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This study aims to use a survey to examine the factors encouraging Malaysian government agencies (MGAs) to adopt chatbots, which are a new artificial intelligence technology.

The minimum sample size was 98 according to the complexity model, and the respondents were top MGA management personnel. The questionnaire was emailed to all MGAs, and data were collected using snowball sampling in the technology–organization–environment (TOE) framework. Out of 348 top management personnel contacted, 341 agreed to participate in the study.

Technology readiness (TR), organizational readiness (OR) and citizen demand were all positively related to chatbot use intention. Government support (GS) strengthened the relationship between OR, TR and chatbot adoption intentions. The findings demonstrated the ability of the TOE framework to ascertain the factors influencing MGA chatbot adoption.

This study was limited to Malaysian federal government agencies.

The findings provide useful information for a better understanding of factors influencing the adoption of chatbots among Malaysian government agencies. Related parties, such as the head of department, could use these findings to plan further action to empower counter service by integrating chatbot adoption.

The study revealed the capability of the TOE framework to identify the factors influencing the decision to adopt chatbots among Malaysian government agencies. GS was found to have moderation effect on the relationship between OR and TR toward the intention to adopt a chatbot.

This paper aims to investigate the motivating factors for Malaysian governmental agencies (MGAs) to embrace chatbot technology.

Based on the technology-organisation-environment (TOE) framework, using purposive and snowball sampling techniques, 262 online data from the MGA top management were gathered. Smart PLS4 was employed to test the hypotheses of the study.

The findings demonstrated positive relationships between technological readiness (TR), big data analytics (BDA), organisational readiness (OR), organisational learning capabilities (OLC) and governmental policies (GP) concerning chatbot adoption intention and also the relationship GP with OR. A mediating effect was also observed, which indicated the OLC role in positively mediating BDA, the OR role in positively mediating OLC and the OR role in positively mediating GP with OR and OLC as sequential mediators in the relationship between BDA and chatbot adoption intention. Furthermore, the presence of citizen demand (CD) strengthened the relationship between TR, OR and chatbot adoption intention.

This study was limited to Malaysian federal government agencies who still not adopting Chatbots.

The findings offer valuable insight into factors affect the adoption of chatbots among Malaysian government agencies. Stakeholders, including department heads, can use these findings to strategically enhance counter service by promoting chatbot adoption.

The study demonstrated that the TOE framework was effective in identifying the factors contributing to the decision-making process for adopting chatbots across MGAs. Organisational readiness and organisation learning capability was found to sequentially mediate the relationship between big data analytic and intention to adopt chatbot. Citizen demand was found to have moderation effect on the relationship between organisational readiness and technological readiness towards the intention to adopt a chatbot.