The interrelationship between intelligent agents’ characteristics and users’ intention in a search engine by making beliefs and perceived risks mediators

Chao, Chih-Yang; Chang, Tsai-Chu; Hui-Chun, Wu; Lin, Yong-Shun; Chen, Po-Chen

doi:10.1016/j.chb.2016.06.031

Cited by 31 publications

(25 citation statements)

References 41 publications

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“…Chao et al [21] Present an investigation of the participating agents when users search for information with search engines, especially studying the beliefs and risks that are taken in their searches Palos-Sanchez and Saura [5] Analyse the Ecosia sustainable search engine using the Unified Theory of Acceptance and Use of Technology (UTAUT) and then analysing the results with PLS-SEM (Partial Least Squares-Structural Equation Modelling) Rangaswamy et al [22] Research the different strategic perspectives of search engines from the point of view of sustainability and sustainable development Keirstead [23] Investigates the searches made with sustainable search engines and the user behaviour Liaw et al [24] Use the TAM with PLS to find what the users feel about the information found with different search engines Kamis and Stohr [25] Develop a PLS model to determine the importance of search engines when an online purchase is made, using factors such as purchase decision, trust or perceived utility, and the behaviour of different users 2 Complexity [29]. Jaca et al [30] showed the importance for businesses of considering society and users' respect for the environment.…”

Section: Authors Descriptionsmentioning

confidence: 99%

Complexity in the Acceptance of Sustainable Search Engines on the Internet: An Analysis of Unobserved Heterogeneity with FIMIX‐PLS

2018

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This paper analyses the complexity of user behaviour when facing the challenge of using sustainable applications, such as Internet search engines. This paper analyses an acceptance model using extended TAM (Technology Acceptance Model) with Trust as an added external variable. It was suggested that Trust indirectly influences the final Intention to Use with the perceptions of Utility and Ease of Use. To test the proposed model, a survey was carried out with users from different geographical areas of Spain (n=445). The second aim of this study was to understand the complexity of marketing segmentation by separating the application users into different user groups. Users were grouped by their preference of favorite Internet search engine. Unobserved heterogeneity was studied using FIMIX-PLS, and three different user behaviours with search engines were identified. These corresponded to the number of inhabitants who live in the user area. In this way, the impact that the environment has on user choice, acceptance, and use of this type of sustainable applications was shown. The results were checked using PLS-SEM and showed that the model for the adoption of sustainable search engines is explanatory and predictive because confidence and acceptance for this TAM were validated. The conclusions are interesting for developers of environmentally sustainable and responsible applications which want to coincide with current trends to ensure that users prefer them.

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Section: Authors Descriptionsmentioning

confidence: 99%

Complexity in the Acceptance of Sustainable Search Engines on the Internet: An Analysis of Unobserved Heterogeneity with FIMIX‐PLS

2018

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“…The second group is TUA-IPA. It can be described that the dependent variables also are divided into four variables consisting of (a) Attitude ( Dastan and Gürler, 2016 ; Kim and Shin, 2015 ), (b) Intention ( Chao et al., 2016 ; Davis et al., 1989 ; Kim and Shin, 2015 ; Koç et al., 2016 ; Serenko, 2005 ; Shin and Jin Park, 2017 ; Simon and Paper, 2007 ), (c) Service Value ( Hah, 2010 ), and (d) Actual Use ( Simon and Paper, 2007 ). Thus, Usefulness has a significant influence on the entire dependent variables except for the study of Smart Watches ( Kim and Shin, 2015 ) and Mobile Information ( Koç et al., 2016 ).…”

Section: Discussion and Findingsmentioning

confidence: 99%

“…According to the related literature review of this study, there are previous studies that used TAM as a base model. Hence, they could be divided into two groups, consisted of technology user acceptance, i.e., the intelligent virtual agent ( Serenko, 2005 ; Chao et al., 2016 ), smartwatch ( Kim and Shin, 2015 ), electronic payment ( Dastan and Gürler, 2016 ), and information system in a mobile phone ( Koç et al., 2016 ), and technology user acceptance of travel and tourism, i.e., includes portable electronic navigator technology ( Peres et al., 2011 ), the virtual agent in restaurant website ( Reza, 2014 ), and social media with luxury hotels ( tom Dieck et al., 2017 ). Therefore, the external variables and belief variables are favorably most studied, following as behavioral intention to use (BI), attitude toward using (attitude), and actual system use (AU), respectively.…”

Section: Literature Review and Research Hypothesesmentioning

confidence: 99%

“…(2018) ; Usoro et al. (2010) ; Phaosathianphan and Leelasantitham (2019) ✓ ✓ ✓ - Technology User Acceptance of related IPA technology Chao et al. (2016) ; Dastan and Gürler (2016) ; Davis et al.…”

Section: Literature Review and Research Hypothesesmentioning

confidence: 99%

“…This refers to the belief of an individual that the use of technology would enhance the efficiency of his/her work (Davis, 1989). As claimed by reviewing the literature, Usefulness is the most referred factor in the literatures of related user acceptance which are consisted of technology acceptance model (Davis et al, 1989), electronic payment (Dastan and Gürler, 2016), intelligent virtual agent (Serenko, 2005;Chao et al, 2016), voice recognition (Simon and Paper, 2007), artificial intelligence (Hah, 2010), smartwatch (Kim and Shin, 2015), information system in mobile phones (Koç et al, 2016), and Internet of Things (Shin and Jin Park, 2017). The studies about tourism technology acceptance included the using of information technology on tourism (Usoro et al, 2010;No and Kim, 2014;Trakulmaykee et al, 2018), portable electronic navigator technology (Peres et al, 2011), and social media with luxury hotels (tom Dieck et al, 2017).…”

Section: Antecedent Variablesmentioning

confidence: 99%

See 2 more Smart Citations

A plenary free individual traveler life cycle for assessment of adoption intelligent travel assistant

Phaosathianphan

Leelasantitham

2020

Heliyon

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This study aims to investigate antecedent variables and IT Processes being suitable factors for ultimately measuring and assessing feature values of an Intelligent Travel Assistant (ITA) related to the actual use of the Free Individual Traveler (FIT). Accordingly, the technology acceptance model (TAM) is extended with the essential factors of travel and tourism (i.e., Quality and Safety), and a plenary FIT life cycle (i.e., Pre-Trip, On-Route, On-Site, and Post-Trip). The data collection is gathered from 382 FITs in Thailand by the online questionnaire through famous social media, i.e., Facebook and Line. Hence, the collected data are analyzed statistically by PASW Statistics and SmartPLS. Therefore, the distinguished finding of this study is obtained from the useful assessment model, which is decided to usage for FITs and investment of ITA operators for travel and tourism firms.

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More trust or more risk? User acceptance of artificial intelligence virtual assistant

Xiong,

Shi,

et al. 2023

Hum Ftrs & Erg Mfg Svc

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Artificial intelligence (AI) virtual assistants are rapidly growing, permeating people's daily lives and work. However, some trust and risk issues prevent the acceptance and use of AI virtual assistants by users. Thus, understanding the roles of trust and perceived risk in user acceptance of AI virtual assistants is crucial. This study develops a comprehensive research model based on unified theory of acceptance and use of technology (UTAUT) to explain user acceptance of AI virtual assistants. This model extends UTAUT by adding users' perception of trust and risk. The research model and hypotheses are validated through structural equation modeling with a sample of 926 AI virtual assistant users. Results show that gender is significantly related to behavioral intention to use, education is positively related to trust and behavioral intention to use, and usage experience is positively related to attitude toward using. UTAUT variables, including performance expectancy, effort expectancy, social influence, and facilitating conditions, are positively related to behavioral intention to use AI virtual assistant. Trust and perceived risk respectively have positive and negative effects on attitude toward using and behavioral intention to use AI virtual assistants. Trust and perceived risk play equally important roles in explaining user acceptance of AI virtual assistants. Theoretical and practical implications of the current AI virtual assistant acceptance model and directions for future research are discussed.

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The interrelationship between intelligent agents’ characteristics and users’ intention in a search engine by making beliefs and perceived risks mediators

Cited by 31 publications

References 41 publications

Complexity in the Acceptance of Sustainable Search Engines on the Internet: An Analysis of Unobserved Heterogeneity with FIMIX‐PLS

Complexity in the Acceptance of Sustainable Search Engines on the Internet: An Analysis of Unobserved Heterogeneity with FIMIX‐PLS

A plenary free individual traveler life cycle for assessment of adoption intelligent travel assistant

More trust or more risk? User acceptance of artificial intelligence virtual assistant

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