Investigation of the Antecedents of Digital Transformation and Their Effects on Operational Performance in the Jordanian Manufacturing Sector

Abstract

Digitalization is viewed as an important promoter of competitiveness, offering future avenues to new value and revenue opportunities. Nevertheless, the factors that determine the drivers of digital transformation (DT) adoption still need to be explored and understood further. Based on the RBV and institutional theory, this study examines the roles played by organizational culture, IT readiness, and customer demands of firms in the implementation of DT and the consequent improvement of operational performance. The results of a survey carried out among 226 manufacturing companies in Jordan indicate that these antecedent factors have a significant and positive effect on the adoption of DT and operational performance. The results also demonstrate that the implementation of DT enhances the operational performance of firms by increasing their efficiency and effectiveness. This research adds to the existing literature on digital transformation through an examination of the antecedents of its adoption. These findings are useful, as they assist firms in viewing digital transformation as an overarching opportunity that needs to be leveraged to improve their operational performance and competitiveness.

1. Introduction

Technology is lifting organizations to a whole new level on almost all fronts and has had an extreme impact on the global economy. These profound changes are re-structuring markets and spurring on the need for new technological advances in a wide range of goods and services sectors. In this context, digital transformation (DT) is a key driver of change within organizations as it allows companies to become creative, simplify processes, and successfully compete in the high-tech economy (C. Liu et al., 2022). In particular, a firm employing digital technologies to develop a new digital business model that helps them to create and appropriate more value is said to be engaging in ‘Digital Transformation’ (Verhoef et al., 2021). DT takes on added importance as the changing business environment exerts constraints on how much a firm can improve its production processes, maintain its position in the market, and provide competitive costs (Vinodh et al., 2009). While DT has revolutionized industries in the world’s core economies, its adoption in certain industrial segments—specifically in growing economies such as Jordan—poses interesting prospects and challenges which remain to be addressed more comprehensively.

The digital transformation of manufacturing industries is accelerating in emerging economies, as part of a global trend toward smarter and more agile production systems. This change, which is based on Industry 4.0 and moving towards Industry 5.0, stems from the adoption of new technologies such as AI, IoT, digital twins, and collaborative robots (Yao et al., 2024). Recent research has emphasized the fact that these technologies are helping manufacturers in developing countries to boost their productivity, customization, and supply chain efficiency (Zafar et al., 2024). In Jordan’s manufacturing sector, digital transformation provides opportunities to modernize processes, implement new business models, and improve competitiveness in the global market. Through the adoption of such technologies, these companies can leverage new opportunities, improve operational productivity, and create higher export potential, allowing them to position themselves better in the worldwide economy. Notwithstanding these benefits, identifying the key factors that allow for the effective adoption of DT is still an underexplored area of study (Jordan Strategy Forum, 2024).

Studies that have been conducted on the topic of digital transformation have zeroed in on certain aspects; most notably, digital servitization (Cenamor et al., 2017; Frank et al., 2019), lean production (Buer et al., 2018; Tortorella & Fettermann, 2018), and logistics research (Hofmann & Rüsch, 2017). Other research has focused on more peripheral aspects such as the role of technologies (Oztemel & Gursev, 2020), technology deployment (Bibby & Dehe, 2018), and technology performance (Dalenogare et al., 2018). In relation to this, there seems to be a gap with regard to empirical evidence that facilitates the exploration of the factors supporting the digital transformation of manufacturing companies, especially in the context of developing economies such as that of Jordan.

This research proposes to fill this gap by focusing on three antecedents that could facilitate the undertaking of transformation—namely, organizational culture, IT readiness, and customer pressures—as well as the influences that these three antecedents have on operational performance. It also assesses the mediating role of DT in these relationships. The research is guided by the following questions:

RQ1: How do antecedents such as organizational culture, customer pressures, and IT readiness impact the adoption of digital transformation in manufacturing firms?

RQ2: To what extent do these antecedents influence operational performance?

RQ3: What mediating role does digital transformation play between these ante-cedents and operational performance?

We test the proposed model using data collected from 226 Jordanian international manufacturers with operations in different markets. PLS-SEM is deployed for structural hypothesis modeling. The overall findings of this research indicate that there is a strong influence on digital transformation due to internal and external factors and that digital transformation has a mediating effect on the operational performance of organizations. This research extends existing knowledge regarding the phenomenon of digital transformation and the wider literature on operational performance in three key aspects. Firstly, this research differs from earlier studies, which have typically focused on examining the direct effect of a firm’s digital transformation on its performance. In contrast, this study aims to address this research gap by providing a broad view of how internal and external antecedents influence digital transformation to contribute to operational performance.

Secondly, this research relies on the Resource-Based View (RBV) and institutional theory to investigate the internal capabilities and external factors motivating digital transformation alongside operational performance in manufacturing firms in Jordan. Under the RBV perspective, organizational culture and IT readiness are considered strategic assets in a firm’s digitally enabled value chain—especially in Jordan, where firms face resource constraints—increasing the likelihood that firms will embrace digital technologies. Concurrently, institutional theory helps to explain customer pressures resulting from increased digital demand as an external transformation driver. With these theories, this study provides a comprehensive account of digital transformation in a resource-based and institutionally driven manner, underscoring its importance for Jordanian public policy that seeks to modernize the industrial backbone of the economy and enhance its digital competitiveness.

Finally, the majority of past research have been performed in developed economies. In this regard, digital transformation scholars have argued that there is a lack of context in these findings and further research in a wider scope is warranted (Vial, 2019). This paper contributes to this need as it focuses on a non-Western context which has not been previously studied; that is, the Jordanian context.

The remainder of this paper is organized in the following manner. In the subsequent section, we provide a background of digital transformation in the Jordanian manufacturing sector. Section 3 outlines the theoretical background and the development of hypotheses. We detail our methodology in Section 4. Our results are presented and discussed in Section 5 and Section 6, respectively. Theoretical and practical implications are outlined in Section 7. Finally, Section 8 and Section 9 detail the conclusions and limitations of the work, along with suggestions for future research.

2. Digital Transformation of the Jordanian Manufacturing Sector

Due to the rapid development and integration of technology into various industries, firms are altering their operational strategies in order to successfully reach their intended objectives. Such transformations influence the internal efficiency of enterprises, as well as their approaches to the advertisement and sale of the goods and/or services produced (Anwer et al., 2019). The modification of borders in the area of the digitalization of processing is also relevant for the manufacturing sector, as an inseparable part of any economy. Notably, these ideas are supported by the fact that technologies which serve to improve the business processes of manufacturing units are being actualized (Chryssolouris et al., 2009).

In the context of the manufacturing sector, those in charge of formulating policies must be cognizant of the fact that there is a need to develop and enforce DT policy in order to boost the competitiveness of firms. It is evident that, despite the nature of the industry, sustained competitive advantage is only achievable through the digitization of processes. Hence, the importance of DT for mature businesses to meet the challenges posed by native digital companies, which have emerged within the last decade and a half, is evident (Kim et al., 2022)

Due to increasing economic development, the local production companies in Jordan have indicated an interest in improving their output through the incorporation of new technologies; for example, the use of accounting software to enhance the standard of their financial reports (Idris & Mohamad, 2017). Furthermore, Jordanian businesses tend to provide a wide range of services via mobile smart applications and single-window services. The aim is to provide beneficiaries with value-added experiences through offering e-payment solutions which do not involve any hard copies. Notably, in Jordanian industries, the control processes are adapted ahead of time and the business management is highly involved in the analysis and classification of risk, using risk measurement methods to determine the probability of certain risks (Organization for Economic Co-operation and Development, 2016).

To enhance efficiency, creativity, and competitiveness in the field, the Jordanian ministry of digital economy and entrepreneurship has implemented the ‘Digital Transformation Strategy 2024’. This strategy aims to promote the utilization of new technologies such as IoT, AI, robotics, and big data. These tools possess smart capabilities, facilitate better operational efficiency, enable faster product development through the wireless interconnection of manufacturers in digital supply chains, and promote data-driven decisions. The strategy focuses on the improvement of the overall workforce’s skills, the protection of the people, the development of more collaborative ecosystems, and the advancement of resource-use efficiency and waste minimization for a more sustainable future. Overall, it is aimed at promoting enhancements in working efficiency, creativity, and international competitiveness, while guaranteeing sound, resilient, and green manufacturing processes (Ministry of Digital Economy and Entrepreneurship, 2023).

3. Conceptual Framework and Hypothesis Development

3.1. Theoretical Foundation of the Study

To analyze the antecedents influencing the adoption of DT (and, hence, enhancing their operational performance), this study relied on the Resource-Based View (RBV) (Barney, 1991) and institutional theory. The RBV integrates notions that provide a basis for competitive advantage, consisting of resources that are valuable, rare, not easily imitated, or which cannot be replaced (Barney, 1991). The theoretical argument of the RBV highlights that the management of firm resources—as the basic units of analysis—explains the differences in performance across organizations. It is believed that, by efficiently developing and deploying assets (e.g., the ability to modify an organization’s culture or advanced technological resources), firms have a greater chance of achieving operational success (Nayak et al., 2022). As a non-physical resource, organizational culture nurtures an ecosystem that enhances learning and innovation, which is consistent with the RBV’s perspective on the importance of intangible resources (Balci, 2021). IT readiness is one of the core capabilities that facilitates the realignment of an organization’s resources and improves its operational efficiency (Ji et al., 2023).

Institutional theory provides a robust framework to understand how external forces shape organizational behaviors, particularly in the context of digital transformation. The theory posits that organizations are not only influenced by internal efficiency-seeking motives but are also subject to external pressures from institutional environments such as customers, regulatory bodies, and competitors (DiMaggio & Powell, 1983). These pressures manifest through three primary mechanisms: coercive pressures, which stem from legal and regulatory demands; mimetic pressures, where organizations emulate successful peers to reduce uncertainty; and normative pressures, arising from professional norms and industry standards (W. R. Scott, 2013). Organizations must conform to these external pressures in order to survive and succeed (Gupta et al., 2020). In the context of digitalization, customer pressures act as a significant antecedent, compelling organizations to adopt advanced technologies to meet evolving demands for quality, customization, and sustainability.

3.2. Hypothesis Development

3.2.1. DT and Operating Performance

Past research has involved comprehensive analysis of the links that exist between technology and varied performance outcomes. Studies have shown that technology adoption in the financial sector enables positive shifts in firm performance (S. V. Scott et al., 2017), organizational performance (Chen et al., 2016), supply chain performance (Zelbst et al., 2010), and innovation performance (Bourke & Roper, 2016). Nonetheless, the link between DT and operational performance is still an issue that requires further investigation (Agrifoglio et al., 2017; Dalenogare et al., 2018).

The existing literature indicates that DT can increase operational performance in some ways. First, DT may reduce both production and management expenditure. The use of the internet, big data, and blockchains can lower labor requirements, cut back the time needed for production, and improve the overall standard of the output, enabling businesses to offer a wide range of affordable customized goods and services (S. C. Liu et al., 2021, p. 9). Furthermore, DT increases the effectiveness of corporate assets, as artificially intelligent machines and equipment can be self-optimized (Zhou et al., 2018). DT equally drives the flow of information, with robotics and real-time data adding increased accuracy, speed of delivery, and customizability (Tracey et al., 1999; Esmaeilian et al., 2016). Additionally, uninterrupted information exchange, agility, and productivity of digital technologies, along with swift product development, can enhance a company’s innovative performance.

Thus, it is evident that DT and its benefits support the efforts of firms in reducing their operating expenses and enhancing efficiency. Based on this, we propose the following hypothesis:

Hypothesis (H1).

DT is positively associated with operating performance.

3.2.2. Antecedents of Digital Transformation

Organizational Culture

Culture is critical in the development of an organization, and it consists of the values and beliefs that people share within a firm and provides a framework for “how things are done” (Vial, 2019). According to Kane et al. (2018), culture can also be an agent of change, as it optimally enhances flexibility, adaptation, and innovation (Leidner & Kayworth, 2006; Vial, 2019). In this regard, Holbeche (2018) maintains that the most progressive organizations regard their culture as a principal vehicle for innovation, due to its ability to not only create the conditions to attract and retain people that will be useful for the mission but also to orient them toward the desired implementation. Such a measure helps managers to enhance commitment to initiatives aimed at the realization of transformations. This has been illustrated by Verdu-Jover et al. (2018), who stressed that an organization with a reflective cognitive tendency to reconsider its internal aspects in response to external aspects is best able to manage change. Furthermore, organizational culture fosters dynamic capabilities, which are important in guiding the organization through the DT process. This, consequently, makes it an important enabler of organizational change (Warner & Wäger, 2019, cm.2; Hogan & Coote, 2014). The elasticity of culture enables companies to more accurately react to technological transformations; hence, culture should be understood as the basis for, and driver of, successful digital change.

H2a. 

Organizational culture has a significant impact on digital transformation.

Corporate culture is of great importance to a company as it provides various strategic resources which can be sources of competitive advantage, while organizational culture guarantees success for firms as it helps them to diversify in the long run (Martínez-Caro et al., 2020). Moreover, having a strong organizational culture is imperative for the overall effectiveness of a firm, as it enables seamless coordination and improved goal achievement (Zheng et al., 2010). In addition to its strategic implications, organizational culture plays an important role in performance outcomes based on the alignment of processes, resources, and strategies. As an example, Sahoo (2021) noted that in the context of manufacturing companies, robust culture promotes the implementation of lean practices, which improve flexibility, cost, and delivery performance. Furthermore, Ha (2020) showed that accounting information systems are more effective in promoting firm culture, helping to optimize decision making and operations in small- and medium-sized firms. In addition, Hardcopf et al. (2021) hold that culture encourages collaboration, creativity, and employee commitment, thus maximizing the gains obtained from lean production and enhancing operational performance.

H2b. 

Organizational culture has a significant impact on operational performance.

A strong organizational culture that supports employee creativity, adaptability, and teamwork greatly influences the success of an organization’s DT efforts (Vial, 2019). Organizations with such a culture are better able to amalgamate new digital strategies and innovations, leading to improved productivity, customer satisfaction, and competitive edge or advantage (Verhoef et al., 2021). Cultural values are important factors in creating a positive attitude toward DT, both in the context of digital maturity models, (Imgrund et al., 2018) that place value on entrepreneurial orientation, and employee involvement (Tekic & Koroteev, 2019). Hence, organizations can create the necessary behaviors for the achievement of DT goals by embedding these values through digital technologies and promoting a common purpose. In the contemporary climate of rapid technological development, fostering digital culture assists companies in maintaining competitiveness and forming a diverse and global workforce that enables collaboration and enhances the pace of change. Thus, the following hypothesis is proposed:

H2c. 

Digital transformation has a mediating role in the relationship between organizational culture and operational performance.

IT Readiness

IT readiness is a crucial factor that determines the successful implementation of digital transformation across various organizations. Research has indicated that IT readiness, involving sound infrastructure deployment, human resources, and digital competencies, is imperative for such transformation. For example, Chwiłkowska-Kubala et al. (2023) accentuated how energy companies are highly dependent on the IT readiness of their employees when carrying out digital transformation. Along these lines, Jafari-Sadeghi et al. (2021) reported that the technological readiness of information systems enhances digital changes in businesses by allowing them to discover and make use of other available digital prospects, thus developing the technological aspects of entrepreneurship within the markets served. Frick et al. (2021) emphasized the need for a preparedness to use new technologies, including artificial intelligence, as part of the prerequisites for IT readiness to promote transformation efforts. Furthermore, Novikov et al. (2018) discussed how IT readiness ensures that the integration of digital transformation initiatives carried out by an organization is risk-free, due to their commitment to technologies such as blockchain.

H3a. 

IT readiness is positively associated with digital transformation.

Possessing advanced IT capabilities has become a strong indicator of an organization’s operational efficiency, as it gives them the ability to become more efficient, flexible, and enhance their decision making. Kliestik et al. (2023) have highlighted the role of IT readiness together with the use of Industry 4.0 technologies in maintaining streamlined processes and increased operational activities in international companies’ value chains, resulting in the increased operational performance of these firms. Additionally, Wanasinghe et al. (2020) showed the tendency of IT readiness to extend through IoT implementation within the oil and gas industry; in particular, IoT devices aid in the collection of data and management of assets in real-time, thus greatly enhancing productivity. The role of IT readiness as a foundational element of operational excellence has been well-demonstrated in terms of how organizations can increase their efficiency through the integration of suitable IT infrastructure with appropriate digital tools and automating tasks to meet consumer and market requirements. In summary, the findings suggest that advanced IT readiness is likely to be more important for the operational performance of firms, especially in technology-intensive and competitive environments.

H3b. 

IT Readiness is positively associated with operational performance.

The inclination towards IT readiness serves as a foundational driver for successful digital transformation, shaping its impact on operational performance. Organizations with robust IT infrastructure and high technological competence face fewer challenges when adopting advanced digital strategies (Chwiłkowska-Kubala et al., 2023; Alkhamery et al., 2021). A firm’s IT readiness is expected to ensure its compatibility with emerging technologies, while also equipping its workforce with the necessary skills to adapt seamlessly to technological upgrades. This readiness acts as a springboard for digital transformation, enabling organizations to integrate innovative solutions into their business processes effectively. Furthermore, IT readiness offers both financial and operational advantages that contribute to improved firm performance; for instance, organizations with well-developed IT infrastructure are more agile, enhancing their ability to respond to dynamic market conditions and competitive pressures (Khan, 2025). Additionally, the strategic use of IT readiness reduces inefficiencies, fosters process optimization, and supports enhanced decision making. The role of digital transformation as a mediator in translating IT readiness into operational benefits, including increased efficiency, innovation, and adaptability.Therefore, firms that strategically align IT readiness with digital transformation initiatives are more likely to achieve superior operational performance.

H3c. 

Digital transformation has a mediating role in the relationship between IT readiness and operational performance.

Customer Pressures

Customer pressure can be regarded as the requests or expectations that customers have with regard to the adoption of available technological and routine processes by firms. Vial (2019) noted that, in order to meet customer expectations regarding fast and optimized services, there is a need to increase the quality of digital services. Customer pressure plays a critical role in this context, as customers’ perspectives and ideas for future products or services assist the organization in the process of innovation; as such, organizations will tend to evolve their digital products in accordance with customer desires (Verhoef et al., 2021). Digital transformation is also affected by competition, as companies do not want to lose their customers and are, therefore, forced to carry out digital development to keep pace with their competitors (Henriette et al., 2016). Additionally, providing superior customer service has proven to be a true motivator for corporations that wish to enhance their digital capabilities and better connect with target customers (Westerman et al., 2014). In summary, customer-oriented policies and practices are the core of the process of the digital transformation of companies, whereas customer pressures can be seen as an important factor affecting companies’ efforts towards digitalization (Kane et al., 2015).

H4a. 

Customer pressures are positively associated with digital transformation.

Customer demands regarding the design and transformation of operational performance are growing, especially in industries where responses to market needs are a priority. As an illustration, customer interest in eco-friendly and sustainable items compels production firms to embed green approaches into their activities, which may lead to improvement of the quality of products and result in greater competitiveness on the market (Nguyen-Viet, 2022). Customization needs are another kind of pressure that customers apply; as X. Liu et al. (2022) demonstrated, if one has to tailor a product to many customers, the focus becomes on minimizing variability in production, which boosts the efficiency of the production process. It has been shown that customer-oriented innovations, such as an increase in the speed of service delivery or the broadening of service content, help to focus and allocate resources and thus enhance certain operational indicators (AlBrakat et al., 2023). Furthermore, customer requirements serve to define manufacturing strategies; for example, altering supply chains and production systems such that they can rapidly respond to changes in expectations, especially in competitive market environments (Qiu et al., 2020).

H4b. 

Customer pressures are positively associated with operational performance.

Customer expectations have increasingly been recognized as a main factor driving digitalization in the manufacturing sector, which subsequently drives greater operational performance in return. As consumers are now insisting on higher levels of quality, customization, and quicker speed, manufacturers have no option but to turn to the integration of new technologies. For instance, according to Chavez et al. (2016), customer demands encourage the adoption of customer-oriented systems focused on the improvement of operational flexibility, quality, and cost, having positive consequent effects such as the greening of the supply chain. As Zhang and Zhang (2023) found, business process digitalization improves productivity by mitigating inefficient factors in supplier and customer management processes. In summary, these studies highlight the fact that digital transformation mediates the relationship between customer pressure and improved performance, as it helps to respond to customers’ requests while maintaining efficiency and innovation.

H4c. 

Digital transformation has a mediating role in the relationship between customer pressures and operational performance.

4. Methodology

4.1. Sample and Data Collection

This research employed a correlational quantitative research design with the aim of identifying antecedents associated with digital transformation, as well as examining the mediating effects of digital transformation in the relationships between the considered antecedents and the operating performance in firms. The sample included large Jordanian manufacturing firms, which were identified through a convenience sampling technique. All firms have been in business for a long time and have more than 250 employees. This guarantees that the firms in our sample possess the financial and technological means to undergo digital transformation. In addition, this study was designed in such a way that questionnaires were administered to chief executives (CEOs) and chief information officers (CIOs), who play key roles in the development of business strategies and the implementation of digital transformation processes.

As the target audience of the survey comprised Arabic speakers, it was warranted that the survey would first be translated from English to Arabic. For purposes of conceptual equivalence, a back-translation approach was employed: a bilingual professional first translated the survey into Arabic, following which it was independently back-translated to English by another bilingual scholar. Prior to data collection, the final Arabic version underwent pre-testing with a focus group of managers in order to evaluate its clarity and relevance. The data were then collected via emails and social media platforms (e.g., LinkedIn and WhatsApp). This convenience sampling method was considered acceptable due to the widespread use of such platforms by senior-level professionals in Jordan’s industrial sector. These platforms facilitate cost-effective communication and direct contact with respondents that may be difficult to reach, thereby enhancing the response rate. While this method does have limitations due to self-selection bias (i.e., as those respondents who are interested in digital transformation may have been more inclined to participate), it achieved the goal of accessing a knowledgeable managerial audience. To counter potential bias, the outreach was aimed at a wide range of industrial sub-sectors. Furthermore, voluntary participation ensured the anonymity of participants, thus reducing bias. Out of 1000 surveys sent out, 227 responses were collected after two reminder emails, resulting in a 23% response rate. This meets best practice recommendations for PLS-SEM, where Hair et al. (2014) indicated the need for a sample size between 150 and 200 to provides reliable parameter estimates and sufficient power for models with complex mediation structures. In addition, these samples fulfill the “10-times rule,” which states that having a sample size that is ten times greater than the maximum number of paths leading to any endogenous constructs is needed. Thus, the final sample of 227 was considered more than sufficient for the analyses conducted in this study. The demographic characteristics of the sample are provided in

Table 1. Sample characteristics (n = 226).

Gender	No	%
Male	151	66.8
Female	75	33.2
Total	226	100.0
Position	No	%
Operations Manager	15	66
President/CEO	51	22.6
Supply Chain Manager	57	25.2
Purchasing Manager	58	25.7
Supplier Relations Manager	45	19.9
Total	226	100.0
Company age	No	%
>3 years	21	9.3
3–6 years	74	32.7
7–10 years	131	58.0
Total	226	100.0
Industry	No	%
Food, beverages and tobacco	33	14.6
Industrial machinery and equipment	27	11.9
Rubber, plastics, and non-metallic products	20	8.8
Electrical, electronics, and semiconductors	31	13.7
Textiles and clothing	37	16.4
Automotive and transport equipment	19	8.4
Wood, cork, and paper	18	8.0
Pharmaceuticals	26	11.5
Furniture	14	6.2
Other	1	0.4
Total	226	100.0
Firm size	No	%
250–500	60	26.5
501–1000	99	43.8
1000–1500	66	29.2
>1500	1	0.4
Total	226	100.0

.

4.2. Measures

All items in the survey are closed, in order to enhance the response rate. The survey employs two main types of questions: rating questions (i.e., Likert-type) and categorical questions. All items in the questionnaire utilized a 5-point Likert scale to minimize cognitive burden and streamline understanding. This approach was taken to ensure that participants from different backgrounds could interpret responses uniformly. Previous research suggests that 5-point and 7-point scales have similar reliability and validity (Dawes, 2008), and within the scope of this study, the 5-point scale provided useful accuracy in cases where clarity and straightforwardness were essential.

The survey comprises two parts (parts 1 and 2). Part 1 includes instructions on how to fill the survey and “for whom” and “what is the company about” sections. This part is designed to collect information such as the respondent’s titles, experience, industry sectors, company size, and company age. Part 2 focuses on the description of the research model and its structure and is divided into three sections as detailed below. See Appendix A.

Section A includes questions that seek to determine the antecedents of digital transformation (i.e., customer pressures, IT readiness, and organizational culture). In this respect, customer pressures are assessed on a five-point scale (ranging from “Strongly Disagree = 1” to “Strongly Agree = 5”), employing the items developed by Tripopsakul (2018). The respondents were asked to what degree customer preferences and expectations act as a barrier to, or catalyst for, the use of social media and other digital platforms in business transactions. IT readiness is measured using items adopted from the study of Sachithra Lokuge et al. (2019). The participants were provided with a set of questions designed to determine the company’s level of readiness to accept digital transformation (with a rating ranging from “Strongly Disagree = 1” to “Strongly Agree = 5”). Organizational culture is additionally measured using the items constructed by Denison and Mishra (1995), known as the Denison Organizational Culture Model. The items were translated into an 8-item scale were scored in a five-point Likert response format (ranging from “strongly disagree = 1” to “strongly agree = 5”).

Section B includes questions pertaining to the degree of digital transformation that a participant’s organization has achieved. Based on the items that were appropriate and verified by Agostino and Costantini (2022), DT is considered using a first-order reflective construct evaluated on a five-point Likert scale (ranging from “Strongly Disagree = 1” to “Strongly Agree = 5”). The respondents were asked about the extent to which their firm has carried out digital transformation.

Finally, Section C assesses the levels of operational performance. Operational performance is classified as a first-order reflective construct and was also calculated on a five-point Likert scale (ranging from “strongly disagree = 1” to “strongly agree = 5”), adapted from the work of Krause et al. (2007). Respondents were asked to assess the extent to which their firm transformed its operational costs, delivery, quality, and flexibility due to its digital transformation.

There were certain control variables that were integrated into the current research; for example, the size and age of firms were relied upon for a number of reasons. First, they may have some degree of interaction with the two independent variables. Additionally, the influences of these variables have been reported in prior accounting and digitalization research (Guo & Xu, 2021). Finally, these variables are a combination of internal and general organizational variables. The total number of employees in the respondents’ companies was applied to compute the firm size, while the period in which the company had been operational was applied to compute the firm age.

5. Results

In this study, analysis was performed using the partial least squares structural equation modeling (PLS-SEM) technique to assess both the measurement and structural models. Prior to discussing specific statistical results, it is important to acknowledge that the empirical results confirmed all the hypotheses, which corroborated the theoretical framework and fulfilled the research objectives.

As for the reasons for adopting PLS-SEM, firstly, it is a valid technique for the analysis of key mediation models which has been widely applied in research in the accounting and digitalization fields (Singh et al., 2021). The choice to adopt the PLS-SEM method instead of the old-fashioned covariance-based technique was particularly affected by the size of the sample, as the approach used is more appropriate when the sample size is fairly small. In addition, this technique considers many different variables and thus enhances the modeling process, which is critical when aiming to develop a prediction model (Henseler et al., 2015). Furthermore, convergent and discriminant validity reliability tests are part of the measurement analysis (Hair et al., 2014).

Measurement Model

Before performing SEM analysis, the reliability and validity of the measurement model was first assessed. Reliability was estimated using the Cronbach’s alpha and composite reliability indices. Previous work has suggested a value of 0.7 as a threshold for both the CR and CA (Hair et al., 2014).

Table 2. Reliability and convergent validity results.

Construct	Item	Factor Loading	AVE	CR	α
Company culture	CUL1 1	-	0.741	0.945	0.930
	CUL2	0.820
	CUL3	0.831
	CUL4	0.918
	CUL5	0.943
	CUL6	0.868
	CUL7	0.776
	CUL8 2	-
Digital transformation	DT1	0.915	0.856	0.947	0.916
	DT2	0.931
	DT3	0.930
IT readiness	ITR1	0.889	0.815	0.930	0.887
	ITR2	0.892
	ITR3	0.927
Operational performance	OP1	0.852	0.826	0.950	0.929
	OP2	0.951
	OP3	0.903
	OP4	0.927
Pressures from customers	CUSP1	0.901	0.846	0.943	0.909
	CUSP2	0.963
	CUSP3	0.893

¹ Item dropped. ² Item dropped.

provides further details on the CR and alpha values for CUL (CR = 0.945, α = 0.930), DT (CR = 0.947, α = 0.916), ITR (CR = 0.93, α = 0.887), OP (CR = 0.95, α = 0.929), and CUSP (CR = 0.943, α = 0.909). As a result, the values can be considered acceptable in terms of the degree of internal consistency achieved. Furthermore, the measurement model’s validity was verified not only at the item level (convergent validity) but also at the construct level (discriminant validity). To measure the construct validity, we also analyzed the AVE and the factor loadings. The factor loading of the items on the constructs exceeded the minimum threshold of 0.5. Furthermore, all other constructs obtained AVE values above the acceptable threshold of 0.5. Thus, it can be concluded that all constructs achieved convergent validity.

In the present study, the authors constructed cross validity using the heterotrait–monotrait (HTMT) ratio method (Henseler et al., 2015). According to this approach, a construct can achieve discriminant validity if the relationships of the items within the same construct are greater than those across the constructs (Henseler et al., 2015). As shown in

Table 3. Discriminate validity (HTMT criteria).

Constructs	1	2	3	4	5
1. Company culture	0.861
2. Digital transformation	0.566	0.925
3. IT readiness	0.434	0.716	0.903
4. Operational performance	0.622	0.727	0.651	0.909
5. Pressures from customers	0.560	0.752	0.687	0.706	0.920

, the HTMT ratios of the constructs were all less than the maximum value of 0.85; therefore, the discriminant validity of the constructs was confirmed (Henseler et al., 2015). Additionally, the variance inflation factor (VIF) test was performed to assess the multicollinearity of the data, where the VIF values should be <10 (Aiken & West, 1991). The VIF values shown in

Table 4. Variance Inflation Factor (VIF).

First Order Construct	VIF Values
Company culture	1.436
Digital transformation	2.912
IT readiness	1.998
Operational performance	2.321
Pressures from customers	1.073

are all less than 10; thus, there was no multicollinearity issue.

Structural Model

The structural model was assessed using the 1000 bootstraps method in the smart-pls software. The minimum threshold for the coefficient of determination (R2), as stated by Wetzels et al. (2009), is 0.10. According to

Table 5. Results of testing the structural model.

Hypothesis	Paths	Std. Beta	Std. Error.	t-Value	p-Value	Remark
H1	DT → OP	0.547	0.111	4.784	p < 0.001	Significant
H2a	CUL → DT	0.191	0.065	2.866	0.002	Significant
H2b	CUL → OP	0.106	0.045	2.187	0.014	Significant
H3a	IT readiness → DT	0.340	0.106	3.417	p < 0.001	Significant
H3b	IT readiness → OP	0.449	0.087	5.345	p < 0.001	Significant
H4a	pc → DT	0.414	0.118	3.399	p < 0.001	Significant
H4b	pc → OP	0.229	0.087	2.440	0.007	Significant
	Control variables
	age → OP	0.052	0.064	0.822	0.205	Not Significant
	Size → OP	0.077	0.045	1.756	0.040	Significant
	R2 (DT) = 0.664 R2 (OP) = 0.650

, the R-square values indicate that the suggested model reflects 66.4% of digital transformation and 65% of operating performance. Therefore, according to the criteria, it is suitably predictive and possesses a high degree of explanatory power.

Direct Effects Models

The findings of hypothesis testing are presented in Table 5 and Figure 1. The first hypothesis states that there is a positive correlation between DT and OP. The findings indicate that DT has a statistically significant and positive effect on OP, with a beta co-efficient of 0.547, a t-value of 4.784, and a p-value of 0.001, demonstrating that a greater degree of digital transformation is linked with substantially enhanced operational outcomes. The validation of H2a and H2b demonstrated that CUL has statistically significant positive connections with DT (β = 0.191, t = 2.866, p = 0.002) and OP (β = 0.106, t = 2.187, p = 0.014). These results suggest that companies which nurture an organizational culture that promotes innovation, learning, and adaptability are more likely to undertake successful implementations of digital transformation and achieve enhanced performance outcomes.

Thus, H2a and H2b were confirmed. In addition, the validation of H3a and H3b indicated that there exist positive and significant relationships between IT readiness and DT (β = 0.340, t = 3.417, p = 0.001) as well as OP (β = 0.449, t = 5.345, p = 0.001). Therefore, H3a and H3b were also confirmed. Practically, this means that firms with appropriate IT infrastructure, trained staff, and digital capabilities are in a better position to implement and capitalize on digital transformation strategies. These firms are also more likely to observe improvements in performance measures such operational efficiency, organizational agility, and overall productivity. Finally, the validation of H4a and H4b indicated the existence of positive and significant relationships between PC and DT (β = 0.414, t = 3.399, p = 0.001) as well as OP (β = 0.229, t = 2.440, p = 0.007). These results indicate that firms facing more stringent external customer requirements, such as demand for expedited delivery and digital or bespoke interfaces, are more likely to pursue digital transformation initiatives and obtain operational performance enhancements.

Mediating Effects of Digital Transformation

A bootstrapping technique was used in this study to evaluate the mediation models. In order to replicate the original sampling process, samples of size n obtained via bootstrapping were repeatedly resampled for analysis, thus providing an empirical representation of the sampling distribution for assessment of the indirect effect. In order to determine the presence of substantial mediating effects, we considered the p-values and whether zero fell outside the lower and upper bounds of the confidence interval; in this way, the existence of an indirect influence can be asserted.

The results regarding digital transformation as a mediating variable in the analysis of the relationships between CUL, IT, PC, and operating performance are presented in

Table 6. Mediation analysis.

Hypothesis	Relationship	Std. Beta	Std. Error.	t-Value	p-Value	BCI LL	BCI UL	Remark
H2c	CUL → DT → OP	0.059	0.035	2.187	0.014	0.015	0.126	Supported
H3c	ITR → DT → OP	0.102	0.053	2.649	0.004	0.037	0.218	Supported
H4c	CP → DT → OP	0.131	0.082	2.440	0.007	0.033	0.281	Supported

. Initially, the findings suggest that the mediating influence of DT in the correlation between CUL and OP is statistically significant (T-value = 2.187; Lower and Upper values = 0.015 and 0.126, respectively; p-value = 0.014). Therefore, H2c was confirmed. Furthermore, the results indicated that the mediating influence of DT in the relationship between ITR and OP is also significant (T-value = 2.649; Lower and Upper values = 0.037 and 0.218, respectively; p-value = 0.004). Therefore, these findings offer evidence in support of H3c. The results further indicate that the mediating influence of DT in the relationship between PC and OP is statistically significant (T-value = 2.440; Lower and Upper values = 0.033 and 0.281, respectively; p-value = 0.007). Hence, these findings also validate H4c.

6. Discussion

Firms in the manufacturing sector are being challenged, in terms of their ability to compete, due to shifting trends in digital technology. In this context, the absence of the understanding of the antecedents that allow for digitalization serves as a barrier for practitioners. This study argues that certain external and internal antecedents, supported by appropriate organizational structures, internal work practices, and the technological state of the firm, drive the successful realization of digital transformation. This research looks at the major antecedents contributing to digital transformation in manufacturing firms and their influence on operational performance, based on the RBV and institutional theory. This research specifically focused on three key antecedents: Organizational culture, IT readiness, and customer pressures.

The results of the analysis provide strong support for the first hypothesis; that is, digital transformation exerts a significant and positive influence on operational performance. This corroborates the findings of other authors (Tracey et al., 1999; Esmaeilian et al., 2016), who have emphasized the influence of technology in the reduction of costs and increased productivity. Cutting-edge tools for digital transformation—particularly artificial intelligence and real-time data analysis—make processes more efficient, shorten production cycles, and allow for the creation of high-quality tailor-made products, thus overriding the assertion that digital transformation is not crucial for outperforming the competition in the marketplace (S. C. Liu et al., 2021). This supports the RBV, as digital capabilities act as strategic resources that enhance performance.

The results of this research clearly indicate the significance of the considered antecedent factors in promoting the digitization of businesses, demonstrating that those factors influencing their integration into the strategy and processes of an organization determine the success of its implementation, as well as the enhancement of operational performance. The results demonstrate that organizational culture is likely to have significant positive impacts on DT and OP (H2a, H2b); moreover, DT mediates the relationship between organizational culture and OP (H2c). Therefore, a partial mediating effect was confirm. This is consistent with the RBV, in the context of which organizational culture is an important intangible resource which nurtures an ecosystem that enhances learning and innovation (Balci, 2021). These findings conform to prior studies (Verdu-Jover et al., 2018; Warner & Wäger, 2019; Hogan & Coote, 2014; Zheng et al., 2010). Research has suggested that organizations with change-oriented and collaborative cultures are in a better position to deploy DT strategies, effectively achieving better operational efficiencies, customer satisfaction, and overall competitiveness in the market (Vial, 2019; Verhoef et al., 2021). Furthermore, a conducive culture encourages individuals to be flexible and innovative, thus helping organizations to implement their transformation strategies as their employees are willing to work towards the associated goals (Martínez-Caro et al., 2020; Kane et al., 2018). The results indicate that business organizations derive value from a culture which encourages digital adoption and bolsters employee engagement and alignment with organizational processes, resulting in improved operational outcomes. Such cultures strengthen an organization’s ability to cope with rapid changes in the digital environment and use new technologies to increase performance (Warner & Wäger, 2019). Consequently, the empirical validation of these relationships addresses critical gaps in the literature by strategically showcasing culture as a lever in achieving digital success, operational enhancement, and addressing the digitally driven culture which operationalizes improvements.

Additionally, the results of this study highlighted the significant impacts of IT readiness on DT and OP in Jordan’s manufacturing sector (H3a, H3b), with DT partially mediating the relationship between IT readiness and operational performance (H3c). IT readiness, including solid infrastructure, appropriate human resources, and digital skills, is crucial for successfully carrying out strategic initiatives in the context of digital transformation, as emphasized by Chwiłkowska-Kubala et al. (2023). In their research, they also determined that IT readiness enables transformation efforts in the case of the energy sector, which is relatively resource-intensive. Furthermore, according to Jafari-Sadeghi et al. (2021), technological readiness supports the innovation activities of a company by aiding in detecting and implementing the digital opportunities available to them. The literature also suggests that IT readiness is a driver of operational performance, enabling companies to improve their operations through the integration of IT systems, the use of digital applications, and the automation of processes to better satisfy customer expectations (Kliestik et al., 2023; Wanasinghe et al., 2020). These findings support the RBV, which claims that the internal capabilities of an organization—such as its IT readiness—function as strategic resources that yield value and competitive advantage. This is particularly true in technology-based contexts. As a result, IT readiness promotes operational performance when a firm undertakes digital transformation, in turn demonstrating the high significance of IT in gaining a competitive advantage in a highly digitalized world.

Finally, the study results revealed that customer pressures greatly influence DT and operational performance (H4a, H4b). Moreover, within the latter relationship, digital transformation (DT) acts as a mediator (H4c), thus confirming a partial mediating effect. Consumer need for custom-made products, quicker delivery, and higher quality are considerable factors affecting the adoption of DT as Jordanian producers aim to improve their operational processes. This is in line with the RBV, in the context of which digital transformation is seen as a strategic resource to be leveraged for competitive edge. This result also aligns with institutional theory, whereby manufacturers experience requirements related to the industry that force them to adapt to increasing customer needs in order to maintain competitiveness (Westerman et al., 2014). Additionally, the study results confirmed the existence of a significant positive correlation between customer pressure and operational performance, as well as with DT. The effects reported here endorse the findings of earlier investigations, including those of X. Liu et al. (2022), and Chavez et al. (2016), suggesting that businesses can register short-term performance advances by meeting customer expectations while undertaking DT. In summary, the presented findings reinforce the idea that both digitization and customer pressures are essential for promoting effective operational performance in Jordan’s industrial manufacturing sector.

7. Theoretical and Practical Contributions

7.1. Theoretical Contributions

This study offers relevant theoretical and practical implications, adding value to the current body of knowledge. It provides an elaborate theoretical framework which has been developed through critical analysis of the existing literature related to digital transformation antecedents, digital transformation itself, and operational performance. The proposed framework synthesizes shared knowledge across antecedents including organizational culture, IT readiness, and customer pressures, while taking digital transformation as a mediating variable that affects operational performance. This study stands out as one of the first studies to consider these specific antecedents alongside digital transformation and operational performance, thus presenting a novel perspective that bridges gaps in the existing literature.

This research seeks to advance the theoretical understanding of factors that lead to digital transformation through identifying its key antecedents—IT readiness, organizational culture, and Customer Pressures. While previous research has largely investigated these elements separately, our model integrates their synergies in facilitating digital transformation. This enables us to fill one specific gap in the literature focused on the cumulative strategic impact of organizational and technological resources on digital transformation efforts. In particular, it seeks to show how internal factors such as IT readiness and organizational culture influence the adoption of DT. The research results demonstrate that IT readiness facilitates the rapid adoption of new technology, thus increasing the level of resilience and effectiveness. As far as the organizational culture is concerned, it was found that companies that support a flexible and cooperative organizational culture tend to implement DT strategies more effectively; as a consequence, improved operational performance, customer satisfaction, and competitiveness can be observed. As for external antecedents, this research contributes to the literature by revealing the important role of customer demands for customization, speed, and quality, which encourage the adoption of DT by manufacturers, in order to promote their efficiency and responsiveness.

A majority of past studies in this area have been confined to developed countries. Therefore, it has been argued by digitalization researchers (Vial, 2019) that it is not feasible to make generalized conclusions based on these results and that more studies in different environments are required. In particular, this is the first study of its kind to be carried out in a non-Western culture—namely, that of Jordan—in order to answer this call.

The study contributes to the understanding of digitalization in manufacturing with regard to operational performance, which is a critical concern in the process of digital transformation, thereby supplementing the firm and financial performance indicators which have been the main focus of prior studies. However, the works of Gunasekaran et al. (2017) has emphasized the relevance of operational performance in this context; specifically, they showed how DT can affect process improvements, productivity increases, and operational competitiveness in manufacturing firms. This study complements their findings regarding those core operational functions; inventory, production, and supply chain functions, along with their integration, can lead to improvements in operational performance in the short run, and give the manufacturer a sustained competitive advantage in the long run. Through the consideration of operational performance and the core areas outlined earlier, to enable a deeper analysis, this research contributes to the existing body of knowledge in the operational area, in which the focus of the organization is deemed to be of paramount importance in the modern, rapidly changing, and ever-evolving world.

7.2. Practical Contributions

This research offers a noteworthy contribution by providing useful guidelines for Jordanian manufacturing firms implementing digital transformation processes. Understanding organizational culture, IT readiness, and customer pressures as vital antecedents, this research provides clear directives to operational managers with respect to how they should focus their efforts to achieve enhanced operational performance. For example, this study points out that there is a need to develop an organizational culture that promotes flexibility, creativity, and collaboration across boundaries, as these factors determine the success of digital initiatives. It is also advised that sufficient IT readiness should be developed, not just through the deployment of new technologies but through the training of individuals and the re-engineering of systems to enable their rapid response to fast-changing requirements. Furthermore, concerns regarding market dynamics are addressed through the consideration of customer pressures, emphasizing the need for digital enhancement to enable greater customer satisfaction and product improvement.

In the case of Jordanian businesses, which tend to compete at a regional level and have unique socio-economic barriers, these insights provide useful recommendations regarding how best to allocate resources in a manner that enhances productivity. Treating these antecedents as a complementary system could result in the minimization of supply chain management waste and optimized building cycle times. Furthermore, the research provides a structure with which industry participants, policymakers, and advisors can better formulate context-conscious solutions such as re-training, IT investments, or the cultural re-engineering of the manufacturing industry in Jordan. Moreover, these implications are not only applicable to the achievement of the goals of separate firms, but also for the enhancement of the competitiveness of the industrial sector of Jordan within the international arena.

8. Conclusions

The purpose of this research was to determine the antecedents affecting the intentions towards the adoption and implementation of digital transformation by Jordanian manufacturing firms. Additionally, a model that advances our understanding of the influence of DT on operational performance was developed, based on the RBV and institutional theory. In contrast to prior studies, which typically focused only on the direct impacts of DT on performance, the present study attempted to delve into the black box to broadly investigate how internal and external factors influence the contributions of digital transformation to operational performance.

The PLS-SEM approach was applied for analysis of the survey questionnaire responses. This allowed for the examination of how several antecedents—organizational culture, IT readiness, and customer pressures—affect the adoption of DT in order to improve the operational performance of firms. The findings indicated that there are positive correlations between each of the considered antecedent factors and DT, as well as with operational performance. In addition, the adoption of DT was found to act as a mediator between each of the antecedent factors and operational performance.

This research fills an important gap in the literature on the drivers of digital transformation in the context of the manufacturing industries in developing countries, in this case, Jordan. The results presented here stress the need for firms to gain a better understanding of the factors determining the adoption of DT, such that they will be able to implement the adoption of DT in a more efficient way; that is, in a manner that optimally improves processes and creates competitive edge.

9. Limitations and Future Work

Despite examining pivotal aspects of the causes driving the digital transformation of businesses in Jordan, this research also has some limitations that offer avenues for further inquiry. A major drawback is context specificity: this study was conducted within the sphere of manufacturing firms in Jordan, which could limit the extent of the application for these findings in other sectors or even other countries in the world. Broadening the scope of future research to encompass distinct industries or nations of varied economic and digital sophistication would enhance the possibility of such generalization. In addition, this study uses convenience sampling, which may impact the generalizability of the results. This approach aided in data collection but may not adequately reflect the diversity of the intended population. Future studies are advised to use random or stratified sampling to enhance the external validity of the findings. Furthermore, although this research delineated the major antecedents of DT, moderators such as leadership types, employee readiness, or external support (e.g., government support) were not assessed in detail. These variables merit investigation in order to obtain a fuller picture of how digital transformation and its implications function. Finally, future research may draw upon a mixed methods approach—for example, incorporating ethnography or qualitative case studies—which could enhance the relevance of the quantitative findings in relation to the actual nature of digital transformation efforts.