Exploring Age Differences in Motivations for and Acceptance of Chatbot Communication in a Customer Service Context

Goot, M.J. van der; Pilgrim, Tyler

doi:10.1007/978-3-030-39540-7_12

Cited by 32 publications

(14 citation statements)

References 16 publications

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“…For computer self-efficacy, Lopez et al [45] found a strong negative correlation between people's ages and their self-efficacy with information communication technology 15 . Similarly, van der Groot & Pilgrim, reported that their participants' age influenced their Motivations to interact with technology [77].…”

Section: Results: Cognitive Styles Meet Gender Demographicsmentioning

confidence: 98%

Human-AI Interaction for Diverse Humans: What Cognitive Style Disaggregation Reveals

Anderson¹,

Li²,

Vorvoreanu³

et al. 2021

Preprint

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AI) is becoming more pervasive through all levels of society, trying to help us be more productive. Research like Amershi et al.'s 18 guidelines for human-AI interaction aim to provide high-level design advice, yet little remains known about how people react to Applications or Violations of the guidelines. This leaves a gap for designers of human-AI systems applying such guidelines, where AI-powered systems might be working better for certain sets of users than for others, inadvertently introducing inclusiveness issues. To address this, we performed a secondary analysis of 1,016 participants across 16 experiments, disaggregating their data by their 5 cognitive problem-solving styles from the Gender-inclusiveness Magnifier (GenderMag) method and illustrate different situations that participants found themselves in. We found that across all 5 cogniive style spectra, although there were instances where applying the guidelines closed inclusiveness issues, there were also stubborn inclusiveness issues and inadvertent introductions of inclusiveness issues. Lastly, we found that participants' cognitive styles not only clustered by their gender, but they also clustered across different age groups.CCS Concepts: • Human-centered computing → User studies; • Computing methodologies → Intelligent agents.

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Section: Results: Cognitive Styles Meet Gender Demographicsmentioning

confidence: 98%

Human-AI Interaction for Diverse Humans: What Cognitive Style Disaggregation Reveals

Anderson¹,

Li²,

Vorvoreanu³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…However, several research studies utilized mixed research methods, such as experimental investigations and questionnaires (e.g. Refs [27,28]) or a combination of interviews and experimental methods [29].…”

Section: Methodsmentioning

confidence: 99%

“…Remarkably, a significant number of researchers have focused on the intention to adopt and use chatbots by investigating factors which affect users in specific research areas, such as health [13], financial services [14][15][16], tourism [5,[17][18][19][20], customer service (e.g. Refs [1,[21][22][23][24][25][26][27][28][29][30][31][32][33][34]), mobile commerce [35][36][37], business [38,39], insurance [12,40] and education [41,42].…”

Section: Users' Intention Toward Chatbots' Adoptionmentioning

confidence: 99%

“…However, TAM was widely applied by being combined with other theories. Particularly, in five studies combined theories have been used (TAM and DOI; TAM and ECM and ISS; TAM & SST; TAM, DOI and TOE) [1,29,35,37,40], whereas in seven (18%) research papers information system (IS) continuance models such as SOR, U&G, TPB, CAT, TRA, SERVQUAL, extended post acceptance model were applied. Cheng et al [52] used SOR theory to explain the behavior of consumers toward chatbots in the context of e-commerce.…”

Section: Behavioral Theoriesmentioning

confidence: 99%

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A literature review on users' behavioral intention toward chatbots' adoption

Gatzioufa

Saprikis

2022

ACI

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PurposeDespite the fact that chatbots have been largely adopted for the last few years, a comprehensive literature review research focusing on the intention of individuals to adopt chatbots is rather scarce. In this respect, the present paper attempts a literature review investigation of empirical studies focused on the specific issue in nine scientific databases during 2017-2021. Specifically, it aims to classify extant empirical studies which focus on the context of individuals' adoption intention toward chatbots.Design/methodology/approachThe research is based on PRISMA methodology, which revealed a total of 39 empirical studies examining users' intention to adopt and utilize chatbots.FindingsAfter a thorough investigation, distinct categorization criteria emerged, such as research field, applied theoretical models, research types, methods and statistical measures, factors affecting intention to adopt and further use chatbots, the countries/continents where these surveys took place as well as relevant research citations and year of publication. In addition, the paper highlights research gaps in the examined issue and proposes future research directions in such a promising information technology solution.Originality/valueAs far as the authors are concerned, there has not been any other comprehensive literature review research to focus on examining previous empirical studies of users' intentions to adopt and use chatbots on the aforementioned period. According to the authors' knowledge, the present paper is the first attempt in the field which demonstrates broad literature review data of relevant empirical studies.

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“…For example, in 2019, over 50 % of US and German consumers were estimated to have used chatbots at least once-with even higher numbers in the UK or France [88]. In consequence, chatbot researchers currently have an unprecedented opportunity for real-world study of users [106], user motivations [14], and implications at scale. In consequence, knowledge on chatbot use has been gathered for a range of contexts-in the private sphere [87], at work [74], and in public spaces [17].…”

Section: State Of the Artmentioning

confidence: 99%

Future directions for chatbot research: an interdisciplinary research agenda

et al. 2021

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Chatbots are increasingly becoming important gateways to digital services and information—taken up within domains such as customer service, health, education, and work support. However, there is only limited knowledge concerning the impact of chatbots at the individual, group, and societal level. Furthermore, a number of challenges remain to be resolved before the potential of chatbots can be fully realized. In response, chatbots have emerged as a substantial research area in recent years. To help advance knowledge in this emerging research area, we propose a research agenda in the form of future directions and challenges to be addressed by chatbot research. This proposal consolidates years of discussions at the CONVERSATIONS workshop series on chatbot research. Following a deliberative research analysis process among the workshop participants, we explore future directions within six topics of interest: (a) users and implications, (b) user experience and design, (c) frameworks and platforms, (d) chatbots for collaboration, (e) democratizing chatbots, and (f) ethics and privacy. For each of these topics, we provide a brief overview of the state of the art, discuss key research challenges, and suggest promising directions for future research. The six topics are detailed with a 5-year perspective in mind and are to be considered items of an interdisciplinary research agenda produced collaboratively by avid researchers in the field.

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Exploring Age Differences in Motivations for and Acceptance of Chatbot Communication in a Customer Service Context

Cited by 32 publications

References 16 publications

Human-AI Interaction for Diverse Humans: What Cognitive Style Disaggregation Reveals

Human-AI Interaction for Diverse Humans: What Cognitive Style Disaggregation Reveals

A literature review on users' behavioral intention toward chatbots' adoption

Future directions for chatbot research: an interdisciplinary research agenda

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