A Study on The Intention to Use Total Cost of Ownership System in Tenaga Nasional Berhad
Fizal Azuan Mhd Sidin1, Maisarah Mahamud Sayuti1, Syed Jamal Abdul Nasir Syed Mohamad1*
1 Arshad Ayub Graduate Business School, Universiti Teknologi MARA, 40450 Shah Alam
*Corresponding Author: [email protected] Accepted: 15 February 2022 | Published: 1 March 2022
DOI:https://doi.org/10.55057/ajrbm.2022.4.1.2
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Abstract: This research aims to discover factors that influence behavioural intention among Tenaga Nasional Berhad (TNB) Grid employee to adopt Total Cost of Ownership (TCO) system as a new technology in daily operation. TCO system is expected to improve and optimize OPEX of TNB Grid. This research adopted Technology Acceptance Model (TAM) by Davis et al.
(1989) as the framework of the study. Despite utilising perceived usefulness and perceived ease of use as the dimension, this research study extends the investigation by incorporating perceived risks as per model by Khasawneh (2015). This research study is based on the data collected from a survey questionnaire that investigates factors that influence behavioural intention to accept TCO system among TNB Grid employee. The quantitative method is used to gather the information in the non-contrived study setting to provide a natural environment of the TNB Grid. The stratified random sampling method is used whereby the target population;
employees dealing with the entire asset lifecycle management of high voltage equipment, is divided into meaningful segments such as job’s position, office location, department and years of service. The research discovered there are strong relationship between perceived usefulness, perceived ease of use and perceived risks with the behavioural intention to adopt TCO system among TNB Grid employees. The research also highlighted the important element to be considered by TNB in developing the efficient TCO system which is the ease-of-use system. The positive attitude will drive behavioural intention to use the system if the user perceives ease of use.
Keywords: Total Cost of Ownership, Technology Acceptance Model, Perceived Usefulness, Perceived Ease of Use, Perceived Risks
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1. Introduction
Stand tall as the vertically integrated utility with several strategic global expansions, TNB has delivered world-class services in electricity industry. Regulation changes, technology disruptions and slow domestic growth are completely summarizing TNB is currently facing the perfect storm. Based on the report of International Transmission Operations & Maintenance Study (2019) for TNB Grid; a division responsible for high voltage transmission, the total OPEX showed a significant increment in the past 3 years; the year 2017 RM55.59 million, the year 2018 RM 61.37 million and year 2019 RM 67.15 million. The increment is alarming and possibly impacting TNB Grid overall performance. The assessment discovered it was due to increment of post-acquisition cost of several equipment.
Hypothetically, by implementing TCO system, the most optimum cost of each equipment will be prevailed. Thus, it helps TNB Grid to determine the best equipment; low TCO, to be acquired. The similar approach has been studied by Indarto (2017) for Perusahaan Listrik Negara Indonesia, where the study proved TCO will optimize the post-acquisition cost which
indirectly may reduce the electricity cost. As the investment cost of adopting the TCO system is quite huge, therefore a study on the intention to use TCO System in TNB is required to ensure worth investment shall be made.
The Total Cost of Ownership (“TCO”) system is one of the most viable technology to be adopted in determining the optimum cost of equipment purchase. Indarto (2017) has proven the application of the TCO system at Perusahaan Listrik Negara, Indonesia to determine the most economical high voltage power transformer to be purchased. Meanwhile, Mohammad (2013), in his research confirmed perceived ease of use and perceived usefulness are the determinant of technology acceptance of the mobile application to monitor patient progress.
Furthermore, Khasawneh (2015) has extended the original Technology Acceptance Model (“TAM”) by Davis et al. (1989) by incorporating perceive risk on mobile banking technology adoption in the Jordan market. Summing up these literature reviews, a suitable framework has been developed in determining factors that will influence the adoption of a new technology in an organization of a targeted population.
The purpose of this study is to determine factors that influence behavioural intention to adopt Total Cost of Ownership (“TCO”) system in Tenaga Nasional Berhad (“TNB”) specifically in Grid Division (“TNB Grid”) which includes Perceived Usefulness, Perceived Ease of Use and Perceived Risks. The adoption of the TCO system as the new technology is expected to improve asset management strategy, especially on the equipment purchase area. Embarking TCO system is expected to improve TNB Grid operation expenses (“OPEX”) and ultimately sustaining a company-wide good financial position.
The study was focusing on TAM by Davis et al. (1989), whereby factors influencing the adoption of TCO system as the technology-focused in TNB Grid will be the main part of the entire research. Theoretically, TAM is used to determine user acceptance towards new technology through Perceived Usefulness (“PU”) and Perceived Ease of Use (“PEOU”) factors, (Davis, 1989; Davis et al., 1989; Davis, 1993). Since the adoption of the TCO system will involve huge investment, definitely there will be a risk element associated with this technology adoption strategy. Thus, incorporating Perceived Risks (“PR”) as one of the factors that will influence the intention to adopt this system will ensure better output shall prevail at the end of this research. This approach is aligned with Dontigney (2016), which specifically mentioned that perceived risk always happens when there is uncertainty at the decision-making stage. In this context, TAM will assist the researcher to identify PU and PEOU factors of adopting the TCO system at the end of the study. PU and PEOU will be the independent variables or factors that are potentially influencing the behavioural intention among TNB Grid employees to adopt the TCO system. According to Venkatesh et al. (2012), TAM recommended that the intention of the technology acceptance is determined directly by attitude, PU and PEOU. As an addition, TAM individuals’ intention to use technology determines the actual use of the application and attitudes toward technology affect the intention (Davis et al., 1989; Davis and Venkatesh, 2004;
Venkatesh et al., 2012).
1.1. Scope of the Study
The study covers four TAM dimensions; Perceived of Usefulness, Perceived Ease of Use and Perceived Risks which hypothetically will influence the Behavioural Intention to adopt the TCO system. The conventional TAM by Davis (1989), has been used for this research since it has been used in many industries and possibly the most frequently used among all other theories (Ma and Liu, 2004; Kim and Chang, 2007; Yarbrough and Smith, 2007). The extension of TAM by incorporating Perceived Risks by Bhasin (2018) and Arrow et al. (1950) will ensure
the reliability and quality of the study since risks are the important element to be associated in any investment.
The study will be focusing on TNB Grid; a department in TNB which responsible to transmit high voltage power to the customer in Peninsular Malaysia. The respondents of the study consist of TNB Grid employees who look after operation and maintenance as well as procurement of high voltage equipment. This study will be based on the quantitative approach and a questionnaire is utilized to meet the objectives of the study. The decision to opt for a quantitative because it helps to describe the trends in a population or a description of the relationships among its variables (Creswell,2011). The target population is around 500 employees, hence around 200 samples are expected to participate in the study (Taherdoost, Hamid, 2017). The target population resides throughout peninsular Malaysia from various department; Asset Operation & Maintenance, Asset Development, Asset Strategy &
Engineering and Procurement. This cross sectional study has been conducted in between November and December 2020.
2. Literature Review
2.1. Technology Acceptance Model (TAM)
TAM by Davis et al.,1989 has been identified as the best model to determine the intention to use Total Cost of Ownership system for TNB Grid employees. The selection was done due to the flexibility and adaptability of the model as it has been continuously studied and expanded from time to time. The two popular major upgrades are TAM 2 (Venkatesh & Davis, 2000) and TAM 3 (Venkatesh & Bala, 2008). Furthermore, TAM is possibly the most frequently used model among all other theories which proved the model is practical enough to be used in this research (Ma and Liu, 2004; Kim and Chang, 2007; Yarbrough and Smith, 2007). Two cognitive beliefs are posited by TAM which is perceived usefulness and perceived ease of use (Park,2009) will be used in this study besides behavioural intention to use. As an addition, perceived risks have been incorporated to TAM since TCO system adoption will require a huge investment which is associated with risks.
2.2. Perceived Usefulness
Davis (1989), defines Perceived Usefulness (“PU”) as “the level to which an individual believes that adopting a specific system would improve his or her performance.” PU can be described as the degree to which an individual believes that organisation that he/she belongs to will be improving by using the technology (Mohammad, 2013). Mohammad added, this construct defined the degree to which a healthcare professional believes that implementing mobile technology for patient tracking will enhance the healthcare industry. The measurement of PU comprises of five items which have been modified to the context of technology adoption in the healthcare industry; (1) accessibility to information (2) real-time data monitoring, (3) data retrieval ability (4) improvement of time (5) improvement of work efficiency.
H1: Perceived Usefulness has a positive relationship towards Behavioral Intention to use TCO System among TNB Grid employee
H0: Perceived Usefulness has no significant relationship towards Behavioral Intention to use TCO System among TNB Grid employee
2.3. Perceived Ease of Use
According to Davis (1989), Perceived Ease of Use (“PEOU”) is the degree to which a person believes that using a particular system would be free of effort. PEOU refers to a level of
easiness that individual of an organisation feels when using the technology in their works (Mohammad, 2013). Based on the research on the healthcare context, Mohammad described PEOU as a level of easiness that doctors and nurses feel when using mobile tracking on patient progress system. The elements of this construct comprised of the easiness level of; (1) learn to use (2) features discovery (3) understand the information provided (4) adaptability to the system (5) adaptability to the system.
H2: Perceived Ease of Use has a positive relationship towards Behavioral Intention to use TCO System among TNB Grid employee
H0: Perceived Ease of Use has no significant relationship towards Behavioral Intention to use TCO System among TNB Grid employee
2.4. Perceived Risks
To provide a solid theoretical basis for examining the intention to adopt TCO system in TNB Grid, this paper draws on the conventional TAM as well as the extension of perceived risks (“PR”) as an additional dimension. According to Lee (2009), PR can be explained as a possible loss when pursuing the desired result. Most scholars suggested that consumers’ perceived risk comprised of a multi-dimensional construct. According to Kaplan et al. (1974), perceived risks consist of financial, performance, social, physical, privacy, and time-loss. The idea has been seconded by Khasawneh (2015) who then incorporated PR in the conventional TAM to study on mobile banking adoption in the Jordan market. Khasawneh also stated that PR shall include 5 dimensions; performance risk, privacy/security risk, time/convenience risk, social risk and financial risk. In this study, the researcher aims to factor that influences the intention of TNB Grid employees to adopt the TCO system. Thus, social risk and privacy/security risk are foreseen as not applicable to this study.
H3: Perceived Risk has a negative relationship towards Behavioral Intention to use TCO System among TNB Grid employee
H0: Perceived Risk has no significant relationship towards Behavioral Intention to use TCO System among TNB Grid employee
2.5. Behavioural Intention
According to Venkatesh et al. (2012), the TAM proposes technology acceptance intention is determined directly by attitude, perceived usefulness and perceived ease of use. Behavioural intention is measured through the degree of an individual’s desirability to make an effort while performing certain behaviours.
Figure 2.1:Conceptual Framework
3. Research Methodology
3.1. Research Design
The research study used a quantitative method whereby the primary data has been used to gather the information to study on the behavioural intention to use TCO system in TNB Grid will be obtained by Target Population and Sample Size.
Sampling Design
The population of this research has included the current employees of TNB Grid that are involved in the entire asset lifecycle management of high voltage equipment. The target population and size has been determined according to Taherdoost (2017). Taherdoost (2017) suggests the sample size is the important feature of an empirical study which aims to outline hypothesis about the population and recommended the appropriate sample size for most research; larger than 30 and less than 500. Taherdoost (2017) also recommends using a 10%
sample size of the parent population within 30 to 500. According to Table 3.1, for the total population for 514 employees in TNB Grid, the sample size will be 217.
Stratified random sampling method has been used because it helps to obtain a sample population that best represents the entire population being studied. The target population was divided into meaningful segments such as job’s position, office location, department and years of service. It was also targeted the population of the employees dealing with the entire asset lifecycle management of high voltage equipment to ensure the quality of the responses.
Table 3.1: Sample Size Decision
Variance of the population P=50%
Confidence level = 95% Margin of error
Confidence level = 99% Margin of error
Size 5 3 1 5 3 1
50 44 48 50 46 49 50
75 63 70 74 67 72 75
100 79 91 99 87 95 99
150 108 132 148 122 139 149
200 132 168 196 154 180 198
250 151 203 244 181 220 246
300 168 234 291 206 258 295
400 196 291 384 249 328 391
500 217 340 475 285 393 485
600 234 384 565 314 452 579
700 248 423 652 340 507 672
800 260 457 738 362 557 763
1000 278 516 906 398 647 943
Source: The Research Advisor (2006) and Krejcie and Morgan (1970)
3.2. Research Instrument
The questionnaire for this research consists of 4 items representing the dependent variable (DV) meanwhile the 3 independent variables (IVs) are represented by 12 items. A 5-point Likert scale was used to measure the relationship of factors that influence the intention to use the TCO system among TNB Grid employees (Sekaran & Bougie, 2016).
Figure 3.1: Operationalization map
3.3. Data Collection and Data Analysis
The quantitative data has been gathered from questionnaires distributed to the target population. The questionnaires have been distributed to the TNB Grid employee who involved in the entire asset lifecycle management from acquisition of the asset, operate and maintain the asset, refurbish and replace the asset and finally disposal of the asset. The questionnaire has been distributed in both ways; personal administered and via electronic (Google Forms). The quantitative analysis performed will provide an opportunity for the researchers to realize information and gather insights which might be overlooked with traditional data analysis techniques (Lawrence, et al, 2013).
All the responses have been entered into the IBMR Statistical Package for Social Science (SPSS). This process was conducted to measure the reliability of items constructed along with running both descriptive analysis and regression analysis to find out the salient influence factors dimensions (IVs) towards intention to use TCO system (DV).
Several techniques have been used to analyze data. It includes frequency, descriptive analysis, reliability analysis, Pearson’s Product Moment Correlation Coefficient and also regression analysis to identify the statistical value or results of data collected (Sekaran &
Bougie, 2013).
4. Findings and Analysis
4.1. Validity test (Exploratory Factor Analysis)
According to Hair et al., (2006; 2019), six basic guidelines as stated below has been used to determine the appropriateness of factor analysis;
(i) KMO measure of sampling adequacy (MSA) greater than .50 (ii) Barlett’s test of sphericity is at least significant at .05
(iii) Anti-image correlation of items greater than .50 (iv) Communalities of items greater than .50
(v) Minimum factor loading (cut-off) of .50 for each items (vi) Minimum eigenvalues of 1
The result shows the validity test conducted on all responses meet the six basic guidelines of the Hair et al., (2006; 2019).
4.2. Reliability test (Cronbach’s Alpha)
Theoretical Constructs and the Cronbach‘s Alpha Coefficients of a total of 219 usable data were collected are as below;
Table 4.1: Cronbach‘s Alpha Coefficients Of A Total Of 219 Usable Data Construct Cronbach’s Alpha
PU 0.924
PEOU 0.953
PR 0.934
BI 0.964
According to Hair et al. (2015), Cronbach’s Alpha reliability test interpreted that α > 0.9 shows an excellent strength of association.
The result reveals that “behavioural intention to use TCO system” has the highest Cronbach’s alpha value of 0.964, followed by “perceived ease of use” with 0.953, “perceived risk” with 0.934, “perceived usefulness” with 0.924. According to Pallant (2001), all the items are considered reliable and shows an excellent strength of higher than α > 0.9.
4.3. Correlation
Pearson Correlation Coefficient (r) has been used to determine whether the independent variable has a significant variation to the dependent variable. Table 4.2a explained on the correlation coefficient range and strength, (Sekaran & Bougie, 2013). Referring to Table 4.2b, the result demonstrated a strong positive relationship between perceived usefulness and perceived ease of use. The result shows r = 0.724 for perceived usefulness and r = 0.746 for perceived ease of use. The r-value is positive and close to 1. This indicates positive associations and there is a strong relationship between perceived usefulness and perceived ease of use. In terms of perceived risk, the result demonstrated a moderate negative relationship where the r value is -0.535.
Table 4.2a: Correlation Coefficient Range and Strength
Correlation Coefficient (r) Strength of correlation
0 No correlation
1 Perfect positive correlation
-1 Perfect negative correlation
0 to 0.25 0 to - 0.25
Weak positive correlation Weak negative correlation
0.25 to 0.74 -0.25 to -0.74
Moderate positive correlation Moderate negative correlation
0.75 to 1 -0.75 to -1
Strong positive correlation Strong negative correlation
Table 4.2b Correlations
Correlations
Per_Use Per_EOU PR BR Per_Use
Per_EOU .775**
Per_Risk -.475** -.552**
Bhv_Int .724** .746** -.535**
**. Correlation is significant at the 0.01 level (2-tailed).
4.4. Regression Analysis
Referring to Table 4.3, in consequence of the standard regression analysis, the model’s degree of predicting the dependent variable was found to be R = 0.791. The model’s degree of explaining the variance in the dependent variable was R2 = 0.626, which means that the independent variable, perceived usefulness, perceived ease of use and perceived risk explains 62.6% of the dependent variable, behavioural intention towards TCO system. Looking at these coefficients, it shows that R2 is a strong regression model where 62.6% of the model fit the regression model. The other 37.4% of the total variation in the dependent variable remain unexplained.
Standard Error of the Estimate is the standard deviation of the residuals. The result shows 0.38561 of Standard Error of the Estimate. When the R2 increases, the Standard Error of the Estimate decreases. This is because a better fit model will have a lower estimation error.
Summary of the regression analysis is as per Appendix Table 4.3, 4.4 and 4.5.
Table 4.3: Summary of The Regression Analysis
Model Summaryb
Mod
el R
R Square
Adjusted R Square
Std. Error of the Estimate
Change Statistics R Square
Change
F
Change df1 df2
Sig. F Change
1 .791a .626 .620 .38561 .626 119.767 3 215 .000
a. Predictors: (Constant), Per_Risk, Per_Use, Per_EOU b. Dependent Variable: Bhv_Int
Table 4.4: ANOVA
ANOVAa Model
Sum of
Squares df Mean Square F Sig.
1 Regression 53.426 3 17.809 119.767 .000b
Residual 31.970 215 .149
Total 85.396 218
a. Dependent Variable: Bhv_Int
b. Predictors: (Constant), Per_Risk, Per_Use, Per_EOU
Table 4.5: Coefficients
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 1.671 .294 5.690 .000
Per_Use .375 .072 .346 5.226 .000
Per_EOU .337 .060 .393 5.616 .000
Per_Risk -.105 .034 -.153 -3.052 .003
a. Dependent Variable: Bhv_Int
Source: Developed for the research study.
Based on the regression analysis results, the regression equation was obtained as it is shown below;
Behavioural Intention towards TCO system = 1.671 + 0.375 Perceived Usefulness + 0.337 Perceived Ease of Use – 0.105 Perceived Risk
From the equation above, the intercept of the equation is 1.671, which means that the dependent variable = 1.671 when the independent variables = 0. The dependent variable is expected to:
i. Increased by 0.375 units if one unit is increased in Perceived Usefulness;
ii. Increased by 0.337 units if one unit is increased in Perceived Ease of Use; and iii. Decreased by 0.105 units if one unit is increased in Perceived Risk;
4.5. Hypothesis Testing
Referring to the regression analysis results in Table 4.8, it yields the regression equation as below and significant value p is less than 0.05
Behavioural Intention towards TCO system = 1.671 + 0.375 Perceived Usefulness + 0.337 Perceived Ease of Use – 0.105 Perceived Risk
Table 4.6 Hypothesis summary
Hypothesis Result
H1: Perceived Usefulness has a positive relationship towards Behavioral Intention to use TCO System among TNB Grid employee
Accepted (p < 0.05) H2: Perceived Ease of Use has a positive relationship towards Behavioral Intention to use TCO
System among TNB Grid employee
Accepted (p < 0.05) H3: Perceived Risks has a negative relationship towards Behavioral Intention to use TCO
System among TNB Grid employee
Accepted (p < 0.05)
5. Discussion and Conclusion
5.1. Research Objective 1
The statistical analysis has confirmed the research objective 1 which is; To determine Perceived Usefulness as the factor that influences behavioural intention to adopt the TCO system. It was determined by proving the hypothesis one H1 below;
H1: Perceived Usefulness has a positive relationship towards Behavioral Intention to use TCO System among TNB Grid employee
5.2. Research Objective 2
The statistical analysis has confirmed the research objective 2 which is; To determine Perceived Ease of Use as the factor that influences behavioural intention to adopt the TCO system. It was determined by proving the hypothesis one H2 below;
H2: Perceived Ease of Use has a positive relationship towards Behavioral Intention to use TCO System among TNB Grid employee
5.3. Research Objective 3
The statistical analysis has confirmed the research objective 3 which is; To determine Perceived Risks as the factor that influence behavioural intention to adopt the TCO system. It was determined by proving the hypothesis one H3 below;
H3: Perceived Risks has a negative relationship towards Behavioral Intention to use TCO System among TNB Grid employee
5.4. Research Objective 4
The statistical analysis has confirmed the research objective 1, 2 and 3 by proving the H1, H2 and H3. The research study has explained the relationship of independent variables or dimension and the dependent variable of the framework; TAM. The statistical analysis summarizes that the independent variable, perceived usefulness, perceived ease of use and perceived risk explains 62.6% of the dependent variable, behavioural intention towards TCO system.
6. Conclusion
Based on the findings, perceived usefulness, perceived ease of use has a positive influence on behavioural intention to adopt the TCO system. Meanwhile, perceived risks have a negative influence on behavioural intention to adopt the TCO system among TNB Grid employees.
These research study findings are consistent with previous studies conducted and have been supported by multiple literature reviews.
The findings explained Perceived Ease of Use is the variable with the highest β value which relatively the most important independent variable followed by Perceived Usefulness and Perceived Risks. TNB Grid should provide focus on the TCO system development to give ease- of-use experience to all employees. Finally, this research study has contributed to another branch of relevant study on utility industry as well as enriching the literature on new technology acceptance and adoptions of TAM.
References
A.Indarto, I. Garniwa, R. Setiabudy and C. Hudaya, "Total cost of ownership analysis of 60 MVA 150/120 kV power transformer," 2017 15th International Conference on Quality in Research (QiR).
Al Imarah, A.A.T., Zwain, A.A.A. and Al-Hakim, L.A.Y. 2013. The Adoption of e- government services in the Iraqi higher education context: An application of the UTAUT model in the University of Kufa. Journal of Information Engineering and Applications, 3(10):77-84.
Amin, M., Rezaei, S., & Abolghasemi, M. (2014). User satisfaction with mobile websites: The impact of perceived usefulness (PU), perceived ease of use (PEOU) and trust. Nankai Business Review International, 5(3), 258-274.
Dontigney, E. (2016). Types of Perceived Risk.
Elliott, K. M., Hall, M. C., & Meng, J. (. (2013). Consumers' Intention to Use Self-Scanning Technology: The Role of Technology Readiness and Perceptions Toward Self-Service Technology. xxxx
Gresham, J. (2020). Manufacturing trends in automated inspection equipment: Linking technology with business change management using the TAM (Order No. 27739078).
Hair, Joseph F Jr.; William C Black; Barry J Babin; Rolph E Anderson. (2019). Multivariate data analysis. Andover, Hampshire, United Kingdom: Cengage, [2019] ©2019
Hsiao, C.H. and Tang, K.Y. 2014. Explaining undergraduates’ behaviour intention of e- textbook adoption: An empirical assessment of five theoretical models. xxxx
Lee, Ming-Chi. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit.
Li, Y.-M. and Yeh, Y.-S. (2010), “Increasing trust in mobile commerce through design aesthetics”,Computers in Human Behaviour, Vol. 26 No. 4, pp. 673-684.
Maduku, D. K. (2015). An empirical investigation of students' behavioural intention to use e- books. Management Dynamics, 24(3), 2-20.
Marghalani, Adel & Shami, Maan. (2018). Estimating Total Cost of Ownership. Xxxx.
10.13140/RG.2.2.32151.06567.
McNish, M., 2001. Guidelines for managing change: A study of their effects on the implementation of new information technology projects in organisations.
Mha, K. (2015). A mobile banking adoption model in the Jordanian market: An integration of TAM with perceived risks and perceived benefits. Journal of Internet Banking and Commerce, 20(3), 1-35.
Normalini, M. K., P. (2019). Revisiting the effects of quality dimensions, perceived usefulness and perceived ease of use on internet banking usage intention. Global Business and Management Research, 11(2), 252-261.
Nurfatiasari, S., & Aprianingsih, A. (2017). A pilot study of technology adoption: An analysis of consumers' preference on future online grocery service.
Pallant, J. (2001). SPSS survival manual: A step-by-step guide to data analysis using SPSS for Windows (Version 10). Crows Nest, N.S.W: Allen & Unwin.
Sekaran. U, Bougie. R. “Research Methods for Business: A Skill Building Approach,” 2016 John Wiley & Sons Ltd. 7 Edition: UK
Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (6th ed.). Boston, MA:
Pearson.
TNB Grid Division, “Asset Maintenance Guidelines” 2018 Grid Strategy Department.
TNB Grid Division, “International Transmission Operations & Maintenance Study (ITOMS)”, 2019
Yu, C.S. 2012. Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of Electronic Commerce Research, 13(2): 104-121.