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Article

Assessing Operational Performance of Manufacturing Companies in the Context of Environmental Dynamism, and Competitive Strategy

by
Arzu Karaman Akgül
Department of Business Administration, Faculty of Economics and Administrative Sciences, Yıldız Technical University, Istanbul 34210, Turkey
Adm. Sci. 2026, 16(4), 179; https://doi.org/10.3390/admsci16040179
Submission received: 4 January 2026 / Revised: 31 March 2026 / Accepted: 31 March 2026 / Published: 8 April 2026
(This article belongs to the Special Issue Building Resilient and Agile SMEs: Strategic Responses to Digital Disruption and Transformation)
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Abstract

Today’s global and competitive environment forces companies to revise their competitive strategies and assess their operations’ performance. Customers are demanding new products and services, and organizations should adapt to the changing requirements of the customers. Companies may achieve excellence in their operations with cost reduction, by reducing time-to-market, and through improvements in delivery and quality. The main contribution of this study is assessing the linkages among operational performance (OP), environmental dynamism (ED), and competitive strategy (CS) in an emerging economy, Turkey. This study also aims to define the dimensions used to assess the operational performance, which are called the competitive manufacturing priorities in the operations management literature. To test the linkages between environmental dynamism, operational performance, and competitive strategy, a structural model is proposed. Analyses are conducted in SPSS 28.0 and AMOS 24.0 programs using the data gathered from Turkish manufacturing companies. Since 99.8% of firms operating in Türkiye are SMEs, most of the companies participating in this study (124 of 211) are also SMEs, and another contribution of this study is understanding the dimensions affecting the operational performance of SMEs According to the results, environmental dynamism has a significant relation to operational performance, and operational performance has a positive linkage with competitive strategy as well. The results also indicate that the most important dimensions used in assessing operational performance are customer satisfaction and supplier performance, as expected for manufacturing companies. Furthermore, the results of this study are expected to support organizations in developing and implementing effective strategies that integrate new capabilities and environmental considerations into their competitive strategy. As expected in SMEs, the most used competitive strategy is found to be “cost leadership,” because they can achieve operational performance by efficiently using resources, and by minimizing the production and transaction costs, they can enhance their competitiveness in the market.

1. Introduction

Rough competition, changing customer needs, and technological developments create an uncertain and dynamic environment (Tracey et al., 1999). The imperatives of competition and the continuous growth of the industrial sector force companies to have competitive capabilities. To survive in the dynamic and competitive environment, companies can use an internal approach, the Resource-Based View (RBV) method, focusing on the company’s assets, expertise, capabilities, and intangible assets (Lubis, 2022). RBV helps companies develop an effective strategy and dynamic capability to gain and/or maintain competitive advantage (Chahal et al., 2020). During the first industrial revolution, manufacturing companies were expected to provide a limited product line, increase productivity, lower costs, and benefit from economies of scale. The post-industrial era requires organizations to adapt to the changing customer needs and to provide value-added products, which are designed, produced, and delivered by improved production systems. In other words, the post-industrial period helps companies to understand the vitality of their inner operations on companies’ strategy (Anatan & Radhi, 2007). Production and operations management (POM) is an area which is responsible for the coordination of production processes inside an organization. POM is responsible for designing and planning these processes, transforming inputs to outputs efficiently, with effective use of resources, including labor, materials, and technology (Salah et al., 2023). In addition, operations management scholars contributing to the operations management area have been focusing on various concepts related to the improvement of operational performance and a sustainable competitive edge (Boyer & Lewis, 2002; Ward et al., 2007; Johannessen & Olsen, 2009).
Drawing on the relevant literature, this study contributes to the existing research on operations management in a number of ways. In the OM literature, there have been several studies dealing with various topics of operations strategy, ranging from its content (Leong et al., 1990; Vickery et al., 1993; Ward et al., 1996) to its significance for gaining competitive advantage (Hitt et al., 1998; Upton et al., 2004). Although there are several studies about operations strategy, in recent years, the term “operational performance” has gained more importance in the production and operations management literature, with its definition by Boyer and McDermott (1999) as the effective implementation of operational strategy. Therefore, the present study aims to link operational performance to the factors related to operations strategy, environmental dynamism and competitive strategy, and provide further evidence to the empirical studies that have discussed the relationship among environmental dynamism (ED), operations strategy (OS), and competitive strategy (CS) (e.g., Ward & Duray, 2000; Ellitan, 2017). Based upon a detailed literature review, new operational performance dimensions are added to the existing list of manufacturing competitive priorities used in the previous studies to better evaluate operational performance.
The remainder of this paper is organized as follows. The next section provides a structural model investigating the linkages among environmental dynamism, operational performance, and competitive strategy, a brief review of the relevant literature, and sets out the study’s hypotheses. The research methodology is presented in the third section. The results and discussion are in Section 4, followed by the conclusion and implications.

2. Theoretical Framework

Drawing on the pertinent literature, there is evidence for the significance of operations function and its performance. However, there is a debate regarding the assessment of operational performance. In the operations management literature, authors proposed and used different dimensions for evaluating the effectiveness of operations and labeled them in different ways, ranging from manufacturing tasks (Skinner, 1969), competitive priorities (Ward et al., 1998), to order winners and qualifiers (Hill, 1989). In this study, the term manufacturing competitive priorities is used to explain the operational performance dimensions.
Some authors like Badri et al. (2000) and Ward et al. (1995) used the term operations strategy, while others (e.g., Ward & Duray, 2000; Boyer & McDermott, 1999; Acquaah et al., 2011) preferred to use the term manufacturing strategy by stressing the effectiveness of a manufacturing firm. The perspective that states that operational strategy is derived from capabilities is consistent with the Resource-Based View (RBV).
In the literature, there is broad agreement about the primary dimensions of operational performance, which are also called competitive priorities: cost, quality, delivery, and flexibility (Acquaah et al., 2011; Hallgren et al., 2011; Jabbour et al., 2012; Schoenherr et al., 2012, Cai & Yang, 2014; Hung et al., 2015). However, given the imperatives of global competition, some authors have recently added new competitive priorities (e.g., new product development (NPD), customer satisfaction, production times, and supplier performance) to the existing body of competitive priorities (Peng et al., 2011; Bayraktar et al., 2012; Shavarini et al., 2013; Huang et al., 2014). Table 1 represents the common set of competitive manufacturing priorities (operational performance dimensions) along with their definitions.
These priorities are increasingly examined through the Resource-Based View (RBV), which emphasizes internal resources and capabilities as the primary drivers of competitive advantage. The VRIN framework (valuable, rare, inimitable, and non-substitutable resources) in RBV offers a theoretical foundation for understanding how manufacturing priorities result in long-term competitive advantage (Barney, 1991; Raduan et al., 2009, Abel et al., 2025).
Based upon the VRIN framework, for accomplishing the “valuable” criterion, companies should understand and respond to changing market needs by improving their manufacturing competencies and capabilities such as flexibility, agility, superior quality, and efficiency (Ward & Duray, 2000; Chavez et al., 2017).
If a company can provide rare competitive manufacturing priorities, like internal cross-functional integration and external integration with key customers and suppliers, instead of common priorities such as cost effectiveness and flexibility, this may enhance its performance and agility in supply chains (Boyer & Lewis, 2002; Dubey et al., 2019).
To be inimitable, companies can use some features like having internal integration, information sharing, joint planning, cross-functional teams, which are difficult to imitate for competitors. These characteristics are so important because, for example, if there is a delay, customers do not care which function caused the delay; they simply want to know whether the order has been fulfilled, and the company should have an integrated customer order fulfillment process, in which all involved activities and functions work together to be unique (Flynn et al., 2010).
Non-substitutability means there are no alternative strategies which can replace the competitive manufacturing priorities. The cumulative capabilities perspective suggests that improvements in quality can support enhancements in delivery and flexibility, eventually leading to cost advantages, thereby reinforcing VRIN characteristics, especially non-substitutability (Rosenzweig & Easton, 2010).
In sum, when the competitive manufacturing priorities are valuable, rare, inimitable, and non-substitutable, they become the key to sustained competitive advantage. The RBV thus draws a theoretical framework for explaining how operations strategy translates into competitive advantage. The literature emphasizes that competitive priorities must be aligned with competitive strategy and environmental dynamism to gain/maintain competitive advantage (Skinner, 1969; Hayes & Wheelwright, 1984; Ward & Duray, 2000).

2.1. Environmental Dynamism

Environmental dynamism is defined as the rate of change and unpredictability in the external environment, especially about technology, markets, and competition (Garg et al., 2003; Liang et al., 2024; Kim et al., 2025; Ma et al., 2025; X. Zhang et al., 2025). In such an environment, companies should try to cope with market unpredictability and accept it as a fact (Kickert, 1985). What is important here is being quicker in adapting to the uncertainties than their rivals (Douglas, 2002; Badri et al., 2000).
In dynamic environments, companies confront various threats and opportunities. Examples of threats could be technological developments, market instability, shorter product life cycle, and unforeseen attitudes of competitors. These issues raise the risk of obsolescence and push firms to develop new products, markets, and technologies. Environmental dynamism provides companies with opportunities when they can adapt to the changing requirements of customers and technological developments by offering new products, processes, and services (Kim et al., 2025).
From an RBV perspective, firms achieve superior performance when they possess resources that are valuable, rare, inimitable, and non-substitutable (VRIN) (Barney, 1991). In a dynamic environment, companies can increase the value of their resources by demonstrating timely responsiveness and rapid and flexible product innovation (Teece et al., 1997). Environmental dynamism also forces companies to develop rare and inimitable resources such as trade secrets and certain specialized production facilities and engineering experience. These resources are called dynamic capabilities, which are difficult to replicate by competitors, since they contain tacit knowledge (Teece et al., 1997).
In dynamic environments, there is a mismatch between the operational resources and the external needs of the market. Companies which have strong dynamic capabilities may see these changes, seize opportunities, and reconfigure their resources accordingly. This resource reconfiguration process could be defined as the mechanism through which environmental dynamism affects operational performance (Teece, 2007).
The scholars who first linked environmental issues to operational strategy are Swamidass and Newell (1987). They measured it with the perception of environmental uncertainty. The dimensions of “environment” are defined as the cost of doing business, availability of workers, competitors, and environmental dynamism in the study of Ward et al. (1995). These dimensions were also employed by Badri et al. (2000) to define environmental uncertainty. In their study, Ward and Duray (2000) also used environmental dynamism as a construct and aligned it with operational strategy and competitive strategy. Their study proposed that the effectiveness of manufacturing strategy depends on environmental dynamism, which supports the RBV-based explanation. Similarly, a recent study of Dubey et al. (2019) supports that environmental dynamism strengthens the positive relationship between operational capabilities and firm performance.
In their study, Amoako-Gyampah and Boye (2001) specifically examined the relationships between the business environment and the operations strategy of companies in the Ghanaian manufacturing industry. Their findings in general confirmed those of Ward et al. (1995) and Badri et al. (2000).
Barney (1991) emphasized that sustainable performance is the result of having valuable, rare, inimitable, and non-substitutable resources. However, valuable resources are dynamic when the environment is dynamic. Environmental dynamism refers to changes in technology, products, and services, and in customers’ tastes and preferences. As the environment changes, the existing valuable resource(s) may become obsolete, and new capabilities emerge. In other words, the valuable resources must be determined by the environment (Priem & Butler, 2001). Thus, companies need to assess and reconfigure their resources according to the changes in the environment.
In dynamic environments, companies use dynamic capabilities (Teece et al., 1997) to enhance their operational performance by increasing flexibility, delivery performance, and reliability. There are several studies emphasizing the importance of dynamic capabilities in dynamic environments (Wu, 2010; Eisenhardt & Martin, 2017; Samsudin & Ismail, 2019; Kero & Bogale, 2023).
However, RBV suggests that companies possess VRIN resources, and it also emphasizes the critical role of resource orchestration, which refers to structuring, bundling, and leveraging resources (Sirmon et al., 2007; Sirmon et al., 2011). Environmental dynamism also causes heterogeneity in terms of resources and capabilities among companies. Some of the organizations can easily adapt to the dynamic environment, while others fail. This heterogeneity leads to a difference between the companies in terms of operational performance (Peteraf, 1993). In addition, in dynamic environments, companies should also have knowledge-based capabilities such as organizational learning. It helps companies to understand the routine and do it better (Cepeda & Vera, 2007).
As a result, for companies assessing their valuable resources according to the environmental dynamism, activating their dynamic capabilities, orchestrating resources, and enhancing organizational learning can improve and/or maintain their operational performance in dynamic environments. Drawing on these arguments and supporting evidence, the following hypothesis is proposed to examine the effect of environmental dynamism on operational performance:
H1. 
Environmental dynamism has a positive and significant relation to operational performance.

2.2. Operational Performance–Competitive Strategy

Operational performance is defined as the efficiency and effectiveness of resources, internal processes, and operations of an organization in accomplishing its strategic goals. There are a wide variety of measures, such as productivity, quality, cost, and customer satisfaction (Adeniji et al., 2024). Operational performance is evaluated by competitive manufacturing priorities, which determine the competitive advantage of a company over its competitors. To take orders from competitors, companies should focus on their operational performance (Antonio et al., 2007). Thus, competitive manufacturing priorities are critical for firms to become more competitive or to maintain their competitiveness.
As discussed before, the Resource-Based View has evolved to include dynamic capabilities, which are defined as the ability to reconfigure the resources in dynamic environments (Teece, 2007). Operational performance ensures alignment between dynamic capabilities and external needs, which enables companies to achieve superior performance. In their study, Jayaram et al. (2014) found that operational capabilities directly affect the competitive strategy and provide a competitive advantage to the companies. Chatha and Butt (2015) demonstrated that operational performance mediates the relationship between manufacturing strategy and firm performance.
Skinner (1969) first discussed the linkage between competitive strategy and operational performance in his seminal study. Davis and Vokurka (2005) defined the competitive strategy as a long-term and comprehensive competition formula of a firm related to its goals and policies for achieving those goals. The firm must be aligned with its operating environment to develop a competitive strategy. The primary environmental factor is the industry in which the company operates. The dynamism in the environment affects all the companies in the industry; the ability of the company to manage the effects is crucial (Porter, 1998). This study uses Porter’s (1998) generic competitive strategies, which are cost, differentiation, and focus, and Porter’s classification covers all other competitive strategy classifications. Porter (1998) indicated that a company will obtain a competitive edge by implementing one of these strategies, which enables the company to outperform its competitors. Competitive strategy should be in accordance with the company’s aim and objectives (Davis & Vokurka, 2005), and to be competitive, the company should align its operational strategy with its competitive strategy (Sahoo, 2021).
Operational performance is defined as companies’ capability of allocating resources efficiently and determines the competitive strategy of the company. Companies providing high-quality and innovative products execute a differentiation strategy (Swink & Harvey Hegarty, 1998), while companies changing their prices in reaction to market conditions dynamically have cost leadership skills (Rosenzweig et al., 2003). In other words, competitive manufacturing priorities used in measuring operational performance have a critical impact on the competitive strategy that the company pursues. Based upon the Resource-based View, competitive manufacturing priorities are the resources of the company which enables it to choose the competitive strategy that maximizes its revenue. Drawing upon these arguments, the hypothesis below is drawn:
H2. 
Operational performance has a positive and significant relation to competitive strategy.
Drawing on the relevant literature, a structural model that incorporates environmental dynamism, operational performance, and competitive strategy is built. The research model of this study is shown in Figure 1.

3. Methodology

3.1. Sample and Procedure

The data are collected by conducting a structured survey with production managers, business managers, and chief managers in Turkish manufacturing companies. The questionnaire began with company-related and demographics questions, which are open-ended, including the age, the scale, and the capital structure of the company. The following section in the survey asks participants to assess their operational performance. The statements that are frequently cited in the literature are identified to define the determinants of these variables.
To ensure validity, the survey is first conducted in two companies. The first company is in the clothing sector, while the other one operates in the vehicle sector. To identify complicated and/or unexplained items in the questionnaires, face-to-face interviews are conducted with the plant managers of these companies. Based upon the comments gathered from these company visits, the items used in the questionnaire are updated, made more understandable, and the questionnaire was finalized. The companies in the sample are drawn from the Istanbul Chamber of Industry’s top 1000 firms in Turkey. Questionnaires are distributed to the top 1000 companies, and it was requested that the questionnaire be completed by a senior executive with some knowledge and expertise in manufacturing operations. After one reminder, a total of 211 usable questionnaires were received, representing an effective response rate of 21.1 percent, which was highly satisfactory, given the confidentiality and the seniority of respondents.
The companies included in the sample come from a wide variety of sectors, including the metal sector (28.1%); clothing sector (17.1%); petrochemicals (14.8%); fast-moving consumer goods (13.3%); vehicle sector (10%); construction materials (8.6%); and paper-based sector (3.8%). Most of them have been operating for more than 20 years (89%). Many of the companies (75.7%) are entirely domestic and operate in both domestic and international markets (76%). Table 2 provides an overview of the sample’s demographic profile.

3.2. Measurement of Variables

For measuring environmental dynamism, the scale used in the early studies of Ward et al. (1995), Li (2000), Ward and Duray (2000), Bulbul and Gules (2004), and Ward et al. (2007) was used. With a three-item scale, companies are asked to rate the speed of environmental dynamism on a standardized 5-point Likert scale from (1) “very slow” to (5) “very quick”.
However, in the literature, some scholars tried to develop and modify Porter’s (1998) generic strategies; these strategies are in line with previous competitive strategy categories and overlap with organizational, environmental, and performance-related aspects (Amoako-Gyampah & Acquaah, 2008). Therefore, Porter’s generic strategy typology is used, and the respondents are asked about which strategy they have been using. The competitive strategy statements are related to cost, differentiation, and focus strategies.
To test the competitive manufacturing priorities, a scale is developed, and respondents are asked to rate the dimensions on a standardized 5-point Likert scale from (1) extremely bad to (5) very good.

4. Results and Discussion

To analyze the data, first, the reliability and validity of the constructs depicted in the research framework are tested by exploratory factor analysis (EFA). Second, confirmatory factor analysis (CFA) is used to test the measurement models for each construct to determine the goodness-of-fit of the offered factors in step 1. Then, the relationships among the variables are discussed according to the hypothesis.

4.1. Exploratory Factor Analysis (EFA)

To evaluate the measurements of the scale, two fundamental concepts, validity and reliability analyses, are conducted with the use of exploratory and confirmatory factor analyses. First, exploratory factor analysis was subjected to all variables, and then, Cronbach’s alpha coefficients were calculated to assess the items’ reliability.
Another significant issue is factor loadings, which describe the link between variables and factors. Variables that have a high factor loading have a strong explanatory power. Hair et al. (2006) recommended the accepted value for the factor loads as 0.50, while Stevens (2002) recommended that they should be greater than 0.40 for interpretation. Cutillo (2019) and Alavi et al. (2020) also proposed that a variable with a factor loading of 0.40 attributes to the factor. Table 3 illustrates that all items exceed the recommended limit for factor loading (0.40) (Stevens, 2002). Cronbach alpha value is defined as acceptable when it is 0.70 and higher (Hair et al., 1998). Table 3 also shows the Cronbach alpha coefficients used to test construct reliability. Cronbach alpha coefficients are in the range between 0.729 and 0.821, which means an acceptable reliability for the constructs in the model.

4.2. Confirmatory Factor Analysis (CFA)

After conducting EFA, CFA is done to determine convergent validity. To evaluate the measurement model fit, three main indicators are used. Table 4 shows the standardized regression weights, composite reliability (CR), and average variance extracted (AVE) values. Composite reliability is defined as an internal consistency measure that shows how much the items represent the construct.
To evaluate the composite reliability, Fornell and Larcker (1981) suggested a cut-off value of 0.70, and our values ranged from 0.72 to 0.84, exceeding the recommended level. AVE is defined as the total variance of the construct’s items, and there is no generally accepted value for AVE. However, Hair et al. (1998) and Fornell and Larcker (1981) suggested a cut-off value of 0.50, and there are studies recommending 0.40 (Diamantopoulos & Siguaw, 2000; Karatepe, 2006; Škrinjar et al., 2008). As illustrated in Table 4, the lowest AVE score is 0.40, while the highest is 0.64, which means the AVE of the constructs exceeds the cut-off value, suggesting a milder restriction of 0.40.
The Heterotrait-Monotrait Ratio (HTMT) is used for evaluating the discriminant validity of the constructs (Henseler et al., 2016). The threshold for HTMT values is below 0.85, which indicates satisfactory discriminant validity. The HTMT matrix demonstrates that the discriminant validity of the constructs was adequate. As it is illustrated in Table 5, the HTMT values ranged from 0.447 to 0.592, all falling below the recommended threshold of 0.85. That means the HTMT criterion supports the overall validity and reliability of the measurement model.
This study used the data gathered from questionnaires with a single respondent from each manufacturing company, and the data shows personal opinions of the respondents, which may lead to common method bias. To avoid bias, Harman’s single-factor test is conducted. As the percentage of variance value is below 0.50 (it is found to be 0.21), there is no common method bias.

4.3. Assessment of Overall Model Fit

Table 6 summarizes various goodness-of-fit indices with their acceptable values. Acceptable limits of goodness-of-fit indices are summarized in the study of Chopra et al. (2019). The third column of Table 6 displays the model’s goodness-of-fit indices. The results indicate a significant value for the chi-square statistic (χ2 = 156.148, p < 0.000). If the model fits well, the chi-square value is expected to be not significant, but for the large sample sizes, the statistic might be significant when there is a small discrepancy between the suggested and real models. For defining a large sample, there is no strict cut-off. Commonly, if the sample size is bigger than 200, it is accepted as moderately large. Based upon the goodness-of-fit values in Table 6, the TLI value is below the recommended value. Since the sample size is bigger than 200, RMSEA emerges as the most promising candidate to check the goodness-of-fit of the model, and as can be seen from Table 6, the RMSEA value for the proposed model is acceptable. Other values also prove a good fit for the overall model (Sharma et al., 2005).
After assessing the overall model fit, the last step is to test the causal linkages and to validate the hypothesized relations. To test the hypotheses, IBM SPSS AMOS 24.0 is used. Figure 2 illustrates the regression weights of the causal paths, and all the regression weights are approved to be significant (p < 0.001).

4.4. Discussion

Based upon the results, the most important dimensions (competitive manufacturing priorities) used for assessing operational performance are “customer satisfaction” (β = 0.761, p < 0.001) and “supplier performance” (β = 0.700, p < 0.001), whereas “flexibility” (β = 0.521, p < 0.001) and “delivery” (β = 0.522, p < 0.001) are the least important ones. This is an expected result for the manufacturing companies. Supplier performance has an important role in operational excellence in manufacturing companies, providing benefits such as cost reduction, quality improvements, reduction in new product development time, and adaptation to technologies (Chen & Paulraj, 2022). Additionally, companies can only adapt to rapid market changes and increase customer satisfaction if they can align their operational capabilities with performance requirements (Mwaka et al., 2025).
For environmental dynamism, “rate of product and service innovations” is found to be the primary item (β = 0.920; p < 0.001), and “rate of process innovations” is the second one (β = 0.821; p < 0.001), while “rate of change in customers’ expectations in the sector where the company operates” was the least important one (β = 0.622; p < 0.001). The most critical aspects of environmental dynamism that we found are closely interrelated. In today’s highly dynamic markets, most of the companies, including manufacturing companies, are increasingly pursuing innovation-based strategies in their value chain to reduce their costs while providing new products to their customers.
The most used competitive strategy is found to be “cost leadership” (β = 0.806; p < 0.001), and the second one is “focus” (β = 0.693; p < 0.001). Since most of the companies are SMEs (124 of 211), it becomes necessary for them to adopt competitive strategies which enable them to remain both competitive and profitable. If SMEs can achieve operational performance by efficiently using resources and minimizing production and transaction costs, they can enhance their competitiveness in the market (Tomee et al., 2025).
Hypothesis 1 is proposed for investigating whether environmental dynamism has a positive and significant relation to operational performance. Figure 2 shows a direct positive and significant relation between environmental dynamism and operational performance (β = 0.604; p < 0.001). It displays substantial evidence for H1 searching for the direct and positive relationship between environmental dynamism and the operational performance dimensions (manufacturing competitive priorities).
Hypothesis 2 is developed to question the relationship between operational performance and competitive strategy. Figure 2 shows a direct positive and significant relation between operational performance and competitive strategy (β = 0.112; p < 0.05). However, the correlation coefficient is small and indicates a weak relationship, which is statistically significant, confirming H2.

5. Conclusions

The aim of this study is to investigate the causal links among environmental dynamism, operational performance, and competitive strategy in Turkish manufacturing companies empirically. The findings provide strong empirical support for the proposed model and offer important theoretical and managerial implications.
Environmental dynamism is significantly and positively linked to operational performance, supporting H1 This finding is in accordance with early research (Swamidass & Newell, 1987; Ward et al., 1995; Badri et al., 2000; Amoako-Gyampah & Boye, 2001; Yu & Ramanathan, 2011).
Kumar and Bhatia (2021) proposed that environmental dynamism pushes firms to adopt technology (i.e., Industry 4.0), which enables them to enhance operational and market performance. Similarly, Ruba et al. (2023) found that environmental dynamism has a strong positive effect on firm performance and highlights the role of innovativeness. These findings are consistent with recent studies based upon RBV, which suggest companies operating under dynamism possess valuable, rare, inimitable, and non-substitutable resources by adopting new technologies, reconfiguring resources, and enhancing operational performance.
The findings also indicate that operational performance has a positive and significant relation to competitive strategy, supporting H2. This result shows that improvements in operational capabilities enable companies’ strategic positioning. Based upon the RBV framework, operational performance can be achieved when the company allocates its resources and capabilities efficiently. High operational performance characterized by competitive priorities such as superior quality, reasonable cost, delivery reliability, and flexibility, is leading the companies’ competitive strategy. These findings are also consistent with the findings of earlier studies of Sahoo (2021), who found that operational practices and environmental dynamism were positively correlated. Furthermore, Dong (2024) emphasized the importance of aligning the operational capabilities with the competitive strategy to sustain competitiveness and growth. However, operational performance is defined as the efficient use of resources, and it also shapes the strategic positioning of the company.
This study contributes to the existing literature by reviewing all the dimensions of operational performance (competitive manufacturing priorities) with the use of various criteria presented by various authors to evaluate the operational performance relative to their rivals in a dynamic business environment. Unlike previous studies that examine a limited number of competitive manufacturing priorities, this study consolidates a broader range of criteria gathered from the literature and provides a more holistic measurement of operational performance. The results empirically validate that the extended list of competitive manufacturing priorities includes significant drivers in assessing operational performance. Furthermore, this study exhibits the application of operational performance theory to an emerging country context by using multiple dimensions of operational performance, thereby providing empirical evidence for the efficient use of competitive manufacturing priorities for sustainable competitiveness in a dynamic environment.

5.1. Managerial Implications

Since this study provides a model synthesizing environmental dynamism, operational performance, and competitive strategy, it has important contributions to the operations management literature. To gain and/or maintain a company’s position, the management should understand how critical it is to assess operational performance in comparison to its rivals. Since 99.8% of firms operating in Turkey are SMEs, 124 of the 211 participants of this study are also SMEs, and the findings contribute to their understanding of the dimensions affecting their operational performance. The production managers, especially the Turkish ones, may learn to sustain in the dynamic and competitive environment.

5.2. Limitations and Future Research

The current study also has some limitations. First, it focuses on the manufacturing companies in Turkey, and to generalize the results, future studies should conduct the survey in other emerging countries. Second, this study used the data gathered from questionnaires with a single respondent from each manufacturing company, and the data shows personal opinions of the respondents, which may lead to response bias. To eliminate this bias problem, researchers may collect data from multiple respondents and/or conduct longitudinal research. Future research may add variables such as firm size, firm age, capital structure, etc., as moderator/moderators to the research model.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Due to privacy restrictions, some data is not available. Other data can be requested from the author.

Conflicts of Interest

The author declares no conflicts of interest.

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Figure 1. Research model of the study.
Figure 1. Research model of the study.
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Figure 2. Results of the Analysis.
Figure 2. Results of the Analysis.
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Table 1. Operational performance dimensions (competitive manufacturing priorities).
Table 1. Operational performance dimensions (competitive manufacturing priorities).
Competitive
Manufacturing
Priorities		Definition of Competitive Manufacturing Priorities
CostProduction is a costly activity. If companies can shorten the cost, they can also shorten the price, and increase their profitability (Schroeder & Flynn, 2001; Chin & Saman, 2004). The dimension “cost” includes cost per unit, cost of labor and materials, fixed cost, and storage cost.
QualityThe ability of products to satisfy the requirements of the consumers, conformance to specifications, and producing free-of-error products are the most common definitions of quality, which is a precondition for today’s global and competitive markets (Li, 2000; Forker et al., 1996; Krajewski & Ritzman, 2005; Akal, 1998).
DeliveryDelivery is defined as a time-based capability by Li (2000). Delivery speed and delivery on time (Schroeder & Flynn, 2001; Chin & Saman, 2004) are the main expectations of today’s customers, requiring reliability as much as speed.
FlexibilityFlexibility refers to responding to the changing conditions in the market quickly (Amoako-Gyampah, 2003). Dimensions of flexibility could be listed as volume, product, process, machine, labor, etc.
Production-related timesProduction-related times are vital for both the companies and the customers. Production-related times must be decreased.
New Product
Development		Companies which can launch novel products and/or make incremental changes are defined as innovative (Chin & Saman, 2004). New product developments are measured by the R&D investment level and consistency, and the number of new products introduced per year.
Customer
Satisfaction		To sustain their organizations, companies should satisfy their customers (Q. Zhang et al., 2003). No. of complaints, rate of returns, and unmet demand creates unsatisfaction. Customer satisfaction is ensured by providing an effective after-sales service and quick response.
Supplier
Performance		Supplier performance refers to suppliers’ capability in satisfying the requirements of original equipment manufacturers (OEM). It means the capability of delivering the right material/part/product to the right manufacturing facility, at the right cost, at the right time, and with minimal shipping defects (Vonderembse, 2002; Omar et al., 2006). In today’s global environment, lead time of the supplier, material quality, on-time delivery rate of supplier, percentage of defective product in transportation, supply of materials whenever needed (flexibility), including the supplier in new product development processes, and integration of suppliers with quality control systems are key points that affect the manufacturing companies.
Table 2. Demographic profile of the sample.
Table 2. Demographic profile of the sample.
FrequencyPercent
Sector
Metal Sector5928.1
Clothing Sector3617.1
Petrochemicals Sector3114.8
Fast-moving Consumer Goods (FMCG) Sector2813.3
Vehicle Sector2110.0
Construction Materials sector188.6
Paper-based Sector83.8
Wood-based Sector52.4
Mining Sector21.0
Electricity Sector21.0
Firm Age (years)
Up to 10199.0
10 to 20 3918.6
20 to 30 4119.5
30 to 40 4822.9
40 to 50 2812.4
50+3315.7
Non-respondent41.9
Company Scale
Small and Medium-Sized Companies (SMEs)12459.0
Large Companies8540.5
Non-respondent10.5
Capital structure of the company
Domestic capital-based companies15975.7
Foreign and domestic capital-based companies5124.3
Table 3. Factor loadings of the items and reliability of constructs.
Table 3. Factor loadings of the items and reliability of constructs.
FactorsFactor
Loading		% of Variance
Explained		Cronbach
Alpha
Environmental Dynamism 74.770.821
Rate of product and service innovations0.910
Rate of process innovations0.883
Rate of change in customers’ expectations in the sector where the company operates0.797
Competitive Strategy 44.450.755
Cost Leadership0.980
Focus0.823
Differentiation0.807
Operational Performance 35.970.729
Customer Satisfaction0.706
Quality0.647
Supplier Performance0.646
Production-Related Times0.635
New Product Development0.600
Delivery0.583
Flexibility0.486
Cost0.453
Notes: Kaiser–Mayer–Olkin (KMO) measure = 0.779; Bartlett test of sphericity = 640.477. p < 0.000.
Table 4. Result of confirmatory factor analysis of the measurement model.
Table 4. Result of confirmatory factor analysis of the measurement model.
ConstructItemsItem
Reliability		Composite
Reliability		Average Variance
Extracted (AVE)
Recommended Value >0.70>0.50
Environmental Dynamism 0.840.64
Rate of change in customers’ expectations in the sector where the company operates ED10.622 *
Rate of product and service innovations ED20.920 *
Rate of process innovationsED30.821 *
Competitive Strategy 0.720.47
Cost LeadershipCS10.806 *
FocusCS20.535 *
DifferentiationCS30.693 *
Operational Performance 0.840.40
CostOP10.689 *
QualityOP20.574 *
DeliveryOP30.522 *
FlexibilityOP40.521 *
Production-related timesOP50.609 *
Customer SatisfactionOP60.761 *
New Product DevelopmentOP70.665 *
Supplier PerformanceOP80.700 *
*: p < 0.001.
Table 5. HTMT Ratio for assessing discriminant validity.
Table 5. HTMT Ratio for assessing discriminant validity.
Environmental
Dynamism		Operational
Performance		Competitive
Strategy
Environmental Dynamism (ED)-
Operational Performance (OP)0.592-
Competitive Strategy (CS)0.5470.447-
Table 6. Goodness-of-fit indices for the proposed model.
Table 6. Goodness-of-fit indices for the proposed model.
Fit IndexRecommended ValueProposed ModelFit (Yes/No)
Chi-square 156.148
Df 75
p value>0.050.000Yes
Chi-square/Df1.00–5.002.082Yes
RMSEA<0.080.072Yes
NFI>0.800.857Yes
IFI>0.900.920Yes
TLI>0.900.885No
CFI0.900.918Yes
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Karaman Akgül, A. Assessing Operational Performance of Manufacturing Companies in the Context of Environmental Dynamism, and Competitive Strategy. Adm. Sci. 2026, 16, 179. https://doi.org/10.3390/admsci16040179

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Karaman Akgül A. Assessing Operational Performance of Manufacturing Companies in the Context of Environmental Dynamism, and Competitive Strategy. Administrative Sciences. 2026; 16(4):179. https://doi.org/10.3390/admsci16040179

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Karaman Akgül, A. (2026). Assessing Operational Performance of Manufacturing Companies in the Context of Environmental Dynamism, and Competitive Strategy. Administrative Sciences, 16(4), 179. https://doi.org/10.3390/admsci16040179

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