Should AI-Based, conversational digital assistants employ social- or task-oriented interaction style? A task-competency and reciprocity perspective for older adults

Chattaraman, Veena; Kwon, Wi‐Suk; Gilbert, Juan E.; Ross, Kassandra

doi:10.1016/j.chb.2018.08.048

Cited by 256 publications

(250 citation statements)

References 41 publications

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“…Because language and personality are at the heart of a successful implementation of an engaging customer experience with conversational interfaces (Go & Sundar, 2019), we believe, in line with Chattaraman et al (2019), that the design of the interaction style employed by the chatbot represents a core challenge for companies as it should be consistent with customers' expectations and preferences, which differ according to customers' needs. From these premises, this study complements past literature on the interaction style of chatbots (Chattaraman et al, 2019) with extensive information on whether the interaction style employed (i.e., socialoriented versus task-oriented) increases younger consumers' social outcomes in terms of social presence and trust, affective outcomes with respect to perceived enjoyment, and behavioral intent outcomes in terms of intention to use the conversational OFD service.…”

Section: Introductionmentioning

confidence: 85%

“It’s on its way”: Chatbots applied for online food delivery services, social or task-oriented interaction style?

Cicco

Silva

Alparone

2020

Journal of Foodservice Business Research

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Research has yet to explore the spread of chatbots into the foodservice delivery sector and its impact on the customer's experience, especially in a moment when the Internet seems to be the tool to meet needs associated with being physically apart. In order to fill this gap, the present study addresses the implications that chatbots' interaction styles have for younger consumers using this channel for conversational online food delivery (OFD) services. Specifically, this study provides theoretical and practical insights into whether the conversational design of chatbots can influence social, affective, and behavioral intent outcomes. The study adopts an experimental design to investigate the effects of a social-versus task-oriented interaction style chatbot on the level of social presence and trust (social outcomes), perceived enjoyment (affective outcome), and intention to use the conversational OFD service in the future (behavioral intent outcome). Findings from a sample of 171 participants show that the interaction with the chatbot set up with a social-oriented interaction style increased users' perception of social presence and perceived enjoyment, while it did not have any direct and significant effect on trust and intention to use. The study further supports the role of social presence, trust, and perceived enjoyment as mediators between the chatbot's interaction style and the intention to use the conversational OFD service.

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Section: Introductionmentioning

confidence: 85%

“It’s on its way”: Chatbots applied for online food delivery services, social or task-oriented interaction style?

Cicco

Silva

Alparone

2020

Journal of Foodservice Business Research

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“…One phenomenon in this wave are conversational agents (CAs), defined as software which users interact with through natural language (McTear et al 2016). Equipped with increasing capabilities to assist users in a variety of tasks (Maedche et al 2016;Morana et al 2017), these agents permeate our private and professional lives (Maedche et al 2019) in different forms, including digital assistants on smartphones (Chattaraman et al 2018), chatbots on social media (Xu et al 2017) or as physically embodied service robots (Stock 2018;Stock and Merkle 2018).…”

Section: Introductionmentioning

confidence: 99%

Designing Anthropomorphic Enterprise Conversational Agents

2020

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The increasing capabilities of conversational agents (CAs) offer manifold opportunities to assist users in a variety of tasks. In an organizational context, particularly their potential to simulate a human-like interaction via natural language currently attracts attention both at the customer interface as well as for internal purposes, often in the form of chatbots. Emerging experimental studies on CAs look into the impact of anthropomorphic design elements, so-called social cues, on user perception. However, while these studies provide valuable prescriptive knowledge of selected social cues, they neglect the potential detrimental influence of the limited responsiveness of present-day conversational agents. In practice, many CAs fail to continuously provide meaningful responses in a conversation due to the open nature of natural language interaction, which negatively influences user perception and often led to CAs being discontinued in the past. Thus, designing a CA that provides a human-like interaction experience while minimizing the risks associated with limited conversational capabilities represents a substantial design problem. This study addresses the aforementioned problem by proposing and evaluating a design for a CA that offers a human-like interaction experience while mitigating negative effects due to limited responsiveness. Through the presentation of the artifact and the synthesis of prescriptive knowledge in the form of a nascent design theory for anthropomorphic enterprise CAs, this research adds to the growing knowledge base for designing humanlike assistants and supports practitioners seeking to introduce them into their organizations.

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“…facilitate learning performance. Sophisticated AI algorithms have been applied in the innovative educational products to support automated instructions as a supplement of face to face lecturing (Chattaraman, Kwon, Gilbert, & Ross, 2019;Theelen, Willems, van den Beemt, Conijn, & den Brok, 2020). Automatic scoring applications (ASA), for example, is a typical application of AI-enabled digital learning technique that automatically assigns grades to learners in an educational setting.…”

mentioning

confidence: 99%

“…Automatic scoring applications (ASA), for example, is a typical application of AI-enabled digital learning technique that automatically assigns grades to learners in an educational setting. AI-based automatic scoring applications are built on natural language processing techniques and sophisticated deep learning algorithms to understand learners' needs by simulating the pronunciation tutoring towards human beings (Chattaraman et al, 2019). Students could get immediate feedback from the AI-enabled automatic scoring applications.…”

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confidence: 99%

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The affordances of AI‐enabled automatic scoring applications on learners’ continuous learning intention: An empirical study in China

Fu¹,

Gu²,

Yang³

2020

Brit J Educational Tech

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Traditional educational giants and natural language processing companies have launched several artificial intelligence (AI)‐enabled digital learning applications to facilitate language learning. One typical application of AI in digital language education is the automatic scoring application that provides feedback on pronunciation repeat outcomes. This research is motivated by the usage of automatic scoring‐empowered digital learning tools by language learners, and set out to uncover the influencing mechanisms of AI‐enabled automatic scoring application affordances on learners’ continuous learning intention. Specifically, based on affordance theory, we found several automatic scoring application affordances through in‐depth interviews. Considering the current lack of investigations on the mechanisms underlying automatic scoring application and its implications for learners’ learning behaviors, we built a model to examine the role of automatic scoring application affordances on cognitive/emotional engagement and following continuous learning intention. We further examined the moderation role of in‐job learners and student learners on the above relationships. The model was tested using a survey of 260 Chinese foreign language learners who used AI‐empowered learning tools to facilitate their language learning practices. This study explores why learners continuously use AI‐enabled automatic scoring applications by identifying the affordances that differentiate it from traditional educational technologies. Practitioners could take the identified affordances into account when designing AI‐enabled language learning applications.

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Should AI-Based, conversational digital assistants employ social- or task-oriented interaction style? A task-competency and reciprocity perspective for older adults

Cited by 256 publications

References 41 publications

“It’s on its way”: Chatbots applied for online food delivery services, social or task-oriented interaction style?

“It’s on its way”: Chatbots applied for online food delivery services, social or task-oriented interaction style?

Designing Anthropomorphic Enterprise Conversational Agents

The affordances of AI‐enabled automatic scoring applications on learners’ continuous learning intention: An empirical study in China

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