Experimental Data on Biodiversity Conservation and Brand Equity
Description
This dataset supports the analyses reported in the manuscript “Building Brand Equity through Biodiversity Conservation: The Role of No Net Loss in Supply Chains under Good and Bad Uncertainty.” The data were collected through a vignette-based experimental study designed to test the hypotheses proposed in the paper. It includes survey responses from participants exposed to six experimental vignettes representing different sustainability and uncertainty scenarios. The key constructs measured include marketing capability, networking capability, customer value, brand equity, and uncertainty. The dataset contains one .csv file used for statistical analysis and one accompanying Excel file detailing variable names, measurement items, and scale information. All participant data have been anonymized, and ethical approval for the study was obtained from the authors’ institutional research ethics committee. This dataset is made available for academic research purposes only. Users are requested to cite both this dataset and the associated manuscript when using the data.
Files
Steps to reproduce
Steps to Reproduce Download files: Obtain the dataset (.csv) and the measurement/codebook file (.xlsx). Inspect & prepare: Verify variable names, scales, coding, and any missing-data handling as per the manuscript. A. JASP — Exploratory Structure Check 3. EFA in JASP: Factor → Exploratory Factor Analysis. Extraction: PCA; Rotation: Varimax; enable KMO/Bartlett. Retain factors via eigenvalues > 1 and scree plot; suppress loadings < .40 (or manuscript threshold). Confirm the expected constructs: marketing capability, networking capability, customer value, brand equity, uncertainty. B. SmartPLS 4.0 — PLS-SEM (structure evaluation only) 4. Project setup: Create project; import .csv; define latent variables and assign indicators per the manuscript; add control variables where specified. 5. PLS-SEM run (structure evaluation): Specify the PLS-SEM model identical to the research model. Use path weighting scheme. Evaluate structure/measurement quality only: Reliability: Cronbach’s α, CR Indicator quality: outer loadings Convergent validity: AVE ≥ .50 Collinearity: VIF Discriminant validity: Fornell–Larcker Report R², (optionally f², Q²) for endogenous constructs. C. SmartPLS 4.0 — PROCESS (model analysis and inference) 6. PROCESS setup: Open PROCESS in SmartPLS; select the same dataset and variables. 7. Specify model: Replicate the proposed conditional process model from the manuscript (mediations via marketing capability, networking capability, customer value; moderators = good and bad uncertainty; include all control variables as specified). 8. Bootstrapping: Run PROCESS with 5,000 subsamples, two-tailed tests, bias-corrected CIs, fixed seed. 9. Report: Export and report β coefficients, 95% confidence intervals, and p-values for: Indirect (mediation) effects, and conditional indirect effects (moderated mediation) All specified control paths. D. Outputs & Consistency 10. Export: Save SmartPLS project; export measurement/quality tables (from PLS-SEM) and all PROCESS results (paths, indirect/conditional effects). 11. Verify: Ensure values match the manuscript’s tables/figures and that EFA structure is consistent with the final measurement model. Software: JASP (EFA); SmartPLS 4.0 (PLS-SEM for structure evaluation; PROCESS for hypothesis testing/conditional process)
Institutions
- TA Pai Management Institute