Cultural Inheritance-Based Innovation Scale

1. Abstract

The Cultural Inheritance-Based Innovation Scale (CIBI Scale) is an advanced psychometric instrument developed by Wang, Li, Ruan, Zhang, and Li (2023) designed to systematically assess and quantify the empirical performance of cultural inheritance-based innovation within heritage tourism destinations (HTDs). Heritage tourism constantly encounters the fundamental paradox of cultural conservation versus commercial commodification. To resolve this tension, the CIBI framework operationalizes how historic, tangible, and intangible cultural assets can be dynamically preserved while undergoing contemporary creative adaptation. Grounded in an extensive inductive-deductive research design that synthesized qualitative grounded theory from in-depth stakeholder interviews with rigorous classical test theory and structural equation modeling, the CIBI Scale provides a robust, multidimensional metric consisting of 13 operational items partitioned across four correlated latent dimensions: Cultural Authentic Production (4 items), Cultural Creative Dissemination (3 items), Cultural Living Display (3 items), and Cultural Transforming Creation (3 items).

Evaluated using field data gathered from domestic and international tourists visiting culturally prominent Chinese heritage destinations, the scale employs a 7-point Likert response format ranging from 1 (Strongly Disagree) to 7 (Strongly Agree). Psychometric analyses demonstrate high-level measurement reliability and rigorous construct validity. Exploratory factor analysis (EFA) demonstrated an unambiguous four-factor structure explaining 82.459% of the total cumulative variance, with clean communalities and primary factor loadings between 0.726 and 0.854. Confirmatory factor analysis (CFA) verified superior fit for both first-order and hierarchical second-order models (χ²/df = 3.727, RMSEA = 0.055, CFI = 0.971, GFI = 0.960, AGFI = 0.941, TLI = 0.963). Reliability testing revealed outstanding internal consistency, with dimension-specific Cronbach’s alpha coefficients exceeding 0.839 and composite reliability (CR) indices surpassing 0.70. Average variance extracted (AVE) values consistently exceeded 0.50, demonstrating robust convergent validity, while predictive modeling confirmed strong positive associations with tourist destination satisfaction (r = 0.749, p < 0.001) and recommendation intentions (r = 0.690, p < 0.001). This measurement tool establishes an empirical foundation for destination managers, cultural sociologists, and environmental psychologists seeking to optimize cultural sustainability, authentic place attachment, and innovative tourism experiences.

2. Keywords

Cultural Inheritance-Based Innovation Assessment, Heritage Tourism Destinations, Cultural Innovation, Sustainable Development, Cultural Transforming Creation, Cultural Living Display, Cultural Creative Dissemination, Cultural Authentic Production, Destination Management, Tourism Psychometrics, Construct Validity

3. Authors

The Cultural Inheritance-Based Innovation Scale was conceptualized, operationalized, and empirically validated by a team of researchers specializing in tourism management, service marketing, and cultural heritage conservation at the College of Tourism, Huaqiao University:

• Mei-Yu Wang: College of Tourism, Huaqiao University, Quanzhou, Fujian, China (ORCID: 0000-0003-0697-110X; Email: [email protected]).
• Yong-Quan Li: Research Center of Tourism and Service Management, College of Tourism, Huaqiao University, Quanzhou, Fujian, China (Email: [email protected]).
• Wen-Qi Ruan (Corresponding Author): College of Tourism, Huaqiao University, Quanzhou, Fujian, 362021, China (Email: [email protected]).
• Shu-Ning Zhang: College of Tourism, Huaqiao University, Quanzhou, Fujian, China (Email: [email protected]).
• Rui Li: College of Tourism, Huaqiao University, Quanzhou, Fujian, China (Email: [email protected]).

4. Purpose

The overarching objective of the Cultural Inheritance-Based Innovation Scale (CIBI Scale) is to resolve an enduring theoretical and managerial dilemma in cultural tourism: how to simultaneously preserve the historical integrity, identity, and authenticity of cultural heritage while fostering continuous innovation, modern relevance, and economic viability. Traditional heritage administration has frequently operated under a defensive preservation paradigm, regarding any modern alteration or commercialization as an existential threat to authentic heritage. Conversely, unchecked market commodification risks transforming sacred, historical traditions into hollow cultural kitsch. The CIBI Scale bridges this dialectic by operationalizing cultural inheritance-based innovation as a holistic, symbiotic construct wherein historical authenticity provides the foundational substance, while modern creative processes serve as the vehicle for intergenerational transmission and contemporary engagement.

In academic literature, the measurement of cultural innovation has historically suffered from fragmented, non-standardized conceptualizations borrowed loosely from industrial manufacturing or broad service marketing frameworks. Generic innovation metrics often emphasize technological disruption, cost reduction, or product novelty while failing to account for symbolic value, collective cultural memory, craftsmanship retention, and emotional resonance. The CIBI Scale addresses this void by offering an empirically validated, specialized measurement instrument tailored specifically to the socio-cultural ecosystems of heritage destinations, historic districts, traditional settlements, and cultural landscapes.

From an applied research and managerial perspective, the CIBI Scale fulfills several critical functions:

• Diagnostic Benchmarking for Heritage Sites: It enables destination management organizations (DMOs), museum curators, and cultural preservation agencies to systematically audit their innovation initiatives across four distinct operational vectors, identifying whether a site leans excessively toward static preservation or over-commercialized superficiality.
• Predictive Consumer Modeling: It serves as an explanatory psychological battery within structural equation models to investigate how tourists perceive, interpret, and emotionally respond to modernized heritage expressions, clarifying downstream effects on existential authenticity, place attachment, visitor delight, revisit intention, and word-of-mouth advocacy.
• Policy and Sustainable Development Assessment: It provides regional and national policymakers with quantifiable metrics to assess whether public cultural investments, cultural and creative industrial parks, and intangible cultural heritage safeguarding programs achieve genuine sustainable cultural transmission or unintended cultural erosion.
• Cross-Cultural Psychometric Exploration: The scale serves as a standardized baseline for comparative studies examining how different national cohorts, generational demographics, and ethnic communities interpret modern reinterpretations of ancestral traditions.

5. Psychological Construct

The theoretical architecture of the Cultural Inheritance-Based Innovation construct is grounded in a multidimensional conceptualization comprising four interrelated yet functionally autonomous dimensions. These subscales capture the lifecycle of cultural revitalisation, spanning the extraction and safeguarding of original cultural characteristics to modern technological curation, multi-channel narrative dissemination, and contemporary utilitarian redesign.

Cultural Authentic Production

Cultural Authentic Production focuses on the foundational preservation, rigorous extraction, and faithful material manifestation of historical core elements. In psychometric terms, it reflects the destination’s capacity to maintain traditional craftsmanship, indigenous materials, rich symbolic meanings, and the intrinsic “living flavor” (social milieu) of the local community. Rather than treating authenticity as a static, frozen state, this subscale captures dynamic continuity—ensuring that modern products, culinary experiences, architectural restorations, and artisanal crafts maintain traceable genetic ties to traditional techniques, historical folklore, and local cultural philosophy.

Cultural Creative Dissemination

Cultural Creative Dissemination evaluates the communicative and pedagogical strategies through which heritage assets are branded, narrated, and diffused into public consciousness. Cultural heritage risks social obsolescence if confined to elite academic spheres or archaic documentation. This dimension captures how destinations utilize modern branding, storytelling, popular cultural media, and contemporary narrative formats to make historical heritage accessible, engaging, and emotionally evocative for modern audiences. It assesses the extent to which the site constructs an unmistakable destination brand identity, synergizes historical motifs with youth culture trends, and leverages interactive social media to convey traditional values, philosophies, and community lifestyles.

Cultural Living Display

Cultural Living Display concerns the experiential staging, interactive curation, and dynamic presentation of cultural assets in real-time settings. Traditional heritage exhibitions historically emphasized passive observation—artifacts sealed behind museum glass cases or static historical markers. In contrast, Cultural Living Display assesses the integration of human-centric live displays, participatory workshops, community immersion, and advanced digital technologies (such as augmented reality, holographic projections, spatial soundscapes, and digital interactive exhibits). This dimension gauges the tourist’s psychological immersion, evaluating how effectively multi-sensory and interactive spaces bridge temporal divides to revitalize ancient customs as vibrant, living social practices.

Cultural Transforming Creation

Cultural Transforming Creation captures the evolutionary adaptation and functional upgrading of heritage elements to meet the pragmatic aesthetic and utilitarian demands of contemporary daily life. Grounded in creative reinvention, this dimension assesses the destination’s ability to extract ancient motifs, philosophical concepts, and craft repertoires and synthesize them with modern ergonomics, industrial design, fashion, and functional services. It evaluates how traditional products are re-engineered so that contemporary consumers not only appreciate them conceptually but seamlessly integrate them into daily modern life, establishing continuous emotional and psychological bonds across temporal boundaries.

6. Theoretical Framework

The construction and validation of the CIBI Scale rests on an integrated theoretical foundation drawing from cultural evolution theory, modern heritage management paradigms, consumer experience psychology, and service-dominant logic.

First, the scale is theoretically anchored in Cultural Evolution and Adaptation Theory, which asserts that cultural systems survive not through fossilization, but through adaptive transmission. As cultural anthropologists have long maintained, an intangible or tangible heritage tradition that loses functional or psychological utility within its host society faces inevitable extinction. Innovation represents an adaptive evolutionary mechanism that retains the central informational code (cultural core) while modifying outward phenotypic traits (forms, media, applications) to resonate with changing socioeconomic environments. The CIBI framework institutionalizes this principle: Cultural Authentic Production protects the underlying informational code, while Cultural Transforming Creation provides the phenotypic adaptation essential for ongoing cultural fitness.

Second, the scale builds upon Authenticity Theory in Tourism and Consumer Psychology, specifically harmonizing constructive authenticity and existential authenticity. Early sociological models posited an absolute, objective authenticity that viewed any commercial or creative intervention as an act of “staged authenticity” or cultural debasement. Modern psychological research, however, demonstrates that tourists often construct authenticity dynamically through psychological engagement, symbolic interpretation, and immersive self-discovery. By demonstrating that high CIBI scores correlate positively with perceived authenticity, the scale reinforces the theoretical view that creative, technological, and living presentations do not dilute perceived authenticity; rather, when anchored in genuine material and philosophical roots, they provide visitors with the psychological scaffolds necessary to experience existential authenticity.

Third, the framework integrates Service-Dominant (S-D) Logic and Value Co-Creation. Within contemporary experiential economies, cultural heritage destinations are not merely static resource repositories offering passive visual encounters; they are interactive service platforms where cultural value is co-created through reciprocal interactions between the destination’s offerings and the visitor’s subjective interpretation. The dimensions of Cultural Living Display and Cultural Creative Dissemination operationalize this co-creative dynamic, demonstrating that cultural value emerges through active engagement, multi-sensory stimulation, and shared symbolic communication.

7. Validity

The psychometric integrity of the CIBI Scale was established through a series of rigorous empirical evaluations examining construct, convergent, discriminant, and criterion-related predictive validity across independent tourist samples at major heritage tourism destinations.

Construct Validity

Construct validity was demonstrated using both exploratory and confirmatory factor modeling. Exploratory factor extraction confirmed that the 13 items loaded unambiguously onto their theorized latent dimensions, with standardized factor loadings ranging from 0.726 to 0.854, substantially exceeding the conservative 0.50 benchmark. Communality statistics across all items remained between 0.744 and 0.860, indicating that the latent factors explain substantial shared variance in each manifest item. In the subsequent confirmatory stage, all standardized indicator loadings exceeded 0.60, collectively explaining 71.392% of the total variance in the latent constructs.

Convergent Validity

Convergent validity evaluates the degree to which items within a specified latent construct share a high proportion of common variance. In the validation study conducted by Wang et al. (2023), convergent validity was verified using Composite Reliability (CR) and Average Variance Extracted (AVE):

• Composite Reliability (CR): The CR values for all four dimensions exceeded the established 0.70 threshold, demonstrating excellent shared internal variance among the latent indicators.
• Average Variance Extracted (AVE): The AVE for each individual dimension consistently surpassed the standard 0.50 cutoff, verifying that the variance explained by each latent construct is greater than the variance attributable to measurement error.

Discriminant Validity

Discriminant validity was established via the Fornell-Larcker criterion. The inter-construct correlation coefficients among the four dimensions ranged from 0.30 to 0.70. Crucially, the correlation coefficient between any pair of latent constructs was strictly lower than the square root of the AVE for each respective construct. This confirmed that while the four dimensions share a common theoretical lineage under the higher-order CIBI construct, they capture distinct operational facets of cultural innovation without redundant collinearity.

Predictive and Nomological Validity

To establish predictive and nomological validity, Wang et al. (2023) modeled the influence of CIBI on key destination outcome measures: tourist satisfaction and recommendation intentions. Structural path analysis and correlation metrics demonstrated robust predictive relationships:

• The overall higher-order CIBI construct exhibited a strong, statistically significant positive effect on tourist destination satisfaction (r = 0.749, p < 0.001).
• The construct demonstrated a substantial positive effect on recommendation intention (r = 0.690, p < 0.001).
• Independent multi-regression models confirmed that each individual dimension (Authentic Production, Creative Dissemination, Living Display, and Transforming Creation) exerted a direct, statistically significant positive impact on both satisfaction and recommendation intention, confirming the scale’s nomological validity.

8. Reliability

The reliability of the CIBI Scale was evaluated across multiple independent samples during the scale purification and formal testing phases, focusing primarily on internal consistency and composite reliability.

During the primary empirical validation phase, internal consistency was assessed via Cronbach’s alpha coefficient (α) for each subscale. The analysis revealed high internal consistency coefficients across all four dimensions, with every dimension exceeding the conservative 0.80 benchmark:

• Cultural Authentic Production: α > 0.839
• Cultural Creative Dissemination: α > 0.839
• Cultural Living Display: α > 0.839
• Cultural Transforming Creation: α > 0.839

The total scale’s internal consistency similarly indicated exceptional cohesion without problematic item redundancies. Furthermore, Composite Reliability (CR) values calculated within the confirmatory structural equation models exceeded 0.70 across all subscales, confirming that the measurement indicators reliably capture the true score variance of the latent constructs rather than random measurement error. Item-total correlation analyses confirmed that all 13 items correlated strongly with their intended subscale totals, with no item deletion yielding a meaningful increase in subscale reliability.

9. Factor Analysis

The structural dimensionality of the CIBI Scale was established through a two-stage factor analytic sequence incorporating Exploratory Factor Analysis (EFA), Principal Component Analysis (PCA), and subsequent Confirmatory Factor Analysis (CFA) across independent split-half calibration and validation datasets.

Exploratory Factor Analysis (EFA)

During the initial purification phase, an exploratory factor analysis using principal component extraction and varimax orthogonal rotation was conducted on the candidate pool of items. Multiple iterations, guided by Kaiser’s eigenvalue-greater-than-one criterion and scree plot examination, converged upon a stable 13-item, 4-factor solution. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s Test of Sphericity confirmed that the correlation matrix was highly suitable for factor analysis. The extracted four factors collectively accounted for 82.459% of the total cumulative variance. All individual factor loadings demonstrated high magnitude, ranging between 0.726 and 0.854, with no cross-loadings exceeding 0.35, while item communalities ranged between 0.744 and 0.860.

Confirmatory Factor Analysis (CFA)

To validate the underlying structural architecture, alternative competitive measurement models were tested using maximum likelihood Confirmatory Factor Analysis in AMOS/LISREL:

• One-Factor Model: All 13 items loaded onto a single undifferentiated global factor (exhibited poor fit, demonstrating that cultural innovation is not unidimensional).
• Two-Dimensional Model: Items grouped into preservation-oriented versus modern-oriented factors (exhibited sub-optimal fit).
• Three-Dimensional Model: Items grouped into production, display, and dissemination (exhibited moderate, yet insufficient fit).
• Four-Dimensional First-Order Model: The hypothesized four correlated latent factors (Authentic Production, Creative Dissemination, Living Display, Transforming Creation). This model exhibited superior fit to the observed empirical data: χ²/df = 3.769, Root Mean Square Error of Approximation (RMSEA) = 0.055, Comparative Fit Index (CFI) = 0.972, Goodness of Fit Index (GFI) = 0.961, Adjusted Goodness of Fit Index (AGFI) = 0.940, and Tucker-Lewis Index (TLI) = 0.963.

Because the four first-order latent dimensions exhibited moderate-to-strong inter-factor correlations, Wang et al. (2023) specified a hierarchical second-order factor model, positioning an overarching “Cultural Inheritance-Based Innovation” construct above the four first-order sub-dimensions. The second-order CFA yielded equally robust model fit indices: χ²/df = 3.727, RMSEA = 0.055, CFI = 0.971, GFI = 0.960, AGFI = 0.941, and TLI = 0.963. This confirmation of the second-order model justifies researchers using either the four multidimensional subscale scores independently or aggregating them into a single composite CIBI index.

10. Instrument / Measurement Tool

The Cultural Inheritance-Based Innovation (CIBI) Scale is structured as an objective, self-report psychometric questionnaire designed for administration in field, digital, or intercept settings. Below is the operational specification of the instrument:

• Test Type: Multi-item self-report psychometric assessment / destination performance rating scale.
• Construct Assessed: Perceived cultural inheritance-based innovation performance in heritage tourism environments.
• Item Inventory: 13 standardized measurement statements grouped across 4 latent subscales:
  • Cultural Authentic Production: 4 items
  • Cultural Creative Dissemination: 3 items
  • Cultural Living Display: 3 items
  • Cultural Transforming Creation: 3 items
• Response Format: 7-point Likert rating scale ranging from:
  • 1 = Strongly Disagree
  • 2 = Disagree
  • 3 = Somewhat Disagree
  • 4 = Neutral / Neither Agree nor Disagree
  • 5 = Somewhat Agree
  • 6 = Agree
  • 7 = Strongly Agree
• Target Population: Adult domestic and international tourists (aged 18 years and older) visiting heritage tourism destinations, historic districts, archaeological sites, or living cultural landscapes.
• Administration Time: Approximately 4 to 7 minutes.
• Scoring Protocol: All items are positively keyed (no reverse-scored items). Subscale scores are calculated by averaging or summing the corresponding item responses within each dimension. An overall higher-order CIBI composite score is computed by calculating the arithmetic mean of all 13 items or averaging the four subscale mean scores. Higher numerical values indicate stronger perceived destination performance in cultural inheritance-based innovation.

11. Permissions & Fee and Test Year

The Cultural Inheritance-Based Innovation Scale was formally published in 2023 by Mei-Yu Wang, Yong-Quan Li, Wen-Qi Ruan, Shu-Ning Zhang, and Rui Li in the Journal of Hospitality and Tourism Management. The scale was established as an academic measurement instrument intended to advance empirical scholarship in tourism geography, cultural heritage preservation, and consumer behavior.

• Usage Permissions: The scale may be utilized free of charge by academic researchers, educators, non-profit heritage organizations, and university students for non-commercial research, academic theses, and teaching applications without formal written copyright permission, provided that appropriate bibliographic credit is attributed to Wang et al. (2023).
• Commercial Licensing: Any commercial utilization—including use within proprietary destination consulting, commercial market research, enterprise benchmarking software, or for-profit tourism auditing—requires direct authorization and licensing consultation with the corresponding authors and the copyright holder (Elsevier / Journal of Hospitality and Tourism Management).
• Contact for Inquiries: Correspondence regarding original linguistic iterations (e.g., standard Chinese administration protocols) or commercial licensing should be addressed to Dr. Wen-Qi Ruan at Huaqiao University (Email: [email protected]).

12. References

The following foundational publications support the conceptualization, psychometric development, and theoretical framework of the CIBI Scale:

• Brouder, P. (2012). Creative tourism in a southern Swedish region: A system approach to institutional change. Tourism Geographies, 14(3), 383–404. https://doi.org/10.1080/14616688.2011.610144
• 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.
• 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
• Richards, G. (2011). Creativity and tourism: The state of the art. Annals of Tourism Research, 38(4), 1225–1253. https://doi.org/10.1016/j.annals.2011.07.008
• 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
• Wang, M.-Y., Li, Y.-Q., Ruan, W.-Q., Zhang, S.-N., & Li, R. (2023). Cultural inheritance-based innovation at heritage tourism destinations: Conceptualization, structural dimensions and scale development. Journal of Hospitality and Tourism Management, 55, 118–130. https://doi.org/10.1016/j.jhtm.2023.03.009
• Wang, N. (1999). Rethinking authenticity in tourism experience. Annals of Tourism Research, 26(2), 349–370. https://doi.org/10.1016/S0160-7383(98)00103-0

13. Items of the Scale