Exploratory Study on Gen Z Students’ Adoption and Behavioural Intention of Edtech Services in Management Education by UTAUT model

Prof. Dinesh Kumar R.
Assistant Professor,
CHRIST (Deemed to be University), Hosur Road, Bengaluru.
Email id: dinesh.kumar@christuniversity.in

Abstract

This paper attempts to analyse Generation Z students’ in Management education and their acceptance and behaviour toward education-technology (Edtech) services using Unified Theory of Acceptance and Use of Technology (UTAUT) model. This study is situated against a rigorous investigation of the aspects influencing students’ adoption intention of Edtech in their higher education. The data for the study was obtained directly from the 120 respondents via questionnaire and analysed for convergent power and composite trustworthiness of the measurement model. Additional regression analyses were performed to examine students’ behavioural intentions when using educational technology (Edtech) services based on various factors or constructs identified. The outcome of this study points out that two constructs viz., Performance Efficiency (PE) and Facilitating Conditions (FC) had a moderately positive effect, while the other three constructs viz., Service Quality(SQ), Effort Expectancy (EE) and Social Influence (SI) had a low affirmative effect on Gen Z students’ adoption intention of Edtech in management education institutions.

Keywords: Edtech, UTAUT model, Gen Z, TAM model, Adoption and Behavioural intention, Higher Educational Institutions (HEIs)

Introduction

Education technology refers to the use of modern media and materials to enhance learning experiences. It can refer to any type of teaching or learning that employs technology. In other words, the use of technological apps, tools, or services to boost learning. Mitra & Singh (2020) underlined the relevance of education technology in their essay, which was revealed by the Ministry of Human Resource Development (MHRD) through the National Education Policy (NEP) introduced in the year 2020. The policy outlines the role of technology in tackling a variety of societal concerns and strives to foster multidisciplinary research.

Higher education institutions (HEIs) have already been advised to establish start-up incubator centres and scientific and technological centres, and a National Research Foundation has now been proposed to promote research culture. The National Educational Technology Forum (NETF) will be established as a forum for open dialogue on the use of technology in higher education institutions to improve learning, assessment, planning, and governance, according to the policy.

The policy further encourages investments in digital facilities, the institution of virtual labs and digital repositories, the training of teachers to become high-quality online content producers, the creation and implementation of online assessments, and the incorporation of benchmark tests for content, technological trends, and pedagogical content knowledge for online teaching-learning.

Literature review

Teräs et al. (2020) examined a fundamental focal point to envisage the possible future issues arising from the haphazard adoption of commercial online educational frameworks whose strategies may not typically be driven by best academic methodologies but by business plans that leverage users’ data for potential future revenue expectations.

Karapanos et al. (2017) examine the deployment of educational technology through the lens of cognitive and behavioural reasoning hypotheses, drawing implications for creating an enabling environment for technology-assisted learning and teaching. They interviewed 103 undergraduates to establish the benefits and disadvantages of using online courses for academic achievement in a university environment. Their findings revealed that students value the autonomy that web-based colleges have to offer first and probably most importantly.

Goswami et al. (2014) explored the effect of advanced technologies on Indian education and its numerous challenges and benefits. It goes on to suggest that in today’s modern world, the role of the educator in classroom instruction is that of a facilitator.

Paluri et al. (2015) investigated student acceptance and behaviour toward e-learning using the technology acceptance model (TAM). Significant correlations were found between user satisfaction, perceived usefulness, attitude, performance expectancy to use, and actual use.

Lazar et al. (2020) identified, substantiated, and reviewed an extended Technology Acceptance Model (TAM) in their study.

Abu-Al-Aish & Love (2013) investigated randomised control trials in the aforementioned Edtech classifications.

Miglani & Burch (2018) held out hope that the study might promote the understanding of how technology can be used to help learning.

Amin et al. (2018) conducted a study to investigate students’ intentions to use Ed-Tech (Educational Technology) at the higher education level in Bangladesh.

Chan et al. (2015) examined the engagement of undergraduates in active plus blended learning approaches with learning technologies.

According to Oyedotun et al. (2020), the unforeseen shift to online pedagogy in developing countries as a consequence of COVID-19 has divulged some inequities and obstacles, as well as perks.

Alshehri et al. (2019) studied how people accept and use the whiteboard style using the UTAUT model.

Attuquayefio & Addo (2013) explored to have a better knowledge of the challenges surrounding tertiary students’ adoption of information and communication technology (ICT).

Research Objectives

1. To understand the behavioural perception of the students towards the adoption of education-technology in their learning.
2. To study the Gen Z student’s preferences in the adoption intention of Edtech services over traditional ways of learning.

Research Methodology

This study is based on a survey method where a questionnaire was constructed and primary data for analysis was collected to study the behavioural intention behind the adoption of Edtech services.

The sample population is restricted to the management education students of higher education institutions (HEIs) in Bangalore between the ages of 16 and 25, thus belonging to Generation Z.

The sampling method employed in this research is systematic sampling (a non-probability sampling method), by which the sample is drawn from a group of people who are easy to contact or reach.

The holistic view based on the technology acceptance (UTAUT) prototype is an augmentation of Venkatesh et al. (2003)’s technology acceptance model.

Constructs of the UTAUT model used in the Research

i. Performance Expectancy (PE)

Performance Expectancy (PE) is defined as the extent to which a person believes that technology progresses job performance.

ii. Effort Expectancy (EE)

Effort Expectancy (EE) has been defined as the degree of comfort associated with using technology.

iii. Social Influence (SI)

Social Influence (SI) is defined as the degree to which an individual comprehends it is important that everyone else believe he or she ought to use new information technology.

iv. Service Quality (SQ)

Service Quality (SQ) is defined as sturdiness and turnaround time, content quality, and security.

v. Facilitating Conditions (FC)

Facilitating Conditions (FC) are defined as a person’s confidence in the existence of a technological and organisational environment that promotes system throughput.

vi. Adoption and Behavioural Intention (ABI)

Adoption and Behavioural Intention (ABI) is defined as the level to which a person has made conscientious decisions about whether or not to implement or accept a specific future behaviour.

Research Hypothesis

• H1: Performance Expectancy (PE) has a statistically substantial impact on acceptance and behavioural intention (ABI) to use Edtech services.
• H2: Effort Expectancy (EE) has a statistically significant impact on adoption and behavioural intent (ABI) to use Edtech services.
• H3: Social Influence (SI) has a statistically significant impact on adoption and behavioural intention (ABI) to use Edtech services.
• H4: Service Quality (SQ) has a statistically noteworthy impact on adoption and behavioural goal (ABI) to use Edtech services.
• H5: Facilitating Conditions (FC) has a statistically weighty impact on adoption and behavioural purpose (ABI) to use Edtech services.

Data Analysis and Discussion

Convergent validity reveals the connection between 2 factors that claim to assess the same concept. Convergent validity can be determined using the three requirements postulated by Fornell and Larcker (1981), which are enumerated below:

1. Factor loadings larger than 0.50 were considered extremely significant;
2. Composite reliability should be significantly larger than 0.7; and
3. The average extracted variance should be stronger than 0.5.

When the Composite Reliability (CR) and Average Variance Extracted (AVE) numbers exceed the above-mentioned criteria, we can infer that our measurement model is valid.

The results, as given in table 1, reveal that all components suit their associated variables quite well. All of the factor loadings are more than 0.50. Except for PE and EE, the composite reliability values (CR) are all greater than 0.7, and the average extracted variances (AVE) are all higher than the acceptable 0.5 level. However, it has been asserted that if your AVE is fewer than 0.5 and the Composite Reliability is larger than 0.6, is therefore considered acceptable (Fornell & Larcker, 1981).

Table 1 – Results for Convergent Validity and Composite Reliability

Table 2 – Validity (Cronbach Alpha) Test

According to Table 3, the combined impacts of all components investigated in our study have a substantial influence on behavioural intention to embrace any Edtech services, with R2 = 0.095.

Table 3 – Model Summary of Multiple Regression Analysis

However, in Table 4 the individual constructs are found to be significantly influential on the behavioural intention for adopting Edtech services with the significance value (p) being less than 0.05 at 95% confidence interval.

Table 4 – Table showing Coefficients of Constructs

The data confirmed the model’s strong internal reliability and predictability, which implies that it has a sizable explanatory potential. This study discovered that Performance Expectancy (PE) and Facilitating Conditions (FC) are critical factors that influence students’ behavioural intentions when it comes to using Edtech services.

Furthermore, the results demonstrated that Service Quality (SQ), Effort Expectancy (EE), and Social Influence (SI) all have a favourable influence but are not deemed to be the most significant.

Based on the responses, the researcher concluded that the majority of students would continue to use Ed-Tech Platforms after the Covid-19 Pandemic, but they would prefer blended learning, where they could benefit from both social interaction and Edtech services introduced alongside their academics.

The researcher further advises that Higher Education Institutions (HEIs) recognise the relevance of technology and encourage students to use Edtech platforms by instituting an obligatory curriculum.

Scope and limitations of the research

This research explores the impact of the adoption of educational technology services by the demand side, i.e., students. Investigating the factors influencing education technology adoption by student users might aid in providing better services and strengthening relationships among education technology vendors and consumers.

Despite its broad scope, this study has certain inevitable drawbacks. The responses to survey questions are heavily influenced by what respondents think to be true.

Furthermore, due to resource as well as time constraints, the sample size is restricted and is not a realistic reflection of the entire population, i.e., India in general.

Conclusion

In many sections of the worldwide community, the COVID-19 pandemic has ushered about a dramatic upheaval that has essentially turned everything upside down.

The rate at which learners adopt technology determines the success of educational technology.

As a result of this research, we were able to learn about students’ preferences, intentions, and objectives for using Edtech services, which will aid in the design and deployment of better learning programmes and, as a result, enhance student adoption of these services.

Tech-enabled education can not only transform the online education experience, but it may also augment and supplement traditional classroom-based pedagogy.

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