Navigating the digital transformation path: The role of digital leadership and technological capability through the dynamic capability model

The dynamic capability model has been linked previously with literary investigations on digital transformation, technology capabilities, digital leadership, and DIGIC (Tagscherer & Carbon, 2023; Zhang et al., 2023). Enterprises have recently assessed the material relevance of digital transformation in institutional strategies. Mihu et al. (2023) established that institutional culture focuses on three capabilities: digital intensity, digital savvy, and conditions and interactions to attain transformative digital capability. Around the world, pharmaceuticals, beauty, and closures industries are integrating digital practices into all levels of operation. Digitality has transformed communication challenges between leaders and command lines (Benitez et al., 2022). In the emergence of effective digital transformation, Cserdi et al. (2022) revealed that the dynamic capability of an enterprise is the fundamental strategic procedure to achieve successful digitalization. Additionally, Teece (2016) commented that dynamic capability is highly significant compared to the resource-based view when an enterprise operates in a volatile environment.

Furthermore, enterprises that want to integrate the digital transformation process must consider the digital culture in the firm’s dynamic capabilities. In Finland, Hautala-Kankaanpää (2022) explained that digital culture was an influencer of manufacturing enterprise platforms to adopt transformation linked to the efficiency of their operation. Employees from the multinational enterprises in Tech were explored by Munsamy et al. (2023) on the digital leadership scales during the fourth industrial revolution. The results from SPSS AMOS analysis demonstrated that six dimensions of digital leadership are highly affiliated with digital culture. Similarly, in 14 high-tech institutions, Balakrishnan and Das (2020) explored SMEs’ digital transformation and innovation. The results illustrate that process innovation aligns with digital transformation and culture influences to achieve enterprise performance. According to Mihu et al. (2023), technological capabilities are the resources capabilities of an organization, such as IT resources, and are the driving force for digital transformation. Although technological capability is simple to integrate in large institutions, this cannot be said for SMEs in emerging nations. In Thai, Tieng et al. (2022) investigated ICT capability and enterprise efficiency among 141 entrepreneurs. The empirical results revealed that entrepreneurs who invest in ICT capabilities have an effect on enterprise efficiency and competencies. Previous literary works have employed the resource’s base view to test for the variables of digital transformation (Bazrkar et al., 2022), digital leadership (Benitez et al., 2022; Davison et al., 2023), and technological capability (Persaud & Zare, 2023; Ren et al., 2024). To close the theoretical gaps, the dynamic capabilities were adopted to explore the affiliation between DIGIT, DIGIL, TECH, and DIGIC variables on enterprise performance in SMEs in an emerging country like Ghana.

Digital leadership refers to individuals’ skills, competencies, and behaviors in guiding and influencing others toward achieving organizational goals. Effective leadership is crucial in driving digital transformation initiatives and navigating uncertainties. Hung et al. (2023) conducted a study examining the role of digital leadership in driving successful digital transformation. The researchers surveyed executives from diverse organizations and found that effective digital leadership was critical in achieving DIGIT success. They identified key DIGIL capabilities such as strategic vision, technological understanding, and the ability to drive cultural change. In China, Davison et al. (2023) explored the impact of digital leadership on enterprise performance in the context of digital transformation. The researchers found a favorable affiliation between digital leadership and enterprise performance using survey data from large organizations. They revealed that digital leaders played a crucial role in aligning DIGIT initiatives with organizational goals, fostering a culture of innovation. In Europe, Benitez et al. (2022) investigated the influence of digital leadership on employee engagement during digital transformation. The researchers surveyed employees of a multinational organization undergoing digital transformation. The findings revealed a significant material affiliation between digital leadership and employee engagement. Tagscherer and Carbon (2023) evaluated 92 articles between 2011 and 2021 on the nexus between DIGIT and digital leadership. The researchers found that digital transformation initiatives were positively associated with digital leadership behaviors. Digital leaders, characterized by their ability to drive innovation, embrace technological advancements, and foster a digital culture, were instrumental in leading successful digital transformation. Therefore, the authors argue that a positive relationship between DIGIL and DIGIT can be elucidated through the lens of DCM and the novel from literary work. Thus, DCM provides theoretical ground for investigating the relationship between DIGIL and DIGIT. Manufacturing enterprises that cultivate dynamic capabilities are poised to effectively respond to DIGIT, leading to improved enterprise performance as they adopt, innovate, and align their strategies with market demands.

Hypothesis 1: DIGIL positively influences DIGIT efforts.

The concept of digital leadership has been part of recent investigations with huge research gaps on the significance of its role in digital technology and performance (Rupeika-Apoga et al., 2022; Tagscherer & Carbon, 2023). The Digital Officer is directly responsible for the company’s digital transformation and has a portfolio that may include several different responsibilities (Davison et al., 2023). DeLone et al. (2018) explained digital technology capabilities as the ability to employ technological applications to create value for customers, suppliers, and the enterprise. In Canada, Persaud and Zare (2023) examined the nexus between absorptive capability and technological models in the context of innovation-driven firms. Through interviews and surveys with 447 innovation managers, the researchers found a favorable affiliation between digital leadership behaviors and technological capabilities. To achieve a proactive strategy within the environment, Bhatia (2021) explored the mediating effect of technology on process innovation and management of the environment. The PLS test illustrated that technology had a significant favorable affiliation with enterprise learning, process innovation, and environmental strategy. In China, manufacturing enterprises, Jiang et al. (2020) investigated the nexus between digital leadership and technological communication in the context of information technology (IT) departments. Through surveys and interviews with IT leaders, the researchers found that digital leadership behaviors favorably influenced technological leadership capabilities. Previous literary articles had confirmed a favorable affiliation between Zhe and Hamid (2021), digital technology and innovation in Malaysia, and Tarighi et al. (2021), learning and technological capability in Iran. Therefore, the study assumes that digital technology capabilities support a digital strategy in upsurging digital transformation when channeled through DIGIL. Considering these positive effects of DIGIL on TECH, the next hypothesis was developed as follows.

Hypothesis 2: DIGIL has a positive effect on DIGIT.

Technology refers to the tools, systems, and innovations that enable digital transformation. It includes emerging technologies such as artificial intelligence, cloud computing, big data analytics, IoT, and others (DeLone et al., 2018). Recently, digital manufacturing has become a sustainable means to reduce emissions and improve the process and product development to achieve a green environment (Wu et al., 2022). Within developed and emerging nations, Gillani et al. (2020) conducted a study evaluating the influence of technological capability on DIGIT in the manufacturing sector. Analysis of 931 enterprises’ data from manufacturing firms revealed that higher technological capability favorably influenced the adoption and effectiveness of digital transformation initiatives. Employing the ISM method and MICMAC analysis, Mohapatra et al. (2022) investigated the difference between technological capability and digital transformation in enterprises during COVID-19. Technological capability, including IT infrastructure, digital skills, and access to digital resources, accounted for digital technologies and transformed their business processes, leading to improved competitiveness and performance. Firms with advanced technological capabilities, such as automated warehousing, fleet management systems, and real-time tracking technologies, were more likely to undergo digital transformation, leading to improved efficiency, visibility, and customer satisfaction. Established enterprises implementing digital strategy have been found to benefit from (a) enhanced digital product or developing entirely new ones and (b) improving employees’ relationships and adaptability to new applications. However, for an effective synergy of DIGIT and TECH activities in an enterprise, DIGIL is crucial. Zhao and Huang (2022) highlighted the significance of DIGIL in producing a desirable performance using technological capabilities. The following hypothesis is developed by extending insight from these studies to the TECH context.

Hypothesis 3: Technological capability positively impacts digital transformation.

Digital transformation involves the integration of digital technologies into various organizational functions, processes, and strategies, leading to improved operational efficiency, innovation, and customer experience (Bazrkar et al., 2022). The relationship between digital transformation and organizational performance has become a growing interest in recent years, as organizations increasingly recognize the importance of leveraging digital technologies to enhance their performance and competitiveness (Masoud & Basahel, 2023). Mohammed and AL-Abrrow (2023) conducted a comprehensive study examining the impact of digital transformation on firm performance. Through a meta-analysis of multiple empirical studies, the article identifies a strong favorable relationship between digital transformation and various dimensions of organizational performance, including financial performance, innovation, and leadership. Using the SEM test and employing the RBV and RD model in SMEs in China, Zhang et al. (2023) investigated the factors of digital transformation on organizational efficiency. The results of 180 enterprises’ empirical investigations produced a positive affiliation between digital transformation and enterprise efficiency. During the COVID-19 era, Rupeika-Apoga et al. (2022) conducted a study examining the impact of technology adoption on organizational performance. The researchers analyzed data from various industries in Latvia and found that organizations that successfully integrated technologies into their operations experienced significant performance improvements. Technology adoption was associated with increased productivity, cost savings, and better decision-making capabilities, leading to enhanced organizational performance. Therefore, it is hypothesized that organizations that undergo successful DIGIT will experience enhanced organizational performance.

Hypothesis 4: Digital transformation positively influences organizational performance.

Organizations must quickly adopt new technological solutions to be competitive in today’s fast-paced world. It is now essential for businesses to foster a digital culture in order to modernize their processes and enrich their relationships with both consumers and staff (Persaud Zare, 2023). A company’s “digital culture” is its norms and values as influenced by its use of digital resources (Sumrit, 2021). The role of DIGIC in the affiliation between digital leadership and DIGIT has emerged as a material area of inquiry in contemporary organizational studies. The term “culture” describes the guiding principles that help an organization be adaptable while it undergoes change or transformation. Honesty, encouragement, selectiveness, consistency, adaptability, no-blame, non-punitive evaluation, failure as a learning opportunity, innovation as business as usual, teamwork, empowerment, and a positive attitude toward customers and staff are all examples of such principles (Balakrishnan & Das, 2020). Hautala-Kankaanpää (2022) surveyed Finnish enterprises, and data from 194 were analyzed using the SEM test. The variable digital culture was adopted as a moderating construct on the affiliation between digital platform and enterprise performance. The results illustrate that digital culture had a material moderating effect on the relationship. Additionally, another strand of authors (Balakrishnan & Das, 2020; Mihu et al., 2023) found that DIGIC compels enterprises to adopt and implement DIGIT practices through pressures from customers, government institutions, and competitors, which in the long term results in the efficiency of manufacturing enterprises. In conclusion, the study argues that DIGIC moderates the link between DIGIL and DIGIT. This means that DIGIC strengthens or weakens the influence of DIGIL initiatives by organizations in their digital strategies. The inference is that from a DCM perspective, the moderation effect of DIGIC between DIGIL and DIGIT highlights an enterprise’s ability to develop dynamic capabilities related to digital transformation. Thus, the following hypothesis was derived:

Hypothesis 5. Digital culture has a favorable influence on digital transformation.

Hypothesis 5a. Digital culture moderates the link between digital leadership and digital transformation.

The inspection outcomes from the literary works have highlighted contradictions in elements, establishing material, immaterial, and mixed findings. Induced from the results, making decisions could affect policy implications for digital transformation and digital leadership on enterprise performance. Different methodologies, principles, and periods have been employed to expound on the affiliation between the variables. This investigation adopted a more robust SMARTPLS SEM to handle complex theoretical models. As evidenced in the literary work, few investigations have been carried out in emerging communities. Therefore, the emerging community context assessment on DIGIT, DIGIC, and TECH will close these gaps. Hence, the evaluation results will serve as a policy document for institutions, academia, and government.

These research papers highlighted the complex interplay between digital transformation, technology, and leadership in an uncertain world. They provide material insights into the challenges leaders face, the strategies required to navigate uncertainty, and the impact of DIGIT on enterprise efficiency. By integrating theoretical frameworks with empirical findings, these research papers offer a deeper understanding of the dynamics at play and provide practical implications for organizations and leaders seeking to thrive in the digital era. The proposed research model aims to examine the affiliation between digital transformation, technology, and leadership in driving enterprise success in the context of manufacturing enterprises. The model incorporates key variables and their hypothesized relationships, as shown in Figure 1.

Figure 1

Conceptual model.

Manufacturing enterprises in Ghana were considered to achieve the research objective and test the hypothesis developed on the relationship between the variables. Ghana is at the genesis of building a manufacturing hub of Africa, with the government’s policy in 2015 to implement the one district-one factory (1D1F). The 1D1F is a policy initiative to have one manufacturing company in each of the 275 districts in Ghana. According to the Ghana Statistical Service, these manufacturing companies have been established to adapt to digital change rather than the traditional form. This will help the sector produce sustainable goods that meet international standards (R1-Q5 and R2-Q6). The research employed quantitative methods to comprehensively understand the relationships between digital transformation, digital leadership, technology capability, digital culture, and enterprise performance. A comprehensive literature assessment underpins the employed deductive technique. This method provides a systematic assessment of observations to develop hypotheses, which are subsequently tested by gathering data through surveys. A convenience sampling technique was employed to provide a diverse and inclusive sample of enterprises. Statistical power analysis was used to establish the sample size, estimate the bias, and guarantee reliable results. The inverse square root (Kock & Hadaya, 2018) and statistical power analysis (G* power), with 0.95 statistical power and 0.03 effect size, were used while considering the sample size. Seventy-four data points were needed, with a 0.95 statistical power and a medium-sized effect of 0.15 anticipated. Furthermore, utilizing the inverse square root, 618 data points were sufficient to obtain a statistical power of 0.80 and a minimal path coefficient of 0.10 to actualize a significant effect of 0.05 (Kock & Hadaya, 2018). A sample size of 386 is suitable for an unknown population (Janes et al., 2017). An average sample size of 360 was deemed fit for the data analysis. Questionnaires were distributed via Google Forms to the managers within the selected manufacturing organizations, and 650 responses were received. About 459 usable responses, representing 70.6% of the sample size, seem appropriate for this study. The survey utilizes five Likert-scale questions to measure perceptions of digital transformation, digital leadership, technology capability, digital culture, and enterprise performance.

Existing literature has indicated that adopting a measuring scale that has been previously tested helps to reduce bias and increase data reliability and validity. Five scales were employed to measure digital transformation, digital leadership, technological capability, digital culture, and enterprise performance. Table 1 illustrates the various measured scales, proxies, and references adopted. The researchers followed the empirical literature to check for common method bias of the developed questionnaires, adopting the suggestions of Podsakoff et al. (2003). The current study employed SPSS 23 and Herman’s one-factor procedure. The results demonstrated that a single construct explained about 43% of the variance. With the threshold of 50%, this result was accepted for further analysis. Similarly, KMO and Bartlett’s test of sample validity had a value of 0.875 and 0.001, illustrating that no standard method bias exists in the questionnaire data gathered.

Previous research has indicated that when multiple dimensions are presented in a model, it is significant to adopt the SEM approach as it predicts all the constructs’ validity and reliability (Streukens & Leroi-Werelds, 2016). Because of its advantages over other statistical methods, the partial least squares structural equation modelling (PLS-SEM) was employed for the statistical study. For example, PLS-SEM is the suggested method when the research goal is theory development and variance explanation (prediction of the constructs) (Hair et al., 2022). Also, the approach works well with complex models and small sample sizes and rarely makes any assumptions about the underlying data. Moreover, it is relatively robust as long as the missing data are maintained below a reasonable threshold. Furthermore, using SEM, endogenous components that explain the most variation are estimated and examined (Roldán & Sánchez-Franco, 2012). Because PLS-SEM does not require normally distributed data, it is a better choice. Finally, given that the study’s primary goal was to assess the hypotheses, PLS-SEM was judged to be more suitable for creating a theoretical model and verifying the proposed causalities. The technique consists of two models: measurement and structural evaluation. Hair et al. (2017) state that the structural and measurement models must be evaluated independently to achieve accurate findings. For this reason, the measurement model was first looked at in this evaluation. Convergent and discriminant tests were run for validity and reliability. These encompass the Cronbach alpha (CA), composite reliability (CR), variance inflation factor (VIF), average variance extracted (AVE), HTMT, and Fornell–Larcker validity (Hair et al., 2022). Next, the route coefficients of the structural model, where the direction of the hypotheses was developed, were determined using the bootstrapping method (5,000 iterations). The study’s significance level concerning the significant routes was established using a 95% confidence interval. Also acquired were the fit indices. Version 4.1.0.6 of the Smart PLS program was used for all these analyses (Ringle et al., 2022).

Table 1 demonstrates the respondents’ demographic profile – gender, age, participants’ education, working experience, and position within the various manufacturing enterprises. Around 238 of the 459 respondents were male, whereas the remaining were women. More than 50% of the participants surveyed ranged between 26 and 45 years old. This meant that enterprises in the manufacturing sector employed the younger generation, who were capable of working. Moreover, about 88% of the respondents have had bachelor’s degrees. Regarding work experience, the majority had worked for over 5 years. Among the participants, more than 77% were junior and middle managers.

Reliability, convergent validity, and discriminant validity were evaluated to validate the measurement model. The degree to which one measure strongly correlates with another measure measuring the same construct is known as convergent validity (Hair et al., 2017, 2022). According to Hair et al. (2014), discriminant validity guarantees that a concept measure is empirically distinct and captures phenomena of interest that other measures in a structural equation model fail to capture. Since every construct in this study was modeled as reflecting, the indicators should share a large percentage of the variance (Hair et al., 2014).

Indicator reliability (outer loadings) and AVE were examined to determine convergent validity, as shown in Table 2. All AVE values were greater than 0.5, which attested to the validity of convergence (Hair et al., 2014). Also, every item’s loading was higher than the criterion value of 0.7, indicating the attainment of dependability. Moreover, CA and CR of the constructs were used to evaluate the measures’ dependability.

According to Hair et al. (2017, 2022), CA estimates reliability based on the intercorrelations of the observed indicator variables, whereas CR prioritizes the indicators based on their dependability. According to Hair et al. (2022), all the CR and CA values were 0.7, showing the reliability of the measurements. The square root of the AVE values was compared with the latent variable correlations, taking discriminant validity into account.

To verify good discriminant validity, the square roots of the AVE for each construct were greater than the correlations for the other components in the study (Fornell–Larcker, 1981) (Table 3). The authors examined the cross-loadings that arise when an indicator’s outer loading on the nexus construct exceeds the aggregate of its cross-loadings and affiliations with all other constructs. From Table 4, the cross-loadings are less than the outer loadings of the indicators, indicating that discriminant validity is achieved. Table 5 illustrates the results of the heterotrait–monotrait ratio of correlations (HTMT) test, which was introduced by Henseler et al. (2015) to remedy the issues related to the Fornell–Larcker. The discriminant validity results are achieved as each deattenuated correlation is less than 0.85.

The study evaluated the structural model to confirm the predictive ability of the model and the significance of the affiliation among the constructs (Hair et al., 2022) after obtaining an acceptable validity and reliability. In our study, three endogenous (technological capability, digital transformation, and firm performance) and two exogenous constructs (digital culture and digital leadership) are present in the model. Given that the VIF values for every item ranged from 1.00 to 4.40 (Hair et al., 2017), the researchers detected that the data show no multicollinearity. Moreover, the significance of the proposed relationships was validated. Accordingly, the path coefficients show the proposed correlations as Streukens and Leroi-Werelds (2016) indicated. The bootstrapping with replacement technique, considering 5,000 resamples, was run in Smart PLS4 to test the proposed correlations and outcomes, as illustrated in Table 5.

The quality of this model was estimated using the standardized root mean square residual (SRMR), R square, Q square, and f square. The SRMR value of 0.068 corroborates a good fit (Henseler et al., 2015). An R-squared value of 0.812 (digital transformation) and 0.586 (enterprise performance) indicates that the independent variables in the model account for more than 50% of the variation in digital transformation and enterprise performance. This suggests that the independent variables (such as digital leadership, technological capability, and digital culture) included have a good explanatory power in predicting DIGIT (Hair et al., 2017, 2022). Also, DIGIT is a key predictor of enterprise performance. Additionally, the blindfolding approach in PLS was utilized to evaluate the Q square of the endogenous constructs – digital transformation (0.547), enterprise performance (0.460), and technological capability (0.369). These values are greater than zero (between 0.369 and 0.547) and have a medium-sized effect, suggesting substantial predictive importance (Stone, 1974). Moreover, the f-squared values, which measure the contribution of each exogenous variable toward the model’s predictive stability, were evaluated utilizing Cohen’s (1988, 1992) threshold (small effect ≥0.02; moderate effect ≥0.15, significant effect ≥0.35). All the f-squared values between 0.10 and 0.88 for the constructs were greater, indicating that a good predictive stability of the model is achieved.

In this study, the evaluation concerning the direct effects hypotheses is represented in Table 6. The bootstrapping approach in Smart PLS 4 was adopted (Ringle et al., 2022). The study hypothesized that digital leadership has a direct positive impact on DIGIT (H1) (β = 0.340; p < 0.000; t = 7.325), while DIGIL has a significant positive influence on technological capability (β = 0.686; p < 0.000; t = 20.70). Therefore, H1 and H2 are supported. A significant t-statistic was found on the relationship between technological capability and digital transformation (β = 0.253; p < 0.000; t = 4.463) to warrant support for H3. Regarding H4, the study proposed that digital transformation positively and significantly influences firm performance (β = 0.766; p < 0.000; t = 29.673) and support was found for this relationship. Moreover, a material and favorable affiliation was between digital culture and digital transformation (β = 0.382; p < 0.000; t = 7.784). Thus, H5 was supported. The noteworthy and robust results of digital culture demonstrate that a substantial theoretical impact is achievable. Hence, the authors evaluated the moderating nexus of digital culture on the connection between DIGIL and digital transformation. The study discovered a considerable relationship between digital leadership and culture. The researchers discovered the impact of the interaction (DIGIC* DIGIL) on digital transformation (β = 0.147; p < 0.000; t = 6.919) was significant as predicted. Therefore, H5a is accepted.

Table 7 depicts the beta values of the mediation effect of technological capability on the nexus between DIGIL and DIGIT. The VIF value of 33.7% indicates that technological capability partially mediates the aforementioned link. Further, consider the specific indirect analysis in the PLS output, digital transformation emerged as a mediator on the affiliation between DIGIC and ENTP (β = 0.271; p < 0.000; t = 7.296), DIGIL and enterprise performance (β = 0.211; p < 0.000; t = 6.300), and technological capability and enterprise performance (β = 0.221; p < 0.000; t = 6.124) (Figure 2).

Figure 2

Structural model: Direct and indirect paths.

The changing ecological landscape of business within the manufacturing sector has seen speed in technology and its effect on consumer taste and product development. The evaluation assessed the interrelationships between digital transformation, digital leadership, digital culture, and technological capability on enterprise performance, with questionnaires on 459 participants and employing the robust Smart PLS analytical approach. The investigation illustrated that digital leadership and technological capability had a favorable material affiliation with digital transformation. Again, digital transformation affirms a partial direct material mediating nexus with enterprise performance. Further, digital culture asserts a moderating relationship between DIGIL and DIGIT. The findings demonstrate a corroborated inspection outcome in the dynamic capability model context. The expounds of the evaluation estimates communicate to managers and policymakers that digital transformation can be accomplished through proper integration of digital leadership, technological capabilities, and digital culture to utilize the enterprise’s resources efficiently.

The current investigation contributes theoretically to the dynamic capability model and digital transformation assessment. First, the theoretical support of DCM gives a solid foundation for understanding why manufacturing enterprises pursue digital transformation practices. One unique theoretical contribution of our study is the empirical validation of the DCM within the context of digital transformation. While DCM has been explored deeply in management and marketing research fields, its application to digital transformation is scarce. This study demonstrates how enterprises can develop dynamic capabilities, such as digital leadership and technology capabilities, to keep pace with innovation and improve enterprise performance. The results of the authors’ investigation reveal that the direct and mediating role of digital leadership and technological capabilities had a positive relationship with digital transformation, enriching DCM and supporting novel studies into enterprise performance. The study identifies specific contextual factors that influence the effectiveness of digital transformation practices in moderating digital culture. This contributes to a more context-aware approach to digital transformation, acknowledging that the impact of these practices can vary depending on institution, industry, and government policy. Again, this study adds to the literary works on enterprise performance investigation by empirically validating a theoretical model premise on a unified model of digital leadership, technological capability, digital culture, and digital transformation practices, bringing together the past studies into an extensive one. The outcome from the research provides novel insights into the existing literary work on digital transformation, digital leadership, digital culture, technological capability, and enterprise performance, through the DCM in the adoption, implementation, and continuous improvement of digital transformation practices. The findings established how enterprises can transform their manufacturing innovation processes through dynamic capabilities to achieve efficiency (R2-Q8). The result of this study provides valuable guidance to practitioners. The evaluation suggests that organizations should foster their performance by recognizing that digital transformation is a game changer regarding strategic choices. Moreover, organizations seeking to improve their performance should embrace digital transformation by having an effective digital leader, embracing cultures that foster innovation, and being technologically capable. The results highlighted that enterprises could appreciate their performance efficiency when digital leadership is channeled through digital transformation. Additionally, culture, previously considered a catalyst for technological adoption and transformation, was found to moderate the affiliation through digital leadership. Hence, it is suggested that institutions within the manufacturing enclave should integrate digital culture with digital leadership. Furthermore, it was evident that various technological capabilities accelerate digital transformation. It is recommended that the government provide enterprises with tax breaks and subsidies to employ various technological strategies.

Although this research offers a new contribution to the digital transformation agenda, it has some drawbacks that present an opportunity for future studies. Our research was limited to the geographical context of Ghana. Even though Ghana is considered the gateway to West Africa, its economy differs from that of other emerging economies. Future research must compare the other outcomes with those of other developing countries such as Bangladesh, Nigeria, and Togo. After that, the findings can be generalized to emerging countries. Again, the study only employed data-collection managers, which may cause data bias. Researching different participants, such as employees and individuals with diverse digital transformation knowledge, is suggested. Furthermore, quantitative research uses questionnaires as a data collection strategy. Future evaluation could employ different methodologies, including qualitative. Finally, the direct effect of the moderating role of digital culture could be explored in different sectors, such as the banking sector (R1-Q4).