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. 2023 Mar 28:1–20. Online ahead of print. doi: 10.1007/s13132-023-01377-8

The Interplay of Digital Transformational Leadership, Organizational Agility, and Digital Transformation

Bora Ly 1,✉
PMCID: PMC10044082  PMID: 40479084

Abstract

The rules of the game are changing in organizations due to several digital advances, ecosystems, industries, and fields. Also, the emergence of new institutional infrastructures and complex systems during this period can result in digital transformation (DT). Thus, new institutional theory should be examined to determine how new digitally-enabled institutions emerge, diffuse, and institutionalize within their contexts. The purpose of this study is to use a questionnaire approach to collect and analyze quantitative data (n = 388) to test the model of how digital transformational leadership (DTL) influences DT through organizational agility (OA). The data suggested that OA mediates the relationship between DTL and DT. Furthermore, it sheds light on the crucial role of DTL in DT through OA, demonstrating that alignment of organizational models and evolving OA are critical to DT. Ultimately, this work provides important insights into leadership styles and organization agility in the public sector concerning digital transformation.

Keywords: Organizational agility, Cambodia, Digital transformational leadership, New institutional theory, Digital transformation, PLS-SEM

Introduction

Digital transformation (DT) technologies have changed contemporary organizational environments and operations. Similarly, DT impacts individuals and society, including the government, mass media, art, medicine, and science. Consequently, public organizations are investigating new opportunities DT presents to increase agility and flexibility in rapidly changing environments. However, public organizations continue to face numerous barriers to DT; thus, governance, data acquisition, and resources must be addressed (Ferraris et al., 2020) and manage the public sector in an uncertain environment (Oliva et al., 2019). Aside from these challenges, introducing DT technology into the public sector has many advantages, including enhanced accountability and transparency, more accessible data, improved supply chains, better services, and support for environmental initiatives (AlNuaimi et al., 2021a; Altayar, 2018; Seepma et al., 2021). However, organizations need to be innovative and commitment-driven and possess new skill sets to remain competitive in this disruptive market setting (Scuotto et al., 2021). Organizations and their leaders face multiple challenges in adopting new digital technologies because of DT (Hanelt et al., 2021; Verhoef et al., 2021; Vial, 2019). However, numerous organizations’ leaders struggle with a strategy-to-execution gap, which results in costly DT failures (McGrath & McManus, 2020; Siebel, 2019). Several reasons contribute to this, including the widening disparity between rhetoric and reality and the persistent gap between strategy and implementation (Li, 2020). In addition to the powerful and unsettling changes created by digital transformation, leadership must adapt in several ways simultaneously (Ford et al., 2021). Likewise, leadership is vital in motivating stakeholders and creating a platform for action (Sainger, 2018). They need to ensure their organizations adopt digital mindsets and are agile to respond to disruptions caused by digital technologies (Vial, 2019). Leaders with a mindset for digital transformation are known as “digital leaders,” who could establish collaborative networks and develop competence in digital areas (Bresciani et al., 2021a; Frankowska & Rzeczycki, 2020). Transformational leadership is highlighted in the literature on digital transformation and leadership in a digital context. Although leadership plays an essential role in a digital transformation undertaking, little is known about this role, and little literature is available on digital transformation leadership (DTL) (McCarthy et al., 2021). As such, the present study examines how these factors interact and contribute to organizational agility (OA) to evaluate how several internal factors can influence DT in the public sector. Therefore, this study aimed to: (1) analyze the relationship between DTL and OA and its influence on DT and (2) identify whether OA is a mediator between DTL and DT. According to Gegenhuber et al. (2022), incorporating institutions and technology into institutional theory is essential for digital technologies. Accordingly, this work was to examine and answer the above objectives using institutional theory (INT) (Greenwood & Hinings, 1996; Greenwood et al., 2017) and new institutional theory (NIT) (Dacin et al., 2008). Therefore, this investigation contributes significantly to both theory and practice. The concept of INT is commonly used to explain organizational changes, including new rules, procedures, and even entire organizational structures (Suddaby et al., 2010). However, few sources of information appear to describe the application of INT to investigating OA. Future research suggested that INT can be used to understand better the influence of OA on DT (Dubey et al., 2018a). As part of this work effort, INT and new institutional theory (NIT) are used to investigate this phenomenon to bridge the gap between theory and practice. Therefore, it could explain how actors or institutions influence organizations or organizational environments. In addition, this work addresses a knowledge gap regarding how OA and leadership could add to DT, particularly when several private and public organizations underline the importance of deploying DT during and after COVID-19 (Li et al., 2021). Also, they further assert that organizations can respond more quickly to environmental volatility by aligning DT with organizational strategy and focusing on internal capabilities, leadership, and connections. Finally, this work contributes to the body of knowledge of DTL on DT and OA in the public sector. However, public organizations have recently released conceptual and qualitative studies on DT (Durão et al., 2019; Guarnieri & Gomes, 2019) or how DT technologies impact the knowledge they possess about supply chains (AlNuaimi et al., 2021a; Nekrasov & Sinitsyna, 2020; Seepma et al., 2021).

New Institutional Theory (NIT)

The NIT model has been extensively employed in the literature on DT to examine various aspects of the process (Shashi et al., 2020; Verhoef et al., 2021). A study on contemporary organizations must consider its perspective because it encompasses a significant corpus of theory and practice that emphasizes the significance of cultural understanding and shared prospects for individuals (David & Bitektine, 2009). Specifically, it examines how organizations adapt to their environment to survive and grow in the face of competition and obstacles. Also, it is believed that organizations are formed through a shared understanding and interpretation of the norms that control collective behaviors, including procedures and practices (Meyer & Rowan, 1977; Parsons, 1956). NIT highlights three factors that determine the effectiveness of organizations. First, there is coercive pressure, often from government-supported agencies, powerful groups, or organizations that control resources. Second, involving other organizations in decision-making creates imitation pressure. In addition, normative pressure is adopted when professionals and other actors implicitly or explicitly promote specific policies and practices (Meyer & Rowan, 1977). Also, it has frequently been used to examine the external influences affecting the practices and cultures of organizations in connection with technological advancements (Adebanjo et al., 2018; Dubey et al., 2019).

Furthermore, NIT has examined transformation and innovation from a sociocultural perspective utilizing two distinct methodologies. First, it analyzes the association between stability and change, considering the continuity and homogeneity and the volatility and heterogeneity of organizations (Greenwood et al., 2017). Second, stability and change may be viewed due to many different activities, structures, individuals, and organizations (Scott, 2013).

Digital Transformation (DT)

A digital transformation involves several deliberate changes that utilize cutting-edge technologies (Bresciani et al., 2021b). It can be defined by the shift of organizations towards big data analytics, cloud computing, and mobile technologies to deliver their products and services via social media networks (Bresciani et al., 2021b; Nwankpa & Roumani, 2016). Also, it presents an opportunity for changing organizational elements, processes, and cultures to become more responsive to the effects of technological innovation in the marketplace (Nasiri et al., 2020). Thus, it involves (1) redefining and reexamining organizational boundaries; (2) providing community feedback and reducing property rights; and (3) restructuring product and organizational identities (Parmentier & Mangematin, 2014). According to NIT, DT would significantly transform the institution, affecting organizations and fields (Del Giudice et al., 2021; Hinings et al., 2018). Also, it is a term used to refer to the combination of various digital innovations and technologies, which result in the creation of new actors, frameworks, practices, beliefs, communities, structures, or values that disrupt or alter existing industries, fields, ecosystems, or even change entire rules of the game (Parmentier & Mangematin, 2014; Scuotto et al., 2020; Westerman et al., 2014). In addition, these changes impact employees’ and leaders’ employment and reshape the organizational culture (Legner et al., 2017; Scuotto et al., 2021). There is a growing concern among public organizations about transforming their organizations to reap the benefits of digitalization (Tangi et al., 2020). Thus, providing digital transformation in the public organization entails new approaches to working with stakeholders, new methods to service delivery, and new relationships (Mergel et al., 2019).

Digital Transformation Leadership (DTL) and DT

The NIT aims to transform organizations by integrating digitally-enabled organizational arrangements across diverse industries. According to Hinings et al. (2018), the success of DT depends on gaining legitimacy through the organization’s belief system. NIT views leadership as integral to organizational values and beliefs; as organizations change, leadership must evolve and adapt (Biggart & Hamilton, 1987). The shift towards DT occurs as organizations fundamentally alter their operations, offer products and services, and foster a flourishing digital culture (Bresciani et al., 2021b; Chierici et al., 2021).​ Thus, leadership that establishes venues for action and mobilizes stakeholders is necessary for this to be possible (Sainger, 2018).

Furthermore, the leader is crucial to ensuring and accelerating the transition to the digital age (Li et al., 2016; Porfírio et al., 2021). According to Swift and Lange (2018), leaders can support organizations in succeeding in the digital age by adopting three behaviors: (1) monitoring emerging technology trends; (2) establishing the trend of digital transformation and strategic initiatives; and (3) guiding the team through the precise and efficient transition. Furthermore, leaders with a DT perspective can create cooperative networks and develop digital capabilities within their organization (Bresciani et al., 2021a; Frankowska & Rzeczycki, 2020). Literature on DT examines transformational leadership in the digital age mainly through the lens of DT. Equally, the transformational leader cultivates trust, develops leadership among others, acts as a moral agent, and encourages followers to pursue goals beyond the present workforce’s needs (Avolio, 1999). Therefore, transformational leadership and digital technologies are two aspects of DTL (De Waal et al., 2016). Furthermore, transformative leadership has been established to enhance organizational innovation, which is critical in successfully transforming into the digital age (AlNuaimi et al., 2021b; Lei et al., 2020; Sasmoko et al., 2019). In addition, Ardi et al. (2020) examined leadership from the transformation perspective and found that DTL positively impacts organizational performance. Therefore, this work posits:

  • H1: DTL has a significant impact on DT.

Organizational Agility (OA) and DT

In the view of INT, the institutional environment plays a crucial role in the (re)formation of organizational structures. According to Scott (1995), cultural and societal variables and concerns about legitimacy may influence decisions. Thus, DT can be seen through organizational influence (Dubey et al., 2018b; Gupta et al., 2020; Liu et al., 2010). An organization is more likely to implement DT due to external forces, such as competitors, customers, or governments, instead of internal factors (Bresciani et al., 2021b; DiMaggio & Powell, 2000). In addition, some pressures, including mimetic, coercive, and normative, can influence an organization to adopt DT (Teo et al., 2003). However, organizational change is a prerequisite for DT, and insufficient adaptation might result in bottlenecks (Teichert, 2019). Therefore, organizations’ processes, structures, and management must change to become more agile.

Agility is the capability to respond to changing needs and external factors quickly and cost-effectively without sacrificing product or service quality (Ganguly et al., 2009). Changing environments require organizations to reorganize their structures to adapt to new processes and resources (Darvishmotevali et al., 2020; Ferraris et al., 2022; Troise et al., 2022). An additional definition of OA is the ability to evaluate and respond efficiently and effectively to unanticipated changes in the external environment, using internal resources and reconfiguring them accordingly to gain a competitive edge (Žitkienė & Deksnys, 2018). Agility can be seen as a concept with cognitive elements from the standpoint of NIT (Dimaggio, 1991). Specifically, as institutions seek to fulfill value commitments and respond to changing circumstances, agile institutions should prioritize representing, utilizing, and growing knowledge structures (Walsh, 1995).

Furthermore, Menon and Suresh (2021) extended the concept to include adopting information and communication technologies. They identified eight possible determinants of organizational agility: (1) environmental sensing capability; (2) organizational structure; (3) ICT adoption; (4) human resource practices; (5) organizational learning; (6) leadership; (7) adaptability; and (8) engagement with stakeholders. In addition to the OA related to DT, there is an increase in organizational agility and fabric resulting from the creation of new occupational profiles (Del Giudice et al., 2018; Jesse, 2018). Equally, Ghasemaghaei et al. (2017) claim that organizations can increase their agility by using data analytics to better match analytical tools and data with employee competencies and organizational goals. On the other hand, organizations must possess DT, technical competencies (Nguyen et al., 2020; Rane et al., 2020), and e-commerce facilities (Li et al., 2020) to improve their agility. Furthermore, DT is essential to achieving agility in information processing (Li et al., 2020). Hence, the study proposes:

  • H2: OA has a significant impact on DT.

Leading Digital Transformation and Fostering OA

The NIT approach contends that for an organization to advance toward institutionally new transformation, it must have a high level of organizational capacity, necessitating the mobilization of these competencies and resources within the organization. Additionally, the NIT affirms that organizations can strengthen their legitimacy by implementing organizational processes and employing leaders with desirable traits and/or practices (Meyer & Rowan, 1977; Scott, 1995). Five human attributes are necessary for agile organizations: (1) establishing a shared purpose; (2) anchoring core values; (3) enhancing work; (4) fostering personal growth; and (5) delivering appropriate benefits (Shafer et al., 2001). As a result of incorporating these human characteristics and the appropriate leadership style, organizations can adjust more quickly and effectively to changes by making reasonable structural adjustments. Therefore, the leadership style employed within an organization could be expected to influence agility. Furthermore, organizations seeking to improve their organizational agility should be able to adjust their strategic alternatives and reverse unsuccessful strategy outcomes (Ahammad et al., 2020). Unfortunately, the research literature does not provide adequate explanations or measures of the level of interdependence between these factors (Aurélio de Oliveira et al., 2012).

This work agrees the definition of OA includes continuous improvement, communication, team maturity, and flexibility. It consists of highly motivated, talented, disciplined, organized, and self-disciplined teams that could easily improvise (Stettina & Heijstek, 2011). Leadership is crucial in handling power, authority, and responsibilities among team members, increasing motivation and confidence (Gunasekaran, 1999). The leadership of an organization is vital to enhancing its commitment to OA (Raeisi & Amirnejad, 2017). The NIT defines leadership capabilities as activities critical to managing institutional transformations. The capabilities consist of charismatic leadership (visioning, persuasiveness, and communicating), instrumental leadership (organizing, directing, rewarding), and institutional leadership (implementing changes) (Nadler & Tushman, 1990). The literature identifies these leaders as transformational leaders; for example, transformational leaders shape their followers’ attitudes, emotions, and values (Bass & Avolio, 1993). These leaders can increase OA by encouraging subordinates to think beyond their immediate requirements and perform effectively in complicated and challenging environments. Transformational leaders equip themselves and their teams to respond appropriately when facing obstacles and possible possibilities (Burke & Collins, 2001; Veiseh & Eghbali, 2014). Indeed, leadership in OA continually adjusts the course of action and development trajectory. For example, an organization like Nokia has faced trouble because its leaders are dissatisfied with periodic strategy reviews (Doz & Kosonen, 2008).

The current investigation found that transformational leadership contributed to adopting e-business (Alos-Simo et al., 2017) and enhanced OA (Akkaya & Tabak, 2020; Veiseh & Eghbali, 2014; Wanasida et al., 2020). Additionally, transformational leadership contributes to organizational innovation (Pirayesh & Pourrezay, 2019), organizational creativity (Veiseh & Eghbali, 2014), and the performance of innovation initiatives (Aurélio de Oliveira et al., 2012). Also, this competence may assist organizations in enhancing their learning orientation, hence increasing their competitiveness, which manifests in OA (Ojha et al., 2018). Finally, it improves service recovery performance within public sector organizations (Lin, 2011). Taking these factors into account, this work posits:

  • H3. DTL has a significant impact on OA.

The OA becomes more successful by distributing agility across organizations, workforces, and systems (Muduli, 2016). Also, it is defined by four essential competencies: adaptability, competence, responsiveness, and speed (Akkaya & Tabak, 2020). All employees could change their behavior by implementing appropriate information systems, providing clear instructions, and supporting leadership (Larjovuori et al., 2016). Culture, leadership, and organizational change are all associated with organizational agility characteristics (Dalvi et al., 2013). Similarly, leadership can contribute to OA and DT by fostering a culture that embodies the organizational mission and helps employees learn the skills needed to reach organizational goals (Babnik et al., 2014). Consequently, this study proposes that OA could mediate between DTL and DT.

  • H4. OA mediates the relationship between DTL and DT.

Methods

This study quantitatively examined the DTL, OA, and DT in Cambodian public organizations to test the hypotheses constructed within the NIT model using a questionnaire-based. The data were analyzed using structural equation modeling (SEM). Equally, this work accepted PLS-SEM over covariance-based testing because it is more accurate in assessing nonparametric and unprecedented studies (Henseler, 2018) and evaluating the emerging complexity of existing theories (Hair et al., 2019). Also, SPSS version 26 was applied for statistical analysis, and a significance level of .05 was specified. In addition, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.86, above the recommended value of 0.60 (Pallant, 2020), and Bartlett’s test of sphericity was significant at p < .001. Also, Fornell and Larcker (1981) claim that acceptable internal consistency requires Cronbach’s alpha > 0.70. Additionally, the structural equation analysis was performed with SmartPLS3.

Measurement Instruments

Scale items for this study have been adapted from prior literature and tailored to suit the context. First, six items were adapted from TFL (Chen & Chang, 2013; Podsakoff et al., 1996) to fit the DT context to measure DTL. Also, OA constructs with four items were adapted from Cegarra-Navarro et al. (2016). Finally, DT comprising five items were adapted from the work of Nasiri et al. (2020) for this study. All scales are rated significant on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree).

In addition, confirmatory factor analysis (CFA) was used to validate the instrument for measuring the constructs. It was used to determine how observed variables are essential to the applied latent construct. This analysis is based on the strength of the regression model connecting the factors to the observed variables rather than the relationship of the variables (Byrne, 2010).

Participants and Procedures

Convenience sampling techniques were used because of their convenience and availability (Bryman, 2016) through self-administered questionnaires in the public organization located in Siem Reap City, Cambodia, that participated in this study from January 2022 to February 2022. In addition, participants distributed survey forms through social media working groups. It took approximately 10 min for participants to complete the anonymous survey and provide informed consent. Equally, the G*Power calculator was used to determine the sample size. Assuming an effect size of alpha = .05 with a two-tailed test, the study’s power was (1- β) = 0.95, suggesting that 327 samples were required. As a result, the report yielded a total return of 388 distributed questionnaires deemed larger than the minimum required. The profile of respondents revealed that 53.4% were male, 46.6% were female, 51.3% were aged 18–35, 44.3% were 36–55, and 4.4% were aged 56 and over. Additionally, 49% were undergraduates, 44.6% were graduates, and 6.4% were post-graduates of educational background.

Assessment of Measurement Model

The evaluation of the measurement model involves examining internal consistency, convergent validity, and discriminant validity (Hair et al., 2016; Hair et al., 2011; Henseler et al., 2009). Outer loading values of 0.7 and above are deemed appropriate (Hair et al., 2016). The t-statistic was calculated for each cross-loading based on the replacement method within SmartPLS, using 5000 samples and a significance level of p < 0.005. Equally, the internal consistency of the constructs was calculated using composite reliability (CR) and Cronbach’s alpha values. All CRs were higher than 0.70, the recommended value (Fornell & Larcker, 1981). Each construct exceeded the threshold for Cronbach’s alpha of 0.70. In addition, there was good converging validity because the average variance extracted (AVE) was higher than 0.50. Table 1 summarizes the key findings of the assessment (loadings, Cronbach’s alpha, CR, and AVE).

Table 1.

Factor loadings, reliability, and validity

Constructs Loadings
Digital transformational leadership (DTL) (Cronbach’s alpha = 0.886, CR = 0.913, AVE = 0.637)
 DTL1-Leaders inspire all members with the DT plans for our organization 0.783
 DTL2-Leaders provide members with a clear DT vision 0.768
 DTL3-Leaders motivate team members to accomplish the same DT goals 0.803
 DTL4-Leaders in our organization encourages all members to reach DT goals 0.821
 DTL5-Leaders in my organization consider the DT beliefs of all members 0.742
 DTL6-Leaders encourage all members to think about DT ideas 0.865
Organizational agility (OA) (Cronbach’s alpha = 0.898, CR = 0.922, AVE = 0.663)
 OA1-We can quickly accommodate individual and government needs 0.776
 OA2-We can rapidly adapt processes and activities to meet demand fluctuations 0.786
 OA3-We can handle issues from suppliers and partners efficiently 0.830
 OA4-We respond quickly to market and government changes 0.778
 OA5-We seek ways to reinvent our organization continually 0.854
 OA6-We see government and market trends that provide for speedy expansion 0.856
Digital transformation (DT) (Cronbach’s alpha = 0.865, CR = 0.902, AVE = 0.649)
 DT1-My organization strives to digitalize everything possible 0.810
 DT2-My organization collects vast amounts of data from several sources 0.835
 DT3-My organization aspires to use digital technologies for additional activities 0.845
 DT4-My organization strives to use digital technology to improve service quality 0.759
 DT5-My organization strives towards digital information sharing 0.776
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Discriminant validity was evaluated using two procedures in this paper. First, it checks the square root of the AVE of each item to the relationship with the other factors. When the square root of the AVE is greater than the corresponding correlations, the construct has adequate discriminant validity (Fornell & Larcker, 1981). As a result of the findings, the square root of AVE for the construct was higher than the inter-construct correlation (see Table 2).

Table 2.

Discriminant validity-Fornell and Larcker criterion

DT DTL OA
DT 0.806
DTL 0.521 0.798
OA 0.488 0.650 0.814
HTMT
DT
DTL 0.580
OA 0.536 0.694 -
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Furthermore, discriminant validity was also assessed by the heterotrait-monotrait ratio of correlations (HTMT). Henseler et al. (2015) set a threshold value of 0.9; therefore, a value of HTMT greater than 0.9 could be problematic when contested discriminant validity. As illustrated (Table 2), the discriminant validity of all constructs was established.

Multicollinearity and Common Method Bias Remedies

All constructs were tested for multicollinearity before this process, and the variance inflation factor (VIF) was calculated to confirm collinearity. According to Hair et al. (2011), PLS-SEM requires a VIF tolerance value between 0.20 and 5.0, and multicollinearity would be problematic if VIF is higher than 5.0 or lower than 0.20. The findings confirmed the absence of multicollinearity because the VIF results ranged from 1.8 to 2.9. Furthermore, the Harman single-factor test was performed to determine the variance explained to rule out common method bias. The findings indicate that no single factor accounted for most variance (only 42.179). This suggests no evidence of common method bias (CMB) contamination in the data since the variance explained was less than 50% (Podsakoff et al., 2003).

Structural Model

After the construct validity and reliability were confirmed, an analysis of structural model findings was carried out. First, the model quality is evaluated to predict endogenous constructs. Then, it is accessed based on the coefficient of determination (R2), cross-validated redundancy (Q2), path coefficients (β), and significance of paths. Standardized path coefficients test the degree to which hypotheses were confirmed. Finally, the goodness of the model is determined by the strength of each structural path (Gallardo-Vázquez & Sánchez-Hernández, 2014).

According to Falk and Miller (1992), the R2 value of the latent dependent variable for each path between constructs should be at least equal to or greater than 0.1, indicating the model is predictive capability. Equally, the R2 value of 0.75, 0.50, or 0.25 for endogenous latent variables might be considered substantial, moderate, or weak (Hair et al., 2011; Henseler et al., 2009). The results (see Table 3) revealed that all R2 values were 0.310 and 0.422 for DT and OA, considered moderate outcomes. Additionally, the Stone-Giesser test or cross-validated redundancy Q2 was used to evaluate the predictive relevance of the endogenous constructs. A Q2 larger than 0 indicates that the model is predictively significant, whereas a Q2 less than 0 suggests that the model is flawed (Castro & Roldán, 2013). Therefore, it can be concluded that the prediction of constructions is significant because constructive Q2 values were 0.189 and 0.256 for DT and OA, respectively (see Table 3).

Table 3.

Hypotheses testing

Path coefficient Standard deviation t value p value Decision
H1: DTL -> DT 0.353 0.083 4.242 0.000 Supported
H2: OA -> DT 0.259 0.058 4.491 0.000 Supported
H3: DTL -> OA 0.650 0.031 21.227 0.000 Supported
R2 Q2
DT 0.310 0.189
OA 0.422 0.256
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It is possible to avoid model misspecification by adopting the standardized root mean square residual (SRMR) in PLS-SEM (Henseler et al., 2016). Hu and Bentler (1999), precisely like Kenny (2020), defined SRMR as the standardized difference between the observed and predicted correlations. Accordingly, a global model fit was quantified by applying SRMR (Ly & Ly, 2022a, 2022b, 2022c). However, no threshold of SRMR has been proposed in a PLS-SEM context yet (Hair et al., 2016). Therefore, it has been suggested that SRMR < .10 is a good model fit (Hu & Bentler, 1998; Kara et al., 2022; Worthington & Whittaker, 2006). This finding has an SRMR of 0.08, indicating that this study was a good model fit.

Further assessment of the goodness of fit, and hypotheses were tested to establish the significance of the relationship. Also, the replacement method was used to bootstrap with 5000 samples to check the significance of each path coefficient. As shown in Table 3, all the path coefficients are statistically significant. This suggests that DTL (β = .353, t = 4.242, p < .001) and OA (β = .259, t = 4.491, p < .001) were significantly and positively impact on DT. Thus, H1 and H2 were robustly supported. Additionally, DTL (β = .650, t = 21.227, p < .001) significantly and positively impacted OA. Hence, H3 was supported.

Mediator Analysis

According to Zhao et al. (2010), the indirect effect of a*b should be measured first to test for mediating effects in PLS. Secondly, determine the size of the mediation. Finally, the results were analyzed using bootstrapping with 5000 subsamples and bias-corrected 95% confidence intervals (CI) (Hair et al., 2016; Zhao et al., 2010). The empirical findings exhibited that the total effect was significant and positive (β = .521, t = 7.892, p < .001). After the mediator was included in the model, the influence diminished, and the direct relationship was statistically significant (β = .353, t = 4.242, p < .001), and an indirect effect with mediator inclusion was also found statistically significant (β = .168, t = 4.289, p < .001). Furthermore, the confidence intervals are not displayed with zeros (see Table 4). Hence, H4 was robustly supported.

Table 4.

Mediation analysis

Total effects Direct effects Indirect effects
β t value β t value Hypotheses Coefficient t value p value
DTL -> DT 0.521 7.892 0.353 4.242 H4:DTL -> OA -> DT 0.168 4.289 0.000
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Also, the study used the variance accounted for (VAF) to estimate relative absorption to assess the strength of this mediator. According to the VAF calculation developed by Hair et al. (2013), the power of the mediation effect (VAF value) on the DTL-DT relationship was 0.32. This suggests that OA is a complementary partial mediation in the DTL-DT relationship because the direct and indirect effects were significant and positive. Therefore, the findings entail that OA mediates the relationship in the model.

Discussion

The present study evaluated the interaction and effects of DTL and OA on DT through the mediating role of OA. Based on these findings, hypotheses were formulated based on the relationships among the identified variables. First, the findings revealed that DTL significantly and positively influenced DT. These results are consistent with existing literature and NIT analysis, indicating that DTL is better prepared to manage organizational change, especially in the digital arena (De Waal et al., 2016). This acknowledges that the transformational leader could embrace digital values and reshape their beliefs to support organizations experiencing any change, including DT (Hinings et al., 2018; Sainger, 2018). Hence, for competitiveness and relevance in the age of Industry 4.0, organizations should seek leaders capable of digitally transforming their operations (Li et al., 2016; Porfírio et al., 2021).

Second, the effect of OA on DT is significant and positive. It has been proven that organizations can benefit from OA capacity, as evidenced by literature reviews (Li et al., 2021). It indicates that organizations seeking agility need to disrupt existing procedures, management, and structures (Darvishmotevali et al., 2020; Teichert, 2019), precisely the method required to implement a change within an organization, such as digital transformation. Thus, organizational learning and knowledge management are essential to ensure that DT efforts are successful (Menon & Suresh, 2021; Walsh, 1995). Menon and Suresh (2021) recommend that organizations embrace agile technological tools and human resource strategies to remain agile in a digital landscape.

Third, these data claimed that DTL significantly and positively affects OA. This study corroborates prior research that shows transformational leaders improve OA by nurturing relationships with followers and encouraging individuals to take measured risks when confronted with opportunities and challenges (Burke & Collins, 2001; Veiseh & Eghbali, 2014). In addition to boosting OA, it drives an organization to adapt rapidly to DT (Wanasida et al., 2020). Therefore, DTL can see situations from multiple perspectives, allowing them to be more agile and adaptable to DT.

Finally, the findings suggest that OA mediates the relationship between DTL and DT. It indicates that public institutions in Cambodia should consider the importance of OA within the public sector since DTL may not accomplish the desired results when it comes to DT without effective OA. Also, a successful DT relies on a dynamic organizational structure that leverages digital technology to increase value and adaptability to a changing competitive landscape. Equally, OA enables organizations to respond to market changes quickly and effectively, enhancing their ability to succeed. Therefore, OA plays an essential and valuable role as a mediator while understanding the implications of the DTL-DT relationship in the organization.

Conclusion

This work indicates a connection between DTL, OA, and DT within public organizations in Cambodia. It suggests that a relationship between organizational change and employee practices enables DTL to affect OA and DT. Also, organizations can benefit from having DTL and developing DT to assist their employees in responding promptly and correctly in the age of digitization.

As a result of the NIT concept, the public sector may be able to consider OA and DT as interrelated organizational challenges, with a particular focus on leadership and technology. Also, various external factors contribute to DT, including competition, customers, and stakeholders. Although it is a precondition, DT can result in a bottleneck if not adjusted accordingly. Similarly, leaders must possess strong leadership capabilities to ensure that DT remains effective. Thus, transformational leaders can assist organizations in becoming more agile by establishing appropriate relationships with subordinates.

Although this study was conducted in the context of public organizations in Cambodia, its findings and implications can be applied to many governmental and non-governmental organizations, including in the business sector. Some important implications can be drawn from this study. First, DT is a significant organizational change characterized by the emergence of new digital skill sets imposed on individuals and the entire organization. This has a negative impact on the acceptance of new technology and impedes its development. Likewise, DT adoption may be most successful when transformational leadership characteristics such as fostering trust and supporting team development are integrated with DT. Second, organizations need to develop policies that promote OA, which creates a favorable environment for DT. Similarly, public organizations are urged to embrace agility to achieve DT and learn from the private sector to reduce procedures and red tape. Additionally, public organizations must be involved in designing their strategies rather than rely on top-down directives.

Furthermore, this study provides insight into organizational behavior and DT. It emphasizes leadership’s critical role in fostering OA in the DT process. Also, this adds to the body of knowledge by examining the relationship between DTL, OA, and DT. Significantly, it contributes to the recent research trend in public organizations to redesign and re-engineer services to meet public expectations and government mandates.

Limitations

There are a few limitations to the current study, and there is still room for future research. First, the results cannot be generalized to other societies since this study only involved a small sample size of public organizations in Siem Reap City, Cambodia. Thus, future research should be cross-country comparisons and analyses of perspectives in other countries. Additionally, this work used a cross-sectional research design. Thus, a longitudinal or case study would be more appropriate for analyzing the implementation of digital transformation over time in future studies. Finally, using only quantitative methods makes it difficult to examine the attitudes and perceptions of Cambodian public employees toward this model relationship more profoundly. Hence, it is crucial to consider qualitative and quantitative methods in future studies to get more detail on these findings.

Declarations

Ethics Approval

Ethics review or permission was unnecessary for the study using human volunteers under local regulations or institutional criteria. The questionnaire informed participants not to provide any identification or information in this work. Participants submitted consent to participate in the study after being adequately informed.

Conflict of Interest

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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