Entrepreneurial alertness and business model innovation: the role of entrepreneurial learning and risk perception

Abstract

Although business model innovation has received increasing attention in recent years, there are gaps in existing literature concerning why innovation occurs. Drawing on the intentions model and knowledge-based view, this study explores the relationship between entrepreneurial alertness and business model innovation, proposing a moderated mediation framework to handle the questions of why some entrepreneurs achieve business model innovation successfully while others do not. Based on a dataset of 150 firms in northwest China, this study finds that (1) entrepreneurial alertness facilitates business model innovation; (2) explorative learning and exploitative learning mediate the relationship between entrepreneurial alertness and business model innovation; and (3) risk perception moderates the mediating effects of different types of learning and then affects the relationship between entrepreneurial alertness and business model innovation. Specifically, with the increase of risk perception, the mediating role through explorative learning is weakened, while the mediating effect through exploitative learning is enhanced.

Full text

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Entrepreneurial alertness and business model innovation:
The role of entrepreneurial learning and risk perception
Abstract Although business model innovation has received increasing attention in recent years, there are gaps
in existing literature concerning why innovation occurs. Drawing on an intentions model and knowledge-based
view, this study explores the relationship between entrepreneurial alertness and business model innovation,
proposing a moderated mediation framework to handle the questions of why some entrepreneurs achieve business
model innovation successfully while others do not. Based on a dataset of 150 firms in northwest China, this study
finds that (1) entrepreneurial alertness facilitates business model innovation; (2) explorative learning and
exploitative learning mediate the relationship between entrepreneurial alertness and business model innovation;
and (3) risk perception moderates the mediating effects of different types of learning and then affects the
relationship between entrepreneurial alertness and business model innovation. Specifically, with the increase of
risk perception, the mediating role through explorative learning is weakened, while the mediating effect through
exploitative learning is enhanced.
Keywords Entrepreneurial alertness · Business model innovation · Exploitative learning · Explorative
learning · Risk perception
Introduction
Calls for “public entrepreneurship and innovation” in China have sparked the startup of many new ventures in
recent years. The majority of these are in the high-technology sector. While the start-up stage for these high
technology ventures is relatively straightforward, it is much harder for these companies to survive and develop.
Because new start-up firms are usually subject to the “liability of newness”, experiencing phases of slack, lack of
resources and lack of legitimacy, they must continue to innovate to survive, develop, and grow (Knockaert,
Bjornali, Erikson 2015). Business model innovation thus becomes a new and important approach for young
ventures in the start-up stage allowing them to sustain themselves and achieve success over the long run. Many
start-up ventures gain success by engaging in business model innovation. For example, Mobike (the shared bike
company) and Alipay (the mobile payment company) both stand out from their competitors precisely because of
their innovative business models. Yet, while business model innovation is critical to the success of new ventures,
an understanding of the emergence of business model innovation is scare (Foss and Saebi 2017), with scholars
noting that the mechanisms leading to business model innovation being under-examined (Snihur and Zott, 2019).
For new ventures in the early stage of development, entrepreneurs play a dominant role (Baron, Tang, and
Hmieleski 2011). For instance, entrepreneurs’ characteristics strongly shape the process of business model
innovation (Sosna, Trevinyo-Rodríguez, and Velamuri 2010; Martins, Rindova, and Greenbaum 2015).
Entrepreneurial alertness is a distinctive characteristic which differentiates entrepreneurs from non-entrepreneurs
(Kirzner 1979). It allows entrepreneurs to scan and search for new information, to associate and connect previously
disparate information, and to evaluate and judge whether the new information represents an opportunity (Tang,

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Kacmar, and Busenitz 2012). Entrepreneurial alertness involves skills and abilities in collecting information and
recognizing opportunities (Baron and Ensley 2006), and can greatly aid entrepreneurs in finding approaches that
achieve business model innovation. Given this, we propose that entrepreneurial alertness is an important
antecedent of business model innovation.
In practice, lots of entrepreneurs identified the overlooked possibilities through their alertness, however, they
don’t know how to convert these possibilities into reality to create new value, in other words, not all the
entrepreneurs who exhibit entrepreneurial alertness eventually achieve the business model innovation (Snihur and
Zott, 2019). Why is it that only a small number of entrepreneurs are successful in achieving business model
innovation, while others are not? In this paper, we work to reveal the mechanism between entrepreneurial alertness
and business model innovation. Based on the intentions model, we argue that intentions and subsequent actions
are driven by perceptions of what is desirable and feasible (Krueger 2007). That is to say, only when entrepreneurs
identify options that are both desirable and feasible can business model innovation be achieved. Entrepreneurial
alertness provides an approach that focuses only on whether the entrepreneur feels that business model innovation
is desirable. To reveal the mechanism of entrepreneurial alertness and business model innovation, we need to put
forward a mediator which can make entrepreneurs feel their actions are more feasible. According to the knowledge-
based view (Kogut 1992; Spender 1996), entrepreneurial learning will play a role in increasing a sense that actions
are feasible. By building and extending the knowledge base, entrepreneurial learning helps entrepreneurs obtain
the knowledge and abilities they need, and thus that it is feasible to take action. In this way, entrepreneurial learning
mediates the relationship between entrepreneurial alertness and business model innovation.
The business model innovation process is perceived as risky because it contains various ambiguous and/or
uncertain steps (Osiyevskyy and Dewald 2015). Under such circumstances, risk perceptions will also affect the
learning type that entrepreneurs prefer to take, and, in turn, will have effects on the business model innovation
process. An entrepreneur’s risk perception is defined as the entrepreneur’s assessment of the risk inherent in a
situation (Jackson and Dutton 1988; Sitkin and Pablo 1992; Sitkin and Weingart 1995). When the new business
model is being conceptualized, entrepreneurs will perceive the uncertainty and unpredictability of the situation
and environment (Giesen, Berman, Bell, and Blitz 2007; Sosna, Trevinyo-Rodríguez, and Velamuri 2010).
Entrepreneurs with different risk perceptions may adopt different types of learning, which may have different
effects on business model innovation.
We empirically examine these ideas based on a sample size of 150 ventures in northwest China. Specifically,
our research empirically investigates the positive effect of entrepreneurial alertness on business model innovation,

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and the role that entrepreneurial learning and risk perception play in this process. Thus, this study contributes to
research on the important topic of business innovation by developing a moderated mediation framework to clarify
the relationship between how entrepreneurship alertness simulates business model innovation through
entrepreneurial learning and risk perception.
Theoretical background
Business model and business model innovation
A business model articulates the logic, the data and other evidence that supports a value proposition for the
customer, and a viable structure of revenues and costs for the enterprise delivering that value (Teece 2010). In
short, it speaks to the question of “how a company generates income or earns money” (Morris, Shirokova and
Shatalov 2013). According to Amit and Zott (2001), the three core elements of the business model are: content,
structure, and governance. Content refers to the activities in the system; the structure is the way activities are linked;
and governance involves the governing transactions, which involves control issues and who is in charge of what
activity (Amit and Zott, 2011; Snihur and Zott, 2019). Thus, the essence of business model innovation involves
adding new activities, linking activities in a new way, or governing transactions in a new way or through new
partners (Zott and Amit, 2007; Snihur and Zott, 2019).
Business model innovation has received wide attention from researchers not only because it reflects value
capture and creation (Amit and Zott, 2011), but also because the usefulness of the business model is expected to
help entrepreneurs increase their chances of success (Trimi and Berbegal-Mirabent, 2012). Thus, research on how
entrepreneurs achieve business model innovation has enormous practical and academic value. However, only a
few studies to date have considered the drivers of business model innovation at the individual level. For example,
some research identifies individual creativity (Svejenova, Planellas, and Vives, 2010), managers’ analogical
reasoning (Martins et al., 2015), or founders’ thinking patterns and behavior (Snihur and Zott, 2019) as having an
influence on business model innovation. Amongst these studies, most involve either conceptual or qualitative
research, with quantitative empirical research on business model innovation remaining quite rare (Amit and Zott
2015).
Intentions model and entrepreneurial alertness
We draw on the intentions model proposed by Krueger to understand how to achieve business model innovation.
It offers a significant framework for understanding and predicting entrepreneurial intentions and activities

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(Krueger, Reilly, and Carsrud 2000). It has been successful in investigating the cognition of individuals and their
resultant behavior (Shepherd, Krueger 2002). The core of the intentions model is that an individual’s perception
of the perceived desirability and the perceived feasibility affects the intentions and subsequent actions (Krueger,
Reilly, and Carsrud 2000; Kruger 2017). The perceived desirability of entrepreneurship can be envisioned as the
psychic utility expected from the outcomes of entrepreneurship (Douglas 2017). The perceived feasibility is linked
to the entrepreneur’s belief that they are capable of successfully completing the tasks involved in the most desirable
entrepreneurial opportunity (Douglas 2017).
The extant literature shows that entrepreneurial intentions and their subsequent actions are significantly affected
by the entrepreneur’s cognition (e.g. Krueger 1993; Krueger and Brazeal 1994; Krueger et al. 2000; Krueger and
Kickul 2008). Moreover, as the drivers of the difference between entrepreneurs and ordinary people in mental
representations and interpretations (Kirzner 1979), entrepreneurial alertness is an important component of
entrepreneurs’ cognition that also affects entrepreneurial activities. Kirzner (1979) defined alertness as an
individual’s ability to identify opportunities which are overlooked by others. Tang, Kacmar and Busenitz (2012)
offered a model involving three distinct elements to define and measure: scanning and search, association and
connection, and evaluation and judgment. The literature has shown that entrepreneurial alertness can create new
solutions for business situations that will lead to more valuable and concrete business opportunities (Puhakka
2011). Previous studies of entrepreneurial alertness had paid much more attention to the definition and antecedents
of alertness, as well the function in discovering opportunities (Busenitz 1996; Ma and Huang 2016; Patel, 2019),
but have focused far less often on how entrepreneurial alertness can affect the potential outcomes of ventures.
From existing research, we know that entrepreneurial alertness helps entrepreneurs to process the cues from the
environment so that they can identify more opportunities. However, only if entrepreneurs convert these
opportunities into reality, will they actually create new value and achieve success. Entrepreneurs will first perceive
the entrepreneurial activity as desirable, based on the opportunities recognized by entrepreneurial alertness. If they
should perceive this activity is feasible, then they will put this into action.
Knowledge-based view and entrepreneurial learning
The knowledge-based view (KBV) highlights the central role of knowledge in the creation of strategic
opportunities. Knowledge is the main source of value creation and competitive advantage (Grant 1996; Spender
1996). It represents new potential sources of revenue (Foss, Lyngsie, and Zahra 2013). According to the KBV,
innovation is the product of a firm combining different knowledge to generate new applications (Kogut and Zander
1992). A firm’s existing knowledge base limits its scope and capacity to comprehend and apply novel knowledge

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to radical innovations (Zhou and Li 2012). From this perspective, the development of sustainable competitive
advantages depends on the firm’s unique knowledge base. Therefore, in order to achieve innovation, knowledge
acquisition is necessary for entrepreneurs.
One important method that new ventures use to acquire knowledge is through entrepreneurial learning, which
is an effective way to create a competitive advantage. Entrepreneurial learning is an experiential process where
individuals continuously develop their entrepreneurial knowledge throughout their professional lives in order to
build their skills and effectiveness in starting up and managing new ventures (Politis 2005). It is through this
process of learning that entrepreneurs accumulate and update their knowledge base (Minniti and Bygrave 2001).
The core of entrepreneurial learning is “what entrepreneurs should or do learn during the process of exploring and
exploiting an entrepreneurial opportunity in the creation of new ventures or management of existing firms” (Wang
and Chugh 2014, p. 24).
Two generic types of entrepreneurial learning identified by March (1991) are: exploration and exploitation.
Exploration refers to firms striving to develop capabilities to excel at the creation or acquisition of new knowledge
(Bierly and Daly 2007). Explorative activities include search, variation, risk taking, experimentation, play,
flexibility, discovery, and innovation (March 1991). Knowledge acquired by explorative learning is often distinct
from a firm’s existing knowledge base (Katila and Ahuja 2002). Exploitation involves firms developing
capabilities to excel at leveraging existing knowledge to create new organizational products and processes in rapid
fashion (Bierly and Daly 2007). Exploitative activities include refinement, choice, production, efficiency, selection,
implementation, and execution (March 1991). Exploitative learning is a directed search that limits the variety of
types of knowledge to be explored (McGrath 2001).
In sum, there is a great deal to be gained from improving our understanding of the relationship between business
model innovation, entrepreneurial alertness, and entrepreneurial learning. To examine the effects of
entrepreneurial alertness on business model innovation, and the mechanism of this process, this research develops
a moderated mediation framework based on the intentions model and knowledge-based view (KBV). Figure 1
summarizes the key constructs and hypotheses and shows the moderated mediation framework.
Insert Fig. 1 Conceptual Model about here
Hypotheses
Entrepreneurial alertness and business model innovation

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Entrepreneurial alertness helps entrepreneurs in searching for, associating, and evaluating environmental
information (Tang, et al. 2012). These processes contribute to the recognition of customers’ unfulfilled needs, new
activities in new markets, and constraints of the environment, all of which greatly aids new ventures in finding the
new activities, new way of linking activities, and / or new ways of governing transactions, thus leading to business
model innovation (Zott and Amit 2007).
First, entrepreneurial alertness helps entrepreneurs to perceive, discover and meet customers' needs by searching
for information in the environment; this is one of the core processes for achieving business model innovation.
Entrepreneurs often struggle with how to understand and satisfy customers’ needs better. Entrepreneurial alertness
makes entrepreneurs focus on the environment and search for the information that helps them perceive and
discover customers’ needs. It enhances entrepreneurs’ willingness to create and capture value by fulfilling
perceived customers’ needs. To meet the customers’ needs, entrepreneurs may experiment with new activities,
new ways of linking activities, or new ways of governing transactions, and all are useful approaches to achieve
business model innovation (Zott and Amit 2007).
Second, in addition to perceiving customers’ needs in the environment, entrepreneurial alertness enables
entrepreneurs to identify new activities in new markets through associating or connecting various information in
a creative manner (Amato, Baron, and Barbieri et al. 2016), which can also drive entrepreneurs to innovate in
business model. Association and connection, involves pulling together disparate pieces of information and building
them into coherent alternatives (Tang et al. 2012). Based on connecting previously unconnected domains of
information, entrepreneurs will not only find new markets but also find new ways to do business (Baron and Ensley
2006). For example, the emergence of Internet-based ventures, advances in the Internet, and the decline in
computing and communication costs have allowed for the development of new ways to create and deliver value
(Zott, Amit, and Massa 2011). Alert entrepreneurs connect information technologies with their business and then
create unconventional exchange mechanisms and transaction architectures (Amit and Zott 2001), thus achieving
business model innovation.
Third, entrepreneurial alertness also helps entrepreneurs to identify the external environmental constraints which
can be regarded as stimuli and creative challenges for innovation (Amit and Zott 2015). Hargadon and Douglas
(2001) suggest that the viability of innovating a new business model depends partly on the degree to which it
complies with important legal, regulatory, technology, and industry norms and requirements. These external
factors affect the range of innovating alternatives that may be considered. In other words, external constraints
affect the desirability and feasibility of intended business model innovation, and also influence the specific ways

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in which activities within the business model can be carried out. The entrepreneurs with entrepreneurial alertness
may evaluate these boundaries, and then combine the information they collected before and make an association.
They may find that boundaries can provide potential solutions for start-ups to adopt new methods for transaction
(Santos and Eisenhardt 2009). Thus, environmental constraints can be viewed as stimuli and creative challenge,
and drive entrepreneurs to explore new business models.
Therefore, the study proposes that:
Hypothesis 1: Entrepreneurial alertness has a positive effect on business model innovation.
The mediating effect of entrepreneurial learning
Given that entrepreneurial alertness can help entrepreneurs identify many different approaches to business
model innovation, why do some entrepreneurs succeed while others do not? The mechanism between
entrepreneurial alertness and business model innovation needs to be revealed. The intentions model offers a basis
for further specifying this relationship. Based on the intentions model, intentions and following actions are driven
by perceptions which are the desirable and feasible behavioral outcomes (Krueger 2007). According to the
intentions model, the conscious personal choice to proceed to action is based on, first, the perceived desirability
of an entrepreneurial opportunity. The individual who wants to proceed to action, then awaits the “green light” of
perceived feasibility before fully forming entrepreneurial intentions (Douglas 2017). In short, only when
entrepreneurs combine desirability and feasibility can business model innovation be achieved. Entrepreneurial
alertness provides entrepreneurs with various approaches to achieve business model innovation, some of which
may be desirable. But entrepreneurs also need to take appropriate measures to ensure they feel these approaches
are also feasible in order to take action, and thus achieve business model innovation in reality. In our model, we
therefore propose that an important mediating mechanism between entrepreneurial alertness and business model
innovation, is the process through which entrepreneurs come to feel that potential approaches are feasible.
The KBV provides us a theoretical foundation that shows that entrepreneurial learning can play the role of this
mediator. If entrepreneurs want to improve the perceived feasibility of various options, they need to develop
appropriate capabilities to influence future situations (Keh, Foo, and Lim 2002). Based on KBV, entrepreneurial
learning can help entrepreneurs obtain the relevant knowledge base and foster the ability to take action. KBV
emphasizes that the principal functions of a firm are the creation, integration, and application of knowledge (Grant
1996; Spender 1996), and that knowledge is the basis of venture capability. The knowledge base of a venture can
enhance its innovative ability, and then help entrepreneurs to gain a perception of control over their future situation.
Entrepreneurs acquire more knowledge through entrepreneurial learning, which can be transformed into an ability

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to influence future situations. By acquiring and transforming knowledge into abilities, they are likely to try new
activities, new connecting structures, and new governance of transactions—in other words, business model
innovation. Entrepreneurial alertness provides approaches to achieve business model innovation (desirability), and
entrepreneurial learning provides the knowledge base and abilities to execute approaches needed (feasibility).
Combining these two, entrepreneurs will eventually achieve business model innovation. In this way,
entrepreneurial learning can serve as the mediator between entrepreneurial alertness and business model
innovation. Entrepreneurial learning consists of two generic types of learning: explorative learning and exploitative
learning (March 1991). Both types can serve as mediators in the relationship between entrepreneurial alertness and
business model innovation.
Explorative learning provides entrepreneurs with a diverse knowledge base and abilities to identify more
components and solve new problems (McGrath 2001), which are useful for innovative outcomes (Vargas et al.
2018). It expands their cognitive range and promotes their abilities to identify customers’ potential needs (Taylor
and Greve 2006). With the knowledge base and abilities acquired by explorative learning, entrepreneurs will be
more willing to try out new business opportunities or new ways of doing business (Chen 2017). In this manner,
they may eventually achieve the business model innovation.
Exploitative learning enhances knowledge depth and the expertise to resolve complex or unusual problems
(Bierly and Daly 2007). Through exploitative learning entrepreneurs can obtain an in-depth knowledge base and
abilities in a specific industrial field (Zhou and Li 2012). The in-depth knowledge base enables entrepreneurs to
gain more experience and know-how about existing technologies and markets (Zhou and Li 2012). This knowledge
and these abilities help entrepreneurs to adopt an efficient process and routine (Tripsas and Gavetti 2000) which
can facilitate the effective realization of business model innovation. Thus, exploitive learning is helpful to business
model innovation.
Overall, whether it is diverse knowledge of multiple domains, or deep knowledge in a specific domain, both
help entrepreneurs foster the knowledge base and obtain abilities to achieve business model innovation (Katila and
Ahuja 2002; Taylor and Greve 2006; Sosna, Trevinyo-Rodríguez, and Velamuri 2010; Zhou and Li 2012). As
shown on Figure 1, both explorative learning and exploitative learning can be the mediator in the relationship
between entrepreneurial alertness and business model innovation. Therefore, the study proposes that:
Hypothesis 2: (a) Explorative learning and (b) exploitative learning mediate the relationship between
entrepreneurial alertness and business model innovation.
The moderated effect of risk perception

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To this point, the study has argued that entrepreneurial alertness stimulates business model innovation through
exploitative learning and explorative learning. Risk perception is a cognitive and perceptual characteristic of an
individual (Weber and Hsee 1998); it varies among individuals. The entrepreneur’s risk perception is important as
it impacts entrepreneurs’ decisions and behaviors (Simon, Houghton, and Aquino 2000). Risk perception affects
the extent to which entrepreneurial alertness promotes exploitative learning and explorative learning. This has an
influence on the likelihood of entrepreneurial alertness enhancing business model innovation. Based on the
characteristics of these two different types of learning, entrepreneurs’ risk perception will have different impacts
on the future results.
Explorative learning helps entrepreneurs acquire the distinct and novel knowledge (Katila and Ahuja 2002) but
has a high level of uncertainty and associated costs. When entrepreneurs' risk perception is low, entrepreneurs
believe that the environment is supportive to making change. Given this, faced with the approaches recognized by
entrepreneurial alertness, entrepreneurs will consider more of these to be viable for the achievement of business
model innovation. In this circumstance, if entrepreneurs adopt explorative learning, which emphasizes learning by
generating variation (McGrath 2001), they will facilitate innovation and lead to breakthroughs in the long term
(Chen 2017). In this way, entrepreneurs who have a low risk perception are more likely to adopt explorative
learning to achieve business model innovation. However, in cases where entrepreneurs have a much higher risk
perception, they will regard the environment as turbulent, hostile and unwelcoming. In this case, there is a higher
possibility that entrepreneurs will encounter failures and losses. With the threat of uncertainty and significant cost,
explorative learning will become a burden for the entrepreneurs who perceive high risks. Hence, entrepreneurs are
often less likely to use explorative learning to acquire abilities for business model innovation. In this way, with
the increase of entrepreneurs’ risk perception, the possibility of adopting explorative learning is decreased.
Exploitative learning helps entrepreneurs develop their capabilities to excel at leveraging existing knowledge
(Bierly and Daly 2007). It is controllable and the return-to-investment period is shorter (March 1991).
Entrepreneurs with low risk perception think the probability of success is high. In this situation, entrepreneurs are
less willing to use exploitative learning. Because exploitative learning builds closely on the existing knowledge
base (Schildt, Maula and Keil 2005), this will limit the growth and performance of the venture in the long term
(Parida, Laht, and Wincent 2016; Chen 2017). However, entrepreneurs who perceive high risk in their environment,
believe the probability of failure is higher. Faced with the approaches recognized by entrepreneurial alertness,
entrepreneurs believe that they may suffer economic loss, so they are more likely to take conservative actions.
Through exploitative learning, entrepreneurs experience lower costs and may receive reliable revenues and profits

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(Govindarajan and Trimble 2010). Thus, entrepreneurs are more likely to adopt exploitative learning to achieve
business model innovation when their risk perceptions are high. In this way, with the increase of entrepreneurs’
risk perception, the likelihood of adopting exploitative learning is also increased.
As the moderated mediation framework presented on Figure 1 illustrates, entrepreneurs with different risk
perceptions tend to achieve business model innovation through different types of learning. Therefore,
Hypothesis 3a: Risk perception negatively moderates the mediating effect of explorative learning in the
relationship between entrepreneurial alertness and business model innovation in such a way that the increase of
risk perception weakens the likelihood of adopting explorative learning
Hypothesis 3b: Risk perception positively moderates the mediating effect of exploitative learning in the
relationship between entrepreneurial alertness and business model innovation in such a way that the increase of
risk perception strengthens the likelihood of adopting exploitative learning.
Method
Sample and data collection
We tested the hypotheses using survey data collected from the incubators and high-tech parks in northwest
China in 2016. By way of background, the economy of northwest China was much less developed than other
regions in China prior to 2000. In recent decades, however, the Chinese government has implemented a strategy
to develop the western region, and as a result of this strategy we have seen increasing importance placed on
northwest China. For instance, in last 10 years, thousands of high-tech parks and incubators have emerged in
northwest China. These high-tech parks and incubators have sparked a huge amount of new ventures and start-up
activity. Given the high level of start-up activity, the northwest region provides a perfect context for studying
business innovation. While it is relatively easy for new ventures to start-up here, it is harder for firms to survive
over time. The high level of new start-up activity in this region therefore provides a valuable context to conduct a
survey on business model innovation.
For our sample, we randomly selected 365 enterprises in incubators and high-tech parks based on lists of
enterprises provided by incubators and high-tech parks. The enterprises were all in the high-technology industry.
We conducted the survey by face-to-face interviews for the enterprises whose founders were available to meet
with us, and collected the questionnaires in the respondents’ presence. For the enterprise founders who could not
make an appointment with us in person, we emailed the questionnaire to their email box and let them send the

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completed questionnaire back to our email box. The study received 225 completed surveys, for a response rate of
61.6%. To check the representativeness of our sample, we performed a t test to check for non-response bias. We
compared the responding and non-responding firms in the sample of 365 firms in terms of their size, age, industry,
and found that all of the t-statistics were insignificant, indicating a low possibility of non-response bias. Among
these completed surveys, we have excluded a small number where there were concerns (for example, where all
answers were marked as 3). In addition, given our interest in younger firms, we exclude from this study analysis
of firms older than 10 years old (guided by the Jin et al. (2017)’s definition about venture enterprises, which
suggested that new ventures refer to firms below 10-year age). On this basis, the analysis presented here is based
on 150 observations.
Looking at this group of firms, we see the industry distributions of samples are concentrated in
technology/telecommunications (16.7%), trade (12.0%), service (15.3%), energy and manufacturing (33.3%).
There is variation as well in the developmental stages of the enterprises. This study uses the firms’ age as an
objective measure of developmental stage, classifying firms aged 1-3 years as in the start-up stage (32.0%), those
aged 4-6 years as in the developing stage (34.0%), and those aged 7-10 as in the developed stage (34.0%). However,
given that different industries have distinct development cycles, this study also included a self-assessed measure
of the developmental stage of the ventures. According to the results of self-assessment, the ventures are mostly
perceived to be in the start-up stage (64.8%), with smaller proportions self-identifying as belonging to the
developing stage (30.3%) and developed stage (4.9%). Table 1 summarizes the sample’s key characteristics.
Insert Table 1 about here
Measurement
The survey questionnaire was developed using established scales that have been widely used in existing
scholarship. The questionnaire was drafted in English, using standard question wording and response items, and
then these items were translated into Chinese. Although this study is not a cross-cultural one, an important
consideration is whether the measures used might have a semantic equivalence issue, which concerns the similarity
in the meaning of a construct across cultures (Tsui, Nifadkar and Ou 2007). In order to check on this issue, the
study reviewed evidence on the validity of the measures in the Chinese context based on previous literature. First,
a standard back-translation procedure was performed following the procedures of Schaffer and Riordan (2003).
Based on the previous literature (e.g. Tsui, Nifadkar, and Ou 2007; Liao, Liu, and Loi 2010; Cheng et al. 2018)

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the back-translation procedure ensures the accuracy of translation but also ensures that all items can be used in the
Chinese context. Second, on the basis of previous cross-cultural methodologies research (Schaffer and Riordan
2003; Tsui, Nifadkar, and Ou 2007), this study examined both insiders’ and outsiders’ perspectives on the
measures’ semantic equivalence. This study conducted a pretest with ten MBA students who were also
entrepreneurs, and five bilingual experts in cross-cultural research outside of the current research project. None of
them identified any measurement items that cannot be generalized to Chinese culture, nor could they suggest any
additional items specific to our sample. Third, this study conducted the exploratory factor analysis (EFA) and
confirmatory factor analysis (CFA), which is discussed in further detail below. Based on the results of EFA and
CFA below, all the measures have adequate validity and reliability. In short, we feel confident the survey
questionnaire can be used in the Chinese context.
We measure the following variables on a five-point Likert scale. We took the average of the responses to
measure each construct. With respect to the specific measures used in the study, we offer the following details
(further information for each variable is shown in Appendix 1):
Entrepreneurial alertness. Our measure of entrepreneurial alertness is from Tang et al. (2012). According to
Tang et al. (2012), entrepreneurial alertness contains three dimensions: scanning and searching; association and
connection; and evaluation and judgement. The “scanning and searching” dimension has six items, asking
respondents if they actively look for information with questions such as: “I always keep an eye out for new business
ideas when looking for information”. The “association and connection” dimension has three items, with questions
such as: “I see links between seemingly unrelated pieces of information”. Finally, the “evaluation and judgement”
dimension has four items, asking respondents about their evaluations and judgements of the existence of profitable
business opportunities. A sample question is “I can distinguish between profitable opportunities and not-so-
profitable opportunities”. Our study used these 13 items to measure the entrepreneurial alertness of respondents in
our sample. To reduce the dimensionality, for each respondent, we calculated an average score for each of the
three dimensions, and then a total score of entrepreneurial alertness calculated as the average score across the three
dimensions. The Cronbach’ s alpha of entrepreneurial alertness is 0.734.
Entrepreneurial learning. Entrepreneurial learning is measured by explorative learning and exploitative
learning based on the study of Atuahene-Gima and Murray (2007). An example of an item of explorative learning
is “we always test the new ideas within the unknown field”; an example of an item of exploitative learning is “we
improve the ability to solve existing customers’ problems”. The Cronbach’s alpha of explorative learning is 0.769;
the Cronbach’s alpha of exploitative learning is 0.780.

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Risk perception. Following the previous research of Keh, Foo, and Lim (2002), we measure risk perception
using a four-item scale. Questions ask respondents to self-evaluate “the overall risk of the business” or whether
“there is a lot of uncertainty when predicting how well the business will do”. The measure of risk perception has
a Cronbach’s alpha of 0.720.
Business model innovation. The measure of business model innovation originates from Zott and Amit (2007).
We use 10 items to measure the business model innovation. An example of an item is “the business model offers
new combination of products, services, and information”. The Cronbach’s alpha of business model innovation is
0.899.
Control variables. The control variables in this study include entrepreneurs’ education level, management
experience, number of pre-employment units, industry experience in current entrepreneurial activities, firm size
and the firms’ industries. Education level is measured by an individual’s highest degree. The experience is
measured by how many years they have been as a manager (management experience) or worked in a certain
industry (industry experience). Entrepreneurs’ education and experience impact their learning behaviors. The firm
size is measured by firm asserts. The sample consists of companies mostly in four industries (technology/
telecommunications industry, trade industry, service industry, energy and manufacturing industry). A small
number of firms were not in these four industries and thus coded as “other industries”. We use four industry dummy
variables, with “other industries” as the baseline (Zhou and Li 2012), and the regression model containing control
variables for Industry 1-4 (as show in Table 2 and Table 3, industry1-4 represent technology/ telecommunications
industry, trade industry, service industry, energy and manufacturing industry respectively).
Reliability and validity
We conducted several tests to ensure the reliability and validity of data. Table 2 reports all variable descriptive
statistics, correlations and the square root of average variances extracted (AVE). No inter-factor correlations are
above the 0.6 threshold, and the largest variance inflation factor (VIF) is 2.25, well below the 10.0 benchmark,
suggesting that our estimations are not likely to be biased by multicollinearity problems.
Insert Table 2 about here
We used internal consistency method (Cronbach’s alpha) to assess the reliability of our measures. Based on
prior research, Cronbach’s alpha values of 0.7 or higher are considered to be of adequate reliability (Cronbach,
1951). The results show that the Cronbach’s alpha of all constructs were greater than 0.7 (details in Appendix 1),
which indicate that the theoretical constructs exhibit good reliability.

14
We undertook several steps to ensure the validity of our measures. First, we assessed the convergent validity by
using the factor loadings on all items in the model. The results showed that all the factor loadings are above 0.6,
indicating high validity. Moreover, the AVEs of all of the constructs were above 0.5, also indicating a high
convergent validity (Fornell and Larcker 1981). Second, as Table 2 exhibits, the diagonal elements are the square
roots of the AVE for each construct, which are greater than the off-diagonal elements. Based on Fornell and
Larcker’s (1981) criterion, these results suggest good discriminant validity. Third, this study also conducted a
confirmatory factor analysis (CFA) to assess the model fit. The CFA model presented a reasonable fit to the data
(/df=1.401, GFI=0.916, CFI= 0.958, IFI=0.9, CFI=0.958, TFI=0.940, RMSEA=0.052).
Common method variance
Podsakoff et al. (2003) pointed out that there are two primary ways to control for common method biases: (1)
the design of the study’s procedures, (2) statistical controls. This study controlled the common method variance
in these two ways. First, we adopt an ex ante method by collecting the independent variables and dependent
variables from different sources to control common method variance. The questionnaire was divided into Part A
and Part B. Part A was the entrepreneurs’ cognitive questionnaire, filled in by the founder CEO of the new ventures.
As noticed, all of these founder CEO are in charge of the operation of the new ventures. Part B was the survey
about the growth and development of entrepreneurial ventures, which was filled in by the co-founders or middle-
level managers familiar with the venture. Entrepreneurial alertness and risk perception were addressed in the
entrepreneur cognitive questionnaire (Part A), and entrepreneurial learning and business model innovation were
addressed in the venture growth and development questionnaire (Part B). In this way, this study controlled for
common method variance through design procedures. Secondly, we also adopt an ex post approach, maker variable
method, to assess any potential common method bias. According to Lindell and Whitney (2001), the marker
variable should theoretically be unrelated to at least one focal variable. We chose a six-item variable which is
“experiential processing” (Cronbach’s alpha=0.707) as our maker variable. We use the lowest positive correlation
between experiential processing and other latent variables (r=0.015) implemented to adjust the correlations
between the variables. The adjusted correlations are shown in the upper triangular matrix space of Table 2. Results
show that none of the correlations between the substantial variables become insignificant after the maker variable
adjustment. These results suggest that common method variance is not a concern in our study.
Results

15
This study conducted a hierarchical multiple regression analysis to test hypotheses by entering the control
variables, independent variable, and mediator variable in separate steps. Table 3 displays the regression results of
the model hypotheses. The dependent variables were explorative learning, exploitative learning and business
model innovation separately.
Insert Table 3 about here
The results of Model 8 (β=0.437, p<0.01) show that entrepreneurial alertness positively affects business model
innovation; thus, Hypothesis 1 is supported.
According to Baron and Kenny (1986), testing of mediation usually includes four steps: (1) testing the
relationship between independent variable and mediating variable; (2) testing the relationship between independent
variable and dependent variable; (3) testing the relationship between mediator and dependent variable; (4)
controlling the effect of mediator variable on the dependent variable, comparing the effect of independent variable
on the dependent variable with the effect on the second step, whether the effect weakens or disappears to judge
the mediation effect is partial or full. The results in Table 3 show that: (1) entrepreneurial alertness is positively
related to explorative learning and exploitative learning (β=0.603, p<0.001, model 2; β=0.367, p<0.05, model 5);
(2) entrepreneurial alertness is positively related to business model innovation (β=0.437, p<0.01, model 8); (3)
both explorative learning and exploitative learning are positively related to business model innovation (β=0.399,
p<0.01; β=0.228, p<0.1; model 9); (4) Contrasting Model 10 and Model 8, the study finds that when the mediator
variable is controlled, the influence of independent variables on dependent variables is disappeared, which
indicates that explorative learning and exploitative learning fully mediate the relationship between entrepreneurial
alertness and business model innovation. Thus, Hypothesis 2a and Hypothesis 2b are supported.
This study proposed a moderated mediation model, and following the research of Preacher, Rucker, and Hayes
(2007) and Edwards and Lambert (2007), this study used the PROCESS to test this moderated mediation
framework with the Bootstrapping technique (Hayes 2013). This study adopted the Bootstrapping method (Table
4) to test the hypothesis with 5000 Bootstrap sample sizes and a 90% confidence level for the confidence interval.
There are three levels representing high, medium, and low levels of risk perception. These are the mean plus a
standard deviation; the mean; and the mean minus a standard deviation, respectively. The value between BootLLCI
and BootULCI excluding 0 indicates that the moderated mediation effect is significant. As Table 4 shows, with
the increase in risk perception, the mediation effect of explorative learning is gradually reduced, but it is still
significant, which indicates that stronger risk perception will weaken the mediating role of explorative learning.

16
However, the mediation effect of exploitative learning gradually increases, and the level of significance changes
from low to high. This shows that with the increase in risk perception, the mediation effect of exploitative learning
gradually increases. The results of Model 3 and Model 6 in Table 3 examine the moderation effects through the
indirect effects of risk perception on explorative learning and exploitative learning. The results show that, first, the
risk perception significantly reduces (β=-0.216, p<0.1, model 3) the positive relationship between entrepreneurial
alertness and explorative learning, as shown in Figure 2; secondly, as shown in Figure 3, the risk perception
significantly enhances the positive relationship between entrepreneurial alertness and exploitative learning
(β=0.235, p<0.1, model 6). Based on the effects that risk perception has on entrepreneurial learning, it appears that
risk perception also influences business model innovation. Combining the results of Table 3 and Table 4, we
conclude that risk perception moderates the indirect relationship between entrepreneurial alertness and business
model innovation through explorative learning and exploitative learning. Thus, Hypothesis 3a and Hypothesis 3b
are supported.
Insert Fig. 2 Risk perception’s moderated effect on explorative learning about here
Insert Fig. 3 Risk perception’s moderated effect on exploitative learning about here
Insert Table 4 about here
Discussion
Theoretical contribution
Based on the intentions model and knowledge-based view, this study examined the effect of entrepreneurial
alertness on business model innovation, and the role of entrepreneurial learning and risk perception. By putting
forward a moderated mediation framework, this study explains why some entrepreneurs achieve business
innovation effectively, but others benefit little, if at all with respect to innovation. Broadly speaking, our study
makes three important contributions to existing research on this topic.
First, our study examines the positive relationship between entrepreneurial alertness and business model
innovation empirically. The results supplement and enrich discussions of entrepreneurial alertness and business
model innovation in the field of entrepreneurship research. As noted in our introduction, prior research focuses
primarily on the definition and antecedents of alertness, as well the function of discovering opportunities (Busenitz

17
1996; Tang et al. 2012; Ma and Huang 2016; Obschonka et al. 2017). This study carries forward the research on
entrepreneurial alertness by showing how entrepreneurial alertness affects important practical actions—
specifically, business model innovation. This is a valuable contribution, because current research, while supporting
studies of individual level antecedents of business model innovation, has paid relatively little attention to the role
of entrepreneurial alertness. Our study thus not only supplements the valuable work of Amit and Zott (2015) on
entrepreneurial alertness as an antecedent of business model innovation, but also offers new empirical evidence
concerning the role of entrepreneurial characteristics for business model innovation as well (Foss and Saebi 2018;
Snihur and Zott 2019).
Second, based on the intentions model and KBV, this research reveals the mechanism between entrepreneurial
alertness and business model innovation by exploring the mediating effect of entrepreneurial learning. The results
of this study not only provide empirical references for research within the intentions model, but also connects the
intentions model with KBV in the field of entrepreneurship. Entrepreneurial alertness reflects a state of
consciousness of entrepreneurs, but for alertness to influence business model innovation, a mechanism is required.
We show how entrepreneurial learning serves as a mediating mechanism, thus supplementing existing scholarship
on how business model innovation is achieved (Giesen et al. 2007; Chesbrough 2010; Sosna, Trevinyo-Rodríguez,
and Velamuri 2010; Foss and Saebi 2017; Malmström and Johansson 2017). We show how this mechanism is
involved in the process of achieving innovation, moving from opportunities/approaches (entrepreneurial alertness),
through abilities (entrepreneurial learning), to business model innovation. This process advances Taylor and
Grave’s (2006) study about the two steps of innovation realization: innovative ideas generation and
implementation. This process partly explains why some entrepreneurs can achieve innovation, while others remain
in their current form and cannot realize innovation. Furthermore, the proposal of entrepreneurial learning as a
mediator is based on the intentions model and KBV. In this way, this study makes a valuable contribution by
connecting these two theories in the entrepreneurship field. In addition, the intentions model is used to predict the
intentions of individuals and their resultant behavior (Shepherd and Krueger 2002). This research also provides
empirical reference for the research of the intentions model (Krueger, Reilly, and Carsrud 2000; Krueger and
Kickul 2008; Douglas 2017; Kruger 2017).
Third, by testing the moderated effect of risk perception on the indirect relationship between entrepreneurial
alertness and business model innovation through entrepreneurial learning, this study shows how risk perception
plays a role in the choice of learning types. More important, through the above tests, this study develops a
moderated mediation framework. This helps to understand why, when faced with the same opportunities or

18
approaches recognized by entrepreneurial alertness, entrepreneurs with a higher risk perception are more likely to
choose exploitative learning to achieve business model innovation, while entrepreneurs with a lower risk
perception prefer explorative learning to achieve business model innovation. These findings supplement the
research of Sosna, Trevinyo-Rodríguez, and Velamuri (2010) by comparing the mediating effects of these two
types of learning. Meanwhile, these results support the studies of Sitkin (1992; 1995), who pointed out that risk
perception has a strong influence on behaviors (Sitkin 1995), and can be applied to understanding the degree to
which organizational decision-makers pursue more radical or more incremental innovations (Sitkin 1992). In this
way, this study further complements and enriches the research of risk perception in the field of entrepreneurship
(Weber and Hsee 1998; Simon, Houghton, and Aquino 2000; Podoynitsyna, Van der Bij, and Song 2012).
Managerial implications
Our study offers several important practitioner implications, helping to illuminate how entrepreneurs achieve
business model innovation. First, it suggests to entrepreneurs that achieving business model innovation for a new
venture involves a systematic approach and framework that emphasizes a number of key elements. Specifically,
they need to be alert to their environment; in doing this, they can frame their initial ideas for business model
innovation. Then, entrepreneurs and their ventures need to foster the relevant abilities that can help them put the
innovative ideas into practice. This involves strengthening their skills and knowledge through explorative learning
and/or exploitative learning. At the same time, in the process of obtaining knowledge through learning,
entrepreneurs need to be attentive to their perception of risk. When entrepreneurs feel the risk is very high, it is
better for them to conduct local learning and/or leveraging of existing knowledge, because exploitative learning is
more beneficial than explorative learning when entrepreneurs’ risk perception is high. In contrast, when
entrepreneurs feel the risk is relatively low, then learning totally new things is more supportive for their business
model innovation process, because explorative learning facilitates innovation better than exploitative learning
when entrepreneurs have a low risk perception.
In short, in order to effectively implement business model innovation, entrepreneurs need to be sufficiently alert
to the environment, to actively taking entrepreneurial learning actions to implement business model innovation
intention, and to carefully choose an appropriate entrepreneurial learning type based on their risk perception. By
doing this, they will not only achieve business model innovation, but also ensure that their ventures are more likely
to survive and develop.
Limitations and future research directions

19
Despite its contributions, this study also has some limitations. First, this study used the cross-sectional design
of questionnaire, which restricts our ability to assess causality relationships. In the future, it would be better to use
longitudinal data or secondary data to test the hypotheses. For example, the researcher could initially collect
information about entrepreneurs’ cognition, and contact the same ventures to collect information about enterprise
growth and development after a certain period. Alternatively, the researcher could collect the information about
enterprise growth and development from the database or Internet. Second, this study gathered data from 365
enterprises, gaining 225 responses, but used only 150 observations in the final analysis. The 150 observations were
ventures from the high-technology industry in northwest China. We acknowledged that our findings may not be
generalizable to other situations. Future research should be conducted with samples from different countries,
different districts and different industries to test the generalizability of our findings. Third, the sample was based
on existing ventures, so survivorship bias may exist. The future research could add samples of failed firms to make
the results more reliable.
Several important new directions for future research emerge based on findings from this study. First, this study
conducted a quantitative research into the relationship between entrepreneurial alertness and business model
innovation. Future research may also use a qualitative research approach, or mixed methods (a combination of
qualitative and quantitative) to explore the effects of entrepreneurs’ cognition on business model innovation. In
this way, researchers can gain rich and valuable information that offers greater insight into the processes involved.
For example, following the findings of Martins et al. (2015), Malmström, and Johansson (2017), researchers could
use interviews, case studies, text analysis or other tools to understand entrepreneurial cognition, and then explore
the relationship and processes linking entrepreneurial cognition and business model innovation. Second, based on
research into the cognitive view of business model innovation, there are many cognitive characteristics that may
have effects on business model innovation (Osiyevskyy and Dewald 2015). Future research could thus shed new
light on the relationships between different cognitive characteristics and business model innovation. Third,
according to Amit and Zott (2015), business model innovation has four themes, and different themes have different
antecedents. Future research could explore the relationship among different entrepreneurial cognitions, different
antecedents and different business model innovation themes.
Conclusion

20
Our study contributes to the entrepreneurship literature by proposing entrepreneurial alertness as a vital
antecedent of business model innovation. To this end, this research puts forward a moderated mediation framework
showing how entrepreneurship alertness stimulates business model innovation through entrepreneurial learning
and risk perception. Thus, this study contributes to the research of business model innovation and relevant practical
fields.
Furthermore, on the basis of intentions model and knowledge-based view, this study makes efforts on revealing
the mechanism between entrepreneurial alertness and business model innovation by introducing entrepreneurial
learning as the mediator and risk perception as the moderator. In this way, this study not only provides empirical
evidence for research of intentions model, but also connects intentions model and knowledge-based view in
conceptual and theoretical discussions in the field of entrepreneurship.

21
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27
Tables
Table 1 Sample characteristics
Characteristics
Proportion
Characteristics
Proportion
1. Entrepreneur age
4. Education
21~30
17.3%
High school or technical secondary school
25.3%
31~40
35.3%
college
25.3%
41~50
38.1%
bachelor
37.4%
51~60
9.3%
master
12.0%
2. Firm age (years)
1~3
32.0%
5. Industry
4~6
34.0%
Technology/Telecommunications
16.7%
7~10
34.0%
Trade
12%
3. Developmental stage
Service
15.3%
Start-up stage
64.8%
Energy and Manufacturing
33.3%
Developing stage
30.3%
Others
22.7%
Developed stage
4.9%

28
Table 2 Descriptive statistical analysis
Variables
1
2
3
4
5
6
7
8
9
10
11
12
13
15
1.Education
N/A
-0.225
-0.045
0.020
0.180*
-0.029
-0.050
-0.016
0.042
0.306**
-0.037
-0.028
0.054
-0.016
2.Management experience
-0.207*
N/A
0.638**
0.216*
-0.119
-0.013
0.117
-0.029
0.072
0.093
0.098
0.18
-0.178
-0.020
3.Employment numbers
-0.029
0.643**
N/A
0.117
-0.018
-0.046
0.023
0.024
0.021
0.180
-0.122
-0.063
0.097
-0.159
4.Industry experience
0.035
0.228*
0.130
N/A
0.049
0.052
-0.030
-0.101
-0.096
0.309**
0.254**
0.290**
-0.097
0.160
5.Industry 1
0.192*
-0.102
-0.003
0.063
N/A
-0.183*
-0.336**
-0.208**
-0.151
0.078
0.067
-0.040
0.065
0.137
6.Industry 2
-0.014
0.002
-0.030
0.066
-0.165*
N/A
-0.280**
-0.175
-0.179
-0.037
-0.094
-0.037
0.014
-0.003
7.Industry 3
-0.034
0.130
0.038
-0.015
-0.316**
-0.261**
N/A
-0.321**
0.159
0.063
-0.071
-0.058
0.077
-0.069
8.Industry 4
-0.001
-0.014
0.039
-0.084
-0.190*
-0.157
-0.301**
N/A
-0.116
-0.002
0.019
0.057
-0.046
-0.031
9. Firm size
0.056
0.086
0.036
-0.080
-0.134
-0.161
0.172
-0.099
N/A
0.137
0.088
-0.092
0.130
0.044
10.Entrepreneurial alertness
0.316**
0.107
0.192
0.319**
0.092
-0.021
0.077
0.013
0.150
0.812
0.422**
0.414**
0.267**
0.403**
11.Explorative learning
-0.021
0.112
-0.105
0.265**
0.081
-0.078
-0.055
0.034
0.102
0.431**
0.770
0.499**
0.073
0.578**
12.Exploitative learning
-0.013
0.192*
-0.047
0.301**
-0.024
-0.021
-0.042
0.071
-0.076
0.423**
0.507**
0.778
-0.036
0.463**
13.Risk perception
0.068
-0.160
0.111
-0.081
0.079
0.029
0.091
-0.030
0.143
0.278**
0.087
-0.020
0.739
0.108
14.Business model innovation
-0.001
-0.005
-0.142
0.173*
0.150
0.012
-0.053
-0.016
0.058
0.412**
0.584**
0.471**
0.121
0.726
Mean
2.289
1.681
1.938
3.338
0.167
0.120
0.333
0.153
0.373
3.839
3.632
3.781
3.316
3.767
S.D.
1.021
0.940
0.852
0.847
0.374
0.326
0.473
0.362
0.767
0.616
0.768
0.791
0.768
0.693
Notes: N=150; † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed), the same below; the diagonal value refer to the square root of AVE, N/A means this item is not adaptive to
analysis.

29
Table 3 Regression model and results
Explorative learning
Exploitative learning
Business model innovation
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Model 9
Model 10
Control variables
Education
0.017
-0.126
-0.143†
0.067
-0.015
-0.010
0.061
-0.027
0.044
0.014
Mgmt. experience
0.304*
0.196
0.182
0.459†
0.396**
0.368*
0.332*
0.261*
0.113
0.130
Pre-employment number
-0.427*
-0.367*
-0.356*
-0.377*
-0.339†
-0.317†
-0.578**
-0.530**
-0.314*
-0.363*
Industrial experience
0.195†
0.139
0.144
0.212†
0.176
0.160
-0.004
-0.046
-0.145
-0.128
Industry 1
0.346
0.183
0.234
-0.165
-0.266
-0.239
0.210
0.097
0.160
0.125
Industry 2
-0.163
-0.310
-0.330
-0.169
-0.260
-0.189
0.330
0.232
0.475†
0.404
Industry 3
-0.111
-0.161
-0.154
-0.216
-0.246
-0.186
-0.215
-0.260
-0.060
-0.103
Industry 4
-0.118
-0.244
-0.332
0.031
-0.033
0.091
-0.221
-0.282
-0.070
-0.165
Firm size
0.144
0.069
0.106
-0.078
-0.125
-0.146
0.112
0.059
0.069
0.060
Independent variables
Entrepreneurial alertness (EA)
0.603***
0.603***
0.367*
0.393*
0.437**
0.177
Mediators
Explorative learning
0.399**
0.304*
Exploitative learning
0.228†
0.200†
Moderators
Risk perception
-0.046
-0.059
EA*Risk perception
-0.216†
0.235†
F
1.772†
3.861**
3.715***
2.584*
3.097**
2.975**
1.805†
2.936**
4.108***
3.702**
R2
0.219
0.408
0.452
0.293
0.36
0.402
0.235
0.361
0.460
0.470
Adj-R2
0.095
0.302
0.330
0.18
0.244
0.267
0.105
0.238
0.348
0.343
△
R2
Notes: N=150; † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed).

1
Table 4 The results of moderated mediation effect
Business model innovation
Risk perception
Effect
Boot SE
BootLLCI
BootULCI
Explorative
learning
2.495 (low level)
0.233
0.124
0.077
0.497
3.318 (medium)
0.187
0.099
0.057
0.382
4.140 (high level)
0.140
0.122
0.011
0.426
Exploitative
learning
2.495 (low level)
0.001
0.082
-0.137
0.124
3.318 (medium)
0.085
0.064
0.009
0.228
4.140 (high level)
0.168
0.127
0.019
0.444
Notes: Bootstrapping sample size is 5000; 90% confidence intervals presented.

2
Figures
Figure 1 Conceptual Model

3
Figure 2 Risk perception’s moderated effect on explorative learning

4
Figure 3 Risk perception’s moderated effect on exploitative learning

5
Appendix Appendix 1 The summary of items and their reliability and validity
Variable
Items
Factor
loading
Cronbach’
s alpha
C.R.
Entrepreneurial
alertness
1. Scanning and searching for new information.
0.791
0.734
0.859
2. Associating and connecting previously-disparate information.
0.767
3. Evaluating and judging whether the new information represents an opportunity.
0.873
Explorative
learning
1. Our company’s employees frequently come up with creative ideas that challenge
conventional ideas.
0.807
0.769
0.853
2. We frequently experiment with radical new ideas (or ways of doing things).
0.838
3. We always test the new ideas within the unknown field.
0.711
4. We collected novel information and ideas that went beyond our current market and
technological experiences.
0.716
Exploitative
learning
1. We improve existing technologies to strengthen the ability of R&D.
0.749
0.780
0.8597
2. We excel at refining existing technologies.
0.857
3. We improve the ability to solve existing customers’ problems.
0.756
4. We consolidate the existing product development skills.
0.746
Risk perception
1. The overall risk of the business.
0.784
0.720
0.8268
2. The probability of failure is high.
0.791
3. The founder stands to lose a lot financially.
0.751
4. There is a lot of uncertainty when predicting how well the business will do.
0.617
Business model
innovation
1. The business model offers new combinations of products, services, and information.
0.643
0.899
0.9177
2. Incentives offered to participants in transactions are novel.
0.684
3. The business model gives access to an unprecedented variety and number of participants.
0.700
4. The business model links participants to transactions in novel ways.
0.785
5. The business model adopts a new way of trading.
0.735
6. The business model creates a new profitable way.
0.708
7. The business model creates a new profit point.
0.745
8. The business model introduces new ideas, methods and commodities.
0.748
9. The business model introduces new operating procedures, practices and specifications.
0.765
10. Overall, the company’s business model is novel.
0.741

6
Appendix 2 The items of each dimension of entrepreneurial alertness
Dimensions
Items
Scanning and search
1. I have frequent interactions with others to acquire new information
2. I always keep an eye out for new business ideas when looking for information
3. I read news, magazines, or trade publications regularly to acquire new information
4. I browse the internet every day
5. I am an avid information seeker
6. I am always actively looking for new information
Association and connection.
1. I see links between seemingly unrelated pieces of information
2. I am good at “connecting dots”
3. I often see connections between previously unconnected domains of information.
Evaluation and judgment
1. I have a gut feeling for potential opportunities
2. I can distinguish between profitable opportunities and not-so-profitable opportunities
3. I have a knack for telling high-value opportunities apart-from low-value opportunities
4. When facing multiple opportunities, I am able to select the good ones