Factors Influencing Intention to Adopt AI Chatbot in Civil Status and Passport Department in Jordan: The Moderating Role of Trust in Technology

This study investigates the determinants of chatbot adoption intention in Jordan's civil status and passport department, with a particular focus on technology trust as a moderating variable. Drawing on the Technology Acceptance Model (TAM), we developed and tested hypotheses regarding chatbot adoption factors. The research employed a quantitative approach, collecting data from 687 valid respondents out of 743 distributed questionnaires. Analysis using Smart-PLS software revealed that most examined factors positively influenced adoption intentions, with one notable exception: ease of use showed no significant impact on user attitudes. The findings confirmed that positive attitudes toward chatbots significantly enhanced adoption intentions, while trust in technology strengthened this attitude-intention relationship. This study extends existing theoretical frameworks by providing empirical evidence from a Middle Eastern context, specifically in Jordan's public sector, offering valuable insights for both researchers and practitioners implementing chatbot solutions in public services. The results particularly emphasize how trust in technology shapes user acceptance in the Jordanian context, contributing new perspectives to the current understanding of chatbot adoption dynamics.

Authors

Institutions

Publication Details

Journal
Journal of Posthumanism
Published
2025-04-16
DOI
https://doi.org/10.63332/joph.v5i3.938
Primary Topic
Organizational and Employee Performance
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Factors Influencing Intention to Adopt AI Chatbot in Civil Status and Passport Department in Jordan: The Moderating Role of Trust in Technology

Amer A. N. Al-Qutaish, Ethar Khalaf Ibrahim ALHumeisat, Hussein N. E. Edrees, Abu Elnasr E. Sobaih et al.
Journal of Posthumanism
Organizational and Employee Performance
article

Factors Influencing Intention to Adopt AI Chatbot in Civil Status and Passport Department in Jordan: The Moderating Role of Trust in Technology

Amer A. N. Al-Qutaish, Ethar Khalaf Ibrahim ALHumeisat, Hussein N. E. Edrees, Abu Elnasr E. Sobaih, Nadir Aliane, Mohammad Mousa Mousa, Mustafa Akaileh, Najwan Ibrahim Jadallah
article en

Abstract

This study investigates the determinants of chatbot adoption intention in Jordan's civil status and passport department, with a particular focus on technology trust as a moderating variable. Drawing on the Technology Acceptance Model (TAM), we developed and tested hypotheses regarding chatbot adoption factors. The research employed a quantitative approach, collecting data from 687 valid respondents out of 743 distributed questionnaires. Analysis using Smart-PLS software revealed that most examined factors positively influenced adoption intentions, with one notable exception: ease of use showed no significant impact on user attitudes. The findings confirmed that positive attitudes toward chatbots significantly enhanced adoption intentions, while trust in technology strengthened this attitude-intention relationship. This study extends existing theoretical frameworks by providing empirical evidence from a Middle Eastern context, specifically in Jordan's public sector, offering valuable insights for both researchers and practitioners implementing chatbot solutions in public services. The results particularly emphasize how trust in technology shapes user acceptance in the Jordanian context, contributing new perspectives to the current understanding of chatbot adoption dynamics.

Journal of PosthumanismVol. 5(3)
Al-Hussein Bin Talal University (JO), Palestine Technical College (PS), Palestine Technical University - Kadoorie (PS), Al-Balqa Applied University (JO), University of Business and Technology (SA), King Faisal University (SA), Tunis El Manar University (TN)
King Faisal University
Openalex Percentile: Top 97%
Organizational and Employee Performance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.