Marketing capability is defined as an organisation’s ability to coordinate, integrate, and deploy market-facing resources and practices that generate value and support commercial outcomes [1,2,3]. Capability theory explains performance differences through firm-specific practices that are valuable, difficult to imitate, and persistent over time [1,4]. This perspective suggests that a positive capability–performance association should be expected and motivates examination of how construct specification and measurement choices influence the magnitude of that association. Empirical studies have linked marketing capability to objective and subjective performance indicators, including profitability, sales growth, changes in market share, and composite indices that combine financial and market outcomes [3,5]. Reported associations vary across studies, reflecting differences in construct specification, measurement quality, performance operationalisation, and market settings. In practical terms, this study answers three questions. How strong is the typical association between marketing capability and performance across published research? What happens to this association when different measurement models of marketing capability are used? And finally, under what market conditions are stronger or weaker associations likely to be observed Against this background, a major driver of heterogeneity concerns construct specification. At least six measurement models are recognised: reflective, formative, reflective–formative, formative–reflective, reflective–reflective, and formative–formative [6]. Reflective models treat observed items as manifestations of a latent capability, requiring evidence of item covariance, validity, and overall fit. Formative models treat observed components as defining the construct, requiring justification of indicator content, checks for multicollinearity, and assessment of weight structures [6,7]. These approaches differ in causal direction, treatment of measurement error, and identification requirements. Shifts between them alter construct meaning and can bias structural coefficients, including correlations with performance. In addition, higher-order structures introduce additional complexity. Reflective second-order factors aggregate lower-order reflective dimensions into a broader capability, whereas formative higher-order composites combine distinct elements such as competitor-oriented scanning and customer-oriented implementation [8]. These alternatives affect observed associations with performance. Aggregating elements with uneven relevance to performance may dilute associations, whereas a narrowly defined first-order reflective construct can capture capabilities more closely aligned with performance-linked practices. Beyond specification, differences in measurement validation also drive heterogeneity. Reflective models require evidence of convergent validity, discriminant validity, and fit measures, via confirmatory factor analysis. Weak or incomplete reporting reduces comparability and increases uncertainty in structural estimates. For formative composites, evaluation depends on the weight structure, multicollinearity diagnostics, and content coverage. Absent or inadequate reporting weakens the interpretive link between indicators and construct [7]. This may further complicate the assessment of the association between antecedents and performance Furthermore, market context shapes findings and interacts with specification and validation. Business-to-business studies emphasise relational coordination, account management, and knowledge integration [9,10,11]. Business-to-consumer studies focus on branding, market segment management, service quality, customer relationship, market-sensing, and channel execution [12,13,14]. Mixed samples combine these settings. These differences influence the average association and the dispersion around that average, and they intersect with specification choices. For example, higher-order composites are used to capture capabilities across industries [10,15,16]. To address these issues, this study addresses three questions. First, what is the pooled correlation between marketing capability and firm performance in eligible empirical research? Second, do construct specification choices, including first-order reflective, second-order reflective, formative first-order where available, and formative higher-order composites, moderate the pooled correlation? Third, do measurement attributes, particularly the reporting of confirmatory factor analysis for reflective structures, moderate the pooled correlation? A related question concerns market setting: do business-to-business and business-to-consumer studies report comparable magnitudes, and how do mixed settings relate to pooled effects. Thus, the study aims to make three contributions. First, it quantifies the average association and provides a prediction interval that describes the dispersion expected in comparable future studies. Second, it demonstrates that construct specification choices have measurable effects on the magnitude of the capability–performance association. Third, it shows that reported measurement validation coincides with differences in both central tendency and dispersion, informing instrument design and reporting practices.

This study contributes to the marketing capability literature in three main ways. First, it provides a quantitative synthesis of the marketing capability–performance association based on 88 effects reported between 2000 and 2025. Second, it compares alternative construct specifications, including reflective first-order, reflective higher-order, and formative models, and examines how these choices relate to both the magnitude and the dispersion of observed effects. Third, it investigates whether studies that report confirmatory factor analysis metrics yield different capability–performance associations from studies without such reporting and whether the association varies across business-to-business, business-to-consumer, and mixed market settings.