Marzia Tamanna and Bijaya Sinha
The purpose of this paper is to provide an in-depth analysis of the challenges associated with using artificial intelligence (AI) in academic research and suggest various…
Abstract
Purpose
The purpose of this paper is to provide an in-depth analysis of the challenges associated with using artificial intelligence (AI) in academic research and suggest various preventive measures that can be taken to address these issues and transform them into opportunities.
Design/methodology/approach
To develop measurement items and constructs, the authors collected 248 responses through an online survey. These responses were then used to establish the structural model and determine discriminant validity through the use of structural equation modeling with SmartPLS 4.0.9.9. Additionally, the authors used SPSS (Version 29) to create graphs and visual representations of the challenges faced and the most commonly used AI tools. These techniques allowed them to explore data and draw meaningful conclusions for future research.
Findings
This research shows that AI has a positive impact on higher education, improving learning outcomes and data security. However, issues such as plagiarism and academic integrity can destroy students. The study highlights AI’s potential in education while emphasizing the need to address challenges.
Practical implications
This paper emphasizes the preventive measures to tackle academic challenges and suggests enhancing academic work.
Originality/value
This study examines how AI can be used to personalize learning and overcome challenges in this area. It emphasizes the importance of academic institutions in promoting academic integrity and transparency to prevent plagiarism. Additionally, the study stresses the need for technology advancement and exploration of new approaches to further improve personalized learning with AI.
Details
Keywords
Using surveys of Amazon and Tmall Global users, this paper aims to empirically investigate the issue of platform technological selection. We explore the impact of switching costs…
Abstract
Purpose
Using surveys of Amazon and Tmall Global users, this paper aims to empirically investigate the issue of platform technological selection. We explore the impact of switching costs on users’ intentions to use an app-enabled cross-border e-commerce (CBEC) platform based on an extended technology acceptance model (TAM). The results suggest that the higher the switching cost of a platform is, the greater the users’ satisfaction and intention to use this platform. Therefore, for the platform, a moderate switching cost will be beneficial for retaining users.
Design/methodology/approach
Based on the TAM, this paper takes the switching costs as the starting point and focuses on exploring the relationships among switching costs, perceived usefulness, perceived ease of use, perceived reliability, satisfaction and intention to use. Online surveys of users of Amazon and Tmall Global are adopted as the main instruments of this research. We collected a total of 408 valid responses from Amazon users and 490 from Tmall Global users. For the data analysis, this study conducts frequency analysis, a test analysis of the reliability and validity of the measures, correlation analysis, and path analysis using a structural equation model.
Findings
The results show that switching costs positively affect the users’ satisfaction and intentions to use a CBEC platform through perceived usefulness, perceived ease of use and perceived reliability.
Research limitations/implications
The questionnaire respondents were predominantly Chinese due to the constraints of the survey conditions. In fact, China has a high penetration rate in CBEC, and Chinese users have rich experience using the Amazon and Tmall Global platforms.
Practical implications
The development of CBEC has ups and downs, and users frequently switch platforms. Considering how platforms can stand out from the crowd and retain users, we believe that a moderate increase in the switching cost of the platform is helpful for companies to address these problems, and the implications of the results are particularly valid for decision-makers of CBEC platforms and companies.
Social implications
Amazon and Tmall Global are the two largest CBEC platforms in the world. Using these two companies as examples for comparison can effectively identify the differences between the platforms and the conclusions are representative. We suggest that platforms can improve user satisfaction and willingness to use by establishing VIP communities, issuing coupons, providing shipping services as well as convenient after-sale complaint channels, and improving the platform’s easy-to-use interface, as ways to further enable the platform to retain more users and stand out in fierce competition.
Originality/value
This paper addresses an interesting and practical issue related to the effects of introducing switching costs in an extended TAM applied to CBEC platforms.