Dynamic value sharing based on employee contribution as a competitiveness-enhancing device

Human capital strongly influences innovation, operational/financial performance, and growth. Traditional value sharing at the employee level is often implemented as static value sharing (SVS), where the proportion allocated to employees is fixed based on an initial assessment and remains unchanged for long periods. SVS can induce free-riding and weaken incentives because later contributions are not reflected in rewards. The paper argues that individual contributions are dynamic and that incentive systems should update accordingly. Motivated by Huawei’s multi-stage value-sharing practices—evolving from capital-based to contribution-based Time Unit Plan—this study asks: How does dynamic value sharing based on contribution (DVS) compare to SVS in supporting enterprise growth via continuous improvements in human capital, profit, and value? How can enterprises implementing DVS evolve quickly to the optimal state, and under what internal/external conditions? Recognizing bounded rationality and social imitation in real settings, the paper employs evolutionary game theory to model and analyze these questions, claiming three contributions: clarifying whether contribution-based value sharing fosters enterprise development; proposing DVS as a fair, dynamic mechanism that can attract and retain high-ability employees; and deriving evolutionarily stable strategies (ESS) under bounded rationality to guide firms toward stable optima.

The literature on value sharing (shared capitalism) encompasses employee ownership, ESOPs, profit sharing, gain sharing, and stock options. Principal–agent and human capital theories suggest benefits such as reduced agency costs and higher productivity, while evidence is mixed regarding employee-level outcomes and firm performance due to free-riding and ownership dispersion. Equity theory posits that perceived fairness—rewards matching contributions—drives motivation. Contribution-based allocation has theoretical and experimental support: distributing equity proportionally to relative contribution can elicit higher effort and screen high contributors. However, prior studies often assume static contributions and full rationality, overlooking that employee capabilities, effort, and learning evolve over time. The literature lacks consensus on the effectiveness of value sharing and under-examines dynamic adjustment of shares with changing contributions. This paper addresses these gaps by analyzing dynamic contribution-based value sharing under bounded rationality via evolutionary game theory, integrating human capital and value-creation perspectives, and comparing SVS versus DVS on human capital accumulation, profit, and firm value.

The study constructs a two-stage competitive framework and applies evolutionary game theory. Stage 1 (consumer market): two firms—one with value sharing (VS) and one without (NVS)—compete in a horizontally differentiated Hotelling model. Consumers are uniformly distributed on [0,1], with mismatch cost e; the VS firm is at 0 and the NVS firm at 1. Consumer utility for each product aggregates base value, negative price utility, positive innovation utility, and mismatch disutility. Innovation utility depends on transformed human capital: VS has incentive capacity r (0<r<1) and transformation capacity λ (0<λ<1) affecting how individual human capital (from high-capability HE and low-capability LE employees) is absorbed and converted into product innovation T1 (VS) versus T2 (NVS). Equilibrium oligopoly profits π1 (VS) and π2 (NVS) are derived as functions of T1, T2, e, and relative baseline advantage g. Stage 2 (talent market): firms choose VS or NVS; employees are HE or LE. Under bounded rationality and social imitation, strategies evolve via replicator dynamics. Firm payoff is enterprise value comprising physical capital (retained profits θπ) and organizational capital (non-transformed human capital internalized as institutional and tacit knowledge). For VS, a share λ of human capital is transformed into innovation and 1−λ into organizational capital; for NVS, organizational capital accrues per assumed conversion. Employee payoff includes private utility from retained human capital (decreasing with r), plus either a share δ of distributed profits under VS or salary w under NVS. Uncertainty about employee type leads firms to form expectations based on the population shares of HE (y) and LE (1−y). SVS case: value-sharing proportion δ is fixed based on initial contribution and not updated. The study formulates the firm and employee replicator dynamic system (I) using expected utilities under SVS and derives conditions for evolutionary stability. DVS case: the firm dynamically updates the value-sharing proportion in proportion to contribution (higher for HE), and low-capability employees may imitate high-capability strategies and invest in improving human capital through learning and training. The study formulates a modified replicator dynamic system (II) under DVS capturing dynamic adjustment of δ and imitation-driven capability upgrading. Numerical simulation: To validate and explore dynamics, the paper calibrates parameters using Huawei’s 2008–2012 data (period of Saturated Rights Issues comparable to SVS) for initial conditions. Key parameters include initial VS adoption x0≈0.55, initial HE share y0≈0.25, incentive capacity r≈0.6, transformation capacity λ≈0.6, overall profit-sharing ratio (1−θ) with θ≈0.4, individual sharing proportion d≈0.0016, prices p1≈1400 and p2≈2500, base values a1≈2100 and a2≈3750, and costs c1≈1250 and c2≈2050. Simulations compare trajectories of y (HE share), profits, and firm value under SVS vs NVS and DVS vs NVS, and conduct sensitivity analyses on y0, r, λ, and (1−θ).

• Under SVS, there is no evolutionarily stable strategy (ESS) between firms and high-capability employees; both the probability of implementing SVS and the HE proportion fluctuate, leading to instability and potential talent loss. • Under DVS, the HE proportion increases over time, stabilizes, and HE employees ultimately dominate value sharing due to dynamic alignment of rewards with contributions and reduced free-riding. • Profit and firm value: With SVS, profit and value exceed NVS but fluctuate significantly. With DVS, profit and value increase rapidly, stabilize, and are much higher than NVS; trajectories show greater stability than SVS. Early NVS profits may dip then recover due to imitation dynamics in the talent market. • Screening effect: DVS effectively attracts and retains high contributors; low contributors exit due to relatively lower payoffs than under NVS. • Sensitivity to initial HE share (y0): Higher y0 shortens the time for HE share and profit to reach equilibrium, indicating firms with more talented employees (e.g., high-tech firms) have inherent advantages when implementing DVS. • Incentive capacity (r): Higher r accelerates convergence to equilibrium and increases equilibrium profits. • Transformation capacity (λ): Higher λ increases equilibrium profit, but the time to reach equilibrium is non-monotonic; for example, λ=0.7 can lead to slower convergence than λ=0.5, suggesting excessive focus on utilization over attraction/accumulation can delay optimality. • Overall profit-sharing ratio (1−θ): The relationship with time-to-equilibrium is non-monotonic. When 1−θ ≤ 0.996, increasing 1−θ speeds convergence of HE share and profit without affecting the maximum profit level; when 1−θ > 0.996, further increases substantially prolong convergence time. • Overall, DVS outperforms SVS and NVS by stably promoting value creation, supporting sustained increases in human capital quality, profits, and firm value.

The findings align with human capital, equity, and principal–agent theories: rewarding contributions fairly enhances motivation, reduces agency costs, and supports accumulation of firm-specific human capital. DVS ties rewards dynamically to evolving contributions, mitigating free-riding prevalent under SVS and providing stronger screening and retention of high-capability employees. This explains mixed empirical results for broad-based stock plans (typical SVS) that often fail to improve performance or reduce turnover. By incorporating bounded rationality and imitation, the model captures realistic diffusion of strategies: visible success of HE under DVS drives capability upgrading and entry of high contributors, reinforcing competitive advantage. The non-monotonic effects found for transformation capacity and overall profit sharing highlight managerial trade-offs between rapid utilization of existing human capital and long-term accumulation and investment. These insights extend prior work by showing why dynamic, contribution-based sharing is more effective and stable than static schemes in building sustainable competitive advantage.

This study compares static (SVS) and dynamic (DVS) contribution-based value sharing using an evolutionary game framework linking talent-market dynamics to consumer-market competition. Main conclusions: (1) Value sharing increases profit and firm value relative to no value sharing; however, SVS lacks an ESS and leads to instability. (2) DVS stabilizes system trajectories and yields higher, more stable profits and firm value than NVS (and more stably than SVS). (3) DVS screens and ultimately concentrates value sharing among high-capability employees who contribute more. (4) Firms with a higher initial share of talented employees benefit more from DVS, achieving equilibrium faster; moderate improvements in transformation capacity and overall profit-sharing ratio further aid convergence, while excessively high overall sharing can slow convergence. Managerial implications: Pair investments in transformation capacity with robust talent attraction and retention policies to avoid delaying equilibrium; set overall profit-sharing at a level that spurs short-term motivation without undermining long-term investment needs (e.g., R&D).

The Hotelling framework assumes a fully covered market and does not consider cases with uncovered demand. The model abstracts from social preferences (e.g., altruism, reciprocity) and risk preferences (e.g., risk aversion) that may influence strategic choices. Future research should extend the evolutionary game to incorporate heterogeneous social and risk preferences and explore partially covered markets.