CEO Decision Style and Firm Performance–Model Questionnaire

Abstract

The CEO Decision Style and Firm Performance–Model Questionnaire (Kruse, Bendig, & Brettel, 2023) is an empirical psychometric instrument designed to evaluate strategic executive cognition and its subsequent impact on organizational outcomes. Rooted in Upper Echelons Theory and the behavioral theory of decision-making, the instrument operationalizes chief executive officer (CEO) decision styles across two foundational dimensions—Alternative Search and High Standards—while mapping their direct and indirect mechanisms through new product development (NPD) speed, NPD innovativeness, and subjective multi-dimensional firm performance. Comprising 23 items measured on seven-point Likert scales, the instrument adapts validated psychometric scales from judgment and decision-making literature (e.g., Misuraca et al., 2015; Durinik et al., 2018) and strategic management literature (Sheng et al., 2013). The scale was systematically translated from English to German utilizing the rigorous back-translation protocol established by Brislin (1970), vetted for semantic clarity by professional management consultants, and pre-tested with corporate executives. Psychometric evaluation via confirmatory factor analysis (CFA) and structural equation modeling (SEM) demonstrated robust construct validity and internal consistency, with composite reliability (CR) values across all latent constructs ranging from 0.72 to 0.90. The empirical five-factor structure exhibited excellent goodness-of-fit indices (χ²[220] = 1171.809, p < 0.001; Comparative Fit Index [CFI] = 0.92; Root Mean Square Error of Approximation [RMSEA] = 0.06; Standardized Root Mean Square Residual [SRMR] = 0.05) and adhered fully to the Fornell and Larcker (1981) criterion for discriminant validity. Furthermore, testing using the comprehensive marker variable technique confirmed the absence of distortive common method variance (CMV), making the instrument a robust diagnostic and academic measurement tool for organizational psychology, entrepreneurship, and strategic management research.

Keywords

CEO Decision Style, Alternative Search, High Standards, Upper Echelons Theory, New Product Development Speed, New Product Development Innovativeness, Firm Performance, Heuristics, Maximizing and Satisficing, Confirmatory Factor Analysis, Structural Equation Modeling, Strategic Leadership

Authors

The scale was developed and empirically validated by a team of organizational scholars specializing in strategic leadership, innovation, and corporate entrepreneurship:

• Sebastian Kruse (Corresponding Author): RWTH Aachen University, Innovation and Entrepreneurship Group (WIN) – Technology and Innovation Management / Entrepreneurship (TIME) Research Area. Correspondence Address: RWTH Aachen University, WIN – TIME Research Area, Kackertstr. 7, 52072 Aachen, Germany. Email: [email protected]
• David Bendig: University of Münster, Institute for Entrepreneurship, Münster, Germany.
• Malte Brettel: RWTH Aachen University, Innovation and Entrepreneurship Group (WIN) – TIME Research Area, Aachen, Germany.

Purpose

The fundamental purpose of the CEO Decision Style and Firm Performance–Model Questionnaire is to quantify the cognitive architectures that top executives bring to strategic decision situations and to trace how these cognitive inclinations translate into tangible operational and financial enterprise outcomes. Historically, strategic management and organizational psychology have acknowledged that business leaders operate under severe conditions of bounded rationality, characterized by information asymmetry, computational limits, and acute temporal pressures. However, executive research has frequently relied on demographic proxies (e.g., executive age, educational background, functional tenure) to approximate CEO cognition—an approach widely criticized for its lack of psychological precision and low predictive granularity.

To overcome these limitations, Kruse, Bendig, and Brettel (2023) developed a psychometrically grounded measurement model that directly captures the micro-foundations of executive decision-making. Specifically, the scale focuses on two cognitive pillars drawn from behavioral economics and judgment literature: (1) Alternative Search, reflecting the extent to which an executive pursues broad, heuristic-driven or exhaustive exploration of alternative courses of action, and (2) High Standards, reflecting the threshold of perfection, rigor, and excellence demanded before a strategic choice is ratified. By isolating these individual cognitive traits, the instrument enables scholars and practitioners to determine how executive choice mechanisms directly influence corporate dynamic capabilities—specifically operationalized as New Product Development (NPD) speed and NPD innovativeness.

From an applied perspective, the instrument serves critical diagnostic functions in organizational consulting, executive coaching, and corporate board evaluations. By administering the scale, executive assessment professionals can identify whether a CEO displays a tendency toward exhaustive information seeking (which may enhance innovation novelty but paralyze launch velocity) or exhibits extreme perfectionism (which may preserve product quality standards at the expense of market responsiveness). In academic research, the instrument provides an empirical bridge linking micro-level executive cognition with macro-level firm performance, supporting complex mediator and moderator modeling in organizational behavior, innovation management, and corporate strategy.

Psychological Construct

The measurement instrument operationalizes a multi-dimensional conceptual model composed of five distinct psychological, behavioral, and organizational latent constructs:

1. Alternative Search (CEO Decision Style Dimension)

Rooted in the conceptual paradigm of maximizing versus satisficing, Alternative Search describes the behavioral disposition of a leader to persistently scan the external and internal environment for alternative solutions, pathways, and strategic possibilities. Rather than accepting an initial satisfactory option, an executive high in alternative search expends substantial cognitive energy and organizational resources to identify alternative strategies, examine competing technologies, and assess multiple contingent scenarios. While this trait fosters expansive creativity and exploratory learning, in volatile competitive landscapes it can introduce cognitive overload and decision paralysis.

2. High Standards (CEO Decision Style Dimension)

The High Standards construct encapsulates the evaluation criteria and aspiration levels enforced by the executive. Individuals possessing high decision standards refuse to settle for “good enough” solutions; they maintain exceptionally demanding criteria regarding strategic quality, operational execution, and resource allocation. Unlike the search dimension—which pertains to the breadth of options evaluated—high standards pertain to the stringency of the evaluation threshold. Executives exhibiting elevated standards compel their teams to refine technical specifications and scrutinize strategic proposals, establishing a corporate climate of perfectionism and meticulous execution.

3. New Product Development Speed (Mediator Construct)

New Product Development Speed reflects an organization’s operational velocity in translating nascent conceptual ideas into finalized, commercialized products in the target marketplace. It captures cycle-time efficiency, responsiveness to competitive maneuvers, and the temporal compression achieved across consecutive R&D phases (e.g., initial ideation, prototyping, testing, validation, and market introduction). In the structural model, NPD speed functions as a vital operational mediator influenced by CEO decision styles, dictating how rapidly an enterprise can capitalize on emerging windows of market opportunity.

4. New Product Development Innovativeness (Mediator Construct)

NPD Innovativeness captures the degree of technological novelty, uniqueness, and disruptive divergence embodied in an organization’s newly introduced products or services. Rather than simple incremental modifications or line extensions, this construct evaluates whether the firm’s R&D outputs shift technological trajectories, alter customer expectations, or establish novel product categories. Highly innovative outputs require cognitive flexibility, willingness to embrace uncertainty, and sustained organizational experimentation—traits influenced directly by the CEO’s cognitive decision style.

5. Firm Performance (Ultimate Outcome Construct)

Firm Performance represents the holistic, multi-dimensional success of the business enterprise relative to its primary industry competitors. Grounded in established strategic management frameworks, this construct assesses competitive achievement across dimensions such as market share growth, profitability, return on assets, sales growth, and overall corporate effectiveness. Capturing performance relative to industry benchmarks enables the instrument to control for broader macroeconomic cycles and industry-specific baseline differences.

Theoretical Framework

The theoretical architecture of the CEO Decision Style and Firm Performance Model is built upon the synthesis of two seminal theoretical paradigms: Upper Echelons Theory and Behavioral Decision Theory.

Upper Echelons Theory

First articulated by Hambrick and Mason (1984) and later refined by Hambrick (2007), Upper Echelons Theory posits that organizational outcomes—including strategic choices, innovation trajectories, and bottom-line performance metrics—are partially predicted by the background characteristics and cognitive orientations of top managers. The central premise states that executives face complex, ambiguous environments filled with an overwhelming volume of stimuli. Because human cognition is intrinsically constrained, executives process these environmental cues through their personalized “cognitive filters,” formed by their values, cognitive styles, and behavioral dispositions. Consequently, strategic choices are not purely objective reactions to economic inputs, but personalized reflections of executive decision-making styles.

Behavioral Decision Theory: Maximizing vs. Satisficing

To provide a rigorous psychological basis for executive cognitive filters, the instrument integrates Herbert Simon’s (1955, 1957) pioneering concepts of bounded rationality and satisficing, supplemented by modern psychological formulations of maximizing tendencies (Schwartz et al., 2002). While Simon posited that human actors inevitably satisfice—choosing alternatives that meet a threshold of adequacy due to cognitive constraints—contemporary psychometricians have demonstrated that decision-makers display stable individual differences in their orientation toward maximizing (Misuraca et al., 2015; Durinik et al., 2018).

Crucially, recent cognitive science demonstrates that maximizing is not a single uniform trait, but a multi-dimensional construct consisting of distinct behavioral strategies: Alternative Search (an externalized, ongoing scanning of options) and High Standards (an internal insistence on optimal excellence). By disaggregating maximizing into these two discrete dimensions, Kruse et al. (2023) established that these cognitive facets exert divergent, sometimes opposing, influences on firm operations. For example, while extensive alternative search may delay product development cycle times, demanding high standards can elevate the creative and technical innovativeness of market offerings.

Validity

The construct, convergent, discriminant, and criterion-related validity of the measurement model was rigorously established using advanced multivariate statistical procedures within an empirical sample of German corporate enterprises:

Content and Face Validity

Initial items were adapted from established psychometric literature measuring decision styles (Durinik et al., 2018; Misuraca et al., 2015) and dynamic capabilities/performance (Sheng et al., 2013). To ensure cross-cultural semantic equivalence, items were translated from English into German using Brislin’s (1970) classic forward- and back-translation methodology. Subsequently, three independent senior management consultants evaluated the inventory to guarantee high face validity, operational clarity, and executive comprehension. A formal pre-test administered across randomly selected business organizations allowed for iterative lexical refinement prior to final deployment.

Convergent Validity

Convergent validity was confirmed via confirmatory factor analysis (CFA). All 23 indicators exhibited robust and statistically significant factor loadings on their designated latent constructs at the p < 0.001 level. Furthermore, the constructs successfully satisfied the classical Fornell and Larcker (1981) criterion: the Average Variance Extracted (AVE) for each latent variable exceeded the square of the inter-construct correlations, confirming that the variance explained by each latent construct is substantially larger than associated measurement error.

Discriminant Validity

To substantiate that Alternative Search, High Standards, NPD Speed, NPD Innovativeness, and Firm Performance represent statistically distinct constructs, competitive CFA models were systematically tested. The hypothesized five-factor model demonstrated a statistically superior fit compared to plausible constrained nested alternatives (e.g., loading the two CEO style dimensions onto a single common factor, or collapsing NPD speed and innovativeness into a single capability dimension), confirming discriminant separation across all five latent variables.

Common Method Variance (CMV) Assessment

Given that survey data were collected from key executive informants, common method bias was rigorously assessed using the comprehensive CFA marker variable technique outlined by Williams, Hartman, and Cavazotte (2010). A theoretical marker variable devoid of substantive conceptual links to the model constructs was incorporated into the baseline CFA. A constrained model was then compared against an unconstrained model in which all substantive survey items simultaneously loaded onto the marker variable. The chi-square difference test yielded no statistically significant variation between models (Δχ² = 0.05, df = 1, p = 0.83). This quantitative finding confirms that common method variance did not artifactually inflate or confound the observed empirical relationships.

Reliability

The scale developers assessed internal consistency across all latent dimensions utilizing Composite Reliability (CR), which provides a more accurate metric of scale reliability than traditional Cronbach’s alpha within latent structural equation modeling by accounting for heterogeneous factor loadings.

Empirical analyses confirmed high internal consistency across all five constructs, with composite reliability coefficients comfortably surpassing standard psychometric thresholds (CR > 0.70; Hair et al., 2010):

• Alternative Search: Exhibited high composite reliability within the upper psychometric echelon, indicating that indicators reliably measure the breadth of executive exploration.
• High Standards: Demonstrated robust internal consistency, confirming unified measurement of executive aspiration thresholds.
• NPD Speed: Demonstrated strong composite reliability, validating its operationalization as a coherent internal process metric.
• NPD Innovativeness: Exhibited high scale reliability, indicating strong coherence among items assessing product novelty and market disruption.
• Firm Performance: Displayed composite reliability values approaching 0.90, confirming high stability across multidimensional financial and competitive operational indicators.

Across the entire measurement model, composite reliability values ranged from 0.72 to 0.90, demonstrating that the 23-item inventory yields stable, reproducible measurements across executive cohorts.

Factor Analysis

The underlying factor structure of the 23-item questionnaire was formally assessed using maximum likelihood Confirmatory Factor Analysis (CFA) using covariance structural modeling techniques. The empirical sample comprised verified CEOs and managing directors of established commercial enterprises based in Germany.

The primary measurement specification specified five correlated latent factors representing: (1) Alternative Search, (2) High Standards, (3) New Product Development Speed, (4) New Product Development Innovativeness, and (5) Firm Performance. Consistent with modern multivariate standards (Hair et al., 2010), goodness-of-fit was evaluated through a multi-index evaluation strategy:

• Chi-Square / Degrees of Freedom: χ²[220] = 1171.809 (p < 0.001)
• Comparative Fit Index (CFI): 0.92 (satisfying the standard > 0.90 baseline for complex structural systems)
• Root Mean Square Error of Approximation (RMSEA): 0.06 (indicating close empirical fit within the 90% confidence interval)
• Standardized Root Mean Square Residual (SRMR): 0.05 (well below the conservative 0.08 cutoff)

Every individual item loaded positively, strongly, and significantly (p < 0.001) on its designated latent factor, with normalized factor loadings confirming substantive explained variance. The comprehensive marker variable CFA (Williams et al., 2010) further corroborated the structural stability of this five-factor model under rigorous methodological controls.

Instrument / Measurement Tool

• Test Type: Structured Organizational & Cognitive Style Questionnaire / Self-Report Inventory
• Target Population: Chief Executive Officers (CEOs), Managing Directors, and Top-Level Corporate Executives
• Administration Format: Self-administered paper-and-pencil or secure web-based survey
• Estimated Completion Time: Approximately 8 to 12 minutes
• Total Item Count: 23 items
• Factor Structure: 5 correlated latent dimensions:
  • Factor 1: Alternative Search (CEO Decision Style)
  • Factor 2: High Standards (CEO Decision Style)
  • Factor 3: New Product Development Speed
  • Factor 4: New Product Development Innovativeness
  • Factor 5: Firm Performance
• Response Scale: Uniform 7-point Likert scale:
  • 1 = Strongly Disagree (Stimme überhaupt nicht zu)
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neutral (Neither Agree nor Disagree)
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree (Stimme voll und ganz zu)
• Scoring and Computational Procedures:
  • Construct scores are computed either as mean composite scores across subscale items or preserved as latent factor scores derived from structural equation modeling.
  • Higher scores on the Alternative Search subscale denote an executive tendency toward comprehensive exploration of alternatives.
  • Higher scores on the High Standards subscale denote rigorous, perfectionist aspiration criteria.
  • Higher scores on NPD Speed and Innovativeness denote rapid commercialization velocity and higher technological novelty, respectively.
  • Higher scores on Firm Performance represent superior competitive and financial achievement relative to peers.

Permissions & Fee and Test Year

The measurement model was formally published in 2023 in the Journal of Management Studies. The original research is copyrighted by the authors and the Society for the Advancement of Management Studies / John Wiley & Sons Ltd.

• Academic / Non-Commercial Use: The scale may be utilized for non-commercial academic, psychological, and organizational research purposes, provided that appropriate scholarly attribution is formally cited.
• Commercial Applications: Commercial assessment, executive testing, and consulting use require formal permission from the corresponding author and copyright holders.
• Fees: There is no licensing fee for academic research purposes.
• Correspondence Contact: Inquiries regarding test access, item phrasing, and usage authorizations should be directed to Dr. Sebastian Kruse ([email protected]), RWTH Aachen University, Innovation and Entrepreneurship Group (WIN) – TIME Research Area, Kackertstr. 7, 52072 Aachen, Germany.

References

• Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/002202217000100301
• Durinik, M., Bavolar, J., & Orosova, O. (2018). Maximizing and its components in everyday decision making: A study on Slovak university students. Frontiers in Psychology, 9, 1642. https://doi.org/10.3389/fpsyg.2018.01642
• 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
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Pearson Education.
• Hambrick, D. C. (2007). Upper echelons theory: An update. Academy of Management Review, 32(2), 334–343. https://doi.org/10.5465/amr.2007.24345254
• Hambrick, D. C., & Mason, P. A. (1984). Upper echelons: The organization as a reflection of its top managers. Academy of Management Review, 9(2), 193–206. https://doi.org/10.2307/258434
• Kruse, S., Bendig, D., & Brettel, M. (2023). How does CEO decision style influence firm performance? The mediating role of speed and innovativeness in new product development. Journal of Management Studies, 60(5), 1205–1235. https://doi.org/10.1111/joms.12913
• Misuraca, R., Faraci, P., Gangemi, A., Carmeci, F. A., & Miceli, S. (2015). The decision making tendency inventory: A new measure to assess maximizing, satisficing, and minimizing. Journal of Behavioral Decision Making, 28(2), 111–123. https://doi.org/10.1002/bdm.1847
• Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., & Lehman, D. R. (2002). Maximizing versus satisficing: Happiness is a matter of choice. Journal of Personality and Social Psychology, 83(5), 1178–1197. https://doi.org/10.1037/0022-3514.83.5.1178
• Sheng, S., Zhou, K. Z., & Lessassy, L. (2013). NPD speed vs. innovativeness: The contingent impact of institutional and market environments. Strategic Management Journal, 34(11), 1384–1396. https://doi.org/10.1002/smj.2005
• Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852
• Simon, H. A. (1957). Models of man, social and rational: Mathematical essays on rational human behavior in a social setting. John Wiley & Sons.
• Williams, L. J., Hartman, N., & Cavazotte, F. (2010). Method variance and marker variables: A review and comprehensive CFA approach. Organizational Research Methods, 13(3), 477–514. https://doi.org/10.1177/1094428110366036

Items of the Scale