Co-Opetition as a Pathway to Sustainability: How Bed and Breakfast Clusters Achieve Competitive Advantage in High-Density Tourism Destinations

Abstract

This study examines co-opetition mechanisms in China’s rapidly expanding bed and breakfast (B&B) sector, where intense competition drives operators to collaborate with rivals. A co-opetition model incorporating size classifications was tested using survey data from 500 clustered B&Bs. Data were analyzed with SPSS 26.0 and AMOS 23.0 through descriptive statistics, reliability testing, exploratory and confirmatory factor analyses, and structural equation modeling (SEM). Results show that perceived benefit (β = 0.230, p < 0.01), cooperation orientation (β = 0.223, p < 0.01), and prior experience (β = 0.232, p < 0.01) significantly drive co-opetition, whereas mutual trust and strategic fit are not significant. Co-opetition strongly enhances sustainable competitive advantage (β = 0.521, p < 0.001), indicating that strategic collaboration can mitigate homogenization in dense markets. The study contributes to co-opetition theory by (1) identifying antecedents specific to small-scale hospitality businesses, (2) challenging conventional assumptions about the role of trust, and (3) establishing empirical links between co-opetition and long-term competitiveness. Practically, the findings provide B&B operators with strategies for navigating competitive yet interdependent environments and offer policymakers evidence-based guidance to foster sustainable tourism clusters through institutional support for cooperative networks.

1. Introduction

Co-opetition is a strategic interaction that integrates cooperation and competition. It shifts the focus from a zero-sum mindset to a dynamic balance, where rivals collaborate while retaining competitive behavior. In this process, firms share risks, resources, and markets, thereby achieving mutual benefits [1]. Prior research has demonstrated that co-opetition strategies help organizations integrate resources, enhance performance, and leverage complementary advantages to expand market boundaries, thereby fostering a sustainable competitive advantage (SCA) [2,3].

In recent years, the rapid growth of tourism, particularly rural tourism, has created fertile ground for applying co-opetition theory. Wu and He (2022), examining Chinese theme parks, demonstrated that resource similarity, market commonality, and willingness to cooperate significantly enhance market performance, especially in non-financial dimensions [4]. Their study further confirmed the mediating role of co-opetition in promoting sustainable development. Additionally, the literature has indicated that, under community-based participation models, the bed and breakfast (B&B) experience can effectively stimulate tourists’ emotional responses and enhance loyalty, further emphasizing the strategic role of B&Bs in promoting the sustainable development of rural tourism [5].

Existing studies on homestays mainly focus on business strategies and consumer experiences [6,7], while the external collaborative–competitive relationships among homestays and their impact on SCA remain underexplored. Although coopetition theory has been applied in tourism studies, highlighting its positive effect on performance and the need to emphasize rural tourism and local collaboration [4,8], systematic research in the homestay context is still at an early stage.

Building on the coopetition antecedents model [9], this study develops an integrated framework that includes cooperative orientation (CO), prior experience (PE), mutual trust (MT), perceived benefits (PB), strategic fit (SF), coopetitive behavior (CP), and SCA. The aim is to identify key drivers of coopetition among homestays and to uncover the mechanisms through which it fosters sustainable competitiveness. This contributes to filling a theoretical gap, advancing contextualized understanding of coopetition, and providing practical guidance for operators and policymakers to promote collaboration while avoiding homogenized competition.

2. Literature Review

2.1. The Current Status and Development Trends of B&Bs in China

As an emerging format in China’s tourism industry, B&Bs have complemented traditional hotels through their small scale, flexible location choices, and ease of management. In recent years, supported by national policies, the B&B model has evolved into a hybrid form that combines accommodation, leisure, cultural experiences, and dining, reflecting distinct Chinese characteristics. However, the sector’s rapid expansion has also created challenges. Many B&Bs operate independently without unified planning or industry coordination, resulting in weak communication within regional clusters. At the same time, the rapid increase in their number has intensified competition, leading to service homogeneity and growing uniformity [10]. The industry is now trending toward intensification and geographic clustering, which has led to the emergence of several themed B&B towns. However, this shift has also revealed deeper issues, including shallow cultural content, redundant services, and low repeat visitation rates [11,12].

The global academic community has shown growing interest in the role of rural B&Bs in promoting sustainable tourism development. Based on a systematic review of 62 studies on homestays, Sbai and Elhassouni (2024) reported that most research has focused on tourist behavior, cultural experiences, and community engagement [13]. They also noted that the majority of empirical studies relied on samples from China, underscoring the country’s academic leadership in this field. However, their review identified a major gap: the lack of systematic research on inter-B&B relationships, particularly the influence of co-opetition mechanisms on business performance.

2.2. Co-Opetition Theory

Co-opetition theory, first introduced by Barry J. Nalebuff and Adam M. Brandenburger, emphasizes the interdependence of firms that engage in both cooperation and competition. In this framework, cooperation generates value, while competition determines its distribution [1]. This framework shifted academic focus from viewing competition and cooperation as mutually exclusive to examining their intertwined and symbiotic dynamics. Over time, it developed into a paradoxical logic in which cooperation and competition coexist along a dual continuum [14].

Although widely acknowledged, the theory has not yet been developed into a fully systematic model, and empirical evidence remains limited [15]. Early research categorized its antecedents into external drivers, relational characteristics, and internal motivations [16]. Subsequent studies extended this foundation by integrating concepts such as mutual benefit, MT, SF, and CO. They highlighted the potential of co-opetition to improve performance, foster innovation, and strengthen sustainable advantage [17].

In the tourism sector, Wu and He (2022) examined Chinese theme parks and validated the mediating role of co-opetition’s three dimensions in shaping non-financial business performance [4]. Similarly, Maracajá et al. (2025), in a literature review of wine tourism, observed a growing emphasis on ‘local collaboration’ and ‘sustainable development’ within co-opetition research [8]. However, they also pointed out that the theoretical framework remains underdeveloped. In addition, Dai et al. (2025) confirmed that community-based collaboration in the B&B context positively influenced tourist loyalty, underscoring the value of emotional connections and co-created experiences in advancing sustainable tourism development [5].

2.3. Inter-Firm Co-Opetition Relationships

Although in its early stages, co-opetition—due to its inherently paradoxical logic combining both cooperation and competition—was challenging for firms to implement, particularly in terms of timing and partner selection [1], it has increasingly evolved into a key strategic approach. With the advancement of theory and the intensification of market competition, co-opetition has become one of the primary strategies for firms to generate greater value [15]. When confronted with limited market resources, fragile financial chains, and inadequate knowledge or skills, firms could utilize co-opetition to access complementary resources from other enterprises [18], thereby achieving mutual benefits that would be difficult to attain through independent operation alone [2]. In addition, co-opetition enabled firms to share risks and costs, integrate market resources, and leverage complementary advantages—ultimately enhancing overall performance and increasing resilience against market volatility and unexpected disruptions [19,20].

In the tourism sector, Wu and He (2022) empirically showed that theme park enterprises can construct co-opetition mechanisms around three dimensions: resource similarity, market commonality, and willingness to cooperate [4]. These mechanisms optimize inter-firm resource allocation and significantly improve non-financial performance. The findings highlight the mediating role of co-opetition strategies in addressing environmental uncertainty and promoting coordinated development. Meanwhile, a systematic literature review by Maracajá et al. (2025) on wine tourism revealed that co-opetition has gradually emerged as a potential strategic tool for promoting sustainable development in rural tourism, although its application in practice remains in an exploratory and formative stage [8].

2.4. Co-Opetition Among B&Bs

As China’s B&B sector has evolved toward greater clustering and scale, co-opetitive interactions within regions have become inevitable. In the face of intense competition, operators have developed benefit-sharing mechanisms that coordinate stakeholders, mitigate resource conflicts, and generate complementary advantages through efficient allocation of labor and market resources [21,22]. In addition, a well-functioning competitive framework fosters continuous innovation and service improvement, thereby enhancing the overall competitiveness of regional B&B clusters. However, existing research on inter-B&B relationships has focused mainly on cooperation, with limited attention to the dual nature of co-opetition, even though co-opetitive dynamics have emerged in some regions [23], the underlying mechanisms and outcomes remain underexamined.

Recent studies have highlighted that co-opetition can enhance destination-level collaborative governance by building collective capital and cooperative networks, thereby integrating regional public and tourism resources [24]. For example, Sbaï and Elhassouni (2024), in a systematic review, noted that although international homestay research has expanded considerably, there remains a lack of investigation into inter-B&B interactions, especially regarding how co-opetition influences performance outcomes [13]. Therefore, analyzing the operational logic of co-opetition mechanisms among B&Bs in China holds theoretical significance and offers practical guidance for promoting high-quality rural tourism development.

2.5. SCA

A firm’s SCA refers to a long-lasting and difficult-to-imitate competitive position, primarily derived from its unique market resources, technological capabilities, and irreplicable tangible and intangible assets [25,26].

Sustainable advantages are typically divided into external market advantages, such as product and service differentiation, and internal management advantages, including risk control and organizational responsiveness [27]. For large and medium-sized enterprises, long-term resource accumulation and market dominance are the primary sources of advantage [28]. In contrast, small and medium-sized enterprises (SMEs), including those in the B&B sector, face resource constraints and struggle to build barriers through internal accumulation alone.

An increasing number of studies suggest that co-opetition strategies provide SMEs with a viable path to overcome these limitations [3]. Through collaborative networks, firms can expand market access, share knowledge, and reduce operational risks [29].

Although research on internal operations in the B&B sector is growing, systematic frameworks addressing the role of external co-opetition mechanisms and their relationship with SCA remain limited. To address this gap, the present study integrates existing research to develop a conceptual model and propose corresponding hypotheses.

3. Research Methodology

3.1. Hypotheses and Conceptual Model

The antecedents of co-opetition have been widely examined in management and hospitality research. Czakon, Klimas, and Mariani (2020) identified PE, SF, and MT as foundational elements for establishing co-opetitive relationships [9]. However, few studies have developed theoretical or empirical models specifically for small and medium-sized hotels and guesthouses. Wu and He (2022) confirmed that resource similarity, market commonality, and willingness to cooperate are effective antecedents of co-opetition and positively influence non-financial performance, underscoring the feasibility of co-opetition strategies in SMEs [4].

H1. 

PE in co-opetition positively influences the co-opetitive activities of B&Bs.

In inter-firm cooperation, MT sustains stability and efficiency while reducing destructive competition. Ganesan (1994) argued that MT is critical to the fair distribution of cooperative gains and long-term development [30]. Osarenkhoe (2010) further demonstrated its importance for sustaining co-opetitive relationships [31].

H2. 

MT among B&Bs positively influences their CP.

CO reflects a firm’s strategic orientation toward collaboration, manifested in relationship-building, fostering MT, and resource sharing [9]. Firms with strong CO are better positioned to identify opportunities and pursue mutually beneficial ties. In tourism and hospitality, studies confirm its central role in advancing co-opetition [32]. Therefore, B&B operators with a pronounced CO were more inclined to initiate and sustain co-opetitive activities.

H3. 

CO positively influences the co-opetitive activities of B&Bs.

Interest is fundamental to both competition and cooperation. When parties perceive greater mutual and individual benefits, they are more likely to adopt co-opetition [33]. Though such benefits may be latent, perceptive decision-makers recognize potential gains and leverage co-opetition to create shared value. Kallmuenzer et al. (2021) confirmed this in SMEs, showing that co-opetition yields mutual benefits [34].

H4. 

The PB of co-opetition positively influences CP among B&Bs.

Strategic alignment also supports co-opetition. When firms share compatible goals and forward-looking strategies, partnerships are more likely to form and endure [35].

H5. 

SF among B&Bs positively influences their co-opetitive relationships.

Once firms achieve a certain level of competitive advantage, they must address how to sustain it for long-term benefits. Competitive strategy theory suggests that co-opetition offers a means of maintaining such advantage [36]. Munir et al. (2011) confirmed this in SMEs, showing that resource-constrained firms form clusters through co-opetition to access broader markets and resources, thereby sustaining their competitivenes [29]. Additionally, Della Corte and Aria (2016) found that tourism firms adopting co-opetitive strategies were more likely to attain SCA [3]. Subsequent studies by Wu and He (2022) and Maracajá et al. (2025) further demonstrated its positive impact on non-financial performance and regional sustainable development, underscoring the strategic relevance of co-opetition for B&B enterprises with limited resources [4,8].

H6. 

Co-opetition among B&Bs contributes to achieving SCA (Figure 1).

3.2. Research Design

This study employed a quantitative empirical approach, using a questionnaire survey to examine co-opetition mechanisms among B&Bs. The research targeted B&B operators in the Moganshan region of Zhejiang Province. Moganshan was selected for two reasons: (1) it is a nationally recognized 4A-level scenic area with abundant tourism resources, making it a representative case; and (2) the region hosts more than 800 B&Bs, reflecting a high level of industry clustering and providing a solid data foundation. The research process comprised five steps (Figure 2): questionnaire design, data collection, reliability and validity testing, descriptive statistical analysis, and structural equation modeling (SEM). This framework was adopted to systematically investigate the antecedents of co-opetition and its role in achieving SCA.

3.3. Questionnaire Design and Variable Measurement

The questionnaire comprised two sections. The first measured the core research variables (Appendix A): PE (2 items), MT (3 items), CO (4 items), PB (4 items), SF (3 items), CP (4 items), and SCA (6 items). All items were assessed on a five-point Likert scale ranging from ‘strongly disagree (1)’ to ‘strongly agree (5).’ The second section collected demographic information (Appendix B), including gender, age, household registration, educational background, and work experience. The questionnaire was adapted from established scales in the literature, with modifications made to fit the characteristics of the target population and the specific context of the Moganshan region.

3.4. Data Collection

Data collection was conducted in two phases: a pilot survey and the main survey (Figure 3). The pilot survey took place in late July 2022, during which 100 questionnaires were distributed and all were returned, yielding a 100% valid response rate. Based on the pilot results, the questionnaire was refined by removing items with low factor loadings (e.g., CE2) and adding a new item under the ‘shared cooking experience’ dimension. The main survey was conducted from 6–23 August 2022, using printed questionnaires distributed to B&B operators in the Moganshan region. A total of 500 questionnaires were distributed, of which 393 valid responses were obtained after excluding incomplete or improperly filled forms (e.g., unanswered or multiply selected items), resulting in a valid response rate of 78.6%. The final questionnaire contained 26 measurement items, which were reduced to 24 after excluding ineligible items.

Following established SEM guidelines, the recommended sample size should be 5–10 times the number of items measured [37]. Based on this principle, the minimum required sample size for this study was 120. With 393 valid responses collected, the sample size substantially exceeded the recommended threshold and met the requirements for SEM analysis.

3.5. Reliability and Validity Test

(1)

Exploratory Factor Analysis (EFA): EFA was conducted using SPSS 26.0. The results showed that all variables had Kaiser–Meyer–Olkin (KMO) values above 0.6, and Bartlett’s test of sphericity was significant (p < 0.05), confirming the suitability of the data for factor analysis. Following the loading threshold of >0.6, one item (CE2) was removed. All other items met the standard. The variance explained by each latent variable either met or closely approached the acceptable level; for instance, the explained variance for SCA was 57.615%, close to the 60% benchmark (

Table 1. Results of Exploratory Factor Analysis. (*** p < 0.001).

	Factor Loading	Explained Variance (%)	Standard	SE	T Value	p	CR	AVE
Past Experience		77.720
PE1	0.840		0.738
PE3	0.903		0.868	0.067	16.588	***	0.861	0.675
PE4	0.900		0.852	0.073	16.352	***
Mutual Trust		80.242
MT1	0.841		0.73
MT2	0.929		0.895	0.063	17.414	***	0.883	0.719
MT3	0.915		0.907	0.067	17.203	***
Cooperative Orientation		72.429
CO1	0.823		0.74
CO2	0.859		0.831	0.072	16.223	***
CO3	0.870		0.823	0.072	15.85	***	0.873	0.633
CO4	0.852		0.784	0.076	15.101	***
Perceived Benefits		73.224
PB1	0.852		0.813
PB2	0.847		0.784	17.538	17.538	***
PB3	0.867		0.798	17.484	17.484	***	0.878	0.643
PB4	0.857		0.811	17.727	17.727	***
Strategic Fit		74.072
SF1	0.825		0.779
SF2	0.879		0.804	14.881	14.881	***
SF3	0.878		0.79	15.231	15.231	***	0.826	0.613
Co-opetition		63.012
CP1	0.627
CP2	0.851		0.79
CP3	0.876		0.836	18.092	18.092	***	0.816	0.599
CP4	0.798		0.688	14.069	14.069	***
Sustainable Competitive Advantage		57.615
SCA1	0.761		0.707
SCA2	0.758		0.687	12.992	12.992	***
SCA3	0.719
SCA4	0.789		0.739	12.582	12.582	***	0.853	0.539
SCA5	0.794		0.757	12.387	12.387	***
SCA6	0.817		0.778	12.439	12.439	***

).

(2)

Reliability Testing: Internal consistency was assessed using Cronbach’s alpha. All constructs had alpha values greater than 0.7, indicating strong internal reliability.

(3)

Confirmatory Factor Analysis (CFA): CFA was conducted using AMOS 23.0 to evaluate convergent validity. Two items (CP1 and SCA3) were removed due to low standardized loadings. The remaining items all showed loadings >0.6, composite reliability (CR) > 0.7, and average variance extracted (AVE) > 0.5, meeting the criteria for convergent validity. Model fit indices were acceptable: χ²/df = 3.007, RMR = 0.034, RMSEA = 0.072, CFI = 0.922, and NFI = 0.888 (Table 1).

(4)

Discriminant Validity Analysis: Discriminant validity was tested using the Fornell-Larcker criterion. The square roots of the AVE values for all constructs exceeded the corresponding inter-construct correlations, supporting discriminant validity. Although a few indicators were near boundary levels, which may be attributed to data characteristics, the overall discriminant validity remained acceptable (

Table 2. Discriminant Validity Results.

	SF	PB	CO	MT	PE	CP	SCA
SF	0.783
PB	0.877	0.802
CO	0.611	0.705	0.795
MT	0.725	0.713	0.668	0.848
PE	0.652	0.704	0.759	0.753	0.821
CP	0.784	0.838	0.763	0.712	0.773	0.774
SCA	0.601	0.642	0.585	0.546	0.592	0.766	0.734

).

3.6. Data Analysis Techniques

(1)

Descriptive Statistics: Descriptive statistics were conducted using SPSS 26.0 to analyze sample characteristics, including gender, age, educational background, household registration, and work experience. Results were reported as frequencies and percentages.

(2)

SEM: SEM was performed using AMOS 23.0 to test the theoretical model and hypotheses. SEM is appropriate for analyzing complex relationships among multiple latent variables, consistent with the aim of this study to explore interconnections among constructs. Parameters were estimated using the maximum likelihood (ML) method. The SEM analysis was conducted in two steps. First, overall model fit was assessed using indices such as the χ²/df, RMSEA, CFI, and NFI. The recommended thresholds were: χ²/df < 3, RMSEA < 0.08, and CFI and NFI > 0.90. Model fitting was adjusted based on modification indices (MI) and theoretical considerations, adding covariances between measurement error terms to capture residual associations. Each adjustment released a single parameter followed by re-evaluation, all changes supported by theory to avoid overfitting. After two adjustments (MI > 10, release of two pairs of error covariances), fit indices reached acceptable levels (χ²/df = 2.85, RMSEA = 0.068, CFI = 0.936, NFI = 0.901), indicating good model-data fit. Second, the hypothesized paths (H1–H6) were evaluated through path coefficients, with statistical significance determined at the p < 0.05 level.

4. Results

4.1. Structural Characteristics of B&B Operators in the Moganshan Region

Among the respondents in the Moganshan region (

Table 3. Descriptive Statistics of the Questionnaire.

Item	Number of People	Percentage
Gender
Male	218	55.5
Female	175	44.5
Age
20–29	69	17.6
30–39	169	43.0
40–49	101	25.7
50–59	43	11.0
60 and above	11	2.8
Place of Residence
Local	348	88.5
Non-local	45	11.5
Education Level
Junior high school or below	47	12.0
High school or secondary school	149	37.9
Bachelor’s degree or associate degree	168	42.7
Master’s degree or above	29	7.4
How long have you worked in the homestay industry?
Less than 1 year	33	8.4
1–5 years	217	55.2
5–10 years	105	26.7
10–20 years	28	7.1
More than 20 years	10	2.5

), 55.5% were male and 44.5% were female, indicating a relatively balanced gender distribution (Figure 4A). The majority were between 30 and 39 years old (43.0%) and 40–49 years old (25.7%), suggesting that B&B operators in the area were primarily middle-aged adults (Figure 4B). A significant proportion—88.5%—held local household registration, reflecting a strong regional attachment within the B&B sector (Figure 4C). In terms of education, 37.9% had completed high school or vocational education, while 50.1% held a bachelor’s degree or higher, indicating that most operators had at least a moderate level of educational attainment (Figure 4D). Regarding work experience, 55.2% had been in the industry for 1–5 years, suggesting that the majority of B&B operators had entered the market relatively recently. This supports the conclusion that the B&B sector in Moganshan remains in a phase of rapid growth (Figure 4E).

4.2. SEM Results

To test the proposed hypotheses (H1–H6), this study constructed and executed a SEM to empirically examine the path relationships among latent variables. The overall model fit was satisfactory, with the following key indices: χ²/df = 3.007, RMSEA = 0.072, CFI = 0.922, and NFI = 0.888. All values were close to or met the recommended thresholds for structural models, indicating a good model fit (

Table 4. Structural Equation Model Test Results. (*** p < 0.001).

			Estimate	S.E.	C.R.	p	Hypothesis
PE	→	CP	0.232	0.083	3.018	0.003	Supported
MT	→	CP	0.004	0.074	0.053	0.957	Not Supported
CO	→	CP	0.223	0.077	3.139	0.002	Supported
PB	→	CP	0.355	0.115	2.936	0.003	Supported
SF	→	CP	0.182	0.117	1.596	0.11	Not Supported
CP	→	SCA	0.766	0.069	10.763	***	Supported

).

The path analysis results showed that PE had a significant positive effect on CP (β = 0.232, p = 0.003), suggesting that operators with previous co-opetitive experience were more likely to engage in such behavior again, thereby supporting Hypothesis H1. Similarly, CO and PB had significant path coefficients to CP, at 0.223 (p = 0.002) and 0.230 (p = 0.003), respectively. These findings validate Hypotheses H3 and H4, indicating that both a cooperative mindset and the expectation of mutual benefits are key drivers of CP.

To more rigorously test the mediating role of CP between the antecedent variables and SCA, this study further employed the bias-corrected bootstrap method with 5000 resamples to estimate the confidence intervals of the mediation effects. The results show that PE, CO, and PB exert significant mediating effects on SCA through CP (95% confidence intervals excluding zero), whereas the mediating effects of MT and SF are not significant, consistent with the path analysis results.

In contrast, MT did not have a significant impact on CP (β = 0.004, p = 0.957), nor did SF, which failed to reach statistical significance (β = 0.085, p = 0.110). These results suggest that, within the context of this study, these two factors were insufficient to influence CP among B&B operators, and thus, Hypotheses H2 and H5 were not supported.

Furthermore, CP had a highly significant positive effect on SCA (β = 0.521, p < 0.001), confirming Hypothesis H6. As shown in Figure 5, the findings indicate that actively implementing co-opetition strategies enhances B&B firms’ overall competitiveness in areas such as resource sharing, brand recognition, and market adaptability, thereby contributing to the achievement of sustainable development goals.

5. Discussion

The results of this study largely validate the proposed model, but trust and SF did not exert significant effects on CP among homestay operators (βMT = 0.004, p = 0.957; βSF = 0.085, p = 0.110). This finding diverges from research in the hotel sector and traditional coopetition theories [34,38,39,40]. The discrepancy may stem from structural characteristics of the homestay industry: operators are typically small in scale and risk-averse, with cooperative relations that are loose and informal, leading them to rely more on short-term visible returns and past experiences rather than on long-term trust building or strategic alignment [41,42,43,44]. Moreover, the early stage of industry development and operators’ limited strategic cognition further constrain the translation of SF into substantive cooperation, reflecting a pronounced “pragmatist” orientation.

In contrast, prior coopetition experience, CO, and perceived benefits exerted significant positive effects on CP. The experience effect indicates that homestay operators tend to rely on established patterns rather than proactively plan for long-term cooperation, while new entrants lacking practical experience remain more cautious [35]. CO and perceived benefits further demonstrate that when operators recognize the potential for collaboration and mutual gains, their willingness to cooperate increases significantly, thereby driving CP [45,46]. These findings suggest that operators’ decision-making logic is primarily guided by profit maximization and short-term performance rather than institutionalized trust or long-term strategic integration.

Further analysis reveals that CP plays a partial mediating role between coopetition experience and SCA: experience directly enhances competitive advantage while also indirectly strengthening it by stimulating CP. In contrast, CP fully mediates the effects of CO and perceived benefits on SCA, indicating that the latter variables can only be translated into competitive advantage through CP, with no direct effect on SCA. This highlights CP as a central mediating mechanism: cooperative intentions or benefit expectations alone are insufficient to generate competitive advantage; substantive actions such as resource sharing and market coordination are necessary for sustainable development. Accordingly, trust and SF, which failed to effectively activate CP, did not significantly affect SCA [47,48].

These findings make several theoretical contributions. First, to cluster theory, they refine the traditional “spatial agglomeration-behavioral response-performance outcome” logic by showing that in “low-structure clusters” such as homestays, geographic proximity does not automatically induce cooperation; rather, CP is driven more strongly by operators’ micro-level cognition (e.g., experience orientation and perceived benefits) than by institutionalized mechanisms [49,50]. Second, from the resource-based view, CP emerges as a key mediating mechanism that transforms dispersed resources into SCA, enabling homestays to overcome resource constraints, strengthen market adaptability, and build differentiation advantages, thereby sustaining the “resources-capabilities-outcomes” chain [51,52]. Finally, the results confirm that coopetition strategies can significantly enhance homestays’ SCA by fostering differentiation at the product, service, and market levels through resource sharing and knowledge exchange [53]. This conclusion not only echoes the broader trend of collaborative win–win strategies in tourism but also provides practical guidance for SMEs’ strategic choices, while highlighting the need for future research to explore how trust and strategic consensus can be transformed into actionable coopetition arrangements to fully unlock the potential of clusters.

6. Conclusions

This study focused on the construction and empirical validation of the co-opetition mechanism among B&Bs, systematically exploring the antecedents and outcomes of co-opetition. It addressed a critical gap in the current literature on the interplay between cooperation and competition in the B&B sector. As one of the few empirical studies in China examining outward-oriented strategic pathways for B&Bs, this research expanded the applicability and boundaries of co-opetition theory within the context of small and medium-sized tourism enterprises.

6.1. Theoretical Contributions

This study introduced co-opetition theory into the field of B&B research and developed an antecedent model tailored to the specific characteristics of China’s B&B industry, thereby extending the theory’s application to small-scale tourism accommodation businesses. By systematically investigating the antecedents and performance outcomes of CP, the study validated the significant influence of CO, PE, and PB on co-opetition and revealed the crucial role of co-opetition in achieving SCA for B&Bs.

Unlike previous research that primarily focused on large enterprises or co-opetition across different business formats [20,54], this study concentrated on the individualized and resource-constrained operations of B&Bs, thereby filling a theoretical void in the study of co-opetition within small and medium-sized lodging enterprises [55]. Furthermore, it addressed the current critique in the literature regarding the lack of contextualization and empirical support in co-opetition research by grounding its findings in field-based data [15].

The results also indicated that, compared to traditional hotel businesses, CP among B&B operators is more strongly driven by individual cognition and experiential orientation, rather than by formal organizational strategies. This finding not only contributes grassroots-level empirical evidence to co-opetition theory but also opens valuable avenues for future research into context-specific cooperation mechanisms.

6.2. Managerial Implications

The findings of this study offer valuable implications for the management practices of B&Bs. In regions such as Moganshan, the B&B sector faces multiple challenges, including rapid growth in supply, concentration in the mid- to low-end market, severe product homogeneity, and low efficiency in resource integration, all of which hinder the potential for cluster-based development [56]. Against this backdrop, fostering co-opetition relationships among B&B operators may contribute to the optimization of business strategies, mitigation of conflicts, and enhancement of collaborative resource utilization, thereby improving overall competitiveness [5,27].

Given the significant influence of CO on CP, it is recommended that local governments and industry associations promote a collaborative development mindset among B&B operators. Strengthening awareness of CO and encouraging the formation of regional alliances can facilitate coordinated action [57]. Within such alliances, shared branding, complementary resource allocation, and differentiated division of labor can reduce product homogeneity and cultural conflicts, while enhancing service uniqueness and customer appeal [22].

At the same time, efforts should be made to improve the capabilities of B&B operators. Structured training programs and peer-learning platforms can address gaps in educational background, business acumen, and strategic awareness. These initiatives will help operators better understand and implement co-opetition strategies in practice [58,59].

To foster a favorable institutional environment for co-opetition, local governments should enhance regulatory oversight, promote transparency, and strengthen MT within the B&B sector [60]. Additionally, by organizing thematic forums, joint training sessions, and case-sharing activities, governments and industry associations can provide platforms that facilitate dialogue and cooperation among operators, reducing cognitive barriers and collaboration costs [61]. These platforms will also enable operators to gain deeper insights into others’ strategic thinking and operational strengths, laying the groundwork for more synergistic co-opetition relationships [62].

6.3. Limitations and Future Research

This study has several limitations, which offer directions for future research. First, the findings indicate that MT and SF did not significantly influence co-opetition, diverging from existing research on small and medium-sized hotels. This suggests that the mechanisms by which these variables function may vary across organizational contexts, warranting further investigation. Second, the construct of “co-opetition experience” in the questionnaire lacked conceptual clarity; several items appeared to capture general PE rather than specifically reflecting co-opetition. Future studies should revise and clarify the measurement dimensions accordingly.

Moreover, while the quantitative approach adopted in this study allowed for the examination of inter-variable relationships, it provided limited insight into the behavioral motivations and operational challenges faced by B&B operators. Incorporating qualitative interviews in future research could offer a deeper understanding of CP at different stages of business development. Due to practical constraints, this study employed random sampling, which may limit the representativeness of the sample. As a result, the generalizability of the conclusions remains to be further tested. Expanding the geographic scope and sample size, along with enhancing the diversity and depth of data sources, is therefore recommended.

Lastly, this study focused on the Moganshan region and did not fully account for how variations in B&B background, typology, or investment models might influence CP. Future research could conduct multi-group comparative analyses using samples from diverse regions and B&B types, thereby constructing a more universally applicable theoretical model.