Investigating the nexus between business process management maturity and digital maturity: an empirical study of organizations in Poland

The main goal of this study is to investigate the relationship between Business Process Management (BPM) maturity and digital maturity, recognizing that higher digital maturity correlates with a more advanced level of digital transformation. BPM and digital transformation (DT) are intricately linked, with BPM serving as a key enabler and catalyst for digital transformation, while also being reshaped by the new realities of the digital era (Baiyere et al., 2020; Fischer et al., 2020; Mendling et al., 2020). As stated by Mendling et al. (2020) “BPM and digital innovation belong together, like two sides of the same coin”. Embracing this synergistic relationship is crucial for organizations seeking to thrive in the digital age.

Organizations are leveraging BPM to initiate and support their digital transformation projects, using BPM outcomes to fundamentally rethink and redesign their business models and processes in light of emerging digital technologies (Fischer et al., 2020; Gabryelczyk et al., 2024). This represents a shift from the traditional focus of BPM on incremental process improvement towards a more transformative role in the digital era. The dual role of BPM in the digital transformation journey–as a driver of disruptive innovation on one hand, and an enabler of continuous improvement on the other–is highlighted in recent research linking both concepts (Baiyere et al., 2020; Fischer et al., 2020; Schmiedel and vom Brocke, 2015). Thus, the interrelationship between BPM and DT is evident and frequently highlighted in opinion papers, consulting companies reports and research papers (Baiyere et al., 2020; Mendling et al., 2020; Thordsen and Bick, 2023). They provide a foundation for exploring the link between process and digital maturity, but it is worth noting that the scientific literature in this area is extremely rare and qualitative in nature (Dharmawan et al., 2019; Szelągowski and Berniak-Woźny, 2022; Thordsen and Bick, 2023). Additionally, the causal relationships between BPM and digital maturity, which have been confirmed by some quantitative studies, remain under-researched (Antonucci et al., 2020; Stjepić et al., 2020). Therefore, providing quantitative evidence for the above statements was our motivation to conduct this research. Moreover, prior research shows that BPM maturity is a critical enabler of digital maturity for only certain and obvious digital transformation areas, especially for improving processes (Antonucci et al., 2020), strategy formulation and governance (Antonucci et al., 2020; Stjepić et al., 2020), and change management (Stjepić et al., 2020; Flechsig et al., 2022). Comprehensive research explicitly addressing the impact of BPM capabilities on a more complete set of digital maturity dimensions is lacking. Our research was motivated by the need for more systematic, multidimensional research across industries to address identified gaps and better integrate under-researched dimensions of digital maturity with BPM maturity.

We understand digital transformation and digital innovation (DI) as related but distinct concepts that are both important for businesses looking to adapt and thrive in an increasingly digital world. While digital transformation refers to comprehensive structural changes to business models and operations, digital innovation focuses on the more targeted development of new digitally-enabled offerings (Plekhanov et al., 2023). Nevertheless, the two concepts share a fundamental emphasis on leveraging technology to rethink how organizations create and deliver value. Digital transformation drives digital innovation by creating an environment in which new offerings and business models can be implemented. As Van Looy (2021) highlights, BPM plays a crucial role in ensuring that these innovations are successfully delivered and integrated within an organization. BPM itself, when applied in an explorative way, following the ambidexterity concept (Rosemann, 2014), can build the innovative capabilities of an organization. We focus on assessing the links between BPM maturity and digital maturity, which can be perceived not only as a proxy for digital transformation but also for digital innovation (Van Looy, 2021) because they are complementary and mutually reinforcing (Plekhanov et al., 2023).

Our study is based on data from Poland. The choice of one country is justified by the need to conduct research in accordance with the concept of contextual intelligence introduced by Khanna (2014). It is crucial to conduct research using data from the chosen countries because management practices and knowledge cannot be applied uniformly across varying cultural and institutional contexts. The obtained results will therefore allow us to provide a preliminary understanding of the phenomenon and gain insights into contextual intelligence and knowledge on the nexus between BPM and digital maturity that can be extended to other environments in future research. Moreover, this exploratory study based on a limited sample in one country may open the field to explore a similar phenomenon in other countries, especially in other transition countries in the CEE region that share the context of economic and political changes in the 90s of the previous century (Gabryelczyk and Roztocki, 2018).

In order to obtain objective, reliable, and replicable results, we proposed a mixed approach leveraging both qualitative and quantitative research methods. In the qualitative part of our paper, we will identify key dimensions of digital maturity, while in the quantitative part, we will verify the following hypothesis:

We use a pragmatic definition of maturity as “a measure of assessing an organization’s capabilities in relation to a specific discipline” (Rosemann and De Bruin, 2005) for both the BPM and DT areas. BPM maturity is a measure of an organization’s advancement in the application of methods and tools for BPM, as well as a measure of excellence in the execution of individual processes (Röglinger et al., 2012). Digital maturity is an ongoing transformational process that makes an organization fit for competing in a digital world by fundamentally changing how it operates across multiple dimensions (Thordsen and Bick, 2023). While BPM maturity assessment models are already established in research, the scientific discourse on digital maturity models is just beginning (Ochoa-Urrego and Peña-Reyes, 2021; Thordsen and Bick, 2023).

Our article is structured as follows: in the background section, we discuss aspects of both digital and BPM maturity, as well as previous research linking these two maturities. Then we discuss the research process carried out and the results obtained, not forgetting their discussion.

Maturity is a measure of an organization’s capability to appropriately respond to its environment and adequately implement practices in key areas in order to achieve its strategic goals (Szelągowski and Berniak-Woźny, 2022). Maturity models are conceptual models consisting of a sequence of discrete levels of maturity within one or multiple areas of organizational activity, representing anticipated, desired, or typical evolutionary improvement paths for the processes (Röglinger et al., 2012; Rosemann and de Bruin, 2005). They provide a structured framework to assess an organization’s current capabilities and guide improvement efforts within the measured area (Röglinger et al., 2012; Szelągowski and Berniak-Woźny, 2022; Van Looy et al., 2011). Higher levels of maturity, both in the area of BPM and digitalization, lead to better outcome control, more efficient management of effectiveness, greater success in achieving designated goals, and enhanced organizational capability for planning and implementing organizational changes (Lockamy and McCormack, 2004). The main functions of maturity assessment are: diagnosing current maturity levels, verifying and harmonizing assessments of various stakeholders, enabling comparative analysis, initiating corrective actions to improve maturity, and developing employee awareness and understanding of organizational change (Ritchie and Dale, 2000). When classifying maturity models, we can consider the criterion of application area. Alternatively, based on the criterion of purpose and scope, we can indicate prescriptive (normative) or descriptive (explanatory) models, including self-assessment models (Tarhan et al., 2016).

Commonly used BPM maturity models include the Capability Maturity Model Integration (CMMI), the Business Process Maturity Model (BPMM) from the Object Management Group, and the Process and Enterprise Maturity Model (PEMM) (Dijkman et al., 2016). It is estimated that there are over 150 BPM maturity frameworks addressing various capability dimensions (Van Looy et al., 2011). These capabilities tend to evolve slowly to match the needs of the digitalization era, e.g., BPMMxIT by Flechsig et al. (2022) puts more emphasis on IT capabilities within the model. Moreover, Kerpedzhiev et al. (2021) propose a revised content of the entire BPM Capability Framework by Rosemann and De Bruin (2005) to accommodate shock impulses generated by a volatile, uncertain, complex, and ambiguous (VUCA) environment.

Most models define five maturity levels, ranging from ad hoc and unpredictable processes at the lowest level to continuously optimized and innovative processes at the highest level (Szelągowski and Berniak-Woźny, 2022). As organizations progress to higher maturity levels, their processes become more formally defined, quantitatively managed, and optimized using consistent practices within the BPM lifecycle (Röglinger et al., 2012).

Digital maturity refers to an organization’s ability to leverage digital technologies to transform business models, operational processes, and customer experience. Digital maturity models are tools used to assess the capabilities status quo (Becker et al., 2009). These models, similarly to BPM maturity models, provide a roadmap for organizations to develop the necessary business capabilities in the key areas important for achieving digital maturity: strategy, culture, people, organization, technology, customer experience, and change management (Röglinger et al., 2012; Thordsen and Bick, 2023). As organizations advance in digital maturity, they are able to adopt new technologies faster, innovate products and services, and drive value through data-driven decision-making. Digital maturity is also used as a linkage to define the stage of digital transformation, as it includes both technological and managerial components (Teichert, 2019).

Digital maturity models have been developed by both business experts (management consulting companies or industry organizations) and IS scholars. The first general digital maturity models were created by management consulting companies in 2011. After a few years, academia joined the discussion while business focused on more specialized models rooted in Industry 4.0. After 2019 scientific digital maturity models seem to prevail (Thordsen and Bick, 2023).

The number of maturity models proposed in the field of BPM has significantly increased over the last 2 decades, and their practical applications are quite established. In contrast, the research on digital maturity models is still underexplored, and the practical utilization and usefulness of these models remain uncertain (Thordsen and Bick, 2023). However, efforts have been made to propose new digital maturity models, such as the Holistic Digital Maturity Model (Aras and Büyüközkan, 2023), which provides a comprehensive approach to evaluating an organization’s digital capabilities and readiness for transformation.

While there are indications that high BPM maturity enables the enhancement of digital maturity, conclusive empirical evidence proving this relationship is still very limited. Thordsen and Bick (2023) suggest an overlap between digital maturity and process maturity and consider a positive relationship between digital maturity and business performance as likely. The research also suggests that organizations with higher BPM maturity are better able to harness digital tools and mindsets to enable more responsive, adaptive, and customer-centric processes (Dharmawan et al., 2019). Moreover, high BPM maturity supports the identification of processes suitable for digitization and effectively aids the expansion of digital technology capabilities (Flechsig et al., 2022; Szelągowski and Berniak-Woźny, 2022; Antonucci et al., 2020). Empirical evidence in existing studies also shows that organizations integrate advanced technologies better when BPM maturity establishes strong governance and adaptability (Antonucci et al., 2020; Kerpedzhiev et al., 2021).

To further explore these dependencies, our research takes the perspective of resource-based theories and draws on the capability approach (Teece et al., 1997). Dynamic capabilities enable organizations to integrate and reconfigure their resources to adapt to changing environments and continuously innovate. BPM and digital maturity models represent sets of capabilities. This alignment between BPM maturity and digital maturity, so far insufficiently researched, allowed us to hypothesize about the impact of capabilities developed for BPM on key capabilities needed for DT as captured in digital maturity models.

To address the research problem, we employed a mixed methods approach integrating both qualitative and quantitative techniques. An initial qualitative phase was conducted to identify key variables, which were subsequently incorporated into a quantitative model for further analysis. Figure 1 explains the research approach of this study.

In the first stage, we examined sources of information about the relationship between BPM maturity and digital maturity, which allowed us to formulate the hypothesis. In the second stage, we applied a qualitative research procedure to identify and review the existing digital maturity models developed by management consulting firms or business associations. This approach was chosen because, within the academic community, the value of digital maturity models is considered uncertain (Berger et al., 2020; Teichert, 2019) since they are often missing the academic validity (Thordsen and Bick, 2023). They are also accused of providing an incomplete picture of digital maturity, not addressing specific critical dimensions of it (Teichert, 2019). Therefore, we decided to focus only on so-called gray publications (Mahood et al., 2013) presenting models that were built on premises of business applicability and represent a practical perspective. The value of expert maturity models is that they are rooted in the analysis of business practice and are a synthetic summary of empirical observations from the day-to-day experience of business consulting. They often serve as intermediaries between theory and practice, however, they prioritize usability over methodology. Consultancies’ models are aligned with decision-making patterns in industries and additionally, they leverage vast cross-industry empirical data because they are widely used (Tarhan et al., 2016; Rosemann and vom Brocke, 2014), We decided to appreciate the practical relevance and actionability of consulting frameworks in the fast-changing environmental context over theoretically developed ones that are relatively unknown and lack practical application and verification in a business context (Felch et al., 2019).

Through the analysis of the practitioner-developed models, in the third stage, we identified key dimensions that were common across these frameworks. These dimensions were treated as the current perception of organizations of the factors that indicate digital maturity. The progress on these dimensions was considered as a measure of an organization’s digital maturity. In the fourth stage, we developed a research survey that included both measures of BPM maturity and digital maturity. The subjective assessment allowed organizations to self-evaluate their BPM maturity, while the objective assessment utilized variables that operationalized the dimensions of digital maturity.

The data collected through the survey were analyzed in stage five using PLS-SEM method to examine the impact of BPM maturity on the dimensions of digital maturity within organizations. Based on the results, we drew conclusions in stage six.

To ensure a systematic approach to the identification of the digital maturity models, the Webster and Watson (2002) approach was used. Our search strategy was to refer to four publication databases: EBSCOhost, Scopus, ProQuest, and ScienceDirect which were researched using the phrases: digital maturity model, digital maturity index, and digital readiness model in the title, summary, and keywords. No time limit was imposed, however, only articles in English in scientific journals were taken into account. Following an initial review of publications based on their titles and abstracts, duplicates and those irrelevant to the topic were eliminated. The backward search ultimately led to the identification of models developed by practitioners, mainly consulting companies. The models were often found as online publications on the websites of the organizations that developed them. The search allowed us to identify 40 digital maturity models developed by both academia and business between 2011 and 2021. The models were reviewed, and only the ones that met the following criteria were chosen for further analysis:

1. were developed by practitioners

2. referred to the entire enterprise;

3. exhaustively presented the evaluated dimensions of digital maturity,

Following this review, 14 models listed in Table 1 were selected for full-text, detailed qualitative analysis.

Then we identified the dimensions that were the most commonly referred to by these models. For this purpose, the coding process was carried out (Saldana, 2021) with the support of NVIVO 12 software. The coding we used was a part of the procedure of the grounded theory methodology.

In the first round, we used the descriptive coding canon as an analysis vehicle for qualitative data (Miles et al., 2014). The purpose was to identify individual digital maturity items. The content of the selected digital maturity models was encoded using codes without any prior assumptions as to their number and structure. We summarized each maturity aspect using a short phrase according to our best understanding of items described in analysed maturity models, then we named the described digital maturity concept. This approach allowed us to apply the “general process of grouping entities by similarity” as proposed by (Bailey, 1994). As a result of the first coding cycle, we obtained 40 characteristics of digital maturity.

The goal of the second round of coding was to combine the first series of codes into conceptual categories that represent organizational domains critical for digital maturity. Therefore, the focused coding canon was applied to combine characteristics into digital maturity dimensions. We thus completed the concept integration phase that let us identify the following nine critical domains of digital maturity: digital culture, digital processes, digital strategy, digital customer value, technological readiness, governance mechanisms, change management capabilities, digital competencies of employees, and use of data.

To perform the quantitative part of the research, we developed a research survey that included both measures of BPM maturity and digital maturity.

The subjective assessment allowed organizations to self-evaluate their BPM maturity. The respondents rated it at one of five defined levels: Initial, Defined, Repeatable, Managed, and Optimized (Rosemann and De Bruin, 2005). Self-assessment of BPM maturity has been used in previous studies (Reijers, 2006). We agree with the statement that “organizations should be able to self-assess the maturity of their specific processes or the overall organization pragmatically with limited effort” (Tarhan et al., 2015). To strengthen the validity of the self-assessment approach, we took steps to minimize potential bias by including control questions to check response consistency and carefully selecting respondents. These respondents were professionals with a deep understanding of both BPM and their organizations, ensuring they comprehended the issues related to BPM maturity assessment.

Digital maturity capabilities that we used were identified as a result of the quantitative part of this research. Each dimension was operationalized in the form of three survey questions. The summary of the variables and their operationalization is presented in Table 2.

The survey was distributed in 2023 to large Polish organizations that had declared their involvement in BPM initiatives and had simultaneously embarked on the path of digital transformation. The survey respondents were professionals responsible for these initiatives, most often in roles such as digital transformation project managers, employees of BPM competence centers, and subject-matter experts. They were able to look holistically on their organizations to assign a maturity rating against the description of the five defined levels.

The data collected through the survey was subjected to quantitative analysis to examine the impact of BPM maturity on digital maturity within organizations. The statistical analysis of the collected data was based on the model presented in Figure 2.

During our statistical analysis, we assessed the factor loadings, average variance extracted (AVE), and composite reliability (CR) following recognized recommendations for determining convergent validity (Byrne, 2016; Hair et al., 2019). These recommendations state that factor loadings should be greater than 0.70, CR should be greater than 0.7, and AVE should be greater than 0.5 (Byrne, 2016; Hair et al., 2019). Further, we used PLS-SEM analysis of the structural equation modeling (Byrne, 2016; Hair et al., 2019) to look for links between different theoretical concepts inside the structural model.

Nine dimensions of digital maturity models represent the capabilities needed for successful digital transformation as seen by practitioners. Their descriptions provided below are based on the most common attributes found in the analyzed models.

Digital culture is an organizational culture that embraces new ways of working and is open to change. The risk of failure is accepted. It builds a sense of influence and engagement among employees with a focus on cooperation and knowledge sharing. It promotes customer-centricity and curiosity about the possibilities brought by new technologies. It triggers organizational, process, and product innovations. Digital processes refer to the level of support that digitized and automated process flows provide for achieving business goals and improving the quality of operational process performance. For digitalized processes to be effectively used, sufficient system support must be in place. The digital strategy shows to what degree the opportunities generated by digitalization are included in business strategy or reflected in a business model. It presents the strategic intent shared by business and IT managers to use new technologies to satisfy customer needs in a new way, and generate sales growth with digital products and services. It also refers to the allocation of the budget for digitalization. Digital customer value involves digitizing customer interactions, utilizing digital channels, enhancing customer experience, redefining the role of a customer and leveraging technology to enrich the product portfolio. Technological readiness is the technological foundation of the organization. It refers to technological advancement as well as to technology management, IT architecture and infrastructure management. It evaluates how well IT systems are integrated to support operations. Governance encompasses the effectiveness of management mechanisms, the managerial style employed by leaders, and the organizational structure for task assignment and result reporting. Digital competencies of employees pertain to the company’s ability to recruit and retain employees with high digital competencies and the ability to develop these competencies internally. Change management capabilities refer to transformation management skills, the strength of the vision for digital change, persuasive communication, and resource allocation for planned changes. It encompasses the quality of leadership in change projects as well as management and leadership skills. Data usage describes the capability and tools available to collect, analyze, and use data to make business decisions.

The key dimensions addressed by survey questions are presented in Table 2.

We received 166 correctly filled surveys, of which 53% of respondents represented the private sector, 40% the public sector, and 7% declared other sectors. The dominant industries represented were: information and telecommunication (21%), technology and professional services (9%), finance and insurance (8%), and the energy industry (4%). The vast majority of respondents (81%) were employed in large organizations.

The first step in the quantitative analysis is the assessment of discriminant validity, which measures the extent to which a construct is distinct from other constructs by being negatively correlated or uncorrelated with measures of other constructs. High correlation of different constructs may indicate that the respondents do not perceive the difference between them. A discriminant validity assessment for the constructs used in the study on digital maturity and BPM maturity is presented in Table 3. Correlations between the constructs are listed in the table, with the square roots of the average variance extracted (AVE) values italicized in the diagonal. For sufficient discriminant validity, the diagonal values should be greater than the off-diagonal correlations.

The digital competencies of employees (COMP) and change management competencies (CHANGE) have a 1.01 correlation, indicating a substantial overlap that compromises discriminant validity. Likewise, there is a strong degree of overlap indicated by the correlation of 0.909 between governance (GOV) and digital processes (PROC). Additionally, the table displays moderate correlations, such as 0.577 between digital customer value (CUST) and data use (DATA) and 0.76 between change management capabilities (CHANGE) and digital culture (CULT). Better discriminant validity is indicated by the reduced correlations between dimensions such as technological readiness (TECH) and digital strategy (STRA).

The assessment of the discriminant validity, while highlighting high correlations between some constructs, underscores the interconnected nature of these dimensions. High correlations do not necessarily indicate measurement flaws but rather reflect theoretical and practical overlaps intrinsic to complex frameworks (Farrell, 2010). For example, digital competencies and change management competencies are mutually reinforcing constructs essential for successful digital transformation, showing inherent synergy between skillsets required to drive innovation and adaptability (Cheung et al., 2024). Similarly, the strong association between governance and digital processes highlights how effective governance structures facilitate the optimization of digital workflows (Rönkkö and Cho, 2022). Statistically, the robustness of the model is supported by the Fornell-Larcker criterion (Fornell and Larcker, 1981), where the AVE values indicate that each construct sufficiently captures the variance of its indicators, even with high inter-construct correlations (Cheung et al., 2024). As Farrell (2010) notes, theoretical overlaps between constructs can enhance rather than undermine the explanatory power of models, especially when constructs are conceptually aligned, as in digital transformation frameworks. Moreover, moderate correlations observed between constructs such as digital customer value (CUST) and data use (DATA) illustrate the nuanced relationships within the broader context of digital maturity and BPM maturity (Cheung et al., 2024). Lower correlations in constructs like technological readiness (TECH) and digital strategy (STRA) further emphasize clear distinctions where theoretical and practical separations are more pronounced (Rönkkö and Cho, 2022).

The factor loading validation of the measurement model is presented in Table 4. Significantly, Cronbach’s Alpha (A), values ranging from 0.547 to 0.869 supported the constructs’ continuous, strong internal consistency and reliability. These values, which occasionally fall below the ideal threshold of 0.7 but typically assert excellent internal consistency and reliability, show that the measurement model is dependable and resilient (Kline, 2023).

The strongest confirmation of the impact of BPM maturity on digital maturity was confirmed in the areas of digital culture, digital strategy, technology readiness, and governance. These four constructs were confirmed by the high factor loadings (between 0.812 and 0.911 and high CR, indicating good internal consistency. AVE shows that each of these constructs accounts for not less than 71% of the variance in these items, significantly beyond the acceptable cutoff of 0.5 (Hair et al., 2019). Furthermore, the constructs’ strong reliability is further confirmed by the Cronbach’s Alpha at 0.824 at the lowest for these constructs (Kline, 2023).

Change management capabilities, competencies of the employees, and data usage are moderately impacted by BPM maturity as confirmed by research results. Factor loadings vary in the range between 0.637 and 0.904, with high CR indicating good internal consistency. AVE shows that each of these constructs accounts for not less than 55–61% of the variance in these items that is beyond the acceptable cutoff of 0.5 (Hair et al., 2019).

We found weaker confirmation for the impact of BPM maturity on digital processes and digital customer value dimensions. The moderate loadings suggest that they are not as strong indicators, while the CR demonstrates adequate internal consistency. They explain 54.7 and 49.5% of the variance in the respective items, meeting or almost meeting acceptable levels. However, Cronbach’s Alpha values of 0.65 and 0.54 fall below the generally accepted cutoff of 0.7, indicating moderate dependability and suggesting that these elements may require further refinement. These two constructs may benefit from additional development and research (Kline, 2023). Nevertheless, we include them in further analysis, recognizing that the various characteristics of the researched organizations might have contributed to different interpretations of terminology used in our survey.

For the hypothesis testing we used Smart PLS analysis of the structural equation modeling (Byrne, 2016; Hair et al., 2019) to look for links between different theoretical concepts inside the structural model. The findings show various levels of explanatory power (R²) and statistical significance (p-value) across the dimensions. The path coefficients (β) indicate the strength and direction of the effect that BPM maturity has on the dimensions of digital maturity within the analyzed model. Table 5. Presents the findings, which offer a comprehensive view on the way how BPM maturity impacts dimensions of digital maturity.

The results demonstrate the model’s predictive power, accounting for a significant portion of the variance in various digital maturity variables. The highest explanatory power was found in change management capabilities, which are strongly influenced by BPM maturity (R² = 8.7%), with a significant positive impact (β = 0.2106, p-value = 0.002). For the dimension of digital processes, BPM maturity naturally explains a substantial portion of its growth (R² = 6.6%) with a stronger impact (β = 0.1836), which is confirmed (p-value = 0.007).

BPM maturity enhances employees’ digital competencies (R² = 5.8%, β = 0.1719, p-value = 0.011) as well. Additionally, governance is partly explained by BPM maturity (R² = 5.5%) with a significant positive effect (β = 0.1676, p-value = 0.014). Technological readiness is minimally explained by BPM maturity (R² = 3.9%), indicating only a slight improvement in this dimension due to BPM maturity. Similarly, only a small portion of digital culture is explained by BPM maturity (R² = 3.2%). However, BPM maturity does contribute to its growth (β = 0.1279) with this relationship being borderline significant (p-value = 0.049).

The last two dimensions yield interesting results. BPM maturity explains 11.2% of the variance in data use (R² = 11.2%). However, the relationship appears to be reversed (β = −0.3534), indicating that higher BPM maturity levels correlate with lower data use. Digital customer value is minimally explained by BPM maturity (R² = 1.9%), showing a non-significant negative effect (β = −0.1217, p-value = 0.074). Figure 3 presents the resulting structural model.

All investigated dimensions of the digital maturity were impacted by the level of BPM maturity in a limited way, explaining 2%–9% of their variability. The outlier in this respect was the data usage dimension, which was impacted to a higher degree (11.2%), albeit in a negative way that will be discussed later. The general low impact on dimensions of digital maturity is not surprising since each of them is shaped by far more factors than just BPM maturity. However, we can state that achieving higher BPM maturity fosters the development of most dimensions crucial for advancing digital transformation.

An especially robust impact of BPM maturity was observed in the dimension of change management capabilities. The research provided strong evidence of BPM maturity’s beneficial effects on transformation management skills. This is understandable since implementing change is an inherent feature of BPM initiatives, and these skills are perfected while conducting BPM projects. The significance of developing change management capabilities is emphasized by both academics and practitioners. According to Kotter (2012), organizations need to develop two “operating systems”. The first one carries out everyday operations. The second one requires a specific set of managerial skills and allows organizations to constantly and quickly introduce changes to the first system. Business practitioners also recognized the importance of change management capabilities for achieving digital maturity by separating the what and the how in their digital maturity models. The what covers various dimensions important for maturity (such as technology, culture, people skills, etc.) while the how refers to change management capabilities (CapGemini, 2023). Therefore, we can state that this research confirmed the positive and strong impact of BPM maturity on the critical organizational capability for conducting digital transformation.

The study strengthens the theoretical knowledge that change management is intrinsically linked to process management techniques by empirically proving the confirmation of the strong relationship between BPM maturity and change management capabilities. Practically, it underscores the importance of building BPM maturity through robust frameworks that enhance change management skills, which are crucial for overcoming the challenges of digital transformation.

The next six dimensions of digital maturity: processes, digital competencies, governance, digital strategy, and technological readiness, are impacted by BPM maturity with similar strength. It is interesting to note that similar dimensions, albeit without a digital focus, are referred to as the six core elements of BPM (Rosemann and vom Brocke, 2014). Strategic alignment (a BPM core element) aligns with our digital strategy dimension, governance (a BPM core element) aligns with the governance dimension, and methods (a BPM core element) that include process management methods partly align with the digital processes dimension. Information technology (a BPM core element) corresponds to technological readiness, people (a BPM core element) align with digital competencies and, finally, culture (a BPM core element) aligns with digital culture. Although our dimensions were defined a decade after the six core elements of BPM and were based on different literature and research approaches, their nature is very similar. Therefore, we propose that achieving maturity on the six core elements of BPM will contribute to the growth of digital maturity.

We suggest that these are quite universal dimensions vital for an organization that transforms. By using BPM frameworks, practitioners can improve staff competencies, technology infrastructure, governance structures, and strategy alignment as a part of a comprehensive and well-coordinated plan for digital transformation. This integration ensures that BPM practices support strategic digital goals of transformation in addition to increasing operational efficiencies (Gabryelczyk et al., 2024).

BPM maturity may not improve customer-centric digital capabilities. Digital customer value is impacted less by BPM maturity than the previous dimensions. Additionally, there is a reverse correlation between BPM maturity and digital customer value. This is an interesting finding that may indicate that digital customer value is not the focal point of BPM projects or is not present in utilized BPM frameworks. BPM projects undertaken by companies traditionally focus on continuous improvement, automation, and standardization (Van den Bergh et al., 2014). The analysis of customers and other stakeholders is limited in the popular BPM methodologies and is often not supported by process analysis methods and tools. For example, the most popular process modeling notation, BPMN, does not propose symbols depicting customers, their needs, expectations, or pain points.

For decades BPM has been following largely an “inside-out” approach where the capabilities were built to make internal business processes more efficient sometimes neglecting customers’ experience (Rosemann, 2014). BPM scholars suggest that BPM capabilities should be enriched with those that enable addressing customer needs and inter-organizational factors, calling for more research in this area (Kerpedzhiev et al., 2021; Van Looy, 2021). Ensuring customer-centric process design, analysis, and improvement is seen as one of the challenges for BPM in the coming 5–10 years (Kerpedzhiev et al., 2021). This may require incorporating methods from the customer relationship management area such as mapping customer journeys or applying the Kaizen principles of continuous process improvements by actively involving stakeholders in feedback (Ahmad and Van Looy, 2020) and consequently–updating existing BPM maturity models.

In addition to that, digital customer value is not solely dependent on BPM maturity. It is impacted by factors like strategy, product innovation capabilities, and effective communication. Our findings confirm the necessity of adopting a more customer-centric approach in BPM endeavors in the digital era in order to bridge the gap between external customer value creation and internal process improvement.

Data usage is impacted with the highest strength, however in the reverse direction. The obtained result indicates that further research on this phenomenon is necessary as current research does not directly address how BPM maturity interacts with data usage. Prior studies have criticized traditional BPM maturity models for their over-reliance on structured and predictable processes, which limits their capacity to accommodate dynamic, data-driven processes (Szelągowski and Berniak-Woźny, 2022). It has also been noted that existing BPM maturity models are not adapted to assess the maturity of organizations operating within data-driven environments and have serious limitations in handling emerging data capabilities (Kerpedzhiev et al., 2021). Trying to explain this counterintuitive phenomenon, we notice that BPM initiatives do not focus on data usage, setting other priorities such as process streamlining, eliminating waste as proposed by lean methodologies, process modeling, standardization, and automation, placing less weight on data-driven process analysis. BPM and data-driven projects are often motivated by different goals: BPM by efficient repeatability, while data-driven initiatives focus on rapid adaptation and innovation based on insights.

Moreover, data-driven initiatives and process-driven initiatives are not well-aligned. Insights generated from data may not be properly leveraged to improve business processes, negating the impact of data use in BPM initiatives. BPM systems may not be fully integrated with data analytics tools, leading to gaps in data flow and insights application. Additionally, process analysts might lack the necessary data analytics skills, while data scientists may not fully understand the intricacies of business processes. Furthermore, achieving higher levels of process maturity does not guarantee the cross-functional collaboration and democratization of data required for organization-wide data exploitation, e.g., as part of AI and data science initiatives (Sjödin et al., 2021). AI and data science initiatives should connect analytics teams with business units to drive business-justified and scalable digital transformation (Sjödin et al., 2021). This only confirms that BPM-driven value creation from data is one of the current major challenges. “As organizations aspire towards embracing data-driven approaches both technically and culturally, the socio-technical barriers for value creation from data are becoming increasingly evident” (Beerepoot et al., 2023).

It is recommended that practitioners explore ways to align BPM and data analytics procedures to ensure BPM methods are designed to effectively leverage data insights. This could entail investing in technologies that enable smooth data integration with process management, creating cross-functional teams that combine BPM and data analytics knowledge, and encouraging a culture of data-driven decision-making. This research identifies a theoretical gap concerning the integration of BPM and data analytics frameworks, suggesting the need for hybrid methods to align process management with data-centric tactics. In summary, while the negative relationship between BPM maturity and data use is counterintuitive, it likely arises from a combination of factors related to the alignment of initiatives and the varying relevance of data for the two types of maturities.

The results obtained in the study should be interpreted in consideration of contextual intelligence, which takes into account the unique socioeconomic conditions, institutional environment, infrastructure, and human capital constraints in the country in which we conducted the research. Poland’s location in the CEE region and the related historical recognition of Poland as a transition country certainly influence the use of management practices. Its economic, cultural, social, legal, and infrastructural situation is quite different and still more uncertain than in developed Western economies (Gabryelczyk and Roztocki, 2018). Polish organizations lag behind Western countries in digital transformation and adoption of advanced process management practices (Gabryelczyk, 2019). They tend to focus on process analysis and have not yet developed strong data analysis capabilities (Sliż et al., 2024). So, although overall BPM maturity may be increasing, the capabilities required to effectively harness data insights might be lagging behind.

The study’s findings have important theoretical implications, particularly on how BPM maturity and digital maturity interact. The assertion that BPM maturity is a fundamental enabler of the majority of dimensions of digital maturity, albeit to varying degrees, is one of the core theoretical contributions. The paper provides insights for organizations to exploit BPM practices in their digital transformation initiatives by showcasing the positive impact of BPM maturity on multiple aspects of digital maturity. Moreover, our study brings to light new research areas that have the potential to develop an explorative approach to BPM, particularly in the area of integration of data analytics and customer-centricity with process management.

While BPM and digital maturity models have distinct characteristics, there are synergies between the two areas. Digitally mature organizations often have well-defined, automated processes as their foundation. Similarly, mature BPM practices enable organizations to effectively implement digital technologies and drive transformation. Therefore, to succeed in the digital age, organizations need to consider the maturity of both their BPM and digital capabilities. Implementing new digital tools is not sufficient for achieving transformation success. It is equally important to make process improvements, drive cultural change, and develop new competencies. Upskilling employees, changing mindsets, and engaging people across the organization are critical components of this journey.

Our research confirms the hypothesis that higher BPM maturity contributes to the enhancement of the majority of the key dimensions of digital maturity. BPM maturity enhances the change management capabilities that are crucial for digital transformation. However, its impact on dimensions such as digital customer value and data usage may depend on the local business context, and in Polish organizations, the positive impact is not confirmed. Addressing these two areas appears to be a struggle for many companies, hindering their ability to effectively utilize data and prioritize customers in BPM initiatives at this stage. Investing in the fundamentals of BPM and data management is likely needed before more advanced digital transformation initiatives, including AI, can gain traction.

In conclusion, our study contributes to BPM theory by empirically demonstrating the varied influence of process maturity on digital capabilities and provides foundations for the development of a more nuanced theoretical framework to explain the relationship between process management and digital transformation. Many BPM initiatives are motivated only by IT-enabled improvement, where organizations aim to leverage a specific IT system with a smaller impact on overall company operations compared to transformation (Baiyere et al., 2020). Large-scale transformation is motivating only 1% of BPM implementations (Gabryelczyk et al., 2024). Consequently, BPM maturity may not correlate with all factors crucial for digital transformation, as these factors were not necessary to achieve the goals of a BPM endeavor.

The limitation of this study is that the assignment of BPM maturity levels relied on respondents’ subjective assessments. While we have done our best to mitigate this bias, we acknowledge the potential for biases such as response style bias and overconfidence that may result in discrepancies between survey responses and actual state of affairs. Additionally, the digital maturity dimensions were defined based on maturity models developed by business, not by academia, which might have limited the catalog of dimensions defined and surveyed. Using all available models could change or supplement the list of digital maturity dimensions. An additional limitation results from the potential sample bias associated with the convenience sampling method we used.

However, our study as exploratory in nature, provides a preliminary understanding of the phenomenon and opens a huge potential avenue for future research, both in terms of qualitative and quantitative analyses. The list of key digital maturity dimensions should be verified using both practical and scientific models. The quantitative study can be replicated on data from other countries, which will enable discussion on the generalization of the results, and also provide additional evidence for the use of contextual intelligence. Regarding capabilities, the new research can focus especially on the gap that results from the different motivations and goals of BPM and digital initiatives. The dimension of customer orientation may be more closely aligned with BPM maturity in organizations that adopt an outside-in approach to BPM. This relationship warrants further investigation. Additionally the research can expend the portfolio of BPM capabilities and methods to fully harness the potential of data inherently linked to processes.

Our findings provided new insights into how the development of BPM capabilities contributes to an organization’s overall digital maturity. The BPM development should be integrated with other initiatives that capture management’s attention, such as data analytics and customer centricity to maximize return on investment.

We acknowledge that our findings may apply primarily to the Polish organizational context characterized by its unique post-transition institutional environment and managerial practices, and further research with a representative sample is needed to confirm generalizability. Future research should examine whether similar practices exist in other contexts before broader generalizations can be made. Although considering past research and the concept of contextual intelligence, we suspect that similar conclusions could be drawn for other post-transition countries from the CEE region. Additionally, contextual comparisons can be made between industries and sectors. Let this conclusion open the space for future research on the contribution of BPM maturity to increasing the digital maturity of organizations and their capability to innovate.