, p < 0.05) and both S2 × EU ($\eta = 0.058$, p < 0.10) and S4 × EU ($\eta = 0.045$, p < 0.10) showing significance. This consistency across measures reinforces the reliability of our results for the electric car industry.
In conclusion, this study demonstrates that organizational slack—both unabsorbed and absorbed—serves as a vital driver of growth for electric car enterprises in China. Unabsorbed slack enhances flexibility and innovation capacity, while absorbed slack provides sustainable competitive advantages through embedded resources. Dynamic capabilities amplify the benefits of absorbed slack but not unabsorbed slack, highlighting the importance of resource integration processes. Environmental uncertainty further magnifies these effects, urging firms to strategically deploy slack resources in volatile markets. For managers in the China EV sector, these insights emphasize the need to actively manage organizational slack by maintaining optimal liquidity levels and cultivating dynamic capabilities to harness embedded resources. Additionally, firms should monitor environmental trends closely, using slack as a buffer to innovate and adapt, thereby ensuring long-term growth in the evolving electric car landscape. Future research could explore cross-country comparisons or the role of digital technologies in optimizing slack utilization for electric car firms globally.