An Entrepreneurial Business of the Beary-x AMR Robots: Antecedents of Factors Affecting Customer Purchase Intention

This study investigates the antecedents of customer purchase intention toward the Beary-X Autonomous Mobile Robots (AMRs) developed by TESR.Specifically, it examines the impact of three key factors: electronic word-of-mouth (eWOM), product quality, and personal innovativeness.A structured quantitative survey was conducted with 427 respondents who expressed interest in robotics technologies.The data were analyzed using Multiple Linear Regression (MLR) to evaluate the influence of each independent variable on purchase intention.The results reveal that all three dimensions of eWOM quality, quantity, and credibility significantly enhance purchase intention.Among product quality dimensions, performance, aesthetics, and special features show strong positive effects, while durability reveals a surprising negative relationship.Personal innovativeness also plays a crucial role, indicating that consumers with a higher propensity to adopt new technologies are more likely to purchase AMRs.These findings contribute to the theoretical understanding of technology adoption behavior in the robotics industry and offer practical implications for marketers and product developers.Businesses can improve purchase intention by boosting eWOM strategies, refining key product attributes, and targeting innovation-oriented consumers.

Authors

Publication Details

Journal
Creative Business and Sustainability Journal
Published
2025-01-01
DOI
https://doi.org/10.58837/chula.cbsj.47.1.3
Primary Topic
Technology Adoption and User Behaviour
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An Entrepreneurial Business of the Beary-x AMR Robots: Antecedents of Factors Affecting Customer Purchase Intention

Sirion Chaipoopirutana, Anon Mosuwan
Creative Business and Sustainability Journal
Technology Adoption and User Behaviour
article

An Entrepreneurial Business of the Beary-x AMR Robots: Antecedents of Factors Affecting Customer Purchase Intention

Sirion Chaipoopirutana, Anon Mosuwan
article en

Abstract

This study investigates the antecedents of customer purchase intention toward the Beary-X Autonomous Mobile Robots (AMRs) developed by TESR.Specifically, it examines the impact of three key factors: electronic word-of-mouth (eWOM), product quality, and personal innovativeness.A structured quantitative survey was conducted with 427 respondents who expressed interest in robotics technologies.The data were analyzed using Multiple Linear Regression (MLR) to evaluate the influence of each independent variable on purchase intention.The results reveal that all three dimensions of eWOM quality, quantity, and credibility significantly enhance purchase intention.Among product quality dimensions, performance, aesthetics, and special features show strong positive effects, while durability reveals a surprising negative relationship.Personal innovativeness also plays a crucial role, indicating that consumers with a higher propensity to adopt new technologies are more likely to purchase AMRs.These findings contribute to the theoretical understanding of technology adoption behavior in the robotics industry and offer practical implications for marketers and product developers.Businesses can improve purchase intention by boosting eWOM strategies, refining key product attributes, and targeting innovation-oriented consumers.

Creative Business and Sustainability JournalVol. 47(1)
Decent work and economic growth
Openalex Percentile: Top 84%
Technology Adoption and User Behaviour
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.