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Article

Business Model Innovation and Performance of Startups: The Moderating Role of External Legitimacy

by
Lu Zhang
1,*,
Xuanzhi Yang
2,
Sulu Zhu
1 and
Zhengyi Xia
1
1
Business School, Hohai University, Nanjing 211100, China
2
School of Management, Jiangsu University, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(6), 5351; https://doi.org/10.3390/su15065351
Submission received: 20 February 2023 / Revised: 15 March 2023 / Accepted: 16 March 2023 / Published: 17 March 2023
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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Abstract

:
Business model innovation (BMI) is a key performance driver for startups. Nonetheless, the reality is that new firms with new business models still face survival pressures. New institutional theory shows that legitimacy factors will affect the performance level of new ventures. Legitimacy is an important subject in the field of institution and organization, which refers to the extent to which an individual or organization’s behavior is accepted by the public and reflects the important influence of external institutional forces on the organization. Consequently, this study collected data from entrepreneurs in Eastern China and conducted a regression analysis, which revealed that novelty-based and efficiency-based business model innovation positively affects the performance of startups. Moreover, this study found that different dimensions of external legitimacy have different effects on the relationship between business model innovation and the performance of startups. Regulative legitimacy and normative legitimacy negatively regulate the relationship between novelty-based business model innovation and the performance of startups. In contrast, normative legitimacy positively regulates the relationship between efficiency-based business model innovation and the performance of startups. The study also found that cognitive legitimacy positively regulates the relationship between novelty-based business model innovation and the performance of startups. In summary, the study highlights the importance of considering the influence of different dimensions of external legitimacy on the relationship between business model innovation and the performance of startups. The findings suggest that legitimacy is a crucial factor affecting startups’ ability to improve their performance through business model innovation.

1. Introduction

The rise of community group buying, live-streaming selling, and the Internet of Things has encouraged entrepreneurs to innovate current business models (BMs) and start new businesses [1,2]. Many startups have successfully created business marvels through their innovative business models [3]. Examples include Meituan, TikTok, and Xiaomi, which seized market opportunities and delivered new values to consumers. Nevertheless, adopting a business model is also a process of facing external legitimacy tests [4]. During the COVID-19 epidemic, the Chinese government regulated several startups for wasting investors’ money to capture the community group-buying market. For instance, in 2021, Meituan, one of the most well-known companies, spent USD 2.8 billion on community group buying, which swung from profit to loss. In addition, a set of problems such as click farming and price deception in live-streaming selling led to heavy supervision from the government and platforms. Meanwhile, some companies build social support and credibility by gaining consumer trust, which drives performance [5]. Hence, the legitimacy problem encountered when launching a new model has a significant impact on the success or failure of startups. In the intensely competitive Chinese entrepreneurial environment, the simultaneous pursuit of external legitimacy and enterprise competitiveness is often imperative for firms to survive, and it is crucial for a startup’s business model innovation (BMI).
BMI refers to an organization’s basic logic to create, transmit, and acquire value together with stakeholders [6,7,8,9]. Earlier research about BMI originated from e-commerce and focused on how firms change their profit models within the integration of new technology and the Internet [4,10]. Later, one of the main streams of research concentrated on the relationship between BMI and performance; it was generally assumed that BMI can enhance the survival and growth of startups [11,12,13,14]. Despite this, numerous businesses owned innovative ideas and technologies fail due to difficulties gaining consumer recognition or sustaining themselves after government policies are tightened. According to new institutional theory, legitimacy constraint is one of the primary causes which leads to the failure of new businesses [15]. Legitimacy, as an institutional element, refers to the order and norm in a social system [16] which enables an organization to keep its own values consistent with the values of the social situation in which the organization is embedded [17]. In the field of strategic management, legitimacy is a crucial factor embedded in the micro-operations of firms and facilitates access to vital strategic resources for startups [18]. Extant literature has investigated the effects of internal legitimacy on the growth of startups [19], but limited attention has been paid to the effects of different dimensions of external legitimacy on the relationship between BMI and the performance of startups. In addition, this study found that Chinese startups provide a suitable context for testing this research gap. In the context of the Chinese transition economy, legitimacy factors from various sources play different roles in the growth of startups, it is of practical significance to investigate the level of external legitimacy for the development of startups.
Legitimacy is one of the core concepts of new institutionalist theory [20]. Weber [16] first introduced legitimacy into sociological research and argued that legitimacy is the alignment of an organization’s values with the social context in which the organization is embedded. Aldrich and Fiol [21] divided legitimacy into sociopolitical legitimacy, which refers to the acceptance of the firm’s behavior by its close stakeholders, and cognitive legitimacy, which refers to the acceptance and understanding of the firm by the public. Scott [22] further divided sociopolitical legitimacy into regulative and normative. Regulative legitimacy refers to companies “doing the right thing” [23]. The source of this legitimacy constraint is mainly the laws, regulations, and institutions stipulated by the government, industry associations, and other institutions, which is a mandatory regulation. Normative legitimacy refers to companies “doing the thing rightly”. The source of this legitimacy is social and moral values, which emphasize the expectation of the general public for the organization to “do the thing rightly” [24]. According to Scott [22] and Harris [24], this study proposes regulative, normative, and cognitive legitimacy as three dimensions of legitimacy.
Further, it is considered that the implications of the legitimacy of startups’ growth should be considered in the realm of organizational management [25]. Legitimacy involves external stakeholders that imply legitimacy needs extrinsic consideration. In other words, legitimacy is one of the main strategic orientations of the organization, which drives the firm to manage the relationship with external stakeholders [26]. The BMI of a startup cannot be separated from the influence of external legitimacy [27]. If the source of legitimacy from stakeholders is lost, the production and operation activities of the organization can hardly be sustained, or they can even lose the foundation of survival [28,29]. First, regarding legal constraints, the emergence of new technologies has spawned new business models and issues such as lagging policy regulation. Due to the rigid nature of laws and regulations, the activities of new businesses must be approved by government agencies [30]. This leads to the institutional convergence of business models and impedes the development of business models. Second, the BMI of startups is subject to re-evaluation by industry stakeholders [31]. New ventures are frequently required to compromise the market and overcome regulative legitimacy constraints by forming virtual ties with competitors, joining industry alliances, and other means [32,33]. Third, cognitive legitimacy constraints define the beliefs and premises of society as a whole, forcing startups to effectively presuppose their roles and behaviors within the existing social system, but this convergence pressure can result in a loss of business model novelty [34]. The relationship between BMI and the performance of startups has been discussed extensively [35,36], but whether novelty-based and efficiency-based BMI in startups is carried out effectively or ineffectively is controversial among scholars [37]. Therefore, this study draws on external legitimacy as a moderating factor and posits that three dimensions of external legitimacy will affect relationships between both novelty-based and efficiency-based BMI and the performance of startups.
In summary, based on the literature we can assume that novelty-based and efficiency-based BMI can affect the performance of startups. As a corollary, this study proposes that three dimensions of legitimacy will moderate the effects of BMI on the performance of startups. To do so, data from 149 Chinese startup firms were collected to depict how BMI can drive the performance of startups. The remainder of this paper is structured as follows. First, the discussion of the relationship between novelty-based and efficiency-based BMI and the performance of startups will be presented, and the role of regulative, normative, and cognitive legitimacy as three dimensions of a moderator will be highlighted. Then, the method, dataset collection, empirical results, and analysis are presented. Following it, conclusion, limitations, and future research directions will be presented in the final section.

2. Theoretical Analysis and Research Hypotheses

2.1. BMI and Performance of Startups

The emergence of new technologies and the rise of the Internet have transformed the profitability of some enterprises, resulting in the birth of several new enterprises that rely on new technologies and the Internet to capture business value [38]. The issue of business model development has been aggressively pursued in the business world, and Drucker [39] defined a business model (BM) as a theory that guides business operations and functioning. Timmers’ [10] first academic definition of a business model, as an architecture containing products, services, and information, was published in 1998. Mitchell and Coles [33] proposed that managers can gain competitive advantages by innovating business models. Since then, business model innovation (BMI) has become the mainstream of business model research [40].
BMI plays a key role in the entrepreneurial process. As Steve Blank [41] said, a startup is an enterprise established to search for a repeatable and scalable business model. New firms need to innovate business models consistently to survive or stay ahead of competitors [42]. It has been pointed out that BMI is one of the key ways for enterprises to gain competitive advantages and improve organizational performance [14,43]. For some startups, an innovative business model means that it is regarded as a tool to restructure the enterprise structure within the enterprise, to improve the efficiency of enterprise communication [44]. For them, whether the business model is compatible with the organizational goals determines whether the enterprise can stand out from the competition [45]. For others, through BMI, they introduce new ideas into the industry and deliver brand-new values to customers [46,47]. Zott and Amit [48] show that different types of BMI have different impacts on the performance of new ventures; new ventures have different ways of using novel and efficient business models. They also proposed the NICE model of BMI, which includes novelty, efficiency, lock-in, and complementarity. However, types of lock-in and complementarity are not the primary mechanisms by which firms improve their performance, so only measurement scales of novelty and efficiency are developed [11]. Novelty-based BMI is the innovation activity where an enterprise adopts new content, structure, and governance mode to gain an advantage. Firms often either create a new market or create a new transaction within an existing market. Efficiency-based BMI is a collection of various innovative activities taken by enterprises to improve transaction efficiency, with the ultimate goal of reducing the transaction costs of participants in economic activities.
The study in this paper is partially based on the Zott and Amit BMI definition and NICE framework [11,49] as a new approach to creating or capturing the value that represents a new activity system for the firm. Two types of BMI exist, novelty and efficiency. Novelty-based BMI refers to a new operating procedure adopted by a new enterprise, utilizing a different value delivery mechanism to provide unique value to customers, which is to offer new solutions to trading partners, build new trading rules, and even cultivate a new business culture [12,50]. Efficiency-based BMI refers to the renewal of existing business models by startups, primarily in terms of improving the efficiency of value acquisition and strict cost control, to ultimately achieve mutual benefits with partners, the essence of which is to provide partners with better products and services and improve transaction efficiency [51,52].
In pursuit of sustainable development, new enterprises need to use novelty-based BMI to improve organizational performance in the dynamic and uncertain market environment [53]. Novelty-based BMI breaks through the traditional product-centric philosophy and instead offers a unique value proposition to one or more groups, which embodies superior value-adding in a financially, socially, and environmentally sustainable manner, resulting in sustainable entrepreneurship and robust competitiveness regimes [54]. Enterprises can use new transaction modes to complete transactions with stakeholders, explore consumer needs that have not been discovered by other competitors, and design new ways of interaction to bring brand-new value feelings to consumers [55]. However, novelty-based BMI will prompt stakeholders to re-adapt to the rules, which requires the enterprise to spend a long time cultivating the new consumption mode of users, and the connection mode with partners also has a running-in process.
It is not the only choice of all enterprises to rely on novelty-based BMI to achieve enterprise growth. For many new enterprises, efficiency-based BMI can improve the efficiency of all links in the transaction process, update or transform inefficient trading mechanisms, and bring tangible benefits to both sides of the transaction [56]. Different from novelty-based BMI, efficiency-based BMI can effectively imitate the market behavior of existing enterprises in the market to achieve the purpose of improving performance. The innovation of efficiency-based BM tends to be defensive, which can not only reduce the risks caused by information asymmetry but also ensure the efficiency of the business operation of both sides of the transaction, thus contributing to the realization of sustainability-oriented performance [57,58]. Therefore, the following hypotheses are suggested:
H1a. 
Novelty-based BMI has a positive impact on the performance of startups.
H1b. 
Efficiency-based BMI has a positive impact on the performance of startups.

2.2. The Moderating Role of Legitimacy

Legitimacy, a significant subject in institutional and organizational areas, is the extent to which the behavior of an individual or an organization is accepted by the public [59,60]. From the strategic perspective, legitimacy is an intangible resource of the enterprise, which can promote the improvement of the enterprise’s ability and is the key factor for the enterprise to achieve performance goals [61]. Moreover, contingency theory provides an assumption that legitimacy exists at the intersection of an organization (the object of legitimation) and its environment [60]. This means that legitimacy is viewed as a relational notion which is constructed and sustained through relationships between an organization and its stakeholders. The higher the recognition degree of the external environment, the stronger the legitimacy of the organization, especially when encountering environmental protection demands and pressures for internal green innovation [62]. In fierce market competition, the importance of legitimacy level for enterprises is obvious. The high legitimacy level of enterprises indicates that enterprises convey the signal of strong environmental adaptability so that it is more convenient to obtain congruence from social subjects, just like obtaining material resources from economic subjects [63].
Regulative legitimacy comes from the constraint of national compulsory laws and regulations [64]. First, in terms of the macro environment, there are still some institutional gaps in Chinese commercial laws and regulations, and there is incoherence between national policies and local policies, leading to those new enterprises needing to constantly adjust their business models to adapt to the policy environment in the process of starting up [65]. Second, novelty-based BM is usually new things breaking social cognition, such as popular live-streaming selling. Enterprises adopting these novelty-based BMs usually have insufficient norms for their own behavior, resulting in situations that do not conform to existing law, such as selling fake and shoddy products, infringing others’ intellectual property rights, and even adopting unfair competition means. The new trading mechanism involved in the novel business model is still not subject to the jurisdiction of laws and regulations, and there is an institutional vacuum. Once faced with external regulations, it is difficult for enterprises to deal with them quickly, resulting in losses. Third, novelty-based BMs usually do not match the existing institutional environment. The background of a transforming market economy increases transaction risks and institutional uncertainties, and new enterprises adopting novelty-based BMs have higher requirements for the external environment [66]. If new enterprises do not pay attention to regulating their own market behavior, they will face significant losses after the policy improvement. At present, the government still plays a large role in resource allocation. At this time, novelty-based BMs with higher risk and complexity are often not the first choice of entrepreneurs. On the contrary, the innovation degree of the efficiency-based BM is not as high as that of the innovative one, and the current policy system is more convenient and acceptable for the commercial activities of new ventures, so it will not pose new challenges to the policy system [67]. Therefore, new ventures do not need to spend excessive time and energy to deal with the policy factors, and choosing an efficient business model is more in line with the current institutional environment. Accordingly, the following hypotheses are put forward:
H2a. 
Regulative legitimacy negatively moderates the relationship between novelty-based BMI and the performance of startups.
H2b. 
Regulative legitimacy positively moderates the relationship between efficiency-based BMI and the performance of startups.
Normative legitimacy reflects the public’s evaluation and acceptance of “doing the thing rightly” by individuals or organizations [23]. By showing their contribution to society, entrepreneurs can break the conceptual barrier and maintain a good relationship with public institutions such as the media to gain recognition [68]. Startups with novelty-based BMI use new methods in their business operations that may harm social interests, for instance, finance companies that provide novelty-based BMI such as cryptocurrency and crowdfunding platforms in the financial services industry [69], so people have low social trust in them [70]. Efficiency-based BMI focuses on optimizing the enterprise value chain, properly handling the relationship with stakeholders, and controlling the risks in the transaction link, which will make its social evaluation degree become higher [71], so the normative legitimacy will have a positive regulating effect on the relationship between efficiency-based BMI and performance of startups.
Thus, the following hypotheses are proposed:
H3a. 
Normative legitimacy negatively moderates the relationship between novelty-based BMI and the performance of startups.
H3b. 
Normative legitimacy positively moderates the relationship between efficiency-based BMI and the performance of startups.
Cognitive legitimacy comes from the evaluation and recognition of stakeholders that are closely related to individuals or organizations [26]. In the overall state of a good competitive atmosphere and loose market environment, the market is inclusive to new ventures adopting novel business models, so new ventures are easier to be accepted [72]. If partners see the opportunities in the market, they will be willing to make more investments. At this time, enterprises will continue to try innovative business models, and the market will encourage the innovation of enterprises, so they will get more benefits from adopting innovative business models. Moreover, the importance of cognitive legitimacy dictates that startups frequently use storytelling to gain the trust of external stakeholders, and new BM are the nucleus of a good story, so new companies can use good stories and new identities to acquire market cognitive legitimacy recognition [30,47]. If the public’s cognition level of novelty-based BMI is relatively tolerant, it may have a positive impact on the performance of new enterprises [73]. Similarly, startups adopting efficiency-based BMI do not pose much threat to vested interests in the market. The public’s approval of startups adopting measures such as improving transaction efficiency can improve the reputation of new ventures; for instance, some startups manage their value chains by improving the circularity of resource usage [74]. Therefore, startups adopting efficiency-based BMI can give full play to their low-cost advantages and improve performance. Hence, the following hypotheses are proposed:
H4a. 
Cognitive legitimacy positively moderates the relationship between novelty-based BMI and the performance of startups.
H4b. 
Cognitive legitimacy positively moderates the relationship between efficiency-based BMI and the performance of startups.

2.3. Theoretical Model

Combined with theoretical analysis, this study develops a model of the relationship between BMI, the performance of startups, and legitimacy (Figure 1). In this model, this study explains that the performance of startups depends on two types of BMI when faced with different types of legitimacy factors. Specifically, differentiation of design thinking creates distinctive BMs. When legitimacy factors exert influence on the actions and results of startups, the inherent logic created by two types of BM will lead startups to take actions to carry out positive or negative countermeasures, and ultimately change the performance.

3. Methods

3.1. Sample and Data Collection

This study adopts a questionnaire to test hypotheses. First, based on the research questions, we choose Chinese startups as samples. According to the research of Li and Atuahene-Gima [75], enterprises established within 8 years are designated as new ventures. Since the questionnaire are issued in June 2022, companies established after June 2015 will be designated as startups. Secondly, the subjects of the questionnaire are mainly managers of startups, covering Jiangsu, Shanghai, and Zhejiang Provinces. These regions have strong entrepreneurial vitality, which can effectively guarantee the quality of the questionnaire. Jiangsu Province, together with Zhejiang Province and Shanghai City, constitutes the Yangtze River Delta City Cluster, which is one of the six major city clusters in the world. The economic and entrepreneurial vitality of this region has been among the highest in China [76]. According to the Report on Chinese Youth Entrepreneurship (2022) [77], released by China Foundation for Youth Entrepreneurship and Employment, Jiangsu Province, Zhejiang Province, and Shanghai City have a high degree of entrepreneurial activity, gathering a large number of high-quality scientific and technologically innovative enterprises. The industries mainly include e-commerce, artificial intelligence, and automobile and transportation, and the above industries are typical industries for business model innovation. Therefore, enterprises in Jiangsu, Zhejiang, and Shanghai are representative to a certain extent. Third, Hair et al. [78] showed that the number of samples should be 5 to 10 times the number of variables theoretically. This study includes 11 variables, so the number of samples should be 55 to 110 theoretically. In terms of data collection, to ensure data authenticity and high quality this study strictly controlled the time, scope, and channel of questionnaire issuance (Table 1). The questionnaires were distributed from June to September 2022. The researchers sent out a total of 200 questionnaires randomly and collected 149 valid responses, the effective questionnaire recovery was 74.5%. The results of this study were acceptable. If it is found that the questionnaire filling time is too short, the options are regular, single, or lacking items, the researchers chose to delete them.

3.2. Measurement of Variables

Variables in this study include BMI, the performance of startups, legitimacy, and control variables. A 7-point Likert scale was used in all scales. We used existing scales in the literature that are validated in original studies. On the scale of business model innovation, the performance of startups and legitimacy range from 1 (strongly disagree) to 7 (strongly agree). The market environment ranges from 1 (very poor) to 7 (very good).
First, based on the research of Zott and Amit [11,49], this study divides BMI into novelty-based and efficiency-based. In addition, since the relationship between the two variables is not completely opposite but related, when one variable is used as the dependent variable, the other variable is treated as the control variable.
Second, the performance of startups draws on the research of Covin and Slevin [79] and Cai and Yin [80] to measure the growth and sustainable operation of startups by integrating both growth and profitability aspects in the context of the actual situation of Chinese startups. Meanwhile, since growth and profitability indicators are closely related, they are combined into a single variable without dimensional differentiation.
Third, legitimacy is a relatively subjective concept, and it is reasonable to measure it through a subjective perception scale. This study selects the scale developed by Su et al. [81] for reference. The scale has been tested and applied many times in China to ensure its reliability and validity.
At last, this paper selects control variables that may affect the performance of new ventures, including firm age, size, property rights, nature, and market environment. The market environment is one of the key factors affecting enterprise performance, so it needs to be controlled as a variable. The scale of Guo and Shen [82] is adopted for the market environment.

4. Empirical Results and Analysis

4.1. Descriptive Statistics

Table 2 shows the descriptive statistical results.

4.2. Reliability and Validity Test

In this study, we used Statistical Product Service Solutions 26.0 (SPSS 26.0) to complete the empirical part. As shown in Table 3, standardized factor loading (Loading in Table 3) was used to judge the measurement validity, and the standardized factor loading of all measurement items was greater than the reference value of 0.50 suggested by Hair et al. [78]. In terms of reliability, all Cronbach’s α (α in Table 3) reliability coefficients exceeded 0.8, indicating high measurement reliability. In terms of validity, composite reliability (CR in Table 3) values were all greater than 0.9, indicating high aggregation validity. In addition, the square root of average variance extracted (AVE in Table 3) value of all variables is greater than the correlation coefficient of the same row, indicating that the measurement discriminative validity meets the requirement [83].

4.3. Correlation

The mean, standard deviation (SD in Table 4), and correlation coefficient of each variable are shown in Table 4. Numbers 1–2 represent independent variables including novelty-based BMI (NBMI in Table 4) and efficiency-based BMI (NBMI in Table 4). Number 3 represents the dependent variable, the performance of startups. Numbers 4–6 represent moderator variables including regulative, normative, and cognitive legitimacy (RL, NL, and CL in Table 4). Numbers 7–11 represent control variables including firm age, scale, ownership, type, and market environment (FA, FS, FO, FT, and ME in Table 4). As shown in the table, novelty-based BMI is significantly positively correlated with the performance of startups (r = 0.726, p < 0.001), and efficiency-based BMI is also significantly positively correlated with the performance of startups (r = 0.798, p < 0.001), which preliminarily supports the hypothesis of this paper.

4.4. Multicollinearity Test

To avoid the multicollinearity of variables, the variance inflation factor (VIF) is used for the diagnosis of multicollinearity. If the VIF value is less than 10, it indicates that there is no serious multicollinearity problem [78]. VIF values of each regression model in this study range from 1.363 to 2.509 (<10), indicating that there is no severe multicollinearity.

4.5. Regression Analysis

As shown in Table 5, Model 1 only contains control variables and tests the relationship between control variables and the performance of new ventures. It can be seen that the market environment has a significant effect on the performance of startups, indicating that it is necessary to control this variable. Model 2 and Model 3 are the two benchmark models in this study. Model 2 added the independent variable novelty-based BMI based on Model 1 and observed the relationship between novelty-based BMI and the performance of startups. It was found that the fitting effect of the model was significantly enhanced, and the regression coefficient was 0.497 (p < 0.001), indicating a significant positive correlation between novelty-based BMI and the performance of startups, that is, H1a was supported. In Model 3, the variable of efficiency-based BMI is added on the basis of Model 1, and the results show that efficiency-based BMI is positively correlated with the performance of new ventures (β = 0.617, p < 0.001), that is, H1b is supported. Both Model 2 and Model 3 pass the test, and H1a and H1b are supported. From the change of R2, the explanation of efficiency-based BMI for the performance of new enterprises is more robust.

4.6. Moderating of Regulative Legitimacy

The hierarchical regression analysis method is adopted to test whether the adjustment effect exists by using the interaction term between the independent variable and the regulating variable. After adding the interaction term into the model, if the result is significant, it proves that the adjustment effect exists.
As shown in Table 6, Model 2 examines the impact of novelty-based BMI on the performance of new ventures. Model 4 examines the relationship between novelty-based BMI, regulative legitimacy, and the performance of startups. Model 5 examines the impact of the interaction term between novelty-based BMI and regulative legitimacy on the performance of startups. On the basis of Model 2, regulative legitimacy was added to Model 4. The regression results of Model 4 show that regulative legitimacy is significantly positively correlated with the performance of new startups (β = 0.234, p < 0.05). Model 5 is based on Model 4 with the addition of interaction terms of novelty-based BMI and regulative legitimacy. If the coefficient is significant after the addition of interaction terms in Model 5, it indicates that the regulating effect of regulative legitimacy plays a role, and hypothesis H2a is verified. Model 5 in Table 6 shows that regulative legitimacy negatively moderates the relationship between novelty-based BMI and the performance of startups (β = −0.108, p < 0.05), assuming that H2a is supported.
Model 6 and Model 7 test the moderating effect of regulative legitimacy on the relationship between efficiency-based BMI and the performance of startups. The results show that the regression of interaction terms between efficiency-based BMI and regulative legitimacy is insignificant (β = −0.023, p > 0.05), and H2b is not supported.
As shown in Table 7, Model 8 and Model 9 test the moderating effect of normative legitimacy on novelty-based BMI and the performance of startups. The regression coefficient of the interaction term between normative legitimacy and novelty-based BMI is significantly negative (β = −0.206, p < 0.001), indicating that normative legitimacy weakened the influence of novelty-based BMI on new venture performance; therefore, H3a is supported. Model 10 and Model 11 test the moderating effect of normative legitimacy on efficiency-based BMI and the performance of startups. The regression coefficient of the interaction term between normative legitimacy and efficiency-based BMI is significantly positive (β = 0.208, p < 0.001), indicating that normative legitimacy strengthens the influence of efficiency-based BMI on the performance of startups; therefore, H3b is supported.
As shown in Table 8, Models 12 and 13 test the moderating effect of cognitive legitimacy on novelty-based BMI and the performance of startups. The regression coefficient of the interaction term between cognitive legitimacy and novelty-based BMI is significantly positive (β = 0.311, p < 0.001), indicating that cognitive legitimacy strengthens the influence of novelty-based BMI on the performance of startups; therefore, H4a is supported. Model 14 and Model 15 tested the moderating effect of cognitive legitimacy on efficiency-based BMI and the performance of startups. The regression coefficient of interaction between cognitive legitimacy and efficiency-based BMI was not significant (β = −0.013, p > 0.05), and hypothesis H4b was not supported.
To show the moderating effect of legitimacy directly, this study draws the moderating effect diagram of legitimacy according to the analysis results. As can be seen from Figure 2, regulative legitimacy is negatively moderated, weakening the positive effect of novelty-based BMI on the performance of startups.
Figure 3 shows the negative effect of normative legitimacy on the relationship between novelty-based BMI and the performance of startups.
Figure 4 shows the positive role of normative legitimacy in positively regulating the relationship between efficiency-based BMI and the performance of startups.
Figure 5 shows that cognitive legitimacy positively moderates the positive effect of novelty-based BMI on the performance of startups.
The specific research hypothesis results are shown in Table 9. In Table 9, BMI means business model innovation, PS means the performance of startups, RL means regulative legitimacy, NL means normative legitimacy, and CL means cognitive legitimacy.

5. Conclusions and Implications

5.1. Conclusions

In this paper, we empirically tested the assumption that novelty-based and efficiency-based BMI can improve the performance of startups. Through providing a model framework, we also proposed how different dimensions of legitimacy moderate the relationship between business model innovation and the performance of startups. Based on startups’ experiences, findings depict that startups can achieve performance in pursuing novelty-based and efficiency-based BMI.
First, our study consolidates prior findings on the implications of BMI in developing countries [11,19] and offers the importance of BMI for entrepreneurial enterprise. Our findings from 149 Chinese startups show not only that BMI works in the context of developing countries but also provides a value network according to the new value proposition. Using specific BMI, startups can attract more quality partners, broaden their sources of information, increase the frequency of enterprise interaction, and reduce transaction costs.
Second, our findings concerning the BMI–PS relationship suggest a better understanding of how BMI influences the performance of startups. The significance of novelty-based and efficiency-based BMI is different. Furthermore, the significance of efficiency-based BMI is stronger, which indicates that compared with novelty-based BMI, many new enterprises tend to choose a more secure and efficient business model to gain a competitive advantage.
Third, we have not found that any study investigated the moderating role of contextual factors such as external legitimacy in the relationship between BMI and the performance of startups. The plot of regulative and normative legitimacy moderating effect on the relationship between novelty-based BMI and performance of startups proposes that the negative impacts of legitimacy diminish under conditions of highly novelty-based BMI. Moreover, normative legitimacy positively moderates the relationship between efficiency-based BMI and the performance of startups. Cognitive legitimacy positively moderates the relationship between novelty-based BMI and the performance of startups. However, hypotheses H2b and H4b are rejected; one interpretation is that regulative legitimacy is a double-edged sword for new startups that adopt efficient business models. In a market with low regulative legitimacy, the absence of government regulation and risk prevention enables enterprises to take advantage of information asymmetry to obtain the advantage of poor information and make profits in the short term. Additionally, the improvement of transaction efficiency represents reducing transaction costs for new ventures and stakeholders. However, faced with rapidly changing market demands, only an organization that is prepared to adopt a new way will lead to the creation of a new BMI; such cognitive legitimacy from the public can be accepted to help startups improve performance. Otherwise, cognitive legitimacy will offset the benefits of improving efficiency [84].

5.2. Theoretical Contribution

Business model innovation is beneficial for enterprises to gain competitive advantages. However, previous studies focused on the business model innovation of incumbent enterprises and paid attention to the internal legitimacy factors, ignoring the business model innovation of startups. The activity and innovation of startups is stronger than that of incumbent enterprises, which makes the business model innovation more quick and successful. However, they are often faced with policy constraints, resource competition from incumbent enterprises, and consumer doubts, which are not common problems faced by incumbent enterprises.
Based on this, there are two aspects of contribution. First, this study enriches the empirical research on the relationship between business model innovation and the performance of startups. Business model innovation is a meaningful action taken by new enterprises, and the institutional situation is a critical external factor affecting enterprise actions, which will impact enterprise performance. By exploring and testing the moderating effect of external legitimacy on BMI and the performance of startups, the empirical research on business model innovation is expanded. Second, in the entrepreneurial context, legitimacy is the essential institutional factor of BMI, and the role of the three dimensions of legitimacy is different. The current research regards legitimacy as the paradigm of the whole analysis unit, and the validity of the three dimensions has not been further tested. This study explores the moderating effects of regulative, normative, and cognitive legitimacy on the relationship between novelty-based, efficiency-based BMI and the performance of startups and proposes that in an institutional environment with high levels of regulative and normative legitimacy, it may be harder for new enterprises adopting novel business models to benefit from them.

5.3. Managerial Implications

In the context of transformation, the existence of an institutional vacuum provides space for the business model innovation of startups, but this space is not in a blank state for a long time. The emergence of novel business models in the market can reconstruct the relationship between transaction entities and reopen the value network, but the existence of this business model will inevitably form a competitive relationship with the current dominant business model. Therefore, it is constrained by the system environment. Currently, new enterprises need to quickly cross the threshold of legitimacy. New enterprises should make their own behavior conform to the expectation of external legitimacy, which is the mandatory correction of enterprises in the process of business model innovation. To achieve long-term development, senior managers need to pay attention to institutional information, consider laws and regulations, regional culture, public and other factors, and expand the channels and legitimacy of resources needed in business model innovation. At the same time, new enterprises can consider embedding in the existing value network and maintain good competitive and cooperative relations with other competitors, which is conducive to typical development.
Managers should be sensitive to laws and regulations, pay attention to the collection of relevant evaluation information from news media and consumers, and adjust business behaviors appropriately according to this information. Enterprise managers should not simply follow the policy, but also give full play to their initiative and positively influence the policy content through the interaction between government and enterprise. Similarly, in the face of media coverage of negative news, managers need to quickly carry out public relations, clarify the facts, and develop solutions. In the face of the doubts and product problems raised by consumers, it is necessary to put forward feasible plans from the consumers’ perspective and learn lessons to deal with product problems.

5.4. Limitations and Future Research

This study still has the following limitations. In terms of data collection, the research has adopted various methods to screen out effective questionnaires as far as possible, but subjective opinions still exist when questionnaires are used. In future research, objective and open data should be collected as far as possible to ensure the reliability of the research. In terms of research context, legitimacy in the entrepreneurial context is of particular significance to startups. Relevant research contexts can be expanded in the future to explore the significance of legitimacy in other contexts to different types of enterprises. In addition, limited by the data confidentiality requirements of respondents, we did not obtain detailed data about the sector and structure of startups, which future studies should consider.
Based on the consideration of the entire paper, the follow-up research can be extended in the following three aspects. First, BMI is a dynamic process, and the results cannot be measured overnight. The follow-up research can start from the dynamic perspective and explore the dynamic matching process between business model innovation and institutional factors through longitudinal case studies. Second, further research should consider the combination or interaction of various institutional factors. Institutional logic does not only affect entrepreneurial actions at a single level. Future research must pay attention to the complexity and interaction of institutional factors and strengthen the emphasis on all levels of legal factors, primarily normative factors. Third, further research should try to find antecedent variables of business model innovation. From the perspective of entrepreneurship, a business model is the process of thinking and innovation of entrepreneurs. Exploring the antecedent variables of business model innovation from the perspective of entrepreneurs can better guide the current entrepreneurial practice. Moreover, the structure of production and service companies can be analyzed, especially the MT and MHT of production companies and the KIS of service companies.

Author Contributions

Conceptualization, L.Z. and X.Y.; methodology, L.Z.; software, L.Z.; validation, X.Y.; formal analysis, S.Z.; investigation, Z.X.; resources, X.Y. and Z.X.; data curation, L.Z.; writing—original draft preparation, L.Z.; writing—review and editing, L.Z.; S.Z.; visualization, L.Z. and Z.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are not publicly available due to privacy.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Theoretical model.
Figure 1. Theoretical model.
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Figure 2. The moderating effect of regulative legitimacy on novelty-based BMI and the performance of startups.
Figure 2. The moderating effect of regulative legitimacy on novelty-based BMI and the performance of startups.
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Figure 3. The moderating effect of normative legitimacy on novelty-based BMI and the performance of startups.
Figure 3. The moderating effect of normative legitimacy on novelty-based BMI and the performance of startups.
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Figure 4. The moderating effect of normative legitimacy on efficiency-based BMI and the performance of startups.
Figure 4. The moderating effect of normative legitimacy on efficiency-based BMI and the performance of startups.
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Figure 5. The moderating effect of cognitive legitimacy on novelty-based BMI and the performance of startups.
Figure 5. The moderating effect of cognitive legitimacy on novelty-based BMI and the performance of startups.
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Table
Table 1. Questionnaire collection.
Table 1. Questionnaire collection.
Form OnlineOfflineTotal
Distribution of questionnaires18020200
Collection of questionnaires14215157
Validation of questionnaires13613149
Validation ratio75.6%65%74.5%
Table
Table 2. Descriptive statistics results.
Table 2. Descriptive statistics results.
VariablesCategoryFrequencyPercentage (%)
Firm age1~4 years5134.23
5~8 years9865.77
Firm scale<502919.46
50~1507550.34
>1504530.20
OwnershipState-owned3422.82
Foreign-funded117.38
Private8657.72
Others1812.08
TypeAgriculture10.67
Manufacturing4127.52
Services10771.81
N = 149
Table
Table 3. Constructs, measurement items, and loadings.
Table 3. Constructs, measurement items, and loadings.
Variables/ItemsαCRAVELoading
Novelty-based BMI0.9290.9450.740
1. Provide customers with new products, services, information 0.859
2. The value brought to customers is unique and easy to perceive 0.862
3. Be able to discover the hidden needs of consumers 0.866
4. Be able to develop new marketing channels and methods 0.861
5. Have a novel way of trading 0.849
6. Acquired new ideas and inventions through existing business operations 0.864
Efficiency-based BMI0.9360.9500.757
1. Focus on the perfection of the product or service 0.891
2. Constantly improve the main products or services to meet customer needs 0.894
3. Tend to follow the innovations or actions of market leaders 0.836
4. Focus on expanding the current market size 0.870
5. Optimize existing operational processes, knowledge and technology continuously 0.872
6. Focus on the existing needs and satisfaction of the partner 0.857
Performance of startups0.9530.9600.727
1. Market share of major products or services 0.842
2. Growth rate of enterprise sales 0.858
3. Growth rate of new employees0.842
4. Growth rate of new products or services0.874
5. Enterprise capital turnover speed0.885
6. Market expansion rate of product or service0.802
7. Return on investment (investment income/investment cost)0.839
8. Corporate net income (net income/total sales)0.848
9. Growth rate of corporate net income0.881
Regulative legitimacy0.9550.9710.917
1. Operation has been approved by relevant national laws 0.958
2. Operation has been recognized by industry standards0.974
3. We have obtained various product quality certification/service certification given by the industry0.940
Normative legitimacy0.9270.9450.822
1. The founder has obtained the honorary title granted by the government or the industry 0.877
2. We has obtained various honorary titles from the government0.945
3. Our public welfare activities (such as donations) are highly praised0.892
4. Positive media coverage of the company0.911
Cognitive legitimacy0.8410.9050.761
1. The company or its public activities can be known to the public 0.856
2. Good corporate culture (such as emphasis on people)0.883
3. We have good cooperation with government, banks, partners, etc.0.877
Market environment0.8870.9160.648
1. Technology changes rapidly in the industry 0.893
2. The industrial market is expanding rapidly 0.884
3. There are many profitable opportunities in the industry 0.884
4. Key products or services in the industry are being updated quickly 0.703
5. Customers have diverse needs 0.762
6. There is excessive competition in the industry 0.673
Table
Table 4. Correlation matrix.
Table 4. Correlation matrix.
Variables1234567891011
1. NBMI1
2. EBMI0.770 **1
3. PS0.726 **0.798 **1
4. RL0.526 **0.592 **0.591 **1
5. NL0.554 **0.570 **0.652 **0.690 **1
6. CL0.629 **0.616 **0.676 **0.677 **0.820 **1
7. FA0.030−0.083−0.108−0.146−0.293 **−0.164 *1
8. FS0.0460.1310.1020.185 *0.325 **0.190 **−0.440 **1
9. FO−0.070−0.165 *−0.152−0.238 **−0.233 **−0.180 **0.188*−0.321 **1
10. FT−0.003−0.114−0.103−0.215 **−0.153−0.1100.076−0.163 *0.0711
11. ME0.616 **0.507 **0.570 **0.510 **0.517 **0.591 **0.045−0.039−0.1020.0611
Mean5.0375.3425.0625.9915.4585.3941.343.082.598.575.152
SD1.3101.1831.0891.1841.3881.2500.4761.5180.9734.4921.130
** p < 0.01, * p < 0.05, (2-tailed); N = 149.
Table
Table 5. Regression results.
Table 5. Regression results.
VariablesPerformance of Startups
Model 1Model 2Model 3
Firm age−0.216−0.271−0.146
Firm scale0.034−0.004−0.009
Ownership−0.058−0.068−0.007
Type−0.029−0.025−0.009
Market environment0.557 ***0.199 *0.225 ***
Novelty-based BMI 0.497 ***
Efficiency-based BMI 0.617 ***
R20.3650.5840.679
△R2 0.2190.314
F16.421 ***33.189 ***50.030 ***
VIF (max)1.3631.3661.431
*** p < 0.001, * p < 0.05, (2-tailed); N = 149.
Table
Table 6. The moderating role of regulative legitimacy.
Table 6. The moderating role of regulative legitimacy.
VariablesPerformance of Startups
Model 2Model 4Model 5Model 3Model 6Model 7
Firm age−0.271−0.222−0.235−0.146−0.128−0.151
Firm scale−0.004−0.013−0.012−0.009−0.014−0.016
Ownership−0.068−0.038−0.038−0.0070.0050.004
Type−0.025−0.014−0.015−0.009−0.005−0.005
Market environment0.199 **0.1350.1290.225 ***0.191**0.181
Novelty-based BMI0.497 ***0.442 ***0.443 ***
Efficiency-based BMI 0.617 ***0.579 ***0.571 ***
Regulative legitimacy 0.234 *0.222 ** 0.1120.083
NBMI*RL −0.108 **
EBMI*RL −0.023
R20.5840.6080.6550.6790.6850.685
Adjusted R20.2190.5880.6290.3140.6830.683
F33.189 ***31.281***27.298 ***50.030 ***43.845 ***38.567 ***
VIF (max)1.3661.8302.2831.4311.8872.509
*** p < 0.001, ** p < 0.01, * p < 0.05, (2-tailed); N = 149.
Table
Table 7. The moderating role of normative legitimacy.
Table 7. The moderating role of normative legitimacy.
VariablesPerformance of Startups
Model 2Model 8Model 9Model 3Model 10Model 11
Firm age−0.271−0.118−0.123−0.146−0.036−0.042
Firm scale−0.004−0.046−0.045−0.009−0.045−0.045
Ownership−0.068−0.045−0.043−0.0070.0040.005
Type−0.025−0.016−0.016−0.009−0.004−0.003
Market environment0.199 **0.1040.1010.225 ***0.133 **0.130 **
Novelty-based BMI0.497 ***0.406 ***0.407 ***
Efficiency-based BMI 0.617 ***0.539 ***0.536 ***
Normative legitimacy 0.245 ***0.240*** 0.205 ***0.201 ***
NBMI*NL −0.206***
EBMI*NL 0.208 ***
R20.5840.6310.6710.6790.7120.712
Adjusted R20.2190.0470.6730.3140.0330.751
F33.189 ***34.448 ***29.945 ***50.030 ***49.809 ***43.367 ***
VIF (max)1.3662.0652.3391.4312.0602.119
*** p < 0.001, ** p < 0.01, (2-tailed); N = 149.
Table
Table 8. The moderating role of normative legitimacy.
Table 8. The moderating role of normative legitimacy.
VariablesPerformance of Startups
Model 2Model 12Model 13Model 3Model 14Model 15
Firm age−0.271−0.183−0.183−0.146−0.093−0.103
Firm scale−0.004−0.023−0.023−0.009−0.025−0.027
Ownership−0.068−0.051−0.051−0.007−0.002−5.25
Type−0.025 *−0.018−0.018−0.009−0.005−0.005
Market environment0.199 **0.1020.1020.225 ***0.133 *0.129 *
Novelty-based BMI0.497 ***0.398 ***0.398
Efficiency-based BMI 0.617 ***0.537 ***0.536 ***
Cognitive legitimacy 0.252 ***0.251 *** 0.203 **0.196 **
NBMI*CL 0.311 ***
EBMI*CL −0.013
R20.5840.6230.6400.6790.7050.718
Adjusted R20.2190.6050.6380.314 ***0.0260.70
F33.189 ***33.340 ***28.966 ***50.030 ***48.116 ***41.937 ***
VIF (max)1.3662.1032.3471.4312.0822.183
*** p < 0.001, ** p < 0.01, * p < 0.05, (2-tailed); N = 149.
Table
Table 9. Hypothesis results.
Table 9. Hypothesis results.
NumberHypothesisResult
H1aNovelty-based BMI has a positive impact on the performance of startupsAccepted
H1bEfficiency-based BMI has a positive impact on the performance of startupsAccepted
H2aRL negatively moderates the relationship between novelty-based BMI and the PSAccepted
H2bRL positively moderates the relationship between efficiency-based BMI and the PSRejected
H3aNL negatively moderates the relationship between novelty-based BMI and the PSAccepted
H3bNL positively moderates the relationship between efficiency-based BMI and the PSAccepted
H4aCL positively moderates the relationship between novelty-based BMI and the PSAccepted
H4bCL positively moderates the relationship between efficiency-based BMI and the PSRejected
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Zhang, L.; Yang, X.; Zhu, S.; Xia, Z. Business Model Innovation and Performance of Startups: The Moderating Role of External Legitimacy. Sustainability 2023, 15, 5351. https://doi.org/10.3390/su15065351

AMA Style

Zhang L, Yang X, Zhu S, Xia Z. Business Model Innovation and Performance of Startups: The Moderating Role of External Legitimacy. Sustainability. 2023; 15(6):5351. https://doi.org/10.3390/su15065351

Chicago/Turabian Style

Zhang, Lu, Xuanzhi Yang, Sulu Zhu, and Zhengyi Xia. 2023. "Business Model Innovation and Performance of Startups: The Moderating Role of External Legitimacy" Sustainability 15, no. 6: 5351. https://doi.org/10.3390/su15065351

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