The data was collected randomly from all staff at UiTM Pahang to measure the readiness of big data adoption in the processes involved as an institution. The respondents were asked about their awareness of big data. From Table 1, the results showed that 5% of UiTM Pahang staff had never heard of this term, 30.6% stated that they had heard of this term but they were not aware of its meaning, 38.7% said that they were aware of this concept but only to some extent, and 17.7% claimed they were very aware of this concept but they did not use it. Meanwhile, only 8% of the staff claimed they were very aware of this concept, and that they used it in their daily lives. The percentages in Table 1 generally show that academicians have a higher awareness of big data compared to administrative staff. This finding is aligned with a study from Braunack-Mayer, Street, Tooher, Feng, and Scharling-Gamba, (2020), who discovered a lack of awareness of the BD concept among students and staff at higher education institutions. Although they realized the potential benefits of using it, concerns about the misinterpretation of data, constant surveillance, poor transparency, inadequate support, and the potential to impede active learning are among the concerns that have been raised by staff and students about the use of BD.
Table 1
Awareness on Big Data among UiTM Pahang Staff (%)
Awareness on Big Data | Academician | Administration | Total |
I am very aware of this concept, and I used it in my life | 9 | 0 | 8 |
I am very aware of this concept, but I do not use it | 21 | 0 | 17.7 |
I am aware of this concept but only to some extent | 34 | 67 | 38.7 |
I have heard this term, but I am not aware of its meaning | 34 | 11 | 30.6 |
Never heard of this term | 2 | 22 | 5 |
Total | 100 | 100 | 100 |
Further questions to assess readiness in terms of BD skills were only asked to those who are very familiar with the concept, regardless of whether they use it or not, which represented only 25.7% of the respondents. The results are shown in Table 2.
Table 2
Skills needed in Big Data (%)
Skills | I don’t know | I have heard this before | I know how to use it | I’m very proficient | Total |
Structured Query Language (SQL) | 50 | 37.5 | 12.5 | 0 | 100 |
Data Mining software (SAS, RapidMiner, Apache Machout etc) | 31.25 | 56.25 | 12.5 | 0 | 100 |
Knowledge in programming Languages (C++, JAVA, Python, R - programming etc) | 31.25 | 37.5 | 31.25 | 0 | 100 |
Public Cloud or Hybrid Cloud (Microsoft Azure, AWS, OpenStack etc) | 18.75 | 62.5 | 18.75 | 0 | 100 |
Based on the results, only 25.7% of the respondents were very aware of the concept. However, in terms of gauging the skills required to use big data, the readiness is very low. Only 12.5% of those who claimed to be well-versed in the subject knew how to use SQL or any data mining software, which is at the heart of BD. Programming languages are known by approximately 31.25% of those who claimed to be very aware. As it is known, to be able to be a good data scientist dealing with big data, they are mostly required to have experience in more than one programming language. Lastly, only 18.75% of them know how to use Public Cloud or Hybrid Cloud. To prepare staff for big data adoption, it is best to raise awareness by increasing proficiency in the bare minimum of skills required to handle big data. As mentioned by Saad and Sarmin (2021), in listing the critical success factors of big data adoption, people with BD skills are one of the requirements for BD success. Although UiTM has already trained the staff and the students under the Data Camp Module, there are probably still staff at UiTM Pahang who lack the awareness and skills required to handle the BD in their work environment.
Four theories have been combined in assessing staff readiness towards BD adoption. The justification of the theories selected is based on past studies conducted on big data adoption. Several theories are commonly used in information systems (IS) adoption for explaining the adoption decisions of IT at an individual or organizational level, such as Technology–organization–environment (TOE) (Tornatzky et al., 1990), Technology acceptance model (TAM) (Davis, 1989), Diffusion of innovations (DOI) (Rogers, 1995), and task-technology fit (TTF) (Goodhue & Thompson, 1995). DOI and TAM have been identified as the most powerful theories used to explore innovation adoption. Since both theories cover the same elements, which are adopters' assessment of innovations on the perception of their characteristics, they have been combined to explore to develop a model to measure the sustainability of education through BD adoption and knowledge management sharing.
Diffusion of Innovations (DOI)
The results in Fig. 1 are the responses obtained from 64.5% of the total respondents. The respondents who claimed to not know or were not aware were excluded from further analysis. The DOI can be categorized into three, which are relative advantage, perceived use of difficulty, and compatibility.
In terms of relative advantage, the majority of respondents (40–60%) agree (35–55%) that BD includes vast accounts of information that can lead to easy retrieval of information, comprehensive and easier searching, and ultimately help to create better education management systems. It can be concluded that no one disagrees with the relative advantage of BD in UiTM Pahang. As mentioned by Riffai, David, Peter and AlBulushi (2016), the implementation of BD in higher education has been seen as the resolution to higher education challenges. The analysis of data not only improves the performance of students, staff, courses, and institutions (Williamson, 2019), but also society, whereby universities will be viewed as a public good instead of a commercial enterprise. Pratsri and Nilsook (2020) summarize six functions of BD and higher education are interactions of humans and technology, such as intelligent teaching systems, predictive analytics, behavior detection, risk prediction, skill estimation, processing, and recruitment.
For the perceived use of difficulty, the disagreement can be seen, whereby 45% agree that BD is difficult to perform using traditional data analysis, 37.5% remain neutral and 17.5% disagree with this statement. The respondents also show almost the same sentiment when they were asked about whether comparing with print materials is necessary for deciding to use BD, whereby only 57.5% agree or strongly agree and more than 15% disagree. However, 80% of the respondents agree and strongly agree that the use of BD will increase their productivity. From the result above, it can be concluded that the staff at UiTM Pahang have a perspective on the difficulty of using BD. This aligns with the study by Verma and Bhattacharyya (2017), as cited by Rogers (2003), on how the acceptance of innovation will be difficult if the user has a negative perception of the technology that will be used. There were two characteristics of new technology to be successfully accepted, which were that it was user-friendly and easy to use.
In terms of compatibility, 15% of respondents do not know about BD before starting professional work at university. About 47.5% of the respondents agree that knowledge coverage in BD is related to their professional and scientific activity, while 25% think it is not. However, according to the opinion of the respondent, 37.5% think it is not hard to learn BD. Overall, the awareness is low which results in low readiness for BD adoption. Nevertheless, the responses on the DOI show that BD concept convey positive perceptions and even though the practical or proficiencies are deficient, they are positively ready for adoption from a mental aspect. According to Shinwei Sun et al. (2016) in exploring BD adoption, although relative advantage, perceived use of difficulty, and compatibility are frequently mentioned by scholars in studying technology adoption, compatibility is less favorable compared to relative advantage as it will outweigh the compatibility perception of users. This is indicated in the finding of UiTM Pahang staff of compatibility in adopting BD. Although the awareness is low, they have a positive perception of BD as they might have heard the benefits of adopting BD in the organization.
Unified Theory and Use of Technology (UTAUT)
There are three crucial elements to discuss for the Unified Theory and Use of Technology (UTAUT), in an attempt to check the readiness to adopt big data, which are effort expectancy, performance expectancy, and social influence.
For effort expectancy, the results in Fig. 2 show that in the perception of the respondents, only a few of them agree (17.5%) and strongly agree (7.5%) that it is easy to be skillful in using BD. Likewise, only 20% agree and 10% strongly agree that it is easy to learn. Conversely, about 62.5% thought that learning BD would be time-consuming. Regardless of what they thought about the effort needed for better adeptness, 75% disagree and strongly disagree with the statement that BD would not be relevant. Nevertheless, only 22.5% and 2.5% agree and strongly agree, respectively, that they have a clear understanding of how BD can be used in their work. This show that the respondents understand that they need to heap effort and are aware of the difficulty to learn, but lack the motivation or clear goal in discrepancy in the results.
For performance expectancy, generally, the respondents agree that BD would help them make intelligent decisions (77.5%), give a positive impact on their work performance (90%) and give valuable insights into UiTM Pahang (80%). The results also show that 82.5% of respondents agree that BD would enable them to use customer data more smartly and increase the quality of customer data that they use. This also would help them to be more customer-focused.
The last element in Unified Theory and the Use of Technology is social influence. The results show that the respondents agree that important people think they should use BD (37.5%). About 67.5% of the respondents also agree that people who influence their behavior also think they should use BD. Yet, only 27.5% of the respondents think most people around them use BD. Overall, 70% of the respondents agree that UiTM Pahang would support the use of BD.
This study adopted a study by Brünink (2016). Out of the four variables from UTAUT, only three have remained. The other variable, facilitating conditions, is excluded because it is related to user behavior and not to adoption intentions. For effort expectancy, it is clearly stated that UiTM Pahang staff have mixed feelings about the perception of using BD. As stated by Brünink (2016), effort expectancy is not a salient factor impacting BD adoption intention. Hence, the influence of the use of BD depends on user perception. They will accept and use BD if it truly improves performance and has already been successfully implemented by other companies (Villarejo-Ramos, Cabrera-Sánchez, Lara-Rubio, & Liébana-Cabanillas, 2021). Therefore, this statement is interrelated with the result for performance expectancy as the respondent's belief in the impact and the benefits of BD, and the social influence affects the intention of using BD in an organization (Brünink, 2016).
Technology Organization Environment (TOE)
Three main elements that affect a firm's adopt technological innovations are Technology Context, Organization Context, and Environment Context (Sun et al., 2018). In the technological context, there are three categories which are complexity, IT infrastructures, and perceived benefit.
Technology Organization Environment (TOE) – Technology Context
As shown in Fig. 3, 17.5% of the respondents disagree that BD analytics would be too complex for UiTM Pahang to use. About 12.5% of them also disagree that the skill needed would be too complex for them. However, with regards to the complexity, mentally, 55% agree that the use of BD would be frustrating and 70% agree that it would require a lot of mental effort.
The technology context also discusses the IT infrastructure of the organization. Only 27.5% of the respondents agree that databases within UiTM Pahang are available for them. Still, only 17.5% agree that the IT infrastructure is sufficient for BD. Lastly, when implementing BD analytics, 35% and 25% agree that UiTM Pahang will have sufficient supporting staff and training, respectively. Hence, from the perspectives of the respondents, a lot more infrastructures need to be provided by the organization to facilitate BD analytics.
The last context in TOE is the perceived benefits. Generally, the majority (80%) of the respondents agree that BD technology will help UiTM Pahang improve student performance, the teaching process, and decision-making. About 80% of the respondents also agree that BD also enables a better understanding of future trends, and therefore helps in reshaping the academic program. Eighty percent of respondents also agree that BD will lead to advancement in resource allocation for institutions. Lastly, 85% of them also agree that BD technology will lead to a better understanding of current and future trends of learning.
According to Baig et al. (2021), in the study of BD adoption using TOE and DOI theories, among the four elements from the technology context, only complexity has a negative influence on BD adoption. The study was probably conducted at an early stage, and thus it is hard for the user to determine the complexity of BD and its use in the organization. Meanwhile, the other study by Marchena Sekli and De La Vega (2021) covered only two elements from the technology context, which are compatibility and complexity as they are more relevant and significant to the adoption of innovation. Thus, this study result aligned with Baig et al. (2021) who found that complexity has negative influences on BD adoption, which is contrary to the study of manufacturing as complexity has a positive influence on BD adoption (Gangwar, 2018; Maroufkhani, Tseng, Iranmanesh, Ismail, & Khalid, 2020). However, in this study, the majority of the UiTM Pahang staff have a negative perception of complexity in the adoption of BD.
Technology Organization Environment (TOE) – Organization Context
The next element in Technology Organization Environment is the organizational context. Under this element, it can be further categorized into top management support and financial readiness. This section will discuss the perceptions of respondents toward the top management of UiTM Pahang in providing support and also in terms of financial readiness.
Table 3
The percentages of responses toward Big Data according to Technology Organization Environment (TOE) – Organization Context
Top management support | Strongly Disagree | Disagree | Neutral | Agree | Strongly Agree | Total |
Top management would provide resources necessary for the adoption of big data analytics | 5 | 5 | 53 | 25 | 13 | 100 |
Top management would provide necessary support for the adoption of big data analytics | 5 | 5 | 48 | 30 | 13 | 100 |
Top management would support the use of big data analytics | 3 | 0 | 45 | 40 | 13 | 100 |
Top managers would be enthusiastic about adopting big data analytics | 0 | 8 | 50 | 33 | 10 | 100 |
Financial Readiness | | | | | | |
UiTM Pahang would have the financial resources for adopting big data analytics. | 8 | 13 | 53 | 18 | 10 | 100 |
Our financial budgets would be significant enough to support the adoption of big data analytics. | 10 | 13 | 55 | 18 | 5 | 100 |
It would be easy to obtain financial support for big data analytics adoption. | 10 | 8 | 65 | 13 | 5 | 100 |
UiTM Pahang would take big data analytics more seriously because of the possible adequate financial support. | 5 | 5 | 50 | 33 | 8 | 100 |
From Table 3, the results show that from the perspectives of the respondents, only 38% agree that top management would provide the resources necessary for the adoption of BD analytics. Meanwhile, 10% disagree with this statement. Only 43% of the respondents agree that top management would provide the necessary support for the adoption of BD analytics, while 10% disagree. About 53% agree that top management would support the use of big data analytics and only 3% strongly disagree. Lastly, 43% of the respondents agree that top managers would be enthusiastic about adopting BD analytics and 8% disagree with this statement. From the perceptions of the respondents, less than 50% of respondents agree with all statements related to top management support. It is advisable that the discrepancy in the view of top management and staff should be addressed before further implementation of big data.
The respondents were also asked to rate the financial readiness of UiTM Pahang as an organization to support BD adoption. Only 28% of the respondents think that UiTM Pahang would have the financial resources for adopting BD analytics, while 21% disagree. Meanwhile, only 23% of the respondents think that UiTM Pahang’s financial budget would be significant enough to support the adoption of BD analytics, while another 23% do not agree. Next, about 18% of the respondents both agree and disagree that it would be easy to obtain financial support for BD analytics adoption. Lastly, 41% of the respondents agree with the statement that UiTM Pahang would take big data analytics more seriously because of the possible adequate financial support. The perceptions of the respondents toward the readiness for BD adoption show that they have low expectations in terms of top management support and financial readiness of the organization. This might be due to the staff being unclear on the actual top management support being provided and financial readiness. Management can overcome this by communicating a clear goal or policy related to the organization's path for future development in BD adoption. As mentioned by Baig et al. (2021), top management support and financial resources are the two important elements in influencing BD adoption in higher education. The decision to adopt new technology is an important decision that requires top management intervention.
Technology Organization Environment (TOE) – Environment context
The last element under TOE is the environmental context. This element focuses on government policies in encouraging new information technology not only in BD, but as a whole. Based on the results in Table 4, about 76% either agree or strongly agree with this statement. This can be seen from the Sustainable Development Growth (SDG), which is also adapted into UiTM Strategic Plan 2025.
Table 4
The percentages of responses toward Big Data according to Technology Organization Environment (TOE) – Environment Context
Question | Strongly Disagree | Disagree | Neutral | Agree | Strongly Agree | Total |
The governmental policies encourage us to adopt new information technology (e.g., big data analytics) | 0 | 0 | 25 | 58 | 18 | 100 |
The government provides incentives for using big data analytics in government procurements and contracts such as offering technical support, training, and funding for big data analytics use. | 0 | 3 | 58 | 30 | 10 | 100 |
There are some business laws to deal with the security and privacy concerns over the big data analytics technology | 0 | 0 | 43 | 45 | 13 | 100 |
Only 40% of the respondents agree that the government provides incentives for using BD analytics in government procurements and contracts such as offering technical support, training, and funding for big data analytics use. Meanwhile, 58% of the respondents agree that there are some business laws to deal with security and privacy concerns over BD analytics technology. The issue of security has also been pointed out in a study by Al Sai et al. (2019) and Veeneman et al. (2018). The effectiveness and efficiency of BD have to deal with security and privacy as it involves processing data from different parties. Therefore, in terms of government policies, it is confirmed that support from the government and regulatory bodies influences the organization, especially education in adopting BD (Baiq et al., 2021).