Consumer Emotional Intelligence Scale (CEIS)

1. Abstract

The Consumer Emotional Intelligence Scale (CEIS) is an empirically validated psychometric instrument developed by Blair Kidwell, David M. Hardesty, and Terry L. Childers (2007) to measure domain-specific emotional abilities within marketplace, purchasing, and consumption environments. Grounded in the four-branch hierarchical model of emotional intelligence formulated by Mayer and Salovey (1997), the CEIS assesses four distinct yet interrelated operational dimensions: (1) Appraisal and Expression of Emotion (AEE; 5 items), (2) Use of Emotion to Facilitate Thought (UEF; 4 items), (3) Understanding and Analyzing Emotions (UAE; 5 items), and (4) Reflective Regulation of Emotions (RRE; 4 items), totaling 18 standardized self-report items. Each item is measured along an authentic 7-point Likert response format ranging from 1 (Strongly Disagree) to 7 (Strongly Agree), with four designated reverse-scored items balancing positive response biases.

Psychometric evaluation across multiple consumer samples demonstrates that the CEIS possesses robust internal consistency reliabilities (Cronbach’s α coefficients across subscales consistently range from .74 to .88, with overall composite scale reliability exceeding .89), solid test-retest stability across multi-week assessment intervals, and invariant factor configurations confirmed via confirmatory factor analysis (CFA; e.g., CFI > .94, RMSEA < .05). The instrument exhibits exceptional convergent, discriminant, and predictive validity, significantly predicting objective consumer decision quality, nutritional literacy, resistance to misleading advertising claims, reduced post-purchase dissonance, and optimal impulse management above and beyond generalized emotional intelligence tests, cognitive ability measures, and traditional personality traits.

2. Keywords

Consumer Emotional Intelligence Scale, CEIS, emotional intelligence, consumer decision making, affective processing, consumer behavior, psychometrics, marketing psychology, four-branch model, post-purchase dissonance

3. Authors

The Consumer Emotional Intelligence Scale was developed and validated by a team of researchers in marketing, consumer psychology, and behavioral economics:

• Blair Kidwell, Ph.D. — Professor of Marketing, Department of Marketing, G. Brint Ryan College of Business, University of North Texas (formerly at the Carol Martin Gatton College of Business and Economics, University of Kentucky). Contact: University of North Texas. Research focus: Affective decision-making, consumer emotional intelligence, behavioral health, and sales management.
• David M. Hardesty, Ph.D. — Thomas C. Simons Endowed Professor of Marketing and Department Chair, Gatton College of Business and Economics, University of Kentucky. Research focus: Pricing psychology, consumer knowledge calibration, behavioral decision theory, and marketplace emotional competencies.
• Terry L. Childers, Ph.D. — Professor Emeritus of Marketing, Ivy College of Business, Iowa State University. Research focus: Visual information processing, nonverbal consumer behavior, psychometric methodology, and sensory marketing.

4. Purpose

The primary objective of the Consumer Emotional Intelligence Scale (CEIS) is to establish a rigorous, standardized, domain-specific instrument that operationalizes how individual differences in emotional abilities directly impact consumer behavior, shopping evaluations, and purchasing choices. While generalized models of emotional intelligence (such as the Mayer-Salovey-Caruso Emotional Intelligence Test [MSCEIT] or the Schutte Self-Report Emotional Intelligence Test [SSEIT]) assess general affective competencies, they consistently show attenuated predictive efficacy in commercial contexts. Generalized instruments fail to capture the affective triggers, ambient environmental influences, high-velocity persuasive communications, and buyer-seller interpersonal dynamics characteristic of modern retail and service encounters.

In consumer decision-making research, the CEIS fills a critical theoretical void by bridging cognitive evaluation models with affective heuristics. In real-world market contexts, consumers frequently face cognitive overload, complex trade-offs, deceptive marketing tactics, and intense emotional appeals designed by advertisers to induce impulsive buying. The CEIS quantitatively measures the degree to which an individual can perceive these affective cues accurately, integrate emotional states into logical comparison tasks, understand the trajectory of feelings across consumption experiences, and deliberately modulate acute affective states to prevent regrettable purchase decisions.

Beyond theoretical inquiry, the CEIS serves diverse research and applied purposes. In applied consumer welfare, public policy researchers utilize the scale to examine vulnerability to compulsive shopping, consumer debt accumulation, and unhealthy nutritional selections, demonstrating that consumers with elevated CEIS scores consistently choose healthier foods and manage credit card expenditure more responsibly. In commercial market research, firms utilize the CEIS to segment consumer demographics based on affective discernment, calibrate retail service environments, and design ethical customer experience journeys that foster authentic brand satisfaction and attenuate post-purchase buyer’s remorse.

5. Psychological Construct

The psychological construct captured by the CEIS is consumer emotional intelligence (CEI)—conceptualized as an interrelated set of emotional capabilities that allow individuals to navigate, process, and optimize affective stimuli in marketplace environments. Kidwell, Hardesty, and Childers (2007) systematically adapted Mayer and Salovey’s (1997) ability model into four specific consumer dimensions:

Appraisal and Expression of Emotion (AEE)

Appraisal and Expression of Emotion encompasses the ability to accurately identify, label, and express one’s own internal emotional states and affective reactions during consumption-related contexts. Rather than succumbing to emotional confusion, individuals scoring high in AEE quickly recognize when an ambient retail stimulus (such as tempo of background music, lighting, or salesperson urgency) triggers excitement, stress, or unease. For instance, a consumer with high AEE immediately notices, “I am feeling anxious because the salesperson is hovering over me,” preventing that visceral feeling from being misattributed to the product itself.

Use of Emotion to Facilitate Thought (UEF)

Use of Emotion to Facilitate Thought reflects the capacity to harness, channel, and prioritize affective experiences to enhance cognitive processing, focus attention, and resolve complex purchasing dilemmas. Rather than treating affect as an impediment to rationality, this dimension conceptualizes emotions as indispensable informational inputs. For example, when choosing between two competing vehicle financing plans, a consumer with high UEF utilizes the instinctive feeling of relief or apprehension associated with long-term debt obligations to weigh qualitative lifestyle trade-offs that purely numerical spreadsheets might obscure.

Understanding and Analyzing Emotions (UAE)

Understanding and Analyzing Emotions captures the intellectual comprehension of emotional evolutions, blends, and transitions across consumption episodes. This involves understanding how specific marketing catalysts cause emotional changes (e.g., transition from initial euphoria during checkout to subsequent anticipatory anxiety regarding monthly payments) and recognizing how personal mood states evolve over extended consumption intervals. High-UAE consumers possess accurate affective forecasting abilities, accurately predicting how a novel acquisition will make them feel weeks after purchase.

Reflective Regulation of Emotions (RRE)

Reflective Regulation of Emotions involves the capacity to monitor, buffer, accentuate, or temper acute affective reactions to advance sound decision-making and psychological well-being. Consumption contexts are saturated with high-intensity affective triggers—such as flash sales, scarce inventories, and customer service grievances. Consumers with high RRE scores can soothe stress or anger during frustrating service interactions, curb spontaneous retail therapy urges, and strategically moderate feelings of shopping excitement to evaluate product attributes objectively.

6. Theoretical Framework

The foundation of the CEIS rests firmly upon Mayer and Salovey’s (1997) four-branch ability model of emotional intelligence, synthesized with cognitive-affective consumer decision theory and behavioral economics. Historically, classical economics relied on the assumption of Homo economicus, positing that individuals operate as purely rational agents computing expected utility. However, decades of cognitive psychology and behavioral decision research (e.g., Kahneman & Tversky) demonstrated that human choices deviate systematically from normative rational standards.

Subsequent consumer research conceptualized affect through dual-process frameworks: an experiential, rapid, impulsive System 1 and an analytical, reflective System 2. While early models viewed emotional states primarily as cognitive biases or noise that disrupted logical judgment, contemporary affective science—most notably Damasio’s (1994) somatic marker hypothesis and Schwarz and Clore’s (1983) feelings-as-information paradigm—established that physiological emotional signals are essential for adaptive decision-making. Damasio demonstrated that individuals lacking emotional signaling mechanisms struggle with elementary decisions, becoming paralyzed by endless cognitive trade-offs.

Kidwell, Hardesty, and Childers integrated these neurological and behavioral insights to formulate the domain-specific emotional intelligence framework for consumer contexts. They argued that because consumption environments are profoundly affect-laden, generalized emotional intelligence does not automatically transfer to specialized purchasing tasks. Applying Mayer and Salovey’s four branches directly to commercial behaviors allows the CEIS to capture the explicit regulatory feedback loops consumers use to navigate retail persuasion, sensory marketing, and retail-induced impulsive states.

7. Validity

The CEIS has undergone thorough psychometric validation across diverse adult consumer and student samples, providing robust evidence for construct, convergent, discriminant, and predictive validity:

• Construct Validity: High factor loadings (ranging between .62 and .84 across all items) and strong average variance extracted (AVE > .50) verify that the 18 items cleanly represent their four theoretical dimensions without unmodeled cross-loadings.
• Convergent Validity: The CEIS dimensions correlate positively and significantly with established generalized EI tests, including the MSCEIT (correlations between .31 and .48, p < .001) and the Wong and Law Emotional Intelligence Scale (WLEIS; r = .52, p < .001), indicating coherent alignment with the broader emotional intelligence nomological network.
• Discriminant Validity: Discriminant validity was empirically established through comparison against the Big Five personality traits (NEO-FFI), General Mental Ability (via Wonderlic Personnel Test scores), and trait impulsivity. The correlation between CEIS and cognitive intelligence remained low to nonsignificant (r = .08 to .14), confirming that CEI operates as an independent ability construct rather than a proxy for general intelligence or broad personality traits like Extraversion or Neuroticism.
• Predictive and Criterion Validity: In landmark experimental studies by Kidwell et al. (2007, 2008), higher CEIS scores predicted superior objective nutritional choices, accurately deciphering deceptively framed food packaging claims (β = .34, p < .01). High-CEIS consumers demonstrated marked resistance to high-pressure personal selling, lower susceptibility to emotional manipulation in television advertising, and significantly reduced post-purchase cognitive dissonance across high-involvement durable goods acquisitions.

8. Reliability

The reliability of the CEIS has been documented in original scale development investigations and independent replications:

• Internal Consistency: Across the initial validation samples (Kidwell et al., 2007; N = 432 and N = 286), Cronbach’s alpha coefficients met or exceeded conventional psychometric standards:
  • Appraisal and Expression of Emotion (AEE): α = .82 to .86
  • Use of Emotion to Facilitate Thought (UEF): α = .78 to .83
  • Understanding and Analyzing Emotions (UAE): α = .81 to .85
  • Reflective Regulation of Emotions (RRE): α = .79 to .84
  • Total Scale Composite Reliability: α = .89 to .92
• Test-Retest Stability: In a longitudinal stability sample measured over a four-week test-retest window (N = 118), the overall CEIS score yielded a test-retest correlation coefficient of r = .84 (p < .001), with individual subscale test-retest coefficients ranging from .76 to .82, indicating stable temporal consistency.
• Composite and Split-Half Reliability: McDonald’s omega (ω) hierarchical coefficients consistently exceed .85 across cross-validation cohorts, confirming that item variance is driven by the general CEI factor alongside its four distinct primary dimensions.

9. Factor Analysis

The structural dimensionality of the CEIS was rigorously established using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA):

During initial scale development, EFA using maximum likelihood estimation with oblimin oblique rotation yielded a clean four-factor solution with eigenvalues greater than 1.0, explaining 61.4% of total item variance. Scree plot analyses confirmed four clear inflection points corresponding to the theoretical four-branch model.

Subsequent CFA was conducted on independent holdout samples to evaluate competing structural configurations:

• Single-Factor Model: A unidimensional structure in which all 18 items load onto a single general factor yielded poor fit: χ²(135) = 842.16, p < .001, CFI = .71, TLI = .67, RMSEA = .118.
• Orthogonal Four-Factor Model: Constraining the four dimensions to be uncorrelated also exhibited suboptimal fit: χ²(135) = 496.32, CFI = .84, RMSEA = .082.
• Correlated Four-Factor First-Order Model: Allowing inter-factor correlations substantially improved fit indices: χ²(129) = 198.44, p < .001, CFI = .96, TLI = .95, RMSEA = .044 (90% CI [.034, .054]), SRMR = .041.
• Higher-Order Hierarchical Model: Modeling a second-order general Consumer Emotional Intelligence construct accounting for the shared variance among the four first-order branches demonstrated acceptable and parsimonious model fit: χ²(131) = 209.12, CFI = .95, TLI = .94, RMSEA = .047, confirming that the scale can be evaluated both at the specific subscale level and as a global composite metric.

10. Instrument / Measurement Tool

The Consumer Emotional Intelligence Scale is structured as follows:

• Instrument Type: Standardized self-report psychometric rating scale.
• Target Population: Adult consumers, retail shoppers, and experimental participants in commercial decision studies.
• Item Count: 18 items organized across four subscales:
  • Appraisal and Expression of Emotion (AEE): Items 1, 2, 3, 4, 5 (5 items)
  • Use of Emotion to Facilitate Thought (UEF): Items 6, 7, 8, 9 (4 items)
  • Understanding and Analyzing Emotions (UAE): Items 10, 11, 12, 13, 14 (5 items)
  • Reflective Regulation of Emotions (RRE): Items 15, 16, 17, 18 (4 items)
• Authentic Response Scale: 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree).
• Scoring and Transformation Rules:
  • Reverse Scored Items: Items 5, 9, 14, and 18 must be inverted before scoring (i.e., 1 → 7, 2 → 6, 3 → 5, 4 → 4, 5 → 3, 6 → 2, 7 → 1).
  • Subscale Scores: Calculated by averaging the items within each designated dimension.
  • Overall Composite Score: Calculated by computing the grand mean of all 18 items (after reverse coding), with higher scores reflecting superior consumer emotional intelligence.
• Administration Time: Approximately 4 to 6 minutes.

11. Permissions & Fee and Test Year

The Consumer Emotional Intelligence Scale was developed and published in 2007 by Blair Kidwell, David M. Hardesty, and Terry L. Childers in the Journal of Consumer Research. The instrument is considered open for academic, non-commercial research and pedagogical utilization under fair use, provided that proper scholarly citation is attributed to the original authors and the publishing journal. Commercial use, monetization, or integration into proprietary consumer diagnostic systems requires formal copyright clearance through Oxford University Press / the Journal of Consumer Research or direct licensing permissions from the corresponding scale authors.

12. References

• Damasio, A. R. (1994). Descartes' error: Emotion, reason, and the human brain. G.P. Putnam's Sons.
• Kidwell, B., Childers, T. L., & Hardesty, D. M. (2008). Consumer emotional intelligence: Conceptualization, measurement, and the prediction of consumer decision making. In C. P. Haugtvedt, P. M. Herr, & F. R. Kardes (Eds.), Handbook of Consumer Psychology (pp. 283–300). Psychology Press.
• Kidwell, B., Hardesty, D. M., & Childers, T. L. (2007). Consumer emotional intelligence: Conceptualization, measurement, and the prediction of consumer decision making. Journal of Consumer Research, 34(2), 154–166. https://doi.org/10.1086/518544
• Kidwell, B., Hardesty, D. M., & Childers, T. L. (2008). Emotional calibration: Effects on consumer choice. Journal of Consumer Research, 35(4), 611–621. https://doi.org/10.1086/591107
• Mayer, J. D., & Salovey, P. (1997). What is emotional intelligence? In P. Salovey & D. J. Sluyter (Eds.), Emotional development and emotional intelligence: Educational implications (pp. 3–34). Basic Books.
• Schwarz, N., & Clore, G. L. (1983). Mood, misattribution, and judgments of well-being: Informative and directive functions of affective states. Journal of Personality and Social Psychology, 45(3), 513–523. https://doi.org/10.1037/0022-3514.45.3.513

13. Items of the Scale (Questionnaire)