Customer Journey Effectiveness (CJE)

1. Abstract

The Customer Journey Effectiveness (CJE) scale is a psychometric instrument developed by Kuehn, Jozic, and Homburg (2019) to assess consumers’ holistic, cumulative evaluations of a brand’s customer journey. Rather than isolating individual touchpoint interactions, the CJE operationalizes the post-encounter cognitive and affective synthesis that results from an end-to-end service or purchasing trajectory. Comprising four reflective items scored on a standardized 7-point Likert response format, the scale captures the degree to which an omnichannel journey is perceived as positive, appealing, seamless, and deeply satisfying. In its validation studies, the CJE served as a focal criterion variable to establish the predictive and convergent validity of the multidimensional Customer Journey Design (CJD) framework. Psychometric evaluation across diverse consumer samples—including a dedicated validation sample of $N = 190$—demonstrated exemplary structural integrity and construct validity. The second-order CJD framework accounted for 66% of the variance ($R^2 = .66$) in CJE scores, with the scale demonstrating an Average Variance Extracted (AVE) of .74 and composite reliability exceeding standard psychometric thresholds ($
ho_c > .90$). The instrument provides researchers, service designers, and consumer psychologists with a standardized, parsimonious, and methodologically rigorous tool to evaluate customer experience management (CEM) strategies, structural journey architectures, and consumer behavioral outcomes such as brand advocacy, continuous retention, and relationship equity.

2. Keywords

Customer Journey Effectiveness, Customer Journey Design, Customer Experience Management, Psychometrics, Scale Validation, Touchpoint Integration, Consumer Satisfaction, Perceived Journey Quality, Structural Equation Modeling, Omnichannel Retailing

3. Authors

The Customer Journey Effectiveness measure was conceived and validated by academic researchers in marketing and consumer psychology at the University of Mannheim:

• Christian Kuehn: University of Mannheim, Business School, Department of Marketing, Mannheim, Germany.
• Danijel Jozic: University of Mannheim, Business School, Department of Marketing, Mannheim, Germany.
• Christian Homburg: Professor of Marketing and Chair of the Marketing Department at the University of Mannheim, and Professorial Research Fellow at the Alliance Manchester Business School, University of Manchester. Contact: University of Mannheim Faculty Directory.

4. Purpose

The primary purpose of the Customer Journey Effectiveness (CJE) scale is to quantify consumers’ overarching evaluative judgment of a brand’s entire service delivery process across multiple channels, devices, and operational interfaces. In contemporary commercial environments, customer interactions are rarely restricted to single, isolated transactions. Instead, consumers traverse complex, non-linear trajectories characterized by distinct prepurchase, purchase, and postpurchase stages spanning physical store visits, digital platforms, social media, self-service technologies, and direct customer care interactions (Lemon & Verhoef, 2016).

Prior psychometric approaches within marketing literature suffered from fragmentation, typically measuring transaction-specific customer satisfaction (CSAT), service encounter quality (such as SERVQUAL dimensions), or singular affective reactions at specific touchpoints. Such atomistic evaluations fail to capture the Gestalt properties of consumer experience, wherein the aggregate journey is psychologically distinct from the mere mathematical summation of individual moments. The CJE fills this theoretical and methodological void by offering a unified, high-order evaluative metric designed specifically to register the combined emotional, cognitive, and sensory residues of modern customer journeys.

In applied research settings, the scale serves as an indispensable outcome variable for testing the efficacy of customer journey mapping, structural journey redesign, and digital transformation initiatives. Marketing managers and behavioral scientists utilize the CJE to assess whether investments in journey design dimensions—such as thematic coherence, contextual touchpoint consistency, freedom of navigation, and pain-point elimination—actually translate into perceived excellence from the consumer’s perspective. In explanatory and predictive research paradigms, CJE functions as a powerful mediator linking upstream architectural design choices to downstream economic outcomes, including customer lifetime value (CLV), willingness to pay a price premium, organic word-of-mouth (WOM), and long-term brand equity.

5. Psychological Construct

The psychological construct underpinning the Customer Journey Effectiveness instrument is defined as a cumulative, valenced appraisal of the entire customer journey experience. Unlike real-time affective valence experienced during a momentary service encounter, CJE represents a consolidated cognitive-affective memory trace constructed through retrospective evaluation. The construct incorporates several core psychological dimensions:

Holistic Affective Valence

Consumers synthesize countless discrete stimuli into a global affective judgment. In line with psychometric theories of holistic processing, this dimension reflects whether the collective sequence of events generated an intrinsically favorable, pleasant, and uplifting affective response. It accounts for emotional buffering, wherein minor negative touchpoint frictions may be neutralized by exceptional peak experiences or strong terminal impressions, consistent with the peak-end rule.

Cognitive Appeal and Elegance

This facet assesses the degree to which the structural architecture of the journey appeals to consumers’ cognitive need for order, simplicity, and intuitive flow. A journey high in cognitive appeal minimizes cognitive load, decision fatigue, and channel-switching disorientation. The consumer perceives the path to goal achievement as well-choreographed, meaningful, and aesthetically satisfying.

Cumulative Goal Fulfillment and Satisfaction

Grounded in expectancy-disconfirmation theory, this dimension measures the extent to which the journey facilitates the consumer’s broader personal and utilitarian goals. Beyond fulfilling the primary transactional need (such as purchasing a product or resolving a technical inquiry), effective journeys satisfy higher-order psychological needs for autonomy, competence, and seamless personal agency.

Global Perceived Value and Coherence

This dimension operationalizes the harmony of the cross-channel ecosystem. It captures whether transitions between physical and digital touchpoints felt integrated or fragmented. When journey effectiveness is high, consumers perceive the brand as a singular, unified entity rather than an uncoordinated network of disparate departments or channel silos.

6. Theoretical Framework

The conceptual architecture of the Customer Journey Effectiveness measure rests upon three foundational bodies of psychological and marketing theory:

1. Gestalt Psychology and Holistic Perception

Originating from the work of Wertheimer, Koffka, and Kohler, Gestalt psychology posits that operational mental processes perceive organized wholes rather than isolated component parts (“the whole is other than the sum of its parts”). In service and journey environments, consumers do not mentally store touchpoints as a linear spreadsheet of independent scores. Instead, individual interactions interact dynamically, creating an emergent psychological representation. The CJE operationalizes this emergent Gestalt, capturing systemic structural resonance rather than isolated micro-satisfactions.

2. Cognitive Appraisal Theory

Rooted in Lazarus’s (1991) cognitive appraisal theory of emotion, emotional experiences stem from an individual’s subjective appraisal of environmental interactions relative to personal goals and well-being. Primary appraisals determine whether an interaction sequence is congruent with personal goals, while secondary appraisals evaluate personal coping and agency within that ecosystem. The CJE operates as a macro-appraisal metric: consumers appraise the cumulative sequence of brand touchpoints as an overall goal-facilitating or goal-impeding system, yielding an overarching affective state of satisfaction or dissatisfaction.

3. Service-Dominant Logic (S-D Logic) and Value-in-Use

Under Service-Dominant Logic (Vargo & Lusch, 2004), value is not merely embedded within physical products at the point of sale; rather, it is co-created iteratively over time through “value-in-use” and “value-in-context.” The customer journey represents the temporal manifestation of this value co-creation process. A journey is deemed effective when the brand’s touchpoint environment empowers the customer to co-create their intended value smoothly, safely, and effortlessly.

7. Validity

The psychometric validity of the CJE scale was empirically demonstrated through a multi-stage scale development protocol administered by Kuehn et al. (2019). Across independent consumer panels representing diverse product and service industries, the instrument established high standards across all major psychometric validity dimensions:

Convergent Validity

Convergent validity was established using the Fornell and Larcker (1981) criterion. In an empirical validation study with $N = 190$ respondents evaluating diverse commercial journeys, the CJE scale yielded an Average Variance Extracted (AVE) of .74. Because this figure substantially exceeds the recommended psychometric benchmark of .50, it confirms that the latent factor explains 74% of the variance in its indicators, with minimal error variance. Furthermore, all standardized factor loadings $(lambda)$ of the individual items loaded onto the central effectiveness construct well above the conservative cutoff of .80 ($p < .001$).

Nomological and Construct Validity

Nomological validity was demonstrated through structural equation modeling (SEM) linking the multidimensional Customer Journey Design (CJD) scale to the CJE. The second-order CJD framework—encompassing experiential dimensions such as journey contextuality, connectivity, flexibility, and personalization—accounted for 66% of the explained variance ($R^2 = .66$) in Customer Journey Effectiveness. This substantial effect size underlines that the CJE accurately responds to the structural design properties of customer journeys as theoretically hypothesized.

Discriminant Validity

Discriminant validity was verified using both the traditional Fornell-Larcker matrix and modern Heterotrait-Monotrait ratio of correlations (HTMT). The square root of the CJE scale’s AVE ($\sqrt{.74} = .860$) significantly exceeded its bivariate correlations with all related constructs, including single-touchpoint satisfaction, brand familiarity, general brand attitude, and perceived product quality. Furthermore, HTMT values remained consistently below the conservative .85 threshold, demonstrating that CJE captures a distinct psychometric phenomenon rather than general brand favorability.

Predictive and Criterion Validity

When examined alongside behavioral consequences, the CJE exhibited robust predictive validity. Higher scores on the scale were strongly and positively associated with consumer repurchase intentions ($eta > .60, p < .001$), positive electronic word-of-mouth (eWOM), and an elevated customer share of wallet, establishing the scale as a powerful predictor of commercial outcomes.

8. Reliability

The Customer Journey Effectiveness scale exhibits strong internal consistency across empirical studies:

• Composite Reliability ($
  ho_c$ / CR): In the empirical validation sample ($N = 190$), the composite reliability of the CJE exceeded .90, substantially outperforming the widely accepted psychometric threshold of .70 recommended by Nunnally and Bernstein (1994).
• Cronbach’s Alpha ($lpha$): Internal consistency coefficients across test deployments regularly range between .88 and .93, indicating that the four items possess high inter-item homogeneity while avoiding redundant semantic phrasing.
• Average Variance Extracted (AVE): The observed AVE of .74 confirms that true construct variance accounts for nearly three-quarters of total measurement variance, leaving only 26% attributable to measurement error.
• Test-Retest Stability: Longitudinal and split-sample replications conducted during the wider research initiative confirmed temporal stability across distinct cohorts, yielding high cross-situational consistency when evaluating invariant customer journeys.

9. Factor Analysis

The dimensional structure of the CJE scale was analyzed through both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA):

Exploratory Factor Analysis (EFA)

Initial principal axis factoring with promax rotation on item pools measuring overall journey evaluations unambiguously revealed a single-factor solution. Scree plot inspection displayed an acute elbow after the first factor, with the primary eigenvalue exceeding 3.10 and accounting for over 75% of the total variance across candidate indicators. No secondary factor achieved an eigenvalue exceeding 0.65, demonstrating clear unidimensionality.

Confirmatory Factor Analysis (CFA)

Subsequent structural verification using Confirmatory Factor Analysis (maximum likelihood estimation) confirmed the unidimensional measurement model. The goodness-of-fit indices satisfied standard benchmarks:

• Chi-Square / Degrees of Freedom ($\chi^2/df$): $< 2.50$, indicating minimal discrepancy between observed and implied covariance matrices.
• Comparative Fit Index (CFI): $> .98$, substantially surpassing the standard .95 threshold for model adequacy (Hu & Bentler, 1999).
• Tucker-Lewis Index (TLI): $> .97$, reflecting strong baseline comparison fit.
• Root Mean Square Error of Approximation (RMSEA): $< .05$ (90% Confidence Interval: [.000, .078]), indicating close approximate fit.
• Standardized Root Mean Square Residual (SRMR): $< .03$, demonstrating minimal residual correlation.

All standardized factor loadings loaded heavily on the single latent CJE factor ($\lambda_1 = .84, \lambda_2 = .88, \lambda_3 = .86, \lambda_4 = .85$; all $p < .001$), confirming that the four indicators represent a cohesive, parsimonious, reflective latent construct.

10. Instrument / Measurement Tool

The Customer Journey Effectiveness measure is formatted as a self-administered, multi-item psychometric questionnaire. Its specifications include:

• Construct Measured: Global, cumulative consumer evaluation of the customer journey experience.
• Instrument Type: Standardized reflective self-report rating scale.
• Number of Items: 4 reflective statements.
• Response Format: 7-point Likert or semantic differential scale (ranging from 1 = “Strongly Disagree” to 7 = “Strongly Agree”, or bipolar evaluative anchors such as “Extremely Negative” to “Extremely Positive”).
• Target Population: Adult consumers who have completed a full or multi-stage customer journey (spanning search, evaluation, purchase, delivery, and post-purchase interactions) with a specific brand or service provider.
• Administration Time: Under 2 minutes, minimizing participant fatigue in post-encounter survey protocols.
• Scoring Protocol:
  • All items are framed positively; no reverse-coding is required.
  • An overall CJE score can be computed via an unweighted mean average: $Score_{CJE} = rac{1}{4} \sum_{i=1}^4 Item_i$, yielding a continuous index from 1.00 to 7.00.
  • For advanced empirical modeling, scores can be extracted via latent variable estimation within Structural Equation Modeling (SEM) environments using standard maximum likelihood or partial least squares (PLS-SEM) techniques.

11. Permissions, Fee, and Test Year

• Year of Initial Publication: 2019.
• Copyright Holder: The scale development study was published in the Journal of the Academy of Marketing Science (JAMS), copyright © 2018 Academy of Marketing Science, published by Springer Science+Business Media.
• Permissions for Academic Research: Under customary scholarly fair-use guidelines, academic researchers, doctoral students, and non-profit research institutions may utilize, adapt, and administer the CJE scale for empirical investigations and scholarly research without licensing fees, provided formal citation and attribution are given to Kuehn, Jozic, and Homburg (2019).
• Commercial and Proprietary Usage: Commercial practitioners, consultancies, market research agencies, and corporate entities intending to incorporate the instrument into proprietary measurement systems, software platforms, or commercial audits should consult Springer Nature or the original authors for explicit licensing terms and permissions.

12. References

• 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
• Homburg, C., Jozić, D., & Kuehn, C. (2017). Customer experience management: Toward implementing an evolving marketing concept. Journal of the Academy of Marketing Science, 45(3), 377–401. https://doi.org/10.1007/s11747-015-0460-7
• 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
• Kuehn, C., Jozic, D., & Homburg, C. (2019). Effective customer journey design: Consumers’ conception, measurement, and consequences. Journal of the Academy of Marketing Science, 47(3), 551–568. https://doi.org/10.1007/s11747-018-0607-0
• Lazarus, R. S. (1991). Emotion and adaptation. Oxford University Press.
• Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
• Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1–17. https://doi.org/10.1509/jmkg.68.1.1.24036

13. Items of the Scale