The perceived usefulness of AI Voice assistants, technological attractiveness, and technological trust can all have an impact on the transactional use of AI-controlled voice assistants for service delivery.
Saeed Badghish, Faculty of Economics and Administration, King Abdulaziz University, Saudi Arabia.
Aqueeb Sohail Shaik, Assistant Professor, Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India.
Nidhi Sahore, Birla Institute of Management Technology Greater Noida, India.
Shalini Srivastava, Jaipuria Institute of Management, Noida, India.
Ayesha Masood, School of Business, Law and Social Sciences, Abertay University, Dundee, Scotland, UK.
This paper examines, through the lens of social learning theory, the possibility of transactional use of AI-controlled voice assistants for service delivery to pick up speed in the near future (SLT). In this work, we use the Partial Least Square Structural Equation Modeling (PLS-SEM), (N = 316), to test the suggested model. The SLT, which contends that learning is a social process that occurs via observation and imitation of other people’s behaviour, is the foundation of the study’s theoretical framework.
The study discovered that the perceived usefulness of AI Voice assistants, technological attractiveness, and technological trust can all have an impact on the transactional use of AI-controlled voice assistants for service delivery. According to the study’s findings, all three variables were directly related to the transactional use of AI-controlled voice assistants and were also mediated by behavioural intention. Results also indicated that increasing users’ perceptions of the technology’s usefulness and ease of use will speed up the adoption of transactional use of AI-controlled VAs for service delivery.
The study also emphasises the significance of customer churn and social resistance in influencing customers’ attitudes towards technology and willingness to adopt it. Findings also highlight the necessity for businesses to consider the elements that impact the customers’ adoption and offer insightful arguments of how the potential of AI-controlled VAs for service delivery is to accelerate in the coming future.
Published in: Technological Forecasting and Social Change
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