Beyond Query: Interactive User Intention Understanding

User intention understanding from scratch

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) ◽

10.1109/splim.2016.7528398 ◽

2016 ◽

Cited By ~ 1

Author(s):

Ziheng Wang ◽

Yonggang Qi ◽

Jun Liu Zhanyu Ma

Keyword(s):

User Intention ◽

Intention Understanding

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Research on trend prediction of internet user intention understanding and public intelligence mining based on fractional differential method

Chaos Solitons & Fractals ◽

10.1016/j.chaos.2019.07.034 ◽

2019 ◽

Vol 128 ◽

pp. 331-338 ◽

Cited By ~ 1

Author(s):

Shaofei Wu ◽

Qian Zhang ◽

Wenting Chen ◽

Jun Liu ◽

Lizhi Liiu

Keyword(s):

Differential Method ◽

Internet User ◽

Trend Prediction ◽

User Intention ◽

Intention Understanding ◽

Fractional Differential

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Modeling User Intention in Pervasive Service Environments

Embedded and Ubiquitous Computing – EUC 2005 - Lecture Notes in Computer Science ◽

10.1007/11596356_97 ◽

2005 ◽

pp. 977-986 ◽

Cited By ~ 5

Author(s):

Pascal Bihler ◽

Lionel Brunie ◽

Vasile-Marian Scuturici

Keyword(s):

User Intention ◽

Pervasive Service ◽

Service Environments

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Aggregating Everyday Outfits by Incremental Clustering with Interactive User Adaptation

IEEE Access ◽

10.1109/access.2021.3104973 ◽

2021 ◽

pp. 1-1

Author(s):

Yasutomo Kawanishi ◽

Hiroshi Murase ◽

Satoshi Komorita ◽

Sei Naito

Keyword(s):

Incremental Clustering ◽

User Adaptation ◽

Interactive User

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Involuntary Motion in Human-Robot Interaction: Effect of Interactive User Training on the Occurrence of Human Startle-Surprise Motion

2021 IEEE International Conference on Intelligence and Safety for Robotics (ISR) ◽

10.1109/isr50024.2021.9419526 ◽

2021 ◽

Author(s):

Robin Jeanne Kirschner ◽

Lisa Burr ◽

Melanie Porzenheim ◽

Henning Mayer ◽

Saeed Abdolshah ◽

...

Keyword(s):

Interaction Effect ◽

Human Robot Interaction ◽

Robot Interaction ◽

User Training ◽

Interactive User

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Intention to adopt mobile payment in physical stores

Journal of Enterprise Information Management ◽

10.1108/jeim-06-2019-0179 ◽

2019 ◽

Vol 33 (2) ◽

pp. 285-308 ◽

Cited By ~ 6

Author(s):

Yonathan Dri Handarkho ◽

Yulius Harjoseputro

Keyword(s):

Theoretical Model ◽

Technology Acceptance ◽

Switching Behavior ◽

Mobile Payment ◽

Herd Behavior ◽

User Intention ◽

Consumer Innovativeness ◽

Content Type ◽

Use Of Technology ◽

Acceptance Model

Purpose The purpose of this paper is to develop a theoretical model based on Push–Pull–Mooring (PPM) framework consisting of direct, indirect and moderating effects, derived from technology acceptance model, unified theory of acceptance and use of technology and other extended theory, to address the main factor influencing an individual in adopting mobile payment (MP) in physical stores. The research, therefore, utilized individual switching behavior as an underpinning to explain MP adoption in an offline context. Design/methodology/approach The theoretical model was tested by collecting data from 459 respondents in Indonesia through online self-administered questionnaires. Findings The finding indicated consumer innovativeness has the most influential direct effect on MP adoption, followed by deal proneness, perceived convenience and perceived herd behavior. Meanwhile, perceived enjoyment and subjective norms were found to have an indirect effect on the adaptation of MP through mediator convenience. Furthermore, age, gender, occupation and income did not have any moderating effect for all the direct influence of MP adoption. Originality/value Previous literature only focused on direct intention. However, this study observed the adoption of MP in a physical store by involving the switching behavior. It specifically puts concern and objective as the factors that influence user intention to switch from their old payment system to the MP system in bricks and mortar store using PPM framework.

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