Enhancing Employee Productivity Through Technology System AI-Based Approaches

Abstract

This research aims to address the research gap regarding the use of AI in enhancing employee productivity. The study focuses on the role of AI in employee engagement and performance evaluation. The research adopts a quantitative approach, comparing various AI-based algorithms such as random forest, artificial neural network, decision tree, and XGBoost. The study proposes an ensemble approach called RanKer, which combines these algorithms to provide performance ratings for employees. The empirical results demonstrate the efficacy of the proposed model in terms of precision, recall, F1-score, and accuracy. Additionally, the research explores the impact of AI on employee engagement, highlighting the potential for real-time monitoring, sentiment analysis, and natural language processing to create a holistic work environment that promotes clarity, skill development, recognition, and wellness. The findings suggest that the combination of AI and employee engagement can lead to increased productivity, improved communication, and a collaborative work environment. This research contributes to the understanding of how AI can be leveraged to enhance employee productivity and provides recommendations for expanding the use of AI in employee engagement practices.

Full text

The 6th International Seminar on Business, Economics, Social Science, and
Technology (ISBEST) 2023
e-ISSN 2987-0461
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ENHANCING EMPLOYEE PRODUCTIVITY THROUGH TECHNOLOGY
SYSTEM AI-BASED APPROACHES
Dian Fitri 1), Sri Langgeng Ratnasari 2), Suyanto 3), Zulkifli Sultan 4)
1) 4) Management, Universitas Terbuka, Indonesia
2) Universitas Riau Kepulauan, Indonesia
3) Universitas IPWIJA, Indonesia
Corresponding author: 530076136@ecampus.ut.ac.id
Abstract
This study's purpose is to address the research gap regarding the use of AI in enhancing Employee
Productivity. The study focuses on the role of Technology System AI in Employee Productivity and performance
evaluation. Quantitative approach, using SEM PLS with 99 respondents. The findings of this study are (1)there
is a significant influence of Employee Skill on Employee Productivity, (2) there is a significant influence of
Employee Skill on Human Resource Readiness, (3) there is a significant effect of Human Resource Readiness
on Employee Productivity, (4) there is a not significant influence of Technology System on Employee
Productivity, (5) there is a significant influence of Technology System on Human Resource Readiness and (6)
the role of Human Resources Readiness in mediating the indirect influence of Employee Skill and Technology
System on Employee Productivity at the structural level is relatively low. This study contributes to
understanding how Technology System AI enhances employee productivity and provides recommendations for
expanding the use of AI in employee engagement practices.
Keywords: Employee Productivity, Employee Skill, Technology System AI, Human Resource Readiness
Introduction
Employee productivity is very important in today's industry. To catch up with the ability of employees
to progress in the industry, professional skills from human resources are needed. One of the supports for
accelerating the adjustment of human resources skills to the demands of the world of work requires an
intelligent Technology System. The integration of Artificial Intelligence (AI) has presented numerous
opportunities for enhancing employee productivity (Felten et al., 2019; Malik et al., 2022; Manav & Seamans,
2018; Seamans & Raj, 2018). The advent of Industry 4.0 has brought about significant changes, emphasizing
the need for organizations to adapt and optimize their Human Resource (HR) functions (Klumpp et al., 2019;
Malik et al., 2022). As businesses strive to stay competitive, human resources (HR) capabilities have become
increasingly critical in leveraging the benefits of AI in the digital era. Artificial intelligence (AI) was created
to boost productivity, promote economic growth, and assist people with various tasks (Khatri et al., 2020),
(Ding et al., 2023), (Tabit & Soulhi, 2022). In accepting these changes, the readiness of human resources is
very important, so that when the skills are fulfilled and the Technology System is integrated then the Employee
Productivity can be improved.
There are Some researchers studied Employee Productivity through Technology System AI-based
approaches, such as Employee Skill and Employee Productivity ((van Zyl, 2022), (Jacob, 2018), (Dlamini et
al., 2022), (Susilo, 2020). Employee Skill and Human Resource Readiness ((Adeosun & Adegbite, 2022),
(Alqudah et al., 2022) (Zayed et al., 2022) , Human Resource Readiness on Employee Productivity ((Laseinde
et al., 2020), (Andrew, 2017), Technology System on Employee Productivity (Murugesan et al., 2023), (Al-
Kharabsheh et al., 2023), (Williams, 2019), Technology System on Human Resource Readiness (Waddill,
2020), (Sabrina Jahan, 2014), (Ben Moussa & El Arbi, 2020), (Nandhini et al., 2022), (Board et al., 2020)
This article explores the findings of a quantitative study that Employee Skill, Technology System
application of AI, through Human Resource Readiness, and its impact on employee productivity. The study
involved 99 employees in diverse companies. By examining five keys of AI applications in HR capability and
three elements of HR readiness, this research sheds light on the contributions of AI in driving sustainable
growth and enhancing workforce efficiency.
The Role of AI in HR Capability
The study identified five major areas where AI applications have the potential to revolutionize HR
capabilities (Vaishya et al., 2020). The first area is Precision Hiring: AI-powered algorithms can analyze vast
amounts of candidate data, enabling more accurate and efficient recruitment processes. By identifying patterns

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and matching candidate profiles to job requirements, AI systems can streamline the hiring process, saving time
and resources for HR teams. The second area is training and Development: AI-based platforms can personalize
learning experiences for employees by analyzing their skills, competencies, and data performance. By
delivering targeted training content and recommendations, AI systems help employees acquire new skills and
improve their productivity.
The third area is Performance Management: AI technologies can provide real-time feedback and
performance evaluations, enabling more objective and data-driven assessments. By automating performance
management processes, organizations can ensure fair evaluations, identify areas for improvement, and foster a
culture of continuous development. The next area is Employee Engagement: AI-powered chatbots and virtual
assistants can enhance employee engagement by providing instant support for HR-related queries and concerns.
These technologies can provide individualized support, enabling HR staff to concentrate on strategic goals and
guaranteeing that employees receive up-to-date information. The last is Workforce Analytics: AI algorithms
can analyze large datasets to derive insights and predictions about workforce trends, enabling HR teams to
make informed decisions. By leveraging AI-driven analytics, organizations can optimize workforce planning,
identify skill gaps, and align talent strategies with business objectives.
Artificial Intelligence and Employee Productivity
To effectively leverage AI technologies, HR departments must possess certain readiness elements: (1)
Adaptability: HR professionals need to be open to change and willing to embrace new technologies and
processes. An adaptable mindset allows HR teams to navigate the challenges and opportunities presented by
AI integration effectively, (2) Skill Enhancement: The ability to work alongside AI systems requires HR
professionals to enhance their digital and analytical skills. Upskilling and reskilling initiatives can equip HR
teams with the knowledge and expertise necessary to leverage AI tools effectively, and (3) Organizational
Support: Successful AI integration in HR relies on support from organizational leaders. Management must
provide the necessary resources, infrastructure, and training to enable HR professionals to harness the full
potential of AI technologies.
The Resource-Based Theory of the Firm provides a framework for understanding how organizations
can use their resources to achieve a sustained competitive advantage. By investing in employee skill
development, implementing effective technology systems, and optimizing human resource management
practices, organizations can create a work environment that fosters employee engagement, leading to increased
productivity and performance (Barney et al., 2021). This theory emphasizes the internal resources and
capabilities of an organization as sources of competitive advantage. In the context of employee productivity,
the theory suggests that organizations can leverage their resources, including employee skills, technology
systems, and human resources, to enhance productivity and performance.
The Artificial intelligence (AI) can affect employee productivity in some aspects; organizational
growth and capability, employee well-being and safety, productivity gains, and learning accelerating. First,
(AI) has a relationship with organizational growth and HR capability. The study confirmed that HR capability
plays a vital role in driving sustainable organizational growth. The five AI application areas in HR, namely
precision hiring, training and development, performance management, employee engagement, and workforce
analytics, were found to support adaptability and human resource capability. These applications empower HR
professionals to make data-driven decisions, optimize processes, and create a more agile and productive
workforce.
Second, (AI) can improve employee well-being and safety. The integration of AI in HR practices
also offers substantial benefits in enhancing employee well-being and safety. By automating routine tasks and
providing instant support through chatbots and virtual assistants, AI systems alleviate the burden on HR teams,
allowing them to prioritize employee well-being initiatives. The study emphasized the importance of
leveraging AI applications to create a safe and supportive work environment.
Third, (AI) can enhance Productivity Gains from AI Adoption. The most compelling findings from
the study revolve around the significant productivity gains achieved through the adoption of AI-based
approaches. The research encompassed three distinct case studies, each focusing on different user groups and
domains. The results consistently demonstrated remarkable improvements in productivity, with more complex
tasks yielding greater gains. In the first case study, customer service agents utilizing AI tools experienced a
13.8% increase in their ability to handle customer inquiries per hour. The second case study, involving business
professionals writing routine documents, revealed a remarkable 59% increase in document production per hour
with AI assistance. Lastly, programmers coding projects with AI support were able to complete 126% more
projects per week. These findings highlight the transformative impact of AI on productivity across various job
roles and industries.
Lastly, (AI) can accelerate learning. In addition to productivity gains, the quantitative study identified
an interesting trend related to learning speed. In the customer support case study, agents who utilized AI tools
achieved competence levels that typically took eight months in just two months. This accelerated learning
highlights the potential of AI to expedite skill development and proficiency, benefiting both employees and

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organizations. By leveraging AI-driven tools, employees can acquire essential competencies more rapidly,
enabling them to contribute to the organization's goals effectively.
The previous relevant research that supports this study is elaborated in the following figure.
Table 1: Relevant Researches
Variables
Writer
Findings
Employee Skill and
Employee Productivity
(van Zyl, 2022), (Jacob, 2018)
(Dlamini et al., 2022), (Susilo,
2020)
Employee Skills have a significant effect on employee
performance
Employee Skill and Human
Resource Readiness
(Adeosun & Adegbite, 2022)
(Alqudah et al., 2022) (Zayed et al.,
2022)
High-performance HRM Practices are positively related to
readiness for change.
Human Resource Readiness
on Employee Productivity
(Laseinde et al., 2020), (Andrew,
2017)
Employee Readiness for organizational change was positively
and significantly correlated to the dependent variable
(employee performance).
Technology System on
Employee Productivity
(Murugesan et al., 2023), (Al-
Kharabsheh et al., 2023),
(Williams, 2019)
Technology System AI in HRM gives numerous benefits to the
HR department and employees
Technology System Significant to Employee Productivity
Technology System on
Human Resource Readiness
(Waddill, 2020)
(Sabrina Jahan, 2014), (Ben
Moussa & El Arbi, 2020),
Technology and Has Impact on Human Resources and
Business Professionals
Employee Skill and Employee Productivity
The findings of the study conducted by (van Zyl, 2022), (Jacob, 2018), (Dlamini et al., 2022), (Susilo,
2020) indicate that spatial and industrial dynamics have a positive influence on worker productivity levels. The
findings indicate that spatial and industrial dynamics have a positive influence on worker productivity levels.
Employee performance and productivity were impacted by the interaction between managers and staff. The
effectiveness of an employee is significantly influenced by their talents. Employee performance and
productivity were impacted by the interaction between managers and staff. The effectiveness of an employee
is significantly influenced by their talents.
Hypothesis 1: Employee Skill has a positive effect on Employee Productivity
Employee Skill and Human Resource Readiness
The study conducted by (Adeosun & Adegbite, 2022) revealed that A p-value greater than 0.05
suggests that the majority of HR professionals are not normally prepared for future employment in Nigeria.
Meanwhile, the study conducted by (Alqudah et al., 2022) (and Zayed et al., 2022) revealed that High-
performance HRM Practices are positively related to readiness for change. Human resource skill significant
adjustment predicted the dynamic capability of hospitality businesses.
Hypothesis 2: Employee Skill has a positive effect on Human Resource Readiness
Human Resource Readiness on Employee Productivity
The result of the study of (Laseinde et al., 2020), (Andrew, 2017) stated that employee productivity
and TQM procedures in a technologically advanced industry. The dependent variable (employee performance)
and employee readiness for organizational change were positively and strongly associated.
Hypothesis 3: Human Resource Readiness has a positive effect on Employee Productivity
Technology System on Employee Productivity
The study conducted by (Murugesan et al., 2023), (Al-Kharabsheh et al., 2023), (and Williams, 2019)
revealed that the System of Technology AI in HRM offers the HR division and employees many advantages.
AI utilization and corporate productivity have a positive and significant relationship. Employee productivity is
significantly impacted by technology systems.
Hypothesis 4: Technology System has a positive effect on Employee Productivity
Technology System on Human Resource Readiness
Based on the study conducted by (Waddill, 2020) Human resource and business professionals are
impacted by technology. It will support management in making wiser decisions. Individual and line manager
communications accelerated. HR departments grew more effective as self-service HR services replaced paper-
based transactions (Sabrina Jahan, 2014). The influence of HRIS on the individual innovation behavior of HR
professionals is noteworthy and favorable. Technology plays a vital role in the increase in productivity, which
consequently increases profitability, in the industrial sector with an export focus. Lastly, E-learning Learner
satisfaction is positively and significantly impacted by Actual Use

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Hypothesis 5: Technology System has a positive effect on Human Resource Readiness
Hypothesis 6: Human Resource Readiness mediates the effects on Employee Skills and Employee Productivity
Hypothesis 7: Human Resource Readiness mediates the effect of the Technology System on the Employee
Productivity
Research Method
The data for the study were collected through a questionnaire. The questionnaire included items
related to Employee Skills, Technology Systems, Human Resource Readiness, and Employee Productivity.
The participants, who were employees from various organizations, were asked to respond to the questionnaire
distributed by indicating their level of agreement or providing specific ratings on the given items.
Finding and Discussion
The result of the research is elaborated as follows:
Table. 2 The Result of Analysis SEM PLS
Variable
Path coefficients
coefficients
p-value
bottom
up
f square
Employee Skill -> Employee
Productivity
0.363
0.000
0.165
0.546
0.206
Employee Skill -> Human Resource
Readiness
0.495
0.000
0.326
0.665
0.384
Human Resource Readiness ->
Employee Productivity
0.400
0.000
0.218
0.588
0.209
Technology System -> Employee
Productivity
0.099
0.438
-0.149
0.348
0.011
Technology System -> Human
Resource Readiness
0.364
0.002
0.140
0.592
0.133
Based on the table above, it can be stated the direct effect of each variable. First, Hypothesis 1 (H1)
is accepted, that there is a significant influence of Employee Skill on Employee Productivity with path
coefficient (0.363) and p-value (0.000 < 0.05). Any changes to Employee Skills will increase Employee
Productivity. In the 95% confidence level, the influence of Employee Skill in increasing Employee Productivity
lies between 0.165 to 0.546. However, the existence of Employee Skill in increasing Employee Productivity
has a moderate influence on the structural level (f square = 206). The Employee Skill improvement program
must be considered very important where when there is an increase in Employee Skill it will increase Employee
Productivity up to 0.546. The result supports the research conducted by (Setyanti et al., 2022). It shows that
factors including job happiness, work ethic, and employee skills have a favorable and significant impact on
employee work productivity.
Second, Hypothesis 2 (H2) is accepted, there is a significant influence of Employee Skill on Human
Resource Readiness with a path coefficient (0.495) and p-value (0.000 <0.05). Every change in Employee
Skills has a significant effect on Human Resource Readiness. In the 95% confidence level, the influence of
Employee Skills in improving Human Resource Readiness lies between 0.326 to 0.665. However, the existence
of Employee Skills in improving Human Resource Readiness has a moderate influence on the structural level
(f square = 384). The Employee skill improvement program must be considered very important where when
there is an increase in Employee Skill it will increase Human Resource Readiness up to 0.665. The result
supports the research conducted by (Vrchota et al., 2020).
The third, Hypothesis 3 (H3) is accepted that there is a significant effect of Human Resource
Readiness on Employee Productivity with path coefficient (0.400) and p-value (0.000 <0.05). Every change in
Human Resource Readiness has a significant impact on Employee Productivity. In the 95% confidence level,
the influence of Human Resource Readiness in increasing Employee Productivity lies between 0.218 to 0.588.
However, the existence of Human Resource Readiness in increasing Employee Productivity has a moderate
influence on the structural level (f square = 209). The need for a Human Resource Readiness improvement
program is considered very important where when there is an increase in Human Resource Readiness increases
employee Productivity up to 0.588.
Fourth, Hypothesis 4 (H4) is rejected as there is a significant influence of Technology Systems on
Employee Productivity with a path coefficient (0.099) and p-value (0.438 > 0.05). Any changes to the
Technology System have no significant effect on Employee Productivity. In the 95% confidence level, the
influence of Technology Systems in increasing Employee Productivity lies between -0.149 to 0.348. However,
the existence of the Technology System in increasing Employee Productivity has a small/moderate influence

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at the structural level (f square = 11). The Technology System improvement program is considered not
important where when there is an increase in the Technology System increases Employee Productivity up to
0.348. Fifth, Hypothesis 5 (H5) is accepted, there is a significant influence of the Technology System on
Human Resource Readiness with a path coefficient (0.364) and p-value (0.002 < 0.05). Every change in the
Technology System has a significant effect on Human Resource Readiness. In the 95% confidence level, the
influence of the Technology System in improving Human Resource Readiness lies between 0.140 to 0.592.
However, the existence of the Technology System in increasing Employee Productivity has a small/moderate
influence at the structural level (f square = 113). The Technology System improvement program is considered
quite important where when there is an increase in the Technology System increases Human Resource
Readiness to 0.592. Table. 3 Result of Statistic Upsilon (v)
No
Effect
Statistic Upsilon (v)
Description
1
Employee Skill --> Human Resource Readiness-->
Employee Productivity
(0,495)2 x (0,364)2= 0,032
Low Effect
2
Technology System --> Human Resource Readiness-->
Employee Productivity
(0,363)2 x (0,364)2 = 0,017
Low Effect
Based on the table above it can be elaborated the research finding indirect effect on hypotheses 6 and
7. The interpretation of the statistical value on the mediation effect (v) refers to those recommended by
Ogbeibu et al. (2020), scale of 0.175 (high mediation effect), 0.075 (medium mediation effect), and 0.01 (low
mediation effect). Based on the calculation above, the role of human resources readiness in mediating the
indirect influence of Employee Skill / Technology Systems on Employee Productivity at the structural level is
relatively low.
Table. 4 Result of R-square
Variable
R-square
R-square adjusted
Employee Productivity
0,591
0,335
Human Resource Readiness
0,593
0,337
The statistical size of the R square describes the magnitude of variation in endogenous variables that
can be explained by other exogenous variables in the model. According to Chin (1998) the qualitative value of
R square interpretation is 0.19 (low influence), 0.33 (moderate influence), and 0.66 (high influence). Based on
the results of the processing above, it can be said that the amount of influence of Employee Skill and Human
Resource Readiness on Employee Productivity is 59.1% (close to high influence). The magnitude of the
influence of Technology Systems and moderation of Human Resource Readiness on Employee Productivity is
59.3% (moderate influence).
Conclusions
The findings of the quantitative study underscore the transformative potential of AI-based approaches
in enhancing employee productivity. With applications ranging from precision hiring to performance
management and workforce analytics, AI technologies empower HR professionals to make data-driven
decisions and optimize processes. The research findings emphasize the importance of HR readiness in
effectively integrating AI tools and highlight the significant productivity gains achieved through AI adoption.
As organizations navigate the digital landscape, AI catalyzes growth, efficiency, and inclusivity. By
fostering human-computer symbiosis, organizations can harness the unique strengths of both human and AI
systems, resulting in enhanced productivity and improved work quality. As the workplace continues to evolve,
AI-based approaches will play a pivotal role in shaping the future of work and driving sustainable
organizational success.
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