SMEs in a Digital Era: The Role of Management

Abstract

This article aims to explore the role of management in translating the external factors’ and internal barriers’ impacts on the level of adoption of digital technologies as a lever for change in business operations and processes in small- and medium-sized enterprises (SMEs). SMEs face a distinct set of challenges when adopting digital technologies, often lacking resources and knowledge. On the other hand, they have certain characteristics, such as simpler organisational structures and processes, that make them more flexible than larger firms in leveraging technologies into new business models. Data for this study are obtained from 989 SMEs in Bulgaria in the manufacturing and services sectors. A PLS–SEM analysis confirms eight hypotheses raised on the relationships between environmental factors and government support and internal factors (management support, organisational flexibility, and risk-tolerant culture) that impact digital business intensity. Environmental factors’ impact is stronger than government support, while internal barriers are found to have no statistically significant relationship. The research findings highlight the important role of management support in guiding digital transformation through supporting organisational flexibility and promoting a risk-tolerant culture.

1. Introduction

Digital technologies have transformed societies and industries. Their rapid development and application to a growing scope of activities will continue to lead to further disruptions and shifts in how firms do business. Justifiably, researchers’ interest in how firms adopt, use, and benefit from digital technologies is growing.

The widespread diffusion of digital technologies and their extensive effects is captured by the term digital era (Schaefer et al. 2018). It is characterised by the increase in speed and breadth of knowledge turnover within societies and economies (Doukidis et al. 2004), reshapes the business landscape, and presents new opportunities to firms (Liu et al. 2024). Digital technologies and technological advancements have major social and economic implications. Shepherd (2004) argues that the digital era will have the same effect on all members of societies and all economies. Importantly, faster technological development speeds up changes in the competitive landscape, requiring organisations to react quicker. Yet, organisations are in different stages of adapting to the digital age, adopting digital technologies, and benefitting from them.

Digital are those technologies that rely on the use of devices, algorithms, and applications that are dependent on computers to store, process, and interpret digitised data (Govers and Van Amelsvoort 2023). Their adoption and impact on business have gone a long way from digitisation, referring to the transfer of analogue into digital data; through digitalisation related to how digital technologies are applied to processes, products, and services; and then to digital transformation (DT) (Dörr et al. 2023). DT emerges as the dominant paradigm (Rêgo et al. 2022) and can be defined from three lenses—technological, organisational, and social (Reis et al. 2018). According to Vial’s definition of DT, it is a process where digital technologies create disruptions which trigger strategic responses and thus ultimately alter value creation paths while managing structural changes (Vial 2019).

Wessel et al. (2021) distinguish DT from IT-enabled organisational transformation along two dimensions—value proposition and organisational identity. DT is related to (re-)defining the value proposition and involves a new organisational identity, while IT-enabled transformation leverages digital technologies to support the value proposition and enhance the existing organisational identity (Wessel et al. 2021). The information-systems perspective, related to IT-enabled transformation, is significantly better researched as compared to DT from the organisational and management studies’ perspective (Vial 2019).

While digital transformation in large enterprises is often a well-structured process and relies on sufficient financial and human resources, the above is rarely true for small- and medium-sized enterprises. SMEs typically face a distinct set of challenges when turning to digital technologies such as limited resources and capabilities (Zhang et al. 2022) that may hamper or delay their digitalisation. OECD (2023) highlights that SMEs lag in digitalisation, which is likely to negatively impact their way to digital transformation.

On the other hand, SMEs’ smaller size and simpler organisational structures and processes make them more flexible (Barann et al. 2019) and able to integrate new technologies much faster (Becker and Schmid 2020). CEOs and founders are typically highly influential in the strategic decision-making in SMEs (Eller et al. 2020; Hortovanyi et al. 2023), which may either speed up DT or block it if they do not perceive it beneficial.

From the organisational studies perspective, DT can be defined as a continuous organisational change (Hanelt et al. 2021) describing how firms redefine their structures, processes, and culture triggered by digital technologies and how they manage the transition towards the desired state (Rêgo et al. 2022). Nguyen et al. (2023) define DT as “the use of digital technologies to trigger significant changes to the firm’s internal and external processes with an aim to improve the organisation”. Similarly, Tangi et al. (2021, p. 2) define DT in the context of the public sector as “second-order organisational changes enabled by digital technologies transforming the way organisations are structured and organised and resulting in a new state, from the point of view of processes, culture, roles, relationships, and possibly all aspects of the organisation”. Creating new value propositions and new business models and ultimately gaining and sustaining a competitive advantage are the aims of this organisational change. However, Hanelt et al. (2021) observe that the organisational change approach used in DT research is rather limited.

This study derives an operational definition of DT from the organisational change perspective and draws on the above authors. It interprets DT as a continuous organisational change that is supported by the adoption of digital technologies and manifested through the alteration of organisational elements such as structure, processes, and culture. DT leverages digital technologies and organisational elements with the aim of gaining and sustaining a competitive advantage through new value propositions and business models.

The number of studies on SME digitalisation has grown significantly over the years with the aim of understanding the digitalisation antecedents, outcomes, and processes. However, SME-related research refers to either a mere techno-centric (investigating classes of technology) or a strategic topic (Meier 2021). Extant literature is short of empirical studies which take the organisational perspective to explain DT in SMEs (Palade and Møller 2023). Moreover, the distinct challenges of SMEs question the applicability of organisational theories and practices that fit large enterprises (Hu et al. 2024). González-Varona et al. (2021) highlight the insufficient research focus on organisational competence to drive DT in SMEs.

Insufficient empirical studies of the organisational changes in SMEs supported by the adoption of digital technologies and the specific role of owners and managers is identified as a research gap addressed in this article. National contexts are characterised by differences in the external factors’ intensity and impact on SME DT; thus, adding empirical research on countries with SMEs that are less advanced on their DT path can provide further proof of the applicability of DT approached as a continuous organisational change.

This study aims to contribute to the above research gaps and to expand research evidence on external factors and capabilities that impact SMEs’ digital transformation. The open-systems theory and the resource-based theory guide this research.

The rest of this paper is structured as follows: The Section 2 develops the study hypotheses based on the literature review. The Section 3 describes the study context, sample, variables and measures used and the analytical procedure employed. The Section 4 presents the results obtained. The Section 5 offers discussion, including the underlying implications and limitations of this study. The Section 6 concludes.

2. Theoretical Background and Hypotheses Development

System theories are theories of organisational change that see organisations as dynamic systems of adaptation and evolution that contain multiple parts interacting with one another and the environment (Amagoh 2008). General systems theory helps explain the behaviour and structure of organised entities, such as organisations (Von Bertalanffy 1972). Within this broad framework, the open-systems approach came as a response to the realisation that the behaviour of organisational systems is hard to explain without considering the environmental relationships and effects, and how organisations interact with their environment (Cummings 2015). The open-systems approach views organisations as using inputs from and producing outputs back to the environment (Hammond 2003). They maintain a degree of non-equilibrium and can be best understood through the dynamic interactions and relationships among their units (Von Bertalanffy 1972). Importantly, the environment is not simply influencing the organisation, but “a high-variety environment is a necessity to an open system” (Pondy and Mitroff 1979).

Open-systems concepts have significantly influenced organisational theory and research, particularly in exploring how organisations interact and adapt to dynamic and unpredictable environments. These concepts help explain the behaviour of organisations in coping with continuous change (Amagoh 2008) and can benefit DT studies that conceptualise it as a continuous change triggered by digital technologies (Dörr et al. 2023; Hanelt et al. 2021; Rêgo et al. 2022). DT does not only change input–output or processes but also strategies and plans to create new value or market opportunities (Dörr et al. 2023); it impacts organisational structure and work systems.

The resource-based theory turns the attention to the internal factors (resources and capabilities) and decisions controlled by the firm, which complement the strategic positioning and thus provide a more complete view of the competitive advantage determinants (Eisenhardt and Martin 2000). It distinguishes between doing (capabilities) and having (resources). The firm resources, its competitors’ resources, and environmental limitations (such as industry and governmental policy) are among the key factors that impact the firm performance (Conner 1991). Yet, the organisation itself is the main source of its competitive advantage (Arend and Lévesque 2010). The firm resources, however, may also act as barriers to certain strategic choices (Wernerfelt 1984).

The theoretical framework illustrated in Figure 1 attempts to integrate the open-systems concept and the resource-based theory. This study conceptualises the organisation as an open system that is influenced by (and influences) its environment and copes with constant change. Digital technologies are one of the significant triggers of change.

Performance is largely influenced by environmental factors (competitors’ resources, customer requirements, supplier resources) and limitations (government policy, among others). Internal resources and capabilities are the main sources of competitive advantage, given the environmental impact, but may also act as limitations, as both are not static and evolve over time. Capabilities include routines to perform individual tasks and routines to coordinate the individual tasks (Helfat and Peteraf 2003).

2.1. External Factors

Studies on DT have explored the role of different external factors that promote or hamper digital technology adoption, such as competition, customers, and other stakeholders. In a bibliometric review, Hu et al. (2024) identify four external factors that determine DT–government policies and regulations, market competition, digital technique, and digital platform. These interact with internal factors (strategy, management, culture, and IT capabilities) to impact the DT process. Other studies also highlight the role of environmental factors, such as customers, competition, and digital technology development (Omrani et al. 2022), in driving DT. Vial (2019) points at consumer behaviour and expectations and the competitive landscape as two disruptions resulting from the fast development and adoption of digital technologies.

SMEs are an important ingredient of national economies, measured by their share in gross domestic product, innovation activities, and employment. Understandably, many national and international policies focus on digital uptake by SMEs (such as (European Commission n.d.b.; OECD n.d.)) and support mechanisms aimed to boost DT in SMEs. Governmental policies and support are devised to address the deficiencies of SMEs in their DT path, such as the often-cited lack of financial resources, training digital skills, etc. Yet, Hu et al. (2024) identify the role of governments and other stakeholders in facilitating SMEs’ DT as an under-researched area.

The role of owner/manager is cited by many authors as one of the important characteristics of SMEs (Palade and Møller 2023). The owner/manager has a key role in translating external signals into strategies and operations. Government incentives and support mechanisms that are perceived as addressing the internal deficiencies are hypothesised to increase the management support for digitalisation, given digitalisation itself is perceived as beneficial.

The following hypotheses are raised:

H1. 

Environmental factors (customers, suppliers, competitors, digital technologies development) directly and positively influence management support for digitalisation in SMEs.

H2. 

Government support (financial support, tax benefits) directly and positively influences management support for digitalisation in SMEs.

2.2. Internal Barriers

When they are valuable, rare, inimitable, and non-substitutable, the resources and capabilities of an organisation may be the source of its competitive advantage and thus sustained performance (Barney 1991). According to Arend and Lévesque (2010), when a firm chooses to alter its resource bundle in line with a better-performing benchmark, it does not know the production function; thus, the choice to restructure is perceived by management as costly and risky, and that might lead to hesitation to initiate such a change in the resource and capabilities base.

Vial (2019) identified several barriers to DT from the internal organisational environment, most significant of which are related to inertia and resistance. These can stem from a lack of flexibility, a mismatch between the available capabilities and resources and the requirements of DT, or tangible and intangible organisational components that result in inertia. Legacy IT systems result in resistance and limit the opportunities to adopt new digital technologies quickly (Al-Emadi and Anouze 2018). A related study found support for the interaction of information technology infrastructure and e-commerce capability (Newbert 2007).

The organisational change studies identify several other barriers that need attention. Valence, or whether employees assess the change as personally beneficial, is crucial for their support or resistance (Holt et al. 2007; Oreg et al. 2011). Change is related to adopting new behaviours, and inadequate employee incentivisation and involvement can provoke negative reactions and resistance. Organisational culture rigidities, such as values and beliefs, may also pose barriers and lead to resistance (Al-Emadi and Anouze 2018). Several authors highlight the lack of managerial understanding of DT as a factor that reduces support (Tangi et al. 2021).

H3. 

Internal barriers (legacy IT systems, lack of understanding and incentives, resistance) directly and negatively influence management support for digitalisation in SMEs.

2.3. The Role of Management

The role of management is broadly agreed to be one of the key success factors in implementing organisational change (Burke 2011; Kotter 2007; Mladenova and Davidkov 2023; Yukl 2012). Similarly, DT studies underline the management’s qualities (Hu et al. 2024), DT awareness, acceleration skills and harmonising skills (Hanelt et al. 2021), support, and role in developing the digital mindset within the organisation (Vial 2019).

SMEs are characterised by flatter and more flexible organisational structures (Inan and Bititci 2015) that enable them to adapt and react to changes faster. The flatter hierarchies allow more informal contacts and collaboration between departments. Organisational flexibility, on the other hand, is pointed out as an important DT factor (Berghaus and Back 2016; Kane 2019; Vial 2019). Becker and Schmid (2020) argue that SMEs that do not have organisational flexibility will achieve it as a result of DT. SME management is involved in both operational and strategic topics; decision-making processes are less standardised and formalised (Inan and Bititci 2015) and are thus expected to have a direct influence on organisational flexibility.

H4. 

Management support directly and positively influences organisational flexibility.

Management support is an antecedent for the implementation of digital strategies and technologies (Al-Emadi and Anouze 2018) in organisations. Management in SMEs has a crucial role in initiating and driving DT (Annosi et al. 2023; Li et al. 2018). The pace of adoption of digital technologies and leveraging them to digitally transform the business is largely influenced by the understanding and support of SME owners/managers. Meier (2021) also argues that management’s openness and commitment are among the key factors supporting SME digitalisation.

H5. 

Management support directly and positively influences digital business intensity.

Leadership is instrumental in shaping climate and culture in organisations (Schneider et al. 2017). An even stronger relationship can be expected in SMEs where leaders are involved more in operational than strategic issues (Inan and Bititci 2015) and thus largely influence the culture through decisions and demonstrations on what behaviours are acceptable and encouraged or not, etc. Culture in organisations reflects the shared beliefs, values, norms, and aspirations of the members of the organisation, which define the behaviours and guide the conversations. The management impacts culture in the desired direction (Schneider et al. 2017), and the cultural context influences the process, contents, and goals of organisational changes (Hempel and Martinsons 2009). DT is related to innovation and taking risks and thus requires a culture that accepts innovation-related risks and encourages open discussion and learning from mistakes. The management support is hypothesised to directly influence such a risk-tolerant culture in SMEs.

H6. 

Management support directly and positively influences risk-tolerant culture.

The challenges of the digital era are associated with complex dynamics between technology, structure, and culture in organisations (Hu et al. 2024). Organisational culture is found to influence flexibility and change and relate to innovation (Shahzad et al. 2017).

A culture that accepts risks and leverages collaboration internally and externally is hypothesised to influence organisational flexibility.

H7. 

Risk-tolerant culture directly and positively influences organisational flexibility.

2.4. Leveraging Digital Technologies for Changing Processes and Operations

The level of digital intensity reflects the degree to which a firm uses different digital technologies across its processes (De Mattos et al. 2023), such as big data, analytics, cloud, etc. SMEs tend to adopt less complex, simpler digital solutions (De Mattos et al. 2023; OECD 2021). Digital business intensity measures the investment in innovative digital technologies (Nwankpa and Datta 2017). The adoption of digital technologies drives new business processes and thus enables innovation and the creation of new business models.

Govers and Van Amelsvoort (2023) highlight that DT refers to a cultural change within organisations to continually challenge their status quo, experiment, and become comfortable with failure. Vial (2019) outlines the key features of digital culture to be a willingness to take risks, to experiment, and to promote learning (from mistakes). A culture that is open to taking risks, openly discussing failure, and striving to develop innovation is hypothesised to positively impact the integration and leveraging digital technologies to renew its business processes.

H8. 

Risk-tolerant culture directly and positively influences digital business intensity.

Adopting digital technologies requires an organisational structure that is flexible enough to adapt to changes (Verhoef et al. 2021). DT encourages firms to develop more adaptive structures (Hanelt et al. 2021). Kane (2019) highlights the role of agility and collaboration. The digital technologies’ impact requires alignment of structures and processes within the organisation (Fichman and Nambisan 2010). An organisational structure that enables quick adaptation to change and leverages collaboration would influence the digital business intensity.

H9. 

Organisational flexibility directly and positively influences digital business intensity.

The above nine hypotheses are illustrated in the research model (Figure 2).

3. Materials and Methods

3.1. Context

This study is conducted in Bulgaria for several reasons. The country has strong traditions in the IT and electronics sectors and is often referred to as the Silicon Valley of Southeastern Europe (European Commission n.d.a.). The official statistics reports 412,124 SMEs, which represent 99.81% of all firms in Bulgaria in 2021 (BSMEPA n.d.). SMEs contributed 74% of the total employment and 66.5% of the gross value added. Bulgarian SMEs face similar challenges as those in other countries, with effective introduction of ICT solutions and digitalisation being an important one (BSMEPA n.d.). The National SME Strategy 2021–2027 highlights the need to support specialisation in high-tech manufacturing and knowledge-intensive services and digitalisation in all economic sectors. Yet, Bulgarian SMEs lag behind large firms in using digital technologies, such as social media, cloud computing, data analytics, etc. (NSI 2023), but also as compared to their European counterparts. According to Eurostat (2024), nearly 60% of EU SMEs reach basic digital intensity, while in Bulgaria, this percentage is less than 30%.

3.2. Sample

The target population for this study included SMEs in Bulgaria in the manufacturing and services sectors. The sample was constructed to represent these sectors, i.e., displaying their share and the number of SMEs in the national economy and in each of the two sectors. The sampling frame was constructed based on the National Business Statistics (Business Registry). SMEs were randomly selected to guarantee the sample was representative. A local market research firm collected data, visiting the respondents’ premises during the period October-December 2023. Data were collected via a standardised questionnaire, with a single respondent for each firm. The data collection method was a face-to-face interview on the respondent’s premises. All questions were mandatory. The questionnaire was scripted in a specialised platform for tablet-assisted fielding.

In total, 989 filled-in questionnaires (59% response rate) were valid and used for the subsequent analysis. The questionnaire included items used to collect demographic information about the firm (economic sector, number of employees, year of establishment, ownership) as well as about the respondent (job position, education, age).

The demographic profile of the firms in the sample is presented in

Table 1. Firms profile (N = 989).

Firms Profile	Number	Percentage
Size (M = 31.21, SD = 44.905)
0–9 employees (micro)	419	42.37
10–49 employees (small)	334	33.77
50–249 employees (medium-sized)	236	23.86
Sector
Manufacturing	474	47.93
Services	515	52.07
Firm age (M = 17.78, SD = 9.107)
0–6 years	132	13.35
7–15 years	254	25.68
16–20 years	214	21.64
21–30 years	299	30.23
>30 years	90	9.10
Total	989	100

, and the job positions of the respondents are presented in

Table 2. Job position of the respondents (N = 989).

Job Position	Number	Percentage
(Co-)Owner	338	34.2
Executive director	308	31.1
Member of the board of directors	36	3.6
Finance director	24	2.4
Sales director	59	6.0
Chief accountant	178	18.0
IT director	46	4.7
Total	989	100

.

3.3. Variables and Measures

This study defines seven reflective variables. The items for their measurement were identified based on the literature review. Items were translated into Bulgarian language and adapted where necessary to reflect the local language specifics. All items are measured using a 5-point Likert scale, where 1 = not at all important/completely disagree/not relevant and 5 = most important/completely agree/most relevant. The operational definitions of the variables, indicators, and sources are presented in Appendix A.

3.4. Analytical Procedure

Data were analysed using RStudio, v.1.3.1073. PLS–SEM was selected as the applicable analytical procedure due to the characteristics of the dataset (non-normal distribution) and the complexity of the research model, which includes many variables, indicators, and relationships (Hair et al. 2019). The analysis follows the guidelines outlined by Hair et al. (2021). The selected analytical procedure is applied to assess the research model’s significance and explanatory power.

4. Results

4.1. Evaluation of the Measurement Model

The evaluation of the measurement model started with an examination of item loadings. The indicators are sufficiently reliable with all loadings > 0.7 (see Appendix A).

To assess the internal consistency reliability, the composite reliability (rhoC) and average variance extracted (AVE) are examined. RhoC values are between 0.876 and 0.932, below the rule-of-thumb threshold of 0.95, suggesting that the composite reliability is good. AVE values range from 0.639 to 0.801 and thus meet the requirement of AVE > 0.5. This indicates that the measures of all variables have high levels of convergent validity. Cronbach’s alpha values are also satisfactory, ranging from 0.812 to 0.903.

Table 3. Reliability metrics.

	alpha	rhoC	AVE	rhoA
EF	0.903	0.932	0.775	0.906
GS	0.868	0.919	0.790	0.882
BD	0.868	0.910	0.716	0.872
MS	0.876	0.923	0.801	0.878
RC	0.812	0.876	0.639	0.817
OF	0.871	0.912	0.722	0.875
DI	0.873	0.922	0.797	0.875

summarises the results of the reliability and validity examination.

The heterotrait–monotrait (HTMT) and Fornell–Larcker criteria are applied to test the discriminant validity. All HTMT values are below 0.90, and the examination of the bootstrapped confidence intervals shows they are also significantly different from 0.90 (number of bootstrap subsamples = 10,000; 5% confidence intervals). The application of the Fornell–Larcker criterion shows that the square root of the AVE values of each construct is higher than the construct’s highest correlation with any other construct (

Table 4. HTMT and Fornell–Larcker criteria.

	HTMT Criterion						Fornell–Larcker Criterion
	EF	GS	BD	MS	RC	OF	EF	GS	BD	MS	RC	OF	DI
EF							0.881
GS	0.461						0.409	0.889
BD	0.050	0.081					0.014	−0.064	0.846
MS	0.655	0.374	0.075				0.586	0.331	−0.065	0.895
RC	0.583	0.468	0.105	0.852			0.504	0.396	−0.091	0.723	0.799
OF	0.715	0.432	0.069	0.869	0.873		0.636	0.378	−0.058	0.761	0.738	0.850
DI	0.356	0.334	0.180	0.419	0.464	0.445	0.316	0.292	−0.156	0.367	0.393	0.388	0.893

). The discriminant validity assessment concludes that the variables are unrelated and capture specific constructs.

4.2. Evaluation of the Structural Model

The examination of the structural model (Figure 1) started with an evaluation of the collinearity issues. All VIF values are <3, indicating that collinearity among the predictor variables is not an issue for this structural model.

The next step is the evaluation of the significance and relevance of the path coefficients. The results of the structural paths bootstrapping are presented in

Table 5. Path coefficient estimates, significance, and confidence intervals.

	Original Est.	Bootstrap Mean	Bootstrap SD	T Stat.	2.5% CI	97.5% CI	Decision
H1: EF→MS	0.544	0.544	0.025	21.751	0.494	0.592	supported
H2: GS→MS	0.104	0.105	0.030	3.532	0.046	0.162	supported
H3: BD→MS	−0.066	−0.070	0.031	−2.103	−0.123	0.004	rejected
H4: MS→OF	0.476	0.476	0.033	14.536	0.411	0.538	supported
H5: MS→DI	0.094	0.094	0.350	2.708	0.025	0.161	supported
H6: MS→RC	0.723	0.723	0.017	41.67	0.688	0.756	supported
H7: RC→OF	0.394	0.394	0.032	12.188	0.331	0.458	supported
H8: RC→DI	0.199	0.200	0.043	4.597	0.116	0.284	supported
H9: OF→DI	0.170	0.169	0.039	4.352	0.094	0.245	supported

. Examining the original path coefficient estimates indicates a strong positive effect of MS on both RC (0.723) and OF (0.476) and a negligible effect on DI (0.094).

MS is positively impacted by EF (0.544). GS has a much lower effect on MS (0.104). The relationship BD→MS is not statistically significant.

The positive impact of RC on OF is strong (0.394) and weaker on DI (0.199). Similarly, OF’s positive impact on DI is rather weak (0.170).

All paths are statistically significant (5% confidence interval) except for the relationship BD→MS, which leads to the rejection of H3. All other relationships are positive while characterised by different strengths of the relationships. The decision is taken to accept the hypotheses with strong positive effects (H1, H4, H6, and H7). The statistically significant relationships with much lower strength (H2, H5, H8, and H9) are also accepted, and mediation effects are further explored to identify how these relationships contribute to the overall model effects.

The model is characterised by complementary mediation. The mediated paths from EF and GS through MS, RC, and OF to DI are calculated with a 5% confidence interval. The total indirect effects of EF on DI (0.200) and of GS on DI (0.038) are positive and significant.

After inspecting the bootstrapped confidence intervals, it is concluded that the effects are significant at the 5% level. The direct effects of EF on DI (0.056) and of GS on DI (0.132) are positive.

Inspecting the bootstrapped confidence interval of the direct effect of EF on DI shows that it contains zero in the 95% interval [−0.021; 0.131]. It can be concluded that the relationship between EF and DI is fully mediated.

The relationship of GS to DI is partially mediated as the bootstrapped confidence interval does not contain zero in the 95% confidence interval [0.072; 0.191].

The explanatory power of the structural model is assessed by examining the coefficient of determination (R²) of the endogenous constructs. Adjusted R² values of OF (0.652), RC (0.522), and MS (0.355) indicate moderate in-sample explanatory power of the model. The adjusted R² value of DI (0.176) is assessed as weak, suggesting that there are other factors of importance that are not included in the design of this study.

The model is tested for differences between two groups–owners (N = 338) and all managerial staff (N = 651) through PLS–MGA. The results indicate statistically significant differences on three paths only: GS→MS, MS→RC, and EF→DI, with p-values < 0.05 (

Table 6. Statistically significant differences between owners and managerial staff.

	Estimate	Owners Beta	Mgmt Beta	Diff.	Owners Beta Mean	Mgmt Beta Mean	p Value
GS→MS	0.104	0.168	0.048	0.120	0.169	0.051	0.022
MS→RC	0.723	0.786	0.675	0.111	0.786	0.676	0.000
EF→DI	0.056	0.147	0.014	0.133	0.149	0.015	0.037

). Owners perceive these relationships as stronger as compared to management staff. For all other paths, there is no statistically significant difference between the perceptions reported by owners and managerial staff.

The results of the analysis are presented in Figure 3.

5. Discussion

The adoption of digital technologies to drive new and improve existing business processes is an important step in the digital transformation of SMEs. It relates to leveraging existing resources and capabilities and reconfiguring them to better adapt to and benefit from the environmental opportunities. The resource-based view highlights the specific role of the capabilities to improve the resources’ productivity, the need to pick resources and build capabilities along the way (Makadok 2000), and is largely adopted in DT studies (Eller et al. 2020).

SMEs are also open systems that interact with environmental factors. The digital technologies’ rapid development, competitors, customers, and suppliers put pressure on but also open new opportunities for SMEs. Digital transformation from the open-systems theory perspective is a change that allows the firm to adapt to and benefit from the environmental dynamics. This continuous organisational change supported by digital technologies is manifested through the alteration of organisational elements, such as structure, processes, and culture, with the ultimate goal of gaining a competitive advantage through new value propositions and business models. Digital technologies enable major improvements in current and the development of new business processes to support new business models. DT should be regarded as a continuous effort that aligns with the ongoing digital technologies development and not as a one-off project.

This research hypothesised the focal role of management support in translating the external factors and internal barriers’ impact on structure, culture, and digital business intensity in SMEs. The analysis confirmed eight out of the nine hypotheses raised. The results obtained are discussed below.

First, the focal role of management is confirmed, and this result reinforces previous research (Tangi et al. 2021; Vial 2019). Management support in SMEs has a strong positive effect on risk-tolerant culture and organisational flexibility necessary to drive digital transformation. Management is instrumental in shaping culture and structure in organisations (Schneider et al. 2017) and even more so in SMEs. SME owners and managers have a strong influence on organisational culture and the presence or lack of flexibility of structure and processes. This study finds a statistically significant difference in the relationship between MS and RC between the two groups, with owners perceiving this relationship stronger (M = 0.786) as compared to management staff (M = 0.676). This might suggest that owners see the strategic importance of the culture but also aligns with previous research highlighting the key role of entrepreneurs and owners in SMEs to shape the culture. Owners and managers’ capabilities help alter the resources of the firm to achieve and sustain competitive advantage in a digital era. An international study on six European regions finds that management support in SMEs strengthens the relationship between social capital and the propensity to adopt I4.0 technologies (Agostini and Nosella 2020). Leso et al. (2023) report a positive influence of cultural alignment on organisational agility in a study of Brazilian SMEs that are in the process of DT.

Second, this research confirms the positive impact of environmental factors on management support and the positive yet lower impact of government support. The pressure from micro-environment players, such as customers, competitors, and suppliers is an important driver of SME digital transformation endeavours. The digital technologies development itself enables the process. In a study of 180 SMEs in China, Zhang et al. (2022) also find that organisational capabilities mediate the influence of technological and environmental factors on DT. Government support has a role, although less significant. This conclusion aligns with Omrani et al. (2022), who also find a marginal influence of factors such as access to finance, legal and administrative environment, and other factors that can be considered representing the governmental policies and support for DT in SMEs. Assessing the differences between the groups of respondents, however, indicates that government support has stronger impact on MS in the eyes of owners (M = 0.169) than managerial staff (M = 0.051). This might suggest that owners perceive access to government support as a stronger argument to embark on the digitalisation path. While the role of government support through access to finance and favourable policies is typically highlighted as an important factor for DT, these results suggest that further investigation is needed.

Surprisingly, these research results rejected the hypothesised impact of internal barriers on management support for adopting digital technologies. Previous research highlights the existing IT systems’ role as one of the main considerations for digital adoption decisions in SMEs (Omrani et al. 2022). Yet, the context of the study may help explain its results. Other recent studies on the digitalisation of Bulgarian firms show that the majority of firms are in initial or medium phases of digitalisation (Siemens and AHK Bulgarie 2021), and Bulgarian SMEs tend to adopt less advanced digital technologies (Omrani et al. 2022). This could mean that the current technological level is low enough not to hamper the adoption of contemporary digital technologies, provided the supporting culture and flexibility are in place.

Environmental factors and government support impact the adoption of digital technologies. This research finds a fully mediated relationship between environmental factors and digital business intensity and a partially mediated relationship between government support and digital business intensity. A possible explanation of these results could be sought in the market incentives. The effects of the environmental factors’ dynamics need to be grasped by the SME decision-makers who would create the necessary internal conditions (risk-tolerant culture and organisational flexibility, among others) to support the adoption of digital technologies. Government support, on the other hand, might not always be fully aligned with the strategic aspirations of SMEs or correspond to the market pressure and conditions that SMEs face. That is, the effects of direct financial support might encourage investment in digital technologies even when the organisational factors are not aligned. An argument could be raised that in such instances, the results from such investments might not lead to their successful adoption and/or would not benefit the organisation. This would support the observations that not all investments in digital technologies realise their full value, and their impact depends on the alignment with organisational factors such as structure, processes, strategies, etc. (Fichman and Nambisan 2010; Nwankpa et al. 2022). Yet, certain caution should be taken when interpreting the results of this research, given the moderate in-sample explanatory power for organisational flexibility, risk-tolerant culture, and management support, and the corresponding weak explanatory power with regard to digital business intensity.

Several implications for theory and practice can be drawn from these research results. Three theoretical contributions are highlighted here. First, the research model explores external factors, internal barriers, and capabilities that impact the digital transformation of SMEs and contributes empirical evidence on the relationships between them. Thus, this study adds to the cumulative theory building on the factors, barriers, and mechanisms through which SMEs advance on their DT journey. Second, the results support the arguments for the focal role of management support and its impact on risk-tolerant culture and organisational flexibility. In a digital era, SME owners and managers have an important role in navigating the organisational changes supported by the adoption of digital technologies and manifested through the alteration of organisational elements such as structure, processes, and culture. Moreover, these research results suggest a strong positive impact of risk-tolerant culture on flexibility. This might elucidate some of the mechanisms through which management support is exerted. Third, the results suggest a stronger impact of the environmental factors as compared to the government support in driving DT in SMEs. The lack of support for a relationship between internal barriers and management support suggests further examination of the interplay between internal capabilities and constraints might be needed in the case of SMEs.

There are three main implications for practitioners. First, management support is crucial for creating and sustaining the necessary risk-tolerant culture and organisational flexibility. Digital transformation requires awareness of the need and benefits from it and continuous support by the top and middle management levels. Digital leadership does not require so much deep understanding of the technology but rather a change-oriented and future-oriented vision (Kane 2019). While SME owners and managers have limited attention (Dörr et al. 2023) and typically take the strategic decisions, they should carefully consider priorities and be persistent in their pursuit. If DT is understood as an important priority, owners and top managers should give it enough support and should enable middle management to do so. Second, SME owners and managers should constantly screen the environmental factors and governmental policies and make use of the opportunities. The adoption of digital technologies is found to have a positive impact on competitiveness (Cardoso et al. 2023) and is a relevant strategic imperative. Third, in order to be able to adopt and utilise the potential of digital technologies, they should pay attention to the organisational culture. Culture is manifested by the shared beliefs, norms, and behaviours within the organisation. And if not shaped proactively and nurtured, it can divert to a non-desirable direction that might hamper the achievement of strategic imperatives, DT included.

This research has its limitations that need to be taken into account when interpreting its conclusions. It is cross-sectional, and data are collected from a single respondent in each SME. It relies on one national context. Future research might extend to more respondents from different hierarchical levels within each firm and to samples from other national contexts that can improve the generalisability of the results.

6. Conclusions

This study approached DT as a continuous organisational change supported by the adoption of digital technologies and manifested through the alteration of organisational elements such as structure, processes, and culture. Data were collected via a structured questionnaire from 989 SMEs in the manufacturing and services sector in Bulgaria. The sample was constructed to represent these sectors. The respondents were randomly selected to guarantee that the sample was representative. Data were analysed in R by a PLS–SEM.

The analysis results revealed the crucial role of management support in SMEs. Management support strongly influences risk-tolerant culture and organisational flexibility that drive the adoption of digital technologies. The impact of environmental factors (customers, suppliers, competitors, and digital technologies development) and the government support (tax benefits and access to finance through subsidies and funded programmes) influence management support. This research finds a fully mediated relationship between environmental factors and digital business intensity and a partially mediated relationship between government support and digital business intensity. The hypothesised relationship between internal barriers and management support is rejected.

This study addresses the limited focus on DT from the organisational change perspective. It adds evidence on the factors and mechanisms that influence the digital transformation of SMEs. These results have certain theoretical contributions and practical implications and pose several research questions that can be investigated to build on current knowledge of digital transformation in SMEs.