Determinants of Environmentally Responsible Behaviour Among Tourists–Model Inventory

1. Abstract

The Determinants of Environmentally Responsible Behaviour Among Tourists—Model Inventory (DERBT-M) is an empirically validated psychometric instrument designed to assess the multidimensional cognitive, affective, and intentional antecedents that drive environmentally responsible behaviors among travelers. Originally developed and validated by Nusrat Batool, Zubair-Ahmad Dada, and Shamim-Ahmad Shah (2023) at the University of Kashmir, this measurement model addresses the growing imperative to quantify tourist impacts on ecologically fragile ecosystems, specifically validated in high-altitude, ecologically sensitive destinations such as the Western Himalayas. The instrument operationalizes four core latent constructs: Emotional Affinity towards Nature (EATN), Environmental Knowledge (EK), Behavioral Intentions (BI), and manifest Environmentally Responsible Behavior (ERB).

Comprising 18 standardized items administered via a 5-point Likert scale ranging from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”), the DERBT-M demonstrates robust psychometric integrity. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) establish satisfactory structural fit across standard goodness-of-fit indices: Goodness of Fit Index (GFI) = .971, Root Mean Square Residual (RMSR) = .05, Comparative Fit Index (CFI) = .944, and Normed Fit Index (NFI) = .997. Internal consistency reliability is confirmed through Cronbach’s alpha coefficients spanning .703 to .890 across subscales, alongside composite reliability (CR) metrics ranging from .943 to .961. Convergent validity is confirmed with Average Variance Extracted (AVE) values consistently exceeding the .50 benchmark and standardized factor loadings surpassing .70. Discriminant validity is supported according to the Fornell–Larcker criterion, wherein the square root of each construct’s AVE exceeds its inter-construct correlations. This psychometric tool serves as an essential measurement architecture for environmental psychologists, destination management organizations (DMOs), and sustainable tourism researchers seeking to identify, predict, and shape pro-environmental tourist conduct.

2. Keywords

Environmentally Responsible Behavior, Emotional Affinity Towards Nature, Environmental Knowledge, Behavioral Intentions, Sustainable Tourism, Psychometrics, Western Himalayas, Structural Equation Modeling, Ecotourism, Conservation Psychology, Pro-environmental Behavior, Destination Management

3. Authors

The DERBT-M was developed and validated by a research team affiliated with the University of Kashmir, specializing in geography, tourism studies, and disaster management:

• Nusrat Batool, Ph.D. Candidate / Scholar
  Affiliation: Department of Geography and Disaster Management, University of Kashmir, Srinagar, Jammu & Kashmir, India.
  ORCID: 0000-0001-6004-3211
  Email: [email protected]
• Zubair-Ahmad Dada, Ph.D.
  Affiliation: Department of Geography and Disaster Management, University of Kashmir, Srinagar, Jammu & Kashmir, India.
• Shamim-Ahmad Shah, Ph.D.
  Affiliation: Professor, Department of Geography and Disaster Management, University of Kashmir, Srinagar, Jammu & Kashmir, India.

Correspondence Address: Nusrat Batool, Department of Geography and Disaster Management, University of Kashmir, Hazratbal, Srinagar, Jammu & Kashmir 190006, India.

4. Purpose

The primary purpose of the Determinants of Environmentally Responsible Behaviour Among Tourists—Model Inventory is to provide a standardized, psychometrically grounded instrument to diagnose, quantify, and model the socio-psychological factors that govern tourist conduct in ecologically vulnerable landscapes. Anthropogenic pressures associated with mass tourism—such as habitat fragmentation, carbon emissions, municipal solid waste generation, water table depletion, and flora/fauna disturbance—pose existential threats to biodiversity hotspots. Mitigating these adverse externalities requires an empirical understanding of why certain individuals adopt stewardship practices while others engage in destructive behaviors.

From an applied perspective, the DERBT-M bridges the gap between passive tourist sentiment and active ecological stewardship. Traditional visitor surveys frequently measure customer satisfaction or generic travel preferences without capturing the underlying cognitive and affective drivers of conservation actions. The DERBT-M fills this void by systematically evaluating how tourists process ecological information (Environmental Knowledge), how deeply they connect with natural settings (Emotional Affinity towards Nature), how these internal drivers formulate actionable decision tendencies (Behavioral Intentions), and the degree to which these translate into overt conservation actions (Environmentally Responsible Behavior).

In destination management and clinical environmental psychology settings, the DERBT-M serves multiple functional purposes:

• Predictive Behavioral Auditing: Enables park managers, heritage conservators, and tourism boards to forecast visitor behavior profiles and quantify baseline ecological mindfulness.
• Targeted Intervention Design: Facilitates the formulation of demographic-specific behavioral nudge strategies, interpretive educational signage, and visitor codes of conduct tailored to whether tourist compliance is bottlenecked by deficient knowledge or lack of emotional connection.
• Policy Evaluation and Benchmarking: Offers a standardized structural framework to evaluate the longitudinal impact of ecotourism policies, conservation fees, and green hotel certification programs on tourist compliance and willingness to pay.
• Theoretical Structural Modeling: Supplies empirical researchers with validated measurement subscales that can be embedded into broader structural equation models alongside socio-demographic indicators (e.g., age, gender, education, origin) to explore complex socio-spatial interactions.

5. Psychological Construct

The DERBT-M conceptualizes pro-environmental engagement as a multifaceted, hierarchical psychological phenomenon comprising four interrelated theoretical dimensions:

1. Emotional Affinity towards Nature (EATN)

Rooted in affective environmental psychology, Emotional Affinity towards Nature reflects an individual’s subjective, visceral, and emotional bond with the natural world. Drawing upon foundational paradigms of nature connectedness, this construct captures deep feelings of psychological oneness, peace, contentment, and sensory resonance experienced in wilderness settings. Within the DERBT-M, EATN is characterized not merely as aesthetic appreciation, but as an experiential state that triggers emotional indignation, moral outrage, and distress when witnessing ecological degradation, alongside feelings of personal responsibility toward non-human organisms.

2. Environmental Knowledge (EK)

Environmental Knowledge encompasses cognitive familiarity with ecological systems, the specific environmental vulnerabilities of visited destinations, and the direct causal pathways linking human recreational activities to ecosystem degradation. Unlike general biological trivia, this construct measures applied, action-oriented, and systemic ecological literacy. It evaluates whether a tourist comprehends how recreation contributes to water pollution, wildlife disturbance, and resource depletion, as well as their understanding of actionable mitigation procedures (e.g., Leave No Trace principles, carbon offset programs, and sustainable product selection).

3. Behavioral Intentions (BI)

Behavioral Intentions represent the conative component of the tourist’s psychological orientation, capturing the conscious subjective probability and motivational readiness to execute pro-environmental actions during travel. In accordance with classic social-cognitive frameworks, behavioral intention serves as the direct proximal antecedent to manifest behavior. Within the inventory, BI measures willingness to sacrifice financial or personal comfort for conservation benefits, including selecting eco-certified accommodations, prioritizing local and organic merchandise, adhering stringently to ecological regulations, and supporting environmental taxation schemes.

4. Environmentally Responsible Behavior (ERB)

Environmentally Responsible Behavior constitutes the manifest, self-reported actions performed by tourists to actively minimize negative ecological footprints and contribute toward resource preservation. ERB encompasses both non-intrusive preservation behaviors (e.g., avoiding littering, conserving municipal water and energy in lodgings, respecting wildlife corridors) and active civic interventions (e.g., waste cleanup, financial donation to host-community initiatives, peer-led behavioral policing, and formal reporting of environmental infractions to regulatory bodies).

6. Theoretical Framework

The construction of the DERBT-M synthesizes three foundational paradigms in environmental and cognitive psychology: the Value-Belief-Norm (VBN) Theory, the Theory of Planned Behavior (TPB), and the Biophilia Hypothesis.

Integration of the Value-Belief-Norm (VBN) Paradigm

Paul Stern’s (2000) Value-Belief-Norm theory posits an unfolding causal chain: general personal values (biospheric, altruistic, egoistic) activate ecological worldviews (such as the New Ecological Paradigm), which subsequently foster an Awareness of Consequences (AC) and an Ascription of Responsibility (AR). In the DERBT-M, Environmental Knowledge serves as the operational mechanism for Awareness of Consequences—educating the tourist on how specific leisure behaviors harm fragile habitats. This cognitive awareness activates personal moral norms, operationalized through Emotional Affinity towards Nature, generating an acute sense of moral and emotional obligation to avert environmental harm.

The Theory of Planned Behavior (TPB) and the Intention-Behavior Gap

Formulated by Icek Ajzen (1991), the Theory of Planned Behavior establishes that manifest behavior is directly driven by behavioral intentions, which are formed by attitudes toward the behavior, subjective norms, and perceived behavioral control. The DERBT-M incorporates the conative transition from internal state to overt action by treating Behavioral Intentions as the mediating bridge connecting affective resonance (EATN) and cognitive awareness (EK) to actual Environmentally Responsible Behavior (ERB). By evaluating both intentionality and active execution, the model allows researchers to examine and identify the parameters that bridge or widen the well-documented “green intention-behavior gap” in tourism research.

Affective Dimensions and the Biophilia Hypothesis

Edward O. Wilson’s (1984) Biophilia Hypothesis asserts an innate evolutionary human tendency to seek connections with nature and living systems. Kals, Schumacher, and Montada (1999) operationalized this concept as emotional affinity toward nature, demonstrating that cognitive environmental knowledge alone is frequently insufficient to compel protective behaviors without an underlying affective, emotional attachment. The DERBT-M explicitly incorporates this premise: emotional affinity functions as a motivational catalyst that transforms detached ecological facts into active stewardship, moral indignation against environmental degradation, and dedicated pro-environmental action.

7. Validity

The structural, convergent, and discriminant validity of the DERBT-M was established through rigorous analytical procedures conducted on empirical datasets gathered across tourist cohorts in the Western Himalayas (Batool et al., 2023):

Content and Face Validity

Measurement items were synthesized and adapted from validated psychometric instruments in tourism and environmental psychology, including Fall-Diallo et al. (2015), Ghazali et al. (2019), Kim et al. (2018), Kiatkawsin and Han (2017), and Li and Wu (2020). Items were subjected to academic expert review in geographic information science, environmental management, and psychometrics to ensure domain representativeness, lexical precision, and cross-cultural applicability before field deployment.

Convergent Validity

Convergent validity evaluates the extent to which indicators of a specific construct share a high proportion of common variance. In accordance with criteria established by Hair et al. (2005):

• Standardized Factor Loadings: All retained survey items exhibited standardized factor loadings exceeding the critical threshold of .70, demonstrating that the observable items strongly reflect their latent targets.
• Average Variance Extracted (AVE): The AVE metrics calculated across all four latent constructs consistently surpassed the recommended .50 threshold, establishing that the variance captured by each construct is greater than the variance attributable to measurement error.

Discriminant Validity

Discriminant validity confirms that each latent construct is statistically distinct from other constructs within the structural system. This was established utilizing the Fornell–Larcker (1981) criterion. The square root of the AVE calculated for each individual construct (Emotional Affinity, Environmental Knowledge, Behavioral Intention, Environmentally Responsible Behavior) was systematically greater than the bivariate inter-construct correlation coefficients between any pair of constructs. This statistical divergence verifies that the DERBT-M successfully assesses conceptually discrete psychological domains rather than overlapping latent phenomena.

8. Reliability

The DERBT-M demonstrates exceptional internal consistency and construct stability across diverse respondent cohorts. Reliability parameters reported in empirical validation investigations are detailed below:

Cronbach’s Alpha Coefficients

Internal consistency estimates using Cronbach’s alpha (α) demonstrated strong scale robustness, comfortably exceeding the widely accepted psychometric benchmark of .70 (Nunnally & Bernstein, 1994):

• Range across subscales: α = .703 to .890
• Construct-specific reliability analyses confirmed that no individual item deletion would yield a substantial increase in composite alpha metrics, confirming the unidimensional integrity of each respective subscale.

Composite Reliability (CR)

Because Cronbach’s alpha assumes tau-equivalence (equal factor loadings across all items) and may underestimate internal consistency in structural equation models, Composite Reliability (CR) was also calculated. The CR metrics across the four operational dimensions ranged between .943 and .961, well above the conventional cut-off value of .70. These high coefficients confirm that the latent constructs are measured with minimal error variance and display exceptional measurement reliability.

9. Factor Analysis

The structural dimensionality of the DERBT-M was evaluated using Confirmatory Factor Analysis (CFA) within a covariance-based Structural Equation Modeling (SEM) framework, executed using Maximum Likelihood estimation.

Model Fit Parameters

The hypothesized four-factor measurement model was tested against empirical data. Goodness-of-fit was appraised using absolute, incremental, and parsimonious fit indices recommended by Hu and Bentler (1995):

• Goodness of Fit Index (GFI) = .971: Substantially exceeds the classical benchmark of ≥ .90, demonstrating that the sample covariance matrix is accurately replicated by the model.
• Normed Fit Index (NFI) = .997: Exceeds the standard threshold of ≥ .95, confirming that the hypothesized model significantly improves fit compared to an independence baseline model.
• Comparative Fit Index (CFI) = .944: Surpasses the acceptable threshold of ≥ .90, indicating stable parameter recovery across sample variations.
• Root Mean Square Residual (RMSR) = .05: Meets the rigorous threshold for acceptable residual variance, demonstrating minimal residual covariance between the observed and implied matrices.

Overall, the CFA verified that the items load unequivocally onto their four designated dimensions without problematic cross-loadings or anomalous error covariances, providing empirical support for the theoretical structure of the DERBT-M.

10. Instrument / Measurement Tool

• Instrument Name: Determinants of Environmentally Responsible Behaviour Among Tourists—Model Inventory (DERBT-M)
• Test Type: Psychometric Self-Report Questionnaire / Structured Survey Inventory
• Target Population: Adult tourists (ages 18 and older, including young adults 18–29, thirties 30–39, and middle age 40–64) visiting natural, protected, or ecotourism destinations
• Number of Items: 18 standardized statements
• Subscales / Dimensions:
  • Emotional Affinity towards Nature (EATN): Items 1–4
  • Environmental Knowledge (EK): Items 5–8
  • Behavioral Intentions (BI): Items 9–12
  • Environmentally Responsible Behavior (ERB): Items 13–18
• Response Scale: 5-point Likert scale (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree)
• Scoring Rules:
  • Individual subscale scores are generated by calculating the mean or sum of the items within each designated domain.
  • Higher scores within each domain reflect elevated levels of nature connectedness, cognitive environmental knowledge, conservation intentionality, and enacted pro-environmental behavior.
  • Subscale scores can be analyzed independently or modeled as a structural latent system within path analysis or SEM.
• Administration Time: Approximately 6 to 10 minutes
• Language: English (validated also with Spanish contextual adaptations in academic literature)

11. Permissions & Fee and Test Year

• Test Year: 2023
• Licensing and Commercial Status: Non-commercial, open academic instrument. The inventory was developed for scholarly and educational research applications.
• Fee: Free for non-commercial academic research, pedagogical investigations, and non-profit conservation evaluation.
• Permissions: For non-commercial academic research, citations of the original validation paper (Batool, Dada, & Shah, 2023) are required. Commercial deployments, proprietary adaptations, or wide-scale integration into commercial destination audits should be directed to the corresponding author: Nusrat Batool, Department of Geography and Disaster Management, University of Kashmir.

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

Batool, N., Dada, Z.-A., & Shah, S.-A. (2023). Sociodemographic determinants of environmental attitude, environmental knowledge, emotional affinity and responsible behaviour among tourists: A case study of the Western Himalayas. PsyEcology, 14(3), 363–388. https://doi.org/10.1080/21711976.2023.2240615

Fall-Diallo, A., Castello, P., & Maza, M. (2015). Ethical consumer behavior in tourism: An empirical study on responsible tourist practices. Journal of Travel & Tourism Marketing, 32(6), 682–697. https://doi.org/10.1080/10548408.2014.932870

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

Ghazali, E. M., Nguyen, B., Mutum, D. S., & Yap, S. F. (2019). Pro-environmental behaviours and value-belief-norm theory among consumers: An empirical evaluation. Ecological Indicators, 98, 234–246. https://doi.org/10.1016/j.ecolind.2018.11.002

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2005). Multivariate data analysis (6th ed.). Pearson Prentice Hall.

Hu, L. T., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76–99). Sage Publications.

Kals, E., Schumacher, D., & Montada, L. (1999). Emotional affinity toward nature as a motivational basis to protect nature. Environment and Behavior, 31(2), 178–202. https://doi.org/10.1177/00139169921972056

Kiatkawsin, K., & Han, H. (2017). Young travelers’ intention to behave pro-environmentally: Merging the value-belief-norm theory and the expectancy theory. Tourism Management, 59, 76–88. https://doi.org/10.1016/j.tourman.2016.06.018

Kim, M. S., Stepchenkova, S., & Babalou, V. (2018). The effect of self-transcendence personal values on environmentally responsible behavior of tourists: The mediating role of nature connection. Journal of Travel Research, 57(6), 720–735. https://doi.org/10.1177/0047287517718354

Li, Q., & Wu, M. (2020). Tourists’ pro-environmental behavior in protected areas: A meta-analysis. Journal of Sustainable Tourism, 28(9), 1269–1289. https://doi.org/10.1080/09669582.2020.1732994

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

Stern, P. C. (2000). Toward a coherent theory of environmentally significant behavior. Journal of Social Issues, 56(3), 407–424. https://doi.org/10.1111/0022-4537.00175

Wilson, E. O. (1984). Biophilia. Harvard University Press.

13. Items of the Scale