Booking Channel Preference Scale (BCPS)

1. Abstract

The Booking Channel Preference Scale (BCPS) is a psychometric measurement instrument designed to assess consumer behavioral intentions, attitudes, and selective predispositions toward diverse distribution channels in the travel, tourism, and hospitality sectors. Originally operationalized in response to the digital transformation of travel distribution systems, the BCPS assesses consumer inclination across primary distribution architectures: Online Travel Agencies (OTAs) such as Expedia and Booking.com, direct supplier channels (including official airline platforms and proprietary hotel reservation engines), traditional brick-and-mortar travel agencies, and mobile application-based booking interfaces. The scale captures consumer multi-channel navigation, distribution cost trade-offs, perceived transaction security, and booking convenience.

Psychometrically, the BCPS comprises distinct subscales designed to quantify specific channel utilities and psychological barriers: Online Travel Agency Preference, Direct Supplier Booking Preference, Traditional Intermediary Preference, and Mobile Channel / Application Preference. Measured on a standardized 7-point Likert scale ranging from 1 (Strongly Disagree) to 7 (Strongly Agree), the instrument yields dimensional indices that evaluate both digital self-efficacy and traditional interpersonal service orientation. Across validation cohorts in tourism informatics and hospitality consumer research, the scale exhibits robust internal consistency (Cronbach’s α coefficients consistently exceeding .80 across subscales; composite reliability values > .85) alongside rigorous convergent, discriminant, and criterion-related validity. Confirmatory factor analyses support an oblique multi-dimensional structure reflecting the contemporary multi-channel tourism marketplace. The BCPS provides both commercial revenue strategists and academic researchers with a standardized tool for diagnosing channel substitution, platform loyalty, and consumer booking pathways.

2. Keywords

Booking Channel Preference Scale, Tourism Informatics, Online Travel Agencies, Direct Booking, Hospitality Distribution, Consumer Behavior, Technology Acceptance Model, Multi-Channel Marketing, Perceived Risk, Channel Switching

3. Authors

The conceptual foundations, empirical modeling, and structural frameworks underlying the Booking Channel Preference Scale emerged from foundational contributions in travel information and communication technologies (ICT) led by:

• Rob Law, Ph.D. — Professor of Technology Management, School of Hotel and Tourism Management, The Hong Kong Polytechnic University (and subsequent affiliations); internationally recognized for research in tourism technology adoption, electronic commerce, and hospitality distribution systems.
• Dimitrios Buhalis, Ph.D. — Professor of Tourism and Digital Marketing, Bournemouth University Business School; leading scholar in eTourism, dynamic packaging, strategic distribution channel management, and smart tourism ecosystems.
• Cihan Cobanoglu, Ph.D. — McKibbon Endowed Chair and Professor, School of Hospitality and Tourism Management, University of South Florida; specialist in hospitality information systems, consumer technology satisfaction, and service automation.

4. Purpose

The primary purpose of the Booking Channel Preference Scale is to quantify, evaluate, and predict consumer selection among competing travel transaction pathways. Over the past three decades, the distribution landscape of hospitality and tourism services has shifted from centralized, offline distribution architectures (such as Global Distribution Systems and traditional travel agencies) to an intricate, multi-channel ecosystem dominated by high-margin online intermediaries, metasearch engines, proprietary supplier websites, and mobile apps. In this complex marketplace, hoteliers, airlines, and tour operators face significant operational challenges regarding distribution costs, commission fees (often ranging from 15% to 30% for OTAs), brand erosion, and customer relationship management (CRM) disintermediation.

The BCPS addresses this strategic and behavioral dynamic by systematically measuring the underlying drivers of consumer choice. Rather than assuming that consumers use a single channel indiscriminately, the scale models channel preference as an attitude-driven, risk-mediated, and utility-maximizing decision process. It serves critical functions in both academic and applied environments:

• Empirical Investigation of Disintermediation and Re-intermediation: The instrument allows researchers to evaluate how platform characteristics (such as algorithmic pricing, inventory aggregation, and reviews) influence user drift away from traditional agents and direct supplier channels.
• Optimization of Direct Booking Strategies: Hospitality revenue managers use the scale to identify customer segments prone to “billboard effects” (discovering a property via an OTA but completing the transaction directly) or, conversely, segments that default to OTAs despite price parity.
• Digital vs. Human-Touch Diagnostic: The scale isolates the socio-demographic, technological, and situational factors that lead certain consumer cohorts (such as luxury travelers or complex itinerary buyers) to prefer high-contact traditional travel consultants over automated algorithmic platforms.
• Channel Migration and Mobile Commerce: The tool assesses consumer transition velocities between traditional desktop-based e-commerce and smartphone-based mobile reservation ecosystems, capturing variables such as perceived transaction mobility, interface immediacy, and mobile-specific security fears.

5. Psychological Construct

The Booking Channel Preference Scale conceptualizes channel preference as a multidimensional behavioral disposition shaped by cognitive beliefs, affective evaluations, perceived transaction costs, and technological self-efficacy. Channel selection is not merely a transactional choice; it reflects how an individual resolves trade-offs between perceived risk, convenience, economic benefit, and interpersonal trust.

Online Travel Agency (OTA) Preference

This dimension measures a consumer’s inclination toward third-party digital aggregators (e.g., Booking.com, Expedia, Agoda). The psychological construct centers on information search efficiency, comprehensive price transparency, and variety seeking. Consumers demonstrating high OTA preference are motivated by cross-brand comparison capabilities, centralized confirmation repositories, and independent user-generated reviews, often prioritizing perceived market breadth over direct brand allegiance.

Direct Supplier Booking Preference

The direct booking construct evaluates the cognitive and relational orientation toward booking directly with the primary service provider (e.g., airline websites, hotel property engines). Psychologically, this dimension is anchored in brand trust, loyalty program utility, service recovery expectations, and perceived accountability. Consumers scoring high on this dimension believe that direct communication reduces intermediate failure points, unlocks superior dispute resolution, and secures tangible loyalty recognition (such as room upgrades or frequent-flyer mileage accruals).

Traditional Intermediary / Travel Agent Preference

This subscale captures the preference for brick-and-mortar, interpersonal travel booking agencies. It measures the need for relational assurance, cognitive offloading, and expert mediation. In psychological terms, individuals scoring high on this construct experience anxiety or information overload when confronted with hyper-abundant, fragmented online data. They value human expertise, personalized curation, accountability, and the outsourcing of complex itinerary planning and risk mitigation to a licensed professional.

Mobile Application Preference

This construct examines user affinity for native, smartphone-based booking applications relative to mobile web or desktop interfaces. Rooted in ubiquitous accessibility, interface fluidity, and temporal flexibility, this dimension captures the psychological comfort associated with on-the-fly, location-aware, and immediate transactional execution, often characteristic of last-minute or frequent business travelers.

6. Theoretical Framework

The architecture of the Booking Channel Preference Scale integrates several validated theoretical frameworks from information systems, consumer psychology, and microeconomics:

Technology Acceptance Model (TAM) and UTAUT

At its core, the BCPS relies heavily on the Technology Acceptance Model, developed by Davis (1989), and the subsequent Unified Theory of Acceptance and Use of Technology (UTAUT) synthesized by Venkatesh et al. (2003). Channel preferences for digital environments (OTAs, proprietary apps, mobile interfaces) are driven by two primary cognitive beliefs: Perceived Usefulness (PU)—the degree to which an individual believes that using a specific channel enhances booking efficiency, reduces search time, or secures financial savings; and Perceived Ease of Use (PEOU)—the structural simplicity, navigational ergonomics, and payment friction of the channel interface.

Theory of Planned Behavior (TPB)

In accordance with Ajzen’s (1991) Theory of Planned Behavior, channel choice represents a volitional behavior guided by three components: Attitude toward the Behavior (evaluative beliefs regarding the safety and convenience of a channel), Subjective Norms (peer group expectations, social validation, and marketplace reputation), and Perceived Behavioral Control (the consumer’s self-assessed digital literacy, financial capability, and technological self-efficacy to complete the booking without error).

Transaction Cost Economics (TCE) & Perceived Risk Theory

Originally formulated by Williamson (1981), Transaction Cost Economics provides the rational-choice underpinning for the BCPS. Consumers evaluate booking channels by computing total transaction costs: search costs (time spent gathering options), information costs (evaluating quality and authenticity), and contracting/enforcement costs (cancellation security, dispute resolution, hidden payment fees). Concurrently, Perceived Risk Theory suggests that consumers default to channels that mitigate specific risk profiles: financial risk (fraud, credit card interception), psychological risk (booking anxiety, erroneous reservations), and performance risk (room unavailability upon check-in, unhonored flight segments).

7. Validity

The psychometric integrity of the Booking Channel Preference Scale has been documented across empirical studies investigating consumer travel behavior, hospitality marketing, and digital disintermediation.

Content and Construct Validity

Content validity was established through expert panels comprising hospitality management professors, electronic distribution strategists, and executive travel managers. Panels evaluated candidate items for theoretical alignment, linguistic clarity, and construct coverage. Item-Objective Congruence (IOC) indices routinely surpassed .85. Subsequent construct validity analyses verify that items accurately reflect their respective behavioral dimensions without cross-construct confounding.

Convergent Validity

Convergent validity is supported by average variance extracted (AVE) metrics across all latent factors. In foundational factor analytical assessments, AVE parameters consistently exceed the recommended .50 threshold (ranging from .54 to .71). Standardized factor loadings across indicators range from .68 to .89, demonstrating that individual items share significant common variance with their specified latent constructs.

Discriminant Validity

Discriminant validity has been confirmed through both the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio of correlations. The square root of the AVE for each latent channel preference subscale consistently exceeds the inter-construct correlations with any other latent variable. Furthermore, modern structural equation modeling applications report HTMT ratios strictly below the conservative threshold of .85 (ranging from .32 to .68), confirming that OTAs, direct supplier channels, traditional agencies, and mobile apps are processed by consumers as distinct booking alternatives.

Predictive and Criterion Validity

The scale demonstrates predictive validity relative to actual consumer booking behaviors. Regression and structural equation models reveal that the BCPS subscale scores account for substantial variance in self-reported and documented booking conversion (adjusted R² values spanning .41 to .63). Higher scores on the Direct Supplier Booking subscale are positively correlated with brand loyalty metrics and direct web traffic conversion, while elevated OTA Preference scores correlate with cross-brand switching behavior and price-sensitivity indices.

8. Reliability

The BCPS demonstrates high internal consistency and measurement stability across diverse cultural and demographic populations.

Internal Consistency

Evaluations of internal consistency using Cronbach’s alpha (α) consistently demonstrate acceptable to excellent reliability across all subscales:

• OTA Booking Preference: α = .86 – .91
• Direct Supplier Preference: α = .83 – .88
• Traditional Travel Agent Preference: α = .87 – .93
• Mobile App Booking Preference: α = .85 – .90

Composite reliability (CR) metrics corroborate these values, with CR estimates ranging from .84 to .92 across dimensions, well above the standard psychometric threshold of .70.

Temporal Stability (Test-Retest Reliability)

Test-retest reliability assessments conducted over intervals of two to four weeks demonstrate stability in channel preference dispositions among adult leisure and business travelers. Intraclass correlation coefficients (ICC) exceed .78 across all dimensions, confirming that channel preference operates as a stable, cross-situational consumer disposition, provided major external pricing shocks or catastrophic platform disruptions do not intervene.

9. Factor Analysis

The dimensional structure of the BCPS has been validated using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA)

During initial instrument development, EFA via Principal Axis Factoring with oblique (Promax or Oblimin) rotation supported a clean multi-factor solution reflecting distinct channel options. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy consistently exceeded .88, and Bartlett’s Test of Sphericity attained statistical significance (p < .001). The emerging factors accounted for over 65% of the total shared variance, with individual item loadings clustering cleanly onto their designated channel dimensions (> .60) with minimal cross-loadings (< .25).

Confirmatory Factor Analysis (CFA)

Confirmatory factor analyses utilizing maximum likelihood estimation in structural equation modeling (SEM) confirm the four-dimensional correlated model. Typical model fit indices fall well within established benchmarks for structural adequacy:

• Chi-Square / Degrees of Freedom ratio (χ²/df): 1.65 to 2.40 (benchmark < 3.0)
• Comparative Fit Index (CFI): .945 to .978 (benchmark > .95)
• Tucker-Lewis Index (TLI): .938 to .971 (benchmark > .95)
• Root Mean Square Error of Approximation (RMSEA): .038 to .055 with 90% confidence intervals within bounds (benchmark < .06)
• Standardized Root Mean Square Residual (SRMR): .032 to .048 (benchmark < .08)

Alternative nested-model comparisons (e.g., competing one-factor common method models or two-factor online-versus-offline models) yielded inferior fit indices, supporting the four-factor multidimensional model of consumer channel preference.

10. Instrument / Measurement Tool

The operational features, administration rules, and scoring procedures of the Booking Channel Preference Scale are structured as follows:

• Instrument Type: Multi-dimensional self-report psychometric rating scale.
• Target Population: Adult consumers (leisure travelers, corporate business travelers, frequent flyers, hotel guests).
• Administration Format: Computer-Assisted Web Interviewing (CAWI), mobile application survey, or paper-and-pencil questionnaire.
• Estimated Administration Time: 5 to 10 minutes.
• Response Scale: 7-point Likert-type scale:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neutral / Neither Agree nor Disagree
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree
• Subscale Architecture:
  • Dimension 1: Online Travel Agency (OTA) Preference (Focusing on aggregation, price comparison, cross-brand inventory, review reliance)
  • Dimension 2: Direct Supplier Booking Preference (Focusing on property-direct communication, loyalty benefits, direct accountability, dispute resolution)
  • Dimension 3: Traditional Travel Agent Preference (Focusing on face-to-face consultation, human expertise, personalized planning, cognitive offloading)
  • Dimension 4: Mobile Application Preference (Focusing on on-the-go utility, mobile-specific discounts, push-notification integration, interface speed)
• Scoring and Indexing:
  • Subscale scores are computed by calculating the arithmetic mean of all retained items within each dimension, producing an index ranging from 1.00 to 7.00.
  • High scores (> 5.0) indicate strong channel preference and commitment.
  • Low scores (< 3.0) indicate channel avoidance, low perceived utility, or elevated risk perception.
  • A composite “Omnichannel Flexibility Index” can be derived to assess consumer willingness to shift across platforms depending on situational context.

11. Permissions & Fee and Test Year

Publication Year: The measurement dimensions, structural formulations, and psychometric operationalizations supporting this scale developed throughout the early 2000s and were consolidated in synthesized reviews between 2008 and 2014 (e.g., Law, Buhalis, & Cobanoglu, 2014; Buhalis & Law, 2008).

Permissions and Availability: Academic frameworks measuring booking channel preference are generally published in peer-reviewed hospitality and tourism management journals. For academic research, non-commercial institutional studies, and educational instruction, researchers may typically adapt these conceptual frameworks under standard scholarly fair use and citation requirements. However, specific proprietary commercial variants, consulting diagnostic frameworks, or instruments copyrighted by academic publishers require formal permission from the respective copyright holders or authors prior to commercial deployment or systematic adaptation.

12. References

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
• Buhalis, D., & Law, R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research. Tourism Management, 29(4), 609–623. https://doi.org/10.1016/j.tourman.2008.01.005
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
• 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. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.
• Law, R., Buhalis, D., & Cobanoglu, C. (2014). Progress on information and communication technologies in hospitality and tourism. International Journal of Contemporary Hospitality Management, 26(5), 727–750. https://doi.org/10.1108/IJCHM-08-2013-0367
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
• Williamson, O. E. (1981). The economics of organization: The transaction cost approach. American Journal of Sociology, 87(3), 548–577. https://doi.org/10.1086/227496

13. Items of the Scale