Structural relationship between attributes of corporate social responsibility, trust, and positive emotion in the case of a food delivery application system
Purpose: The purpose of this research is to expand the explanatory power of stakeholder theory and explore the validity of its application in the area of food delivery application systems. Design/methodology/approach: The attributes of stakeholder theory are price fairness, healthiness, environmenta...
Gespeichert in:
Veröffentlicht in: | Global Business & Finance Review (GBFR) 2024, 29(3), , pp.136-148 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Purpose: The purpose of this research is to expand the explanatory power of stakeholder theory and explore the validity of its application in the area of food delivery application systems. Design/methodology/approach: The attributes of stakeholder theory are price fairness, healthiness, environmental packaging, restaurant welfare, and delivery person welfare. The explained attributes of this work are trust and positive emotion. A total of 343 samples were used for data analysis using Amazon Mechanical Turk for data collection. This work implemented structural equation model analysis to test the research hypotheses. Findings: The results revealed that price fairness and restaurant welfare positively affected trust and that delivery person welfare positively impacted positive emotion. Research limitations/implications: This study is worthwhile to ensure the explanatory power of stakeholder theory in the area of food delivery application systems. Originality/value: The study investigated stakeholder management effects on trust and positive emotions in the food delivery application systems. This research aims to analyze the influence of stakeholder management on trust and positive emotions in food delivery application systems. Additionally, it explores the sustainability of future AI platform businesses. |
---|---|
ISSN: | 2384-1648 1088-6931 2384-1648 |
DOI: | 10.17549/gbfr.2024.29.3.136 |