Digital Platform Capabilities for Transforming Cultural Heritage Business: Exploring the Mediating Role of Business Model Experimentation and Competitive Advantage

Abstract

Digitalisation has evolved as a multidimensional phenomenon and impacts the business world. SMEs heavily invest in digital platform capabilities to keep track of digital transformation, enabling them to perform business model experimentation to generate and develop innovation. This paper explores the role of these two crucial growth-promoting variables in the performance of art and craft-based firm’s performance. Through this paper, the researchers contest the argument that, although digital platform capabilities accelerate business model experimentation for firm performance, competitive advantage plays a significant mediating role. Along with these arguments, this study also explores the role of digital platform capability in business model experimentation. It examines the mediating role of business model experimentation in the forming of a competitive advantage. The research model under examination belongs to the explorative school of research; hence, the researchers have used partial least square–structural equation modelling (PLS-SEM) on a sample of 211 Indian firms belonging to the category of art and craft-based businesses. The hypothesis testing results facilitate exciting insights about the direct and indirect effects of digital platform capabilities, business model experimentation, and competitive advantage on firm performance. In light of the research findings, policymakers, SME consultants, and managers may obtain practical insights in order to develop an intervention mechanism. Researchers working in this area will glean a fresh look at the antecedents of SME performance as this model is explorative; future research may explore the testing of the model in different geographic locations and industry contexts.

1. Introduction

Digital transformation has become a cornerstone of sustainable competitive advantage in the contemporary business landscape, especially for small and medium-sized enterprises (SMEs) navigating resource constraints and volatile market environments (Liao et al., 2024; N. Wang et al., 2023). This transformation is particularly significant for cultural heritage businesses, which traditionally rely on localised knowledge, artisanal practices, and physical engagement with heritage assets. Indian handicrafts symbolise the country’s rich cultural heritage and were a significant source of employment for the people during the colonial period. However, building a new India was incomplete without the contributions of small and medium-size enterprises (SMEs). For decades, tangible and intangible heritage have been inextricably linked to activities that generate subsistence for livelihood (Beall & Kanji, 1999; C. Grant, 2020). Indian art and crafts have been enriched because of India’s global strategic location and broad exposure to diverse civilisations (Guha-Thakurta, 2004; Sandhu, 2020). Regardless of their size and the nature of their operations, these firms have played a critical role in empowering the Indian economy (Venkatesh & Muthiah, 2012; Zhu et al., 2022) by providing the biggest source of employment after agriculture and contributing significantly to India’s gross industrial output and exports (Mukherjee & Mukherjee, 2022). Despite their importance, SMEs are profoundly unorganised, lack capital access, and have less technology exposure (Singh & Kaur, 2021). Moreover, SMEs in India have witnessed intensified competition due to the liberalisation, privatisation, and globalisation adopted as fundamental economic reforms in 1991, coupled with domestic reforms and the establishment of the WTO in 1995 (Paul, 2015). Though seen as game changers for the business landscape in India, these shifts laid the foundation for the transformation of indigenous enterprises (Chittoor et al., 2008). During the same decade (the 1990s), countries around the globe saw major disruptions in business models (Cassidy, 2003) because of the Internet boom. The disruption caused issues for economies but provided opportunities as well.

Responding to this competitive pressure, many SMEs use digital platforms to leverage their business strategies (Li et al., 2016). Scholars have therefore been working to apply emerging technologies such as digital platforms (Sarwar et al., 2024; N. Wang et al., 2023). Digital platforms have transformed traditional business models, enabling SMEs to leverage technology for enhanced operational efficiency and competitive advantage. The integration of digital platform capabilities—defined as the firm’s competencies to utilise digital infrastructures to create, deliver, and capture value—has emerged as a potent enabler of strategic renewal and operational agility in this sector (Cenamor et al., 2019; Falahat et al., 2020).

Cultural heritage SMEs may increase market reach, customer contact, and value delivery via digital platforms. These systems make heritage experiences dynamic and participatory through resource integration, co-creation, and data-driven decision-making (Liao et al., 2024). Many cultural heritage SMEs struggle with digital platforms despite these benefits. Strategic stagnation, low absorptive abilities, and a mismatch between digital operations and the firm’s identity often generate this conflict (Cenamor et al., 2019). Business model experimentation (BME) can fill these gaps and unleash digital platforms’ transformative potential. BME tests, adapts, and refines new value creation, delivery, and capture configurations (Bouwman et al., 2019). BME helps cultural heritage SMEs develop new business models, embrace hybrid solutions, and adjust to shifting consumer expectations using adaptive learning. BME enables cultural heritage SMEs to implement adaptive learning to develop new business models, hybrid solutions, and rapidly adjust to consumer expectations. Experimentation turns digital capabilities into strategic results.

This change leads to a competitive advantage—a distinctive position of the company which enables it to outperform its competitors. The competitive edge in digitalised markets is increasingly based on intangible assets such as innovation capacity, customer intimacy, and knowledge use, which are deeply linked to digital skills and experimental agility. Competitive advantage in cultural heritage may come from differentiated narratives, immersive digital experiences, or networked community participation facilitated by digital platforms and enhanced by BME. This study uses the resource-based view (RBV) and dynamic capabilities theory to argue that digital platform capabilities are scarce, valuable, and strategically reconfigurable assets that enable organisational flexibility and value creation (Liao et al., 2024). RBV stresses digital assets’ uniqueness, while dynamic capabilities theory emphasises organisational agility in reconfiguring them to adapt to changing market and technology landscapes.

Despite growing scholarly interest in digital transformation, few studies have systematically examined the mechanisms through which digital platform capabilities impact the performance of cultural heritage SMEs. In particular, the mediating roles of business model experimentation and competitive advantage remain underexplored. This study contributes to the growing body of literature on digital transformation by providing empirical evidence on the interplay between digital platform capabilities, business model experimentation, and competitive advantage in a niche yet critical domain.

2. Literature Review, Conceptual Model, and Hypotheses

The role of competitive advantage in a company’s success has been extensively discussed in the literature (Barney, 1991; R. M. Grant, 1991; Porter, 1985). A central concern remains how firms acquire and sustain this advantage over time (Barnett & McKendrick, 2004; Nayak et al., 2022; Newbert, 2008; C.-H. Wang & Juo, 2021). Traditional studies—particularly those rooted in the resource-based view (RBV)—have emphasised that resources which are valuable, rare, inimitable, and non-substitutable (VRIN) are central to achieving and maintaining a competitive edge (Barney, 1991; Newbert, 2008). However, recent scholarship has challenged the sufficiency of merely possessing such resources in today’s dynamic and volatile business environment. Even firms with similar resource endowments may exhibit divergent strategic behaviours and performance outcomes (Eisenhardt & Martin, 2000; C.-H. Wang, 2014). To address these limitations, the dynamic capabilities framework was introduced by (Teece et al., 1997) as an extension of the RBV, offering a more adaptive lens to explain how firms sustain competitiveness and drive superior long-term performance amid environmental turbulence (Helfat & Raubitschek, 2018; Sousa-Zomer et al., 2020). Technological advancement, digital transformation, and sustainability regulations have increased environmental uncertainty (N. Wang et al., 2023). In this evolving landscape, the ability to continuously reconfigure and optimise resource allocation has emerged as a critical source of competitive advantage (Tiberius et al., 2021).

Dynamic ability is the ability of an enterprise to identify opportunities and threats, seize new opportunities, and reorganise internal and external resources to respond effectively (Teece, 2007). Capacities are accumulated and are shaped by unique internal and external structures and interactions that are difficult to emulate (R. M. Grant, 1991; Soto-Acosta & Meroño-Cerdan, 2008, 2009). In other words, a capability may be a source of competitive advantage if it is durable, non-transparent, non-transferable and difficult to duplicate. Many studies have examined organisational capacity and performance without addressing competitiveness, while some have examined competitiveness as a proxy. (Weerawardena, 2003) found that marketing skill boosts competitive advantage in 324 manufacturing enterprises, emphasising its strategic importance.

Scholars have studied how dynamic capabilities—especially digital dynamic capabilities—impact organisations’ strategic behaviour and outcomes as digital technologies spread. Digital skills help organisations adapt to shifting market needs and technology advances, optimising resource allocation and seizing new possibilities (Teece, 2018). Adaptation is a sustainable competitive advantage in a fast-paced, digital world (Chen et al., 2024). This study examines SMEs’ competitive advantage capabilities, recognising that competitive advantage and performance are distinct but interrelated variables. The question is whether competitive advantage mediates the link between these competencies and business performance.

Digital platform capabilities and business model experimentation are studied as SME performance determinants via competitive advantages. The conceptual model needs these variables to add academic literature knowledge. Digital platform capabilities and business experimentation help SMEs obtain a competitive edge and perform better. A mediator in the conceptual model helps explain how underlying constructs affect SME performance. Thus, the below hypotheses were proposed. Figure 1 visually exhibit the conceptual model.

2.1. The Impact of Digital Platform Capability on Business Model Experimentation

Digital platform capabilities (DPCs) are increasingly needed for SMEs to innovate and test their business strategies. Liao et al. (2024) state that powerful digital platform capabilities can increase enterprises’ openness to external interactions, enabling collaboration and resource sharing, which are essential for business model testing. The flexibility and standardisation of digital platforms enable SMEs to quickly access varied resources and insights to test business strategies. SMEs must adapt and innovate to meet market demands, and (Cenamor et al., 2019) found that digital platforms streamline business processes and improve decision-making. According to Kiyabo and Isaga (2020), integrating digital technologies into corporate operations encourages experimentation and creativity. Digital platforms facilitate business model experimentation and increase SMEs’ agility to respond quickly to changing market conditions. Falahat et al. (2020) found that business model experimentation helps organisations find and capitalise on market possibilities, increasing their competitive position. Resources for business model experimentation play a significant role in business model experimentation (Bouwman et al., 2019), and in this case, the critical resource is digital platform capability. Therefore, we propose the following hypothesis.

H1: 

The digital platform capability (DPC) of SMEs positively influences the firms’ business model experimentation (BME).

2.2. The Impact of Digital Platform Capability on Competitive Advantage

The theory of competitive advantage originates from competition theory, which posits that competitors and market dynamics are essential conditions (Bain, 1956). Competitive advantage reflects a firm’s superior performance, often achieved through cost leadership or product differentiation (Porter, 1985). The resource-based view (RBV) adds that such advantage stems from key firm-controlled resources and capabilities (Barney, 1991). However, in rapidly changing environments, these resources can depreciate or be imitated, limiting their sustainability (Mikalef & Pateli, 2017; Teece et al., 1997; G. Wang et al., 2015). Liao et al. (2024) claim that high-level digital platform enterprises can gain first-mover advantages over their competitors. Speed and agility are key in changing markets. Digital platforms enable SMEs to streamline operations and make strategic decisions, lowering costs and improving efficiency and competitiveness. Cenamor et al. (2019) found that digital platforms improve resource allocation and market response, which are crucial for competitiveness. Kiyabo and Isaga (2020) found that digital platform-using enterprises can differentiate themselves from competitors by creating unique value propositions. Today’s market, where customer expectations change, requires competitive distinctiveness. Falahat et al. (2020) also note that digital platforms enable SMEs to develop and adapt, which is crucial for competitive advantage. Therefore, we propose the following hypothesis.

H2: 

The digital platform capability (DPC) of SMEs positively influences the firms’ competitive advantage (CA).

2.3. The Impact of Business Model Experimentation on Competitive Advantage

Kiyabo and Isaga (2020) note that BME lets organisations test new business models to create distinct value propositions and differentiate them from the competition. This supports the resource-based perspective that unique competencies and resources sustain competitive advantages. Furthermore, Bouwman et al. (2019) found that BME-invested enterprises are more likely to discover and capitalise on emerging market possibilities, improving performance. BME’s iterative nature lets SMEs improve their strategy based on real-time feedback and market data, which is vital in today’s fast-paced business environment. As companies try out new business models, they enhance operational efficiency and produce customer-friendly solutions, strengthening their competitive position. Falahat et al. (2020) found that innovative business models can help organisations increase performance.

H3: 

The business model experimentation (BME) of SMEs positively influences the firms’ competitive advantage (CA).

2.4. The Mediating Role of Business Model Experimentation

Digital platforms give SMEs the means and resources to engage in BME, giving them a competitive edge. DPC boosts a firm’s ability to innovate and adapt, while BME turns these capabilities into competitive advantages, according to Liao et al. (2024). BME mediates the dynamic capabilities framework’s recommendation that enterprises experiment and modify their business models to changing market conditions. Cenamor et al. (2019) add that the agility and reactivity of digital platforms enable enterprises to test different business strategies, improving market positioning. Bouwman et al. (2019) found that BME enterprises that use digital platforms fare better. Therefore, we propose the following hypothesis.

H4: 

Business model experimentation (BME) positively mediates the relationship between digital platform capabilities (DPCs) and competitive advantage (CA).

2.5. The Impact of Competitive Advantage on Overall Performance

According to Kiyabo and Isaga (2020), enterprises with a strong competitive edge can grow sales, profitability, and market share. The resource-based view holds that distinctive resources and competencies help a corporation outperform its competition. Falahat et al. (2020) found that competitive advantage drives international performance, which may be applied to overall performance measurements. A strong competitive advantage helps increase performance by differentiating products and services, reducing costs, and improving customer satisfaction. The literature also shows that organisations that use their competitive advantages to stay ahead are more likely to succeed. Therefore, we propose the following hypothesis.

H5: 

SMEs’ competitive advantage (CA) has a significantly positive impact on their overall performance.

2.6. The Mediating Role of Competitive Advantage

Research shows that BME promotes innovation and adaptation, but competitive advantage improves performance indicators. Kiyabo and Isaga (2020) note that BME enterprises are more likely to build distinctive capabilities that give them competitive advantages and boost performance. Falahat et al. (2020) found that competitive advantage helps BME improve performance. BME’s iterative nature lets organisations adjust their plans depending on market input, improving operational efficiency and consumer happiness. As organisations innovate their business models, they generate customer-friendly value propositions, strengthening their competitive position and boosting performance. On the other hand, Liao et al. (2024) claim that enterprises with excellent digital platform skills can leverage their competitive advantages to function well due to the agility and reactivity of digital platforms. Cenamor et al. (2019) show that DPC streamlines processes and informs strategic decisions, improving competitive posture. According to the resource-based concept, unique skills create sustainable competitive advantages that drive performance. Falahat et al. (2020) found that organisations that exploit their digital capabilities perform better. In the light of Rua et al. (2018) and Falahat et al. (2020), research senses a research gap to validate the mediating role of competitive advantage between the firm’s capabilities (digital platform capacities and business model experimentation) and performance. Hence, we propose the following hypothesis.

H6: 

Competitive advantage (CA) positively mediates the relationship between business model experimentation (BME) and SME performance (P).

H7: 

Competitive advantage (CA) positively mediates the relationship between digital platform capabilities (DPCs) and SME performance (P).

3. Research Methodology

This study aims to explore the relationship among diverse concepts belonging to a diverse theoretical background. Hence, the researchers adopted the second-generation multivariate statistical tool “structural equation modelling (SEM)” using SmartPLS (version 4).

Data collection and the sample: To test the proposed hypotheses, we conducted a survey targeting art and craft-based SMEs in India. Furthermore, our survey included only small and medium-sized enterprises (SMEs) that changed their business model because of the digitisation process in the last 24 months. Only respondents who answered the second question in the affirmative were included in our sample. The questionnaire was reviewed and tested by reading it out loud to executives and academics to improve the clarity of the questions. We collected the data in 2024. We conducted a survey at a trade fair organised in India’s National Capital Region (NCR). However, the respondents were contacted in person at the tradeshow. The benefit of contacting art and craft-based tradeshow exhibitors was that the research team obtained precise contact information on art and craft-based businesses from all parts of India. Detailed descriptive statistics are available in the annexture 1 (Refer Supplementary Material). The study sample consisted of 211 art and craft-based businesses. A detailed breakdown of business characteristics is presented in annexture 1. Regarding ownership structure, 57.8% of the businesses were operated as family-run enterprises. In comparison, the remaining 42.2% were part of Self-Help Groups (SHGs) or cooperative associations. This indicates a strong presence of both individual and collective models of business ownership in the sector. In terms of scale, measured by the number of artisans associated with each business, 44.6% of the businesses employed fewer than 10 artisans, while 37.0% had between 11 and 50 artisans, and 18.5% engaged more than 50 artisans. These figures suggest a dominance of small-scale operations, though a significant number of medium-sized and large artisan groups were also represented. The sample included a diverse range of product categories. The most prominent category was fashion jewellery, accounting for 18.9% of the sample, followed by festive decoration, gift items (14.2%), and lamp, light, and lawn accessories (13.3%). Other categories—such as house furniture (12.3%); candles, potpourri, and aromatics (11.8%); and carpets and rugs (9.5%)—also had considerable representation. A small portion of the businesses focused on bathroom accessories (7.1%), while 12.8% engaged in miscellaneous or less commonly found product types. As for the age of the business operations, the majority of the businesses (36.0%) had been active for 2 to 5 years, followed by 27.0% operating for 5 to 10 years, and 18.9% being relatively new with less than 2 years of experience. A further 18.0% of the sample represented long-established businesses operating for over 10 years, indicating a mix of emerging and experienced ventures in the craft-based sector. This descriptive overview highlights the heterogeneity of ownership patterns, scales, product diversification, and business maturity within the art and craft-based entrepreneurial ecosystem.

Measurement Scale: All variables examined under the model were measured based on the standard scales available in the existing literature: digital platform capability (Cenamor et al., 2019), firm performance and business model experimentation (Bouwman et al., 2019), and competitive advantage (Falahat et al., 2020). All variables were measured using a 7-point scale (1—Strongly Disagree to 7—Strongly Agree). A detailed list of measurement scales is available in

Table 1. Measurement Scale.

Variable and Scale Source	Measurement Scale
Firm Performance (Bouwman et al., 2019)	We are pleased with our business’s overall performance
	The enterprise’s revenue growth
	The enterprise’s profit growth
	Market Share
	Speed to Market
	Penetration Rate
	Market Value
	Net Income
	Return on Investment (ROI)
Business Model Experimentation (Bouwman et al., 2019)	Business model innovation required
	Changing the entire business model
	Changing only some components of the business model
	Changing the product/service offering before changing the business model
	Modify the product/service offering before altering the business model
	Changes to the business model and product/service offering occur concurrently
	Trying out new BMs in practice first before making final changes
	In-depth analyses before starting to change the business model
Competitive Advantage (Falahat et al., 2020)	Please rate your company’s competitive advantages relative to your primary competitors
	Our cost
	Our selling price
	Product quality
	Uniqueness in terms of packaging/branding/product design
	Make/modify products according to customer requirements/needs
	Product accessibility
	Technical support and after-sales service
	Delivery speed and reliability
	End-customer rating of service quality
Digital Platform Capabilities (Cenamor et al., 2019)	Please rate on a scale of 1 (strongly disagree) to 7 (strongly agree)
	Our platform enables seamless integration of our partners and IT systems (e.g., forecasting, production, manufacturing, shipment, etc.)
	Our software automatically aggregates pertinent data from our partners’ databases (e.g., operating information, business customer performance, cost information, etc.)
	Our platform is built on a modular software architecture, most of which may be reused in other business applications.
	Our platform is easily extensible to accept additional information technology applications, functions, or partners.

.

4. Results

The model in the research is exploratory, and to achieve the goal, the study adopts a partial least square for hypothesis testing (Hair et al., 2011) using SmartPLS. The researchers collected data from 211 handicraft-based business entities utilising a mix of online and offline surveys. As the study is exploratory in nature, the researchers identified PLS-based structural equation modelling as the best-suited second-generation multivariate statistical tool to test the proposed hypothesis. Adhering to all protocols related to PLS-SEM, the researchers confirmed content validity and reliability using two sets of data analysis PLS algorithm-based measurement model evaluation and structural model evaluation using a 5000-subsample bootstrapping procedure. However, the researchers acknowledge that the study is based on cross-sectional data, and testing whether the analysis is free from common-method bias is very much needed. The researchers followed the recommendation of Kock (2015) that suggests checking whether the VIF score (refer to Annex 1 for the Inner VIF Scores) is less than 5. After conducting this test, we found that the data are free from common-method bias. Further, the researchers checked the predictive relevance of the model and performed PLSpredict/CVPAT and found the Q square scores (refer to Annex 1) are more than 0, establishing the predictive relevance of the examined model.

4.1. Measurement Model Evaluation

For this study, the researchers first analysed the measurement model using the PLS algorithm results and found the measured model to be sufficient in order to proceed with further analysis. The key measurement model indicators are convergent validity, assessed using outer loading; average variance extracted (AVE); and internal consistency reliability using composite reliability and Cronbach’s alpha. The last test is related to discriminant validity using HTMT scores. All of the scores were measured well under the prescribed limits (refer to

Table 2. Measurement Model Evaluation Statistics Score.

			Construct Reliability and Validity				Discriminant Validity
Variable	Item Code	Outer Loading	Cronbach’s Alpha	rho_A	Composite Reliability	Average Variance Extracted (AVE)	HTMT Less than 1
Business Model Experimentation (BME)	BME_1	0.864	0.951	0.955	0.959	0.769	YES
	BME_2	0.835
	BME_3	0.904
	BME_4	0.838
	BME_5	0.871
	BME_6	0.909
	BME_7	0.914
Competitive Advantage (CA)	CA_1	0.911	0.958	0.96	0.964	0.751	YES
	CA_2	0.892
	CA_3	0.896
	CA_4	0.868
	CA_5	0.857
	CA_6	0.777
	CA_7	0.833
	CA_8	0.869
	CA_9	0.888
Firm Performance (FP)	FP_1	0.648	0.908	0.933	0.915	0.548	YES
	FP_2	0.656
	FP_3	0.695
	FP_4	0.693
	FP_5	0.708
	FP_6	0.732
	FP_7	0.846
	FP_8	0.805
	FP_9	0.847
Digital Platform Capabilities (DPCs)	DPC_1	0.882	0.907	0.907	0.935	0.781	YES
	DPC_2	0.867
	DPC_3	0.901
	DPC_4	0.885

); after this measurement model evaluation, the researchers considered the model to be adequate for structural analysis.

4.2. Structural Model Evaluation

For structural model assessment, the researchers followed the first four steps suggested by (Hair et al., 2011). These steps are as follows: Step 1—“Assessment of structural model for collinearity”; Step 2—“Assessment of significance and relevance of structural model relationship”; Step 3—“Assessment of the level of R²”; and Step 4—“Assessment of the f² effect size”. Detailed structural model evaluation and hypothesis testing statistics scores are exhibited in

Table 3. Structural Model Evaluation and Direct Hypothesis Testing.

Hypothesis	Path	Path Coefficient	R Square	f Square	T Statistics	p-Value	Inference
H1	Digital Platform Capabilities (BPCs) -> Business Model Experimentation (BME)	0.663		0.786	14.57	0.000	Accepted
H2	Digital Platform Capabilities (DPCs) -> Competitive Advantage (CA)	0.381		0.134	4.504	0.000	Accepted
H3	Business Model Experimentation (BME) -> Competitive Advantage (CA)	0.309		0.089	3.588	0.000	Accepted
H5	Competitive Advantage (CA) -> Firm Performance (FP)	0.734		1.171	16.421	0.000	Accepted
Dependent Variables in the Model	Business Model Experimentation (BME)		0.437
	Competitive Advantage (CA)		0.391
	Firm Performance (FP)		0.537

. Based on the supporting measurement and structural analysis results, the researchers used the bootstrapping results to test the direct and indirect (mediating effect) hypothesis—see Table 3 for immediate hypothesis results and

Table 4. Mediating Effect Testing.

		Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	T Statistics (|O/STDEV|)	p-Values	Inference
H4	Digital Platform Capabilities (DPCs) -> Business Model Experimentation (BME) -> Competitive Advantage (CA)	0.205	0.207	0.06	3.406	0.001	Accepted
H6	Business Model Experimentation (BME) -> Competitive Advantage (CA) -> SME Performance (P)	0.227	0.23	0.067	3.375	0.001	Accepted
H7	Digital Platform Capabilities (DPCs) -> Competitive Advantage (CA) -> SME Performance (P)	0.279	0.278	0.062	4.484	0.000	Accepted

for mediating effect testing. The bootstrapped results in Table 3 and Table 4 indicate that all the hypotheses have a p-value of 0.000, indicating that all of the proposed hypotheses were accepted.

5. Discussion

The existing literature reports that adopting digital technologies and platforms has become a priority for several SMEs (Chen et al., 2024). However, there is a limited understanding of the implications of adopting digital technologies and platforms (Frishammar et al., 2018). Moreover, only adopting digital technologies is insufficient unless adequate digital technology capabilities are developed (Helfat & Raubitschek, 2018; Teece, 2018). This study fills this research gap by examining the relationship between digital platform capabilities, digital transformation, and SME performance. The results reveal that digital platform capability significantly positively impacts firm performance antecedents. These findings corroborate the findings of Cenamor et al. (2019) and re-emphasise the claim that digital platform capabilities must be aligned with orientations and firm-specific capabilities. Previous studies have focused on firm capabilities and firm performance (Cenamor et al., 2019; Helfat & Raubitschek, 2018; Teece, 2018) and posited the need to examine the potential mediating effects. To bridge this gap in the literature, this study also examined the impact of digital platform capabilities on business model experimentation and found a positive mediating role. This finding creates a scholarly debate and posits a contrasting result to Bouwman et al. (2019), in which the authors found a partial mediation relationship between resources for business model experimentation and firm performance. In this paper, we argue that the capabilities of digital platforms can be an antecedent for business model experimentation, which can lead to innovation pursuits and ultimately to improve the firm performance.

This paper theoretically contributes to a better understanding of the effect of digital platform capabilities on business model experimentation and their combined effect on the competitive advantage of the firm. The firm’s competitive advantage, ultimately leading to improved firm performance. This study implies that the capability of digital platforms also plays a significant role in business model innovation through rapid and lean experimentation. Here, it becomes essential to understand the mediating role of business model experimentation in digital platform capability. Business model experimentation plays a positive mediating role in digital platform capability. In previous studies by Bouwman et al. (2019), business model experimentation also positively influenced firm performance. This is one of the significant contributions of this study to the exploration of the factors underpinning business model experimentation and firm performance. However, it is essential to recall that digital platform capability positively leads to a competitive advantage. It is encouraging to report that competitive advantage leads to firm performance and significantly mediates the impact of digital platform capabilities and business model experimentation.

By integrating the existing literature and robust data analysis, this work provides a framework with which to address the new layer of complexity introduced by the digital transformation of cultural (art and crafts) heritage organisations. As a unit of analysis, the business model provides an insight into how businesses create, deliver, and capture value. These processes are affected by digital transformation, which involves a change in the architecture of processes, products, services, and information flows, taking into account the new role of the customer in engagement and satisfaction, the role of multiple players, and the integrated benefits.

The authors developed a framework for a digital business model using a well-known conceptualisation and based on selected digital business model constructs from an operational point of view. It has been found that business model experimentation and competitive advantage work together to mediate the relationship between digital platform capabilities and company performance, making it possible for these heritage arts and crafts to compete in a different way in the digital age.

Digital technologies enable numerous players to collaborate on integrating their data, information, and content creation skills, producing a competitive advantage. They form a new partnership engine based on resource integration rather than collaboration. Digital technologies’ pervasiveness, inherent dematerialization, and collaboration make this very promising. The concept applies to the enterprises’ new resource structure. By integrating different actors’ resources, resources become meta-informational and constantly replenished. Thus, the actors’ resource integration and data, information, and content development are crucial to enterprises’ competitive advantage.

However, the digital transformation of these arts and crafts organisations does not stop at digitising the offer; it must also facilitate the seamless transfer between and among channels, both online and offline, with a wider collective dimension. This is not just a channel change, but a vast network of interactions and touchpoints where the company and the consumer are not the only players. Numerous actors are involved in these processes and can influence the brainstorming process. Finally, the business model must be re-engineered with a new ecosystem and a collective perspective to ensure that all value network activities are informed. The multiplicity of actors to be considered may contribute to a competitive edge. Through economic and social capital, businesses can create and distribute value beyond the typical consumer. Technology facilitates mutual interaction, the basis for long-term improved corporate performance.

6. Conclusions, Limitations, and Future Research Direction

The findings of this study are based on a unique sample (heritage art and craft-based SMEs) context that is considered to be traditional and has less interaction with modern technologies. The results indicate that a firm’s digital platform capabilities significantly influence its digital transformation and overall performance. However, this relationship is not linear, as prior researchers have highlighted the need to investigate the factors underpinning the relationship between digital platform capabilities and firm performance. In practice, digital platform capabilities impact a firm’s business model experimentation, temperament, and competitive advantage. This study has made a significant theoretical contribution by examining the mediating role of business model experimentation on a firm’s competitive advantage. This posits an inference of the managerial implication that to make a firm’s competitive advantage robust and agile, one needs to promote the practice of business model experimentation. To envision competing values, firms need to exercise business model experimentation; this is possible if the firm’s digital platform capabilities are robust. Investing resources in in-house digital platform development or prudently scouting the right vendor is essential to achieve the posited model’s outcomes. As the study concludes that a robust competitive advantage achieved by a firm leads to holistic performance, it is essential to note that the indirect effect of digital platform capabilities and enabling business model experimentations is also significant.

However, this study has rightly investigated the factors underpinning SMEs’ digital transformation-led performance; future researchers may extend the model by introducing other factors with moderating or mediating roles (firm, manager, and entrepreneur characteristics or micro-/macro-economic factors). The study’s major limitation is its sample design, as this was based on cross-sectional data, meaning the data captured represent the situation at one point in time. This limits the inference of causality among the variables. Future researchers may explore the possibilities of designing the same study using a longitudinal data collection mode and utilising multi-group analysis of the two time intervals. The current study investigated only the causality with structural equation modelling; in further research, one may explore the importance/performance role of individual variables and associated measurement items by utilising advanced PLS-SEM techniques like Importance–Performance Map Analysis.

Overall, this study provides a novel approach to examining the antecedents of firm performance in light of new technological interventions in order to make firms agile with strong digital platform capabilities and business model experimentation.