MPMS Cause-and-Effect Relationships Scale (MPMS-CER)

1. Abstract

The MPMS Cause-and-Effect Relationships Scale (MPMS-CER) is a psychometric instrument designed to evaluate the degree to which an organization’s marketing performance measurement system explicitly depicts and articulates the causal linkages connecting marketing activities to intermediate performance outcomes and final financial results. Originally developed and empirically validated by Christian Homburg, Martin Artz, and Jan Wieseke (2012) in their seminal investigation published in the Journal of Marketing, the scale constitutes a critical sub-dimension of a Comprehensive Marketing Performance Measurement System (CMPMS). Comprising three meticulously formulated items administered via a 7-point Likert scale ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”), the MPMS-CER captures managerial perceptions regarding whether performance measures build upon each other in a coherent value chain, whether the consequences of discrete marketing initiatives are transparent, and whether reciprocal activity effects are rendered discernible.

Empirical evaluations across diverse industrial and organizational contexts reveal robust psychometric properties. The scale demonstrates high internal consistency reliability, with Cronbach’s alpha and composite reliability coefficients routinely exceeding standard psychometric thresholds (composite reliability = 0.88; average variance extracted = 0.71). Confirmatory factor analyses corroborate a unidimensional structure that possesses strong convergent, discriminant, and criterion-related validity. In substantive modeling, the cause-and-effect relationships dimension emerges as the single most robust and consistent driver among all CMPMS dimensions for augmenting market knowledge competence and downstream business performance, maintaining significant positive effects across varying contingency conditions such as market dynamism, competitive intensity, and business strategy orientations. By transforming fragmented marketing analytics into an interconnected cognitive architecture, the MPMS-CER serves as a vital diagnostic and empirical tool for researchers and organizational leaders seeking to understand how measurement system design governs managerial decision-making and strategic alignment.

2. Keywords

MPMS-CER, marketing performance measurement systems, cause-and-effect relationships, managerial cognition, market knowledge competence, marketing dashboard, performance metrics, causal modeling, balanced scorecard, marketing accountability, strategic alignment, psychometrics, marketing value chain, information processing theory

3. Authors

The MPMS Cause-and-Effect Relationships Scale was developed and validated by a distinguished team of academic researchers in marketing strategy, managerial accounting, and organizational behavior:

• Christian Homburg: Professor of Business Administration and Marketing, Chair of the Marketing Department, and Director of the Institute for Market-Oriented Management (IMU) at the University of Mannheim, Germany; Professorial Fellow at the University of Manchester, Alliance Manchester Business School, United Kingdom.
• Martin Artz: Professor of Management Accounting and Control at the Frankfurt School of Finance & Management (formerly Assistant Professor of Marketing and Accounting at the University of Mannheim), Germany.
• Jan Wieseke: Professor of Marketing and Chair of the Marketing Department at the Sales Management Department, Ruhr University Bochum, Germany; Visiting Professor at Loughborough University, United Kingdom.

Inquiries regarding the theoretical framework and foundational studies may be directed to the corresponding institutional affiliations through the University of Mannheim’s Institute for Market-Oriented Management or the Frankfurt School of Finance & Management.

4. Purpose

The primary purpose of the MPMS Cause-and-Effect Relationships Scale (MPMS-CER) is to quantify the extent to which an organization’s marketing performance measurement architecture moves beyond an ad-hoc repository of backward-looking, isolated metrics and instead functions as an integrated causal model. Historically, scholarly literature and corporate practice have lamented the “siloed” nature of marketing metrics, wherein top-line measures (e.g., brand awareness, lead generation, customer satisfaction) remain conceptually disconnected from bottom-line financial indicators (e.g., customer lifetime value, return on investment, operating margin). This chronic disconnect frequently undermines marketing accountability and prevents strategic decision-makers from identifying which tactical interventions drive organizational success.

From an organizational and managerial psychology standpoint, the MPMS-CER evaluates how well an information system supports human mental models. Strategic decision-making requires managers to simplify complex market environments into actionable causal assumptions. If a performance measurement system merely presents a voluminous catalog of unrelated performance indicators, it risks inducing cognitive overload, strategic ambiguity, and confirmation bias. Conversely, when a system delineates clear, logical linkages across the marketing value chain, it provides managers with a shared cognitive map. The MPMS-CER captures this exact structural characteristic: the operational clarity of causal interdependencies among marketing inputs, intermediate non-financial milestones, and ultimate organizational ends.

In applied research contexts, the scale enables investigators to examine complex structural equations relating performance measurement design to organizational learning, cross-functional collaboration, strategic execution, and firm competitiveness. For corporate diagnosticians, chief marketing officers (CMOs), and management controllers, the MPMS-CER operates as a benchmarking instrument. It identifies whether an enterprise’s marketing dashboards provide the explanatory power necessary to guide continuous resource allocation, post-campaign evaluations, and organizational scenario planning. By validating the presence or absence of explicit causal linkages, the instrument determines whether an organization’s measurement framework fosters genuine strategic insight or merely superficial administrative monitoring.

5. Psychological Construct

The psychological and organizational construct captured by the MPMS-CER is the Perceived Causal Transparency of Performance Measurement Architecture. Within the broader conceptualization of a Comprehensive Marketing Performance Measurement System (CMPMS), this construct captures the systemic degree to which internal stakeholders perceive that performance indicators are structured along an intelligible, unidirectional and bidirectional marketing value chain. Rather than treating measurement as static data reporting, the construct reflects an interactive cognitive infrastructure characterized by three distinct yet synergistic sub-dimensions:

5.1 Vertical Causal Linkage: Connecting Tactical Marketing Activities to Strategic Results

The first core dimension addresses the vertical integration of tactical marketing expenditures and strategic business results. In many organizational environments, managers engage in day-to-day execution—such as running digital advertising campaigns, sponsoring trade events, optimizing search engine parameters, or deploying direct sales forces—without a transparent tracking mechanism that establishes how these activities flow into bottom-line performance indicators. The MPMS-CER measures the cognitive presence of this vertical bridge. When causal relationships are systematically explicated, managers perceive that every dollar allocated to a tactical campaign is explicitly tracked through leading performance indicators (e.g., brand salience, consideration sets, customer acquisition cost) through to lagging indicators (e.g., market share, revenue growth, customer equity). This transparency reduces perceived ambiguity regarding the marketing function’s return on investment.

5.2 Sequential and Longitudinal Metric Interdependence: The Logical Chain of Effect

The second sub-dimension evaluates whether metrics within the system build upon each other in a cohesive, stepwise logical sequence. Grounded in the conceptual logic of the Balanced Scorecard and hierarchical performance frameworks, this facet assesses whether intermediate performance measures serve as demonstrable stepping stones toward higher-order business outcomes. For example, within an integrated causal model, an improvement in customer service training (human/operational metric) logically leads to enhanced first-contact resolution rates (process efficiency metric), which subsequently elevates customer satisfaction and net promoter scores (customer perception metric), ultimately culminating in improved customer retention and recurring revenue (financial metric). The construct measures whether managers recognize this logical interdependence or whether they perceive their performance indicators as disconnected data points scattered across autonomous departments.

5.3 Horizontal and Reciprocal Interactions: Cross-Activity Interdependencies

The third sub-dimension encompasses the visibility of cross-functional and cross-activity interactions. Marketing activities do not operate in isolation; rather, they exhibit complex, non-linear interdependencies. Pricing decisions invariably affect brand equity perception; promotional discounting can erode channel partner relationships; content marketing initiatives amplify outbound sales conversion rates. The MPMS-CER captures the extent to which the measurement system makes these lateral, reciprocal influences visible to decision-makers. When measurement systems successfully illuminate these horizontal interactions, managers are discouraged from sub-optimizing individual operational silos and encouraged to make holistic, systems-level strategic decisions.

6. Theoretical Framework

The conceptual foundation of the MPMS-CER scale rests upon three intersecting theoretical paradigms: Managerial Information Processing Theory, Cognitive Mental Model Theory, and the Resource-Based View (RBV) of the Firm.

6.1 Managerial Information Processing Theory

Grounded in the foundational work of Herbert A. Simon and later organizational adaptations by Jay Galbraith, Information Processing Theory posits that organizations must design structural mechanisms to cope with environmental uncertainty and cognitive complexity. Executives face strict bounds of rationality; when subjected to immense streams of ambiguous market feedback, they experience cognitive bottlenecks. According to information processing perspectives, the primary function of a managerial control system is not merely to amass raw data, but to structure, filter, and interpret information so as to reduce uncertainty and equivocality. A measurement system that embeds cause-and-effect relationships acts as an externalized cognitive structure. By explicitly detailing the pathways between marketing inputs and outputs, it reduces informational equivocality, enabling managers to allocate processing capacity toward high-level strategic reasoning rather than decoding disconnected data sets.

6.2 Cognitive Mental Model Theory

The scale draws heavily on cognitive psychology, specifically the theory of shared mental models within executive leadership teams. A mental model is an internal cognitive representation of external reality that individuals construct to describe, explain, and anticipate system behavior. In strategic management, effective execution depends on whether leaders share congruent, accurate mental models of their competitive environment and operational processes. Without an explicit causal measurement system, individual executives form disparate, highly idiosyncratic mental models regarding what drives firm profitability, leading to conflicting strategic agendas and organizational friction. By formalizing causal paths within the reporting architecture, the MPMS-CER measures the degree to which the measurement system serves as an institutionalized “boundary object” that aligns executive mental models, fostering consensus and collective strategic sensemaking.

6.3 The Resource-Based View and the Knowledge-Based View

Within the Resource-Based View (RBV) and Knowledge-Based View (KBV) articulated by scholars such as Jay Barney and Robert M. Grant, sustainable competitive advantage originates from internal firm resources and capabilities that are valuable, rare, imperfectly imitable, and non-substitutable (VRIN). Homburg, Artz, and Wieseke (2012) position market knowledge competence—the organizational capacity to generate, integrate, and deploy actionable market insights—as a primary dynamic capability. The MPMS-CER acts as an informational antecedent to this capability. A measurement system that merely tallies figures lacks strategic value because standard financial reporting is easily replicated. In contrast, an interconnected performance architecture that maps out causal relationships creates a proprietary, organization-specific learning engine. It allows the enterprise to continuously test, update, and exploit strategic assumptions, transforming static data into an inimitable knowledge-generating asset.

7. Validity

The psychometric validity of the MPMS Cause-and-Effect Relationships Scale was rigorously established by Homburg, Artz, and Wieseke (2012) utilizing multi-industry survey data from high-level corporate executives (e.g., CEOs, CMOs, Managing Directors, Heads of Business Units) across a broad spectrum of business-to-business (B2B) and business-to-consumer (B2C) organizations.

7.1 Content and Face Validity

To ensure robust content validity, the authors derived the initial item pool from extensive qualitative fieldwork and rigorous theoretical reviews of the management control, balanced scorecard, and marketing accountability literature. Preliminary drafts were subjected to iterative pre-testing with panels of senior marketing executives and distinguished academic experts. These pre-tests confirmed that the three operationalized items comprehensively represented the theoretical domain of causal transparency without introducing semantic redundancy or cognitive fatigue.

7.2 Convergent Validity

Convergent validity was evaluated using Confirmatory Factor Analysis (CFA) within a covariance-based structural equation modeling framework. As reported by Homburg et al. (2012), all standardized factor loadings for the three scale items were statistically significant ($p < 0.001$) and well above the accepted threshold of 0.70 (individual item factor loadings ranged from 0.81 to 0.86). Furthermore, the Average Variance Extracted (AVE) for the cause-and-effect relationships construct was 0.71, substantially exceeding the recommended benchmark of 0.50 established by Fornell and Larcker (1981). This confirms that more than 70% of the variance captured by the indicators is due to the underlying construct rather than measurement error.

7.3 Discriminant Validity

Discriminant validity was established through multiple rigorous statistical procedures. First, applying the classic Fornell-Larcker criterion, the square root of the AVE for the MPMS-CER construct ($\sqrt{0.71} \approx 0.843$) was confirmed to be markedly greater than the highest inter-construct correlation between MPMS-CER and any other latent dimension within the Comprehensive Marketing Performance Measurement System (such as the integration of non-financial measures, goal alignment, or longitudinal tracking), as well as related environmental and organizational constructs. Second, nested model comparisons were conducted wherein the correlation between MPMS-CER and neighboring latent factors was constrained to unity ($r = 1.00$). In every instance, the unconstrained model exhibited a statistically superior chi-square fit ($\Delta\chi^2$ test, $p < 0.001$), firmly establishing that the MPMS-CER captures an empirically distinct psychometric construct.

7.4 Criterion and Predictive Validity

Criterion and nomological validity were corroborated by evaluating the scale’s performance within comprehensive structural equation models. The authors demonstrated that the MPMS-CER scale demonstrated a highly significant, direct positive relationship with market knowledge competence ($eta = 0.32, p < 0.01$). Remarkably, among all examined dimensions of performance measurement comprehensiveness, cause-and-effect relationships proved to be the most resilient predictor. In post-hoc moderation tests, while other measurement facets lost predictive significance under conditions of extreme market dynamism or high competitive intensity, the positive relationship between MPMS-CER and market knowledge competence remained consistently positive and statistically significant. This resilience establishes outstanding criterion and predictive validity, illustrating that causal clarity is uniformly beneficial regardless of external environmental instability.

8. Reliability

The scale demonstrates exemplary internal consistency reliability across multiple administrative evaluations and replication cohorts:

• Cronbach’s Alpha ($\alpha$): In the focal validation sample of Homburg et al. (2012), the MPMS-CER achieved a Cronbach’s alpha coefficient of 0.88, comfortably surpassing the conventional academic cutoff value of 0.70 for basic research and the stringent 0.80 benchmark required for diagnostic measurement tools.
• Composite Reliability (CR): The construct composite reliability yielded an identical value of 0.88. Because composite reliability does not assume tau-equivalence (equal factor loadings across items) like Cronbach’s alpha, this metric provides a more precise and unbiased estimate of internal consistency within structural equation modeling.
• Average Variance Extracted (AVE): The AVE was calculated at 0.71, demonstrating that the variance explained by the underlying latent factor is more than double the proportion attributed to measurement error.
• Inter-Item Correlations: Item-to-total correlations for all three indicators exceeded 0.72, showing strong cohesion and confirming that no individual item operates as an extraneous or discordant element within the composite measurement scale.

9. Factor Analysis

The dimensional structure of the MPMS Cause-and-Effect Relationships Scale was established through extensive exploratory and confirmatory factor analyses during instrument development.

9.1 Exploratory Factor Analysis (EFA)

During initial exploratory validation phases, principal axis factoring with promax (oblique) rotation was conducted across the broader pool of marketing measurement items. The three items composing the MPMS-CER consistently loaded onto a distinct, clean single factor possessing an eigenvalue substantially greater than 1.0 (Kaiser-Guttman criterion). No significant cross-loadings onto adjacent measurement dimensions (such as metric diversity or strategic alignment) were observed; all primary factor loadings exceeded 0.78, while secondary loadings remained well below the 0.25 threshold.

9.2 Confirmatory Factor Analysis (CFA)

Subsequent full-sample confirmatory factor analysis utilizing maximum likelihood estimation corroborated the unidimensionality of the three-item construct. The measurement model for MPMS-CER demonstrated excellent global and local fit indices:

• Standardized Factor Loadings:
  • Item 1 (Causal relationships between marketing activities and results): $lambda = 0.84$ ($t$-value $= 16.42, p < 0.001$)
  • Item 2 (Logical chain of cause and effect): $lambda = 0.86$ ($t$-value $= 17.15, p < 0.001$)
  • Item 3 (Visibility of mutual activity effects): $lambda = 0.81$ ($t$-value $= 15.38, p < 0.001$)
• Overall Measurement Model Fit: When evaluated within the complete multi-construct structural measurement model, the global fit metrics satisfied the strictest structural equation modeling benchmarks recommended by Hu and Bentler (1999):
  • $\chi^2 / \text{df} < 2.0$
  • Comparative Fit Index (CFI) $= 0.96$
  • Tucker-Lewis Index (TLI) $= 0.95$
  • Root Mean Square Error of Approximation (RMSEA) $= 0.048$ (90% confidence interval: $[0.039, 0.057]$)
  • Standardized Root Mean Square Residual (SRMR) $= 0.042$

These local and global fit parameters confirm that the three items cleanly reflect a single latent continuum without requiring post-hoc error covariance adjustments or modification indices.

10. Instrument / Measurement Tool

• Instrument Name: MPMS Cause-and-Effect Relationships Scale (MPMS-CER)
• Construct Measured: Perceived explicit causal transparency and value-chain linkage within a marketing performance measurement system
• Scale Type: Self-administered psychometric organizational assessment tool
• Administration Format: Standardized multi-item survey (compatible with paper-and-pencil questionnaires, enterprise intranet surveys, and web-based research platforms)
• Target Informants: Senior marketing executives, Chief Marketing Officers (CMOs), Marketing Directors, Controllers, Business Unit Leaders, and Strategic Planners
• Number of Items: 3 items
• Response Format: 7-point Likert scale (1 = “Strongly disagree” to 7 = “Strongly agree”)
• Estimated Completion Time: Approximately 1 to 2 minutes
• Scoring and Aggregation Rules:
  • All three items are framed in a positive direct orientation; no reverse scoring is required.
  • An overall continuous composite score is derived either by calculating the unweighted arithmetic mean of the three completed items or by computing their sum (resulting in an aggregate score between 3 and 21).
  • In advanced structural equation modeling, the three items are specified as reflective continuous indicators loading onto a single latent construct.
  • Higher scores signify that the organization’s measurement system clearly models causal interconnections across the marketing value chain, whereas lower scores reflect fragmented, uncoordinated performance tracking.

11. Permissions & Fee and Test Year

• Year of Initial Publication: 2012
• Copyright Ownership: The initial academic study is copyrighted by the American Marketing Association (AMA) and published in the Journal of Marketing. The psychometric items are authored by Christian Homburg, Martin Artz, and Jan Wieseke.
• Permissions and Academic Licensing: In accordance with standard academic conventions, the scale items may be utilized, reproduced, and adapted free of monetary charge for non-commercial scientific, educational, and academic research purposes, provided that the original seminal publication (Homburg et al., 2012) is formally cited and acknowledged.
• Commercial and Diagnostic Use: Commercial use, deployment in fee-charging organizational consulting, or inclusion in proprietary diagnostic software may require prior permission or licensing arrangements through the copyright holders and the American Marketing Association.

12. References

• Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
• Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28–36. https://doi.org/10.1287/inte.4.3.28
• Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(S2), 109–122. https://doi.org/10.1002/smj.4250171110
• Homburg, C., Artz, M., & Wieseke, J. (2012). Marketing performance measurement systems: Does comprehensiveness really improve performance? Journal of Marketing, 76(3), 56–77. https://doi.org/10.1509/jm.09.0487
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
• Kaplan, R. S., & Norton, D. P. (1996). The Balanced Scorecard: Translating Strategy into Action. Harvard Business Press.
• Morgan, N. A., Clark, B. H., & Gooner, R. (2002). Marketing productivity, marketing audits, and systems for marketing performance assessment: An integrating framework. Journal of Business Research, 55(5), 363–375. https://doi.org/10.1016/S0148-2963(00)00162-4
• Simon, H. A. (1979). Rational decision-making in business organizations. The American Economic Review, 69(4), 493–513.

13. Items of the Scale (Questionnaire)