The role of digital management control systems on TQM practices and employee performance: an empirical study on Saudi service organizations

This study explores how Digital Management Control Systems (MCS) affect employee performance in Saudi service organizations by focusing on the role of Total Quality Management (TQM 4.0) that aims to address the growing need for digital integration in the Kingdom's evolving economy. Data was collected from 108 employees across different service organizations. Using statistical methods like linear regression and mediation analysis to test the relationships. Additionally, quality tools such as Pareto charts and fishbone diagrams were used to identify implementation barriers. The results show a full mediation effect. This means that digital systems do not improve performance directly instead, they improve quality practices first, which then strongly boosts employee performance. This suggests that quality management serves as the essential mechanism through which digital tools generate value. The study also found that employees’ resistance to change is the biggest challenge to digital transformation. This highlights the importance of addressing human factors alongside technical upgrades to achieve successful organizational change. Finally, the research proposes a digital dashboard mockup to help managers easily monitor performance and solve these issues. Also, a study proves that investing in technology is most effective when combined with strong quality management practices.

In recent years, the way organizations operate, monitor performance, and achieve strategic objectives has fundamentally transformed because of the rapid advancement of digital technologies. Digital transformation has become a critical priority for organizations, especially those looking to enhance their efficiency, improve service quality, and maintain competitiveness in increasingly dynamic environments. With this transformation, traditional monitoring mechanisms have evolved into Management Control Systems (MCS), which are intelligent digital platforms that support real-time decision-making, performance evaluation, and continuous improvement.

In Saudi Arabia, this transformation is strongly aligned with Saudi Vision 2030, which emphasizes digital innovation, organizational excellence, and the adoption of advanced technologies in both the public and private sectors. In addition, the Vision encourages organizations to integrate digital solutions, improve operational efficiency, strengthen institutional performance, and enhance a culture of continuous quality improvement. Consequently, integrating Digital Management Control Systems with Total Quality Management TQM 4.0 has become increasingly important for supporting sustainable organizational performance and enhancing employee productivity within Saudi service organizations.

Despite the growing adoption of digital technologies, organizations still face challenges in translating technological investments into measurable improvements in employee performance. While previous study has investigated digital transformation, TQM, and Management Control Systems separately, limited empirical research has examined how Digital MCS improve employee performance through the implementation of TQM 4.0 practices, particularly within the context of Saudi service organizations.

Therefore, this study seeks to address this research gap by examining the mediating role of TQM 4.0 in the relationship between Digital Management Control Systems and employee performance. To understand the practical impact of digital quality (Mostafa et al., 2025), conducted a quantitative case study at King Khalid University in Saudi Arabia. Surveying 350 staff members sought to measure how digital tools influence educational quality. The study found a clear result: digital infrastructure is not just a support tool but a primary driver for Total Quality Management (TQM). Specifically, they found that using data for decision-making significantly boosts continuous improvement and proving that digital transformation directly enhances the quality of education. On the other hand (Elg et al., 2021), explored the challenges of this transition through a qualitative multiple case study in Sweden by interviewing quality managers across different organizations; they investigated how digitalization affects their daily roles. The study found that many quality professionals deal with unclear responsibility and struggle to balance their traditional duties with new digital demands. This research highlights that the shift to digital quality is not just technical but requires a major cultural adaptation for employees. Another important study by (Ali et al., 2022) examined how TQM practices both soft and hard to help organizations become ready for Industry 4.0. The researchers used a quantitative method by sending an online survey to 209 small and medium companies in Malaysia. After analyzing the data, they are using statistical tools like SEM and Fin that Hard TQM, which focuses on digital systems and process control, is a major factor in making technology work effectively. The study also showed that top management support is essential to connect digital tools with quality goals. Moving to another study (Nguyen et al., 2023), performed a systematic review of over 200 articles to build a solid foundation for the future. Using Pareto Analysis, they identified the most critical practices needed for modern quality. The outcome was a complete TQM 4.0 framework that rests on five pillars, emphasizing that a Smart Factory cannot succeed without a Smart Organization that values human skills as much as technology. Focusing on the bigger picture (Aichouni et al., 2024), analyzed how quality management intersects with environmental responsibility. Through systematic review they demonstrated that integrating Industry 4.0 with TQM creates dynamic capabilities that help organizations adapt to change. So, the study concluded that digital tools are essential for sustainability, also enabling companies to reduce waste and improve environmental performance to operational efficiency. In terms of evolution (Liu et al., 2023), traced the journey from traditional TQM to Quality 4.0 by analyzing 46 key studies. Their review mapped out how quality practices are shifting from reactive inspection (fixing errors) to predictive analytics (preventing errors). They provided a clear research agenda that identified that while the technology exists, we still lack sufficient models that explain how to merge these advanced tools with standard management practices (Purba et al., 2025). focused on the internal mechanics of organizations by analyzing literature from 2021 to 2025. They examined the relationship between TQM and Management Control Systems, and the analysis concluded that these two systems must be integrated to achieve strategic alignment to ensure that the drive for quality is properly guided by organizational goals. To clarify how we measure success (Tambare et al., 2022), reviewed the technical standards and performance indicators used in Industry 4.0. They explained that with the massive amount of data coming from sensors, organizations need a structured performance measurement system like ISO 22400 to filter this information. The study points out that without these standards big data becomes meaningless information; with them, it becomes actionable intelligence for quality management (Hananta and Susyanti, 2024). Provided a broad overview of TQM implementation across different sectors, including healthcare and education. Their review confirmed that the primary benefits of TQM, like enhanced staff engagement and creativity, are widely applicable, and this study serves as a reminder that the principles of quality remain effective and relevant regardless of the industry or the technological tools being used. The evolving landscape of digital organizations necessitates a more sophisticated integration between Total Quality Management (TQM) and Management Control Systems (MCS). This synergy is not merely technical, as (Alsaqer et al., 2024) highlight; it establishes a structured framework that drives strategic alignment and operational efficiency. When these frameworks are further merged with Industry 4.0 technologies like the Internet of Things and Big Data, they foster the dynamic capabilities essential for organizational sustainability and adaptability (Aichouni et al., 2024) This digital shift is fundamentally redefining the nature of MCS itself, transitioning from rigid, traditional budgeting and reporting into the more flexible, predictive models proposed by (Syed, 2023) The relevance of such systems in today's business environment is paramount, particularly for ensuring that digital transformation efforts are effectively channeled toward sustainable decision-making and process control (Putri et al., 2024). Ultimately, as (Latif et al., 2023) demonstrate, the success of modern organizations relies on leveraging these integrated digital controls to achieve a measurable impact on sustainable performance and responsiveness (Bobe and Teklay, 2025). conducted a systematic literature review highlighting the relationships between TQM and MCS and how MCS can support TQM. The study proposed an integrative framework linking TQM principles with MCS mechanisms, showing that this integration enhances organizational performance, strategic alignment, and operational excellence, while reducing waste. Another literature review conducted by (Fähndrich, 2023) explored the impact of digitalization on management control systems. The study showed how digitalization can change control mechanisms like forecasting, reporting, budgeting, analytics, and automation. These mechanisms can be used to measure TQM, such as Big Data, Cloud Computing, and Artificial Intelligence. This transformation shifts management control from a reactive to a more proactive and predictive model (Cunha et al., 2023). Examined the effectiveness of performance measurement systems (PMS) in continuous improvement environments. Using the Ishikawa (6M) framework, finding 175 obstacles across six categories, where most challenges were system, indicators, and people, accounts for almost 70% of obstacles (van Elten and van der Kolk, 2025). Conducted an empirical study that was specific in the healthcare sector to examine the relationship between performance management and trust. The results showed that performance measurements were affected positively in employee performance when supported by high-quality metrics, such as accuracy, sensitivity, and verifiability. This study also highlighted the role of cultural control in increasing trust within organizations.

Similarly (Pangaribuan and Nuryati, 2023), focused on the role of TQM and performance measurement systems in the education sector. The paper found that TQM practices and especially reward systems significantly enhance productivity and employee motivation, and quality management principles remain effective across different sectors (Fontalvo Herrera et al., 2025). Applied a quantitative approach using Six Sigma and statistical quality control in the hospitality sector. Their findings showed that monitoring performance using statistical indicators enables organizations to achieve high-quality service levels in the upper five. Moreover, it is supported by continuous improvement by informed decision-making.

Overall, the literature confirms that the integration of digital technologies with TQM and MCS enables organizations to move from reactive quality control to data-driven and predictive management systems. This study aims to address this gap. TQM 4.0 confirms real-time monitoring, continuous improvement, and data-driven decision-making, supported by Industry 4.0 technologies such as IoT, Big Data, and AI. However, despite these advancements, there is still a lack of empirical studies about how Digital MCS directly influences TQM practices and employee performance, particularly in the context of Saudi service organizations.

Objectives

The main objective of the study is: To examine the impact of Digital Management Control Systems (MCS) on employee performance.

To investigate the mediating role of TQM 4.0 practices in the relationship between Digital MCS and performance.

To identify and diagnose the primary challenges and barriers faced by employees in Saudi service sectors when implementing digital quality systems.

To propose a digital dashboard mockup that integrates SPC principles for continuous performance monitoring.

Research hypotheses

Based on the literature review and the identified research gap regarding the digital transformation of quality management in Saudi service organizations, this study proposes the following hypotheses:

H1: Digital Management Control Systems (MCS) have a significant positive impact on the implementation of TQM 4.0 practices.

H2: The implementation of TQM 4.0 practices has a significant positive impact on employee performance.

H3: TQM 4.0 practices positively mediate the relationship between Digital MCS and employee performance.

Figure 1 illustrates the conceptual framework of this study, presenting the proposed relationships and the mediation effect between Digital MCS, TQM 4.0 practices, and employee performance.

Research design

The research is designed to examine the relationships between Digital Management Control Systems (Digital MCS), TQM 4.0 practices, and employee performance in Saudi service organizations. This study adopted TQM 4.0 as its core method, integrating Statistical Process Control (SPC) tools for diagnostic and analytical purposes. A survey was designed to standardize measurement of perceptions and practices across a various set of organizations and enable testing the proposed hypotheses (H1-H3).

Population and sampling

The study targets the employees who are working in Saudi service organizations and dealing directly with digital performance monitoring (e.g., dashboards, KPI reports, automated monitoring).

To ensure relevance to the research context, the survey started with screening questions to confirm the target employees; the first question was (1) employment in a Saudi service organization, and question (2) was direct use of digital dashboards or KPI reports in their daily work. To measure digital control practices across service organizations, we used a non-probability convenience sampling strategy due to practical access constraints. Furthermore, this sampling method is particularly justified in this study as it allows for the efficient collection of specific data from targeted employees who actively interact with digital dashboards across various dispersed organizations.

The study aimed to collect more than 100 valid responses to support reliable statistical analysis. As a result, the final sample size included 108 valid responses collected through this convenience sampling method.

Questionnaire design and measures

A questionnaire was developed and organized on validated scales from prior studies: (1) demographics and screening questions, (2) Digital MCS, (3) TQM 4.0 practices, (4) employee performance, and (5) implementation challenges.

Used a 5-point Likert scale to measure all items:

1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.

Digital MCS (independent variable)

Digital Management Control Systems (Digital MCS) refer to the use of digital technologies such as dashboards, automated key performance indicators (KPIs), analytics, and alerts to monitor and control performance. This study was measured using adapted items reflecting the diagnostic and interactive use of performance, adopted by Henri (2006), Simons (1995). In this paper, Digital MCS are measured using five Likert-scale items in Saudi service organizations.

TQM 4.0 practices (mediator)

TQM 4.0 represents the integration of traditional quality management principles with digital technologies, real-time, and system connectivity (Sony et al., 2020) It emphasizes data-driven decision-making, enabling system integration, and predictive problem prevention based on Nguyen et al. (2023) Aligned with Industry 4.0, its transformation from traditional quality management toward digitally enabled continuous improvement systems. TQM 4.0 is measured using four Likert-scale items.

Employee performance (dependent variable)

Employee performance refers to the degree to which employees achieve their work effectively, including improvements in accuracy and productivity. Employee performance is measured using four Likert-scale items adapted by (Hananta and Susyanti, 2024).

In digital transformation, performance is affected by the availability of real-time data, performance feedback, and decision- supports systems.

Data analysis

Data analysis was conducted in multiple stages to ensure systematic data collection. The steps were applied: Descriptive statistics were used in this study to summarize the data, including frequencies and percentages.

Reliability analysis was conducted using Cronbach's Alpha to assess internal consistency.

Composite scores for each construct were calculated by averaging the items.

Regression analysis was performed to examine relationships between variables.

Mediation analysis was used to test the indirect effect between variables.

Pareto analysis was used to identify the most significant challenges.

A fishbone diagram was developed to analyze root causes of the identified issues.

Integration of quality tools

In addition, TQM 4.0 is used as a methodology, and Statistical Process Control (SPC) for diagnostic analyses. To identify the most critical challenges based on their frequency of occurrence, we used Pareto analysis, which helps prioritize issues according to their impact. A fishbone diagram was developed to analyze the root causes of the challenges by categorizing them into people, technology, measurement, and management. Furthermore, a Scatter diagram was used to verify the correlations between the main variables.

Finally, the study concludes with a Proposed Digital Mockup that serves as a practical application, incorporating automated Control Charts for real-time monitoring. To ensure internal consistency among the items, the reliability of the measurement instrument was assessed using Cronbach's Alpha. The value above 0.70 is considered acceptable for this study to ensure content validity by adapting questionnaire items from validated scales in previous studies and aligning them with the study context.

Diagnostic quality analysis

Root cause analysis of implementation barriers

To find out what stops companies from successfully using digital systems, TQM 4.0 analyzed the responses collected from the survey using a Pareto chart (Figure 2). This chart is based on the 80/20 rule, which means that solving the biggest few problems brings the best results. Based on the participants' answers, the chart shows that Resistance to Change and Lack of System Integration are the top reported issues. These are followed by Lack of Technical Training and Data Inaccuracy these four problems cause about 80% of all the difficulties faced by the respondents. Therefore, to improve employee performance, the managers must focus their time and resources on solving these four main problems first.

Diagnosing the root causes of implementation barriers

Following the Pareto analysis, an Ishikawa Fishbone diagram was developed to deeply diagnose the root causes of Resistance to Change which shows as the most critical implementation barrier. This specific barrier was chosen based on the Pareto principle to focus on the most critical issue. The subcauses in the Fishbone diagram were identified through researchers brainstorming and supported by the literature review on digital transformation challenges.

The root causes were systematically categorized into four main areas: Technology, People, Management, and Measurement. Technology issues primarily include system complexity and a lack of integration, while human factors are dominated by a lack of technical training. Management barriers relate to the unclear digital strategies. In the measurement category a significant gap was identified regarding the weak utilization of Statistical Process Control tools, which makes it difficult to simultaneously monitor quality practices and employee performance, leading to ambiguity and employee resistance. By mapping these interconnected root causes, organizations can design targeted solutions to ensure a smooth digital transition.

This section presents the findings of statistical analysis conducted on the primary data collected through the structured questionnaire. The survey was carefully designed to evaluate participants' perceptions regarding Digital Management Control Systems (MCS), the implementation of TQM 4.0 practices, and their subsequent impact on Employee Performance (EP). The data analysis process is structured into three main stages: first, providing a descriptive overview of the respondents' demographic characteristics; second, verifying the reliability and internal consistency of the questionnaire items and finally, testing the research hypotheses through regression and mediation analyses to draw meaningful conclusions.

Descriptive statistics

Figure 3 shows the distribution of the study sample across different service sectors in Saudi Arabia. The total number of valid responses was 108. The data shows that the Government Services sector had the highest representation, accounting for 26.9% (n = 29) of the total respondents. This is followed by the Financial & Banking sector with 16.7% (n = 18) and the Tourism & Hospitality sector with 14.8% (n = 16). The presence of respondents from diverse sectors such as healthcare, telecommunications, and education enhances the generalisability of findings in the Saudi organizations' service.

Reliability analysis

To test the reliability of the survey, Cronbach's Alpha was calculated for the three main variables. As shown in Table 1, all values are greater than the acceptable level of 0.70. Specifically, Digital MCS scored [0.931], TQM 4.0 scored [0.911] and Employee Performance scored [0.944]. This proves the survey questions are highly reliable and consistent, and we can safely proceed to test the study hypotheses.

Hypothesis testing

Before conducting the regression analysis, statistical assumptions were tested and met. The Variance Inflation Factor (VIF) values for all variables were strictly less than 3.0, indicating no multicollinearity issues. The data exhibited normal distribution, with skewness and kurtosis values falling within the acceptable range of −2 to +2.

As shown in Tables 2, 3, the results of the simple linear regression analysis indicate a significant positive impact of Digital Management Control Systems on Employee Performance. The coefficient of determination (R 2 = 0.633) reveals that Digital MCS explains 63.3% of the variance in employee performance. The path coefficient is statistically significant (Estimate = 0.797, p <.001). These findings provide strong empirical evidence to support first hypothesis H1, confirming that higher levels of digital system integration led to enhanced performance levels within the surveyed Saudi service organizations.

Tables 4, 5 present the results of the mediation analysis conducted to test H2 and H3. The results in Table 5 show that Digital MCS has a significant positive impact on TQM 4.0 practices (Estimate = 0.861, p <.001), and TQM 4.0 practices in turn significantly enhance Employee Performance (Estimate = 0.723, p <.001) thus supporting Hypothesis H2.

Regarding the mediation effect, Hypothesis H3 Table 5 shows that the indirect effect is highly significant (Estimate = 0.623, p <.001). Interestingly, the direct effect of Digital MCS on EP became nonsignificant (p = 0.090) when the mediator TQM 4.0 was included in the model. This indicates a state of Full Mediation meaning that Digital MCS does not improve performance directly but rather through its capability to effectively implement TQM 4.0 practices. As a result, Hypothesis H3 is fully supported, highlighting the critical role of quality practices as a bridge between digital tools and organizational excellence.

The proposed conceptual dashboard

Following the root cause analysis, Figure 4 presents a conceptual mockup of the Unified TQM 4.0 & Performance Portal, designed specifically for Saudi service organizations. This proposed digital solution directly translates the studying of statistical findings into a practical management tool. The dashboard layout reflects the proven full mediation model through three interconnected pillars data input representing Digital MCS and addressing Lack of System Integration and the Quality Engine operationalizing TQM 4.0 practices, also tracking Employee Performance and training needs. Furthermore, its user-friendly interface is specifically designed to minimize System Complexity and mitigate Resistance to Change which were identified as primary implementation barriers in the earlier analyses. By providing real-time and integrated visibility across these domains, the dashboard empowers decision-makers to effectively leverage digital systems for sustained quality and performance excellence.

Overall, the statistical results and quality analyses presented in this section strongly confirm that integrating Digital MCS with TQM 4.0 practices is essential for enhancing employee performance. The proposed digital dashboard provides practical solutions to overcome implementation barriers, particularly resistance to change. Most importantly, these findings directly align with the strategic objectives of Saudi Vision 2030, which emphasizes digital transformation and operational excellence within the Kingdom's service sectors.

The findings of this study provide a comprehensive understanding of the interplay between Digital Management Control Systems (MCS), TQM 4.0 practices, and employee performance within the Saudi service sector. The results of the hypothesis testing and root cause analysis offer several critical insights.

The critical mediating role of TQM 4.0

The most striking result of this research is the Full Mediation effect of TQM 4.0 practices on the relationship between Digital MCS and Employee Performance (H3). While the initial regression model showed that Digital MCS significantly predicts performance (R^2 = 0.633), this direct impact disappeared once TQM 4.0 was introduced. This interprets that technology alone such as digital dashboards and automated monitoring does not directly boost employee productivity. Instead, digital systems act as an “enabler” that enhances quality practices (like real-time data accuracy and process control), which in turn drives performance. This aligns with the “Smart Organization” concept proposed by Nguyen et al. (2023), emphasizing that digital tools only generate value when embedded within a solid quality management framework This means that investing in technology in Saudi service sectors might not succeed without first building a strong quality culture. Therefore, organizations must focus on human factors like training and change management along with upgrading their IT systems to actually improve employee performance.

Alignment with Saudi vision 2030 and previous studies

Our findings support Mostafa et al., 2025, who highlighted that digital infrastructure is a primary driver for quality in Saudi organizations. The high mean scores in our descriptive statistics suggest that employees in the government and banking sectors (representing 43.6% of our sample) are increasingly reliant on digital KPIs. Furthermore, the fact that 67.6% of respondents felt motivated by digital goals contradicts earlier traditional management theories that viewed digital monitoring as a source of stress. Instead, in the context of Saudi's digital transformation, it appears to provide clarity and structural support, echoing the findings of (Ali et al., 2022) regarding the necessity of “Hard TQM” (digital process control) for organizational readiness.

Overcoming implementation barriers: the human element

Despite the statistical success of the models, the Pareto analysis (Figure 2) reveals that the path to digital quality is not without hurdles. Resistance to Change was identified as the most significant barrier (36.7%). When diagnosing this through the Fishbone diagram (Figure 5), it becomes evident that the resistance is rooted in “People” and “Management” factors rather than technical failure. System complexity and a lack of technical training lead to psychological barriers. This interpretation explains why (Elg et al., 2021) found that quality managers struggle during digitalization; it is not the data that is the problem, but the cultural adaptation required to handle it.

Practical implications: the dashboard as a solution

The proposed Conceptual Dashboard (Figure 4) serves as a practical response to the identified barriers. By integrating Digital MCS and TQM 4.0 into a single “Quality Engine,” organizations can reduce the “Lack of System Integration” (35.8%) cited by participants. Providing a user-friendly interface addresses the “System Complexity” found in the Fishbone analysis, ultimately facilitating a smoother transition toward the digital goals of Saudi Vision 2030.

This study examined the critical role of TQM 4.0 in mediating the relationship between Digital Management Control Systems and employee performance within Saudi service organizations. The findings confirm that while digital transformation provides the necessary infrastructure for monitoring and control, it is the integration of quality management practices that ultimately translates these technological capabilities into enhanced organizational performance. The study successfully validated that a “digital-first” approach must be balanced with a “quality-centric” culture to achieve the objectives of Saudi Vision 2030.

Managerial implications and recommendations

Based on the empirical evidence, several recommendations are proposed for decision-makers in the service sector: Strategic Integration: Managers should not implement digital dashboards as isolated IT projects. Instead, they should be integrated into the core TQM framework to ensure data accuracy and relevance.

Focus on Human Capital: To mitigate the 36.7% resistance to change identified, organizations must invest in continuous technical training and foster a culture that embraces digital quality tools.

Real-Time Performance Feedback: Utilizing the proposed Unified TQM 4.0 Portal can provide employees with clear real-time KPIs which was shown to increase motivation and role clarity for 67.6% of the participants.

Limitations and future research

Despite its insights, this study has limitations that provide avenues for future inquiry: Sample Scope: The study focused on a sample of 108 employees within the Saudi service sector. Although this sample size was sufficient for the applied statistical models, future research should expand the sample to a larger number of participants to enhance the generalizability of the findings across different organizational levels and regions.

Cross-Sector Analysis: While the findings are highly relevant to service organizations, future studies should explore how this mediation model performs in the Manufacturing or Healthcare sectors, where operational constraints differ.

Technological Evolution: As AI and Machine Learning become more prevalent, future research should investigate how these advanced tools can further automate TQM 4.0 practices to predict and prevent quality issues before they occur.