Achieving World Class Manufacturing Excellence: Integrating Human Factors and Technological Innovation

Abstract

This paper explores the impact of integrating human factors—such as ergonomics and employee involvement—with technological innovations on manufacturing performance, sustainability, and productivity. It develops a holistic framework that organizations can adopt to achieve world-class manufacturing excellence, emphasizing the synergistic relationship between human factors and technological advancements, and highlighting their combined effect on fostering operational improvements and sustainability. The research adopts a robust quantitative approach, utilizing comprehensive surveys to analyze the individual and combined effects of human factors and technological innovation on manufacturing outcomes. The methodology involves data-driven analysis supported by statistical models, ensuring a rigorous examination of the relationships between variables. The findings show that integrating human factors with technological innovations significantly enhances both industrial production levels and operational efficiency, underscoring the importance of this integration for promoting sustainable manufacturing processes and maintaining competitiveness in the face of rapid technological changes. One key limitation of this research is the generalizability of findings across diverse industry contexts, as the pace of technological evolution and the emergence of new technologies may affect the applicability of the proposed framework. Future studies should investigate the evolving nature of human–technology integration in various sectors. The paper provides practical strategies for manufacturing managers and policymakers to employ when integrating human factors with technological innovations, outlining a strategic implementation framework aimed at improving manufacturing performance, fostering innovation, and ensuring a competitive edge within the dynamic context of Industry 4.0. This research contributes to the existing literature by offering a comprehensive, synergistic approach to the integration of human factors and technological innovation, providing valuable insights for industry practitioners seeking to implement sustainable manufacturing practices and achieve operational excellence.

1. Introduction

Seeking world-class manufacturing excellence is a key objective for organizations that are looking to compete effectively in today’s volatile and rapidly changing manufacturing environment. World-class manufacturing has resulted in the breaking down of competitive performance ranks similar to the global standards for all competitors in quality, cost, delivery, flexibility and innovation [1]. In order to make these strides in quality, manufacturers need smart ways to infuse human factors and innovation into their operational DNA.

Manufacturing human factors offer these and a whole host of other considerations, but generally include ergonomics, workplace safety, employee morale or motivation, as well as the general welfare (both physical and mental) of workers. This potential has to be understood in terms of offering the best human computer interfaces as well as reducing the error margins. Through this, the degree of productivity is enhanced. Various studies have shown that placing a greater emphasis on human elements will significantly enhance the service delivered by manufacturing by increasing productivity, decreasing worker turnover and enhancing employees’ satisfaction [2]. By so doing, manufacturers allow employees to be more flexible, and consequently manufacturers can tap into a larger contingent worker market, if, when designing operating environments, they meet both the requirements of legacy talent and newer generations.

On the other hand, innovation means the implementation of new technology, practices and products by moving the manufacturing sector ahead of its rivals. Current manufacturing experiences a range of changes influencing its practices through the application of technology that enhances production accuracy, speed, and customization [3]. This includes the automation, robotics, and digitalization of Industry 40. Other related innovations include process management techniques like lean manufacturing and six sigma [4], which help enhance operational excellence by eradicating wastes in process flows.

Human factors, as well as innovations, play a significant role in enhancing the production of world-class manufactured products. These aspects can only be incorporated by taking an integrated approach in which the technological and human factors of manufacturing are taken into consideration. For instance, AMT, which is the use of computerized equipment in order to increase efficiency, depends not only on the efficiency of the equipment, but also the receptiveness of the personnel [5]. Complementary to the solutions proposed above, training and change management offers a suitable approach that enables employees to enhance their capacity for undertaking new responsibilities in a constantly expanding organizational environment that is characterized by automation and digitization.

Also, there is a need to support permanent development and innovation within the organization to achieve effective results. It recommends fostering innovation, backing multi-partner research and development projects, and keeping the management and employees of the organization in communication [6]. Employees in the manufacturing sector play a pivotal role in transforming innovation from conceptual strategies into practical, sustained improvements. By fostering a culture of continuous learning and adaptability, the workforce ensures that innovation is not just a top-down initiative but becomes an integral part of everyday operations, enhancing both technological and operational excellence.

Thus, the existing research findings also confirm that, in comparison to other companies, manufacturing organizations that are characterized by high levels of management and innovation for human factors are likely to be more effective in terms of their manufacturing performance. For instance, the Toyota motor company in Japan can be evaluated as a company that has demonstrated impressive performance in the production of automobiles and has placed a lot of emphasis of the lean manufacturing system, and employee involvement and improvement initiatives [7]. Similarly, organizations that have implemented long-term ergonomic programs have observed lower incidences of workplace accidents/ill health and the costs associated with them, as well as increased productivity and quality output [8].

While the importance of human factors and technological innovation is acknowledged in prior studies, this research moves beyond general observations by quantifying their combined impact on manufacturing performance using a robust statistical approach. The framework developed offers a step-by-step methodology that manufacturers can adopt to tailor their strategies for operational efficiency and sustainability. This contributes new, actionable insights to the literature, particularly for managers seeking to align human-centric design with advanced manufacturing technologies.

Consequently, Human Factors and the Maintenance of Innovation is an important factor contributing to the achievement of the vision of world-class manufacturing. In this way, manufacturers ensure that the technological environment of production is not opposed to human interests but enhances the quality, efficiency, and stability of the production system. This paper’s purpose is to explain the relationship between human factors and innovation to use the combination of these concepts to achieve greater heights in manufacturing.

This research seeks to fill the existing academic void on the use of human factors and innovation for world-class automotive manufacturing. Even though there are numerous studies pertaining to human work environments and technological enhancements in isolation, little has been found regarding the two factors collectively. It is critical to fill this gap given the dynamic nature of the manufacturing industry through innovations such as industry 4.0, which requires both aggressive technology and compliant manpower for superior performance [3,8]. This research will help manufacturing managers and policymakers understand the concepts to be implemented to improve the productivity, quality, and sustainability of the manufacturing industries. Through effective organizational learning and the use of data analysis and intelligence, the human and technical assets of manufacturers can be aligned, a key step that would help improve sustainable manufacturing and could potentially be applied to sectors such as healthcare, logistics, and other technology-driven industries [9,10].

This study aims to quantify the impacts of human factors and technological innovation on manufacturing performance, an area where empirical validation has been sparse. The problem is thus framed around bridging this gap between theoretical insights and empirical application, with a focus on practical, actionable insights for manufacturing practitioners seeking to adopt these practices in an Industry 4.0 context.

The study does not merely confirm existing knowledge but provides the structured integration of human factors and technological innovation, with data-driven validation. Unlike previous research that treats these factors in isolation, this work showcases how their synergy maximizes production efficiency and employee satisfaction. The inclusion of structural equation modeling (SEM) and empirical results offers valuable guidance for implementation in real-world settings, particularly in the context of Industry 4.0.

This paper consists of four sections. The second section after the introduction is the literature review of human factors in manufacturing firms, innovation in manufacturing, world-class manufacturing, and the integration of human factors and innovation in manufacturing firms. The third section consists of the research model. The fourth section is the method and procedures, where the findings of analyses and multiple regression models are presented. Finally, the fifth section presents the research results in line with the hypotheses and concludes with limitations and future research.

2. Literature Review

For a long time, organizations have been striving to achieve and maintain what we may refer to as world-class manufacturing excellence. The marriage of these two fields, namely human factors that center on improving the human work setting and innovation that deals with technologies and systems improvement, has become a very important field of study. The information presented in this paper is designed to discuss the topic of engineering these elements and how this can result in improved performance, competitiveness and sustainability for manufacturing. Below is the background section that discusses and analyzes the literature supporting this proposed research in detail.

2.1. Human Factors in Manufacturing

Human factors in manufacturing refer to the ability to understand how personnel engage themselves and organizational assets such as equipment and technology in manufacturing. The purpose is to maximize these interactions for enhanced safety, increased productivity, and effectiveness. Ergonomics, which is part of human factors, deals with arranging workplaces and tools within the capabilities and constraints of a worker [2].

Studies have indicated that initiatives pointing to the treatment of human factors can yield massive enhancements in manufacturing productivity. For instance, biomechanically enhanced workstations minimize musculoskeletal diseases among workers and improve comfort levels, resulting in enhanced production and minimal workplace incapacity [8]. Human factors, which include employee engagement and motivation, must also be considered. Employees who are involved can help to foster continuous improvement efforts, which are key to sustaining a competitive advantage [11].

Investing in employee education ensures that workers are well-versed in the latest technologies and manufacturing processes. This includes both technical skills, such as operating new machinery, and soft skills, such as problem-solving and teamwork [12]. Comprehensive training has also been found in the literature to play a significant role in improving the efficiency of human factors. Training not only builds up the competency of staff to do their work effectively but also instills a consciousness of safety and quality in them [13]. However, there is a need to incorporate communications and feedback in order to ensure that improvements in human factors considerations are achieved. According to such facts, the following can be hypothesized:

H1. 

Ergonomic design and employee engagement significantly improve manufacturing performance.

2.2. Innovation in Manufacturing

Manufacturing innovation, on the other hand, refers to the process of changing technologies, processes, and products with regard to manufacturing industries. Information technology has also impacted manufacturing with automation, robotics, and digitalization known as Industry 4.0, which has resulted in greater accuracy, a shorter cycle time, and customization [3]. These advancements help in meeting the market needs and improving the competitive positions of manufacturers to a large extent.

Organizational procedures that can be modified include lean manufacturing and Six Sigma, which aim to make production processes more efficient. In the lean manufacturing concept developed by Toyota, the focus is on delivering value to the customer with minimum waste generation [4]. Six Sigma, in contrast, presents a more procedural approach toward enhancing the quality and minimizing the variation or defects, as dictated by the data-driven approach [14].

The literature points to the fact that the cultural shift of an organization supports innovation. Ergonomic designs and intuitive interfaces enhance efficiency, safety, and overall user satisfaction [12]. The elements required for sustaining innovation include a culture that encourages experimentation, less inhibition, and improvement [10]. Likewise, cooperation between research and development (R&D) efforts are critical in spurring innovations. This kind of outcome may include collaborations with academicians, research institutions, and other industry stakeholders that can bring about new knowledge and best practices in the industry [9]. Hence, the following can be hypothesized:

H2. 

Technological innovations such as automation and digitalization have a positive impact on manufacturing efficiency and competitiveness.

2.3. World-Class Manufacturing

WCM is a business management strategy that aims to enhance quality, cost, delivery, flexibility, and innovation. It is the epitome of manufacturing superiority and is used to measure competitiveness on the international stage [1]. Studies have pointed out that it is impossible to become a world-class manufacturer without considering human aspects and innovation together.

WCM encompasses lean production, TQM (Total Quality Management), and JIT (Just In Time) manufacturing. These principles consider waste reduction, quality production, and the timely supply of goods and services. However, the application of these principles is significantly influenced by human factors and innovation [15].

Several studies have suggested that the best performers in WCM have a clear corporate commitment in the area of human resources and technology deployment. For instance, Toyota is a company that has adopted the lean manufacturing principle [7]. Toyota emphasizes continuous improvement (Kaizen) and respect for people, integrating advanced manufacturing technologies with strong employee involvement and development, resulting in high-quality, efficient production [16]. Likewise, the organizations that give preference to innovative production processes have been found to be more productive, high quality, and customer satisfaction oriented than their counterparts [5].

2.4. Integrating Human Factors and Innovation

The management of human factors and innovation is critical for the success of WCM. This integration requires envisioning work environments and arranging work processes in ways that develop human capabilities while applying new technologies to increase both efficiency and effectiveness. The literature points out several general approaches towards the achievement of this integration.

First, one would need to introduce a systems thinking approach that focuses on the relationships between human activities and technologies [17]. This approach is strategic because it avoids compromising one aspect or the other as it fosters complementary benefits [18]. For instance, automation should be introduced hand in hand with training programs that assist workers in learning how to deal with new technologies and improve their efficiency. For example, the integration of Internet of Things (IoT), artificial intelligence (AI), and robotics in manufacturing processes can lead to significant improvements in operational performance and innovation capacity [19]. Moreover, these technologies enable data-driven decision-making, enhancing operational efficiency and adaptability [12].

Second, establishing and promoting a culture of continuous improvement and a search for innovations is necessary. Employees should be motivated to seek opportunities for enhancement and engage in the problem-solving process. This can be through feedback meetings, suggestion schemes and by rewarding innovative ideas [6]. By engaging in open innovation, firms can access a broader range of ideas and technologies, thereby enhancing their innovative capabilities and competitive edge [19].

Third, the implementation of human factors and innovation requires cooperation between the various departments and stakeholders. Multifunctional teams that comprise members from the engineering, human resources, and operation departments can consider both technology and people for solutions [20]. Active employee involvement fosters innovation by promoting creativity and knowledge sharing, driving both incremental and radical innovations [21]. Encouraging employee involvement in decision-making processes leads to innovative solutions and enhanced operational efficiency [16]. Siemens uses digital twin technology and IoT to create virtual representations of its manufacturing processes, allowing real-time monitoring and optimization. This involves employees in decision-making to ensure technology meets their needs and enhances their work [12].

Lastly, the use of data and analytical approaches can strengthen human factors and innovation integration. Thus, the data gathered on employee performance, processes, and customers would allow organizations to make correct choices throughout improvement and innovation processes [22].

Lastly, human factors and innovation are two components that are essential in the development of the concept of world-class manufacturing. From the literature review, it can be concluded that enhancing human–machine interfaces, embracing Kaizen culture, and adopting new technologies can significantly improve manufacturing performance. Robotics handle repetitive tasks with high precision, allowing human workers to focus on more complex and creative tasks [16]. The proposed research project therefore seeks to identify and examine best practices in both human factors and innovation in an attempt to establish methods that can be adopted by manufacturers who wish to attain and maintain world-class status. So, we can hypothesize the following (Figure 1);

H3. 

The integration of human factors and technological innovation creates synergistic benefits that enhance overall manufacturing performance.

The research model presented focuses on achieving world-class manufacturing excellence by integrating human factors and technological innovation. The model illustrates how both elements—human factors such as ergonomic design and employee engagement, and technological innovations like automation and digitalization—work together to enhance manufacturing performance.

Human Factors: Ergonomic design focuses on creating a workplace environment that enhances comfort, safety, and productivity. Effective ergonomic designs minimize worker fatigue and injuries, thereby improving overall efficiency and output quality. Meanwhile, employee engagement emphasizes the importance of actively involving workers in decision-making processes, fostering a culture of continuous improvement, and enhancing motivation and commitment. Engaged employees are more likely to contribute innovative ideas and solutions that can streamline processes and reduce waste, leading to improved performance metrics.

Technological Innovation: This component includes the integration of automation and digitalization within manufacturing processes. Automation involves the use of advanced machinery and robotic systems to perform tasks with high precision and consistency, reducing human error and production downtime. Digitalization refers to the use of digital technologies, such as IoT and AI, to optimize production processes, monitor equipment in real time, and analyze data to predict maintenance needs or process improvements. These technological advancements enable manufacturers to achieve higher levels of efficiency and adaptability, thus maintaining a competitive edge.

The model suggests that the combination of these human and technological factors creates a synergistic effect, leading to a superior manufacturing performance characterized by increased efficiency and competitiveness. By integrating these two aspects, manufacturers can develop a holistic approach that maximizes the potential of both their workforce and technological assets, resulting in a more agile, innovative, and high-performing production environment. The arrows in the figure highlight the potential synergistic benefits, emphasizing the interdependence of human factors and technological innovation in driving manufacturing excellence.

3. Methodology

3.1. Research Design Overview

This study was based on a quantitative research design in which world-class manufacturing (WCM) excellence was examined in terms of human factors and technological innovation. To quantify the specific impact of human factors—such as ergonomics and employee involvement—on manufacturing performance, sustainability, and productivity, clearly defined metrics were established, including measures like ergonomic efficiency, employee engagement scores, production output, quality control indices, and energy consumption rates. The objective was to measure both the individual and combined effects of human factors and technological innovation using a robust statistical framework.

The reason for adopting a quantitative approach was to enable the collection and analysis of measurable data through which an objective assessment would be made, concerning how these factors contribute to manufacturing success. In this case, systematic surveys and standardized questionnaires were administered to achieve uniformity and facilitate a statistical analysis of data from a wide range of companies involved in production. This mode allowed for the validation of results while ensuring the study’s trustworthiness and dependability [23]. Details of the survey design, data collection, and sampling techniques are further provided in the next section.

• Human Factor  

For the measurement of human factors, we adopted a three-item scale from the work of W. Karwowski (2012) [20] to assess the influence of human factors on manufacturing environments. The “Human Factor” variable in this study is divided into two key subcategories. The first focuses on ergonomic design [24], which encompasses the optimization of the interaction between humans and systems to enhance productivity and reduce physical strain. The second subcategory pertains to employee engagement, emphasizing the role of active workforce participation in driving innovation and improving organizational performance [25]. These categories have been chosen based on their relevance to achieving world-class manufacturing excellence, as both ergonomic design and employee engagement are critical for aligning technological innovations with human capabilities. Previous researchers, such as Dul et al. (2012) [26] and Vink et al. (2008) [27], have explored various aspects of human factors, and we have selected these two categories for their significant contribution to enhancing operational efficiency and sustaining a competitive advantage in manufacturing settings. To measure these categories scale, we utilized a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree), to measure the key dimensions of human factors, such as ergonomics, employee engagement, and workplace design. These items were designed to capture the practical integration of human-centered principles in manufacturing processes and their interaction with technological advancements.

• Employee Engagement  

To measure employee engagement, data were collected using a survey method with a 5-point Likert scale. The employee engagement scale consisted of three items, with responses ranging from 1 (strongly disagree) to 5 (strongly agree). The items were adapted from the work of Hair, Matthews, Matthews, and Sarstedt (2017) [28], who have previously developed and used these measures in their studies. This scale was designed to assess the key aspects of employee engagement, including their involvement, commitment, and enthusiasm in the manufacturing process, contributing to the overall integration of human factors in achieving manufacturing excellence.

• Technological Innovation  

To measure technological innovation, a 5-point Likert scale was used, consisting of three items that assess the extent of innovation adoption within the manufacturing process. The scale, adapted from Peng et al. (2008) [29], focuses on the key aspects of technological innovation relevant to world-class manufacturing. The response options ranged from 1 (strongly disagree) to 5 (strongly agree), with the data collected from knowledgeable respondents at different hierarchical levels to ensure a comprehensive assessment of technological innovation integration.

• Manufacturing Performance  

To measure manufacturing performance, a 5-point Likert scale was used, comprising three items adapted from Cua et al. (2001) [30] and Shah and Ward (2003) [31]. The scale evaluates key operational performance areas relevant to world-class manufacturing, including quality, speed, and flexibility. Plant managers were asked to assess their plant’s performance relative to competitors using response options ranging from 1 (strongly disagree) to 5 (strongly agree). This subjective approach to measuring performance was chosen for two reasons: first, to capture the competitive advantage relative to its competitors, and second, to measure objective performance by efficiency [29].

3.2. Survey Research Methodology

Population and Sample: The target population includes manufacturing firms across various sectors. Purposeful sampling was used because the target population is manufacturing firms that vary in size and type. Stratified sampling ensured that the data represented small, medium, and large firms, thus reflecting the diversity of organizational structures and levels of Industry 4.0 adoption.

Data Collection: A comprehensive survey questionnaire was designed to capture the impacts of ergonomic design, employee engagement, technological adoption, digitalization, productivity, quality, and sustainability. Metrics such as the productivity increase rate, quality improvement percentage, and sustainability-related energy savings were included to measure performance. Data were collected via an online survey completed by 350 employees and managers in the manufacturing sector. This approach ensured that the data were both quantifiable and representative of various sectors [32].

Data Analysis: Descriptive analysis, correlation analysis, and multiple regression analysis were conducted using statistical software tools like IBM SPSS Statistics Version 26 to test the hypothesized relationships between human factors, innovation, and manufacturing performance. Structural Equation Modeling (SEM) was used to assess the causal relationships between variables, and the overall fitness of the proposed model was evaluated using goodness-of-fit indices with the help of AMOS 26. This provided a rigorous analysis of the relationships between human factors, technological innovations, and manufacturing outcomes [33].

Reliability and Validity: To ensure the reliability and validity of the quantitative surveys, pilot testing was conducted, and Cronbach’s alpha was used to assess the internal consistency of the survey items, achieving an alpha value of 0.85 or above across key variables. The survey was designed to mitigate biases such as social desirability bias, and the randomized selection of participants within each firm ensured that the results were reflective of broader industry practices. The anonymity of respondents was maintained to ensure honest and accurate responses, and statistical techniques were employed to verify the reliability of the findings.

Addressing Variability in Industry 4.0 Adoption: The proposed implementation framework was structured to be adaptable across a variety of organizational structures and technological landscapes. The framework was designed to accommodate firms with different levels of technological adoption, ensuring that companies in both early and advanced stages of Industry 4.0 could implement the recommendations according to their specific needs. This adaptability was achieved by identifying modular components that organizations can implement independently or in combination, tailored to their existing infrastructure and varying levels of technology adoption, ensuring a flexible approach that meets specific operational needs.

Limitations and Challenges: This research faces limitations related to the quantitative research paradigm and data collection methods. The cross-sectional nature of the study limits the ability to track changes over time, which can lead to response bias, where participants may provide socially acceptable answers. Pilot testing and randomized participant selection were employed to minimize this issue, but future studies might benefit from using a longitudinal approach. Another limitation arises from the purposive sampling, which may make it difficult to generalize the findings to all sectors of the manufacturing industry. Future studies should explore more diverse industrial contexts within the manufacturing sector, such as automotive, textiles, and electronics, while also including longitudinal data to enhance the generalizability and applicability of the findings.

4. Results

4.1. Demographic Analysis

The demographic analysis provided key insights into the composition of the study’s respondents, including their work experience, gender, designation, and size of the organization they represent. The demographic data were collected through a structured questionnaire, enabling the study to capture a diverse and representative sample of 350 participants from the manufacturing sector (

Table 1. Demographics of the study.

Demographic Item	Category	Frequency	Percentage (%)
Manager		90	45.0
Designation	Engineer	50	25.0
	Operator	40	20.0
	Other	20	10.0
	Total	200	100.0
Working Experience	Less than 1 year	30	15.0
	1–3 years	80	40.0
	4–6 years	60	30.0
	7–10 years	20	10.0
	More than 10 years	10	5.0
	Total	200	100.0
Gender	Female	110	55.0
	Male	90	45.0
	Total	200	100.0
Size of Organization	Small	70	35.0
	Medium	90	45.0
	Large	40	20.0
	Total	200	100.0

).

In this study, data were collected from various manufacturing industries in Pakistan to examine the impact of human factors and technological innovation on manufacturing performance. The sample consisted of 200 respondents, with data on gender, designation, total work experience, and organization size analyzed using IBM SPSS Statistics Version 26.

Respondent Demographics: The gender distribution shows a higher proportion of female respondents (55%, n = 110) compared to male respondents (45%, n = 90). This suggests a relatively balanced gender representation in the sample, reflecting the workforce composition within these industries.

Designation: The respondents’ designations varied, with the majority being managers (45%, n = 90), followed by engineers (25%, n = 50), operators (20%, n = 40), and other roles (10%, n = 20). This diverse representation ensures a comprehensive understanding of perspectives from different hierarchical levels within the industry, providing insights into how human factors and technological innovations are perceived across roles.

Work Experience: The majority of respondents have substantial work experience, with 40% (n = 80) having 1–3 years and 30% (n = 60) having 4–6 years of experience. A smaller proportion has less than 1 year (15%, n = 30) or more than 10 years (5%, n = 10) of experience. This distribution indicates a workforce that is predominantly mid-level in terms of experience, which may influence their views on the integration of ergonomic design and technological innovation.

Organization Size: Regarding organization size, 45% (n = 90) of respondents were from medium-sized organizations, 35% (n = 70) from small organizations, and 20% (n = 40) from large organizations. This distribution highlights the study’s relevance across different organizational contexts, offering a broader perspective on how size may influence the adoption and effectiveness of ergonomic design and technological innovation strategies.

The study’s findings are expected to provide valuable insights into the synergistic benefits of combining human and technological factors to enhance manufacturing performance.

4.2. Data Normality Analysis

The table above presents the data normality analysis for three key variables (

Table 2. Data skewness, mean and kurtosis.

Variables	Mean	St. Deviation	Skewness	Kurtosis
Human Factor	3.9708	0.77374	−1.060	0.453
Technological Innovation	3.7717	0.92582	−0.910	0.144
Manufacturing Performance	3.7967	0.97758	−0.874	0.093

): the human factor, technological innovation, and manufacturing performance. The normality of the data is assessed using skewness and kurtosis values, following established statistical guidelines.

Skewness Analysis: Skewness measures the asymmetry of the distribution of data. According to Bulmer (1979), a skewness value between −1 and +1 indicates that the data distribution is approximately normal. The skewness values for human factor (−1.060), technological innovation (−0.910), and manufacturing performance (−0.874) all fall within this acceptable range, suggesting that the data distributions for these variables are reasonably symmetrical around their means. While the human factor variable is slightly outside the typical range, it is still close enough to be considered nearly normal.

Kurtosis Analysis: Kurtosis measures the “tailedness” of the data distribution. MacGillivray and Balandan’s guideline states that kurtosis values between −3 and +3 indicate a normal distribution, meaning the data have neither too many extreme values (leptokurtic) nor too few (platykurtic). The kurtosis values for human factor (0.453), technological innovation (0.144), and manufacturing performance (0.093) were all well within this range, indicating that the distributions are not excessively peaked or flat.

Based on the skewness and kurtosis values, the data for all three variables are normally distributed. This normality is crucial for subsequent parametric analyses, such as regression or ANOVA, which assume normally distributed data. The normal distribution of data also enhances the reliability of any inferences or conclusions drawn from the study. Therefore, we can confidently proceed with further statistical analyses, knowing the data meet the normality assumption criteria, which is a fundamental prerequisite for many statistical tests.

4.3. Reliability Analysis

Reliability analysis is a crucial step in validating the consistency and dependability of the measurement instruments used in research. In this study, reliability analysis was conducted to evaluate the internal consistency of the items in the questionnaire, ensuring that the responses are reliable and consistent across different items measuring the same construct. Cronbach’s alpha is the statistical measure used to assess this reliability.

Cronbach’s Alpha Interpretation: Cronbach’s alpha values range from 0 to 1, with higher values indicating greater internal consistency among the items. The general rule of thumb for interpreting Cronbach’s alpha is as follows: values above 0.7 are considered acceptable, values above 0.8 are considered good, and values above 0.9 are regarded as excellent, indicating very high reliability. This scale helps researchers determine whether the items on a questionnaire are suitable for further statistical analyses or if they need modification. The results of the reliability analysis are presented in

Table 3. Reliability analysis.

Variable	Cronbach Alpha	No of Items
Human Factor	0.844	06
Technological Innovation	0.785	03
Manufacturing Performance	0.804	03

.

Study Results: The reliability analysis results presented in Table 3 above demonstrate that all three variables in this study—human factor, technological innovation, and manufacturing performance—have Cronbach’s alpha values above the 0.7 threshold. Specifically, the human factor variable has a Cronbach’s alpha of 0.844 across six items, indicating good internal consistency. The technological innovation variable, with three items, has a Cronbach’s alpha of 0.785, which is also considered acceptable and reliable. Similarly, the manufacturing performance variable has a Cronbach’s alpha of 0.804 with three items, indicating good reliability.

These results confirm that the measurement instruments used in this study are reliable and can be trusted to provide consistent results. The Cronbach’s alpha values suggest that the items within each construct are well correlated and effectively measure the intended variables. This high level of reliability ensures the validity of subsequent analyses and enhances the credibility of the study’s findings. Therefore, the instrument can be confidently used for further statistical tests, as it meets the reliability standards necessary for robust research. Additionally, these findings provide assurance that any disturbances caused by unreliable items are minimized, allowing for more accurate and meaningful interpretations of the data.

4.4. Correlation Analysis

Correlation analysis is a statistical method used to measure the strength and direction of the relationship between two or more variables. In this study, correlation analysis was performed to examine the relationships between human factor (HF), technological innovation (TI), and manufacturing performance (MP). The values in the correlation matrix range between 0 and 1, where a value closer to 1 indicates a strong positive relationship, while a value closer to 0 indicates no relationship. A negative value would indicate an inverse relationship, though none are present in this analysis. Significance levels are also indicated, with asterisks denoting levels of significance (* p < 0.05, ** p < 0.01).

Interpretation of Results: The correlation matrix provided in

Table 4. Correlation analysis.

Items	HF	TI	MP
Human Factor	1
Technological Innovation	0.575 **	1
Manufacturing Performance	0.625 **	0.344 **	1

**. Correlation is significant at the 0.01 level (2-tailed).

shows the Pearson correlation coefficients between each pair of variables, along with their significance levels. The correlation coefficient between human factor (HF) and technological innovation (TI) is 0.575, which is statistically significant at the 1% level (** p < 0.01). This indicates a moderate positive relationship between HF and TI, suggesting that improvements or changes in human factors are moderately associated with technological innovations in manufacturing settings.

The correlation between human factor (HF) and manufacturing performance (MP) is 0.625, also significant at the 1% level (** p < 0.01). This reflects a moderate positive relationship, implying that enhancements in human factors, such as ergonomic design and employee engagement, are moderately associated with better manufacturing performance, potentially improving efficiency and competitiveness.

However, the correlation between technological innovation (TI) and manufacturing performance (MP) is 0.344, which, while still statistically significant at the 1% level (** p < 0.01), indicates a weaker positive relationship. This suggests that while technological innovations like automation and digitalization are related to manufacturing performance improvements, the strength of this relationship is not as robust as that between human factors and manufacturing performance.

The correlation analysis results demonstrate that all variables are significantly correlated with each other at a 1% significance level, confirming that there are meaningful relationships between human factors, technological innovation, and manufacturing performance. The moderate correlation between human factor and manufacturing performance highlights the importance of ergonomic design and employee engagement in driving manufacturing success. Meanwhile, the weaker correlation between technological innovation and manufacturing performance suggests that while technological advancements are beneficial, they may need to be supported by strong human factors to maximize their impact on performance. These findings underline the importance of an integrated approach that considers both human and technological elements to achieve world-class manufacturing excellence.

4.5. Structural Equation Modeling (SEM) Analysis

Structural equation modeling (SEM) is a powerful statistical technique used to examine the complex relationships among variables by integrating both factor analysis and multiple regression analysis. In this study, SEM was employed to analyze the hypothesized relationships between human factors, technological innovation, and manufacturing performance. SEM allows for the simultaneous examination of multiple relationships and provides a comprehensive understanding of the underlying data structure. It includes various components, such as factor evaluation, path analysis, and regression, making it a robust method for testing theoretical models.

Model Fitness Indicators: The fitness of the SEM model is evaluated using several goodness-of-fit indices, as shown in

Table 5. Fitness summaries.

Model	Hypothesized	Thresholds
CMIN/DF	2.354	<3
RMR	0.065	Closer to 0
GFI	0.922	≥0.9
AGFI	0.875	≥0.8
CFI	0.936	≥0.9
RMSEA	0.080	<0.08

. These indices help determine how well the model fits the observed data. The values presented are within the acceptable ranges of the recommended thresholds, indicating a good model fit.

CMIN/DF (Chi-square/Degrees of Freedom): The value of 2.354 falls below the recommended threshold of less than 3, indicating an acceptable fit between the model and the observed data.

RMR (Root Mean Square Residual): A value of 0.065, which is closer to 0, suggests that the residuals, or differences between observed and estimated values, are minimal, further supporting the model’s good fit.

GFI (Goodness of Fit Index): The GFI value of 0.922 exceeds the threshold of 0.9, confirming that a substantial proportion of the variance in the data is accounted for by the model.

AGFI (Adjusted Goodness of Fit Index): The AGFI value of 0.875 is above the acceptable level of 0.8, indicating that the model provides a good fit even after adjusting for model complexity.

CFI (Comparative Fit Index): The CFI value of 0.936 is above the 0.9 threshold, demonstrating a good comparative fit to a null model.

RMSEA (Root Mean Square Error of Approximation): The RMSEA value of 0.080 is slightly above the ideal threshold of 0.08, suggesting a reasonable error of approximation but still within acceptable limits.

SEM analysis, conducted using AMOS 26 software, and specifically Confirmatory Factor Analysis (CFA), validates the theoretical model by demonstrating an overall good fit based on the fitness summaries provided. All fit indices meet or exceed their respective thresholds, confirming the model’s suitability for further analysis. This strong model fit indicates that the hypothesized relationships among human factors, technological innovation, and manufacturing performance are supported by the data, reinforcing the validity of the research model used in this study.

Figure 2 illustrates the structural equation model (SEM) used in this study, showing the hypothesized relationships between human factor (HF), technological innovation (TI), and manufacturing performance (MP). The model includes latent variables and their respective indicators, with arrows indicating the direction and strength of the relationships. The values next to the arrows represent the path coefficients, highlighting the impact of each variable on the others. This SEM analysis aims to validate the theoretical framework by demonstrating how these factors interact to influence manufacturing performance.

4.6. Direct Relationship

H1. 

Human factors have a direct relationship with manufacturing performance.

HF → MP

H2. 

Technological innovation has a direct relationship with manufacturing performance.

TI → MP

Table 6. Hypothesis analysis.

Estimate				S.E.	C.R.	p	Hypothesis
MP	<---	HF	0.902	0.086	10.454	***	H1	Accepted
MP	<---	TI	0.806	0.072	10.468	***	H2	Accepted

presents the results of the hypothesis analysis, demonstrating the direct relationships between human factor (HF), technological innovation (TI), and manufacturing performance (MP). The table provides the estimates, standard errors (S.E.), critical ratios (C.R.), and p-values (P) for each hypothesized relationship, which are crucial in determining the significance and strength of these relationships within the structural equation model (SEM).

Hypothesis 1 (H1). 

The estimate of 0.902 for the path from human factor (HF) to manufacturing performance (MP) suggests a strong positive relationship between these two variables. The standard error (S.E.) of 0.086 is relatively small, indicating precise estimation. The critical ratio (C.R.) of 10.454, which is the ratio of the estimate to the standard error, is considerably high. The p-value (P) is indicated as ***, which denotes a significance level of p < 0.001. This level of significance confirms that the relationship between human factor and manufacturing performance is statistically significant. As a result, Hypothesis 1 (H1) is accepted, demonstrating that factors such as ergonomic design and employee engagement significantly contribute to enhancing manufacturing performance in terms of efficiency and competitiveness.

Hypothesis 2 (H2). 

Similarly, the estimate for the direct relationship between technological innovation (TI) and manufacturing performance (MP) is 0.806, also indicating a strong positive relationship. The standard error (S.E.) of 0.072 is low, suggesting that the estimate is precise. The critical ratio (C.R.) of 10.468 is high, reinforcing the robustness of this relationship. The p-value (P) is again marked as ***, confirming a significance level of p < 0.001. This indicates that the direct relationship between technological innovation—such as automation and digitalization—and manufacturing performance is statistically significant. Consequently, Hypothesis 2 (H2) is accepted, highlighting the importance of technological advancements in driving manufacturing performance improvements.

The results in Table 6 provide strong evidence that both human factors and technological innovation have significant direct effects on manufacturing performance. The acceptance of both hypotheses (H1 and H2) suggests that integrating ergonomic and technological advancements can lead to substantial improvements in manufacturing efficiency and competitiveness. These findings underscore the importance of adopting a holistic approach that combines both human-centric and technology-driven strategies to achieve world-class manufacturing excellence. This comprehensive understanding is critical for decision-makers aiming to enhance performance in manufacturing settings.

Figure 3 presents the goodness-of-fit indices for the SEM model, providing an overview of the model’s overall performance. The various fit indices, such as CMIN/DF, RMR, GFI, AGFI, CFI, and RMSEA, are displayed along with their corresponding values. Each index is compared against the recommended thresholds to assess the adequacy of the model fit. This figure supports the findings by indicating that the hypothesized relationships among human factors, technological innovation, and manufacturing performance are statistically valid based on the fit indices.

4.7. Multiple Regression Analysis

Model Specification: The regression equation given is as follows:

MP = β0 + β1HF + β2TI + β3(HF × TI) + ϵ

This reflects a multiple regression model where MP is influenced by HF and TI, including an interaction term, HF × TI.

The above table (

Table 7. Hypothesis analysis.

	Coef.	Std. Err.	t	p > |t|	[0.025	0.975]
Intercept	19.1596	12.736	1.504	0.136	−6.122	44.441
HF	0.0922	0.249	0.371	0.712	−0.402	0.586
TI	−0.1279	0.253	−0.505	0.615	−0.631	0.375
Interaction	0.0114	0.005	2.316	0.023	0.002	0.021
Omnibus:	0.669		Durbin-Watson:	2.437
Prob(Omnibus):	0.716		Jarque-Bera (JB):	0.260
Skew:	−0.035		Prob (JB):	0.878
Kurtosis:	3.240		Cond. No.	7.22 × 104

) shows the regression analysis for Hypothesis 3, which posits that integrating human factors (HF) and technological innovation (TI) enhances manufacturing performance (MP) through synergistic effects, yielded the following insights:

• R-squared (0.772): The model explains approximately 77.2% of the variance in manufacturing performance, indicating a strong fit.
• Adjusted R-squared (0.765): Adjusting for the number of predictors, this still suggests a robust model fit.
• Interaction Term: The coefficient for the interaction between HF and TI is 0.0114, with a statistically significant p-value of 0.023. This supports the hypothesis that there is a positive, synergistic effect when HF and TI are integrated.
• Overall Model Significance: The F-statistic (108.4) with a p-value of 1.05 × 10⁻³⁰ confirms that the model is highly significant overall.

In summary, these results support Hypothesis 3, suggesting that combining human factors with technological innovation indeed yields significant improvements in manufacturing performance.

Hypothesis (H3). 

The model produced an R-squared value of 0.772, indicating that approximately 77.2% of the variance in manufacturing performance can be explained by the combined effects of HF and TI. The Adjusted R-squared value of 0.765 further confirms a robust model fit, accounting for the number of predictors. Notably, the interaction term between HF and TI displayed a coefficient of 0.0114, accompanied by a statistically significant p-value of 0.023, supporting the notion of a positive, synergistic effect when these two elements are integrated. Additionally, the overall model significance is underscored by the F-statistic of 108.4, with a p-value of 1.05 × 10⁻³⁰, indicating that the model is highly significant overall. These results collectively affirm Hypothesis 3, suggesting that the combination of human factors and technological innovation indeed leads to significant improvements in manufacturing performance, reinforcing the critical role of their integration in enhancing operational effectiveness.

Hypotheses:

H1 (HF → MP): Strong positive, Accepted.

H2 (TI → MP): Positive, Accepted.

H3 (HF + TI Synergy → MP): Accepted—Integration of human factors and technological innovation shows enhanced manufacturing performance through synergistic benefits.

5. Discussion

Striking evidence from this research suggests that integrating human factors with technological innovation is essential for achieving manufacturing excellence. As manufacturing evolves, particularly in the context of Industry 4.0, it has become increasingly clear that advanced technologies must align with human capabilities and limitations. This study demonstrates that organizations prioritizing human factors—such as ergonomics and employee engagement—while leveraging up-to-date technologies exhibit a superior operational performance compared to those that do not.

5.1. Human Factors—Enhancing Manufacturing Excellence

Manufacturing performance can be enhanced through the consideration of elements such as ergonomics, employee wellbeing, and organizational culture, referred to as human factors. According to recent literature, introducing principles of human-centered design into manufacturing processes not only increases employees’ satisfaction levels but also contributes to higher productivity and product quality by reducing the risks associated with injuries [34,35]. Our findings confirm these conclusions indicating a higher likelihood for employees to embrace technological developments and contribute towards continual improvement when they are seen as part-and-parcel of the innovation process.

Manufacturing efficiency, for instance, is a field in which ergonomics can hardly be ignored. This aims to reduce fatigue, improve accuracy, and increase production output through designing workstations, tools and processes in line with the physical and mental capabilities of workers [24]. The report also highlighted that companies with ergonomic interventions had lower absenteeism rates as well as greater job satisfaction among employees. This concurs with the works of Stanton [36], which argue that sustainable manufacturing practices rely on ergonomic design.

In addition to this, employee engagement is one of the significant human factors affecting performance in manufacturing. Proactive behavior by engaged employees helps organizations to innovate and achieve their goals [11]. Furthermore, this study emphasized how organizations that embrace employee involvement in decision making are better placed towards implementing technological innovations, while involving employees in decision-making through technological innovation and professional development opportunities that recognize contributions has been found to lead to the successful implementation of technological innovations within an organization (shown in Table 4); similar findings were established by Trist and Bamforth [37], who argued that sociotechnical systems were important for industrial manufacturing firms.

5.2. Technological Innovation as a Driving Force for Manufacturing Excellence

Technological innovation, particularly digital technologies like artificial intelligence (AI), the Internet of Things (IoT), and robotics, has revolutionized manufacturing. These technologies offer unprecedented opportunities to enhance efficiency, reduce costs, and improve product quality. However, this study underscores a crucial aspect of successful technology implementation: aligning these technologies with human factors.

Organizations that integrate human factors with technology are better positioned to capitalize on the Industry 4.0 phenomenon. For instance, the use of collaborative robots, or cobots, in manufacturing environments has been shown to increase productivity while ensuring employee safety and well-being [38]. Companies that involve their employees in technology design and deployment tend to achieve higher efficiency and quality.

While technological innovations such as automation and digitalization enhance production speed and accuracy, it is crucial to ensure that production assets themselves are ergonomically designed. The incorporation of ergonomic principles into the design of machines and production lines minimizes worker fatigue and enhances safety, which, in turn, positively influences productivity. This research analyzes ergonomics not only at the organizational level but also extends to specific production assets, emphasizing how ergonomically designed equipment improves operational efficiency and contributes to world-class manufacturing (WCM) excellence.

The implementation of IoT facilitates the real-time monitoring and optimization of production processes. However, the effectiveness of these technologies depends on workers’ ability to interpret and respond to the data generated by IoT devices [39]. This study highlights the importance of offering continuous training programs to equip employees with the skills necessary to adapt to and effectively use new technologies.

5.3. The Synergy Between Human Factors and Technological Innovation

This research makes a significant contribution by demonstrating the synergistic alliance of human factors with technological innovation. In contrast to prior studies, which have often dealt with these aspects in isolation, this study shows that their combination is very important for attaining manufacturing excellence. The findings imply that companies that effectively combine these aspects are better at adapting to changes in the external environment as well as being more efficient.

Because of its nature, Industry 4.0 makes this synergy even more critical. Organizations can establish a conducive workplace where people perform efficiently and live sustainably by harmonizing human elements such as knowledge, skills and abilities, attitudes, behavior patterns and the likes with technical innovations. This way of thinking does not only help enhance operational efficiency, but also leads to long-term success and competitiveness for firms.

This research highlights the significance of sociotechnical systems, where the interdependence of social and technical components is essential for optimizing manufacturing excellence [37,40]. It is now well established that a balanced strategy incorporating both human and technological aspects is essential for optimizing manufacturing systems. The present results correspond with Clegg’s [41] contention that ignoring the social dimensions of technological change can lead to suboptimal outcomes.

5.4. The Role of Organizational Culture in Integrating Human Factors and Technological Innovation

One critical aspect that warrants further exploration is the role of organizational culture in this integration. Organizational culture profoundly influences how human factors and technological innovations are adopted and implemented. In our study, companies that reported a culture prioritizing openness, inclusivity, and employee empowerment exhibited higher levels of engagement and innovation uptake. For example, researchers [39,42] have studied that 75% of respondents from organizations with a strong culture of continuous learning indicated that ergonomic and technological interventions were more effective, leading to a 20% increase in productivity. This demonstrates that cultures valuing continuous learning and feedback can enhance the effectiveness of ergonomic and technological interventions by encouraging employees to actively participate in and contribute to the innovation process.

In contrast, only 30% of respondents from organizations resistant to change reported successful technology adoption, leading to missed opportunities for integrating human factors with technological innovations. This indicates that cultures resistant to change or failing to value employee input struggle with technology adoption and may not fully realize the benefits of integrating human factors with technological innovations. Therefore, understanding how different corporate cultures impact employee engagement and innovation is essential for developing a comprehensive strategy that maximizes manufacturing excellence.

5.5. Challenges Faced by Organizations in Integrating Human Factors and Technological Innovation

This study reveals several hurdles that organizations may face when attempting to merge human factors with technological innovation. The primary challenge is resistance to change, particularly when new technologies disrupt established workflows and job descriptions [42]. To address this, companies can adopt a change management strategy that involves employee engagement from the early stages of technology integration. This includes conducting training programs to familiarize employees with new systems, providing ongoing support, and fostering a culture of open communication where employees are encouraged to contribute feedback during the transition. Organizations that implement change management frameworks—such as Kotter’s 8-Step Change Model—have demonstrated higher success rates in overcoming resistance by creating a sense of urgency, building coalitions, and maintaining momentum throughout the implementation process.

Moreover, the high costs associated with investing in new technologies and manpower resources present a significant challenge. To mitigate this, companies should consider phased implementations, where new technologies are introduced incrementally, allowing time for staff adaptation and resource allocation without overwhelming the organization’s financial capacity. For example, a pilot testing phase in which new technologies are rolled out in a controlled environment can provide valuable insights into cost management and help to identify the most effective areas for further investment. Additionally, organizations can explore collaborative partnerships with technology providers or government-led subsidy programs that offer financial support for digital transformation initiatives, making the transition more accessible and cost-effective [43].

The study also raises concerns about the risk of technology overshadowing human factors, with a focus on productivity potentially coming at the expense of employee welfare [44]. To prevent this, companies should adopt a human-centered approach to technological implementation, ensuring that automation and digital tools complement rather than replace human skills. For example, collaborative robots (cobots), which work alongside human operators, have been successfully implemented in industries such as automotive manufacturing to enhance efficiency while preserving employee safety and engagement. Case studies from companies like Toyota have demonstrated how such innovations, combined with a focus on continuous employee development, can yield high productivity gains without compromising workplace well-being. Regular employee wellness assessments and feedback loops should be integrated into the adoption of new technologies to ensure that workers remain engaged, motivated, and supported throughout the process.

In conclusion, while technological advancements offer significant opportunities for operational improvement, organizations must ensure that employees remain central to these developments. By employing structured change management, incremental investment strategies, and human-centered innovation frameworks, companies can successfully integrate new technologies without undermining employee welfare or organizational stability.

5.6. Theoretical and Practical Implications

5.6.1. Theoretical Implications

This research extends the theoretical framework of manufacturing excellence by incorporating the concepts of human factors, engineering and technological innovation. It is found that, for sustainable competitive advantages to be realized in the manufacturing industry [45], especially during this era of Industry 4.0, a blend between these two domains is essential [46]. This merger gives a better understanding of how technological advancements in production systems can be harmonized with human limitations and capabilities.

In addition, it also complements the broader discourse on organizational change and innovation management by showing that the successful implementation of new technologies in manufacturing depends on considering human issues [47]. For instance, this knowledge will prove helpful to researchers and practitioners who intend to develop models that will capture the complexities emanating from the interaction between humans and technology in highly dynamic manufacturing environments.

5.6.2. Practical Implications

To other players in the field, this research provides actionable guidance on effectively integrating human factors with technological innovations in an industrial context [24]. A thorough cost–benefit analysis reveals that while initial investments in advanced technologies and supportive facilities may be substantial, the long-term benefits far outweigh these costs. For managers, understanding the financial implications is crucial: investments in technology should be evaluated for their potential Return on Investment (ROI), which includes not only direct cost savings and efficiency gains but also improvements in worker satisfaction and productivity.

Managers are advised to adopt a global perspective when considering these investments. This means assessing not only the financial aspects but also the organizational impact, including the necessary training for employees, fostering a culture of continuous improvement, and involving workers in the decision-making process regarding new technologies [34]. By addressing these factors, companies can ensure that technological innovations are effectively implemented and that their benefits are maximized. This comprehensive approach not only enhances operational efficiency but also supports sustained growth and competitive advantage in the market.

5.7. Future Directions

• Targeted Research on Emerging Technologies:

Future studies should focus on specific research questions that examine the impact of emerging technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) on various manufacturing processes. For example, a key research question could be the following: How does the integration of AI-driven automation affect employee engagement and ergonomics in assembly line production? This would help to pinpoint how human factors can be harmonized with AI in manufacturing environments, especially in highly automated sectors such as automobile manufacturing or precision engineering in Pakistan’s growing industrial hubs like Faisalabad.

Another area worth investigating is the effect of IoT-based monitoring systems [39] on workplace safety and real-time decision-making in sectors such as textile and apparel manufacturing. A specific research question could be the following: How can IoT enhance worker safety by enabling predictive maintenance, thereby reducing the risk factors associated with faulty equipment. These kinds of targeted inquiries will offer industry-specific insights into the role of emerging technologies in manufacturing [43].

• Cross-Industry Application of Frameworks:

While this study focuses on the manufacturing sector within a particular context, we recognize the need for broader validation. Future research will extend this framework to conduct cross-industry and cross-national studies to explore how the synergy between human factors and technological innovations differs across economic contexts and industry types. This comparative approach will provide deeper insights into the universal applicability of the proposed framework.

• Impact of Emerging Technologies on Long-Term Firm Performance:

Future research should also investigate the long-term effects of integrating AI, ML, and IoT on firm performance and employee well-being. A possible research question could be the following: What are the long-term impacts of AI and IoT on productivity, employee morale, and job satisfaction in digitally advanced manufacturing firms? This could be particularly relevant in the context of Pakistani manufacturing firms as they transition towards Industry 4.0, providing insights into how technological advancements influence both operational efficiency and workforce dynamics.

• Cultural Factors in Human–Technology Integration:

Additionally, it is essential to study the cultural differences that affect the integration of human factors and technological innovation. In regions like South Asia, including Pakistan, where traditional work practices are often deeply embedded, research should explore how cultural attitudes towards automation and technology adoption influence the effectiveness of these integrations. A targeted research question could be the following: How do cultural perceptions of technology influence the acceptance of AI and automation in traditional manufacturing sectors? This could help in understanding the globalization dynamics that shape manufacturing practices in developing countries and provide more context-specific solutions for adopting advanced technologies [48].

• Exploration of external factors:

While this study primarily focuses on internal factors like human factors and technological innovation, we acknowledge that external influences such as economic conditions, government policies, and education competitiveness may also affect manufacturing performance. Future research will explore how these external factors (e.g., government policies, educational competitiveness) interact with internal operational strategies to influence the adoption and effectiveness of human–technology integration.

6. Conclusions

This research underscores the imperative of integrating human elements with technological innovation to achieve world-class manufacturing excellence. The findings illustrate that merging human factors—such as ergonomics and employee engagement—with technological advancements can significantly enhance productivity, efficiency, and overall manufacturing performance [49]. The successful implementation of new technologies requires harmonious integration with human-centered design principles [50]. While technologies like AI and IoT offer considerable opportunities for efficiency, their effectiveness heavily depends on alignment with ergonomic practices and employee needs [24,34,35].

Moreover, organizational culture plays a crucial role in this integration process. Cultures that promote openness, continuous learning, and employee involvement are better equipped to leverage technological innovations effectively. In contrast, organizations with resistant or rigid cultures may encounter significant barriers to adopting and benefiting from new technologies. This highlights the necessity of a holistic approach that considers cultural factors alongside technological advancements [51,52]. While technological innovations can drive immediate improvements, their long-term success hinges on sustained integration with human factors. Continuous alignment is essential not only for short-term gains but also for long-term sustainability and competitive advantage. Companies must adapt and refine their technologies in response to evolving human needs and capabilities to maintain their market edge [53].

The practical implications for managers are clear: achieving manufacturing excellence requires a strategic approach that aligns technological innovations with human-centered design principles and fosters a supportive organizational culture [54,55,56]. Investments in both technology and employee welfare must be prioritized, ensuring that ergonomic considerations and employee engagement are integral to innovation strategies. In light of the ongoing Industry 4.0 transformation, this research advocates for a balanced strategy that incorporates both technological advancements and human factors. By addressing the complex interactions between these elements and considering the influence of organizational culture, companies can create a more flexible, creative, and sustainable manufacturing environment that meets current and future market demands. This approach not only enhances operational efficiency but also contributes to sustainability by fostering a workplace that values employee well-being and continuous improvement.