When reputational power shapes commitment: industry reputation, supplier adaptation and product quality Open Access

During the 1980s, the literature on logistics, supply chain, and marketing channels shifted attention away from the study of channel power (see Lusch and Brown, 1982; Hibbard et al., 2001), driven by a massive wave of relationship marketing (RM) research (see Palmatier et al., 2006), which emphasized trust, commitment, and collaboration over power. These critical studies demonstrated that supply chain relationships succeed or fail based on a host of human factors. While this relationship-marketing stream advanced understanding of trust and commitment, it tended to de-emphasize the structural foundations of power and dependence that had been central to earlier research on marketing channels (e.g. Emerson, 1962; Frazier, 1983; Gaski, 1984). Building on this well-established tradition, our study revisits how power operates today—not as coercion or resource control, but as a reputational form of influence that shapes modern buyer–supplier relationships.

We know that power asymmetry influences trust, dependence, and commitment in channels (e.g. Andaleeb, 1996; Goodman and Dion, 2001; Nyaga et al., 2013; Patrucco et al., 2020). What remains less explored is how non-coercive power—rooted in social approval and industry standing—operates alongside relational mechanisms. We extend this dialog by conceptualizing supplier reputation as a distinct, socially embedded power base within the power-dependence framework. Commitment remains a cornerstone of supply chain success, yet not all commitment is created equal. Research distinguishes between affective commitment, rooted in relational goodwill and emotional connection, and calculative commitment, which is driven by cost-benefit assessments and switching costs (de Ruyter et al., 2001; Abe et al., 2021; Gounaris, 2005). As buyer-supplier relationships evolve, firms update the mix of these commitments in response to performance, flexibility, and trust.

Recent research highlights the increasing importance of supplier responsiveness, often achieved through structural or procedural adjustments, in fostering affective commitment (Richey et al., 2022; Ukko et al., 2020). These relationship-specific adaptations (RSAs), defined as “investments in adaptations to process, product, or procedures specific to the needs or capabilities of an exchange partner” (Cannon and Perrault, 1999, p. 443), strengthen relational bonds and may also create dependency. Relationship-specific adaptations foster commitment and potentially create power asymmetry in a dependent relationship (Nyaga et al., 2013). By contrast, some partnerships thrive because the supplier provides superior goods and services to its relative competitors. We will refer to this as “product quality.” Calculative commitment may emerge from this perceived product quality, particularly when that quality is difficult to replicate or replace. In either case, dependency can arise, but the underlying mechanism differs.

While the influence of power asymmetry has long been recognized in channels research (Frazier, 1983; Gaski, 1984; Stern and Reve, 1980), its integration with contemporary RM frameworks has been limited. Power imbalances persist in practice and can lead to adverse outcomes such as psychological uncertainty (Ma et al., 2021) and strained partnerships. While firms may mitigate these risks through mechanisms like business continuity planning (Sadeghi, 2022), success still hinges on the structure and quality of communication. Simply put, trust and commitment matter, but so does reputational power.

By situating our work within this ongoing tradition, we aim not to rediscover power but to reinterpret how its reputational aspects shape commitment in modern supply chains. We define reputational power as the buyer's perception that a supplier possesses influential standing, prestige, and legitimacy within the industry, which grants the supplier non-coercive influence over the relationship by shaping how buyers interpret and respond to supplier behaviors. This study re-centers the role of reputational power in supply chain dyads, investigating how buyer perceptions of a supplier's industry reputation and RSAs affect commitment. In our framework, relationship-specific adaptations and product quality are distinct supplier behaviors that elicit two forms of commitment, while reputation moderates these behavioral effects. In other words, reputation does not duplicate adaptation or quality—it defines the conditions under which each form of commitment is strengthened or weakened. Building on the idea that every buyer wants to be a “customer of choice” (Patrucco et al., 2024), we examine whether suppliers with stronger reputations can elicit commitment even when they are less adaptable, and whether buyers are more likely to overlook performance gaps when their partner holds reputational power.

Although recent work has re-engaged with power dynamics (e.g. Minerbo et al., 2021; Touboulic et al., 2014), few studies consider industry reputation as a form of non-coercive power. A well-regarded supplier across the industry may attract more partners, command more loyalty, and shape the terms of engagement, even without adapting to each buyer's specific needs. Simply put, the supplier's reputation matters (Morsy, 2017), and we position this as the focal moderating construct in our model, strengthening or softening the pathways between supplier behavior (e.g. RSA or product quality) and buyer commitment.

We contribute to this conversation in several ways. First, we isolate affective commitment as the mediating mechanism linking RSAs to performance outcomes such as loyalty and strategic and financial performance. Second, we model calculative commitment as the parallel mechanism linking product quality to those same outcomes, emphasizing the distinction between relational and economic drivers of dependency. Third, and most critically, we explore how supplier reputation moderates both pathways, revealing that power dynamics influence not only whether buyers commit, but how and why they commit.

To test these ideas, we conduct two field studies and analyze our data using Hayes' PROCESS in SPSS and CB-SEM in AMOS to validate our model. We adopt the buyer's perspective, responding to Nyaga and Whipple's (2011) call to examine the effects of power inequity on supply chain relationship quality, and Patrucco et al.’s (2020) suggestion to investigate how both internal and external variables influence supplier commitment. We also extend Schwieterman and colleagues’ bilateral measure of supply chain buyer-supplier dependence. While they found that top-performing supply chains had low supplier dependency, we take the buyer's perspective to determine how the buyer's dependency on suppliers influences performance outcomes. While prior work often models supplier perspectives or focuses on satisfaction (e.g. Chang et al., 2012), we examine buyer-perceived reputation as a non-coercive source of power that can alter commitment dynamics.

Drawing on Social Exchange Theory (SET), we aim to answer a fundamental research question: How does reputational power, operationalized as industry reputation, influence buyer commitment and performance in supply chain relationships? Leveraging the notion of social capital, we demonstrate how buyers weigh relational investments and economic outcomes in light of perceived reputational power. Supplier reputation, in particular, can amplify or override these judgments, revealing how reputational power and commitment intersect to shape performance. In doing so, this study not only reintroduces reputational power to the conversation but reframes it as a reputational asset with strategic consequences.

There is general agreement that trust and commitment are key mediating variables leading to longer supply chain relationships (Morgan and Hunt, 1994; Palmatier et al., 2006). Stronger relationships can lead to positive outcomes for firms in a supply chain setting, including loyalty (e.g. Čater and Čater, 2010), innovation (e.g. Zhang and Zhu, 2019), cost performance (e.g. Patrucco et al., 2020), transparency (Morgan et al., 2018), and financial performance (Padgett et al., 2024). Hence, it can be mutually beneficial to work toward a robust buyer-supplier relationship, as it strengthens the position of both firms in the marketplace. A strong relationship can elevate a firm to a higher standing than either could achieve independently (e.g. Palmatier et al., 2007).

As noted, supply chain relationships succeed or fail based on the partner's commitment to the relationship, which is governed by their satisfaction with the relationship (e.g. Piechota et al., 2021; Geyskens et al., 1996). This means there must be an incentive for both parties to continue the arrangement. Affective commitment is built around relational goals (e.g. satisfaction, affiliation, enjoyment), while calculative commitment is built around economic goals (e.g. revenue, distributive justice, financial benefits) (de Ruyter et al., 2001). In both cases, however, firms must strike a balance between the social value they provide and the value they receive in the relationship. Social exchange theory (SET) posits that individuals seek rewards and avoid punishments, making calculations to determine whether the benefits outweigh the risks (Cook et al., 2013). Often deployed in supply chain research (e.g. Patrucco et al., 2020), at this level firms conduct a cost-benefit analysis of the overall value the other party brings to the relationship (Lambe et al., 2001). If both parties conclude that they are receiving fair value, the relationship strengthens, and each firm becomes committed to the other (Cropanzano and Mitchell, 2005). If the analysis reveals that the derived value is not worth the costs, then one party may choose to leave the relationship.

A strong partnership may have additive (history) effects, increasing net value for both firms via social capital. “Social capital […] can be described as the joint benefits embedded in a relationship between two or more parties. The stronger the relationship, the greater the embeddedness of the parties that exist in that relationship” (Hitt, 2011, p. 11). This creates an incentive for both parties to invest more in the relationship (e.g. responsiveness, product quality, delivery speed) to maximize overall relationship value. It also sends signals. For instance, Glas (2018) found that partner attractiveness was influenced by both trust and commitment, which ultimately led to preferential treatment compared to competitors. “(O)ver time, each party in the exchange relationship compares the social and economic outcomes from these interactions to those that are available from exchange alternatives, which determines their dependence on the exchange relationship” (Lambe et al., 2001, p. 5–6). Firms make tradeoffs between relational and social benefits and outcomes associated with the relationships, consistent with SET.

Firms use current (and past) exchanges to predict future exchanges with that party (Kelley and Thibaut, 1978). If outcomes have been positive, expectations are that future outcomes will also be positive (e.g. Blau, 1968); neither party would wish to end the partnership. These outcomes range from social to economic, and their sum is constantly weighed against the possibility of achieving the same benefits through working with another party (e.g. Homans, 1958; Thibaut and Kelley, 1959). The cost-benefit analysis is not conducted in a vacuum with respect to that relationship; instead, it is also a comparison to some uncertain ratio of an alternative relationship.

The challenge is that the parties rarely view the exchange in the same way (e.g. Nyaga and Whipple, 2011). Differences exist in perceived fairness, levels of satisfaction, and, most importantly for this study, relationship balance, which refers to dependence arising from unequal reputational power (Blau, 1964). Power can be wielded by either the buyer or supplier in a supply chain relationship, but given that there are typically more buyers competing for fewer suppliers, it is the latter who usually wields it (Porter, 2008; Ukko et al., 2020). As noted, we use the term reputational power to refer to a supplier's perceived influence within a buyer–supplier dyad, derived from its industry reputation and standing. In other words, reputational power represents a non-coercive form of power grounded in social approval and legitimacy, rather than control or sanctions. This framing allows us to treat reputational power as a reputational resource that shapes dependence and commitment, rather than as a coercive control mechanism. This power creates a dependence, which influences multiple supply chain outcomes (e.g. Andaleeb, 1996; Goodman and Dion, 2001; Izquierdo and Cillian, 2004; Mukherjee and Nath, 2007; Hernandez-Espallardo and Arcas-Lario, 2008; Mukherji and Francis, 2008). Benefits accrue to the partner with whom the other firm depends (e.g. Kim and Henderson, 2015). Since the supplier usually holds the power, there is an opportunity cost associated with maintaining their buying partner. Considering power as an industry reputation (as in our study), buyers may become more invested in the supplier if they perceive it as a leader. The opportunity cost should change based on the buyer's recognition of supplier brand prominence, and the combination of level and type of dependence determines the relevant outcomes. Reputation is neither a behavioral antecedent nor a proxy for quality, but rather a perceived non-coercive power base that affects interpretation. Figure 1 presents our conceptual framework for investigating these relationships.

Drawing on Social Exchange Theory (Blau, 1964), we treat commitment as stemming from two kinds of exchange logic. One is relational, built on trust, identification, and socioemotional value. The other is economically grounded in utility gains, performance, and the costs associated with switching. Relationship-specific adaptations (RSA) made by the supplier sit squarely in that relational camp. They signal goodwill and partner orientation, prompting reciprocity and a sense of obligation, but they do not make the buyer more dependent or increase their exit costs. So, RSA should not drive calculative commitment. These boundaries could blur if RSA requires the buyer to invest in relationship-specific assets or if quality improvements are perceived as acts of goodwill; however, these peculiarities are beyond the scope of our study and can be considered in future research.

Product quality, on the other hand, is a key economic indicator. High quality boosts satisfaction and reinforces rational loyalty, but it is still a performance outcome, not a gesture of care or shared values. It sustains exchange but rarely creates attachment. Quality supports calculative commitment, not affective commitment. RSA operates through relational trust, and quality works through instrumental utility. Each map corresponds to a distinct form of commitment, which is why we do not model cross-path effects.

Building from the literature (Scheer et al., 2010; Frazier, 1983), examine how trust leads to performance and loyalty through two types of dependence. Relationship value dependence (RVD) is the desire to maintain a partner because of the unique, non-substitutable value derived from that relationship (Scheer et al., 2015). Conversely, switching cost dependence (SCD) stems from the latent costs that would be incurred if the relationship with a specific partner were to terminate. RVD represents the current valuation of the partnership. In contrast, SCD represents the predicted depletion resulting from the loss of the relationship (i.e. the perceived cost of disengaging from the existing partner plus the costs of searching, qualifying, and engaging with a new partner).

We position relationship-specific adaptation (RSA) as our first independent variable, responding to the call from Chang et al. (2012) to investigate the antecedents and mediating mechanisms of relationship dependence. But first, we establish the direct effect. It is logical to assume that RSA would lead to a level of satisfaction with the partnership not otherwise enjoyed. RSA can signal to a partner the intention to act in the long-term interest of the relationship, particularly when RSAs are made by a more powerful partner and viewed as a sign of benevolence (Wang and Jap, 2017). Positive outcomes come from RSA only happen because there is first a level of affective commitment with that partner.

Prior research highlights that suppliers' adaptive behaviors foster relational goodwill and emotional attachment by demonstrating flexibility and concern for mutual success (Heide and John, 1992,Joshi and Stump, 1999). Adaptations reduce perceived vulnerability and signal a willingness to accommodate partner needs, reinforcing norms of reciprocity and gratitude (Lusch and Brown, 1982; Gounaris, 2005). Over time, such behavior cultivates affective commitment—a sense of identification and emotional investment in the relationship (Geyskens et al., 1996; Morgan and Hunt, 1994). When suppliers engage in relationship-specific adaptations, buyers are likely to respond with stronger affective commitment. Formally.

Next, we suggest that this commitment will increase a host of firm outcomes. Affective commitment stems from the general goodwill between firms and the pleasure derived from doing business together. It could be classified as relational capital, or “social capital with relational embeddedness” (Hitt, 2011, p. 11). Affective commitment has direct effects on different types of firm-level performance (Hitt et al., 2006), and we believe this will translate to financial performance in our model.

High product quality reduces operational risk and strengthens economic interdependence between buyer and supplier (Anderson and Weitz, 1992; Forker et al., 1997). When buyers depend on a supplier's superior-quality inputs, switching costs rise—not only due to financial considerations but also to process alignment and customer expectations (Krause et al., 2007). This dependence reflects calculative rather than affective attachment, as the buyer remains committed because the economic and performance calculus favor continuity (Geyskens et al., 1996). Thus, perceived product quality reinforces calculative commitment grounded in utilitarian and risk-reduction motives. Formally.

Finally, we predict the mediating mechanism. We assert that RSA translates into more loyal partners and higher performance, in part because they enjoy the relationship with that specific partner. Leveraging SET logic, we predict that RSA will enhance strategic performance through affective commitment, as the benefits of a committed relational partner should outweigh the costs, resulting in a competitive advantage (e.g. Jain et al., 2014; Scheer et al., 2015). Under SET, firms respond to both cost-benefit analysis and expectations. If the buyer expects the supplier to adapt their products to meet their needs, relational benefits should accrue through affective commitment (Padgett et al., 2020), which in turn enhances performance. Finally, according to SET, loyalty is cumulative, meaning it builds over time through commitment (e.g. López-Sánchez et al., 2011). Loyalty has been shown to result from affective commitment (e.g. Davis-Sramek et al., 2009); the more embedded the social capital, the higher loyalty is expected to be.

While loyalty, strategic performance, and financial performance are more straightforward, the impact on RVD is more nuanced. We base this prediction on the fact that relational value is created when suppliers adapt to their buyers' needs. Affective commitment does not bind firms financially, but it is an essential emotional tie between partners that creates shared goals (e.g. Čater and Čater, 2010; Geiger et al., 2012; Jain et al., 2014). Many suppliers do not customize for their buyers; the stronger the commitment, the greater the dependency on that supplier. While the dependency is less tangible, it is no less important. Consider an automotive company transitioning to electric vehicles that relies on several parts manufacturers. If a supplier consistently customizes their products (e.g. parts, components) and services (e.g. scheduling, delivery, inventory management), the auto company would be more reliant on that supplier than one that does not offer customization. This supplier customization signals to the manufacturer that the supplier is willing to invest in the relationship. This action can encourage additional similar investments by the manufacturer (e.g. Mukherji and Francis, 2008), thereby strengthening the long-term commitment to the relationship (Chiou et al., 2010). Formally,

The commitment buyers seek depends on the firm's overarching goals. Above, we predicted that firms seeking partners willing to adapt to their wants and needs will lead to affective commitment based on relational values. Turning to the financial perspective (bottom panel of Figure 1), firms that focus on a more utilitarian function, such as product quality, may exhibit a more calculative commitment. Product quality is an equally important aspect of the buyer-supplier dyad, as it also influences the level of commitment partners have to the relationship (e.g. Čater and Čater, 2010). In fact, it is often a basic criterion in vetting potential suppliers, as most customers are unwilling to negotiate on an agreed-upon quality level (e.g. Samson and Terziovski, 1999). In line with Ulaga (2003), we operationalize product quality (from the buyer's perspective) as the supplier's ability to consistently meet and exceed their standards. The following hypothesis is straightforward. Product quality should lead to higher levels of commitment because its benefits extend to both sides of the dyad.

Supplier reputation provides a social information context that shapes how buyers interpret supplier behaviors. In high-reputation conditions, buyers attribute adaptive behaviors to genuine partnership intent rather than opportunism (Carter et al., 2020). This reputational credibility amplifies the emotional and relational meaning of adaptation, reinforcing affective commitment. Conversely, when reputation is weak or ambiguous, the same adaptations may be viewed as self-serving or tactical. Thus, reputation strengthens the positive effect of relationship-specific adaptations on affective commitment. Formally.

Calculative commitment “represents the decision to remain in a relationship based on the economic value derived from the partnership” (Padgett et al., 2024, p. 888). It is fundamentally a cost-benefit analysis in which partners make decisions to maintain relationships based on the financial advantage it provides (Geyskens et al., 1996). This economically driven collaboration, however, does not mean that firms are less loyal (e.g. Gilliland and Bello, 2002). Often, relational and financial advantages work in tandem (Padgett et al., 2024), and we follow this work to suggest that both financial and relational outcomes will be positively influenced by calculative commitment.

Similarly, supplier reputation enhances the calculative logic of commitment by reducing perceived risk and increasing perceived reliability. Buyers interpret high-quality offerings from reputable suppliers as evidence of technical competence and long-term dependability (Roberts and Dowling, 2002; Nyaga et al., 2013). This perception amplifies the economic rationale for staying with the suppliers; switching would risk losing a trusted and capable partner. Reputation intensifies the link between product quality and calculative commitment. Formally.

Like in the previous section, we now make a case for mediation. Past research has shown a positive relationship between product quality and performance indicators (e.g. Chiou et al., 2010). We extend these studies to position calculative commitment as the mechanism that helps quality translate to these outcomes. Leveraging SET, Wilson (1995) notes that among buyers and sellers, “trust and friendships [are] supported by quality products and services” (p. 336). These relationships then become strategic, and as a result, both firms accelerate the relationship-building process to reap the benefits of a trusted partner (Lambe et al., 2001). It is logical, then, that product quality would lead to strategic and financial performance, but we suggest that commitment is once again the mediating mechanism. However, this commitment is not relational because the partnership thrives on the discrete attribute of product quality, not the less tangible willingness to adapt. We model calculative commitment as the mediator in this context.

Firms can become dependent on each other based on the value derived from the relationship itself or on the financial cost of switching partners. Calculative commitment is built around the cost of losing that supplier and refers to the financial benefits of maintaining the relationship. Padgett et al. (2020) found that as suppliers invest in the relationship, buyers become more committed. This led to a dependence on that relationship, based either on social value or on economic value (i.e. switching costs). Again, leveraging SET, Product quality has been shown to lead to behavioral loyalty through the mechanism of calculative commitment (Cater and Cater, 2010). Here, we suggest that it will mediate the relationship between switching cost dependence and switching behavior. Formally.

Power is a fundamental feature of buyer–supplier relationships and often reflects the relative dependence between parties (Patrucco et al., 2020; Krafft et al., 2015). In this study, we operationalize reputational power through industry reputation—the buyer's perception of a supplier's prestige and standing within the industry. This reputational form of power is non-coercive, shaping buyer evaluations not through control or sanctions but through admiration, legitimacy, and social influence. Power is critical to any buyer-supplier relationship and is often related to issues of dependence (Patrucco et al., 2020; Krafft et al., 2015). There are two types of power (Hunt and Nevin, 1974). Power can be coercive (Lui et al., 2006), in which threats and punishments drive dependence (e.g. Kumar, 2005) alternatively, it can be non-coercive, in which dependence arises from rewards and incentives (Gundlach and Cadotte, 1994). Reputational power, as conceptualized in our study, refers to a supplier's position within the industry. It is the buyer's perception of their supplier, and is non-coercive. The buyer would not depend on the supplier, for example, because the supplier threatens to charge more per unit or penalizes the buyer when a certain quota is not met. Instead, dependence is derived from the reward of being associated with the partner. To borrow another cliché, “a rising tide raises all boats,” or “the halo effect,” and in this case, the buyer benefits from the visibility of partnering with a reputable brand. In our model, we suggest that it will weaken the influence of adaptation on the level of commitment.

If the supplier is well regarded in the industry, the buyer is more likely to accept a less favorable relationship. Through an SET lens, Blau (1968) asserts that “the most important benefits involved in social exchange do not have any material value on which an exact price can be put at all, as exemplified by social approval and respect” (p. 455). We take this to mean that reputation outweighs concrete financial benefits in a supply chain dyad. In a relational capacity, the buyer will remain committed to the supplier even if they are less willing to adapt to requests or customize parts. For instance, Intel is the premier manufacturer of core processors and a supplier to several laptop manufacturers (e.g. Dell, HP, etc.). As the world's largest semiconductor company (McFarlane, 2021), it is the most recognized brand name in the industry. If one of their buyers requested a change to the inventory or delivery procedures—and they chose not to comply—there is reason to suspect the affective commitment may not change. Buyers may not want to “rock the boat” with such a strong partner. The relationship may not hinge on the supplier's willingness to adapt if that supplier has a strong enough reputation. A prominent industry name may have greater capacity to call the shots and decide not to grant requests to its buyers, a luxury not enjoyed by less notable or influential brands. In sum, suppliers that adapt to their buyers' needs should gain affective commitment. Still, they may not need to invest as much in adaptation to gain that social capital, given that they are highly regarded in the industry. Their reputational power grants them some latitude in the buyer-supplier dyad. Formally.

Similar to Hypothesis 3, we model reputational power as a moderator whose influence will be seen in the relationship between product quality and calculative commitment. Switching is one of the fundamental risks of supply chain partnerships (Padgett et al., 2020); buyers attempt to minimize their dependence on any single supplier. If substitute products are available to the buyer from another supplier, the supplier's reputational power will be perceived as low by the buyer (Porter, 2008). Product quality should lead to stronger calculative commitment because there are no substitutes for the best product on the market. Referring once again to Intel, the quality of their products is unmatched in the industry, as perceived by the buyers (e.g. Dell, HP, and Lenovo). This has logically increased their reputational power with buyers because their reputation (built from quality manufacturing) is now also unmatched in the industry. For instance, AMD, Intel's closest competitor, can be argued to produce better processors objectively (https://www.avast.com/c-amd-vs-intel-cpu). Still, it is the perception of “Intel Inside” that resonates with both buyers and the end consumer. In sum, it is the buyer perceived quality—not the quality itself—that suppliers can leverage.

We argue that Intel's brand value is so strong that its product quality could slip, but its buyers would remain committed to the relationship. They would still perceive themselves as receiving fair value simply by adding the Intel sticker to their devices. We are not suggesting that reputational power can overcome inferior quality; instead, we argue that it provides a margin for error. With industry credibility comes a buffer that suppliers can enjoy who earn it. Buyers may be less eager to switch suppliers because the cost of losing that visible partnership outweighs the benefits of finding a new, lower-reputation partner, even if the new partner is equal to or higher in quality. Further, the nature of any buyer-supplier partnership may influence these variables. Consider that the supplier knows the actual, realized financial benefits upon completion of the exchange, while the buyer must anticipate financial outcomes based on future downstream sales. A personal computer manufacturer like Dell can forecast demand, but it needs each supplier's components to assemble its products for the market. Intel's financial accounting (i.e. revenues and costs) is known well before Dell's, making Dell dependent on their upstream partner. This may come down to the risk buyers wish to minimize, which they seek to do by working with the best suppliers perceived as the best in the industry. We assert that this adds to the power dynamic in a supply chain relationship. In summary, while product quality leads to commitment based on economic viability, this relationship is softened when the supplier holds power in the relationship. Formally.

We modeled relational (Study 1) and economic (Study 2) pathways separately to preserve theoretical clarity and avoid conflating two distinct forms of commitment within Social Exchange Theory. Affective commitment, driven by relationship-specific responses, reflects socio-emotional attachment and relational capital. In contrast, calculative commitment, driven by product quality, reflects cost–benefit dependence grounded in tangible performance outcomes (see. Cater and Cater (2010), de Ruyter et al. (2001), Geyskens et al. (1996), Gilliland and Bello (2002), and most recently, Padgett et al. (2024)).

These two forms of commitment have different cognitive and emotional foundations, operate through distinct exchange logics, and respond differently to boundary conditions such as supplier reputation. Integrating them into a single structural model risked misspecifying the mechanisms, obscuring the moderating role of reputational power across pathways, and diminishing the parsimony of our theoretical contribution. By modeling them separately, we follow prior research (e.g. Čater and Čater, 2010; Padgett et al., 2024), treating affective and calculative commitment as parallel but independent processes, allowing us to test their antecedents, mediators, and moderators with greater conceptual precision. While boundary conditions may exist in practice, the literature supports modeling the two commitment paths independently. Given, the two studies were conducted to test each of the proposed conceptual models presented in Figure 1. Study 1 focused on the relational perspective, whereas Study 2 assessed the conceptualization of the financial perspective. Study 1 provides insights related to H1-H3 and H7, while Study 2 addresses H4-H6 and H8.

The survey instrument used included scales measuring multiple constructs via 7-point, Likert-type measures. All scales utilized in both studies were derived from the existing literature, where their respective reliability and validity had been established. Minor modifications were made to several constructs to represent the study's needs. Study 1 included measures of Relationship-Specific Adaptation, Affective Commitment, Relationship Value Dependence, and the moderator, Reputational Power. Dependent measures for both Study 1 and 2 included scales assessing Loyalty, Strategic, and Financial Performance. Measures unique to study 2 included Product Quality, Calculative Commitment, and Switching Cost Dependence. We follow past work (e.g. Jain et al., 2014; Čater and Čater, 2010; Padgett et al., 2024) in developing our commitment scales. A list of items for each construct, along with their sources, is available in  Appendix 1.

Data for both studies were collected from a professionally managed online panel (Prolific). Online panels have been consistently utilized in representative studies in both marketing and supply chain/logistics research (c.f. Locander et al., 2020). A digital survey instrument was created in Qualtrics, with potential respondents solicited via Prolific's pre-screening process, which included an embedded link to the survey.

All respondents were screened using Prolific's pre-screening macro to ensure the sample accurately reflected experience with the supplier/customer relationship. Our goal was to sample individuals with extensive experience dealing with their suppliers to gain an in-depth understanding of their perceptions of that experience. Many studies within the marketing and supply chain literature have assessed workers' individual perceptions of the purchaser/supplier relationships (c.f. Jain et al., 2014; Čater and Čater, 2010, 2009).

To this end, in identifying an appropriate sampling frame, we first selected the “work” pre-screener in Prolific, and then, under this title, we selected “work function.” Potential respondents were asked, “Which of the following describes your function in your organization? By function, we mean the types of activities you complete in your role – not what your company does overall.” Of the 21 possible selections, we selected Business Management and Administration, Transportation, Distribution and Logistics, Government and Public Administration, Manufacturing, Marketing and Sales, and Retail. This process yielded a sampling frame of 12,742 respondents. Each project in Prolific requires a Study Description for screening to determine whether respondents meet the initial inclusion criteria for the target population. Our description stated, “We are looking for individuals who work in positions (i.e. purchasing, customer service, negotiations, etc.) for their company/agency where they deal with their suppliers on a daily basis.” After these stages, the respondents accessed the instrument URL to begin completing the survey. The first filter question, following the acknowledgment of informed consent, asks respondents:

With a yes/no response to this question, respondents who answered no were redirected to the end of the survey and were thanked for their time (these individuals did not receive full compensation for their participation). Those who responded ‘yes’ were retained as part of our target population. After this stage, respondents were primed with the following statement:

Respondents were then asked the following two questions.

1. Please provide a brief statement that best describes the industry that your supplier is in (you can name the product or service that they provide you with if you wish).

2. Please provide a brief statement that best describes the industry that your company is in.

To ensure accuracy in responses, respondents were asked (to the best of their ability) to state how long their company had had a relationship with the supplier and how long the company had employed them. A one-year term was set as the minimum response for each of these questions.

These procedures yielded a usable sample of 306 responses, comprising 59% females, an average age of 39 years, and 13% of respondents who were company owners. Of the companies represented, 44% were large (with more than 250 employees). The respondent's average time with the firm was 7 years, and 56% had a relationship with their supplier for more than 5 years. Additionally, 79% had a contract with their supplier. Furthermore, 64% of respondents held managerial roles, and 13% were owners/co-owners of their firm.

Content analysis was applied to responses for questions 1 and 2 above. From these assessments, a set of 11 customer/buyer categories (i.e. accounting and financial services, automotive sales and service, computer and information technology, construction, education, healthcare, general repair services and utilities, manufacturing, and retail/hospitality, among others), and 16 supplier categories (i.e. education, raw materials, resell goods, equipment, computer/electronics/information technology, manufacturing, financial services, healthcare, and general business services), covering both good and service offerings were identified. To validate the category descriptions, two expert judges were briefed on the study's purpose and asked to match each response from the 306 respondents to one of the associated categories. Percent of agreement statistics demonstrated inter-rater reliability across both groups (purchasers: 91% and suppliers: 89%).

To assess the scale's psychometric properties, two confirmatory factor models (one for each study) were created using AMOS 28. Noting  Appendix 1, examination of factor loadings, coefficient alpha, composite reliability, and average variance extracted scores provided evidence of reliability. Discriminant validity was assessed via the Fornell-Larker Criteria (see Fornell and Larker, 1981) and the Heterotrait-Monotrait ratio of correlations (Henseler et al., 2015). The Fornell-Larker criterion is met if, for each of two constructs, the average variance extracted (shared variance within) is greater than the squared correlation between constructs (shared variance between). A cut-off of ≤ 0.85 for HTMT scores provides evidence of discriminant validity.

Multiple tests were conducted to assess common method variance (CMV). All tests were run separately for Study 1 and then for Study 2. The Harman Single-Factor test, the Common Latent Factor test, and the Marker Variable test, developed by Podsakoff et al. (2003) and Lindell and Whitney (2001), were all applied. The Harman test proposes a threshold of <50% variance explained by a single factor to alleviate CMV concerns. For Study 1, the variance explained was 40.13%, while for Study 2 it was 35.26%. A common latent factor was added to the confirmatory model and loaded onto the items for each construct included in the model, with all loadings constrained to be equal across the constructs. The square of the common loading provides evidence of CMV. Following this test, a marker variable was selected based on recommendations from Simmering et al. (2015). We chose the Attitude toward the Color Blue scale, with items developed and used by Miller and Simmering (2022), as our marker. As with the common latent factor test, equally constrained paths were loaded on each of the items of the marker variable. This process was undertaken separately for each set of constructs involved in Study 1 and then Study 2. For Study 1, the common factor and marker variable tests yielded constrained loadings of 0.702 (49%) and 0.472 (22%). The accepted cut-off value for each of these tests is 0.50, meaning that the variance explained by the common method should not exceed 50%. As noted, these results are well below this threshold. CMV did not appear to be an issue for Study 2, as the results for the common factor and marker variable tests were 0.462 (21.16%) and 0.262 (6.9%), respectively. All test results sufficiently demonstrate that common method variance is not a problem for either study.

To test each model and hence derive decisions relating to each posited hypothesis. Hayes (2017) utilized the PROCESS macro for SPSS. In this case, two separate moderated mediation models were created using Model 7, allowing assessment of each of the eight posited hypotheses. Four control variables used in representative studies (cf. Padgett et al., 2020) were incorporated into each model. These included the number of employees at the purchasing firm, tenure with their supplier, respondent tenure with the company, and whether the respondent was the company owner or manager. Specific results for each study are as follows.

As stated previously, the purpose of study 1 was to test the relational perspective model as presented in Figure 1.  Appendix 1 provides the output for psychometric measure assessment. Overall model fit proved acceptable with a χ² = 502.9; df = 275; p ≤ 0.000; AGFI = 0.859; RMSEA = 0.052; NFI = 0.919; IFI = 0.962 and CFI = 0.96. Further, all factor loadings exceed the 0.70 cutoff, with all coefficient alpha and composite reliability scores exceeding 0.72 and average variance extracted scores exceeding 0.679; all above recommended cutoffs, thus providing evidence of reliability and convergent validity (see Panel B). Discriminant validity was also supported based on the Fornell-Larker Criteria and the HTMT score for each measure (all were below the 0.85 cutoff). The highest squared correlation equals (AC and RVP), 0.632, which is below the AVE scores for each construct and the HTMT scores, which ranged from 0.788 to 0.20, are also below the 0.85 cutoff. Study 2 examined the financial perspective model. This model demonstrated acceptable fit (χ² = 430.3; df = 0.230; p ≤ 0.000; AGFI = 0.867; RMSEA = 0.053; NFI = 0.924; IFI = 0.9623 and CFI = 0.963). All measures exhibited reliability, convergent and discriminant validity, as did the relational model (see Table A1, Panel C).

Noting the results in Table 1, significant coefficients (p-values ≤0.05) associated with respective relationships are in bold. H1 posited that relationship-specific adaptation has a positive effect on affective commitment. This hypothesis is supported because the p-value for the regression coefficient (0.604) is less than 0.05. Significance is determined based on the bootstrap confidence interval. If the interval does not contain a value of zero, then significance at the 0.05 level is supported. The coefficient depicts the direction of the relationship (±).

H2 examines the positive, direct effect of affective commitment on (a) relationship value dependence, (b) loyalty, (c) strategic performance, and (d) financial performance. Noting the results in Table 1, each of the sub-hypotheses is supported, as the coefficients for relationship value dependence (0.749), loyalty (0.503), strategic performance (0.186), and financial performance (0.276) are all significant (p-values ≤0.05).

The H3 group of sub-hypotheses examines the mediation effect of affective commitment on the direct effect of relationship-specific adaptation on (a) relationship value dependence, (b) loyalty, (c) strategic performance, and (d) financial performance. Noting the results presented in Table 1, under Indirect Effects (Relationship Specific Adaptation × Affective Commitment × DV), we see that for H3a, the coefficient of 0.243 is significant, thus providing evidence of support. The same result holds for H3b, H3c, and H3d, with coefficients equal to 0.070, 0.063, and 0.092, respectively, all of which are significant (p-values ≤0.05).

An additional finding herein concerns the strong R2 between relationship-specific adaptation and relationship value dependence. Consistent with Social Exchange and Power-dependence (Emerson, 1962) theories, dependence is a function of perceived value. Further, Powers and Reagan (2007) conceptualized five stages of a long-term, buyer–seller relationships. These authors demonstrate that adaptation is considered extremely valuable to the relationship and is most important during the “creating relationship value” and the “relationship maintenance” stages. Given that, it is not surprising that the variance in relationship dependence would be highly attributable to increased adaptation on the part of the supplier.

Further, Relationship-based constructs are inherently closer to the mechanisms that our model actually explains (proximally), so higher explanatory power is expected. In contrast, financial outcomes sit further downstream and can be influenced by numerous external and firm-level factors that our behavioral model does not attempt to capture directly. Low R² values for financial performance are, therefore, not surprising in behavioral research. Prior studies using similar constructs report comparable levels of explained variance when linking relational mechanisms to financial or operational results (e.g. Palmatier et al., 2006; Nyaga et al., 2010). The fact that our relational inputs strongly predict relational outcomes, but only modestly explain financial outcomes, reinforces the theoretical distinction between our relational and financial studies.

The next series of hypotheses examines relationships from a financial perspective. As presented in Table 2, H4 is supported as product quality has a direct and positive influence on calculative commitment (coefficient = 0.813; p-values ≤0.05) and calculative commitment has a positive and significant effect on switching cost dependence (H5a, coefficient = 0.510), loyalty (H5b, coefficient = 0.431), strategic performance (H5c, coefficient = 0.512), and financial performance (H5b, coefficient = 0.449).

Having supported all sub-hypotheses under H5, H6 investigates the indirect paths between product quality, calculative commitment, and each of the four dependent variables. As shown in Table 2, calculative commitment does significantly (p-values ≤0.05) mediate the relationships that exist between product quality and switching cost dependence (H6a), loyalty (H6b), strategic performance (H6c), and financial performance (H6d). These relationships are mediated by calculative commitment.

H7 and H8 each hypothesize a moderated mediation effect of reputational power on the mediation effects of affective (H7) and calculative commitment (H8). These two hypotheses differ as H7 posits a negative effect whereas H8 posits a positive effect. First, examination of the results in Table 1 shows that the interaction effect of −0.0784 is significant (p-value ≤0.05) and directionally consistent with the hypothesized effect for each of the four dependent measures. Additionally, the Index of Moderated Mediation for each dependent variable is significant at the 0.05 level and negative, as hypothesized. Careful analysis of the conditional indirect effects for each dependent variable reveals that, as reputational power increases (from Low to Medium to High), the regression coefficient decreases. The takeaway is that the mediating effect of affective commitment grows weaker as reputational power increases. Figure 2 illustrates the graphical representation of this phenomenon, in which affective commitment remains a significant mediating factor. However, its effect weakens as the lines move closer together, indicating that the supplier's power increases, thereby weakening the mediating effect of affective commitment.

As stated, H8 posits that reputational power will have a positive effect on the mediating effect of calculative commitment. Noting the results in Table 2. Neither the interaction effect (−0.0275) nor any of the indices of Moderated Mediation are significant (p-values ≥0.05), thus clearly demonstrating that reputational power does not affect calculative commitment's mediation effect, and support for H8 does not exist.

Based on the hypothesis test results, a question arises about why reputational power moderated the strength of the mediation effect in the relational perspective model. Yet, it did not serve to mediate calculative commitment in the financial perspective model. Turning to the existing literature, we find that several studies have treated aspects of reputational power as a mediator, rather than a moderator (c.f. Ha et al., 2025; Maloni and Benton, 2000; Williams and Moore, 2007). To gain additional insights into the role of power from relational and financial perspectives, we tested two additional models that posit reputational power as a mediator.

Each model adds power in the relationship held by the supplier as an additional mediator. Preacher and Hayes (2008a, b) suggest that, in situations involving multiple indirect (mediating) effects, simultaneous estimation is advised, as assessing indirect effects separately will significantly increase the likelihood of a Type II error. Analysis was conducted using covariance-based structural equation modeling (CB-SEM). This was deemed appropriate, as it not only enabled the assessment of both mediators (power and affective/calculative commitment) but also allowed the inclusion of all four dependent variables within the relational and financial models.

Table 3 presents the results associated with the relational perspective, detailing the indirect effects, critical values, parameter estimates, and p-values. The model resulted in acceptable model fit with χ² = 562.9; df = 219; p ≤ 0.000; AGFI = 0.810; RMSEA = 0.072; NFI = 0.900; IFI = 0.921 and CFI = 0.932. Interpreting the indirect effect of relationship-specific adaptation, through power to each dependent variable, we find that for each variable, relationship value dependence (p-value = 0.628), loyalty (p-value = 0.721), strategic perspective (p-value = 0.159), and financial performance (p-value = 0.085), none of these indirect effects are significant. We also note that the presence of power as a mediator renders the indirect effect of affective commitment on strategic performance no longer significant (p-value = 0.136).

Table 4 presents the output of the financial perspective structural model. The model fit was also acceptable, with χ² = 704.5, df = 261, p ≤ 0.000, AGFI = 0.810, and RMSEA = 0.075.901; IFI = 0.910 and CFI = 0.92. As to interpretation of the indirect effects, all are significant except for the product quality - > power - > loyalty effect (p-value = 0.907). Hence, while power was not a moderator of the mediation effects of calculative commitment on the financial perspective, it does serve as a mediator, along with calculative commitment, for three of the four independent variables included in the financial perspective model. The discussion and interpretation of the practical implications of the hypothesis test results and post hoc testing follow in the next section.

Of the eight posited series of hypotheses, seven were supported. Looking first at the hypotheses associated with the relational perspective, it is found that relationship-specific adaptation has a positive effect on affective commitment (H1). Relationship-specific adaptation refers to a supplier's willingness to tailor its offerings and processes to meet customers' specific needs. Firms can gain insights here as the ability to modify to meet customers' needs results in perceptions of value and efficiency, as conceptualized by affective commitment.

The H2 and H3 hypotheses suggest a clear pattern: affective commitment directly enhances outcomes, including relationship value, loyalty, and stronger strategic and financial performance. Adaptability also influences these outcomes, but its impact is mediated by affective commitment. When partners genuinely value the relationship, both sides stand to gain—emotional depth enhances performance, and performance strengthens the bond. The importance of affective commitment to suppliers lies in its fostering of greater perceived value from their customers. Suppliers are encouraged to offer products that provide superior value to their customers. In contrast, customers are advised to actively seek suppliers who are willing and able to meet their specific needs.

H7 assesses the moderating role of power on the mediating presence of affective commitment. In this case, the support for H7 suggests that differential power in the supplier/buyer relationship is detrimental to a customer's commitment to their supplier and thus could result in customers seeking suppliers whom they perceive as less threatening. Perceptions of power herein could be consistent with suppliers demanding that their customers comply with their processes and procedures, which would be counterintuitive if their goal is to create long-term loyalty and commitment.

From a financial perspective, the H4, H5, and H6 series of hypotheses assess the roles of product quality and calculative commitment in shaping switching-cost dependence, loyalty, and strategic/financial performance. The financial perspective focuses more on “bottom line” issues as opposed to the “goodwill” aspects of the relational perspective. Here, product quality has a significant impact on calculative commitment (H4), which, in turn, has a substantial influence on each of the customers' outcomes (H5a-d). Furthermore, the relationship between product quality and outcomes is strongly mediated by calculative commitment (H6a-d). Herein, the financial perspective focuses on the customer's cost-benefit analysis of the relationship. Power does not play a role in this case, as H8 was not supported. The implication here for suppliers is that focusing primarily on one's product offering is not necessarily a terrible thing, provided the perceived quality results in customers seeing a significant benefit relative to the cost they incur. Power does not appear to affect the strength of calculative commitment as a mediator. Firms considering this approach are advised to adopt cost-effective pricing rather than relational adaptation to serve their customers’ needs best.

The follow-up, post hoc analysis took a deeper dive into the existence of power within the supplier/purchaser dyad. Power was conceptualized as a mediator across both perspectives and in relation to each form of commitment. Power did not mediate any of the relationships from the relational perspective. This seems intuitive given the focus on adaptability and efficiency in this perspective. Yet, for the financial perspective, power served (along with calculative commitment) to mediate three of the four relationships; all were significant, except for loyalty. For suppliers focusing on the superior quality of their product as a primary strategic advantage, having a perceived powerful position increases dependence, as well as strategic and financial performance; yet it does not lead to greater customer loyalty to a powerful supplier. The implications of these findings extend to both theory and practice.

Our post hoc analysis helped clarify why the relational and financial pathways behave differently. In the relational model, reputational power did not mediate the effects of RSA or affective commitment, suggesting that social and emotional bonds operate independently of perceived industry reputation. Buyers respond to adaptive behaviors because they reflect goodwill—not because of the supplier's standing. In contrast, the financial model revealed an important insight: power lies at the intersection of product quality and calculative commitment. In plain terms, reputational power strengthens the business side of the relationship, drawing partners closer together when results drive the connection. Buyers tend to stick with a powerful, high-quality supplier even when there's not much emotional pull. Power matters most when commitment is based on calculation, rather than a sentimental reminder that every exchange has both social and economic aspects.

In supply chains, relationships are the backbone of success—but deciding whether to keep working with a buyer or supplier depends on more than goodwill. Many factors come into play. Our findings bring power asymmetry back into the center of supply chain relationship research—a field long guided by the relationship marketing paradigm. While relationship marketing has emphasized trust, commitment, and mutual benefit (Morgan and Hunt, 1994), it has often overlooked the more complex realities of power imbalances that shape how partners actually work together. That blind spot leaves a significant gap in our understanding of today's volatile business landscape. Re-centering power, we address recent calls for a more nuanced view of buyer–supplier dynamics (Ma et al., 2021; Azadegan et al., 2022; Patrucco et al., 2020), grounded in reputational power. We demonstrate that buyers commit not only because they trust or value their suppliers, but also because they perceive reputational risk or benefit in the relationship, which aligns with our central argument that supplier reputation serves as a power mechanism rather than a stand-alone attribute (Patrucco et al., 2024).

Whereas prior work has often treated power as coercive or purely economic (e.g. Gaski, 1984; Kumar et al., 1998), our results contribute to a more socially embedded view of power as a reputational asset. This extends the relationship-specific adaptation (RSA) literature by highlighting how affective commitment is shaped by partner investments and by buyers' perceptions of that partner's industry standing. This also adds dimension to calculative commitment: while RM literature tends to view it as less desirable or “cold,” we show that calculative commitment driven by product quality remains robust even in the presence of power asymmetry.

This study closes the long-standing gap between relationship marketing and power-dependence theory in logistics and supply chain management. It shows that today's supply chains run on both relational goodwill and structural leverage—two forces that do not compete so much as coexist. The point hits home given recent disruptions, shifting risk appetites, and growing interdependence across industries. Buyers consider emotional, economic, and reputational trade-offs when selecting and committing to suppliers. This should be a renewed focal point for both theory and practice.

This study extends Social Exchange Theory by unpacking how reputational power—industry reputation—shapes buyer commitment through distinct underlying mechanisms. While prior SET-based research often treats commitment as a singular construct (e.g. Morgan and Hunt, 1994; Nyaga et al., 2010), we distinguish between affective and calculative commitment and demonstrate how they respond differently to both supplier behavior and perceptions of reputational power. Our results show that supplier reputation moderates the pathway between RSAs and affective commitment, but does not influence the link between product quality and calculative commitment. This asymmetry deepens theoretical insight into SET by illustrating that buyers interpret social and economic value through different cognitive and emotional lenses, particularly in contexts of perceived power imbalance.

Second, our study reconceptualizes power as a non-coercive, reputational asset rather than merely a source of control or forced compliance. Traditional power-dependence theory tends to focus on coercion—such as threats, sanctions, or withholding resources (Gaski, 1984; Kumar et al., 1998). Our findings suggest a distinct form of power. A strong supplier reputation builds affective commitment and cushions gaps in adaptability or quality—not by force, but through respect and perceived relational capital. This view aligns with recent work on psychological uncertainty (Ma et al., 2021) and preferred customer status (Patrucco et al., 2024), positioning reputation as a strategic resource that shapes how buyers weigh social-exchange investments.

Third, our results challenge the existing literature's assumption that supplier reputation uniformly enhances commitment. We recognize that reputation matters and can even determine who holds the more powerful position in the relationship (Morsy, 2017). Past research tends to treat reputation as a universal good—something that lifts every aspect of a relationship. Our results tell a more selective story. Reputation reinforces the relational side of exchange through RSAs and affective commitment, but it does not reach the economic side, which is built on product quality and calculative commitment. Buyers know the difference. They may cut a reputable supplier some slack for emotional reasons, but they still expect measurable performance where it counts. This nuanced perspective refines our understanding of commitment formation under asymmetry and expands SET to include more differentiated exchange processes.

Our study contributes to the resurgence of power asymmetry as a key concern in supply chain research. While the RM paradigm has emphasized collaboration and trust, our work revisits structural imbalance as a force in buyer–supplier relationships. One that shapes not only dependence but the cognitive framing of value. In doing so, we join recent calls to revisit how power is exercised in today's supply chains—systems characterized by volatility, fierce competition for supplier attention, and ongoing global disruptions (e.g. Azadegan et al., 2022). We demonstrate that power and commitment are not mutually exclusive forces. They interact and evolve in response to both tangible investments and reputational standing.

Our study underscores the strategic significance of supplier reputation in buyer–supplier relationships. It demonstrates that long-term collaboration can be achieved, even when buyers hold the power advantage, through transparent communication and information sharing (Wang et al., 2016). Importantly, our findings suggest that suppliers with strong industry recognition may enjoy a “margin for error,” wherein buyers remain committed to the relationship even when suppliers reduce their level of customization or adaptability. This means that well-reputed suppliers can occasionally afford to be less responsive to specific buyer needs—yet still retain loyalty—because buyers value the perceived stability, credibility, and status associated with partnering.

For suppliers, building brand equity, visibility, and goodwill is not just a marketing move—it is a strategic choice in managing relationships. Reputation should not mask weak performance; it should serve as a foundation for joint planning, clearer expectations, and investment in buyers who create long-term value. Managing reputational power effectively strengthens influence while maintaining sustainable partnerships. From the buyer's side, reputational power can breed dependence through both affective and calculative commitment. As our results show, cost-based loyalty—staying to avoid switching costs—can harden into structural dependence. To counter this, buyers should maintain leverage by benchmarking supplier performance, crafting smart contracts, and occasionally rotating partners. These practices keep power in balance and protect against overreliance on any single supplier.

Moreover, reputational power dynamics have broader implications for supply chain governance. Highly reputed suppliers may influence buyer behavior without necessarily offering superior product quality or relational customization. Buyers must remain vigilant to ensure that loyalty is earned through value creation rather than merely conferred by reputational power. Managerial teams should consider conducting internal audits of their supplier portfolios to assess whether suppliers' loyalty is justified. This vigilance is crucial for maintaining a balanced, value-driven supply chain.

Finally, this study raises ethical and regulatory considerations. Habib et al. (2015) provide strategic tools for weaker actors in asymmetric relationships, such as fostering transparency and forming coalitions with other buyers. Such tactics may help buyers counteract supplier complacency or stagnation. For policymakers, these dynamics underscore the importance of oversight when dominant suppliers leverage their reputational standing to maintain loyalty despite declining value. For firms, the key managerial takeaway is to treat reputation not as a license to underperform, but as an obligation to lead responsibly and deliver value consistently, even when the power dynamic is in their favor.

Although our study refers to “supply chain” relationships, our data do not capture full supply chain dyads or network interactions. The respondents shared their perceptions as individual actors situated within supply chain organizations, rather than as matched buyer–supplier pairs. The findings reflect how managers interpret relational and economic mechanisms rather than how those dynamics unfold jointly across firms. This limits our ability to model true interdependence or trace the reciprocal exercise of power within the dyad. Future research could address this by collecting paired data or network-level measures to examine how reputational power operates across multiple tiers of the supply chain.

Our investigation into supply chain relationships and the influence of reputational power has limitations that inform future research. First, our buyer-side lens offers a fresh perspective on the usual seller-side focus, but a complete picture requires both views. Future work should explore what happens when those views clash—for example, when a supplier's reputation looks different from the buyer's perspective. Capturing that alignment could add depth to future models. Second, our data is just a snapshot, and relationships evolve. One interesting angle might be to study how partnerships—and perceptions of status and power—change as the relationship matures.

In our study, we utilize panel data to provide results that are generalizable to a large population of players within the buyer-seller dyad. While the majority of our sample consisted of individuals in managerial roles, we assessed individual perspectives rather than taking a more overarching organizational perspective. Future assessments of power issues in the supply chain, as they relate to commitment and outcomes, may be served well by incorporating a more organizational perspective. We also acknowledge the limitations of using cross-sectional and panel data. Respondent bias and industry-specific noise can distort the results, making it more challenging to generalize the findings. However, while this certainly imposes limits, we attempted to be meticulous in our pre-screening of respondents and in our assessment of potential problems in the data through multiple common methods variance tests. We feel that the apparent absence of such issues via these tests should mitigate some of these concerns.

While we draw insights from a more “universal” perspective in our study, it may be prudent to investigate more deeply the differences across industries. Such an inquiry could assess differences among firms focusing on goods versus services, for-profit versus non-profit versus government entities, and manufacturers versus resellers, among others. Additionally, supply chain members have numerous relationships, and the power dynamics shift between partners. Future research could examine the interplay of power among multiple partners simultaneously, uncovering nuanced approaches to gaining and sustaining commitment. Such research could prove helpful in predicting which paths in our conceptual models are more strongly influenced by external power perceptions. Our models show a stronger relational pull, but this may vary by industry or culture. Hence, we urge scholars to examine and assess these possibilities in the future.

Researchers in logistics and supply chain management need to return to the roots of Power Dependency Theory to truly understand the dynamics of power. French et al.'s (1959) foundational typology of power, along with Raven's (1993) extension that includes informational power, offers significant opportunities to extend the power–commitment framework. Our work focuses on supplier reputational power as a single, referent-based source of power. Future studies could disaggregate reputational power into multiple bases—reward, coercive, legitimate, referent, expert, and informational—to test how each uniquely interacts with affective and calculative commitment. Countervailing power (Galbraith, 1952) examines how partners on the weaker side of an asymmetrical power relationship can counterbalance their commitment to a powerful supplier. A buyer with strong countervailing capabilities may resist the pull of supplier coercive or legitimate power, instead favoring relationships that foster mutual trust and shared goals. Future research could model these interactions explicitly to explore how the interplay between reputational power bases and buyer countervailing resources shapes the commitment–performance link. We detail a large number of potential studies in  Appendix 2.

Future research can extend our power–commitment framework by incorporating emerging themes from sustainability and technology management. Chowdhury et al. (2025) show how artificial intelligence can support green servitization, prompting new questions about what truly drives commitment. Do sustainability efforts foster affective commitment through shared values, or calculative commitment through efficiency gains? How might reputational power in sustainability settings shape or amplify those effects? Similarly, one might question the performance impact of supply chain technology (Story et al., 2021), which could act as a relationship-specific adaptation in digital form. Generative AI technology will further reshape the role of reputational power in technology-driven exchanges (Richey et al., 2022). Finally, people still manage these relationships. And given the current global business climate, scholars must examine the consequences of power use across national borders. Understanding the importance of power will influence who we hire to work as boundary-spanning expatriates (Harvey et al., 2012). Cultural and institutional distance can shift the balance between affective and calculative commitment, especially when power is not evenly shared. In global settings, supply chain teams often act as embedded relational capital—bridging gaps, reinforcing supplier reputation, and shaping how commitment takes form. Future work should examine how diverse sources of reputational power—such as sustainability leadership, digital capability, expectation alignment, and global team effectiveness—interact with relational and economic commitment mechanisms to shape buyer–supplier outcomes.