Survey Dataset on Academic Branding among Indonesian Lecturers (N = 165)

Published: 9 November 2026| Version 1 | DOI: 10.17632/cvpt92b5tk.1
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Description

This dataset contains survey responses from 165 lecturers at Indonesian higher education institutions. It was collected to examine how lecturers' academic branding relates to their perceived contribution to institutional reputation through digital presence, scholarly visibility and academic credibility, whether ethical and cultural negotiation moderates the link between academic branding and digital presence, and whether these relationships differ between lecturers with up to ten and more than ten years of teaching experience. Data were collected through an anonymous, self-administered online questionnaire in Indonesian between January and May 2026. Participation was voluntary and informed consent was given electronically. Of 179 responses received, 14 with missing values on the measurement items were excluded, leaving 165 complete cases. The questionnaire measured six constructs with 28 items on a six-point Likert scale (1 = strongly disagree, 6 = strongly agree): academic branding (5 items), digital presence (5), scholarly visibility (5), academic credibility (5), perceived contribution to institutional reputation (3) and ethical and cultural negotiation (5). The files comprise: (1) the respondent-level item data in XLSX format, with a codebook, the English wording of all items and an aggregate sample profile, and in CSV format; (2) SmartPLS 4 outputs for the PLS algorithm, bootstrapping, PLSpredict, measurement invariance (MICOM) and multi-group analysis by teaching tenure; and (3) figures of the estimated structural model. The released data contain no names, e-mail fields, timestamps or individual demographic variables; the respondent profile is provided only in aggregate form. The dataset supports a manuscript currently under review and can be reused to replicate the analyses or to study academic branding, scholarly visibility and institutional reputation in higher education.

Files

Steps to reproduce

1. Open 1_Data/Academic_Branding_Dataset_N165.xlsx (sheet "Data") or the CSV file. The Codebook sheet explains all variables; the Items sheet gives the item wording. 2. Import the CSV file (or the Data sheet) into SmartPLS 4. Indicators AB1–AB5, DP1–DP5, SV1–SV5, AC1–AC5, IR1–IR3 and EC1–EC5 are on a six-point scale with no missing values. 3. Specify six reflective constructs: Academic Branding (AB), Digital Presence (DP), Scholarly Visibility (SV), Academic Credibility (AC), Institutional Reputation (IR) and Ethical and Cultural Negotiation (EC). 4. Specify the structural model: AB predicts DP, SV, AC and IR; DP, SV and AC each predict IR (DP, SV and AC are parallel mediators). Add EC as a moderator of the AB → DP path using the two-stage approach. 5. Run the PLS-SEM algorithm with the path weighting scheme. Compare results with 2_SmartPLS_Output/01_PLS_Algorithm.xlsx. 6. Run bootstrapping with 5,000 subsamples (percentile confidence intervals, two-tailed, α = 0.05). Compare with 02_Bootstrapping.xlsx. 7. Run PLSpredict with 10 folds and 10 repetitions. Compare with 03_PLSpredict.xlsx. 8. For the group comparison, generate groups from TENURE_GROUP (1 = ≤ 10 years, n = 77; 2 = > 10 years, n = 88). Run MICOM with 1,000 permutations and compare with 04_MICOM_Teaching_Tenure.xlsx. Then run the bootstrap multi-group analysis (5,000 subsamples), the Welch–Satterthwaite test and the permutation test, and compare with 05_MGA_Teaching_Tenure.xlsx. 9. Because bootstrapping and permutation involve random resampling, p-values and confidence intervals may differ slightly from the reported outputs; point estimates should match.

Institutions

Categories

Management, Structural Equation Modeling, Higher Education, Human Capital

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