Trust and digital privacy: willingness to disclose personal information to banking chatbot services

A systematic review of AI-based chatbot usages in healthcare services

PURPOSE

This systematic literature review aims to provide a comprehensive and structured synthesis of the existing knowledge about chatbots in healthcare from both a theoretical and methodological perspective.

DESIGN/METHODOLOGY/APPROACH

To this end, a systematic literature review was conducted with 89 articles selected through a SPAR-4-SLR systematic procedure. The document for this systematic review was collected from Scopus database. The VoSviewer software facilitates the analysis of keyword co-occurrence to form the fundamental structure of the subject field.

FINDINGS

In addition, this study proposes a future research agenda revolving around three main themes such as (1) telemedicine, (2) mental health and (3) medical information.

ORIGINALITY/VALUE

This study underscores the significance, implications and predictors of chatbot usage in healthcare services. It is concluded that adopting the proposed future direction and further research on chatbots in healthcare will help to refine chatbot systems to better meet the needs of patients.

Exploring chatbot trust: Antecedents and behavioural outcomes

An awareness about the antecedents and behavioural outcomes of trust in chatbots can enable service providers to design suitable marketing strategies. An online questionnaire was administered to users of four major banking chatbots (SBI Intelligent Assistant, HDFC Bank's Electronic Virtual Assistant, ICICI bank's iPal, and Axis Aha) in India. A total of 507 samples were received of which 435 were complete and subject to analysis to test the hypotheses. Based on the results, it is found that the hypothesised antecedents, except interface, design, and technology fear factors, could explain 38.6% of the variance in the banking chatbot trust. Further, in terms of behavioural outcomes chatbot trust could explain, 9.9% of the variance in customer attitude, 11.4% of the variance in behavioural intention, and 13.6% of the variance in user satisfaction. The study provides valuable insights for managers on how they can leverage chatbot trust to increase customer interaction with their brand. By proposing and testing a novel conceptual model and examining the factors that impact chatbot trust and its key outcomes, this study significantly contributes to the AI marketing literature.