Tourists’ post-adoption continuance intentions of chatbots: integrating task–technology fit model and expectation–confirmation theory

IF 2.3 Q3 REGIONAL & URBAN PLANNING Foresight Pub Date : 2022-09-15 DOI:10.1108/fs-10-2021-0207
Neeraj Dhiman, M. Jamwal
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引用次数: 2

Abstract

Purpose Despite the proliferation of service chatbots in the tourism industry, the question on its continuance intentions among customers has largely remain unanswered. Building on an integrated framework using the task–technology fit theory (TTF) and the expectation–confirmation model (ECM), the present study aims to settle this debate by investigating the factors triggering customers to continue to use chatbots in a travel planning context. Design/methodology/approach The research followed a quantitative approach in which a survey of 322 chatbot users was undertaken. The model was empirically validated using the structural equation modelling approach using AMOS. Findings The results reveal that users’ expectations are confirmed when they believe that the technological characteristics of chatbots satisfy their task-related characteristics. Simply, the results reveal a significant and direct effect of TTF on customers’ confirmation and perceived usefulness towards chatbots. Moreover, perceived usefulness and confirmation were found to positively impact customers’ satisfaction towards chatbots, in which the former exerts a relatively stronger impact. Not surprisingly, customers’ satisfaction with the artificial intelligence(AI)-based chatbots emerged as a predominant predictor of their continuance use. Practical implications The findings have various practical ramifications for developers who must train chatbot algorithms on massive data to increase their accuracy and to answer more exhaustive inquiries, thereby generating a task–technology fit. It is recommended that service providers give consumers hassle-free service and precise answers to their inquiries to guarantee their satisfaction. Originality/value The present work attempted to empirically construct and evaluate the combination of the TTF model and the ECM, which is unique in the AI-based chatbots available in a tourism context. This research presents an alternate method for understanding the continuance intentions concerning AI-based service chatbots.
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游客使用聊天机器人后的延续意愿:整合任务-技术契合模型和期望-确认理论
目的:尽管服务聊天机器人在旅游业中越来越多,但客户对其继续使用意愿的问题在很大程度上仍未得到解答。基于任务-技术契合理论(TTF)和期望-确认模型(ECM)的集成框架,本研究旨在通过调查促使客户在旅行规划环境中继续使用聊天机器人的因素来解决这一争论。设计/方法/方法本研究采用定量方法,对322名聊天机器人用户进行了调查。利用AMOS结构方程建模方法对模型进行了实证验证。研究结果表明,当用户认为聊天机器人的技术特征满足了他们的任务相关特征时,他们的期望就得到了证实。简单地说,结果揭示了TTF对客户对聊天机器人的确认和感知有用性的显著和直接影响。感知有用性和确认性正向影响客户对聊天机器人的满意度,其中感知有用性的影响相对更强。毫不奇怪,客户对基于人工智能(AI)的聊天机器人的满意度成为了预测它们是否会继续使用的主要指标。这些发现对开发人员有各种各样的实际影响,他们必须在海量数据上训练聊天机器人算法,以提高其准确性,并回答更详尽的查询,从而生成适合任务技术的算法。建议服务提供商为消费者提供无忧的服务和准确的解答,以保证消费者的满意度。原创性/价值本研究试图从经验上构建和评估TTF模型和ECM的组合,这在旅游业中基于人工智能的聊天机器人中是独一无二的。本研究提出了一种理解基于人工智能的服务聊天机器人的持续意图的替代方法。
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来源期刊
Foresight
Foresight REGIONAL & URBAN PLANNING-
CiteScore
5.10
自引率
5.00%
发文量
45
期刊介绍: ■Social, political and economic science ■Sustainable development ■Horizon scanning ■Scientific and Technological Change and its implications for society and policy ■Management of Uncertainty, Complexity and Risk ■Foresight methodology, tools and techniques
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