The impact of digital platforms on new startup performance: Strategy as moderator

Abstract

Recent studies have acknowledged the contribution of digital platforms to entrepreneurial activity. However, little has been discovered regarding the way digital platforms impact new start-ups' performance and how strategy affects this relationship. This study investigated this phenomenon using a structural equation model based on entrepreneurial bricolage and environmental strategy performance theory. The current findings indicate that broader market outreach, cost-effectiveness, and network effect produce a favorable, direct, and substantial effect on new startup performance. Furthermore, strategy moderates the effects of broader market outreach, network effect, and cost-effectiveness on new startup performance. Furthermore, strategy moderates the impact of broader market outreach, network effect, and cost-effectiveness on new startup performance. The Chinese government in particular, and governments of various emerging nations generally, must pay close attention to the results of this research to design policies that will not only lengthen the lifespan of small businesses but also promote rapid expansion as well as innovation of digital start-ups.

1. Introduction

China has become Asia's fastest-growing economy and one of the world's booming sharing economies [1]. Despite substantial facts and figures, entrepreneurship performance, particularly that of new startups, is viewed as poor in China. Access to capital, scarce resources, business process planning and management, and access to cutting-edge information that is viable for marketing and product innovation [2,3] are all posing challenges for new venture growth. Thus, it is prudent to begin formulating long-term viable solutions to spark innovation and entrepreneurship in China, as doing so could help new start-up businesses perform better. Startup enterprises that have chosen digital platforms have years of relevant experience in growing their businesses. This is due to the potential prospects provided by digital platforms, which modified the underlying risk of firm management from a social and economic perspective [4], resulting in the performance growth of new companies. Apart from facilitating the establishment of the new venture, digital platforms enable new enterprises to break their original organizational boundaries by utilizing platform resources to extensively and deeply scan and search the external environment [4]. Despite the significant impact of digital platforms on new startup performance, literature is generally silent on how digital platforms influence the performance growth of new startup enterprises.

Digital startups operating their business on digital platforms differ from traditional businesses in terms of market outreach, cost-effectiveness, and resources. This study investigates this phenomenon using the entrepreneurial bricolage theoretical approach, which discusses how entrepreneurship can be accomplished with limited resources [5,6]. For platform-based digital startups with limited resources looking to accelerate their growth, entrepreneurial bricolage may be a viable option. Moreover, utilizing platform resources alone is not sufficient as strategy is also required to spur the performance growth of new startups. It is critical to implement an appropriate strategy that will allow new startups to maximize their profits through effective resource allocation [7], by identifying and comprehending the relationship between resources and competitive advantage [8]. Studies such as those of Chesbrough [9] and Thompson [10] argued that being on digital platforms necessitates relinquishing some control over the development of the edge market and accepting some commercial uncertainty that needs the development and implementation of a unified goal which is very challenging. Thus to gain a more compelling description of the linkage between digital platforms and startup performance within organizational and environmental situations, this study introduces strategy as a moderating variable for the underlying relationship.

As digital platforms become increasingly important in the generation of new opportunities and outcomes, there has been a significant shift in the study of business enterprises into how digital platforms drive business performance and mechanisms that mediate the impact of platforms on digital start-ups, in recent years [11,12]. However, the focus of these studies was primarily on the impact of platforms on the economy [13], product development [14] the development of business models triggered by innovative and new businesses using online platforms [15], the influence of digital platforms as outside enabling factors on entrepreneurship processes [16], and platforms openness action mechanism to explore the mechanism of their boundary conditions [17]. Furthermore, Yenchun [18] and more recently Mishra & Tripathi [19] and Poniatowski et al. [20], have investigated the impact of digital platforms on new startup growth performance with a focus on digital platform classification, pros and cons, and the future implications of digital platforms on the successful adoption for large startups such as IBM, Protector, and Gamble. In general, these research findings applaud the impact of platforms on the free startup stage of the digital startup, which only includes the identification of entrepreneurial opportunities for the start of a new venture, and the successful adoption of platforms by large startups such as IBM, gamble, and protector [9]. Furthermore, the research settings of these studies were mostly qualitative and conceptual-based, with a focus on what constitutes platforms, how digital platforms operate, and how to successfully operate within the platform ecosystem. As a result, since digital platforms do not only influence new venture creation but also its operation processes and growth performance, in-depth knowledge of how digital platforms influence the performance growth of new startup enterprises and the existing mechanism that moderates the link between digital platforms and performance of new startup is worth investigating. The outcome of this research will add to the expanding corpus of work on digital platforms and strategy research on the continuing debate over how digital platforms shape the future [13].

This paper proceeds with a theoretical framework and a quick assessment of pertinent research on digital platforms, firm strategy, as well as startup success to formulate propositions that are related to the model of the present research. Section three contains the method, which is followed by the results and comments. Finally, the conclusion discusses theoretical and practical impacts, in addition to research limits.

2. Theoretical underpinning

The concept of digital platforms is universal and covers a variety of contexts, including the development of a new product and innovation, as well as economics and technology [[21], [22], [23]]. The idea of digital platforms is rooted in engineering and product development literature, which means a modular technical or product architecture involving a steady, common set of basic elements as well as a diverse set of ancillary elements [2,14,24]. The digital platform is defined as a facility and architecture that supports online commercial transactions between two or more parties, one of which is usually a manufacturer and the other a customer [25]. The present research investigation considers digital platforms in terms of technology assets and architecture that provide room for other enterprises to join and run on top of it. Examples of digital platforms include Alibaba, Amazon, Airbnb, Google search engine, Facebook, Instagram, and Knowledge platforms like Stack Overflow and Rest. These are all platforms with different business models and rules that enable the operations of other apps on it and support business interaction between suppliers and customers. There are two approaches for digital platforms: becoming a platform owner or starting a new venture on third-party platforms. However, due to a lack of funds and technological prowess, startup businesses often find it hard to build their digital platforms [26]. They seek to leverage third-party platforms to create new businesses, survive, and even thrive. Digital platforms have been found to provide bundles of opportunities to entrepreneurs, which entails the development of complementary products and services [27,28]. Therefore, new startup enterprises have plenty of advantages within the context of the digital platform through their role play as platform followers by observing the rules and regulations created by the platform's leadership [29].

However, most studies of digital platforms are vague on how digital platforms influence the startup process of new startup enterprises. Li and Kang [17] demonstrate the effect of platforms' openness on the identification of opportunities and incorporation of entrepreneurship resources. Nassar and Malik [16] studied the impact of digital platforms as external enabling technologies in the entrepreneurship process and discovered that platforms have made venture creation procedures less bounded and facilitate digital startups with limited resources to enhance their growth performance by integrating available resources smoothly and efficiently. The study findings of Yenchun [18] show that the use of platforms' open innovation ecosystem facilitates entrepreneurial activities preparedly the establishment of a new business venture. However, contrary to other majority studies on digital platforms, the studies of Nambisan, Siegel, and Kenney [4] demonstrated how open innovation and digital platforms may hinder the venturing process or the continual survival of new startup enterprises. They show that the feature creeps strategy of a digital platform and platforms’ related envelopment strategies are the existential threat to the entrepreneur and his venture. Thomas, Autio, and Gann [21], found that digital platforms allow the coordination of activities among various players in a way that leverages value co-creation. Other studies have focused on how the interaction and collaboration of enterprises with other players enhance the performance growth of new start-up enterprises [30]. Heyne, Boettke, & Prychitko [31] discovered that new startup enterprises can enhance their growth performance through partnerships with their complementary partners using advanced technologies and organizational strategies within the platform ecosystems. Huang et al. [32], illustrate the value of digital platforms as an outlet for entrepreneurial activities by allowing new startups to deepen their knowledge whilst reimbursing their manufacturing, distribution, and marketing capacities. Thus digital startup enterprises must acquire certain skills that can enable them to handle a variety of collaborative issues, including resource sharing and intellectual property rights to operate successfully in the ecosystem of the platform [33,34]. Taking new startups as an example, we can easily see how different types of company strategies provide the generating process that shapes new startup performance [[35], [36], [37]] and affects their sales and value creation [13,38]. Consequently, strategy is employed in the present study to interplay the mediation role on the relationship effect of digital platforms on new startups' performance.

Therefore, unlike the present research, most of the prior research has concentrated on conceptual theory building and has adopted qualitative research techniques. Therefore, apart from the need for qualitative research on the influence of digital platforms on new startup enterprises' performance, empirical research settings exploring digital platforms' influence on the growth performance of new startups and how strategy interplays the moderation role of platforms affects new startup performance received little attention.

3. Conceptual framework and hypothesis development

The environment in which businesses operate provides the resources that the business requires. It presents risks and possibilities and shapes numerous strategic decisions that entrepreneurs have to take. Digital platforms serve as the firm's external resource pool that allows for the collaborative use of resources across borders, regions, and geographical locations. This is particularly beneficial to the growth of startup entrepreneurs' network resources to prevent them from being driven out of the market by giant firms, which makes survival even more difficult [39]. However, simply joining a digital platform alone won't automatically yield gains since businesses can accomplish effective creation of value only if they decisively and spontaneously devise diverse strategies on a digital platform [40]. Without developing a unique strategic measure, firms will be unable to drive significant value on digital platforms to improve their performance. As such, this study developed a model rooted in entrepreneurial bricolage theory and environment strategy performance theory that supports the influence of digital platforms on startup performance and that strategy plays a crucial role in moderating digital platforms' impact on startup performance.

Environmental strategy performance theory advocates the need for new or changes in the strategic approach to managing a business's ecological operations to meet the demands of customers and suppliers in an ever-changing environment [41,42]. This theory investigates the interaction between an enterprise's external environment, performance outcomes, and strategic choices. The environment in the context of digital platforms consists of the rapidly changing technology landscape and its competitive dynamics. Startup enterprises should quickly adjust to rapidly evolving conditions and increase firm performance by carefully observing and utilizing internal information resource flow [39]. Studies have shown that despite being young, several firms outperform their more defined rivals in the marketplace [11,30]. Startups can develop strategies to take advantage of digital platforms by strategically matching their business models to the functions and power of the platforms [39]. In doing this, startups may increase their brand awareness, customer enthusiasm, and income generation. For instance, startups could employ an ecosystem-oriented strategy to collaborate with complementary digital platforms to increase their audience and provide comprehensive solutions.

Entrepreneurial bricolage on the other hand entails using resources at hand creatively to deal with or solve new difficulties and possible scenarios [43]. It suggests a resourceful and inventive attitude to dealing with new issues and potential situations. Entrepreneurial bricolage enables startup enterprises to build strong and rapidly growing enterprises despite their limited resources [44,45]. Startup enterprises should transform their available scarce resources into innovative products and services in an advanced way instead of simply acknowledging their existing potential using entrepreneurial bricolage [46]. Previous research has classified the bricolage of entrepreneurs across input bricolage, market bricolage, and institutional bricolage contingent upon the content of resources [43,47]. (1) Market bricolage is the process of transforming an existing network of business enterprises, such as customers, suppliers, and competitors, into new customers in a market where competitors compete. Market bricolage enables new startups to broaden their market outreach at a minimal cost via economies of scale while also establishing strong relationships with business partners [46]. (2) Input bricolage is an approach to applying human and material resources to fresh opportunities and challenges in novel ways. It is a practical approach to strengthen the operational efficiency of new startup enterprises with limited resources. It also makes the best use of available resources to get the lowest possible labor cost for a variety of business operations [16]. (3) Institutional Bricolage implies the adaptive and innovative strategies used by firms or individuals when they meet discrepancies or inconsistencies within the regulatory or institutional frameworks. This method entails repurposing, restructuring, or merging available resources to address issues or capitalize on possibilities within a specific institutional setting [16]. Institutional bricolage is especially important in instances where formal regulations and established standards are inadequate or fail to adequately convey the complicated nature of a situation. Institutional bricolage is critical for digital startup enterprises to overcome routine inertia [47] as they frequently operate in complex institutional frameworks that may not fully support their innovative and dynamic nature. It also entails restructuring the existing resources of new startup enterprises and integrating them in such a way that results in the establishment of new entities [16].

Given that gaining innovative tactical resources in the free market is tricky for startup enterprises due to their size and newness [46], entrepreneurial bricolage, which advocates the use of inferior resources to create superior resources, is appropriate for studying the link between digital platforms and startup performance. Startup enterprises can use digital platform architecture and technical qualities to restructure resources. It was evident that the modularity, uniformity, and openness of the platform empower startup enterprises to configure, arrange, and coordinate their resources through the lens of entrepreneurial bricolage, which aids in the development of novel resource combinations and overall firm performance [39]. Based on the theories of entrepreneurial bricolage and environmental strategy performance, this study proposes that broader market outreach, cost-effectiveness, and network effect all have a substantial impact on the performance of new startups, and that strategy plays a crucial part in this relationship. This study introduces these concepts in the following subsections.

Broader Market Outreach: This refers to the expansion of the market size beyond the initial organizational boundaries. Market bricolage advocates repurposing the current network of business organizations, such as clients, suppliers, and rivals to obtain new customers in a competitive market. It was evident that numerous customers started as friends or grew into friends on the digital platform [16]. Therefore, startup enterprises can simply increase their market outreach via market bricolage. The use of digital platforms enables startup enterprises to break their conventional time and space constraints and interact with the different types of customers and suppliers of the same or different products around the world [48]. The more extensive the reach of digital platforms, the more extensive the market reach of new startup enterprises, and the broader the customer base and innovation knowledge as well as information obtained from the customer feedback that can be utilized to create more value output and innovative ideas as well as provide a better solution for customers [40]. The digital platform is frequently used as an intermediary between new digital firms and a variety of consumer groups that would otherwise struggle to interact directly in an offline marketplace. Thus in the present research, any enterprises that have a wider connection with a variety of customers and suppliers from different geographical locations worldwide are expected to grow faster.

H1

Broader Market outreach has a direct positive influence on a new startup's performance

Cost-effectiveness: this can be seen as the rate at which business costs decrease while business efficiency increases. Input bricolage encourages maximizing existing resources to obtain the lowest feasible labor cost for several commercial processes. By integrating platform resources and extending distribution networks, startup enterprises can serve new clients at a reduced cost and with greater revenue development through input bricolage [48]. Digital platforms have significantly reduced inputs and communication costs by establishing user network connections across multiple jurisdictions, allowing firms to quickly meet the global base of manufacturers, customers, and consumers at merely zero cost [49]. It helps new startup businesses cut their marketing and operating costs [46]. For example, billboards, radio spots, direct mail, television ads, and print ads were all used in the early days of product or service marketing. Though these are still effective marketing techniques even in the digital world, they result in additional costs [48]. Whereas new startup enterprises that launch or operate their businesses on digital platforms can easily market their products at no cost and improve their business performance. Furthermore, the centralization of self-service touchpoints for automated facilities on a platform simplified cost management of new startup enterprises by allowing maintenance costs to be encapsulated and closely handled [17]. The digital platform however provides economies of scale to new startup enterprises through the centralization of the digital service lifecycle; more customers can be generated without incurring any additional costs [16,46]. Thus, this study assumes that cost savings are only the beginning of the advantages gained by new enterprises from digital platforms.

H2

Cost-effectiveness has a direct positive influence on new startup performance.

Network Effect: the network effect at its best entails the creation of a strong vicious circle that traditional companies cannot possibly attain. The phenomenon of network effects occurs when a product, service, or platform's net worth improves alongside an increase in users. Telephone and platforms such as Alibaba Taobao, Airbnb, Uber, and social network platforms such as Facebook and Instagram are examples of items with a high network impact. The more people use Uber or Didi platforms, the more value it can create for all users (producers and customers). Digital platforms establish powerful dynamics through network effects that faster the performance growth of enterprises. It has been argued that the network effect creates switching costs for enterprises [50]. The cost of obtaining a customer decreases as network effects take force. More buyers constitute further demand, which in turn generates a fresh supply. An increase in supply means the availability of more goods, more choice, and pricing competition, all of which contribute to drawing in fresh customers [51]. Following the core principles of entrepreneurial bricolage, a startup can leverage the existing customer base and interactions inside the platform to innovate and develop novel goods or services that adapt to the platform's user society. Thus by ingeniously integrating existing resources and interactions inside platforms, startup enterprises can improve their goods and services while tapping into network effects to build consciousness and reputation [16]. Furthermore, environmental strategy performance theory holds that firm performance is influenced by the interaction between a firm strategic decision with its outside environment [41]. Therefore, startup enterprises need to strategically coordinate their business models to leverage and magnify the platform network effects. This may entail adjusting their offers to attract and keep users, producers, or stakeholders who are capable of contributing to the platform's network effects. For example, a startup enterprise may position itself for greater development and success by aligning its strategy to the platform's network dynamics. As a result, this study assumes that the more extensive the digital platform becomes, the higher value it can generate that enhances the growth performance of new startup enterprises.

H3

Network effect has a direct positive impact on new startups' performance.

4. The moderating role of strategy

As more platforms evolve and change constantly, prior studies demand that startup enterprises develop new or change their strategies to survive and thrive in the ecosystems [52,53]. This is because the creation and capture of value using digital platforms or within platform ecosystems are limited by the existing challenges in any given situation. The strategy enables the business firm to distinguish itself from rivals and/or achieve a competitive edge in the process of technology commercialization [52]. For an enterprise to establish itself as trustworthy in a digital platforms marketplace, it must expand far more strategic effort than in a traditional business. Customers must develop trust in the products, or sellers before they are willing to risk their resources or even their lives in some cases. Therefore, when analyzing the mitigating impact of strategy in the link between digital platforms and the performance of new startups, it is presumed that digital platforms' influence on startup performance varies contingent upon business strategy. The business's performance will improve so far as the startup strategy and the utilization of digital platforms are compatible. However, empirical evidence that examined this kind of relationship using a moderating approach is limited.

Strategy is regarded as a tool for enterprises to enhance competitiveness and performance. The Firm's strategies are the foundations of activities involved in enterprise management that determine organizational importance in long-term performance [42]. An effective strategy is defined by the execution of multiple activities from rivals or comparable activities in various approaches [37,53]. The best strategic decision constitutes three main organizational factors; the definition of parameters, where the strategy adds value, and the control and protection of imitation [53]. According to Fréry [54], strategic decisions should be long-term to achieve a competitive edge since the goal of every strategy is to achieve sustainable value creation. He added that strategy shouldn't be based on short-term issues such as operational efficiency. Casadesus-Masanell & Richart [55] have developed a conceptual model that differentiates the concept of strategy and business model. They discovered that distinguishing between strategy and business model concepts is difficult in a simple competition with a one-on-one depiction of strategy and business model. When several major uncertainties must be considered in a well-designed strategy, the ideas of strategy and business model diverge.

Although existing research has established the fit of strategies in the use of digital platforms and their combined effect on startup performance, the findings of these studies produce mixed results. For instance, Chandler, and Hanks [7] built on the contingency theory and studied the combined effect of a competitive strategy and resource availability on an enterprise's performance. They discovered that just two of the six scenarios were analyzed to support the relationship between competitive strategies and available resources [7, p. 343]. Oltra and Flor [56] found that business strategy regulates the connection between operation strategy and firm outcomes. Rhee and Mehra [57] used a moderating technique to examine the compatibility between operation strategy and business strategy. Their research is focused on a service environment, especially the financial sector, and the integration with a marketing strategy is taken into account. On the contrary, Brush and Chaganti [58] discover no significant connections between strategy and different kinds of firms' resources. Hence, contrary to the previous research studies, this study considers the mitigating effect of strategy on the association between digital platforms and the success of new startups. As a result, the following notion is advanced by this study.

H4

Strategy strengthens the relationship between broader market outreach and startup performance.

H5

Strategy strengthens the link between cost-effectiveness and startup performance.

H6

Strategy strengthens the relationship between network effect and startup performance.

5. Methodology

5.1. Samples and data

To validate this research hypothesis, this paper collects first-hand data by administering survey questionnaires to digital start-up enterprises that run their businesses on the digital platform's ecosystem. In this study, the questionnaire questions are scored on a 5-point Likert scale. The scales read (1) strongly disagree, (2) disagree, (3) neutral, (4) agree, and (5) strongly agree. It is interesting to note that all participants in this study were given informed consent. The processes for creating a questionnaire are as follows: First, the scale items required for this study were assessed using a systematic review of the relevant mature scales in the existing literature, followed by repeated discussions with the research group, and the original English scale was translated into Chinese. Secondly, to ensure that the scale is consistent with the management practice of China's new ventures in the digital context, we organized a special seminar and invited some entrepreneurs to repeatedly discuss the specific items in the questionnaire and revise the corresponding items in the questionnaire. Next, the study performed a preliminary survey by administering an online questionnaire survey to some founders at Shenyang and Beijing digital incubation centers. Finally, based on the preliminary survey response, the present research questionnaire was altered and updated to produce the final research questionnaire.

Since digital startups are defined as businesses that use digital platforms and other infrastructure to create value, digital startups are typical representatives of this research study. This study uses China as the research setting because China is a global hub in the digital economy and digital entrepreneurship, and there has recently been an influx of a significant number of digital start-up firms and unique business models in the country. This survey area mostly includes Beijing, Zhejiang, Guangzhou, Shandong, Shenyang, Hunan, and other locations. We randomly select sample enterprises from a list of enterprises provided by local government agencies or business incubators to ensure the randomness of the sample selection procedure. These study sample characteristics are appropriate for the current study because one of the major concerns for startups is how to improve their performance while operating in a digital platform environment.

Furthermore, the survey instruments of the present research are developed after careful examination of the former research and conversations with professionals who have a better understanding of digital platforms and entrepreneurship. Table 1 shows how certain questionnaire items were reframed to fit the digital background. The NSP and STG mean new startup performance and strategy whereas BMO, NWE, and CE refer to broader market outreach, network effect, and cost-effectiveness respectively. Due to a lack of established measurement items, this study develops seven measurement items for new startup performance by reviewing the work of Tippins and Sohi [59] and Rauf [60]. By reviewing the work of Singh et al. [61], this study develops four measurement items for broader market outreach and five measurement items for cost-effectiveness. This is to investigate their influence on new startup performance. To investigate the network effect influence on the performance of new startups, this study creates four measurement items following the research of Park & Chang [52] and Chen et al. [62]. This research also draws upon the work of Bush and Chaganti [58] and Edelman, Brush, and Manolova [42] in developing five strategy measurement items. This study also develops four measurement items for demand uncertainty and three measurement items for competition uncertainty based on the work of Zhou, Yim & Tse, [63]; Jaworski, & Kohli [64], and Miller, [65]. It should be noted that the five-Likert scale was used to build each of the produced measurement items.

Table 1.

Measurable Items of five constructs.

Measurable items		Sources
NSTP1	I'm experiencing growth in my sales for the past 2year	Tippins & Sohi, (2003), Rauf (2007)
NSTP2	There is an expansion of sales in my business every month
NSTP3	Since joining the platform, I've seen constant revenue growth.
NSTP4	I cannot remember when last I experienced a decrease in sales in this business since I joined this platform
NSTP5
NSTP6	The gap between last year's and this year's sales is substantial
NSTP7	My sales this year alone are enough to build another same business
BMO1	Since joining the platforms, the nature and extent of my market risk have been redefined.	Authors made
BMO2	I have effectively expanded my consumer base beyond the confines of the primary market.
BMO3	Due to the broad nature of the platform market, it is difficult to anticipate the precise dimensions of our market scope.
BMO4	Since joining the platform, I've seen a significant boost in customer involvement from a variety of geographic zones.
NEW1	Enhance performance with the achievement of others in the platform ecosystem	Chen et al. (2018); Park & Chang (2021)
NEW2	I create more value with the increase in the number of new ventures and customers joining our selling platforms
NEW3	The success of our business changes with the number of people who use these business platforms
NEW4	Saving a lot of cost through the use and reuse of shared platform resources free of charge
CE1	Enhance performance through interaction with highly innovative complementors within the platform ecosystem	Singh et al. (2021)
CE2	Ensure the operability of a new venture with innovation that meets platform requirements thereby reducing risk
CE3	Enhance performance through collaboration with other players in the platform ecosystem
CE4	Cost saving by expanding the scope of our innovation through the reuse of shared resources in the platform's ecosystem.
CE5	Creation of value and searching out external organization boundaries without extra cost from the use of shared resources.
STG1	My products/services are more innovative and better user experience than our competitors	Bush & Chaganti, (1999); Edelman, Brush & Manolova, (2005)
STG2	I'm constantly searching for new customer groups and markets to promote products/services
STG3	I can often find business opportunities in new or growing markets
STG4	I continue to develop and implement innovative activities to enhance user stickiness
STG5	I continue to provide new products/services to increase customer stickiness

5.2. Endogeneity, control variables, and common method bias

Furthermore, the following Variables including education level, respondent age, gender, business age, demand, and competition uncertainty that might affect our study results were controlled for in this study. These control variables were chosen based on prior research on digital platforms and entrepreneurship. Furthermore, This study adjusted for the subsequent factors that might influence this study result: the respondents' age, education level, income, and the number of employees. These control variables were selected based on prior research on digital ecosystems and entrepreneurship. Prior research has discovered that the entrepreneur's age, gender, and educational background affect the performance of startup enterprises [66,67]. Business age, competition uncertainty, and demand uncertainty were all controlled variables because they affect the performance of new startup firms [68,69].

Given that the study's goal is to examine how people behave while responding to self-reported survey questions, common method variance (CMV) may be an issue. The occurrence of common method variance (CMV) may be taken into account since the study is intended to investigate people's behavior using self-reported survey items. Therefore, to account for the possibility of common method bias (CMB), the single-factor test by Harman was applied. The statistical estimation of exploratory factor analysis (EFA) revealed that the possibility of CMB in the data set was not a concern since 36.73% of the entire variance was reached. The Gaussian copula technique [70] was employed to address the issue of endogeneity further in this investigation by controlling variables that potentially affect study outcomes. Endogeneity was tested on a broader market outreach, cost-effectiveness, and network effect using the Gaussian copula approach. The copula coefficients found were negligible, indicating that there is no possibility of endogeneity in our investigation.

In this survey investigation, a sum of 460 questionnaires has been collected. The study eliminated a total of 107 questionnaires that either failed to meet the criteria of digital startups or were invalid because such questionnaires were not carefully filled. This study uses (353) 76.73 % response rate in its empirical analysis. The study measurement items are depicted in Table 1.

5.3. Analysis

This paper applies a structural equation modeling using (Smart pls 3.3.3v statistical package) to effectively capture the conceptual interrelationships between digital platforms (broader market outreach, Cost-effectiveness, and network effect), strategy, and a new startup's performance. Structural equation modeling (SEM) is described by Stein et al. [71] defined as a “multivariate statistical technique for estimating the parameters of a system of simultaneous equations”. This method is used to evaluate a linear equation system to check out the appropriateness of the proposed causal model [71]. The SEM allows the hypothesized relations to be fully evaluated in the sense of the entire model. It is highly advantageous in the assessment of mediation and moderation variables because all paths are precisely examined. However, complexities as in error measurement and feedback, are explicitly integrated within the model [72,73]. The digital platform is translated into broader market outreach, cost-effectiveness, and network effect in this study. Hence, broader market outreach, cost-effectiveness, and network effect are independent variables in this study. New startup performance is the measurement or dependent variable. The research model for this investigation is depicted in Fig. 1.

Fig. 1.

Research structure.

The structural equation model, on the other hand, is composed of two sections. The structural model and the measurement model. This study's measurement model is made up of the following equations written in Bollen's standard notation [74].

$$W_1={\mathrm{\lambda }}_1\Upsilon +{\delta }_1$$ (1)

$$W_2={\mathrm{\lambda }}_2\Upsilon +{\delta }_2$$ (2)

$$W_3={-\mathrm{\lambda }}_3\Upsilon +{\delta }_3$$ (3)

$$W_4={\mathrm{\lambda }}_4\Upsilon +{\delta }_4$$ (4)

$${\begin{array}{c}X\end{array}}_1={\mathrm{\lambda }}_5\eta +{\epsilon }_1$$ (5)

$$X_2={\mathrm{\lambda }}_6\eta +{\epsilon }_2$$ (6)

$$X_3={\mathrm{\lambda }}_7\eta +{\epsilon }_3$$ (7)

$$X_4={\mathrm{\lambda }}_8\eta +{\epsilon }_4$$ (8)

$$Y_1={\mathrm{\lambda }}_9Ϝ+{Ϸ}_1$$ (9)

$$Y_2={\mathrm{\lambda }}_{10}Ϝ+{Ϸ}_2$$ (10)

$$Y_3={\mathrm{\lambda }}_{11}Ϝ+{Ϸ}_3$$ (11)

$$Y_4={\mathrm{\lambda }}_{13}Ϝ+{Ϸ}_4$$ (12)

$$Y_5={\mathrm{\lambda }}_{14}Ϝ+{Ϸ}_5$$ (13)

$$Z_1={\mathrm{\sigma }}_{01}\upsilon +{\mu }_1$$ (14)

$$Z_2={\mathrm{\sigma }}_{02}\upsilon +{\mu }_2$$ (15)

$$Z_3={\mathrm{\sigma }}_{03}\upsilon +{\mu }_3$$ (16)

$$Z_4={\mathrm{\sigma }}_{04}\upsilon +{\mu }_4$$ (17)

$$Z_5={\mathrm{\sigma }}_{05}\upsilon +{\mu }_5$$ (18)

where the v's, w's, x's y's, and z's are observed indicators for latent variables (BMO's, NEW's, CE's, and STG's shown in Table 1). The σ′s, ϒ’s, η′s, Ϝ’s, and φ′s are latent variables (BMO1-4, NEW1-NEW4, CE1-5, and STG1-5 shown in Table 1), the λ′s, σ′s and τ are factor loadings depicted in Table 2. All the study factors loading are expected to be positive in these equations. As illustrated in Table 2 the factor loadings column values indicate a strong link between each observed indicator with its corresponding latent variables. The δ′s, ε′s, Ϸ’s, and μ′s are error, or disturbance terms. The measurement model is expressed as follows in standard matrix notation:

$$W={\forall }_x\gamma +\delta$$ (19)

$$X={\forall }_y\eta +\epsilon$$ (20)

$$Y={\forall }_zϜ+Ϸ$$ (21)

$$Z={\forall }_z\sigma +Ϫ$$ (22)

Table 2.

Measurement variables.

	Constructs	Items	Factor Loadings	Cronbach's Alpha	C/Reliability	AVE
New Startups Performance	Growth in Sales	NSP1	0.645	0.785	0.792	0.543
		NSP2	0.668
		NSP3	0.812
		NSP4	0.705
		NSP5	0.833
Digital Platforms	Broader market Outreach	BMO1	0.781	0.768	0.770	0.590
		BMO2	0.765
		BMO3	0.794
		BMO4	0.730
	Network Effect	NWE1	0.759	0.767	0.773	0.588
		NWE2	0.796
		NWE3	0.773
		NWE4	0.738
	Cost Effectiveness	CE1	0.833	0.811	0.820	0.638
		CE2	0.819
		CE3	0.810
		CE4	0.729
Strategy	Strategy	STG1	0.829	0.791	0.793	0.614
		STG3	0.796
		STG4	0.781
		STG5	0.726

Items removed: indicator items are below 0.5: NSTP6, NSTP7, CE5, and STG2.

a. All item loadings >0.5 indicate Reliability (Hulland, 1999, p. 198).

b. All Cronbach's alpha >0.7 indicates indicator reliability (Nunally, 1978).

c. All average Variance Extracted (AVE) > 0.5 indicates convergent Reliability (Fornell and Larker, 1981).

d. All composite reliability (CR) > 0.7 indicates internal; consistency (Gefen et al., 2000).

However, the structural model is made up of the following equations:

$$ῤ={\beta }_0+{\beta }_1{\xi }_1+{\beta }_2{\xi }_2+{\beta }_3{\xi }_3+{\beta }_4ϻ+{\beta }_5ϻ*{\xi }_1+{\beta }_6ϻ*{\xi }_2+{\beta }_7{ϻ*\xi }_1+ђ$$ (23)

$$ῤ={\beta }_0+{\beta }_4ϻ+\left({\beta }_1+{\beta }_2+{\beta }_3*ϻ\right)*{\xi }_1{\xi }_2{\xi }_3+ђ$$ (24)

where ῤ is the dependent variable (NSTP) and the ${\beta }_1,{\beta }_2,and{\beta }_3$ are the coefficients of the endogenous variables (BMO, NEW, and CE). The ${\beta }_4$ is the coefficient of the exogenous variable (STG) and ${\beta }_5,{\beta }_6,and{\beta }_7$ are the coefficients of the moderator ϻ in the equation. The $ђ$ is the error term. The + sign notation means a positive relationship between BMO, NEW, CE, and ῤ and also that STG strengthens this relationship. Here, we can assess causal linkages between unobserved factors, the influence of the independent variables (endogenous variables) on the dependent variable, and the moderation effect of the ϻ variable on the relationship between ${\xi }_1,{\xi }_{2}and{\xi }_3$ and ῤ.

However, if ${\beta }_4$ in the equation xxvii is not equal to zero, the relationship between ${\xi }_1,{\xi }_{2}and{\xi }_3$ and ῤ depend on the value of ϻ which indicates a moderation effect.

5.4. Measurement model validity and evaluation

The measurement scale validity was examined by evaluating its loading factors, convergent and discriminants validities as well as internal consistency. The measurement model's findings are shown in Table 2 along with the indicator items included in the model: Cronbach's alpha, Outer loadings, composite reliability (CR), and Average variance extracted (AVE). It demonstrates that the score values of Outer loadings, Cronbach's alpha, and CR values are greater than 0.7, and the AVE value for each construct is greater than 0.5, indicating a better level of reliability and validity [75].

However, model evaluation begins with evaluating discriminant validity [76]. Discriminant validity confirms a measuring construct's uniqueness and shows that the phenomenon being investigated is not captured by other measures [77]. This study employs both the Fornell–Larcker and heterotrait-monotrait ratio (HTMT) criteria for the evaluation of discriminant validity. However, According to research on Partial Least Square-Structural Equation Modeling (PLS-SEM), researchers often use the Fornell-Larcker criterion and cross-loading to test the discriminant validity in variance-based SEM. According to the Fornell–Larcker criterion for discriminant validity testing, the square root of AVE must be higher than the correlated value of the domain across each other domains in the model's structure. Table 3, Table 4 show the test for discriminant validity. Table 3 indicates that the value of AVE meets the conditions and that the constructs and measurement model are sufficiently differentiated. The HTMT values (0.339–0.897) were within the acceptable range. As a result, it is concluded that the model is without any worries about reliability and validity concerns.

Table 3.

Hetrotrait-monotrait ratio (HTMT).

	Correlation of the constructs							Collinearity statistics
	1	2	3	4	5	6	7	VIF
								New startup performance
BMO								1.425
CE	0.494							2.368
NWE	0.491	0.836						2.315
NSP	0.529	0.826	0.897
STG	0.541	0.839	0.825	0.830				2.496
STG*BMO	0.493	0.375	0.416	0.448	0.542
STG*CE	0.339	0.638	0.633	0.754	0.683	0.654
STG *NWE	0.365	0.622	0.715	0.697	0.722	0.882

Table 4.

Fornell-larcker criterion.

	BMO	CE	NWE	NSP	STG
BMO	0.768
CE	0.391	0.799
NWE	0.378	0.662	0.767
NSP	0.411	0.663	0.706	0.737
STG	0.424	0.671	0.644	0.661	0.784

However, the variance inflation factor (VIF) was examined in this research to see if there was any multicollinearity. The recommended VIF values are greater than 0.1 and lower than 5, respectively [78]. Consequently, the independent variables are free from the issues of multicollinearity.

6. Result analysis

6.1. Characteristics of the sample

Table 5 shows that 53 % of those polled are men, while 47 % are women; as a result, there is only a 6 % gender disparity among respondents. The respondents within the youth age bracket 22–35 years accounted for 46.2%, are the highest respondents. This is followed by an age range of 36–50 years to account for 25.5%. With regards to education, those with a higher college certificate and high school certificate possess the largest percentage of participation of involvement in this report, at 41.1%, and 32.3% respectively. 7.9% of the population has a bachelor's degree or higher. However, concerning the duration of enterprises under study, it shows that the enterprise within the 1–3 years age bracket has the highest response rate in this survey. This is followed by those enterprises within the 4–9 years age bracket of operation. This shows that the questionnaire has significantly covered the targeted population.

Table 5.

Demographic information (n = 405).

		Frequency	Percentage
Gender	Male	187	53.0
	Female	166	47.0
	Below 20yrs	50	14.2
Age	20–35yrs	163	46.2
	36–50yrs	90	25.5
	51–65yrs	45	12.7
	Above 65yrs	5	1.4
	Junior School and Below	66	18.7
Education level	High School/Technical school education	114	32.3
	Higher College	145	41.1
	Bachelor's Degree and Above	28	7.9
Business Age	Below 1yr	73	20.7
	1–3yrs	140	39.7
	4–9yrs	93	26.3
	10yrs and Above	47	13.3

6.2. Structural analysis

The structural model was used to evaluate the study's presented hypotheses. This is accomplished by assessing the significant correlations between the research variables and their direct and indirect path coefficients, as well as the total variables' R2 explanatory power. This study analysis is in the form of two models. The first model comprises the path effect analysis of the direct effect of broader market outreach, cost-effectiveness, and network effect on new startups' performance, and the second model forms the moderation effect analysis of strategy on the link between broader market outreach, cost-effectiveness, network effect, and startup performance. Partial Least Square (PLS) issued the resampling method of bootstraps to determine the importance of path coefficients [79].

6.3. Empirical findings

To investigate the hypothesized correlations, a bootstrapping process with 5000 re-samples was carried out. Table 6 and Fig. 2 presents the results of the bootstrapping procedure. All hypothesized associations (H1, H2, H3, H4, H5, and H6) related to the direct and moderation effects are supported. Specifically, the result reveals that broader market outreach (BMO) has a substantial positive impact on new startup performance (NSP) (β = 0.116, t = 2.776, p-value = 0.006) at a 5 % level of significance. Cost-effectiveness (CE) and network effect (NWE) were also observed to have a substantial positive direct impact on new startup performance at a 5 % level of significance with their coefficient and t-statistics (β = 0.134, t = 2.421, p-value = 0.015) and (β = 0.345, t = 5.509, p-value = 0.000) respectively. There is an 11.6% increase in new startup performance with a change in broader market outreach, a 13.4% increase in new startup performance with the increase in cost-effectiveness, and a 34.5% rise in a new startup with the rise in network effect. Furthermore, the result further shows that strategy (STG) moderates the relation effects of BMO, CE, and NWE on NSP. As depicted in Table 6, the strategy interaction term on the associational effect between STG*BMO→NSPand STG*CE→NSP was found to be positive and statistically significant at a 5 % level with their coefficient and t-statistics (β = −0.067, t = 2.093) and (β = −0.0.256, t = 5.705). The result further shows the interaction effect of strategy on the relationship between STG*NWE→NSP to be positive and statistically significant (β = 0.093, t = 2.008) at a 5% level. The empirical result of this study shows that H4, H5, and H6 were fully supported. It shows that strategy strengthens the effect of a broader market outreach, cost-effectiveness, and network effect on new startup performance.

Table 6.

The result of structural analysis.

Hypothesis	Relationships	Beta value	Std. Dev.	T-Stat.
H1	Broader Market outreach - > New Startup Performance	0.116	0.042	2.776
H2	Strategy * Broader Market Outreach - > Startup Performance	0.067	0.032	2.093
H3	Cost Effectiveness - > Startup Performance	0.134	0.055	2.421
H4	Strategy * Cost-Effectiveness - > Startup Performance	0.256	0.045	5.705
H5	Network Effect - > Startup Performance	0.345	0.063	5.509
H6	Strategy * Network Effect - > Startup Performance	0.093	0.046	2.008

Fig. 2.

Structural model.

To broaden the interpretation of the moderation impact, this study plotted a two-way level interaction effect of new startup performance using the Jeremy Dawson simple slope analysis [80]. The data is categorized into two parts according to the values of institutional bricolage, which are low and high. Low entails a standard deviation below the mean in the blue line whereas high implies a standard deviation above the mean in the green or dot line. The impacts of broader market outreach, cost-effectiveness, and network effect on new startup performance were estimated for both levels. Fig. 3a and 3b show that network effect and broader market outreach are strongly related to new startup performance when the strategy is high. The slope of broader market outreach is moderate which means the relationship between broader market outreach and new startup success is higher if the strategy is high, and the effect is weaker when the strategy is low as shown in Fig. 3a. However, Fig. 3c depicts that the effect of cost-effectiveness on new startup performance is weaker whenever the firm's strategy is low.

Fig. 3a.

Broader market outreach and strategy interaction effect.

Fig. 3b.

Network effect and strategy interaction effect.

Fig. 3c.

Cost effectiveness and strategy interaction effect.

7. Discussion

This research outcome supports the basic notion that broader market outreach, cost-effectiveness, and network effect influence the performance of new startup enterprises and that strategy moderates this relationship. Specifically, this study's findings show support for both direct and moderating effects hypotheses. First, it discovers that broader market outreach, cost-effectiveness, and network effect have a substantial direct association with new startup performance. The outcome of this paper complies with the entrepreneurial bricolage theory, which posits that converting what is available into new possibilities and markets through the incorporation of unique procedures and processes can assist startup firms in achieving growth performance [43,47]. This implies that utilizing platform resources allows new businesses to increase their market space while lowering the cost of business operations and production [21,39]. The network effect has also been demonstrated to possess a direct and significant influence on new startups' performance. This research result is consistent with Park and Chang's [52] research, which revealed that new startup can overcome their drawbacks by pursuing early internationalization, particularly when direct network benefits outweigh indirect network effects.

Second, this study also conforms to the environmental strategy performance theory, which emphasizes the need for enterprises to adopt strategies to co-evolve in a continually changing environment [20,28]. The findings support the proposition that strategy strengthens the interaction between broader market outreach, cost-effectiveness, and network effect with new startup performance. This suggests that new startup firms with excellent strategies can effectively leverage the platform's enormous number of resources to mitigate the adverse impacts of inadequate business resources and improve their performance [62]. The present results are congruent with the findings of Oltra and Flor [56], who show that strategy has a moderating impact on the association between operation strategy and firm outcomes. In essence, strategy is critical for startup enterprises not only to enhance their performance but also to survive and thrive in the ecosystems [38,81].

8. Conclusion and research limitations

This study addresses an important research issue on how digital platforms influence the performance of new start-up enterprises and the significance of strategy in mitigating the interaction effect of digital platforms and new start-up performance. This is a research environment in which empirical findings on the digital platform as well as firm strategy are rarely investigated. According to this finding, broader market outreach, cost-effectiveness, and network effect have both significant direct positive impacts on new startup performance, and their relationship effect with new startup performance is positively moderated by strategy. The present work offered theoretical and managerial advances.

Theoretical implications are found in the expansion of conceptual knowledge of how digital platforms influence the performance of new startups. The notion of digital platforms remains a fairly fresh study that requires the contributions of many scholars to the development of this literature topic. The extant literature on digital platforms concentrated mostly on the monetary effect of digital platforms, product developments, recognition of opportunities, and the free start-up stage of entrepreneurship [16,19,27,28], with less attention being paid to how digital platforms influence the growth performance of new startups enterprises. However, most of these studies have a conceptual basis for qualitative research studies. Therefore, empirical demonstration of the importance of broader market outreach, cost-effectiveness, and network effects on startup performance enriches not just the digital platforms literature but also provides practical knowledge for entrepreneurs and scholars as well.

Moreover, this study went further and deeply explores the mechanism that stimulates digital platforms' effect on enterprise performance through the introduction of a strategy as a moderator. The results of the present investigation emphasize the importance of strategic decision-making in increasing the positive impact of digital platforms for entrepreneurs. This makes up for the lack of in-depth analysis of the specific mechanism and enriches the present theoretical research perspective of both platforms and strategy, allowing for more research into their combined influence on startup success.

From a managerial standpoint, this study's conclusions have practical consequences for startups, entrepreneurs, and policymakers. Prior studies indicated that the survival rate of start-up businesses in China is low. According to Tsinghua University's (2017) China entrepreneurship investigation, the average lifetime of a Chinese small business is 3–4 years. The empirical findings of this study show that digital platforms can be used as strategic assets by startups to obtain greater market reach, cost-effectiveness, and network effects. Given that the primary goal of any nation worldwide now is to create more jobs and stimulate economic development, this research result is critical to the government of China and any emerging economies in formulating policies that can improve not only the lifespan of small businesses but also the growth and innovation of digital startups.

The outcomes of this research further extend into policy-making sectors. While innovation-driven policies are still important, this study emphasizes the significance of recognizing the marketing, distribution, and commercialization phases of startup growth. Chinese policies governing digital startups are typically focused on innovation processes aimed at producing innovative output. Unfortunately, these policies fail to recognize the importance of the marketing and distribution phases in the overall growth performance of new startups. Therefore, it is suggested that government support policies should explicitly target commercialization techniques to improve the growth performance and survival rate of digital startups.

In addition, prior studies have stressed the need for startup firms to seek startup resources from banks, relatives, and government aid, in addition to establishing public policies that could ease access to financial resources in the emerging market [82,83] to transform their businesses into digital. However, startup enterprises usually encounter low levels of trust and management and less serious business partnerships. Thus, this study discovered that digital platforms are a clear approach for startup firms to improve their performance. As a result, startup enterprises with limited resources should consider using digital platforms to enhance their performance.

Despite the valuable contributions, this research has drawbacks that could be used to guide future research. First, to meet the digital entrepreneurial situation, some of the scales developed in this paper are relatively new, and their reliability requires further investigation. Second, this research focuses on digital startup businesses, the majority of which are new ventures. Thus it is impossible to observe the interaction effect between digital platforms and strategy for a short period.

Fund project

This study was funded by the 111 Project (B16009), and the National Natural Science Foundation of China (71,672,029, 72,172,031).

Data availability statement

The survey data associated with the study will be made available based on the request. The descriptive statistics of the survey data have been attached as supplement materials in the paper.

Additional information

No additional information is available for this paper.

CRediT authorship contribution statement

Magaji Abdullahi Usman: Conceptualization, Data curation, Formal analysis, Methodology, Validation, Writing – original draft, Writing – review & editing. Xinbo Sun: Funding acquisition, Project administration, Supervision.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Prof. Dr. Sun Xinbo reports financial support was provided by The National Natural Science Foundation of China and 111 Project. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following are the Supplementary data to this article.