Research on the Impact of BMI on Enterprise Performance Based on the Antecedence of Risk Perception

Abstract

Despite research showing that business model innovation (BMI) can enhance performance and create competitive advantages for small- and medium-sized businesses, these firms often do not achieve the expected outcomes. A business model can undergo irreversible fundamental changes due to Business Model Innovation (BMI), resulting in high levels of risk, uncertainty, and ambiguity. An analysis of 350 Chinese small- and medium-sized enterprises (SMEs) was conducted to determine the impact of innovating a business model on firm performance. Based on BMI calculations, this study investigates whether organizational capabilities and growth strategies impact business performance. Even though BMI is not directly correlated with firm performance, growth in efficiency and novelty, organizational capacities, and revenue are all fully mediated by growth in those factors. Revenue growth, organizational capacity, and efficiency affect a firm’s performance. The model is proven to be valid by this study. In addition to providing guidelines on implementing BMI based on a company’s strategy, this study contributes to the existing literature on BMI efforts for small- and medium-sized businesses.

1. Introduction

Due to the rapid advancement of digital technologies, the business environment has changed, and more innovative ways of doing business have emerged [1,2]. A new competitor is not necessarily a significant established player but can potentially be a start-up offering a different business model than the incumbent [3,4,5]. The game’s rules have changed significantly in some industries due to the introduction of various business models [6]. Additionally, companies must constantly innovate and alter their business models due to these factors [7]. Innovations to a firm’s business model and infrastructure that are not trivial are called business model innovations [8]. BMI enables firms to generate new activities beyond product and process innovation [9] and has been identified as a source of sustainable competitive advantages [10].

In recent years, studies have begun to explore the antecedents of advancing BMI in firms by facilitating proactive responses to environmental changes [11,12,13]. Nevertheless, firms might have to cannibalize current revenue streams to generate uncertain future revenues through BMI [11,14,15]. Due to this uncertainty, innovative business models are often characterized by unpredictability regarding cost, duration, and outcome [16]. Because large companies usually have adequate resources, they can often try new business models [17]. In addition, they can devise new prototype business models as spinoffs without risking the firm’s survival [18]. Because SMEs have fewer resources, they are more unlikely to experiment with new business models. Small- and medium-sized enterprises are particularly susceptible to the risks and challenges associated with BMI if they fail a new business model [19].

Several complex conditions may attenuate the BMI–performance relationship [20] despite BMI’s ability to enhance value appropriation. Emerging evidence suggests that business model novelty does not necessarily lead to high performance [21]. Scholars have increasingly called for studies to identify the “key conceptual, theoretical, and empirical gaps” in BMI research. Considering that BMI literature does not explicitly address boundary conditions [22], this study examines how the relationship between BMI and firm performance can be explained and tested in the context of its dimensionality and the effects of prevalent multilevel contingency factors.

A resource-based perspective [23,24,25] aligns with previous research [26] when we investigate the BMI–performance relationship [26]. As Simon et al. said [27], BMI is a mechanism where existing firms structure, bundle, and utilize resources and capabilities to build or enhance value. This view considers BMI as a specialized capability of a firm that is difficult for competitors to reproduce [28,29,30]. While it is demonstrably true that a firm’s ability to use its resources is at least as important as its ability to acquire them [31], we must also acknowledge that it is essential to effectively utilize the resources and capabilities of other firms to carry out business model activities on the focal firm’s behalf. Therefore, our theoretical framework is based on the central premise that superior performance is related to managers’ ability to leverage resources (e.g., BMI) to create a resource-based advantage and demonstrate how certain factors affect firms’ ability to gain value from innovation in their resource bundles and capabilities [32,33]. Amit and Zott [34] extend their theoretical model beyond the firm boundaries to examine the BMI–performance relationship across various levels of analysis [33]: the business model, the company, the industry type, and the macroeconomic environment.

SMEs must be able to identify innovative opportunities and threats that come from within and outside their borders to minimize uncertainties and make informed decisions [35], as well as sense and leverage these threats to maximize success [36]. A dynamic capability theory article [37,38,39] states that organizations (such as SMEs) need exceptional knowledge-management abilities to identify and process existing and new knowledge to generate new business opportunities [40,41]. When an organization has knowledge-management capabilities, it can acquire, convert, and apply external knowledge sources and utilize internal knowledge [42]. Using technologies for screening customer data, distributing new knowledge to employees, or managing organizational processes for acquiring, storing, and using knowledge are examples of knowledge-management capabilities. For BMI to be adequate, it is assumed that gathering internal and external expertise at the right time is crucial [16]. For SMEs to innovate their business models, they need to understand what knowledge-management capabilities they possess.

Researchers have proposed that knowledge management may enable BMI [6,43,44,45], but they have not empirically explored how different knowledge-management capabilities affect BMI in small- and medium-sized businesses. Research previously conducted [46,47] shows that different types of innovation require different kinds of knowledge sources; we assume that the type of knowledge-management capability is necessary in SMEs and specific knowledge management skills are needed to manage BMI effectively, as they differ from those required for product and process innovation. Specific knowledge-management capabilities likely influence a firm’s general strategy. An organization’s risk-taking tolerance (the ability to accept risks while taking advantage of innovative opportunities) affects its knowledge processing and utilization [48,49]. To determine if capabilities can impact BMI in small- and medium-sized enterprises with varying levels of risk-taking tolerance, we examine the conditional effects of powers on BMI.

Risk perception usually has poor consequences because of the social amplification effect. Accidents, pollution, epidemic outbreaks, and unknown risks can be magnified or narrowed, and ignoring risks and potential threats can be embroidered or restricted by it [50]. Researchers have studied risk perception since the 1960s, when Slovic et al. [51] used a psychometric paradigm model to measure risk perception across different dimensions. In the study by Langford, Ian H. et al. [52], a conceptual model is presented that contains multiple factors that explain the change in risk perception, demonstrating that everyone’s risk perception is dynamic and that various risk assessment criteria are involved. Several significant disasters affected the business model of enterprises in 2020, caused by the COVID-19 outbreak. Will this cause enterprises to change their risk practices? Is risk perception being incorporated into strategic plans by companies? It is unknown to scholars, and no deep study has been conducted on these questions.

Chen et al. (2018) [53] argue that corporate green innovation results from institutional pressures based on data from Chinese firms. As shown by Long et al. (2017) [54], environmental innovations perform better for the environment than economic innovations increase firm performance. Chinese manufacturing firms focus on green growth and the link between innovation and green development, among other findings by Guo et al. [55,56]. It is, however, still unclear at the firm level, where some questions remain. The influence of green patents on Chinese firms’ innovation–performance relationship is, therefore, of great interest.

The authors of Reference [57] proposed the theory of dynamic capability, which suggests that an organization can integrate, establish, and reconstruct internal and external capabilities to adapt to rapidly varying conditions. There is a great deal of complexity involved in dynamic capabilities. To achieve competitive advantages, enterprises must improve their current capabilities and build future capabilities to maintain their advantages [58,59]. The ability to perceive risks, develop new products, and differentiate services are dynamic abilities that need to be studied as a prerequisite for BMI.

Main Contribution

A study in Reference [4] showed that dynamic capabilities (i.e., new product research and development and differentiated services) could positively impact an enterprise’s financial performance and competitive advantage. Few scholars have examined the impact of new product development and differentiated services on BMI from the perspective of dynamic capability theory. A primary objective of this study is to determine how business model innovation is related to firm performance by examining the antecedents of BMI. We aim to achieve the following specific objectives:

• Examining the relationship between efficiency BMI and risk perception.
• Compared to novelty BMI, efficiency BMI is evaluated for the effect of risk perception.
• Using novel BMI as a measure, this study explores how risk perception is affected by novelty BMI.

This paper is organized as follows. An analysis of the relevant literature, hypotheses, and model for the research are presented in Section 2. The methodology and data collection are described in Section 3. Results are reported in Section 4, and findings are discussed in Section 5. In Section 6, we present practical suggestions and limitations. Toward the end of this paper, future research directions are provided in conclusion.

2. The Literature Review and the Hypotheses

2.1. Risk Perception

Since the 1920s, researchers have been looking at the risk problem as a problem that must be addressed, and many fruitful results have been found in theory and practice. However, with further research, more phenomena have emerged that existing ideas cannot reasonably explain. According to Raymond Bauer, Perceived Risk is a subjective experience of objective risk that was presented at the 1960 annual meeting of the American Marketing Association. This concept was introduced into studies of human behavior for the first time by combining real and personal feelings. This section aims to sort out the relevant research results on risk perception from three perspectives—concept, connotation, and composition—to establish a theoretical framework for further research on the effects of BMI on risk perception.

A psychological concept called risk perception evolved from a psychological idea. Bauer first introduced the theory to the study of behavioral problems in 1960. According to him, a certain degree of loss is associated with decisions with uncertain results. There is a concept known as risk perception that deals with how an individual perceives these risks. Behavioral risk perception includes the uncertainty of a behavior’s outcome due to the behavior [60,61]. Cox refines Bauer’s definition by dividing conduct into two phases: before and after it occurs. When individuals perceive risk in the first stage, the primary source of their risk perception is the probability that their behavior is likely to lead to adverse consequences due to their behavior. In contrast, when they perceive risk in the second stage, they experience loss related to adverse effects due to their behavior [62].

The concept of risk perception was then simplified by scholars such as Stone, who proposed that it is based on the decision maker’s subjective expectation of loss [63]. As a result of reviewing the definitions of risk perception that scholars have developed over the past 30 years, Mellers pointed out that uncertainties are at the core of risk perception. This uncertainty and distortion are not objective phenomena but depend on individuals’ subjective attitudes and intuitive judgments [64], which profoundly affect their everyday behavior and significant life decisions [65].

2.2. Business Model Innovation (BMI)

BMI has received significant academic attention in recent years [20]. The purpose of BMI is usually to “discover a fundamentally different business model in an existing organization” [66] or to search for a firm’s new business logic and new methods of creating and capturing value for its stakeholders [67]. There has also been an increase in the demand for academic conferences and management workshops regarding business models and BMI. The BMI concept is still considered a “slippery construct.” [67]. Operations aspects must be addressed and understood to run a business successfully, including processes, links, and structures [68,69,70]. A business model’s strategic function is associated with developing the business. Accordingly, a business model supports management in defining and developing a company’s strategy [71]. As a result of BMI, new firms have a better chance of surviving for longer [72,73,74]. According to the reference, BMI plays a critical role in financial performance and sustainability [75].

2.3. Hypotheses Development

2.3.1. Aspects of Business Model Design

Based on configuration theory, business model design measures can be developed by considering holistic arrangements of design elements [76]. As a result of their interdependence, configurations combine design elements to form patterns [77]. Design elements of a business model are content, structure, and governance. Rather than seeing configuration as a deviation from an ideal type, this paper follows Miller’s [78] recommendation by viewing it as a variable. Configuration is the degree of coordination and connection between the elements of an organization, according to Miller [78].

Business models are typically designed to maximize innovation and efficiency, according to Miller [78]. Entrepreneurs can create value under uncertainty by focusing on innovation and efficiency, which makes his choice particularly appropriate for studying business models adopted by entrepreneurial firms. Entrepreneurs and existing designs can create new designs that can be reproduced and copied [79]. The emphasis on lower costs is often associated with imitation-based approaches to business creation [80]. There is no longer a need to choose between novelty and efficiency in business model design since the two can coexist simultaneously.

2.3.2. In the Design and Performance of Business Models, the Environment Plays a Modulating Role

According to our hypotheses, entrepreneurial firms’ performance is influenced by their innovative and efficient business models. There are two effects of business model design: one depends on how much value can be created by a firm’s business model; the other depends on how well the firm may use that value.

A business model can increase customer willingness to pay by improving transaction efficiency or reducing supplier and partner opportunity costs by increasing customer willingness to pay. Similarly, business models generate value based on how powerful they are compared to rival business models, or in other words, how successful they are. Each stakeholder in the business adds value to the company. The focal firm is, therefore, limited to capturing a specific value [81].

Various stakeholders claim varying amounts of value from a business model depending on its design. Based on Brandenburger and Nalebuff [82] and Brandenburger and Stuart [81], we reason that the business model design of the focal firm has a positive impact on its performance. Suppose the focal firm’s business model design creates value and does not reduce its bargaining power at the same level of competition with other stakeholders in the same business model. In that case, the focal firm will not lose weight.

An entrepreneurial firm’s performance appears moderated by environmental conditions [83]. The environment has three essential dimensions: magnitude, dynamism, and complexity [84]. Additionally, Randolph and Dess [85] and McArthur and Nystrom [83] argue that it influences new firms’ entry into the market and their survival and growth. The concept of generosity is a focal dimension of environmental uncertainty arising from the resource-dependency view of organizations [86,87]. An entrepreneurial organization that depends on external resources and is sensitive to capital and market receptivity may benefit from this approach.

2.4. Hypotheses

2.4.1. Risk Perception and Firm Performance

A risk perception concept encompasses an understanding of psychology, marketing, economics, and management, as well as rational content and perception. Risk perception significantly influences personal behavior, and modes of action are affected differently depending on the research background. The dimensional composition of risk perception will change because the research objects have other attributes. According to many scholars, six dimensions of risk are more important: financial risk, privacy risk, time risk, social risk, operational risk, physical risk, and psychological risk, while others add other dimensions based on research object characteristics such as source, delivery, and green risk. Empirical research looks at two dimensions of financial trouble, psychological and privacy time risks, based on business model innovation. The paper proposes that enterprises adopt sustainable strategies due to their perception of risk based on the dimensions summarized by previous scholars. The following are our hypotheses:

Hypotheses 1 (H1).

In high-tech SMEs, firm performance is positively affected by risk perception.

2.4.2. Efficiency BMI and Firm Performance

According to data analysis, e-business value is predominantly driven by transaction efficiency BMI. Transaction efficiency BMI increases as transaction costs decrease, in agreement with transaction costs theory [88,89,90]. An e-business with greater transaction-efficiency BMI gains reduces costs and provides more value since its value increases with lower transaction-efficiency BMI gains. In comparison to offline businesses and other online businesses, there are several ways to improve BMI’s efficiency. Providing accurate and comprehensive information will reduce information asymmetries between buyers and sellers. With the internet, data can be transmitted conveniently and quickly because of its speed and ease of use. The cost of search and bargaining can also be reduced by better understanding the customer’s needs [91,92] and opportunistic behavior [89]. By improving telecommunications, business-to-business e-businesses improve their transaction efficiency through the cheaper interconnectivity of virtual markets, enabling them to make better-informed decisions more quickly.

The advantages of e-commerce are that it simplifies transactions, reduces distribution costs, streamlines inventory management, and offers a more varied selection at lower costs. As a result of demand aggregation and bulk purchasing, individual customers can benefit from scale economies, the supply chain can be streamlined, transactions can be processed faster, and orders can be fulfilled more quickly to benefit both parties. Study shows that using an online auction format for trading cars between businesses can result in a 50% reduction in transaction costs [93]. An efficient e-business can reduce marketing, sales, transaction processing, and communication costs and maximize its value-creating potential.

In this case, Autobytel.com [94] is a good example. To assist potential auto buyers in the selection process, extensive and comprehensive information is provided about the differences between motor vehicles and the costs incurred by dealers for each model. As a result, buyers can make well-informed decisions quickly. As a result, bargaining costs are reduced substantially, and the buying process is simplified and accelerated. Sales volumes increase without marginal costs for vendors, even though margins might be lower on each sale. Although Autobytel.com’s efficiency BMI gain partially depends on the quality of contributions from its partners, the overall gain is nonetheless significant [95]. Suppose car dealers cannot deliver products without delays to customers, for example. This could result in efficiency BMI losses related to the decision-making process due to inefficiencies associated with implementing the customer’s decision. According to management literature, a high degree of networking can improve industries’ BMIs. Research on highly networked Japanese firms suggests the ability to reduce transaction costs for specialized assets is primarily due to improved information flows and reduced knowledge asymmetries [96]. Information technology is believed to reduce the cost of coordinating and executing transactions [97]. As a result, we formulate the following hypotheses:

Hypotheses 2a (H2a).

SME high-tech firms perform better when they have an efficiency BMI.

2.4.3. Novelty BMI and Firm Performance

According to Schumpeter [98], innovations have the potential to create value. An invention traditionally involves introducing a new product or service, a new production method, distribution, marketing, or tapping into new markets. Data analysis, however, shows that e-businesses are also capable of innovating in how they structure transactions, which is how they operate. In the early days of customer-to-customer bidding, eBay was the first to introduce such a service. A successful trade was possible among consumers because of this architecture. Purchasing needs and reservation prices are conveyed by individual buyers to sellers in reverse markets on Priceline.com [99]. With Autobytel [94], consumers can now shop for cars around the clock by connecting with car dealers, financial companies, and insurance companies. Throughout these cases, the companies implemented new transactional methods and alignments. Furthermore, innovative transaction methods connect previously unconnected parties, reduce inefficient buying and selling processes, discover latent consumer needs (for example, the ability to buy a car from home without haggling), or open up new markets.

There seems to be no end to the possibilities for innovation because of virtual markets. In addition to providing an array of complementary products and services, e-business firms can incorporate various exciting new ideas into their bundles of offerings. It is also essential to select the appropriate parties to participate in the innovation process in e-business. Using affiliate programs, companies can direct and intensify website traffic by enabling third parties to allow transactions from their websites.

Innovative e-businesses may be able to gain significant first-mover advantages [100]. Being the first to market with a new business method allows you to capture ‘mindshare’ and establish brand awareness and reputation, thereby creating easier switching costs. Innovations in e-business can learn and obtain proprietary knowledge, accumulate proprietary knowledge, and access scarce resources. Among our four value drivers, novelty BMI and lock-in are connected in two significant ways. E-business innovators can attract and retain customers by combining their brands with solid marketing. Being the first to market is essential to succeeding in markets with increasing returns [101,102]. Having a critical mass of suppliers and customers before others is easier for first movers because of network externalities [103,104]. An advantage over the competition is crucial when entering a market where the winner takes the most [102].

In addition to novelty BMI, complementarities also play a role. Many e-businesses combine complementary elements to differentiate their products, including resources and capabilities [105,106,107]. Using a computer retailer that provides only internet-based services, Cyberian Outpost, a U.S customer, can browse a database of 170,000 products, including information on their functionality and compatibility, and select computer configurations, accessories, and peripherals. An extensive range of complementary products from partners can be found in the database. We propose the following hypotheses based on this information:

Hypotheses 2b (H2b).

SME innovation performance is positively affected by novelty BMI.

2.4.4. The Relationship between Risk Perception and Efficiency BMI and Novelty BMI

The effectiveness of BMI business models is another reason entrepreneurs should embrace them and use them to grow their businesses. During the process of designing a new product, entrepreneurs must balance different factors, such as familiarity and unfamiliarity [108], conformity and differentiation [109], and reliability and distinctiveness [110], which are all distinct features that make up a product’s design. The ability of entrepreneurs to balance these factors is essential to establish credibility [111], which is a prerequisite for growth and success to occur [112] in their ventures. To achieve positive performance impacts, it can be necessary to combine novelty with efficiency. A novel business model will likely yield a higher return when using an efficiency BMI design. The novelty BMI impacts the switching costs of a business model and can also improve the distinctiveness of a business model due to the presence of fewer comparable alternatives. For the focus firm to gain efficiency, it may be beneficial to emphasize the novelty of its business model.

The novelty return on design may also increase due to concentrating on high-efficiency BMI designs. Business models geared towards efficiency BMI could appeal to a broader audience by creating a novel business model. As entrepreneurs, it is possible that they can create more value if they emphasize efficiency and novelty simultaneously rather than only focusing on one of these attributes. Due to the limited resources available to entrepreneurs, large amounts of design effort will likely not yield adequate results due to the limited availability of resources.

Hypotheses 3a (H3a).

High-tech SMEs’ firm performance is positively affected by efficiency BMI.

Hypotheses 3b (H3b).

Novelty BMI positively influences firm performance in high-tech SMEs. Figure 1 shows the theoretical model of the study.

3. Data Collection and Methodology

3.1. The Sample and the Procedure

In this study, high-tech SMEs in China were surveyed through a questionnaire survey. A lot of energy has been devoted to mass entrepreneurship and innovation by China since the national policy was introduced in recent years, which has led to a rise in the number of SMEs and rapid development of the Chinese economy. The importance of high-tech SMEs is exceptionally high. According to the National Bureau of Statistics, the top-20 cities had a combined economic output of 16.16 trillion dollars or 35.84% of the national economy. Over the past year, the percentage has increased by 1.2 points. According to the first half of 2019, Chengdu, the capital of Sichuan Province, had a GDP of 770.24 billion yuan, ranking eighth in China, with an actual growth rate of 8.2%. From 2016 to 2020, Chengdu ranked first in economic growth. When the mass entrepreneurship policy was implemented, Chengdu ranked 38th among Chinese cities in population and entrepreneurship.

As part of the province’s initiative to promote the start-up of science-based companies, five high-tech incubation parks have been opened, with 2000 experts on hand to guide entrepreneurs. In addition, ten decrees have been formulated to facilitate the start-up of science-based companies. SMEs in high-tech industries in Sichuan, China, were the subjects of this study. Companies that provide high-tech products and services for sustainable growth to small- and medium-sized businesses are considered high-tech SMEs in China, which possess high numbers of scientists and engineers engaged in scientific research, development, and intellectual property protection. Additionally, small- and medium-sized enterprises must have less than 1000 employees or less than 400 million yuan in total income.

Using a questionnaire survey, the data were collected. Based on systematic research on a wide range of pertinent literature at home and abroad, as well as in-depth enterprise field research, this questionnaire was developed following a strict questionnaire-development program. Survey samples are mainly distributed in 18 provinces in the East, Central, and West. March 2018 marked the start of the pre-survey phase, which ended in April 2018. A total of 310 individuals were asked to complete the questionnaires. One hundred thirty-two of these completed questionnaires were valid. Formal questionnaires are constructed based on the data obtained in the pre-survey stage and a reliable and accurate test of the data collected in the pre-survey stage.

Between June and August 2018, a formal investigation was conducted. Several enterprises were investigated, including state-owned and private companies, three foreign-funded companies, domestic holding companies, and companies engaged in electronics, appliances, machinery, new materials, energy, software, and communication. Also included in the investigation are several high-tech companies. The survey is, therefore, primarily aimed at managers in the middle and upper management ranks (including the chairman and general manager, as well as the senior managers of significant departments). To ensure the survey is objective and accurate, these aspects must be familiar with and understood by respondents who have been employed in the organization for at least three years. A total of 390 valid questionnaires were recovered, whereas 350 invalid questionnaires were recovered. There were 28 cities in the sample, and Chengdu, Mianyang, and other central cities account for 52.2% of them. A total of 34.2% of the sample comes from Ganzi, Ya’an, and other southern cities. A total of 13.6% of the sample came from the western provinces of Luzhou and Nanchong.

3.2. Measurement of Variables

As a result of the research, three primary constructs were identified, namely, firm perception, efficiency BMI, and vendor perception. Documentation of the existing literature was conducted to develop the measurement items since the questionnaire survey was primarily conducted among senior executives and well-educated business students. The higher the number of participants in the survey, the greater the reliability and validity. There is, however, a risk that the data may degrade further down the line if they are not in the proper condition. According to this survey, respondents are asked to place a rating on a 7-point scale, with 1–7 indicating the degree of agreement or disagreement they hold with each item. There is a better match between the number of respondents and the questions; the more respondents, the better. Numbers 1–7 have specific meanings, as follows: 1 implies a significant disagreement, 2 implies a significant disagreement, 3 implies a slightly disagree, 4 indicates a slight uncertainty, 5 indicates a slight agreement, 6 indicates an agreement, and 7 indicates a very good agreement.

This study tests the relationship between firm performance and the dependent variable: explanatory variables in the model include firm performance. Financial data of many companies in China (for example, those not listed) are not open to the public, and access to accurate information is difficult through formal channels. Many Chinese enterprises protect their financial data as trade secrets. The financial performance of enterprises may be influenced by the industry in which they are located, according to some scholars [78]. Direct comparisons between businesses from different industries are likely to cause misunderstandings. When analyzing samples from different industries, it is important to pay special attention to [113]. Previous studies have shown a significant correlation between objective and subjective enterprise performance measures [114]. A subjective measure can be used instead of an objective measure when we cannot obtain financial data with exact figures [115]. There has been widespread use of subjective performance in research. In this study, subjective measurement indicators are more appropriate than objective ones [116]. As a result, the scale developed by [116] is used in this paper to calculate enterprise performance by combining short-term financial performance with long-term market performance. A study conducted by [117] uses four items to measure innovations in SMEs: the company is more innovative in developing new products, developing more recent technologies, and launching new products more rapidly than other companies in the same industry. A significant portion of this company’s revenue comes from its new product sales and because it launches new products faster than most other companies.

Independent variable: The Enterprise Risk Perception measures organizations’ capacity and policies when managing risk. Psychology encompasses the concept of risk perception. Risk perception can be defined in a variety of ways. The authors of Sitkin and Weingart (1995) [118] define risk perception as a person’s assessment of the risk associated with different situations. A key component of this process is determining the probability of uncertainty in various situations, the controllability of this uncertainty, and the level of confidence in the assessment. According to Ortwin Renn (1998) [119], a domestic scholar, risk perception is defined by previous studies as people’s intuitive judgments and attitudes towards risk and is the psychological feeling and subjective understanding of individuals when faced with various objective risks. Scholars have widely recognized risk perception through the definition given by Zhao et al. (2021) [120]. Similarly, this paper adopts his definition of risk perception and cites his measurement.

Four items from [121] survey enterprise risk perception with reliability. In emerging firms, these items are credible and have satisfactory convergent validity. Identifying significant risks and opportunities is part of our standard procedure.

Mediator variables: Model explanatory variables include business model innovation. Previous studies have focused on case analysis, so it has always been a problem worth studying regarding business model innovation measurement methods. Business model innovation empirical research has always been lacking in scale development, and large samples are relatively scarce for empirical analysis. In the research Cheng conducted, the key point was that differences in industry differentiation led to varying levels of business model innovation that greatly affected the universality of the research. Using empirical research methods, Zott and Amit (2007) [4] developed appropriate scales and tried to make research on business model innovation more comparable by employing relevant scales. In their research, Zott and Amit (2008) [69] conducted empirical verification to measure the effectiveness of the designed scale and obtained positive reliability and validity results. Several scholars also recognized it at the same time. Business model innovation is also measured using this scale in this study. A strategic BMI scale developed by [72] is used to measure BMI, which is divided into efficiency BMI and novelty BMI. It is relatively easy to create multiple products and services based on the same resource because there are few conversion costs and difficulties. It is possible to convert a resource into various products and services quickly. The same resource can often be used for more than one purpose. The efficiency BMI provides a way for enterprises to identify opportunities in the future and respond to them faster than they are presently able to. Existing companies are better equipped to discover and combine new resources than competitors, and potential competitors have more time to explore new markets than existing ones can. Organizational systems can be changed more rapidly by potential competitors and enterprises to support their strategic adjustments.

Controls: Schumpeter’s theory states that large companies are more likely to invest in technological innovation. As well as that, certain companies are almost innovative if they can survive and grow for a long time on the market. We use R&D investments, enterprise personnel sizes, and enterprise ages to control for these variables.

3.3. Data Analysis Method

This paper analyzes the data using Spss22.0 and Amos26.0 to perform exploratory factor analysis and confirmatory factor analysis on the data to analyze the factors. Afterward, SEM is used to conduct analysis after building a model, fitting it, evaluating it, and correcting the situation. There is no doubt that reliability is an indicator of test results’ consistency, reliability, and stability. Correlation coefficients are used to determine how reliable a hand is. Test results are more likely to be reliable and stable when the reliability coefficient is higher.

In addition to validity and reliability having connections, there are also differences between them. Validity is primarily used for judging whether or not the measurement results are accurate and reliable as a result of the measurement. To test the model’s fit and the validity of the measurement scale based on the measurement model established in Amos26.0 software, a confirmatory factor analysis was conducted to determine the validity of the measurement scale and the model’s fit.

After the structural validity of each scale has been verified, it is generally necessary to calculate the correlation between each of the variables. In terms of statistical analysis, correlation analysis is one of the methods used to study the relationship between two or more variables identified at the same point in time. When trying to express how a variable is correlated with another variable, we often employ the concept of the correlation coefficient. This paper uses Spss22.0 software to determine whether there is a correlation between the variables by conducting a correlation analysis while utilizing Spss22.0 software to analyze the correlation. Using the structural equation model analysis method, the relationship between independent and dependent variables can be explained by combining confirmatory factor analysis and path analysis. To analyze the structural equation model, three steps were followed in this study:

(1)

Developing a structural equation model from scratch. A measurement model and a structural model for each research variable are generated according to the research hypotheses and then converted into a measurement model and structural model according to the measurement model. A structural equation model is then constructed based on this initial structure equation model.

(2)

The structural equation model is modified to account for the updated results. Incorporating the survey data into the Amos26.0 software, running it to generate the initial structural equation model operation results, and then modifying the structural equation model based on the fitting index value until the optimal structural equation model has been obtained are the steps you must take to get the optimal structural equation model.

(3)

Verify the hypotheses developed in the research. Amos26.0 software should be used to evaluate the ideal structural equation model and discuss the results to test whether the hypotheses are valid.

4. Results

4.1. Reliability and Validity of the Scale

This paper uses the statistical analysis software Spss22.0 to test the reliability of the scale of risk perception and novelty BMI, efficiency BMI, firm performance, and Amos 24.0 test validity. The test results are shown in

Table 1. Using AVE, C.R. and alpha values to load items is considered standard procedure.

Factors to Consider	Item	Std. Distinction	Skewness	Kurtosis	Loading Standardization	AVE	CR	ALPHA
F3 Firm performance	FP1	1.726	−1.016	0.236	0.654	0.59	0.85	0.76
	FP2	1.748	−0.941	−0.01	0.808
	FP3	1.785	−1.06	0.19	0.846
	FP4	1.799	−0.855	−0.137	0.772
F4 Novelty BMI	NB1	1.787	−0.989	0.062	0.767	0.54	0.77	0.88
	NB2	1.763	0.418	−0.864	0.698
	NB3	1.701	0.61	−0.46	0.611
	NB4	1.711	0.509	−0.655	0.632
F2 Efficiency BMI	EB1	1.73	0.531	−0.563	0.782	0.50	0.80	0.72
	EB2	1.89	0.649	−0.474	0.708
	EB3	1.805	−0.928	0.079	0.693
	EB5	1.681	−1.076	0.316	0.655
F1 Risk perception	RPT1	1.7	0.566	−0.61	0.669	0.51	0.85	0.68
	RP2	1.921	−1.02	0.326	0.649
	RP2	1.659	−1.094	0.367	0.769
	RP3	1.696	−1.016	0.236	0.747
	RP4	1.675	−0.941	−0.01	0.736
	RP1	1.574	−1.06	0.19	0.654

.

We find that Cronbach’s a value of all items of the four variables is greater than 0.7. Therefore, all items of the questionnaire meet the requirements, and there is no need to delete any items, which also proves the reliability of the scale.

Because the validated maturity scale is used, confirmatory factor (CFA) analysis is used in the validity test. First, look at the construct validity of the scale. According to the output results, x²/df is 1.502, less than 3, which is ideal for adaptation; the value of RMSEA is 0.034, less than 0.05, and the adaptation is ideal; GFI is 0.929; more than 0.9, ideal adaptation; AGFI is 0.953. If it is greater than 0.9, the adaptation is ideal; TLI is 0.919; if it is greater than 0.9, the adaptation is ideal. In general, risk perception, novelty BMI, efficiency BMI, and firm performance models are well adapted. Then, look at convergence validity, which is shown in Table 1. Using the data shown in Table 1, there is more than a 0.5-factor load across all the observed variables, exceeding the acceptance standard of significance for factor loading. Furthermore, the test results show that the C.R. value represents more of a factor than the 0.7 value. At the same time, the AVE value of the questionnaire is also acceptable by the accepted standard of 0.5, which suggests that the questionnaire is generally valid and convergent. Indicators of discriminant validity are positive in Table 1, as one can see from the results of the discriminant validity test. A simple correlation coefficient is observed between the variables, and the square root of their AVE values is found to be lower than the correlation coefficient of the variables. The scale used in this paper complies with both the requirements of validity and reliability as a result.

4.2. Hypothesis Testing

Several latent variables in management are neither accurate nor directly measurable, which SEM can address adequately. Further, it is equipped with a measurement model capable of handling multiple dependent variables simultaneously, is highly elastic, and allows independent and dependent variables to be included with errors simultaneously. The advantage of this method is that it allows you to estimate the specific factor structure and the relationships between the factors. It can also estimate the degree of fit of the whole model by using the model’s regression coefficients. Models are constructed, fitted, evaluated, and corrected during statistical analysis. The ranges of fitting degrees used by SEM and their commonly used indexes are presented in

Table 2. The range of evaluations for fitting degrees.

Title	Dimensions	The Value of a Judgment
x2/df	–––	p < 2
CFI	0~1	>0.09
IFI	0~1	>0.09
TLI	0~1	>0.09
SRMR	0~1	<0.05
RMSEA	0~1	<0.05

.

Based on the research model proposed in Figure 1, the conceptual model built in this study, AMOS26.0, is used to build the path map of the initial SEM. The figure contains 4 latent variables and 18 explicit variables. It includes an external derivative variable of risk perception (i.e., the independent variable in the study), and three endogenous variables of novelty MBI, efficiency BMI, and firm performance (i.e., dependent variables in the study).

In the model, there are residual variables of 18 explicit variables in e1–e18 and 3 internal derivative variables in e19–e21. The default value of their path coefficients is 1. This study will verify the five influence paths set in the model, which can be seen in

Table 3. Analyzing fit indices based on the recommended and actual values.

Coefficient of Correlation for Overall Fitting
x2/df	RMSEA	TFI	CFI	NFI
I	–––	F	–––	I
2.259	0.061	0.910	0.925	0.87
0.81	–––	–––	–––	–––

and

Table 4. AMOS estimated results.

			Estimate	S.E.	C.R.	p
F2	←	F1	0.552	0.085	6.472	***
F4	←	F1	1.4	0.112	12.515	***
F3	←	F2	−0.137	0.041	−3.319	***
F3	←	F1	0.869	0.135	6.456	***
F3	→	F4	0.209	0.093	2.247	*

Note(s): *, p < 0.05; ***, p < 0.001.

. The results of the initial SEM model are shown in Table 4, which illustrates the relevant results based on these results. All available evidence supports the hypotheses.

In the context of this paper, the x²/df statistic can be used to test the similarity between a sample covariance matrix and an estimated covariance matrix by directly comparing both. According to theory, x²/df should have an expected value of 1. In general, the closer x²/df is to 1, the greater the similarities between the covariance matrices of the sample and the estimated covariance matrices, and therefore, the better prediction we can expect from the model. A structural equation model based on the initial data was developed with Amos26.0 to analyze the results of the operations to clarify how well the correlation made between the variables matched the actual data of the operation. It is evident from Table 3 that the initial model is equipped with an x²/df value of 2.259 (degree of freedom = 646), which indicates a good fitting effect. A TLI of 0.910 and a CFI of 0.925 are both significant values for both the initial model and the test model; these values are all more significant than 0.9, which is the standard that all values must meet; RMSEA is 0.061, and SRMR is 0.81, both of which are well below the average of 0.8. The model initially fit well to the data given by the sample, as shown by the model fitting index at the beginning. All the hypotheses in this paper have been confirmed, and the hypothesis test results are shown in

Table 5. Hypothesis test results.

Hypothesis	Results
Firm performance is positively affected by risk perception	supported
SME high-tech firms perform better when they have an efficiency BMI	supported
SME innovation performance is positively affected by novelty BMI	supported
High-tech SMEs’ firm performance is positively affected by efficiency BMI	supported
Novelty BMI positively influences firm performance in high-tech SMEs. Figure 1 shows the theoretical model of the study	supported

and in Appendix A.

5. Discussion

This study aims to examine the effect of risk perception on the performance of small- and medium-sized enterprises and whether strategic MBI can be used as an intermediary in improving firm performance through manipulating risk perception. The study suggests that SMEs in the high-tech sector are more likely to perform well if they perceive risk positively and engage in strategic MBI. However, capacity tenderness is more important when it comes to high-tech SMEs.

The complexity, cost, and risks of innovation are evident from this. As a result of risk perception, technology-based SMEs may have difficulty achieving successful innovations using their resources. According to quantitative research, small- and medium-sized enterprises’ performance is impacted by risk perception. Meanwhile, organizational MBI improves firm performance by acquiring external knowledge and resources and reducing innovation costs and risks. In this case, the conclusion supports RBV theory and is similar to Rehman and Anwar, 2019 [121] and Yang et al., 2018 [122]. According to their findings, RBV theory enhances enterprise firm performance.

As a result, capability tenderness has a more significant influence on firm performance. A management MBI is often a function within the context of one management strategy. An example is when a small- or medium-sized enterprise is technology-driven and mainly represented by one strategy. A technology, environmental, and long-term strategy can be included as part of this strategy. Additionally, there is a technological innovation management capability, which consists primarily of the capability of the company to innovate, conduct research and development projects, and manage and reserve its R&D resources to produce technological innovations. Managing the current market demand for a product is the third capability to increase current market demand and predict and develop future market demand for the same product.

BMI was significantly impacted by enterprise risk perceptions in this study. A strategic performance model for innovative organizations should include risk management practices, as reported by Etges et al. (2017) [123]. Many companies, however, still choose not to employ any risk management procedures in their BMI processes [124]. Boic and Dimovski (2019) [125] found that BMI significantly impacted the effectiveness of early-stage entrepreneurial ventures and was positively linked to their ability to pursue both exploratory and exploitative innovation. Furthermore, innovation positively impacts business performance [126]. According to Liem (2018) [127], enterprises’ perception of risk is also associated with their performance in the banking industry. A recent study revealed, however, that there is no significant connection between BMI and the environmental performance of financial firms.

A study examining the impact of risk perceptions on firm performance found that the indirect influence of risk perception influenced firm performance significantly. In contrast, the direct impact of BMI was not significant. This means that BMI can fully mediate the relationship between enterprise risk perception and firm performance from a firm perspective. Taran et al. (2013) [124] described how enterprise risk perceptions improve firms’ business models, which leads to their profitability. However, enterprise risk perception practices’ direct and indirect impacts significantly affect firm performance. A recent study by Dellermann et al. (2017) [128] found that firms’ customer-directed value is enhanced by implementing risk strategies through digital BMI.

Models for Evaluating Risks during Innovation

Different approaches have been taken to the issue of innovation in the literature [129]. Hansen and Wakonen (1997) [130] define innovation as a novel asset or a revolutionary process. Undoubtedly, “innovation” is becoming an essential part of the work of companies, but it is also being seen as a barrier to sustainable growth by Pittaway et al. (2004) [131]. From an economic perspective, Freeman and Soete (2008) [132] can be translated as a distinct combination of resources that plays a role in innovation commercialization.

Since uncertainty is a natural part of the innovation environment and given the definition under which risk is conceptualized as the effect of uncertainty on objects, and given the fact that uncertainty is an inherent part of the innovation environment, it is evident that innovative enterprises have a significant amount of risk involved. According to the International Organization for Standardization, uncertainty is the state of a deficit in information concerning an event. To facilitate the ability of these companies to anticipate how they will deal with the uncertain environment of the market analyzed, the analysis and management of these risks can be translated into activities that aim to assist them in doing so. By fostering actions that may well end up contributing to the company’s bottom line [133], these actions may well contribute to the company’s bottom line. Taking into consideration O’Connor et al. (2008) [134], we can see the necessity to encourage companies to deal with their risks systematically so that they can advance their innovations in a way that enables them to balance the high degree of uncertainty with dynamic and flexible environments that allow for successful innovation to happen.

A risk management process is designed to assist a company in analyzing its risks and managing them accordingly. It can be summarized that risk management is the process of guiding companies in identifying the risks they must face, those that they should ignore, and which are to be hedged or eliminated to maximize the performance of their organization [135]. It also assists them in identifying the risks that they should exploit to maximize their performance. There is a need for corporate risk management to become tailored to the specific business needs and to be analyzed at a corporate level, as emphasized by Frigo and Anderson (2011) [136]. If only applied to analyzing the feasibility of a particular project, as they do, it offers only an individual analysis. Therefore, it does not provide a complete perspective on the risk exposure of a company’s business when applied only to evaluating a particular project’s feasibility. This article emphasizes the issue of the lack of risk management models in the innovative market, as mentioned in the introduction of the article, which is one of the reasons for which this article has been written. Another factor that throws the importance of risk management into relief is the period between the emergence of an innovative idea and the launch of the product itself. In most cases, this is because there is a direct relationship between the risk element’s importance and the project element’s overall duration [137]. A significant and motivating factor in utilizing analytical practices and risk management strategies has been considering the time difference between the idea’s conception and the moment the newly created product is launched on the market [135].

For the COSO methodology to add value to the risk management process and focus on corporate risk specific to organizations, it has been developed as part of the Treadway Commission (Committee of Sponsoring Organizations, 2007). Enterprise risk management is defined as a process that identifies the events that can potentially negatively impact the company’s operations and address them before they occur. This concept gave rise to enterprise risk management being applied to the company’s strategy. COSO put forward a new system of risk management to achieve this goal, using the Exchange of Information and Risk Management (ERM) as a tool for creating and preserving the value of a company, as discussed in Hayne and Free (2014) [138]. As a structure, enterprise risk management can be defined as assessing all the risks an organization is exposed to, including the risks at the corporate level and the risks at each business unit. It proposes that risk management be a continuous process that should be integrated into all aspects of the organization.

PMBOK, or Project Management Body of Knowledge, is a collection of methodologies describing how a project management process can be structured so that the management of risks gets preference and projects are given priority (Project Management Institute, 2008) [139]. Although companies worldwide widely use PMBoK, certain researchers criticize it because it does not contain the concept of how value flows to a project as it pertains to a project [139]. There is no doubt in their minds that non-traditional project management methods, such as those that take into account the flow of activities that do not contribute to the development of the project plan, as opposed to PMBoK, have already been applied to both the software and construction industries and have a significant reduction in risks because they allow the opportunity to work with uncertainty during a project as it emerges.

6. Conclusions

According to the results of our research, senior managers and policymakers should take into account some of the implications of this work. As we found in our study, there is no direct correlation between firms’ profitability and enterprise risk perception practices. However, BMI does have a significant effect on this path. Our findings indicated that enterprises’ risk perception practices indirectly influenced financial performance by developing an effective model. According to our research, enterprise risk perception practices impact nonfinancial and environmental performance equally.

This study found that BMI mediated the relationship between enterprise risk perceptions and nonfinancial performance by partially affecting both enterprise risk perceptions and nonfinancial performance and partly affecting enterprise risk perceptions and financial performance. It should be noted, however, that financial firms can still construct an adequate BMI by analyzing enterprise risk perceptions. In light of the findings we have produced, we found that promoting enterprise risk perception practices contributes positively or negatively to the performance of financial firms directly or indirectly. This is an era of globalization where managers and responsible executives need to be competitive in the market to gain superior performance in their companies. However, building a BMI is not automatic; it takes talent and resources to do so [140,141].

The findings of our study suggest that financial institutions should apply enterprise risk perceptions to produce a useful BMI since it is a significant predictor. Instead of measuring economic performance, BMI measures both financial and nonfinancial performance. The banking sector, in particular, should be able to increase its institutions’ financial and nonfinancial performance by taking advantage of their BMI. As a result, financial firms are advised to investigate other factors related to environmental performance to help them better understand why BMI may not be the most accurate indicator.

Two critical implications appear from this study: firstly, financial firms need to take steps to improve their enterprise risk perception practices, as these practices are responsible for facilitating their BMI, which is a key factor in their high performance. Second, financial companies and banks need to study why BMI does not influence their environmental outcomes and how to make it better to engage in social and ecological activities. We recommend that policymakers and the Chinese government implement programs to improve enterprise risk perception practices within financial institutions and banks. It is possible to reduce potential business threats by educating employees on financial literacy, integrating enterprise risk perception practices, predicting financial outcomes, mitigating risks, and forecasting future outcomes. It is further recommended that financial institutions adopt a structured framework for establishing an adequate BMI that can be used for improving economic and nonfinancial performance by establishing an adequate framework for enterprise risk perception. In addition, companies’ top management and responsible executives are encouraged to investigate practices that enhance the environment thoroughly. To ensure the validity of the findings, these recommendations can be applied to other countries, such as emerging economies and developed economies, so that the results are applicable and scalable.

To explain how enterprise risk perceptions affects financial firms’ performance in the long run, this study attempted to investigate how enterprise risk perceptions influence financial firms’ performance in the long run by examining how enterprise risk perceptions affect the performance of financial firms. A total of 350 firms in the Chinese financial sector were surveyed using a structured questionnaire. Using SEM, we tested the research hypotheses. As the results indicate, a significant impact on financial firms’ performance is seen as a result of enterprise risk perception practices. As a result of this analysis, there is strong evidence that BMI affects companies’ financial and nonfinancial performance. The environmental performance, however, does not appear to be influenced by it in a significant way. In terms of how enterprise risk perception practices are related to financial performance, the BMI fully mediates the relationship between the two factors. Enterprise risk perception practices partially mediate the relationship between nonfinancial performance and those that entail enterprise risk perception practices. Still, the relationship between those practices and those that involve enterprise risk perception practices and sustainability does not include that practice.

Limitations and Future Directions for Research

While this study has substantial policy implications, it has some limitations that will need to be addressed in future research. As a result of this study’s sampling frame and population, we have found the first limitation. According to the instant analysis, only Chinese companies were surveyed, so it may not represent how financial firms work in emerging and European markets, which may generate an unfair picture. When looking at the evidence from other markets and integrating the evidence into one’s own, one can gain a much broader perspective. In addition, the fact that our study applied standard method variance to data collected from a cross-sectional sample was also a disadvantage. Several managers and executives should be interviewed to better understand the threat, which would benefit the investigation. If possible, the model should be tested with reported data since these data will assist researchers in providing trustworthy insight into future studies, allowing them to use the model for testing. There was a third limitation associated with the conceptual framework in this study. The performance of banking and financial firms (financial, nonfinancial, and environmental) can be affected by enterprise risk perception practices using BMI as a mediator. In addition to the limitations mentioned above, this study also has other limitations that should be considered when interpreting the findings. Quite a few studies have shown that cross-sectional data are useful in research relating to business and management, but, despite their usage in business and management research, cross-sectional data represent a single point in time, making it difficult to determine the cause and effect or impact of changes over time. In our selection questions, we explicitly used a 24-month time frame to establish whether the SMEs were involved in BMI, and we tested models with alternative causal pathways, and some performance indicators were observed. However, we would like to suggest a more robust test of longitudinal data [142] that will provide a more comprehensive picture of the performance indicators. Several complicated factors are involved in longitudinal research, such as the need to obtain larger samples to account for sample mortality and care more about controlling external, dynamic factors. There is also the possibility that the sample size for specific subcategories can be somewhat skewed because of the small sample size. Furthermore, there is no sampling frame covering all SMEs involved in business process outsourcing. Final note: although there was a high degree of relevant knowledge among the respondents—primarily top executives—all measures, including evaluation of firm performance, were based on subjective self-evaluations by all respondents.

The existing framework makes a clear contribution. As a result, the model can be extended to include other relevant moderators and mediators to make the findings more meaningful. Several studies have suggested that risk management is associated with innovation, including da Silva Edges and Cortimiglia [143]. There is a great deal of uncertainty about how enterprise risk perception practices are related to the types of innovations that are promoted—products, processes, organizations, and marketing. We also could not control the demographics of the managers, like their qualifications and ages, as well as their income and experience, which is a fourth factor that contributed to their performance. Therefore, these factors can be controlled in future studies to reduce spurious results.

Moreover, it is important to note that enterprise risk perception significantly impacts enterprise strategic planning in managing enterprise risk [144]. To eliminate the common bias associated with research methods, future studies should collect objective measurements to eliminate subjective or objective biases. However, it may be difficult to gather microdata from sources such as statistical offices that are both subjective and objective. However, it is important to note that we focused mainly on internal items within the organization. However, we are also aware that external forces may often reduce the effectiveness of BMI programs, depending on the external circumstances.

Furthermore, we have found from many case studies that we have conducted and published that those external dynamics can also play a disruptive role in BMI. Quantitative validation of some of our qualitative findings is, however, what we aim to do in this paper. This paper focuses on the mediating role of organizational capabilities (including the importance of a positive, innovative, and entrepreneurial culture) that contribute to achieving BMI and how that impacts firm performance.