Leadership-Driven Pricing and Customization in Collaborative Manufacturing: A Platform Dynamics Perspective

Abstract

Fueled by advances in cloud technologies and industrial platforms, networked collaborative manufacturing platforms (NCMPs) are reshaping how products are priced and customized. As decision rights increasingly shape value creation within these platforms, platform leadership—whether driven by the manufacturer or the designer—emerges as a critical determinant of product strategy. However, the effects of different leadership structures on pricing and customization remain unclear. To address this issue, we develop game models comparing manufacturer-led and designer-led platforms. Our analysis reveals that under manufacturer-led platforms, dual-product strategies remain viable across a wider range of customization conditions, ensuring pricing stability and broader demand coverage. In contrast, designer-led platforms are more sensitive to the commission rate—excessive commissions tend to crowd out standard product offerings and distort pricing incentives. Moreover, platform control does not always guarantee superior profit: while designers consistently outperform manufacturers under manufacturer-led platforms, profit dominance in designer-led settings shifts with commission rates. Notably, by jointly optimizing product strategy and pricing mechanisms, firms can achieve more balanced value distribution and sustain collaboration. These findings offer a strategic framework for manufacturers and designers to align platform governance with product architecture, contributing new insights into collaborative pricing, platform leadership, and dual-product innovation in industrial platform ecosystems.

1. Introduction

The manufacturing industry is undergoing a profound transformation driven by the Industrial Internet of Things (IIoT), artificial intelligence, and big data analytics [1,2]. In this context, Networked Collaborative Manufacturing Platforms (NCMPs) have gained new vitality and become the core infrastructure for industrial enterprises to integrate design, production, and supply activities through a digitally connected ecosystem [3]. These platforms are particularly vital in complex product sectors such as heavy equipment and advanced manufacturing, where they enhance customization capability, production flexibility, and operational efficiency [4].

As NCMPs gain traction, a strategic question arises: who should lead the platform when both manufacturing enterprises and design firms are capable? Manufacturers excel in operational execution, cost control, and large-scale production, whereas design enterprises drive innovation, customization, and user-centered product development [4,5]. The choice of platform leadership not only influences the distribution power within the ecosystem but also has profound implications for operational decisions such as product pricing, customization strategies, and value capture [6,7].

From a strategic perspective, the platform’s leadership directly influences the firm’s approach to product-line design and differentiation [8,9]. Conventional wisdom suggests that designer-led (PD) platforms focus on innovation and customization, while manufacturer-led (PM) platforms prioritize standard offerings and cost efficiency. However, this dichotomy is increasingly challenged. In practice, many platforms—such as those based on 3D printing—pursue a dual-product strategy, offering both customized and standard products to balance efficiency and personalization [10,11]. This raises a critical question: can different leadership structures support a hybrid product strategy—and if so, under what conditions?

The leadership structure also plays a key role in shaping pricing mechanisms [7,12,13]. Under the PM model, the manufacturer leads the NCMP, aggregating customer requirements and offering two product pathways: standard and customized. For standard products, the designer licenses ready-made blueprints to the manufacturer, who handles production and delivery while paying a licensing fee. For customized products, the platform facilitates coordination between the designer and manufacturer: the designer provides tailored design solutions based on the consumer’s needs and charges a design fee, while the manufacturer is responsible for production. Platforms such as Shapeways exemplify this model, where manufacturers set prices and designers earn revenue through design services [11].

In contrast, under the PD model, designers lead the NCMP and are responsible for product coordination. For standard products, manufacturers display products on the platform and pay commissions upon sales. For customized products, the platform promotes end-to-end co-creation: the designer translates customer needs into design solutions, while manufacturers complete production tasks. This model forms a dual-path coordination mechanism that integrates standard transactions with collaborative customization. The decision-making logic behind platform leadership is summarized in Figure 1.

Figure 1 illustrates how platform leadership structures shape key strategic decisions in collaborative manufacturing platforms. In practice, leadership—whether manufacturer-led (e.g., COSMOPlat) or designer-led (e.g., Shapeways)—determines the decision sequence, particularly who sets prices for standard products and how the product line is designed. This leadership-driven allocation of pricing authority and decision rights directly impacts operational coordination and value distribution. Clarifying these mechanisms is crucial for guiding leading industrial enterprises through platform transformation.

These practical observations highlight a clear gap in the literature: although platform governance has received growing attention, how leadership structure affects product strategies, pricing, and profit allocation in NCMPs remains underexplored. To address this gap, we develop a formal analytical model that compares manufacturer-led (PM) and designer-led (PD) platforms, focusing on how leadership roles shape key strategic decisions. Specifically, we investigate the following research questions:

(1)

How does platform leadership structure affect the viability and configuration of dual-product strategies in NCMPs?

(2)

Under what conditions is the manufacturer or the designer better suited to lead the NCMP in terms of profit allocation and pricing efficiency?

(3)

How can manufacturers and designers collaborate to determine an efficient leadership–product strategy combination under varying market and platform conditions?

This study makes three primary contributions to the literature on collaborative manufacturing platforms and product strategy. First, we construct a model of product pricing and customization under alternative leadership structures, capturing the strategic interactions between designers and manufacturers. By analyzing the feasibility conditions for dual-product strategies under varying product substitutability and commission rates, it enriches the theoretical framework of NCMPs and extends the product-line literature in industrial platform contexts. Second, we reveal the asymmetric effects of platform leadership on pricing, market demand, and profit allocation. In particular, we identify threshold conditions under which the profit dominance shifts between manufacturers and designers, thereby shedding light on the trade-offs in value distribution under different governance structures. Third, we propose a decision-support framework to align leadership structure with product strategy. Our findings offer not only theoretical insight into hybrid strategy design for NCMPs but also practical implications for pricing mechanisms, collaboration, and value capture in B2B manufacturing ecosystems.

The rest of this paper is organized as follows. Section 2 reviews related literature. Section 3 presents the modeling framework. Section 4 derives the equilibrium results. Section 5 conducts a comparative analysis of the two leadership models. Section 6 discusses key findings, theoretical contributions, and practical implications. Section 7 concludes the paper and outlines its limitations. Proofs are provided in Appendix A and Appendix B.

2. Literature Review

2.1. Product Customization Strategy

The rising demand for individualized solutions and the expansion of collaborative manufacturing platforms have driven firms to explore product strategies that balance customization value and operational efficiency [14,15]. In modern platform contexts, particularly industrial settings where both manufacturers and designers participate in product development, the challenge is no longer whether to offer customized or standard products but how to provide both effectively—and under what leadership structure.

Early research began uncovering the viability of hybrid product lines that combine standard and customized offerings. For instance, Syam and Kumar [16] demonstrate that partial customization can help firms mitigate price competition, especially when consumers differ in their willingness or ability to customize. Building on this, Gu and Tayi [17] show that the profitability of customization depends on consumer customizability—firms benefit only when users can meaningfully engage with product choices. Basu and Bhaskaran [18] further model co-design as a process shaped by both the firm’s design capability and the consumer’s effort, emphasizing that consumers are more likely to participate when standard and customized options coexist.

However, these studies mostly center on mass customization products, where the cost of customization is relatively low due to modularity and scalability. In contrast, in industrial platforms—such as networked collaborative manufacturing environments—customization often involves substantial costs across multiple dimensions: design, communication, and reconfiguration [15,19]. These costs are nontrivial and vary across stakeholders, making the decision to offer both product types more complex and strategic.

Recent research has begun to examine this complexity under platform structures. Zhang et al. [3] demonstrate that product substitutability, commission rates, and setup costs significantly impact firms’ hybrid product-line strategies. Dai et al. [20] further demonstrate that channel competition and platform-imposed fees can tilt firms toward or away from customization. Huang et al. [21] highlight how different platform governance modes—reselling vs. agency—shape product-line and pricing decisions. Zong et al. [22] reinforce this perspective by showing that platform leadership itself shapes product strategy. Their study on green and non-green products finds that retailer-led platforms tend to adopt dual-product lines consistently, while supplier-led platforms may abandon less profitable offerings. Though their focus is on sustainability, the implication is general: leadership structure can shift the strategic balance between product types.

Nonetheless, much of the existing literature focuses on E-consumer platforms [9,23,24]. Instead, our research examines single-unit or small-batch production on networked collaborative manufacturing platforms, where customization costs are nontrivial and leadership structure plays a more pronounced role. We model how consumer interaction cost, design cost, and product substitutability jointly determine whether hybrid product offerings are viable and how these outcomes shift depending on whether the manufacturer or designer leads the platform.

2.2. Platform Leadership Structure and Its Impact on Product and Pricing Decisions

Recent studies in platform retailing have provided useful insights into how sales modes—reselling, agency, or hybrid—are linked to pricing rights and product decisions [25,26,27]. Ha et al. [28] show that platforms often support both agency and reselling channels simultaneously, allowing for flexible control over service efforts and retail pricing. Their analysis reveals that the choice of channel structure and, thus, pricing authority depends on how leadership incentives align with the platform’s service investment.

The relationship between pricing authority and commission structures is further explored by Hu et al. [26], who examine agency versus wholesale selling modes under different retail pass-through conditions. While agency selling enables suppliers (e.g., designers) to set retail prices and platforms to charge commissions, wholesale selling transfers pricing power to the platform (e.g., the manufacturer). Their findings suggest that the choice of selling mode—and, thus, platform leadership—depends critically on cost structures and cross-brand demand responses. This logic aligns closely with NCMPs, where either party may gain pricing leverage depending on customization cost and substitutability.

Huang and Chen [29] investigate a platform–manufacturer setting with multiple competing platforms and examine the manufacturer’s incentive to encroach. They find that the manufacturer’s willingness to exert leadership is sensitive to platform commission rates and consumer surplus concerns. Under certain conditions, increased platform control reduces the manufacturer’s incentive to lead, highlighting the interplay between platform governance and product strategy.

Ke et al. [27] further study suppliers’ strategic selection among agency, reselling, and hybrid selling modes in the presence of store brands. Their results demonstrate that the optimal mode depends on platform commission rates and consumer brand preferences. Importantly, the hybrid mode—akin to a dual-product strategy—becomes more viable under moderate commission levels. This supports the intuition in our model that leadership structure and commission policy jointly determine whether the platform adopts a single- or dual-product strategy.

Despite these insights, most existing literature focuses on consumer-facing platforms and does not fully capture the co-creation dynamics in manufacturing contexts, where both standardization and customization incur significant costs. Moreover, few studies address how leadership affects the feasibility of offering both standard and customized products when pricing rights, design fees, and communication costs are jointly considered. Our study contributes to this gap by modeling a collaborative manufacturing platform where either the designer or manufacturer may lead. We examine how this leadership structure influences the optimal product strategy, retail pricing decisions, and platform profit division, particularly under varying levels of product substitutability and commission rates.

2.3. Key Differences and Contributions

This study makes both theoretical and practical contributions to the literature on collaborative manufacturing, particularly in the context of product strategy and platform governance. Our contributions can be contextualized along four key dimensions that differentiate our study from existing work: customization cost, power structure, commission impact, and dual-product strategy.

Table 1. Comparison of this research with the related literature.

Paper	Customization Cost	Power Structure	Commission Impact	Dual-Product Strategy
Syam and Kumar [16]				✓
Gu and Tayi [17]	✓			✓
Basu and Bhaskaran [18]	✓			✓
Zhang et al. [30]	✓		✓	✓
Ha et al. [28]		✓	✓
Hu et al. [26]		✓	✓
Huang and Chen [29]		✓	✓
Ke and Zhou [27]		✓	✓	✓
This paper	✓	✓	✓	✓

Note: “✓” indicates that the paper includes the given element.

summarizes how our research compares with representative studies across these dimensions.

First, while earlier studies (e.g., Refs. [16,17,18]) have considered hybrid product lines involving standard and customized products, they rarely consider how the power structure and pricing authority impact the feasibility and profitability of these strategies. Our study extends this literature by modeling a dual-product strategy within an NCMP environment that features alternative leadership structures and endogenous pricing mechanisms.

Second, a growing body of platform literature (e.g., Refs. [26,27,28]) has increasingly explored governance and commission mechanisms. However, these studies are typically rooted in retail-oriented platforms and overlook the collaboration of design and manufacturing in industrial contexts. Our research contributes by analyzing how leadership roles in NCMPs shape pricing decisions, participation incentives, and supply-chain profit allocation.

Third, we propose a unified modeling framework that connects product substitutability, cost asymmetry, and commission structure with platform viability. This integration enables a more comprehensive understanding of when and how dual-product strategies can be sustained under different platform governance models.

3. Modeling Framework

3.1. Analytical Modeling Approach

To analyze pricing and customization strategies under different platform leadership structures, we develop a sequential decision-making model based on game theory—an effective framework for capturing strategic interactions between stakeholders with potentially conflicting interests. This approach is well-suited for modeling the dynamic interactions between designers and manufacturers and reveals how leadership influences both decision-making and pricing authority.

For each leadership structure (PM and PD), we construct a game in which decisions unfold in a predefined sequence that mirrors typical processes in collaborative manufacturing, including licensing, design, production, and pricing. The sequence is based on in-depth observations of collaborative manufacturing platforms, ensuring the model’s practical relevance.

We solve the game using backward induction to derive the subgame-perfect equilibrium, ensuring optimal and credible strategies at every decision stage. This rigorous analytical framework allows us to investigate how leadership structures influence product-line strategies, pricing mechanisms, and overall supply-chain performance.

3.2. Assumptions

To ensure analytical tractability while capturing the key features of realistic market settings in NCMPs, we adopt the following assumptions:

Assumption 1.

The platform offers two types of products: a customized product (labeled by $c$) and a standard product (labeled by $s$), which are partially substitutable. Such a dual-product strategy is widely adopted in industrial manufacturing platforms [11].

Assumption 2.

The manufacturer incurs a unit production cost of $c_i$ for product $i\in \{c,s\}$, with $c_c>c_s$. Customized products require more production resources and, hence, incur higher costs.

Assumption 3.

Consumers choose between a customized product and a standard product based on utility maximization. The utility functions are defined as $U_c = v - p_{c }-{ c}_m$ for the customized product and $U_s = \theta v - p_s$ for the standard product, where $v$ denotes the consumer’s valuation for the customized product; $p_c$ and $p_s$ are the prices of the customized and standard products, respectively; and $c_m$ is the customization-related interaction cost. The parameter expressed as $\theta \in (0,1)$ represents the consumer’s relative valuation of the standard product compared to the customized product. A small $\theta$ implies greater product differentiation and lower substitutability. This formulation follows the utility structure proposed by Joshi et al. [31] and captures the intuitive notion that consumers generally value customized products more than standard ones.

3.3. Model Setup

3.3.1. Game Structure and Sequence

Due to the distinct resource advantages of each party, two types of leadership structure may arise within the NCMP: one led by the manufacturer and the other led by the designer. We refer to these as the PM model and the PD model, respectively. The structural differences and operational flows of these two models are illustrated in Figure 2.

The game under each model proceeds as follows:

In the PM model, the designer provides the blueprints for the standard product in exchange for a licensing fee and sets a design fee for the customized product. The manufacturer is responsible for production and sells the products directly to consumers. The sequence of events is illustrated in Figure 3. First, the designer decides the licensing fee ($w_l$) for the standard product’s blueprints and the design fee ($m$) for the customized products. Next, the manufacturer sets the retail prices ($p_s$ and $p_c$) for the standard and customized products. Finally, consumers make their purchasing decisions based on utility maximization.

In the PD model, the manufacturer sells the standard product through the platform and collaborates with the designer to fulfill customized product orders. The sequence of events is illustrated in Figure 4. The platform first exogenously sets the commission rate ($\rho$) on standard product sales. Then, the manufacturer determines the production fee ($w_h$) for the customized product and sets the retail price ($p_s$) for the standard product. Subsequently, the designer sets the retail price ($p_c$) for the customized product. Finally, consumers make purchasing decisions.

3.3.2. Market Segmentation

Given the consumer utility structure defined in Assumption 3, we now analyze how consumers make purchasing decisions and how the market can be segmented accordingly.

Consumers choose the option that yields the highest utility. The decision follows the principle of utility maximization, expressed as ${\max \{U}_s, U_c,0\}$. Consumers will purchase customized products if $U_c\ge U_s$ and $U_c\ge 0$, which implies $v\ge max\{p_c+c_m, (p_c+c_m-p_s)/\left(1-\theta \right)\}$. They will choose the standard product if $U_s\ge U_c$ and $U_s\ge 0$, which implies $v\le p_c+c_m-p_s)/\left(1-\theta \right)$ and $v\ge p_S/\theta$. When $U_s=U_c\ge 0$, it makes no difference to the consumer which product they buy. Based on the degree of quality differentiation between the two products, market segmentation can be categorized into three scenarios, as shown in

Table 2. Market segmentation.

Interval	${\mathit{Q}}_{\mathit{c}}({\mathit{p}}_{\mathit{c}},{\mathit{p}}_{\mathit{s}})$	${\mathit{Q}}_{\mathit{s}}({\mathit{p}}_{\mathit{c}},{\mathit{p}}_{\mathit{s}})$
$0<\theta \le {\displaystyle \frac{p_s}{p_c+c_m}}$	${1-(p}_c+c_m)$	0
${\displaystyle \frac{p_s}{p_c+c_m}}<\theta <1-\left(p_c+c_m\right)+p_s$	$1-{\displaystyle \frac{\left(p_c+c_m\right)-p_s}{1-\theta }}$	${\displaystyle \frac{\left(p_c+c_m\right)-p_s}{1-\theta }}-{\displaystyle \frac{p_s}{\theta }}$
$1-\left(p_c+c_m\right)+p_s\le \theta <1$	0	$1-{\displaystyle \frac{p_s}{\theta }}$

.

As shown in Table 2, product demand depends on the degree of product quality differentiation ($\theta$) and the retail prices ($p_c$ and $p_s$). When the quality differentiation is small ($\theta > {\displaystyle \frac{p_s}{p_c}} + c_m$), consumers are likely to purchase standard products. However, as the quality of standard products improves, demand will shift: when $\theta \ge 1-\left(p_c+c_m\right)+p_s$, consumers may either choose to consider purchasing standard products or opt against purchasing them altogether.

The notations used in this paper are summarized in

Table 3. List of notations.

Symbol	Description
$v$	Consumer’s valuation of the customized product, uniformly distributed on $\left[0,1\right]$
$\theta$	Relative valuation of the standard product compared to the customized product; also reflects the degree of product differentiation ($\theta \in (0,1)$)
$m$	Design fee charged by the designer for customized products
$e$	Unit design cost incurred by the designer for each customized product
$w_h$	Production fee paid by the platform to the manufacturer for customized products in the PD model
$w_l$	Licensing fee paid by the manufacturer to the designer for standard product blueprints in the PM model
$c_m$	The interaction cost incurred by consumers during the customization process
$c_c$	Unit production cost of the customized product
$c_s$	Unit production cost of the standard product
$p_s$	Retail price of the standard product
$p_c$	Retail price of the customized product
$Q_c,{ Q}_s$	Demand for the customized product and the standard product, respectively
$\rho$	Commission rate charged by the platform for standard product sales in the PD model

.

4. Equilibrium Results

4.1. PM Model

Under the PM model, the profit function of the designer is expressed as follows:

$${\pi }_D(m,w_l)=\left(m-e\right)Q_c+w_l\ast Q_s$$

(1)

where $m$ is the design fee for the customized product and $e$ is the unit design cost.

The profit function for the manufacturer is expressed as follows:

$${\pi }_M(p_c,p_s)=\left(p_c-m-c_c\right)Q_c+\left(p_s-w_l-c_s\right)Q_s.$$

(2)

Using backward induction, the optimal decisions in the PM model are derived, leading to the following proposition.

Proposition 1.

Under the PM model, there exists an optimal licensing fee for the standard product blueprints ($w_l^{\ast }$) and an optimal design fee for the customized product ($m^{\ast }$) chosen by the designer, as well as optimal retail prices for the standard customized products ($p_s^{\ast }$ and $p_c^{\ast }$) chosen by the manufacturer, such that the profits of both the designer and manufacturer are maximized.

The detailed proof is provided in Appendix A. The equilibrium outcomes under the PM model are summarized in

Table 4. Equilibrium outcomes under the manufacturer-led platform (PM model).

Interval	$0<\mathit{\theta }\le {\underset{\_}{\mathit{\theta }}}^{\mathit{P}\mathit{M}}$	${\underset{\_}{\mathit{\theta }}}^{\mathit{P}\mathit{M}}<\mathit{\theta }\le \overline{\mathit{\theta }}$	$\overline{\mathit{\theta }}<\mathit{\theta }<1$
$p_c^{\ast }$	${\displaystyle \frac{{3-3c_m+e+c}_c}{4}}$	${\displaystyle \frac{3-3c_m+e+c_c}{4}}$	/
$p_s^{\ast }$	/	${\displaystyle \frac{{3\theta +c}_s}{4}}$	${\displaystyle \frac{3\theta +c_s}{4}}$
$m^{\ast }$	${\displaystyle \frac{1-c_m+e-c_c}{2}}$	${\displaystyle \frac{1-c_m+e-c_c}{2}}$	/
$w_l^{\ast }$	/	${\displaystyle \frac{\theta -c_s}{2}}$	${\displaystyle \frac{\theta -c_s}{2}}$
$Q_c^{\ast }$	${\displaystyle \frac{1-a}{4}}$	${\displaystyle \frac{1-a-\theta +c_s}{4(1-\theta )}}$	0
$Q_s^{\ast }$	0	${\displaystyle \frac{a\theta -c_s}{4\theta (1-\theta )}}$	${\displaystyle \frac{\theta -c_s}{4\theta }}$
${\pi }_D^{\ast }$	${\displaystyle \frac{{\left(1-a\right)}^2}{8}}$	${\displaystyle \frac{c_s^2+\theta [\left(1-2a\right)\left(1-\theta \right)+a\left(a-2c_s\right)]}{8\theta (1-\theta )}}$	${\displaystyle \frac{{\left(\theta -c_s\right)}^2}{8\theta }}$
${\pi }_M^{\ast }$	${\displaystyle \frac{{\left(1-a\right)}^2}{16}}$	${\displaystyle \frac{c_s^2+\theta [\left(1-2a\right)\left(1-\theta \right)+a(a-2c_s)}{16\theta (1-\theta )}}$	${\displaystyle \frac{{\left(\theta -c_s\right)}^2}{16\theta }}$

Note: ${\underset{\_}{\theta }}^{PM}={\displaystyle \frac{c_s}{a}}$, $\overline{\theta }=1-a+c_s$, and ${a=c}_m+c_c+e.$

.

Proposition 1 characterizes the equilibrium outcomes under the PM model across three product differentiation regimes. Specifically, when product differentiation is high (i.e., $0<\theta \le \underset{\_}{\theta }$), all consumers either prefer the customized product or exit the market, resulting in zero demand for the standard product. As $\theta$ increases (${\underset{\_}{\theta }}^{PM}<\theta \le \overline{\theta }$), consumers with lower valuation begin purchasing the standard product, and both product types coexist. However, when $\theta$ is too low, i.e., ${\underset{\_}{\theta }}^{PM}<\theta <1$, the customized product becomes unappealing, and consumers opt for the standard alternative.

Thus, under the PM model, the feasibility condition for adopting a dual-product strategy is $\theta \in \left({\underset{\_}{\theta }}^{PM}, \overline{\theta }\right)$. Within this interval, the platform can maintain non-zero demand for both products, provided the differentiation level lies within a moderate range. Notably, this feasible region shrinks as the unit production cost of the standard product ($c_s$) decreases, i.e., ${\displaystyle \frac{𝜕\left({\underset{\_}{\theta }}^{PM}-\overline{\theta }\right)}{𝜕c_s}}<0$. However, when the production cost of the standard product satisfies $c_s>a^2$, the feasible region, i.e., $\left({\underset{\_}{\theta }}^{PM}, \overline{\theta }\right)$, expands with increasing customization cost ($a$), indicating that higher cost asymmetry between product types can enhance the viability of a dual-product strategy.

4.2. PD Model

Under the PD model, the profit functions of the manufacturer and designer are expressed as follows:

$${\pi }_M(w_h,p_s)=\left(w_h-c_c\right)Q_C+\left[\left(1-\rho \right)p_s-c_s\right]\ast Q_s$$

(3)

$${\pi }_D(p_c)=\left(p_c-w_h-e\right)Q_c+\rho \ast p_s\ast Q_s$$

(4)

By backward induction, the optimal decisions in the PD model are presented in Proposition 2.

Proposition 2.

Under the PD model, there exists an optimal pricing decision: the manufacturer chooses the production price for customized products ($w_h^{\ast }$) and the retail price for standard products ($p_s^{\ast }$), while the designer chooses the retail price for the customized product ($p_c^{\ast }$), such that each party maximizes their respective profits.

The proof is provided in Appendix B, and the equilibrium outcomes are presented in

Table 5. Equilibrium when the designer leads the platform.

Interval	$0<\mathit{\theta }\le {\underset{\_}{\mathit{\theta }}}^{\mathit{P}\mathit{D}}$	${\underset{\_}{\mathit{\theta }}}^{\mathit{P}\mathit{D}}<\mathit{\theta }\le \overline{\mathit{\theta }}$	$\overline{\mathit{\theta }}<\mathit{\theta }<1$
$p_c^{\ast }$	${\displaystyle \frac{3+e+c_c-\theta \left(2+\rho \right)-c_m\left[3-2\theta (1+\rho )\right]}{2\left(2-\theta -\theta \rho \right)}}$	${\displaystyle \frac{3-3c_m+e+c_c-\theta }{4}}+{\displaystyle \frac{c_s\left(1+\rho \right)}{4(1-\rho )}}$	/
$p_s^{\ast }$	/	${\displaystyle \frac{\theta }{2}}+{\displaystyle \frac{c_s}{2(1-\rho )}}$	${\displaystyle \frac{\theta }{2}}+{\displaystyle \frac{c_s}{2(1-\rho )}}$
$w_h^{\ast }$	${\displaystyle \frac{1-c_m-e+c_c-\theta \rho }{2}}$	${\displaystyle \frac{1-c_m-e+c_c-\theta \rho }{2}}$	/
$Q_c^{\ast }$	$1-{\displaystyle \frac{c_s+\theta }{\theta (2-\rho )}}$	${\displaystyle \frac{1-a+c_s-\theta }{4\left(1-\theta \right)}}$	0
$Q_s^{\ast }$	0	${\displaystyle \frac{1}{4}}({\displaystyle \frac{1+a-c_s-\theta }{1-\theta }}-{\displaystyle \frac{2c_s}{\theta (1-\rho )}})$	${\displaystyle \frac{1}{2}}-{\displaystyle \frac{c_s}{\theta (1-\rho )}}$
${\pi }_D^{\ast }$	${\displaystyle \frac{\left(1-\theta \right){\left(1-a\right)}^2+\theta \rho [\left(1-a^2\right)+\theta \rho \left(\theta \rho -3+\theta \right)]}{4{(2-\theta -\theta \rho )}^2}}$	${\displaystyle \frac{1}{16}}({\displaystyle \frac{{\left(1-a-\theta +c_s\right)}^2}{1-\theta }}-{\displaystyle \frac{4c_s^2}{\theta {\left(1-\rho \right)}^2}}+4\theta \rho +{\displaystyle \frac{4c_s^2}{\theta (1-\rho )}})$	${\displaystyle \frac{[{\theta }^2{\left(1-\rho \right)}^2-c_s^2]\rho }{4\theta {(1-\rho )}^2}}$
${\pi }_M^{\ast }$	${\displaystyle \frac{{\left(1-a-\theta \rho \right)}^2}{4(2-\theta -\theta \rho )}}$	${\displaystyle \frac{1}{8}}[{\displaystyle \frac{{\left(a-c_s\right)}^2}{1-\theta }}+{\displaystyle \frac{2c_s^2}{\theta \left(1-\rho \right)}}+1-2\left(a+c_s\right)+\theta \left(1-2\rho \right)]$	${\displaystyle \frac{{\left[c_s-\theta (1-\rho )\right]}^2}{4\theta (1-\rho )}}$

Note: ${\underset{\_}{\theta }}^{PD}={\displaystyle \frac{1}{2}}(1+a+{\displaystyle \frac{c_s\left(1+\rho \right)}{1-\rho }}-\sqrt{{\left(1+a-c_s\right)}^2+{\displaystyle \frac{4c_s^2}{({1-\rho )}^2}}-{\displaystyle \frac{4c_s(1-a+c_s)}{1-\rho }}})$, $\overline{\theta }=1-a+c_s$, and ${a=c}_m+c_c+e$.

.

Proposition 2 presents the equilibrium outcome under the PD mode and identifies the feasible interval for adopting a dual-product strategy: ${\underset{\_}{\theta }}^{PD}<\theta \le \overline{\theta }$. The lower bound (${\underset{\_}{\theta }}^{PD}$) is determined by the platform’s commission rate ($\rho$), implying that $\theta$ and $\rho$ jointly shape the platform’s ability to sustain positive demand for both product types. To ensure that ${\underset{\_}{\theta }}^{PD}<\overline{\theta }$, $\rho$ must satisfy $0<\rho <{\displaystyle \frac{1-a}{1-a+c_s}}$. We further analyze these interactions in Section 5.

A closer look at Proposition 2 reveals two additional insights. First, the pricing mechanisms for the two product types differ fundamentally. The standard product price ($p_s^{\ast }$) increases with the commission rate ($\rho$), as the manufacturer passes platform fees on to consumers. In contrast, the customized product price ($p_c^{\ast }$) is unaffected by $\rho$, making it more stable. This asymmetry suggests that platform governance has a stronger influence on standard product pricing than on customized offerings. Second, the profit expressions of both the designer and manufacturer exhibit nonlinear dependencies on $\theta$, $\rho$, and $c_s$. Terms such as $\theta \rho$, ${\left(1-\rho \right)}^2$, and $c_s^2$ indicate that profit allocation is highly sensitive to the platform’s incentive structure. Notably, this creates the potential for profit reversals between the two parties as $\rho$ varies—a phenomenon further explored in Proposition 5.

5. Comparative Analysis

5.1. Vertical Comparison of Enterprise-Led Platforms

5.1.1. Comparison of Feasibility Ranges for Dual-Product Strategy

Proposition 3.

The feasibility interval for adopting a dual-product strategy differs between the PD and PM models. While the upper bound of product differentiation is identical under the two models (${\overline{\theta }}^{PM}={\overline{\theta }}^{PD}$), the lower bound depends on the commission rate ($\rho$). Specifically, if ${\displaystyle \frac{1-a}{1+a}}\le \rho <{\displaystyle \frac{1-a}{1-a+c_s}}$, ${\underset{\_}{\theta }}^{PM}<{\underset{\_}{\theta }}^{PD}$; if $0<\rho <{\displaystyle \frac{1-a}{1+a}}$, ${\underset{\_}{\theta }}^{PM}>{\underset{\_}{\theta }}^{PD}$.

From the modeling perspective, the lower bound of the product differentiation parameter ($\theta$) for adopting a dual-product strategy differs across platform leadership structures. Specifically, the relative positions of the ${\underset{\_}{\theta }}^{PM}$ and ${\underset{\_}{\theta }}^{PD}$ thresholds depend on the commission rate ($\rho$) charged on standard product sales in the PD model. When the commission rate is relatively high (${\displaystyle \frac{1-a}{1+a}}\le \rho <{\displaystyle \frac{1-a}{1-a+c_s}}$), the feasibility range for the PD model narrows, and the manufacturer-led platform can sustain a dual-product strategy, even under high differentiation (i.e., low $\theta$). In contrast, when the commission rate is low ($0<\rho <{\displaystyle \frac{1-a}{1+a}}$), the designer-led platform is more likely to adopt a dual-product strategy.

These results highlight that while the upper bound of the feasible differentiation range remains invariant across models, the lower bound is shaped by the incentive structure, particularly the platform commission. This finding lays the foundation for understanding how platform leadership and pricing mechanisms jointly affect the viability of hybrid product strategies, which we further explore in Proposition 4.

5.1.2. Comparison of Product Price and Market Share

Proposition 4.

When both models adopt a dual-product strategy, i.e., when $\underset{\_}{\theta }<\theta \le \overline{\theta }$, where $\underset{\_}{\theta }=max\{{\underset{\_}{\theta }}^{PM},{\underset{\_}{\theta }}^{PD}\}$, and the commission rate ($\rho$) satisfies ${\displaystyle \frac{1-a}{1+a}}<\rho <{\displaystyle \frac{\theta -c_s}{\theta +c_s}}$, then we have $p_c^{PD}<p_c^{PM}$, $p_s^{PD}<p_s^{PM}$, and $Q_s^{PD}>Q_s^{PM}$. Conversely, if ${\displaystyle \frac{\theta -c_s}{\theta +c_s}}<\rho <1-{\displaystyle \frac{2c_s(1-\theta )}{\theta (1+a-c_s-\theta )}}$, then the above inequalities are reversed. Moreover, when $0<\rho <{\displaystyle \frac{1-a}{1-a+c_s}}$ and $\underset{\_}{\theta }<\theta \le \overline{\theta }$, the demand for the customized product remains equal across the two models, i.e., $Q_c^{PM}=Q_c^{PD}$.

Building on Proposition 3, where the feasibility of the dual-product strategy depends on $\rho$, Proposition 4 shows how $\rho$ further impacts pricing and market outcomes. A moderate $\rho$ enables the PD model to set lower prices for both products, resulting in higher demand for standard products. However, when $\rho$ exceeds ${\displaystyle \frac{\theta -c_s}{\theta +c_s}}$, the PD model loses its pricing edge due to increased commission burden, leading to demand loss and higher prices. This pattern is evident in the case of YunGongChang, a PD-type industrial platform. Initially offering both product types, the platform raised $\rho$ to boost revenue, discouraging manufacturers from selling standard products. As a result, the platform shifted toward customization-only offerings, reflecting both the feasibility constraint in Proposition 3 and the pricing-performance shift in Proposition 4.

In contrast, PM platforms such as COSMOPlat (by Haier) avoid such commission-induced distortions. By directly managing production based on design inputs, PM platforms maintain more stable pricing and can sustain dual-product offerings, even under high differentiation. Together, these results demonstrate that $\rho$ not only determines whether dual-product strategies are viable (Proposition 3) but also whether they are efficient in terms of price and demand (Proposition 4).

5.1.3. Profit Comparison

Proposition 5.

Profit comparison under different platform leadership structures:

(1) 

Under the PM model, for any $\theta \in (0,1)$, the designer’s profit is always twice that of the manufacturer, i.e., ${\pi }_D^{PM}=2{\pi }_M^{PM}$.

(2) 

Under the PD model, when the platform adopts a dual-product strategy (i.e., ${\underset{\_}{\theta }}^{PD}<\theta \le \overline{\theta }$), the profits of the designer and the manufacturer are jointly influenced by the key parameters of $a$, $c_s$, $\theta$, and $\rho$. Specifically, there exists a threshold value (${\rho }_1\left(a,c_s,\theta \right)$) such that if ${\rho <\rho }_1\left(a,c_s,\theta \right)$, ${\pi }_D^{PD}<{\pi }_M^{PD}$ and if ${\rho >\rho }_1\left(a,c_s,\theta \right)$, ${\pi }_D^{PD}>{\pi }_M^{PD}$.

Proposition 5 identifies contrasting profit patterns under different platform leadership structures. In the PM model, the designer consistently earns twice as much as the manufacturer across all $\theta \in (0,1)$. This result may appear counterintuitive, as the platform is led by the manufacturer. However, it stems from the structure of value flow: the designer receives fixed blueprint and design fees for every unit sold, while the manufacturer bears the cost of production and market variability. Thus, the designer occupies a secure and margin-stable position, while the manufacturer’s profit is diluted by downstream cost risk and pricing sensitivity.

In the PD model, profit allocation is no longer fixed but varies with the commission rate ($\rho$). Due to the analytical complexity of the profit comparison, we resort to computational analysis to identify the threshold (${\rho }_1\left(a,c_s,\theta \right)$) at which the profit ranking reverses: when ${\rho <\rho }_1$, the manufacturer earns more than the designer, and when ${\rho >\rho }_1$, the designer earns more.

Figure 5 illustrates this shift across three cost scenarios. The intersection points of the profit curves mark ${\rho }_1$, where profits are equal. But more importantly, Figure 5 reveals a non-monotonic response of the designer’s profit to $\rho$.

Observation 1.

(Non-monotonic designer profit under PD model) As $\rho$ increases, the designer’s profit first rises, then falls, forming an inverted U-shaped curve, while the manufacturer’s profit monotonically decreases with $\rho$.

This behavior highlights a crucial insight: although a higher commission allows the platform (designer) to extract more value from standard product sales, excessively high commission eventually discourages manufacturer participation in that product line. Thus, there exists a profit-maximizing range of $\rho$ for the designer, beyond which marginal gain turns into marginal loss.

This finding underscores a broader implication: in designer-led platforms, the commission rate not only governs revenue sharing but also indirectly shapes the viability of dual-product strategies. Effective platform design requires the balancing of value capture with partner participation incentives—a key tension in collaborative manufacturing ecosystems.

5.2. Sensitivity Analysis

To examine how key parameters influence the equilibrium outcomes, a sensitivity analysis is conducted below. By varying core parameters such as the product substitutability ($\theta$) and platform commission rate ($\rho$), we analyze the corresponding changes in key decision variables, including pricing, market share, and profit.

Proposition 6.

The effects of product differentiation ($\theta$) and commission rate ($\rho$) on pricing and profits differ under the PM and PD models.

(1) 

PM model: When the platform adopts a dual-product strategy, an increase in the product differentiation parameter ($\theta$), i.e., lower product differentiation, leads to a higher retail price and licensing fee for the standard product, as well as higher profits for both the designer and manufacturer (${\displaystyle \frac{{𝜕p}_s^{\ast }}{𝜕\theta }}>0$, ${\displaystyle \frac{{𝜕w}_l^{\ast }}{𝜕\theta }}>0$, ${\displaystyle \frac{𝜕{\pi }_D^{\ast }}{𝜕\theta }}>0$, ${\displaystyle \frac{𝜕{\pi }_M^{\ast }}{𝜕\theta }}>0$) while the customized product price and design fee remain unaffected (${\displaystyle \frac{{𝜕p}_c^{\ast }}{𝜕\theta }}={\displaystyle \frac{𝜕m^{\ast }}{𝜕\theta }}=0$).

(2) 

PD model: (i) As $\theta$ increases, the customized product price decreases, while the standard product price and the production fee for customization increase (${\displaystyle \frac{{𝜕p}_c^{\ast }}{𝜕\theta }}<0$, ${\displaystyle \frac{{𝜕p}_s^{\ast }}{𝜕\theta }}>0$, ${\displaystyle \frac{{𝜕w}_h^{\ast }}{𝜕\theta }}>0$). (ii) As the platform’s commission rate ($\rho$) increases, both product prices rise, while the customized product’s production fee declines (${\displaystyle \frac{{𝜕p}_c^{\ast }}{𝜕\rho }}>0$, ${\displaystyle \frac{{𝜕p}_s^{\ast }}{𝜕\rho }}>0$, ${\displaystyle \frac{{𝜕w}_h^{\ast }}{𝜕\rho }}<0$).

Proposition 6 highlights how product differentiation ($\theta$) and the commission rate ($\rho$) shape the pricing and profit outcomes under different platform leadership models. In the PM model, standard product pricing and associated profits are positively related to $\theta$, reflecting the manufacturer’s sensitivity to substitution when balancing market breadth and profitability. In contrast, the pricing and fee structure of customized products remains invariant to $\theta$, suggesting that customization in PM settings operates under a stable cost-plus approach, likely due to the manufacturer’s control over downstream pricing.

Under the PD model, both $\theta$ and $\rho$ jointly influence pricing strategies and profit distribution. As $\theta$ increases, the designer reduces the customized product price to sustain demand, while standard product prices and production fees increase, rebalancing revenue between product types. Moreover, a rising $\rho$ leads to higher retail prices for both products but exerts downward pressure on the manufacturer’s production fee, reflecting designers’ need to preserve their own margins under a commission-based revenue structure.

These theoretical results are reflected in internationally recognized platform practices. Under the PM model, Stratasys, through its GrabCAD platform, offers a stable pricing structure for customized parts while dynamically adjusting the pricing and availability of standard offerings based on application specificity—consistent with our finding that $\theta$ influences standard product-related decisions more directly.

Conversely, under the PD model, Dassault Systèmes’ 3DEXPERIENCE Marketplace provides a designer-led ecosystem where design houses set product pricing and platform terms and manufacturers act as executors. As $\theta$ and $\rho$ vary, the platform exhibits flexible pricing and renegotiation of production fees to maintain manufacturer participation. This reflects our model’s result whereby, in PD settings, pricing and value allocation are more sensitive to differentiation and commission incentives.

While the profit formulas under the PD model can be derived, their expressions are too complex for direct interpretation. To better understand how profits change with key parameters, we use numerical examples and visualize the results with 3D plots (see Figure 6), which illustrate how the designer’s and manufacturer’s profits respond to changes in $\theta$ and $\rho$ under the PD model, ad we obtain Observation 2.

Observation 2.

The effect of product differentiation ($\theta$) on profits is moderated by the commission rate ($\rho$): when $\rho$ is low, the manufacturer’s profit increases and the designer’s profit decreases as θ rises; when $\rho$ is high, the trend reverses.

The result reveals a nuanced trade-off: when the commission rate ($\rho$) is low, manufacturers benefit from greater substitutability (higher $\theta$), as standard products become more appealing and cost-effective. Designers, however, see reduced gains due to weakened demand for customized offerings. In contrast, a higher commission rate shifts the advantage toward designers, who retain more value through customization, while manufacturers bear increasing costs, leading to declining profits. This reinforces the importance of coordinating platform fee structures with product strategy.

6. Discussion

6.1. Key Findings

This study investigates how platform leadership structures in NCMPs affect product line configuration, profit allocation, and pricing outcomes. Through a game-theoretic model comparing manufacturer-led (PM) and designer-led (PD) governance, we derive several key findings.

First, we find that platform leadership exerts a decisive influence on the viability and configuration of dual-product strategies (standard and customized products). Propositions 3 and 4 demonstrate that while the PM and PD models share the same upper bound of product substitutability in terms of supporting dual offerings, the lower bound in the PD model is endogenously linked to the commission rate ($\rho$ρ). This indicates that a designer-led platform can support a dual-product strategy only when $\rho$ ρ falls within a specific intermediate range.

This result complements the work of Ke et al. [27], who argue that hybrid product strategies are viable under moderate commission regimes, yet we go further by showing that the viability threshold is structurally asymmetric across governance modes. Unlike prior literature focusing on modular mass customization (e.g., Syam and Kumar [16]; Gu and Tayi [17]), our results reflect the reality of NCMPs, where customization involves costly coordination. The PM model supports a dual-product strategy across a broader range of product differentiation, which explains and extends the empirical observation of Zong et al. [22] that supplier-led platforms tend to preserve diverse offerings, even under significant cost pressure.

Moreover, our analysis contributes to the underexplored issue of market segmentation structure under different leadership forms. We show that the presence or absence of standard products under PD governance depends not only on consumer preferences but also on platform-imposed fees. This bridges platform governance and product positioning—two dimensions that have often been studied separately in the literature.

Second, our model demonstrates that platform leadership substantially affects profit outcomes—importantly, not always in favor of the platform leader. Proposition 5 and Observation 1 reveal that in the PM model, the designer earns higher profit than the manufacturer, despite the latter’s pricing power. This finding differs from earlier studies (e.g., Ha et al. [28]), which generally suggest a monotonic relationship between control and revenue capture.

In the PD model, the profit shares of both parties depend nonlinearly on the commission rate. Specifically, the designer’s profit follows an inverted U-shaped curve with ρ, while the manufacturer’s profit decreases monotonically. This difference reflects the structural tension between pricing power and coordination cost, which is largely absent in consumer-focused platform models (e.g., Abhishek et al. [25]; Zhang et al. [9]). Our results also resonate with Huang and Chen [29], who find that greater platform control may inhibit leadership incentives due to operational burden—but we further clarify the specificity of our study by determining when and why this reversal occurs in a dual-product context.

Third, we identify the conditions under which manufacturers and designers can jointly benefit by selecting a suitable leadership–product strategy combination. Proposition 6 and Observation 2 indicate that mutual improvement is achievable through collaborative leadership–product alignment, especially when customization costs are moderate and product substitutability allows for partial overlap between consumer segments. Unlike studies that treat leadership structure as exogenous or fixed (e.g., Huang et al. [21]), our model treats it as a strategic choice shaped by economic incentives and cost asymmetries.

Notably, our findings suggest that efficient coordination is not necessarily achieved by maximizing control or expanding product lines but by aligning leadership authority with relative cost efficiency and pricing flexibility. This insight refines the broader platform governance literature, which tends to focus on market reach or brand control as primary motives for leadership choice.

In summary, our analysis reveals three core insights. First, platform leadership not only affects power dynamics but also reshapes the feasibility of offering both standard and customized products under varying customization costs. Second, profit allocation in NCMPs is driven more by pricing authority and cost asymmetry than by who leads, challenging the assumption that leadership necessarily implies greater payoff. Third, efficient outcomes depend not on maximizing control or product variety alone but on aligning platform parameters—such as the commission rate and product substitutability—to balance incentives across stakeholders. These findings collectively extend prior theories of platform governance by highlighting the interplay between leadership structure, product strategy, and pricing design in shaping outcomes.

6.2. Theoretical Contributions

This study makes several theoretical contributions to the literature on platform leadership, product customization strategy, and collaborative manufacturing ecosystems by uncovering how governance structure endogenously interacts with product and pricing decisions in dual-role platforms involving both manufacturers and designers.

First, we advance the theory of platform leadership and governance by showing that leadership structure is not merely an institutional arrangement but a strategic lever that shapes the feasibility and configuration of product strategy. While prior literature often treats platform leadership as exogenous or focuses on its effects on pricing or access control (e.g., Rochet and Tirole [32]; Parker and Van Alstyne [33]), our model reveals that the choice of leader alters the structural boundaries of dual-product viability. Specifically, in designer-led platforms, the feasibility of hybrid offerings is subject to a lower bound as a function of the commission rate, whereas in manufacturer-led platforms, the same boundary is invariant to it. This finding shifts the theoretical lens from “who sets the rules” to “how rule-setting reshapes the product-market equilibrium”, extending theories of platform power into the domain of B2B customization.

Second, our results challenge the prevalent theoretical intuition that platform control yields greater payoff for the leading party (e.g., Ha et al. [28]; Abhishek et al. [25]). We show that operational control over pricing and strategy may not translate into profit advantage, especially when the leader also bears the cost of customization. In particular, even when the manufacturer leads the platform, the designer consistently earns more under a wide range of parameter settings. Moreover, under designer leadership, profit shares are sensitive to the commission rate in a non-monotonic way—highlighting hidden trade-offs between coordination and monetization. This insight refines existing models of value capture in platform-based supply chains by demonstrating that profit asymmetry is not only a function of value creation but also of cost absorption and pricing constraints—a nuance often overlooked in existing theoretical work.

Third, we contribute to the theory of product-line strategy in two-sided platforms by identifying the conditions under which offering both configurable and co-innovation products is endogenously optimal. Unlike traditional customization models that treat product types as exogenous choices (e.g., Syam and Kumar [16]; Gu and Tayi [17]), our model embeds product strategy within a triadic system involving consumer segmentation, product substitutability, and platform governance. We show that dual-product strategies are more stable and profitable under manufacturer leadership when customization costs are high because control and operational burden are aligned. This insight moves the field toward a more integrated theory of platform–product alignment, especially in decentralized manufacturing ecosystems where different players possess complementary but asymmetric capabilities.

Finally, we offer a strategic synthesis across multiple streams of platform research—governance, pricing, and product design—by modeling their interaction rather than treating them as separate optimization layers. This holistic approach reflects the realities of industrial platforms, where decisions about who leads, what to offer, and how to price are jointly determined. By identifying the endogenous conditions under which governance–product pairings become viable and mutually beneficial, our work lays a theoretical foundation for the study of negotiated governance structures in co-creation platforms—an area currently underdeveloped in both economics and operations literature.

In summary, this study enriches theory by reframing platform leadership not as a static attribute but as a dynamic choice that reshapes product and pricing feasibility, realigns profit flows, and calls for negotiated rather than imposed governance in collaborative innovation ecosystems.

6.3. Practical Implications

The findings provide valuable managerial insights for practitioners managing leadership structures and product strategies in collaborative manufacturing platforms.

First, our analysis highlights that platform governance should not be determined solely by formal authority but by structural fit. In designer-led models, an excessive commission rate reduces the viability of dual-product offerings, as observed in the case of YunGongChang, which transitioned from offering both standard and customized products to a customization-only model after raising its platform fees. This evolution echoes our theoretical finding that higher commission burdens limit pricing flexibility and suppress standard product participation under PD structures.

In contrast, manufacturer-led platforms such as COSMOPlat maintain broader product portfolios and stable pricing mechanisms by integrating design inputs into the production process, thereby avoiding the distortions caused by excessive platform fees. This reinforces our conclusion that aligning pricing rights with cost-bearing responsibilities enables more sustainable hybrid strategies.

Our results also shed light on how product substitutability and cost structures influence pricing behavior under different governance models. In the PM model, standard product pricing varies with differentiation, while customization follows a stable, cost-plus logic. The GrabCAD platform by Stratasys exemplifies this approach by offering consistent pricing for customized parts while adjusting standard offerings based on demand specificity. Conversely, in PD models like Dassault Systèmes’ 3DEXPERIENCE Marketplace, pricing decisions and production fees vary jointly with product substitutability and commission rates, requiring dynamic negotiation and profit rebalancing to maintain platform participation.

Finally, firms should be cautious in using platform control to extract excessive value, as profit asymmetries may deter cooperation. Our model shows that profit allocation can be non-monotonic and even unfavorable to the leading party under certain conditions. A well-calibrated fee structure that accounts for both product type and customization complexity may foster more equitable outcomes and long-term platform stability.

7. Conclusions and Limitations

7.1. Conclusions

This study investigates how platform leadership structures shape product strategy decisions and value distribution within NCMPs that simultaneously offer standard and customized products. By modeling and comparing designer-led and manufacturer-led platforms, we reveal how commission rates, customization costs, and product substitutability interact to affect the feasibility and performance of hybrid product strategies. The analysis contributes to a more nuanced understanding of platform-based industrial coordination. Through game theory modeling and analysis, we derive the following insights.

First, we show that platform leadership significantly alters the conditions under which dual-product strategies remain viable, particularly by reshaping pricing power and demand segmentation. Second, our results indicate that the profitability of platform participants under each leadership model depends not only on who leads but also on how commission rates and product differentiation affect value sharing—highlighting that control may, under certain conditions, lead to inferior outcomes for the leading party. Third, we identify specific parameter ranges under which coordination between manufacturers and designers can yield mutually beneficial results through appropriate combinations of governance and product strategies. These findings underscore the importance of aligning leadership structures with incentive mechanisms to ensure both platform sustainability and collaborative efficiency in industrial ecosystems.

7.2. Limitations and Future Research

This study, while offering novel insights into platform leadership and product strategy design in NCMPs, has several limitations that suggest directions for further research.

First, the model assumes complete information and static decision-making, which may not capture the dynamic and adaptive nature of real-world platform ecosystems. Future research could explore evolutionary games to examine how strategies evolve under uncertainty, learning, or bounded rationality.

Second, while this study focuses on pricing and product strategies under specific leadership structures, it does not account for how collaborative investments may actively shape product-line strategy. In practice, NCMPs are not merely transactional orchestrators; they often act as strategic investors, offering co-innovation capabilities, modular design systems, and digital infrastructure to support product innovation. Future research could examine how varying levels of collaborative investment under different leadership models influence the product-line strategy, thereby offering a synergy-driven perspective on product-line decisions in collaborative manufacturing.

Third, although this paper emphasizes collaborative innovation products, the coordination logic for configurable or modular products deserves further investigation. In the context of manufacturing, where products are typically highly customized, many firms adopt configurable product strategies to provide modularized personalization. Future research could extend the current model by generalizing standard products to configurable products, enabling a more realistic exploration of manufacturer–designer cooperation and value-sharing mechanisms under partial customization.

These extensions would enrich our understanding of platform strategy beyond pricing mechanics, addressing important dimensions such as contractual design, investment alignment, and multi-role collaboration in applied industrial contexts. Additionally, future research could empirically validate the theoretical predictions of this study using case studies, industry data, or survey-based methods in real-world NCMP settings.