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Article

Partner Business Model Alignment for Mitigating Operational Conflicts in Exploitation Alliance: Evidence from Chinese Residential Joint Ventures

by
Jinxiu Wang
1,* and
Li Wang
2
1
School of Management, Fujian University of Technology, Fuzhou 350118, China
2
School of Architecture and Civil Engineering, Xihua University, Chengdu 610039, China
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(7), 3337; https://doi.org/10.3390/su18073337
Submission received: 6 February 2026 / Revised: 26 March 2026 / Accepted: 28 March 2026 / Published: 30 March 2026
(This article belongs to the Special Issue Sustainable Development of Organization Performance for Economic and Business Aspects)
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Abstract

The dynamic process through which latent differences in business models of partners escalate into daily operational conflicts within exploitation alliances remains insufficiently explained. This study examines how alignment in partner business models influences operational conflicts, a key determinant of exploitation alliance sustainability. Questionnaire data from 110 experts in Chinese residential joint ventures (JVs) were used to test the proposed hypotheses. The findings indicate that key resources (KRs) and profit formula (PF) indirectly affect operational conflicts through jointly established core business standards (CBSs). Counterintuitively, these standards significantly increase operational conflict risks (OCRs) when they institutionalize underlying misalignments, thereby acting as a full mediator. The results advance theory by clarifying the micro-process of institutionalized misalignment and refining the Resource-Based View (RBV) in alliance contexts. Practically, the study highlights the importance of conducting thorough ex-ante business model analysis, co-creating operational standards, and undertaking continuous alignment reviews to mitigate conflict and enhance JV viability.

1. Introduction

Exploitation alliances are frequently structured as JVs to integrate existing resources and enhance competitiveness within complex markets [1,2]. Initial alliance design typically prioritizes partner selection based on resource complementarity [3] and strategic goal alignment [4]. However, even alliances with sound structural foundations often encounter significant operational challenges post-formation [5]. A primary source of these difficulties extends beyond initial resource asymmetries to the inherent divergence in partners’ underlying business models [2,6]. Such divergence manifests across critical dimensions, including strategic objectives, profit logic, operational tempo, and risk tolerance [7,8]. If not proactively managed, these latent misalignments can escalate during routine operations, precipitating tangible conflicts such as decision-making impasses, resource allocation disputes, and communication breakdowns [9,10]. Ultimately, these conflicts threaten the economic viability, environmental and social performance, and capacity for long-term value creation of the JV [8,11,12,13,14].
Extant literature has extensively examined alliance formation motives, governance structures, and performance outcomes through dominant theoretical lenses such as Transaction Cost Economics (TCE), the RBV, and Social Network Theory [15]. These studies predominantly adopt a relatively static and structural perspective, focusing on initial conditions or governance blueprints [16,17], while offering limited insight into the ongoing micro-interactions after an alliance commences [18]. Consequently, a significant research gap persists regarding the dynamic processual mechanisms through which latent business model differences between partners escalate into concrete operational conflicts that jeopardize alliance stability and long-term sustainability [19].
Specifically, although prior research acknowledges the importance of partner compatibility and conflict management [20,21], the precise causal pathways through which business model misalignment translates into daily operational friction remain underexplored. Scholarship on alliance governance and JV conflict often treats business models as monolithic constructs or examines isolated elements, neglecting how the complex interplay of multiple business model components unfolds and crystallizes through joint operational practices [22]. Similarly, research on business model compatibility tends to emphasize strategic fit at the formation stage [23], yet provides insufficient explanation for how latent discrepancies become institutionalized within alliance routines and are subsequently activated during execution, leading to conflict.
This theoretical shortcoming is further accentuated by the contemporary imperative of sustainable transition [24]. The growing global emphasis on Environmental, Social, and Governance (ESG) principles and sustainable development introduces new layers of complexity into alliance management [25,26,27]. While literature on sustainable business models has expanded, discussing value creation for the triple bottom line and the dynamic capabilities required for sustainability integration [28,29,30], its intersection with the practical governance and daily conflict dynamics of exploitation alliances remains notably sparse. There is a pressing need to understand how sustainability goals interact with, and potentially exacerbate or mitigate, tensions arising from business model misalignment.
Chinese residential JVs present an ideal empirical setting to theorize this process [12]. Typically formed between hands-on developers seeking scale expansion and financial institutions targeting staged financial returns [31], these JVs epitomize the “capabilities-capital” complementary exploitation alliances [32]. Their governance structures generally rely on board-based co-management and require unanimous consent for major decisions [31], which make effective operational coordination and conflict resolution paramount. Concurrently, China’s real estate sector is undergoing profound transformation characterized by tightening financing conditions, sluggish market sales, and increasingly institutionalized, policy-driven regulation [33]. These external pressures amplify latent tensions rooted in business model misalignment, rendering the linkage between strategic divergence, operational conflict, and alliance sustainability particularly salient for empirical investigation.
This study therefore adopts an integrative, process-oriented perspective to investigate how partner business model alignment influences operational conflicts and, ultimately, the sustainable development of exploitation alliances. Leveraging questionnaire-based data from Chinese residential JV experts and employing hypothesis testing, this research seeks to identify critical components of business model alignment with exploitation alliances and delineate the underlying mechanisms through which misalignment generates operational conflict. The findings aim to offer actionable insights for exploitation alliance partners to proactively design and manage their collaborations, enhancing both operational harmony and long-term viability within an increasingly sustainability-conscious business landscape.

2. Theoretical Background and Hypotheses Development

2.1. Exploitation Alliance Characteristics

Exploitation alliances are fundamentally oriented toward deepening, refining, and extracting maximum value from a firm’s existing competencies and resources, rather than pursuing exploration into novel domains [1,2,32,34]. Characterized by a distinct emphasis on learning efficiency, these alliances enable partners to enhance their capacity for coordinated action and thereby maximize the value-creation potential inherent in integrated resources [35]. Operationally, such collaboration focuses on streamlining processes, improving task efficiency, and achieving mutually defined performance targets within predefined alliance boundaries [36].
From a structural perspective, these alliances are designed to emphasize stability, control, and reciprocal commitment [2]. Yet they consistently encounter the underlying challenge of balancing cooperative engagement with the need for operational oversight [37]. Typically formed through deliberate upfront planning, they prioritize selecting partners based on complementary resource endowments [3] and aligned strategic intentions [4]. Governance commonly follows a co-management model, requiring consensus on major decisions and thus formalizing interdependence among the partners [38].
This formalized interdependence, while reinforcing shared control, also introduces operational complexity [39]. Partners must continuously navigate the tension between collaboration and competition inherent in their relationship [40]. Such a competitive dynamic can generate relational risks, whereby self-interested or non-aligned behavior by any partner may compromise the alliance’s collective objectives [41]. In turn, such behaviors can precipitate operational conflicts that threaten the alliance’s continuity [42]. Especially, underlying differences between partners that originate from their distinct business models and manifest as disparities in strategic goals, operational rhythms, profit expectations, and risk tolerance [39,43] are likely to intensify these conflicts. This escalation may ultimately lead to the restructuring or dissolution of the alliance [42].

2.2. Residential Developer’s Business Model Elements

While bearing industry-specific characteristics [44], the competitiveness of a business model fundamentally originates from a firm’s internal resources and capabilities [45]. The conceptualization of business models as structured frameworks for value creation and capture has been extensively developed in the international literature. For instance, Johnson et al. [46] proposed a framework comprising customer value proposition, PF, KRs, and key processes, which effectively delineates the pathway from resources and capabilities to profit realization. This framework, along with other influential perspectives such as the activity system view by Zott et al. [47], provides a universal analytical lens for examining business model alignment and misalignment across different contexts.
Correspondingly, Wang [31] adapted this framework to the residential development industry, decomposing the business model into four modules: customer orientation (CO), PF, KRs, and CBSs. This adaptation highlights the contextual specificity of business model applications. The Chinese residential market, characterized by strong policy intervention, high capital intensity, and a predominant focus on high-turnover strategies [6], presents a distinct environment compared to more mature markets in Europe and North America, where regulatory frameworks, market cycles, and development models may differ substantially [48]. These contextual differences suggest that while the fundamental constructs of business model misalignment are universally relevant, their manifestation and impact may be particularly acute in the Chinese context. The pressure for rapid scale, coupled with stringent financing constraints and policy volatility, can amplify latent inconsistencies between partners’ business models, making their alignment through jointly established standards both more critical and more challenging.
CO refers to a developer’s ability to translate heterogeneous land conditions into precise customer segmentation, which directly influences project-level design decisions and subsequent market performance. Liu et al. [6] show that the high-turnover model adopted by Chinese developers explicitly targets first-home buyers and middle-class owner-occupiers. Similarly, Brege et al. [49] demonstrate that Swedish timber-frame residential developers configure their customer value proposition around volume segments, including students, the elderly, and young families, to achieve economies of scale. These findings underscore the universal importance of CO.
The profit module defines the revenue logic, cost structure, and margin targets. According to Brege et al. [49], the dominant PF in Swedish industrialized housing can be categorized into three types, which include high turnover, cost efficiency, and asset management. Each of these types is characterized by distinct revenue rhythms and profit structures. Liu et al. [6] identify two primary profit approaches among Chinese residential developers. One approach relies on low construction or financing costs to achieve high margins, while the other is driven by high turnover to realize economies of scale. Johnson et al. [46] argue that such a formula must balance price points against cost structures and resource velocity.
Residential developers’ KRs encompass the development management team, capital strength, product portfolio, supplier resources, funding channels, customer base, development brand, and property management brand [31]. Liu et al. [6] find that firms adopting an asset management model leverage deep collaborations with banks and trust companies to secure low-cost capital. Those employing a cost-efficiency model possess strong independent development capabilities, maintaining low operational costs through cost leadership strategies. Developers implementing a high-turnover model widely adopt standardized operational systems covering product design, construction, and sales, enabling rapid inventory monetization that supports continuous expansion. Grounded in the RBV [45], resources that are rare, difficult to imitate, and non-substitutable form the basis of sustained competitive advantage in residential development.
CBSs include profit margin on sales, financing cost, and operational key performance indicators (KPIs) [31]. Liu et al. [6] reveal that Chinese firms following the cost-efficiency model predefine a minimum gross margin at project inception and refrain from bidding on land that threatens this threshold. Experts also note that internal institutional requirements in residential development firms impose constraints on the annualized financing cost of individual projects. In the capital-intensive residential development industry, rules that maintain a cost advantage in capital can constitute a source of sustained competitive advantage. Parmenter [50] argues that operational KPIs, when properly designed around critical success factors, directly influence operational behavior and project outcomes. By focusing on a limited set of high-impact indicators, such as the schedule performance index and cost performance index, project managers can promptly detect deviations from planned scope, time, or budget, allowing them to take timely corrective actions to protect profit margins.

2.3. Interrelationships Among Business Model Elements

The RBV offers a core theoretical lens for explaining how KRs shape and facilitate CO. Grant [51] emphasized that competitive advantage specifically arises when unique resources and capabilities are aligned with market opportunities. This alignment proves especially vital for CO, as it enables firms to effectively deploy resources to comprehend and address customer needs. Furthermore, strategic resource deployment facilitates essential outcomes associated with customer knowledge orientation, including market leverage, market lock-in, market trust, and market innovation [52]. For instance, a market leverage, supported by strategic assets like customer data, information, and knowledge, enhances a firm’s market networking and flow capacity. Similarly, market trust, cultivated through the consistent delivery of high-quality services, acts as a relational resource that strengthens CO by reinforcing long-term market relationships. In the context of Chinese real estate enterprises, Li et al. [53] illustrate that strong customer reputation and brand halo help accelerate absorption rates and sustain pricing power, thereby reinforcing a customer-first strategy. Osterwalder and Pigneur [54] further emphasize that KRs must be able to support target customer segments, deliver value propositions, maintain distribution channels, foster customer relationships, and sustain revenue streams. Their work underscores that CO is not solely a marketing function but requires the coherent integration of KRs throughout the entire business model to achieve sustained market success. Based on the foregoing analysis, the following research hypothesis is proposed:
H1a. 
KRs positively affect CO.
Empirical studies in the real estate sector consistently demonstrate that KRs held by residential developers exert a direct, positive, and economically significant influence on CBSs. For instance, the quality and stability of the development-management team have been shown to enhance profit margins and improve operational KPIs [43]. Similarly, abundant internal cash reserves and diversified funding channels not only reduce interest expenses but also enable developers to strategically time land acquisitions and construction launches to coincide with market upswings, thereby widening gross margins [53]. Developers with a broad product portfolio, covering affordable, mid-end, luxury, rental, and retirement segments, can reallocate capital across categories in response to policy shocks. This operational flexibility shortens inventory turnover and elevates net operating margins [55]. Moreover, established relationships with trusted suppliers and access to offshore funding sources help shorten payment cycles and reduce working capital requirements, directly lowering financing costs and enhancing cash-flow KPIs [56]. A strong development brand further reduces equity contribution requirements from lenders, thereby decreasing the weighted average cost of capital. Additionally, a large installed customer base, including repeat buyers and membership clubs, increases presale velocity, allowing developers to lock in prices ahead of project completion and hedge against margin compression during market downturns [53]. Collectively, these KRs raise the upper bound of achievable profit margins, lower the cost of capital, and improve key operational performance indicators. Based on this synthesis, the following hypothesis is proposed:
H1b. 
KRs positively affect CBSs.
Recent literature converges on the view that the PF acts as a primary lever for upgrading CBSs in residential development. Brege et al. [49] demonstrate that Swedish industrialized builders who redefined their formula around turnkey value instead of cost-plus obtained wider gross margins because the new financial logic internalized risk and rewarded faster, defect-free delivery. Liu et al. [6] observe similar dynamics among high-turnover Chinese developers. Their findings indicate that a PF emphasizing rapid inventory turnover and strict cost ceilings directly reduced financing expenses and improved operational KPIs. Johnson et al. [46] contend that once a revenue, margin, and velocity blueprint are established, all subsequent resource allocation decisions are constrained by it, leading to automatic improvements in site-level indicators such as cycle time and cost per unit. Robin [48] notes further that phasing sales in alignment with construction progress, a strategy embedded in the profit plan, lowered lenders’ risk premiums and consequently reduced interest costs. Pérez-Sánchez et al. [55] conceptualize profitability as an intermediate variable that mediates between innovation choices and competitiveness, confirming that the PF defines the feasible upper limit for both operational performance and capital efficiency. Collectively, these studies illustrate that deliberate design of the PF enhances sales margins, lowers financing costs, and tightens operational KPIs, thereby elevating overall CBSs in housing projects. Based on the foregoing analysis, the following hypothesis is proposed:
H1c. 
PF positively affects CBSs.

2.4. Partner Business Model Alignment Effect on Operational Conflicts

In Chinese residential JVs, the alignment of partner business models relies heavily on whether they share common CBSs [31]. When these standards diverge, operational conflicts become more pronounced, particularly regarding financing strategies and daily operational control. Empirical studies have shown that Chinese real estate developers can be categorized into distinct business model types, each characterized by specific target profit margins, leverage preferences, and performance indicators [6]. Partners from different strategic backgrounds must establish a shared framework for decision-making. Otherwise, they are likely to experience repeated friction.
Profit margin expectations shape key activities such as land bidding, pricing strategy, and promotional spending. If one partner emphasizes sales volume while another targets premium positioning, their cash flow patterns will differ, leading to disagreements over financing approaches. De Dreu and Weingart [57] observe that even task-focused conflicts, when unresolved, can weaken collective confidence and provoke competitive behavior in decisions such as capital injections or refinancing schedules. This highlights the need to align profit objectives before equity investment; failing to do so may turn financial deliberations into arenas for underlying strategic disputes.
Alignment in financing cost assumptions is similarly important. Studies such as Liu et al. [6] show that cost-driven firms often adopt higher leverage to reduce capital expenditure, while other strategic types, such as those focused on public projects or asset management, maintain more conservative financial structures. These differences frequently result in disputes over loan agreements, refinancing thresholds, and the use of presale revenues. Such conflicts can delay fund utilization and compromise expected returns. Hempel et al. [18] further note that competitive interactions often originate in financial negotiations and later extend into project implementation management disagreements, underscoring the importance of reconciling risk preferences during initial partnership agreements.
Operational KPIs help coordinate the speed of construction, sales effectiveness, and cost management. Liu et al. [6] point out that high-turnover developers often impose demanding sales targets and strict cycle times, while efficiency-focused partners emphasize tight cost controls. Without prior agreement on these indicators, partners tend to blame each other for delays or budget overruns, worsening disputes over supplier selection, progress payments, and managerial autonomy.
Overall, evidence suggests that partners who clearly define margin benchmarks, financing constraints, and operational metrics before project commencement can convert potential conflicts into coordinated performance management. This finding aligns with the business-model-fit perspective advocated by Brege et al. [49], which posits that successful JVs depend not on identical resources but on consistent decision-making principles. Such principles ensure that financial and operational actions remain within a range acceptable to all parties involved. Consequently, the following research hypothesis is proposed:
H2. 
Discrepancies in partners’ CBSs positively affect operational conflicts in residential JVs.
Based on the preceding analysis, the conceptual framework of this study is presented in Figure 1.

3. Materials and Methods

3.1. Measurement Instrument

A structured questionnaire was employed to examine the structural relationships among components of residential developers’ business models and their association with operational conflicts in residential JVs. All variables were evaluated using multi-item Likert scales ranging from 1 to 5. Respondents answered based on the construct items presented in Table 1.
This study employed single-item measures for the two constructs, CO and PF. Within the specific context of residential JV development, CO was operationalized as the capability to translate heterogeneous land conditions into precise customer segmentation, and PF was operationalized as the strategic choice between high turnover rate and high gross margin. These two concepts enjoy a high degree of consensus and have clear, specific meanings in industry practice. Utilizing a single, focused item effectively captures their core essence, avoiding the potential redundancy or ambiguity that multi-item scales might introduce [58,59]. Therefore, in partial least squares structural equation modeling (PLS-SEM), employing single items for such clearly defined, industry-specific constructs is acceptable when supported by a strong theoretical rationale [60].
The measurement variables for KRs included KR1-KR8, and the measurement variables for CBSs consisted of CBS1–CBS3. These measurement variables for business model components were derived by applying general theoretical frameworks [46] to the context of the Chinese residential development industry and were subsequently validated through expert interviews [31]. Furthermore, OCRs in residential JVs were assessed across two dimensions, OCR1 and OCR2.
The questionnaire was administered in Chinese. To ensure linguistic accuracy and conceptual consistency, a back-translation procedure recommended by Brislin [61] was adopted. Specifically, two bilingual management scholars independently translated the questionnaire into English. Then, two other translators, who had not seen the original version, independently translated it back into Chinese. Multiple rounds of comparison and revision were conducted until a consensus version was achieved. Prior to the formal study, a pretest was conducted with three investment directors and two alliance managers. Based on their professional feedback, the wording of the items was refined to eliminate potential ambiguities.

3.2. Sampling and Data Collection

This study investigates the key actors involved in residential development JVs. The first group consists of partner selection specialists, typically investment directors or senior executives, who possess the expertise to identify and engage partners that can reliably advance project objectives. The second group includes JV operational managers who gain firsthand insight into how a partner’s operational approach either fuels or mitigates conflicts.
To ensure the research reflects current industry practice and involves qualified professionals, the study drew its sample from the top 200 Chinese real estate developers based on their 2023 sales performance. Respondents were required to satisfy two screening criteria. They needed to have participated in at least one residential development JV between 2020 and 2024 and to have held a formal decision-making role specifically related to either partner selection or JV operation management.
Respondents were recruited through a combination of multiple channels. Initial contacts were made using the research team’s professional network within the real estate industry. Survey invitations were also distributed indirectly through industry associations and other relevant organizations, and direct outreach was conducted to selected target companies. Data collection took place from June to December 2024. Invitations containing a link to the online questionnaire were sent via email to eligible professionals, followed by two rounds of reminder emails.
During this period, 300 questionnaires were distributed, and 128 were returned. After the removal of 18 responses that were either incomplete or exhibited patterned answers, 110 valid questionnaires remained, yielding a usable response rate of 36.67 percent. The final respondent composition included 18 senior executives, 55 investment directors, and 37 alliance managers. In terms of experience, seven respondents reported 3 to 5 years, 69 reported 6 to 10 years, and 34 reported more than 11 years. Geographically, participants were distributed across several regions of China: 5 from the North, 29 from the East, 20 from the South, 29 from Central China, 21 from the Southwest, and 6 from the Northwest.
Although respondents from the same company were encouraged to provide independent answers based on their respective project responsibilities, it cannot be entirely ruled out that a small number of participants may have come from different subsidiaries of the same parent company. In the analysis, company affiliation was included as a control variable in preliminary tests, and no systematic differences were detected.
To assess the potential threat of non-response bias, the time-trend extrapolation approach recommended by Armstrong and Overton [62] was adopted. This method compares early and late respondents across all key constructs, with these groups defined as the first and last 50% of returned questionnaires, respectively. Independent-samples t-tests revealed no statistically significant differences in any of these comparisons (p > 0.05), suggesting that non-response bias is unlikely to pose a serious threat to the study’s findings.

4. Analysis and Results

The proposed model was analyzed using PLS-SEM with SmartPLS 4. The sample size of 110 satisfies the minimum requirement relative to the number of hypotheses tested in this study [60]. Common method bias was assessed using Harman’s single-factor test, which showed that the first unrotated factor accounted for 36.955% of the total variance, which falls below the recommended threshold of 50% [63]. Furthermore, the inner variance inflation factor (VIF) values for all structural paths ranged from 1.000 to 1.006, all well below the conservative cutoff of 3. These diagnostic tests collectively suggest that common method bias does not pose a significant concern for the validity of the findings.

4.1. Measurement Model Assessment

As summarized in Table 2, the measurement model demonstrates satisfactory properties. All indicator loadings exceeded the recommended value of 0.708. Both Cronbach’s alpha and composite reliability (CR) values were above 0.70 for all constructs, supporting high internal consistency [60]. The average variance extracted (AVE) for each construct was above 0.50, confirming adequate convergent validity. Furthermore, as shown in Table 3, all heterotrait–monotrait ratio (HTMT) values were below the conservative threshold of 0.85, establishing discriminant validity [64]. These results collectively affirm that the measurement model meets the requisite standards of reliability and validity for proceeding with structural model analysis in PLS-SEM.

4.2. Structural Model Assessment

Evaluation of the structural model indicated no multicollinearity issues, with all inner variance inflation factor (VIF) values falling between 1.000 and 1.006. The R2 values for CO, CBSs, and OCRs are 0.083, 0.260, and 0.442, which, respectively, indicate low, moderate, and moderate-to-substantial levels of explained variance in the model. The corresponding Q2 values are 0.048, 0.268, and 0.181, all greater than zero, which confirms the model’s predictive relevance for each construct. The model fit was further supported by a standardized root mean square residual (SRMR) value of 0.094, which is within the acceptable limit of <0.10.
The direct effect hypotheses were tested, and the results are presented in Table 4. Hypotheses H1a, H1b, H1c, and H2 were supported. Specifically, KRs had significant positive effects on CO (β = 0.288, t = 2.735, ** p < 0.01) and CBSs (β = 0.631, t = 10.099, *** p < 0.001). The PF also demonstrated a significant influence on CBSs (β = 0.168, t = 2.070, * p < 0.05). Moreover, CBSs were found to significantly increase OCRs (β = 0.510, t = 4.721, *** p < 0.001). The path coefficients of the full model are depicted in Figure 2.
Mediation analysis results, displayed in Table 5, indicate that CBSs significantly mediate the relationship between KRs and OCRs (β = 0.322, t = 4.001, *** p < 0.001), as well as between PF and OCRs (β = 0.086, t = 2.052, * p < 0.05).

5. Discussion

This study empirically examines how the alignment of partner business models influences OCRs in Chinese residential JVs. The findings reveal significant relationships among business model elements and their impact on operational conflicts, offering nuanced insights into JV management. The discussion elaborates on these results by integrating them with extant literature on business models and conflict management.

5.1. Interrelationships Among Business Model Elements

The results confirm that KRs and the PF significantly shape a venture’s CBSs. This aligns with the foundational view of a business model as a systemic architecture where elements are interdependent [65,66]. KRs, such as brand or human capital, form the productive foundation of a firm [45,67]. The data analysis shows these resources directly strengthen CBSs (β = 0.631), which can be interpreted as the formalized processes, rules, and metrics that govern daily operations [54,68]. Similarly, the PF encompassing the revenue model, cost structure, and margin targets also positively influences these standards (β = 0.168). This supports the argument that how a firm intends to create and capture value (its PF) dictates the operational rules and performance metrics it establishes [69]. The strong interrelationship echoes the framework proposed by Johnson et al. [46], wherein customer value proposition, PF, KRs, and key processes must cohere for a business model to be effective. In the context of a JV, when partners bring distinct resources and financial expectations, their alignment or misalignment directly crystallizes into the JV’s operational protocols.

5.2. Effects of Business Model Elements on Operational Conflicts

A central finding is that CBSs, once established, significantly increase OCRs (β = 0.510). This appears counterintuitive but is explicable through the lens of partner interaction. CBSs represent the formalized integration of partners’ business model elements. If these standards are built upon misaligned or incompatible resources and profit motives from the partners, they become a source of friction rather than harmony. The mediation analysis further elucidates this pathway by demonstrating that CBSs fully mediate the effects of both KRs (β = 0.322) and PF (β = 0.086) on operational conflicts. This suggests that it is not the resources or financial goals per se that cause conflicts, but rather how they are institutionalized into the JV’s joint operating procedures.
This finding resonates with the established literature on JV conflicts, which identifies that conflicts often arise from differing operational routines, decision-making norms, and goal incompatibility [70]. Beamish and Lupton [71] highlight the absence of effective management mechanisms as a key failure factor. This study specifies that even when mechanisms exist, if they emanate from poorly aligned underlying business models, they can become the very vehicle for conflict. This extends the transaction cost theory [72] by showing that ex-post operational conflict is not merely a coordination cost but is directly shaped by the ex-ante design of the collaborative business interface.
Particularly critical is this study’s emphasis on the core mechanism of institutionalized misalignment. When partners enter a JV relationship with their independent business models, the inherent discrepancies in their strategies, processes, and value propositions do not simply vanish. Instead, through negotiation and compromise, these differences can become codified into seemingly unified CBSs. These standards thus become formal frameworks that embed and solidify the initial divergences [70]. In daily operations, when decisions are made according to these standards, the contradictions inherent within them become explicit, manifesting as disagreements over financing plans, disputes about budget allocation, variations in quality control benchmarks, and differing interpretations of operational KPIs [50]. Therefore, conflict does not stem from a lack of standards but from the fact that the standards themselves represent an unresolved and formalized state of misalignment.
This explains why, in JV practice, the creation of detailed contracts and operational manuals sometimes fails to prevent conflict and may even provide clear terms of reference for subsequent disputes [71]. The empirical results of this study reveal that JV governance should not only focus on whether formal rules and standards have been established but must also deeply examine whether these rules and standards genuinely reflect a deeply integrated business logic rather than a superficially assembled one [46,73]. Only CBSs built upon a foundation of sufficiently aligned business models can perform their intended functions of coordination and control. Otherwise, they yield the opposite effect.

5.3. The Double-Edged Effect of Institutionalized Misalignment in CBSs

A central and compelling finding of this study is the significant positive influence of jointly established CBSs on OCRs. This result contradicts the conventional expectation that standards promote coordination, instead revealing a critical micro-mechanism for conflict emergence within exploitation alliances [12].
The analysis indicates that conflicts are not merely transient phenomena arising from an initial break-in period in JVs [5]. Rather, when CBSs institutionalize latent yet fundamental disagreements between partners concerning KRs and PF [44], and these disagreements remain insufficiently negotiated, such standards become persistent triggers for conflict. For example, a CBS incorporating an aggressive sales profit target, formulated primarily under pressure from a capital partner focused on short-term financial returns, may fail to adequately account for the operational capabilities and resource constraints of a development partner prioritizing brand building and long-term market share. Consequently, such a standard can continuously spark disputes over cost control, material selection, and marketing investment across all project phases, from design and construction to sales. Conflict thus permeates the entire project lifecycle, potentially erupting at any point where standard application deviates from practical realities [10].
The OCRs measured herein primarily pertain to dysfunctional conflicts, such as decision deadlocks and resource contention, which impair collaborative efficiency [74]. However, this finding also implies a potential constructive pathway. Proactively surfacing underlying divergences and fostering constructive debate during the CBS formulation stage could stimulate functional conflict [74]. This process might subsequently lead to the co-creation of more inclusive and resilient operational standards.
This finding extends and challenges the extant literature on alliance governance [37]. The traditional perspective emphasizes formal contracts and standards as control mechanisms that reduce ambiguity and curb opportunism [38]. In contrast, the present study reveals that in contexts of fundamental business model misalignment, a premature or superficial pursuit of standardization risks ossifying flexible, negotiable strategic differences into rigid, irreconcilable operational oppositions. This insight aligns with research on the double-edged effect of contracts [75], which posits that formalization, while coordinating action, may simultaneously stifle adaptability and trust. The contribution of this research lies in anchoring this mechanism to a specific mediating pathway, namely the institutionalization of business model misalignment through CBSs [76]. This provides a refined theoretical lens for understanding the dynamic interplay between cooperation and conflict within exploitation alliances.

5.4. Implications for Sustainability Performance and Governance

This study reveals that differences in partners’ KRs and PF exacerbate operational conflicts through the institutionalization of jointly established CBSs, offering a novel micro-level perspective on the mechanisms underlying sustainable performance in JVs. Sustainability extends beyond long-term economic viability to emphasize the creation of integrated value across environmental, social, and governance dimensions [29]. The findings of this research can be linked to sustainability performance indicators and governance practices.
Conflicts directly undermine the foundation of economic sustainability. Operational conflicts, such as disagreements over financing plans and disputes over operational control, lead directly to decision-making gridlock, project delays, redundant resource allocation, waste, and unforeseen cost overruns [57,77]. These conflicts consume financial and managerial resources that would otherwise be directed toward value creation, thereby weakening the JV’s profitability and financial resilience and directly impairing its economic sustainability. Preventing and mitigating conflicts through ex-ante business model alignment and ongoing dynamic assessment constitutes a core managerial activity for safeguarding the alliance’s economic objectives and maintaining its long-term financial health.
Conflicts signal internal governance failure and impede the creation of social value. Frequent and intense operational conflicts fundamentally reflect a lack of trust, misaligned strategic objectives, and a weak shared vision among partners [7,78]. This indicates a failure in the JV’s internal governance mechanisms. Sound governance serves as the cornerstone of a sustainable business model, requiring the establishment of transparent, equitable, and effective decision-making and oversight systems to harmonize the interests of all parties [7]. This study demonstrates that when CBSs institutionalize underlying misalignments, they fail to function as governance instruments and instead become catalysts for conflict. This highlights a critical distinction in alliance governance between procedural compliance, which involves having common standards, and substantive effectiveness, which requires standards grounded in deep consensus. The sustainable business model literature emphasizes that broad stakeholder engagement is essential for successfully implementing sustainable practices and attaining social legitimacy [79,80,81]. Extending this insight to the internal dynamics of a JV, the partners themselves represent the most critical stakeholders. Ensuring their deep involvement and alignment on core business model elements is a prerequisite for building effective internal governance, preventing destructive conflicts, and thereby creating stable social value for employees, customers, and communities.
The consistency of standards influences the pathway to environmental performance. Although this study does not directly measure environmental indicators, its theoretical framework carries significant extended implications. In an era where green building and sustainable development are increasingly becoming industry norms, the CBSs of residential JV projects are likely to incorporate specific environmental performance commitments, such as targets for green building certification, energy and water efficiency metrics, proportions of eco-friendly materials used, or carbon reduction requirements [29,82]. If partners hold fundamentally divergent PFs, for instance, one prioritizing short-term cash flow maximization while the other emphasizes long-term green brand premium, or possess uneven capabilities in KRs, such as one partner having expertise in green technology while the other does not, conflicts over the level of environmental investment, choice of technological pathways, or cost-sharing will become salient during operations. Consequently, underlying business model misalignment in the environmental dimension, mediated through CBSs, manifests as conflicts over environmental goals and execution deviations during the project implementation phase, ultimately affecting the project’s overall environmental performance.

5.5. Theoretical Implications

This study advances theoretical understanding through three distinct yet interconnected contributions. By building a conceptual bridge between business model theory and JV conflict management, the research establishes a micro-process model that delineates how the composition of partners’ business models translates into operational conflicts. Whereas business model literature has traditionally emphasized value creation and competitive advantage [83,84], and JV research frequently examines control and performance [43], the current framework systematically traces the pathway through which business model combinations influence operational conflicts as a critical relational outcome. This model thereby enhances comprehension of the micro-foundations of inter-organizational cooperation [85] and illuminates the transformation process between strategic intent and collaborative results.
The study also substantiates and expands the concept of business model alignment by introducing the critical dimension of contextualization. The efficacy of business model alignment and the mechanisms underlying conflict generation are significantly moderated by external contingencies. In the backdrop of China’s real estate sector, which is characterized by strong policy intervention, market cyclicality, and high capital intensity, the study reveals how these unique contextual forces intensify the consequences of misalignment. For instance, stringent presale fund regulations heighten partners’ sensitivity to cash flow disagreements [31], while frequent policy shifts can quickly render a once-aligned PF obsolete, triggering new conflicts. By testing and extending theories predominantly derived from manufacturing and service sectors in this distinct setting [6,49], this work not only validates their core logic but also delineates their boundary conditions. This contributes to a more contextually informed body of knowledge on strategic alliances, particularly relevant for understanding enterprise behavior and partnership governance in policy-intensive industries within emerging economies.
Furthermore, the findings offer an important refinement to the RBV in alliance contexts. While RBV posits that resources confer advantage [45], this study reveals that within JVs, merely pooling valuable resources proves inadequate. The decisive factor lies in whether these resources can be effectively integrated into unified operational standards. When integration fails, otherwise complementary resources may instead become sources of conflict. This insight both aligns with and extends the relational view, which emphasizes that competitive advantage derives from how partners uniquely combine and leverage their heterogeneous resources [86]. The present research specifies the concrete mechanisms through which resource integration and institutionalization occur.
Importantly, the core mechanism identified in this study suggests a potentially generalizable micro-process. This mechanism posits that latent differences in partners’ KRs and PF exacerbate operational conflicts when they become institutionalized through jointly established CBSs. Its relevance may extend beyond the specific context of Chinese residential exploitation alliances. For example, in research and development or service alliances in Europe or North America, underlying disagreements over intellectual property rights, which function as a KR, or revenue-sharing models, which constitute a PF, could similarly become sources of operational conflict if formally codified into a joint development agreement that serves as the CBS.

5.6. Practical Implications

For practitioners managing or entering Chinese residential JVs, this study offers actionable guidance. First, due diligence must go beyond financial and legal checks to include a deep analysis of potential partners’ business models. Partners should explicitly discuss and map their respective KRs, PF, and operational philosophies prior to JV formation, as suggested by the business model generation process [54].
Second, the design of joint CBSs is a critical negotiation point. Managers should not treat operational manuals and governance rules as mere administrative tasks. They are the embodiment of the business model alignment. The research findings suggest that investing time in co-creating these standards, which ensures they equitably reflect and integrate both parties’ models, can preemptively reduce operational conflicts. This echoes the conflict management recommendation from Hempel et al. [18], who indicated that collaborative conflict management approaches enhance trust and performance.
Third, continuous alignment review is necessary. Business models and market conditions evolve [73]. Regular reviews of whether the JV’s operational standards remain aligned with any shifts in partners’ individual strategies or resources can help avoid emergent conflicts. This is akin to the dynamic capability perspective applied to alliance management.
Furthermore, this study reveals that operational conflicts stemming from business model misalignment can disrupt project delivery, quality consistency, and operational continuity. Conversely, ensuring JV internal robustness through deliberate partner selection, business model alignment, and flexible conflict management supports the timely and quality-compliant completion of residential projects. Such outcomes address housing needs and foster more resilient communities. At the project level, the success of residential JVs contributes tangibly to sustainable urban development objectives, which aim to integrate economic vitality, social inclusiveness, and environmental livability [87]. Therefore, managing business model alignment and conflict in JVs represents both a governance imperative and a practical contribution to shaping sustainable urban futures.

6. Conclusions

This study demonstrates that operational conflicts in JVs are fundamentally linked to the structural alignment of partners’ business models. The elements of a business model, particularly KRs and the PF, work through the mediation of jointly established CBSs to influence conflict levels. These insights enrich theories of business models, inter-organizational relationships, and conflict management, while providing a clear roadmap for partners to build more harmonious and effective JVs.
Importantly, the findings establish a meaningful connection to the growing literature on sustainable business models. The sustainable business model is defined as “the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural, or other contexts, in a sustainable way” [87], with its core objective being the creation of value across the triple bottom line encompassing economic, social, and environmental dimensions. This study proposes that ex-ante alignment and continuous review of partners’ business models serve as a foundational governance mechanism for developing a sustainable business model within a joint venture. This process resonates with the emphasis in sustainability scholarship on dynamic organizational capabilities, including the capacity to adapt, integrate stakeholders, and reconfigure value creation logic in pursuit of long-term viability and resilience [29]. For real estate developers committed to ESG principles and sustainable urban development, ensuring alignment with partners on economic objectives and critical resources transcends operational necessity to become an essential management practice. Such alignment functions as a critical antecedent condition for preventing conflicts, fulfilling project commitments, and ultimately cocreating a joint venture business model that is robust, enduring, and capable of delivering sustainable value to diverse stakeholders.
Despite offering valuable insights, this study has several limitations. The sample consists exclusively of 110 professionals drawn from China’s leading real estate developers. While this approach ensures access to highly experienced informants, it potentially restricts the generalizability of findings to JVs involving small or medium-sized developers or to those operating outside the residential sector. The cross-sectional research design captures perceptual data at a single point in time, which inherently limits the capacity to draw causal inferences concerning the dynamic interplay between business model alignment and operational conflict. Furthermore, the research context is confined to China’s real estate sector, a policy-intensive industry marked by strong regulatory intervention, substantial capital requirements, and pronounced market cyclicality. External contingencies of this nature, including frequent adjustments to purchase restrictions and financing policies, may influence business model alignment and conflict in ways that the current model does not fully capture. In addition, although the study conceptualizes operational conflict as a threat to sustainable performance, its empirical emphasis remains primarily on economic and operational dimensions. Environmental and social indicators from the ESG framework have yet to be directly incorporated into the measurement of business model alignment or conflict.
These limitations, however, open several promising directions for future investigation. Longitudinal case study designs could trace the evolution of business model alignment and conflict throughout the JV lifecycle, thereby generating richer insights into causal mechanisms and temporal dynamics. Subsequent research might also integrate environmental and social indicators into the business model alignment framework, thus extending the analytical scope toward a more comprehensive sustainability perspective. Another avenue involves testing and refining the proposed model across diverse institutional and market contexts, including more mature real estate markets such as those in Europe or North America, as well as other policy-sensitive industries within emerging economies. Such comparative work would help clarify the boundary conditions of the theory and bolster its external validity. Finally, expanding the sample to encompass smaller developers or non-residential JVs would further enhance the generalizability of the conclusions and yield a more nuanced understanding of how firm size and sectoral context moderate the relationship between business model alignment and conflict.

Author Contributions

Conceptualization, J.W.; methodology, J.W.; software, J.W.; validation, J.W.; formal analysis, J.W.; investigation, J.W.; resources, J.W.; data curation, J.W.; writing—original draft preparation, J.W.; writing—review and editing, L.W.; visualization, J.W.; supervision, J.W.; project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Research Start up Fund of Fujian University of Technology, grant number GY-Z220197.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Science and Technology Ethics Committee of Fujian University of Technology (protocol code 20240510004 and date of approval 1 May 2025).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author, subject to privacy restrictions.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. March, J.G. Exploration and exploitation in organizational learning. Organ. Sci. 1991, 2, 71–87. [Google Scholar] [CrossRef]
  2. Koza, M.P.; Lewin, A.Y. The co-evolution of strategic alliances. Organ. Sci. 1998, 9, 255–264. [Google Scholar] [CrossRef]
  3. Glaister, K.W.; Buckley, P.J. Strategic motives for UK international alliance formation. J. Manag. Stud. 1996, 33, 301–332. [Google Scholar] [CrossRef]
  4. Hitt, M.A.; Dacin, M.T.; Tyler, B.B.; Park, D. Understanding the differences in Korean and U.S. executives’ strategic orientations. Strateg. Manag. J. 1997, 18, 159–167. [Google Scholar] [CrossRef]
  5. Reuer, J.J.; Zollo, M.; Singh, H. Post-formation dynamics in strategic alliances. Strateg. Manag. J. 2002, 23, 135–151. [Google Scholar] [CrossRef]
  6. Liu, G.; Li, K.; Shrestha, A.; Martek, I.; Zhou, Y. Strategic business model typologies evident in the Chinese real-estate industry. Int. J. Strateg. Prop. Manag. 2018, 22, 501–515. [Google Scholar] [CrossRef]
  7. Kumar, M.V.S. Differential gains between partners in joint ventures: Role of resource appropriation and private benefits. Organ. Sci. 2010, 21, 232–248. [Google Scholar] [CrossRef]
  8. Muñoz de Prat, J.; Escriva-Beltran, M.; Gómez-Calvet, R. Joint ventures and sustainable development: A bibliometric analysis. Sustainability 2020, 12, 10176. [Google Scholar] [CrossRef]
  9. Abd-Karim, S.B.; Abg Marani, D.A.; Mohd Danuri, M.S.; Mohamed, O. Managing conflicts in joint venture projects. Int. J. Prop. Sci. 2014, 4, 1–15. [Google Scholar] [CrossRef][Green Version]
  10. Das, T.; Teng, B.-S. Instabilities of strategic alliances: An internal tensions perspective. Organ. Sci. 2000, 11, 77–101. [Google Scholar] [CrossRef]
  11. Hoetker, G.; Mellewigt, T. Choice and performance of governance mechanisms: Matching alliance governance to asset type. Strateg. Manag. J. 2009, 30, 1025–1044. [Google Scholar] [CrossRef]
  12. Wang, J.; Qiu, D.; Wang, L. Contractual governance design for sustainable performance of exploitation alliances: Clause-level evidence from Chinese residential joint ventures. Sustainability 2026, 18, 1531. [Google Scholar] [CrossRef]
  13. Brogi, M.; Lagasio, V. Do corporate governance frameworks affect sustainability reporting? Corp. Soc. Responsib. Environ. Manag. 2025, 32, 3928–3943. [Google Scholar] [CrossRef]
  14. Chopra, S.S.; Senadheera, S.S.; Dissanayake, P.D.; Withana, P.A.; Chib, R.; Rhee, J.H.; Ok, Y.S. Navigating the Challenges of Environmental, Social, and Governance (ESG) Reporting: The Path to Broader Sustainable Development. Sustainability 2024, 16, 606. [Google Scholar] [CrossRef]
  15. Das, T.K.; Teng, B.-S. Trust, control, and risk in strategic alliances: An integrated framework. Organ. Stud. 2001, 22, 251–283. [Google Scholar] [CrossRef]
  16. Saxton, T. The effects of partner and relationship characteristics on alliance outcomes. Acad. Manag. J. 1997, 40, 443–461. [Google Scholar] [CrossRef]
  17. Jiang, X.; Jiang, F.; Arino, A.; Peng, M.W. Uncertainty, adaptation, and alliance performance. IEEE Trans. Eng. Manag. 2017, 64, 605–615. [Google Scholar] [CrossRef]
  18. Hempel, P.S.; Zhang, Z.X.; Tjosvold, D. Conflict management between and within teams for trusting relationships and performance in China. J. Organ. Behav. 2009, 30, 41–65. [Google Scholar] [CrossRef]
  19. Roshan, R.; Chandra Balodi, K. Sustainable business model innovation of an emerging country startup: An imprinting theory perspective. J. Clean. Prod. 2024, 475, 143687. [Google Scholar] [CrossRef]
  20. Luo, Y. Procedural fairness and interfirm cooperation in strategic alliances. Strateg. Manag. J. 2007, 29, 27–46. [Google Scholar] [CrossRef]
  21. Luo, Y. How important are shared perceptions of procedural justice in cooperative alliances? Acad. Manag. J. 2005, 48, 695–709. [Google Scholar] [CrossRef]
  22. Bocken, N.; Lenssen, M.P.A.I.G.; Short, S.; Rana, P.; Evans, S. A value mapping tool for sustainable business modelling. Corp. Gov. Int. J. Bus. Soc. 2013, 13, 482–497. [Google Scholar] [CrossRef]
  23. Joyce, A.; Paquin, R.L. The triple layered business model canvas: A tool to design more sustainable business models. J. Clean. Prod. 2016, 135, 1474–1486. [Google Scholar] [CrossRef]
  24. United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development; United Nations General Assembly: New York, NY, USA, 2015. [Google Scholar]
  25. Alareeni, B.A.; Hamdan, A. ESG impact on performance of US S&P 500-listed firms. Corp. Gov. Int. J. Bus. Soc. 2020, 20, 1409–1428. [Google Scholar]
  26. Khamisu, M.S.; Paluri, R.A.; Sonwaney, V. Stakeholders’ perspectives on critical success factors for environmental social and governance (ESG) implementation. J. Environ. Manag. 2024, 365, 121583. [Google Scholar] [CrossRef]
  27. Zhang, Y.; He, Y. How does the green financial system affect environmentally friendly firms’ ESG? Evidence from Chinese stock markets. Energy Econ. 2024, 130, 107287. [Google Scholar] [CrossRef]
  28. Bocken, N.M.P.; Short, S.W.; Rana, P.; Evans, S. A literature and practice review to develop sustainable business model archetypes. J. Clean. Prod. 2014, 65, 42–56. [Google Scholar] [CrossRef]
  29. Amui, L.B.L.; Jabbour, C.J.C.; de Sousa Jabbour, A.B.L.; Kannan, D. Sustainability as a dynamic organizational capability: A systematic review and a future agenda toward a sustainable transition. J. Clean. Prod. 2017, 142, 308–322. [Google Scholar] [CrossRef]
  30. Oliveira-Dias, D.; Kneipp, J.M.; Bichueti, R.S.; Gomes, C.M. Fostering business model innovation for sustainability: A dynamic capabilities perspective. Manag. Decis. 2022, 60, 105–129. [Google Scholar] [CrossRef]
  31. Wang, J. Study on the Member Selection of Bidding Alliance in the Residential Land Auction; Economic Science Press: Beijing, China, 2024. [Google Scholar]
  32. Yang, H.; Zheng, Y.; Zhao, X. Exploration or exploitation? Small firms’ alliance strategies with large firms. Strateg. Manag. J. 2014, 35, 146–157. [Google Scholar] [CrossRef]
  33. Xiong, Z.; Liu, J.; Yan, F.; Shi, D. Corporate ESG performance when neighboring the Environmental Protection Agency. J. Environ. Manag. 2024, 349, 119519. [Google Scholar] [CrossRef]
  34. Rothaermel, F.T.; Deeds, D.L. Exploration and exploitation alliances in biotechnology: A system of new product development. Strateg. Manag. J. 2004, 25, 201–221. [Google Scholar] [CrossRef]
  35. Kale, P.; Singh, H. Building firm capabilities through learning: The role of the alliance learning process in alliance capability and firm-level alliance success. Strateg. Manag. J. 2007, 28, 981–1000. [Google Scholar] [CrossRef]
  36. Ireland, R.D.; Hitt, M.A.; Vaidyanath, D. Alliance management as a source of competitive advantage. J. Manag. 2002, 28, 413–446. [Google Scholar] [CrossRef]
  37. Gulati, R.; Singh, H. The architecture of cooperation: Managing coordination costs and appropriation concerns in strategic alliances. Adm. Sci. Q. 1998, 43, 781–814. [Google Scholar] [CrossRef]
  38. Poppo, L.; Zenger, T. Do formal contracts and relational governance function as substitutes or complements? Strateg. Manag. J. 2002, 23, 707–725. [Google Scholar]
  39. Hamel, G. Competition for competence and inter-partner learning within international strategic alliances. Strateg. Manag. J. 1991, 12, 83–103. [Google Scholar] [CrossRef]
  40. Parkhe, A. Strategic alliance structuring: A game theoretic and transaction cost examination of interfirm cooperation. Acad. Manag. J. 1993, 36, 794–829. [Google Scholar] [CrossRef]
  41. de Man, A.-P.; Roijakkers, N. Alliance governance: Balancing control and trust in dealing with risk. Long Range Plan. 2009, 42, 75–95. [Google Scholar] [CrossRef]
  42. Inkpen, A.C.; Beamish, P.W. Knowledge, bargaining power, and the instability of international joint ventures. Acad. Manag. Rev. 1997, 22, 177–202. [Google Scholar] [CrossRef]
  43. Yan, A.; Gray, B. Bargaining power, management control, and performance in United States–China joint ventures: A comparative case study. Acad. Manag. J. 1994, 37, 1478–1517. [Google Scholar] [CrossRef]
  44. Zott, C.; Amit, R. The fit between product market strategy and business model: Implications for firm performance. Strateg. Manag. J. 2008, 29, 1–26. [Google Scholar] [CrossRef]
  45. Barney, J. Firm resources and sustained competitive advantage. J. Manag. 1991, 17, 99–120. [Google Scholar] [CrossRef]
  46. Johnson, M.W.; Christensen, C.M.; Kagermann, H. Reinventing your business model. Harv. Bus. Rev. 2008, 86, 50–59. [Google Scholar]
  47. Zott, C.; Amit, R.; Massa, L. The business model: Recent developments and future research. J. Manag. 2011, 37, 1019–1042. [Google Scholar] [CrossRef]
  48. Robin, E. Performing real estate value(s): Real estate developers, systems of expertise and the production of space. Geoforum 2022, 134, 205–215. [Google Scholar] [CrossRef]
  49. Brege, S.; Stehn, L.; Nord, T. Business models in industrialized building of multi-storey houses. Constr. Manag. Econ. 2014, 32, 208–226. [Google Scholar] [CrossRef]
  50. Parmenter, D. Key Performance Indicators: Developing, Implementing, and Using Winning KPIs; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2015. [Google Scholar]
  51. Grant, R.M. The resource-based theory of competitive advantage: Implications for strategy formulation. Calif. Manag. Rev. 1991, 33, 114–135. [Google Scholar] [CrossRef]
  52. Chiu, W.-H.; Dai, Z.-J.; Chi, H.-R.; Lin, P.-K. Customer knowledge orientation as a key to business model innovation of free-to-fee switch. J. Knowl. Manag. 2022, 26, 401–426. [Google Scholar] [CrossRef]
  53. Li, B.; Li, R.Y.M.; Wareewanich, T. Factors influencing large real estate companies’ competitiveness: A sustainable development perspective. Land 2021, 10, 1239. [Google Scholar] [CrossRef]
  54. Osterwalder, A.; Pigneur, Y. Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers; Wiley: Hoboken, NJ, USA, 2010. [Google Scholar]
  55. Pérez-Sánchez, L.À.; Velasco-Fernández, R.; Giampietro, M. Factors and actions for the sustainability of the residential sector: The nexus of energy, materials, space, and time use. Renew. Sustain. Energy Rev. 2022, 161, 112388. [Google Scholar] [CrossRef]
  56. Wilkinson, T.J.; Thomas, A.R.; Hawes, J.M. Managing relationships with Chinese joint venture partners. J. Glob. Mark. 2009, 22, 109–120. [Google Scholar] [CrossRef]
  57. De Dreu, C.K.W.; Weingart, L.R. Task vs relationship conflict, team performance, and team member satisfaction: A meta-analysis. J. Appl. Psychol. 2003, 88, 741–749. [Google Scholar] [CrossRef]
  58. Diamantopoulos, A.; Sarstedt, M.; Fuchs, C.; Wilczynski, P.; Kaiser, S. Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. J. Acad. Mark. Sci. 2012, 40, 434–449. [Google Scholar] [CrossRef]
  59. Sarstedt, M.; Diamantopoulos, A.; Salzberger, T.; Baumgartner, P. Selecting single items to measure doubly concrete constructs: A cautionary tale. J. Bus. Res. 2016, 69, 3159–3167. [Google Scholar] [CrossRef]
  60. Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
  61. Brislin, R.W. Back-translation for cross-cultural research. J. Cross-Cult. Psychol. 1970, 1, 185–216. [Google Scholar] [CrossRef]
  62. Armstrong, J.S.; Overton, T.S. Estimating nonresponse bias in mail surveys. J. Mark. Res. 1977, 14, 396–402. [Google Scholar] [CrossRef]
  63. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
  64. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2014, 43, 115–135. [Google Scholar] [CrossRef]
  65. Amit, R.; Zott, C. Value creation in e-business. Strateg. Manag. J. 2001, 22, 493–520. [Google Scholar] [CrossRef]
  66. Teece, D.J. Business models, business strategy and innovation. Long Range Plan. 2010, 43, 172–194. [Google Scholar] [CrossRef]
  67. Wernerfelt, B. A resource-based view of the firm. Strateg. Manag. J. 1984, 5, 171–180. [Google Scholar] [CrossRef]
  68. Casadesus-Masanell, R.; Ricart, J.E. From strategy to business models and to tactics. Long Range Plan. 2010, 43, 195–215. [Google Scholar] [CrossRef]
  69. Magretta, J. Why business models matter. Harv. Bus. Rev. 2002, 80, 86–92. [Google Scholar]
  70. Kumar, R.; Nti, K.O. Differential learning and interaction in alliance dynamics: A process and outcome discrepancy model. Organ. Sci. 1998, 9, 356–367. [Google Scholar] [CrossRef]
  71. Beamish, P.W.; Lupton, N.C. Managing joint ventures. Acad. Manag. Perspect. 2009, 23, 75–94. [Google Scholar] [CrossRef]
  72. Williamson, O.E. Market and Hierarchies Analysis and Antitrust Implications; Free Press: New York, NY, USA, 1975. [Google Scholar]
  73. Demil, B.; Lecocq, X. Business model evolution: In search of dynamic consistency. Long Range Plan. 2010, 43, 227–246. [Google Scholar] [CrossRef]
  74. Jehn, K.A. A multimethod examination of the benefits and detriments of intragroup conflict. Adm. Sci. Q. 1995, 40, 256–282. [Google Scholar] [CrossRef]
  75. Cao, Z.; Lumineau, F. Revisiting the interplay between contractual and relational governance: A qualitative and meta-analytic investigation. J. Oper. Manag. 2014, 33–34, 15–42. [Google Scholar] [CrossRef]
  76. Faems, D.; Janssens, M.; Madhok, A.; Looy, B.V. Toward an integrative perspective on alliance governance: Connecting contract design, trust dynamics, and contract application. Acad. Manag. J. 2008, 51, 1053–1078. [Google Scholar] [CrossRef]
  77. Sampson, R.C. The cost of misaligned governance in R&D alliances. J. Law Econ. Organ. 2004, 20, 484–526. [Google Scholar]
  78. Gulati, R. Does familiarity breed trust? The implications of repeated ties for contractual choice in alliances. Acad. Manag. J. 1995, 38, 85–112. [Google Scholar] [CrossRef]
  79. Comin, L.C.; Aguiar, C.C.; Sehnem, S.; Yusliza, M.-Y.; Cazella, C.F.; Julkovski, D.J. Sustainable business models: A literature review. Benchmarking Int. J. 2020, 27, 2028–2047. [Google Scholar] [CrossRef]
  80. Massa, L.; Tucci, C.L.; Afuah, A. A Critical Assessment of Business Model Research. Acad. Manag. Ann. 2017, 11, 73–104. [Google Scholar] [CrossRef]
  81. Marczewska, M.; Kostrzewski, M. Sustainable business models: A bibliometric performance analysis. Energies 2020, 13, 6062. [Google Scholar] [CrossRef]
  82. Xie, J.; Nozawa, W.; Yagi, M.; Fujii, H.; Managi, S. Do environmental, social, and governance activities improve corporate financial performance? Bus. Strategy Environ. 2018, 28, 286–300. [Google Scholar] [CrossRef]
  83. Zott, C.; Amit, R. Business model design and the performance of entrepreneurial firms. Organ. Sci. 2007, 18, 181–199. [Google Scholar] [CrossRef]
  84. Afuah, A. Business Models: A Strategic Management Approach; McGraw-Hill: New York, NY, USA, 2004. [Google Scholar]
  85. Shaker AZahra, D.U.; Newey, L.R. Social knowledge and SMEs’ innovative gains from internationalization. Eur. Manag. Rev. 2009, 6, 81–93. [Google Scholar] [CrossRef]
  86. Dyer, J.H.; Singh, H. The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Acad. Manag. Rev. 1998, 23, 660–679. [Google Scholar] [CrossRef]
  87. Nosratabadi, S.; Mosavi, A.; Shamshirband, S.; Kazimieras Zavadskas, E.; Rakotonirainy, A.; Chau, K.W. Sustainable business models: A review. Sustainability 2019, 11, 1663. [Google Scholar] [CrossRef]
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Figure 1. Research framework.
Figure 1. Research framework.
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Figure 2. The path coefficients of the full model.
Figure 2. The path coefficients of the full model.
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Table 1. Questionnaire items for each construct.
Table 1. Questionnaire items for each construct.
ConstructQuestionnaire ItemsReference
COYour company’s capability to translate specific site conditions into product positioning for targeted customer segments (1 = very weak, 5 = very strong).Based on industry expert interviews [31]
PFDoes your company tend to prioritize high asset turnover or high gross profit margin on sales (1 = strongly emphasize turnover, 5 = strongly emphasize gross margin)?Based on business model theory [46]
KRsPlease evaluate the importance of the following resources for project operations (1 = very unimportant, 5 = very important):
KR1: Development management team
KR2: Capital strength
KR3: Product portfolio
KR4: Supplier resources
KR5: Funding channels
KR6: Customer base
KR7: Development brand
KR8: Property management brand		Based on the theoretical framework [46] and industry expert interviews [31]
CBSsPlease evaluate the degree of influence of the following jointly established standards on JV project operations (1 = very low, 5 = very high):
CBS1: Profit margin on sales
CBS2: Financing cost
CBS3: Operational KPIs		Based on the theoretical framework [46] and industry expert interviews [31]
OCRsIn JV projects, what is the likelihood of the following situations occurring (1 = very low, 5 = very high)?
OCRs1: Operational control conflict risks
OCRs2: Financing plan conflict risks		Based on the theoretical framework [46] and industry expert interviews [31]
Table 2. Evaluation of the measurement model.
Table 2. Evaluation of the measurement model.
ConstructsItemsMeanStandard DeviationItems LoadingAVECRCronbach’s Alpha
COCO13.131.0931.000
PFPF14.450.6291.000
KRsKR13.930.9650.769 0.5080.8670.860
KR23.521.0200.775
KR33.300.9630.793
KR43.941.1190.724
KR53.680.9280.749
KR63.551.0800.810
KR73.271.1800.800
KR83.430.8830.754
CBSsCBS14.160.8940.777 0.6490.7420.732
CBS24.240.9760.834
CBS34.090.7960.806
OCRsOCR14.080.9000.894 0.7750.7160.711
OCR24.100.8340.866
Table 3. Value of HTMT.
Table 3. Value of HTMT.
COPFKRsCBSsOCRs
CO
PF0.090
KRs0.2910.094
CBSs0.3190.2470.797
OCRs0.0420.1940.4220.680
Table 4. Results of direct effects measurement.
Table 4. Results of direct effects measurement.
Model HypothesesPath Coefficients (β)t Statisticsp Values
H1a0.2882.7350.006 **
H1b0.63110.0990.000 ***
H1c0.1682.0700.038 *
H20.5104.7210.000 ***
*** p < 0.001; ** p < 0.01; * p < 0.05.
Table 5. Results of mediation effects.
Table 5. Results of mediation effects.
Path Coefficients (β)t Statisticsp Values
KRs → CBSs → OCRs0.3224.0010.000 ***
PF → CBSs → OCRs0.0862.0520.040 *
*** p < 0.001; * p < 0.05.
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Wang, J.; Wang, L. Partner Business Model Alignment for Mitigating Operational Conflicts in Exploitation Alliance: Evidence from Chinese Residential Joint Ventures. Sustainability 2026, 18, 3337. https://doi.org/10.3390/su18073337

AMA Style

Wang J, Wang L. Partner Business Model Alignment for Mitigating Operational Conflicts in Exploitation Alliance: Evidence from Chinese Residential Joint Ventures. Sustainability. 2026; 18(7):3337. https://doi.org/10.3390/su18073337

Chicago/Turabian Style

Wang, Jinxiu, and Li Wang. 2026. "Partner Business Model Alignment for Mitigating Operational Conflicts in Exploitation Alliance: Evidence from Chinese Residential Joint Ventures" Sustainability 18, no. 7: 3337. https://doi.org/10.3390/su18073337

APA Style

Wang, J., & Wang, L. (2026). Partner Business Model Alignment for Mitigating Operational Conflicts in Exploitation Alliance: Evidence from Chinese Residential Joint Ventures. Sustainability, 18(7), 3337. https://doi.org/10.3390/su18073337

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