Acceptance of ICT Usage Among Indigenous Students: Empirical Findings Based on UTAUT
Abstract
Information Communication Technology (ICT) should be able to simplify and help students by providing continuous education during the pandemic. However, due to the limited ICT infrastructure and different cultural background of the Indigenous people, the usage of ICT was unable to be utilised in full capacity. Peninsular Malaysia is home to the indigenous Orang Asli community, the majority of whom continue to live a traditional lifestyle. The Orang Asli's living conditions have been improved through a number of initiatives by the Malaysian government. JAKOA, for instance, has started a number of social development initiatives aimed at improving the well-being of the Orang Asli community. Thus, this study aims to identify the factors affecting the behavior and usage intention towards technology among Indigenous students in Intellectual Center Aboriginal Students or Pusat Intelek Pelajar Orang Asli (PIPOA). We discovered that all hypotheses are supported positively and significantly by using primary data and all information obtained through the distribution of questionnaires using Google form to the targeted respondents. This indicates that the UTAUT constructs contribute to the intention to use technology among the sample of this study. This study shed light to the new education era, providing assurance to equal education quality and lifelong learning opportunities for young generation of the Indigenous community.
Keywords: UTAUT, ICT, Indigenous Students, Malaysia
Introduction
Indigenous people or in Malay term “Orang Asli” are the original people who lived in Peninsular Malaysia. They are classified into the Negrito, Senoi, and Proto-Malay tribes. In Malaysia, there are reported to be 953 Indigenous communities with a combined population of 206,777 Indigenous people (Department of Orang Asli Development, 2021). Indigenous people in Malaysia were given the same rights as other ethnic groups in terms of growth, social advancement, and educational opportunities (Singar & Zainuddin, 2017). However, the issue of dropouts among Indigenous students is an ongoing concern within the context of Malaysia education (Norwaliza, 2022). Ministry of Education (MOE) is committed to help the young generation of Indigenous people by implementing “Transformasi Pendidikan Orang Asli dan Pribumi” through “Pelan Pembangunan Pendidikan Malaysia (PPPM) 2013-2025” (Bernama, 2022). All of this is carried out to ensure that Indigenous children can fit into the existing educational programme as Malaysia moves toward the Fourth Industrial Revolution (IR 4.0).
During the COVID-19 pandemic, schools and higher institutions were forced to shut down their operation due to the enforcement of Movement Control Order (MCO). There are no standards, practices, guides, or examples to manage these institutions during the pandemic as all the event occurred abruptly (Lopez et al., 2022). The initiative to ensure the continuity of educational service was through online learning. However, Indigenous communities need special attention as situations may vary compared to urban settings (Ching & Loke, 2016). They are not well equipped to adapt with the transition from traditional learning to online learning. Hence, Malaysian educational sector is facing tremendous challenges in delivering educational services during an unprecedented event to the Indigenous students (Shamsir et al., 2021).
Online learning means that the students will use ICT to study (Lin et al., 2017). However, global distribution of ICTs in the most Indigenous villages is not as advanced as in the urban area (Bala & Tan, 2021). Their community's geographic location restricts their access to ICT and worsens the digital gap, which slows the expansion of ICT in education within the Indigenous community (Luaran et al., 2016). As they live and work in specific locations and speak only their own culture's language, Indigenous students who use ICT sparingly have difficulty understanding and using instructional software that is often produced in English and is text-heavy (Kenrick & Lewis, 2004; Stephens et al., 2006). Therefore, the aim of this study is to examine the factors that contribute to the behavior intention and usage intention of technology among Indigenous students in Intellectual Center Aboriginal Students or Pusat Intelek Pelajar Orang Asli (PIPOA).
The structure of this study is followed by a literature review & hypotheses development, research methods, findings and lastly conclusion and recommendation.
Literature Review
The Indigenous group in Malaysia
Indigenous Malaysians made up about 0.63% of the country's population of 32.37 million people as of 2020, according to estimation. Based on Figure 1, the Indigenous people have 18 subgroups within three (3) major categories which are Negrito (2.94%), Senoi (55.09%) and Melayu Proto (41.97%) (Department of Orang Asli Development, 2020).
According to Figure 2, based on the census of 2020 done by Department of Orang Asli Development, the distribution of the Indigenous people settlements in Pahang according to the 3 major categories are Negrito (1,127), Senoi (35,654) and Melayu Proto (41,644) which resulted in a total of 78,425 people, from the total of 206,777 Indigenous people in Malaysia altogether, and it is the highest population compared to other states (Department of Orang Asli Development, 2020).
Although the Indigenous people were among the first cultures to settle in Malaysia, not everyone is aware of their existence. When compared to other races in Malaysia, Indigenous people still have the lowest socioeconomic standing in a number of areas, including health, education, and technology.
Technology use among the Indigenous group in Malaysia
According to Bala and Tan (2021), the perceptions of the Indigenous people and their general receptivity to change and new information were favourable. Additionally, they think that ICT will increase their income (by 50%) and help they live healthier lives (by 53%). Additionally, 61.9% of respondents anticipate that ICT will alter their community. According to Ahlan and Arshad (2011) and Zal (2011), local researchers concur that Indigenous people do not oppose ICT growth because they see ICT as a platform for their advancement and success in the contemporary world. However, it is commonly known that Indigenous people in Malaysia adopt ICT at a much slower rate than other communities as reported in Department of Orang Asli Development Strategic Plan 2011-2015 (Department of Orang Asli Development, 2011). The limitations in education and the economy are among the key causes of this problem (Zal, 2011).
The Unified theory of technology acceptance and use (UTAUT) model
UTAUT is a paradigm for measuring user acceptability of information technology that was developed to promote a united viewpoint (Venkatesh et al., 2003). They also mentioned that UTAUT seeks to explain user intents to use an information system and subsequent usage behaviour. This analysis used the UTAUT model because of its thoroughness and robust explanatory power. Additionally, UTAUT has gained widespread acceptance as the most up-to-date fundamental notion for expressing user acceptance of an invention, particularly in technology. UTAUT is comprised of four major themes: performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). According to empirical research, out of these four constructs, the facilitating conditions are directly linked to behaviour adoption. The importance of these elements as direct predictors of user’s level of acceptance and use behaviour cannot be overstated (Ayaz & Yanartaş, 2020).
Hypotheses development
Performance expectancy (PE) and behavioral intention (BI)
Venkatesh et al. (2003) mentioned that PE relates to how individuals believe that new technology will assist and benefit them in performing their job better. It also refers to the level of confidence of a person using the technology would do better. Venkatesh et al. (2003) also revealed that the construct of PE is the strongest determinant of a user’s behavioral intention to adopt a technology. In other previous studies that used UTAUT model, PE has been found to significantly affect intention to use technology. Almaiah et al. (2019) discovered that PE has been found to positively affect university students' BI to use mobile learning systems. In addition, Persada et al. (2019) found that Generation Z perceived digital learning in their study, thus it positively relates with the performance expectancy and behavioral intention in using technology. While Ahmet Ayaz and Yanartaş (2020) discovered that PE is the most significant determinant of BI in their study. Hence, based on the evidence of previous research, the following formation of hypothesis can be made:
H1: There is a significant relationship between PE and BI in Indigenous students' use of technology
Effort expectancy (EE) and behavioral intention (BI)
Effort expectation is defined as the ease at which a system is used and the amount of work a consumer anticipates a certain task will demand (Venkatesh et al., 2003). The efforts required to use the system, regardless of how simple or complex it is, is hence referred to as EE. Technology that is user-friendly might be more quickly accepted and embraced by users. Most consumers prefer technology that provides them with comfort, flexibility, and utility (Catherine et al., 2017). It is a critical component of the UTAUT model and is widely used to assess users' intentions to use new technology. There is some evidence to support the idea that EE and BI are significantly and positively correlated (Ayaz & Yanartaş, 2020; Almaiah et al., 2019). However, this is contrary from study of Chen and Hwang (2019) where they found that EE does not contribute to the factor of intention to use technology. From the discussion mentioned above, the second hypothesis is suggested as follow:
H2: There is a significant relationship between EE and BI in technology usage among Indigenous students
Social influence (SI) and behavioral intention (BI)
Venkatesh et al. (2003; 2012) defined SI as “the extent to which consumers believe other people believe they should use technology”. It refers to the extent to which someone important to the individual (such as relatives, peers, friends, and teachers) believes he or she should use the new system. According to Catherine et al. (2017), word-of-mouth influences the adoption of communication technologies through reference groups, which include friends and IT experts. SI might come from social forces, subjective norms, or images. Image refers to the enhancement of a single person's reputation or social class through an apparent new system (Venkatesh et al., 2003). Persada et al. (2019) found that Generation Z are influenced by their peers, relatives and teachers in using technology in their study, thus this shows that SI has a significant effect towards the BI. Ayaz and Yanartaş (2020) has supported the significant findings of Persada et al. (2019). In contrast, Almaiah et al. (2019) and Chen and Hwang (2019) found no strong evidence of SI and BI in using technology in their study. Based on this, the following hypothesis has been put forward:
H3: There is a significant relationship between SI and BI in Indigenous students' use of technology.
Behavioral intention (BI) and use intention (UI)
According to Abdulwahab (2010), behavioural intention is the level to which a person intends to carry out specific acts. The behavioural intention of a person will reveal their purpose to accomplish something (Zolait, 2016). A feeling that is motivated by the desire to accomplish something might be described as behavioural intention (Oh & Yoon, 2014). It makes sense that a person's behaviour while performing work is the power of concealed items that can only be seen from that person's behaviour (Krismadinata et al., 2019). Use intention is thought to be significantly influenced by behavioural intention as a variable. Behavioural intention functions as a mediator in the UTAUT paradigm. It is reliant on the outcomes of several factors. All factors affecting BI are influenced by a user's beliefs or attitudes. The majority of studies have found a significant and positive relationship of BI on UI. These outcomes were found by Ma et al. (2010), Im et al. (2011), and Yu (2012) when they observed users’ attitude about mobile banking and e-banking, respectively. Therefore, the fourth hypothesis is as follows:
H4: There is a significant relationship between BI and user intention in technology usage among Indigenous students.
Faciliting condition (FC) and use intention (UI)
The FC is the degree to which a person believes that the technological is in place to facilitate system use. It is a user's perception of available resources and assistance when performing a task (Venkatesh et al., 2003; 2012). Instead of focusing on behavioural intention, the UTAUT model has built conducive factors to influence real technology usage (Ayaz & Yanartaş, 2020; Schaper & Pervan, 2007; Venkatesh et al., 2003). Joa and Magsamen-Conrad (2022) found none of the UTAUT constructs including facilitating condition has significant relationship. However, Md. Sari et al. (2022) found that the factor measuring facilitating conditions had the greatest mean, which confirmed that their respondents had the necessary infrastructure to employ technology for educational purposes. In addition to the discussion, the findings of Vaithilingam et al. (2022) are consistent with previous research, which discovered that FC affects the level of technology adoption. Therefore, the following hypothesis is developed:
H5: There is a significant relationship between FC and UI in technology usage among Indigenous students.
Method
The information for this study was all gathered from the distributed questionnaires to the intended respondents using a Google Form. The questionnaire is divided into three sections. Section A and B of this study contain the dependent and independent variables, while Section C contains the demographic information on the respondents. We used existing scales validated in the original theory of UTAUT. The variables were assessed using a 5-point Likert scale. They need to determine their level of agreement or disagreement to the statements in the survey by using five different scale rates ranging as Strongly Agree (5), Agree (4), Moderate (3), Disagree (2) and Strongly Disagree (1).
Our team has instructed and distributed the online questionnaire to all participants at the end of a technology-based program that has conducted PIPOA, Rompin. This face-to-face program has been conducted on 25 and 26 June 2022. PIPOA is a center that place excellent Aboriginal students from all around peninsular Malaysia and provide the opportunity for students to obtain a complete and better education for their future. PIPOA originally was established to accommodate Indigenous students excelling in the Primary School Achievement Test (UPSR) and Third Form Assessment (PT3). Therefore, the age range of PIPOA students are from 13 until 17 years old. As on 1 June 2022, there are 96 students in PIPOA, however only 90 participants aged 13 until 15 years old had involved in this program.
Hence, purposive sampling technique has been used in this study. Out of 96 populations of PIPOA students, 90 of them had participated. However, there are only 65 final samples that have been included in this study after excluding the invalid and incomplete response from participants. In certifying the validity and reliability of the scale selection, facilitator has monitored the participants throughout the survey process. Table 1 shows the distribution of demographic profile of 65 respondents:
In order to determine whether certain variables are associated, this study employs PLS SEM. It has two assessments: measurement model and structural model assessments (Hair et al., 2017). In order to evaluate a measurement model, two criteria must be met: convergent validity and discriminant validity. The SEM-PLS model typically has two components namely measurement model and structural model. Convergent validity, discriminant validity, and reliability are applied to quantify the measuring model's ability to identify the target population.
Alternatively, the structural model is constructed to display and describe the connection between latent variables in the research model. Next, we will look at how R2 and Q2 statistics can assess the quality of the SEM-PLS model's structural components. If the t-value is larger than 1.96 and the P-value is less than 0.05, or 5%, for each pathway, then the hypothesis is accepted.
Findings
Model reliability and validity
Internal consistency reliability was determined using composite reliability (see Table 2). The composite reliability ratings ranged from 0.812 to 0.924, showing that internal consistency reliability was outstanding, as predicted by the assessment system (Hair & Alamer, 2022). Outer loadings are used to report the indication dependability of specific model elements. All of the absolute standardised outer loadings were more than 0.600; Chin (2010) argued that the aforesaid criterion is acceptable when other items measure the same concept. The model's absolute standardised loadings ranged from 0.523 to 0.771, with all extracted average variances greater than 0.500 (Fornell & Larcker, 1981). These figures assure the employment of valid indicators in the present study.
The heterotrait-monotrait (HTMT) technique is used to evaluate discriminant validity (refer to Table 3). HTMT values greater than HTMT0.85 of 0.85 (Kline, 2015) or HTMT0.90 of 0.90 (Gold et al., 2001) usually imply that discriminant validity is not a concern. The current study discovered that all of the results were over the cut-offs of HTMT0.90 (Gold et al., 2001) and HTMT0.85 (Kline, 2015).
The predictive power of the model
The model's predictive power was then assessed using R2 (Hair et al., 2019). The endogenous variables use intention and behaviour intention had R2 values of 0.655 and 0.548 respectively, indicating that the model explained 65.5% and 54.8% of the variation in use intention and behaviour intention respectively. R2 values of 0.250, 0.500, and 0.700 are considered to have weak, moderate, and strong explanation power, respectively. As a result, the explanatory power is low to moderate. Given that the endogenous latent variable in this study was reflective, the predictive relevance of the endogenous construct was next examined using Stone-Q2 Geisser's test (Geisser, 1983; Stone, 1974). The Q2 score was greater than zero using cross-validated redundancy (Behavior intention = 0.386, usage intention = 0.457). This indicated that the model used was predictive (Hair et al., 2014; Henseler et al., 2015) (Refer Figure 3).
Hypotheses testing
The t-statistics were computed using a bootstrapping method with 1000 resamples, and the hypotheses were tested using this method (Hair et al., 2019). According to the findings presented in Table 4 and illustrated as Figure 3, which can be seen below, all of the hypotheses are supported favourably and significantly. In order to rule out the possibility of there being problems with multicollinearity, we looked at the variance inflation factor (VIF). In every single model of regression analysis, the VIFs were lower than 2.3, and these values were lower than the critical value of 5 that was proposed by Hair et al. (2019). Thereby, multicollinearity was not serious (O’brien, 2007). According to the findings in Table 3, every single construction satisfies the requirement.
Discussion and Conclusion
This study aims to look into the factors that influence Indigenous students' behaviour and technology use intentions in PIPOA. This study reveals that all hypotheses are strongly and significantly supported. It indicates that all of the UTAUT constructs employed by this study contribute to the intention to use technology by them especially for educational purposes. As for PE and BI, the p-value is 0.093, therefore hypothesis 1 (H1) is accepted. This finding is consistent with Almaiah et al. (2019), Persada et al. (2019) and Ayaz and Yanartaş (2020). This demonstrate that Indigenous students believe that technology can assist them in performing better in their study for example in completing their homework easily and efficiently. Hence, they have a positive intention toward the usage of technology.
Hypothesis 2 (H2) examines the relationship between EE and BI. From the result, it shows that the p-value is 0.017, hence H2 is accepted. The finding of this study is consistent with Ayaz and Yanartaş (2020) and Almaiah et al. (2019). From the result, we can conclude that the respondents found user-friendly technology have influenced their intention in using technology. They found that technology used for education purpose is flexible, useful and easy to use, therefore it is effortless to understand the technology and it directly influenced their intention.
In similar vein, social influence (H3) also shows a significant relationship toward the behavioral intention of using technology. This might be caused by the place they stay at, since of all the Indigenous students are staying at one center (PIPOA) with similar objective, thus all parties (e.g: teachers, friends and management) in the center may become the factor in influencing them to use technology. According to Catherine et al. (2017), the reference groups play a major role in the adoption of communication technologies. Consistently, Persada et al. (2019) found that Generation Z are easily influenced by their peers, relatives and teachers in using technology in their study. Nonetheless, the finding of the study is inconsistent with Almaiah et al. (2019) and Chen and Hwang (2019) where they found no strong evidence of social influence and behavioral intention in using technology among students.
As all the three constructs in UTAUT found have significant relationship with the behavioral intention (H4), next, we proceeded to test the relationship between behavioral intention and use intention. The result shows the p-value is 0.000 indicating that the relationship is significant, thus H4 is accepted. Having good behavioral intention will directly influence good use intention in using technology. In the UTAUT model, behavioural intention is a mediator, so a number of variables affect it. All factors influencing behavioural intention reflect a user's belief or personal attitude. According to Oh and Yoon (2014), behavioral intention can be interpreted as a feeling driven by the desire to do something. This study’s finding is supporting the previous study of Ma et al. (2010), Im et al. (2011) and Yu (2012) where they found a significant relationship when examining their respondents’ attitudes towards e-banking and mobile banking.
The last hypothesis tested is between facilitating condition and use intention of technology among Indigenous students (H5). We test direct relationship between facilitating and use intention as per suggested by Venkatesh et al. (2003) as this construct is believed to establish impact toward usage of technology rather than behavioral attention as compared to the other three construct. From the result, we can see that the p-value is 0.006, thus H5 is accepted. For this study, we can conclude that the respondents perceive that if there are the resources and facilities provided at the time of usage, it might influence the intention to use the technology. This is consistent with Md Sari et al. (2022) and Vaithilingam et al. (2022) which they found that facilitating conditions have significant effect on technology adoption.
In conclusion, Indigenous students in PIPOA have a positive perception towards the usage of technology and they believe technology will make them better and more efficient in their education. As this study is focusing on the Indigenous students, we believe that this will help contribute to the new perspective of education among Indigenous students. It will also support the fourth SDG, which promotes inclusive quality education lifelong learning opportunities for all people, including Indigenous peoples.
Like all researches, this study has several limitations even though it has achieved its objective. First, the respondents of this study are among group of selected/intelligent Indigenous students that have been gathered in PIPOA. We believed that their way of thinking might be slightly different from the other groups of students. Next, the survey is conducted after the technology-based program, therefore this might also influence their perception towards the behavior intention as well as the use intention of technology adoption. Therefore, the aforementioned limitations could have uncovered the path for further study. The aforementioned constraint will not, however, render the study's conclusions meaningless.
Acknowledgments
This research was fully funded by research grant provided by Universiti Tenaga Nasional BOLD Grant 2022 with the code J510050002/2022046.
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Publication Date
18 August 2023
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eBook ISBN
978-1-80296-963-4
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European Publisher
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1
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1st Edition
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1-1050
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Multi-disciplinary, Accounting, Finance, Economics, Business Management, Marketing, Entrepreneurship, Social Studies
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Nik Azman, N. A. N., Wan Hanafi, W. N., Mohd Salleh, S., & Ridzwan, I. U. (2023). Acceptance of ICT Usage Among Indigenous Students: Empirical Findings Based on UTAUT. In A. H. Jaaffar, S. Buniamin, N. R. A. Rahman, N. S. Othman, N. Mohammad, S. Kasavan, N. E. A. B. Mohamad, Z. M. Saad, F. A. Ghani, & N. I. N. Redzuan (Eds.), Accelerating Transformation towards Sustainable and Resilient Business: Lessons Learned from the COVID-19 Crisis, vol 1. European Proceedings of Finance and Economics (pp. 929-941). European Publisher. https://doi.org/10.15405/epfe.23081.86