Customer Non-Transactional Values Measure

Abstract

The Customer Non-Transactional Values Measure (Shin & Perdue, 2023) is an empirically validated, 15-item psychological and behavioral instrument developed to evaluate non-transactional values co-created through customer engagement behaviors (CEBs) within digital hospitality brand communities. While conventional marketing and consumer psychology paradigms traditionally prioritize direct economic transactions, such as purchase volume, booking frequency, and immediate revenue generation, the rise of collaborative consumer ecosystems has highlighted the imperative of non-transactional contributions. This measure addresses an established void in consumer research and hospitality analytics by operationalizing the multifaceted value customers generate through interactive, social, and communicative participation. Developed through a rigorous multi-stage psychometric protocol—incorporating an extensive literature review, content adequacy assessments via quantitative sorting algorithms, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA)—the instrument establishes a robust, second-order hierarchical structure comprising four core first-order latent dimensions: Influential-Experience Value, Customer-to-Business (C-to-B) Innovation Value, Relational Value, and Functional Value. Evaluated among verified participants of the Marriott Bonvoy Insider Community, the scale demonstrates exceptional psychometric properties. Internal consistency reliability is confirmed by high Cronbach’s alpha coefficients ($lpha = 0.84 ext{ to }0.92$) and composite reliability indices ($ ext{CR} = 0.85 ext{ to }0.92$). Convergent and discriminant validity are evidenced by average variance extracted figures ($ ext{AVE} = 0.66 ext{ to }0.74$) exceeding established thresholds and outperforming squared inter-construct correlations. Furthermore, structural equation modeling validates its nomological network, establishing distinct motivational antecedents (intrinsic and extrinsic drivers) and downstream relational consequences (brand attachment and brand loyalty). This instrument provides researchers and practitioners with a psychometrically sound diagnostic tool to capture collaborative value beyond monetary transactions.

Keywords

Customer Non-Transactional Values, Customer Engagement Behaviors, Online Brand Communities, Influential Experience Value, Customer-to-Business Innovation, Relational Value, Functional Value, Service-Dominant Logic, Brand Attachment, Brand Loyalty, Scale Validation, Hospitality Psychometrics

Authors

The scale was developed and psychometrically validated by:

• Hakseung Shin, Ph.D. (Corresponding Author)
  Affiliation: Department of Tourism, College of Social Science, Hanyang University, Seoul, Republic of Korea.
  ORCID: 0000-0002-9209-7430
  Email: [email protected]
  Mailing Address: Hanyang University, College of Social Science, Department of Tourism, 222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.
• Richard R. Perdue, Ph.D.
  Affiliation: Howard Feiertag Department of Hospitality and Tourism Management, Pamplin College of Business, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, Virginia, United States.

Purpose

The primary purpose of the Customer Non-Transactional Values Measure is to capture, operationalize, and quantify the non-monetary value generated when consumers interact with a brand and other consumers within digital platforms. In modern consumer ecosystems, particularly in experiential sectors such as travel, tourism, and hospitality, a substantial portion of customer-derived organizational capital emerges outside direct financial exchanges. Traditional customer valuation models—such as Customer Lifetime Value (CLV)—historically prioritized recency, frequency, and monetary (RFM) transaction metrics. However, this purchase-centric perspective neglects crucial behavioral investments, including peer-to-peer assistance, unsolicited product innovation, peer influence, brand defense, and reciprocal social support. The Customer Non-Transactional Values Measure systematically identifies and quantifies these distributed, collaborative behaviors.

From an applied research and psychometric perspective, the measure was engineered to resolve critical gaps in the theoretical literature surrounding customer engagement behaviors (CEBs) and Service-Dominant (S-D) logic. Prior scales frequently conflated internal psychological engagement (e.g., cognitive absorption, emotional dedication) with behavioral manifestation, or focused narrowly on transactional outcomes such as repurchase intent. By conceptualizing value as an active co-creation phenomenon, Shin and Perdue (2023) developed this instrument to differentiate the specific value dimensions generated by consumers when they post in brand forums, provide constructive feedback, solve problems for other tourists, and curate relational ties. The tool addresses the following core objectives:

• Deconstructing Distributed Value Creation: It provides a metric for evaluating how customer online participation translates into tangible and intangible organizational assets, such as crowdsourced operational innovations and peer-driven marketing reach.
• Diagnostic Benchmarking for Community Managers: It allows digital community managers, consumer behavior specialists, and brand strategists to benchmark the health and performance of their online brand platforms based on social capital rather than simple click-through rates.
• Structural Testing of Consumer Motivation and Loyalty: It serves as an empirical bridge in structural equation models, enabling scholars to analyze how intrinsic and extrinsic customer motivations drive specific engagement behaviors, which subsequently foster brand attachment and long-term brand equity.

Psychological Construct

The Customer Non-Transactional Values Measure operationalizes customer-perceived, co-created value as a multidimensional second-order construct composed of four distinct first-order dimensions. Each dimension represents a specific manifestation of non-transactional behavioral contribution within online brand environments:

1. Influential-Experience Value

Influential-Experience Value encapsulates the behavioral investment made by consumers when sharing vivid, authentic, and detailed narratives of their consumption experiences that subsequently guide, persuade, and influence the consumption choices of other community members. Grounded in the psychology of social influence, word-of-mouth (WOM), and information transmission, this dimension reflects consumers’ willingness to act as brand ambassadors and trusted advisors. Within the hospitality context, tourists frequently face high perceived risk due to the experiential, intangible nature of services. When experienced consumers articulate rich subjective insights, post photographic documentation, or recommend targeted amenities, they generate experiential capital that reduces uncertainty for prospective buyers while lowering acquisition costs for the firm.

2. Customer-to-Business (C-to-B) Innovation Value

The C-to-B Innovation Value subscale measures customer behaviors oriented toward co-developing, refining, and innovating the firm’s service offerings, operational workflows, and product portfolios. Moving beyond passive consumption, highly engaged consumers systematically channel their domain expertise, feedback, and creative problem-solving capabilities directly back to corporate decision-makers. In an online brand community, this manifests through submitting actionable suggestions for service modifications, reporting systemic operational flaws, participating in crowdsourcing initiatives, and proposing novel experiential concepts. This dimension aligns with the open-innovation framework, capturing how customer intellectual capital directly benefits the firm’s Research and Development (R&D) trajectory without requiring monetary investment.

3. Relational Value

Relational Value captures the socio-emotional contributions made by consumers toward building, maintaining, and strengthening social ties, mutual trust, and communal solidarity among platform members. Rooted in social capital theory and the psychological need for relatedness, this dimension assesses behaviors that foster an inclusive, supportive, and emotionally secure peer environment. In brand forums, relational value is generated when consumers provide empathetic encouragement, validate the emotional experiences of others, welcome newcomers, mitigate conflicts, and celebrate communal milestones. These bonding and bridging activities transform an impersonal brand platform into a vibrant, psychologically safe brand community, enhancing psychological ownership and emotional stickiness.

4. Functional Value

Functional Value reflects instrumental, problem-solving, and informational utility provided by consumers to assist their peers in navigating practical consumption challenges. Distinct from experiential storytelling, functional value is focused on informational accuracy, tactical advice, and utility. Behaviors exemplifying this construct include clarifying booking policies, outlining loyalty program mechanics, advising on navigational logistics, resolving technical dilemmas, and answering specific service-related inquiries. By answering questions that would otherwise be routed to corporate customer care centers, consumers who deliver functional value effectively reduce operational overhead for the brand while enhancing the immediate problem-resolution speed across the consumer collective.

Theoretical Framework

The conceptual architecture of the Customer Non-Transactional Values Measure is grounded in a convergence of contemporary marketing paradigms, behavioral economics, and social psychology theories:

Service-Dominant (S-D) Logic

The overarching paradigm informing this instrument is Service-Dominant Logic, introduced by Stephen Vargo and Robert Lusch (2004, 2008). In contrast to the traditional Goods-Dominant (G-D) paradigm—which positions value as an embedded property of physical outputs realized entirely at the transactional point of sale (value-in-exchange)—S-D logic posits that value is fundamentally collaborative, dynamic, and experienced during usage (value-in-use and value-in-context). In this framework, the customer is always an active co-creator of value. Shin and Perdue (2023) extend S-D logic to digital hospitality ecosystems, proposing that customer engagement behaviors generate substantial non-transactional value-in-use for both the customer collective and the focal firm.

Customer Engagement Behavior (CEB) Theory

The measure heavily incorporates the foundational tenets of Customer Engagement Behavior theory as conceptualized by van Doorn et al. (2010), Brodie et al. (2011), and Kumar and Pansari (2016). CEBs are defined as customers’ behavioral manifestations that have a brand or firm focus, beyond purchase, resulting from motivational drivers. Historically, researchers struggled to measure the structural multidimensionality of CEB outcomes. By treating non-transactional values as structured behavioral outcomes of customer engagement, the authors operationalized specific categories of brand-directed (C-to-B innovation) and customer-directed (functional, relational, and influential) engagement activities.

Social Exchange Theory and Self-Determination Theory

The motivational architecture driving non-transactional value creation is underwritten by Social Exchange Theory (Emerson, 1976; Blau, 1964) and Self-Determination Theory (SDT) (Deci & Ryan, 2000). Social Exchange Theory posits that individuals engage in non-contractual interactions based on expectations of reciprocal benefits, status enhancement, and socio-emotional returns. Complementarily, SDT differentiates between intrinsic motivations (e.g., altruism, internal enjoyment, mastery, communal identification) and extrinsic motivations (e.g., social recognition, tangible rewards, tier status). Shin and Perdue incorporated these motivational drivers as nomological antecedents, establishing how internal psychological mechanisms direct distinct types of community engagement.

Validity

The Customer Non-Transactional Values Measure underwent psychometric validation adhering to standard scale development frameworks (e.g., Churchill, 1979; Hinkin, 1998; DeVellis, 2017). Empirical validation was established through multiple evaluative phases:

Content and Face Validity

Item generation commenced with an exhaustive review of consumer behavior, hospitality, and digital engagement literature (e.g., Baldus et al., 2015; Chen et al., 2018; Kumar & Pansari, 2016; Ranjan & Read, 2016; So et al., 2014), yielding an initial pool of 38 candidate items representing five theoretical forms of non-transactional engagement. Content adequacy was assessed following the quantitative correspondence sorting method outlined by Hinkin, Tracey, and Enz (1997). Independent expert panels evaluated each item’s conceptual match against theoretical definitions. Items displaying ambiguous categorization, semantic redundancy, or insufficient definitional correspondence were systematically eliminated.

Convergent and Discriminant Validity

The measurement model was subjected to evaluation using Confirmatory Factor Analysis (CFA). Convergent validity was demonstrated across all four constructs:

• Standardized factor loadings for all 15 retained indicators were statistically significant ($p < .001$) and exceeded the recognized 0.60 standard, ranging from 0.62 to 0.91.
• The Average Variance Extracted (AVE) for every latent construct exceeded the established 0.50 threshold (Fornell & Larcker, 1981), with values ranging between 0.66 and 0.74.

Discriminant validity was established via the Fornell-Larcker criterion. The AVE for each construct exceeded the squared correlation between that construct and any other construct in the model. This confirmed that despite shared covariance within the overarching higher-order construct, the four dimensions capture empirically distinct facets of non-transactional value.

Nomological and Criterion-Related Validity

Nomological validity was verified through structural equation modeling (SEM), testing theoretically established structural trajectories linking motivational antecedents to downstream relational assets:

• Motivational Antecedents: Intrinsic motivation demonstrated a statistically significant, positive relationship with all four non-transactional value engagement dimensions: Influential-Experience Value, C-to-B Innovation Value, Relational Value, and Functional Value. Extrinsic motivation exhibited a targeted, significant positive influence specifically on engagement behaviors producing Functional Value.
• Relational Outcomes: Engagement behaviors yielding C-to-B Innovation Value, Relational Value, and Functional Value exerted statistically significant positive impacts on brand attachment. Furthermore, engagement behaviors that generated Influential-Experience Value and C-to-B Innovation Value demonstrated statistically significant positive impacts on downstream brand loyalty. These structural relationships corroborate the functional utility and placement of the scale within consumer behavior networks.

Reliability

The reliability of the Customer Non-Transactional Values Measure was evaluated across internal consistency metrics, item-total correlation assessments, and composite construct indices:

Internal Consistency Reliability

Across the four dimensions, the scale exhibited strong internal consistency, surpassing Nunnally and Bernstein’s (1994) recommended threshold of 0.70 for established empirical instruments:

• Cronbach’s Alpha ($lpha$): Coefficients for the four latent constructs ranged from 0.84 to 0.92, indicating high scale reliability and homogeneity among indicators without problematic item redundancy.
• Composite Reliability (CR): Composite reliability scores for all four latent factors ranged between 0.85 and 0.92, confirming construct reliability under structural equation modeling assumptions where item loadings are not constrained to equality.

Item Loadings and Variance Metrics

During scale purification, all retained items demonstrated corrected item-total correlations exceeding 0.50, with no item deletion yielding a significant increase in the respective subscale alpha coefficient. The standardized factor loadings ($0.62 le lambda le 0.91$) further confirmed minimal measurement error across the 15 indicators, verifying that the empirical variance observed stems directly from the targeted latent constructs.

Factor Analysis

The latent structure of the Customer Non-Transactional Values Measure was established using a sequential two-phase factor-analytic design consisting of Exploratory Factor Analysis (EFA) followed by Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA)

EFA was executed on the refined item pool using principal component analysis paired with orthogonal (varimax) rotation to identify initial factor groupings based on the Kaiser criterion (eigenvalues > 1.0; Hair et al., 1998). The initial four-factor extraction accounted for 75.9% of the total variance across candidate indicators. Items were evaluated against rigorous retention rules: items exhibiting factor loadings below 0.50, notable cross-loadings onto non-target factors (> 0.35), or detrimental effects on subscale internal consistency were systematically removed. A final principal component analysis conducted on the purified 22-item intermediate set yielded a clean four-factor model explaining 78.3% of the total variance.

Confirmatory Factor Analysis (CFA)

To establish structural stability and finalize the dimensional boundaries, CFA was administered using maximum likelihood estimation. Based on modification indices, covariance inspections, and theoretical parsimony, two closely related sub-dimensions were merged, resulting in a refined 15-item measurement structure. The finalized architecture was specified as a second-order factor model, wherein the four first-order latent constructs (Influential-Experience, C-to-B Innovation, Relational, and Functional) load onto a single higher-order latent construct: Customer Non-Transactional Value.

The goodness-of-fit statistics demonstrated acceptable fit between the theoretical model and the observed empirical data:

• Chi-Square Statistic: $\chi^2 = 220.632$ ($p < .001$)
• Comparative Fit Index (CFI): 0.946 (exceeding the standard 0.90 threshold, approaching the strict 0.95 cutoff)
• Tucker-Lewis Index (TLI): 0.935 (demonstrating strong incremental fit)
• Standardized Root Mean Square Residual (SRMR): 0.052 (well below the conservative 0.08 cutoff)
• Root Mean Square Error of Approximation (RMSEA): 0.088 (within the acceptable range for complex behavioral models)

Instrument / Measurement Tool

The technical parameters and administration protocol for the Customer Non-Transactional Values Measure are detailed below:

• Instrument Name: Customer Non-Transactional Values Measure
• Scale Authors: Hakseung Shin and Richard R. Perdue (2023)
• Instrument Type: Psychometric Self-Report Questionnaire / Multi-Item Behavioral Inventory
• Target Population: Adult consumers participating in online brand communities, virtual consumer forums, loyalty portals, and interactive social brand pages (validated among members aged 18 to 65+ within the Marriott Bonvoy Insider Community)
• Administration Modality: Online computer-assisted self-interviewing (CASI) or web-based survey administration
• Total Number of Items: 15 items
• Structural Composition: Second-order factor model with four first-order subscales:
  • Influential-Experience Value: Measures sharing consumption narratives and experiential guidance.
  • Customer-to-Business (C-to-B) Innovation Value: Measures co-developing ideas, providing service feedback, and proposing improvements to the firm.
  • Relational Value: Measures social bonding, peer encouragement, and communal solidarity.
  • Functional Value: Measures informational support, troubleshooting, and utilitarian assistance to other consumers.
• Response Scale: Typically administered using a standard 7-point or 5-point Likert scale ranging from 1 = Strongly Disagree to 7 = Strongly Agree (or 1 = Strongly Disagree to 5 = Strongly Agree).
• Scoring Protocol:
  • Subscale Scores: Calculated by computing the unweighted mean (arithmetic average) or sum of items corresponding to each respective dimension.
  • Global Non-Transactional Value Score: Computed by averaging all 15 indicators, or by estimating second-order latent factor scores via structural equation modeling software.
  • Reverse Scoring: All 15 indicators are positively framed; no reverse-scored items are present.

Permissions & Fee and Test Year

The Customer Non-Transactional Values Measure was formally published and validated in 2023 by Hakseung Shin and Richard R. Perdue in the Journal of Travel Research.

• Commercial Status: Non-commercial. The scale is intended for academic research, education, and institutional benchmarking.
• Usage Fee: No fees are assessed for non-commercial, scholarly research applications.
• Permissions and Licensing: The instrument and its foundational research are copyrighted by SAGE Publications and the authors. Academic researchers seeking to adapt, reproduce, or employ the instrument in proprietary settings should contact the corresponding author, Dr. Hakseung Shin ([email protected]), and observe fair-use citation practices referencing the original 2023 publication in the Journal of Travel Research.

References

The theoretical framework, psychometric foundations, and empirical validation of the measure are grounded in the following academic literature:

• Baldus, B. W., Voorhees, C., & Calantone, R. (2015). Online brand community engagement: Scale development and validation. Journal of Business Research, 68(5), 978–985. https://doi.org/10.1016/j.jbusres.2014.09.035
• Blau, P. M. (1964). Exchange and power in social life. John Wiley & Sons.
• Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer engagement: Conceptual domain, fundamental premises, and emerging research directions. Journal of Service Research, 14(3), 252–271. https://doi.org/10.1177/1094670511411703
• Chen, T., Guo, W., Gao, X., & Liang, Z. (2018). AI-based self-service technology in public service organizations: Consumer value co-creation and customer satisfaction. Tourism Management, 68, 82–93.
• Churchill, G. A., Jr. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73. https://doi.org/10.1177/002224377901600110
• Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
• DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). SAGE Publications.
• Emerson, R. M. (1976). Social exchange theory. Annual Review of Sociology, 2(1), 335–362. https://doi.org/10.1146/annurev.so.02.080176.002003
• 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. (1998). Multivariate data analysis (5th ed.). Prentice Hall.
• Hinkin, T. R. (1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 1(1), 104–121. https://doi.org/10.1177/109442819800100106
• Hinkin, T. R., Tracey, J. B., & Enz, C. A. (1997). Scale development: Developing objective measurement tools for hospitality and tourism research. Cornell Hotel and Restaurant Administration Quarterly, 38(6), 100–120. https://doi.org/10.1177/001088049703800619
• Kumar, V., & Pansari, A. (2016). Competitive advantage through engagement. Journal of Marketing Research, 53(4), 497–514. https://doi.org/10.1509/jmr.15.0044
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
• Ranjan, K. R., & Read, S. (2016). Value co-creation: Concept and measurement. Journal of the Academy of Marketing Science, 44(3), 290–315. https://doi.org/10.1007/s11747-014-0397-2
• Shin, H., & Perdue, R. R. (2021). Customer engagement in online brand communities: A social exchange theory perspective. Journal of Travel & Tourism Marketing, 38(5), 488–503. https://doi.org/10.1080/10548408.2021.1952148
• Shin, H., & Perdue, R. R. (2023). Developing a multi-dimensional measure of hotel brand customers’ online engagement behaviors to capture non-transactional value. Journal of Travel Research, 62(3), 593–609. https://doi.org/10.1177/00472875211073618
• So, K. K. F., King, C., & Sparks, B. (2014). Customer engagement with tourism brands: Scale development and validation. Journal of Hospitality & Tourism Research, 38(3), 304–329. https://doi.org/10.1177/1096348012451456
• van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010). Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research, 13(3), 253–266. https://doi.org/10.1177/1094670510375599
• 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
• Vargo, S. L., & Lusch, R. F. (2008). Service-dominant logic: Continuing the evolution. Journal of the Academy of Marketing Science, 36(1), 1–10. https://doi.org/10.1007/s11747-007-0069-6

Items of the Scale