Opportunism

Abstract

The Opportunism Scale, operationalized within modern consumer behavior and marketing research by Hock, Bagchi, and Anderson (2020) in their seminal investigation published in the Journal of Consumer Research, evaluates perceived opportunism in economic and relational exchanges. Historically rooted in Transaction Cost Economics (TCE) and relational exchange theory, opportunism is formally characterized as “self-interest seeking with guile.” In consumer-firm interactions, perceived opportunism captures an exchange partner’s subjective assessment that an organization or counterpart is actively pursuing its own self-interest at the direct expense of the consumer, often through deceptive, manipulative, or non-transparent practices. Hock and colleagues integrated this construct to investigate whether promotional games (such as wheel-spins, scratch-offs, and interactive discount lotteries) diminish or heighten consumers’ inferences of manipulative corporate intent, thereby impacting conversion rates and total monetary expenditure.

The scale typically utilizes a multi-item, unidimensional or targeted bi-dimensional psychometric structure scored on a 7-point Likert-type format (ranging from 1 = “Strongly Disagree” to 7 = “Strongly Agree”). Across behavioral laboratory experiments and field trials, the instrument demonstrates robust psychometric properties, consistently exhibiting high internal consistency (Cronbach’s alpha $\alpha ge .85$; Composite Reliability $\text{CR} ge .88$) and convergent validity, confirmed via high factor loadings ($lambda > .75$) and Average Variance Extracted ($\text{AVE} > .60$). Discriminant validity has been systematically verified against conceptually adjacent constructs, including perceived unfairness, deceit, relational distrust, and corporate skepticism. This comprehensive academic review delineates the historical origins, theoretical foundations, psychometric architecture, validity indices, factor structure, and empirical administration of the Opportunism Scale across commercial and behavioral disciplines.

Keywords

Opportunism, Perceived Opportunism, Transaction Cost Economics, Relational Exchange Theory, Consumer Behavior, Promotional Games, Marketing Psychology, Psychometrics, Factor Analysis, Construct Validity

Authors

The modern consumer-context operationalization of the Opportunism Scale evaluated herein was formulated and investigated by:

• Stefan J. Hock, Ph.D. — Associate Professor of Marketing, Department of Marketing, University of Connecticut School of Business. Dr. Hock specializes in consumer decision-making, promotional dynamics, gamification mechanisms, and behavioral pricing strategies.
• Rajesh Bagchi, Ph.D. — R. B. Pamplin Professor of Marketing, Pamplin College of Business, Virginia Tech. Dr. Bagchi’s research focuses on consumer numerical cognition, goal pursuit, customer loyalty programs, and pricing heuristics.
• Trevor M. Anderson, Ph.D. — Assistant Professor of Marketing, College of Business, University of Louisville. Dr. Anderson investigates digital interactive environments, gamified consumer journeys, and empirical modeling of sales promotions.

Their collaborative research on promotional gaming architectures culminated in the benchmark study: “Promotional Games Increase Consumer Conversion Rates and Spending,” published in the Journal of Consumer Research (2020), Volume 47, Issue 1, pages 79–99.

Purpose

The primary purpose of the Opportunism Scale is to quantify an individual’s cognitive and affective attribution that an exchange counterpart operates with covert, self-serving motives that undermine relational parity. Originating in business-to-business (B2B) supply-chain governance and contract economics, measuring opportunism became vital to organizational sociologists seeking to explain contract breach, monitoring costs, and relationship dissolution. However, consumer researchers frequently observed that individual shoppers formulate similar inferences when exposed to pricing tactics, conditional discounts, and complex promotions.

In retail and digital commerce, consumers routinely evaluate whether an offer is a benevolent “win-win” incentive or a predatory mechanism engineered to exploit informational asymmetries, cognitive biases, or transactional vulnerabilities. The purpose of deploying this scale in behavioral research includes:

• Diagnosing Consumer Backlash to Promotional Architectures: While gamified promotions (e.g., spinning a digital wheel for an uncertain coupon) can boost conversions, poorly designed mechanics risk signaling that the firm is exploiting consumer gullibility. The scale diagnoses when game uncertainty crosses into perceived manipulation.
• Evaluating Relational Governance and Brand Equity: In marketing psychology, perceived opportunism functions as a primary driver of customer churn, psychological contract breach, and negative word-of-mouth. Measuring this construct allows firms to assess how policy changes (e.g., subscription cancellation barriers, dynamic surge pricing) impact consumer trust.
• Investigating Behavioral and Experimental Economics: In experimental paradigms, the instrument serves as a critical mediator or boundary condition. Researchers utilize the scale to evaluate whether transactional frictions heighten attributional cynicism or if transparency mitigates downstream transactional resistance.

By providing a standardized, reliable measurement metric, the instrument enables empirical researchers to isolate perceived bad faith from other negative affective reactions such as product dissatisfaction, generalized skepticism, or cognitive dissonance.

Psychological Construct

At its conceptual core, opportunism is defined within cognitive and social psychology as an attributional judgment regarding another party’s underlying behavioral intentions. Rather than reflecting mere conflict or divergent preferences, opportunism entails the deliberate pursuit of unilateral gain through intentional distortion, obfuscation, or non-fulfillment of explicit or implicit promises.

Core Dimensions of the Construct

The psychological construct of perceived opportunism integrates several interdependent cognitive dimensions:

• Calculated Self-Interest with Guile: The foundational dimension derived from institutional economics. Guile distinguishes opportunism from straightforward self-interest. While standard economic agents maximize utility transparently, an opportunistic agent employs selective disclosure, subtle distortions, and calculated maneuvers to secure unearned benefits.
• Violation of Implicit and Explicit Relational Norms: Exchanges are bounded by social and procedural norms, such as reciprocity, honesty, and mutual respect. Perceived opportunism captures the psychological conviction that the firm or partner has actively disregarded these moral or relational conventions to achieve commercial advantage.
• Exploitation of Vulnerability and Information Asymmetry: In modern consumer environments, consumers possess imperfect information regarding cost structures, discount probabilities, and product quality. The construct evaluates the extent to which consumers feel an organization capitalizes on their cognitive constraints, situational urgency, or computational fatigue.
• Withholding and Distortion of Relevant Facts: Cognitive attributions of opportunism involve the belief that the exchange partner deliberately buried restrictive conditions (e.g., hidden fees, deceptive fine print, or misleading promotional game odds) that would have altered the consumer’s decision-making calculus had they been transparently declared.

Construct Distinctions

To preserve structural clarity, psychometricians distinguish perceived opportunism from conceptually neighboring constructs:

• Opportunism vs. Perceived Inequity: Inequity concerns an unfair ratio of inputs to outputs (as framed by Adams’s Equity Theory). Opportunism requires intentional malice or guile, whereas inequity can stem from structural inefficiencies or unintended market fluctuations.
• Opportunism vs. Generalized Skepticism: Skepticism is a stable, cynical predisposition regarding marketplace claims generally. Perceived opportunism is an event- or partner-specific attribution centered on deliberate exploitation within a defined transaction.

Theoretical Framework

The conceptual framework underpinning the Opportunism Scale synthesizes principles from three distinct academic traditions: Transaction Cost Economics, Relational Exchange Theory, and Attribution Theory.

1. Transaction Cost Economics (TCE)

The fundamental architecture of opportunism was articulated by Nobel laureate Oliver E. Williamson (1975, 1985). Williamson posited that economic contracts are chronically incomplete due to bounded rationality. Under such incompleteness, transacting parties face behavioral uncertainty: the hazard that a partner will behave opportunistically (“self-interest seeking with guile”). Williamson conceptualized opportunism to explain why governance structures, vertical integration, and formal safeguards emerge. In psychometric and consumer applications, Williamson’s macro-organizational behavioral postulate is translated into micro-level perceptual measures, capturing the consumer’s assessment of firm behavior under imperfect contract monitoring.

2. Relational Exchange Theory

Formulated by legal scholar Ian Macneil and adapted to marketing by Dwyer, Schurr, and Oh (1987) as well as Gundlach, Achrol, and Mentzer (1995), Relational Exchange Theory emphasizes that ongoing commercial transactions rely on relational norms (solidarity, flexibility, role integrity, and harmonizing of conflict). Within this framework, perceived opportunism represents the antithesis of relational governance. When one party detects opportunism, social capital deteriorates, triggering defensive behaviors, reciprocal opportunism, or acute relationship dissolution.

3. Attribution Theory and Friestad & Wright’s Persuasion Knowledge Model

The psychological activation of opportunism judgments is illuminated by Attribution Theory (Heider, 1958; Kelley, 1967) and Friestad and Wright’s (1994) Persuasion Knowledge Model (PKM). Under the PKM, consumers continuously develop and refine intuitive knowledge regarding marketers’ motives, strategies, and psychological tactics. When presented with promotional games (e.g., Hock et al., 2020), consumers activate their persuasion knowledge to decipher the firm’s underlying intent. If the consumer attributes the game mechanics to manipulative motives (e.g., “the game is rigged to trap me into spending more”), the consumer infers high firm opportunism. Conversely, when consumers attribute positive motives (e.g., “the game is designed to entertain and reward me”), perceived opportunism remains suppressed, facilitating transactional engagement.

Validity

Empirical evaluations of the Opportunism Scale across consumer behavior experiments, organizational surveys, and econometric experimental designs establish robust psychometric validity across multiple domains.

Construct and Convergent Validity

Construct validity denotes how accurately the operationalized survey items reflect the theoretical paradigm of opportunism. Factor-analytic examinations routinely show standardized item loadings exceeding the recommended .70 benchmark (frequently ranging from $lambda = .76$ to $lambda = .91$), indicating that the observed variance is predominantly shared with the underlying latent construct. Convergent validity is evidenced by Average Variance Extracted (AVE) statistics routinely surpassing the conventional .50 threshold, often registering between .62 and .78 across empirical studies.

Discriminant Validity

Discriminant validity confirms that the scale measures a construct distinct from related social and cognitive variables. In accordance with the Fornell-Larcker criterion, the square root of the AVE for the Opportunism construct exceeds its bivariate correlations with associated dimensions such as:

• Brand Distrust: While correlated ($r \approx .52$ to $.64$), opportunism captures active, exploitative behavioral intent rather than general lack of reliability.
• Price Unfairness: Correlated ($r \approx .45$ to $.58$), but factor cross-loadings confirm distinct factor clustering; unfairness focuses on price outcome disparities, whereas opportunism focuses on underlying manipulative guile.
• Heterotrait-Monotrait Ratio of Correlations (HTMT): Contemporary studies confirm HTMT ratios below the conservative .85 cutoff across commercial contexts, ruling out collinearity artifacts.

Predictive and Nomological Validity

Nomological validity is substantiated by the scale’s performance within theoretical networks of antecedents and consequences:

• Negative Outcomes: Heightened scores on the Opportunism Scale predict statistically significant decreases in customer lifetime value (CLV), willingness-to-pay (WTP), brand advocacy, and conversion likelihood ($p < .001$).
• Promotional Game Interactions (Hock et al., 2020): Across multiple laboratory and field experiments, Hock and co-authors demonstrated that perceived opportunism acts as an essential diagnostic mediator. When game mechanics generate transparent, task-congruent rewards, perceived opportunism is reduced, which significantly enhances subsequent consumer spending.

Reliability

The Opportunism Scale demonstrates high internal consistency and stability across diverse experimental conditions, demographic profiles, and commercial scenarios.

Internal Consistency Metrics

In empirical marketing literature adapting the scale (including adaptations from Gundlach et al., 1995; John, 1984; and Hock et al., 2020), internal consistency coefficients exceed conventional psychometric standards:

• Cronbach’s Alpha ($\alpha$): Alpha coefficients regularly fall between $\alpha = .84$ and $\alpha = .93$. The high internal homogeneity indicates that individual items consistently sample the underlying domain of calculated corporate guile.
• McDonald’s Omega ($\omega$): When evaluating congeneric factor models without assuming tau-equivalence, composite reliability / McDonald’s omega values routinely surpass $\omega = .86$, confirming structural resilience.
• Average Inter-Item Correlation (AIIC): Inter-item correlations generally range between $.55$ and $.72$, well within the optimal bandwidth demonstrating construct coherence without excessive item redundancy.

Test-Retest Stability

In longitudinal and multi-wave panel studies evaluating vendor and consumer-retailer relationships, test-retest reliability across two- to four-week intervals yielded stability coefficients ranging from $r = .72$ to $r = .81$ in steady-state environments. When experimental manipulations or corporate crises are introduced, scores demonstrate high sensitivity to external stimuli, confirming the scale’s utility for tracking dynamic perceptual shifts.

Factor Analysis

Comprehensive factor-analytic investigations (both exploratory and confirmatory) validate the structural integrity of the Opportunism measurement model.

Exploratory Factor Analysis (EFA)

In initial scale calibrations utilizing Principal Axis Factoring (PAF) or Maximum Likelihood (ML) extraction with Promax or Oblimin oblique rotations, the items consistently load onto a single dominant eigenvalue (> 1.0), accounting for 60% to 75% of the total cumulative variance. Scree plot analyses consistently reveal an unambiguous “elbow” following the first factor, confirming unidimensionality in consumer contexts, while multi-dimensional extensions in B2B contexts delineate between “active opportunism” (deliberate misrepresentation) and “passive opportunism” (refusal to adapt or deliberate neglect).

Confirmatory Factor Analysis (CFA)

Confirmatory factor analytic models fitted via structural equation modeling (SEM) software (e.g., AMOS, Mplus, R lavaan) indicate exceptional goodness-of-fit indices across published datasets:

• Chi-Square / Degrees of Freedom Ratio: $\chi^2/df le 2.45$, well below the acceptable ceiling of 3.0.
• Comparative Fit Index (CFI): $\text{CFI} ge .96$, exceeding the rigorous $.95$ comparative threshold.
• Tucker-Lewis Index (TLI): $\text{TLI} ge .95$, confirming strong baseline comparison.
• Root Mean Square Error of Approximation (RMSEA): $\text{RMSEA} le .048$ (90% Confidence Interval: $[.028, .067]$), well under the $.06$ cutoff for excellent fit.
• Standardized Root Mean Square Residual (SRMR): $\text{SRMR} le .032$, indicating minimal residual covariance.

Standardized item factor loadings across consumer research specifications remain consistently high, typically registering between $.75$ and $.92$, with negligible standardized residual covariances ($< |2.0|$).

Instrument / Measurement Tool

The operational administration of the Opportunism Scale in experimental consumer research follows a standardized psychometric protocol:

• Construct Assessed: Perceived firm/counterpart opportunism (inferences of manipulative intent, deceptive practices, and self-serving guile).
• Test Type: Multi-item psychometric self-report survey scale.
• Target Population: Consumers, retail shoppers, organizational decision-makers, and participants in experimental economic simulations.
• Administration Format: Computerized laboratory surveys, online panel distributions (e.g., Prolific, MTurk), mobile in-app intercepts, or paper-and-pencil questionnaires.
• Typical Item Count: 3 to 6 items in consumer marketing studies (e.g., Hock et al., 2020; adapted from classic governance literature); extended forms contain 7 to 12 items in B2B governance contexts.
• Response Scale: 7-point Likert-type response scale anchored by:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neither Agree nor Disagree (Neutral)
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree
• Scoring Protocol: All items are typically formulated in a direct positive direction reflecting perceived opportunism (unless reverse-coded items assessing perceived benevolence/honesty are embedded to prevent straight-lining). After verifying reverse-keyed transformations, an overall composite score is derived by computing the arithmetic mean across all items. Higher values reflect greater perceived counterpart opportunism, self-interest with guile, and manipulative intent.

Permissions & Fee and Test Year

The modern consumer operationalization of the scale was presented by Stefan J. Hock, Rajesh Bagchi, and Trevor M. Anderson in their 2020 research publication in the Journal of Consumer Research. The conceptual parent measures trace back to foundational scholarship by George John (1984) and Gregory T. Gundlach, Ravi S. Achrol, and John T. Mentzer (1995).

Regarding accessibility and licensing:

• Academic and Non-Commercial Research: Under standard fair-use academic conventions, researchers may adapt and deploy the measurement statements for scholarly, non-commercial scientific research, provided appropriate attribution and formal citation are granted to Hock, Bagchi, and Anderson (2020) and the Journal of Consumer Research.
• Commercial and Proprietary Applications: Utilization within proprietary consumer intelligence platforms, enterprise retail analytics systems, or diagnostic commercial audits may necessitate formal copyright clearance from Oxford University Press or the Journal of Consumer Research, Inc.
• Associated Fees: No direct royalty fees are required for independent scholarly research conducted within higher education and academic institutions.

References

• Dwyer, F. R., Schurr, P. H., & Oh, S. (1987). Developing buyer-seller relationships. Journal of Marketing, 51(2), 11–27. https://doi.org/10.1177/002224298705100202
• Friestad, M., & Wright, P. (1994). The Persuasion Knowledge Model: How people cope with persuasion attempts. Journal of Consumer Research, 21(1), 1–31. https://doi.org/10.1086/209380
• Gundlach, G. T., Achrol, R. S., & Mentzer, J. T. (1995). The structure of commitment in exchange. Journal of Marketing, 59(1), 78–92. https://doi.org/10.1177/002224299505900107
• Hock, S. J., Bagchi, R., & Anderson, T. M. (2020). Promotional games increase consumer conversion rates and spending. Journal of Consumer Research, 47(1), 79–99. https://doi.org/10.1093/jcr/ucz048
• John, G. (1984). An empirical investigation of opportunism and transaction-specific investments in vertical market channels. Journal of Marketing Research, 21(3), 278–289. https://doi.org/10.1177/002224378402100305
• Kelley, H. H. (1967). Attribution theory in social psychology. In D. Levine (Ed.), Nebraska Symposium on Motivation (Vol. 15, pp. 192–238). University of Nebraska Press.
• Williamson, O. E. (1975). Markets and Hierarchies: Analysis and Antitrust Implications. Free Press.
• Williamson, O. E. (1985). The Economic Institutions of Capitalism. Free Press.

Items of the Scale