The Influence of AI-Based Chatbots and Their Design on Users’ Trust and Information Sharing in Online Loan Applications

Zierau, Naim; Flock, Korbinian; Janson, Andreas; Söllner, Matthias; Leimeister, Jan Marco

doi:10.24251/hicss.2021.666

Cited by 15 publications

(8 citation statements)

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“…To measure the information disclosure, we looked at the response rate to the questions asked by the chatbot using the notes from the RA. Since most research focuses on willingness to disclose [79,80] and on minimizing the risk of results falling into the online privacy paradox, the present research differentiates itself by looking at the actual disclosure of users when information disclosure nudges are present versus absent. During the experiment, the RA noted the questions that were not answered by each participant.…”

Section: Methodsmentioning

confidence: 99%

“…This creates a dilemma in research: whether to measure users' willingness to disclose information or their actual disclosures. To date, most research that has studied information disclosure in human-chatbot interactions focuses on users' willingness to disclose [79,80]. However, the online privacy paradox implies that these studies' results may be skewed, and users would in practice disclose more than they report.…”

Section: Online Privacy Paradoxmentioning

confidence: 99%

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Users’ Information Disclosure Behaviors during Interactions with Chatbots: The Effect of Information Disclosure Nudges

et al. 2022

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Drawing from the tension between a company’s desire for customer information to tailor experiences and a consumer’s need for privacy, this study aims to test the effect of two information disclosure nudges on users’ information disclosure behaviors. Whereas previous literature on user-chatbot interaction focused on encouraging and increasing users’ disclosures, this study introduces measures that make users conscious of their disclosure behaviors to low and high-sensitivity questions asked by chatbots. A within-subjects laboratory experiment entailed 19 participants interacting with chatbots, responding to pre-tested questions of varying sensitivity while being presented with different information disclosure nudges. The results suggest that question sensitivity negatively impacts users’ information disclosures to chatbots. Moreover, this study suggests that adding a sensitivity signal—presenting the level of sensitivity of the question asked by the chatbot—influences users’ information disclosure behaviors. Finally, the theoretical contributions and managerial implications of the results are discussed.

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Section: Methodsmentioning

confidence: 99%

Section: Online Privacy Paradoxmentioning

confidence: 99%

Users’ Information Disclosure Behaviors during Interactions with Chatbots: The Effect of Information Disclosure Nudges

et al. 2022

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“…Online trust is essential, and studies have differentiated between two types of online trust; conversions depend on the extent of confidence people have in the e-service provider and how easily they can relate to or are familiar with the interfaces of the website [44][45][46][47]. There are three types of trust that experts have identified when discussing e-services: patients' trust in the medical provider, general trust in the internet, and e-service trust.…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Prediction model for behavioral intention to use E-HRM through awareness in Jordanian five-star hotels

Alsheikh,

Al-Ghalabi,

Altarawneh

et al. 2024

Heritage and Sustainable Development

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Using web-based technologies in various HR procedures is the concept of Electronic Human Resource Management or E-HRM. It makes a big difference to the efficiency of the business by providing several options for getting things done quickly. Nevertheless, there has been scant investigation into this field, and its maximum potential remains untapped, despite its meteoric rise. The present research also suggests adding the Technology Acceptance Model (TAM) to the E-HRM continuity model to find out what factors affect workers' behavioral intention to use E-HRM systems, and how awareness plays a role in this. In order to help advance E-HRM as a field, this study aims to expand the theoretical bounds of existing research. 400 prospective employees were scouted from Jordanian five-star hotels as part of the convenience sampling procedure used to get the data. Starting in January 2022, the data was collected over one month. We used a Structural Equation Model (SEM) with Smart PLS on the dataset to evaluate our hypothesis. The study found that behavioral intention to use E-HRM is significantly influenced by perceived usefulness, perceived ease of use, trust, and subjective norms. The results only backed up two of the four hypotheses on the moderator effect. Hotel workers will be better able to handle high levels of stress and conduct their jobs more efficiently if the industry adopts E-HRM technology.

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“…Second, we conducted the cross-sectional survey using online panel data provided by Amazon Mechanical Turk (Section 4.2.3). Using online panel data has been shown to be suitable for studying trust-related phenomena [e.g., 34,39]. Research has demonstrated that the results of surveys using MTurk have high reliability and provide high-quality data comparable to student samples or online convenience samples [e.g., 40].…”

Section: Necessary Condition Analysis Applicationmentioning

confidence: 99%

Understanding the Necessary Conditions of Multi-Source Trust Transfer in Artificial Intelligence

Renner¹,

Lins²,

Söllner³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

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Trust transfer is a promising perspective on prevalent discussions about trust in AI-capable technologies. However, the convergence of AI with other technologies challenges existing theoretical assumptions. First, it remains unanswered whether both trust in AI and the base technology is necessary for trust transfer. Second, a nuanced view on trust sources is needed, considering the dual role of trust. To address these issues, we examine whether trust in providers and trust in technologies are necessary trust conditions. We conducted a survey with 432 participants in the context of autonomous vehicles and applied necessary condition analysis. Our results indicate that trust in AI technology and vehicle technology are necessary sources. In contrast, only vehicle providers represent a necessary source. We contribute to research by providing a novel perspective on trust in AI, applying a promising data analysis method to reveal necessary trust sources, and consider duality of trust in trust transfer.

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The Influence of AI-Based Chatbots and Their Design on Users’ Trust and Information Sharing in Online Loan Applications

Cited by 15 publications

References 0 publications

Users’ Information Disclosure Behaviors during Interactions with Chatbots: The Effect of Information Disclosure Nudges

Users’ Information Disclosure Behaviors during Interactions with Chatbots: The Effect of Information Disclosure Nudges

Prediction model for behavioral intention to use E-HRM through awareness in Jordanian five-star hotels

Understanding the Necessary Conditions of Multi-Source Trust Transfer in Artificial Intelligence

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