From perceived value to customer loyalty behavioral intentions: a social exchange theory perspective on value-based selling

In the current industrial marketing environment, characterized by intensifying competition and high complexity, customer demands are becoming more diversified and personalized. The traditional product- and price-oriented sales models can no longer meet modern customers’ expectations for high-value-added solutions (Jiao et al., 2003; Latinovic and Chatterjee, 2022). This shift has led companies to evolve their sales strategies from product-oriented approaches to customer demand and value creation-centered models (Chowdhury et al., 2023; Kindström and Kowalkowski, 2014; Salonen, 2011). In this context, value-based selling (VBS) has become a key strategy for companies to gain and sustain a competitive advantage. This is due to its ability to uncover customer value potential, build differentiated competitive advantages and foster long-term relationships (Terho et al., 2015; Terho et al., 2012). Unlike non-VBS contexts where value is often interpreted as a static, transactional tradeoff between price and quality, value in VBS is dynamic, cocreated and quantifiable, focusing on personalized economic (e.g. cost savings, efficiency gains) (Kienzler, 2018; Terho et al., 2015), and relational benefits (e.g. trust-building through customized solutions) that align with customers’ strategic goals (Keränen et al., 2023; Töytäri and Rajala, 2015).

In recent years, the concept of VBS has attracted significant attention in both academia and practice due to its emphasis on delivering customer-centric value. However, existing research in this field has primarily focused on the seller’s operational perspective (Liu et al., 2023; Liu and Zhao, 2021; Terho et al., 2015), with limited attention given to how customers perceive and respond to such value propositions. This has created a critical gap in understanding the psychological and relational mechanisms through which VBS influences customer behavioral outcomes, particularly loyalty. Moreover, prior studies have often relied on case-based or descriptive approaches, lacking empirical examination of the causal pathways involved (Keränen et al., 2023; Raja et al., 2020).

Customer loyalty (e.g. repurchase intention and word-of-mouth [WoM] recommendation) is a primary goal of marketing efforts (Watson et al., 2015) and one of a company’s most enduring assets (Pan et al., 2012). Customer loyalty not only helps reduce customer churn and marketing costs (Cudby, 2020; Jahromi et al., 2014) and strengthens corporate competitiveness (Chen, 2015; Lu et al., 2021), but also enhances brand influence through WoM effects, effectively attracting new customers (Mason, 2008). However, the formation of customer loyalty is influenced by multiple factors, including customers’ perceptions of the supplier’s value proposition, the establishment of trust and the enhancement of relationship satisfaction (Hüttinger et al., 2012). Especially in the context of VBS, these factors not only exert individual effects but also interact to jointly shape customer loyalty behaviors. At the core of the VBS model lies the creation and delivery of value to customers to build long-term and stable relationships. Therefore, the interplay among perceived value, trust and satisfaction is critical to the formation of customer loyalty. This study will further explore how these factors work together within the VBS framework to drive customer loyalty behaviors.

To address the aforementioned research gaps, this study aims to develop a systematic theoretical framework based on perceived value theory (PVT) and social exchange theory (SET), exploring the role of VBS – as a strategic sales approach – in customer behavior decision-making. VBS is conceptualized here as a process-oriented strategy that emphasizes value co-creation and delivery to foster long-term relationships, distinct from theoretical frameworks but informed by PVT (for value assessment) and SET (for reciprocal exchanges). Specifically, this study examines how customer perceived value – assessed through dimensions like product quality, technical competence, flexibility and responsiveness – influences repurchase intention and WoM recommendation intention through the mediating roles of trust and relationship satisfaction. Customers interpret value in VBS by evaluating not only economic gains but also relational reciprocity, as per SET (Cropanzano et al., 2017). Using empirical analysis in the computerized numerical control (CNC) machine tool industry, this study aims to answer the following key questions:

• In the context of VBS, what key factors drive the formation of customer perceived value?

• Does customer perceived value have a significant impact on customer trust and relationship satisfaction with the supplier?

• Do customer trust and relationship satisfaction with the supplier have a significant impact on their repurchase intention and WoM recommendation intention?

• Does customer perceived value indirectly influence repurchase intention and WoM recommendation intention through the mediating roles of trust and relationship satisfaction?

This study has significant theoretical and practical value. Theoretically, based on PVT and SET, this study constructs a systematic causal path model, revealing how customer perceived value influences loyalty through trust and relationship satisfaction, thereby filling the gap in existing literature regarding customer psychological mechanisms. At the same time, from a customer perspective, it extends VBS research by deepening the understanding of customer behavior formation mechanisms. Practically, this study provides empirical support for industrial companies in designing precise marketing strategies, particularly in how to maximize customer loyalty by enhancing perceived value, trust and relationship satisfaction. Furthermore, the findings have important implications for business to business (B2B) companies in dealing with complex market environments, optimizing customer relationship management and improving sales team capabilities.

The structure of this paper is as follows: Section 1 is the introduction, which introduces the research background, research questions and significance of the study. Section 2 presents the theoretical foundations and literature review, systematically reviewing the progress of VBS research and introducing PVT and SET as the theoretical foundation. Section 3 describes the research model and hypotheses, proposing the conceptual model and research hypotheses. Section 4 outlines the methodology, detailing the design and validation of measurement tools, data collection and cleaning processes, and the control and evaluation of common method biases. Section 5 presents the data analysis and results, evaluating the reliability and validity of the measurement model and conducting path analysis on the structural model to verify the direct relationships and mediating effects among variables. Section 6 is the discussion and implications, discussing the research findings and offering theoretical, managerial contributions, as well as suggestions for future research directions.

As outlined in the introduction, VBS has emerged as a pivotal strategy in B2B markets, emphasizing customer-centric value creation over traditional transactional approaches. The core concept of VBS revolves around identifying, delivering and realizing customer value, aiming to build long-term cooperative relationships between buyers and sellers by deeply understanding customer needs and precisely communicating value propositions (Terho et al., 2015). This differs markedly from non-VBS paradigms, where value is typically viewed through a lens of immediate utility or cost-benefit without the emphasis on co-creation and long-term relational reciprocity (Kienzler, 2018). In VBS, value interpretation involves customers actively assessing not just functional benefits but also the supplier’s role in enhancing their business outcomes, often through SET-driven exchanges (Liu and Zhao, 2021; Terho et al., 2012).

VBS represents a process-oriented sales approach, encompassing multiple stages of value creation – from identifying customer needs and formulating value propositions to delivering and confirming value (Liu and Zhao, 2021; Ma et al., 2024). In this process, the professional capabilities of the sales team and effective resource allocation within the company play a critical role in the successful implementation of VBS. For example, sales teams need to possess sharp customer insights, exceptional value communication skills and expertise in quantifying customer value benefits (Liu et al., 2023). In addition, a customer-oriented culture and flexible, efficient resource allocation within the organization are seen as key factors influencing the success of VBS (Liu and Zhao, 2021). With the acceleration of digital transformation, the implementation of VBS has increasingly incorporated technological tools. For instance, the application of digital platforms and data analysis tools not only enhances the efficiency of value co-creation but also strengthens the interaction between suppliers and customers (Alamäki and Korpela, 2021).

To synthesize prior research, existing VBS studies can be categorized into three main streams:

1. seller’s operational perspective, focusing on sales team capabilities and internal resource allocation (Liu et al., 2023; Liu and Zhao, 2021);

2. value co-creation processes, emphasizing stages from need identification to value delivery (Terho et al., 2015; Ma et al., 2024); and

3. digital integration, exploring tools for enhancing efficiency and interactions (Alamäki and Korpela, 2021).

While these streams offer valuable insights into supplier-side dynamics, they reveal critical gaps: First, an overemphasis on the seller’s viewpoint neglects customer psychological mechanisms, such as how perceived value is internalized to drive loyalty. Second, limited empirical testing of causal pathways, particularly mediated relationships involving trust and satisfaction, leaves relational outcomes underexplored. Third, few studies adopt a customer-centric lens to examine behavioral intentions like repurchase and WoM in high-complexity industries.

These gaps underscore the need for a customer-perspective model examining causal relationships. In this study, VBS is positioned as a strategic context where PVT and SET intersect to explain customer-side dynamics, addressing these unresolved areas.

Perceived value is the overall evaluation formed by customers after weighing the benefits received against the costs incurred (Zeithaml, 1988). This concept combines perspectives from economics, consumer behavior and psychology, emphasizing the balance between customer subjective experience and cost-benefit analysis. In industrial marketing (B2B), perceived value is not limited to economic aspects but also includes emotional, social and technological added value (Arslanagic-Kalajdzic and Zabkar, 2017; Leek and Christodoulides, 2012).

Prior B2B literature has treated perceived value as a multidimensional construct, often examined through lenses such as value-for-money tradeoffs, relational benefits and strategic alignment. For instance, early works like Zeithaml (1988) and Lapierre (2000) focused on its role in buyer-supplier relationships, highlighting dimensions like quality, service and cost. More recent studies have expanded this to emerging contexts, such as the circular economy (CE), digital servitization and mobile payments. In CE, perceived value incorporates ethical and systemic elements (Sairanen et al., 2024), while digital servitization underscores the influence of entrepreneurial orientation and digital capabilities on value perception (Simonsson and Agarwal, 2021). Research on mobile payments emphasizes factors like trust and mindfulness, moderated by technological anxiety (Srivastava et al., 2025). Across these, customers in B2B environments assess value by cognitively balancing benefits against costs, interpreting them in terms of strategic and relational outcomes (Sairanen et al., 2024; Zietsman et al., 2020).

However, while these advancements provide a robust foundation for understanding perceived value in general B2B settings, its specific application within the VBS framework remains underexplored. VBS positions perceived value as dynamic, cocreated and quantifiable, focusing on personalized economic and relational benefits aligned with customer goals (Terho et al., 2015; Keränen et al., 2023). Yet, empirical evidence on how customers form and respond to perceived value in VBS – particularly through psychological mechanisms like trust and satisfaction – is limited, with most studies prioritizing supplier perspectives (Liu et al., 2023). This gap justifies our focus on perceived value as a central construct, integrating it with VBS to examine customer-side decision-making.

Key dimensions of perceived value in B2B include product quality, technical competence, flexibility and responsiveness. Perceived product quality refers to customers’ subjective evaluation of the product’s excellence, reliability and technological advancement, serving as a foundational antecedent of perceived value (Sweeney and Soutar, 2001). It is one of the key antecedents of perceived value (Sweeney and Soutar, 2001). Perceived quality determines customers’ judgment of whether the product is worth the time, money and effort they invest. In the VBS context, high-quality products (e.g. precision, durability and stability) not only meet functional needs but also facilitate value co-creation by enabling quantifiable efficiency gains and long-term relational benefits (O'Cass and Ngo, 2012; Terho et al., 2015). They not only directly impact the product’s actual performance but also significantly enhance the customer’s positive perception of the economic value offered by the supplier (O'Cass and Ngo, 2012). Second, technical competence is a critical driver in enhancing the customer’s functional value perception (Hänninen and Karjaluoto, 2017). Suppliers, through their technical expertise, help customers solve production problems, improve process efficiency, optimize resource allocation and offer customized solutions that improve business performance. Within VBS, this competence supports cocreated value by aligning technical solutions with customer strategic objectives (Kamalaldin et al., 2021). Furthermore, flexibility is particularly important in dynamic market environments (Anand and Ward, 2004). Suppliers with high flexibility can provide personalized solutions tailored to specific customer needs and quickly respond to production adjustments or technical requirements. In VBS, flexibility enhances perceived value by reducing risks in uncertain environments and fostering adaptive partnerships (Töytäri and Rajala, 2015). Finally, responsiveness, as a key indicator of supplier service capabilities, is crucial for reducing customer uncertainty and perceived risk (Handfield and Bechtel, 2002). By responding quickly and efficiently solving problems (e.g. deploying technicians for equipment repairs or addressing technical issues during the debugging process), suppliers can significantly enhance customer evaluations of service value. This dimension is especially salient in VBS, where timely responsiveness reinforces trust and relational reciprocity (Keränen et al., 2023).

To address the gaps in VBS research identified in the literature review, this study integrates PVT and SET to construct a systematic framework examining customer decision-making in VBS contexts. PVT posits that customers evaluate value by comparing perceived benefits against costs (Zeithaml, 1988). In VBS, these benefits include functional (e.g. product quality, technical competence) and relational (e.g. trust, satisfaction) dimensions, which customers internalize through cognitive and emotional processes (Sairanen et al., 2024). Complementing PVT, SET provides a foundational perspective on the relational dynamics in customer-supplier interactions, emphasizing reciprocity, cost-benefit analysis, and the pivotal roles of trust and fairness in sustaining relationships (Cook et al., 2013; Cropanzano et al., 2017). Originally proposed by Homans (1958), SET views social behavior as resource exchanges aimed at maximizing benefits and minimizing costs. Blau (1964) extended this to interorganizational contexts, underscoring trust and power in exchange processes, while Cook and Emerson (1978) highlighted its utility in complex interactions.

In B2B marketing, SET elucidates the formation and maintenance of long-term relationships, serving as a lens for understanding cooperation mechanisms (Cropanzano et al., 2017). B2B exchanges encompass not only economic benefits but also intangible social elements, such as information sharing, technical collaboration and capability enhancement. Recent applications in industrial marketing demonstrate SET’s depth: For instance, in green customer integration, relational trust drives noncoercive strategies with moderating effects from big data and social capital (Zhou et al., 2024); in supplier relationship management, it fosters innovation and collaboration (Yang et al., 2023); and in information sharing, justice dimensions enhance performance (Huo et al., 2023), while mitigating opportunism through goal congruence (Tran et al., 2022).

Despite its broad use, SET’s application in VBS remains underdeveloped, offering a critical viewpoint on how social benefits evolve relationships and complement economic value analyses. In VBS, SET helps identify how factors like trust and satisfaction influence customer behavior, balancing economic and social benefits to optimize loyalty and long-term performance. SET complements this by explaining how reciprocal exchanges foster trust and satisfaction, which in turn drive loyalty outcomes like repurchase intention and WoM recommendation (Blau, 1964; Cropanzano et al., 2017).

The proposed model (Figure 1) posits that customer perceived value, driven by product quality, technical competence, flexibility and responsiveness, influences loyalty (repurchase intention and WoM recommendation) through the mediating roles of trust and relationship satisfaction. This model extends VBS research by empirically testing these relationships from the customer perspective in a high-complexity industrial context, integrating PVT’s value assessment with SET’s relational reciprocity to provide a cohesive explanation of theory, scope (e.g. B2B loyalty mechanisms) and implications (e.g. enhanced customer retention strategies).

Based on the PVT (Zeithaml, 1988), the multidimensional capabilities of suppliers are seen as key factors in enhancing customer perceived value, especially in the context of VBS, where customers’ perceptions of the value they receive are often based not only on the product itself but also on the interactions and service quality provided by the supplier. This study explores the impact of four dimensions – product quality, technical competence, flexibility and responsiveness – on customer perceived value.

In the industrial marketing context, perceived product quality mainly refers to customers’ perceptions of the product’s objective performance, durability, technological advancement and other attributes. It is considered a key driver of success in the manufacturing industry (Colledani et al., 2014). High-quality products not only meet customers’ functional requirements but also provide stability and reliability, helping to reduce maintenance costs and improve production efficiency. Zeithaml (1988) pointed out that when customers evaluate perceived value, they often focus on the balance between the benefits they receive and the costs they incur. Product quality directly affects the customer’s experience, which in turn influences their assessment of return on investment (Lapierre, 2000). Therefore, suppliers with higher product quality can make customers feel they are receiving value beyond the cost, thereby enhancing their overall evaluation and perceived value. Building on this, we propose:

Technical competence is a key factor in value creation by suppliers, especially in complex industrial marketing environments, where their role becomes increasingly important (Lapierre, 2000). Customers not only care about the basic functionality of the product but also expect suppliers to solve their specific needs through technical support and innovation capabilities (Kamalaldin et al., 2021). Suppliers with strong technical competence can help customers optimize production processes, reduce operational costs, and improve efficiency through customized solutions and effective technical services (Javaid et al., 2022). This technology-driven value-added service can significantly improve customers’ overall evaluation of the supplier, further enhancing the customer’s perceived value. Thus:

In the fast-changing industrial marketing environment, flexibility is an important decision-making criterion for customers when choosing a supplier (Ho et al., 2010; Lapierre, 2000). Supplier flexibility is reflected in their ability to quickly adapt to changes in customer needs, including product customization, adjusting delivery schedules and optimizing after-sales services (Lee et al., 2022; Schulz et al., 2023). Increased supplier flexibility not only helps customers effectively cope with uncertainty but also provides higher added value by reducing potential risks and pressures (Töytäri and Rajala, 2015). In competitive market environments, this flexibility helps enhance the customer experience and further increases the customer’s perceived value (Zhang et al., 2003). Therefore:

Responsiveness is the supplier’s ability to quickly respond to customer needs or urgent issues and effectively resolve them (Bernardes and Hanna, 2009; Lapierre, 2000). In industrial marketing, customers have high expectations for response time because responsiveness directly affects the continuity of operations and downtime costs (Cannon and Homburg, 2001; De Matteis et al., 2023). Suppliers with high responsiveness can quickly meet customer demands, provide timely support and maintain efficient communication, which effectively reduces customer uncertainty and enhances the perceived value of the partnership. Hence:

In the B2B environment, trust is considered a critical factor for successful collaboration (Dowell et al., 2013; Gansser et al., 2021). B2B transactions often involve high switching costs, strong interdependence and complex, prolonged procurement processes, all of which make trust especially important in industrial marketing (Akrout and Diallo, 2017). According to SET, trust arises from the reciprocal relationship between parties in the exchange of resources and benefits (Cropanzano et al., 2017; Molm et al., 2000). When customers perceive that the value provided by a supplier exceeds their expectations and delivers significant economic or noneconomic benefits, trust is established (Ferro et al., 2016). Furthermore, suppliers can strengthen customer trust in their performance and integrity by providing high-value solutions, continuous technical support and stable service (Nguyen et al., 2019a). In VBS, suppliers not only need to offer high-quality products and services but also provide customized solutions that meet the specific needs of customers, which may further enhance customer trust in the supplier’s ability to fulfill their commitments. Thus:

In the industrial marketing context, relationship satisfaction not only reflects the customer’s evaluation of the supplier’s products or services but also the overall perceived value of the collaboration (Eggert and Ulaga, 2002; Ulaga and Eggert, 2006). In VBS, an increase in perceived value means that customers are receiving more economic or non-economic benefits, which directly drives higher customer satisfaction (Zietsman et al., 2020). When customers perceive that the partnership has led to significant benefits, such as cost savings, efficiency improvements, technical support or quick response, they are more likely to exhibit higher satisfaction with the relationship (Padgett et al., 2020). In addition, perceived value not only provides tangible returns but also enhances customers’ recognition of the collaboration process, further boosting relationship satisfaction (Zietsman et al., 2020). In long-term B2B partnerships, high perceived value can significantly increase customer satisfaction, further motivating the customer to maintain a stable relationship (Sharma, 2022). Therefore:

In the industrial marketing environment, trust is considered one of the key factors in establishing and maintaining long-term relationships between customers and suppliers (Ryciuk and Nazarko, 2020; Spekman and Carraway, 2006). According to SET, trust reflects not only the customer’s recognition of the supplier’s ability to fulfill commitments, integrity and reliability but also serves as a key driver in relationship development (Cropanzano et al., 2017; Molm et al., 2000). In the context of VBS, trust is especially important because it influences customers’ perception of the value provided by the supplier, which in turn impacts their satisfaction with the long-term partnership (Mungra and Yadav, 2020). When customers have a high level of trust in the supplier, they are more likely to recognize the supplier’s value contribution in long-term collaboration and show higher satisfaction with the partnership. Therefore, the following hypothesis is proposed:

In this study, customer loyalty is specifically divided into two dimensions: repurchase intention and WoM recommendation intention, which reflect the customer’s tendency to continue the partnership in the future and their willingness to recommend the supplier’s products or services to others. This multidimensional definition helps to gain a more comprehensive understanding of the psychological and behavioral mechanisms behind the formation of customer loyalty and improves the accuracy in measuring the effectiveness of marketing strategies such as VBS. Customer loyalty refers to a customer’s preference for a company’s products or services based on factors such as quality, price and service, and their ongoing behavior of repeat purchases (Dawes et al., 2021; Uncles et al., 2003). Loyal customers not only generate direct revenue through repeat purchases but also significantly reduce the supplier’s marketing costs through WoM recommendations, thereby enhancing the company’s sustainable competitive advantage (Arslan, 2020).

In the VBS context, both trust and relationship satisfaction are seen as key drivers of customer loyalty, and both are central elements of SET. Trust is one of the important dimensions of SET and a key factor in promoting customer loyalty (Paparoidamis et al., 2019). Trust can effectively reduce the uncertainty customers perceive during the transaction process (Grabner-Kraeuter, 2002), increasing their dependence on the supplier’s products and services and prioritizing them over competitors (Handfield and Bechtel, 2002). Therefore, when customers have high trust in the supplier, they are more likely to establish a long-term partnership, exhibit a stronger intention to repurchase and be more willing to spread positive WoM to recommend the supplier. Thus:

Relationship satisfaction is another important component of SET, reflecting the customer’s overall evaluation of the reciprocal nature of the collaboration with the supplier (Rauyruen and Miller, 2007). High levels of relationship satisfaction arise from customers’ overall recognition of the supplier’s product quality, technical support and after-sales service (Padgett et al., 2020). When customers feel highly satisfied with the partnership, their emotional dependence on the supplier and long-term commitment significantly increase, which further promotes repeat purchase behavior (Čater and Čater, 2010; Eriksson and Vaghult, 2000). At the same time, satisfied customers tend to enhance the supplier’s market reputation through positive WoM, further increasing brand influence and expanding the customer base (File and Prince, 1992; Nguyen et al., 2019b). Therefore:

Drawing upon the interrelations posited in H2–H6, SET delineates a sequential mechanism wherein perceived value cultivates trust, which subsequently augments relationship satisfaction, thereby influencing behavioral intentions (Cropanzano et al., 2017). Within the VBS paradigm, this pathway elucidates the progression from value perceptions to cognitive trust, affective satisfaction and ensuing loyalty behaviors (Paparoidamis et al., 2019). Accordingly:

This study uses a quantitative research method, collecting data through a structured questionnaire survey to empirically test the research hypotheses. The questionnaire design was based on existing literature, combined with the practical application context of VBS. A multiitem measurement method was employed, using a five-point Likert scale (1 = “Strongly Disagree”, 5 = “Strongly Agree”) to assess respondents’ agreement with the relevant statements. The specific measurement items for each construct are detailed in  Appendix. These items were appropriately adjusted and optimized based on relevant literature, considering the context of this study.

During the questionnaire design process, the research team employed a dual validation approach of expert review and presurvey testing to ensure the content validity and clarity of structure of the measurement tool. The questionnaire design process, the research team first submitted the draft questionnaire to three professors in the field of marketing and three professors in the field of information systems. They were invited to provide feedback on two aspects:

1. evaluating whether each measurement item accurately reflected the corresponding theoretical construct; and

2. checking whether the wording of the items was clear, concise and easy to understand.

After collecting feedback, the research team revised the questionnaire items and made the necessary preliminary adjustments.

Next, to verify the suitability and clarity of the questionnaire, the research team conducted a pilot survey with 30 participants. The feedback indicated that participants had no difficulty understanding the questionnaire items, with only minor issues regarding phrasing. Based on the pilot results, further revisions were made to the items, and the Cronbach’s alpha coefficient for each construct was calculated. The results showed that all constructs had a Cronbach’s alpha above 0.7, meeting the recommended threshold, indicating that the scale had high internal consistency and reliability. After completing the necessary adjustments and correcting any language flaws identified in the pilot survey, the final version of the questionnaire was determined and used for the formal survey.

The data for this study were sourced from a high-end precision machinery manufacturing company in China, which has long adopted a VBS model. The company’s business includes equipment sales, process solutions and automation services, with over 1,500 customers. The CNC machine tool industry is particularly suitable for VBS research due to its high technical complexity, demand for customized solutions, and emphasis on long-term relational value co-creation (Colledani et al., 2014; Javaid et al., 2022). These characteristics make it representative of industrial B2B sectors where suppliers must demonstrate quantifiable value to mitigate risks and foster loyalty, contrasting with low-complexity industries.

The survey subjects for this study were procurement team managers from the client companies of the enterprise, who are directly involved in or make decisions regarding the procurement process, with an average relationship duration of over two years, aligning with the post-onboarding retention stage of the customer journey. This study employed purposive sampling to ensure that the collected data is highly representative and valid. The principle of purposive sampling was to ensure that respondents have procurement decision-making authority and direct knowledge of the company’s VBS model.

The data for this study were collected through an online questionnaire survey, which lasted for four weeks. To improve the response rate, the research team implemented two rounds of follow-up reminders. The first reminder was sent one week after the questionnaire was distributed to respondents who had not yet replied. The second reminder was sent one week before the survey deadline to those who had not completed the survey, to increase the final response rate. At the end of the survey, a total of 258 questionnaires were collected, resulting in an overall response rate of 45%.

To ensure data quality and detect potential response bias, this study employed two measures: nonresponse bias testing and data cleaning. First, following the method proposed by Armstrong and Overton (1977), a paired t-test was conducted to compare the differences between the first 20 respondents who submitted their questionnaires and the last 20 respondents. The results indicated no significant difference between the two groups (p > 0.05), suggesting that there is no substantial response bias in this study. Second, questionnaires with evidently low response quality were removed, including those with extremely short completion times and those with repeated answers across all items (e.g. selecting the same response for every question). After data cleaning, 32 invalid questionnaires were discarded, leaving 226 valid questionnaires, resulting in an effective response rate of 39.4%.

Table 1 presents the demographic characteristics of the 226 respondents. The sample was predominantly male, accounting for 86.7%, while females comprised 13.3%. In terms of age distribution, the largest group of respondents was in the 40–49 age range (55.7%), followed by those aged 30–39 (28.8%), with 10.2% in the 20–29 age range and 5.3% aged 50 or above. Regarding education level, the majority of respondents held at least a college degree (61.0%), with 24.8% having high school or below education and 14.2% holding a master’s or doctoral degree.

In terms of industry type, the respondents represented several sectors, with the largest group coming from automotive manufacturing (31.8%), followed by 3C electronics (25.2%), medical equipment (20%) and aerospace (23%). Regarding firm size, medium-sized enterprises (200–1,000 employees) represented the largest group (45.6%), followed by small enterprises (28.7%) and large enterprises (over 1000 employees, 25.7%). This demographic distribution provides a broad perspective for the target population of the study, ensuring diverse opinions from various industries and organizational sizes.

To control and assess for potential common method bias (CMB) in the survey, this study implemented both procedural and statistical measures (Kock, 2015; Podsakoff et al., 2003).

We implemented a series of procedural control measures during the data collection process to minimize the potential impact of CMB, including:

• assuring respondents of the anonymity and confidentiality of the data to reduce social desirability bias;

• emphasizing that the survey questions have no right or wrong answers and only focus on respondents’ true opinions, thereby reducing potential common-source bias;

• conducting a pretest of the questionnaire prior to the formal survey to ensure that the questions are clear and easy to understand, thus minimizing potential cognitive bias; and

• ensuring that respondents possess a certain level of knowledge related to VBS to improve the accuracy and validity of their responses.

To further assess the impact of CMB, we employed two widely recognized methods for testing:

1. Harman’s single factor test. The results showed that the variance explained by a single factor was only 27.047%, well below the recommended threshold of 40% (Babin et al., 2016), indicating a low risk of CMB; and

2. Variance inflation factor (VIF) analysis. The results revealed that the VIF values for all variables were below 3.3 (ranging from 1.356 to 2.811), further confirming that the influence of multicollinearity and CMB in this study was minimal.

This study evaluated the model fit using the standardized root mean square residual (SRMR) value. The result showed an SRMR value of 0.077, which is below the threshold of 0.08, indicating a satisfactory model fit (Hair et al., 2017).

Furthermore, the measurement quality was assessed based on construct reliability, convergent validity and discriminant validity. Construct reliability was measured using Cronbach’s alpha and composite reliability (CR) (Hair et al., 2019). The results in Table 2 show that all constructs in the study had Cronbach’s alpha and CR values above the standard value of 0.70, indicating that the measurements used had high reliability (Hair et al., 2019).

To assess convergent validity, this study used standardized factor loadings and average variance extracted (AVE) as criteria. As shown in Table 2, the factor loadings for all constructs were above the recommended threshold of 0.708, with the lowest value being 0.724 for RS2. In addition, the lowest estimated AVE was 0.608 for Trust, which is above the 0.5 standard. These results indicate that the research model demonstrates good convergent validity.

This study evaluates discriminant validity based on the Fornell and Larcker (1981) method and the heterotrait–monotrait ratio (HTMT) test. The results, which can be seen in Table 3, indicate that the square roots of the AVE for each latent variable are greater than the correlation coefficients with other variables, and the HTMT values are all below the 0.85 threshold. These findings suggest that this study exhibits good reliability, convergent validity and discriminant validity.

First, collinearity issues were examined. The results indicated that the VIF values for all variables were below 5, suggesting that multicollinearity was not a major concern in this study. After confirming the model’s reliability, validity and absence of severe collinearity, we proceeded to analyze the structural model to test the research hypotheses. Table 4 presents the path coefficients and significance test results of the structural model.

The results showed that supplier product quality (β = 0.190, t = 3.943, p < 0.001), technical competence (β = 0.454, t = 9.977, p < 0.001), flexibility (β = 0.362, t = 7.958, p < 0.001) and responsiveness (β = 0.199, t = 3.726, p < 0.001) all had significant positive effects on customer perceived value, supporting hypotheses H1a through H1d. Furthermore, customer perceived value positively influenced supplier trust (β = 0.672, t = 17.146, p < 0.001) and relationship satisfaction (β = 0.262, t = 4.878, p < 0.001), verifying hypotheses H2 and H3.

Customer trust in the supplier had a significant positive effect on relationship satisfaction (β = 0.576, t = 10.025, p < 0.001), supporting H4. In addition, trust (β = 0.496, t = 7.597, p < 0.001) and relationship satisfaction (β = 0.301, t = 4.365, p < 0.001) were found to positively influence customers’ repurchase intention, supporting H5a and H5b. Further analysis revealed that trust (β = 0.340, t = 4.839, p < 0.001) and relationship satisfaction (β = 0.361, t = 5.329, p < 0.001) significantly and positively impacted customers’ WoM recommendation intentions, supporting H6a and H6b.

In addition, this study examined the effects of control variables such as gender, age and education level on repurchase intention and WoM recommendation intention. None of these effects were found to be significant. Similarly, company size and industry type did not have significant effects on repurchase intention or WoM recommendation intention.

This study employed the three-step method proposed by Hair et al. (2014) and Bollen (1989) to test the mediation effects of trust and relationship satisfaction. To assess serial mediation from perceived value through trust and relationship satisfaction to behavioral intentions, bootstrapping (5,000 resamples) was used in structural equation model (SEM) to compute indirect effects, confidence intervals and variance accounted for (VAF) (Preacher and Hayes, 2008). The specific results are shown in Tables 5 and 6, supplemented by Table 7 for serial mediation pathways.

The results in Table 5 indicate that trust (β = 0.315, p < 0.001) and relationship satisfaction (β = 0.188, p < 0.001) significantly mediated the relationship between perceived value and repurchase intention. Similarly, trust (β = 0.217, p < 0.001) and relationship satisfaction (β = 0.225, p < 0.001) also significantly mediated the relationship between perceived value and WoM recommendation intention.

Table 6 presents the VAF results. According to the standards proposed by Hair et al. (2021), trust (VAF = 0.385, 20% ≤ 38.5% ≤ 80%) and relationship satisfaction (VAF = 0.272, 20% ≤ 27.2% ≤ 80%) demonstrated partial mediation between perceived value and repurchase intention. Similarly, trust (VAF = 0.329, 20% ≤ 32.9% ≤ 80%) and relationship satisfaction (VAF = 0.337, 20% ≤ 33.7% ≤ 80%) exhibited partial mediation between perceived value and WoM recommendation intention.

Table 7 delineates significant serial mediation: The pathway from perceived value through trust and relationship satisfaction to repurchase intention yields β = 0.116 (95% CI [0.072, 0.168], p < 0.001). Analogously, for WoM recommendation intention, β = 0.139 (95% CI [0.088, 0.197], p < 0.001). VAF metrics (0.248 for repurchase intention; 0.292 for WoM recommendation intention) affirm partial serial mediation.

This study, grounded in the VBS context, elucidates the mechanisms by which customer perceived value, trust, relationship satisfaction and behavioral intentions interrelate. The empirical results affirm that customer perceived value, propelled by product quality, technical competence, flexibility, and responsiveness, significantly shapes repurchase intention and WoM recommendation intention via the mediating roles of trust and relationship satisfaction. These outcomes resonate with the core principles of PVT and SET, accentuating the synergy of functional and relational elements in fostering customer loyalty within VBS frameworks (Zeithaml, 1988; Cropanzano et al., 2017). To align with the research questions, the following interprets the findings sequentially. Our findings align with Paparoidamis et al. (2019), who emphasize trust’s role in B2B loyalty post-onboarding, particularly in sustaining long-term relationships through perceived reliability and integrity. Mapping to the customer journey, perceived value drives initial assessment during the evaluation phase, while trust and satisfaction sustain retention through reciprocal exchanges in the post-onboarding stage. This study extends prior work by providing empirical evidence of these mediated pathways in the CNC machine tool industry, where high technical complexity and long-term relationships amplify the importance of value co-creation (Colledani et al., 2014).

The results indicate that product quality, technical competence, flexibility and responsiveness significantly enhance customer perceived value, supporting H1a–d. In contemporary VBS literature, this underscores value as cocreated and quantifiable, with product quality enabling efficiency gains (Liu et al., 2023; Terho et al., 2015). Technical competence aligns with customized solutions in dynamic markets (Ma et al., 2024), while flexibility and responsiveness mitigate risks, fostering adaptive partnerships (Keränen et al., 2023). These drivers extend general B2B value dimensions by embedding them in VBS’s relational reciprocity (Zhou et al., 2024).

Perceived value positively influences trust and relationship satisfaction (H2 and H3), which in turn affect repurchase and WoM intentions (H5a/b and H6a/b), with trust enhancing satisfaction (H4). Through the VBS lens, these findings illustrate how value propositions build trust via reciprocal exchanges, leading to satisfaction and loyalty (Keränen et al., 2023; Latinovic and Chatterjee, 2022). This integrates B2B relationship literature, where trust reduces uncertainty in complex transactions (Gansser et al., 2021), and satisfaction reinforces commitment (Sharma, 2022).

This study further verifies the significant positive impact of perceived value on trust and relationship satisfaction. By enhancing customer trust in the supplier’s capabilities and commitment, perceived value reduces customer perceived risk, supporting recent VBS emphases on relational value (Keränen et al., 2023), particularly in technology-intensive industries (Liu et al., 2023). At the same time, relationship satisfaction, as an overall evaluation of the cooperation, is the result of both perceived value and trust, and its importance in customer relationship management is widely recognized in contemporary literature (Padgett et al., 2020; Mungra and Yadav, 2020).

Trust and relationship satisfaction also significantly influence customer behavioral intentions. Trust reduces perceived risk and strengthens customer reliance on the supplier, significantly enhancing repurchase intention and WoM recommendation intention (Jahromi et al., 2014). Relationship satisfaction, by reinforcing emotional identification and accumulating positive experiences, further boosts customer loyalty, a result that aligns with Hennig-Thurau et al. (2002), which highlights satisfaction as a key precursor for behavioral loyalty and WoM communication.

Moreover, trust and relationship satisfaction play partial mediating roles between perceived value and customer behavioral intentions. The serial mediation pathway from perceived value through trust and relationship satisfaction to behavioral intentions further illuminates a chained relational dynamic, wherein trust serves as a pivotal intermediary, converting value perceptions into satisfaction-mediated loyalty, in alignment with SET’s sequential reciprocity framework (Cropanzano et al., 2017). Trust strengthens the impact of perceived value on repurchase and recommendation intentions by enhancing customers’ positive perceptions of the supplier’s integrity and competence. Relationship satisfaction further consolidates customers’ long-term cooperation willingness and recommendation behavior by transforming perceived value into emotional recognition and loyalty. This addresses RQ4, demonstrating mediated mechanisms in VBS, where trust and satisfaction not only derive directly from perceived value but also sequentially shape behavioral intentions, advancing our comprehension of customer loyalty formation (Keränen et al., 2023; Chowdhury et al., 2023).

The theoretical contributions of this study can be summarized in three key areas.

First, this research extends the PVT within the context of VBS by systematically examining how customer perceived value affects behavioral intentions. While prior studies have primarily focused on the supplier perspective, this research shifts the focus to the customer’s psychological and decision-making processes, thereby addressing a critical gap in the literature. By anchoring in contemporary VBS works (Keränen et al., 2023), it demonstrates perceived value as a dynamic, cocreated construct, enriching PVT with empirical insights from high-complexity B2B settings. By demonstrating that perceived value is not only a functional evaluation but also a psychological mechanism shaping trust and relationship satisfaction, this study provides a more comprehensive framework for understanding customer behavior in industrial marketing.

Second, this study integrates SET to explain how customer perceived value impacts customer loyalty through the mediating roles of trust and relationship satisfaction. Through the incorporation of serial mediation, our analysis elucidates relational sequences in VBS, extending antecedent research on economic dimensions to encompass progressive psychological trajectories. While previous research has explored the role of economic and functional benefits in VBS, our findings emphasize the relational and psychological dimensions of customer-supplier interactions. This extends SET applications in VBS (Zhou et al., 2024), revealing how reciprocal exchanges foster sequential loyalty pathways, thus broadening theoretical boundaries. This research reveals the underlying pathways through which perceived value fosters long-term customer commitment, offering a novel perspective that extends the theoretical boundaries of VBS.

Third, by developing a causal path model incorporating perceived value, trust, relationship satisfaction and customer loyalty, this study establishes a new theoretical foundation for analyzing customer behavior in B2B settings. Unlike traditional models that focus solely on transactional factors, our model highlights the interplay between cognitive, emotional and relational factors, providing a holistic framework that future studies can build upon. Integrating recent B2B literature (Yang et al., 2023), it suggests testing in diverse contexts to refine generalizability, paving the way for cross-cultural VBS explorations. Moreover, this study underscores the importance of considering industry-specific and cultural contexts, suggesting that future research should test the model across different markets and industries to enhance its generalizability.

This study provides valuable managerial insights for firms implementing VBS strategies in complex market environments.

First, this study offers empirical support for designing targeted marketing strategies. By validating the mechanisms through which customer perceived value influences trust, relationship satisfaction and loyalty, this research helps firms refine their value positioning strategies. In line with contemporary VBS practices (Ma et al., 2024), managers should leverage digital tools for personalized value co-creation to sustain competitive edges in B2B sectors. In an increasingly competitive industrial market, businesses must go beyond product-centric approaches and focus on delivering high-perceived value propositions that foster deeper customer engagement and long-term loyalty.

Second, the study highlights the importance of enhancing customer relationship management. In light of the serial pathway, organizations should strategically prioritize initial trust cultivation (e.g. via reliable value provision) to engender subsequent satisfaction and enduring loyalty within VBS frameworks. Firms should focus on building trust through transparent communication, fair pricing and consistent value delivery. Moreover, strengthening customer-centric service processes – such as proactive problem-solving, personalized support and after-sales engagement – can significantly enhance emotional connection and relationship satisfaction, ultimately reinforcing customer loyalty. Drawing from recent B2B insights (Gansser et al., 2021), this implies training sales teams in relational reciprocity to mitigate churn in high-stakes industries.

Third, the findings underscore the critical role of salesforce capability development. Companies should train their sales teams not only in value communication but also in relationship-building techniques. Equipping sales personnel with consultative selling skills, data-driven customer insights and adaptive negotiation strategies can help reinforce the firm’s value proposition in the minds of customers. Aligning with Liu et al. (2023), this advocates for cross-functional teams to address VBS complexities, ensuring sustained performance. This shift from a transactional to a relationship-oriented sales approach is key to sustaining competitive advantage in VBS implementation.

Finally, firms should consider segmenting their customer base based on value sensitivity. Different customers may prioritize different value dimensions – some may focus on product performance, while others may value flexibility, responsiveness or service quality. By adopting a differentiated value delivery approach, businesses can enhance customer retention and strengthen their market positioning. Future-oriented, this suggests piloting AI-driven analytics for real-time value adaptation in evolving B2B landscapes.

While this study provides a new theoretical framework and practical insights for the implementation of VBS, it also has certain limitations.

First, the study’s focus on customers of a single company in China’s CNC machine tool industry constrains its generalizability across diverse industries and cultural contexts. This industry-specific and geographically limited sample may not fully capture variations in market dynamics or customer behaviors elsewhere (Colledani et al., 2014). Future research should use cross-industry and cross-cultural studies, incorporating varied firm sizes and regions, to enhance the model’s external validity and robustness.

Second, while this study focuses on four dimensions of perceived value – product quality, technical competence, flexibility and responsiveness – it does not encompass other pertinent dimensions such as relational, social, knowledge and emotional value, which are increasingly recognized in B2B contexts (Arslanagic-Kalajdzic and Zabkar, 2017; Keränen et al., 2023). These dimensions, including long-term cooperative trust, industry social networks, supplier expertise and psychological brand connections, may further influence loyalty in VBS settings. Future research should empirically investigate these dimensions’ mechanisms and relative contributions, potentially employing mixed methods (e.g. surveys and qualitative interviews) to deepen insights into value perception processes.

Finally, different types of products (e.g. high-risk vs low-risk, high-value vs low-value) may influence the role of trust in the mechanism of repurchase intention. Future research could further introduce product categories as a moderating variable to test the applicability of the findings in different product contexts.