The Mediating Role of Environmental Uncertainty in the Impact of Information Technology on Supply Chain Performance

Abstract

In a period in which competition and globalization are increasing day by day, the competition of companies exists among supply chains. To have and sustain competitiveness in the supply chain context, information technologies should be used, supply chain performance should be measured at certain intervals, and environmental uncertainty should be taken into account. In this framework, the purpose of this study is to examine the relationship between information technologies and supply chain performance and to investigate whether environmental uncertainty has a mediating role in this relationship. For this purpose, 192 data points were obtained via a survey obtained from the top 500 international companies active in Turkey and analyzed with SAS software. Resulting from the analysis, it was determined that there is a significant relationship between information technologies, environmental uncertainty, and supply chain performance, and between information technologies and environmental uncertainty. It is concluded that environmental uncertainty has a significant effect on supply chain performance, and information technologies have a significant effect on environmental uncertainty. In addition, it has been determined that information technologies have a significant effect on supply chain performance, and environmental uncertainty has a medi-ating role in this effect. According to the results of this study, managers are suggested to increase their supply chain performance by using information technologies and to consider environmental uncertainty to achieve a sustainable competitive advantage.

1. Introduction

With globalized and digitalized world trade, supply chains (SCs) have strategic importance for companies. To be successful in the field of SCs, information flow must be carried out precisely, continually, and smoothly. In this framework, companies can create a virtual network with SC members by using information technologies (ITs) [1]. As the complexity of SC activities and number of SC members increase, the need for IT increases [2]. With the introduction of Industry 4.0, IT has caused great changes [3], especially in the decision-making phase and critical management of SCs. Therefore, with the digital transformation provided by Industry 4.0, digital SCs have emerged recently, and many companies, especially sector leaders that intend to be successful in information flow, have started to invest in IT considerably [4]. Today, those companies that are able to adapt the changes are successful and they are in competition with each other to benefit more from the IT.

With the increasing importance of the SC, it has become critical to regularly measure the SC’s performance for its success. By measuring the SC performance, companies can have the opportunity to compare their performance with past periods. Improvements should be made if low performance results are obtained; thus, productivity and quality can be increased [5]. In addition, new goals can be set by strategic planning [6]. Although companies use IT to measure their SC performance, environmental uncertainties can affect the decisions and results. Environmental uncertainties can be defined as “changes in internal and external conditions that affect the SC structure, performance, and strategy” [7]. Due to the rapid changes in technology and environmental uncertainties [8], it is difficult for companies to forecast the innovations that might be introduced by the competitors [9]. Increasing levels of uncertainty cause difficulties in the decision-making process, which may lead to erroneous decisions. Although environmental uncertainties can cause crises in companies, they may create some opportunities as well [10]. To this end, companies need to analyze the environment accurately and act swiftly against opportunities and threats [11]. This strategy has been supported by the studies conducted by several researchers in the literature. If companies are agile, strategically flexible, and entrepreneurial, they can perform better in the case of high environmental uncertainty. Additionally, they may seize various opportunities by acting innovatively and properly [12,13,14,15,16]. Therefore, IT and environmental uncertainty have a significant impact on the success of companies in SCs. In this context, this study aims to examine the relationship between IT and SC performance and to investigate whether environmental uncertainty has a mediating role in this relationship. To this end, we focus on the question “Does information technology affect supply chain performance and is there a mediating role of environmental uncertainty on this effect?”.

In the literature, there is no study examining the relationship between the variables and the subdimensions of them in the mediator variable dimension. Therefore, we intend to contribute to this field by presenting a robust model that can predict the variables accurately and precisely. The top 500 companies, which have great impact on the economy and a functional SC structure, are selected as the research population. The results of this study may play a critical role in the decision-making process of these companies. The first step is to gather data about IT, SC performance, and environmental uncertainty and then to devise a model based on the obtained findings. At the final stage, the contributions of the model to theory and practice, its limitations, and possible suggestions are discussed.

2. Conceptual Framework

2.1. Strategic Importance of Information Technologies in Supply Chains

IT, expressed using various terms such as information systems, information communication technologies, and information and communication technologies, has led remarkable changes all over the world [17,18]. Transitioning from an industrial society to an information one enables us to access and share information anywhere and at any time [19]. Thanks to the developments in IT, information has become as important as the goods or services produced by companies [20]. Managing information flow is the prominent factor for success [1], and IT provides a useful tool to achieve this.

In a globalized and digitalized world trade, companies compete at SCs. To ensure successful SC management, it is essential to create a continual virtual link between companies and IT [1]. In the presence of intense competition, there must be a complete, timely, and uninterrupted flow of information among SC members [21]. The enhanced complexity of the SC activities has increased the need for IT and has made it paramount [2]. To this end, the information obtained in all national and international activities should be stored and analyzed and shared among the members instantly via IT [21].

Since the success of the SC is based on speed and communication [22], IT can be utilized in the decision-making and management stages in the SC. Managers in the SC need various information to forecast customer needs, stock levels, and inventory transactions [23]. IT has started to be used widely in performance measurement because of the increasing competition [24]. Moreover, the use of IT has become essential to ensure integration between chain members and business departments. As a result, companies invest substantially in IT, also known as the brain of SCs, for many processes such as collecting, analyzing, and distributing information [23].

The utilization of IT, also used by companies to prevent failures in SCs, especially in sectors where high competition exists, has a long history [25]. Recently, Industry 4.0 has started to be employed in various areas in the SC and has provided huge improvements [3]. It has accelerated the digitalization process, and these technological developments have started to be called a “digital supply chain” in the SC [4]. Queiroz et al. [26] define digital SC management as “an intelligent best-fit technological system that is based on the capability of massive data disposal and excellent cooperation and communication for digital hardware, software, and networks to support and synchronize interaction between organizations by making services more valuable, accessible and affordable with consistent, agile and effective outcomes”. Not only factories but also the structure of the SC become smarter by means of the digital transformation provided by Industry 4.0. Hence, the importance and use of IT is expected to increase exponentially [27].

The digital transformation enables the realization of SCs and logistics activities tailored to customers [28], and is shown in Figure 1. In the digital SC, planning, procurement, production, and logistics activities should be digitalized using IT. In addition, it has become a necessity to integrate, automate, restructure, and analyze the SC at these stages. It is important to ensure the integration of resources, production, and delivery processes in the digital SC. To do so, efforts should be spent on the use of IT in companies. Thanks to the use of IT, a digital SC is created and savings surge in terms of time, money, and resources. It is high likely for annual productivity to increase by 4.1% and for income to increase by 2.9% [29]. Therefore, companies in different sectors such as DHL, Turkish Airlines, Amazon, Alibaba, and DB Schenker are increasing their investments day by day to create a digital SC by using IT [30]. Companies can identify opportunities and threats more easily by means of digitalization in the SC.

Furthermore, the digital transformation of products and services may open a door to interaction with customers, suppliers, and other channel members. As a result of digitalization in planning, procurement, production, and logistics, the structure of the SC can be reorganized; it can be integrated and analyzed. In this framework, companies can obtain a competitive advantage in the digital SC by using the latest technologies such as electronic data interchange (EDI), internet, enterprise resource planning (ERP), radio frequency identification (RFID), big data, smart factories, artificial intelligence, smart logistics, unmanned aerial vehicle, cloud-based systems, robotics, augmented reality, simulations, 3D printers, digital twins, Internet of Things, augmented reality, and blockchain [19,31,32,33]. Evidently, the use of IT may differ among companies as well as the countries (Organization for Economic Co-Operation and Development [34]). Therefore, companies that follow these technologies closely can integrate the IT solutions into their systems and take advantage of significant opportunities in the sector compared to other companies.

Figure 1. Digital supply chain [35]. Copyright A.T. Kearney, 2015. All rights reserved. Reproduced with permission.

Figure 1. Digital supply chain [35]. Copyright A.T. Kearney, 2015. All rights reserved. Reproduced with permission.

Information systems, which are groundbreaking in the context of Industry 4.0, connect companies to their suppliers and customers [19], enabling companies to gain benefits in various areas. Thanks to information systems, enterprises gain a sustainable and competitive advantage by providing infrastructure support to the value chain, reducing costs and gaining cost leadership [36]. Therefore, companies are investing in IT and using it to share information more easily and increase cooperation with customers and suppliers in the value chain [37]. Thus, the information required for issues such as customer demand, production capacity, product flow, etc. [38], can be shared more easily at a lower cost among the members of the SC, and business activities can be carried out more effectively [39]. IT contributes to the integration of the SC [40] and the reduction in the whiplash effect [41], and has great importance in the effective use of existing resources and the division of labor [42]. In addition, IT may be also utilized efficiently to increase productivity, develop new products, and enhance the quality of products and services [19]. Although IT helps companies make the right decisions and deal with uncertainties [38], many problems, such as the whiplash effect, increase in uncertainties and costs, decrease in quality, and customer dissatisfaction and irregularity in information flow can arise if it is utilized improperly [43]. Using only one or a few ITs in the SC may not provide required strategic benefits to the SC.

2.2. Supply Chain Performance

The concept of performance, which has French origin, is defined as “the degree of directing and fulfilling the processes related to the objectives and tasks of enterprises” [44]. Performance measurement is defined as “the ascertainment of the efficiency of the methods applied to achieve the objectives” [45]. Accordingly, strategic planning can be conducted, and the level of achievement of the targets can be determined [6]. Garvin stated in 1993 that “If you can’t measure, you can’t manage”, which emphasizes the importance of performance measurement even in an era when there was no intense competition [46]. Therefore, companies need to conduct regular performance measurement and analyze the results. By means of performance measurement, the enterprises can have the opportunity to compare themselves with the competitors and past periods. In addition, it has become crucial to measure performance with respect to increasing competition and a complex SC [47,48]. Thus, activities with low performance can be identified, and necessary improvements can be made to increase productivity and quality [49].

SC performance is “a measurement system used to determine the efficiency of the SC and compare it with other systems” [50]. Measurement of SC performance is vital in terms of determining the problems and accomplishment of companies, analyzing their processes, and determining whether or not customer demand is met and the degree of accomplishment of the targets [51]. To make improvements in the current activities in a company, first, the current situation should be analyzed with respect to performance measurement, and then the necessary actions should be taken to reach the targets. However, performance measurement may not always be perceived positively by some employees and managers. Managers avoid performance measurement because they think it means accountability. Likewise, employees do not prefer performance measurement because they know it adds extra responsibility [52,53]. However, performance measurement provides advantages to managers to make decisions [5]. In addition, the degree of performance of companies not only affects the managers and employees, but also lending institutions, including other members of the SC [54]. Therefore, all SC members can benefit from the measurement of the performance of the SC.

Numerous studies have been conducted in the literature on the measurement of SC performance, and many criteria have been used for the evaluation. However, there is still no consensus on these criteria to be used [41]. While financial criteria were focused on before the 1980s, new criteria were introduced and started to be used as a result of insufficient financial data in parallel with the changes in the world market after the 1980s [55]. Based on the literature, the researchers commonly use cost (resource), flexibility, customer satisfaction (output), and logistics performance as criteria in performance measurement, and these criteria were used in this research.

2.3. Environmental Uncertainty

The uncertainty is defined as “not knowing which outcome of an option with many outcomes will be realized” [56]. Environmental uncertainty is “changes in internal and external conditions that affect SC structure, performance, and strategy” [7]. In particular, developments in technology may cause an increase in environmental uncertainty for companies [8]. In the face of environmental uncertainty, companies find it difficult to predict short-term [9] technological changes, and competitors may introduce new products and services [57]. As uncertainty increases, decision making becomes more difficult, and confidence in the decisions is decreased. Making wrong decisions because of uncertainty can cause significant risks for the future of the companies [10].

If there is a low level of uncertainty in the environment in which companies are active, the demand can be estimated accurately and a company can make more realistic plans. Hence, the achievement level of goals can be high [58]. As environmental uncertainty increases, forecasting the demand can become more difficult as competition increases [59]. Nevertheless, companies have to monitor their activities in a constantly changing environment. Many decisions such as forecasting the demand, capacity plans, establishment location, and production plans have to be taken under the pressure of an uncertain environment that is constantly changing. Since companies do not isolate themselves from the environment, they strive to reduce this uncertainty and be successful by using SC strategies, SC integration, and IT to make the right decisions in an uncertain environment. Consequently, the companies that are open-minded, respond to changes instantly, act agile, and take risk, when necessary, can be successful [60].

Companies should not consider environmental uncertainty as a problem, but should consider it as an opportunity and turn threats into opportunities by developing suitable strategies [61]. In practice, companies may turn environmental uncertainty into an opportunity if they use agile, lean, and postponement SC strategies [62,63,64]. In addition to these SC strategies, having safety stocks and using different simulations are also important for companies [65]. In this context, by adapting the companies to the environment, it can be possible to find a way to minimize the effects of environmental uncertainty. For example, to cope with fluctuations in customer demand, companies can develop and use advanced forecasting techniques to minimize failures and better predict demand. Thus, the effect of demand uncertainty can be minimized [65].

Uncertainties in the SC may also cause problems that are hidden or not noticed by companies [66]. The best example of this is in the recent COVID-19 pandemic. The COVID-19 pandemic, which no one could predict, caused serious interruptions in the SC [67]. The chip crisis took place as a result of it. This demonstrates how environmental uncertainty can have negative effects on SC processes. Consequently, companies have started to focus more on the SC and are investing more in ITs and starting to use them widely [68].

Although environmental uncertainty can lead to crises for companies, sometimes it can lead to opportunities and innovative activities. Hence, it is necessary to analyze and interpret the environment accurately. While the companies that can act agile and respond quickly to opportunities and threats perform better, the performance of others might be inferior [11]. In the literature, environmental uncertainty, which may have a direct impact on the success of companies, is commonly analyzed in four dimensions: technology uncertainty, demand uncertainty, competition uncertainty, and supplier uncertainty.

Technological uncertainty takes place as a result of the technologies that are used in industries, and includes uncertainty about new technological features. The relationship between new technology and the current one may lead to uncertainty as well as the selection of alternative technologies [69]. Technological uncertainty can be decreased by the product lifecycle and time required to introduce new products to the relevant market [70]. In the era of constantly changing technology, enterprises should focus more on investment costs to have automation-based technologies, as they can design and launch a new product in a very short time [60].

If demand uncertainty, which is defined as “the unpredictability of interest and demand for the products offered for sale by the enterprise”, is high, customer orders may change very frequently and, thus, may harm production and planning [71]. Industry trends, conditions related to the distribution channel, size differences in the desired product design, and market segment can lead to the uncertainty in demand [72]. To reduce demand uncertainty, which has a great impact on the performance of the companies, it is necessary to share more information among SC members in a complete and timely manner and to ensure SC integration by using IT [73]. Competitive uncertainty, which is defined as “changes in the competitive environment and uncertainties in the attitudes and methods of competitors” [74], arises when companies have difficulties in predicting the actions of their competitors [75]. Companies may encounter competitive uncertainty because of the uncertainty about future competitors; even if it is predictable, it is not possible to expect what kind of moves those competitors will make [76].

Supplier uncertainty is “the unpredictability of the quality and delivery of the product from the supplier” [73]. The degree of supplier uncertainty [72] arises depending on other active companies in the sector, and increases as the dependence on the supplier increases [69]. Supplier uncertainty, which includes uncertainties such as the cost of the supplied product and product quality, is a significant risk that can cause delays or even halt production, posing a substantial threat to the company [73]. Regarding this uncertainty, companies can take actions against it by establishing long-term trust-based relationships with more suppliers.

3. Aim and Importance of the Study

As the competition among companies is starting to be experienced among SCs, the SC has become more important. Companies that are successful in SC management gain a competitive advantage and can become successful, while companies that fail in this regard may have to withdraw from the market. One of the most important keys to success in the SC is information flow. Uninterrupted, complete, and timely information flow ensures that SC management is effective. Therefore, companies are spending effort to provide the uninterrupted flow of information among chain members by using many various ITs, especially with the help of the increasing digitalization of Industry 4.0. In addition, it is essential for companies to measure SC performance at certain intervals to reveal the results of the strategies used in SC management and the realization of the decisions that are taken. To be better and make the right decisions, the current situation of the business can be determined and necessary improvements can be made by measuring performance.

However, environmental uncertainty may affect the decisions of the enterprise and the performance of the SC. As environmental uncertainty increases, making decisions may become more difficult, and confidence in those decisions may decrease. Since erroneous decisions might also affect the sustainability of the business, necessary steps should be taken to combat uncertainties. In this context, the purpose of this study is to determine the relationship between IT and SC performance and whether environmental uncertainty has a mediating role in this relationship. To best to our knowledge, there is no study in the literature that examines the relationships among the variables and subdimensions of them in the mediator variable dimension. Therefore, this study can contribute to this topic in the literature and provide a new model to the literature by developing a unique model that can be used in future academic studies. In addition, by selecting the top 500 companies determined by the ICI as the research population, we also aim to contribute to these companies, which are prominent in the Turkish economy, with the results of the research. Therefore, the results of this study are important for both the research population and the Turkish economy.

4. Research Model and Hypotheses

Based on the domestic and foreign literature review, the research model was created, as shown in Figure 2. In this study, the relationship among the dependent and independent variables and the mediating role of environmental uncertainty are investigated. The SC has become strategically important for companies as the competition among companies has started to occur through SCs. For the success and effective management of the SC, information flow must be uninterrupted and timely. In this context, companies are taking advantage of intense use of IT to establish a virtual link among SC members and company departments. By planning, executing, and controlling SC activities with IT, they can increase both profitability and customer satisfaction. IT is among the indispensable elements for companies, as those that adapt to digital transformation can ensure the continuity of the company by gaining a competitive advantage today, as in the past. In addition to IT, periodic measurements of the results of the activities in the SC is critical for companies to realize their weaknesses and make comparisons. Although companies use IT and measure their SC performance because they operate in an uncertain environment, it is difficult for them to maintain their competitive advantage. However, doing so is essential for their success and customer satisfaction. Although environmental uncertainty can threaten companies, it can also provide opportunities, leading to the emergence of innovative activities. In this framework, IT, SC performance, and environmental uncertainty variables, which are vital for the continuity of companies, are included in the research model. While IT and environmental uncertainty are commonly analyzed as a single dimension in the literature, it has been determined that SC performance is evaluated within the framework of subdimensions, including cost performance, customer performance, flexibility performance, and logistics performance. A model has been created based on these variables and subdimensions. However, there is currently no study in the literature that examines the relationship among these variables and the role of the mediating variable in this relationship. Therefore, this study is anticipated to contribute to the literature and the researchers who intend to study this subject.

Regarding the literature review, as we mentioned before, the four main hypotheses were constituted and examined with respect to the developed model. In the literature, the relationship between IT and SC performance is widely based on the resource-based approach [77,78,79,80,81]. The resource-based approach is a theory that emphasizes that a sustainable competitive advantage can be achieved via the resources and capabilities of companies [78,82,83]. IT can be also utilized as a tool that increases performance by improving the capabilities of enterprises [78,80]. As IT facilitates the flow of information in the SC, companies can also provide better performance and competitive advantage by obtaining time and cost advantages [78,84,85,86,87,88]. Therefore, companies recognize IT as a part of SC management and invest more in IT [37]. However, some studies in the literature show that ITs do not affect SC performance [86,89]. Hence, it is thought that it is crucial for companies to determine the effect of ITs, which require high costs occasionally, on SC performance; thereby, Hypothesis 1 was established.

H1. 

Information technologies positively and significantly affect supply chain performance.

Given the difficulty for companies to maintain their competitive advantage in an uncertain environment, combating uncertainty is essential for success, sustainability, competitiveness, and customer satisfaction. For this reason, companies benefit from IT to reduce the impact of environmental uncertainty by circumventing the uncertainty or turning it into an opportunity by predicting it. The use of IT aims to minimize the uncertainties that may occur in the SC [90]. According to Lee [91], companies that can carefully follow the developments in IT and integrate them into their system are able to eliminate technological uncertainties as well as eliminate other uncertainties. Although companies implement various strategies to address environmental uncertainty, they may still encounter its negative effects. Therefore, Hypothesis 2 was established to determine the effect of IT, which might be used as a toll to turn the threats created by environmental uncertainty into opportunities.

H2. 

Information technologies positively and significantly affect environmental uncertainty.

In the face of environmental uncertainty, companies determine and implement new methods and practices to adapt to their surroundings and turn threats into opportunities. Thus, an enterprise operating in a highly uncertain environment can recognize various opportunities as well as threats [92]. These environmental uncertainties of companies can create positive or negative effects on their SC performances [65,93]. Ahammad et al. [11] reported that in a condition with high environmental uncertainty, if companies can react to opportunities and threats on time and quickly, SC performance can increase, otherwise performance can decrease. In the literature, it has been indicated that variables increase SC performance in conditions with high uncertainty. In this study, to determine the effect of environmental uncertainty on SC performance, Hypothesis 3 was established.

H3. 

Environmental uncertainty has a positive and significant effect on supply chain performance.

High SC performance is a foremost component of achieving sustainable competitive advantage by increasing market share and profitability. The methods for increasing SC performance are frequently investigated by industry professionals and academics. In some studies, it is reported that by integrating advanced IT into the SC of companies, a successful information flow can be ensured and SC performance can increase. With this idea, companies are investing in IT more and more. However, some studies conclude that IT does not contribute to the increase in SC performance. In addition, it is suggested that environmental uncertainty, which is not known when it occurs, should be taken into consideration by companies because the emergence of unexpected uncertainties may lead to unresolvable results in the SCs of companies. However, companies acting in an environment with high environmental uncertainty may obtain opportunities as well as threats [92]. In the literature, studies focusing on the moderating role of environmental uncertainty in the relationship among various variables are quite widespread [9,94,95,96]. However, there is no study that investigates the mediating role of environmental uncertainty in the effect of information technologies on SC performance. Therefore, Hypothesis 4 was established in this study.

H4. 

There is a mediating role of environmental uncertainty in the effect of information technologies on supply chain performance.

5. Research Population, Sample, and Data Collection Method

The research population consists of Turkey’s top 500 industrial companies in 2022 as determined by the Istanbul Chamber of Industry (ICI) [97]. Among the top 500 companies, 16 of them did not disclose their names and the survey was sent online to the remaining 484 companies. Since the evaluation was made based on the companies, a total of 192 data points were received by collecting one piece of data from each company.

The survey method was used as a data collection method and the scale was created according to the 5-point Likert type. The questionnaire form used in the research consists of four sections. In the first section, there are statements for determining the demographic characteristics of the participants. In the second section, there are statements regarding the IT scale. In the third part, there are statements regarding the SC performance scale. Lastly, in the fourth section, there are statements regarding environmental uncertainty.

The “Information Technology Scale” used in this study is the scale developed by Desarbo et al. [98] and translated to Turkish by Karagöz [99], and validity and reliability analyses were performed. The customer service and cost performance scale used in the SC performance scale is the scale developed by Piprani et al. [100], and the flexibility performance is the scale developed by Huo et al. [101]. The scale translated to Turkish by Gülaslan [78], with its validity and reliability analyzed by her, was used. The logistics performance scale was developed by Tao [102] and translated to Turkish by Yazgan [23], and its validity and reliability analyses were conducted by him. The environmental uncertainty scale was used by Wong vd. [96] and translated to Turkish by Çankaya [103], and its validity and reliability were analyzed by Çankaya.

6. Analysis and Findings of Research Data

In this study, structural equation modeling (SEM) was used to determine whether or not the environmental uncertainty has a mediating role in the relationship between the independent variable IT and the dependent variable SC performance. To this end, the SAS statistical software package (Version 9.4) was used for data analysis.

6.1. Demographic Findings

Table 1. Distribution of participants according to sociodemographic characteristics.

Variables		n	%
Gender	Female	89	46.4
	Male	103	53.6
Educational status	High school and below	15	7.8
	Associate degree	36	18.8
	License	102	53.1
	Postgraduate (Master’s/Ph.D.)	39	20.3
	Dealer/Retailer	22	11.5
	Distributor/Wholesaler	21	10.9
	Logistics Service Provider	44	22.9
	Producer	79	41.1
	Primary Supplier	18	9.4
	Secondary Supplier	8	4.2
Sector in which the business operates	Food	52	27.1
	Metal/Steel	10	5.2
	Energy	8	4.2
	Transportation and Storage	20	10.4
	Textile	17	8.9
	Heating Cooling	6	3.1
	Machinery/Industry	7	3.6
	Furniture/Wood Works	5	2.6
	Plastic packaging	12	6.3
	Logistics	5	2.6
	Electrical/Electronics	5	2.6
	Agricultural Products	12	6.3
	Defense Industry and Aviation	3	1.6
	Mining	5	2.6
	Automotive	17	8.9
	Chemistry/Medicine	8	4.2
Number of employees in the enterprise	Fewer than 50	31	16.1
	50–249	35	18.2
	250–500	60	31.3
	More than 501	66	34.4
Status in the enterprise	Manager/General Manager	14	7.3
	Assistant director	13	6.8
	Purchasing manager	9	4.7
	Production manager	8	4.2
	Supply Chain/Logistics Manager	45	23.4
	Planning Specialist	10	5.2
	Warehouse Problem	22	11.5
	Operations manager	14	7.3
	Entrepreneur	19	9.9
	Finance Manager	11	5.7
	Marketing Manager	7	3.6
	Human resources manager	11	5.7
	Others	9	4.7
Total		192	100.0

shows the distribution of participants in the survey according to their demographic characteristics. The participants consist of 46.4% female and 53.6% male. The majority of them (53.1%) are undergraduate graduates. According to the participants with SC position, 41.1% are manufacturers, 22.9% are logistics service providers, 11.5% are dealers/retailers, 10.9% are distributors/wholesalers, 9.4% are primary suppliers, and 4.2% are secondary suppliers. According to the number of employees, 34.4% of the participants are companies with more than 501 employees, 31.3% are companies with 250–500 employees, 18.2% are companies with 50–249 employees, and 16.1% are companies with fewer than 50 employees. In terms of sector, the top five sectors are food (27.1%), transport and storage (10.4%), automotive (8.9%), textile (8.9%), plastics/packaging (6.3%), and agricultural products (6.3%). When their positions in the enterprise are analyzed, it is indicated that 23.4% of them are SC/logistics responsible.

6.2. Reliability, Validity Analysis, and Normality Test

Skew values were calculated to determine whether the data obtained from the research were suitable for normal distribution. For the data to be considered suitable for normal distribution, skewness values should fall within the range of +2 to −2. When

Table 2. Mean, standard deviation, and skewness values of the scale and subdimensions.

	Mean	Std. Dev.	Minimum	Maximum	Skewness
Cost Performance	3.4257	1.1045	1.0000	5.0000	−0.6299
Customer Performance	3.9652	0.8400	1.0000	5.0000	−1.3955
Flexibility Performance	3.5729	1.1543	1.0000	5.0000	−0.9523
Logistics Performance	3.7250	0.9169	1.0000	5.0000	−1.1671
Supply Chain Performance	3.6992	0.8036	1.0000	5.0000	−1.0288
Information Technologies	3.7847	1.0805	1.0000	5.0000	−1.0550
Environmental Uncertainty	3.2395	0.9750	1.0000	5.0000	−0.1593

is examined, it is observed that the skewness values are within the range of +2 to −2 and are close to zero. Therefore, it is concluded that all of the scales and subdimensions are suitable for normal distribution, allowing for the application of parametric statistical analysis methods

Cronbach’s alpha reliability coefficients were calculated to test the internal consistency reliability of information technologies, environmental uncertainty, SC performance factors, cost performance, customer performance, flexibility performance, and logistics performance subdimensions used in the research. The results obtained are presented in

Table 3. Cronbach’s alpha coefficients of factors and subdimensions.

Scales	Cronbach’s Alpha
Cost Performance	0.937559
Customer Performance	0.881852
Flexibility Performance	0.964207
Logistics Performance	0.867136
Supply Chain Performance	0.943012
Information Technologies	0.967616
Environmental Uncertainty	0.836233

and it is indicated that the internal consistency coefficients vary between the lowest of 0.836233 and the highest of 0.967616. Cronbach’s alpha is required to be at least 0.7 [104]. Therefore, the scales used in this study are highly reliable scales.

6.3. Results of the Relationship between Scales

Correlation analysis was performed to reveal the relationship among IT, environmental uncertainty, and SC performance factors used in this study. The cost performance, customer performance, flexibility performance, and logistics performance subdimension scores were analyzed. The obtained results are presented in

Table 4. Findings on the relationship between factors and subdimensions.

	Cost Performance	Customer Performance	Flexibility Performance	Logistics Performance	Supply Chain Performance	Information Technologies	Environmental Uncertainty
Cost Performance	1.00000
Customer Performance	0.45666	1.00000
	<0.0001
Flexibility Performance	0.54684	0.58110	1.00000
	<0.0001	<0.0001
Logistics Performance	0.61403	0.55262	0.50359	1.00000
	<0.0001	<0.0001	<0.0001
Supply Chain Performance	0.78957	0.80538	0.83525	0.80813	1.00000
	<0.0001	<0.0001	<0.0001	<0.0001
Information Technologies	0.53163	0.48284	0.45719	0.54410	0.61690	1.00000
	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001
Environmental Uncertainty	0.31071	0.36630	0.32652	0.46112	0.44904	0.32252	1.00000
	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001	<0.0001

. According to the relationship among SC performance and its subdimensions (cost performance, customer performance, flexibility performance, and logistics performance), there are positive, high, and statistically significant relationships (r = 0.78957, p = 0.0001; r = 0.80538, p = 0.0001; r = 0.83525, p = 0.0001; r = 0.80813, p = 0.0001, respectively). Cost performance, customer performance, flexibility performance, and logistics performance subdimensions were found to have positive, moderate, and statistically significant relationships with each other. There were positive, moderate, and statistically significant relationships among SC performance, IT, and environmental uncertainty (r = 0.61690, p = 0.0001; r = 0.44904, p = 0.0001, respectively). There was a positive, moderate, and statistically significant relationship between environmental uncertainty and IT (r = 0.32525, p = 0.0001).

6.4. Testing the Theoretical Structural Model with Path Analysis

The main focus of this research is to reveal whether the SC performance scale, IT scale, and environmental uncertainty scales, which have direct, indirect, and mediating relationships with each other, can be explained by a theoretical structural equation model. The theoretical model tested in this study is presented in Figure 3. The PROC CALIS procedure of SAS 9.4 package program was used to test this theoretical model structure. In this model, it is indicated that information technology and environmental uncertainty scale have a direct effect on SC performance and an indirect effect through IT and environmental uncertainty scale.

In this study, a two-step approach was applied in modeling and analyzing the structural model; one of them is confirmatory factor analysis and the other one is structural equation modeling. Therefore, before constructing the structural model and applying the structural equation modeling, it was necessary to validate all measurement models of latent constructs to verify the measurement models in terms of unidimensionality, validity, and reliability. First, confirmatory factor analysis (CFA) was conducted based on 192 samples to verify the reliability and validity of the proposed theoretical model in Figure 3. Furthermore, the structural equation modeling (SEM) method was applied to test the proposed model.

To determine whether the tested theoretical model was supported by the data of the study, it was necessary to examine the goodness of fit indices values obtained as a result of structural equation analysis. Although a large number of fit index values are used in many studies in many fields, the most commonly used ones are included in this study. One of them is the chi-square (χ²) value, which tests whether the data obtained from our sample are compatible with the model proposed theoretically by the researcher. A nonsignificant chi-square value indicates a good fit. The value obtained by dividing the chi-square value by the degrees of freedom (df) (χ²/df) is equal to or less than two, which indicates that the model is perfectly compatible, and the value between two and five or less than five indicates that the model is acceptable. The other frequently used fit indices are GFI (goodness of fit index), AGFI (adjusted goodness of fit index), RMSEA (root mean square error approximation), CFI (comparative fit index), and NFI (normed fit index).

Table 5. Goodness of fit index acceptance criteria.

Compatibility Index	Acceptance Criteria	Reference
χ2/sd	≤3 = perfect fit ≤5 = acceptable fit	[105]
RMSEA/SRMR	≤0.05 = perfect fit ≤0.10 = acceptable fit	[106]
GFI/AGFI	≥0.90 = perfect fit ≥0.85 = acceptable fit	[107]
CFI	≥0.90 = perfect fit ≥0.85 = acceptable fit	[107]
NFI	≥0.90 = perfect fit ≥0.85 = acceptable fit	[107]

shows the robustness of fit indices and good fit value ranges.

6.5. Confirmatory Factor Analysis Results

Confirmatory factor analysis (CFA) was conducted in this study, and the standardized factor loadings, t-test results, and their significance obtained for each item are presented in

Table 6. Confirmatory factor analysis: Items, standardized loadings, and significance.

	Scales and Items	Average	Std. Deviation	Standardized Loads	Std. Error	t	p
Cost Performance	SCPCP1	3.3854	1.1610	0.8767	0.0203	43.046	<0.0001
	SCPCP2	3.4791	1.1931	0.8474	0.0249	34.023	<0.0001
	SCPCP3	3.4375	1.2306	0.9048	0.0181	49.996	<0.0001
	SCPCP4	3.4010	1.2282	0.9007	0.0179	50.269	<0.0001
Customer Performance	SCPCP1	4.0312	1.0176	0.8979	0.0196	45.622	<0.0001
	SCPCP2	4.0885	0.9960	0.8977	0.0198	45.326	<0.0001
	SCPCP3	4.0156	1.0459	0.8044	0.0293	27.382	<0.0001
	SCPCP4	4.0104	1.0682	0.7446	0.0458	14.047	<0.0001
	SCPCP5	3.9375	0.9954	0.7176	0.0479	12.888	<0.0001
	SCPCP6	3.7083	1.2609	0.7853	0.0566	8.5762	<0.0001
Flexibility Performance	SCPFP1	3.4791	1.2235	0.8481	0.0211	40.117	<0.0001
	SCPFP2	3.5104	1.2236	0.8783	0.0174	50.252	<0.0001
	SCPFP3	3.6302	1.2548	0.9294	0.0111	83.539	<0.0001
	SCPFP4	3.6354	1.2204	0.9665	0.0072	133.400	<0.0001
	SCPFP5	3.6093	1.2484	0.9080	0.0140	64.504	<0.0001
Logistics Performance	SCPLP1	3.5468	1.1385	0.8200	0.0319	25.690	<0.0001
	SCPLP2	3.6197	1.2134	0.6815	0.0425	16.031	<0.0001
	SCPLP3	3.8020	1.0646	0.7483	0.0351	21.293	<0.0001
	SCPLP4	3.8645	1.0985	0.7576	0.0362	20.909	<0.0001
	SCPLP5	3.7916	1.1659	0.8042	0.0336	23.923	<0.0001
Information Technologies	IT1	3.7187	1.1995	0.8878	0.0164	53.827	<0.0001
	IT2	3.8072	1.1528	0.8997	0.0149	60.072	<0.0001
	IT3	3.8072	1.1574	0.9561	0.0078	121.400	<0.0001
	IT4	3.7864	1.1850	0.9596	0.0074	128.500	<0.0001
	IT5	3.7760	1.1653	0.8570	0.0204	41.987	<0.0001
	IT6	3.8125	1.1285	0.8910	0.0161	55.256	<0.0001
Environmental Uncertainty	EU1	3.2135	1.1806	0.6760	0.0493	13.702	<0.0001
	EU2	3.1614	1.1667	0.8312	0.0356	23.290	<0.0001
	EU3	3.1770	1.1933	0.8881	0.0327	27.133	<0.0001
	EU4	3.4062	1.2286	0.7915	0.0504	11.736	<0.0001

. According to the table, it is indicated that all of the standardized factor loadings of the items are above 0.40 and the correlation relations among the variables are significant.

The robustness of fit indices of the confirmatory factor analysis are presented in

Table 7. Robustness of fit indices of confirmatory factor analysis.

Compliance Measures	Perfect Fit	Acceptable Fit	Results of the Model	Compatibility
RMSEA	0 ≤ RMSEA ≤ 0.05	0.05 ≤ RMSEA ≤ 0.10	0.0853	Acceptable Fit
SRMR	0 ≤ SRMR ≤ 0.05	0.05 ≤ SRMR ≤ 0.10	0.0991	Acceptable Fit
GFI	0.90 ≤ GFI ≤ 1.00	0.85 ≤ GFI ≤ 0.90	0.8640	Acceptable Fit
AGFI	0.90 ≤ AGFI ≤ 1.00	0.85 ≤ AGFI ≤ 0.90	0.8510	Acceptable Fit
NFI	0.90 ≤ NFI ≤ 1.00	0.85 ≤ NFI ≤ 0.90	0.8582	Acceptable Fit
CFI	0.90 ≤ CFI ≤ 1.00	0.85 ≤ CFI ≤ 0.90	0.9112	Perfect Fit
χ2/df	0 ≤ χ2/df ≤ 3	3 ≤ χ2/df ≤ 5	2.389	Perfect Fit

. According to the table, it is indicated that RMSEA, SRMR, GFI, AGFI, and NFI have acceptable fit values and CFI and χ²/df have excellent fit values. Therefore, confirmatory factor analysis results show that the overall fit of the measurement model is statistically quite adequate.

6.6. Convergent and Divergent Validity

For the robustness of fit index values, average explained variance (AVE) and compo-site reliability (CR) values were analyzed for convergent validity. Maximum squared shared variance (MSV) and average squared shared variance (ASV) values were analyzed for divergent validity. The necessary conditions for convergent and divergent validity are also presented in

Table 8. Necessary conditions for convergent and divergent validity.

Convergent Validity	Divergent Validity
Standardized factor loadings > 0.7	AVE > MSV
AVE > 0.5	AVE > ASV
CR > 0.7	$\sqrt{\mathrm{A}\mathrm{V}\mathrm{E}}$ > interfactor correlation
CR > AVE	MSV > ASV
	$\sqrt{\mathrm{A}\mathrm{V}\mathrm{E}}$ > 0.5

.

AVE, CR, MSV, and ASV values were obtained via convergent and divergent validity, and are presented in

Table 9. AVE, CR, MSV, and ASV values regarding the convergent and divergent validity and reliability of the scales.

	CR	AVE	MSV	ASV	Supply Chain Performance	Information Technologies	Environmental Uncertainty
Supply Chain Performance	0.976	0.672	0.381	0.292	0.820	0.381	0.202
Information Technologies	0.966	0.827	0.381	0.243	0.617	0.909	0.104
Environmental Uncertainty	0.839	0.572	0.202	0.153	0.449	0.323	0.765

. When the convergent validity results of the scales are analyzed from this table, it is indicated that the average explained variance (AVE) values of all scales are greater than 0.5 and the composite reliability values (CR) of all scales are greater than the average explained variance (AVE) values of the scales. This is an indication of a high degree of convergent validity [108]. The fact that all of the composite reliability values calculated for the three scales used in this study are above 0.7 indicates a satisfactory level in terms of internal consistency [109]. In the literature, it is also possible to find opinions that AVE values up to 0.40 can be accepted [109]. In addition, according to [109], if CR values are appropriate, low AVE values in that subdimension can be acceptable.

Finally, to test the divergent validity, which is used to determine that the scales used in this study are related to each other but have different structures from each other, as stated in Table 8, in addition to the MSV < AVE condition, the square root value of the AVE of the subdimensions is expected to be greater than both 0.50 and the correlation values calculated using the other factors of the scale [108,110,111]. According to the divergent validity values given in Table 9, it is indicated that the maximum shared variance squared (MSV) values of all three factors are smaller than the average variance explained (AVE) values, and the average shared variance squared (ASV) values of all three factors are smaller than the maximum shared variance squared (MSV) values. In addition, the square root of the AVE values of each scale is expected to be greater than the correlation of any two scales. In Table 9, the values written in bold font on the diagonal are √AVE values and, as a result of comparing each of them with the correlation values in the column and row in which they are located, it is determined that all three of the three correlation coefficients are smaller than the √AVE value they are compared with. This indicates that the scales fulfil all the relevant criteria and have an acceptable level in terms of divergence validity [112].

These results reveal that the model has a sufficient level of convergent (CR > 0.70; AVE > 0.50; CR > AVE) and divergent validity (MSV < AVE; ASV < AVE; correlation between factors < √AVE). According to the tests performed, it is evaluated that the scales used in this study are reliable and valid at acceptable levels, and, at the same time, they are compatible with the predicted theoretical structure (one-factor model).

Below the diagonal are correlations, above the diagonal are squared correlations, and above the diagonal are √AVE. AVE: average explained variance; CR: composite reliability; MSV: maximum shared variance squared; ASV: mean square of shared variance

6.7. Structural Equation Modeling Results

The fit index values obtained by testing the structural model proposed in Figure 3 with path analysis are given in

Table 10. Robustness of fit indices obtained from structural equation model analysis.

Compliance Measures	Perfect Fit	Acceptable Fit	Results of the Model	Compatibility
RMSEA	0 ≤ RMSEA ≤ 0.05	0.05 ≤ RMSEA ≤ 0.10	0.0795	Acceptable Fit
SRMR	0 ≤ SRMR ≤ 0.05	0.05 ≤ SRMR ≤ 0.10	0.0240	Perfect Fit
GFI	0.90 ≤ GFI ≤ 1.00	0.85 ≤ GFI ≤ 0.90	0.9849	Perfect Fit
AGFI	0.90 ≤ AGFI ≤ 1.00	0.85 ≤ AGFI ≤ 0.90	0.9206	Perfect Fit
NFI	0.90 ≤ NFI ≤ 1.00	0.85 ≤ NFI ≤ 0.90	0.9793	Perfect Fit
CFI	0.90 ≤ CFI ≤ 1.00	0.85 ≤ CFI ≤ 0.90	0.9882	Perfect Fit
χ2/df	0 ≤ χ2/df ≤ 3	3 ≤ χ2/df ≤ 5	2.2063	Perfect Fit

. In this table, it is seen that RMSEA has an acceptable fit and the remaining AGFI and χ²/df, SRMR, GFI, NFI, and CFI have excellent fit values. Therefore, it is determined that the proposed theoretical model is valid and reliable.

The fit of the initially proposed structural model (Figure 3) can also be determined by examining the estimates and the significance tests of the errors. The unstandardized path coefficient estimates from the path analysis are presented in

Table 11. Path analysis standardized results.

	Path		Guess	Standard Error	t	p	R2
Cost Performance	⟵	Supply Chain Performance	0.7344	0.0488	15.023	<0.0001	0.539
Customer Performance	⟵	Supply Chain Performance	0.6696	0.0501	13.362	<0.0001	0.448
Flexibility Performance	⟵	Supply Chain Performance	0.7043	0.0523	13.465	<0.0001	0.496
Logistics Performance	⟵	Supply Chain Performance	0.8268	0.0454	18.205	<0.0001	0.683
Information Technologies	⟶	Supply Chain Performance	0.5724	0.0548	10.445	<0.0001	0.551
Environmental Uncertainty	⟶	Supply Chain Performance	0.3230	0.0599	5.385	<0.0001
Information Technologies	⟶	Environmental Uncertainty	0.3225	0.0648	4.974	<0.0001	0.104

. Figure 4 shows the standardized coefficient estimates of the relationships together with their significance. In the factor analysis literature, these path coefficients are also referred to as factor loads. The statistical significance of the t-values should be examined to reveal the relationship among the observed variables (subdimensions) and factors. In a typical factor analysis study, almost all of these t-values are required to be significant to claim that factor-variable relationships are different from zero. If most of the t-values of the path coefficients are not significant, the validity of our factor model is questioned.

From Table 11, it is indicated that the IT and environmental uncertainty scales have statistically highly significant effects on SC performance and the IT, and the environmental uncertainty scale (p < 0.0001). This means that all factor-variable relationships are supported. When the estimation coefficients of these interactions are analyzed, it is indicated that all of them are positive. Therefore, the hypotheses H1, H2, and H3 determined in this study are acceptable.

Squared multiple correlations (R², squared multiple correlations, coefficient of determination) give an idea about the robustness of the models used in this study because these values are interpreted as the percentage of variation in the dependent variables (endogenous variables) explained by the independent variables (exogenous variables). Squared multiple correlations obtained from this study are given in the last column of Table 11. As shown in this table, the R² values range from 0.104 to 0.683 and are at medium and high levels. This means that the model is predicted well by the independent variables.

The path diagram obtained as a result of the path analysis with the standardized coefficients and their statistical significance (with asterisks) is shown in Figure 4. In this graph, the double-headed arrows pointing to two variables express the covariance between these two variables. Single-headed arrows indicate the functional relationship between the two variables. From the coefficients on the one-way arrows indicating the functional relationship among the variables, it is indicated that all of the binary relationships (IT scale and environmental uncertainty scales on SC performance) are statistically significant.

Table 12. Standardized direct, indirect, and total effects (effect/std. error/t-value/p-value).

	Direct Impacts			Indirect Impacts			Total Impacts
	Environmental Uncertainty	Supply Chain Performance	Information Technologies	Environmental Uncertainty	Supply Chain Performance	Information Technologies	Environmental Uncertainty	Supply Chain Performance	Information Technologies
Environmental Uncertainty	0	0	0.3225	0	0	0	0	0	0.3225
			0.0648						0.0648
			4.9748						4.9748
			<0.0001						<0.0001
Flexibility Performance	0	0.7043	0	0.2275	0	0.4766	0.2275	0.7043	0.4766
		0.0523		0.0452		0.0506	0.0452	0.0523	0.0506
		13.4659		5.0286		9.4177	5.0286	13.4659	9.4177
		<0.0001		<0.0001		<0.0001	<0.0001	<0.0001	<0.0001
Logistics Performance	0	0.8269	0	0.2671	0	0.5595	0.2671	0.8269	0.5595
		0.0454		0.0504		0.0469	0.0504	0.0454	0.0469
		18.2057		5.2951		11.9411	5.2951	18.2057	11.9411
		<0.0001		<0.0001		<0.0001	<0.0001	<0.0001	<0.0001
Cost Performance	0	0.7345	0	0.2373	0	0.4970	0.2373	0.7345	0.4970
		0.0489		0.0463		0.0490	0.0463	0.0489	0.0490
		15.0237		5.1233		10.1340	5.1233	15.0237	10.1340
		<0.0001		<0.0001		<0.0001	<0.0001	<0.0001	<0.0001
Customer Performance	0	0.6697	0	0.2163	0	0.4531	0.2163	0.6697	0.4531
		0.0501		0.0438		0.0513	0.0438	0.0501	0.0513
		13.3620		4.9380		8.8269	4.9380	13.3620	8.8269
		<0.0001		<0.0001		<0.0001	<0.0001	<0.0001	<0.0001
Supply Chain Performance	0.3230	0	0.5725	0	0	0.1042	0.3230	0	0.6766
	0.0600		0.0548			0.0280	0.0600		0.0487
	5.3852		10.4456			3.7223	5.3852		13.8822
	<0.0001		<0.0001			0.000197	<0.0001		<0.0001

shows the standardized direct, indirect, and total impacts. In this table, total effects are the sum of direct and indirect effects. These results explain in detail that the structural equation model (SEM) effect analysis shows some discrepancies that cannot be analyzed accurately by the linear regression analysis method. Therefore, this table provides more detailed results of SEM effect analysis and more refined results in terms of the overall theory. When Table 12 is examined in detail, the statistical significance values for direct effects are the same as the standardized values in Table 11. When the direct, indirect, and total effects are analyzed, all of the effects are statistically significant (p < 0.001).

One of the aims of this study is to investigate whether or not there is a mediating role of environmental uncertainty in the relationship between IT and SC performance. Before analyzing the mediating role of environmental uncertainty in the effect of IT on SC performance, the effect of the independent variable on the dependent variable was analyzed. To mention the mediating role, the effect of the independent variable on the dependent variable must be significant. As a result, it can be concluded that ITs have a statistically significant and positive effect on SC performance (p < 0.001). When the analysis results in Table 12 are analyzed, it is indicated that the degree of indirect effect of the IT variable on SC performance decreases compared to the direct effect. When the change in the direct effect of IT on SC performance was 0.57, the change in the indirect effect of IT on SC performance decreased to 0.10. According to these results, it can be said that environmental uncertainty has a partial mediating role in the effect of IT on SC performance. Therefore, the hypothesis H4 determined in this study is acceptable.

7. Discussion and Conclusions

Consequently, 192 data were collected and analyzed via the questionnaire method from the top international 500 companies in Turkey determined by the ICI. It is concluded that there is a significant and positive relationship between IT and SC performance, and hypothesis H1 was confirmed. Therefore, companies that intend to increase their SC performance should benefit effectively from IT. In the age of digital transformation, companies can strategically use the SC, which is the key to competition, by utilizing IT. The sector-leading companies have achieved significant success, largely due to their effective use of IT, which plays a crucial role in their information flow. Therefore, the results obtained from the research reveal that companies need IT to achieve successful SC performances. In addition, our results are consistent with the literature. Researchers such as Salleh vd. [113], Gülaslan [78], Yang ve Su [114], Zhao et al. [24], Liu vd. [58], Yarar [43], Collins vd. [115], McFarland [116], Zheng vd. [117], Çemberci vd. [118], and Fawcett vd. [119] have also found a significant relationship between IT and SC performances in their studies. By managing the processes effectively, SC performance can increase, leading to improvements in customer satisfaction, profitability, and market share. By integrating advanced IT into the SC, many advantages can be obtained by achieving success in information flow. Therefore, integrating IT into all processes of the SC implies that improvements can be achieved in the processes in the SC.

As a result of the analysis performed to test hypothesis H2, it was determined that IT has a significant effect on environmental uncertainty, and the hypothesis was confirmed. Environmental uncertainty may pose great risks to companies’ decisions and may cause wrong decisions and even lead to bankruptcy. The obtained result reveals that IT must be utilized to cope with these uncertainties and turn them into opportunities by acting swiftly. This is consistent with the results of Sevinç [120] and Bülbül et al. [121], which are among the few studies in the literature. Therefore, it is beneficial for companies to turn environmental uncertainty into an opportunity by making more use of IT to cope with environmental uncertainty.

This study shows that the analysis of results shows a significant effect of environmental uncertainty on SC performance. Based on this, the H3 hypothesis was decided to be correct. To increase SC performance, environmental uncertainty should also be taken into consideration. Also, Sun et al. [122] demonstrated that environmental uncertainty has a significant effect on SC performance.

Another aim of this study was to determine whether environmental uncertainty plays a mediating role in the effect of IT on SC performance. To this end, hypothesis H4 was examined, and it was concluded that environmental uncertainty plays a mediating role in the relationship between IT and SC performance. As for the type of mediation, it was determined that environmental uncertainty has a partial mediating role. Therefore, while IT has a significant effect on SC performance, this effect can be decreased when environmental uncertainty is involved. In this case, companies should benefit from IT effectively more to increase their SC performance.

In the literature, there is no study investigating the mediating role of environmental uncertainty in the relationship between IT and SC performance. Therefore, this study focused on a subject that has not been empirically studied before, aiming to contribute to the literature. Therefore, it is also expected to be a source for researchers in future studies. In addition, by developing an original and new model, a valid and reliable model can be added to the literature. At the same time, the success of the companies that make up the research target population is of strategic importance for Turkey, one of the 20 largest economies in the world, to achieve its economic goals. In developing economies such as Turkey, where small- and medium-sized enterprises are predominant, these companies act as locomotives in ensuring sustainable growth. The results of this study are critical for these companies or other companies aiming to be successful in SC management.

As a result of digital transformation, companies may struggle to use scarce resources efficiently if they do not make adequate use of IT. Companies that can use IT effectively may be advantageous compared to their competitors in predicting the risks and dangers they may encounter in an intense competitive environment. According to the findings, it is recommended that companies increase their level of IT usage. In this framework, it is beneficial that the plans and strategies related to IT should be in line with the plans and strategies of the enterprise. In addition, in a period when the importance of information is increasing day by day, enterprises should closely follow the developments in IT carefully. It is believed that enterprises investing in these technologies should act consciously and invest in ITs that meet their needs regarding benefits and costs. For this reason, managers and employees have great responsibilities in making a sensitive planning process before investing. Since existing ITs may lose their validity with the rapid changes in technology, it might be advantageous for managers to follow new technologies and integrate the right technologies into their companies. It is predicted that it may be more beneficial to integrate these technologies into all departments of the enterprise and SC members. Having a qualified labor force in the use of these integrated technologies is also critical in terms of obtaining the expected benefit. In addition to in-service training, providing and increasing university-industry cooperation can provide great convenience in the training of qualified personnel.

The findings suggest that SC managers should also consider environmental uncertainty for companies to utilize their SCs as a competitive advantage and to sustain that advantage. Because it is of strategic importance for the continuity of the companies, company managers should understand that environmental uncertainty may cause critical decision misjudgments in the companies. According to results, enterprises can use IT effectively to cope with unexpected events in an environment with uncertainty. Since the perspectives of enterprises on environmental uncertainty are important, it is recommended that sector actors should implement practices that can turn environmental uncertainty into an opportunity instead of perceiving it as a threat and avoiding it. It is thought that it would be appropriate for business managers to use IT and adopt appropriate strategies to manage environmental uncertainty in the face of changes by constantly analyzing the environment. Thus, an increase in the SC performance of enterprises can be achieved. The findings emphasize the need for IT to achieve a sustainable performance. It is also recommended that appropriate criteria should be selected for the performance measurement; it should be measured at certain intervals and necessary arrangements should be made. In all these stages, both the support and control mechanism of the top management are important. Therefore, as a result, it is recommended to business managers to increase SC performance by utilizing IT, to take environmental uncertainty into account and turn it into an opportunity and to provide sustainable competitive advantage.

There are some limitations in this study. Since IT and environmental uncertainty variables are not commonly discussed with subdimensions in the literature, they are absent in this study. However, some studies categorize both variables into subdimensions. In future studies, the addition of subdimensions to the model may enhance the literature. On the other hand, this study is limited to the companies determined by the ICI. Investigating other companies may lead to different results. These companies, which constitute the research population, operate in different sectors. By investigating companies that are active in only one sector, the results can be compared, and, at the same time, the relationships related to demographic variables can be analyzed. In addition, it is expected that studies investigating which ITs reduce environmental uncertainty and increase SC performance will be beneficial for companies.