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1 – 10 of 352One-Ki Daniel Lee, Ramakrishna Ayyagari, Farzaneh Nasirian and Mohsen Ahmadian
The rapid growth of artificial intelligence (AI)-based voice-assistant systems (VASs) has created many opportunities for individuals to use VASs for various purposes in their…
Abstract
Purpose
The rapid growth of artificial intelligence (AI)-based voice-assistant systems (VASs) has created many opportunities for individuals to use VASs for various purposes in their daily lives. However, traditional quality success factors, such as information quality and system quality, may not be sufficient in explaining the adoption and use of AI-based VASs. This study aims to propose interaction quality as an additional, yet more important quality measure that leads to trust in an AI-based VAS and its adoption.
Design/methodology/approach
The authors propose a research model that highlights the importance of interaction quality and trust as underlying mechanisms in the adoption of AI-based VASs. Based on survey methodology and data from 221 respondents, the proposed research model is tested with a partial least squares approach.
Findings
The results suggest that interaction quality and trust are critical factors influencing the adoption of AI-based VASs. The findings also indicate that the impacts of traditional quality factors (i.e. information quality and system quality) occur through interaction quality in the context of AI-based VASs.
Originality/value
This research adds interaction quality as a new quality factor to the traditional quality factors in the information systems success model. Further, given the interactive nature of VASs, the authors use social response theory to explain the importance of the trust mechanism when individuals interact with AI-based VASs.
Contribution to Impact
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Kyung Young Lee, Lorn Sheehan, Kiljae Lee and Younghoon Chang
Based on the post-acceptance model of information system continuance (PAMISC), this study investigates the influence of the early-stage users' personal traits (specifically…
Abstract
Purpose
Based on the post-acceptance model of information system continuance (PAMISC), this study investigates the influence of the early-stage users' personal traits (specifically personal innovativeness and technology anxiety) and ex-post instrumentality perceptions (specifically price value, hedonic motivation, compatibility and perceived security) on social diffusion of smart technologies measured by the intention to recommend artificial intelligence-based voice assistant systems (AIVAS) to others.
Design/methodology/approach
Survey data from 400 US AIVAS users were collected and analyzed with Statistical Product and Service Solutions (SPSS) 18.0 and the partial least square technique using advanced analysis of composites (ADANCO) 2.1.
Findings
AIVAS technology is presently at the early stage of market penetration (about 25% of market penetration in the USA). A survey of AIVAS technology users reveals that personal innovativeness is directly and indirectly (through confirmation and continuance) associated with a stronger intention to recommend the use of the device to others. Confirmation is associated with all four ex-post instrumentality perceptions (hedonic motivation, compatibility, price value and perceived security). Among the four, however, only hedonic motivation and compatibility are significant predictors of satisfaction, which lead to use continuance and, eventually, intention to recommend. Finally, technology anxiety is found to be indirectly (but not directly) associated with a lower intention to recommend.
Originality/value
This is the first study conducted on the early-stage AIVAS users that evaluates the influence of both personal traits and ex-post instrumentality perceptions on users' intention for continuance and recommendation to others.
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This study aims to understand a customer-purchase mechanism in the artificial intelligence (AI)-powered chatbot context based on the elaboration likelihood model (ELM) and…
Abstract
Purpose
This study aims to understand a customer-purchase mechanism in the artificial intelligence (AI)-powered chatbot context based on the elaboration likelihood model (ELM) and technology acceptance model (TAM). The first objective is to examine how to boost chatbot adoption. The second objective is to investigate the role of information characteristics, technology-related characteristics and attitude toward AI in purchase intention.
Design/methodology/approach
Data was collected from a sample of 492 users in Vietnam, who are potential customers of chatbots for purchase. Structural equation modeling was applied for data analysis.
Findings
Results illustrate that chatbot adoption is significantly influenced by information credibility, technology-related factors (i.e. interactivity, relative advantage and perceived intelligence), attitude toward AI and perceived usefulness. Moreover, information quality and persuasiveness motivate information credibility. Information credibility and attitude toward AI are the essential motivations for perceived usefulness. Finally, chatbot adoption and information credibility determine purchase intention.
Practical implications
The results are insightful for practitioners to envisage the importance of chatbot use for customer purchase in the AI scenario. Additionally, this research offers a framework to practitioners for shaping customer engagement in chatbots.
Originality/value
The value of this work lies in the incorporation of technology-related characteristics into the two well-established theories, the ELM and TAM, to identify the importance of AI and its applications (i.e. chatbots) for purchase and to understand the formation of perceived usefulness and chatbot use through information credibility and attitude toward AI.
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Angelo Ranieri, Irene Di Bernardo and Cristina Mele
Service research offering a view of both the dark and bright sides of smart technology remains scarce. This paper embraces a critical perspective and examines the conflicting…
Abstract
Purpose
Service research offering a view of both the dark and bright sides of smart technology remains scarce. This paper embraces a critical perspective and examines the conflicting outcomes of smart services on the customer experience (CX), with a specific focus on chatbots.
Design/methodology/approach
This study uses empirical research methods to examine a single case study where an online retail service provider implemented a chatbot for customer service. Using discourse analysis, we analysed 7,167 conversations between customers and the chatbot over a two-year period.
Findings
The analysis identifies seven general themes related to the effects of the chatbot on CX: interaction quality, information gathering, procedure literacy, task achievement, digital trust, shopping stress and shopping journey. We illuminate both positive (i.e. having a pleasant interaction, providing information, knowing procedures, improving tasks, increasing trust, reducing stress and completing the journey) and negative outcomes (i.e. having an unpleasant interaction, increasing confusion, ignoring procedures, worsening tasks, reducing trust, increasing stress and abandoning the journey).
Originality/value
The paper develops a comprehensive framework to offer a clearer view of chatbots as smart services in customer care. It delves into the conflicting effects of chatbots on CX by examining them through relational, cognitive, affective and behavioural dimensions.
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Gunjan Malhotra and Mahesh Ramalingam
This study explores features that impact consumers' purchase intention through artificial intelligence (AI), because it is believed that through artificial intelligence…
Abstract
Purpose
This study explores features that impact consumers' purchase intention through artificial intelligence (AI), because it is believed that through artificial intelligence, consumers' intention to purchase grows significantly, especially in the retail sector, whereby retailers provide lucrative offers to motivate consumers. The study develops a theoretical framework based on media-richness theory to investigate the role of perceived anthropomorphism toward an intention to purchase products using AI.
Design/methodology/approach
The study is based on cross-sectional data through an online survey. The data have been analyzed using PLS-SEM and SPSS PROCESS macro.
Findings
The results show that consumers tend to demand anthropomorphized products to gain a better shopping experience and, therefore, demand features that attract and motivate them to purchase through artificial intelligence via mediating variables, such as perceived animacy and perceived intelligence. Moreover, trust in artificial intelligence moderates the relationship between perceived anthropomorphism and perceived animacy.
Originality/value
The study investigates and concludes with managerial and academic insights into consumer purchase intention through artificial intelligence in the retail and marketing sector.
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Christian Dietzmann, Timon Jaeggi and Rainer Alt
AI-based robo-advisory (RA) represents a FinTech application that is already replacing retail investment advisors. In private banking (PB), clients also increasingly expect…
Abstract
Purpose
AI-based robo-advisory (RA) represents a FinTech application that is already replacing retail investment advisors. In private banking (PB), clients also increasingly expect service provision across different digital channels, but with a higher degree of personalization. Hence, the present study investigates the impact of intelligent RA on the PB investment advisory process to derive both process (re)design knowledge and strategic guidance for artificial intelligence (AI) usage for PB investment advisory.
Design/methodology/approach
The present study applies an AI process impact analysis approach by decomposing AI-based RA into three AI application types: conversational agent, customer segmentation and predictive analytics. The analysis results along a reference PB investment advisory process reveal sub-process transformations which are applied for process redesign integrating AI.
Findings
The study results imply that AI systems (1) enable seamless client journeys, (2) increase advisor flexibility, (3) support the client–advisor relationship by applying an omnichannel approach and (4) demand advisor skills to be augmented with technical and statistical knowledge.
Originality/value
The research study contributes (1) an AI process impact analysis approach, (2) derives process (re)design knowledge for AI deployment and (3) develops strategic guidance for AI usage in PB investment advisory.
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Cristian Morosan and Aslihan Dursun-Cengizci
Given the rapid development in artificial intelligence (AI), the hotel industry is deploying AI-based systems. In line with this important development, this study aims to examine…
Abstract
Purpose
Given the rapid development in artificial intelligence (AI), the hotel industry is deploying AI-based systems. In line with this important development, this study aims to examine the impact of trust in the hotel and AI-related performance ambiguity on consumers’ engagement with AI-based systems. This study ultimately examined the impact of engagement on consumers’ intentions to stay in hotels offering such systems, and intentions to tip.
Design/methodology/approach
This study developed a conceptual model based on the social cognition theory. The study used an online survey methodology and collected data from a nationwide sample of 400 hotel consumers from the USA. The data analysis was conducted with structural equation modeling.
Findings
Consumers’ engagement is strongly influenced by their trust in the hotel but not by performance ambiguity associated with AI. In turn, engagement strongly influenced consumers’ intentions to stay in hotels that have such systems and their intentions to tip.
Originality/value
As AI systems capable of making decisions for consumers are becoming increasingly present in hotels, little is known about the way consumers engage with such systems and whether their engagement leads to economic impact. This is the first study that validated a model that explains intentions to stay and tip for services facilitated by autonomous AI-based systems that can make decisions for consumers.
研究目的
鉴于人工智能领域的快速发展, 酒店业正在部署基于人工智能的系统。为此, 本研究探讨了客人对酒店的信任和与AI相关的性能模糊性对消费者与基于AI的系统互动的影响。最终, 本研究考察了参与度对客人在提供此类系统的酒店住宿意愿和小费意愿的影响。
研究方法
本研究基于社会认知理论开发了一个概念模型。研究采用在线调查方法, 从美国全国范围的400名酒店消费者中收集数据, 并采用结构方程建模进行数据分析。
研究发现
消费者的参与度受酒店的信任强烈影响, 但不受与AI相关的性能模糊性的影响。反过来, 参与度强烈影响了消费者在提供此类系统的酒店住宿和给小费的意愿。
研究创新
随着能够代表消费者做出决策的人工智能(AI)系统在酒店中日益普及, 人们对消费者如何与这类系统互动以及他们的互动是否会产生经济影响知之甚少。这是第一项验证了一个可以解释在自主的基于AI系统的服务下住宿和给小费意愿的模型的研究。
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Qian Qian Chen and Hyun Jung Park
With the continuous improvement of artificial intelligence (AI) technology, intelligent personal assistants (IPAs) based on AI have seen unprecedented growth. The present study…
Abstract
Purpose
With the continuous improvement of artificial intelligence (AI) technology, intelligent personal assistants (IPAs) based on AI have seen unprecedented growth. The present study investigates the effect of anthropomorphism on cognitive and emotional trust and the role of interpersonal attraction in the relationship between anthropomorphism and trust.
Design/methodology/approach
A structural equation modeling technique with a sample of 263 consumers was used to analyze the data and test the conceptual model.
Findings
The findings illustrate that the anthropomorphism of IPAs did not directly induce trust. Anthropomorphism led users to assign greater social attraction and task attraction to IPAs, which in turn reinforced cognitive or emotional trust in these assistants. Compared with task attraction, social attraction was more powerful in strengthening both cognitive trust and emotional trust. The present study broadens the current knowledge about interpersonal attraction and its role in AI usage by examining two types of interpersonal attraction of IPAs.
Originality/value
As trust plays an important role in the rapid development of human–computer interaction, it is imperative to understand how consumers perceive these intelligent agents and build or improve trust. Prior studies focused on the impact of anthropomorphism on overall trust in AI, and its underlying mechanism was underexplored. The findings can help marketers and designers better understand how to enhance users' trust in their anthropomorphic products, especially by increasing social interactive elements or promoting communication.
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Rakibul Hasan, Park Thaichon and Scott Weaven
The main objective of this chapter is broadening the understanding of anthropomorphic artificial intelligence (AI) (e.g. avatars, humanoid robots, chatbots) in both physical and…
Abstract
The main objective of this chapter is broadening the understanding of anthropomorphic artificial intelligence (AI) (e.g. avatars, humanoid robots, chatbots) in both physical and digital environments. The chapter strives to demonstrate how organisations can curate relationship marketing and enhance customer experience by employing anthropomorphic AI. To achieve this, the chapter extends existing understanding in three ways. First, it explains the interconnectivity between relationship marketing and customer experience. Second, it presents anthropomorphic AI along with its different characteristics and technologies. Third, it offers some real-life uses cases and examples of such AI drawing from practical insights into five selected industries. Overall, the chapter provides some food of thoughts concerning the successful application and deployment of anthropomorphic AI in marketing practices.
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Patrick Bedué and Albrecht Fritzsche
Artificial intelligence (AI) fosters economic growth and opens up new directions for innovation. However, the diffusion of AI proceeds very slowly and falls behind, especially in…
Abstract
Purpose
Artificial intelligence (AI) fosters economic growth and opens up new directions for innovation. However, the diffusion of AI proceeds very slowly and falls behind, especially in comparison to other technologies. An important path leading to better adoption rates identified is trust-building. Particular requirements for trust and their relevance for AI adoption are currently insufficiently addressed.
Design/methodology/approach
To close this gap, the authors follow a qualitative approach, drawing on the extended valence framework by assessing semi-structured interviews with experts from various companies.
Findings
The authors contribute to research by finding several subcategories for the three main trust dimensions ability, integrity and benevolence, thereby revealing fundamental differences for building trust in AI compared to more traditional technologies. In particular, the authors find access to knowledge, transparency, explainability, certification, as well as self-imposed standards and guidelines to be important factors that increase overall trust in AI.
Originality/value
The results show how the valence framework needs to be elaborated to become applicable to the AI context and provide further structural orientation to better understand AI adoption intentions. This may help decision-makers to identify further requirements or strategies to increase overall trust in their AI products, creating competitive and operational advantage.
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