Published at : 05 Feb 2024
Volume : IJtech
Vol 15, No 2 (2024)
DOI : https://doi.org/10.14716/ijtech.v15i2.6689
Amalia Suzianti | Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus Baru UI Depok, 16424, Indonesia |
Gusti Ayu Rifamutia Krishna Devi | Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Kampus Baru UI Depok, 16424, Indonesia |
Safira Nurul Fathia | Department of Industrial Engineering and Management, KTH Royal Institute of Technology, Kungliga Tekniska Högskolan, SE-100 44, Stockholm, Sweden |
The introduction of technology has caused an improvement in
the recruiting sector, replacing old methods with more simplified and effective
procedures. Recruiters can now access a wide range of technical developments by
integrating digital platforms and tools, which helps them more successfully
attract and hire top talent. This study employs the Unified Theory of
Acceptance and Use of Technology (UTAUT) model and Partial Least Squares
Structural Equation Modeling (PLS-SEM) to examine blue-collar workers' intentions
to use an e-recruiting platform. The study, which included 212 respondents,
provided insight into the variables of performance expectation, effort
expectation, social impact, and facilitating conditions that affect people's
propensity to use the job-seeking platform. The analysis of the data showed
that effort expectations and social influence had an impact on behavioral
intention, and behavioral intention had an impact on usage behavior. A thorough
strategy was developed through data analysis, utilizing the Strengths,
Weaknesses, Opportunities, Threats (SWOT) matrix and the Reach, Impact,
Confidence and Effort (RICE) rating system. The
conclusions and prioritized methods provided by the researchers serve as
valuable tools for those engaged in the recruitment sector. This information
empowers them to leverage technology effectively and make informed decisions to
enhance the acceptance and utilization of e-recruiting platforms among
blue-collar employees.
Blue-collar workers; E-recruitment; PLS-SEM; Technology adoption; UTAUT model
To meet the needs of life, people look for work to earn income. However, in practice, finding a suitable job is not easy. Obstacles in obtaining available job vacancy information are one of the problems faced by job seekers. Job seekers are required to be able to access the job vacancy information. With a lack of information regarding job vacancies, job seekers use conventional methods such as visiting individual shops, homes, or companies to only find out about job availability (Huda and Apriyanto, 2019). The emphasis on technological advancement in developing economies has significantly transformed the hiring procedures within industries (Felgenhauer et al., 2017). The result of this transformation is e-recruitment (Stone and Dulebohn, 2013). Job-seeking application is the practice of using the internet to connect employers with the most qualified individuals. Automating boring, routine tasks is possible. Numerous vocations are progressively changing as a result of the widespread use of digital technologies (Rodionov et al., 2022).
These technologies include job search engines, career
pages, applicant tracking systems, pre-screening/self-assessment tools, talent
pool systems, and video ads. There are also social media, candidate
relationship management systems (Lee, 2011), and candidate seekers. A system
is needed because it can help connect the two parties to meet the needs of
finding and recruiting job candidates. Numerous employers turned to digital
technology as their contingency plan to continue operating via teleworking and
solutions such as video conferencing, cloud services, and virtual private
networks (Moganadas
and Goh, 2022). Indeed, it is an opportunity, especially for an
e-recruitment platform company, to offer job search services in the form of
mobile applications (Cahyono, 2016). In Indonesia, a high
unemployment rate occurs among blue-collar employees.
Blue-collar workers are workers who occupy non-managerial
positions or tend to be low-skilled. The classification is based on Klasifikasi
Baku Jabatan Indonesia (KBJI) according to the International Standard
Classification of Occupations (ISCO). Based on Eurofound (2010),
the KBJI/ISCO codes 6, 7, 8, and 9, which correspond to skilled agriculture and
fisheries workers, craft and allied trades employees, plant and machine
operators and assemblers, and elementary vocations, are included in the
category of blue-collar workers (Eurofound, 2010).
According to data from the Indonesian Central Bureau of
Statistics (BPS) (2018), the unemployment rate for
informal workers in Indonesia was 5.99% in February 2021. The large percentage
of informal workers aligns with the country’s low education level. In addition,
there are still many small and medium enterprises that have not been able to
increase their economies of scale and develop the skills of their workers.
Furthermore, digitalization has encouraged many young people to seek more
flexible jobs. However, workers in the informal sector have a relatively high
level of work risk.
Therefore, the 77.9 million informal workers in Indonesia
underscore the urgent need to tackle the unemployment problem faced by
blue-collar workers and the high unemployment rate of 5.99% among blue-collar
workers, further highlights the need for successful interventions. This number
will be reduced by the implementation of online applications for recruitment,
specifically for blue-collar workers.
Though the focus is on blue-collar workers adopting
online applications for recruitment, no study has examined the factors of
blue-collar workers in adopting these e-recruitment job applications. Few
studies in the literature have researched to explore the factors in students’
behavioural intention to use job search apps (Dhiman and Arora, 2018; Hosain
et al., 2016), while research on individuals who are
already employed, such as blue-collar or white-collar, is still quite limited.
Therefore, this study contributes to helping the individuals who are employed,
specifically blue-collar workers, to adopt the e-recruitment app to decrease
the unemployment rate in Indonesia.
As a result, this study aims to understand the
factors that allow the application developer to create specific strategies that
address their needs and concerns, driving faster application adoption. This
online e-recruitment platform will offer organizations and job seekers the
opportunity to search for jobs based on their best match. Mobile job search
apps, being powerful tools readily accessible to job seekers, require a deep
understanding of the variables influencing their usage to maximize their
impact. This raises the research question, ‘What factors influence the use of
job search applications for blue-collar workers?’. Understanding these elements
allows us to create specific strategies that address their needs and concerns,
driving faster application adoption. Accordingly, the author thinks that using
digital solutions will assist in resolving these problems gradually (Chan et al., 2022).
In this research, two main
methodologies were used to determine the factors influencing blue-collar
workers to adopt an e-recruiting platform: the UTAUT model and the PLS-SEM
method. After determining the factors, the strategies are then formulated using
the SWOT Matrix and assessed using the RICE Scoring Model. The methodology that
was carried out in this study consisted of several stages. The
first stage involved establishing research objectives, identifying research
limitations, defining the research methodology, and establishing a systematic
approach to research writing to ensure a systematic and well-structured
presentation. In order to conduct this research, the author consulted various
sources, including books, journals, and previous studies, to gather literature
studies.
The next stage is data collection, where
the author distributes questionnaires to respondents who fit the criteria. Data
obtained from the questionnaire results were then processed using Structural
Equation Modelling (PLS-SEM). Next, the results were analysed and used as a
reference in designing strategies. Strategy planning is done by reviewing
previous literature and mapping out the strategy with a SWOT Matrix, which
experts will validate and prioritize using the RICE scoring method to
prioritize the strategy. The findings obtained from data processing and
analysis are concluded in the concluding section at this stage. This stage is
also explained regarding the final results of the strategy recommendations that
have been validated. Furthermore, suggestions are written as a form of
improvement and strategy recommendations for related stakeholders. Lisrel 8.80
SEM was employed. Measures of the UTAUT model and e-service quality measures as
the independent variable and intention to use as the dependent variable make up
the measuring model. Additionally guaranteed were validity, dependability, and
uni-dimensionality (Albugami and Zaheer, 2023).
2.1.
Unified Theory of Acceptance and
Use of Technology (UTAUT) Model
The research
uses this model to check and explain the significant variables that control
blue-collar workers' adoption of e-recruitment tools. One of the newest models
for technology adoption produced by Venkatesh, Thong, and Xu, (2012) is
called UTAUT. According to Venkatesh et al. (2003),
UTAUT was more effective compared to the other eight theories with response up
to 70% of user variance. Seven factors that appeared to be the essential direct
drivers of behavioural intention or use behaviour in each model were discovered
after analysing the eight models (Venkatesh, Thong, and Xu, 2012).
The new conceptual model recognizes that
supportive environments indirectly impact usage behaviour by influencing
individual’s behavioural intentions to adopt technology, thus incorporating
this adjustment. This model was an updated representation of the Unified Theory
of Acceptance and the Use of Technology Model (UTAUT) by Venkatesh et al.
(2003), which more accurately depicts the interactions between
the variables in the research context and reflects a more nuanced view of the
process as shown in Figure 1.
Some of the variables
employed are drawn from various previous research. These variables are
performance expectancy, effort expectancy, social influence, facilitating
condition, behavioural intention, and usage behaviour. The variable shown in
the modified model is one of the references in developing the initial
conceptual model in this research, as shown in Table 1.
Figure 1 Conceptual Model of the Research
Table 1
Hypothesis Construction
Hypothesis |
Definition |
References |
H1 |
Performance expectancy has
significant impacts on the blue-collar workers’ intention to use this feature
of the application in job seeking. |
(El-Ouirdi et al., 2015) |
H2 |
Effort expectancy has
significant impacts on the blue-collar workers’ intention to use this feature
of the application in job seeking. |
(Venkatesh
et al., 2003) |
H3 |
Social Influence has a
significant impact on the blue-collar workers’ intention to use this feature
of the application in job seeking. |
(Dhiman and Arora, 2018) |
H4 |
Facilitating Conditions has
significant impacts on the blue-collar workers’ intention to use this feature
of the application in job seeking. |
(Venkatesh
et al., 2003) |
H5 |
Behavioral intention has
significant impacts on the blue-collar worker's usage behavior. |
(El-Ouirdi et al., 2015) |
This
modification in the conceptual model, specifically the change from facilitating
condition to usage behaviour and then to behavioural intention, is supported by
relevant research on the adoption of e-recruitment mobile apps (Dhiman and Arora,
2018). The research, entitled “Adoption of E-Recruitment
Mobile Apps: An Integrated Model of UTAUT and Innovation Diffusion Theory”,
provides empirical evidence and theoretical insights that support the mediated
contact between facilitating conditions, behavioural intention, and usage
behaviour.
2.2. Questionnaire Design
The survey aimed to collect data in the form of perceptions related to users' continued usage of the application. The questions, as shown in Table 2, were structured using an instrument adapted from Venkatesh, Thong, and Xu (2012).
Table 2
Questionnaire Construction (Adapted from Venkatesh, Thong, and Xu (2012))
Performance Expectancy |
·
I feel
that the “Apply” feature in the job-seeking Application helps me in my job
search |
·
Using
the “Apply” feature in the job-seeking application increased my productivity | |
·
I found
the “Apply” feature in the job-seeking application useful in my life | |
·
Using
the “Apply” feature in the job-seeking application allows me to find work
much faster | |
Effort Expectancy |
·
I think
the “Apply” feature in the job-seeking application is easy to use |
·
Learning
to operate the “Apply” feature in the job-seeking application was easy for me | |
·
My
interaction with the “Apply” feature in the job-seeking application was clear
and easy to understand | |
·
It's
easy for me to become proficient in using the “Apply” feature in the
job-seeking application | |
Social Influence |
·
People
who are important to me recommend using the “Apply” feature in the
job-seeking application |
·
People
who influence my behavior want me to use the “Apply” feature in the
job-seeking application to find work | |
·
People
whose opinions I value support me using job search with the “Apply” feature
in the job-seeking application | |
·
In
general, my surroundings support me to use the “Apply” feature in the job-seeking
application | |
Facilitating Condition |
·
I have
enough resources to use the “Apply” feature in the job-seeking application |
·
I have
sufficient knowledge to use the “Apply” feature in the job-seeking
application | |
·
People
(or groups) will be willing to help me with difficulties using the “Apply”
feature in the job-seeking application | |
·
Special
instructions (such as video tutorials and steps for using the application
from the job-seeking social media) regarding the “Apply” feature in the
job-seeking application | |
Behavioral Intention |
·
I
frequently use the “Apply” feature in the job-seeking application for job
searching |
·
I
recommend the “Apply” feature in the job-seeking application to others who
are looking for a job | |
·
I
intend to use the “Apply” feature in the job-seeking application in the next
12 months | |
·
I plan
to use the “Apply” feature in the job-seeking application over the next 12
months | |
Usage Behavior |
·
I use
the “Apply” feature in the job-seeking application to improve the quality of
myself to increase the chances of being accepted for work |
·
I use
the “Apply” feature in the job-seeking application to find various job
vacancies that are guaranteed credibility | |
·
I used
the “Apply” feature in the job-seeking application to apply for various job
vacancies |
2.5.
Data Collection and Processing
The
category of data used in this study is primary, where the data retrieved comes
from a direct collection of respondents through a questionnaire. Primary data
is used to answer questions concerning the observed research variables (Creswell and Creswell, 2017). Subsequently, additional
data collection is carried out to perform a more strategic analysis, continuing
from the results of the PLS-SEM analysis, which was provided by multiple
experts who participated as respondents. The expert respondents have a
background in being Product Managers or Quality Assurance with experience of
more than five years.
2.6.
Respondent Distribution
The estimation technique employed in this research
follows Bentler and Chou (1987), which advocates a sample
size in structural equation modeling adhering to the 5:1 principle (for every
one indicator, there are five measuring respondents) (Bentler and Chou, 1987). Thus, based on the theory of Bentler and Chou (1987), the minimum number of respondents required, or
the minimum sample size, is at least 115 respondents. After the distribution of
the research survey, a total of 212 responses were obtained. The majority of
the respondents were living in Jakarta, accounting for a percentage of about
26.3% or 57 respondents; most of the respondents were high school graduates
with a percentage of about 46.9% or 100 respondents, and the most significant
portion of the respondents were born in the range of 1997-2006, accounting for
about 60.1% or 127 respondents.
2.7.
Partial Least Squares Structural
Equation Model
Scientific studies have increasingly
used structural equation modeling (SEM)—a potent multivariate tool to verify
and evaluate multivariate causal linkages—as a method. In contrast to other
modeling techniques, SEMs look at the direct and indirect effects on suggested
causal links. SEM is a statistical method that has been around for about a
century and has evolved through three periods. The initial generation of SEMs
built the causal modeling logic using path analysis (Wright, 1934).
There are two main methods for
implementing SEM. Joreskog and Sörbom (1993) and Joreskog (1969) popularized
covariance-based SEM in the beginning of the 1980s (Joreskog and Sörbom, 1993; Joreskog,
1969). The following primary SEM methodology is variance-based SEM, commonly
referred to as composite-based SEM. Between variance-based SEM, partial least
squares structural equation modeling (PLS-SEM) is regarded as a fully developed
and general approach (Hair, Sarstedt, and Ringle, 2019). The access
that is most frequently used in social sciences studies is PLS-SEM, which was
first introduced by Wold (1980), made famous by Chin (1998), and more
recently by (Hair et al.,
2012; Chin, 1998; Wold, 1980).
2.8.
SWOT Matrix and RICE Scoring Model
Strategy
design in this research used the combination of the SWOT matrix and RICE
Scoring Model. SWOT matrix is used for developing recommendation strategies
using the SWOT matrix method, while strategies were prioritized and selected
using the RICE scoring tool from the expert for assessment. The scoring
methodology uses the RICE scoring algorithm as it prioritizes the strategies
with higher scores above those with lower scores, which could help stakeholders
determine the most optimal strategy to implement. Subsequently, three
strategies will be identified based on the highest RICE value.
This
study employs the modified version of the Unified Theory of Acceptance and the
Use of Technology (UTAUT) model to measure the behaviour of blue-collar workers
based on the adoption of technology. The survey questionnaire was designed
regarding relevant studies, and additional data collection involved seeking
input and suggestions from experts to assess the strategy questionnaire that had
been prepared. This research specifically focuses on blue-collar employees in
the Greater Jakarta and Bandung areas who have utilized their e-recruitment
application to apply for jobs online. The questionnaire results were then
processed using SmartPLS4 software to evaluate the PLS model.
3.1.
Data Processing and Analysis
After developing the questionnaire and defining the
conceptual model of the research, a pilot test is administered to assess its
validity and reliability before distributing it widely.
The
pilot test involves a smaller group of respondents, typically at least 30
individuals (Aziati,
Juhana, and Hazana, 2014). This research conducted pilot
testing with a sample of 63 respondents. The validity test was carried out by
comparing the r count and r table of each question indicator. If the value of r
count > r table, then the questionnaire questions are considered valid.
Based on the results, all of the items' r values are more than the r table,
meaning they are valid. Testing was done with IBM SPSS Statistics 20 software.
Furthermore, the reliability test was carried out on the research survey. Test
reliability is done in order to know the consistency of answers from
respondents to questionnaires to be trusted. Reliability leads to the sense that
the instrument used in research proved reliable (Hair et al., 2012).
A research tool is said to be reliable if the value of Cronbach's Alpha is >
0.6. In this research, the Cronbach alpha is 0.867 for 23 indicators;
therefore, it is considered reliable.
To produce a robust model, a path
diagram is designed, as depicted in Figure 2. This illustration provides
insights into the connections between latent variables, and the direction of
the relationship between latent variables and indicator variables, where the
type of indicator construct is used is a reflective indicator construct.
Figure
2 Path Diagram of the Research
3.1.1
Measurement Model Testing (Outer Model)
In testing the outer
model, measurements were made using convergent validity (outer loadings and
average variance extracted or AVE values), internal consistency reliability
values (CA & CR values), and discriminant validity (HTMT values and
Fornell-Larcker criterion). Convergent validity is used to measure the
correlation of an indicator variable with other indicator variables. The outer
loading value of the indicator variable must be greater than 0.7. Meanwhile,
AVE is used to measure the ability of latent effect to explain the variance of
the indicator variables. The AVE value for good convergent validity is greater
than 0.5 (Hair, Sarstedt, and Ringle, 2019). To measure the discriminant validity of the HTMT
criteria, the parameters of the HTMT value need to be less than 0.9 (Hair, Sarstedt,
and Ringle, 2019). In this study, all indicators of the outer model are valid and meet
the requirements.
3.1.2
Measurement Model Testing (Inner Model)
In testing the inner
model, measurements are made using collinearity measurements (VIF values),
coefficient of determination (R2), stone-geisser's Q2, and path coefficient.
VIF value parameters. According to Hair, Sarstedt, and Ringle (2019), VIF values within the range of greater than 0.2
and less than 5 are considered acceptable parameters. On the other hand,
according to Bentler and Chou (1987), in social and behavioral research, the
determination coefficient given if it is greater than 0.26 is said to be
strong; if it lies between 0.13 and 0.26, then it is considered moderate, and
if it is greater than 0.02 and smaller than 0.13, then it is considered small (Bentler and Chou, 1987). Moreover, the value of the coefficient of determination, or Q2, is a
measure of the predictive relevance ability of the endogenous latent variables
of a model, where this value is used to predict data outside the model. The
good Q2 value, according to Hair, Sarstedt, and Ringle (2019), is greater than 0 (Hair, Sarstedt, and Ringle, 2019).
3.1.3
Model Hypothesis Testing
The hypothesis is
considered acceptable if the p-value is less than 0.05 and the t-value is
greater than 1.96 (Hair, Sarstedt, and Ringle, 2019). As depicted in
Table 3, H1, H3, and H5 are accepted, while H2 and H4 are rejected. The two
hypotheses were rejected because they did not meet the requirements for a
p-value <0.05. For H2, it means that users do not view the effort needed to
use the job search app as a significant obstacle or deterrent. For H4, it
implies that job seekers have previous experience or exposure to job search
apps, which makes them comfortable and knowledgeable about how to use them
effectively.
Table 3
Hypothesis Construction
Hypothesis |
Definition |
t-value |
p-value |
Details |
H1 |
PE - BI |
2.724 |
0.007 |
Accepted |
H2 |
EE - BI |
0.186 |
0.852 |
Rejected |
H3 |
SI - BI |
3.190 |
0.002 |
Accepted |
H4 |
FC - BI |
1.642 |
0.101 |
Rejected |
H5 |
UB - BI |
2.954 |
0.003 |
Accepted |
From
this research, performance expectancy is one of the outputs expected to be created
with behavioural intention. The relationship between performance expectancy
(PE) and behavioural intention (BI) has a p-value below 0.05. Thus, the
hypothesis is accepted. As highlighted by (El-Ouirdi et al., 2015),
Performance Expectancy stands out as a crucial determinant of technology
adoption. Therefore, app developers should prioritize enhancing the perceived
usefulness of these apps, taking into account their specific target audience.
As for the third hypothesis, which is accepted, social influence (SI)
significantly affects behavioural intention (BI). According to Dhiman and Arora
(2018), in the context of job search apps, if someone hears
that their friends have found success in their job search using a particular
app, they are more likely to adopt it themselves (Dhiman and Arora, 2018).
Behavioural intention is defined by the motivational factors that influence a
given behaviour, where the stronger the intention to perform the behaviour, the
more likely the behaviour will be performed. Since the p-value is less than
0.05, the relationship between the two variables, behavioural intention, and
usage behaviour, can be deemed significant, and thus, the hypothesis is
accepted. The intention to use information technology will influence the actual
use of the information technology (El-Ouirdi et al., 2015).
However, two hypotheses are rejected.
The second hypothesis shows that Effort Expectancy (EE) does not significantly
influence Behavioural Intention (BI). This means that users do not consider the
effort required to use the job search app to be a significant barrier to
adoption (Decman,
2015). Moreover, it is also shown that Facilitating Condition
(FC) does not influence Behavioral Intention (BI). One possible reason is that
the job seekers were familiar with the use of job search apps and believed that
an organizational and technical infrastructure existed to support their use of
the technology.
3.3.
SWOT Matrix
These
strategies, developed based on the TOWS Matrix analysis, enable organizations
to make the most of their internal strengths, overcome weaknesses, seize
external opportunities, and mitigate potential threats in the recruitment
process, ultimately improving their chances of finding suitable candidates for
their available positions. Furthermore, for each strategy, the relationship to
the hypothesis is mapped on the model. This strategy is structured for all
existing hypotheses, as shown in Table 4.
Table 4
Strategy Construction
No |
Strategy |
Linking to
Hypothesis |
References |
1 |
Uses
video games to learn about a job applicant's behavioral traits and soft
skills, such as creativity, perseverance, extroversion, and leadership
potential. |
H1 |
(Chen and Haymon, 2016) |
2 |
Provide
education and certifications that are directly related to company
requirements. |
H3 |
(Chen and Haymon, 2016) |
3 |
Before
positions are fully filled, candidates are informed right away of the receipt
of their applications. Rejected candidates are also informed of pre-screening
results directly. |
H1 |
(Cappelli, 2001) |
4 |
Online
Referral/Reward based recruitment service. |
H3 |
(Chen and Haymon, 2016) |
5 |
In
order to avoid further limiting their talent pool by eliminating candidates
on the basis of a potentially irrelevant individual difference variable
(offline job fair), it may be advisable to consider putting in place a backup
offline recruitment process. |
H5 |
(Chen and Haymon, 2016) |
6 |
Increasing
the applicant pool (selecting candidates from a wider geographic range). |
H1 |
(Chapman and Webster, 2003) |
7 |
It
has been demonstrated that increasing the applicant pool through social media
recruitment is effective from the target perspective. |
H3 |
(Kaur and Kaur, 2021) |
8 |
To
a large pool of possible employers, job seekers can acknowledge their
abilities, experience, references, and other qualities (such as soft skills
and digital badges that validate a certain competence), and freelancers can
showcase their work and offer recommendations. |
H1 |
(Chen
and Haymon, 2016) |
9 |
The
creators of e-recruitment platforms should keep improving the platforms'
integrity, user-friendliness, and management and adding more opportunities
for value-added activities. |
H1 |
(Kaur
and Kaur, 2021) |
10 |
Recruiters
should include crucial details like pay, location, job descriptions, and
other pertinent information to help candidates make decisions. |
H5 |
(Kaur
and Kaur, 2021) |
11 |
It's crucial for companies offering e-recruitment
services to instruct job searchers on how to register for an account,
complete and submit application forms, upload resumes, and do other tasks. |
H3 |
(Kaur
and Kaur, 2021) |
12 |
Keeping applicant information with personal features,
sending numerous job applications, and receiving real-time responses from the
system. |
H1 |
(Chan and Lau, 2002) |
3.4.
RICE Scoring Method
After developing strategic recommendations using the SWOT
matrix method, strategy recommendations were validated and assessed using the
RICE scoring method. Reach, Impact, Confidence, and Effort are the letters in
the acronym RICE. Each element receives a score, and the items are ranked and
prioritized using the overall score. In this phase, the stakeholders—the
researcher and three e-recruitment application experts, including the Product
Lead, the Quality Assurance Lead, and the Associate Product Manager—explicitly
define the RICE scoring model's four components: Reach, Impact, Confidence, and
Effort, by choosing a scale or scoring system for each component (1 to 10 or
low, medium, or high). After conducting in-depth interviews with three experts
from e-recruitment applications, the aim was to gain insights and
recommendations to prioritize the strategies. Out of the 12 proposed
strategies, the interviews identified three strategies that emerged as
high-priority items for e-recruitment applications to focus on. These
strategies were crucial for accelerating the adoption rate and enhancing the
platform's growth. The three strategies with the highest rice score are:
1.
To help
candidates make decisions, recruiters should provide meaningful information on
the salary, place, job description, and other relevant details.
2.
It has been
demonstrated that using social media to recruit has increased applications from
the intended audience.
3.
The creators
of e-recruitment platforms should continue to improve the platforms'
credibility, simplicity of use, and ease of management and create new
opportunities for activities that add value.
The reason for using three choices as a final strategy
recommendation is that the level of effectiveness is more optimal compared to
four or five choices (Vegada et al., 2016).
After carrying out the research, there are several
recommendations that can be made in regard to future research on similar
topics:
a.
Research can be further developed
into conducting an in-depth analysis of a larger sample of blue-collar workers,
utilizing a multi-group analysis approach.
b.
Further research can also be done
to check how the strategies recommended have actually the job-seeking
application adoption rate towards blue-collar workers.
c.
To increase the structural
model’s fitness, the respecified model can be further reviewed and evaluated,
supported by more literature research on the different re-specification
techniques employed.
d.
A further research recommendation
is to analyze additional variables related to blue-collar workers' adoption of
e-recruitment applications, such as hedonic motivation, price value, and habit,
in order to gain a comprehensive understanding of their decision-making process
and behavioral patterns.
This study's main objective is to determine the level of
adoption of job search applications for blue-collar workers. The research
adopted the research model that was carried out by the UTAUT model. Data
processing is done using the Partial Least Squares-Structural Equation Modelling
(PLS-SEM) method. PLS-SEM was selected as the method for this study because it
has exploratory qualities. Variables used in this study are Performance Expectancy,
Effort Expectancy, Social Influence, Facilitating Condition, and Behavioural
Intention. Based on the hypothesis analysis, it was found that the most
influential factors in the adoption of job search applications include: a)
experience from Performance Expectancy will affect Behavioural Intention
significantly, b) experience from Social Influence will affect Behavioural
Intention significantly, and c) experience from Behavioural Intention will
affect Usage Behaviour significantly. In addition, three strategies are
selected based on the SWOT Matrix and RICE Scoring Model method. These
strategies are devised through a combination of latent variable analysis and a
literature review employing the SWOT matrix. Validation by experts and the RICE
scoring method aid in selecting the most impactful and feasible strategies from
a pool of twelve, making resource allocation more efficient. The strategies
include: 1) to help candidates make decisions, recruiters should provide
meaningful information on the salary, place, job description, and other
relevant details; 2) it has been demonstrated that using social media to
recruit has increased applications from the intended audience, and 3) the
creators of e-recruitment platforms should continue to improve the platforms'
credibility, simplicity of use, ease of management, and create new
opportunities for activities that add value.
This paper
was funded by Seed Funding Grant, Faculty of Engineering Universitas
Indonesia, Grant Number: NKB-1929/UN2.F4.D/PPM.00.00/2022.
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