Intention to Use E-Recruitment System: Empirical Evidence from Jobseekers in the Advertising Industry in Malaysia


Chuah Kee Woon, Jugindar Singh Kartar Singh

Chuah Kee Woon 1
Jugindar Singh Kartar Singh 2

  1. IPE Management School Paris, France 1

  2. Asia Pacific University of Technology and Innovation, 57000 Kuala Lumpur, Malaysia 2

Pages: 76-86

DOI: 10.18488/journal.62.2019.62.76.86

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Article History:

Received: 08 November, 2018
Revised: 14 December, 2018
Accepted: 22 January, 2019
Published: 11 March, 2019


Abstract

Advancements in technology have a massive impact on the workplace including recruitment of staff.  The aim of this study was to examine jobseekers’ behavior towards e-recruitment system usage by jobseekers in Malaysia. The study population was jobseekers intending to apply for jobs in the advertising industry. This was a quantitative study and self-administered questionnaires were used to collect information from a sample of 100 jobseekers. The SPSS statistical tool was used to generate descriptive statistics and inferential statistics.  The results revealed that perceived usefulness was the key determinant of e-recruitment adoption. However, the impact of perceived information content quality (PICQ) and perceived search engine optimization (PSEO) was insignificant. The implications of this study are that recruitment agencies and human resources practitioners need to focus on the perceived usefulness of the e-recruitment system to attract and create the desire in jobseekers to use e-recruitment system.

Keywords: E-recruitment, Perceived usefulness, Perceived information content, quality, Perceived search engine, Optimization.

Received: 8 November 2018 / Revised:14 December 2018 / Accepted:22 January 2019/ Published: 11 March 2019

Contribution/ Originality

This study is one of very few studies which have investigated jobseekers’ intention to use e-recruitment system in Malaysia. This study provides insights for human resources practitioners on the effective use of e-recruitment system to attract a greater pool of applicants.


1. INTRODUCTION

Advancements in technology is continually improving the human resource process of acquisition and hiring of employees (White, 2018). Technology has the capacity to improve the recruitment process and, the precision of smart technology will allow recruiters to be more efficient and result in a faster hiring process (White, 2018). Technologies such as applicant tracking systems and new AI software have made it easier to apply to jobs and find potential candidates anywhere in the world (White, 2017). The number of online job seekers has increased tremendously and based on a survey by Pew Research Center, 54 percent of Americans use the internet to research available jobs in 2016 compared to only 26 percent in 2005 (Smith, 2015). Roughly one-third of Americans have looked for a new job in the last two years, and 79% of these job seekers utilized online resources in their most recent search for employment (Smith, 2015). In Malaysia, there are approximately were 24.5 million Internet users (MCMC, 2017). As reported by MCMC (2017) the Internet remained as an important source of information for 86.9% users in Malaysia. The survey my (MCMC, 2017) found that 35.4% of online users did online job applications. In Malaysia, Monster Employment Index reported a strong 36% growth year-on-year in online hiring. (Digital News Asia, 2017). 

Recruitment of human resources plays a crucial role in the attraction of talent and development of human capital (Liviens and Chapman, 2010). The advancements in technology is impacting various business operations and functions of organizations. Technology advancements and the growth in the number of internet users is affecting several human resource operations and recruitment of right resource is becoming important (Tong and Sivanand, 2005). E-recruitment or internet recruiting is a recruiting process that organizations conducts through web based technologies and tools, such as an organizations public internet site or specialized sites like Monster.com (Kering and Kettley, 2003). The e-recruitment system has transformed the traditional recruitment process into a time and space-independent, collaborative hiring process (Holm, 2012). Job-search websites provide convenience, cost savings, and greater efficient and effective solutions to job applicants and organizations (Leonard, 2000).  In today’s environment, the attraction of applicants is becoming reliant on e-recruitment sources (Holm, 2012).

Past researchers and scholars have identified several factors that influence the adoption of e-recruitment by job seekers and organizations. Some have pointed out that e-recruitment enables organizations to have an access to a diverse and wider pool of job applicants (Doherty, 2010). Hafeez and Farooq (2017) also found that e-recruitment had a positive impact on attracting applicants. The growth of e-recruitment is facilitated by several factors. Leonard (2000) stated that the lower cost is one of the factors for the growth of e-recruitment. Studies also found that found that applicants prefer web-based quality web sites instead of traditional job postings (Zusman and Landis, 2002; Hafeez and Farooq, 2017). Several studies have been done on the adoption of e-recruitment and most studies used the Technology Acceptance Model (TAM) to identify the factors influencing the intention to adopt e-recruitment system. The constructs identified as having a positive influence on the adoption of e-recruitment encompasses perceived usefulness, perceived ease of use, attitude and perceived risk (Yoon, 2009; Kashi and Zheng, 2013). The adoption of e-recruitment is growing rapidly and playing a crucial role in attracting a pool of candidates. Therefore, the factors that influence the job applicant adoption of e-recruitment must be understood.

In view of the importance of e-recruitment system that is efficient and effective, it is worth understanding the factors that have a positive impact on intention to use e-recruitment system. It is also worth doing a study that is focused on a particular sector. Although a number of researchers have studied the intention to use e-recruitment by potential applicants, the relationship between the intentions to use e-recruitment has been studied but the influence of perceived usefulness, perceived information content quality (PICQ) and perceived search engine optimization (PSEO) towards intention to use e-recruitment has not been empirically tested in Malaysia. Therefore, a study of the influence of perceived usefulness, perceived information content quality and perceived search engine optimization towards intention to use e-recruitment is useful for human resource practitioners and third party recruiters.  This study will provide further insight for third party recruiters and human resources practitioners on the strategies to increase the attractiveness of e-recruitment systems.

2. LITERATURE REVIEW

2.1. Intention to Use

The theories and literature covering the intention to adopt or use technologies is drawn based on several factors (Davis et al., 1989; Venkatesh et al., 2003). The Technology Acceptance Model (TAM) is the most widely used model that has two elements namely perceived usefulness and perceived ease of use (Davis et al., 1989). The TAM can explain the intention or behavior to use new technologies and several researchers have used it to predict and understand the use of new technologies (Yoon, 2009; Wu and Chen, 2017). The TAM is an extension of the Theory of Reasoned Action (TRA) which can be applied to understand and predict behavior (Ajzen and Fishbein, 1980). The TRA considers the behavioral intentions and Ajzen and Fishbein (1980) pointed out that an individual’s actual behavior can be determined by the individual’s prior intention. Based on the TAM, Davis et al. (1989) explained that the actual adoption or use of technology is an individual’s response that can be predicted or explained by the individual’s motivation. Venkatesh et al. (2003) further developed the Unified Theory of Acceptance and Use of Technology (UTAUT). The TAM and TRA were incorporated in the UTAUT model. The UTAUT model developed by Venkatesh et al. (2003) can be used to find out the likelihood of new technology adoptions and understand the factors that contribute towards their acceptance. Therefore, the TAM, and UTAUT models can be used to explain intention to use systems such as e-recruitment systems.

Previous research on intention to use new technologies and systems found support for some constructs towards behavioral intention to use and actual use of the systems (Teo and Milutinovic, 2015; Joo et al., 2018). A study by Teo and Milutinovic (2015) found that attitude had a positive and direct influence on intention to use technology. Another research by Joo et al. (2018) found that self-efficacy, perceived ease of use and perceived usefulness had a positive influence on intention to use new technology. Wu and Chen (2017) found that both attitude and perceived usefulness contributes positively towards the intention to use new systems and technologies. Past studies have also shown that culture can also influence the intention to use new technologies. A study by Tarhini et al. (2015) revealed that adoption of technology was significantly higher in among the British respondents as compared to the Lebanese respondents. The results showed that adoption was significantly higher, but also modest, in the British context at 36%. In the research by Tarhini et al. (2015) there were differences between perceived usefulness and perceived ease of use among the British and Lebanese respondents. Similarly, past studies found a positive influence of several constructs towards to intention to use e- recruitment. The study by El Ouirdi et al. (2016) found a positive impact of effort expectancy, performance expectancy and social influence towards intention to adopt new technology. Based on the signaling theory, Kashi and Zheng (2013) found that impression of the organizational website created an interests in organization and prompted the desire of applicants to use e-recruitment. Therefore, there are several factors that can influence the intention to use or adopt the e-recruitment system.

2.2. Perceived Usefulness Behavioral Intention to Use e-recruitment

Perceived usefulness is one of the constructs in the Technology Acceptance Model (TAM). Davis et al. (1989) defined perceived usefulness as “the degree to which a person believes that using a particular system would enhance his or her job performance”. Based on the TAM model by Davis et al. (1989) several past studies found that perceived usefulness was a predictor of behavioral intention (Kashi and Zheng, 2013; Park et al., 2014).  A study based on the TAM model by Park et al. (2014) found that perceived usefulness was positively related to behavioral intention to use technology.  Similarly, other studies also found that perceived usefulness had a positive and significant impact on applicants’ behavioral intention to use electronic or e-services (Horst et al., 2007; Kashi and Zheng, 2013). The results of a study by Amoako-Gyampah (2007) further suggested that and improvement of the perceptions of the usefulness and relevance of the technology will contribute towards the usage of technology.

In this study it was hypothesized that perceived usefulness is related to intention to use e-recruitment system. Several studies were done specifically on the intention to use e-recruitment (Lin, 2010; Brahmana and Brahmana, 2013). A study by Brahmana and Brahmana (2013) that was based on the TAM model found that perceived usefulness was one of the determinants of intention to use e-recruitment system by job seekers.  Similarly, Lin (2010) found that perceived usefulness had a significant effect on attitude towards intention to use e-recruitment system. Results of another study by Priyadarshini et al. (2017) indicated that the information quality dimensions positively influence perceived usefulness, and perceived usefulness subsequently affects attitude towards a website. Based on the past research findings discussed above and logically extending them, the following hypothesis was formulated for testing in the present study.

H1. Perceived usefulness is related to behavioral intention to use e-recruitment system.

2.3. Perceived Information Content Quality and Behavioral Intention to use e-recruitment

The key attributes of websites encompass design, information content and quality. The D&M IS Success Model by DeLone and McLean (1992) is one of the highly cited model of information systems success.  The model defined by DeLone and McLean (1992) has six distinct dimensions of IS success namely system quality, information quality, use, user satisfaction, individual impact, and organizational impact. As stated by Urbach and Müller (2012) the success dimension information quality encompasses the characteristics that are desirable of an information system’s output. Urbach and Müller (2012) further explained that the quality of the information that the system produces is an antecedent of usefulness for the user and user satisfaction.  Another study by Indipenrian et al. (2015) pointed out that information provided to users should be complete, precise, accurate, consistent, up to date, and beautiful. Therefore, information quality is one of the constructs that contributes towards success of a e-recruitment system and affect subsequent intention to use or use (Urbach and Müller, 2012). In this study it is hypothesized that perceived information quality is related to intention to use e-recruitment system. Several studies were done specifically on the relationship between perceived information content quality and intention to use a system (Lin and Lu, 2000; Indipenrian et al., 2015).  Indipenrian et al. (2015) pointed out that social factors and information quality influences the behavioral intention of individuals to adopt a system. Lin and Lu (2000) further asserted that the quality of information provided by a website influences people in the usefulness of a website. The study by Lin and Lu (2000) found a positive relationship information quality on perceived usefulness but not on perceived ease of use. The results of another study by Almahamid et al. (2010) stated that a system is perceived as useful, easy to use and have a high level of information quality. The results of the study by Almahamid et al. (2010) revealed that there was a relationship between perceived information quality and intention to use a system. Kumar et al. (2007) stated that people’s perception of the online information provided by systems could significantly increase the intention to use but the perceived usefulness is dependent on perceived ease of use. Based on the past research findings discussed above and logically extending them, the following hypothesis was formulated for testing in the present study.

H2. Perceived information content quality is related to behavioral intention to use e-recruitment system.

2.4. Perceived Search Engine Optimization and Behavioral Intention to use e-recruitment.

Search engines refers to web sites which index and class other web sites according to their keywords, explanations and contents (Yalçın and Köse, 2010). More specifically, search engine is some kind of software, which collects data about web sites. The objective of search engine is to ensure faster and easier access to information and results. As stated by Yalçın and Köse (2010) Search Engine Optimization (SEQ) is the one of the widely used technique that provides faster reach to web sites. Gandour and Regolini (2011) stated that Search Engine Optimization (SEO) is a set of techniques used by websites in order to be better indexed by search engines. As pointed out by Ortiz‐Cordova and Jansen (2012) traffic flow from search engines is highly important for most online businesses, with the majority of visitors to many websites being referred by search engines. Therefore, search engine traffic and the speed and ease of access is important to attract users and improve the intention to use.

As pointed out by Onaifo and Rasmussen (2013) certain websites' characteristics have an influence on how well websites are ranked by search engines. Furthermore, the reputation of a website and the number of its search engine indexed webpages could increase its ranking (Onaifo and Rasmussen, 2013). Similarly, another study by Lee et al. (2016) highlighted that SEO techniques could help increase the exposure of the specific services and the number of visitors through search engines. Moreno and Martinez (2013) further added that any SEO project should include the proper design of accessible web content, inasmuch as search engines will interpret the web accessibility achieved as an indicator of quality. This will be able to make the search engine more accessible and index the resulting web content (Moreno and Martinez, 2013). Based on the past research findings discussed above and logically extending them, the following hypothesis was formulated for testing in the present study.

H3. Perceived Search Engine Optimization is related to behavioral intention to use e-recruitment system.

3. METHODOLOGY AND RESEARCH DESIGN

3.1. Research Design 

The research design which is the blueprint for achieving the objectives and questions was developed for this study Cooper and Schindler (2006). The research design for this study encompasses research philosophy, methods, strategy, approach, time horizon data collection, sampling plans and data analysis (Saunders et al., 2012). In this quantitative study, a survey strategy was selected to collect a snap-shot of primary data by using a self-administered questionnaire. Non probability sampling was used and questionnaires were administered directly to the respondents. The statistical tool SPSS version 20 was used.

3.2. Sampling Technique and Sample Size

The first step in sampling was identifying the target population. As stated by Cooper and Schindler (2006) a population is the total collection of elements about which we wish to make some inferences. In this study, the target populations were individuals intending to apply for jobs in the advertising industry in Malaysia. A qualifying question was included to identify qualified respondents. By using convenience sampling, the researcher selected any readily available individuals as participants who qualified to participate in this study. In this study the sample size was set at 100. The formula by Tabachnick and Fidell (2013) proposed a formula “50 + 8m” where “m” is the number of variables that are used to calculate the sample size.  According to Hair et al. (2010) the minimum sample size should be 100.

3.3. Instrumentation

An important step is the structure of the questionnaire and the wording that should be used (Cooper and Schindler, 2006). The questionnaire for this study had 2 parts. In the first part of the questionnaire, demographic questions were included. The second part covered questions to measure the dependent variables and independent variables. All questions were closed ended and the fixed alternative questions required the respondents to choose the best answer based on a five- point Likert-type scale. The self-administered questionnaire offered the most cost-effective method for securing data from the respondents (Cooper and Schindler, 2006). The questions used in this questionnaire were adopted from past research. The researcher tested the questionnaire with a sample of 20 respondents. The objective of the testing was to contain the cost and to detect weaknesses in design before a full survey is undertaken (Cooper and Schindler, 2006).

3.4. Data Collection and Editing

The self-administered questionnaires were delivered by hand to collect primary data from the qualified respondents. The direct distribute and collect method was used because of the faster speed in getting responses and respondents that could be reached personally. A cover letter was attached to the questionnaire. Follow up calls were made to respondents to fill up the questionnaire. The response rate was encouraging and higher than expected. The data was edited to locate omissions and check consistency across respondents (Cooper and Schindler, 2006). In this survey method, the edited data was then tabulated using codes the researcher had devised for analysing the data (Cooper and Schindler, 2006).

3.5. Data Analyses

In this study, the statistical tool used was SPSS Version 20. The first step was to present descriptive statistics of the respondents of this study. The frequency tables array data from lowest to highest values with counts and percentages. In addition, bar charts and pie charts were appropriate for relative comparisons of nominal data. For normality testing histograms were optimally used with continuous variables where intervals group the responses (Cooper and Schindler, 2006). The researcher established the alternative hypothesis and a two-tailed test was used. The Pearson correlation coefficient that reveal the magnitude and direction of relationships was calculated and the values varies over a range of +1 through 0 to -1 (Cooper and Schindler, 2006). Multiple regression analysis was used to obtain further insight into the relationship between the independent variables and dependant variable (Cooper and Schindler, 2006). 

4. RESULTS

4.1. Demographic Profiles of the Respondents

The respondents included 48% (n=48) female and 52% (n=52) male. The respondents age included 29% between the age range of 18 to 24 years, 45% between the age 26 to 34 years and 26% above the age of 34 years. The respondents included 78% singles and 22% were married. In terms of qualification, there were 12 postgraduates, 73 were degree holders and 15 respondents had either a Diploma/Certificate or lower qualification.

4.2. Descriptive Statistics and Normality

To describe central tendency and other descriptive statistics, the means, standard deviations, skewness and kurtosis were generated using SPSS. Skewness and kurtosis values showed the normality of data distribution. The distribution was left-skewed or also referred to as negatively-skewed. In this research the mean is mean is also to the left of the peak. The values for kurtosis and skewness was within the range specified by Hair et al. (2010). The values for skewness are between the ranges of -.169 to -.502 and the values for kurtosis are between the ranges of -.143 to .502. The values of skewness and kurtosis values are within +1 and -1 standard deviations from its mean and normality was not violated (Hair et al., 2010). 

Table-1. Descriptive Statistics.

Variables
Minimum
Maximum
Mean
Std. Dev.
Skewness
Kurtosis
Statistic
Statistic
Statistic
Statistic
Statistic
Std. Error
Statistic
Std. Error
Behavioral Intention  
2.5
5
3.8775
0.476
-0.208
0.22
0.379
0.437
Perceived Usefulness 
3
5
4.256
0.451
-0.502
0.22
-0.143
0.437
Perceived Information Content Quality 
2
5
3.62
0.574
-0.481
0.22
0.573
0.437
Perceived Search Engine Optimization
2.67
5
4.01
0.573
-0.169
0.22
-0.311
0.437

4.3. Reliability    

As stated by Cooper and Schindler (2006) reliability is the degree to which what we measure is free from random error. The SPSS tool was used to test the internal consistency reliability. The Cronbach’s alpha value is a reliability coefficient that indicates how well the items in a set are positively correlated to one another (Sekaran and Bougie, 2010). The Cronbach alpha values were 0.683, 0.714, 0.830, and 0.668 respectively for the dependent variable and the three dependent. The results show that the Cronbach’s Alpha coefficient lies above or close to the commonly suggested threshold of 0.70 These reliability statistics complies with the recommendations by Nunnally and Bernstein (1994) who specified that the reliability coefficient or Cronbach’s alpha should be .6 or higher.

4.4. Pearson Correlation

Table 2 shows the all the bivariate correlations between independent variables and the dependent variable namely behavioral intention, perceived usefulness, perceived information content quality and perceived search engine optimization. were positive The size of the correlation indicates the strength of the relationships between the variables (Pallant, 2010).  In this study, the correlation between perceived usefulness and behavioral intention was moderate. However, the correlation between the other two independent variables and the dependent variable was low (Pallant, 2010). Only the relationship between perceived usefulness and perceived information content quality towards behavioral intention was statistically significant at the 0.01 level (2-tailed).

Table-2. Pearson Correlation Coefficient.

Variables
Behavioural Intention
Perceived Usefulness
Perceived Info. Content Quality
Perceived Search Engine Optimization
Behavioural Intention  
1
.479**
.276**
0.174
Perceived Usefulness
.479**
1
Perceived Information Content Quality
.276**
1
Perceived Search Engine Optimization
0.174
1

In Table 3, the relevant values to address issues concerning both overall model fit as well as the estimation of the regression model are shown (Hair et al., 2010). The R value of .486 indicates that the correlation between the independent and dependent variables is moderate. The R square is the coefficient of determination that measures the explanatory power of the variables included in a regression model. The coefficient of determination (R square) in this study 0.269. This means that only 27 percent of the variance in the dependent variable is being explained by the three independent variables. The coefficient of determination is one of the widely used statistics but its value is affected by many factors, some of which are associated more closely with the data collection or the research design than with how close the regression equation actually fits the observations (Cornell and Berger, 1987).  The use of R2 alone as a model-fitting criterion is often risky and other statistics should be used to assess the model (Cornell and Berger, 1987).

Table-3. Overall Model Fit.

Multiple R
0.486
Coefficient of Determination (R square)  
0.269
Adjusted R square  
0.232
Standard Error of estimate  
0.42258
F Change
9.881
Sig F Change
0

In Table 4, the standardized Beta coefficient shows the strength of relationship between the dependent and independent variables (Hair et al., 2010). The results show that only perceived usefulness had a significant impact on behavioral intention to use e-recruitment. Perceived Usefulness was the most predictive of the behavioral intention to use e-recruitment. A standardized regression value of .512 indicates that perceived usefulness construct was moderately responsible for explaining the dependent variable and had the highest impact. However, perceived information content quality and perceived search engine optimization did not have a significant impact on behavioral intention to use e-recruitment. Based on the results shown above, hypothesis H1 was proven. Hypothesis H2 and H3 were rejected.

Table-4. Variables entered in the Regression Model.

Unstandardized Coeff.
Std.  Coeff.
B
Std. Error
Beta
t
Sig
Perceived Usefulness
0.54
0.122
0.512
4.413
0
Perceived Information Content Quality
0.022
0.091
0.026
0.238
0.813
Perceived Search Engine Optimization
-0.019
0.087
0.095
0.908
0.366

5. DISCUSSION AND CONCLUSION

The results of this study showed there is positive and significant relationship between perceived usefulness and behavioral intention to use e- recruitment system by job seekers in Kuala Lumpur. The findings are consistent with results from other studies (Lin, 2010; Brahmana and Brahmana, 2013).  Brahmana and Brahmana study that was based on the TAM model found that perceived usefulness was one of the determinants of intention to use e-recruitment system by job seekers.  The results of this study showed that job seekers behavioral intention to use e recruitment system was influenced by their perceived usefulness and convenience. SHRM (2018) stated that recruiting via online job sites is one of the primary strategies of external candidate sourcing today. Job seekers find such sites useful because many employers have a website with an employment/recruiting component to it that is linked to the organization's HRIS or applicant tracking system. The online job sites provide information and explain the benefits of working for the company and how to apply for jobs.

A surprising finding was on the relationship between perceived information content quality and intention to use e-recruitment. The insignificant relationship between information content quality and intention to use e-recruitment deviated from past studies that found a positive relationship between them (Lin and Lu, 2000; Indipenrian et al., 2015).  Contrary to the results of this study, the research by Indipenrian et al. (2015) pointed out that social factors and information quality influences the behavioral intention of individual to adopt a system.  A plausible explanation is that the perceived information quality on established internet recruiting sites such as Monster.com are generally good and therefore less attention is paid to the quality. Another possible explanation is that most of the respondents in this study were young adults. Flanagin and Metzger (2000) found that few users are rigorously verifying the information obtained via the Internet. Flanagin and Metzger (2000) further stated that less experienced users are less likely to verify the information. However, it must be noted that internet savvy and more experienced users may be somewhat more likely to view the information content quality and verify the information.   

Similarly, the relationship between perceived search engine optimization and behavioral intention to use e-recruitment was not significant.  The results of this study deviated from past studies that found a positive relationship between perceived search engine optimization and behavioral intention to use e-recruitment (Onaifo and Rasmussen, 2013; Lee et al., 2016). Results from this study failed to show significant support for the relationship between perceived search engine optimization and behavioral intention to use e-recruitment. One plausible explanation is that young job seekers currently do not have much concerns over the search engines. Most organizations have invested in search engine optimization to get higher and faster search results. As stated in the Search Engine Journal, the dominant search engine used by most users today is Google with over 75 percent of the search market share (Davies, 2018). Google is moving searchers away from clicking through to websites and toward fulfilling their needs and intents directly on the Google website (Davies, 2018).
From the theoretical perspective, this research shed some light on the factors that influence the intention to use e-recruitment systems by jobseekers. Firstly, although previous researchers have shed some light on the influence of several factors towards the intention to use e-recruitment by job seekers, there were some new findings in this study. This study found that perceived usefulness is the key criteria to attract job seekers. The study contributed to the existing knowledge and further demonstrated that contrary to previous studies, perceived information content quality and perceived search engine optimization were found to have an insignificant impact on intention of job seekers to use e-recruitment. This could be due to several other extraneous variables of the lesser importance placed on these variables.   

The findings of this study pointed out the perceived usefulness of using internet by job seekers. Therefore, from the practical perspective, organizations should focus on the perceived usefulness and further encourage job seekers to use e-recruitment. Organizations and third party online recruiters should focus on improving perceived usefulness. Organizations and other online job providers should stress on the quality and performance of e-recruitment systems. Perceived usefulness can provide more enjoyment and organizations need to consider this construct as the most important factor. Hence, organizations should include perceived usefulness as an integral part of e-recruitment systems to attract higher number of applicants and select the best candidate.

In this study several limitations were noted. The limitations pave the way for future research directions. Firstly, the information for this research was collected via and non-random convenience sampling method that can be subject to biasness and the limits of validity need to be considered. Secondly, this research only considered three independent constructs. Other constructs such as perceptions of information credibility and perceived enjoyment and attitude can also influence the behavioral intention of job seekers. Therefore, there remains a great deal of research using random sampling technique and examining other constructs. This research covered the behavioral intention to use e-recruitment. The behavioral intention may not translate into actual usage of e-recruitment systems. Therefore, it is further recommended that future research should consider actual usage rather than only intention to use. This study did not examine the demographic profile of the respondents. The demographic profile such as gender can moderate the relationship between the constructs. Future studies should probe further the differences that may be attributed to particular groups and the moderating role of demographic factors such as gender. Future studies may further provide valuable insights if the type of job or level of job is studied. 

Funding: This study received no specific financial support.   
Competing Interests: The authors declare that they have no competing interests. 
Contributors/Acknowledgement: Both authors contributed equally to the conception and design of the study.

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