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1 – 10 of 828Ruby Wenjiao Zhang, Xiaoning Liang and Szu-Hsin Wu
While the proliferation of chatbots allows companies to connect with their customers in a cost- and time-efficient manner, it is not deniable that they quite often fail…
Abstract
Purpose
While the proliferation of chatbots allows companies to connect with their customers in a cost- and time-efficient manner, it is not deniable that they quite often fail expectations and may even pose negative impacts on user experience. The purpose of the study is to empirically explore the negative user experience with chatbots and understand how users respond to service failure caused by chatbots.
Design/methodology/approach
This study adopts a qualitative research method and conducts thematic analysis of 23 interview transcripts.
Findings
It identifies common areas where chatbots fail user expectations and cause service failure. These include their inability to comprehend and provide information, over-enquiry of personal or sensitive information, fake humanity, poor integration with human agents, and their inability to solve complicated user queries. Negative emotions such as anger, frustration, betrayal and passive defeat were experienced by participants when they interacted with chatbots. We also reveal four coping strategies users employ following a chatbots-induced failure: expressive support seeking, active coping, acceptance and withdrawal.
Originality/value
Our study extends our current understanding of human-chatbot interactions and provides significant managerial implications. It highlights the importance for organizations to re-consider the role of their chatbots in user interactions and balance the use of human and chatbots in the service context, particularly in customer service interactions that involve resolving complex issues or handling non-routinized tasks.
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Qian Chen, Yeming Gong, Yaobin Lu and Xin (Robert) Luo
The purpose of this study is twofold: first, to identify the categories of artificial intelligence (AI) chatbot service failures in frontline, and second, to examine the effect of…
Abstract
Purpose
The purpose of this study is twofold: first, to identify the categories of artificial intelligence (AI) chatbot service failures in frontline, and second, to examine the effect of the intensity of AI emotion exhibited on the effectiveness of the chatbots’ autonomous service recovery process.
Design/methodology/approach
We adopt a mixed-methods research approach, starting with a qualitative research, the purpose of which is to identify specific categories of AI chatbot service failures. In the second stage, we conduct experiments to investigate the impact of AI chatbot service failures on consumers’ psychological perceptions, with a focus on the moderating influence of chatbot’s emotional expression. This sequential approach enabled us to incorporate both qualitative and quantitative aspects for a comprehensive research perspective.
Findings
The results suggest that, from the analysis of interview data, AI chatbot service failures mainly include four categories: failure to understand, failure to personalize, lack of competence, and lack of assurance. The results also reveal that AI chatbot service failures positively affect dehumanization and increase customers’ perceptions of service failure severity. However, AI chatbots can autonomously remedy service failures through moderate AI emotion. An interesting golden zone of AI’s emotional expression in chatbot service failures was discovered, indicating that extremely weak or strong intensity of AI’s emotional expression can be counterproductive.
Originality/value
This study contributes to the burgeoning AI literature by identifying four types of AI service failure, developing dehumanization theory in the context of smart services, and demonstrating the nonlinear effects of AI emotion. The findings also offer valuable insights for organizations that rely on AI chatbots in terms of designing chatbots that effectively address and remediate service failures.
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Nika Mozafari, Welf H. Weiger and Maik Hammerschmidt
Chatbots are increasingly prevalent in the service frontline. Due to advancements in artificial intelligence, chatbots are often indistinguishable from humans. Regarding the…
Abstract
Purpose
Chatbots are increasingly prevalent in the service frontline. Due to advancements in artificial intelligence, chatbots are often indistinguishable from humans. Regarding the question whether firms should disclose their chatbots' nonhuman identity or not, previous studies find negative consumer reactions to chatbot disclosure. By considering the role of trust and service-related context factors, this study explores how negative effects of chatbot disclosure for customer retention can be prevented.
Design/methodology/approach
This paper presents two experimental studies that examine the effect of disclosing the nonhuman identity of chatbots on customer retention. While the first study examines the effect of chatbot disclosure for different levels of service criticality, the second study considers different service outcomes. The authors employ analysis of covariance and mediation analysis to test their hypotheses.
Findings
Chatbot disclosure has a negative indirect effect on customer retention through mitigated trust for services with high criticality. In cases where a chatbot fails to handle the customer's service issue, disclosing the chatbot identity not only lacks negative impact but even elicits a positive effect on retention.
Originality/value
The authors provide evidence that customers will react differently to chatbot disclosure depending on the service frontline setting. They show that chatbot disclosure does not only have undesirable consequences as previous studies suspect but can lead to positive reactions as well. By doing so, the authors draw a more balanced picture on the consequences of chatbot disclosure.
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Shichang Liang, Rulan Li, Bin Lan, Yuxuan Chu, Min Zhang and Li Li
This study explores how chatbot gender and symbolic service recovery may improve the satisfaction of angry customers in the context of service failures. It provides a strategy for…
Abstract
Purpose
This study explores how chatbot gender and symbolic service recovery may improve the satisfaction of angry customers in the context of service failures. It provides a strategy for companies to deploy chatbots effectively in customer anger.
Design/methodology/approach
This research relies upon a systematic literature review to propose three hypotheses, and we recruit 826 participants to examine the effect of chatbot gender on angry customers through one lab study and one field study.
Findings
This research shows that female chatbots are more likely to increase the satisfaction of angry customers than male chatbots in service failure scenarios. In addition, symbolic recovery (apology vs. appreciation) moderates the effect of chatbot gender on angry customers. Specifically, male (vs. female) chatbots are more effective in increasing the satisfaction of angry customers when using the apology method, whereas female (vs. male) chatbots are more effective when using the appreciation method.
Originality/value
The rapid advancements in artificial intelligence technology have significantly enhanced the effectiveness of chatbots as virtual agents in the field of interactive marketing. Previous research has concluded that chatbots can reduce negative customer feedback following a service failure. However, these studies have primarily focused on the level of chatbot anthropomorphism and the design of conversational texts, rather than the gender of chatbots. Therefore, this study aims to bridge that gap by examining the effect of chatbot gender on customer feedback, specifically focusing on angry customers following service failures.
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Heba Salah Zaki and Bassam Samir Al-Romeedy
Artificial intelligence-based chatbots are frequently used to handle customer complaints in the hospitality and tourism sectors; however, little is known about their recovery…
Abstract
Purpose
Artificial intelligence-based chatbots are frequently used to handle customer complaints in the hospitality and tourism sectors; however, little is known about their recovery strategies. Further, the widespread usage of chatbots is anticipated to affect customers' favorable responses. Therefore, this study aims to examine how chatbots’ symbolic recovery influences customer forgiveness through customer empathy and explore the moderating effect of time pressure on it. Moreover, it investigates the effect of customer forgiveness on customer reconciliation and customer continuous trust.
Design/methodology/approach
Structural equation modeling was used to analyze data collected from 994 customers who have experienced chatbot recovery in tourism and hospitality during the past four months.
Findings
The results show that chatbots’ symbolic recovery stimulates customer forgiveness, which subsequently positively affects customer reconciliation and customer continuous trust. Moreover, customer empathy partially mediates the effect of chatbots’ symbolic recovery on customer forgiveness, and time pressure plays a moderating role in the relationship between chatbots’ symbolic recovery and customer forgiveness.
Practical implications
The results offer highly persuasive insights that may be used to promote chatbots’ symbolic recovery in tourism organizations. The effectiveness of chatbots’ symbolic recovery in achieving customer forgiveness will motivate tourism organizations to use chatbots efficiently in service recovery.
Originality/value
This study extends the theoretical scope of chatbot research by investigating the symbolic recovery capabilities of chatbots. Moreover, it expands the application of SOR theory in the context of chatbot service recovery and reveals the underlying mechanism behind the impact of chatbots’ symbolic recovery on customer forgiveness, thus building and testing an integrative model of chatbot service recovery.
研究目的
系统评估阻碍因素作为技术接受模型(TAM)的先驱方面存在一定的空白。本研究调查了三个阻碍因素, 即不适感、不安全感和风险。此外, 本研究提出了调节变量 - 个人能力(PC), 并测试其对感知有用性(PU)、感知易用性(PEU)和行为意图(BI)之间关系的影响。
研究方法
使用量化数据分析验证了通过Smart PLS4使用的调整模型。对327名有效受访者的数据进行了分析。
研究发现
不适感是影响PU和PEU的显著先驱因素。不安全感和风险分别是PEU和PU的抑制因素。本研究在稀缺文献中贡献了个人能力的调节效应, 积极调节PU和BI之间影响的研究。
研究创新
本研究通过纳入阻碍因素并探索个人能力在AR眼镜方面的调节作用, 为TAM提供了一种新的拓展。此外, 该研究还使创新公司能够通过用户的反馈来增强其产品和服务的设计。
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Yimin Zhu, Jiemin Zhang and Jifei Wu
This study aims to explore the recovery performances of chatbots (vs human employees) and help firms use chatbots to carry out effective service recovery.
Abstract
Purpose
This study aims to explore the recovery performances of chatbots (vs human employees) and help firms use chatbots to carry out effective service recovery.
Design/methodology/approach
Two experiments were conducted to test the proposed hypotheses.
Findings
The results show that compared with human employees’ recovery, chatbots’ recovery leads to lower customer satisfaction and revisit intention. This effect is more significant for symbolic recovery instead of economic recovery. Perceived distributive and interactional justice mediate the interaction effect of recovery provider and recovery strategy on recovery performance. Using immediate recovery rather than delayed recovery can attenuate chatbots’ poor performances in symbolic recovery.
Originality/value
This study enriches the chatbot research and the service recovery literature by deploying chatbots into the service recovery setting. Using an integrated theoretical model including recovery strategy and recovery timing, this study provides substantive insight into how firms can enhance chatbots’ recovery performances.
研究目的
本研究旨在探索聊天机器人(与人类员工相比)的服务补救表现, 并帮助公司使用聊天机器人进行有效的服务补救。
研究设计/方法/途径
本研究进行了两个实验来检验提出的理论假设
调查发现
结果表明, 与人类员工的服务补救相比, 聊天机器人的服务补救导致顾客满意度和再惠顾意愿降低。 这种效应对于象征补救而非功利补救更为显著。 分配公平和互动公平在服务补救提供者和补救策略的交互作用对补救表现的影响中起到了中介作用。 使用立即补救而不是延迟补救可以减轻聊天机器人象征补救方面的不良表现。
研究原创性/价值
本研究通过将聊天机器人部署到服务补救环境中丰富了聊天机器人研究和服务补救文献。 本研究通过构建包括服务补救策略和补救时机在内的综合理论模型, 为企业如何提高聊天机器人的服务补救表现提供了实质性的见解。
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Cheng Yanxia, Zhu Shijia and Xiao Yuyang
Chatbots are increasingly engaged in service marketing. Some academics and managers think using anthropomorphism chatbots will improve positive attitudes and behaviors in the…
Abstract
Purpose
Chatbots are increasingly engaged in service marketing. Some academics and managers think using anthropomorphism chatbots will improve positive attitudes and behaviors in the customer journey, but at a high degree of anthropomorphism, consumers may experience negative emotions such as fear and disgust due to the feeling that the robots resemble humans too much, which is known as the uncanny valley effect. Therefore, the authors aim to explore whether chatbot anthropomorphism will promote or limit the development of the customer journey and explore the moderating factors and the antecedent factors affecting consumers' perceptions of chatbot anthropomorphism.
Design/methodology/approach
The authors collected 72,782 unique data points from 42 articles and 82 samples using a meta-analysis. Based on the stimuli-organism-response (SOR) model, the impact of anthropomorphic chatbots on the consumer journey was discussed.
Findings
The authors’ findings show that chatbot anthropomorphism positively impacts the customer journey but not their negative attitudes. Further moderator analysis reveals that the impact depends on service result, chatbot gender and sample source. The chatbot anthropomorphism is significantly influenced by social presence cues, emotional message cues and mixed cues.
Originality/value
This research contributes to the chatbot anthropomorphism literature and offers guidance for managers on whether and how to enhance chatbot anthropomorphism to facilitate the customer journey and improve service sustainability.
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Mengmeng Song, Xinyu Xing, Yucong Duan and Jian Mou
Based on appraisal theory and social response theory, this study aims to explore the mechanism of AI failure types on consumer recovery expectation from the perspective of service…
Abstract
Purpose
Based on appraisal theory and social response theory, this study aims to explore the mechanism of AI failure types on consumer recovery expectation from the perspective of service failure assessment and validate the moderate role of anthropomorphism level.
Design/methodology/approach
Three scenario-based experiments were conducted to validate the research model. First, to test the effect of robot service failure types on customer recovery expectation; second, to further test the mediating role of perceived controllability, perceived stability and perceived severity; finally, to verify the moderating effect of anthropomorphic level.
Findings
Non-functional failures reduce consumer recovery expectation compared to functional failures; perceived controllability and perceived severity play a mediating role in the impact of service failure types on recovery expectation; the influence of service failure types on perceived controllability and perceived severity is moderated by the anthropomorphism level.
Originality/value
The findings enrich the influence mechanism and boundary conditions of service failure types, and have implications for online enterprise follow-up service recovery and improvement of anthropomorphic design.
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Jan Hendrik Blümel, Mohamed Zaki and Thomas Bohné
Customer service conversations are becoming increasingly digital and automated, leaving service encounters impersonal. The purpose of this paper is to identify how customer…
Abstract
Purpose
Customer service conversations are becoming increasingly digital and automated, leaving service encounters impersonal. The purpose of this paper is to identify how customer service agents and conversational artificial intelligence (AI) applications can provide a personal touch and improve the customer experience in customer service. The authors offer a conceptual framework delineating how text-based customer service communication should be designed to increase relational personalization.
Design/methodology/approach
This paper presents a systematic literature review on conversation styles of conversational AI and integrates the extant research to inform the development of the proposed conceptual framework. Using social information processing theory as a theoretical lens, the authors extend the concept of relational personalization for text-based customer service communication.
Findings
The conceptual framework identifies conversation styles, whose degree of expression needs to be personalized to provide a personal touch and improve the customer experience in service. The personalization of these conversation styles depends on available psychological and individual customer knowledge, contextual factors such as the interaction and service type, as well as the freedom of communication the conversational AI or customer service agent has.
Originality/value
The article is the first to conduct a systematic literature review on conversation styles of conversational AI in customer service and to conceptualize critical elements of text-based customer service communication required to provide a personal touch with conversational AI. Furthermore, the authors provide managerial implications to advance customer service conversations with three types of conversational AI applications used in collaboration with customer service agents, namely conversational analytics, conversational coaching and chatbots.
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Lu (Monroe) Meng, Jiuqi Chen, Mengya Yang and Yijie Wang
This paper aims to explore the effectiveness of customer inoculation strategies in the context of AI service failures in the hospitality and tourism industries. Furthermore, it…
Abstract
Purpose
This paper aims to explore the effectiveness of customer inoculation strategies in the context of AI service failures in the hospitality and tourism industries. Furthermore, it examines how these strategies can enhance customer complaint behavior and satisfaction with service recovery, thereby improving the overall service experience.
Design/methodology/approach
Four distinct studies were conducted: Study 1 investigated the influence of customer inoculation on complaint behavior post-AI service failure. Study 2 assessed the impact of service remedies on customer satisfaction. Study 3 explored the implications of initial purchase and usage intentions. Finally, Study 4 validated the findings using a large-scale online survey.
Findings
The results indicated that customer inoculation significantly increases customer complaint behavior and satisfaction with service remedies following AI service failures. They also showed that this relationship is mediated by psychological distance. Furthermore, customer inoculation positively affects initial purchase and usage intentions, demonstrating effectiveness at various customer engagement stages.
Practical implications
This study enriches the literature on AI hospitality service failure and recovery by introducing the novel concept of customer inoculation. Additionally, it significantly contributes to the inoculation theory literature, which covers diverse fields. Practically, this study proposes an efficient and low-cost strategy for marketers.
Originality/value
This study introduces the concept of customer inoculation in the context of AI service failures, a novel approach in the hospitality and tourism literature. It provides empirical evidence of the efficacy of the strategy, bridging a crucial gap in understanding customer behavior in the face of technological disruptions.
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