A Standardized Field-Survey Protocol for Visitor Conservation Willingness and Self-Reported Responsible Travel Behavior at Longmen Grottoes

Summary

This protocol standardizes a minimum-disruption visitor survey at Longmen Grottoes by linking public-area interception, anonymous questionnaire administration, predefined screening, measurement validation, model checking, and reproducibility archiving.

Abstract

Heritage-site visitor surveys must generate usable behavioral evidence without disturbing site operations or weakening conservation priorities. However, recruitment boundaries, exclusion rules, measurement checks, model diagnostics, and data archiving are often incompletely reported in heritage tourism studies. This protocol presents a minimum-disruption field-survey workflow for measuring visitor conservation willingness and self-reported responsible travel behavior at Longmen Grottoes, Luoyang, China. The workflow integrates public-area visitor interception, anonymous electronic questionnaire administration, eligibility screening, attention checks, daily raw-data locking, predefined exclusion rules, construct scoring, reliability testing, confirmatory factor analysis, regression and path-model diagnostics, sensitivity checks, and reproducibility archiving. The questionnaire separates heritage knowledge, perceived heritage value, place attachment, pro-environmental message quality, perceived crowding, facility and service support, conservation attitude, conservation willingness, and self-reported responsible travel behavior. Representative implementation showed that the workflow produced an analyzable dataset with auditable exclusions, acceptable measurement diagnostics, and interpretable model outputs. The protocol is intended for heritage-site studies that need transparent visitor-behavior evidence rather than a single satisfaction measure. It can be adapted to other heritage settings by retaining the core procedural sequence while revising site-specific constructs, survey points, language versions, behavioral items, and fieldwork restrictions according to local conservation risks and visitor composition.

Introduction

Cultural heritage sites require visitor access for public education, cultural transmission, and tourism value, but visitor use can also place pressure on fragile material, spatial, and symbolic resources¹. This tension is especially visible at high-visitor-volume sites where movement routes are limited, interpretation areas are dense, and conservation rules must be understood during a short visit. Longmen Grottoes in Luoyang, China, provides an appropriate setting for a conservation-oriented visitor-survey workflow because it combines rock-carving heritage protection, public interpretation, visitor-flow management, and conservation education within one active field environment. In this setting, visitor measurement cannot stop at satisfaction. A useful survey must document whether visitors understand heritage value, recognize protection rules, accept conservation restrictions, and report responsible conduct during or immediately after the visit.

Heritage tourism studies commonly measure perceived value, destination image, place attachment, satisfaction, or general pro-environmental intention². The operational weakness is not the use of these constructs, but the limited reporting of how field evidence is produced. Recruitment points, sampling intervals, suspension rules, attention checks, exclusion thresholds, raw-data locking, construct scoring, measurement validation, model diagnostics, and reproducibility archiving are often reported briefly or after results are known. This weakens comparison across studies and limits the usefulness of survey evidence for heritage management. The present protocol addresses this gap by fixing the workflow sequence before substantive interpretation: questionnaire preparation, pilot testing, public-area recruitment, anonymous administration, data screening, measurement checking, model interpretation, sensitivity analysis, and archiving.

The construct structure follows the practical order of a conservation-oriented heritage visit. Heritage knowledge records whether visitors understand the historical and cultural meaning of the site. Perceived heritage value captures whether visitors regard the site as worthy of protection³. Place attachment measures the personal connection formed through the visit⁴. Pro-environmental message quality records whether conservation messages are visible, understandable, and useful for guiding conduct. Perceived crowding captures density and movement pressure that may affect attention to rules⁵. Facility and service support records whether signs, barriers, routes, staff reminders, and visitor services make responsible conduct feasible. These six site-related constructs are separated from conservation attitude, conservation willingness, and self-reported responsible travel behavior so that perception, evaluation, intention, and reported conduct are not collapsed into one general positive-response score.

Conservation attitude and conservation willingness are also treated as distinct constructs. Conservation attitude refers to the visitor’s evaluative belief that Longmen Grottoes should be protected and that conservation should be prioritized. Conservation willingness refers to the visitor’s readiness to accept or support conservation-related requirements, including rule-following, inconvenience, route restrictions, and protective management⁶. Self-reported responsible travel behavior refers to conduct reported during the visit, such as following signs, avoiding harmful contact, staying within permitted areas, and reducing disturbance to the heritage environment⁷. A visitor may agree that conservation is important but still be unwilling to accept restrictions. Responsible conduct may also occur because the site environment makes compliance easy. Separating these constructs helps identify whether a management issue is closer to knowledge, message clarity, willingness, facility support, or reported conduct.

Longmen Grottoes is used as a testing context because visitor measurement must occur close to the visit while preserving low disruption, anonymity, and operational boundaries. The workflow is restricted to public visitor-service areas and post-visit or near-exit spaces, not restricted conservation zones or direct observation of protected surfaces. This boundary matters because heritage-site fieldwork takes place in active management settings where survey activity must not interrupt movement, staff duties, visitor safety, or conservation practice⁸. Fixed survey windows, systematic interception, suspension rules, predefined exclusions, and daily raw-file locking reduce undocumented field decisions and post hoc data cleaning.

The protocol is transferable at the workflow level, but the questionnaire is not intended to be copied unchanged. The components that should remain unchanged are survey-boundary definition, target-population definition, finalized questionnaire version control, systematic visitor interception, eligibility screening, attention checks, raw-data locking before exclusion, predefined data-quality rules, construct scoring, measurement validation, model diagnostics, sensitivity checks, and reproducibility archiving. The adjustable components are conservation-risk wording, behavioral items, survey points, language versions, visitor-composition variables, and management indicators. Archaeological parks may require stronger wording on surface contact and route restriction⁹. Historic towns may require items on resident disturbance and shared street-space behavior¹⁰. Museums and sacred landscapes may require gallery-flow, exhibit-protection, ritual-space, or culturally sensitive conduct items¹¹. This structure allows site-specific adaptation without weakening procedural comparability.

Representative results are used only to demonstrate whether the workflow produces usable visitor-behavior data. They are not used to claim direct observation of behavior, carrying-capacity estimation, intervention effects, or equivalence between self-reported and observed conduct. The contribution of the protocol is the standardized, auditable sequence for collecting, screening, validating, analyzing, and archiving conservation-oriented visitor-survey data under minimum-disruption heritage-site conditions.

This standardized workflow measures visitor conservation willingness and self-reported responsible travel behavior at Longmen Grottoes, Luoyang, Henan Province, China. It includes questionnaire design, pilot testing, public-area visitor interception, anonymous questionnaire administration, raw-data locking, quality screening, construct scoring, measurement checking, model diagnostics, and study-file archiving.

Protocol

The study was conducted in accordance with the Declaration of Helsinki and was approved by the SEGi Research Ethics Committee (Approval No. SEGiEC/SR/FOELPM/163/2025-2027) on 27 June 2025.Written or electronic informed consent was obtained before the questionnaire began.

NOTE: No names, identity card numbers, telephone numbers, facial images, home addresses, device identifiers, GPS coordinates, account identifiers, photographs, audio recordings, video recordings, or other directly identifiable information were collected. IP address collection was disabled before formal questionnaire release. No formal written site-management permission was obtained from the Longmen Grottoes management authority. Therefore, fieldwork was restricted to publicly accessible visitor-service areas and post-visit or near-exit spaces. Survey assistants did not enter grotto interiors, restricted conservation zones, staff-only areas, narrow passageways, crowded viewing platforms, ticket-checking queues, emergency routes, religious or commemorative spaces, or any location where recruitment could interfere with visitor movement, site operations, visitor safety, staff duties, or conservation practice. Recruitment was suspended immediately during heavy rain, emergency announcements, official crowd-control measures, staff requests, abnormal crowding, or any situation that could interfere with visitor flow, safety, or site management.

1. Define the field site, constructs, sample target, and survey window

1. Use Longmen Grottoes scenic area in Luoyang, Henan Province, China, as the field site. Define the target population as adult visitors who have completed, or are about to complete, their same-day visit to Longmen Grottoes.
2. Use three public survey points: the visitor exit area, the public rest area outside the main visiting route, and the visitor service area near the transportation connection point. Use only spaces where visitors can stop voluntarily without blocking visitor movement or interfering with site operations.
3. Suspend recruitment during heavy rain, emergency announcements, official crowd-control measures, staff requests, abnormal crowding, guided-tour congestion, blocked pathways, or any situation that could interfere with visitor flow, visitor safety, staff duties, or site management.
4. Use heritage knowledge (HK), perceived heritage value (PHV), place attachment (PA), pro-environmental message quality (PMQ), perceived crowding (PC), and facility and service support (FSW) as site-related predictors.
5. Use conservation attitude (CA) and conservation willingness (CW) as intention-related constructs. Define CA as the visitor’s belief that Longmen Grottoes should be protected and that conservation should be prioritized. Define CW as the visitor’s readiness to accept or support conservation requirements, including rule-following, inconvenience, route restrictions, and protective management.
6. Use self-reported responsible travel behavior (RTB) as the behavioral outcome. Define RTB as conduct reported during the visit, including following signs and barriers, avoiding harmful contact with heritage surfaces, staying within permitted areas, reducing disturbance to other visitors and the heritage environment, and keeping the site clean.
7. Set the planned raw sample size at 480 completed questionnaires and the minimum valid sample size at 400 questionnaires after quality screening.
8. Conduct formal data collection from 1 April 2026 to 20 May 2026 . Use two survey periods per fieldwork day: 10:00–12:00 and 14:30–17:30.
9. Figure 1 summarizes the full procedure from questionnaire design and pilot testing to public-area field collection, screening, scoring, measurement validation, model checking, sensitivity analysis, and study-file archiving.
   NOTE: Longmen Grottoes was selected because this World Heritage cultural site combines high visitor volume, rock-carving heritage conservation, public interpretation, and visitor-management demands within one active field setting¹².

2. Construct, screen, translate, and pilot the questionnaire

1. Organize the questionnaire into five sections: informed consent, eligibility screening, heritage-site perception items, conservation attitude, conservation willingness and responsible travel behavior items, and demographic and travel-related variables.
2. Use a 5-point Likert scale for all substantive perception, attitude, willingness, and behavior items: 1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, and 5 = strongly agree.
3. Include 31 substantive Likert items across nine constructs: HK, PHV, PA, PMQ, PC, FSW, CA, CW, and RTB. Use at least three observed items for each construct, with four items for PHV, CA, CW, and RTB.
4. Keep CA, CW, and RTB wording distinct. CA items assess the perceived importance, priority, and responsibility of conservation. CW items assess willingness to accept or support conservation-related requirements. RTB items assess conduct reported during the same-day visit.
5. Word each item around the Longmen Grottoes visit rather than general environmental concern. Avoid double-barreled wording, leading wording, professional conservation terminology, and wording that implies a socially desirable answer.
6. Align willingness and behavior items with behavioral-intention and perceived-control logic in applied visitor research¹³. Adapt place attachment and responsible-behavior items to the Longmen Grottoes visit¹⁴. Use heritage-site perception items to measure value perception, visitor-site relationship quality, and conservation-related interpretation specific to Longmen Grottoes¹⁵.
7. Keep the average reading length of each item below 28 characters where possible and keep the full questionnaire completion time within 6–10 min during pilot testing. Do not include open-ended free-text questions in the formal questionnaire.
8. Add screening items for age, same-day visit status, occupational role, and repeat participation. Terminate the questionnaire if the participant is younger than 18 years, did not visit Longmen Grottoes on the survey day, is a scenic-area staff member, tour guide working during the visit, travel-agency employee working during the visit, vendor working inside or near the survey area, member of the research team, or has already completed the questionnaire on the same day.
9. Add two attention-check items. Place the first after the first one-third of substantive items: “To confirm that you are reading the questions carefully, please select ‘Agree’ for this item.” Code it as passed only when the participant selects 4. Place the second after the second one-third of substantive items: “For quality-control purposes, please select ‘Disagree’ for this item.” Code it as passed only when the participant selects 2.
10. Prepare the working questionnaire in preferred language. Ask two bilingual researchers with tourism, heritage, or behavioral survey experience to compare the items with the intended English construct meanings. Resolve wording disagreements before pilot testing.
11. Conduct the pilot test from 20 March 2026 to 25 March 2026 with 30 adult visitors or recent visitors to a cultural heritage attraction. Record completion time, skipped items, misunderstood wording, repeated participant questions, item hesitation, platform-display problems, and confusion about CA, CW, or RTB wording.
12. Revise any item if more than 20% of pilot participants report that it is difficult to understand. Retain an item if its corrected item-total correlation is at least 0.30. Retain a construct if Cronbach’s alpha is at least 0.70.
13. Retain a construct with Cronbach’s alpha between 0.65 and 0.69 only if all corrected item-total correlations are at least 0.30 and no single item deletion increases Cronbach’s alpha by more than 0.03. Mark any construct in this range as marginally reliable.
14. Exclude pilot-test responses from the formal analytical dataset. Finalize the questionnaire after the revised items are checked by the two bilingual reviewers.
15. Table 1 summarizes the construct definitions, item codes, item wording, scoring direction, analytical position, and source or adaptation basis for the nine questionnaire constructs.
16. Supplementary File 1 provides the final questionnaire, informed-consent page, screening questions, attention-check items, questionnaire-platform display record, and pilot-review record.
    PAUSE POINT: Stop after the pilot test if any construct has Cronbach’s alpha below 0.65. Revise the item wording and repeat the pilot test before formal fieldwork.

3. Train survey assistants and conduct systematic intercept sampling

1. Recruit survey assistants who are not involved in scenic-area management, ticketing, tour operation, souvenir sales, visitor-flow control, conservation enforcement, or any work role that could influence visitors’ willingness to participate.
2. Assign each survey assistant a fieldwork code before training. Use personnel codes such as SA01, SA02, SA03, and SA04 in the fieldwork log instead of personal names.
3. Train all survey assistants for 2 h before formal data collection. Cover eligibility criteria, consent procedure, invitation script, refusal handling, sampling interval, travel-group selection rule, recruitment suspension rule, privacy requirements, quality-control rules, fieldwork-log completion, and the distinction among CA, CW, and RTB.
4. Ensure each survey assistant completes five supervised practice invitations before formal data collection.
5. Use the following invitation script: “Hello, we are conducting a de-identified academic survey about visitor experience, heritage protection, and responsible travel behavior at Longmen Grottoes. The questionnaire takes about 6–10 min. Participation is voluntary, and you may stop at any time.”
6. Do not mention expected hypotheses, desired response directions, model relationships, conservation-behavior scores, or any assumed positive effect during recruitment.
7. At the beginning of each survey window, randomly select a starting number between 1 and 5. Approach the visitor corresponding to the selected starting number, and then approach every fifth adult visitor passing the selected survey point after completing, or nearly completing, the visit.
8. Reset the counting sequence after each completed questionnaire, refusal, or ineligible visitor.
9. Invite only one person from the same travel group. When multiple eligible visitors from the same travel group volunteer, invite the person whose birthday is closest to the survey date.
10. End the invitation immediately if the visitor declines, appears rushed, is caring for children or older companions, is blocking visitor flow, is listening to a guided explanation, is entering a crowded route segment, or is in any situation where recruitment may cause inconvenience.
11. Maintain a fieldwork log for each survey window. Record survey date, time window, survey point, survey assistant code, visitors approached, ineligible visitors, refusals, started questionnaires, completed questionnaires, suspended periods, reason for suspension, and sampling deviations.
12. Use the fieldwork log to calculate approach rate, refusal rate, completion rate, and valid-response rate.
13. Continue fieldwork until 480 raw questionnaires are collected. Ensure that each survey point contributes at least 120 raw questionnaires.
14. Monitor gender, age group, first-time visit status, and travel mode at the end of each survey day. Do not reject eligible visitors to force demographic balance. Keep first-time visitors between 45% and 70% and organized group-tour participants below 45% of the valid sample where possible. Treat these values as monitoring ranges, not quotas.
15. Table 2 presents the fieldwork schedule, public survey points, sampling interval, sample targets, suspension rules, and fieldwork-recording variables.
16. Supplementary File 2 provides the fieldwork-log form, role-assignment record, data-management log, exclusion-log form, and codebook structure.
    NOTE: Record any deviation from the sampling interval, survey point, time window, or suspension rule in the fieldwork log on the same day.

4. Administer the questionnaire and check daily records

1. Show the informed-consent page before the questionnaire begins. Ask each participant to confirm that they are at least 18 years old, have read the consent statement, and agree to participate voluntarily.
2. Administer the questionnaire through an electronic questionnaire platform on a tablet or on the participant’s mobile phone. Present one item or one short item group per screen and require a response for each substantive Likert item.
3. Explain only the literal meaning of a word or phrase if clarification is requested. Do not suggest, imply, or confirm any response direction.
4. Record start time, end time, completion time, date, survey point, time window, and survey assistant code. Do not collect free-text comments during the formal questionnaire.
   CHECKPOINT: At the end of each survey day, confirm that all completed questionnaires contain start time, end time, survey point, time window, and survey assistant code.

5. Export, screen, and score the dataset

1. Export the raw questionnaire file in .xlsx format at 20:00 on each survey day. Name each file using the format LongmenSurvey_raw_YYYYMMDD.xlsx and store it in a password-protected folder accessible only to authorized study personnel.
2. Maintain a role-assignment log using personnel codes rather than names. Record the personnel code responsible for each survey session, daily export, raw-file storage, raw-file locking, exclusion coding, independent screening verification, statistical analysis, and final dataset freeze.
3. Merge all daily raw files after formal data collection ends. Assign each case a unique respondent ID using the format LGV0001 to LGV0480. Save one unedited master file as LongmenSurvey_raw_locked.xlsx, and record the lock date, file size, and SHA-256 checksum in the data-management log.
4. Conduct all screening, scoring, and analysis in copied working files. Do not edit the locked raw master file.
5. Exclude cases with eligibility failure, including age below 18 years, no same-day visit, excluded occupational role, or repeated same-day participation. Exclude cases that fail either attention-check item.
6. Exclude cases completed in less than 180 s, cases with the same Likert response across 15 or more consecutive substantive items, cases with a standard deviation below 0.25 across all substantive Likert items, and confirmed duplicate cases.
7. Review cases completed in more than 1,800 s. Retain a long-duration case only if it has no attention-check failure, straight-lining, eligibility contradiction, duplicate response pattern, or other fieldwork problem.
8. Check duplicate cases using substantive Likert items, demographic variables, survey point, time window, and completion time rounded to the nearest second. If a complete duplicate is found, retain the earlier case and exclude the later case.
9. Create an exclusion variable with the categories: eligible, eligibility failure, attention-check failure, fast completion, long-duration review failure, straight-lining, duplicate response, and other fieldwork problem. Retain only cases coded as eligible. Continue fieldwork under the same rules if fewer than 400 valid cases remain after screening.
10. Complete independent screening verification before construct scoring. The first reviewer applies all exclusion rules, and the second reviewer checks eligibility status, attention-check results, completion time, straight-lining, duplicate decisions, and final exclusion codes against the locked raw file.
11. Code all Likert items from 1 to 5. Reverse-code negatively worded items using reversed score = 6−original score.
12. Code demographic and travel variables, including gender, age group, education, monthly personal income, first-time visit, travel mode, survey point, and time window. Record all coding rules in the codebook.
13. Calculate each construct score as the arithmetic mean of its retained items and keep all construct scores on the original 1–5 scale. Do not impute item-level missing values because all substantive items are required fields.
14. Table 3 summarizes the eligibility, attention-check, and quality-screening rules applied before construct scoring.
15. Table 4 presents the demographic coding, travel-variable coding, reverse-coding rule, exclusion-status coding, and construct-score calculation plan.
    CHECKPOINT: Proceed to measurement testing only after the final analytical dataset contains at least 400 valid questionnaires, all retained cases have complete construct scores, and independent screening verification has been completed.

6. Assess reliability, measurement structure, and common-method risk

1. Calculate Cronbach’s alpha and corrected item-total correlation for each multi-item construct. Treat Cronbach’s alpha ≥0.70 as acceptable. Treat Cronbach’s alpha between 0.65 and 0.69 as marginally reliable only when all corrected item-total correlations are ≥0.30 and no single item deletion increases alpha by more than 0.03.
2. Retain items with corrected item-total correlation ≥0.30. Remove an item only if its corrected item-total correlation is below 0.30 and its removal increases Cronbach’s alpha by at least 0.03. Mark perceived crowding as marginally reliable if its alpha remains between 0.65 and 0.69 after item review.
3. Conduct confirmatory factor analysis for the nine-factor measurement model using all retained substantive items. Report CFI, TLI, RMSEA, SRMR, standardized factor-loading range, composite reliability, and average variance extracted.
4. Treat the measurement model as acceptable when CFI and TLI are ≥0.90 and RMSEA and SRMR are ≤0.08. Retain items with standardized factor loading ≥0.50. Review any item with loading below 0.50 or large residual correlation with a conceptually similar item.
5. Assess discriminant validity using latent construct correlations and HTMT. Flag construct pairs if HTMT exceeds 0.85, or 0.90 for conceptually adjacent constructs. Review CA, CW, and RTB carefully if they show excessive latent overlap.
6. Compare the nine-factor model with a one-factor model as a common-method risk screen. Conduct Harman’s single-factor test only as a descriptive screen, and do not use it as proof that common-method bias¹⁶ is absent.
7. Conduct exploratory factor analysis only as a secondary item-clustering diagnostic, using principal axis factoring with oblimin rotation.
8. Table 5 summarizes the pre-specified thresholds used for reliability, item retention, CFA fit, discriminant validity, common-method screening, correlation review, model diagnostics, indirect-effect testing, and sensitivity checks.
9. Report the full item-level reliability results, standardized CFA loadings, composite reliability, AVE values, HTMT matrix, one-factor comparison model, and exploratory item-clustering diagnostics in Supplementary File 3.
   PAUSE POINT: Stop after measurement testing if more than one construct has Cronbach’s alpha below 0.65, if CA, CW, and RTB cannot be separated in the measurement model, or if any HTMT value exceeds the pre-specified threshold.

7. Generate descriptive diagnostics and estimate models

1. Calculate frequency and percentage for categorical variables. Calculate mean, standard deviation, median, minimum, maximum, skewness, and kurtosis for all construct scores.
2. Treat absolute skewness below 2.00 and absolute kurtosis below 7.00 as acceptable for the planned models. Flag a construct if more than 60% of valid participants select the same rounded response category or if its standard deviation is below 0.50.
3. Calculate Pearson correlations among construct scores. Treat correlations above 0.70 as possible construct redundancy and recheck item coding, reverse coding, and construct calculation before modeling.
4. Standardize continuous construct scores and dummy-code categorical controls. Use age group, gender, education, monthly personal income, first-time visit, and travel mode as control variables.
5. Estimate three regression models. Regress CA on HK, PHV, PA, PMQ, PC, FSW, and controls. Regress CW on HK, PHV, PA, PMQ, PC, FSW, CA, and controls. Regress RTB on HK, PHV, PA, PMQ, PC, FSW, CA, CW, and controls.
6. Record standardized coefficient, standard error, t value, p value, 95% confidence interval, R², adjusted R², and VIF for each regression model.
7. Estimate a path model following the construct sequence: site-related predictors → CA → CW → RTB. Test indirect effects from site-related predictors to CW and RTB through CA, and from CA to RTB through CW, using 5,000 bootstrap samples.
8. Report standardized direct effects, indirect effects, total effects, bootstrap 95% confidence intervals, and model fit indices. Treat p <0.05 as statistically significant and p values between 0.05 and 0.10 as marginal.
9. Do not describe non-significant paths as confirmed effects, and do not infer mediation without indirect-effect testing.
10. Present only the summary measurement and model diagnostics in the main text. Report the complete correlation matrix, regression coefficients, path-model estimates, indirect effects, and total effects in Supplementary File 3.

8. Assess model diagnostics and sensitivity

1. Calculate the variance inflation factor for each predictor in each regression model. Treat VIF < 3.00 as acceptable, inspect values between 3.00 and 5.00, and justify any model retained with VIF > 5.00.
2. Flag cases with absolute standardized residuals greater than 3.00. Calculate Cook’s distance for each regression model and flag cases with Cook’s distance greater than 4/n, where n is the number of valid cases included in the model.
3. Treat R² between 0.15 and 0.35 as plausible for cross-sectional visitor behavior data. Recheck the data if all hypothesized paths are significant, all standardized coefficients exceed 0.40, or any R² exceeds 0.60.
4. Repeat the three regression models after removing cases with completion time longer than 1,800 s, excluding organized group-tour participants, recalculating construct scores using median item scores, and excluding cases flagged by Cook’s distance.
5. Repeat the path model after excluding cases flagged by Cook’s distance.
6. Treat results as stable if the main coefficient directions remain unchanged and the key significant or marginal paths remain in the same direction. Report any coefficient-direction change, loss of significance, or large adjusted R² change in the representative results.
7. Report the complete sensitivity-check outputs in Supplementary File 3.

9. Run the analysis and archive the study files

1. Conduct data cleaning in copied working files and complete statistical analysis using the R version and package versions listed in the Table of Materials.
2. Run the analysis script using only the de-identified valid dataset. Set the random seed to 20260401 before resampling, bootstrap estimation, or sensitivity checks.
3. Save the R session information using sessionInfo() after the analysis script has finished.
4. Export the de-identified valid dataset, codebook, exclusion log, fieldwork-log summary, role-assignment record, analysis script, session information, complete model outputs, and figure and table source data using the file names listed in the codebook.
5. Verify that the exported files reproduce the final valid sample size, construct scores, reliability results, CFA indices, HTMT values, regression estimates, path-model estimates, indirect effects, and sensitivity-check results.
6. Create one study archive folder after all analyses have been completed. Place the de-identified valid dataset, codebook, exclusion log, fieldwork-log summary, role-assignment record, analysis script, sessionInfo() output, complete model outputs, and figure and table source data in the folder.
7. Store the locked raw dataset separately in encrypted institutional storage for 5 years after publication. Do not place locked raw export files, platform-generated identifiers, IP addresses, GPS coordinates, device identifiers, staff-identifiable field notes, or potentially identifying free-text information in the study archive folder.
8. Confirm that every variable in the de-identified dataset is defined in the codebook and that every excluded case is linked to one exclusion category in the exclusion log. Record the archive date, file version, file size, and checksum for the de-identified valid dataset in the data-management log.
9. Deposit the de-identified reproducibility package in a permanent public repository before final publication. The package should include the de-identified item-level dataset, final questionnaire, informed-consent page, screening questions, attention-check items, codebook, fieldwork-log summary, exclusion log, data-management log, role-assignment record, analysis script, session information, complete model outputs, and figure and table source data. Deposit the package in Zenodo (https://doi.org/10.5281/zenodo.21640066).
10. Supplementary File 3 and Supplementary Tables 1–7 provides the analysis script, sessionInfo() output, measurement-output workbook, model-output workbook, sensitivity-check outputs, and figure/table source-data checklist.
    PAUSE POINT: Do not share any analytical file until direct identifiers, platform-generated identifiers, location traces, staff-identifiable notes, and potentially identifying text have been removed.

Results

Participant retention and fieldwork profile

A total of 480 questionnaires were obtained during formal fieldwork at Longmen Grottoes. After eligibility screening and data-quality checks, 430 questionnaires were retained for analysis, giving a valid-response rate of 89.6%. The final valid sample exceeded the pre-specified minimum requirement of 400 valid questionnaires. Excluded cases included eligibility failures, failed attention checks, fast completion, straight-lining or nea...

Discussion

This protocol addresses a methodological problem in heritage tourism research: visitors may agree with heritage protection in principle, but their reported behavior during a visit is shaped by interpretation messages, route guidance, crowding pressure, facility support, and conservation rules at the same time. For Longmen Grottoes, the protocol therefore does not treat self-reported responsible travel behavior as a detached outcome. It places site-related perceptions, conservation attitude, conservation willingness, and ...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
car (Regression diagnostic package)	Comprehensive R Archive Network	version 3.1–5	Used to calculate variance inflation factors and support regression diagnostics.
dplyr	Comprehensive R Archive Network	version 1.2.1	Data manipulation package; Used to filter valid cases, recode variables, create exclusion codes, calculate construct scores, and summarize fieldwork and model variables.
Electronic questionnaire platform	Wenjuanxing, Changsha Ranxing Information Technology Co., Ltd., Changsha, China		Web-based platform; registered-account access; accessed April–May 2026; Used to build the electronic questionnaire, set required-response fields, insert screening and attention-check items, generate the survey QR code, record completion time, and export raw responses in .xlsx format.
Field-administration tablet	Apple Inc., Cupertino, CA, USA		iPad, 10th generation; 10.9-inch display; 64 GB; Wi-Fi model; Used when participants completed the questionnaire on a research-team device at the survey point.
ggplot2	Comprehensive R Archive Network	version 4.0.3	Figure and diagnostic-plot package; Used to prepare diagnostic plots and figure source data when graphical statistical output was required.
lavaan	Comprehensive R Archive Network	version 0.7–2	Confirmatory-factor-analysis and path-model package; Used for the nine-factor confirmatory factor analysis, model-fit indices, standardized factor loadings, path model, and bootstrap indirect effects.
lm.beta	Comprehensive R Archive Network	version 1.7–3	Standardized regression package; Used to obtain standardized coefficients from linear regression models.
Microsoft Excel	Microsoft Corporation, Redmond, WA, USA	2021	Used for daily raw-file inspection, file naming, exclusion-log preparation, codebook preparation, and manual verification of .xlsx exports.
openxlsx	Comprehensive R Archive Network	version 4.2.8.1	Spreadsheet export package; Used to export cleaned datasets, codebooks, exclusion logs, reliability tables, correlation tables, regression outputs, and sensitivity-check tables.
Participant mobile-device access	Participant-owned smartphone or tablet	NA	Used when participants completed the questionnaire by scanning the QR code or opening the survey link on their own device.
psych	Comprehensive R Archive Network	version 2.6.5	Reliability and exploratory-factor-analysis package; Used to calculate Cronbach’s alpha, corrected item-total correlations, descriptive statistics, Kaiser-Meyer-Olkin value, Bartlett’s test, exploratory factor analysis, and Harman’s single-factor screen.
R Statistical computing environment	R Foundation for Statistical Computing, Vienna, Austria	version 4.6.1	Used for reliability testing, measurement diagnostics, descriptive statistics, regression analysis, path analysis, sensitivity checks, and output export.
readxl	Comprehensive R Archive Network	version 1.5.0	Spreadsheet import package; Used to import .xlsx raw data, codebooks, and exclusion logs into R.
semTools (Structural-equation-modeling utility package)	Comprehensive R Archive Network	version 0.5–9	Used to calculate composite reliability, average variance extracted, and HTMT values from the fitted measurement model.