在线食品送货上门的千禧年体验:来自印度尼西亚的经验

D. Suhartanto, D. Dean, G. Leo, Ni Nyoman Triyuni
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引用次数: 37

摘要

目的/目的:研究千禧一代对在线外卖服务的满意度,包括电子服务质量、食品质量和感知价值作为决定因素和行为意愿作为结果。背景:在世代群体中,千禧一代是许多零售商(包括餐馆)要求很高的目标群体。尽管有许多研究调查了千禧一代在餐馆环境下的行为,但几乎没有研究发现千禧一代在在线食品送货上门服务环境下的态度和行为。方法:在这项研究中,332名千禧一代在印度尼西亚完成了一项自我管理的调查。为了评估满意度及其决定因素和后果之间的关系,本研究采用偏最小二乘模型。贡献:本研究通过强调食品质量、电子服务质量和感知价值是千禧一代在线食品购买满意度的主要决定因素,扩展了千禧一代对在线食品配送服务满意度的现有知识。此外,本研究从千禧一代的视角为网络外卖服务的溢出理论提供了支持。研究结果:本研究揭示了电子服务质量和食品质量对千禧一代在线外卖服务满意度的重要直接双重影响。此外,本研究指出,电子服务和食品质量也通过感知价值间接影响满意度。此外,满意的千禧一代客户更有可能再次购买,向他人推荐,并以更高的价格再次购买。对从业者的建议:对于中小型餐厅,建议他们只专注于提供食物这一核心业务。如果他们想提供电子服务,他们应该与一个或多个在线服务提供商发展战略合作。对研究人员的推荐:千禧一代倾向于重复购买,推荐,并愿意在未来支付更多的钱,扩展了现有的模型,研究质量,满意度和行为意图之间的关系。因此,在网上餐饮采购服务中,电子服务质量和食品质量都应该纳入未来的研究模型。对社会的影响:这项研究可以帮助餐饮业提高他们的经营绩效,并间接地通过提供高质量的食物,就业机会和税收对整个社会产生影响。未来的研究:未来的研究人员可以在不同的国家和/或与其他世代的队列重新评估模型,以及包括其他变量,如信任,形象,参与,以及社会人口因素。
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Millennial Experience with Online Food Home Delivery: A Lesson from Indonesia
Aim/Purpose: To examine millennial satisfaction towards online food delivery services, including e-service quality, food quality, and perceived value as the determinants and behavioral intention as the consequence. Background: Among the generational cohorts, millennials are a demanding target group for many retailers, including restaurants. Despite many studies examining millennial behavior in the restaurant context, almost no research on millennial attitudes and behavior in the context of online food home delivery service can be found. Methodology: For this research, 332 millennials completed a self-administered survey in Indonesia. To assess the associations between satisfaction and its determinants and consequences, this study employs Partial Least Square modeling. Contribution: This research extends existing knowledge of millennial satisfaction toward online food delivery service by highlighting that food quality, e-service quality and perceived value are the main determinants of satisfaction for online food purchasing among millennials. Further, this study offers support for the spillover theory in the online food home delivery service from millennial perspective. Findings: This study uncovers the important direct dual influences of e-service quality and food quality on millennial satisfaction with online food delivery services. Further, this study notes that e-service and food quality also have an indirect influence on satisfaction via perceived value. Moreover, satisfied millennial customers are more likely to re-purchase, recommend to others, and re-purchase at an increased price. Recommendations for Practitioners: For small and medium restaurants, it is suggested that they need to focus solely on their core business of providing food. If they want to offer an e-service, they should develop strategic cooperation with one or more online service providers. Recommendation for Researchers: Millennials tend to repurchase, recommend, and be willing to pay more in the future extends the existing models that look at the associations among quality, satisfaction and behavioral intention. Thus, in online restaurant purchasing services, both e-service quality and food quality should be included in the future research models. Impact on Society: This study could help restaurant industries to increase their business performance and, indirectly, impact on society as a whole by providing high quality food, employment opportunities, and tax revenues. Future Research: Future researchers can reassess the model in different countries and/or with other generation cohorts as well as including other variables such as trust, image, involvement, as well as socio-demographic factors.
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来源期刊
CiteScore
2.30
自引率
0.00%
发文量
14
期刊最新文献
IJIKM Volume 18, 2023 – Table of Contents Factors Affecting Individuals’ Behavioral Intention to Use Online Capital Market Investment Platforms in Indonesia Investigating the Adoption of Social Commerce: A Case Study of SMEs in Jordan The Influence of Big Data Management on Organizational Performance in Organizations: The Role of Electronic Records Management System Potentiality Customer Churn Prediction in the Banking Sector Using Machine Learning-Based Classification Models
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