Development of a Web-Based KPI Evaluation System Using SAW and Design Science Research ^†

Abstract

Key Performance Indicators (KPIs) play a crucial role in systematically evaluating employee performance. This study integrates the Simple Additive Weighting (SAW) method with Design Science Research (DSR) to develop and validate a web-based KPI assessment system for IT Support staff. This system categorizes performance into three key areas: supervisor evaluation, routine tasks, and request-based tasks. SAW is chosen due to its effectiveness in multi-criteria decision-making and the objective ranking of employees. This research presents a scalable and practical solution for performance management through a structured web-based application. A simulation utilizing Bootstrap 5.1 incorporates real-time monitoring, ticket tracking, and KPI dashboards, enhancing data visualization and reporting. The results indicate that the proposed system enhances transparency, minimizes evaluation bias, and supports objective, data-driven decision-making. By integrating SAW within a structured decision support system, this approach fosters standardized performance assessments, ensuring fairer and more consistent employee evaluations. Future work should focus on real-world implementation and empirical validation.

1. Introduction

Employee performance evaluation is critical to ensuring organizational efficiency, particularly in IT Support roles. These roles require a combination of technical expertise and problem-solving skills to address both routine and request-based tasks. However, existing evaluation systems often lack standardization and transparency, leading to inefficiencies and subjective assessments. Simple Additive Weighting (SAW) method is effective in multi-criteria decision-making and provides objective employee ranking [1].

Recent studies highlight the growing need for objective and systematic KPI-based assessment systems. The SAW method has been demonstrated to be effective in ranking employee performance based on the weighted criteria [2]. Moreover, integrating a systematic decision support system (DSS) enhances the evaluation process by reducing bias and improving consistency [3]. Additionally, the use of Bootstrap methodology in statistical analysis has been recognized as an effective resampling technique to enhance data-driven decision-making [4]. By leveraging SAW and DSR methodologies, this study aims to improve fairness, scalability, and usability through a web-based simulation.

2. Related Work

Several methodologies have been used for employee performance evaluation, including weighted scoring and multi-criteria decision-making methods. Design Science Research has been widely used as a systematic framework for artifact development and evaluation in information systems [5]. while Melo et al. explored the use of SAW for evaluating staff performance in educational institutions, demonstrating its accuracy and efficiency [2], recent works also highlight the combination of SAW with other decision-making techniques, such as TOPSIS, to enhance decision-making in performance evaluations [3] Furthermore, Bootstrap techniques have been widely adopted in performance data analysis to improve the accuracy of statistical estimations [4], previous research also explored employee evaluation using the SAW method in various organizational contexts, showing its effectiveness in ranking and decision support [6,7,8]. Moreover, fuzzy approaches and advanced multi-criteria methods have been proposed to improve evaluation accuracy [9,10,11], a systematic literature review grounded in Human Performance Technology further contextualizes KPI-based employee evaluation in practice [12].

3. Methodology

This study employs a structured methodology to develop and validate a web-based KPI evaluation system for IT Support staff. The methodology consists of three primary components: the Simple Additive Weighting (SAW) method for performance assessment, the Design Science Research (DSR) approach for system development, and the Bootstrap methodology for data resampling and validation. Each component plays a critical role in ensuring an objective, systematic, and scalable performance evaluation framework.

3.1. SAW Method

The Simple Additive Weighting (SAW) method is widely recognized for its effectiveness in multi-criteria decision-making, particularly in ranking alternatives based on weighted criteria [1]. In this study, SAW is utilized to assess employee performance through a structured evaluation process that involves two key steps: the normalization of KPI scores and weighted summation.

3.1.1. Normalizing KPI Scores

Each KPI score is normalized to ensure comparability across different criteria using the following formula:

$$Rij={\displaystyle \frac{Xij}{max\left(Xij\right)}}$$

• Rij: Normalized score for criterion j of alternative i;
• Xij: Actual score for j of alternative i;
• max(Xij): Maximum score across all alternatives for criterion j.

3.1.2. Weighted Summation

After normalization, the final performance score for each employee is determined through weighted summation, calculated as follows:

$$Si=\sum _{j=1}^{n}Wj \times Rij$$

• Si represents the final composite score for employee i;
• Wj is the weight assigned to criterion j;
• Rij is the normalized score for criterion j of employee i.

3.1.3. Example Calculation

To illustrate the application of the SAW method,

Table 1. KPI (Key Performance Indicator) Weighted Score Calculation Table.

KPI	Weight (Wj)	Score (Xij)	Max Score (Xij)	Normalized (Rij)	Weighted Score (Wj × Rij)
Politeness	0.20	80	90	${\displaystyle \frac{80}{90}}=0.89$	0.20 × 0.89 = 0.178
Decision-Making	0.25	90	100	${\displaystyle \frac{90}{100}}=0.90$	0.25 × 0.90 = 0.225
Problem-Solving	0.25	85	95	${\displaystyle \frac{85}{95}}=0.89$	0.25 × 0.89 = 0.223
Routine Task	0.20	75	80	${\displaystyle \frac{75}{80}}=0.89$	0.20 × 0.94 = 0.188
Request-Based Tasks	0.10	70	75	${\displaystyle \frac{70}{75}}=0.93$	0.10 × 0.93 = 0.093

presents an example calculation for Employee A, considering five KPIs: politeness, decision-making, problem-solving, routine tasks, and request-based tasks.

• Final score calculation:

The final score Si is calculated by summing the weighted score:

Si = 0.178 + 0.225 + 0.223 + 0.188 + 0.093 = 0.907 → 0.91

3.2. Design Science Research (DSR) Approach

DSR is used to systematically design and validate the KPI evaluation system. Prior research has emphasized the need for a structured framework in performance evaluation systems to ensure transparency and reliability [13].

3.2.1. Problem Identification

This study begins by analyzing the existing challenges in IT Support performance evaluation, particularly subjectivity, lack of standardization, and inefficiency in assessment processes.

3.2.2. Objective Definition

Based on the identified challenges, the primary objectives of the KPI evaluation system are defined, including:

• Standardizing the evaluation criteria to minimize subjectivity;
• Enhancing transparency in employee performance assessments;
• Improving efficiency by automating data collection and reporting;
• Providing real-time performance insights for better decision-making;
• Ensuring scalability and adaptability of the evaluation system to meet organizational needs.

3.2.3. System Design and Development

The KPI evaluation system is designed and developed using the Bootstrap 5.1 framework (Bootstrap Team, San Francisco, CA, USA), incorporating the following features:

• KPI dashboard with progress bars and performance metrics;
• Ticket and work order management to track real-time employee tasks;
• Automated performance reporting for decision-makers;
• Interactive UI/UX elements to enhance user experience.

3.2.4. Demonstration and Validation

The system undergoes rigorous testing through:

• Hypothetical performance scenarios to assess output accuracy;
• Stakeholder feedback analysis for usability evaluation;
• Limited user testing to validate system efficiency and functionality.

3.3. KPI Structure

The KPI structure is divided into:

• Supervisor assessment: politeness, decision-making, problem-solving.
• Routine tasks: preventive maintenance, daily reports, network check, attendance.
• Request-based tasks: work orders and user-generated tickets.

3.4. Bootstrap Methodology

To improve data accuracy and reliability, the Bootstrap resampling method is employed. This approach enhances performance assessment by:

• Reducing the impact of outliers on employee performance data;
• Providing robust confidence intervals for decision-making;
• Ensuring stability in employee performance trends over time [4].

The distribution of weights assigned to each KPI is presented in

Table 2. List of Key Performance Indicators (KPIs) and Assessment Weights.

KPI	Description	Weight (%)
Politeness	Level of politeness	20%
Decision-Making	Ability to make decisions	25%
Problem-Solving	Effectiveness of problem solving	25%
Routine Tasks	Routine tasks such as daily reports	20%
Request-Based Task	Tasks on request	10%

. This table illustrates the distribution of weight for each KPI used in the evaluation system.

As shown in Figure 1, the system’s workflow includes KPI input, normalization, weighting, performance monitoring, final score calculation, dashboard visualization, and report generation.

4. Result and Discussion

The development of the proposed KPI evaluation system, which integrates the Simple Additive Weighting (SAW) method within a web-based environment, has shown significant improvements in real-time monitoring, assessment transparency, and decision-making efficiency. The simulation results confirm that this system effectively reduces evaluation subjectivity while enhancing organizational efficiency, aligning with findings from previous studies on web-based KPI assessment systems [14]. Moreover, the incorporation of Bootstrap methodologies in performance data analysis further improves the reliability and robustness of the assessment framework [4]. The effectiveness of employee performance assessment systems has also been analyzed in prior research, confirming the managerial value of structured evaluation [15].

4.1. Simulation Scenarios

To validate the system’s functionality, three performance scenarios were simulated: High Performance, Medium Performance, and Low Performance. Each scenario represents a different level of employee effectiveness, with KPI scores normalized and weighted before computing the final score. The results are summarized in

Table 3. Simulation scenarios and performance scores.

Scenario	Politeness	Decision-Making	Problem-Solving	Routine Tasks	Request-Based Tasks	Final Score
High Performance	90	100	95	80	75	1.00
Medium Performance	75	85	80	70	60	0.84
Low Performance	60	70	65	55	45	0.68

.

This table illustrates simulated scenarios with varying performance levels to validate the proposed KPI system. Each KPI is scored and normalized, followed by weighted calculations to determine the final score for each scenario.

4.2. System Design and Features

The proposed web-based KPI evaluation system was developed using Bootstrap 5.1 and integrates multiple interactive features to enhance user experience and decision-making efficiency. The key features include:

Dashboard: A real-time KPI visualization panel displaying performance metrics and progress indicators;

• The dashboard serves as a real-time KPI visualization panel displaying performance metric and progress indicators (Figure 2);

• The system also provides a ticket submission page that allows users to input their requests efficiently (Figure 3);

• Performance monitoring and supervisor evaluation: A scoring mechanism that allows supervisors to assess employee performance in a structured and systematic manner (Figure 4).

These system design elements align with the research findings of Kurniawan et al. [16], who emphasize that integrating performance dashboards into KPI systems enhances evaluation efficiency by offering real-time data visualization and trend analysis. Optimization approaches from other domains, such as improved ABC algorithms for network coverage [17], also demonstrate how metaheuristic techniques can complement decision-making systems to enhance reliability.

4.3. Simulation and Validation

To assess the practical effectiveness of the proposed system, performance scores were calculated for individual employees using the SAW methodology.

Table 4. Employee performance scores.

Employee	Politeness	Decision-Making	Problem-Solving	Routine Tasks	Request-Based Tasks	Final Score
Employee A	80	90	85	75	70	0.91
Employee B	70	80	75	65	55	0.79

presents the final scores for two sample employees.

Final scores are calculated using the SAW method based on the weight distribution outlined (Table 4).

The final scores confirm that employees with higher KPI ratings receive correspondingly higher performance scores, validating the effectiveness of SAW as an objective ranking mechanism. These findings are consistent with prior research by by Rizan et al. [18] and Fatkhudin et al. [6], who demonstrated that SAW-based evaluation systems enhance objectivity and reduce subjectivity in performance assessments.

4.4. Comparative Analysis: Before and After Implementation

A comparative study was conducted to analyze the effectiveness of the web-based KPI evaluation system before and after implementation. Key performance aspects such as transparency, efficiency, and subjectivity were examined, as presented in

Table 5. Comparison before and after implementation.

Aspect	Before Implementation	After Implementation
Transparency	Low	High
Assessment Efficiency	Slow	Fast
Subjectivity	High	Low

.

This table compares key aspects of the evaluation system before and after the implementation of the proposed KPI system (Table 5).

These results indicate that the proposed system significantly improves evaluation transparency, enhances assessment speed, and reduces subjective bias. The findings align with prior research by Parseh and Asplund [19], who argue that automated KPI-based evaluation frameworks improve consistency and efficiency in organizational performance assessments.

4.5. Discussion and Implications

The findings from this study highlight several key implications for employee performance assessment and organizational decision-making, as follows:

4.5.1. Enhanced Objectivity

The integration of SAW and real-time KPI dashboards minimizes subjective bias, ensuring standardized performance evaluations.

4.5.2. Improved Efficiency

The transition from manual to automated KPI-based evaluations significantly reduces evaluation time and administrative workload.

4.5.3. Data-Driven Decision-Making

The combination of SAW and Bootstrap enhances the reliability of performance assessments, enabling more accurate employee ranking and reward allocation.

Furthermore, these results align with prior research by Terra et al. [20], which demonstrated that decision support systems incorporating structured ranking models enhance managerial decision-making efficiency. These results are in line with prior studies that highlighted the role of performance appraisal and structured evaluation in improving productivity and fairness [21,22,23,24,25,26].

4.6. Limitations and Future Research Directions

Future research can also incorporate coaching-oriented performance evaluation [21] or measurement technologies in other industrial settings [22] to broaden the applicability of KPI assessment systems.

Despite its effectiveness, this study has certain limitations that should be addressed in future research:

• The scope of this study is limited to IT Support employees, and further research is needed to assess its applicability across diverse organizational roles;
• While the simulation results validate system functionality, real-world testing with actual employee data is necessary to measure long-term adoption and impact;
• Future studies should explore hybrid decision-making models, such as SAW–TOPSIS or AHP–SAW, to further optimize performance evaluations.

5. Conclusions

This study integrates SAW and DSR methodologies to develop a comprehensive KPI-based performance evaluation system for IT Support staff. The simulated system showcases essential functionalities, including KPI dashboards, ticket management, and real-time monitoring. The validation through simulation demonstrates its effectiveness in enhancing transparency and reducing bias in employee evaluations.

Although this study is limited to simulations using Bootstrap 5.1, future research should focus on real-world implementation and empirical validation to assess its practical impact more comprehensively. Additionally, further development should explore the deployment of the system in an actual IT Support environment, assessing its effectiveness in real operational settings and gathering feedback from end-users for potential refinements.