MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
Virtual Conference on Melaka International Social Sciences, Science and Technology 2025
ISSN: 2454-6186 | DOI: 10.47772/IJRISS | Special Issue | Volume IX Issue XXIII October 2025
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Evaluating on Mismatches and Inaccurate Recommendations of Job
Matching Platform: The Effectiveness of Mobile Job Matching
Platform in Malaysia
Aliyyatul Muna Ahmad Roslan, Nur Faizah Mohd Pahme, Intan Maizura Abdul Rashid Abdul Rahim
University Technology MARA (UiTM) Campus Perak, Seri Iskandar Branch, 32610 Seri Iskandar,
Perak
DOI: https://dx.doi.org/10.47772/IJRISS.2025.923MIC3ST250021
Received: 12 August 2025; Accepted: 20 August 2025; Published: 24 October 2025
ABSTRACT
In Malaysia, the top job matching platforms are Linkedin, Jobstreet and Maukerja were the leading platforms
with millions of user (Wei, 2023; Samah et al., 2022; Maukerja, n.d.). This platform offers advantages to both
job seekers and employers. For the job seekers, the usage of smart technology can save time and help match
jobs that suit job seekers’ skills and experiences. Then, this job matching platform can find suitable candidates
faster and saves costs for employers. By hiring the right people, companies can improve performance and
reduce employee turnover (Shift iQ, 2023). However, this platform also provides inaccurate recommendations
and mismatched jobs for the users. Therefore, a qualitative study was conducted for this study and document
analysis was used to collect data regarding cause mismatches and inaccurate job suggestions to users of the job
matching platform on the mobile application. Then, thematic analysis was carried out to analyze the data that
has been collected. This study provides useful information to enhance the effectiveness of the job matching
platform by offering better recommendations to users as well as improve user experience by fulfilling the
needs and preferences of users.
Keywords— Mobile Application, Job Matching Platform, User Interface, Malaysia
INTRODUCTION
Research Background
Job matching platform uses advanced technology to facilitate exploration by job seekers and employers, by
providing a two-way matching system that considers both parties (World Bank, 2023). This platform is also
powered by advanced algorithms and data analytics to improve the recruitment process. Unlike traditional job
boards, this platform allows employers to be proactive in identifying and hiring suitable candidates, which will
reduce the time for the recruitment process (Indeed, 2024b). The increase in the usage of job matching
platforms can be proven by several platforms that are used globally. Among those platforms are LinkedIn,
Indeed, Monster, Glassdoor, ZipRecruiter, and CareerBuilder. LinkedIn, which records more than 900 million
users globally, provides reasonable costs based on extensive user data of the platform (Bondar, 2023). The
Glassdoor platform has approximately 63 million monthly visitors and 2.5 million employer profiles on the
platform (Glassdoor, n.d.). While in Malaysia, among the platforms with the highest users are LinkedIn,
Indeed, Jobstreet, and Maukerja. With a total of 7 million users, LinkedIn has reached 20.5% of the Malaysian
population in early 2023 (Wei, 2023). The Jobstreet platform has more than 10 million individuals that are
looking for jobs and more than 90,000 employers who use the platform (Samah et al., 2022). The next
platform, Maukerja, has 4 million active job seekers and over 2 million followers (Maukerja, n.d.). Figure 1
illustrates the number of users for these three platforms, which is Linkedin, Jobstreet and Maukerja. Despite
the high number of users of job matching platforms both globally and in Malaysia, there are problems of
mismatching that exist between job seekers and job openings posted by employers. These issues are a concern
for both job seekers and employers (Langhans, 2023).
MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
Virtual Conference on Melaka International Social Sciences, Science and Technology 2025
ISSN: 2454-6186 | DOI: 10.47772/IJRISS | Special Issue | Volume IX Issue XXIII October 2025
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Fig. 1 Number of users for LinkedIn, Jobstreet and Maukerja (Source: Wei, 2023; Samah et al., 2022;
Maukerja, n.d.)
Problem Statement
The current issue on the job matching platform is often face inaccurate and mismatched job recommendations.
This problem is caused by the utilisation of semantic information that is not comprehensive in job descriptions
and resumes, where it is important for an effective text matching process (Yang et al., 2022). Previous research
identified this problem as a semantic matching challenge (Wang et al., 2022). Semantic ambiguity, where
words have many meanings depending on the context, can lead to inaccurate job recommendations on mobile
job matching platforms. For example, the word bank can mean riverbank and financial institution, causing
confusion in the job matching algorithm (Seyidova, 2023). Traditional information retrieval techniques used
on this platform are not suitable for research seekers and employers today. As a result, job seekers often
encounter many irrelevant search results, requiring more time and effort to review job suggestions one by one
(Alsaif et al., 2022).
Research Question
How is it possible for the mobile job matching platform to provide inaccurate recommendations and
mismatches job?
Research Objective
In line with the research question above, the objective of this study is;
To evaluate the reasons that lead to mismatches and inaccurate job recommendations on mobile job matching
platform.
LITERATURE REVIEW
Definition and characteristics of job matching platforms
A job matching platform is an important tool to connect job seekers with suitable positions, by aligning a
person's skills and preferences with the employer's needs. This process can increase the level of job
satisfaction, employee retention and good workforce stability (Osborne & Vandenberg, 2023). The
characteristic of job matching is the skill requirement. When matching between people and jobs, skills are the
main key to this process. With skills, it can help build a strong team and complete tasks better. Skills can be
various according to career level and company culture (Indeed, 2024). Among the important skills are ambition
and communication. Ambitious people have clear goals and can lead to success. Sharing successful goals can
attract the attention of employers to choose the individual. Furthermore, having good communication shows
that the individual is clear and concise in speech, writing, listening and body language (Herrity, 2023). Both
skills are crucial for employers. Clear communication and ambitious goals can help individuals get jobs and
contribute to the team's success.
MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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Job matching platforms help simplify the hiring process by connecting job seekers with employers in one
place. Unlike traditional job boards, these platforms use technology to match job seekers with the right jobs
(Indeed, n.d.). Among the characteristics of the job matching platform is intelligent matching. Using artificial
intelligence for potential matches between skills, qualifications and experience is more than a simple keyword
search. The second characteristic is skill-based matching. This technology focuses on specific abilities and
expertise to find a suitable match for both parties. Finally, advanced algorithms. The use of complex
algorithms for the analysis of skills and abilities to make an accurate match (World Bank, 2023).
Impact of job matching platforms on traditional job search methods
The first impact of job matching platforms on traditional job search methods are more comprehensive search.
Job matching platforms such as LinkedIn provide a wider reach than traditional job searches. With millions of
users worldwide, this platform can connect job seekers, employers and companies. This makes it easy to share
resumes, learn about jobs opening and network with professionals around the world (Hosain & Liu, 2020).
Secondly, is job matching platform can help enhance visibility and help build professional relationships of job
seekers more effectively. Job seekers can connect with companies, follow organizations, and share activity
posts to increase online presence (Hosain & Liu, 2020). By creating a detailed profile and actively
participating in discussions, job seekers can market themselves as experts in certain fields. This can lead to
meaningful networking opportunities and mentorship relationships (Markus, 2023).
Next, platforms like LinkedIn make it convenient and faster to apply for a job compared to traditional job
search methods. Features such as "Easy Apply" that are shown in Figure 2 allow job seekers to apply for jobs
directly on the platform. In addition, this platform centralizes job listings where job seekers can save time from
searching from various sources (Hosain & Liu, 2020). Digital tools such as online profiles, video resumes and
digital portfolios can help showcase skills more effectively and quickly. This approach can speed up the job
search process and increase the chances of getting a job (Man, 2023).
Fig. 2 Easy Apply feature on LinkedIn (Source: Nunez, 2023)
Benefits of using job matching platforms for job seekers
Job seekers can use platforms like Glassdoor to get useful information about the advantages and
disadvantages of various companies for applying for jobs like shown in Figure 3 (Ahamad, 2020). By
collecting anonymous reviews regarding salary and other information from current or former employees,
Glassdoor provides users with information before making a choice (Bergstrom, 2021). Through this benefit,
job seekers can get a deep understanding of company culture, work environment and employee experience,
allowing users to align their career aspirations with the appropriate company.
MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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ISSN: 2454-6186 | DOI: 10.47772/IJRISS | Special Issue | Volume IX Issue XXIII October 2025
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Fig. 3 Glassdoor Community Reviews (Source: Glassdoor, n.d.)
The second characteristic is skill-based matching. This technology focuses on specific abilities and expertise
to find a suitable match for both parties. Finally, advanced algorithms. The use of complex algorithms for the
analysis of skills and abilities to make an accurate match (World Bank, 2023). The second benefit is like on
LinkedIn, which makes it easier for job seekers to find jobs through features such as 'Open to Work' and
customizable job suggestions (Markus, 2023). Users can change the feed according to their preferences, see
the jobs listed, save the desired position and apply directly on the platform (Goyal et al., 2023). Features in
LinkedIn make it a tool for job seekers to efficiently identify and reach suitable job opportunities.
Moreover, the benefit provided on the Indeed platform is the use of data as information to provide a more
dynamic job market, giving useful information for job seekers and employers including details related to
open positions, popular positions and salary trends (Markus, 2023). By using advanced algorithms, this
platform changes job search results based on user behavior, keywords and desired locations, allowing
efficient job searches. In addition, Indeed has user-friendly features such as job notification, bookmark job
and track application to streamline the search process and improve the overall user experience (Goyal et al.,
2023).
AI-powered job matching platforms such as Talentprise, Autojob, Arytic, and Pajama Jobs, change the job
search process by offering smart technology to connect job seekers with ideal opportunities. Instead of
manually matching countless job listings, this platform suggests relevant positions that align with the user's
experience, skills and career aspirations. By aligning job searches, this platform saves time and energy and at
the same time allows job seekers to explore promising opportunities more efficiently (Esparza, 2023).
Adoption and usage trends of job matching platforms globally and in Malaysia
Job matching platforms powered by Artificial Intelligence (AI) and Machine Learning (ML) are becoming
increasingly popular (Foresight Insight, 2024). The platform helps job seekers and companies find suitable
matches more efficiently. While private companies embrace this technology, government companies are still
chasing to improve accessibility and effectiveness (World Bank, 2023). AI job matching platforms can save
time and energy by recommending suitable jobs based on job seeker's skills and experience.
Many platforms including LinkedIn, Glassdoor, Simply Hired, CareerBuilder, SEEK, Recruit, Monster,
Zhilian, Dice Holdings, 51job, Naukri, and StepStone, compete to create the best job matching platform.
LinkedIn, Indeed, CareerBuilder, and Monster are among the popular platforms with a high number of users
(Foresight Insight, 2024). Although the online temporary job search platform has gained popularity in the last
decade, some criticize that this platform hinders the ability of workers to cope with economic problems
(Jones & Manhique, 2022).
LinkedIn and Jobstreet are the most popular job matching platforms in Malaysia, with many of registered
users. LinkedIn, which has over millions registered members, is particularly popular among young
MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
Virtual Conference on Melaka International Social Sciences, Science and Technology 2025
ISSN: 2454-6186 | DOI: 10.47772/IJRISS | Special Issue | Volume IX Issue XXIII October 2025
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professionals (Wei, 2023). Jobstreet, a long-standing platform, continues to provide a large user base
consisting of job seekers (Samah et al., 2022). Maukerja, another prominent platform, has achieved
significant traction through its mobile app by attracting a lot of job seekers and followers (Maukerja, n.d.).
These platforms dominate the Malaysian job market with offers of various options to job seekers and
employers.
RESEARCH METHODOLOGY
Based on figure 3, this study used document analysis as a method to collect data related to the objective of this
study.
Fig. 4 Research Method Process
Data Collection: Document Analysis
To achieve the objective of this, a document analysis was conducted. For qualitative document analysis it has
been suggested to use two or more resources for the analysis (Indeed, 2024a; Bowen, 2009). This approach
requires a complete analysis of eleven documents, including journal articles, theses, conference proceedings,
web pages and reports published between 2019 until present. Document analysis provides several advantages
such as, ensuring data consistency by being guided by the latest written text, reducing the potential for bias by
observations or interviews. Secondly, this approach is more time efficient, allowing researchers to access and
analyze large amounts of data more quickly. Finally, this approach reduces ethical concerns because it involves
the analysis of public documents, which can be accessed for free and subject to public scrutiny (Morgan,
2022). However, there are also limitations of this approach, including some documents that are difficult to
access due to privacy or physical location. In addition, documents are not created for research purposes. Due to
that, these documents always lack specific and in-depth information that researchers may need. Finally, it is
difficult to know if the document is the latest one, especially if it is from an online source. Researchers may
need to contact the organization to confirm if there is a recent document (Cardno, 2018).
Data Analysis: Thematic Analysis
Thematic analysis is a data analysis approach that involves identifying, analyzing and interpreting patterns or
themes with qualitative data. By systematically examining textual data, researchers can explore meaning and
insight (Kampira, 2021). This method is especially suitable for document analysis because it allows for a
comprehensive exploration of written material (Morgan, 2022). One of the advantages of this method is
flexibility. The researcher can adapt the approach to suit the specifics of the research question and interest,
making this approach a versatile tool for qualitative research. In addition, this approach is simple and easy to
use, including manual and software based, making it easy to access by researchers with various skill levels
(Rosairo, 2023). However, the flexibility of this thematic analysis can be seen as a potential limitation. The
researcher needs to be careful to consider the theoretical and methodological framework that supports the
analysis to ensure realism and validity. In addition, the process of identifying and analyzing themes can be
subjective, requiring careful observation for critical reflection details (Kiger & Varpio, 2020).
In the study, there are three themes identified through data collected in document analysis, namely
MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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"Information Overload" (IF), "Evolving Job Markets" (EJM) and "Imbalance between Job Demand and
Supply" (IJDS). Recommendations to manage information overload by developing various capabilities and
using filtering, technology and prioritizing tools (Shahrzadi et al., 2024). Additionally, for the evolving job
market, the way to manage it is through upskilling and reskilling. Upskilling helps employees increase
existing abilities to perform better in current positions while reskilling trains employees to differentiate
positions within the same company (Hasan et al., 2024). For the last theme, which is the imbalance between
job demand and supply, among the suggestions to overcome this problem are focus on career plans for
college students, policy reform by the government, structural changes in the industry and active support from
enterprises (Lian, 2023).
Conceptual Framework and Hypotheses
Based on Figure 5, it shows that information overload, evolving job market, and imbalance between job
demand and supply contribute to inaccurate recommendations and job mismatches, which will then have an
impact on the effectiveness of mobile job matching platforms in Malaysia.
Fig. 5 Conceptual Framework of the Study
Therefore, guided by the conceptual framework presented above, the researcher made four hypotheses, which
are as follows;
H1: Information overload causes inaccurate recommendation and mismatches job
H2: Evolving job market causes inaccurate recommendation and mismatches job
H3: Imbalance between job demand and supply causes inaccurate recommendation and mismatches job
H4: Inaccurate recommendation and job mismatches affect the effectiveness of mobile job matching
platforms in Malaysia
FINDINGS & ANALYSIS
Data Collection and Analysis
Based on the analysis that has been carried out there are three factors that lead to mismatches and inaccurate
job recommendations on mobile job matching platforms which include algorithm limitations, complexity of
MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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job requirements and evolving job markets. Table 1 organizes the data into three parts, namely, factors,
documents and contents.
TABLE I FACTORS THAT LEAD TO MISMATCHES AND INACCURATE JOB RECOMMENDATIONS ON MOBILE JOB
MATCHING PLATFORM
Factors
Documents
Contents
Information
overload
(Green, 2023)
● Recruiters and job seekers struggle with an
overwhelming number of candidate profiles and job listings.
● The problem is not only the quantity of data but also the
inefficiency of filtering tools and methods.
● The latest methods to identify suitable candidates and
jobs have been required for a long time and are resource
intensive.
(Le et al., 2019)
● Companies are grappling with the increasing complexity
of recruitment caused by the increasing global economy and
diverse talent pool.
● An automated system that can accurately match hiring
with qualified candidates is very important.
● While online platforms offer convenience, they also
contribute to information overload making it more difficult to
find a more suitable match.
● Improved methods are needed to measure the
compatibility between qualified candidates and job
requirements.
(Widodo et al.,
2024)
● Job recommendation systems face a variety of problems
in managing large, diverse datasets and modifying
recommendations to individual users.
● Collaborative filtering and content-based filtering are
two commonly used techniques in matching systems, and each
has its own strengths and weaknesses.
● Employment companies offer a platform that allows job
seekers to find relevant positions that can be applied for.
● Job boards compete to provide better services such as
profile creation, resume writing, and personalized job
recommendations.
● Job seekers often face an overwhelming number of
search results resulting in taking a long time to find a suitable
job.
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(Alsaif et al.,
2022)
(World Bank,
2023)
● Traditional information retrieval methods may not be
effective in handling the large amount and complexity of job
search data.
● Even the existence of online job portals has increased
but finding relevant information has become one of the
problems.
● Despite the abundance of information, job boards also
often provide low-quality matches to job seekers and
employers.
● Many online platforms that easily list or aggregate open
jobs make it difficult to filter out the best candidates and
positions.
Evolving job
markets
(Kokkodis &
Ipeirotis, 2023)
● The latest method to access hiring preferences by
employers treats past decisions the same regardless of when
decisions occurred.
● Employers' hiring preferences may change over time
due to experience, adjustment to remote work and familiarity
with the platform.
● A single assessment system may not accurately capture
current employer preferences leading to less relevant
recommendations.
(Shanfari, 2024)
● Changes to the nature of work have created both
opportunities and challenges for job seekers and employers.
● The biggest challenge is the gap between the skills
possessed by employees and the skills required by employers.
(Green, 2023)
● The job market is constantly changing with new jobs
requiring new skills and existing skills becoming outdated.
● The process of matching job seekers to jobs is
constantly changing as well and requires adaptive solutions.
● The automatic job matching system must be able to
adapt to the changing job market landscape.
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(Langhans,
2023)
● Skills are very important in the job market and these
skills also often change.
● Many job seekers are struggling to adapt to these
changes, creating a mismatch between employer needs and
skills.
● Problems with these skills lead to frustration and
inefficiency in the job matching process
Imbalance between
job demand and
supply
(Chaudhry et al.,
2022)
● While higher education has grown, this has led to more
graduates being born causing more and more people to struggle
to find work related to their respective fields of study.
● This mismatch exists from the imbalance between the
supply and demand of work including the lack of vacant jobs in
specific fields.
● The effects of imbalances may increase demand on
certain fields of study.
(Jones et al.,
2024)
● Mismatches can occur not only in educational
qualifications but also in professional skills and specific fields
of study.
● Imbalance between the supply and demand of work that
occurs after an economic downturn such as the Great Recession
can lead to job and sector mismatches.
● Oversupply in a job can depress wages and lead to less
unemployment or acceptance to less desirable jobs.
● Mismatch can also increase due to factors such as
undesirable working conditions and long journeys.
DISCUSSION OF THE RESULTS
RQ: How is it possible for the mobile job matching platform to provide inaccurate recommendations and
mismatches job?
RO: To evaluate the reasons that lead to mismatches and inaccurate job recommendations on mobile job
matching platform
Findings:
Based on the data analysis that has been done, there are three factors to mismatches and inaccurate job
recommendation on mobile job matching platforms. These factors include information overload, evolving job
markets and imbalance between job demand and supply.
Overall, the findings revealed three factors as mismatches and inaccurate job recommendation on mobile job
MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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matching platforms. However, there is one factor that is mentioned the most in five documents, which is the
information overload factor. The recruitment landscape that encompasses job seekers and recruiters has
increased in complexity due to the volume of data and the inefficiency of traditional matching techniques.
Although the online platform has streamlined this process, this platform has also brought new problems such
as information overload and low matching quality.
CONCLUSIONS & RECOMMENDATIONS
To achieve the objective of this study, a qualitative study was conducted. Job matching platforms that
streamline the job search process are seen to have high usage in Malaysia, especially on some popular
platforms in Malaysia, such as LinkedIn, Jobstreet, Indeed and Maukerja. However, the mobile job matching
platform also has issues that prevent it from achieving this goal, which are problems of mismatches and
inaccurate job recommendations caused by several factors such as information overload, evolving job markets
and imbalance between job demand and supply. These factors are critical because the factors are not only
technical factors but also deep-rooted and behavioral problems that will have an impact on user engagement
and the job market (Faberman et al., 2012; Gunaratne et al., 2020). Addressing these problems is very
important so that job matching platforms in Malaysia can match suitable job opportunities with job seekers
more effectively and efficiently. However, there are also limitations to this study, which is the lack of available
data regarding problems with job matching platforms. Lastly, among the potential topics for future research are
study on user interface and user experience on the mobile job matching platform as well as the effectiveness of
using AI and machine learning on the job matching platform.
ACKNOWLEDGMENT
I want to express sincere gratitude to the College of Creative Art, UiTM Perak Branch Campus Seri
Iskandar, for guidance and support throughout this study. I am especially grateful to my supervisor for
invaluable insights and support. This study would not have been possible without the help and resources of
UiTM, Perak. Finally, I would like to thank the organizers of CASSIC 2025 for providing a platform to
present and discuss my research.
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