The Effects of Parasocial Relationships in the Adoption of Mobile Commerce Application: A Conceptual Model

Xin, Deqiang; Jian-qi, Mao; Liu, Min

doi:10.1109/icee.2010.45

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…As discussed in [37], the results derived "through the mediating role of 'parasocial' interaction" [37] show that when chatbots adopt an informal language style customers' reactions are positive with increased use and a positive brand awareness. Parasocial interaction (PSI) refers to a psychological relationship experienced by an audience in their mediated encounters with performers in the mass media and on-line platforms [37][38][39][40][41].…”

Section: Large Language Models and Chatbotsmentioning

confidence: 99%

A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

Tuan,

Moore,

Thanh

et al. 2024

Applied Sciences

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ChatGPT plays significant roles in the third decade of the 21st Century. Smart cities applications can be integrated with ChatGPT in various fields. This research proposes an approach for developing large language models using generative artificial intelligence models suitable for small- and medium-sized enterprises with limited hardware resources. There are many generative AI systems in operation and in development. However, the technological, human, and financial resources required to develop generative AI systems are impractical for small- and medium-sized enterprises. In this study, we present a proposed approach to reduce training time and computational cost that is designed to automate question–response interactions for specific domains in smart cities. The proposed model utilises the BLOOM approach as its backbone for using generative AI to maximum the effectiveness of small- and medium-sized enterprises. We have conducted a set of experiments on several datasets associated with specific domains to validate the effectiveness of the proposed model. Experiments using datasets for the English and Vietnamese languages have been combined with model training using low-rank adaptation to reduce training time and computational cost. In comparative experimental testing, the proposed model outperformed the `Phoenix’ multilingual chatbot model by achieving a 92% performance compared to `ChatGPT’ for the English benchmark.

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Section: Large Language Models and Chatbotsmentioning

confidence: 99%

A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

Tuan,

Moore,

Thanh

et al. 2024

Applied Sciences

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“…Mcommerce is a wireless network e-commerce function, particularly the internet [12]. M-commerce is a subclass of electronic commerce related to online transactions carried out on smartphones [13]. Buyers will be able to conduct online purchases using smartphones and receive an unlimited number of personalized services at any time and from any place because of m-commerce [14].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid SEM and Neural Network Approach to Understand and Predict the Determinants of Consumers’ Acceptance and Usage of Mobile-Commerce Application

Saleh

Enaizan²,

Eneizan³

et al. 2022

Int. J. Interact. Mob. Technol.

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The growth of mobile commerce marketplaces worldwide has been boosted by modern advances in digital technology. However, Privacy and security are still concern in m-commerce application. Since the previous study has researched the link between security and privacy and purpose to use, the factors that influence the formation of privacy and security in m-commerce are mostly unidentified. On the basis of UTAUT2, this study investigates the factors of security and privacy effecting mobile commerce acceptance. A hybrid SEM-ANN method was utilized to identify non-linear and compensatory interactions. Compensatory and Linear models are based on the idea that a deficiency in one component might also be compensated for by other variables. Linear and Non-compensatory models, on the other hand, seem to overcomplicate buyer decision mechanisms. Survey criteria have been conducted to obtain 890 mobile commerce consumer’s datasets utilizing an application on m-commerce. The following are the results. (1) M-commerce acceptability and use were positively influenced by five determinants (Security, performance expectancy, effort expectancy, habit, and price value). (2) Un-authorization, Error, secondary usage, collection, control, and awareness were all shown to directly and significantly negatively impact M-COMMERCE acceptance and use. (3) Three additional variables (social influence, hedonic motivation, and facilitating conditions) did not affect customers' intentions to use m-commerce applications in Jordan. In m-commerce, the integrated model expects a 45% percent increase in security and privacy.

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“…Mobile commerce is an e-commerce application that is done in a wireless environment, especially the Internet (Abdelkarim & Nasereddin, 2010). As a subset of electronic commerce, m-commerce also refers to electronic transactions conducted through mobile devices (Xin, Mao & Liu, 2010). M-commerce will enable consumers to do online transactions using mobile devices and access unlimited types of personalized services anytime and from anywhere without location restrictions (Sreenivasan & Noor, 2010).…”

Section: Introductionmentioning

confidence: 99%

Adoption of Mobile Commerce Technology

Abu-Shanab

Ghaleb

2012

International Journal of Technology Diffusion

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This research extended the Technology Acceptance Model (TAM) with perceived trust and perceived risks (security and privacy concerns) constructs to identify the impact of these factors on Jordanian users’ intentions to adopt mobile commerce (m-commerce). An empirical test was used utilizing 132 responses from students in two public universities in Jordan. Results indicated that perceived trust, perceived usefulness, and perceived ease of use are major influencers of mobile commerce adoption. On the other hand, perceived risk factors (security and privacy concerns) were not significant in this relation. Discussion, conclusion and future work are stated at the end of this paper.

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The Effects of Parasocial Relationships in the Adoption of Mobile Commerce Application: A Conceptual Model

Cited by 5 publications

References 27 publications

A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

A Hybrid SEM and Neural Network Approach to Understand and Predict the Determinants of Consumers’ Acceptance and Usage of Mobile-Commerce Application

Adoption of Mobile Commerce Technology

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