Sage Journals: Discover world-class research

Abstract

Artificial Intelligence, at the forefront of innovation and intelligence, is redefining the pace of life and work, notably within education. This study investigates the determinants influencing Gen Z's behavioral intentions (BI) to integrate AI-powered tools within Indian Higher Educational Institutions (HEIs) by extending the UTAUT2 model with four additional constructs: trustworthiness, personal innovativeness, perceived task excellence, and perceived privacy concern. The data gathered from 430 respondents within Indian HEIs through an online survey following purposive sampling was meticulously analyzed using the structural equation modeling approach in AMOS. The findings validate the applicability of the UTAUT2 model for understanding AI tool integration in the Indian context, with an explanatory power of 34.2%. The study highlights the beneficial impact of hedonic motivation, perceived task excellence, facilitating conditions, and performance expectancy on Gen Z's intention to integrate AI tools. Additionally, the study suggests recommendations for future research and outlines implications based on these findings.

Keywords

AI tools AMOS CB-SEM higher education Gen Z UTAUT2

Stemming from Alan Turing's foundational question posed in 1950, “Can machines think?” (Turing, 2009, p. 23), research in Artificial Intelligence (AI) has made significant strides across multiple domains, resulting in a vast and engaging body of literature (Zhang & Aslan, 2021). Notably, AI tools utilizing different AI technologies have found expanded applications in education, holding exceptional potential for dynamic assessments, personalized learning, and fostering meaningful interactions within mobile, online, or blended learning environments (Roschelle et al., 2020; Zhu et al., 2020).

The emergence of advanced Generative AI and Predictive AI-powered tools further expands higher education possibilities (Pelletier et al., 2023). Generative AI (GenAI) utilizes sophisticated machine learning algorithms to discern patterns and generate new data samples resembling existing datasets (Salah et al., 2023), creating novel content across various mediums, including text, audio, images, videos, and code (Lim et al., 2023). Noteworthy examples of GenAI tools include ChatGPT, Scribe, Gemini, Jasper, Copy.ai, Wordtune, and Dall-E. On the other hand, Predictive AI systems employ cutting-edge machine learning algorithms, statistical techniques, and data analysis to generate predictions, identify patterns, and extend insights for adaptive learning platforms, enrolment management, career recommendations, and learning analytics (Sghir et al., 2022). Furthermore, Gen AI is poised to significantly impact the global economy, potentially boosting annual labor productivity growth by 0.1 to 0.6% through 2040, according to McKinsey's report (2023). Specifically, for India, a joint report by NASSCOM and the Boston Consulting Group (BCG) (2024) projects that the domestic AI market will reach USD 17–22 billion by 2027, positioning India as a significant contributor to global AI-driven growth. This highlights AI's transformative power in reshaping India's economy and driving global growth. Moreover, AI is set to revolutionize learning and administration in the education sector, with the global AI market in education projected to reach $21.13 billion by 2028, growing at a CAGR of 39.7%, according to the Global Market Report (2024).

Given this backdrop, considering the immense potential of AI in reshaping higher education (Slimi, 2021), it is imperative to understand how the current generation, particularly Generation Z learners in higher education institutions in India (HEIs), integrates these tools into their daily activities, including learning. Generation Z, or Gen Z, born between 1995 and 2010, are often called digital natives as they are the first generation to grow up with the internet, smartphones, and social media as integral components of daily life (Axcell & Ellis, 2023). Gen Z's frequent access to digital platforms makes them prefer hybrid learning methods integrating technology and multimedia materials, distinguishing them from previous generations (Seemiller & Grace, 2017). Their key characteristics, such as a drive for success, a persistent desire to improve, and efficiency (Saiyed & Srivastava, 2022), enhance their problem-solving skills, quick access to knowledge, and adaptability. Consequently, they are more inclined to adopt and utilize AI tools in different domains of their life.

With 472 million inhabitants, India has the highest Gen Z population worldwide (Hameed & Mathur, 2020). Additionally, the Gross Enrolment Ratio (GER) for higher education in India has reached 28.4% for the 18–23 age group (AISHE 2021–2022, Government of India 2023), highlighting a significant opportunity to explore the integration of AI tools among this demographic. Further, with over half of India's population, approximately 759 million individuals, actively using the Internet, a figure projected to rise to 900 million by 2025 (Internet in India, 2022), the stage is set for a comprehensive investigation into the integration of AI tools and the factors influencing Gen Z learners’ behavioral intention (BI) regarding such integration in higher education institutions (HEIs).

While prior researchers have investigated the instructional applications of various AI tools across different educational levels (Rodway & Schepman, 2023; Yilmaz & Karaoglan Yilmaz, 2023), there remains a critical gap in understanding how Gen Z learners, particularly in India, adopt and integrate AI tools into their daily activities, including learning. Previous research has explored Gen Z's adoption of digital payments, online learning, healthcare wearables (Bagdi et al., 2023; Nayak et al., 2022; Srivastava et al., 2024), and other digital technologies, including MOOCs (Meet et al., 2022), among the Indian Gen Z population. However, no studies have addressed the unique characteristics and contextual factors influencing AI tool adoption among Indian Gen Z learners, leaving a significant gap in the literature. This study marks an initial investigation into these unexplored areas, extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework ( Venkatesh et al., 2012) with four additional constructs: Trustworthiness (TW), Personal Innovativeness (PI), Perceived Task Excellence (PTE), and Perceived Privacy Concern (PPC). The decision to incorporate these additional constructs is grounded in previous research that highlights their relevance in the context of AI technology adoption. For instance, Trustworthiness or the quality of being trusted has been identified as a critical factor in AI adoption, influencing users’ confidence in AI systems (Salloum, 2024; Sun, 2021). Additionally, Personal Innovativeness reflects the degree to which an individual is open to new technologies, which has been shown to significantly impact the adoption of AI tools (Hanji et al., 2024; Lee et al., 2021). Perceived Task Excellence refers to the belief that AI tools can enhance task performance, a concept supported by studies emphasizing the proven efficacy of AI in improving educational outcomes (Boubker, 2024; Wu & Yu, 2024). Finally, Perceived Privacy Concerns have been recognized as a barrier to technology adoption, particularly in AI applications where data privacy is a significant concern (Hu & Min, 2023; Kronemann et al., 2023). This approach allows us to address the determinants of AI tool adoption that have not been studied before, especially in the context of Indian Gen Z learners, thereby strengthening our research's theoretical contributions and novelty. The subsequent research questions form the foundation of the investigation.

Research questions:

What determinants influence Gen Z's behavioral intention in integrating AI tools?

How do the following determinants, Trustworthiness, Personal Innovativeness, Perceived Task Excellence, and Perceived Privacy Concern, influence Gen Z's behavioral intention to integrate AI tools?

In summary, this study makes several significant contributions to the existing literature. Firstly, it pioneers the research on Gen Z's BI towards integrating AI tools into daily life, including learning. Secondly, it expands the UTAUT2 framework by incorporating additional constructs, providing a more comprehensive understanding of the factors influencing AI tool integration. Thirdly, it validates the UTAUT2 theoretical framework for usage intention research, further enhancing its applicability in diverse contexts. These contributions underscore the importance of this study in shaping the future of AI integration in higher education, extending beyond India to a global scale, where a substantial proportion of learners belong to the Gen Z cohort (Jayatissa, 2023).

This paper follows a structured organization. The subsequent section offers a comprehensive literature review, outlines the development of hypotheses, and introduces a research model centered on the determinants influencing Gen Z's behavioral intention to integrate AI tools, building upon and extending the UTAUT2 framework. Subsequent sections elaborate the methodology, detailing the research design and data collection process, followed by data analysis, presentation of results, and subsequent discussion. Further, the paper presents the conclusion by addressing implications and limitations and offering suggestions for future research.

Theoretical Framework

AI tools are software applications or systems that use AI algorithms to automate tasks, provide predictive capabilities, or aid decision-making processes (Al Ka’bi, 2023; Marzuki et al., 2023). These tools, including chatbots, image recognition software, data analytics platforms, and virtual assistants, aim to improve efficiency and address challenges across various fields such as business, medicine, and education (Bezverhny et al., 2020; Horodyski, 2023; Madarász et al., 2023). The researcher is always interested in knowing how people accept and use new advancements in information and communication technologies (Dwivedi et al., 2019). The Unified Theory of Acceptance and Use of Technology (UTAUT), developed by Venkatesh et al. (2003), is a well-regarded framework for predicting user adoption of novel technologies. The eight primary theories included in the UTAUT are the theory of reasoned action (TRA) (Fishbean & Ajzen, 1975), the Technology Acceptance Model (TAM) (Davis, 1989), Social Cognitive Theory (SCT) (Compeau & Higgins, 1995), Theory of Planned Behavior (TPB) (Ajzen, 1991), Motivational Model (MM) (Davis et al., 1992), Model of Personal-Computer utilization (Thompson et al., 1991), Combined TAM and TPB (C-TAM-TPB) (Taylor & Todd, 1995) and Innovation Diffusion Theory (IDT) (G. C. Moore & Benbasat, 1991). It incorporates the four exogenous constructs, Performance Expectancy (PE), Effort Expectancy (EE), Facilitating Conditions (FC), and Social Influence (SI), and two endogenous constructs, Behavioural Intention (BI) and Use Behaviour (UB). Additionally, it includes four moderator variables: Gender, Age, Experience, and Voluntariness of use. The available literature shows that the UTAUT is one of the most extensively used and well-researched theories for describing learners’ behavioral intentions in e-learning environments (Bansal et al., 2022; Hunde et al., 2023).

Building on the UTAUT framework, Venkatesh et al. (2012) developed the extended version, UTAUT2, which combined three additional exogenous constructs: Hedonic Motivation (HM), Price/Value (PV), and Habit (HT), enhancing its focus consumer acceptance. This extended model significantly improves predictive capacity, estimating user Behavioral Intention (BI) by up to 74% (Venkatesh et al., 2012). Researchers have validated the UTAUT2 model in various contexts (Azman Ong et al., 2023; Parhamnia, 2022), including educational settings. For instance, Al Farsi (2023) examined the model's applicability in understanding Virtual Reality technology acceptance among students, while Faqhi and Jaradat (2021) investigated the adoption of Augmented Reality technology in educational settings by integrating UTAUT2 with the Technology Task Fit (TTF) model. Additionally, Arain et al. (2019) investigated mobile learning acceptance among higher education students using the extended UTAUT2.

Literature Review and Hypotheses Development

Research has also demonstrated the applicability of both UTAUT and UTAUT2 models in understanding AI technology acceptance among students and educators (Habibi et al., 2023; Lavidas et al., 2024). For example, Zhu et al. (2024) applied the UTAUT2 model to explore university students’ adoption of Gen AI products, while An et al. (2023) utilized the UTAUT model to investigate senior secondary students’ perceptions of AI-assisted language learning. Alhwaiti (2023) also employed the UTAUT2 model to assess educators’ acceptance of AI applications in the post-COVID era, underscoring the model's robustness across various educational contexts.

To enhance the explanatory power of UTAUT2 in the context of AI tool adoption among Gen Z, we have extended the model by incorporating four additional constructs: Trustworthiness (TW), Personal Innovativeness (PI), Perceived Task Excellence (PTE), and Perceived Privacy Concern (PPC). These additions are based on Gen Z users’ unique characteristics and concerns and are supported by relevant literature.

Trustworthiness (TW): Trust is a fundamental factor influencing technology acceptance, particularly in contexts involving sensitive data and privacy concerns (Janssen et al., 2018). Gen Z, who are highly aware of digital privacy issues (Bekman & Al, 2023), place significant importance on trust in determining their willingness to adopt AI tools. Studies have shown that the quality of being trusted significantly impacts technology adoption behaviors (Kuen et al., 2023), making it a critical variable for understanding Gen Z's AI tool integration.

Personal Innovativeness (PI): This construct reflects an individual's tendency to embrace new technologies for innovative solutions (Jackson et al., 2013). Gen Z is known for its openness to new technologies and a solid readiness to explore novel digital tools (Molinillo et al., 2023). Research indicates that personal innovativeness is a significant predictor of technology adoption, particularly among younger users who are more receptive to innovations (Aldahdouh et al., 2020; Chen, 2022).

Perceived Task Excellence (PTE): Gen Z tends to prioritize efficiency and high performance in their use of technology (D. Janssen & Carradini, 2021). While performance expectancy refers to the belief that using technology will improve overall performance (Al-Adwan & Al-Debei, 2023), PTE is centered on Gen Z users’ expectations of achieving high standards and exceptional task outcomes (Chillakuri, 2020; Pataki-Bittó & Kapusy, 2021), significantly determining their acceptance.

Perceived Privacy Concern (PPC): Data privacy and security concerns are increasingly prominent in the digital age, especially among younger users (Vimalkumar et al., 2021). PPC addresses the apprehensions regarding how personal data is handled by AI tools, which can significantly impact adoption decisions (Baruh et al., 2017). Research has shown that perceived privacy risks can influence technology adoption (Guhr et al., 2020), underscoring the importance of addressing privacy concerns in technology acceptance models (Al-Qaysi et al., 2020; Tamilmani et al., 2021).

By integrating these constructs, our study aims to provide a comprehensive understanding of the factors influencing Generation Z's Behavioral Intention to integrate AI tools into their daily lives, including learning. This approach extends the UTAUT2 model and addresses the specific needs and concerns of a key demographic in the educational sector.

Performance Expectancy (PE)

The PE is the degree to which a technology's use will benefit stakeholders in certain activities (Venkatesh et al., 2003, p. 447). Individuals are more likely to utilize a technology if they are convinced it will help their performance (Sewandono et al., 2023). Different studies have demonstrated the significant effect of PE on behavioral intention to adopt technology. For instance, studies by Thongsri et al. (2018), Inder et al. (2022), and Alomari and Abdullah (2023) confirm the pivotal role of PE in influencing users’ intention to adopt innovative technologies. In this context, PE indicates how much an individual believes that AI tools can aid them in productivity. This study assumes that the technologically proficient Gen Z learners in HEIs may opt for AI tools to increase productivity and work efficiency (Magano et al., 2020). As a result, the following hypothesis is proposed:

H1: PE significantly influences Gen Z's BI toward integrating AI tools

Effort Expectancy (EE)

The EE refers to the ease of utilizing a technology or system, which affects users’ adoption decisions (Duong et al., 2023). In various research contexts, EE has been identified as a crucial predictor of technological acceptance (Al-Riyami et al., 2023; Chiu & Tsuei, 2023). Given the understanding that Gen Z is often considered adept at using digital devices and navigating online environments (Weinswig Deborah, 2016), it is proposed that they may require assistance in integrating AI tools due to their complexity or unfamiliarity. Therefore, understanding EE can provide insights into the cognitive and behavioral factors that affect the adoption of AI tools among Gen Z learners. As a result, the following hypothesis is proposed:

H2: EE significantly influences Gen Z's BI toward integrating AI tools

Social Influence (SI)

SI refers to the degree to which an individual's willingness to utilize technology is influenced by their social environment, including family and friends (Joa & Magsamen-Conrad, 2022). Recent studies have identified SI as a significant predictor of BI for utilizing technology (Pagán & Medina, 2021), even among younger generations (Baudier et al., 2020; Persada et al., 2019), who often rely on social cues and peer opinions in their decision-making processes. In the context of this study, understanding SI can help elucidate the social dynamics that contribute to AI adoption among Gen Z learners. As a result, the following hypothesis is proposed:

H3: SI significantly influences Gen Z's BI toward integrating AI tools

Facilitating Conditions (FC)

FC refers to users’ perceptions of the support and resources available to help them complete a specific task (Brown & Venkatesh, 2005). Previous studies have indicated that the FC directly influences technology adoption (Altalhi, 2021; Fianu et al., 2020). In today's AI-driven technological landscape, whether used by individuals or organizations, it is crucial to have a supportive infrastructure that enables optimal utilization of AI tools (Alshammari & Alshammari, 2024). As a result, the following hypothesis is proposed:

H4: FC significantly influences Gen Z's BI toward integrating AI tools

Hedonic Motivation (HM)

HM refers to the delight that individuals experience from using modern technology (Sitar-Tăut, 2021). Previous studies have confirmed the positive relationship between HM and BI (Al-Azawei & Alowayr, 2020; Moorthy et al., 2019). Given Gen Z's proficiency in adopting new technology and high activity on social media platforms (Mahapatra et al., 2022), they are likelier to adopt AI tools that offer hedonically rewarding experiences. As a result, the following hypothesis is proposed:

H5: HM significantly influences Gen Z's BI toward integrating AI tools

Price Value (PV)

PV is the cognitive trade-off an individual user makes between the claimed benefits of adopting technology and its cost (Alalwan et al., 2018). If individuals perceive benefits from adopting and utilizing technology, they will be willing to bear the associated expenses (Abu Gharrah & Aljaafreh, 2021). PV becomes particularly significant in platforms employing a freemium pricing strategy, where basic services are offered for free with the option to pay for premium features or upgrades. As a result, users’ perceptions of the value proposition compared to the cost of upgrading to premium features heavily influence their adoption and engagement behavior. Since Gen Z prioritizes efficiency and innovation (Aggarwal et al., 2022) while recognizing the benefits and revolutionary features that AI brings in various areas of their life, they are anticipated to consider the advantages supplied by AI tools over the costs. As a result, the following hypothesis is proposed:

H6: PV significantly influences Gen Z's BI toward integrating AI tools

Habit (HT)

The extent to which individuals carry out behaviors automatically due to repeated learning and practices is recognized as a habit (Limayem et al., 2007). Research has consistently shown that habit significantly influences people's BI to integrate technology into their daily lives (Schomakers et al., 2022; Strzelecki, 2023). The digital-first Gen Z is inclined to adopt AI in various facets of their life, including work and daily life activities, due to its capacity to amplify potency, network, and access to knowledge (Imjai et al., 2024). As a result, the following hypothesis is proposed:

H7: HT significantly influences Gen Z's BI toward integrating AI tools

Trustworthiness (TW)

Regarding privacy, information security, and task accomplishment, trust continues to be highlighted as a critical factor in adopting new technology (Dionika et al., 2020; Thiebes et al., 2021). TW, defined as the quality of being trusted (Peels & Bouter, 2023), is essential in this context, as it directly influences users’ perceptions and acceptance of technology. For instance, Xiong et al. (2024) demonstrated the significant impact of trust on user acceptance of AI virtual assistants, establishing that trust positively affects the acceptance of AI tools. Similarly, Mohd Rahim et al. (2022) investigated postgraduate students in Malaysian HEIs who belong to the Gen Z demographic, showing that trust significantly influences chatbot adoption. Together, these studies highlight the crucial role of trust in technology adoption, underscoring its importance for Gen Z's acceptance of AI tools. Therefore, establishing TW is foremost for fostering user confidence and encouraging the widespread adoption of AI tools among Gen Z. As a result, the following hypothesis is proposed:

H8: TW significantly influences Gen Z's BI toward integrating AI tools

Personal Innovativeness (PI)

PI is the extent to which an individual is open to novel ideas and makes creative choices regardless of others’ experiences (García de Blanes Sebastián et al., 2022). In the realm of information technology, individuals with an elevated level of personal innovativeness are more likely to appraise technological advancements (Al-Adwan et al., 2023) favorably and possess the capacity to overcome the challenges associated with adopting new technology (Ciftci et al., 2021). Gen Z is renowned for their innovativeness and discerning judgments on the utility of emerging technologies (Singh & Sibi, 2023), presenting a prime target for such strategies. Therefore, understanding PI is crucial for targeting this demographic effectively. As a result, the following hypothesis is proposed:

H9: PI significantly influences Gen Z's BI toward integrating AI tools

Perceived Task Excellence (PTE)

Task excellence outlines consistently accomplishing remarkable results and meeting expectations in a particular activity or endeavor. Due to their tech-savvy nature (Turner, 2015), Gen Z is particularly inclined to utilize AI tools to enhance task excellence (Bińczycki et al., 2023), leveraging these technologies to accelerate workflows, maximize productivity, and achieve high-quality results in their dynamic digital lives (Alruthaya et al., 2021). Therefore, understanding PTE is essential for effectively targeting and engaging Gen Z. As a result, the following hypothesis is proposed:

H10: PTE significantly influences Gen Z's BI toward integrating AI tools

Perceived Privacy Concern (PPC)

Individuals often worry about how their personal information gets collected, stored, and misused online, leading to privacy concerns (Kumar et al., 2021) in the digital world. AI technologies can easily manage and store users’ personal information, such as biometric data, behavior logs, and identifiers (Ioannou et al., 2021), leading to a growing concern about public security and privacy (Walsh, 2023). As a result, individuals may hesitate to adopt AI tools due to fears of privacy breaches and unauthorized access to their sensitive data (Alam, 2023; Mhlanga, 2023). Therefore, investigating PPC is essential for effectively addressing barriers to AI tool adoption and fostering a secure and privacy-conscious digital environment. As a result, the following hypothesis is proposed:

H11: PPC significantly influences Gen Z's BI toward integrating AI tools

Figure 1 represents the study's proposed research model.

Figure 1.

The research model.

Methodology

Using the CB-SEM technique, our study employed a thorough quantitative analytical approach to evaluate and validate the research model (Gao, 2023). Structural Equation Modeling (SEM) is a technique for carrying out high-quality statistical analysis on a dataset with multiple variables (Alhwaiti, 2023) to specify the relationships between both observed and latent variables simultaneously by combining path analysis, factor analysis and multiple regression analysis (Babalola & Nwanzu, 2021). Additionally, it has the power to be sensitive to the potential measurement error of the variables included in a research model (Wang & Wang, 2019). CB-SEM is one of the widely used methods of SEM, and it relies on covariance and proves to be well-suited for factor-based models like the one used in the present study (Dash & Paul, 2021). Furthermore, our study is primarily confirmatory, and CB-SEM enables us to extensively analyze a well-established conceptual framework. The study used IBM SPSS Statistics 25 to analyze the constructs’ reliability. CFA and SEM were performed on the collected data using IBM SPSS AMOS 22 to evaluate the measurement model and the path analysis (Batucan et al., 2022). IBM SPSS AMOS 22 is chosen as the analysis software due to its widespread recognition and unique graphical interface, designed explicitly for CB-SEM analysis, facilitating comprehensive assessment of the measurement model and path analysis (SPSS Amos | IBM, n.d.; Thakkar, 2020). By employing these rigorous analytical techniques and software, the study ensures robustness and reliability in evaluating the proposed research model and examining the relationships between variables

Research Design

The study deployed a quantitative research approach with the UTAUT2 model and the CB-SEM technique to examine the proposed research model. Before conducting the study, a thorough literature review was undertaken to ascertain the existing research gaps. Subsequently, a survey questionnaire has been developed as the research tool. In addition to collecting socio-demographic information and a consent letter, the study's questionnaire had two sections. The first section adopts the Venkatesh et al. (2012) instrument, customized to fit the context of AI tool integration. It contains 29 items that assess the seven constructs of UTAUT2. The second section includes 15 self-developed items for the four additional constructs incorporated into the model aligned with the specific requirements and objectives of the study. These items were developed by referring to the research works of Alhadabi and Karpinski (2020), Camacho-Morles et al. (2021), Herrero et al. (2017), Meyliana et al.(2020), and Twum et al. (2022), and modifying them to align with the contextual and demographic characteristics of the Gen Z population in Indian HEIs. Further, each item underwent expert validation and pilot testing, aimed at ensuring clarity and alignment with theoretical constructs, thus enhancing the precision of our measurements and contributing to a better understanding of AI adoption among Gen Z. The 5-point Likert scale (de Rezende & de Medeiros, 2022) was used to measure the respondents’ degree of agreement with 1 (Strongly Disagree) to 5 (Strongly Agree) as the range.

Data Collection and Respondents

The study population comprised Gen Z learners from Indian HEIs, specifically targeting individuals aged 20 to 28. After ensuring expert validity, a pilot study was conducted among 150 sample Gen Z learners (not included in the final survey) of HEIs using a convenience sampling technique to clarify the phrases and remove non-identical items from the questionnaire. The experts consulted for validating the questionnaire were distinguished professors from the discipline of education, both national and international, who specialize in scale development and validation, selected for their extensive experience and contributions to the field. Convenience sampling was chosen for the pilot study due to its practicality and efficiency, enabling quick access to a diverse group of participants within a limited timeframe to gather preliminary data and refine the questionnaire before the main study. Further, the pilot study's reliability was strengthened with a Cronbach's Alpha value of 0.910, facilitating the final survey. The final survey's minimum required sample size was determined utilizing Daniel Soper's (2023) a priori sample method for structural equation modeling - an online sample size calculator incorporating methodologies outlined by Cohen (1988) and Christopher Westland (2010). With an estimated effect size of 0.3, a desired statistical power level of 0.8, 12 latent variables, and 44 observed variables at a significance level of 0.05, a minimum sample size of 200 respondents was recommended. The final survey collected primary data from 430 respondents in the fourth quarter of 2023 using a non-probabilistic purposive sample technique. This technique was chosen to target individuals who fit the specific age criteria of Gen Z and were actively engaged in higher education (Campbell et al., 2020). In India, higher education typically enrolls students aged 18–23, aligning with the Gen Z demographic (AISHE 2021–2022, Government of India 2023). Participants were identified and recruited from this group, with data collection focused on those aged 20–28, ensuring an accurate representation of Gen Z learners in Indian HEIs. Google forms were distributed to students’ email addresses, with prior permission obtained from the departmental heads. Additionally, Google forms were shared with some students in person for comprehensive outreach. Respondents received detailed information about its purpose and procedures through a consent letter at the beginning of the survey questionnaire to ensure voluntary participation and comprehension of the research. Further, efforts were made to include diverse respondents by reaching out to students from various academic disciplines and institutions, ensuring representation across different fields of study. The sociodemographic characteristics of the sample are outlined in Table 1. Initially, IBM SPSS Statistics 25 was used to analyze the data, which examined missing data, uncommitted responses, outliers, and data leveling.

Table 1.

Socio-Demographic Profile of the Respondents (n = 430).

Demographic Variable	Description	Frequency	Percentage %
Gender	Female	277	64.4
Gender	Male	153	35.6
Age	20–28	430	100
Present Level of Education	Under Graduation	173	40.2
	Post Graduation	151	35.1
	PhD	106	24.7
Stream of Education	STEM	187	43.5
	Arts	51	11.9
	Social Science	124	28.8
	Management/Commerce	28	6.5
	Humanities	40	9.3
Use AI Tools	Yes	377	87.7
Use AI Tools	No	53	12.3
Frequency use of AI tools (Last Week)	0hr	53	12.3
	1hr-5hr	312	72.5
	6hr-10hr	36	8.3
	11hr-15hr	4	0.9
	15hr above	25	6

Data Analysis

A two-step process was used to perform the data analysis procedure. The first phase assessed the model's validity, reliability, and fit using confirmatory factor analysis (CFA). In the second phase, the hypotheses were tested using the SEM technique.

From Table 1, the total sample consisted of 64.4% females and 35.6% males, reflecting the higher female enrolment in Indian higher education (AISHE 2021–2022, Government of India 2023). While this skewed gender distribution was noted, the study's model did not explicitly reflect gender influence, thus mitigating potential biases. Additionally, 40.2% were pursuing under graduation, 35.1% were pursuing post-graduation, and 24.7 were progressing with research. 43.5% of the participants were from the STEM stream, 11.9% were from the arts stream, 28.8% were from the social science stream, 6.5% were from the management/ commerce stream, and the remaining 9.3% were from the humanities stream. 87.7% of the participants were employing AI tools, and out of which 72.5% were employing AI tools between 1hr to 5hr in the last week, 8.3% were using between 6hr to 10hr, 0.9% were employing between 11hr to15hr, and 6% were utilizing AI tools more than 15hr in last week. 12.3% of the participants have yet to explore any AI tools.

Measurement Model Assessment

The data's normality assumptions were examined before proceeding to the model's estimation analysis. This was achieved using the Skewness and Kurtosis measures (Kline, 2016). The Skewness and Kurtosis measures were within the acceptable range of − 3 and + 3, and − 10 to + 10, as Brown (2006) and Thornberg et al. (2023) recommended. Furthermore, the potential for multi-collinearity and Common Method Bias (CMB) were assessed using the Variance Inflation Factor (VIF) and Harman's single factor test (Sundus et al., 2022) with IBM SPSS Statistics 25. VIF values ranging from 1.312 to 2.322 below the threshold of 3 (Hair et al., 2019; Ostic et al., 2021) (Table 2) indicated the absence of multi-collinearity among the independent variables. Additionally, loading all observed items of the dataset simultaneously resulted in a percentage variance of 19.136%, below the threshold of 50% (Podsakoff et al., 2003), signifying the absence of CMB.

Table 2.

Constructs Reliability and Convergent Validity.

Constructs	Items	Standardised Factor Loadings	α	CR	AVE	VIF
Performace Expectancy (PE)	PE1	0.833	0.848	0.854	0.595	1.808
	PE2	0.789
	PE3	0.740
	PE4	0.719
Effort Expectancy (EE)	EE1	0.684	0.823	0.825	0.543	1.834
	EE2	0.796
	EE3	0.733
	EE4	0.729
Social Influence (SI)	SI1	0.792	0.818	0.820	0.534	1.672
	SI2	0.713
	SI3	0.735
	SI4	0.678
Facilitating Conditions (FC)	FC1	0.747	0.820	0.822	0.537	1.690
	FC2	0.676
	FC3	0.723
	FC4	0.782
Hedonic Motivation (HM)	HM1	0.649	0.791	0.795	0.566	2.002
	HM2	0.788
	HM3	0.81
Price Value (PV)	PV1	0.723	0.804	0.809	0.586	1.955
	PV2	0.781
	PV3	0.791
Habit (HT)	HT1	0.66	0.824	0.826	0.544	2.059
	HT2	0.737
	HT3	0.71
	HT4	0.834
Trustworthiness (TW)	TW1	0.75	0.826	0.827	0.545	2.322
	TW2	0.701
	TW3	0.675
	TW4	0.82
Personal Innovativeness (PI)	PI1	0.701	0.821	0.825	0.543	1.733
	PI2	0.677
	PI3	0.817
	PI4	0.745
Perceived Task Excellence (PTE)	PTE1	0.725	0.845	0.846	0.578	2.317
	PTE2	0.818
	PTE3	0.746
	PTE4	0.75
Perceived Privacy Concern (PPC)	PPC1	0.891	0.841	0.847	0.651	1.312
	PPC2	0.828
	PPC3	0.689
Behavioural Intention (BI)	BI1	0.538	0.816	0.821	0.615	-
	BI2	0.870
	BI3	0.894

Subsequently, the measurement models’ (Figure 2) convergent validity and reliability of the constructs were examined with maximum likelihood by using the following well-established measures: standardized factor loadings, Construct Reliability (CR), Average Variance Extracted (AVE), and Cronbach's alpha (α) (Cheung et al., 2023). First, the standardized factor loadings were assessed, revealing values ranging from 0.538 to 0.894, all exceeding the recommended threshold value of 0.50, as suggested by Hair et al. (2009).

Figure 2.

The measurement model.

Moving forward, the Average Variance Extracted (AVE) was calculated, which measures the amount of variance captured by a construct to the amount of variance due to measurement error (dos Santos & Cirillo, 2023). The AVE values ranged from 0.534 for SI to 0.651 for PPC, exceeding the generally recommended threshold value of 0.5 (Fornell & Larcker, 1981). Finally, two well-recognized indicators, Cronbach's alpha (α) and Construct Reliability (CR), were used to assess the constructs’ reliability. The values for these reliability indicators were all found to be above the threshold value of 0.70, following guidelines by Hair et al. (2009) and Lance et al. (2006). Therefore, all the constructs of this study, given in Table 2, are highly reliable, and the convergent validity results ensure the internal consistency of the indicators in measuring the same construct.

The Discriminant validity (DV) ensures that the exogenous constructs in the study are not strongly linked with each other (Ab Hamid et al., 2017). This was confirmed through the two prominent measures: AVE/SV or Fornell and Larcker criterion and the Heterotrait- Monotrait (HTMT) ratio (Kuppelwieser et al., 2019). According to Fornell and Larcker's (1981) criterion, DV will be attained when the squared root of each construct's AVE is higher than any correlations with other constructs. The Table 3 presents the evaluation of this criterion and validates DV. On the other hand, HTMT measures the correlation of indicators across the constructs to the correlation of indicators within the constructs (Hair et al., 2021). According to the criterion of Henseler et al. (2015), all HTMT ratios in Table 4 were below the threshold value of 0.85, confirming that all the constructs in the study differ from other constructs. In conclusion, the above findings established the DV of the study's constructs and validated the measurement model.

Table 3.

Fornell and Larcker Criterion.

Construct	PE	EE	SI	FC	HM	PV	HT	TW	PI	PTE	PPC
PE	0.771
EE	0.137	0.737
SI	0.057	0.347	0.730
FC	0.186	0.395	0.279	0.733
HM	0.067	0.217	0.233	0.209	0.752
PV	−0.02	0.214	0.115	0.142	0.373	0.765
HT	0.237	0.366	0.275	0.326	0.181	0.154	0.740
TW	0.131	0.357	0.256	0.337	0.674	0.397	0.282	0.738
PI	0.036	0.307	0.325	0.283	0.469	0.324	0.418	0.418	0.737
PTE	0.175	0.388	0.356	0.227	0.272	0.147	0.477	0.375	0.353	0.76
PPC	−0.01	0.016	0.068	0.071	0.102	0.357	0.18	0.269	0.033	0.11	0.807

Note. Bold digits represent the square root of AVE.

Table 4.

HTMT Criterion.

Construct	PE	EE	SI	FC	HM	PV	HT	TW	PI	PTE	PPC
EE	0.154
SI	0.060	0.342
FC	0.186	0.394	0.287
HM	0.073	0.212	0.228	0.312
PV	0.071	0.214	0.098	0.147	0.328
HT	0.241	0.355	0.257	0.409	0.181	0.152
TW	0.148	0.356	0.309	0.345	0.651	0.390	0.273
PI	0.033	0.329	0.332	0.295	0.506	0.333	0.412	0.414
PTE	0.185	0.390	0.366	0.227	0.264	0.125	0.464	0.367	0.407
PPC	0.004	0.005	0.057	0.067	0.096	0.368	0.150	0.239	0.021	0.094
BI	0.049	0.283	0.132	0.025	0.407	0.038	0.280	0.021	0.273	0.109	0.512

Structural Model Assessment

The general fit of the proposed research model was examined by deploying the fit measurements, such as CMIN /DF (Chi-square /Degree of freedom), Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA) and Standardized Root Mean Square Residual (SRMR). The findings shown in Table 5 confirm that the model fits with the data against the threshold values.

Table 5.

Model Fit.

Fit Index	Threshold Value	Model Value	Sources
CMIN/DF	< 3	1.790	(Hu & Bentler, 1999; Kline, 2005)
GFI	>0.95 (0.90 too)	0.945	(Hu & Bentler, 1999; Kline, 2005)
AGFI	>0.90	0.924	(Shi & Maydeu-Olivares, 2020)
CFI	>0.95	0.966	(Hair et al., 2020)
RMSEA	<0.05	0.043	(Chen et al., 2008)
SRMR	<0.05	0.0466	(Maydeu-Olivares et al., 2018)

For further assessing the structural model, the explanatory power of the endogenous construct has been measured as R-squared (R²). The R² value is the extent to which the exogenous constructs explain the variations of the endogenous construct (Kang & Ahn, 2021). The R² value ranges from 0 to 1, where a higher value indicates substantial explanatory power (Sapra, 2014). Hence, Social sciences deal with the dynamic nature of human behavior and relationships, which are susceptible to ongoing change influenced by individual self-interest, emotions, group dynamics, and other factors (Cziko, 1989). Consequently, predicting and modeling human behavior still needs improvement (Kennedy, 2012), as a singular model struggles to accurately encompass all the factors influencing behavior at a given time (Marien et al., 2019). Therefore, a model with an R-squared between 0.10 and 0.50 can be considered effective in capturing the complex dynamics of human behavior in the social sciences with some of the significant exogenous constructs (Ozili, 2022). In the present study, the BI was found to have an R² value of .342, given that the exogenous constructs explain 34.2% of the endogenous construct. The above result indicates that the research model stipulated a 34.2% for measuring the BI of Gen Z in integrating AI tools.

In the final step, the structural relationships between the exogenous and endogenous constructs were tested using the path coefficients (β) (Figure 4) (Hair et al., 2017). From the SEM results (Figure 3) in Table 6, H5 and H10 were tested as significant at 0.001 significance level, and H1 and H4 were tested as significant at 0.01 significance level. That is, HM (β = 0.363), PTE (β = 0.320), FC (β = 0.17), and PE (β = 0.15) appeared to be significantly correlated with Gen Z's BI towards integrating AI tools. The hypotheses H2, H3, H6, H7, H8, H9, and H11 were not significant at 0.001 level of significance. Therefore, EE (β = 0.12), SI (β = −0.045), PV (β = −0.12), HT (β = −0.044), PI (β = −0.003), TW (β = 0.120), and PPC (β = −0.08) were not correlated with the BI of Gen Z learners from the HEIs in integrating AI tools.

Figure 3.

The structural model results.

Figure 4.

Visual paths of the structural model. Note. ***(p < 0.001), **(p < 0.01), Indicate Insignificant Paths.

Table 6.

SEM Result - Path Coefficient and p-Value.

Hypothesis	Path	β Values	p-Values	Decision
H1	PE → BI	0.15	0.006**	Supported
H2	EE → BI	0.12	0.068	Unsupported
H3	SI → BI	−0.045	0.447	Unsupported
H4	FC → BI	0.17	0.007**	Supported
H5	HM → BI	0.363	***	Supported
H6	PV → BI	−0.12	0.066	Unsupported
H7	HT → BI	−0.044	0.517	Unsupported
H8	TW → BI	0.120	0.062	Unsupported
H9	PI → BI	−0.003	0.961	Unsupported
H10	PTE → BI	0.320	***	Supported
H11	PPC → BI	−0.08	.178	Unsupported

Note. ***(p < 0.001), **(p < 0.01).

Results and Discussion

The present study intends to validate and expand upon the UTAUT2 model framework within the realm of AI tools. Additionally, it recognizes the determinants that impact Generation Z's intentions to incorporate AI tools. The R² value of .342 further confirms the UTAUT2 model's adequate explanatory power towards the BI of Gen Z in integrating AI tools. The model analysis found that the exogenous constructs or the predictor variables (Petersen, 2001) PE, FC, HM, and PTE significantly influence BI, which indicates that they play an essential role in the Gen Z learners’ BI to integrate AI tools. Among the four exogenous constructs, HM (β = 0.363) seemed to exert a significant substantial positive impact on Gen Z learners’ BI towards integrating AI tools in their daily lives, including learning. The constant interaction of Gen Z with the online environment (Szymkowiak et al., 2021) makes them more inclined to integrate AI tools (Ameen et al., 2023), aligning with their preferences for engaging and enjoyable learning experiences (Diwan et al., 2023; Sayed et al., 2023). PTE also appeared to significantly influence Gen Z learners’ BI toward integrating AI tools. Gen Z, also called Gen tech (Dolot, 2018), is well known for valuing technology (Hernandez-de-Menendez et al., 2020) that aids in quick task accomplishments. Thus, the task-enhancing capabilities of AI tools and technologies play a persuasive factor in encouraging their integration among Gen Z learners (Dai et al., 2023). PE significantly influences Gen Z learners’ BI toward integrating AI tools. Gen Z's desire for efficiency and innovation (Seemiller & Grace, 2015) makes them embrace the advancements in AI technology, aligning with their tech-savvy and outcome-driven approach (Pichler et al., 2021), which in turn contributes to PE. The noteworthy positive influence of FC (Jakkaew & Hemrungrote, 2017) on Gen Z learners’ BI towards integrating AI tools can be attributed to convenience and accessibility. When user-friendly, necessary conditions and resources are available (Alyoussef, 2021), it can reduce the barriers and enhance Gen Z's willingness to incorporate different AI tools. This emphasizes the significance of establishing an atmosphere that simplifies the use of AI tools, in line with Gen Z's propensity for effortless and successful technological interactions (Pillai et al., 2023; Tang et al., 2023).

Contrary to other studies (El-Masri & Tarhini, 2017; Handoko, 2020), EE has an insignificant influence on Gen Z learners’ BI towards integrating AI tools. This can be due to the technologically proficient traits of this generation. Gen Z individuals are often proficient in navigating cutting-edge technologies (Moore et al., 2017), making EE less of a determining factor in their decision to incorporate AI tools. Gen Z, also known for their efficiency and adaptability, might prioritize other factors, such as enjoyment or utility, over the perceived effort involved in integrating AI tools, rendering EE less influential in shaping their intentions. The exogenous construct SI has no significant influence on Gen Z learners’ BI toward integrating AI tools. The increasing autonomy and individualistic inclinations can characterize this (Jang & Chiang, 2023) of Gen Z. The Gen Z preference for individual choice and distinctive experiences indicates that external viewpoints and societal pressures have less influence in shaping their decisions concerning integrating AI tools. This result emphasizes the strategies for promoting individualized benefits and intrinsic motivations for endorsing Gen Z's incorporation of AI tools.

The insignificant influence of PV (Rudhumbu, 2022) on Gen Z learners’ BI to integrate AI tools can be attributed to different factors. Gen Z's priority for experiences over material considerations can be attributed to a greater emphasis on AI tools’ functionalities, benefits, and usability or enjoyment rather than their cost-effectiveness. Furthermore, the limited exposure to a wide range of AI tools could contribute to the non-significant influence of PV on Gen Z learners’ BI (Tewathia et al., 2020). The insignificant impact of HT, TW, PI, and PPC on Gen Z learners’ BI aligns with the early phase of AI in the Indian education sector (Market Trends, 2022). Considering the ongoing emergence of new AI tools, habitual behavior related to AI integration is still in the developmental stage among Gen Z learners. As AI tools become more prevalent and are employed regularly, the influence of habit on BI could become more noticeable over time. Being discerning in nature (Chicca & Shellenbarger, 2018), Gen Z individuals could require specific and convincing reasons to trust AI technologies. Factors like accessibility, prior interactions, and the reliability of AI systems can significantly influence trust and, consequently, impact their decision to integrate AI tools. Additionally, Gen Z's innate comfort and familiarity with advanced technologies (Pueschel et al., 2020) could reduce the distinctiveness of PI in determining BI. More importantly, the insignificant impact of PPC on Gen Z learners’ BI towards integrating AI tools could possibly be related to the lack of sound knowledge about the privacy and security risks associated with these tools (Mylrea & Robinson, 2023). Conversely, if Gen Z feels that AI tools can safeguard their privacy and are secure, this could lead to no significant impact of PPC on their decision to integrate AI tools. Further exploration into specific domains and contextual factors influencing TW, PI, and PPC is required to provide more clarity on these non-significant relationships.

Implications

Theoretical Implications

The study contributes to the corpus of literature regarding factors determining the widespread deployment of advanced technologies, notably those based on the Internet. By integrating novel constructs such as TW, PI, PTE, and PPC within the UTAUT2 framework, this research extends current theoretical models to better explain the determinants influencing Gen Z learners’ BI toward AI tool integration. These additions highlight the evolving landscape of technology adoption, especially in contexts where privacy and trust in AI systems are pivotal (Brusilovsky, 2024). Moreover, this study underscores the contextual relevance of these frameworks by examining these dynamics within the Indian educational landscape, offering insights applicable to similar emerging markets. The findings further emphasize the importance of updating theoretical models in response to technological advancements and demographic shifts, encouraging future research to explore these dimensions in detail. Additionally, the study adopts an interdisciplinary approach by integrating insights from psychology, information systems, and education, enriching theoretical foundations with diverse perspectives on technology adoption.

Practical Implications

The present study's findings on Gen Z learners’ BI towards integrating AI tools offer insightful information with practical implications for educators, technology developers, and policymakers. The strong positive influence of HM (Ramírez-Correa et al., 2019) on Gen Z's BI suggests that educational content and AI tools should be designed with a focus on enjoyment and engagement. Educators can utilize AI tools to bring gamified elements, interactive features, and multimedia content to align with Gen Z's preferences for enjoyable learning experiences (Alam, 2022; Ng et al., 2023). Emphasizing the task-enhancing capabilities of AI tools is crucial because the study highlights the notable beneficial impact of both PTE and PE on the BI of Gen Z. Educational platforms should prioritize showcasing how these AI tools can optimize tasks, provide fast information retrieval and improve overall efficiency in the learning process (Gonge et al., 2021). FC is significant for Gen Z, indicating their user-friendly design priority (Zwain, 2019). The development and deployment of AI tools with user-friendly interfaces, simple functionality, and easy accessibility should be the primary focus of developers and educators (Goldenthal et al., 2021). This will ease hurdles and encourage the preference of Gen Z to integrate these tools into their educational journeys. Even though PPC had no significant influence on BI, the study implies that privacy concerns must be addressed for a successful AI integration. Educational institutions, policymakers, and developers should focus on robust privacy measures and communicate these measures transparently among Gen Z learners (Nguyen et al., 2023). The study also necessitates establishing a national-level policy to ensure equity in AI integration within the education domain. The Government should acknowledge the initial stage of AI implementation in the education sector and view AI as a catalyst to achieve the country's Sustainable Development Goal 4 by 2030 (Saini et al., 2023).

Limitations and Directions for Future Research

Despite the present study offering insightful information on the Gen Z learners’ BI to integrate AI tools, further studies need to be undertaken to overcome limitations and improve the applicability of the findings. The study deployed a cross-sectional design with the sample. First, for better generalizability, a longitudinal study can be conducted to provide a more comprehensive overview of how Gen Z's attitudes and intentions evolve over time. Different aspects of longitudinal investigation can include changes in performance and effort expectancy as AI tools become more integrated into educational settings, shifts in trustworthiness and privacy concerns as Gen Z gains more experience with these tools, and the evolving impact of personal innovativeness and perceived task excellence. Additionally, understanding how continuous exposure to AI tools influences long-term behavioral intentions and actual usage patterns can provide deeper insights into sustained technology adoption and the potential for AI tools to enhance educational outcomes over time. Second, the model's ability to explain the determinants influencing Gen Z's BI in integrating AI tools is 34.2%, leaving 65.8% unexplained. Thus, additional factors like AI literacy, Perceived Autonomy, and Digital well-being could be included in the UTAUT2 model to improve the model's explanatory power. Third, future research must also investigate the effects of different moderating variables on Gen Z's BI in incorporating AI tools. Fourth, learners from K-12 levels should be the focus of future investigation into their attitudes and perceptions of AI tools integration in learning. Fifth, future studies could investigate the role of AI literacy in forming the attitudes of Gen Z towards AI tools, therefore providing better insights into the factors influencing their acceptance. Sixth, comparative studies can be conducted among different generations to gain insights into the specific aspects of Gen Z related to AI apart from other generations.

Conclusion

AI is rapidly emerging as the ubiquitous buzzword of recent times, with applications across diverse sectors, most notably in education. Recognizing the growing significance of AI, particularly in education, this study explores different determinants impacting the BI of Gen Z learners in HEIs in India concerning the integration of AI tools. The current investigation further confirms and extends upon the UTAUT2 model with four additional constructs: trustworthiness, personal innovativeness, perceived task excellence, and perceived privacy concerns. The R² value of 0.342 confirms the satisfactory explanatory power of the UTAUT2 model towards the BI of Gen Z regarding the integration of AI tools. The notable positive impact of PE, FC, HM, and PTE on BI indicates their vital role in molding Gen Z's outlook on AI tools. Notably, the substantial positive influence of HM demonstrates the importance of enjoyable and engaging learning experiences in driving Gen Z's inclination toward AI tool integration. In contrast to some existing literature, the study indicates that EE, SI, PV, HT, TW, PI, and PPC were not found to influence Gen Z's BI significantly. Additionally, this study adds to the body of current literature on the UTAUT2 model by evaluating the model's suitability to Gen Z's BI regarding integrating AI tools, specifically in the Indian context. Further, the study contributes to the ongoing discourse by providing sound groundwork for informed strategies and policies that can effectively integrate AI into the quality and equitable educational journeys of Gen Z learners in India.

Footnotes

Authors’ Contributions

Both authors, K. Kavitha, and V. P. Joshith, have made substantial and indispensable contributions throughout the entire research process, ensuring the depth and quality of this article. K. Kavitha and V. P. Joshith engaged in brainstorming sessions, shaping the core ideas and devising the research framework. Both authors played essential roles in acquiring, organizing, and meticulously analyzing the dataset. K. Kavitha wrote the first draft of the manuscript, and V. P. Joshith commented on the manuscript and made necessary grammatical corrections. V. P. Joshith supervised the study, offering guidance and expertise at every stage of the research process. Both authors revised the draft and contributed to the final compilation of the manuscript.

Availability of Data and Material

The datasets used during the current study are available from the corresponding author upon reasonable request.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

K. Kavitha

Author Biographies

K. Kavitha is a research scholar in the Department of Education at the Central University of Kerala, India. She is also a recipient of the Junior Research Fellowship award. Ms. Kavitha received her Master's in Education from the Department of Education at the Central University of Kerala and her Master's in Physics from TASC, Kannur University, Kerala, India. Her research focuses on educational technology (EdTech), specifically emphasizing Artificial Intelligence (AI) and AI-related technologies and their applications in Education and Science Education. Additionally, Ms. Kavitha has so far co-authored articles indexed in Scopus and Web of Science related to her research area.

V. P. Joshith, is a professor and Head at the Department of Education, Central University of Kerala, Kasaragod. He holds a master's degree in education and chemistry, as well as a doctoral degree in education. He has 20 years of teaching and research experience in various fields of Teacher Education. He is a recipient of the UGC Post-Doctoral Research Award. He has worked on multiple funded projects, MOOCs, and e-content development. He has authored two books and 47 research articles in the areas of TPACK, MOOCs, OER, Sustainable Development, and Higher Education. He has visited many premier institutions abroad, including NIE Singapore. Presently, he is the national coordinator for two MOOCs on the SWAYAM platform and a resource person for various seminars and workshops conducted at the national and international levels.

References

Ab Hamid

M. R.

Sami

Mohmad Sidek

M. H.

(2017). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. Journal of Physics: Conference Series, 890, Article 012163. https://doi.org/10.1088/1742-6596/890/1/012163

Abu Gharrah

Aljaafreh

(2021). Why students use social networks for education: Extension of UTAUT2. Journal of Technology and Science Education, 11(1), Article 53. https://doi.org/10.3926/jotse.1081

Aggarwal

Sadhna

Gupta

Mittal

Rastogi

(2022). Gen Z entering the workforce: Restructuring HR policies and practices for fostering the task performance and organizational commitment. Journal of Public Affairs, 22(3), https://doi.org/10.1002/pa.2535

AI in Education Global Market Report 2024. (2024, January). The Bussiness Research Company. https://www.thebusinessresearchcompany.com/report/ai-in-education-global-market-report

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Al-Adwan

A. S.

Al-Debei

M. M.

(2023). The determinants of Gen Z’s metaverse adoption decisions in higher education: Integrating UTAUT2 with personal innovativeness in IT. Education and Information Technologies, 29, 7413–7445. https://doi.org/10.1007/s10639-023-12080-1.

Al-Adwan

A. S.

Al-Adwan

Abbasi

G. A.

Albelbisi

N. A.

Habibi

(2023). Extending the technology acceptance model (TAM) to predict university Students’ intentions to use metaverse-based learning platforms. Education and Information Technologies, 28(11), 15381–15413. https://doi.org/10.1007/s10639-023-11816-3

Al-Azawei

Alowayr

(2020). Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two middle eastern countries. Technology in Society, 62, Article 101325. https://doi.org/10.1016/j.techsoc.2020.101325

Al-Qaysi

Mohamad-Nordin

Al-Emran

(2020). Employing the technology acceptance model in social media: A systematic review. Education and Information Technologies, 25(6), 4961–5002. https://doi.org/10.1007/s10639-020-10197-1

10.

Al-Riyami

Al-Maskari

Al-Ghnimi

(2023). Faculties behavioural intention toward the use of the fourth industrial revolution related-technologies in higher education institutions. International Journal of Emerging Technologies in Learning (IJET), 18(07), 159–177. https://doi.org/10.3991/ijet.v18i07.37051

11.

Alalwan

A. A.

Dwivedi

Y. K.

Rana

N. P.

Algharabat

(2018). Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40, 125–138. https://doi.org/10.1016/j.jretconser.2017.08.026

12.

Alam

(2022). A Digital Game based Learning Approach for Effective Curriculum Transaction for Teaching-Learning of Artificial Intelligence and Machine Learning. 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), 69–74. https://doi.org/10.1109/ICSCDS53736.2022.9760932

13.

Alam

(2023). Developing a Curriculum for Ethical and Responsible AI: A University Course on Safety, Fairness, Privacy, and Ethics to Prepare Next Generation of AI Professionals (pp. 879–894). https://doi.org/10.1007/978-981-99-1767-9_64

14.

Aldahdouh

T. Z.

Nokelainen

Korhonen

(2020). Technology and social Media usage in higher education: The influence of individual innovativeness. SAGE Open, 10(1), Article 215824401989944. https://doi.org/10.1177/2158244019899441

15.

Al Farsi

(2023). The efficiency of UTAUT2 model in predicting student’s acceptance of using virtual reality technology. International Journal of Interactive Mobile Technologies (IJIM), 17(12), 17–27. https://doi.org/10.3991/ijim.v17i12.36951

16.

Alhadabi

Karpinski

A. C.

(2020). Grit, self-efficacy, achievement orientation goals, and academic performance in university students. International Journal of Adolescence and Youth, 25(1), 519–535. https://doi.org/10.1080/02673843.2019.1679202

17.

Alhwaiti

(2023). Acceptance of Artificial Intelligence Application in the Post-COVID Era and Its Impact on Faculty Members’ Occupational Well-being and Teaching Self Efficacy: A Path Analysis Using the UTAUT 2 Model. Applied Artificial Intelligence, 37(1), 584–604. https://doi.org/10.1080/08839514.2023.2175110.

18.

Al Ka’bi

(2023). Proposed artificial intelligence algorithm and deep learning techniques for development of higher education. International Journal of Intelligent Networks, 4, 68–73. https://doi.org/10.1016/J.IJIN.2023.03.002

19.

Alomari

A. S. A.

Abdullah

N. L.

(2023). Factors influencing the behavioral intention to use Cryptocurrency among Saudi Arabian public university students: Moderating role of financial literacy. Cogent Business & Management, 10(1), 1–21. https://doi.org/10.1080/23311975.2023.2178092.

20.

Alruthaya

Nguyen

T.-T.

Lokuge

(2021). The Application of Digital Technology and the Learning Characteristics of Generation Z in Higher Education. ACIS 2021 Proceedings.65. https://aisel.aisnet.org/acis2021/65

21.

Alshammari

S. H.

Alshammari

M. H.

(2024). Factors affecting the adoption and use of ChatGPT in higher education. International Journal of Information and Communication Technology Education, 20(1), 1–16. https://doi.org/10.4018/IJICTE.339557

22.

Altalhi

(2021). Toward a model for acceptance of MOOCs in higher education: The modified UTAUT model for Saudi Arabia. Education and Information Technologies, 26(2), 1589–1605. https://doi.org/10.1007/s10639-020-10317-x

23.

Alyoussef

I. Y.

(2021). Factors influencing Students’ acceptance of M-learning in higher education: An application and extension of the UTAUT model. Electronics, 10(24), Article 3171. https://doi.org/10.3390/electronics10243171

24.

Ameen

Hosany

Taheri

(2023). Generation z’s psychology and new-age technologies: Implications for future research. Psychology & Marketing, 40(10), 2029–2040. https://doi.org/10.1002/mar.21868

25.

Chai

C. S.

Zhou

Yang

(2023). Modeling students’ perceptions of artificial intelligence assisted language learning. Computer Assisted Language Learning, 1–22. https://doi.org/10.1080/09588221.2023.2246519

26.

Arain

A. A.

Hussain

Rizvi

W. H.

Vighio

M. S.

(2019). Extending UTAUT2 toward acceptance of Mobile learning in the context of higher education. Universal Access in the Information Society, 18(3), 659–673. https://doi.org/10.1007/s10209-019-00685-8

27.

Axcell

Ellis

(2023). Exploring the attitudes and behaviour of gen Z students towards branded Mobile apps in an emerging market: UTAUT2 model extension. Young Consumers, 24(2), 184–202. https://doi.org/10.1108/YC-03-2022-1491

28.

Azman Ong

M. H.

Yusri

M. Y.

Ibrahim

N. S.

(2023). Use and behavioural intention using digital payment systems among rural residents: Extending the UTAUT-2 model. Technology in Society, 74, Article 102305. https://doi.org/10.1016/j.techsoc.2023.102305

29.

Babalola

S. S.

Nwanzu

C. L.

(2021). The current phase of social sciences research: A thematic overview of the literature. Cogent Social Sciences, 7(1), 1–20. https://doi.org/10.1080/23311886.2021.1892263.

30.

Bagdi

Bulsara

H. P.

Sankar

Sharma

(2023). The transition from traditional to digital: Factors that propel generation Z’s adoption of online learning. International Journal of Educational Management, 37(3), 695–717. https://doi.org/10.1108/IJEM-01-2023-0003

31.

Bansal

Jain

Seth

(2022). Digitalization in education: Application of UTAUT to use learning management system. Journal of content. Community and Communication, 15(8), 260–275. https://doi.org/10.31620/JCCC.06.22/18

32.

Baruh

Secinti

Cemalcilar

(2017). Online privacy concerns and privacy management: A meta-analytical review. Journal of Communication, 67(1), 26–53. https://doi.org/10.1111/jcom.12276

33.

Batucan

G. B.

Gonzales

G. G.

Balbuena

M. G.

Pasaol

K. R. B.

Seno

D. N.

Gonzales

R. R.

(2022). An Extended UTAUT Model to Explain Factors Affecting Online Learning System Amidst COVID-19 Pandemic: The Case of a Developing Economy. Frontiers in Artificial Intelligence, 5, 1–13. https://doi.org/10.3389/frai.2022.768831 .

34.

Baudier

Ammi

Deboeuf-Rouchon

(2020). Smart home: Highly-educated students’ acceptance. Technological Forecasting and Social Change, 153, Article 119355. https://doi.org/10.1016/j.techfore.2018.06.043

35.

BCG + nasscom. (2024). AI Powered Tech Services: A Roadmap for Future Ready Firms; AI & GenAI’s Role in Turbocharging the Industry. https://nasscom.in/knowledge-center/publications/ai-powered-tech-services-roadmap-future-ready-firms

36.

Bekman

(2023). The effects of self-esteem and online privacy concern on Generations’ attitudes towards Instagram. Akdeniz Üniversitesi İletişim Fakültesi Dergisi, 43, 134–160. https://doi.org/10.31123/akil.1303174

37.

Bezverhny

Dadteev

Barykin

Nemeshaev

Klimov

(2020). Use of chat bots in learning management systems. Procedia Computer Science, 169, 652–655. https://doi.org/10.1016/j.procs.2020.02.195

38.

Bińczycki

Łukasiński

Dorocki

(2023). Determinants of Motivation to Work in Terms of Industry 4.0—The Gen Z Perspective. Sustainability (Switzerland), 15(15), Article 12069. https://doi.org/10.3390/su151512069

39.

Boubker

(2024). From chatting to self-educating: Can AI tools boost student learning outcomes? Expert Systems with Applications, 238, Article 121820. https://doi.org/10.1016/j.eswa.2023.121820

40.

Brown

T. A.

(2006). Confirmatory Factor Analysis for Applied Research. In 2006. The Guilford Press.

41.

Brown, & Venkatesh (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), Article 399. https://doi.org/10.2307/25148690

42.

Brusilovsky

(2024). AI In education, learner control, and human-AI collaboration. International Journal of Artificial Intelligence in Education, 34(1), 122–135. https://doi.org/10.1007/s40593-023-00356-z

43.

Camacho-Morles

Slemp

G. R.

Pekrun

Loderer

Hou

Oades

L. G.

(2021). Activity achievement emotions and academic performance: A meta-analysis. Educational Psychology Review, 33(3), 1051–1095. https://doi.org/10.1007/s10648-020-09585-3

44.

Campbell

Greenwood

Prior

Shearer

Walkem

Young

Bywaters

Walker

(2020). Purposive sampling: Complex or simple? Research case examples. Journal of Research in Nursing, 25(8), 652–661. https://doi.org/10.1177/1744987120927206

45.

Chen

(2022). Adoption of M-learning apps: A sequential mediation analysis and the moderating role of personal innovativeness in information technology. Computers in Human Behavior Reports, 8, Article 100237. https://doi.org/10.1016/j.chbr.2022.100237

46.

Chen

Curran

P. J.

Bollen

K. A.

Kirby

Paxton

(2008). An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models. Sociological Methods & Research, 36(4), 462–494. https://doi.org/10.1177/0049124108314720

47.

Cheung

G. W.

Cooper-Thomas

H. D.

Lau

R. S.

Wang

L. C.

(2023). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41(2), 745–783. https://doi.org/10.1007/s10490-023-09871-y.

48.

Chicca

Shellenbarger

(2018). Connecting with generation Z: Approaches in nursing education. Teaching and Learning in Nursing, 13(3), 180–184. https://doi.org/doi.org/10.1016/j.teln.2018.03.008

49.

Chillakuri

(2020). Understanding generation Z expectations for effective onboarding. Journal of Organizational Change Management, 33(7), 1277–1296. https://doi.org/10.1108/JOCM-02-2020-0058

50.

Chiu

J.-I.

Tsuei

(2023). Effects of a motion graphic on escalator safety on college Students’ behavioural intention, determined using the unified theory of acceptance and use of technology. Education and Information Technologies, 28(5), 5453–5470. https://doi.org/10.1007/s10639-022-11195-1

51.

Christopher Westland

(2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487. https://doi.org/10.1016/j.elerap.2010.07.003

52.

Ciftci

Berezina

Kang

(2021). Effect of personal innovativeness on technology adoption in hospitality and tourism: Meta-analysis. In Information and communication technologies in tourism 2021 (pp. 162–174). Springer International Publishing. https://doi.org/10.1007/978-3-030-65785-7_14

53.

Cohen

(1988). Statistical Power Analysis for the Behavioral Sciences (Second). Lawrence Erlbaum Associates. https://www.utstat.toronto.edu/∼brunner/oldclass/378f16/readings/CohenPower.pdf

54.

Compeau

D. R.

Higgins

C. A.

(1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), Article 189. https://doi.org/10.2307/249688

55.

Cziko

G. A.

(1989). Unpredictability and indeterminism in human behavior: Arguments and implications for educational research. Educational Researcher, 18(3), Article 17. https://doi.org/10.2307/1174887

56.

Dai

Liu

Lim

C. P.

(2023). Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education. Procedia CIRP, 119, 84–90. https://doi.org/doi.org/10.1016/j.procir.2023.05.002

57.

Dash

Paul

(2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, Article 121092. https://doi.org/doi.org/10.1016/j.techfore.2021.121092

58.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008

59.

Davis

F. D.

Bagozzi

R. P.

Warshaw

P. R.

(1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x

60.

de Rezende

N. A.

de Medeiros

D. D.

(2022). How rating scales influence responses’ reliability, extreme points, middle point and respondent’s preferences. Journal of Business Research, 138, 266–274. https://doi.org/10.1016/j.jbusres.2021.09.031

61.

Dionika

Irwansyah

Safira

(2020, January 15). The Acceptance And Use of Digital Public Service: An Influence of Trust and An Application of UTAUT2. https://doi.org/10.4108/eai.9-10-2019.2291112.

62.

Diwan

Srinivasa

Suri

Agarwal

Ram

(2023). AI-based learning content generation and learning pathway augmentation to increase learner engagement. Computers and Education: Artificial Intelligence, 4, Article 100110. https://doi.org/10.1016/J.CAEAI.2022.100110

63.

Dolot

(2018). The characteristics of generation Z. E-Mentor, 2(74), 44–50. https://doi.org/10.15219/em74.1351 .

64.

dos Santos

P. M.

Cirillo

M. Â.

(2023). Construction of the average variance extracted index for construct validation in structural equation models with adaptive regressions. Communications in Statistics - Simulation and Computation, 52(4), 1639–1650. https://doi.org/10.1080/03610918.2021.1888122

65.

Duong

C. D.

Bui

D. T.

Pham

H. T.

A. T.

Nguyen

V. H.

(2023). How effort expectancy and performance expectancy interact to trigger higher education students’ uses of ChatGPT for learning. Interactive Technology and Smart Education, 21(3), 356–380. https://doi.org/10.1108/ITSE-05-2023-0096.

66.

Dwivedi

Y. K.

Rana

N. P.

Jeyaraj

Clement

Williams

M. D.

(2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y

67.

El-Masri

Tarhini

(2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the unified theory of acceptance and use of technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743–763. https://doi.org/10.1007/s11423-016-9508-8

68.

Faqih

K. M. S.

Jaradat

M.-I. R. M.

(2021). Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: Perspective from a developing country. Technology in Society, 67, Article 101787. https://doi.org/10.1016/j.techsoc.2021.101787

69.

Fianu

Blewett

Ampong

G. O.

(2020). Toward the development of a model of student usage of MOOCs. Education + Training, 62(5), 521–541. https://doi.org/10.1108/ET-11-2019-0262

70.

Fishbean

Ajzen

(1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley Publishing Company.

71.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), Article 39. https://doi.org/10.2307/3151312

72.

Gao

(2023). Understanding smart education continuance intention in a delayed benefit context: An integration of sensory stimuli, UTAUT, and flow theory. Acta Psychologica, 234, Article 103856. https://doi.org/10.1016/j.actpsy.2023.103856

73.

García de Blanes Sebastián

Sarmiento Guede

J. R.

Antonovica

(2022). Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants. Frontiers in Psychology, 13, Article 993935. https://doi.org/10.3389/fpsyg.2022.993935

74.

Goldenthal

Park

Liu

S. X.

Mieczkowski

Hancock

J. T.

(2021). Not all AI are equal: Exploring the accessibility of AI-mediated communication technology. Computers in Human Behavior, 125, Article 106975. https://doi.org/10.1016/j.chb.2021.106975

75.

Gonge

S. S.

Khobragade

R. N.

Thakare

V. M.

Deshpande

V. S.

Dhore

M. L.

(2021). An innovative step for enhancement in student results and teaching–learning process using educational technology. In A. Deyasi, S. Mukherjee, A. Mukherjee, A. K. Bhattacharjee, & A. Mondal (Eds.), Computational intelligence in digital pedagogy (pp. 235–249). Springer Singapore. https://doi.org/10.1007/978-981-15-8744-3_12

76.

Government of India. (2023). All India Survey on Higher Education (AISHE) 2021-22. Ministry of Education, Department of Higher Education. https://aishe.gov.in/aishe-final-report/

77.

Guhr

Werth

Blacha

P. P. H.

Breitner

M. H.

(2020). Privacy concerns in the smart home context. SN Applied Sciences, 2(2), Article 247. https://doi.org/10.1007/s42452-020-2025-8

78.

Habibi

Muhaimin

Danibao

B. K.

Wibowo

Y. G.

Wahyuni

Octavia

(2023). ChatGPT in higher education learning: Acceptance and use. Computers and Education: Artificial Intelligence, 5, Article 100190. https://doi.org/10.1016/j.caeai.2023.100190

79.

Hair

J. F.

Babin

B. J.

Krey

(2017). Covariance-Based structural equation modeling in the journal of advertising : Review and recommendations. Journal of Advertising, 46(1), 163–177. https://doi.org/10.1080/00913367.2017.1281777

80.

Hair

J. F.

Black

W. C.

Babin

B. J.

Anderson

R. E.

(2009). Multivariate Data Analysis (7th ed.). Pearson Education.

81.

Hair

J. F.

Howard

M. C.

Nitzl

(2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069

82.

Hair

J. F.

Hult

G. T. M.

Ringle

C. M.

Sarstedt

Danks

N. P.

Ray

(2021). Evaluation of reflective measurement models. In J. F. Hair Jr., G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, & S. Ray (Eds.), Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (pp. 75–90). Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7_4

83.

Hair

J. F.

Risher

J. J.

Sarstedt

Ringle

C. M.

(2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

84.

Hameed

Mathur

(2020). Generation Z in India: Digital natives and makers of change. In E. Gentina & E. Parry (Eds.), The new generation Z in Asia: Dynamics, differences, digitalisation (pp. 89–104). Emerald Publishing Limited. https://doi.org/10.1108/978-1-80043-220-820201010

85.

Handoko

B. L.

(2020). UTAUT 2 Model for Entrepreneurship Students on Adopting Technology. 2020 International Conference on Information Management and Technology (ICIMTech), 191–196. https://doi.org/10.1109/ICIMTech50083.2020.9211185

86.

Hanji

S. V.

Navalgund

Ingalagi

Desai

Hanji

S. S.

(2024). Adoption of AI Chatbots in Travel and Tourism Services (pp. 713–727). https://doi.org/10.1007/978-981-99-3236-8_57

87.

Henseler

Ringle

C. M.

Sarstedt

(2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

88.

Hernandez-de-Menendez

Escobar Díaz

C. A.

Morales-Menendez

(2020). Educational experiences with generation Z. International Journal on Interactive Design and Manufacturing (IJIDeM), 14(3), 847–859. https://doi.org/10.1007/s12008-020-00674-9

89.

Herrero

Á.

San Martín

Garcia-De los Salmones

M. d. M.

(2017). Explaining the adoption of social networks sites for sharing user-generated content: A revision of the UTAUT2. Computers in Human Behavior, 71, 209–217. https://doi.org/10.1016/j.chb.2017.02.007

90.

Horodyski

(2023). Recruiter’s perception of artificial intelligence (AI)-based tools in recruitment. Computers in Human Behavior Reports, 10, Article 100298. https://doi.org/10.1016/J.CHBR.2023.100298

91.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

92.

Min

(Kelly). (2023). The dark side of artificial intelligence in service: The “watching-eye” effect and privacy concerns. International Journal of Hospitality Management, 110, Article 103437. https://doi.org/10.1016/j.ijhm.2023.103437

93.

Hunde

M. K.

Demsash

A. W.

Walle

A. D.

(2023). Behavioral intention to use e-learning and its associated factors among health science students in mettu university, southwest Ethiopia: Using modified UTAUT model. Informatics in Medicine Unlocked, 36, Article 101154. https://doi.org/doi.org/10.1016/j.imu.2022.101154

94.

Imjai

Aujirapongpan

Jutidharabongse

Usman

(2024). Impacts of digital connectivity on Thailand’s generation Z undergraduates’ social skills and emotional intelligence. Contemporary Educational Technology, 16(1), ep487. https://doi.org/10.30935/cedtech/14043

95.

Inder

Sood

Grima

(2022). Antecedents of behavioural intention to adopt internet banking using structural equation modelling. Journal of Risk and Financial Management, 15(4), Article 157. https://doi.org/10.3390/jrfm15040157

96.

Internet in India. (2022). https://www.iamai.in/sites/default/files/research/Internet%20in%20India%202022_Print%20version.pdf

97.

Ioannou

Tussyadiah

Miller

Weick

(2021). Privacy nudges for disclosure of personal information: A systematic literature review and meta-analysis. PLOS ONE, 16(8), Article e0256822. https://doi.org/10.1371/journal.pone.0256822

98.

Jackson

J. D.

M. Y.

Park

J. S.

(2013). An empirical test of three mediation models for the relationship between personal innovativeness and user acceptance of technology. Information & Management, 50(4), 154–161. https://doi.org/10.1016/j.im.2013.02.006

99.

Jakkaew

Hemrungrote

(2017). The use of UTAUT2 model for understanding student perceptions using Google Classroom: A case study of Introduction to Information Technology course. 2017 International Conference on Digital Arts, Media and Technology (ICDAMT), 205–209. https://doi.org/10.1109/ICDAMT.2017.7904962

100.

Jang

Y.-T.

Chiang

I.-T.

(2023). Incorporating desire and persistence into understanding Gen Z learners’ continuance intention toward using YouTube for learning in digital learning context. Education and Information Technologies, 29(8), 10043–10068. https://doi.org/10.1007/s10639-023-12202-9.

101.

Janssen

Carradini

(2021). Generation Z workplace communication habits and expectations. IEEE Transactions on Professional Communication, 64(2), 137–153. https://doi.org/10.1109/TPC.2021.3069288

102.

Janssen

Rana

N. P.

Slade

E. L.

Dwivedi

Y. K.

(2018). Trustworthiness of digital government services: Deriving a comprehensive theory through interpretive structural modelling. Public Management Review, 20(5), 647–671. https://doi.org/10.1080/14719037.2017.1305689

103.

Jayatissa

K. A. D. U.

(2023). Generation Z – A new lifeline: A systematic literature review. Sri Lanka Journal of Social Sciences and Humanities, 3(2), 179–186. https://doi.org/10.4038/sljssh.v3i2.110

104.

Joa

C. Y.

Magsamen-Conrad

(2022). Social influence and UTAUT in predicting digital immigrants’ technology use. Behaviour & Information Technology, 41(8), 1620–1638. https://doi.org/10.1080/0144929X.2021.1892192

105.

Kang

Ahn

J.-W.

(2021). Model setting and interpretation of results in research using structural equation modeling: A checklist with guiding questions for reporting. Asian Nursing Research, 15(3), 157–162. https://doi.org/10.1016/j.anr.2021.06.001

106.

Kennedy

W. G.

(2012). Modelling human behaviour in agent-based models. In Agent-Based models of geographical systems (pp. 167–179). Springer Netherlands. https://doi.org/10.1007/978-90-481-8927-4_9

107.

Kline

R. B.

(2005). Principles and practice of structural equation modeling, 2nd ed. In Principles and practice of structural equation modeling, 2nd ed (pp. 1-534). Guilford Press.

108.

Kline

R. B.

(2016). Principles and Practice of Structural Equation Modeling - Fourth Edition. https://www.researchgate.net/publication/361910413

109.

Kronemann

Kizgin

Rana

Dwivedi

Y. K.

(2023). How AI encourages consumers to share their secrets? The role of anthropomorphism, personalisation, and privacy concerns and avenues for future research. Spanish Journal of Marketing - ESIC, 27(1), 3–19. https://doi.org/10.1108/SJME-10-2022-0213

110.

Kuen

Westmattelmann

Bruckes

Schewe

(2023). Who earns trust in online environments? A meta-analysis of trust in technology and trust in provider for technology acceptance. Electronic Markets, 33(1), Article 61. https://doi.org/10.1007/s12525-023-00672-1

111.

Kumar

Braud

Lee

L. H.

Hui

(2021). Theophany: Multimodal Speech Augmentation in Instantaneous Privacy Channels. Proceedings of the 29th ACM International Conference on Multimedia, 2056–2064. https://doi.org/10.1145/3474085.3475507

112.

Kuppelwieser

V. G.

Putinas

A.-C.

Bastounis

(2019). Toward application and testing of measurement scales and an example. Sociological Methods & Research, 48(2), 326–349. https://doi.org/10.1177/0049124117701486

113.

Lance

C. E.

Butts

M. M.

Michels

L. C.

(2006). The sources of four commonly reported cutoff criteria: What did they really say? Organizational Research Methods, 9(2), 202–220. https://doi.org/10.1177/1094428105284919

114.

Lavidas

Voulgari

Papadakis

Athanassopoulos

Anastasiou

Filippidi

Komis

Karacapilidis

(2024). Determinants of humanities and social sciences Students’ intentions to use artificial intelligence applications for academic purposes. Information, 15(6), Article 314. https://doi.org/10.3390/info15060314

115.

Lee

K. Y.

Sheehan

Lee

Chang

(2021). The continuation and recommendation intention of artificial intelligence-based voice assistant systems (AIVAS): The influence of personal traits. Internet Research, 31(5), 1899–1939. https://doi.org/10.1108/INTR-06-2020-0327

116.

Lim

W. M.

Gunasekara

Pallant

J. L.

Pallant

J. I.

Pechenkina

(2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. International Journal of Management Education, 21(2), Article 100790. https://doi.org/10.1016/j.ijme.2023.100790

117.

Limayem, Hirt, & Cheung (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), Article 705. https://doi.org/10.2307/25148817

118.

Madarász

Mészáros

L. A.

Köte

Á.

Farkas

Nagy

Z. K.

(2023). AI-based analysis of in-line process endoscope images for real-time particle size measurement in a continuous pharmaceutical milling process. International Journal of Pharmaceutics, 641, Article 123060. https://doi.org/10.1016/J.IJPHARM.2023.123060

119.

Magano

Silva

Figueiredo

Vitória

Nogueira

Pimenta Dinis

M. A.

(2020). Generation Z: Fitting project management soft skills competencies—A mixed-method approach. Education Sciences, 10(7), Article 187. https://doi.org/10.3390/educsci10070187

120.

Mahapatra

G. P.

Bhullar

Gupta

(2022). Gen Z: An emerging phenomenon. NHRD Network Journal, 15(2), 246–256. https://doi.org/10.1177/26314541221077137

121.

Marien

Custers

Aarts

(2019). Studying human habits in societal context: Examining support for a basic stimulus–response mechanism. Current Directions in Psychological Science, 28(6), 614–618. https://doi.org/10.1177/0963721419868211

122.

Market Trends. (2022). How the Indian Education Sector Adopted AI Systems for Students? Analytics Insights. https://www.analyticsinsight.net/how-the-indian-education-sector-adopted-ai-systems-for-students/.

123.

Marzuki, Widiati

Rusdin

Darwin

, & Indrawati, (2023). The impact of AI writing tools on the content and organization of students’ writing: EFL teachers’ perspective. Cogent Education, 10(2), Article 2236469. https://doi.org/10.1080/2331186X.2023.2236469

124.

Maydeu-Olivares

Shi

Rosseel

(2018). Assessing fit in structural equation models: A Monte-Carlo evaluation of RMSEA versus SRMR confidence intervals and tests of close fit. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 389–402. https://doi.org/10.1080/10705511.2017.1389611

125.

McKinsey. (2023). The state of AI in 2023: Generative AI’s breakout year. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year.

126.

Meet

R. K.

Kala

Al-Adwan

A. S.

(2022). Exploring factors affecting the adoption of MOOC in generation Z using extended UTAUT2 model. Education and Information Technologies, 27(7), 10261–10283. https://doi.org/10.1007/s10639-022-11052-1

127.

Meyliana

Santoso

H. A. E.

Surjandy

S. W.

Fernando

Condrobimo

A. R.

(2020). Improving the Quality of Learning Management System (LMS) based on Student Perspectives Using UTAUT2 and Trust Model. 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), 1–6. https://doi.org/10.1109/ICICoS51170.2020.9298985.

128.

Mhlanga

(2023). Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning (pp. 387–409). https://doi.org/10.1007/978-3-031-37776-1_17.

129.

Mohd Rahim

N. I.

Iahad

N. A.

Yusof

A. F.

Al-Sharafi

M. A.

(2022). AI-Based Chatbots adoption model for higher-education institutions: A hybrid PLS-SEM-neural network modelling approach. Sustainability, 14(19), Article 12726. https://doi.org/10.3390/su141912726

130.

Molinillo

Rejón-Guardia

Anaya-Sánchez

Liébana-Cabanillas

(2023). Impact of perceived value on intention to use voice assistants: The moderating effects of personal innovativeness and experience. Psychology & Marketing, 40(11), 2272–2290. https://doi.org/10.1002/mar.21887

131.

Moore

G. C.

Benbasat

(1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192

132.

Moore

Jones

Frazier

R. S.

(2017). Engineering education for generation Z. American Journal of Engineering Education (AJEE), 8(2), 111–126. https://doi.org/10.19030/ajee.v8i2.10067

133.

Moorthy

Tzu Yee

Chun T’ing

Vija Kumaran

(2019). Habit and hedonic motivation are the strongest influences in Mobile learning behaviours among higher education students in Malaysia. Australasian Journal of Educational Technology, 35(4), Article 35. https://doi.org/10.14742/ajet.4432

134.

Mylrea

Robinson

(2023). Artificial Intelligence (AI) Trust Framework and Maturity Model: Applying an Entropy Lens to Improve Security, Privacy, and Ethical AI. Entropy, 25(10) Article 1429, https://doi.org/10.3390/e25101429

135.

Nayak

Bhattacharyya

S. S.

Kumar

Jumnani

R. K.

(2022). Exploring the factors influencing adoption of health-care wearables among generation Z consumers in India. Journal of information. Communication and Ethics in Society, 20(1), 150–174. https://doi.org/10.1108/JICES-07-2021-0072

136.

D. T. K.

Lee

Tan

R. J. Y.

Downie

J. S.

Chu

S. K. W.

(2023). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, 28(7), 8445–8501. https://doi.org/10.1007/s10639-022-11491-w

137.

Nguyen

Ngo

H. N.

Hong

Dang

Nguyen

B.-P. T.

(2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w

138.

Ostic

Qalati

S. A.

Barbosa

Shah

S. M. M.

Galvan Vela

Herzallah

A. M.

Liu

(2021). Effects of Social Media Use on Psychological Well-Being: A Mediated Model. Frontiers in Psychology, 12, Article 678766. https://doi.org/10.3389/fpsyg.2021.678766

139.

Ozili

P. K.

(2022). The Acceptable R-Square in Empirical Modelling for Social Science Research. SSRN Electronic Journal, 1–9. https://doi.org/10.2139/ssrn.4128165.

140.

Pagán

Medina

(2021). The Acceptance of MOODLE Learning Management System in Higher Institution during COVID-19 Pandemic. 8024–8034. https://doi.org/10.21125/inted.2021.1620.

141.

Parhamnia

(2022). Investigating mobile acceptance in academic library services based on unified theory of acceptance and use of technology model (UTAUT-2). The Journal of Academic Librarianship, 48(5), Article 102570. https://doi.org/10.1016/j.acalib.2022.102570

142.

Pataki-Bittó

Kapusy

(2021). Work environment transformation in the post COVID-19 based on work values of the future workforce. Journal of Corporate Real Estate, 23(3), 151–169. https://doi.org/10.1108/JCRE-08-2020-0031

143.

Peels

Bouter

(2023). Replication and trustworthiness. Accountability in Research, 30(2), 77–87. https://doi.org/10.1080/08989621.2021.1963708

144.

Pelletier

Robert

Muscanell

Mccormack

Reeves

Arbino

Grajek

Birdwell

Liu

Mandernach

Moore

Porcaro

Rutledge

Zimmern

(2023). 2023 EDUCAUSE Horizon Report. https://www.educause.edu/horizon-report-teaching-and-learning-2023

145.

Persada

S. F.

Miraja

B. A.

Nadlifatin

(2019). Understanding the generation z behavior on D-learning: A unified theory of acceptance and use of technology (UTAUT) approach. International Journal of Emerging Technologies in Learning, 14(5), 20–33. https://doi.org/10.3991/ijet.v14i05.9993

146.

Petersen

(2001). Endogeneity: Methodology. International Encyclopedia of the Social & Behavioral Sciences, 4511–4513. https://doi.org/10.1016/B0-08-043076-7/00778-6

147.

Pichler

Kohli

Granitz

(2021). DITTO For gen Z: A framework for leveraging the uniqueness of the new generation. Business Horizons, 64(5), 599–610. https://doi.org/10.1016/j.bushor.2021.02.021

148.

Pillai

Sivathanu

Metri

Kaushik

(2023). Students’ adoption of AI-based teacher-bots (T-bots) for learning in higher education. Information Technology & People, ahead-of-print(ahead-of-print). https://doi.org/10.1108/ITP-02-2021-0152.

149.

Podsakoff

P. M.

MacKenzie

S. B.

Lee

J.-Y.

Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

150.

Pueschel

Johnson

R. C.

Dhanani

L. Y.

(2020). Putting gen Z first: Educating with a generational mind-set. Industrial and Organizational Psychology, 13(4), 594–598. https://doi.org/10.1017/iop.2020.103

151.

Ramírez-Correa

Rondán-Cataluña

F. J.

Arenas-Gaitán

Martín-Velicia

(2019). Analysing the acceptation of online games in mobile devices: An application of UTAUT2. Journal of Retailing and Consumer Services, 50, 85–93. https://doi.org/10.1016/j.jretconser.2019.04.018

152.

Rodway

Schepman

(2023). The impact of adopting AI educational technologies on projected course satisfaction in university students. Computers and Education: Artificial Intelligence, 5, Article 100150. https://doi.org/10.1016/j.caeai.2023.100150

153.

Roschelle

Lester

Fusco

(2020). AI and the Future of Learning: Expert Panel Report. https://circls.org/reports/ai-report.

154.

Rudhumbu

(2022). Applying the UTAUT2 to predict the acceptance of blended learning by university students. Asian Association of Open Universities Journal, 17(1), 15–36. https://doi.org/10.1108/AAOUJ-08-2021-0084

155.

Saini

Sengupta

Singh

(2023). Sustainable development goal for quality education (SDG 4): A study on SDG 4 to extract the pattern of association among the indicators of SDG 4 employing a genetic algorithm. Education and Information Technologies, 28(2), 2031–2069. https://doi.org/10.1007/s10639-022-11265-4

156.

Saiyed

Srivastava

(2022). Entrepreneurship and Ethics-Perspective of Gen Z. Article in International Journal of Special Education. https://doi.org/10.5281/zenodo.6140996.

157.

Salah

Al Halbusi

Abdelfattah

(2023). May the force of text data analysis be with you: Unleashing the power of generative AI for social psychology research. Computers in Human Behavior: Artificial Humans, 1(2), Article 100006. https://doi.org/10.1016/j.chbah.2023.100006

158.

Salloum

S. A.

(2024). Trustworthiness of the AI (pp. 643–650). https://doi.org/10.1007/978-3-031-52280-2_41.

159.

Sapra

R. L.

(2014). Using R2 with caution. Current Medicine Research and Practice, 4(3), 130–134. https://doi.org/10.1016/J.CMRP.2014.06.002

160.

Sayed

W. S.

Noeman

A. M.

Abdellatif

Abdelrazek

Badawy

M. G.

Hamed

El-Tantawy

(2023). AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platform. Multimedia Tools and Applications, 82(3), 3303–3333. https://doi.org/10.1007/s11042-022-13076-8

161.

Schomakers

E.-M.

Lidynia

Vervier

L. S.

Calero Valdez

Ziefle

(2022). Applying an extended UTAUT2 model to explain user acceptance of lifestyle and therapy Mobile health apps: Survey study. JMIR MHealth and UHealth, 10(1), Article e27095. https://doi.org/10.2196/27095

162.

Seemiller

Grace

(2015). Generation Z Goes to College. John Wiley & Sons. https://www.google.co.in/books/edition/Generation_Z_Goes_to_College/A5dPCwAAQBAJ?hl=en&gbpv=0

163.

Seemiller

Grace

(2017). Generation Z: Educating and engaging the next generation of students. About Campus: Enriching the Student Learning Experience, 22(3), 21–26. https://doi.org/10.1002/abc.21293

164.

Sewandono

R. E.

Thoyib

Hadiwidjojo

Rofiq

(2023). Performance expectancy of E-learning on higher institutions of education under uncertain conditions: Indonesia context. Education and Information Technologies, 28(4), 4041–4068. https://doi.org/10.1007/s10639-022-11074-9

165.

Sghir

Adadi

Lahmer

(2022). Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022). Education and Information Technologies, 28, 8299–8333. https://doi.org/10.1007/s10639-022-11536-0.

166.

Shi

Maydeu-Olivares

(2020). The effect of estimation methods on SEM fit indices. Educational and Psychological Measurement, 80(3), 421–445. https://doi.org/10.1177/0013164419885164

167.

Singh

Sibi

P. S.

(2023). E-loyalty formation of generation Z: Personal characteristics and social influences. Journal of Tourism, Heritage and Services Marketing, 9(1), 3–14. https://doi.org/10.5281/zenodo.8054004

168.

Sitar-Tăut

(2021). Mobile learning acceptance in social distancing during the COVID-19 outbreak: The mediation effect of hedonic motivation. Human Behavior and Emerging Technologies, 3(3), 366–378. https://doi.org/10.1002/hbe2.261

169.

Slimi

(2021). The Impact of AI Implementation in Higher Education on Educational Process Future: A Systematic Review. https://doi.org/10.21203/rs.3.rs-1081043/v1

170.

Soper

D. S.

(2023). Free A-priori sample size calculator for structural equation models - Free statistics calculators. https://www.danielsoper.com/statcalc/calculator.aspx?id=89.

171.

SPSS Amos IBM. (n.d.). Retrieved November 6, 2023, from https://www.ibm.com/products/structural-equation-modeling-sem.

172.

Srivastava

Mohta

Shunmugasundaram

(2024). Adoption of digital payment FinTech service by gen Y and gen Z users: Evidence from India. Digital policy. Regulation and Governance, 26(1), 95–117. https://doi.org/10.1108/DPRG-07-2023-0110

173.

Strzelecki

(2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1–14. https://doi.org/10.1080/10494820.2023.2209881

174.

Sun

C.-C.

(2021). Analyzing determinants for adoption of intelligent personal assistant: An empirical study. Applied Sciences, 11(22), Article 10618. https://doi.org/10.3390/app112210618

175.

Sundus

K. I.

Hammo

B. H.

Al-Zoubi

M. B.

Al-Omari

(2022). Solving the multicollinearity problem to improve the stability of machine learning algorithms applied to a fully annotated breast cancer dataset. Informatics in Medicine Unlocked, 33, Article 101088. https://doi.org/10.1016/j.imu.2022.101088

176.

Szymkowiak

Melović

Dabić

Jeganathan

Kundi

G. S.

(2021). Information technology and gen Z: The role of teachers, the internet, and technology in the education of young people. Technology in Society, 65, Article 101565. https://doi.org/10.1016/j.techsoc.2021.101565

177.

Tamilmani

Rana

N. P.

Wamba

S. F.

Dwivedi

(2021). The extended unified theory of acceptance and use of technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57, Article 102269. https://doi.org/10.1016/j.ijinfomgt.2020.102269

178.

Tang

K.-Y.

Chang

C.-Y.

Hwang

G.-J.

(2023). Trends in artificial intelligence-supported e-learning: A systematic review and co-citation network analysis (1998–2019). Interactive Learning Environments, 31(4), 2134–2152. https://doi.org/10.1080/10494820.2021.1875001

179.

Taylor

Todd

(1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), Article 561. https://doi.org/10.2307/249633

180.

Tewathia

Kamath

Ilavarasan

P. V.

(2020). Social inequalities, fundamental inequities, and recurring of the digital divide: Insights from India. Technology in Society, 61, Article 101251. https://doi.org/10.1016/j.techsoc.2020.101251

181.

Thakkar

J. J.

(2020). Applications of Structural Equation Modelling with AMOS 21, IBM SPSS (pp. 35–89). https://doi.org/10.1007/978-981-15-3793-6_4.

182.

Thiebes

Lins

Sunyaev

(2021). Trustworthy artificial intelligence. Electronic Markets, 31(2), 447–464. https://doi.org/10.1007/s12525-020-00441-4

183.

Thompson

R. L.

Higgins

C. A.

Howell

J. M.

(1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), Article 125. https://doi.org/10.2307/249443

184.

Thongsri

Shen

Bao

Alharbi

I. M.

(2018). Integrating UTAUT and UGT to explain behavioural intention to use M-learning. Journal of Systems and Information Technology, 20(3), 278–297. https://doi.org/10.1108/JSIT-11-2017-0107

185.

Thornberg

Jungert

Hong

J. S.

(2023). The indirect association between moral disengagement and bystander behaviors in school bullying through motivation: Structural equation modelling and mediation analysis. Social Psychology of Education, 26(2), 533–556. https://doi.org/10.1007/s11218-022-09754-y

186.

Turing

A. M.

(2009). Computing machinery and intelligence. In R. Epstein, G. Roberts, & G. Beber (Eds.), Parsing the turing test (pp. 23–65). Springer Netherlands. https://doi.org/10.1007/978-1-4020-6710-5_3.

187.

Turner

(2015). Generation Z: Technology and social interest. The Journal of Individual Psychology, 71(2), 103–113. https://doi.org/10.1353/jip.2015.0021

188.

Twum

K. K.

Ofori

Keney

Korang-Yeboah

(2022). Using the UTAUT, personal innovativeness and perceived financial cost to examine student’s intention to use E-learning. Journal of Science and Technology Policy Management, 13(3), 713–737. https://doi.org/10.1108/JSTPM-12-2020-0168

189.

Venkatesh, Morris, Davis, & Davis (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

190.

Venkatesh, Thong, & Xu (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), Article 157. https://doi.org/10.2307/41410412

191.

Vimalkumar

Sharma

S. K.

Singh

J. B.

Dwivedi

Y. K.

(2021). ‘Okay google, what about my privacy?’: User’s privacy perceptions and acceptance of voice based digital assistants. Computers in Human Behavior, 120, Article 106763. https://doi.org/10.1016/j.chb.2021.106763

192.

Walsh

(2023). Will AI end privacy? How do we avoid an Orwellian future. AI & SOCIETY, 38(3), 1239–1240. https://doi.org/10.1007/s00146-022-01433-y

193.

Wang

(2019). Structural Equation Modeling. Wiley. https://doi.org/10.1002/9781119422730

194.

Weinswig Deborah (2016). Gen Z: Get Ready for the Most Self-Conscious, Demanding Consumer Segment. https://www.deborahweinswig.com/wp-content/uploads/2016/08/Gen-Z-Report-2016-by-Fung-Global-Retail-Tech-August-29-2016.pdf

195.

(2024). Do AI chatbots improve students learning outcomes? Evidence from a meta-analysis. British Journal of Educational Technology, 55(1), 10–33. https://doi.org/10.1111/bjet.13334

196.

Xiong

Shi

Liu

(2024). More trust or more risk? User Acceptance of Artificial Intelligence Virtual Assistant. Human Factors and Ergonomics in Manufacturing & Service Industries, 34(3), 190–205. https://doi.org/10.1002/hfm.21020

197.

Yilmaz

Karaoglan Yilmaz

F. G.

(2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, Article 100147. https://doi.org/doi.org/10.1016/j.caeai.2023.100147

198.

Zhang

Aslan

A. B.

(2021). AI technologies for education: Recent research & future directions. In Computers and Education: Artificial Intelligence (Vol. 2). Elsevier B.V. https://doi.org/10.1016/j.caeai.2021.100025.

199.

Zhu

Huang

Zhou

Shi

Ying

Wang

(2024). Could AI ethical anxiety, perceived ethical risks and ethical awareness about AI influence university Students’ use of generative AI products? An Ethical Perspective. International Journal of Human–Computer Interaction, 1–23. https://doi.org/10.1080/10447318.2024.2323277

200.

Zhu

Liu

O. L.

Lee

H. S.

(2020). The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing. Computers & Education, 143, Article 103668. https://doi.org/10.1016/J.COMPEDU.2019.103668

201.

Zwain

A. A. A.

(2019). Technological innovativeness and information quality as neoteric predictors of users’ acceptance of learning management system. Interactive Technology and Smart Education, 16(3), 239–254. https://doi.org/10.1108/ITSE-09-2018-0065

Factors Shaping the Adoption of AI Tools among Gen Z: An Extended UTAUT2 Model Investigation Using CB-SEM

Abstract

Keywords

Theoretical Framework

Literature Review and Hypotheses Development

Performance Expectancy (PE)

Effort Expectancy (EE)

Social Influence (SI)

Facilitating Conditions (FC)

Hedonic Motivation (HM)

Price Value (PV)

Habit (HT)

Trustworthiness (TW)

Personal Innovativeness (PI)

Perceived Task Excellence (PTE)

Perceived Privacy Concern (PPC)

Methodology

Research Design

Data Collection and Respondents

Data Analysis

Measurement Model Assessment

Structural Model Assessment

Results and Discussion

Implications

Theoretical Implications

Practical Implications

Limitations and Directions for Future Research

Conclusion

Footnotes

Authors’ Contributions

Availability of Data and Material

Declaration of Conflicting Interests

Funding

ORCID iD

Author Biographies

References