DOI: 10.5281/zenodo.16732287
title: Dataset of Extracted Effect Sizes for Meta-Analysis: From Brochures to Bytes – Rewiring Destination Branding through Social, Mobile, and AI

description: This dataset supports the systematic review and meta-analysis titled "From Brochures to Bytes: Rewiring Destination Branding through Social, Mobile and AI—A Systematic Review with Narrative Synthesis and Meta-Analysis" which investigates how digital transformation, particularly social media, mobile technology, and artificial intelligence, impacts destination-branding outcomes. The dataset contains detailed effect sizes extracted from 60 studies included in the meta-analysis.

The main variables in the dataset include:





Study ID: Unique identifiers for each of the 60 studies included.




Sample sizes (N): The number of participants before and after digital interventions.




Group Means and Standard Deviations: Raw scores used to calculate the effect sizes (Cohen's d).




Cohen’s d and Variance: Standardized mean differences indicating effect magnitude and their variance for meta-analytic pooling.




Moderator Variables: Categorical moderators including Outcome_Category, Platform_Type, Content_Strategy, Influencer_Tier, Interactivity_Level, Destination_Type, facilitating subgroup analyses.



The dataset is structured to enable replication and transparency in meta-analytic procedures, moderator testing, and further research exploring the evolution of destination-branding effectiveness in digital contexts.
description: Data Dictionary Tab

The following data dictionary defines every field in the dataset in attached file and the coding scheme used. It contains a list of all column names (from the Effect Sizes data tab and any other tabs) alongside detailed descriptions:





Study ID: Explanation of the format (e.g., numeric ID matching reference list) and notes that it corresponds to the primary reference for that study in the manuscript’s reference list. 




N / Sample Size Columns: Clarification of N_Treatment, N_Control, etc., and how total N is derived. E.g., “N_Before = sample size before intervention (if pre-post design); N_After = sample after intervention. If a control-experimental design, N_Before represents control group size, N_After experimental group size.”




Mean/SD Columns: Description of what “Before” and “After” mean in context (before vs after a campaign, or control vs treatment group mean on the outcome measure). It also notes units or scale ranges if relevant (e.g., “Brand attitude measured on 5-point Likert, higher = more positive attitude”).




Pearson’s r: Defined as the effect size for correlational findings; notes that it’s the Pearson correlation between digital exposure/engagement and branding outcome. Also indicates if any r was derived from other stats (with reference to transformation formulas).




Fisher’s z: Explains that this is the Fisher-transformed correlation used for meta-analysis, and gives the formula z=0.5ln⁡1+r1−rz = 0.5 \ln\frac{1+r}{1-r}z=0.5ln1−r1+r. It references that variance is 1/(N−3)1/(N-3)1/(N−3) for each z.




Cohen’s d / Hedges’ g: Defines Cohen’s d as the standardized difference in means. Explains that Hedges’ g is a small sample bias-corrected d. It notes if any specific formula or adjustment was used (e.g., Hedges’ correction J=1−34df−1J = 1 - \frac{3}{4df-1}J=1−4df−13 applied to d). This dictionary also states that positive g indicates a favorable effect of the digital medium (consistent directionality across studies).




Variance columns (Var_z, Var_g): Describes how these were calculated for each effect. For Var_z, simply 1/(N−3)1/(N-3)1/(N−3). For Var_g, provides the general approach (based on standard error of d formula) with a citation to Borenstein et al. (2009) or Viechtbauer (2010) for further reading.




Outcome Category: Lists the five categories (Awareness, Image, Attitudes, Loyalty, Engagement) and what each entails (e.g., “Engagement Intentions – measures of a tourist’s intent to engage with the destination online or via revisit intention, as defined in individual studies”). 




Platform Type: Documentation of code values (e.g., 1=Facebook, 2=Instagram, 3=Twitter(X), 4=TikTok/YouTube, 5=TripAdvisor/Other). TikTok and YouTube were combined due to both being highly visual platforms, and that a study focusing on TripAdvisor UGC was categorized as “Other” (text value provided).




Content Strategy: States that “UGC” = user-generated content (e.g., tourist posts, reviews), “DMO” = official destination marketing content, “Mixed” = combination. Perhaps coded as U, D, M or 0/1 dummy variables for UGC vs DMO. 




Influencer Tier: Defines Micro, Mid, Macro as per follower thresholds given (with examples if available). E.g., “Micro: typically <50,000 followers on the platform (small, niche influencers); Mid: 50K–500K; Macro/Mega: >500K followers.” It notes that if a study did not involve influencers, this field is not used.




Interactivity Level: Explains the binary coding (High vs Low). “High” indicates interactive or two-way communication elements (comments, user participation, etc.), whereas “Low” indicates a broadcast style. Cites the finding that high interactivity more than doubled the effect size in our results.




Destination Type: Details the categories: “Emerging/Lesser-known” (small or new destinations with low prior awareness), “Well-known City/Flagship” (major tourist cities or flagship destinations), “Nation-branding” (country-level campaigns). Also indicates any assumptions (for instance, if a study did not specify, we inferred based on description).



In summary, the above data dictionary serves as a codebook ensuring anyone using the dataset can interpret each column correctly, reproduce the computations, and understand how subgroup labels correspond to the narrative synthesis. This clarity is important for open science compliance, allowing external researchers to reuse the dataset or verify our results with ease.