1. Introduction
Organizations are frequently obliged to produce novel items for a competitive marketplace in this era of rapid technological innovation. Small and medium-sized enterprises (SMEs) significantly contribute to the worldwide expansion of the global economy and creative output, but they confront specific hurdles to producing new goods [1]. Small and medium-sized enterprises (SMEs) are more capable of adapting than large corporations, demonstrating their advanced adaptability to advancements in technologies, higher-income distribution, and promotion, and indicating that they improve decision making [2]. As marketplaces have become more competitive, small and medium-sized enterprises have attempted to establish themselves through innovative product advancements to compete with major corporations in order to maintain a form of sustainability. As a result, innovation plays a critical role in acquiring competitive strength [3] as it is focused on unique items, innovative marketing and management strategies, and real technology [4].
Total quality management has been highlighted as a tool that may help businesses in a variety of sectors cope with the marketplace’s fast transformation. Escrig-Tena [5] acknowledged total quality management as a globally advanced strategy for attaining quality goods and services that results in operational performance excellence. Recently, total quality management implementation seems to have had a substantial impact on businesses’ effectiveness [6,7]. “Total quality management” is a business management philosophy that emphasizes customer satisfaction and the organization’s overall performance by ensuring that customers’ expectations are met. Consequently, firms have used total quality management to boost corporate performance by distinguishing their goods and obtaining a competitive market position [8].
Economic development is an important role for most developing nations. By boosting productivity, creating jobs, and reducing inequality, small and medium-sized enterprises (SMEs) have an important role to play [9,10]. There appears to be a substantial and expanding corpus of academic study on the speed of innovation, which is defined as the time passed between the original idea and the final market introduction of a new product. Nigeria, as a developing economy, is confronted with the difficulty of matching specifications and expectations in terms of sales and service supply, which has resulted in significant economic waste and incalculable difficulties in the country [11,12]. According to information acquired from the Nigerian Bureau of Statistics, the contributions made by Nigerian small and medium-sized firms in the last five years amount to around 48 % of GDP. They number around 17.4 million people and account for roughly half of the national GDP [13]. Approximately 90% of manufacturing jobs are available. In general, it is a management concept that aims to incorporate all organizational tasks to objectively meet consumers’ needs. Its goal is to make quality less of an operational issue and more of a concern for the whole organization. All units, departments, and divisions of the company will be responsible for total quality management.
Businesses today operate in increasingly competitive and dynamic markets that require goods to be brought to the market faster. Global competitiveness, an exponential increase in innovation, and changing consumer expectations are promoting product quality improvements and requirements for rapid product development. A sustained enterprise keeps its ability to acquire both short-term and long-term benefits in a leading competitive market. This is essential for future growth. In addition, this is something that can be accomplished through a slow and steady expansion over an extended period [14]. Almost every product market sector has customers that place increasing value on speed in addition to basic product and benefit criteria such as items and outstanding expenses [15].
In the context of small and medium-sized manufacturing industries, only few academics have addressed total quality management practices in SMEs’ operational performance [16]. Scholars have claimed [6,17,18,19,20] that there exists a positive relationship between total quality management and SMEs’ operational performance. Nevertheless, [21,22,23,24,25,26] believes that TQM has a negative impact and a less-than-optimal outcome on the performance of SMEs. As a result of these contradictory and inconsistent findings, numerous researchers have proposed the inclusion of additional elements as mediators in an attempt to affect the current relationship [27,28]. These other aspects will be considered as mediators in this investigation to see how TQM impacts SME performance, which will provide new insights on how to further our current understanding of this relationship. Since there is a shortage of research that explores innovation speed as a mediator of the relationship between TQM and SME performance, especially in the Nigerian setting, an investigation is truly needed to bridge this gap.
A deeper knowledge of TQM practices within the innovative context of Nigerian firms will be gained from this research. Specifically, this study will try to answer the following questions: Are there any direct impacts of total quality management practices on SME operational performance in Nigeria, what influence does TQM have on innovation speed among SMEs in Nigeria, is there a relationship between innovation speed and SME operational performance, and what is the role of innovation speed in strengthening the relationship between TQM and OP among SMEs in Nigeria?
Despite a profusion of research, relatively minimal attention has been devoted to the function of Innovation Speed (IS), much less to the relationship mediating the effect that exists between Total Quality Management (TQM) and operational performance (OP), particularly the comprehensive dimension of total quality management, and in the milieu of Nigerian manufacturing small and medium-sized enterprises.
2. Literature Review and Hypotheses Formulation
2.1. Total Quality Management
Quality may be defined as an acknowledged standard for everything, whether it is a product, a material, or a person. Because of the complexities of today’s business environment and results, preventative and customer-focused procedures are required to produce service or an actual quality product from a comprehensive plan of strategy [29]. The approach of total quality management (TQM) explains the quality of the services and procedures of all the individuals required in the development and use of services by businesses, employees, and suppliers, requiring management and customers to continue to meet the expectations of the customers [30].
Total quality management also refers to a company’s management and workers’ ongoing efforts to maintain long-term customer satisfaction and loyalty. Total quality management practically guarantees that everyone tries to enhance work culture, procedures, services, and systems for long-term success to be achieved [31]. Total Quality Management (TQM) is defined as an all-encompassing technique and management structure that aims to improve all organizations’ capabilities and processes to develop and deliver products or services that fulfill customers’ needs or requirements by being cheaper, safer, and faster than competitors or workers that are organized with their boss [32,33,34].
Total quality management was initially used mostly in the manufacturing industry, and later it became known as one of the most essential criteria for gaining a competitive advantage in the service and other sectors [35,36,37]. Total Quality Management (TQM), according to [38], is a good way to improve performance no matter where an organization works, as long as the processes of total quality management are used correctly.
2.2. Innovation Speed (IS)
According to [39], the definition of innovation is the commercialization of new concepts. It is also the incorporation, adoption, or creation of anything new to gain a competitive advantage in goods, services, working procedures, or management techniques. Lueke and Katz [40] define the term “business innovation” as an effective introduction to generating value from innovative ideas, such as a technique, product, or service [41].
Innovation speed represents how quickly a company acquires a product or a method in comparison to its competitors in the industry. There is a considerable variation in the cost and risk of an undertaking and its potential for success or failure based on the pace at which it is adopted. Depending on the company’s resources, size, and strategic goals, an early or a delayed adoption is permissible. As a result, the concept of innovation speed refers to the idea of accelerating activities from the preceding spark to the finished product, which includes all the actions that take place during the product’s development cycle. The speed at which a company can demonstrate the invention and commercialization of new things is sometimes called the “innovation speed.”
The empirical studies on innovation speed are limited. Most studies, according to [42,43], explain that innovation speed focuses on early pioneers because there are too few studies about the context and consequences of speed. Several studies suggest that the speed of innovation is closely related to the current implementation of the subject matter [41,43,44], although some stress the importance of affiliation [45,46]. At the moment, it is hard to say how important the link between IS and the survival of new goods is [47].
Despite an increase in academic studies on IS as a function of company size, there are nevertheless very few. At the same time, this trait is important because small and medium-sized businesses (SMEs) use different techniques for planning, less formal approaches, and less bureaucratic structures [48,49].
2.3. Total Quality Management (TQM) Practices and SMEs’ Performance
Several empirical studies have demonstrated that total quality management systems may enhance the operational performance of small and medium-sized enterprises. In a selection of 141 small and medium-sized enterprises in the Turkish textile sector surveyed by [32], Total Quality Management (TQM) showed a positive correlation with Operational Performance (OP). Akgün et al. [50] report that using Total Quality Management (TQM) helps companies improve their long-term bottom lines.
According to a recent study, total quality management also increases firm performance in several research circumstances [33,51,52,53,54]. Moreover, despite the significant correlation between quality and operational performance, the impact of Total Quality Management (TQM) on Operation Performance (OP) has not been well established [55].
In his 1995 study, [56] concluded that total quality management was associated with organizational performance in 54 different organizations. The results show that total quality management can bring economic benefits to an enterprise. Total Quality Management (TQM) seems to be more effective when the CEO is committed and the employees are empowered than when using benchmarks, flexible manufacturing, apprenticeships, and improved measurements. This is in line with resource-based principles of complementary resources, implying that work should be aimed toward the development of the performance of total quality management. Powell [56] argues that explicit knowledge resources, rather than Total Quality Management (TQM) methods and procedures, determine total quality management performance and that organizations possessing these resources may gain a competitive advantage with or without a total quality management economic philosophy.
Madu and Kuei [57] compared quality methods in American and Taiwanese manufacturing enterprises. Their research reveals connections between quality features and small and medium-sized enterprise performance, but no causal relationships. Moreover, these relationships differ according to the age or size of the company, and even within the same categories of companies there are differences between countries. As a result, we arrived at the following hypothesis:
Total Quality Management (TQM) has a positive impact on small and medium-sized enterprises’ (SMEs) operational performance.
2.4. Total Quantity Management and Innovation Speed
It is essential to emphasize that total quality management (TQM) elements such as top management quality practices, process improvements, the knowledge of the company’s employees and training, customer orientation, and employee quality management are critical components in developing innovative capabilities that foster and produce skills and competencies in organizations [52,58]. Human resource management, often referred to as a people-centered approach, is critical to building a culture of quality in organizations and ultimately fostering an innovative culture. Despite this positive association, there have been inconsistent and contradictory findings on total quality management and innovation when examining the previous relevant research [59,60]. Other researchers found that there are a variety of reasons why TQM adoption on innovation performance may have a negative impact. One of the factors could be that total quality management alone can stifle organizational progress or incremental innovation, resulting in companies becoming constrained.
The variety of Total Quality Management (TQM) procedures and elements, as well as the diversity of innovation typologies, all contribute to complexity. An analysis of the association between total quality management (TQM) and innovation speed has shifted from a minor to a major relationship in one industry after an analysis of the association between total quality management and innovation speed was conducted [61]. This also contributes to the creation of a culture and environment that encourages innovation both in its technical and human dimensions. Total Quality Management (TQM) is primarily reliant on client orientation because innovation is the result of integrating various practices, such as research and development, continuous improvement, design, branding, business overhauling, and the development of employees [62,63]. Total quality management practices could help with this by making it easier to connect activities that have more than one purpose.
Total Quality Management (TQM) has a positive impact on Innovation Speed (IS).
2.5. Innovation Speed and SMEs Performance
Oke et al. [64] discovered that small and medium-sized enterprises (SMEs) pay greater attention to incremental and radical innovations. As a result of these findings, there appears to be a relationship between innovation speed and performance as measured by small and medium-sized enterprises’ (SME) sales growth. This verifies the influence of innovation speed on a company and creates a solid foundation for small and medium-sized enterprises (SMEs) to re-energize their innovative activities. Innovative organizations will be more productive in attending to customer demands and developing new talent, resulting in improved performances and more revenues [19,65]. Innovation is essential for improving operational performance and efficiency [66].
Consequently, researchers are increasingly interested in the influence of various characteristics of innovation on performance [67,68]. The concept of innovation speed allows one to make a statement about an organization’s cumulative innovation speed for each area by comparing the results—whether products, processes, or service improvements—with the prospects, and considering the method by which those outcomes were achieved [69,70]. While originality and ingenuity are often associated with innovation, so are quality ideas such as standardization, low tolerance, and rigorous procedures. In terms of goods or services, characteristics such as effectiveness, quantity, features, reliability, timeliness, pricing, complexity, customer experience, and others may be used to characterize innovation speed.
However, innovation speed is seen as the most important factor for organizations implementing an innovation strategy to remain competitive. It may be more difficult to determine due to various limitations in identifying catalysts and the necessity for adding measures of so-called soft characteristics such as a comparative citation rate, patents based on citations, a linkage in science, and so on [71,72]. Rather than relying solely on total quality management, [73] stated that additional strategic resources could influence operational performance. These researchers suggested that other researchers focus on innovation because, along with other things, it will make it easier for organizations to use Total Quality Management (TQM).
Dedy et al. [74] have added to the results of innovation as a mediator. They were able to illustrate the significance of process innovation as a moderator in establishing a link between total quality management and business success. Furthermore, [19] shows that innovation acts as a bridge between Total Quality Management (TQM) and Operational Performance (OP), with employee performance as well as innovation serving as intermediates. Considering the aforementioned evidence, it is critical to explore the significance of innovation speed (IS) as a mediator in the relationship between TQM and the operational performance (OP) of SMEs, especially in a developing nation such as Nigeria.
In terms of the influence of total quality management on innovation, it has been proven that strategic considerations have a positive effect on the performance of small and medium-sized enterprises (SMEs). As a result, the following hypotheses were proposed:
Innovation speed is positively related to operational performance.
Innovation speed mediates the relationship between total quality management and the operational performance of small and medium-sized enterprises.
Using the four hypotheses formulated for the present study, Figure 1 (below) depicts the heuristic model, showing all the relationships existing between the variables of the study.
3. Methodology
3.1. Research Design, Population, and Sample Size
A quantitative cross-sectional survey was conducted with the help of an adapted survey that had been prepared and was easy to administer to acquire relevant information from targeted respondents, and the results were published. The focus of this research is on manufacturing businesses that are listed in the small and medium-sized enterprises (SMEs) business directory (SMEDAN). Out of a total population of 41.5 million registered businesses [75], 385 small and medium-sized manufacturing enterprises were selected using sampling software, which was the smallest sample size estimated. Afterwards, the sample size that was determined was doubled to resolve the problem of non-response while also minimizing the sampling error [76]. In total, 484 questionnaires were valid for the study out of 770 surveys, resulting in a 63 percent response rate for the study. To assess the appropriateness of the sample size, a post hoc analysis was performed using the G*Power 3.1.9.7 tool [77]. The findings of the power analysis indicated that a minimum sample size of 385 is required to attain a statistical power of 0.99 for the structural model, at a 5 percent significance and effect size threshold of 0.15. Thus, findings from the determined sample size of the 484 people interviewed in this study indicated an adequate and well-represented study when compared to the required sample size [78].
3.2. Measures
To obtain data, a closed-ended questionnaire was used, with questions drawn from prior relevant studies. Total quality management (TQM) practices were operationalized using 28 items based on five dimensions: top management quality practices (7), employee quality management (EQM-6), process management (PM-6), employee knowledge and training (EKE-5), and customer orientation (CF-4) [79]. In this study, innovation speed (IS) was measured using a five-item scale developed by [80], as cited by [81,82], and operational performance (OP) was measured using a scale developed by [83,84], as used by previous studies, which contained six (6) items. The scale was adapted and modified from several studies [81,84,85,86]. Thus, the Likert scale was used to score and measure the responses to the questionnaire items.
4. Results
4.1. Data Analysis
Descriptive statistics provide information on the respondents’ characteristics, allowing the researcher to decide whether the selected respondents are suitable for the study. Table 1 indicates the demographic profile of the respondents, while Table 2 presents the correlation among the variables under study.
Table 2 depicts the inter-correlations of the latent and observable variables. Statistically substantial connections between total quality management, innovation speed, and operational performance confirmed all of the assumptions. Explicitly, it is evident that Innovation Speed (IS) has a positive relationship to Operational Performance (OP) (r = 0.43, p < 0.01) and Total Quality Management (TQM) (r = 0.05, p < 0.01), with large and low effect sizes, respectively. Total quality management and operational performance were shown to have a moderately positive relationship. (r = 0.30, p < 0.01).
4.2. Reliability Test
A two-stage partial least squares (PLS) model was used to evaluate both the measurement and structural models. The model was put to the test using convergent validity. The reliability of a tool that is used to investigate a single idea is affected by several elements. For the factor loadings, AVE, and composite reliability, the converging validity was utilized.
As recommended by [87,88], all the items have outer loadings (λ) above 0.5 for composite reliability and its sister metrics (Cronbach’s alpha and rho_A) are above the threshold of 0.7 for composite reliability and its sister metrics [89]. In the measurement model, there is a convergent item–construct structure. According to the earlier study, the construct’s convergence validity is still adequate, as evidenced by the values of AVE being greater than 0.5 [46,52,90,91]. The findings are summarized in Table 3.
4.3. Validity Test
A method based on [90] was used to determine the discriminant validity, the correlation values between the constructs, and the square root of the AVEs for each construct. Table 4 shows the square root of all AVEs in the connection between constructs. In cases where the square root of the AVE is greater than the correlation between each construct inside a construct, the measurement model is deemed to be acceptable. From Table 4, the evidence reveals that the square root of the AVE in bold and diagonal is greater than the inter-construct correlation for each construct in the measurement model. Hence, this connotes the presence of discriminant validity.
Recently, the validity of the criterion from [90] for determining the presence or absence of discriminant validity has been called into question due to concerns about its reliability [92].
Therefore, an HTMT correlation ratio was suggested, and a Monte Carlo simulation was performed to demonstrate the heterotrait–monotrait (HTMT) technique’s superiority over the [90] method. The HTMT test result is shown in Table 5. When the discriminant validity was assessed using the heterotrait–monotrait (HTMT) ratio, the two thresholds indicated by [93,94] were achieved. On average, the HTMT values for all the components were found to be less than 0.90, demonstrating the existence of discriminant validity amidst the constructs in the model.
Statistical techniques for both descriptive and inferential analysis were used in this investigation. The proposed structural model was subjected to a series of string tests, namely, psychometric and multi-collinearity tests, with confirmation by the Partial Least Square Structural Equation Modeling (PLS-SEM) using Smart PLS 3.0 version to reflect the nature of the relationship between the variables in the investigation (PLS-SEM). Human resource management and marketing studies currently use this method to forecast equations for the study model and produce variable correlations, as practiced by [47,78]. The unrotated exploratory factor analysis (EFA) was also utilized to address common technique biases to determine the single factor that accounted for the majority of the variance in all the research components. Since the single factor variance shows a technique bias, we can conclude the verification of the results of [86]. The results showed that just 27.04 percent of the total variation could be explained by the Eigenvalue of a single factor, which is less than 50 percent. In other words, it demonstrates that there is not a problem with technique bias.
4.4. Hypotheses Test
The results of the PLS-SEM are presented in Table 6. In this work, the structural model as well as the measurement model were both examined in detail. Using a bootstrapping technique with 5000 replicate samples, the structural model is primarily used to analyze the causal relationship between the constructs in an instrument by estimating the path coefficient, R-squared, t-statistic, p-value, and f2 using the constructs in the instrument.
The direct relationship between the predictor variables and outcome variables was examined, and the findings indicate that total quality management (TQM) has a positive impact on operational performance (OP). People, as a dimension of total quality management, have a positive and significant relationship with operational performance (H1: β = 0.281, t = 6.224, p < 0.05) and TQM has a positive impact on innovation speed (H2: β = 0.253, t = 4.111, p < 0.05), while the path between innovation speed and with respect to operational performance is found to be significant (H3: β = 0.418, t = 9.059, p < 0.05). Simultaneously, it is believed that the indirect effect of IS on the relationship between total quality management and operational performance is significant (H4: β = 0.221, t = 4.100, p < 0.05). As an outcome, the direct and indirect hypotheses of the research model were validated in Figure 2 and Figure 3.
In contrast, [95,96] recommend reporting the substantive significance (f2), also known as the effect size. Additionally, the beta coefficients (β), statistical significance (p-value), and explained variance (R2) illustrate the true magnitude of the observed effects. The effect size (f2) for the path TQM → OP is 0.107, indicating that total quality management had a small antecedent effect on the operational performance, as suggested by [95], because (f2) was less than 0.15. Moreover, it is noted that the effects of the IS → OP and TQM → IS paths are moderate, as the (f2) values (0.238 and 0.203, respectively) fall within the range of the medium effect size. The findings indicate that there is a substantial and statistically significant connection between total quality management (TQM) and operational performance (OP) in small and medium-sized manufacturing enterprises (SMEs), with innovation speed serving as a mediating factor in this relationship.
5. Discussion
This study establishes positive relationships between total quality management components and operational performance, as well as the mediating role of innovation speed among Nigerian manufacturing small and medium-sized enterprises. As small and medium-sized enterprises (SMEs) make significant contributions to the development of a nation’s economy, the findings of this study have revealed the urgent necessity of quality practices for their survival, as these practices have a significant impact on their performance and ability to continue operations [97]. The accomplishment of a company’s goals cannot be isolated from the quality of its products or processes. To be competitive, the corporation must constantly enhance the quality of its products. As a result, one of the greatest impediments to the expansion of small and medium-sized enterprises (SMEs) is a lack of quality [98].
This also indicates that small and medium-sized manufacturing organizations should encourage total quality management procedures, with an emphasis on top management quality practices, customer orientation, staff quality management, process management, employee education, and training. This is supported by the RBV hypothesis, which aims to ensure that businesses make the best use of their limited internal resources to maximize their earnings [99]. In the findings of Ali et al. [100], there was a strong emphasis on the consistency of the findings with earlier research, which was corroborated by [101,102], who found that incorporating the Total Quality Management (TQM) component positively impacts operational performance (OP).
This was also proven in the studies conducted by other researchers [6,17,20], who were able to establish the positive relationship that exists between the elements of total quality management and operational performance, which in turn agrees with the findings in hypothesis one of the current study.
Furthermore, the findings are congruent with those of [103], emphasizing that excellent quality across business operations leads to improved performance. In particular, [20,79,90,104,105] discovered a positive and substantial relationship between total quality management and operational performance in the context of small and medium-sized enterprises. On the other hand, this result disagrees with other studies that could not find a significant direct effect of TQM on operational performance [57,58].
When conducting commercial or company research, innovation speed is one of the most crucial factors [28]. Innovation speed techniques have a substantial impact on an organization’s operational performance [43]. Most of the research conducted by [52,59,60,63] on the connection between total quality management and innovation speed after identifying the numerous innovative procedures in the area of process and product merchandise has established a positive relationship between the two variables (TQM & IS). Thus, this also falls in line with the results obtained during the performance of this study.
The importance of the dimensions of innovation speed in mediating the connection between total quality management (TQM) and operational performance (OP) is modest, emphasizing an indirect impact of the innovation phase during the acceleration to attain greater performance in small and medium-sized enterprises (SMEs). This is supported by the findings of the study conducted by [73], who argue that rather than depending solely on total quality management, various strategic resources may affect performance should be considered.
Similarly, the relevance of risk-taking as a moderator of the link between human resource management as a total quality management component and operational performance has been demonstrated. As a result, this research is consistent with the previous studies in that total quality management can only be accomplished if people (human resources) are properly taught and actively participate in quality management to increase operational efficiency. Manufacturing firms may extend their operations by concentrating more on innovation, which would surely improve their performance. People think that the rate of innovation inside companies is good for operational efficiency and long-term competitiveness [106].
Total quality management is broadly viewed as a firm-wide philosophy of management covering facets that include aspects such as product quality, process quality, service quality, and the quality of the outcomes from other activities in the business. This research thoroughly investigates the relevance of innovation speed in mediating the relationship between total quality management and operational performance. These findings offer vital insights into the increased performance of Nigerian small and medium-sized enterprises (SMEs) in the manufacturing sector, concluding that total quality management and innovation alone are unable to improve operational performance.
6. Theoretical and Managerial Implications
By investigating the total quality management component, this research will contribute to the current understanding of total quality management and innovation speed to help determine operational performance. This research has several theoretical and practical implications. First, among such contentious results is the uniqueness of this research, particularly with the model demonstrating the mediating impact of innovation speed in the link between total quality management and operational performance. However, if examined as a dimensional construct rather than an individual element contributing to operational performance outcomes, the total quality management dimension may have a distinct influence on the intervention of innovation speed. The model developed for this study provides insight into the performance of Nigeria’s manufacturing small and medium-sized enterprises (SMEs) from the perspectives of total quality management and information systems. The results of the statistical analysis and empirical findings provide significant contributions to the theory and practice of the subject area. Lastly, the study’s results will prove to be thought-provoking for total quality management developers and experts in the manufacturing industry around the world because they show the link between TQM and the speed of new ideas.
Several internal and external factors that influence the performance of Nigerian manufacturing companies have a significant impact on their overall performance. In an operational management environment, this study provides new insights on the alignment between the total quality management dimension and innovation speed as a component of operational performance, thereby assisting managers in shifting their focus to maintaining and improving quality alongside innovation speed to achieve competitive advantages. The speed with which enterprises produce new goods and services, as well as the speed with which such goods and services are brought to the market, are two facets of innovation speed. Some businesses place a higher value on one than the other. Innovation leaders, for example, prioritize development, while quick followers prioritize delivery.
The relevancy of innovation speed may be observed in the necessity to constantly monitor a product’s or process’ innovativeness and to maintain the quality standards necessary for its operation, from customer orientation through management orientation to organizational elements.
Therefore, speed has long been seen as a key quality of successful inventors. Companies that foster innovation are more likely to keep up with changing consumer trends, keep rivals at bay, achieve even lower prices, and enhance quality.
Thirdly, since an improved operating performance is achieved as a result of an effective total quality management implementation, top management must continue to provide employees with regular training in problem-solving with interpersonal abilities, quality improvement abilities, and creative ideas for maintaining high-quality product and service delivery as required [107,108].
Finally, small and medium-sized enterprises (SMEs) in the manufacturing sector are expressly advised to encourage innovative learning activities to improve the effectiveness and efficiency of organizational operations as well as to enhance sustainable innovative abilities in both the short and long term [109].
7. Conclusions
Total Quality Management is a cogent construct in manufacturing firms, and previous research has highlighted its impact on several financial and non-financial organizational outcomes. The current study aimed to identify the influence of TQM on OP with the mediating effect of innovation speed. The results revealed that TQM positively influences the innovation speed and operational performance of small and medium-sized manufacturing firms in Nigeria. The findings suggest that the significance of innovation speed in this relationship is that firms achieving accelerated innovativeness, both in process and product, will enhance their operational performance. The study also revealed that for any manufacturing firm (small or medium) to flourish in its day-to-day operations, there is a need to actively integrate all total quality management practices to get their benefits. However, it is recommended that management empowers and regularly involves its employees. Continuous improvement should be part of all the processes and job descriptions. Top management should create the right culture through proper hiring. Business process reengineering can help small and large manufacturing companies capture customers’ hearts.
This study’s findings can help Nigerian manufacturing managers understand the importance of quality management and continual improvement. It may inspire managers to dedicate organizational resources to TQM adoption for greater performance. Organizations should always engage in business innovations speedily to outweigh their competitors and embed day-to-day practices focused on better understanding the needs of their customers for the further improvement of their offered products, and should practice constant value-making through employee training for the fast transformation and actualization of the enterprise’s goals.
8. Limitations and Future Studies
Despite the contributions made by this study, there are a few drawbacks. Firstly, the collaboration of other dimensions of Total Quality Management (TQM) might have either a positive or negative influence on their interactions, and future studies should investigate how innovation speed and processes jointly affect operational performance as well as their exploration in related industries such as services, where quality truly matters.
Second, the results presented herein suggest that top management quality practices and employee quality management are dimensions of total quality management that have little influence on improving operational performance. Therefore, future studies would be fruitful if they explore the top management support and CEO-related factors that may influence and strengthen this relationship. Thus, if the moderating influence of innovation speed and its dimensions can be investigated, this research will help develop future studies.
Conceptualization, O.N.A.; Software, B.R.O.; Supervision, M.G. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Girne American University.
Not applicable.
Not applicable.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Demographic characteristics of the respondents.
| Variables | Categories | Freq (n = 484) | Percentage |
|---|---|---|---|
| Gender | Male | 364 | 75.2 |
| Female | 120 | 24.8 | |
| Age | Below 30 years | 70 | 14.4 |
| 30–35 years | 171 | 35.3 | |
| 36–40 years | 115 | 23.8 | |
| Above 40 years | 128 | 26.5 | |
| Working experience | Below 5 years | 84 | 17.4 |
| 5–10 years | 23 | 4.7 | |
| 11-15 | 151 | 31.2 | |
| Above 15 years | 226 | 46.7 |
The observable and latent variables are correlated.
| Variables | Mean | SD | 1 | 2 | 3 |
|---|---|---|---|---|---|
| 1. Innovation Speed | 3.834 | 0.990 | 1.000 | 0.433 | 0.053 |
| 2. Operational Performance | 4.328 | 0.932 | 1.000 | 0.303 | |
| 3. Total Quality Management | 1.000 |
Measurement Model.
| Convergent Validity | Internal Consistency | Discriminant Validity | |||||
|---|---|---|---|---|---|---|---|
| Latent Variables | Indicators | Loadings (λ) | CA | rho_A | CR | AVE | F-L |
| Total Quality Management (TQM) | |||||||
| Customer Focus (CF) | 0.874 | 0.874 | 0.914 | 0.725 | 0.852 | ||
| CF1: “Customers are encouraged to submit complaints and proposals for quality improvement” | 0.858 *** | ||||||
| CF2: “Customers’ complaints, satisfaction level, and proposals for quality improvement are selected” | 0.863 *** | ||||||
| CF3: “Customers’ needs, requirements, desires, and expectations are recorded and analyzed” | 0.830 *** | ||||||
| CF4: “Customers’ needs, requirements, desires, and expectations are recorded and analyzed” | 0.857 *** | ||||||
| Employee knowledge education (EKE) | 0.913 | 0.914 | 0.935 | 0.742 | 0.861 | ||
| EKE1: “Educational programs are evaluated” | 0.832 *** | ||||||
| EKE2: “The employees have knowledge and know-how” | 0.893 *** | ||||||
| EKE3: “The employees are educated in subjects concerning their specialty and daily work” | 0.873 *** | ||||||
| EKE4: “Educational subjects are absorbed by employees” | 0.853 *** | ||||||
| EKE5: “Resources are provided for educational reasons” | 0.855 *** | ||||||
| Employee quality management (EQM) | 0.906 | 0.937 | 0.926 | 0.677 | 0.823 | ||
| EQM1: “Employees who improve quality are awarded” | 0.762 *** | ||||||
| EQM2: “Employees are evaluated” | 0.859 *** | ||||||
| EQM3: “Employees are motivated to improve their performance” | 0.830 *** | ||||||
| EQM4: “Data are collected from employees regarding their satisfaction and suggestions for improvements” | 0.758 *** | ||||||
| EQM5: “Employees take initiatives” | 0.859 *** | ||||||
| EQM6: “Quality data are taken into consideration from employees during their daily work” | 0.860 *** | ||||||
| Process management (PM) | 0.874 | 0.882 | 0.905 | 0.616 | 0.785 | ||
| PM1: “Process and product nonconformities are detected through internal audits” | 0.827 *** | ||||||
| PM2: “Process quality data are recorded and analyzed” | 0.780 *** | ||||||
| PM3: “The critical processes are determined-evaluated” | 0.842 *** | ||||||
| PM4: “The points/places where time is lost are detected to minimize the cost of the internal processes” | 0.837 *** | ||||||
| PM5: “All employees are provided with work instructions” | 0.714 *** | ||||||
| PM6: “Specific organizational structure has been formulated to support quality improvement” | 0.696 *** | ||||||
| Top management quality practices (TMC) | 0.726 | 0.723 | 0.846 | 0.647 | 0.804 | ||
| TMC1: “Top management sets the quality issues on the agenda of the managers’ meetings” | 0.837 *** | ||||||
| TMC2: “Top management actively participates in quality improvement efforts” | 0.823 *** | ||||||
| TMC5: “Top management gives the authority to employees to manage quality problems” | 0.750 *** | ||||||
| Innovation Speed | (IS) | 0.806 | 0.817 | 0.865 | 0.563 | 0.750 | |
| IS1—“Our organization is quick in coming up with novel ideas as compared to key competitors” | 0.647 *** | ||||||
| IS2—“Our organization is quick in new product launching as compared to key competitors” | 0.777 *** | ||||||
| IS3—“Our organization is quick in new product development as compared to key competitors” | 0.806 *** | ||||||
| IS4—“Our organization is quick in new processes as compared to key competitors” | 0.761 *** | ||||||
| IS5—“Our organization is quick in problem-solving as compared to key competitors” | 0.751 *** | ||||||
| Operational Performance | (OP) | 0.939 | 0.941 | 0.953 | 0.803 | 0.896 | |
| OP1—“Customer satisfaction of our organization is better as compared to key competitors” | 0.896 *** | ||||||
| OP2—“Quality development of our organization is better as compared to key competitors” | 0.904 *** | ||||||
| OP3—“Cost management of our organization is better as compared to key competitors” | 0.873 *** | ||||||
| OP4—“Responsiveness of our organization is better as compared to key competitors” | 0.902 *** | ||||||
| OP5—“Productivity of our organization is better as compared to key competitors” | 0.906 *** | ||||||
Notes: CA = Cronbach’s Alpha, CR = Composite Reliability, rho = rho_A reliability indices, AVE = Average Variance Extracted, (F-L) = Italicized values are the square root of AVE. *** OP6, TMC3. TMC4, TMC6, TMC7 were removed due to poor loadings.
Validity in discrimination (Fornell–Larcker Criterion).
| CF | EKE | EQM | IS | OP | PM | TMC | TQM | |
|---|---|---|---|---|---|---|---|---|
| CF | 0.852 | |||||||
| EKE | 0.679 | 0.861 | ||||||
| EQM | 0.069 | 0.100 | 0.823 | |||||
| IS | 0.007 | 0.047 | −0.002 | 0.750 | ||||
| OP | 0.273 | 0.254 | −0.138 | 0.433 | 0.896 | |||
| PM | 0.700 | 0.569 | 0.078 | 0.051 | 0.265 | 0.785 | ||
| TMC | 0.034 | 0.068 | 0.257 | 0.248 | 0.230 | 0.036 | 0.804 | |
| TQM | 0.788 | 0.762 | 0.152 | 0.053 | 0.303 | 0.708 | 0.107 | 0.575 |
Validity in discrimination (Heterotrait–Monotrait Ratio criterion).
| CF | EKE | EQM | IS | OP | PM | TMC | TQM | |
|---|---|---|---|---|---|---|---|---|
| CF | ||||||||
| EKE | 0.760 | |||||||
| EQM | 0.071 | 0.106 | ||||||
| IS | 0.051 | 0.071 | 0.068 | |||||
| OP | 0.300 | 0.272 | 0.157 | 0.489 | ||||
| PM | 0.790 | 0.636 | 0.087 | 0.079 | 0.290 | |||
| TMC | 0.047 | 0.083 | 0.321 | 0.315 | 0.269 | 0.052 | ||
| TQM | 0.875 | 0.852 | 0.566 | 0.145 | 0.381 | 0.875 | 0.415 |
Path Analysis Result.
| Relationship | Std. Beta | Std. Error | t-Value | p-Value | F2 | R 2 | Decision |
|---|---|---|---|---|---|---|---|
| H1: TQM |
0.281 | 0.044 | 6.224 | 0.000 ** | 0.107 | 0.266 | Supported |
| H2: TQM |
0.253 | 0.045 | 4.111 | 0.001 ** | 0.203 | 0.203 | Supported |
| H3: IS |
0.418 | 0.046 | 9.059 | 0.000 ** | 0.238 | 0.266 | Supported |
| H4: TQM |
0.221 | 0.039 | 4.100 | 0.002 ** | - | - | Supported |
** Significant at p < 0.10 level (two-tailed).
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; Olaleye, Banji Rildwan 2
1 Faculty of Business and Economics Department, Girne American University, North Cyprus, Via Mersin 10, Kyrenia 99428, Turkey;
2 Department of Business Administration, Faculty of Management Sciences, Federal University Oye Ekiti, Oye 360101, Nigeria;