Damayanti, Tissa Aulia
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How Social Media and e-WOM Build Perlang Tourist Village’s Brand Damayanti, Tissa Aulia; Pusparini, Marissa; Barasa, Robert Tua
Jurnal Ilmu Manajemen Advantage Vol. 9 No. 2 (2025): Desember 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/adv.v9i2.1713

Abstract

This study examines the impact of social media quality and credibility on enhancing destination brand awareness through electronic word-of-mouth (e-WOM), utilizing Perlang Tourist Village in Bangka Belitung as a case study. Despite its national recognition, Perlang faces challenges in digital branding and infrastructure, necessitating strategic social media engagement to address these issues. Employing a quantitative explanatory design, data were collected from 307 respondents via purposive sampling and analyzed using PLS-SEM. The results show that both social media quality and credibility significantly influence destination brand awareness, with e-WOM serving as a key mediating variable. All seven hypotheses were supported, confirming the conceptual framework. The findings suggest that high-quality and credible content fosters trust, encourages user advocacy, and strengthens brand recognition. This research contributes to the theoretical understanding of digital branding in community-based tourism and offers practical implications for destination marketers seeking to optimize social media strategies.
ARTIFICIAL INTELLIGENCE INTEGRATION IN TALENT MANAGEMENT AND HUMAN RESOURCE DEVELOPMENT: A MULTI-CASE QUALITATIVE STUDY IN INDONESIA’S BANKING INDUSTRY Barasa, Robert Tua; Damayanti, Tissa Aulia; Pusparini, Marissa
Journal of Economic, Bussines and Accounting (COSTING) Vol. 8 No. 6 (2025): COSTING : Journal of Economic, Bussines and Accounting
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/27mz5703

Abstract

This study aims to explore how Artificial Intelligence (AI) is integrated into talent management and human resource development (HRD) within Indonesia’s banking sector, covering both conventional and digital banks. This phenomenon is increasingly relevant due to the acceleration of digital transformation, intensifying competition in the financial industry, and the growing demand for adaptive HR governance. A qualitative multi-case study design was employed. Data were collected through in-depth interviews with HR managers, HR staff, and digital transformation project leaders in five banks (three major conventional banks and two digital banks), complemented by participant observation and organizational document analysis. Purposive sampling was applied with criteria of at least two years of experience in HR or digital transformation, later expanded through snowball sampling until data saturation was achieved. Data analysis followed a thematic approach, strengthened by triangulation of methods and sources to enhance validity. The findings reveal that AI significantly accelerates recruitment processes, enables data-driven career pathing, and provides measurable feedback for competency development. Digital banks demonstrate greater adaptability in adopting AI due to their agile culture and technological readiness, whereas conventional banks face obstacles related to infrastructure limitations, bureaucracy, and employee resistance. Beyond opportunities, the study also identifies risks of algorithmic bias and ethical concerns requiring fairness, explainability, and human oversight. Theoretically, this study contributes by extending the Technology-Organization-Environment (TOE) framework and STAR-HRIS Society 5.0, while incorporating Indonesia’s cultural context into the understanding of AI adoption. Practically, the findings provide strategic guidance for banks especially HR managers and regulators in designing inclusive, ethical, and sustainable HRD strategies. Future research is recommended to assess the long-term dynamics of AI adoption and its impact on employee engagement.