Abstract
Purpose: This study examines when and how green entrepreneurial orientation (GEO) influences business model innovation (BMI) in start-ups, focusing on boundary-spanning search (BSS) as a conversion mechanism and big data capability (BDC) as a boundary condition.
Design/methodology/approach: Grounded in resource-based theory and organisational search theory, the research employs an empirical approach using survey data collected from 307 start-ups. The study examines the mediating effect of BSS and the moderating role of BDC through quantitative analysis.
Findings/results: The analysis reveals three key findings: (1) GEO has a positive impact on BMI. (2) Boundary-spanning search mediates the relationship between GEO and BMI. (3) Big data capability positively moderates the link between BSS and BMI.
Practical implications: For start-ups, the results imply that ‘going green’ is more likely to lead to BMI when firms design a focused external-search portfolio and build minimum viable data capabilities (e.g. data governance, cross-functional information sharing and decision-linked analytics) to reduce information overload and accelerate experimentation.
Originality/value: The study advances an orientation–conversion perspective by explaining heterogeneous BMI outcomes amongst green-oriented ventures and highlighting the contingent value of BSS. The findings are particularly informative for start-ups in emerging-market contexts (including South Africa and many African economies), where resource constraints and uneven digital infrastructure can make the conversion of sustainability intent into a scalable business model change highly contingent.
Keywords: green entrepreneurship orientation; boundary-spanning search; business model innovation; big data capability; start-ups.
Introduction
Business model innovation (BMI) is critical for start-ups competing in turbulent digital markets, as it reconfigures value creation, delivery and capture to sustain growth (Khan et al., 2024). Green entrepreneurial orientation (GEO) has been widely discussed as a potential catalyst for BMI, as it integrates economic objectives with environmental integrity and social equity (Khan et al., 2023). This orientation emphasises balancing ecological sustainability and economic development across production, marketing and product design, thereby shaping start-ups’ competitive positioning (Jiang et al., 2018). Yet the outcomes of green entrepreneurship are far from uniform: some start-ups translate a green mindset into substantive business model change, whereas others adopt green practices without innovating their underlying business models. This study, therefore, asks when and how GEO is converted into BMI. The question is especially salient in emerging-market contexts – such as South Africa and many African economies – where tighter resource constraints, institutional complexity and uneven digital infrastructure can make the conversion of green intent into business model change far from automatic.
Business model innovation refers to firms’ efforts to reconfigure value creation, delivery and capture in response to environmental change and is widely recognised as a key source of competitive advantage (Ancillai et al., 2023). Prior research explains BMI from multiple angles, including inter-organisational relationships, managerial characteristics and firm resources (Anand et al., 2021; Neumeyer & Santos, 2017). Drawing on the resource-based view (RBV), GEO can be understood as an intangible strategic resource that shapes how start-ups allocate attention and resources to green opportunities. However, evidence on whether GEO consistently promotes innovation outcomes remains mixed. On the one hand, GEO may strengthen collaboration with external stakeholders and facilitate knowledge exchange that supports business model redesign (Arfi et al., 2018). On the other hand, green initiatives can require substantial investments in capital, technology and human resources, potentially crowding out experimentation and slowing business model change in resource-constrained start-ups (Jiang et al., 2018). This opportunity–burden tension suggests that GEO may be necessary but insufficient for BMI, making it important to identify the mechanisms and boundary conditions through which GEO is converted into BMI. Importantly, empirical evidence from emerging markets – particularly African contexts such as South Africa, where institutional complexity and uneven access to resources and digital infrastructure can shape innovation processes – remains comparatively limited, constraining our understanding of when and why similar green orientations yield different BMI outcomes.
Organisational search theory suggests that under environmental uncertainty and resource constraints, firms must look beyond existing routines to acquire new information and knowledge to adapt their strategies and structures (Xie et al., 2022). Complementing the RBV, boundary-spanning search (BSS) captures such outward-looking behaviour by which firms acquire and recombine heterogeneous knowledge – such as technologies, practices and managerial expertise – across organisational or industry boundaries (Stuart & Podolny, 1996). This is particularly relevant for start-ups because entrepreneurial orientations often generate rapidly evolving resource demands that cannot be met internally, making external search an important route to access complementary resources and capabilities (Evans et al., 2017). In emerging-market contexts, including South Africa and many African economies, external knowledge is often fragmented across universities, industry bodies, large incumbents and regulators, reflecting institutional complexity and uneven infrastructure – conditions that can heighten both the value and the difficulty of BSS. Because business models span partners, activities and value exchanges, GEO-driven BSS can enable the recombination of external knowledge needed for BMI; however, such a search is not costless and may create information overload and integration challenges for resource-constrained start-ups.
Furthermore, as open systems, start-ups operate under external complexity and uncertainty, which makes it insufficient to merely acquire resources; they must also integrate and coordinate heterogeneous inputs to realise BMI (Carnes et al., 2017). Big data capability (BDC) represents an information-processing and reconfiguration capability that enables firms to capture, integrate and analyse data and externally sourced knowledge for evidence-based decision-making (Jenkinson et al., 2024). When BSS generates large volumes of diverse information, BDC helps filter noisy signals, prioritise relevant insights and reduce information overload, thereby strengthening the conversion of external knowledge into coherent business model design choices. This logic is particularly salient in emerging-market contexts, including South Africa and many African economies, where information is often fragmented across actors and data availability and quality can be uneven. Building on these arguments, we develop a conceptual model linking GEO, BSS and BMI, with BDC as a key boundary condition. We test the model using survey data from 307 start-ups.
This study makes three theoretical contributions to research on green entrepreneurship and BMI. Firstly, rather than treating GEO as a universally beneficial antecedent, we theorise and test an orientation–conversion view that helps explain heterogeneous BMI outcomes amongst green-oriented start-ups: GEO can be a valuable strategic resource, yet its benefits are contingent on whether firms mobilise complementary knowledge and capabilities to redesign value creation and capture. Secondly, we identify BSS as a central behavioural mechanism through which GEO is converted into BMI. By foregrounding the benefits–cost tension of external search (access to non-redundant knowledge versus attention and integration burdens), our model clarifies why ‘being green’ does not automatically translate into business model change. Thirdly, we conceptualise BDC as a conversion-enabling boundary condition that strengthens the transformation of externally sourced, heterogeneous information into coherent business model design choices. These insights are especially relevant for start-ups in emerging-market contexts – such as South Africa and many African economies – where external knowledge is often fragmented and digital capabilities are uneven, making the effectiveness of BSS highly dependent on firms’ information-processing capacity.
The remainder of the article is organised as follows: Section 2 develops the theoretical background and research hypotheses. Section 3 describes the research design, including sample selection, data collection and variable measurement. Section 4 presents the empirical results, including reliability and validity tests, correlation analysis and hypothesis testing. Section 5 discusses the findings and outlines theoretical and managerial implications, limitations and directions for future research.
Theoretical background and hypotheses
Drawing on the RBV and organisational search theory, we conceptualise GEO as an intangible strategic resource that shapes how start-ups identify environmental opportunities and commit to experimentation, whilst BSS captures the behavioural mechanism through which firms access and recombine heterogeneous external knowledge for BMI. Importantly, search is not costless: broad, heterogeneous search can create attention constraints, integration difficulties and information overload. We therefore introduce BDC as an organisational information-processing and reconfiguration capability that strengthens the conversion of externally sourced knowledge into actionable business model design choices, which is particularly salient for resource-constrained start-ups operating in emerging-market contexts. The value of such search depends not only on whether firms look beyond their boundaries, but also on whether they possess the digital capabilities to process and recombine heterogeneous information into coherent business model design choices.
Green entrepreneurship orientation and business model innovation
Green entrepreneurship involves identifying and capitalising on economic opportunities arising from environmental market failures (Dean & McMullen, 2007). It requires a proactive, risk-taking approach to promoting green products or services (Awuni et al., 2025), reflected in practical activities through a green entrepreneurial mindset (Jiang et al., 2018). This orientation aims for a balance between economic, environmental and social development, treating environmental factors as business opportunities. Business model innovation, a source of competitive advantage, modifies existing business models using new technologies and innovative thinking, enhancing efficiency through complementarity (Amit & Zott, 2012).
From an RBV perspective, GEO can be viewed as a strategic, difficult-to-imitate organisational resource that guides how firms allocate attention and resources toward environmental opportunities. However, GEO does not automatically translate into BMI unless it triggers changes in how firms design value creation and value capture. Specifically, GEO can foster: (1) opportunity framing (treating environmental constraints as market opportunities), (2) experimentation and risk acceptance, and (3) stakeholder-oriented value propositions, all of which are central to reconfiguring business model elements.
Green entrepreneurial orientation, marked by innovation, foresight and risk-taking (Jiang et al., 2018), provides a foundation for sustainable business development. On the one hand, companies that embrace a GEO demonstrate a spirit of continuous innovation, integrating green innovation into their company culture and strategy. For instance, Pacheco et al. (2010) suggest that companies introducing green products, services, or technologies to meet customer needs are more likely to explore and capture new markets by adopting a proactive approach to pursuing green growth. Fernando and Wah (2017) emphasise that green entrepreneurship considers the needs of nature, the economy and society, prioritising environmental protection and efficiency whilst engaging in green innovation practices for sustainable development. On the other hand, green entrepreneurs are also willing to undertake riskier ventures and pursue more business opportunities compared to their counterparts in traditional sectors. Chen and Chang (2013) argue that a GEO places higher ecological demands on companies, encouraging the search for low-pollution, low-waste approaches to conducting activities and creating green products, processes and services to address environmental challenges. Building on this, Shirokova et al. (2016) suggest that green entrepreneurship-oriented companies are more receptive to risk-taking strategies and maintain an optimistic outlook even when investing in uncertain projects. Alwakid et al. (2021) propose that the green entrepreneurship sector values eco-efficiency, enabling companies to pursue both economic and ecological impacts, leading to BMI. In resource-constrained start-ups, such orientation is especially relevant because BMI often represents a ‘leapfrogging’ route to compete without relying on scale advantages. Therefore, we propose the following hypothesis:
H1: Green entrepreneurial orientation has a positive impact on business model innovation.
Boundary-spanning search and business model innovation
The mismatch between internal resources and external needs can hinder a company’s development, preventing it from maintaining a competitive advantage if it relies solely on its capabilities. However, the development of digital technology has helped companies open their organisational boundaries and obtain resources from outside sources (Yang et al., 2021b). Rosenkopf and Nerkar (2001) first introduced the concept of BSS, which is a process through which companies search for heterogeneous knowledge across existing organisational boundaries to solve innovation problems. From a strategic management perspective, BSS is defined as the process of capturing heterogeneous knowledge from external networks and reconstructing existing knowledge systems to overcome path dependency in a dynamic environment. In this context, companies must navigate the challenges of technological innovation and market evolution.
Organisational search theory suggests that when firms face uncertainty and novelty demands, they engage in search beyond local knowledge domains to access non-redundant ideas (Kruger & Steyn, 2024). For BMI, such a search is particularly important because business model design requires recombining technological, market, regulatory and stakeholder knowledge that often resides outside the focal firm. At the same time, BSS entails costs, and the benefits of search depend on the firm’s ability to absorb and recombine what it finds. This benefit–cost tension motivates our later focus on BDC as a key boundary condition.
In turbulent environments, firms confront uncertain development landscapes where their growth potential is severely tested. Relying solely on existing knowledge hinders effective responses to external changes. Boundary-spanning search enables companies to acquire commercially valuable knowledge, enhancing their ability to learn from external sources and becoming a vital competitive advantage (Sidhu et al., 2007). Corredoira and Banerjee (2015) confirm that BSSs foster innovative development by increasing technology patent output. Tippmann et al. (2017) argue that BSS offers innovative solutions for optimising internal resources, serving as a bridge between knowledge transfer and inventive solutions. By crossing organisational boundaries, companies can expedite business model design, access external knowledge, explore environmental opportunities and develop new network values and social relationships (Lee & Huh, 2016). Schweisfurth and Raasch (2018) emphasise the pivotal role of both internal and external knowledge resources, as well as internal and external market opportunities, in a firm’s innovation process. Firms should proactively search for and integrate external resources to accelerate innovation through knowledge exchange. Consequently, BSS becomes a strategic choice for enterprises to gain a competitive edge, enhancing their ability to detect and understand the external environment, compensating for resource deficiencies and fostering BMI. Therefore, we propose the following hypothesis:
H2: Boundary-spanning search has a positive impact on business model innovation.
Green entrepreneurial orientation and boundary-spanning search
Rooted in organisational search theory, BSS is a vital approach for companies to acquire external knowledge and enhance their existing organisational knowledge structure (Leone et al., 2022). Resource-based theory posits that an organisation’s competitive advantage stems from its possession of diverse resources and relationships (Varadarajan, 2023). In the context of green entrepreneurship, companies need varied knowledge resources to develop eco-friendly products and services. Given the focus on green entrepreneurship, internal resources often fall short of these demands. Hence, companies employ BSS to connect with key resource owners, compensating for deficiencies and enhancing operational efficiency (Schierjott et al., 2018). Limited internal resources in SMEs hinder the adaptation to changing technology and market demands for green start-ups. Hence, exploring beyond technology or market boundaries is essential to find valuable external resources (Shafique et al., 2021). Boundary-spanning integration allows the incorporation of external information and user groups, fostering synergies for market expansion and product development. With the influence of Internet technology, boundary-spanning integration has become crucial for corporate growth.
Beyond ‘resource shortage’, GEO also shapes the motivation and scope of search. Specifically, GEO sensitises firms to environmental opportunities and stakeholder or regulatory expectations, increasing the likelihood of problem-driven and opportunity-driven search across organisational boundaries. Because green opportunities often span technologies, supply chains, community actors and regulatory domains, GEO-oriented start-ups have stronger incentives to seek heterogeneous partners and knowledge sources outside their current network.
Additionally, in response to potential green business opportunities, green entrepreneurship-oriented companies tend to act faster than their competitors to gain a competitive edge, with resources being a key element in gaining that advantage. Green entrepreneurial orientation represents an innovative and pioneering behaviour that prioritises the acquisition of complementary or alternative knowledge from external sources. These diverse and heterogeneous resources can support business growth (Sofka & Grimpe, 2010). Singh et al. (2022) suggest that green entrepreneurship is more likely to seize green business opportunities and requires a keen awareness of the external environment and information exchange between different organisations, effectively enhancing businesses’ adaptive capacity. Building upon the work of Makhloufi et al. (2022), GEO is believed to have a behavioural tendency that influences firms’ access to resources from external sources. Therefore, we propose the following hypothesis:
H3: Green entrepreneurial orientation has a positive impact on boundary-spanning search.
The mediating effect of boundary-spanning search
Compared to traditional enterprises, green entrepreneurship faces more pronounced resource bottleneck problems. As companies increasingly rely on external resources and foster closer collaborative relationships with innovation agents, the ability to access information becomes a crucial factor in determining the success or failure of an enterprise’s innovation. Thus, intensifying market exploration and actively assimilating and exploiting potential external market opportunities provide companies with a sustainable impetus for innovating their business models. Wu and Shanley (2009) argue that a GEO possesses both social and environmental attributes, enabling companies to discover new combinations of knowledge, thereby providing a valuable knowledge base for green innovation and facilitating the creation of green value. Inkinen (2016) affirms that a GEO helps companies clarify their direction and search strategies, facilitating the acquisition of new knowledge and ultimately improving innovation performance. Moreover, a GEO drives firms to seek environmentally relevant knowledge and resources (e.g. environmental standards, green technologies, etc.) that enhance their understanding of government regulations for the environment and consumer demand for green products, thereby enhancing their ability to innovate green business models. Thus, green entrepreneurship orientation motivates firms to leverage external knowledge and novel technologies to innovate business models that generate value for the firm.
Conceptually, GEO reflects an orientation and commitment, but it does not guarantee that a start-up already possesses the complementary knowledge required for BMI. Boundary-spanning search, therefore, serves as the central mechanism that converts GEO into new resource combinations: by sourcing heterogeneous external knowledge, firms can redesign value propositions and reconfigure value creation and capture activities. This mechanism also helps explain why some green-oriented start-ups successfully innovate their business models whilst others fail – differences in search intensity and effectiveness translate the same orientation into different innovation outcomes. Therefore, we propose the following hypothesis:
H4: Boundary-spanning search plays a mediating role between green entrepreneurial orientation and business model innovation.
The moderating effects of big data capabilities
Big data refers to extensive volumes of structured and unstructured data accessible in real-time (Ferraris et al., 2019). Its true business value emerges when organisations elevate big data resources to the level of big data capabilities (Wang et al., 2018). Hence, accurately identifying significant data resources becomes crucial. These identified resources can then undergo in-depth mining and analysis, ultimately facilitating BMI. Big data capability encompasses processing, analysing and visualising digital resources. This empowers organisations to create data-driven plans, make informed decisions and conduct business operations (Dubey et al., 2019).
Consistent with an information-processing view, BDC reflects an organisational capability system that enables firms to acquire, integrate, analyse and transform data into actionable insights for decision-making and reconfiguration. This capability is especially important when BSS generates large volumes of heterogeneous information, which can otherwise overwhelm managerial attention and slow down integration.
Compared to traditional information management and analysis, big data capabilities open new horizons for companies. They facilitate the use of big data analytics to tap into a broader array of external resources, enabling companies to offer high-value products and services and transform their business models. Barrett et al. (2015) argue that big data capabilities accelerate the process of knowledge awareness, uptake and utilisation, playing a vital role in integrating external knowledge and designing business models. Similar views are expressed by Santoro et al. (2019), who emphasise that the retail industry can undergo business model shifts by leveraging big data or employing innovative methods to access data, information and knowledge. Sorescu (2017) underscores that big data capabilities capture changes in external market conditions, providing real-time insights for businesses. Mikalef et al. (2019) suggest that big data resources and technologies help companies acquire new market knowledge, aiding the development of value propositions that enhance customer experience and significantly contribute to BMI. Consequently, BDC has become a crucial strategic resource. It enables enterprises to identify, integrate and utilise external resources effectively, discover new market opportunities, transform operational processes and drive BMI. In other words, when BDC is high, firms are better able to filter noisy signals, identify valuable patterns across external sources and recombine knowledge into coherent design choices for BMI. Therefore, the positive effect of BSS on BMI should be stronger for firms with higher BDC. Therefore, we propose the following hypothesis:
H5: Big data capabilities positively moderate the impact of boundary-spanning search on business model innovation.
Combining the above analysis, the conceptual model of this study is proposed, as shown in Figure 1.
Research design
Sample and data collection
This study employs a questionnaire survey method to collect data on GEO, BSS, BDC and BMI (Appendix 1). To test the hypotheses, start-ups from four regions in China – Shandong Province, Jiangsu Province, Fujian Province and Zhejiang Province – were selected. The primary respondents were founders, co-founders and key members of entrepreneurial teams. Drawing on Baum et al. (2011), firms less than 4 years old were defined as start-ups and included in the sample. Data collection was conducted from January 2022 to June 2022, primarily using online survey platforms and on-site distribution of paper questionnaires. To reduce common method bias, participation was voluntary and anonymous; respondents were assured there were no right or wrong answers; items were mixed and clearly separated by construct, and established; clearly worded scales were used and adapted to the start-up context.
A total of 483 questionnaires were distributed, resulting in the collection of 376 questionnaires. After eliminating incomplete questionnaires and those with evident errors in responses, 307 valid questionnaires remained, achieving a valid recovery rate of 63.56%. The descriptive statistics of the sample data are presented in Table 1.
| TABLE 1: Sample distribution characteristics. |
Measurement of variables
The research model primarily encompasses four variables: GEO, BSS, BMI and BDC. To ensure measurement validity, this study adopts established scales from prior international studies and adapts them to the start-up context. A 5-point Likert scale is employed for data collection:
Independent variable: GEO. Green entrepreneurial orientation was measured using a five-item scale developed by Jiang et al. (2018). A sample item is: ‘Our firm places strong emphasis on green practices such as green R&D, green technology leadership, and green innovation’. Mediating variable: BSS. Boundary-spanning search was measured based on the scale developed by Laursen and Salter (2006). Following prior studies, the original items were adapted and refined through field interviews to better reflect the start-up context, resulting in six items capturing key external knowledge sources relevant to innovation. Respondents were then asked to identify the primary channels used for searching external knowledge. Dependent variable: BMI. Business model innovation is the creation or enhancement of a firm’s competitive advantage to achieve differentiation and growth. Based on Zott and Amit (2007)’s research, this study is designed to respond to the results of BMI in start-ups by scoring high or low on the novelty-based business model design theme, with nine questions. Moderating variable: BDC. Big data capability was measured using a seven-item scale adapted from Wamba et al. (2017) and Ringel and Skiera (2016), with wording adjusted to reflect the start-up context. A sample item is: ‘Our firm continuously explores strategic opportunities for the application of big data analytics’. Control variables: Following prior studies, firm age, industry sector, asset size, entrepreneur gender, entrepreneur age and educational background were included as control variables.
Research results
Common method bias
Common method bias was assessed using Harman’s one-factor test, yielding a Kaiser–Meyer–Olkin (KMO) value of 0.881. The approximate chi-square value was 2924.333, with 351 degrees of freedom (df) and a significance level of 0.000. The first common factor extracted in the unrotated state accounted for 24.724% of the total variance, which falls below the 50% threshold. This result suggests that common method bias is unlikely to be a serious concern in this study.
Reliability and validity tests
The reliability test results for the four variables, namely GEO, BSS, BDC and BMI, are presented in Table 2. The findings indicate that all four variables have Cronbach’s α coefficients greater than 0.8, and their composite reliability (CR) values exceed 0.8, indicating high reliability for each scale. Reliability and validity were assessed by examining convergent and discriminant validity. The factor loading values for all four variables were above 0.7, and the average variance extracted (AVE) ranged from 0.502 to 0.558, all surpassing the threshold of 0.5, indicating strong convergent validity. Furthermore, the square root of the AVE for each variable exceeded the correlation coefficients between variables, confirming the presence of discriminant validity.
| TABLE 2: Results of reliability and validity analysis. |
In addition to Harman’s one-factor test, we conducted a confirmatory factor analysis (CFA)-based model comparison to assess the potential severity of common method bias. As reported in Table 2, the hypothesised four-factor measurement model (GEO, BSS, BDC and BMI) showed a good fit (χ2 = 431.445, df = 318, χ2/df = 1.357, comparative fit index (CFI) = 0.958, Tucker-Lewis Index (TLI) = 0.953 and root mean square error of approximation (RMSEA) = 0.034), whereas a one-factor model in which all items loaded on a single factor fits the data poorly (χ2 = 1553.212, df = 324, χ2/df = 4.794, CFI = 0.540, TLI = 0.501 and RMSEA = 0.111). This substantial deterioration in fit suggests that common method variance is unlikely to be a serious concern in this study.
Correlation analysis
Descriptive statistics and inter-variable correlation analysis were conducted for each variable, and the results are presented in Table 3. Significant positive correlations were observed between GEO and BSS (r = 0.297, p < 0.01), and between GEO and BMI (r = 0.345, p < 0.01). Boundary-spanning search was also positively correlated with BMI (r = 0.240, p < 0.01). These correlation patterns are consistent with the proposed hypotheses and provide preliminary support for subsequent regression analyses. Multicollinearity was further assessed in the regression analysis stage, where all variance inflation factor (VIF) values were below the conservative threshold of three, indicating no serious multicollinearity.
| TABLE 3: Correlation coefficient matrix for the main study variables (N = 307). |
Hypothesis testing
Main and mediating effects
In this study, hierarchical regression analysis was employed to assess the main and mediating effects, with the results reported in Table 4. Firstly, the main effect was examined. M1 includes only control variables, whereas Model M2 adds GEO. The results show that GEO has a significant positive effect on BMI (β = 0.268, p < 0.001), thereby supporting H1.
Secondly, the mediating role of BSS in the relationship between GEO and BMI was examined. M3 explored the impact of BSS on BMI, revealing a significant positive effect (β = 0.229, p < 0.001), thus supporting H2. When compared to M2, the coefficient representing the effect of GEO on BMI in M4 decreased from 0.268 to 0.234. This reduction indicated that BSS partially mediated the relationship between GEO and BMI, thereby providing support for H4. Additionally, M6 represents a regression model with BSS as the dependent variable and only control variables included. M7 incorporated GEO into M6, revealing a significant positive effect of GEO on BSS (β = 0.238, p < 0.001), thus supporting H3.
To further validate the mediating role of BSS between GEO and BMI, the results of the bootstrap mediating effect test proposed by Hayes, using the process plug-in in SPSS, are presented in Table 5. The indirect effect of GEO on BMI via BSS is estimated at 0.034. The 95% confidence interval (0.003, 0.070) does not include 0, and the relative effect value is determined to be 12.74%. These findings further support the mediating role of BSS in the relationship between GEO and BMI, providing additional support for H4.
| TABLE 5: Bootstrap test results for mediation effects. |
Moderating effects
The moderating effect was examined using hierarchical regression analysis, as reported in Table 4. Building on M3, M5 incorporates BSS, big data capabilities and their interaction term into the regression analysis. The results show a positive and significant coefficient for the interaction term between BSS and big data capabilities (β = 0.502, p < 0.001). To further assess the robustness of this moderating effect, a bootstrap analysis was conducted, yielding an interaction effect of 0.4626 (p < 0.001), with the 95% confidence interval excluding zero (Lower = 0.193, Upper = 0.732). These results indicate a significant moderating effect of big data capabilities, providing support for H5.
Figure 2 illustrates the moderating effect of big data capabilities. The plot demonstrates that the slope is considerably steeper at high levels of big data capabilities than at low levels, indicating that the positive effect of BSS on BMI is strengthened when big data capabilities are high and weakened when they are low.
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FIGURE 2: The moderating effects of big data capabilities. |
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Robustness tests
To assess the robustness of the regression results, an alternative measurement approach was employed. Specifically, the robustness test involved re-measuring the dependent variable using an alternative operationalisation and re-estimating the regression models. In the main analysis, BMI was measured using the nine-item scale developed by Zott and Amit (2007). For the robustness test, this measure was modified following Guo et al. (2013) and adapted to the Chinese start-up context. Two items were excluded, and the remaining seven items were used to construct the alternative measure of BMI. The robustness test results, reported in Table 6, are consistent with the main findings, providing additional confidence in the stability of the results.
| TABLE 6: Robustness of test findings by replacing key variable measures. |
Discussion and conclusion
This study examines how GEO influences BMI by focusing on the roles of BSS and big data capabilities. Based on the analysis of survey data from 307 start-ups, three main conclusions can be drawn. Firstly, GEO emerges as a strategic organisational resource that is positively associated with BMI in start-ups. Secondly, BSS partially mediates the relationship between GEO and BMI. Specifically, GEO motivates start-ups to engage in BSS to access heterogeneous external knowledge and alleviate resource constraints, which in turn facilitates BMI. Thirdly, BDC positively moderates the effect of BSS on BMI. In particular, start-ups with stronger big data capabilities are better able to translate BSS activities into BMI outcomes.
Theoretical contributions
This study advances the literature on green entrepreneurship and BMI in three ways. Firstly, it extends green entrepreneurship research by shifting the focus from performance outcomes to BMI as a strategic reconfiguration outcome, and by clarifying why a green entrepreneurial posture does not uniformly translate into business model change (Makhloufi et al., 2022). Conceptualising GEO as an intangible strategic resource, we argue and show that its value lies not only in ‘being green’ but in enabling start-ups to redesign value creation and capture when complementary knowledge is mobilised, thereby responding to calls for more empirical work on the antecedents and micro-foundations of BMI (Appiah et al., 2025; Foss & Saebi, 2017).
Secondly, the study contributes to organisational search theory by specifying BSS as the conversion mechanism through which GEO is transformed into BMI. Importantly, we foreground the benefits–cost tension inherent in external search: whilst BSS provides access to non-redundant knowledge needed for business model redesign, it can also impose attention and integration burdens on resource-constrained start-ups (Yang et al., 2021a). By empirically establishing partial mediation, our findings help explain heterogeneity in outcomes amongst green-oriented ventures, by clarifying why some ventures translate sustainability intent into BMI whereas others do not.
Thirdly, the study advances the big data and digital capability literature by theorising and testing BDC as a conversion-enabling boundary condition, rather than merely a direct driver of innovation outcomes. The results indicate that BDC strengthens the BSS–BMI link by enhancing firms’ ability to filter, integrate and recombine heterogeneous external information into coherent business model design choices (Mikalef et al., 2019; Suoniemi et al., 2020). This reconceptualisation opens avenues for examining digital capabilities as moderators in sustainability-oriented entrepreneurship, particularly in emerging-market contexts, such as South Africa and many African economies, where knowledge ecosystems are fragmented and digital capability is uneven, making the returns from BSS more contingent.
Managerial contributions
Against the backdrop of climate and sustainability pressures, the findings offer practical guidance for start-ups seeking to translate green entrepreneurial intent into BMI, particularly in emerging-market settings such as South Africa and many African economies, where resource constraints, fragmented ecosystems and uneven infrastructure can make execution challenging.
Firstly, make ‘green’ a business model design problem, not only a CSR initiative. Founders can operationalise GEO by explicitly redesigning value propositions, partner configurations and revenue–cost logic around environmental value. A useful practice is to run short ‘minimum viable green’ experiments that test both customer willingness-to-pay and operational feasibility (e.g. piloting cleaner inputs with one supplier, testing a repair or take-back scheme with a single channel partner, or trialling pay-per-use models that reward durability). In contexts with tighter financing constraints, framing sustainability as measurable customer value (cost savings, reliability, compliance readiness and waste reduction) helps avoid overinvestment in symbolic green practices that do not change the underlying business model.
Secondly, treat BSS as a focused portfolio, anchored in the local ecosystem. Start-ups should deliberately combine near sources (customers, suppliers, distributors and competitors) with a small set of distant sources that provide non-redundant knowledge. For South African/African ventures, high-leverage distant sources often include universities and applied research centres, industry associations, standards and certification bodies, large incumbents’ supplier-development programmes and public or municipal initiatives related to sustainability. To prevent search from becoming unfocused, teams can assign an ‘owner’ to each search channel, set a monthly cadence for harvesting insights and use a simple decision rule (e.g. ‘keep/kill/pivot’ after two review cycles) so that external ideas are quickly translated into concrete business model choices.
Thirdly, build a ‘minimum viable’ BDC to convert external information into decisions. Because BSS can generate noisy and heterogeneous information, managers should prioritise light-weight information-processing routines before expensive technology investments. A practical sequence is: (1) define a small set of decision-critical metrics (unit economics, customer adoption and retention, partner performance and compliance costs), (2) implement basic data governance (data ownership, definitions and quality checks), (3) establish cross-functional information sharing (regular reviews that link data to decisions) and (4) add simple analytics tied to specific choices (pricing, channel selection, supplier switching and service design). This approach is especially useful where data availability and quality are uneven and teams are small: the goal is not to ‘do big data’, but to build enough capability to filter signals, integrate external inputs and accelerate business model experimentation. Managers should also recognise pitfalls: overly broad search can overwhelm attention, and data initiatives without clear decision use-cases risk becoming costly technology projects rather than capability building.
Limitations and future research
Whilst this study yields meaningful findings, several limitations should be acknowledged. Firstly, although the sample size meets the requirements for empirical analysis, the data were collected from a limited number of regions, which may constrain the generalisability of the findings. Future research could extend data collection to broader geographical contexts and incorporate qualitative approaches, such as case studies or longitudinal designs, to enhance external validity. Secondly, this study focuses on GEO as an overall strategic orientation and does not disentangle the potentially heterogeneous effects of its specific dimensions. Future research could examine how different components of GEO or additional contextual factors influence BMI. Finally, the study relies on self-reported survey measures collected at a single point in time. Future studies could employ multi-source data, alternative operationalisations, or mixed-method approaches to further strengthen measurement robustness.
Acknowledgements
The authors thank colleague Pengzhen Liu for reading the manuscript and providing comments for improvements. The authors also thank the editors and reviewers for their helpful comments.
Competing interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
CRediT authorship contribution
Yueting Shao: Writing – original draft. Liang Qu: Methodology, Resources. Pengzhen Liu: Software, Writing – review & editing. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication and took responsibility for the integrity of its findings.
Ethical considerations
This article followed all ethical standards for research without direct contact with human or animal subjects.
Funding information
This work was supported by the Quzhou University Research Project (Grant no.: KYQD007225003).
Data availability
The data used to support the findings of this study are included within the article, and further inquiries can be directed to the corresponding author, Pengzhen Liu.
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The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings, and content.
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