Despite the unprecedented time of the COVID-19 pandemic, there is a constant hunt for the finest talent by employers, and strategies must be established to procure the best talents present on the market. The competitive issue of identifying, retaining, and hiring the best candidates has become an essential issue for Malaysia’s leading employers. The internet has changed how individuals communicate and work (Giles, 2010; Islam et al., 2016). The group of social networking sites (SNS), keep the site users connected to each other, more than any other group, by their constant stream of information. SNS are quickly turning into a worldwide consumer fact since two out of three internet users are a visitor of an SNS (Nielsen Company, 2019).
Employers can acquire a great deal of individual data about candidates with an end goal to improve recruiting decisions (Hosain et al., 2020). The consolidation of new digital platforms, to the recruitment and selection process, has been a path for employers to take an attempt at the best talent. Employers no longer wait for applicants to apply for a position, and these SNS provide employers the opportunity to search for prospects. The spectrum of these SNS is increasingly expanding and public contrasts are taking place. As indicated by Ala et al. (2020), there is a selection of well known SNS by country, area, and continent. In virtually every continent, Facebook is used and is the SNS with the most registered users. In recent years the number of SNS has grown staggeringly and continues to expand (Mohammad et al., 2020). Though rapidly changing technology opens new possibilities for correspondence, it leads to a maze of applications and uncertainty (Diglel & Yazdanifard, 2015).
As SNS are entering the corporate world, a strategy is required to keep pace and be acquainted with the best SNS within the social networking platforms. Leading employers in Malaysia tend to be uncertain sometimes about which SNS to select for their e-recruitment needs (Basit et al., 2020). Moreover, there is a paucity of material on SNS, and their impact on intention to use SNS for e-recruitment from the employer’s perspective, in the context of Malaysia. Most of the studies on the impact of SNS on recruitment were done from a user/applicant/employee perspective (El Ouirdi et al., 2016). As Malaysia is a lucrative hub for fortune 500 companies, there is a critical need of research on the extent that features of SNS influence the employers’ intention to use SNS for e-recruitment in Malaysia, and how these features can hold employers captivated, to keep utilizing that platform in future. In order to bridge this gap, this study aimed to identify the key features of SNS that lead to the intention to use SNS for e-recruitment in Malaysia.
2. Literature Review
Social networking sites (SNS) are defined by Ansari and Khan (2020) as applications that allow users to interact by building profiles with personal information, inviting contacts and peers to access those profiles, and exchanging texts with each other. The utilization of technology at work has affected all aspects of business, with technology giving critical and favorable approaches to upgrade organizational recruitment practices (Abraham et al., 2020). Likewise, SNS can be utilized in human resource management, for recruitment and selection as well (Aqeel & Siddiqui, 2019). The incorporation of recruitment technologies is becoming a necessity for employers who want to have a competitive brand image and engage in scarce niche talent search (D’Silva, 2020; Tong, 2009). Research has shown that Facebook and LinkedIn are the most popular recruitment SNS (Hosain & Liu, 2020; Kaur & Arianayagam 2020). Facebook and LinkedIn are Malaysia’s most prominent SNS from the employer’s point of view. Also, Glassdoor is getting high regard from employers in Malaysia nowadays.
The utilization of SNS for recruitment is still considered a new trend in human resource management (HRM). There are benefits of utilizing SNS in the e-recruiting process. A few examples of SNS advantages include reduced recruiting costs and decreased recruitment time, the opportunity to contact a younger demographic and a larger number of candidates, the attractiveness of passive applicants, and the availability of an alternative method to forecast the success of a prospective employee (Villeda et al., 2019). Employers agree that it is appropriate to use SNS for virtual background checks to decide on recruitment decisions, to obtain a candidate’s brief character description (Kaur & Arianayagam 2020). SNS profiles enable recruiting specialists to gather data on their educational backgrounds and technical professions. Similarly, recruiters are likely to contact prospective talents and engage in a non-committal manner with them. Recruitment experts may search for future talents through the use of SNS and establish long-term engagement with them. SNS like LinkedIn will act as a talent pool with a spectrum of social capital for seasoned professionals and new graduates.
2.1. Underpinning Theory
In the information technology literature, the technology acceptance model (TAM) is the most dominant theory used to evaluate the acceptance of technologies (Tong, 2009). To aid organizations in decision-making around technology selection, TAM was invented to forecast consumer behavior and discover what persuaded users to implement modern technology (Pankowska et al., 2020). Though reproducible testing attempts were used to authorize TAM consistently, the model did not necessarily do what was expected to explain variables influencing perceived usefulness and perceived ease of usage. The desire to recognize additional contributing elements, intensified efforts to extend and produce TAM-2 (Pankowska et al., 2020). A longitudinal report formed the basis for the creation of TAM-2 by more than four partnerships and four distinctive technology structures (Venkatesh & Davis, 2000). One goal of the longitudinal study was to integrate additional primary determinants of perceived usefulness and perceived ease of use while also considering how these determinants are influenced by time. TAM-2 involves mechanisms of social control, subjective norm, desire, and image. It also involves cognitive instrumental processes, job relevance, quality of output, demonstrability of results, and perceived ease of usage, (Venkatesh & Davis, 2000).
For this study, the expanded TAM-2 model was adapted from the Venkatesh and Davis (2000) study and further customized to be used as the reference model for testing. The expanded model incorporates social influences and cognitive functional determinants into one model for the creation of crucial research and comprehension of the theory, where scientists have used behavioral intent as the principal conceptual construct to be evaluated by the important indicator constructs, such as the perceived ease of usage and perceived usefulness of a system (Lai, 2017).
2.2. Intention to use SNS for e-Recruitment (IER)
Recruitment is described as a means of recruiting and convincing potential candidates to apply for an opportunity in an organization (van Esch et al., 2021). There is a significantly large distinction between traditional recruiting and e-recruitment. Traditionally, mainstream recruitment is seen as hiring talent through media commercials, career fairs, and job portals (Muduli & Trivedi, 2020). Whereas e-recruitment is defined as viewing online sources to recruit talent from job portals, career websites, and SNS that afford an alternative option. Likewise, organizations utilize e-recruitment to recruit leading talent online (Binney et al., 2014). Conventional methods of recruitment will not be adequate, and new avenues are required (Munro, 2018). Laurențiu and Mădălina (2020) stated traditional methods of recruitment process must be expected to change into e-recruitment. It creates ways to reduce the expense of hiring and enter margin economies. Opportunities and skills continually turn out to be more global. Considering those realities, e-recruitment is successful and financial crises likewise turn organizations towards e-recruitment to search for new opportunities for global recruitment (Waheed et al., 2019). Researchers saw that organizations using the e-recruitment approach had incurred lower recruitment expense, almost 88% lower of their hiring costs, compared to the traditional method (Vatansever, 2017). E-recruitment has also increased the efficiency of the recruitment strategy which is one of the key advantages given to the HR Manager in dealing with the process. Online recruitment, otherwise called e-recruitment, has numerous benefits to the organization, for example, better match, low cost, fast, less time, and wider scope (Anand & Devi, 2016).
Abia and Brown (2020) attempted the fundamental efficient review of the role of the internet on recruitment and set forth the view that the internet was still an alternative apparatus in the hiring process. From that point on, things have changed dramatically and now the large use of SNS may be seen as essential for the e-recruitment approach, both from the standpoint of an applicant and that of the organization. D’Silva (2020) demonstrated that the changing idea of recruiting strategy from more conventional and time-consuming manual strategies to a more automated approach can result in greater savings of time and financial outlay for the association. With the rise of global competitiveness, corporate productivity is dictated by the ability to recruit qualified talent (Ismail et al., 2020), as a substantial part of critical talent acquisition, corporations need to accomplish this task of hiring their potential prospects (Villeda et al., 2019).
SNS platforms can achieve considerable cost-cutting for the association (Miqdad & Oktaviani, 2021; Villeda et al., 2019). Also, the use of e-recruitment networks and in particular, SNS platforms with their global scope, is likely to enable companies to recruit globally with greater convenience. From a candidate’s perspective, van Esch and Mente (2018) discovered, that those actively seeking new opportunities, value the instantaneousness of response by the employer on e-recruitment systems. However, their study did not investigate the candidate’s view of the utilization of SNS as a feature of the e-recruitment measure. They proposed, “an ever-increasing number of organizations right now recruit online, some because of cost savings and competitive pressure and rest of them, it is the most ideal approach to reach their target group of candidates” (van Esch & Mente, 2018).
Past studies have indicated that recruitment decisions significantly affect various elements of the recruitment results, such as the diversity of candidates (Gilch & Sieweke, 2020). As indicated by them, talent acquisition is a set of processes that associations attain to source the right expertise for their openings, the crucial component to convince future employees. Hiring involves multiple tasks and is a loop that is characterized by separate measures. Starting with the distinction of recruiting targets, creating a framework, and hiring practices such as job advertisements, interviews, etc. (Gilch & Sieweke, 2020). For any recruiting team, this cycle is a challenge since they must first set up recruitment targets. What classifies as successful e-recruitment? The opportunity to hire and acquire top talents successfully derives from a corporate commitment to hiring the best talents (Ezam et al., 2018). This study focuses on electronic recruitment (e-recruitment) since SNS are a significant part of e-recruitment. Likewise, the recruiting scenario continues to change as organizations adopt SNS platforms for e-recruitment. To sum up the discussion, different attributes are necessary for the intention (Hoang et al., 2020) to use SNS for e-recruitment practice. The attributes of an e-recruitment measure are studied as follows.
2.3. Features of Social Networking Sites
2.3.1. Data Quality (DQ)
Data quality is the quality of spreading data that the SNS creates or conveys. It is portrayed by conveying applicable, updated, and straightforward data (Vatansever, 2017). In their research, Vatansever (2017) claimed that the higher the quality of the data, the greater the number of website users. In other words, the higher the standard of SNS data is, the higher the number of users would be. As previously mentioned, in the analysis of Vatansever (2017), data quality scored outstandingly and is now an important consideration for this study. Credibility, data measurement, and content fulfillment are essential data quality pointers. The data standard of the SNS, related to the investigation, is referred to as the data on the prospective candidate’s profile. It is crucial for recruitment practitioners that the data on the applicants meets these particular requirements in a way to construct an accurate picture of the applicant. Therefore, the following hypothesis is developed:
H1: There is an impact of data quality on intention to use SNS for e-recruitment.
2.3.2. Reliability (RL)
Reliability is one of the most critical and widely discussed moral concerns in information technology and the media as it is a key area (Korunovska et al., 2020) to determine intention to use within SNS(s). The volume or number of people who are on these platforms often triggers the inference that there is a clear measure of confidence. Saeidi (2020) discovered that users of Facebook have a more notable measure of confidence and share more data. Whatever it is, do organizations find SNS reliable? As reliability is a big concern in the field of information technology, this study takes this specifically into account. Reliability means how well the user recognizes that the website is trustworthy, protected from disruption and that personal data is preserved (Abraham et al., 2020).
Company data is confidential information and associations are making great strides towards full data protection. Reliability is measured in this study by improper access and (internally/externally) unauthorized auxiliary usage. Inappropriate access considers that data about people is freely available to individuals and that they are duly allowed to display or work on this data (Korunovska et al., 2020). Unapproved secondary usage means that data obtained for one cause by individuals is used for another intent without the consent of the same individuals. Therefore, the following hypothesis is developed:
H2: There is an influence of reliability on intention to use SNS for e-recruitment.
2.3.3. Recognition (RN)
Recognition amidst candidates/applicants is the level of exposure of the SNS in the group of relevant applicants. In their study, Urchaga et al. (2020) wrote that as a rule, a high degree of recognition means that the information presented by the website is useful or desirable to its users. Alluding to hiring through social networks, the more SNS that are used by people, the better is the probability of having the best applicants. Recognition amidst applicants is measured by the official subscribers of the network and the reputation of these pages. In this study, the reputation of the social networks is evaluated by the respondent’s assessment regarding the recognition of the platforms amid the job seekers and whether the website is regularly covered in the mainstream or not. Therefore, the following hypothesis is developed:
H3: There is an effect of recognition on intention to use SNS for e-recruitment.
2.3.4. Networking Spectrum (NS)
Networking spectrum is the range of the social network and adequacy of the users that are required to get associated within the platform. The networking spectrum is characterized as the interaction individuals have as an outcome of network impact, alluding to the analysis by Abraham et al. (2020). It may be essential for associations that the SNS ensures a high degree of significant connections and has a strong networking spectrum. SNS, on the other hand, allows users to meet international candidates and, as a result, increases the networking impact. The networking effect is the process through which a service gets more value as more people access it, implying that the number of users will continue to grow (Kluemper & Rosen, 2009). The use of social media sites has vastly improved; LinkedIn, for example, took 16 months to sign up its first million subscribers, whereas the most recent million arrived in just eight days (Hosain et al., 2020). Therefore, the following hypothesis is developed:
H4: There is an association between networking spectrum and intention to use SNS for e-recruitment.
2.3.5. Result Demonstrability (RD)
Result demonstrability is defined as the tangibility of the results of the technologies being used. Venkatesh and Davis (2000) hypothesize and find evidence for the conclusion that shows ability has a positive effect on the perceived utility of technology as a result (Hossain et al., 2017). Venkatesh and Davis (2000) hypothesize and discover support for the inference that shows demonstrability positively affects the perceived usefulness of the technology. It is believed that if the results of utilizing an information technology system are apparent, this will increase the perceived usefulness of a system by a person. Referring to this to SNS and e-recruitment, this implies that employers will have more perceived usefulness of an SNS if an employer can identify the positive results of the SNS. In other words, if the demonstrability of the result of an SNS is poor, SNS users can contribute their accomplishment to work behavior rather than system use. Lin et al. (2020) find that if it is possible to quickly distinguish the benefits of a website, the more valuable it would be deemed. Therefore, the following hypothesis is developed:
H5: There is a relationship between result demonstrability and intention to use SNS for e-recruitment.
2.3.6. Simplicity of Navigation (SN)
A website’s navigation simplicity guarantees that it has features that make it easy for users to find what they’re looking for. It means it has a good search engine and allows the user to easily and efficiently go back and forth through the pages (Abraham et al., 2020). The ease of use and convenience of the sites are used to determine the navigational simplicity in this study, which includes the fact that using SNS is not difficult to master and is designed to be simple to comprehend. In addition, the platform should make it possible that a novice user can quickly become an expert. To discover key elements of the website, Musa et al. (2020) led a review on website simplicity, architecture, and efficiency assessments. Navigation of the websites was one feature of the investigation and navigability was defined by Musa et al. (2020) as the sequencing of pages, structure, and continuity of navigation conventions. Then, they theorized that more navigable websites will be correlated with more expected recognition by web page users, and this speculation is retained. As a consequence, navigability is an important factor in the performance of a website and prompts the presumption that is a major determinant of the SNS feature. Therefore, the following hypothesis is developed:
H6: There is an influence of navigation simplicity on intention to use SNS for e-recruitment.
2.4. Conceptual Framework
For this study, the extended TAM-2 model of Venkatesh and Davis (2000) has been adopted and the following conceptualization is made in regard to the research baseline model (Figure 1). In the context of the study, the theoretical perspective of TAM-2 has been modified where intention to use SNS for e-recruitment is used as the dependent variable (DV) to be explained by the other influencing independent variables (IV) of the model which are perceived usefulness including data quality, reliability, recognition, networking spectrum and result demonstrability, and perceived ease of use redefined as simplicity of navigation.
Figure 1: Proposed Conceptual Framework
3. Research Methods and Materials
In this cross-sectional study, a deductive method was selected. To meet the descriptive purpose for this study, the survey method was considered be the right technique to apply (Saunders et al., 2019). The target population for the analysis is recruitment professionals, HR professionals, talent acquisition managers, and recruitment directors from leading employers in Malaysia. Due to the wide geographical area of Malaysia and the conditional movement control (CMCO) in place, it was necessary to take a population frame. The population frame or unit of analysis was recruitment and HR professionals based in the Klang Valley area. Leading employers in Malaysia is the context selected for this study, to determine the key features of SNS that lead to intention to use SNS for e-recruitment in Malaysia.
The researcher distributed 250 questionnaires to respondents by means of convenient sampling. A notification email was sent out to all respondents after a few weeks to collect adequate data for the study. For data collection, the average time is 3–4 weeks. A total of 203 sets of questionnaires were returned, with a response rate of 81.2%. Out of 203 responses, 198 responses were found usable and the other five were found unusable due to a great deal of missing information.
The primary data in this paper is collected through SurveyMonkey, where the questionnaires were disturbed in the form of a survey. The respondents are recruitment professionals, HR professionals, talent acquisition managers, and recruitment directors who are working for leading employers. The area of sampling collection was Klang Valley, Malaysia.
Due to the COVID-19 pandemic and CMCO, this quantitative study is conducted through the use of an online survey using SurveyMonkey. A survey has the advantage of allowing for the collection of a large amount of data from a large number of people. Therefore, applying a survey to ask recruitment professionals, HR professionals, talent acquisition managers, and recruitment directors from different leading employers in Malaysia, based in Klang Valley, Malaysia was possible.
4. Results and Discussion
The demographic profile of the 198 respondents is presented in Table 1. It shows that the majority of the respondents were recruitment professionals with a response rate of 48% (95 respondents), followed by HR professionals (24%), talent acquisition managers (14%), recruitment manager (13%) and only 4 respondents (2%) were recruitment directors. In addition, out of 198 respondents, 27 were from information technology (14%), 92 were from staffing and recruiting (46%), 9 from financial services (5%), 13 from telecommunications (7%), 14 were from healthcare (7%), 6 were from oil & gas (3.92%), 8 were from airlines (4%), 12 were from biotechnology (6%), and 17 of them were from other industries, comprising consumer electronics, access solution, retail, FMCG, construction, manufacturing, and commodity (9%). Moreover, it illustrates the per cent of total hiring that are being covered by social networking platforms in respondent’s organization. It was categorized into four groups. Majority of the respondents (total 160 or 81%) stated that their organizations cover 25%–75% of their total recruitment through SNS in Malaysia.
Table 1: Demographic Analysis
4.1. Reliability and Validity Analysis
In this study, the evaluation of the measurement model, aimed to check the relationship between each variable and its associated development and also to ensure that each construct varies from the others (Hair et al., 2017). Several measures suggested by Hair et al. (2017) were used and satisfactory results were obtained. The output is shown in Table 2.
Table 2: Measurement Model Evaluation for Reliability and Validity
The composite reliability should be 0.70 and above to obtain “Internal Consistency Reliability” (Hair et al., 2017). Moreover, it is proposed that the outer loadings are greater than 0.70. In Table 2, it shows that the composite reliability and factor loading for each construct is more than 0.70, confirming the model’s internal consistency reliability. In order to determine the validity, Hair et al. (2017) have also suggested checking the convergent validity and discriminant validity. To obtain convergent validity, the AVE should be 0.50 or higher (Henseler et al., 2015). The composite reliability (CR) should also be more than 0.70. For both AVE and CR value, it indicates that the required threshold has achieved and hence, the convergent validity for all the constructs has been achieved. Besides, Heterotrait-Monotrait Ratio (HTMT) serves as the indicator for discriminant validity. As recommended by Hair et al. (2017), all HTMT values are below 0.85, and thus, discriminant validity for all the constructs has also been achieved.
4.2. Structural Model Analysis
The five-steps proposed by Hair et al. (2017) is followed to assess the structural model that includes multicollinearity assessment (VIF values), path coefficient (β), coefficient of determination (R²) for the model’s predictive potential, effect size (f²), and Predictive Relevance (Q²) to assess the model’s predictive validity. The satisfactory values were obtained for each category which is presented in Figure 2, Tables 3 and 4.
Figure 2: Structural Model
Table 3: Structural Model Analysis
Table 4: Hypothesis Test Results
Note: p-value ≤ 0.05. Significant at the 0.05 level.
4.3. Hypothesis Testing
The next step is to analyze the significance between the structures of the hypothesized relationship. The PLS relies on the bootstrap mechanism that is not parametric (Rigdon, 2012). Bootstrapping measured the t-values and was used to verify if the distribution of path coefficients ranged considerably from zero or not. The non-parametric bootstrapping system was applied using 5, 000 sub-samples generated by the software.
H1: The results indicate that data quality (DQ) has a statistically significant impact on the intention to use SNS for e-recruitment (IER) (β = 0.255, T-Statistics = 3.626, T ≥ 1.96). Study conducted by Waheed et al. (2019) also established a statistically significant association between DQ and IER. Therefore, this study accepts the hypothesis H1.
H2: It also shows that reliability (RL) has a statistically significant influence on intention to use SNS for e-recruitment (IER) (β = 0.155, T-Statistics = 2.418, T ≥ 1.96). Likewise, past researches have demonstrated a significant association between RL and IER (e.g. Masa’d, 2015; Waheed et al., 2019). Therefore, this study also accepts the hypothesis H2.
H3: The result also shows that recognition (RN) has a statistically insignificant effect on intention to use SNS for e-recruitment (IER) (β = 0.025, T-Statistics = 0.332, T ≤ 1.96). Other researchers (Abel, 2011) also found that there is no significant relationship between RN and IER. Therefore, this study rejects the hypothesis H3.
H4: Networking spectrum (NS) is also statistically significant with intention to use SNS for e-recruitment (IER) (β = 0.145, T-Statistics = 2.189, T ≥ 1.96). Similarly, other researchers and studies (e.g. Waheed et al., 2019)) had discovered a significant association between NS and IER. Therefore, this study also accepts the hypothesis H4.
H5: Further, the finding shows that result demonstrability (RD) has a statistically insignificant impact on intention to use SNS for e-recruitment (IER) practices in Malaysia (β = 0.087, T-Statistics = 1.317, T ≤ 1.96). The findings are similar to Siamagka et al. (2015) who found no significant association between RD and IER through SNS. Therefore, this study also rejects the hypothesis H5.
H6: Finally, the outcomes reveal that the relationship between simplicity of navigation (SN) and intention to use SNS for e-recruitment is statistically significant (β = 0.201, T-Statistics = 2.711, T ≥ 1.96). Similarly, past studies conducted on the relationship between SN and IER have identified similar results (e.g. Waheed et al., 2019). Therefore, this study accepts the hypothesis H6.
This study examined the influence of SNS’s features on intention to use SNS for e-recruitment in Malaysia and addressed the inquiry of which features of social networking platforms are the predictors of intention to use SNS for e-recruitment in depth. The findings are based on the understanding of leading employers in Malaysia of 198 recruitment and HR practitioners for a given time. This study found that data quality is a predictor for less time to hire which is a part of intention to use SNS for e-recruitment. According to Waheed et al. (2019), the greater the quality of the data generated by the applicant’s social networks, the greater the reputation of the applicant would be. Hence, a full picture of the applicant reinforces a smoother pre-selection of the applicants, which inspires the entire e-recruitment process to minimize time.
Besides, reliability of SNS have an impact on intention to use SNS for e-recruitment. Reliability is a moral topic of the mainstream that is frequently addressed (Korunovska et al., 2020). Therefore, organizations need to work with this issue and view it as an element of intention to use SNS for e-recruitment. Although companies continue to pursue intention to use SNS for e-recruitment across social networking websites, they have to resolve the reliability question of the networking sites.
On the contrary, the result shows that there is no significant relationship between recognition and intention to use SNS for e-recruitment. Thus, recognition of the SNS is not applicable for efficient e-recruitment which is also supported in the past studies (Abel, 2011; Masa’d, 2015). This study also identified that recognition is not a key feature of the SNS in order to attract a bigger pool of candidates.
The results of this analysis also indicates that the networking spectrum is a good indicator of successful e-recruitment. The networking spectrum is viewed by leading employers in Malaysia as an asset for e-recruitment activities, as there is a strong correlation between networking spectrum and intention to use SNS for e-recruitment. Networking spectrum is influential for employers and ensures a greater degree of reliable connections, and increases the scope of networking. Moreover, the greater scope of networking makes it possible to reach more candidates and target the right candidate group in the market (Abdul-Latif et al., 2019). The broader spectrum of networking makes it possible to attract more individuals and strike the right talent group (Giles, 2010). The use of SNS for e-recruitment has intensified enormously and gives employers new insights to boost the candidates’ matching efficiency. The networking reach of these platforms must be taken into consideration if employers wish to practice successful e-recruitment enabled by social networking sites. If recruiters wish to attract the right target for recruiting, they should benefit from the networking spectrum of these networks.
Past studies conducted on the relationship between result demonstrability and intention to use SNS for e-recruitment (IER) have indicated contrasting outcomes. For example, Wu et al. (2011) discovered a statistically significant relationship between result demonstrability and intention to use SNS for e-recruitment that influences employer’s perceived usefulness of SNS. In contrast, Siamagka et al. (2015) found no significant association between result demonstrability and intention to use SNS for e-recruitment through SNS. According to the findings of this study, the majority of leading employers in Malaysia have started using SNS for their e-recruitment practices not long ago. Therefore, they are not certain on which SNS platform they engage for their e-recruitment activities. The tangibility of the outcome of utilizing SNS for e-recruitment is an important feature for organizations to select the right platform. Yet, the findings of this study bestow that the result demonstrability is not a significant factor to influence their intention to use SNS for e-recruitment activities in Malaysia.
Finally, according to the findings in this study, the simplicity of navigation of SNS is a significant factor for intention to use SNS for e-recruitment from leading employers’ perspective in Malaysia. This finding is similar to Waheed et al. (2019) where the authors demonstrated a statistically significant association between simplicity of navigation and intention to use SNS for e-recruitment.
The main objective of this research was to identify the key features of SNS that lead to intention to use SNS for e-recruitment in Malaysia. Therefore, this study has taken an endeavour to explore the extent to which features of SNS influence the intention of employers to adopt particular SNS for their e-recruitment needs in Malaysia and how these features can hold employers captivated, to keep utilizing that platform in the future. Companies that wish to use SNS for their e-recruitment purposes should rely on the key features of these channels and see them as an advantage to their e-recruitment activities. Referring to the main findings of this study, data quality, reliability, networking spectrum and simplicity of navigation of SNS are the key predicting factors for intention to use SNS for e-recruitment and have the greatest influence on e-recruitment practices amidst the leading employers in Malaysia. As a consequence, if employers put up a strategy for their e-recruitment activities through SNS, they should acknowledge these key features of SNS to achieve their e-recruitment goals.
There is an underlying problem regarding the lack of a robust framework for employers to adopt or at least adhere to, while considering incorporating or continuing to use SNS in their e-recruitment activity. As more and more organizations are adopting SNS as their e-recruitment tool, more information is required to strategize their usage to achieve competitive advantage. Hence, the outcome of this study will help the leading employers in Malaysia to construct a strategy for their e-recruitment through SNS.
The use of social networking sites for e-recruitment has shifted the focus away from traditional hiring and selection processes. It is clear that SNS are commonly used in the search and acquisition of new employees and are projected to expand in the near future as an e-recruitment tool. Thus, this study significantly contributes to the understanding of various SNS features that impact the intention to use SNS for e-recruitment practices among leading employers in Malaysia and offers an analysis of this fast-moving topic.
- Abdul-Latif, M., Boateng, F., & Eneizan, B. (2019). Success and relevance of social networking sites (SNS) integration in e-recruitment: HR personnel perspectives in Ghana. International Journal of Academic Information Systems Research, 3(8), 12-19.
- Abel, S. (2011). The role of social networking sites in recruitment: Results of a quantitative study among German companies. [Enschede, Netherlands: Master dissertation] University of Twente. http://essay.utwente.nl/61154/1/MSc_S_Abel.pdf
- Abia, M., & Brown, I. (2020) Conceptualizations of e-recruitment: A literature review and analysis. In: Hattingh M., Matthee M., Smuts H., Pappas I., Dwivedi Y. K., & Mantymaki, M. (Eds). Responsible Design, Implementation and Use of Information and Communication Technology. (I3E 2020). Lecture Notes in Computer Science, 12067. Springer.
- Abraham, K., Han, J., Bowe, M., Bug, T., & Tessema, M. (2020). Technology-based recruitment and selection approaches of an American multinational company. International Journal of Human Resource Studies, 10(3), 117-133. https://doi.org/10.5296/ijhrs.v10i3.17082
- Ala, M., Rasul, T., & Nair, S. (2020). Social network and social commerce. In: Hoque, M. K., & Bashaw, E. (Eds.), Cross-Border E-Commerce Marketing and Management. IGI Global.
- Anand J., & Devi C. S. (2016). The Impact of e-recruitment and challenges faced by HR professionals. International Journal of Applied Research, 2(3), 410-413. https://www.allresearchjournal.com/archives/2016/vol2issue3/PartG/2-2-84.pdf
- Ansari, J. A. N., & Khan, N. A. (2020). Exploring the role of social media in collaborative learning the new domain of learning. Smart Learning Environments, 7(1). https://doi.org/10.1186/s40561-020-00118-7
- Aqeel, S., & Siddiqui, D. (2019). The effect of social media following on recruitment in service industries of Pakistan. Business Management and Strategy, 10(1), 41-77. https://doi.org/10.5296/bms.v10i1.14633
- Basit, A., Tiong, N., & Hassan, Z. (2020). Determinants of social media adoption and its effects on social media returns among small medium enterprises in Malaysia. Solid State Technology, 63, 263-277.
- Binney, J., Burnett, B., Kordopatis., G., McMillan., P. J., Sharma., S., & Zwitter, T. (2014). New distances to RAVE stars. Monthly Notices of the Royal Astronomical Society, 437(1), 351-370 https://doi.org/10.1093/mnras/stt1896
- Diglel, A., & Yazdanifard, A. (2015). The utilization of social networking websites by companies as a hiring tool; Its effectiveness and reliability from the applicants and the employers perspective. European Journal of Business and Social Sciences, 4, 11-21. http://www.ejbss.com/recent.aspx-/
- D'Silva, C. (2020). A study on increase in e-recruitment and selection process. International Journal of Research in Engineering, Science and Management, 3(8), 205-213. https://www.journals.resaim.com/ijresm/article/view/162
- El Ouirdi, M., El Ouirdi, A., Segers, J., & Pais, I. (2016). Technology adoption in employee recruitment: The case of social media in Central and Eastern Europe. Computers in Human Behavior, 57, 240-249. https://doi.org/10.1016/j.chb.2015.12.043
- Ezam, Q., Ahmad, N., & Jalbani, A. (2018). HR personnels perception for the importance and success of social networking sites integration in recruitment. International Journal of Experiential Learning & Case Studies, 3(2), 189-203. http://dx.doi.org/10.22555/ijelcs.v3i2.2309.g483
- Gilch, P., & Sieweke, J. (2020). Recruiting digital talent: The strategic role of recruitment in organisations' digital transformation. German Journal of Human Resource Management, 35(1), 53-82. https://doi.org/10.1177/2397002220952734
- Giles, M. (2010). A World of Connections-A special report on social networking. The Economist, Special report. London. https://www.economist.com/special-report/2010/01/30/a-world-of-connections
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks, CA: Sage Publications.
- Henseler, J., Ringle, C.M. & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115-135. https://doi.org/10.1007/s11747-014-0403-8.
- Hoang, L. V., Vu, H. M., & Ngo, V. M. (2020). Corporate social responsibility and job pursuit intention of employees in Vietnam. The Journal of Asian Finance, Economics and Business, 7(12), 345-353. https://doi.org/10.13106/jafeb.2020.vol7.no12.345
- Hosain, M., & Liu, P. (2020). LinkedIn for searching better job opportunity: Passive jobseekers' perceived experience. The Qualitative Report, 25(10), 3719-3732. https://doi.org/10.46743/2160-3715/2020.4449
- Hosain, Md., Hena, A., Arefin, M., & Hossin, M. (2020). E-recruitment: A social media perspective. Asian Journal of Economics, Business and Accounting. 16(4), 51-62. https://doi.org/10.9734/ajeba/2020/v16i430248
- Hossain, M., Hasan, M., Chan, C., & Ahmed, J. (2017). Predicting user acceptance and continuance behavior towards locationbased services: The moderating effect of facilitating conditions on behavioral intention and actual use. Australasian Journal of Information Systems, 21. https://doi.org/10.3127/ajis.v21i0.1454
- Islam, Z. U., Bangish, S. B., Muhammad, S., & Jehan, A. S. (2016). The impact of HR practices on job satisfaction: A case study of hotel industry in Pakistan. The Journal of Asian Finance, Economics and Business, 3(1), 43-48. https://doi.org/10.13106/jafeb.2016.vol3.no1.43
- Ismail, U., Pervaiz, M. K., & Rehman, M. S. U. (2020). Distributive justice towards innovative work behavior with mediating effects of human resource management practices in the hotel industry of Pakistan. International Journal of Business and Administrative Studies, 6(3), 136-149. https://dx.doi.org/10.20469/ijbas.6.10002-3
- Kaur, J., & Arianayagam, S. (2020). The usage of social media networks as a tool by employers to screen job candidates. The European Journal of Social & Behavioural Sciences, 29. https://doi.org/10.15405/ejsbs.282
- Kluemper, D.H., & Rosen, P.A. (2009). Future employment selection methods: Evaluating social networking web sites. Journal of Managerial Psychology, 24(6), 567-580. https://doi.org/10.1108/02683940910974134
- Lai, P. (2017). The literature review of technology adoption models and theories for the novelty technology. Journal Information System and Technology Management, 14(1), 21-38. https://doi.org/10.4301/s1807-17752017000100002
- Laurentiu, G., & Madalina, M. (2020). Attitudes toward e-recruitment. An explorative study in Romania. Academicus International Scientific Journal, 21, 73-88. https://doi.org/10.7336/academicus.2020.21.07
- Lin, C. H., Shih, K. H., Wang, W. C., Chuang, L. F., Tsai, W. C., & Huang, C. F. (2020). Factors influencing the purchase of travel insurance over mobile banking. International Journal of Mobile Communications, 18(2), 158-174. https://doi.org/10.1504/ijmc.2020.105843
- Masa'd, F. (2015). Deployment of social media in the recruitment process. Journal of Knowledge Management, Economics and Information Technology, 1(2), 1-24.
- Miqdad, M., & Oktaviani, S. A. (2021). The contribution of social media value to company's financial performance: Empirical evidence from Indonesia. The Journal of Asian Finance, Economics and Business, 8(1), 305-315. https://doi.org/10.13106/jafeb.2021.vol8.no1.305
- Mohammad, J., Quoquab, F., Sulaiman, A., & Salam, Z. (2020). I voice out because I care: The effect of online social networking on employees' likelihood to voice and retention. Asia-Pacific Journal of Business Administration, 13(1), 117-137. https://doi.org/10.1108/APJBA-05-2020-0142
- Muduli, A., & Trivedi, J. (2020). Social media recruitment: The role of credibility and satisfaction. Evidence-based HRM: A global forum for empirical scholarship. Evidence-based HRM, 8(2), 237-251. https://doi.org/10.1108/EBHRM-08-2019-0069
- Munro, N. (2018). An exploration of the role of social media platforms in the employee recruitment process. [Doctoral dissertation] University of Salford. http://usir.salford.ac.uk/id/eprint/48015/
- Musa, R., Ahmad, N. S., & Janiffa, S. (2020). Hypothesized model of determinants and consequences of social media content marketing experience quality (Smcmeq) in muslimah fashion advertisement. Malaysian Journal of Consumer and Family Economics, 24(1), 247-261. https://www.majcafe.com/wp-content/uploads/2020/10/Vol-24-S1-Paper-15.pdf
- Nielsen Company. (2019). Global Face and Networked Places: ANielsen Report on Social Networking's New Global Footprint. Analysis & Policy Observatory, Australia. https://www.nielsen.com/wp-content/uploads/sites/3/2019/04/Global-Faces-and-Networked-Places-A-Nielsen-Report-on-Social-Networkings-New-Global-Footprint.pdf
- Pankowska, M., Pyszny, K., & Strzelecki, A. (2020). Users' adoption of sustainable cloud computing solutions. Sustainability, 12(23), 9930. https://doi.org/10.3390/su12239930
- Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5-6), 341-358. https://doi.org/10.1016/j.lrp.2012.09.010
- Saeidi, S. (2020). A new model for calculating the maximum trust in online social networks and solving by Artificial Bee Colony algorithm. Computational Social Networks, 7(3). https://doi.org/10.1186/s40649-020-00077-6
- Saunders, M., Lewis, P., & Thornhill, A. (2016). Research Methods for Business Students (7th ed.). London, UK: Pearson Education.
- Siamagka, N. T., Christodoulides, G., Michaelidou, N., & Valvi, A. (2015). Determinants of social media adoption by B2B organizations. Industrial Marketing Management, 51(11), 89-99. https://doi.org/10.1016/j.indmarman.2015.05.005
- Tong, D. Y. K. (2009). A study of e-recruitment technology adoption in Malaysia. Industrial Management & Data Systems, 109(2), 281-300. https://doi.org/10.1108/02635570910930145
- Urchaga, J., Ingelmo, R. M., Cabaco, A., & Moral-Garcia, J. (2020). Life satisfaction, physical activity and quality of life associated with the health of school-age adolescents. Sustainability, 12(22), 9486. https://doi.org/10.3390/su12229486
- van Esch, P., & Mente, M. (2018). Marketing video-enabled social media as part of your e-recruitment strategy: Stop trying to be trendy. Journal of Retailing and Consumer Services, 44(9), 266-273. https://doi.org/10.1016/j.jretconser.2018.06.016
- van Esch, P., Black, J. S. & Arli, D. (2021). Job candidates' reactions to AI-enabled job application processes. AI Ethics, 1, 119-130. https://doi.org/10.1007/s43681-020-00025-0
- Vatansever, K. (2017). Using multi criteria decision making approaches for evaluating and selecting website: A literature review. International Journal of Current Advanced Research, 6(4), 3388-3399. http://dx.doi.org/10.24327/ijcar.2017.3399.0281
- Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.126.96.36.19926
- Villeda, M., McCamey, R., Essien, E., Amadi, C., & Anani, R. (2019). Use of social networking sites for recruiting and selecting in the hiring process. International Business Research, 12, 66. https://doi.org/10.5539/ibr.v12n3p66
- Waheed, A., Xiaoming, M., Waheed, S., & Ahmad, N. (2019). The role of social networking sites in intention to use e-recruitment: A study of telecom sector in context of Pakistan. KSII Transactions on Internet and Information Systems, 13, 3842-3861. https://doi.org/10.3837/tiis.2019.08.002
- Wu, M.-Y, Chou, H.-P, Weng, Y.-C, & Huang, Y.-H. (2011). TAM-2-based study of website user behavior-using web 2.0 websites as an example. WSEAS Transactions on Business and Economics, 8, 133-151. http://www.wseas.us/e-library/transactions/economics/2011/53-665.pdf
- Digital Accounting Implementation and Audit Performance: An Empirical Research of Tax Auditors in Thailand vol.8, pp.11, 2021, https://doi.org/10.13106/jafeb.2021.vol8.no11.0121