Strategic factor markets: a critical review, insights and future directions

Strategic factor markets (SFMs) are “where firms buy and sell the resources necessary to implement their strategies” (Barney, 1986: 1232). These markets underpin competitive advantage through control of idiosyncratic resource bundles (Barney, 1986, 1991). Competitive advantage arises when firms identify and acquire resources at prices below their true value. Such opportunities to control undervalued resources emerge from two interconnected factors: inherent market imperfections (Barney, 1991) and differences in managerial cognition (Rahmandad, Denrell, & Prelec, 2021). Barney (1986: 1233) notes, SFMs often disrupt “perfect information models of competition.” As a result, strategic resource prices fail to reflect their true future value (e.g. Adegbesan, 2009; Asmussen, 2015; Barney, 1986; Leiblein, 2011; Maritan & Florence, 2008). Additionally, differences in managerial cognition influence how resources are evaluated, creating heterogeneous expectations of their future value (Amit & Schoemaker, 1993; Helfat & Peteraf, 2015; Rahmandad, Denrell, & Prelec, 2021). SFM theory suggests that firms with better information about resources’ future value (Barney, 1986; Makadok & Barney, 2001) and superior insights (Demsetz, 1973) can acquire undervalued resources and gain a competitive advantage.

SFMs hold a unique position in the organizational management literature by linking the resource-based view, which emphasizes firm-specific resource heterogeneity as the foundation for sustained competitive advantage, with economic theories highlighting market imperfections, such as uncertainty, information asymmetries and resource scarcity. These market imperfections create strategic opportunities in the form of undervalued resources. This intersection allows SFM theory to explain competitive advantage as arising not only from possessing valuable resources but also from strategically navigating imperfect markets where these resources are evaluated, priced, and contested. Understanding both the foundations and empirical findings of SFM theory is thus essential for guiding firms in how resources are controlled under varying conditions.

Despite its enduring influence, SFM theory has evolved in a fragmented manner. Empirical studies, conceptual contributions, and formal models have proceeded in parallel, yielding insights but lacking integration. This matters now more than ever. As firms face mounting pressure to make timely resource decisions amid rapid technological change and resource scarcity, core premises of SFM theory are being reshaped. The rise of artificial intelligence (AI), for example, is redefining how firms perceive and act on opportunities, threatening to erode traditional sources of advantage. Without a synthesis, scholars and practitioners are left with a framework developed for an earlier era, one that may no longer explain contemporary strategy.

Moreover, as Post, Sarala, Gatrell, and Prescott (2020) argue, theory-building reviews are essential not only when a field lacks integration, but also when fragmentation limits our response to emerging empirical and practical challenges. Reviews can advance theory by identifying tensions, clarifying competing logics, testing frameworks and establishing boundary conditions for when models hold, or fail. Our review takes on this dual task. First, we organize a scattered literature into a framework spanning capabilities, valuation and pricing, and cognition. Second, we draw on developments such as AI and shifting global interdependencies to define new boundary conditions for SFM theory, identifying where its assumptions may no longer apply.

From a practical standpoint, managers face increasing pressure to make timely, accurate resource acquisition decisions in environments characterized by uncertainty, resource scarcity and imperfect information. SFMs today require managers not only to value resources accurately but also to leverage internal capabilities effectively and integrate managerial cognition with emerging tools like AI. Yet, practitioners lack a framework that clearly connects these elements to actionable insights. Revisiting and synthesizing the SFM literature helps address this gap by clarifying how firms can strategically develop and deploy capabilities, distinguish valuation from pricing, and enhance judgment through cognitive and technological advances. This review seeks to revitalize SFM theory by integrating its historical insights and extending their applicability to contemporary strategic challenges.

Given these theoretical and practical considerations, a structured synthesis and extension of SFM theory is timely and necessary. Nearly four decades after Barney’s (1986) foundational article, no comprehensive review has been undertaken. Building on Post et al. (2020), this review contributes by organizing fragmented scholarship into a coherent framework and articulating boundary conditions for SFM theory in light of emerging developments such as AI. Drawing on 97 articles, 46 of which explicitly engage with SFM theory, we identify three core themes: capabilities, valuation and pricing, and cognition, including AI’s potential transformative role.

This study adopts an integrative review methodology to synthesize and reframe the fragmented literature on SFMs. This method is well-suited for mature yet conceptually dispersed domains, enabling new theoretical perspectives by integrating diverse conceptual, empirical, and methodological contributions (Torraco, 2005; Snyder, 2019). It helps identify underexplored themes, theoretical tensions, and emerging areas while offering a cohesive framework for advancing SFM theory.

We conducted a structured search in the Web of Science Core Collection, chosen for its comprehensive indexing of peer-reviewed articles across business and management. This search aimed to capture both foundational and emerging work on SFMs. We included articles that referencing “strategic factor markets,” “factor markets,” “resource market,” “resource acquisition”, or “resource investments” in the main text. This yielded 97 articles published between 1986 and 2024, reflecting diverse perspectives and varying degrees of engagement with SFM-related constructs.

To refine this data set and ensure conceptual precision, we applied inclusion criteria focused on explicit engagement with SFM theory. We retained only articles that included one or more of the terms in the title, abstract, or keywords, signaling a direct theoretical or empirical contribution. This resulted in a final set of 46, forming the core literature for this review. These span conceptual, empirical, simulation-based, and formal modeling approaches, collectively offering a robust foundation for synthesizing insights and identifying future research opportunities within the SFM domain.

To analyze the selected studies systematically, we developed a thematic framework grounded in foundational literature and patterns observed across the 46 articles directly engaging with SFM theory. This framework centers on three analytical dimensions: capabilities, valuation and pricing, and cognition, that provide a structured lens for interpreting how firms interact with SFMs.

To enhance transparency, we coded the 46 selected articles by methodological orientation: 18 empirical (39%), 13 conceptual (28%), nine simulation-based (20%), and six formal modeling (13%). The thematic framework – capabilities, valuation and pricing, and cognition – was developed inductively through iterative reading and grounded in both foundational theory and recurring patterns. One author conducted the initial coding and theme development; a second researcher competent in SFM theory independently reviewed the coding. No major disagreements arose, and minor clarifications were resolved collaboratively.

The first analytical dimension is capabilities in SFMs, encompassing how firms build, apply, and adapt capabilities to identify, acquire, and integrate strategic resources (e.g. Lippman & Rumelt, 2003; Makadok & Barney, 2001; Makadok, 2001; Moliterno & Wiersema, 2007). These include resource-picking skills, foresight, and dynamic capabilities that help firms sense undervalued resources, seize such opportunities, and reconfigure their resource base to align with shifting markets (Makadok, 2001; Leiblein, 2011). This theme shifts attention from internal capabilities to how they are shaped through firms’ engagement with external factor markets. It also captures how firms in emerging or institutionally weak contexts deploy relational and organizational capabilities to address market imperfections (e.g. Manikandan & Ramachandran, 2015; Hoskisson et al., 2013).

The second dimension is valuation and pricing of resources. Though related, these present distinct decision-making aspects in SFMs. Valuation refers to a firm’s internal estimate of a resource’s future contribution, shaped by firm-specific complementarities, existing resource bundles, and strategic foresight (Coen & Maritan, 2011; Maritan & Peteraf, 2011). Pricing is related to market dynamics such as competition, resource scarcity, bargaining, and institutions that determine acquisition cost (Adegbesan, 2009; Lippman & Rumelt, 2003; Markman, Gianiodis, & Buchholtz, 2009; Ross, 2012). This dimension highlights how SFM imperfections create a gap between intrinsic value and market price, and how firms must manage this gap to generate rents (e.g., Lippman & Rumelt, 2003; Poppo & Wiegelt, 2000). While valuation is more theoretically developed, pricing remains underexplored and ripe for future research.

The third dimension is cognition in SFMs. This theme explores how decision-makers perceive, interpret, and act on incomplete or ambiguous information. It incorporates the behavioral foundations of resource acquisition, including managerial judgment, bounded rationality, learning, and heuristics (Amit & Schoemaker, 1993; Kim, Hoskisson, & Lee, 2015; Knott, 2003; Leiblein, 2011; Poppo & Wiegelt, 2000). It also addresses how cognitive differences across firms create heterogeneous expectations about resource value, shaping acquisition decisions and outcomes (Ahuja et al., 2005; Barney, 1986; Maritan & Peteraf, 2011).

These three dimensions collectively serve as the organizing structure for our analysis, helping map the intellectual landscape of SFM research, highlight key theoretical developments, and propose future research agenda. The following sections examine each dimension in depth, unpacking how existing literature conceptualizes capabilities, valuation and pricing, and cognition in SFMs. We also incorporate AI’s emerging role across all three dimensions to evaluate how algorithmic tools are reshaping traditional assumptions and creating new boundary conditions. Each theme is discussed with attention to its theoretical contributions, empirical support, and underexplored areas.

Building on the methodology above, we created a literature matrix categorizing the 46 core articles by their methodological approach (e.g., conceptual, empirical, formal modeling, and simulation) and their contributions to the three central themes: capabilities in SFMs, valuation and pricing of resources, and cognition in SFMs. Table 1 summarizes how each article contributes to these domains. Table 2 also provides definitions of main constructs in SFM literature.

Capabilities are central to achieving and sustaining competitive advantage, reflecting firms’ consistent ability to create value (e.g., Amit & Schoemaker, 1993; Winter, 2003). In SFMs, consistently acquiring undervalued strategic resources ahead of competitors is a critical capability that strengthens competitive positioning (Barney, 1986, 1991). This involves not only identifying valuable resources but processing information, anticipating trends, and acting decisively under uncertainty (Leiblein et al., 2017). In imperfect markets, firms that interpret signals and predict accurately are better positioned to secure resources before their strategic value becomes widely recognized (Ahuja et al., 2005; Capron & Chatain, 2008; Collis, 1994).

While SFM literature does not define a fixed set of essential capabilities, it emphasizes two recurring skills: superior foresight and entrepreneurial ability (Koparan & Aksaray, 2024). Superior foresight allows firms to predict the future resource value and make informed acquisitions (e.g., Barney, 1986; Demsetz, 1973; Kogut & Kulatilaka, 2001). Makadok (2001) refers to this as “resource-picking skills,” accurately assessing the future true value to identify and secure undervalued resources (Capron & Chatain, 2008; Chatain, 2014). Developing these capabilities involves managing information costs and navigating valuation uncertainties (e.g. Kaufman, 2015; Leiblein, Chen, & Posen, 2017; Makadok & Barney, 2001; Maritan & Florence, 2008; Maritan & Peteraf, 2011).

While some scholars argue that superior foresight may overlook complementary resources in value realization (Adegbesan, 2009), this critique reflects a narrow interpretation. As Barney (1986) originally proposed, effective foresight entails both external analysis and internal evaluation of how a resource fits with existing capabilities and strategy. This inward perspective helps firms interpret resource value through their unique configurations, making their acquisitions more strategic and harder to replicate (Ahuja et al., 2005).

The SFM literature also explores how external dynamics affect firms’ strategies to buy (resource-picking) or build (capability-building) resources. Several studies show that SFM conditions in home and host countries shape these decisions for multinational and emerging economy firms (Hoskisson et al., 2013; Kim, Hoskisson, & Lee, 2015). For instance, more developed SFMs increase the likelihood of acquisitions over greenfield investments (Chen et al., 2017). Similarly, emerging-market multinationals often pursue strategic resources such as technology, managerial know-how, and talent to accelerate competitive catch-up (Cui et al., 2017; Hoskisson et al., 2013; Kim et al., 2015).

The second core skill, entrepreneurial ability, is a firm’s capacity to generate opportunities through creative resource combinations. This involves seeing beyond immediate use and imagine new or enhanced value when resources are paired (Denrell, Fang, & Winter, 2003). In SFMs, where resource interactions are complex and future value is hard to estimate, creative recombination is vital (Lippman & Rumelt, 2003). Entrepreneurial action helps navigate these challenges (Barney, 1986, 1989; Denrell et al., 2003), allowing firms to generate superior complementarities by identifying creative synergies and configuring resource bundles in unique ways (Lippman & Rumelt, 2003). It also enables proactive strategies like resource preemption, leapfrogging, or assembling defensive resource portfolios to block competitors (Capron & Chatain, 2008; Grimpe & Hussinger, 2014; Maritan & Florence, 2008), thereby reshaping market dynamics (Markman, Gianiodis, & Buchholtz, 2009).

Beyond strategic positioning, entrepreneurial ability also includes integrating newly acquired or underused resources to enhance firm performance (Deng, 2009), particularly in uncertain and imperfect markets where alignment, cospecialization, and knowledge transfer are key (Townsend & Busenitz, 2008). Even nonstrategic complementary resources can become critical under scarcity or interdependence, highlighting the need for entrepreneurial vigilance throughout the supply chain (Ellram, Tate, & Feitzinger, 2013). These insights suggest that entrepreneurial ability shapes not only what resources firms pursue in SFMs, but how they mobilize and combine them to achieve distinct, hard-to-replicate advantages.

Aligned with these perspectives, several studies emphasize the role of SFM investments in capability development (Maritan & Peteraf, 2011; Maritan & Florence, 2008). While intangible capabilities cannot be directly bought, firms can acquire resources in SFMs to develop them or pursue a “buy-to-build” strategy (e.g. Adegbesan, 2009: 464; Clougherty & Moliterno, 2010; Coen & Maritan, 2011; Maritan & Peteraf, 2011). Supporting this, Hsu and Cohen (2020) show that SFM characteristics like diversity, complementarity, and specialization enhance firms’ abilities to develop and apply intangible capabilities by improving R&D effectiveness.

Beyond the first-order capabilities for acquiring undervalued resources, firms must also develop dynamic capabilities to keep their SFM capabilities aligned with changing environments (Teece, Pisano & Shuen, 1997; Teece, 2012; Winter, 2003). In dynamic SFMs, reconfiguration is key, while in more stable contexts, sensing and seizing capabilities dominate (Koparan & Aksaray, 2024). Coen and Maritan (2011) show that superior search abilities aid resource capture when initial endowments are low, but matter less when endowments are high. Kim and Hoskisson (2015) also demonstrate that emerging-market firms use adaptive resource strategies to navigate institutional complexity, underscoring dynamic capabilities’ role in aligning acquisitions with institutional conditions for sustained competitive advantages.

Taken together, the literature shows that SFM capabilities involve more than isolated routines; they reflect a dynamic interplay between foresight and entrepreneurial ability. Firms must not only anticipate resource value but also integrate acquisitions to support strategic direction. Resource acquisition is thus a continuous process linking valuation, configuration, and adaptation. This research, while rich, remains fragmented, with overlapping but uncoordinated discussions of foresight, entrepreneurial ability, and dynamic capabilities. As Post et al. (2020) note, this fragmentation presents an opportunity to test new theory by synthesizing dispersed insights and evaluating competing models. Variation across institutional settings and market structures further positions SFM capability research as fertile ground for theorizing boundary conditions. As these conditions shift with AI adoption, global supply changes, and institutional volatility, foundational assumptions about SFM dynamics must be revisited.

Both superior foresight and entrepreneurial ability emphasize the importance of accurately assessing a resource’s value creation and integrating it with existing bundles. Despite the emphasis, the literature lacks a clear definition of the specific set of capabilities (i.e., SFM capabilities) required to navigate these markets. Consequently, guidance on how organizations can systematically develop and enhance such capabilities remains limited (Ahuja et al., 2005; Dierickx & Cool, 1989; Maritan & Peteraf, 2011). This highlights a broader gap in process-oriented perspectives and addressing the “how” questions (Maritan & Peteraf, 2011: 1383). Future research could examine internal mechanisms that support SFM capability development. For instance, longitudinal case studies could explore how firms structure their decision-making processes, information systems, and cross-functional collaboration to assess, build, and integrate resources. It is also worth examining whether these capabilities are teachable or transferable and what learning routines or knowledge management practices facilitate their diffusion.

This line of inquiry also raises a critical question: do SFM capabilities inherently serve as isolating mechanisms, given their tacit nature and nontradability (Barney, 1989; Dierickx & Cool, 1989; Knott, 2003)? Without such mechanisms, even superior SFM capabilities risk diffusion, weakening their competitive value (Dierickx & Cool, 1989; Knott, 2003; Makadok, 2002). Can (dynamic) SFM capabilities function as isolating mechanisms by generating causal ambiguity (Kim & Hoskisson, 2015; Lippman & Rumelt, 1982), thereby making them hard to replicate? For example, firms may adopt different internal models to cultivate superior insights and evaluation capabilities – some use centralized supervision, others incentivize agents to spot opportunities. Yet, as Ross (2012) suggests, the effectiveness of these models varies with competition: higher competitive intensity increases the cost of incentivization, prompting a shift toward supervision.

Researchers could investigate when firms benefit more from centralized supervision versus decentralized, incentive-based systems. For instance, in industries like tech or pharmaceuticals, where resource acquisitions require specialized knowledge and quick action, incentivizing local managers or business development teams may prove effective. Future research could also examine hybrid systems, for example, using centralized oversight for major acquisitions and incentivized agents for smaller, time-sensitive deals.

Barney’s (1986) foundational argument that firms gain superior returns by acquiring resources below their future value has shaped much of the research in SFMs. This premise rests on the idea that valuation outcomes differ across firms due to uncertainties, information asymmetries, and firm-specific complementarities (Adegbesan, 2009; Asmussen, 2015; Coen & Maritan, 2011). Consequently, the same resource may hold different strategic value depending on a firm’s existing assets, capabilities, and expectations under uncertainty.

In this context, increasing the accuracy of firm-specific resource valuation is a key focus in the literature. Valuation accuracy depends on firm’s ability to effectively interpret uncertain information and make informed acquisition decisions. Coen and Maritan (2011) show this often hinges on the interplay between search capabilities and existing resource endowments. Maritan and Florence (2008) further emphasize that assessing both current and future option value relative to rivals enhances firms’ ability to place optimal bids in SFMs.

Valuation also involves navigating agency issues that distort investment decisions when decision-makers’ (agents) interests diverge from those of the firm (principals). Managers may avoid risky but beneficial acquisitions to protect their short-term performance evaluations or pursue high-profile assets for personal gain. Makadok (2003) highlights these challenges in SFMs, where resource value is hard to assess under uncertainty. Firms must balance incentives and monitoring to align behavior with firm goals while refining valuation expectations. Mitigating these risks improves decision quality and capital allocation.

External factors such as institutional environments also influence valuation (Huang, Xie, & Wu, 2021). Deng (2009), for example, shows how Chinese firms assess value under domestic institutional voids and leverage government support to acquire strategic assets abroad. These studies suggest that valuation is a dynamic, multilevel process embedded in organizational and environmental contexts.

Another perspective on valuation suggests that the value of resources extends beyond their contribution to a firm’s value-creation activities, encompassing their preemptive value (Capron & Chatain, 2008; Chatain, 2014; Grimpe & Hussinger, 2014). Similarly, Harvey and Turnbull (2020) illustrate how Ryanair leverages rivalry restraint by securing cost-advantageous agreements with regional airports, creating barriers to entry and limiting competitors’ access to key resources.

The valuation literature in SFMs agrees that resource value is not objective or stable, but a contextually interpreted judgment shaped by complementarities, strategic positioning, and expectations under uncertainty. Rather than a purely technical process, valuation is embedded in firms’ cognitive frameworks and routines. This view highlights interpretive variation: firms with similar information may reach different conclusions based on internal fit, synergies, and risk tolerance. The literature outlines several valuation logics such as intrinsic value, preemptive value, and strategic fit, each suited to specific contexts. As Post et al. (2020) note, this diversity calls for synthesis to clarify each logic’s conceptual foundations and boundary conditions. These include institutional context, market volatility, and resource type, which determine when and why particular valuation approaches apply. Viewing valuation as both firm-specific and context-dependent opens paths for theory-building on when valuation leads to strategic advantage.

SFM literature primarily focuses on valuation – estimating a resource’s future true value to the firm (e.g. Barney, 1989, 1986; Leiblein, 2011; Leiblein et al., 2017; Makadok & Barney, 2001). This focus often treats pricing – estimating the market price – as a function of valuation, where firms form their bids based on their assessment of intrinsic value (e.g. Adegbesan, 2009; Asmussen, 2015; Denrell et al., 2003; Maritan & Florence, 2008; Ross, 2012). However, while valuation and pricing are related, they are not synonymous. Pricing involves distinct dynamics, influenced by both intrinsic value and prevailing market conditions.

Accurate valuation is critical for generating positive rents by identifying high-potential investments (e.g., Adegbesan, 2009; Asmussen, 2015; Barney, 1986, 1989). However, pricing also matters. Well-priced acquisitions increase rent potential (Lippman & Rumelt, 2003), while poor pricing risks missed opportunities or the winner’s curse. For instance, in the 2021 auction of the US Constitution, Citadel outbid ConstitutionDAO, a decentralized crypto collective, by a mere 1% (Koparan & Koparan, 2025; Li & Picker, 2021). The DAO’s transparent fundraising gave Citadel an information advantage that enabled precise prediction of its competitor’s bidding limit. This example illustrates that accurate pricing, just above the market-clearing bid, is critical for capturing value in SFMs. Conversely, when firms lack such precision, they risk either overpaying or missing out on valuable opportunities altogether, both of which can erode potential value (Maritan & Florence, 2008).

Moreover, like valuation, pricing also operates under uncertainty (Baker et al., 2012), leading to bounded rationality (Einhorn & Hogarth, 1981; March, 1978; Simon, 1972). However, pricing is shaped more by external market dynamics. Competitive intensity, for instance, can compress bid margins, forcing firms to act quickly and with limited information. Resource scarcity can inflate prices' unpredictably amid supply shocks or regulation (Chatain, 2014; Ross, 2012). Bargaining power also affects acquisition terms (Moliterno & Wiersema, 2007). In addition, resource traits, such as additive or supplantive also matter (Asmussen, 2015), as do mobility and modularity, which ease redeployment (Markman et al., 2009). Finally, host country’s institutional development influences transaction transparency, contract enforcement, and pricing benchmarks (Hoskisson, Wright, Filatotchev, & Peng, 2013). Together, these factors underscore that pricing is not merely a derivative of valuation, but a distinct and complex decision domain.

Future research should explore the factors that improve the accuracy of pricing decisions in SFMs. By recognizing pricing as a critical component of resource acquisition alongside valuation, firms can better position themselves to generate economic rents. Thus, while valuation remains a key focus, understanding the distinct yet interconnected nature of pricing is crucial for fully grasping value generation in SFMs.

Beyond pricing, valuation research also reveals gaps. Markman, Gianiodis, and Buchholtz (2009) highlight how resource characteristics such as versatility, mobility, and captivity influence competitive dynamics and valuation. Future research could investigate additional characteristics like visibility, durability, and modularity. For example, studies might explore whether hard-to-observe resources (e.g., proprietary algorithms or tacit knowledge) are consistently misvalued, how firms in fast-changing sectors like renewable energy assess resources vulnerable to obsolescence, and which SFM capabilities matter in such settings. Similarly, examining modularity could clarify whether firms overvalue modular resources due to their flexibility and lower integration costs.

Cognitive processes play a crucial role in SFMs by influencing how decision-makers evaluate resource investments and envision novel combinations (Kunc & Morecroft, 2010; Oliver, 1997; Rahmandad et al., 2021; Felin et al., 2016). Behavioral aspects are particularly important, as decision-makers often encounter biases from uncertainty, ambiguity, and incomplete knowledge (Amit & Schoemaker, 1993; Leiblein, 2011). Managing these biases is key to identifying valuable opportunities. Managers’ mental models both shape and are shaped by the firm’s resource base (Mahoney, 1995). Traditional SFM theory has been critiqued for overlooking these cognitive dynamics by focusing too narrowly on information gathering. Yet, cognitive differences significantly affect resource strategies and positions (Maritan & Peteraf, 2011), as unique mental models enable creative resource recombinations and competitive advantage (Felin et al., 2016).

Moreover, research challenges the traditional view of bounded rationality by demonstrating that entrepreneurs may intentionally deviate from best practices, suggesting cognitive limits can enable strategic flexibility (Knott, 2003). Differences in learning capabilities also matter, influencing decision accuracy and acquisition success over time (Leiblein, Chen, & Posen, 2017). Nonrational expectations may evolve into rational ones as decision-makers gain experience through repeated market interactions (Makadok, 2002). This learning process is evident in multinational enterprises, which improve performance by adapting to and learning from resource environments in host countries (Kim et al., 2015).

The literature on cognition in SFMs shows that resource acquisition is shaped not only by external conditions or organizational capabilities but also by how decision-makers interpret uncertainty, structure search and evaluation, and adapt through learning. Cognition is presented not as a static constraint but as a dynamic capability that varies across individuals, tasks, and contexts. Cognitive processes, from intuitive judgment to analytical reasoning, play a central role in how firms assess value under risk, ambiguity, and uncertainty. This perspective moves the field beyond bounded rationality, emphasizing heterogeneity in cognitive routines and their strategic implications. Building on Post et al. (2020), this work supports theory testing and boundary-setting by asking how and when cognitive forms, like intuitive expertise or belief updating, generate performance advantages. As firms navigate different levels of market imperfection and complexity, cognition must be seen as a contextually shaped mechanism that drives variation in opportunity recognition and action.

To further our understanding of the cognition’s role in creating differentiated resource positions within SFMs, we propose examining cognition through three distinct lenses: environmental factors, task-specific processes and decision-making authority. This structured approach highlights several important yet unexplored areas in SFM literature, detailed in the following section.

From an environmental perspective, the level of imperfection within SFMs critically shapes cognitive processes. Variations in resource types, such as resource rarity heighten uncertainty about future value and price, increasing cognition’s role in acquisition decisions (Koparan & Aksaray, 2024). In such cases, a key question arises: what cognitive capabilities (e.g., intuition, intuitive expertise, information gathering) enable more accurate and timely acquisition decisions that yield competitive advantage? For unique, one-of-a-kind resources lacking valuation or pricing benchmarks, for example, how can decision-makers consistently achieve superior acquisition decisions?

The role of learning in shaping cognitive processes within SFMs remains debated. Makadok (2002) argues that nonrational expectations evolve into rational ones through repeated SFM interactions, emphasizing learning’s importance. Conversely, Cai, Hughes, and Yin (2014) find no significant learning effect, underscoring the need for further investigation. Kim, Hoskisson, and Lee (2015) integrate organizational learning theory with SFM perspectives, demonstrating how new multinationals adapt through experiential learning, unlearning, and resource augmentation, particularly in resource-rich countries. Bilgili, Kedia, and Bilgili (2016) add that emerging market firms tailor their learning strategies across varied SFM conditions, shaped by institutional and resource contexts. Leiblein et al. (2017) underscore effective information processing and belief updating as critical to navigating SFM uncertainty. However, questions remain: how does learning effectiveness vary across industries or levels of market imperfection? What organizational routines best support adaptation and learning in uncertain and dynamic SFMs?

Tasks in resource acquisition such as searching, evaluating, and deciding underlie different cognitive processes (Capron & Mitchell, 2009; Makadok & Barney, 2001). Intuition often aids the search phase, while analytical skills guide evaluation (Amit & Schoemaker, 1993; Makadok, 2001, 2002; Simon, 1997). However, further research is necessary on how firms balance or integrate these cognitive modes during resource acquisition. Specifically, future studies could explore when intuitive versus analytical thinking yields better outcomes (e.g., valuation and pricing accuracy and speed), and how the ideal balance varies by industry, firm characteristics, or resources type?

Decision-making authority can also shape cognitive processes in SFMs. Autocratic structures may restrict cognitive flexibility or amplify biases that hinder effective resource assessment. In contrast, democratic structures can enhance cognitive performance by encouraging diverse perspectives and collective deliberation (Harrison & Freeman, 2004; Kerr, 2004). Yet, questions remain for future research: when do democratic structures yield better cognitive outcomes than autocratic ones? How does their effectiveness vary with complexity, uncertainty, or urgency of resource acquisition decisions? Additionally, how do firms adjust their decision-making authority in response to strategic environment or the nature of resources being pursued?

AI technologies, such as machine learning, predictive analytics, and natural language processing, are reshaping how firms search for value, and acquire strategic resources under uncertainty and information asymmetry. Unlike traditional decision aids, AI acts as a generative tool that identifies opportunities not yet priced in the market, making it highly relevant to SFMs where acquiring undervalued resources offers competitive advantage. For instance, SignalFire’s proprietary AI identifies early-stage startups before they appear on traditional venture capital (VC) radars, enabling investment at favorable valuations and follow-on positions as firms grow (MillerShah Attorneys at Law, 2025). Similarly, EQT Ventures’ Motherbrain and InReach Ventures analyze data from product usage to online behavior to flag emerging companies ahead of market attention (Zhukov, 2024). In other sectors like real estate, AI helps spot investment opportunities more quickly (Carr, 2024). Overall, AI institutionalizes the foresight once attributed to managerial cognition (Barney, 1986) and information gathering and processing (Makadok & Barney, 2001), now executed algorithmically at scale, strengthening firms’ sensing, seizing, and recombination capabilities.

In terms of valuation and pricing, AI enhances firms’ ability to assess both intrinsic and market-based resource value with greater precision. In VC, AI models estimate startup risk, return potential and viability by analyzing funding patterns, founder backgrounds and sector trends (Zhukov, 2024). These systems automate due diligence and market research via natural language processing (e.g., parsing reports, news, and filings) and adjust valuations in real time based on market signals. In real estate and asset management, firms use machine learning to integrate diverse data sources into pricing models that outperform traditional heuristics [Global Real Estate Sustainability Benchmark (GRESB), 2023; Topraklı, 2025; Riaz, 2024]. These applications align with SFM theory’s core idea that firms earn rents by exploiting price–value mismatches, but AI detects these gaps through granular, real-time analytics, enabling faster, more accurate arbitrage.

In the cognition dimension, AI enhances strategic decision-making by reducing human biases and improving the precision of forecasts. Its ability to process vast data and deliver analytical clarity allows firms to make more accurate resource evaluations. However, environmental factors such as resource uniqueness and market novelty can limit the applicability of AI’s predictive logic. In highly imperfect SFMs, those involving rare, ambiguous, or unfamiliar resources, AI’s effectiveness diminishes without human interpretive input. In such contexts, human cognition still plays a critical role in framing problems, exercising judgment where objectives cannot be fully codified, and adapting insights to strategic context.

At the same time, AI’s widespread adoption may fundamentally reshape SFM competitive dynamics. Historically, firms gained advantage through superior information processing and managerial insight. As AI democratizes data access and prediction accuracy (Agrawal et al., 2018), these asymmetries erode. McKinsey Survey (2024) report notes AI adoption rose from 50% to 72% in one year, with 65% of firms now regularly using generative AI. As firms increasingly rely on similar AI systems for strategic evaluation, cognitive variation, once a key source of expectation heterogeneity, may decline. This raises a key question: how can firms maintain differentiation when evaluation methods converge?

As Carr (2024) argued in the context of IT, ubiquity erodes strategic value. Kerr (2004) reinforces this logic for AI, pointing to its generic algorithmic design, reliance on explicit and easily transferable knowledge, and narrow task orientation as limitations to its ability to generate firm-level rents. These traits challenge the resource conditions, rarity, inimitability and nonsubstitutability, required for sustained advantage.

Overall, the competitive value of AI in SFMs hinges not on adoption alone, but on how firms uniquely embed it into resource acquisition decisions. Sustained advantage requires distinct complementarities such as proprietary data access, firm-specific human–AI integration, and adaptive judgment under uncertainty. Without these, AI may improve operational accuracy but remains a generic input, intensifying competition and accelerating behavioral convergence. As similar AI systems proliferate, the window to identify and secure undervalued resources narrows, reducing arbitrage-based rents. In this context, AI is necessary to compete, but not sufficient to win. Advantage will depend on how distinctively firms use AI to shape expectations and act ahead of rivals. Consistent with Post et al. (2020), this analysis establishes new boundary conditions for SFM constructs like foresight, valuation, and strategic asymmetry in an era of automated prediction.

This review synthesizes and extends four decades of SFM research by providing a conceptual framework centered around three interdependent themes: firm capabilities, valuation and pricing, and cognition, including AI’s emerging role. Beyond summarizing existing knowledge, we highlight conceptual tensions, propose new theoretical extensions, and explore underexamined links between traditional strategy frameworks and contemporary developments. Analyzing 46 core articles, we identify where SFM theory has matured, where it overgeneralizes, and where future research can advance it.

First, our review deepens capability-based theories by framing capabilities as dynamic, externally oriented processes shaped through ongoing interaction with SFMs. While prior work highlights foresight and entrepreneurial skill, we emphasize how firms develop dynamic sensing, seizing, and reconfiguring capabilities in response to market imperfections, institutional complexities and environmental turbulence. This market-facing view underscores the reciprocal relationship between internal capability development and external conditions, presenting new opportunities to explore how firms adapt SFM capabilities for sustained competitive advantage.

Second, we extend theory by explicitly distinguishing valuation from pricing in resource acquisition decisions, an often-overlooked distinction in SFM literature. Valuation reflects firm-specific, capability-driven judgments, while pricing is shaped by market forces. Separating these concepts open new research avenues on how firms strategically balance internal valuations with external market behaviors, especially under high uncertainty.

Third, our analysis advances theory by integrating cognition research and AI into SFM thinking. Although previous literature acknowledges heterogeneity in expectations and cognitive constraints, it rarely details specific mechanisms or decision architectures. By linking cognition and AI, we offer a new lens on how human judgment and machine analytics interact. This raises critical questions about whether AI will level competitive advantages traditionally derived from superior human insight or, create new ones through distinctive combinations of analytics, intuition and strategic foresight.

Beyond the individual roles of capabilities, valuation and pricing, and cognition in SFM behavior, promising avenues exist for research that bridges these dimensions. For example, how do resource acquisition capabilities interact with cognitive biases to shape valuation outcomes? Studies could explore why firms with similar capabilities reach divergent decisions based on how uncertainty and payoffs are interpreted. Such inquiry could explain persistent performance differences in resource markets despite comparable routines or information.

Future work might also examine misalignment across dimensions. What happens when valuation emphasizes firm-specific complementarities, but cognition is anchored in industry norms? Or when acquisition speed outpaces cognitive adaptation? Investigating how alignment, or its absence, across these areas shapes firm outcomes could yield fresh insights into dynamic capabilities and performance heterogeneity.

From a practical standpoint, this review offers managers a more sophisticated perspective on SFMs, as strategic contexts influenced by capabilities, market structures, and cognitive processes. Instead of passively reacting to market fluctuations, managers can proactively build capabilities, adopt strategic pricing behavior, and integrate AI tools to shape competitive advantage. By outlining these considerations, our review helps practitioners navigate increasingly complex SFM dynamics.

Finally, we identify key gaps for future research, including pricing dynamics, cognitive and learning processes, and institutional heterogeneity in global contexts. Addressing these can produce robust empirical evidence, advancing theory and practice in resource acquisition. In conclusion, this review not only synthesizes four decades of research but extends SFM theory by introducing new distinctions and considerations. We offer a foundation for future research and practice that better captures the complexity, uncertainty and technological change shaping SFMs today.