Investigation of Technology Acceptance and Adoption Of E-government Job Matching System


Aim of this study is to indicate the elements that affect the jobseekers’ intention to use JobsMalaysia system. The research analyzed the relationship between five variables perceive usefulness, perceive compatibility, perceive ease of use, attitude toward using and intention to use Jobs Malaysia system. Throughout the study, three hypothesis is not supported and has no significant relationship out of total eight hypothesis tested in this study, which is the correlation between Perceived compatibility and Attitude towards using, Perceived ease of use and Attitude towards using, Perceived ease of use and Intention to use JobsMalaysia system. The findings has highlighted that the information in JobsMalaysia are not compatible to the expectations of the user. Significant indicators towards the intention to use Jobs Malaysia system are provided by researchers to help the government in understanding how the job seeker accept and adopt such the system. Besides, several limitations and recommendation had been suggested by the researchers for future researchers to refer.

Keywords: Perceive usefulnessperceive compatibilityperceive ease of useattitude toward usingintention to use


In era of technology, the use of internet has made revolutionary changes in today’s world, especially for recruitment process. Previously, job seekers need to go organization to seek for job and find the vacancy manually (Brahmana & Brahmana, 2013). This gives companies a significantly disadvantage on taking more time to target the right people and high cost on doing promotion. Therefore, to ensure that job seekers get the jobs they want, business companies are desperate to attract new knowledge workers to achieve competitive labor market potential. As a result, more companies are implementing technology to recruit new workers from Technology Recognition Technology (TAM) who are forced to use technology. Farahat (2012) stated there are 93% of 500 global companies use the advance of internet and new cloud-based technology for recruitment process. Moreover, Maurer and Liu (2007) reported 87% new employees are hired when using online recruitment. Hence, job seekers are more prefer to use online recruitment for vacancy searching compared to traditional recruitment.

In Malaysia, there is an online recruitment system that provide by government which is JobsMalaysia System ( JobsMalaysia system is governed by the Ministry of Human Resources (MOHR) function as an automated online job matching system. The JobsMalaysia system is a system of automatic online employment provided by the Department of Human Resources (MOH). The JobsMalaysia system in Malaysia provides facilities for all job seekers to get the right job and also for companies to get the right candidates. Job seekers and employers can also use the JobsMalaysia line as a job application and link recruitment where the Malaysian government provides easy access to all levels of users without any charges. JobsMalaysia system is one of the online recruitment system offers facilities to job seekers fill up job application and employers to find suitable candidate. This is a win-win application where employers can post company information and job vacancies requirement and job seekers can obtain job that meet their expectation (Rosita & Nadianatra, 2007).

Problem Statement

According to Human Resources Online (n.d.), it states that 60% of job seekers in Malaysia preferred to use online recruitment system to seek for the job vacancy and preferred to apply for a job vacancy using a digital resume or application through online recruitment system. However, there is less number of job seekers in using the JobsMalaysia system compare to others online recruitment system in Malaysia such as, and others.

This study was conducted to provide some solution for government. The updated information also allows the job seekers to find suitable jobs. The organizations are able to find quality and potential employees. This study also enables government to understand the graduates’ skills and knowledge level when they register the website such as JobsMalaysia system. Government need to encourage people to use JobsMalaysia system. JobsMalaysia system enables the government agencies and also private sector Company to recruit potential employees.

Based on the data that researchers taken from JobsMalaysia online transaction report, it obvious to see that there was a large huge gap between the number of visitors and the number of new jobseekers registered in year of 2017 and 2016. From the data which researchers had taken from JobsMalaysia online transaction report, it shows that there were over 8 million of people had visited the JobsMalaysia system in year of 2017 but only around 87,315 people had register as the new jobseeker of JobsMalaysia system after visited the JobsMalaysia website. In other word, it means there was about 8,716,762 of job seekers had no yet register as the new job seeker user of Jobs Malaysia system after they had visited this online recruitment system. Whereas for year of 2016, there was over 7 million of people had visited the Jobs Malaysia system but only around 116,613 people had register as the new jobseeker registered of Jobs Malaysia system. There was about 7,023,057 of job seekers had no yet register as the new job seeker users of JobsMalaysia system after they had visited this online recruitment system throughout year of 2016. Hence, this may indicate that job seekers were less intention to use and accept the JobsMalaysia system.

Research Questions

According to the study of Batkovic and Batkovic (2015), the relationship between perceive compatibility to intention to use through perceive of usefulness is not direct. Wu and Wang (2005) indicated that perceive compatibility was directly influence the perceive usefulness of E-marketing (Kanchanatanee, Suwanno, & Jarernvongrayab, A., 2014). The study of Plouffe et al. (2001) stated that a greater adoption intention than traditional TAM construct do in the diffusion of innovation theory. The study of Chen, Gillenson, & Sherrell, (2002); Wu & Wang (2005) indicate the construct is to determine the perceive usefulness of mobile-retail (Batkovic, 2015).

According to Moqbel, Bartelt and Al-Suqri (2014), the result of the study shows perceive compatibility has significant relationship with perceive usefulness. This perception of compatibility and perception of usefulness is related. As state in the study of Moqbel et al. (2014), they stated that the people will have greater perceive of compatibility and will cause the perception of people that technology is useful. They will choose to use personal cloud computing if it is compatible with them.

H1: Perceive compatibility had significant relationship on the perceive usefulness of the system.

Vijayasarathy (2004) study revealed that compatibility correlated with the attitude on E-marketing use. Research by Chen et al. (2002); Vijayasarathy (2004) found that compatibility positively affects the attitude of using online spending. Amaro and Duarte (2015) show that people will have the attitude to using online shopping if they find that online shopping is compatible with their way of life. Studies show that people who use the internet in daily work time and jobs prefer to spend online (Zendehdel & Paim, 2015). Zendehdel and Paim (2015) also pointed out that compatibility seems important to online attitudes. People will change their attitude towards using online shopping because online shopping is compatible with their lives to always use the internet (Zendehdel & Paim, 2015). This will help them save time.

H2: Perceive compatibility has significant positive relationship on attitude toward using Jobs Malaysia system

Perceived ease of use and perceived usefulness have an effect on behavioral intentions to use a system in the context of TAM as mentioned by Davis et al. (1989) and Elkaseh, Kok, and Chun (2016). Elkaseh, Kok, and Chun (2016) also prove that there is a direct influence on the perceived ease of use and perceived usefulness in using e-learning in Libya's higher education. Roldan and Sánchez-Franco (2005) as well as Basri (2016) also found that the relationship between perceived ease of use and perceived usefulness was significant and positive in the study of marriage acceptance. Under e-recruitment, the system is easy to use for the swift progress of the e-recruitment, where teachers forward their resume to the e-recruitment site (Basri, 2016).

Ramayah and Ignatius (2014) also conclude that there is a positive influence on the perceived ease of use of online shopping. This is because internet users are trying to create perceptions about Internet spending based on their experience in making online purchases and the ease in which the task is implemented and easy to understand. Based on the above review, hypotheses are summarized as below:

H3: Perceived ease of use has a significant positive relationship on the perceived usefulness of the JobsMalaysia system.

Juniwati (2014), found that perceived ease of use had a huge impact on online shopping attitude. More people who see technology are easy to use, their positive attitude towards technology (Davis, 1989; Juniwati, 2014). Therefore, it is similar in the context of online purchases; because people see that online shopping can access websites, learn techniques, compare products and prices, to bargain the favorite product, their online shopping attitude tend be more positive. In addition, Teo's research (2011); Seif, Sarmadi, Ebrahimzadeh, and Zare, (2012); Elkaseh et al. (2016) identified a direct effect between easy to use and attitudes in the perspective of e-learning acceptance and aspects affecting teachers and students using technology. Elkaseh et al. (2016), found that easy-to-use affects student attitudes towards behavior using e-learning in higher education in Libya.

H4: Perceived ease of use has a significant positive relationship on attitude towards using the JobsMalaysia system.

Referring to the ease of use is defined as the extent to which people trust usage of multiple systems is a business (Davis et al., 1989; Al-wain & Alnawas, 2013). According to research conducted by Igbaria & Iivari (1995); Porter and Donthu (2006); Al-alak and Alnawas (2013) shows that users can avoid learning something new due to the complications and threats related with studying the problem. According to previous studies conducted by Wang et al. (2003) and Amin (2009); Al-alak and Alnawas (2013), reports that ease of use has a great influence on behavior and behavioral usability.

H5: Perceived ease of use has a significant positive relationship on intention to use JobsMalaysia system.

Farahat (2012) suggests Perceptions of Use has a direct effect on the determination of the intention of using mobile learning with Technology Acceptance Model (TAM). According to Technology Readiness Assessment (TRA), it is a controversy among researchers that the behavior of social media users using social media is decided by their intention to carry out their behavior and purpose, therefore, the function that benefit from social media. The benefits to be considered will be positive to the attitude of the user, so if suspected, the attitude will be positive. As a general rule, TRA proposes that more reasonable behavior is, more deliberately the person in the behavior of the questioner.

H6: Perceived usefulness has significant positive relationship on the attitude towards using of the JobsMalaysia system.

The technology acceptance model considers the use of exceptional technology, leading to actual use (Davis, 1989 Venktatesh and Davis, 2000). Users want to use all the technology because of its benefits, if one's perceived usefulness enables people to meet their needs more quickly, to reduce time for unproductive activities; so people often use it. Furthermore, the significant impact on the uses seen in behavioral intentions has been used to be verified by numerous studies by Lee, Yoon, & Lee, (2009); Li, Duan, Fu, & Alford, (2012); Saadé, Nebebe, and Tan (2007); Roostika (2012) also confirmed many previous studies which found that measurable values ​​had a significant effect on consumption. The ability to be considered the most proportionate contributes to the value of this research. Roca and Gagné (2008) also discovered that perceived usefulness was the sturdiest predictor of the intention to continue to study who are the workers from four international agencies of the United Nations.

H7: Perceived usefulness has significant positive relationship on the intention to use of the JobsMalaysia system.

Ajzen and Fishbein describe the attitude as an affective assessment of the task (Teo & Zhou, 2014). The attitude of the technology use denotes to the stage in which the user use technology and also not use the technology. Intent is the indicator of factors that want behavior through the use of technology. Theory of Reaction Action (TRA) shows that individual attitudes are a measure of behavioral tendencies as a function of their specific consequences. As TRA tells us, this relationship will be behavioral predictions when attitude and beliefs are defined consistently, and behavior is explained by time, purpose and context.

Attitudes towards TAM represent the attitude to the use of technology. Personal attitude plays important factor affecting one's behavior in receiving technology. Davis et al. (1989), Description of online user behavior determined by their attitude. Shen and Chiou (2015) find that attitude is directly related to the intensity of using a technology system based on positive behavior towards them. This is similar with prior unified studies showing a positive relationship between attitude toward usage and purpose of use (Cheung & Vogel, 2013, Chang, Yan Tseng, 2012).

H8: Attitude towards using has a significant positive relationship on intention to use JobsMalaysia system.

Purpose of the Study

The current research is to help the government better understand the factors that could affect job seekers users to accept the JobsMalaysia system otherwise the system should be considered. So it will help the government to identify effective ways to increase the competence of the future JobsMalaysia system to meet the needs of consumers. In addition, this research will help the government calculate the return on investment in the government information system project in the future.

Research Methods

Quantitative data will be more efficient and able to test the hypotheses as the data comes in the form of numbers and statistical. The target population were focus on job seekers. However, the target population is infinite due to the exact amount of target population for job seekers is unknown.

Sampling frame is the list of all the people in the appropriate population (Sekaran & Bougie, 2013). Researchers as the sampling locations for this research have chosen job seekers across Malaysia. Researchers selected the job seekers across Malaysia as the main sampling elements. However, the criteria for job seekers is wide, as it could be everyone across Malaysia, such as student, high school leaver, undergraduate of college or university, fresh graduate of college or university, employee of government or private organization and unemployed person. These population were targeted by researchers as they have experience and understanding in using the online recruitment system such as Jobs Malaysia and others system to search job vacancy. Thus, they may provide researchers with more accurate information through the survey instrument.

Whereas for convenience sampling, it is a non-probability design that acquired individuals or units conveniently available and the units selecting is made randomly (Sekaran & Bougie, 2013).

Due to the exact amount of target, population is unknown and infinite, thus the research will decide to use G power to calculate the sample size. According to G power calculator, it state that the power needed will be 0.85 and the G power result stated that the amount of sample needed for this research will be 120. However, researchers managed to proceed with the sample size of 150 as this will be more sufficient and more than enough to justify the reliability and validity of the research.

The questionnaires are set according to the theoretical frameworks that investigate the relationship between one of TAM determinants, which is perceived compatibility by Rogers (2003), perceived usefulness, perceived ease of use, attitude towards using and intention to use by Davis (1989).


Table 1 will indicated the detailed information of all the job seeker’s demographics profile who involved in the data collection process.

Table 1 -
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6.1. Analysis Descriptive Statistics of Study Variable

Table 2 -
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Table 2 shows the standard deviation and mean for each of the variable which in our study. In the independent variables, there are 72.09% of the respondents are perceived compatibility toward Jobs Malaysia system which referring to the mean for the perceived compatibility among the respondents and their standard deviation is 0.36. Besides, there is 73.6% of the respondents are perceived ease of use toward Jobs Malaysia system while the standard deviation is 0.37. Furthermore, the perceived usefulness of the respondents toward Jobs Malaysia system is 77.23% and the standard deviation for perceived usefulness is 0.35.

For the mediating variable, there is 87.14% of the respondents are attitude towards using toward the Jobs Malaysia system while the standard deviation is 0.44. Lastly, the dependent variable, which is intention to use toward the Jobs Malaysia system among the respondents, is 76.9% and their standard deviation is 0.37.

6.2. Rating Outer Model (Measurement Model)

Based on the study, researchers had used the Smart PLS version 3 to test hypothesis. The PLS software had been increasing used by business-related of investigation and it can be easily obtained through the internet. Before researchers measuring the outer model, researchers need to identify 3 important criteria which are convergent validity, composite discriminant validity and reliability (Silaparasetti, Srinivasarao & Khan, 2017). Full depictions of the SEM to evaluate outer Smart PLS model as shown as below.

Figure 1: Path Coefficients Among The Independent Variables, And Dependent Variable.
Path Coefficients Among The Independent Variables, And Dependent Variable.
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Note: Perceived Compatibility (PC), Perceived ease to Use (PEU), Perceived Usefulness (PU), Attitude towards Using (ATU), Intention to use Jobs Malaysia System (ITU)

The Figure 1 indicated the factors that will affect the intention to use in the JobsMalaysia System which are perceived compatibility, perceived ease to use, perceived usefulness and attitude toward using which are measured by indicators on each. PC1, PC5, PC6 are the indicators for perceived compatibility. PEU1, PEU2, PEU3 are the indicators for perceived ease to use. PU1, PU2, PU3, PU4, PU5 are the indicators for perceived usefulness. ATU1, ATU2, ATU4 are the indicators for attitude towards using. Intention to use is measured by four indicators, namely ITU1, ITU2, ITU3 and ITU4. The relationships of those hypotheses are pointed by arrows between the variable, which the attitude toward using act as the mediating in this study. Throughout this analysis, researchers had deleted seven indicators in order to make the model fit which are PC2, PC3, PC4, PEU4, PEU5, PEU6 and ATU3. Based on Hair (2016), remove 25% of the indicators to obtain a fit model.

6.3. Measurement Model

Before researches run the hypothesis test, it is necessary to checking on the measurement model. This will assist researches to ensure that the model was fit by having the assessing on the measurement model accurateness. The validity of the measurement was the reason that researchers need the measurement model analysis, which this will directly affect the underlying theoretical constructs.

6.3.1.Testing Outer Model (Measurement Model)

The Outer Assessment Model (Measurement Models) comprises of three criteria, which is Convergent Validity, Discriminant Validity and Composite Reliability. From the figure 1 , it shows the full structural equation model to assess outer Smart PLS models by using version 3.

Table 3 -
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From the table 3 , it shows that the outer models variable is Attitude towards Using (ATU), Intention to use (ITU), Perceived of Compatibility (PC), Perceived Ease of Use (PEU), and Perceived Usefulness (PU). In all construct indicators, the value convergent validity must be with loading factor more than 0.5. In figure 1 , researchers are aware that all data is accurate. In table 3 , the loading results for all variables are greater than 0.6, which means that all these variables of the study are completely reliable.

To show the relationship between variables, researchers use different validity tests, if the cross correlation value contains hidden variables higher than other latent correlations, the cross correlation value contains all the indicators used to formulate unacceptable hidden variables. Table 4 shows that the value of the cross correlation loading corresponds to the variable.

Table 4 -
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Table 5 -
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From the table 5 , it clearly showed the square root AVE value for individual construct is more than 0.5. Hence, the divergent validity was all been achieved. Besides, the table has pointed out that all variable have a greater value as compare to other constructs of their square root AVE value. As a conclusion, the criteria for the Partial Test Least Square Models with Outer size (Measurement Model) had all been met in this research.

Table 6 -
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The discriminant validity assessment has the aim to make sure a reflective construct has the strongest relationships with its own indicators such as comparison with than any other construct in the PLS path model (Hair et al., 2017). According to Campbell and Fiske (1959), they stated that the HTMT result less than 0.85 indicates that discriminant validity likely exists between the two scales whereas HTMT result greater than 0.85, will indicated that the two constructs overlap greatly and they are likely measuring the same thing. From the table 6 , it clearly shown that all constructs were less than 0.85. Hence, researchers can concluded that the discriminant validity exists between all the constructs. In order words, most of the items of constructs were not measuring the same thing and it did not contains the overlapping items from the respondents’ perception in the affected constructs.

6.3.2. Testing Inner Model

Table 7 -
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R Square used to identify the coefficient for determination in the dependent constructs. According to Chin (1998), he state that for a strong R square need 0.67, while for moderate need 0.33 and for a weak R square need 0.19. Besides, according to Hair et al. (2016), the R square of 0.75 is strong, 0.5 is moderate, and 0.25 is weak. Next, Falk and Miller (1992) recommended that R square should be equal to or bigger than 0.10 in order, for the variance explained of a particular endogenous construct to be deemed adequate. Hence, based on this study, the R square for researcher’s study is moderate (0.438).

6.4. Structural Model

The output for the hypothesis testing with the bootstrapping was obtained by using the Smart PLS version 3 software.

Figure 2: T-value among the dependent variables and independent variables.
T-value among the dependent variables and independent variables.
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6.4.1 T-Statistic

Table 8 -
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Table 7 shows all the hypothesis stated down from H1 to H8, and in the table it also contain the T-statistics value for each hypothesis. When the hypothesis is significant, the t-value is more than 1.645 (p<0.05), t-value more than 2:33 (p <0.01) for 1-tail test, t-value more than 1.96 (p<0.05) or t-value more than 2:58 (p <0.01). From Table 8 indicates that there are three hypothesis, Perceived compatibility -> Attitude towards using, perceived ease to use -> Attitude towards using and perceived ease to use -> intention to use JobsMalaysia system are insignificant because the lower limit is in a negative value while the upper limit for the hypothesis is in a positive value, so the hypothesis had become insignificant. At the same time, for the entire remaining hypothesis were supported.


Based on the analysis of the researcher, it shows that perceived compatibility is no direct influence the jobseeker attitude toward using JobsMalaysia system. It can say that if the jobseeker has perceived JobsMalaysia system is compatible; their attitude towards using JobsMalaysia system will be higher. The reason may because the some of the information on JobsMalaysia is not compatible with the jobseekers’ lifestyle.

In the case of perceive ease of use, it is not significant with intention to use JobsMalaysia system. Researcher found the hypothesis result between perceived ease of use and intention to use JobsMalaysia system is not supported. To increase their intention to use, JobsMalaysia system can utilize some advertisement to attract jobseeker also setting up vendor in the public area and preparing computers to apply jobs will increase the usage.

According to Technology Acceptance Model (TAM), the researcher found perceived usefulness will affect the attitude towards using. The researcher also found that the jobseekers will perceive JobsMalaysia system is useful to them when they feel it is compatible. It can be compatible with the jobseekers’ lifestyle. However, the questionnaire of study is asking the perception of jobseekers about using the JobsMalaysia system. The researchers also analyze the different perception of jobseeker towards using JobsMalaysia system.


This research work is part of Final year project of the Thesis (UTAR) Supported by Dr Ramesh Kumar Moona Haji Mohamed.


  1. Al-alak, B. A., & Alnawas, I. A. (2013). Measuring the acceptance and adoption of e-learning by academic staff. Knowledge Management & E-Learning: An International Journal (KM&EL), 3(2), 201-221.
  2. Amaro, S., & Duarte, P. (2015). An integrative model of consumers' intentions to purchase travel online.Tourism management, 46, 64-79.
  3. Basri. W. (2016). E-recruitment Adoption Strategy in the Universities of Saudi Arabia. European Journal of Business and Management, 8(34).
  4. Batkovic, I., & Batkovic, R. (2015). Understanding consumer acceptance of mobile-retail. An empirical analysis of the revised technology acceptance model.
  5. Brahmana, R., & Brahmana, R. (2013). What factors drive job seekers attitude in using E-Recruitment?. The South East Asian Journal Of Management, 7(2).
  6. Cheung, R., & Vogel, D. (2013). “Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning”. Computers & Education, 63, 160-175.
  7. Chin, W. W. (1998). The partial least squares approach for structural equation modeling. Modern Methods for Business Research, p. 295-236.
  8. Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81-105.
  9. Chen, L. da, Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information and Management, 39(8), 705–719. DOI:
  10. Farahat, T. (2012). Applying the Technology Acceptance Model to Online Learning in the Egyptian Universities. Procedia - Social and Behavioral Sciences, 64, 95–104. DOI:
  11. Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587–605. DOI:
  12. Lee, B. C., Yoon, J. O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers and Education, 53(4), 1320–1329. DOI:
  13. Li, Y., Duan, Y., Fu, Z., & Alford, P. (2012). An empirical study on behavioural intention to reuse e-learning systems in rural China. British Journal of Educational Technology, 43(6), 933–948. DOI:
  14. Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585–1604. DOI:
  15. Roldán, J. L., & Sánchez-Franco, M. J. (2005). Web acceptance and usage model: a comparison between goal-directed and experiential web users. Internet Research, 15(1), 21–48. DOI:
  16. Saadé, R. G., Nebebe, F., & Tan, W. (2007). Viability of the ‘ Technology Acceptance Model ’ in Multimedia Learning Environments : A Comparative Study Research Model and Hypotheses. Learning, 3, 175–184. DOI:
  17. Seif, M. H., Sarmadi, M. R., Ebrahimzadeh, I., & Zare, H. (2012). A model for predicting intention to use e-learning based on epistemological beliefs. Life Science Journal, 9(2), 926–929. Retrieved from
  18. Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. DOI:
  19. Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: a structural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124-142.
  20. Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce? Information & Management, 42(5), 719–729. DOI:
  21. Zendehdel, M., & Paim, L. H. (2015). Investing the perceive innovation attributes, online shopping in Malaysia. Asian Journal of Social Sciences & Humanities, 4(2), 15-23.

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Sinniah, S., Siew, J. H., Mohd Nor, C. S., Ramendran, C., & Mohamed*, R. K. M. H. (2019). Investigation of Technology Acceptance and Adoption Of E-government Job Matching System. In M. Imran Qureshi (Ed.), Technology & Society: A Multidisciplinary Pathway for Sustainable Development, vol 62. European Proceedings of Social and Behavioural Sciences (pp. 41-54). Future Academy.