Technology readiness, UTAUT2 and continued use of digital wellness services - A configurational approach

Mezei, J. Zs.; Sell, Anna; Walden, Pirkko

doi:10.24251/hicss.2022.181

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Although e-learning initiatives taken by universities and by private players have been successful, the realization of intended goals has been slow to be achieved due to increased e-learning drop-out rates (Al-Fraihat et al, 2020). This is because each user has a different personality trait toward technology (Chen et al, 2009;Mezei et al, 2022), attitude toward continued system usage, and the way the elearning platform is used (Akaslan & Law, 2011). Moreover, India is a developing country, so there is a digital divide in infrastructural facilities, and universities and institutions lack good infrastructure and are scarce in resources for online learning (Paliwal & Singh, 2021).…”

Section: Introductionmentioning

confidence: 99%

Measurement of Doctoral Students’ Intention to Use Online Learning: A SEM Approach Using the TRAM Model

Singh Bhati,

Srivastava,

Singh Rathore

2023

JITE:IIP

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Aim/Purpose: The study aims to supplement existing knowledge of information systems by presenting empirical data on the factors influencing the intentions of doctoral students to learn through online platforms. Background: E-learning platforms have become popular among students and professionals over the past decade. However, the intentions of the doctoral students are not yet known. They are an important source of knowledge production in academics by way of teaching and research. Methodology: The researchers collected data from universities in the Delhi National Capital Region (NCR) using a survey method from doctoral students using a convenience sampling method. The model studied was the Technology Readiness and Acceptance Model (TRAM), an integration of the Technology Readiness Index (TRI) and Technology Acceptance Model (TAM). Contribution: TRAM provides empirical evidence that it positively predicts behavioral intentions to learn from online platforms. Hence, the study validated the model among doctoral students from the perspective of a developing nation. Findings: The model variables predicted 49% of the variance in doctoral students’ intent. The TRAM model identified motivating constructs such as optimism and innovativeness as influencing TAM predictors. Finally, doctoral students have positive opinions about the usefulness and ease of use of online learning platforms. Recommendations for Practitioners: Academic leaders motivate scholars to use online platforms, and application developers to incorporate features that facilitate ease of use. Recommendation for Researchers: Researchers can explore the applicability of TRAM in other developing countries and examine the role of cultural and social factors in the intent to adopt online learning. Future Research: The influence of demographic variables on intentions can lead to additional insights.

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Section: Introductionmentioning

confidence: 99%

Measurement of Doctoral Students’ Intention to Use Online Learning: A SEM Approach Using the TRAM Model

Singh Bhati,

Srivastava,

Singh Rathore

2023

JITE:IIP

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Money at my Fingertips: Decoding the Role of Referent Network Size and Financial Knowledge in Reinforcing Continuance Intention of m-Payment Services

Kumari,

Biswas

2024

Journal of Organizational Computing and Electronic Commerce

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Factors Influencing the Adoption of Generative AI for Art Designing Among Chinese Generation Z: A Structural Equation Modeling Approach

Wang,

Zhang

2023

IEEE Access

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The integration of generative artificial intelligence (GenAI) technology in the realm of art and design has demonstrated significant positive effects on designers and related industries. The current study aimed to explore and evaluate the factors and personal traits that drive Generation Z to embrace GenAI-assisted design. The study model incorporated factors derived from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the Technology Readiness Index, and the concept of trait curiosity. Empirical validation was conducted using data collected from 326 participants in the southeast of Chinese Mainland. The results of structural equation modeling indicated that: (1) Factors such as effort expectancy, price value, and hedonic motivation from UTAUT2 have a positive influence on the intention to use GenAI, while performance expectancy does not show a statistically significant effect. (2) Both optimism and creativity significantly contribute to performance expectancy, effort expectancy, price value, and hedonic motivation. (3) Trait curiosity has a significant positive impact on both optimism and the intention to use GenAI. The research findings suggest the need for further improvements in the construction and operational strategies of GenAI platforms and provide practical insights for enhancing Generation Z's intention to utilize such platforms.

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Technology readiness, UTAUT2 and continued use of digital wellness services - A configurational approach

Cited by 3 publications

References 25 publications

Measurement of Doctoral Students’ Intention to Use Online Learning: A SEM Approach Using the TRAM Model

Measurement of Doctoral Students’ Intention to Use Online Learning: A SEM Approach Using the TRAM Model

Money at my Fingertips: Decoding the Role of Referent Network Size and Financial Knowledge in Reinforcing Continuance Intention of m-Payment Services

Factors Influencing the Adoption of Generative AI for Art Designing Among Chinese Generation Z: A Structural Equation Modeling Approach

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