Determinants of an effective digitalization strategy in entrepreneurial lifestyle and high-growth firms: a configurational approach

The Fourth Industrial Revolution (4IR) is fundamentally changing how society and business operate and interact. As Schwab (2017) noted, this revolution blurs physical and digital boundaries through the fusion of technologies, including artificial intelligence, robotics, the Internet of Things, and quantum computing (Aboderin and Havenga, 2024). This disruptive transformation requires firms to not only adopt new technologies but also shift their strategic mindset and approach to business operations.

Given the far-reaching impact of 4IR technologies on both business and society, firms across all sectors must develop digitalization strategies to build and maintain competitive advantage. However, the challenge of digital transformation is not uniform across all types of enterprises. This study focuses on two distinct types of entrepreneurial firms: lifestyle firms and high-growth firms. Lifestyle firms align business activities with founders’ personal passions and desired quality of life, prioritizing work-life balance over rapid expansion (Ghafar et al., 2021; Ivanycheva et al., 2024). In contrast, high-growth firms focus primarily on rapid expansion in revenue, market share, or employment (Monteiro, 2019).

Despite their different strategic objectives, both firm types exhibit entrepreneurial characteristics reflected in their strategic posture, commonly referred to as Entrepreneurial Orientation (EO). While existing research has examined digitalization strategies broadly, there remains a limited understanding of how these different entrepreneurial firm types configure EO dimensions with technology readiness and Fourth Industrial Revolution knowledge to achieve effective digitalization outcomes. This gap is particularly important because the configurational nature of successful digital transformation suggests that multiple pathways may exist, varying by firm type, competencies, or strategic orientation. To address this research gap, we employ fuzzy set qualitative comparative analysis (fsQCA), which is specifically designed to identify different combinations of conditions that lead to successful outcomes. This approach is particularly suitable for understanding digitalization strategies because digital transformation success likely results from multiple, interconnected pathways rather than single linear relationships.

Digitalization strategy involves using technology and 4IR initiatives to enhance business performance through new product creation and process renewal (Olmstead, 2022). Unlike digitization (converting analog to digital format) and digital transformation (internal system-level restructuring), digitalization specifically focuses on innovating business models and processes to capture digital opportunities (Unruh et al., 2017; Matt et al., 2015).

While no universally agreed-upon definition exists (Shehadeh et al., 2023), digitalization fundamentally changes competitive direction by creating new technological advantages and outlining actionable implementation steps. The strategic approach broadens customer bases, identifies new sales opportunities, and improves internal efficiencies (Anton, 2024; Ding et al., 2024). Benefits include reduced customer acquisition costs and enhanced international expansion capabilities (Bellakhal and Mouelhi, 2023).

Recent empirical evidence demonstrates clear success outcomes from digitalization strategies. Proksch et al. (2024) found that digital strategy significantly influences new venture digitalization effectiveness, with digital capabilities and culture serving as crucial mediators. Similarly, Wen et al. (2022) showed that digitalization enhances corporate innovation performance, particularly when aligned with competitive strategies. The success of digital transformation depends heavily on organizational factors, with AlNuaimi et al. (2022) highlighting the critical nexus between leadership agility and digital strategy effectiveness. Furthermore, Meyer et al. (2023) demonstrated that digital strategies enable enhanced international business performance, supporting firms’ global expansion capabilities.

Recent research emphasizes that successful digitalization requires holistic organizational transformation beyond mere technology adoption. Companies must redefine culture, processes, and customer interactions (Chang et al., 2024). Established firms often struggle with digital transformation anxiety, requiring comprehensive business model amendments, including management approaches, operational structures, and staffing (Verhoef et al., 2021).

The evolution toward a Fifth Industrial Revolution (5IR) represents a paradigm shift from 4IR’s automation focus to human-centric technology collaboration. Noble et al. (2022) describe 5IR as emphasizing “harmonious human-machine collaboration,” particularly transforming retail and service sectors. Similarly, Taj and Zaman (2022) conceptualize Industrial Revolution 5.0 as integrating explainable artificial intelligence with human-centered approaches. This evolution suggests that effective digitalization strategies must increasingly balance technological advancement with human-centered organizational capabilities.

Entrepreneurial Orientation (EO) refers to “processes, practices, and decision-making activities that lead to new entry” (Lumpkin and Dess, 1996, p. 136). It represents a critical factor in business growth and competitive advantage (Kraus et al., 2012). It encompasses the behaviors, decisions, and actions that define how organizations operate and engage in entrepreneurial activities, playing a vital role in shaping entrepreneurial strategies and influencing organizational success (Lumpkin and Dess, 1996; Ibrahim and Mahmood, 2016; Rauch et al., 2009).

Following Miller (1983) and in line with the broad majority of research (Kreiser et al., 2013), we consider EO to encompass the three dimensions risk-taking, proactiveness, and innovativeness. Risk-taking involves taking bold actions and committing resources to uncertain opportunities, while proactiveness refers to staying ahead of competitors by introducing new products or services (Rauch et al., 2009). Innovativeness focuses on creating novel solutions, competitive aggressiveness involves challenging rivals for market dominance, and autonomy represents the ability to independently drive ideas and initiatives to completion (Lumpkin and Dess, 1996).

While the majority of EO research has focused on explaining performance differences among firms, research has pointed to the importance of addressing a broader spectrum of EO outcomes (Wales et al., 2021). Recent research increasingly demonstrates EO’s critical role in digital transformation contexts. Kraus et al. (2023) highlight how EO drives digital entrepreneurship and disruptive innovation, emphasizing the strategic importance of entrepreneurial dimensions in digitalization success. Similarly, Alnoor et al. (2024) demonstrate that EO logic provides crucial insights into digital strategy determinants, with entrepreneurial dimensions serving as foundational elements for effective digital transformation.

Following the landmark study of Lumpkin and Dess (1996), current EO research emphasizes configurational theory as important theoretical scaffolding (e.g. Wales et al., 2021). Building on observations that the EO dimensions may vary independently and may have different effects on outcomes (Lumpkin and Dess, 1996; Kreiser et al., 2002), research has engaged with addressing configurational aspects in EO from different perspectives, including uni-vs. multi-dimensional approaches to EO (e.g. Kreiser et al., 2002), merging dimensions (e.g. Anderson et al., 2015), analyzing unique and shared effects (e.g. Lomberg et al., 2017), or identifying context specific combinations of EO dimensions (Lisboa et al., 2016) or even combinations of EO dimensions and other variables (e.g. Palmer et al., 2019) through a fuzzy set qualitative comparative analysis in order to increase the understanding of EO’s effects on outcomes.

The Fourth Industrial Revolution (4IR) represents a profound transformation reshaping the global economy through rapid integration of advanced technologies, including artificial intelligence, robotics, IoT, and quantum computing (Schwab, 2017; O’Callaghad, 2018). This revolution fundamentally alters how organizations manufacture, communicate, and conduct business by blurring boundaries between digital and physical worlds (Schwab, 2017). The 4IR enables minimization of human-centric tasks, enhanced automation, network integration, and machine-driven efficiency, with particularly transformative implications for manufacturing processes and productivity (Erboz, 2017).

Technology readiness represents a critical determinant of 4IR adoption success, defined as an organization’s preparedness to adopt and integrate new technologies (Schumacher et al., 2016; Sony and Naik, 2019). Recent comprehensive reviews emphasize that digitalization readiness encompasses multiple dimensions, including technological infrastructure, organizational capabilities, and human capital preparedness (Yusof et al., 2025). This multifaceted concept extends beyond mere technology availability to include an appropriate mindset, skills, and strategic orientation toward digital transformation.

Contemporary research demonstrates varied approaches to assessing and implementing digitalization readiness. Murali and Krishna Mohan (2023) provide empirical evidence through case studies showing that systematic readiness assessment approaches significantly improve digital transformation outcomes. Similarly, Schneider (2023) highlights the importance of readiness for digitization in controlling and management contexts, particularly emphasizing AI integration capabilities. The German context is particularly relevant, with Bergmann (2025) demonstrating how skilled labor technology readiness influences digital transformation success, highlighting the critical role of human capital in digitalization processes.

Parasuraman’s (2000) Technology Readiness Index (TRI) remains foundational, encompassing innovativeness, optimism, discomfort, and uncertainty dimensions. However, recent research extends this framework to consider global digitalization impacts, with Moeini Gharagozloo et al. (2022) showing that digital readiness significantly affects international business flows and economic integration. This expanded understanding suggests that technology readiness operates at multiple levels (i.e. individual, organizational, and national) with implications for how entrepreneurial firms navigate digital transformation in globally competitive environments (Marnewick and Marnewick, 2020).

Building on these foundational concepts, researchers have developed comprehensive frameworks for assessing 4IR readiness across multiple organizational dimensions.

The convergence of 4IR technologies with entrepreneurial capabilities creates significant opportunities for business transformation. Organizations with high technology readiness levels are better positioned to leverage 4IR technologies for innovation and competitive advantage (Borodako and Ardelean, 2021; Liao and Lee, 2017). However, successful 4IR adoption requires strategic alignment between technology readiness and organizational entrepreneurial orientation to navigate the complexities of digital transformation effectively.

This study is grounded in Resource-Based Theory (RBT, Barney, 1991), which posits that firms achieve competitive advantage through valuable, rare and inimitable resources, provided they are adequately organized (VRIO). Following the notion that “EO represents how a firm is organized in order to discover and exploit opportunities” (Wiklund and Shepherd, 2003, p. 1310), EO research has repeatedly applied the VRIO framework to explain outcome differences between firms (e.g. Kollmann and Stöckmann, 2014), also linked to the context of digitalization (e.g. Kollmann et al., 2021). Contemporary RBT research emphasizes the theory’s evolution to address new contexts, concepts, and methods, particularly in digital transformation settings (Helfat et al., 2023). The digital age has necessitated adjustments to traditional RBT frameworks, with Valentowitsch et al. (2024) proposing modified resource-based models that account for digitalization’s unique characteristics and requirements. In entrepreneurial firms, effective strategy-making combines strategic approaches with organizational resources to enable competitive advantage (Kollmann and Stöckmann, 2014), particularly relevant in digitalization contexts where EO dimensions interact with technological capabilities to drive transformation success.

In digital contexts, RBT takes on enhanced significance as firms must strategically combine traditional resources with digital capabilities to create competitive advantages (Giustiziero et al., 2023). Digital firms achieve hyper-specialization and hyper-scaling through unique resource configurations that blend technological assets with entrepreneurial capabilities. For SMEs specifically, Chaudhuri et al. (2022) demonstrate how digital capabilities serve as crucial resources that enable value creation for customers when combined with organizational competencies.

The configurational nature of resource combinations aligns with qualitative comparative analysis (QCA) approaches, which examine how different combinations of resources lead to desired outcomes (Helfat et al., 2023). Rather than assuming linear relationships between individual resources and performance, RBT’s configurational perspective suggests that multiple resource combinations can achieve the same strategic outcome—a principle fundamental to understanding digitalization strategy effectiveness across different firm types.

The 4IR signifies a profound period of transformation wherein innovations in AI, biotechnology, and information technology integrate seamlessly, fundamentally altering industrial processes and human interactions with technology. This revolution demands enhanced focus on lifelong learning and adaptability, making entrepreneurial orientation crucial for navigating technological disruption.

EO represents a strategic resource configuration encompassing risk-taking, proactiveness, and innovativeness dimensions that enable firms to harness 4IR technologies for competitive advantage. Mayer (2024) underscores the necessity for leaders to adopt a 4IR mindset and cultivate both hard and soft skills, while entrepreneurial skills can be nurtured within ICT contexts (Nchu, 2024). This highlights the crucial role of continuous skill enhancement and strategic orientation in leveraging 4IR potential for entrepreneurial prosperity.

From an RBT perspective, EO dimensions represent intangible resources that, when combined with technological capabilities, create unique value-generating configurations. Different combinations of EO dimensions may substitute for each other in achieving digitalization success, supporting the configurational logic underlying successful digital transformation strategies.

The 4IR signifies a profound period of transformation wherein innovations in AI, biotechnology, and information technology integrate seamlessly, leading to much higher levels of interconnectedness and fundamentally altering industrial processes, societal structures, and human interactions with technology. This revolution demands a fresh set of skills and knowledge, with an enhanced focus on lifelong learning and adaptability. EO, in this context, refers to the strategic stance of organizations to innovate, embrace risks, and actively seek new opportunities. By weaving 4IR insights and practices into their EO, businesses can harness 4IR technologies such as artificial intelligence and the Internet of Things to spark innovation and maintain a competitive edge. Mayer (2024) underscores the necessity for leaders to adopt a 4IR mindset and cultivate both hard and soft skills to navigate the complexities of this new era. Similarly, entrepreneurial skills can and should be nurtured within Information and Communication Technology (ICT) (Nchu, 2024). This sentiment highlights the crucial role of continuous skill enhancement and strategic orientation in harnessing the potential of 4IR for entrepreneurial prosperity.

Technological transformation has the potential to disrupt business and human activities, particularly in sectors where human involvement is still prevalent, such as in the service sector and education. They stress understanding digital technology’s full capabilities for proper application. The 4IR equally affects and disrupts small businesses and entrepreneurs, who are now required to adapt their processes and adopt new technologies, but are also faced with new challenges and opportunities (Ngomana, 2023). These insights emphasize the need for ongoing skill development and strategic orientation to harness 4IR for entrepreneurial success.

Schwab (2017) provides a comprehensive overview of how the convergence of new technologies is reshaping businesses and society. He emphasizes that embracing an entrepreneurial mindset is essential for leveraging the opportunities presented by 4IR. Similarly, Liao and Lee (2017) highlight how the 4IR is transforming business practices, underscoring the importance of EO in adapting to and capitalizing on these technological advancements. The ability to innovate, take risks, and proactively seek new opportunities is highlighted as a key factor for success in the rapidly changing business landscape. Signé (2023), as well as Naudé (2017), suggest that fostering EO, combined with 4IR knowledge, can significantly enhance entrepreneurial success and contribute to global economic development. Continuous skill enhancement and strategic orientation are thus pivotal in leveraging the full potential of 4IR.

The concepts of technology readiness and EO are closely intertwined, as both play a crucial role in driving innovation and business success in the 4IR. Technology readiness refers to an organization’s preparedness to adopt and integrate new technologies, which is essential for staying competitive in a rapidly evolving digital and competitive landscape. On the other hand, EO encompasses an organization’s strategic posture towards innovation, risk-taking, and proactiveness. When combined, these two concepts create a powerful synergy that enables businesses to not only adopt new technologies but also leverage them to create innovative products and services, thereby enhancing their market position and overall performance (Borodako and Ardelean, 2021; Liao and Lee, 2017).

Recent studies have shown that organizations with high levels of technology readiness and EO are better equipped to navigate the challenges and opportunities presented by the 4IR. For instance, Borodako and Ardelean (2021) found that technological knowledge and EO significantly contribute to entrepreneurial success, with psychological capital acting as a mediator. Similarly, Kreiterling (2023) highlighted the importance of digital innovation and EO in fostering entrepreneurship and economic growth, particularly in competitive markets. These findings highlight the importance of fostering both technology readiness and EO within organizations to drive sustainable growth and innovation (Kreiterling, 2023; Mayer, 2024; Naudé, 2017).

The TRI stands as a widely embraced instrument for evaluating individuals’ preparedness to embrace and interact with technology, uncovering insights into the psychological factors affecting technology acceptance (Agarwal and Prasad, 1999). A practical use of the TRI has been the incorporation into the organizational framework. Penz et al. (2017) investigated the correlation between technology readiness and entrepreneurial orientation, discovering the presence of technology-related components within the dimensions of optimism and innovativeness in the TRI. Furthermore, their research identified aspects related to risk-taking, proactive behaviors, and innovativeness among participants, indicating their entrepreneurial orientation.

RBT’s configurational perspective suggests that different resource combinations can achieve similar strategic outcomes, which explains why multiple pathways exist for successful digitalization strategies. Lifestyle firms, focused on work-life balance and personal fulfillment, may require different resource configurations compared to high-growth firms pursuing rapid expansion (based on your introduction).

The configurational approach aligns with the fsQCA methodology, which identifies multiple sufficient combinations of conditions leading to desired outcomes. In digitalization contexts, this suggests that lifestyle and high-growth firms can achieve effective digital strategies through different combinations of EO dimensions, technology readiness, and 4IR knowledge, reflecting their distinct strategic priorities and resource constraints.

This theoretical foundation supports our expectation that digitalization success emerges from multiple, context-dependent configurations rather than universal prescriptions, with firm type moderating which specific combinations prove most effective for achieving digital transformation objectives.

This leads us to the following assumptions:

This study employs a quantitative research design using fuzzy set qualitative comparative analysis (fsQCA) to explore the configurational antecedents of effective digitalization strategies in lifestyle and high-growth firms. fsQCA is particularly suited to this research for several reasons. First, digitalization success likely results from multiple, interconnected pathways rather than single linear relationships, making traditional regression-based approaches less appropriate. Second, fsQCA identifies different combinations of conditions that lead to the same outcome (equifinality). This aligns with our expectation that lifestyle and high-growth firms may achieve digitalization effectiveness through different configurations. Third, the method accommodates the complexity inherent in entrepreneurial contexts where multiple factors interact in non-linear ways to produce outcomes (Schneider and Wagemann, 2010).

The fsQCA approach is grounded in set-theoretic relationships and Boolean algebra (Thiem, 2022), enabling the identification of necessary and sufficient conditions for achieving high digitalization strategy effectiveness. Unlike traditional statistical methods that assume uniform effects across cases (Rutten, 2022), fsQCA recognizes that the same outcome can result from different combinations of causal conditions, making it ideal for understanding the configurational nature of digitalization strategies across different firm types.

In order to explore the antecedents leading to a high digitalization strategy within lifestyle and high-growth firms, data were collected from German SMEs in 2023. We made use of a survey as our research instrument to gather the variables needed to address the study objective. The study adhered to strict ethical guidelines, including reassuring the participants that all data would be anonymous and confidential and only reported in aggregate format. Participants had to provide consent before starting the survey. This, to a certain extent, ensured that participants replied to the posed questions truthfully and not by providing false information they might think was more socially desirable (Kelley et al., 2003). This can improve the quality and trustworthiness of the data collected. Standardized questions were asked of all participants to obtain accurate and comparable responses (Bell et al., 2016).

The data collection process was outsourced to a professional data collection company. Several briefing meetings ensured that the refinement of the research instrument, online programming and sample selection criteria were correctly administered. The data collection company made use of first-party data sets and participant panels based on the selection criteria. For this study, the selection criteria included that the participating business owner operated their business in Germany, was active as a business owner, senior executive or senior manager and had an employee base of no larger than 249. A total of 408 participants passed the selection criteria, which were set as screening questions at the beginning of the online survey. Of these, 153 terminated the survey before completing, and 27 responses were disregarded as more than 10% of the questionnaire was not completed. This resulted in a final sample of 208, resulting in a response rate of 50.98%. Table 1 depicts the sample demographics.

The demographic breakdown of the sample reveals interesting patterns. The sample is split almost equally between those aged 31 and 50 (49.5%) and 51 and above (46.6%). This reflects a rather mature sample. The presence of younger participants (3.7%), however, is relatively small, suggesting that younger individuals may face barriers to starting their own businesses. Education levels varied significantly among respondents. Many completed secondary education (25.5%) or hold post-secondary qualifications such as diplomas and certificates (22.6%). A smaller percentage have pursued higher degrees, including bachelor’s, master’s, and doctorates. Interestingly, a fraction (1%) reported having a doctoral degree.

Men make up a significant majority of the sample (66.8%), highlighting a gender gap in business ownership. The number of women involved is considerably lower, which raises questions about the opportunities and challenges they might encounter in the entrepreneurial space. Looking at business longevity, a substantial share of respondents have been running their firms for over a decade (58.2%). Firms that have been operating for a shorter time are much less common, pointing to the persistence and resilience of many entrepreneurs in sustaining their enterprises over an extended period. When it comes to the reasons behind starting a business, independence is cited most often (38.5%). Many participants also mention personal growth (16.8%) and seizing an opportunity (20.7%) as important drivers. A smaller number started their businesses out of necessity owing to unemployment, showing that entrepreneurship can serve as an alternative when other job options are limited.

The study made use of several variables. These included the maturity of a digitalization strategy, entrepreneurial orientation consisting of three sub-variables (Risk-taking, Proactiveness and Innovation), knowledge of the 4IR and Technology Readiness. Table 2 presents these variables and their sources (see  Appendix for more details).

The fsQCA method follows a systematic three-step process as outlined by Ragin (2017). First, ordinary data is transformed into fuzzy membership scores, allowing for partial membership in sets rather than the binary membership assumed by traditional QCA. This transformation is particularly important for organizational phenomena where variables often exhibit degrees of presence rather than simple absence or presence.

Following established fsQCA procedures, we transformed all antecedent variables (EO risk-taking, EO proactiveness, EO innovativeness, 4IR knowledge, and technology readiness) and the outcome variable (digitalization strategy) into fuzzy membership scores. Using the three-anchor approach recommended by Ragin (2017), we set values of 5, 3, and 1 from our five-point Likert scales to correspond to full membership (95%), cross-over point (50%), and full non-membership (5%), respectively. This calibration ensures that the fuzzy sets capture meaningful gradations in the data while maintaining theoretical relevance.

The next step involves constructing a truth table that identifies all possible logical combinations of antecedent conditions and their relationship to the outcome. We specified a consistency cutoff value of 0.8 and a minimum number-of-cases threshold of 1. The consistency threshold ensures that only sufficiently consistent configurations are considered as leading to the outcome, while the frequency threshold prevents the analysis from being influenced by isolated cases with unusual combinations.

Using the fsQCA 4.0 software, we employed standard analysis procedures to generate intermediate solutions. The intermediate solution strikes a balance between parsimony and complexity, incorporating both empirical evidence and theoretical knowledge to produce configurations that are both statistically robust and theoretically meaningful. This approach identifies the most parsimonious combinations of conditions that consistently lead to high digitalization strategy effectiveness.

The analysis generates several key metrics: consistency measures indicate how closely a perfect subset relation is approximated, with values above 0.8 considered acceptable for sufficiency claims. Coverage measures indicate the proportion of cases with the outcome that are covered by each configuration, providing insight into the empirical relevance of identified pathways.

Following established entrepreneurship literature, we categorized firms into lifestyle and high-growth types based on their primary strategic orientation and growth objectives. This categorization enables comparative analysis of how different entrepreneurial firm types configure resources for digitalization effectiveness, addressing our research questions about configurational differences across firm types.

The analysis reveals 142 cases (68.3%) classified as lifestyle firms and 66 cases (31.7%) classified as high-growth firms, indicating that lifestyle firms significantly outnumber high-growth firms in the German SME sample. The participants in this study were predominantly male (66.8%) and aged between 41 and 60 years (55.7%). Regarding educational background, 52.8% held either a master’s degree or below secondary school qualification. In terms of firm size, 53.3% of the businesses employed fewer than five employees. Geographically, the majority of firms were located in Nordrhein-Westfalen, Bayern, Baden-Württemberg, or Hessen (58.2%). Furthermore, 59.1% of the firms had been established for more than 10 years. As for industry distribution, 52% operated within the trade, education, or service sectors (see Table 3).

Descriptive statistics show moderate levels across all constructs (means 2.67–3.16), with adequate variance for configurational analysis, and values of Cronbach’s alpha range from 0.63 to 0.94 (see Table 4).

The fsQCA analysis identified seven distinct pathways to achieving high digitalization strategy effectiveness: three for lifestyle firms and four for high-growth firms. All configurations demonstrate high consistency values (≥0.86), indicating strong sufficiency relationships, while coverage values show meaningful empirical relevance (see Table 5). Paths A1, A2 and A3 represent lifestyle firms. Path A1 is a pathway where high levels of all three EO dimensions (risk-taking, proactiveness, and innovativeness) combine to achieve digitalization effectiveness. This configuration suggests that when lifestyle firms exhibit strong entrepreneurial characteristics across all dimensions, additional technological factors become less critical. Path A2 can be interpreted as demonstrating a proactive technology-ready approach, combining high EO risk-taking and proactiveness with strong technology readiness, while notably showing low 4IR knowledge. This pathway suggests that practical technology implementation capabilities can compensate for limited theoretical 4IR understanding.

Path A3 follows an innovation-driven technology pattern, pairing high risk-taking and innovativeness with technology readiness, again with low 4IR knowledge. This indicates that innovative lifestyle firms can achieve digitalization success through risk-taking and practical technology application.

Paths B1, B2, B3 and B4 represent high-growth firms.

Path B1 mirrors the lifestyle firms’ approach with full EO (A1), indicating that strong entrepreneurial orientation across all dimensions is universally effective regardless of firm type.

Path B2 presents a low-risk technology pathway unique to high-growth firms, where proactiveness and technology readiness drive success despite low risk-taking and 4IR knowledge. This suggests high-growth firms can achieve digitalization through calculated, technology-focused approaches.

Path B3 represents a knowledge-intensive innovation approach, combining proactiveness, innovativeness, and high 4IR knowledge with low technology readiness. This pathway is unique to high-growth firms, suggesting they can leverage theoretical knowledge to overcome technology readiness limitations.

Path B4 follows a risk-taking innovation pattern, similar to lifestyle firms’ Path A3, but indicating how high-growth firms can combine entrepreneurial risk-taking with innovation and technology readiness.

The configurational analysis reveals several important differences in how lifestyle and high-growth firms achieve effectiveness in digitalization. Most notably, the two firm types demonstrate distinct approaches to risk management in their digitalization strategies. High-growth firms exhibit a unique low-risk pathway (B2) where proactiveness and technology readiness drive success despite minimal risk-taking, suggesting these firms can pursue calculated, technology-focused approaches to digital transformation. In contrast, lifestyle firms consistently require high risk-taking in their successful configurations, indicating that these firms may need to embrace greater uncertainty to achieve effectiveness in digitalization, possibly due to their smaller scale and more limited resources requiring bolder strategic moves.

The utilization of 4IR knowledge also differs significantly between firm types. High-growth firms demonstrate a distinctive pathway (B3) that leverages high 4IR knowledge to compensate for lower technology readiness. This suggests these firms may have superior learning capabilities or access to specialized knowledge that enables them to overcome technological infrastructure limitations through a strategic understanding of 4IR concepts. Lifestyle firms, conversely, do not exhibit any pathways requiring high 4IR knowledge, indicating that practical implementation may be more critical than theoretical understanding for these firms’ digitalization success.

Technology readiness requirements further distinguish the two firm types. High-growth firms show greater flexibility in their technology readiness requirements, with pathway B3 demonstrating that other factors, such as 4IR knowledge and strong EO dimensions, can compensate for low technology readiness. Lifestyle firms, however, consistently require high technology readiness in their technology-focused pathways (A2 and A3), suggesting that these firms may lack alternative compensatory mechanisms and must rely more heavily on established technological infrastructure to achieve digitalization success.

Finally, the overall pathway diversity reveals that high-growth firms exhibit four distinct pathways compared to three for lifestyle firms, indicating greater strategic flexibility in achieving digitalization success. This diversity suggests that high-growth firms may have access to more varied resource combinations or possess greater strategic agility in configuring their entrepreneurial orientation and technological capabilities. The additional pathway options may reflect these firms’ typically larger scale, greater resource availability, or more complex organizational structures that enable multiple routes to digital transformation effectiveness.

Table 6 further summarizes the intermediate solutions of fsQCA for both lifestyle and high-growth firms. In addition to identifying successful configurations, we also examined the conditions leading to the absence of success. The results reveal three causal configurations (C1, C2, and C3) that are sufficient for low digitalization strategies among lifestyle firms, as well as three configurations (D1, D2, and B3) associated with low digitalization strategies in high-growth firms. This complementary analysis highlights not only the pathways to success but also the alternative configurations that hinder digitalization strategies across firm types.

This study extends entrepreneurial orientation research by demonstrating its configurational nature within digitalization contexts. Our findings support and advance Covin et al.’s (2020) proposition that EO dimensions function as substitutes in certain configurations, requiring alignment with supporting organizational factors to yield positive performance. The identification of seven distinct pathways to digitalization effectiveness provides empirical evidence that entrepreneurial success emerges from multiple, context-dependent resource configurations rather than universal prescriptions.

The application of Resource-Based Theory to digital transformation contexts reveals that digitalization capabilities represent complex bundles of entrepreneurial and technological resources. Our results align with Giustiziero et al. (2023) and Valentowitsch et al. (2024) in showing that digital-age competitive advantages emerge from unique combinations of traditional entrepreneurial resources with technological capabilities. Importantly, the finding that 4IR knowledge plays a complementary rather than central role suggests that practical implementation capabilities may be more valuable than theoretical understanding, supporting Chaudhuri et al.’s (2022) emphasis on applied digital capabilities in SME contexts.

The configurational approach employed in this study contributes to the growing body of literature advocating for complexity-aware research methods in entrepreneurship and digital transformation studies. Our use of fsQCA reveals insights that would be obscured by traditional linear analytical approaches, demonstrating the value of equifinality concepts in understanding how different firm types achieve similar outcomes through distinct pathways.

While EO research in general and also fsQCA-based EO research have equipped us intensively with recommendations on how to increase performance in firms, our research has extended this knowledge to digitalization strategy as an outcome of interest. Mirroring existing research, we find support for both a view that underscores that risk-taking, proactiveness, and innovativeness jointly affect outcome variables (supporting Miller’s view), in this case digitalization strategy, but also for a view that emphasizes that not all EO dimensions have to be present to affect an outcome (supporting the view of Lumpkin and Dess). Notably, when all EO dimensions are present—and only then—other context-related variables, such as 4IR knowledge and technology readiness, are not required to make a difference. This pattern distinguishes this research from patterns identified in other fsQCA studies on EO (e.g. Palmer et al., 2019; Kollmann et al., 2021). This pattern not only reinforces the relevance of EO itself but may also indicate that, at least when EO is used to explain digitalization strategy differences, the whole is greater than the sum of its parts, thus contributing to the core conceptualization of EO.

In line with previous research on the EO-performance relationship using fsQCA, our finding that proactiveness and innovativeness can independently of each other affect digitalization strategy counters Anderson et al. (2015) suggestion to merge these two dimensions into one factor.

In line with existing research, no negative effect of being proactive is identified in any of the paths, which underlines its importance for achieving positive outcomes. The fact that this also holds true for innovativeness supports the meaningfulness of this central aspect. Finding at least one digitalization strategy configuration benefiting from low-risk taking underscores the need to be careful when deciding on the degree of risk in the firm, also in view of decisions regarding digitalization. This reinforces the importance of acknowledging the potential different associations of EO dimensions with other variables and thus considering a multidimensional view of EO. It also supports Anderson et al. (2015) claim that risk-taking is functionally different from the other two EO core dimensions.

The observation that we identify one success path for lifestyle ventures and one for high-growth firms, which are identical in four aspects but differ in terms of risk-taking, highlights the context sensitivity of this particular factor. We also find several additional differences between the two types of firms, suggesting that further research on firm-type differences is warranted.

Our study contributes to digitalization strategy theory by demonstrating that effective digital transformation requires configurational rather than linear thinking. The identification of multiple pathways challenges the “one-size-fits-all” approach often advocated in digital transformation literature, supporting calls for more nuanced, context-specific digitalization strategies (Proksch et al., 2024; AlNuaimi et al., 2022).

The finding that technology readiness and EO dimensions interact in complex ways advances our understanding of how organizational capabilities and strategic orientation combine to enable digital transformation. This supports recent work by Kraus et al. (2022) on the role of entrepreneurial orientation in digital entrepreneurship, while providing specific configurational insights that extend beyond their general propositions.

The configurational nature of our findings has significant implications for managers and entrepreneurs pursuing digitalization strategies. First, the identification of multiple pathways suggests that firms should not adopt universal digitalization approaches but instead leverage their unique combination of entrepreneurial and technological resources. Lifestyle firms, given their consistent need for high risk-taking, should embrace bold digital initiatives that align with their entrepreneurial foundation, while ensuring strong technology readiness to support implementation.

High-growth firms, with their greater pathway diversity, have more strategic flexibility in pursuing digitalization. These firms can choose between high-risk approaches (similar to lifestyle firms) or calculated low-risk strategies that leverage technology readiness and market proactiveness. The unique pathway showing high 4IR knowledge compensating for low technology readiness suggests that high-growth firms should consider investing in strategic knowledge acquisition when technological infrastructure is limited.

The complementary role of 4IR knowledge across both firm types indicates that while theoretical understanding is beneficial, it should not be prioritized over practical implementation capabilities. Managers should focus resources on developing applied digital capabilities and ensuring adequate technology readiness rather than pursuing extensive theoretical 4IR knowledge without corresponding implementation capacity.

Resource allocation strategies should reflect firm type differences. Lifestyle firms should prioritize technology infrastructure development alongside entrepreneurial capability building, while high-growth firms can consider alternative approaches, including knowledge-intensive strategies that leverage theoretical understanding to overcome resource constraints.

Our findings have several important policy implications for supporting SME digitalization. First, the identification of different pathways for lifestyle and high-growth firms suggests that policy interventions should be tailored to firm type rather than offering universal support programs. Lifestyle firms may benefit most from technology infrastructure support and risk-taking encouragement, while knowledge transfer programs and strategic consulting support might better serve high-growth firms.

The finding that 4IR knowledge plays a complementary rather than central role suggests that policymakers should prioritize practical implementation support over theoretical training programs. This implies that government digitalization initiatives should focus on providing accessible technology solutions, implementation guidance, and infrastructure support rather than extensive educational programs about 4IR concepts.

The configurational nature of digitalization success suggests that policy support should be flexible and allow for multiple pathways rather than prescriptive approaches. This might involve creating modular support systems where firms can combine different types of assistance based on their unique resource configurations and strategic orientations.

This study has several limitations that present opportunities for future research. First, our focus on German SMEs limits generalizability to other national contexts, regulatory environments, and cultural settings. Future research should examine whether similar configurational patterns emerge in different countries, particularly those with varying levels of digital infrastructure and entrepreneurial culture.

The cross-sectional nature of our data provides a snapshot of digitalization strategies rather than tracking their evolution over time. Longitudinal studies could provide insights into how firms transition between different pathways and whether certain configurations prove more sustainable over time, as digitalization is an ongoing process rather than a one-time achievement.

Our reliance on self-reported survey data, while mitigated by anonymity assurances, may still be subject to social desirability bias. Future research could incorporate objective performance measures and multi-source data collection to validate our findings and provide more robust causal evidence.

The study focuses specifically on lifestyle and high-growth firms, excluding other entrepreneurial firm types that might exhibit different configurational patterns. Future research could extend this analysis to include other firm categories, such as scalable start-ups, social enterprises, or family businesses, to provide a more comprehensive understanding of entrepreneurial digitalization strategies.

Finally, our analysis focuses on the presence of a high digitalization strategy rather than examining configurations leading to digitalization failure. Future research could provide more comprehensive insights by examining both successful and unsuccessful digitalization configurations, potentially revealing critical pitfalls and necessary conditions for avoiding digitalization failure.