RUNNING HEAD: DYNAMICS OF ...

RUNNING HEAD: DYNAMICS OF ENTREPRENEURIAL UNCERTAINTY AND OPPORTUNITY IDENTIFICATION

1

A Dynamic Model of Entrepreneurial Uncertainty and Business Opportunity Identification: Exploration as a Mediator and Entrepreneurial Self-Efficacy as a Moderator

Schmitt, A., Rosing, K., Zhang, S. X., Leatherbee, M. Schmitt, A., Rosing, K., Zhang, S. X., Leatherbee, M. (2018). A Dynamic Model of Entrepreneurial Uncertainty and Business Opportunity Identification: Exploration as a Mediator and Entrepreneurial Self-Efficacy as a Moderator. Entrepreneurship Theory and Practice, accepted for publication

This is the pre-proof version of the manuscript accepted for publication in Entrepreneurship Theory and Practice. Please refer to the journal Entrepreneurship Theory and Practice for the proof-read final version of the manuscript. Link: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1540-6520/

Corresponding author: Antje Schmitt University of Kassel Department of Business Psychology Pfannkuchstrasse 1, 34121 Kassel, Germany and University of Bamberg Department of Work and Organizational Psychology An der Weberei 5 96047 Bamberg Tel.: +49 (0)951-863-1894 Fax: +49 (0)951-863-2049 E-mail: [email protected]

Kathrin Rosing University of Kassel Psychology of Entrepreneurial Behavior Holländische Straße 36-38, 34127 Kassel, Germany E-mail: [email protected]


Stephen X. Zhang Pontificia Universidad Católica de Chile (Catholic University of Chile) Department of Industrial & System Engineering Edificio Raúl Devés, Piso 3, Avenida Vicuna Mackenna 4860, Macul, Santiago, Chile E-mail: [email protected]

Michael Leatherbee Pontificia Universidad Católica de Chile (Catholic University of Chile), Department of Industrial & System Engineering Edificio Raúl Devés, Piso 3, Avenida Vicuna Mackenna 4860, Macul, Santiago, Chile E-mail: [email protected]

Acknowledgements We are grateful for the support of The Chilean Economic Development Agency (CORFO) and the executive team at Start-Up Chile. We further acknowledge the support from Núcleo Milenio Research Center in Entrepreneurial Strategy Under Uncertainty (130028). We thank Michael Frese for his helpful suggestions and comments on an earlier version of this manuscript. We are grateful to Tobias Koch for his statistical support.

2


3

A Dynamic Model of Entrepreneurial Uncertainty and Business Opportunity Identification: Exploration as a Mediator and Entrepreneurial Self-Efficacy as a Moderator

Abstract This study focuses on the identification of business opportunities when entrepreneurs' perceived level of environmental uncertainty changes. We suggest that within persons, exploration mediates this relationship and entrepreneurial self-efficacy moderates whether entrepreneurs explore more or less with increasing uncertainty. To test our moderated mediation model we conducted a monthly field study with 121 early-stage entrepreneurs. Multilevel regression analyses reveal that an increase in the level of perceived uncertainty within entrepreneurs predicted the identification of opportunities through exploration for entrepreneurs high in self-efficacy, but not for those low in self-efficacy. Entrepreneurial selfefficacy acts as a personal resource that helps entrepreneurs to transform increasing perceptions of uncertainty into exploration and opportunity identification.

Keywords: Entrepreneurship, uncertainty, business opportunity identification, exploration, entrepreneurial self-efficacy


4

INTRODUCTION Entrepreneurs create and identify business opportunities in often ever-changing and fast-moving environments (Sarasvathy, 2001; Shane & Venkataraman, 2000). While entrepreneurs perceive relatively high levels of uncertainty in general, their perception of uncertainty is not static but instead fluctuates dynamically over time. Thus, a critical question is how do entrepreneurs identify business opportunities under varying levels of uncertainty over time? This question remains unanswered albeit that scholars have debated philosophically whether entrepreneurs identify opportunities by discovering them or by creating them (Ramoglou & Tsang, 2016; Sarasvathy, 2001; Shane & Venkataraman, 2000). In this study, we add to the literature on entrepreneurial opportunity identification by examining how dynamic fluctuations of perceived uncertainty are related to the number of business opportunities identified by entrepreneurs. The literature on the effects of uncertainty on entrepreneurial behaviors has primarily looked at static levels of, and individual differences in, global patterns of uncertainty (Brinckmann, Grichnik, & Kapsa, 2010; Matthews & Scott, 1995). Yet, entrepreneurs’ perception of uncertainty is not typically static over time but fluctuates in accordance with their changing environments. Likewise, entrepreneurs’ potential response to uncertainty, that is, engaging in entrepreneurial behavior and identifying business opportunities, may fluctuate dynamically over time. To date, it remains unclear whether and how within-person variations in perceived uncertainty may affect entrepreneurs’ identification of business opportunities. We address this gap by studying the dynamic relationship between perceived uncertainty and opportunity identification within entrepreneurs (Uy, Foo, & Aguinis, 2010). To examine the relationship on how changes in perceived uncertainty may trigger opportunity identification, we focus on one key process that may mediate the relationship. Specifically, we posit that entrepreneurs’ exploration acts as a response to uncertainty.


5

Exploration refers to actively engaging in entrepreneurial activities such as the experimentation with new approaches and processes, redesigning one’s products or services, and searching for novel domains and new opportunities with respect to markets or products (Laureiro-Martínez, Brusoni, & Zollo, 2010; Mom, Van Den Bosch, & Volberda, 2007). Taking a within-person focus, we examine whether entrepreneurs respond to fluctuations in the levels of perceived uncertainty by increasing or decreasing exploration and the ensuing identification of business opportunities. The literatures in applied psychology and entrepreneurship offer conflicting predictions of the relationship between uncertainty and behavioral processes such as exploration. Some approaches suggest that perceived uncertainty may increase exploration in entrepreneurs, while others suggest precisely the opposite. On the one hand, uncertainty may provide an optimal starting point to stimulate exploration and may facilitate the identification of business opportunities (Shane & Venkataraman, 2000; van Gelderen, Frese, & Thurik, 2000) as it prescribes diverse approaches and experimentation (Griffin, Parker, & Neal, 2008). On the other hand, theory suggests that uncertainty may induce a state of psychological entropy in which individuals experience conflicting perceptual and behavioral possibilities (Hirsh, Mar, & Peterson, 2012). This experience is linked to the feeling of anxiety and doubt, which often leads to avoidance behaviors and consequently a decrease in exploration and opportunity identification (Hirsh, et al., 2012; March, 1991; McMullen & Shepherd, 2006). When faced with uncertain situations, this second stream of research would predict that entrepreneurs stick to well-known strategies and routine behaviors instead of actively exploring their surroundings (cf. van Gelderen, et al., 2000). In this study, we aim to untangle the contradictory perspectives on the relationships between perceived uncertainty, exploration, and opportunity identification. Our starting point is that uncertainty perceptions fluctuate within entrepreneurs over time. We posit that the


6

critical question is not just how entrepreneurs behave at absolute high or low levels of uncertainty, but also how they respond to deviations from their typical or average levels of perceived uncertainty in terms of exploration and business opportunity identification. In addition, we examine the boundary conditions that distinguish whether within-person deviations in perceived uncertainty are positively or negatively related to exploration, and subsequently, the identification of business opportunities. Specifically, we draw on social cognitive theory (Bandura, 1986) and include the dispositional belief of entrepreneurial selfefficacy as a boundary condition to influence within-person relations. Entrepreneurial selfefficacy is the domain-specific belief of an entrepreneur in his or her abilities to successfully execute entrepreneurial tasks (Zhao, Seibert, & Hills, 2005). Prior research has investigated the role of self-efficacy in the regulation of behavior and motivation (Bandura, 1986, 1997; Hmielski & Baron, 2008) showing that entrepreneurial self-efficacy is an important personal resource for new venture success (Baum & Locke, 2004; Boyd & Vozikis, 1994; Drnovšek, Wincent, & Cardon, 2010; Tumasjan & Braun, 2012). However, little is known about the role of self-efficacy as a boundary condition in the dynamic relationships of uncertainty perception, exploration, and business opportunity identification. We predict that entrepreneurs high in self-efficacy are more likely to increase exploration and the identification of business opportunities in the face of uncertainty. Specifically, we believe entrepreneurs high in entrepreneurial self-efficacy respond favorably to dynamic changes in uncertainty and embrace highly uncertain situations as challenges, visualizing the potential upside and positive outcomes that might result from these situations (Drnovšek, et al., 2010; Jex, Bliese, Buzzell, & Primeau, 2001). This, in turn, might trigger exploration and facilitate the identification of business opportunities. In contrast, entrepreneurs who are low in entrepreneurial self-efficacy might tend to develop a pessimistic or even fearful approach when situations are perceived as more uncertain than


7

usual, thus tending to decrease exploration when uncertainty increases. The conceptual model is presented in Figure 1. “Insert Figure 1 About Here” We test our model in a sample of high-potential early-stage entrepreneurs in a business accelerator program. This sample is particularly suitable to address our research question because of the close match between our theorized phenomena and the broader population of interest. Early-stage entrepreneurs are in the process of developing their ventures based on an incipient product or service, and through this process are constantly validating, shaping, and refining their business models (cf. Leatherbee & Katila, 2017). The opportunities they identify do not necessarily need to be radically innovative. Opportunities are more likely to be incremental and aimed at dynamically modifying their existing ventures by providing changes to their business models, products or services (Ardichvili, Cardozo, & Ray, 2003; Ucbasaran, Westhead, & Wright, 2008). Moreover, the success of the startup largely depends on the entrepreneurs’ characteristics, decisions, and strategies to deal with changing environmental demands such as uncertainty (McMullen & Shepherd, 2006; van Gelderen, et al., 2000). In sum, our study adds to the entrepreneurship literature that lacks empirical studies examining dynamic within-person relationships and processes (Uy, et al., 2010) especially regarding opportunity identification. Specifically, our study contributes to a better understanding in which entrepreneurs face the challenge of identifying business opportunities in environments of changing uncertainty. It also extends previous work on entrepreneurial self-efficacy (e.g., Hmieleski & Corbett, 2008; Hmielski & Baron, 2008) by examining its function as a boundary condition that explains why early-stage entrepreneurs differ in their sensitivity to fluctuations of uncertainty regarding exploration and opportunity identification.


8

We begin our study by examining the dynamic relationship between uncertainty and exploration. Next, we explore the role of entrepreneurial self-efficacy as a boundary condition that moderates the relationship between fluctuations in perceived uncertainty and exploration. Finally, we analyze whether exploration acts as a mechanism through which a change in perceived uncertainty is linked to the identification of business opportunities. THEORY AND HYPOTHESES Uncertainty and Exploration in Entrepreneurs Uncertainty is one of the important challenges faced by entrepreneurs (McMullen & Shepherd, 2006; Milliken, 1987; Sarasvathy, 2001). It arises from perception processes grounded in people’s interpretation of their environment (Duncan, 1972; Milliken, 1987). People usually act based on what they perceive (Milliken, 1987; Perrewé & Zellars, 1999). Accordingly, in the current study, uncertainty is defined as a subjective and perceptual phenomenon in terms of entrepreneurs’ perceived inability to predict changes in the environment, also known as state uncertainty (Baas, de Dreu, & Nijstad, 2011; Milliken, 1987). State uncertainty is the most frequently addressed type of uncertainty in the entrepreneurship literature (McKelvie, Haynie, & Gustavsson, 2011). Entrepreneurs are confronted with fluctuating levels of competition, market changes, the challenge of acquiring financial resources, and unpredictable behaviors of stakeholders, all of which may create situations of uncertainty. Specifically, in this study we examine two components of the environment that have shown to provide an important source for entrepreneurial uncertainty (McKelvie, et al., 2011): technological innovations and product or service demands. Both technology aspects and product or service demands have frequently been considered by entrepreneurship research as the most important sources of uncertainty (e.g., Behrens, Ernst, & Shepherd, 2014; Song & Montoya-Weiss, 2001). First, entrepreneurs may be unable to accurately predict aspects of the technological environment that are rapidly and


9

continuously changing (Song & Montoya-Weiss, 2001). Technology aspects include internal dimensions (such as the familiarity with technology and the technological skills that are required to develop one’s products and services), and external dimensions (such as the development of technology by third parties). Second, product or service demands cannot typically be controlled by the entrepreneur (Chen, Reilly, & Lynn, 2005; Peidro, Mula, Jiménez, & del Mar Botella, 2010), especially early-stage entrepreneurs lack routines to address unpredictable issues that emerge from unfamiliar markets (McKelvie, et al., 2011; McMullen & Shepherd, 2006). Uncertainty has occupied a central place in theories on entrepreneurship (Duncan, 1972; McMullen & Shepherd, 2006), and there is a long history of research on its role during the venture startup process (Brinckmann, et al., 2010; Liao & Gartner, 2006; Matthews & Scott, 1995; McMullen & Shepherd, 2006; van Gelderen, et al., 2000). Specifically, uncertainty has often been studied in association with the use of planning strategies, and as a stable moderator linking business planning and venture performance (Brinckmann, et al., 2010; Liao & Gartner, 2006; Matthews & Scott, 1995; van Gelderen, et al., 2000). In our study, we shift this perspective towards studying how entrepreneurs respond to fluctuations in uncertainty in terms of exploration and opportunity identification. Whereas previous research has conceptualized exploration usually at the organizational level (cf. Gupta, Smith, & Shalley, 2006; March, 1991), exploration can also be understood in terms of an entrepreneur’s exploratory behavior at the individual level (Laureiro-Martínez, et al., 2010; Mom, et al., 2007). Exploration by entrepreneurs covers behaviors such as looking for new markets for one’s products and services, thinking of and experimenting with new business ideas, or more structured analytical search processes (e.g., the use of algorithms or heuristics) (Laureiro-Martínez, et al., 2010; Leatherbee & Katila, 2017; Mom, et al., 2007).


10

At the individual level, gaining knowledge through exploration is important because an organization’s capacity to explore is, to a large extent, rooted in the exploration behavior of the organizational members (March, 1991; Nonaka, Von Krogh, & Voelpel, 2006). This is particularly relevant for early-stage entrepreneurs where the new venture's capacity for exploration is largely determined by the decisions and behaviors of the entrepreneur (cf. Laureiro-Martínez, et al., 2010; March, 1991). In this study we address exploration at the level of the entrepreneur (Laureiro-Martínez, et al., 2010) and analyze its relationship with entrepreneurs’ month-to-month changes in perceived uncertainty. Competing Theoretical Explanations about Uncertainty and Exploration Based on prior literature there are good reasons to expect that increases in perceived uncertainty may either increase or decrease exploration by entrepreneurs. The assumption that uncertainty reduces the level of exploration can be based on the entropy model of uncertainty (EMU) (Hirsh, et al., 2012). According to this model, subjectively experienced uncertainty emerges from competing perceptual possibilities (e.g., “Will the environmental conditions change or will they remain stable?”) (Hirsh, et al., 2012). The conflict between competing perceptual possibilities is largely experienced as a threat to personal stability and an emotional state directly related to the subjective experience of anxiety. Doubts and anxiety related to an increase in perceived uncertainty may inhibit approach-oriented entrepreneurial activities and impulses (i.e., the investigatory behavior of a person toward a desired stimulus or goal) such as exploration, refinement, and search for alternatives. These states are likely to cause reluctance and avoidance behavior (i.e., engaging in behavior away from adverse stimuli or goals) such as procrastination in entrepreneurs (McMullen & Shepherd, 2006). For example, in situations when the entrepreneur is getting more uncertain about whether and how the environment will change, he or she might be tempted to wait for changes to materialize before acting and consequently decrease exploration in the face of uncertainty.


11

Under perceptions of increased uncertainty, people tend to exert less effort rather than enacting more effort-intensive behaviors, which they know they should reasonably invest their energy in (Markman, Baron, & Balkin, 2005). Accordingly, there is some evidence from the entrepreneurship literature suggesting that an increase in uncertainty triggers habitual and conventional behavior and impedes approach-related, goal- and future-oriented action (cf. McMullen & Shepherd, 2006; van Gelderen, et al., 2000) such as exploration (McMullen & Shepherd, 2006). In contrast, giving rise to the assumption that increases in perceived uncertainty may boost exploration, Schumpeter (1934) claimed that entrepreneurs can bear uncertainty and even take advantage of it by creating something new. Entrepreneurs might be aware of the fact that the need to act, explore, and develop their products, ideas, and services is especially important in times when they perceive an increase in environmental uncertainty to cope with (Griffin, et al., 2008; McKelvie, et al., 2011; McMullen & Shepherd, 2006). Negative emotions that are triggered by an increase in uncertainty can be activating, engaging, and may temporarily enhance people’s alertness and perseverance (Baas, et al., 2011). These negative emotional states might signal that more effort is needed toward goal fulfillment and should prompt individuals to work harder towards goal attainment (Carver, 2004; Cervone, Kopp, Schaumann, & Scott, 1994). When anxiety results from entrepreneurs’ increasing perceptions of uncertainty this might signal that more effort and active behavior is needed to control the situation. Entrepreneurs may then be prompted to engage in exploration in order to better clarify and control the situation. Moreover, according to the EMU model (Hirsh, et al., 2012), uncertainty poses a critical challenge for an individual, such that people are motivated to find ways to keep it at a manageable level. Entrepreneurs might thus utilize exploration as a functional approach to relieve and reduce the negative emotional experiences caused by increasing uncertainty


12

(Baumeister, Vohs, DeWall, & Zhang, 2007; Hirsh, et al., 2012). In this vein, prior literature has argued that increased uncertainty provides an optimal context that stimulates information search (e.g., through the active use of learning techniques) (Fisher, 2012), feedback seeking, and creative and proactive behaviors (Grant & Ashford, 2008). These behaviors may in turn facilitate the identification of new business opportunities (Shane & Venkataraman, 2000; van Gelderen, et al., 2000). Hence, there is theoretical and empirical evidence from the psychology and entrepreneurship literature suggesting that increases in perceived uncertainty could either increase or decrease exploration. Given the competing positions regarding the relationship between changing levels of uncertainty and exploration, we do not argue for an overall positive or negative effect of uncertainty on exploration. Instead, we assume that withinperson changes in uncertainty might increase or decrease exploration as a function of personal characteristics. Specifically, we argue that the relationship between changing perceptions of uncertainty and exploration is contingent on entrepreneurs’ internal resources in terms of their entrepreneurial self-efficacy to successfully execute entrepreneurial tasks. The Role of Entrepreneurial Self-Efficacy The concept of self-efficacy is grounded in social cognitive theory (Bandura, 1986) which explicates the role of individuals’ beliefs in their capability to exercise control over their environment. Self-efficacy is a person’s belief and expectation in one’s ability to successfully accomplish a set of tasks and activities (Bandura, 1997). It determines how individuals perceive situations and respond to them as it is closely linked to action and the intentionality of action (Boyd & Vozikis, 1994; Gielnik et al., 2015). In this study, we concentrate on the domain-specific form of entrepreneurial selfefficacy. It describes a person’s confidence in his or her own entrepreneurial abilities and the belief that one is able to successfully execute the various roles and tasks related to


13

entrepreneurship (e.g., to develop new business opportunities, think creatively, create new products or services) (Drnovšek, et al., 2010; Zhao, et al., 2005). Theoretical approaches and recent empirical research suggest that a minimum level of entrepreneurial self-efficacy is necessary for entrepreneurial action (Frese, 2009; Townsend, Busenitz, & Arthurs, 2010). Social cognitive theory has been widely applied to the literature on entrepreneurial intentions (Boyd & Vozikis, 1994; Zhao, et al., 2005) and the study of entrepreneurial opportunity recognition and venture success (Ardichvili, et al., 2003; Ozgen & Baron, 2007; Tumasjan & Braun, 2012). For instance, entrepreneurial self-efficacy was found to determine entrepreneurial outcomes such as venture performance and growth (Baum & Locke, 2004; Boyd & Vozikis, 1994; Drnovšek, et al., 2010). In addition, some previous research investigated the interplay of entrepreneurial self-efficacy and environmental characteristics as antecedents of entrepreneurial outcomes (Hmieleski & Corbett, 2008; Hmielski & Baron, 2008). Tang (2008) found that environmental munificence (i.e., the extent to which the environment supports sustained growth) predicted entrepreneurial alertness especially for entrepreneurs with high levels of entrepreneurial self-efficacy. In our study, we focus on the influence of entrepreneurs’ self-efficacy on whether exploration is positively or negatively affected by within-person changes in uncertainty perceptions, which may in turn facilitate entrepreneurial outcomes. We argue that entrepreneurs’ confidence in their ability to run a business helps them to transform changes in perceived uncertainty into exploration. In other words, we suggest that entrepreneurial selfefficacy is one main personal resource that influences whether entrepreneurs increase or decrease exploration in the face of changing perceptions of uncertainty. Recent research reveals that high self-efficacy indicates individual differences in people’s ability to effectively regulate positive and negative emotions that result from the perception of demanding and threatening situations (Bledow, 2013). Accordingly, we argue


14

that entrepreneurs high in entrepreneurial self-efficacy can more effectively regulate their emotions related to the perception of increased uncertainty and tend to be less susceptible to threatening and anxiety-proving thoughts. Therefore, they have more cognitive resources available to exert more effort and actively engage in further exploration and refinement of their products and ideas. This argument is in line with previous research that entrepreneurs with higher self-efficacy beliefs tend to embrace uncertain situations as challenges (Drnovšek, et al., 2010; Jex, et al., 2001). Further, for entrepreneurs high in entrepreneurial self-efficacy, exploration might be a means to deal with increased uncertainty and overcome anxiety and threat (Baumeister, et al., 2007; Hirsh, et al., 2012). They are more likely to intuitively anticipate the positive emotions and the favorable outcomes that result from exploration and the potential discovery of new opportunities, because they have better access to aspects of themselves that indicate how they have previously been effective in managing difficult situations (Bullough, Renko, & Myatt, 2014). High entrepreneurial self-efficacy should be especially important for engaging in exploration when perceived uncertainty is higher than usually experienced, but less so when it is lower than usual. In times when uncertainty is perceived to be lower than usual, high self-efficacy entrepreneurs may not necessarily see a need in taking action, such as increasing their levels of exploration. Hence, when uncertainty is lower than typically experienced they may tend to engage in less exploration. Low entrepreneurial self-efficacy, in contrast, indicates that people lack the belief that they can effectively deal with demanding situations. Consequently, these entrepreneurs might have difficulties in activating access to their personal resources (Kuhl, 2001) and in developing mastery beliefs in the performance of complex entrepreneurial tasks. As entrepreneurs low in entrepreneurial self-efficacy cannot extensively draw on the experience


15

of successfully managing demanding entrepreneurial situations, increasing uncertainty is unlikely to trigger exploration as a means to deal with the situation. In times when environments are perceived as being more uncertain than usual, entrepreneurs who are low in entrepreneurial self-efficacy are more likely to respond with a passive approach such as withdrawal behavior, because they doubt their ability to actively deal with the situation and thus reduce exploration. In contrast, when low self-efficacy entrepreneurs perceive the environment as more predictable and secure than usual, they should be better able to actively explore the environment and experiment with potential opportunities, as they might feel safer and better protected. Hence, when uncertainty is lower than usual, these entrepreneurs will tend to explore more than in situations of increased uncertainty. Overall, we argue that when faced with increasing uncertainty that may emerge from changing technological aspects and fluctuations in the demand for one’s products or services, entrepreneurs high in entrepreneurial self-efficacy may respond more favorably to uncertainty than low self-efficacy entrepreneurs in terms of exploration. They may better cope with this unpleasant situation and have more resources available for showing approachoriented behavior such as exploration. In contrast, entrepreneurs low in entrepreneurial selfefficacy may lack confidence to cope with the challenging demands of heightened uncertainty, tend to be uneasy in uncertain situations, and thus more likely to avoid or decrease exploration. Thus, they will be more likely to increase exploration when the environment is perceived to be less uncertain. Hypothesis 1: Entrepreneurial self-efficacy moderates entrepreneurs’ responses to perceived uncertainty such that the within-person relationship between uncertainty and exploration is positive for entrepreneurs showing high entrepreneurial self-efficacy and negative for entrepreneurs showing low entrepreneurial self-efficacy. Exploration and Business Opportunity Identification


16

Entrepreneurial opportunities are fundamental to the development of new ventures, entrepreneurial performance, and venture growth (Gruber, MacMillan, & Thompson, 2008). However, different perspectives on opportunities exist. According to the viewpoint of opportunity recognition, opportunities exist objectively in the environment and can be discovered, for instance, through the deliberate search for and combination of new information (Gielnik, Frese, Graf, & Kampschulte, 2012; Shane & Venkataraman, 2000). In contrast, the perspective of opportunity creation takes opportunities as subjective, such that they are developed and created by the entrepreneur through creative and social construction processes (Sarason, Dean, & Dillard, 2006; Sarasvathy, 2001). Further, some research suggests that entrepreneurs are information processors that may use a combination of both approaches (Vaghely & Julien, 2010). In this study, we concentrate on the identification of opportunities and examine how changes in perceived uncertainty may trigger opportunity identification in terms of the self-perceived number of business opportunities identified. Exploration is a key antecedent for discovering one’s environment and to refine and develop one’s products or service. An increase in exploration may provide a functional behavior and a good starting point for entrepreneurs to identify opportunities. Opportunity identification is a dynamic process of continuous refinement and iteration. Entrepreneurs constantly think through their initial business ideas and engage in activities to shape, develop, or change them. This process may then result in the adjustment or the abortion of one’s initial products or services as a consequence of the identification of new opportunities (Ardichvili, et al., 2003). Based on an action regulation framework of entrepreneurship (Frese, 2009; Gielnik, et al., 2015), entrepreneurs’ effort and an active approach is of key importance for business opportunity identification (Gielnik, Krämer, Kappel, & Frese, 2014). Accordingly, the identification of opportunities is largely dependent on cognitive capacities and processes such


17

as intensively searching for new information, information processing, and carefully investigating market needs (Gielnik, et al., 2014; Tang, Kacmar, & Busenitz, 2012). Hence, building upon prior theory and research, we argue that when entrepreneurs increase their levels of actively exploring their environments, they are more likely to identify new opportunities that may complement or modify their existing business ideas than in times when their exploration behavior is lower than usual. Hypothesis 2: The within-person level of exploration is positively related to the within-person number of business opportunities identified. In this study, we are interested in the role of entrepreneurial self-efficacy in predicting the dynamic relationship between fluctuating perceptions of environmental uncertainty, exploration, and the identification of business opportunities by early-stage entrepreneurs. Entrepreneurial self-efficacy is assumed to function as a boundary condition that determines whether people differ in their sensitivity to within-person changes in uncertainty and increase or decrease their engagement in exploration. Entrepreneurial self-efficacy has already been shown to play an important role for the pursuit of entrepreneurial tasks and for opportunity identification (Ozgen & Baron, 2007; Tumasjan & Braun, 2012). We advance this perspective by arguing that entrepreneurs high in self-efficacy more likely benefit from their skills and experiences to effectively motivate themselves in demanding situations than those low in self-efficacy. Exploration is assumed to be activated with increasing uncertainty by entrepreneurs high in entrepreneurial self-efficacy in order for them to identify new business opportunities. Integrating the literature on the dynamic state of perceived uncertainty and its inconsistent effects on exploration, the literature on entrepreneurial self-efficacy, and research on the antecedents of opportunity identification, we propose the following moderated mediation model: The effect of fluctuations of perceived uncertainty on


18

opportunity identification is mediated by entrepreneurs’ exploration, and the direction of this effect depends on entrepreneurs' entrepreneurial self-efficacy. Hypothesis 3: Entrepreneurial self-efficacy moderates the indirect within-person effect of uncertainty on the number of business opportunities identified through a change in exploration. Specifically, the indirect within-person effect is positive for entrepreneurs high in entrepreneurial self-efficacy, but negative for those low in entrepreneurial selfefficacy. METHOD Sample and Procedure Our sample consists of 121 early-stage entrepreneurs who participated in the Start-Up Chile business accelerator program. This program was launched in 2010 with the objective of attracting early-stage entrepreneurs from across the globe to start their businesses in Chile. In exchange for a $40,000 equity-free grant and free access to a co-working space, participating entrepreneurs are required to stay in Chile for at least six months. One goal of the program is the promotion of social interaction between entrepreneurs from across the globe with the aim of changing the entrepreneurial culture of domestic entrepreneurs in Chile (Leatherbee & Eesley, 2014). In addition to hosting both domestic and foreign entrepreneurs in a co-working space, it offers specialized entrepreneurship schooling. The schooling services have been found to significantly increase startup performance, while the basic services of cash and coworking space seem to accelerate the performance of high-potential startups and encourage low-potential startups to explore better opportunities (Gonzalez-Uribe & Leatherbee, 2017). Thus, our setting is particularly useful for our research question as entrepreneurs in our sample are continuously exposed to new information about the validity and viability of their business ideas.


19

Acceptance into the program is based on entrepreneurs’ growth potential and is highly competitive, with an acceptance rate close to 12%. Applications are randomly assigned to and assessed by at least one external judge and one former Start-Up Chile participant. External judges are investors, entrepreneurs, and business-related academics and professionals. Applications are ranked based on the average score awarded by the judges. The rank-ordered list is presented to a committee of Chilean reviewers who can veto an application ranked among the best 100 or promote an application ranked below 100. Each cohort is roughly comprised of these top 100 ranked applications. We sent an invitation email to the 195 entrepreneurs who participated in the sixth and seventh cohort of the Start-Up Chile program in 2013/2014 (cohort 6: February – July 2013; cohort 7: August 2013 – January 2014) and informed them about this research project and the procedure. Participation in this study was voluntary. Participants received program credits for answering the surveys. These credits were part of a broader initiative of Start-Up Chile to quantify participant’s service towards the entrepreneurial community. One hundred and forty one participants agreed and took part in the study (response rate: 72%). We excluded 20 participants because of missing data in the main variables of interest. Of the remaining 121 entrepreneurs, 104 (86.0%) were male and 17 (14.0%) were female, ranging from 21 to 56 years of age (M = 31.2 years, SD = 5.77). Participants came from 24 countries with a majority from the United States (24, 19.8%), Chile (20, 16.5%), Argentina (11, 9.1%), and India (10, 8.3%). Most of the participants (88%) had between one and three co-founders. In terms of their highest degree of formal education, six (5.0%) of the participants held a high school degree, fifty-seven held a college degree (47.1%), fifty-two (43.0%) held a Master’s degree, and three (2.5%) earned a PhD. Three participants (2.5%) did not indicate their degree of highest education. Prior to joining the Start-Up Chile program, 64.5% of the participants held positions such as CEO, senior executive, functional


20

manager or direct supervisor; 35.5% were students or not formally employed. The early-stage entrepreneurs in our sample venture on a variety of services and products, such as the development of new technologies (e.g., large format 3D printers; mobile applications to compare restaurant menus, book tables, order deliveries), finance (e.g., a stock market simulator; a trustworthy payment platform to change online payment), medicine, health, and care (e.g., tools for cancer screening; a technical support system for elderly people using mobile web technology), and sports (e.g., innovative bicycle components). First, the participants completed an online questionnaire with items on their general levels of entrepreneurial self-efficacy, trait positive and negative affect, and demographic variables. Second, they completed online questionnaires on a monthly basis over a period of four months including measures on perceived uncertainty, exploration, and the amount of business opportunities identified. We believe that time lags of one month are appropriate for investigating business opportunity identification and its antecedents such as uncertainty. Monthly questionnaires can sufficiently capture fluctuations in entrepreneurs’ experiences and behavior without being too intrusive (cf. Sonnentag, Arbeus, Mahn, & Fritz, 2014). The identification of business opportunities should not vary on a daily basis to the extent that shorter time lags (e.g., daily surveys) would be useful. Monthly questionnaires were responded by participants on average 3.52 times resulting in 439 monthly observations. Measures Measures at the Person Level Entrepreneurial self-efficacy was measured with four items by Zhao, Seibert, and Hills (2005). The items are: “How confident are you in: successfully identifying new business opportunities?; creating new products?; thinking creatively?; commercializing an idea or new development?” Participants gave their answers on a scale ranging from 1 (no confidence) to 5 (complete confidence). Cronbach’s alpha of the scale was .77.


21

Control variables. We measured and controlled for early-stage entrepreneurs’ gender, age, education, and entrepreneurial experience (i.e., amount of businesses they have previously founded or co-founded) (cf. Gielnik, et al., 2012; McKelvie, et al., 2011; Ucbasaran, et al., 2008). Further, we controlled for participants trait positive and negative affect. Trait differences in positive and negative affect should predict approach-related behavior such as exploration (cf. Lyubomirsky, King, & Diener, 2005; Watson, Wiese, Vaidya, & Tellegen, 1999) and the identification of business opportunities (Baron, 2008). Trait affect was assessed with five items each based on Mackinnon et al.’s (1999) short version of the positive and negative affect scales (PANAS). For trait positive affect the items are: inspired, alert, excited, enthusiastic, determined. For trait negative affect the items are: afraid, upset, nervous, scared, distressed. Participants gave their answers on a scale ranging from 1 (not at all) to 5 (extremely). Cronbach’s alpha was .76 for the trait positive affect scale and .87 for the trait negative affect scale. Measures at the Monthly Level Uncertainty was defined as the perceived inability to predict changes in the environment due to technological innovations and fluctuations in the demand for one’s products or services (McKelvie, et al., 2011). Uncertainty was measured by two items based on Milliken’s (1987) concept of state uncertainty. Participants were confronted with pairs of statements referring to high versus low uncertainty and they had to indicate a mark closer to the statement that best represented their current situation on a 5-point scale (McKelvie, et al., 2011). The items are: “The fluctuation in the demand for your product is moderate and steady” (low uncertainty) versus “The demand for your product will ﬂuctuate signiﬁcantly“ (high uncertainty) and “Future technological innovations affecting the viability of the product seem likely, but they are likely to be incremental (not discontinuous)” (low uncertainty)


22

versus “Future technological innovations affecting the viability of the product are likely to be frequent and major” (high uncertainty). We formed a composite mean score of technology and product/service demand uncertainty. The two items refer to different content domains or environmental elements and we do not expect these different types or manifestations of uncertainty to be highly correlated on a monthly basis as they do not necessarily co-occur within this restricted time frame. Yet, both items are conceptualized and operationalized as indicators of state uncertainty and their common theme is that they refer to the lack of information and future predictability of the environment (McKelvie, et al., 2011). Exploration was assessed with four items developed by Mom, van den Bosch, and Volberda (2007). The items are: “To what extent did you spend time on the following activities during the last four weeks?: “Focusing on reviewing or redesigning products/ services or processes”, “Evaluating diverse options with respect to products/services, processes or markets.”, “Searching for new business opportunities with respect to products/services, processes or markets.”, “Activities requiring quite some adaptability of you.” Participants gave their answers on a scale ranging from 1 (not at all) to 5 (extremely). Cronbach’s alpha was computed based on the multilevel approach by Geldhof, Preacher, and Zyphur (2014). Cronbach’s alpha was .69 at the monthly level and .85 at the person level. Business opportunity identification was measured with one item based on the approach used by Gielnik et al. (2014) and Ucbasaran et al. (2008). In the monthly questionnaire we posed the question “How many new opportunities have you identified since the last survey (the last four weeks)?”. These monthly varying opportunities were assumed to be incremental, aimed at modifying existing products or services (Ardichvili, et al., 2003; Ucbasaran, et al., 2008). Narrow, definite, and uni-dimensional constructs that refer to a specific time frame can be reliably measured by a single item (Rossiter, 2002; Wanous,


23

Reichers, & Hudy, 1997). The distribution of business opportunity identification was positively skewed. Thus, we used logarithm (log) transformations to reduce the skewness and kurtosis of the distribution (Cohen, Cohen, West, & Aiken, 2003). In our data, the minimum value for opportunity identification was zero. Because the logarithm of zero is undefined, we added a constant of one to each value. Analytical Strategy Our data had a two-level structure with repeated measures at the monthly level nested within entrepreneurs. The stable personal characteristic of entrepreneurial self-efficacy as well as entrepreneurs’ gender, age, education, entrepreneurial experience, and trait affect as our control variables were measured at the person level. We intended to uncover whether monthly fluctuations or deviations from an average level of uncertainty influence an individuals’ exploration and opportunity identification and whether this effect varies as a function of stable entrepreneurial self-efficacy as the cross-level moderator. We applied multilevel regression analysis to test the hypotheses and account for the dependency in the data (Bryk & Raudenbush, 1992) by using the Mplus 7 statistical software (Muthén & Muthén, 1998-2012). With Mplus one can estimate multilevel moderated mediation models which refer to testing mediational models for different values of a continuous moderator variable (Preacher, Rucker, & Hayes, 2007). Specifically, we extended the lower level (within-person) mediation model (or 1-1-1 mediation model) as suggested by Preacher et al. (2007) with random slopes by including entrepreneurial self-efficacy as a stable, person level moderator variable. Variables at the person level (entrepreneurial self-efficacy and control variables) were grand-mean centered in our analyses (i.e., centered on the mean of the whole sample of 121 early-stage entrepreneurs). We used person-mean centering to center the monthly level variables (i.e., uncertainty, exploration). Person-mean centering is recommended when one is


24

primarily interested in relationships at the within-person level or interested in estimating cross-level interaction effects (as in our case). For this type of centering, the person mean of the respective within-person predictor (e.g., person mean of uncertainty) is subtracted from each monthly within-person observation of the predictor variable (Aguinis, Gottfredson, & Culpepper, 2013; Enders & Tofighi, 2007). This type of centering enables us to investigate person-specific monthly deviations from a participant’s average level of perceived uncertainty and exploration. Person-mean centering yields an unbiased estimation of the pooled within-person regression coefficient as it removes all between-person variation from the independent variables. Hence, the relations among the monthly level variables are not biased due to person-differences (e.g., social desirability, stable personality effects) (Enders & Tofighi, 2007). The control variables as measured at the person level explain variance in within-person intercepts; they cannot explain variance in the slopes addressing the monthly level relationships. In multilevel modeling, reporting effect sizes is not as straightforward as in traditional multiple regression as different variance components can be used to estimate the explained variance. Because we are interested in how the predictors are able to explain within-person variance in exploration and business opportunity identification, we calculated an approximate R12 in line with recent recommendations (LaHuis, Hartman, Hakoyama, & Clark, 2014; Snijders & Bosker, 2012). This coefficient refers to the amount of variance in the criterion within individuals that is explained by the predictors in each respective model (see Table 2) based on random-intercept models. We first estimated the unconditional model to determine the amount of variance in exploration and log-transformed business opportunity identification at the within-person level. Second, we added predictor variables as specified in Models 1-3 (Table 2) and compared the residual variance components resulting from these models to the components as provided by the unconditional model (Bryk & Raudenbush, 1992; LaHuis, et


25

al., 2014). Maximum-likelihood estimation with standard errors and confidence intervals was used to estimate the moderated mediation model (Johnson, Lanaj, & Barnes, 2014; Preacher, et al., 2007). We estimated the conditional indirect effects of monthly uncertainty on the logtransformed number of business opportunities identified through monthly exploration for high (1 SD above the mean) and low (1 SD below the mean) values of entrepreneurial selfefficacy. RESULTS Descriptive statistics and inter-correlations among all variables are shown in Table 1. For providing correlations based on the person level (as shown in Table 1 below the diagonal), the monthly level variables were aggregated to the person level. The correlations above the diagonal represent the monthly level of analysis (N= 424-439 observations). Here, we followed the recommendations by Snijders and Bosker (1999) and used person-mean centered scores. The results indicated that entrepreneurial self-efficacy was positively related to exploration (r = .24, p < .01), opportunity identification (r = .33, p < .01), and unrelated to perceived uncertainty (r = .02, ns). Exploration was unrelated to uncertainty (r = .09, ns) at the person level, but slightly positively correlated with uncertainty at the monthly level (r = .12, p < .05). Further, exploration was positively related to opportunity identification (r = .24, p < .01 at the person level and r = .27, p < .01 at the monthly level). “Insert Table 1 Here” Next, we examined how much of the variance in the monthly level variables resides at the monthly level and at the person level respectively by estimating unconditional models with random effects. The analyses revealed that a substantial amount of 34.2% of variance in uncertainty was found on the monthly or within-person level (65.8% at the person level). For exploration, 65.1% of the variance was at the monthly level and 34.9% was at the person


26

level. For the log-transformed variable of opportunity identification, 66.3% was monthly level variance and 33.7% was variance at the person level. Hence, the results indicated that using multilevel analysis was appropriate as there is enough variance to be explained at both levels of analysis (Bryk & Raudenbush, 1992). Hypothesis 1 stated that entrepreneurial self-efficacy moderates the monthly level relationship between uncertainty and exploration. Within individuals, the relationship was predicted to be positive for entrepreneurs showing high self-efficacy and negative for entrepreneurs showing low self-efficacy. Results of the hypothesis test are displayed in Table 2. “Insert Table 2 Here” Analyses showed that entrepreneurial self-efficacy acted as a cross-level moderator of the monthly level slope of uncertainty on exploration (B = 0.09, SE = 0.03, p < .01) (Table 2, Model 2). The 95% bias-corrected bootstrap confidence interval did not include zero [0.03, 0.15]. The predictors in Model 2 explain 3.05% of the variance in exploration at the monthly level. Figure 2 illustrates the interaction effect. For highly efficacious entrepreneurs, the relationship was positive, but for entrepreneurs low in self-efficacy, there was no significant within-person relationship between uncertainty and exploration. Figure 2 further indicates that in months when uncertainty was reported to be lower than usual, the relationship with exploration did not differ for entrepreneurs high versus low in entrepreneurial self-efficacy. Hypothesis 1 was thus supported for entrepreneurs high in entrepreneurial self-efficacy, but not for those low in entrepreneurial self-efficacy. Hypothesis 2 predicted that the within-person level of exploration is positively related to the within-person number of business opportunities identified. The results indicate that the slope between monthly exploration and business opportunity identification was positive and significant while controlling for monthly uncertainty as independent variable (B = 0.08, SE =


27

0.02, p < .01, see Table 2, Model 3). The 95% bias-corrected bootstrap confidence interval did not include zero [0.05, 0.11]. The predictors in Model 3 explain 5.88% of the variance in business opportunity identification at the monthly level. Because we centered exploration at individuals’ mean values, this result suggests that when entrepreneurs engage in exploration activities above their average level of exploration they are more likely to identify new opportunities. Thus, Hypothesis 2 was supported. Hypothesis 3 is the conditional indirect effects hypothesis, which predicts that entrepreneurial self-efficacy moderates the lower level indirect effect of uncertainty on the number of business opportunities identified through changing levels of exploration. In support of Hypothesis 3, the results suggest that the indirect effect was positive for entrepreneurs high in entrepreneurial self-efficacy (indirect effect: B = 0.02, SE = 0.01, p < .05, see Table 3). The 95% bias-corrected bootstrap confidence interval was [0.00, 0.02]. Yet, the indirect effect was non-significant for those moderate or low in entrepreneurial selfefficacy. This finding is consistent with the interpretation of the slopes at higher and lower levels of entrepreneurial self-efficacy as depicted in Figure 2. Hence, Hypothesis 3 was partly supported 1. “Insert Table 3 Here”

1

We further repeated all analyses with the two uncertainty items as separate variables (Howell, Breivik, & Wilcox, 2007). Entrepreneurs’ gender, age, education, entrepreneurial experience, and trait affect were again included as control variables. The results did not differ when following this approach and the coefficients reported in Table 2 and Table 3 were largely unaffected by this approach. Providing support for Hypothesis 1, entrepreneurial self-efficacy acted as a cross-level moderator of the monthly level slope of technology and product/service demand uncertainty on exploration (B = 0.07, SD = 0.03, p < .05 for technology uncertainty and B = 0.08, SE = 0.03, p < .01 for product/service demand uncertainty). Further, the relationship between monthly exploration and business opportunity identification was significant while controlling for monthly technology uncertainty (B = 0.08, SE = 0.02, p < .01) and product/service demand uncertainty (B = 0.08, SE = 0.02, p < .01) as independent variables. These findings support Hypothesis 2. The indirect effects of both technology and product/service demand uncertainty on log-transformed opportunity identification through exploration were positive for entrepreneurs high in entrepreneurial self-efficacy, but non-significant for those moderate or low in entrepreneurial self-efficacy (conditional indirect effect: B = 0.01, SE = 0.00, p < .05 for technology uncertainty and B = 0.04, SE = 0.02, p < .05 for product/demand uncertainty). Thus, Hypothesis 3 was partly supported.


28

DISCUSSION How do entrepreneurs respond to fluctuations in perceived uncertainty in terms of exploration and opportunity identification? Does this response differ depending on their entrepreneurial self-efficacy? Perceived uncertainty is an important concept in the entrepreneurship literature, yet existing theory and research offer conflicting predictions about the characteristics and mechanisms specifying when and how perceived uncertainty leads to exploration and opportunity identification (McKelvie, et al., 2011; McMullen & Shepherd, 2006; Milliken, 1987). This study helps to resolve the competing theoretical explanations and to extend our knowledge on the dynamic relationship of uncertainty and opportunity identification within entrepreneurs. We proposed and tested a moderated mediation model which specifies that in times when entrepreneurs experience their environment to be more uncertain and unpredictable than usual, entrepreneurial self-efficacy facilitates exploration and business opportunity identification in entrepreneurs. Whereas previous research suggests that self-efficacy plays an important role in human functioning and particularly for entrepreneurial success (Bandura, 1997; Boyd & Vozikis, 1994; Drnovšek, et al., 2010), little is known about the processes that might be triggered by entrepreneurs high versus low in self-efficacy when faced with situations that are perceived to be more or less uncertain. In our study, individual differences in entrepreneurial self-efficacy influenced patterns of within-person processes, such that the relationships between fluctuations in perceived uncertainty, exploration and opportunity identification were stronger for those high versus low in entrepreneurial self-efficacy. In line with our theorizing, the findings indicate that entrepreneurial self-efficacy is especially important for exploration in times when uncertainty is perceived to be higher than usual, but not when uncertainty is perceived to be relatively low. An increase in the level of uncertainty may temporarily enhance people’s activation, alertness and perseverance in


29

highly efficacious entrepreneurs. It might signal that more effort and an active search for information is needed to control the situation, prompting individuals to work harder towards attaining entrepreneurial goals (Carver, 2004; Cervone, et al., 1994). Moreover, those high in entrepreneurial self-efficacy might utilize exploration as a functional approach to relieve and reduce the negative emotional experiences caused by uncertainty (Baumeister, et al., 2007; Hirsh, et al., 2012). In contrast, when entrepreneurs perceive uncertainty to be lower than usual, those high and low in entrepreneurial self-efficacy do not differ in how much they engage in exploration. Such reasoning and findings are consistent with previous literature showing that personal resources become more important and valuable when the situational demands are perceived to be relatively high (cf. Bakker, Hakanen, Demerouti, & Xanthopoulou, 2007). Similarly, Hmielski and Baron (2008) showed that the relationship between self-efficacy and firm performance was weak in stable environments with higher levels of transparency and predictability, as compared to dynamic environments. These findings may inspire researchers to further study the interplay between entrepreneurial selfefficacy and perceived environmental attributes to identify models showing that entrepreneurial self-efficacy is not an equally important resource in all kinds of situations. Our research design is based on repeated monthly measures gathered from an international sample of early-stage entrepreneurs. Albeit testing within-person variability is of substantial interest to the field of entrepreneurship where various important constructs exhibit changing patterns within the individual entrepreneur, studies such as this one are especially rare (Uy, et al., 2010). By capturing perceptions and behaviors in entrepreneurs on a monthly basis, we were able to gain insights into the dynamic processes that link uncertainty perceptions with entrepreneurial outcomes. In addition, including entrepreneurial self-efficacy as a boundary condition at the person level enabled us to examine how interindividual differences in this personal resource influence relations within individuals.


30

Our study contributes to the entrepreneurship literature in several ways. First, by integrating the psychology and entrepreneurship literatures it advances research on the dynamics of, and behavioral responses to, perceived environmental uncertainty. The findings further help to better understand the process of opportunity identification. By investigating the role of exploration as a mediator, we extend previous research on the role of planning processes that are triggered by perceptions of uncertainty within entrepreneurs (van Gelderen, et al., 2000). Finally, this study extends previous work on the role of entrepreneurial selfefficacy (e.g., Hmieleski & Corbett, 2008; Hmielski & Baron, 2008; Tumasjan & Braun, 2012; Zhao, et al., 2005) by examining its function as a boundary condition that determines why early-stage entrepreneurs differ in their sensitivity to variations in uncertainty and change their levels of exploration and opportunity identification. Limitations and Directions for Future Research Our study has some limitations that provide appealing opportunities for future research. First, the approximate effect sizes resulting from our analyses were relatively small. Study limitations such as a small and non-random sample, or missing potentially relevant control variables might have affected the effect size estimates (Ferguson, 2009). Second, while our mediator, exploration, is a cornerstone of entrepreneurial strategy, when entrepreneurs experience an increase in uncertainty they may use a variety of strategies besides exploration. For instance, entrepreneurs might focus on exploiting previously developed ideas (i.e., using and developing their available opportunities and knowledge). As compared to exploration, exploitation is a risk-avoiding strategy. Entrepreneurs operating in an uncertain environment are challenged to explore new possibilities but they are also under pressure to exploit the opportunities they have previously identified to meet the current demands (March, 1991; Shane & Venkataraman, 2000). Achieving a balance between exploration and exploitation is essential for innovation and organizational survival in the long


31

run (Gupta, et al., 2006; March, 1991). Thus, a better understanding of the causes and consequences of this trade off during organizational emergence is warranted. Third, other resources apart from entrepreneurial self-efficacy might play a moderating role in our model. For instance, tolerance for ambiguity and a high promotion focus (i.e., striving for gains, ideals and goal attainment, rather than avoiding losses) are other dispositional factors that might determine how entrepreneurs cope with changing uncertainty (Eckhardt & Shane, 2003; Tumasjan & Braun, 2012). Fourth, our short measures on monthly uncertainty and business opportunity identification may be criticized. In line with McKelvie and colleagues (2011), we formed a composite mean score of technology and product/service demand uncertainty because these two items refer to a lack of information and future predictability of the environment and have shown to indicate state uncertainty. Yet, the replication of our findings using additional items to assess a broader state uncertainty construct or different facets of perceived uncertainty would be a useful approach for future research. Fifth, we tested the proposed model in a sample of early-stage entrepreneurs who typically face high levels of perceived uncertainty that vary over time, need to experiment with new opportunities and continuously refine and validate their initial products or services (Butler, Doktor, & Lins, 2010; McMullen & Shepherd, 2006; van Gelderen, et al., 2000). Future research is needed to examine whether the findings can be replicated in samples of entrepreneurs by and large and include other dependent variables relevant to entrepreneurship that might become more important at later stages of the entrepreneurial process. Later-stage entrepreneurs might have successfully adapted to varying levels of uncertainty, and changes in the perception of uncertainty might play a less important role for entrepreneurial behavior and the identification of new opportunities (McMullen & Shepherd, 2006).


32

Sixth, we sampled participants that are screened through a competitive selection process and are privileged to participate in a business accelerator program that provides multiple advantages, such as a stimulating environment. The screening of study participants and favorable working conditions might have an impact on the extent to which these entrepreneurs engage in the exploration and identification of opportunities on a monthly basis. This could have led to a potentially biased inference and a limited generalizability of the findings (Heckman, 1979). Seventh, as most of the early-stage entrepreneurs in our sample had at least one cofounder, team dynamics could have affected our variables of interest (Harper, 2008). For example, entrepreneurs’ exploration and the identification of business opportunities could have been determined by how the founding teams collectively perceive the environments, communicate, and decide to allocate existing resources. Although recent theorizing has emphasized the importance of entrepreneurial team characteristics for outcomes such as venture success and growth, there is still a lack of models on the role of team structures and team dynamics on entrepreneurial outcomes (Harper, 2008). All of the variables were self-reported and assessed by the same source. This could have biased the findings due to common method variance (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003; Siemsen, Roth, & Oliveira, 2010). Yet, common method variance should not be a major problem in this study. Response tendencies as one source of common method variance that are subject to respondents’ personal characteristics are eliminated in this study as we used person-mean centering in the analyses (i.e., centered the independent variables at the respective person mean). This method of centering removes any between-person variance in estimates of within-person relations among the variables, such that the relations among the within-person variables are unbiased estimates of the within-person slope and are not confounded by stable personality characteristics or general response biases (Aguinis, et al.,


33

2013; Enders & Tofighi, 2007). Further, moderation and moderated mediation effects that are the main focus of this study are not inflated by common method variance but deflate the observed relationships between constructs, thus lowering the chances to detect effects statistically (Siemsen, et al., 2010). Even though we treat entrepreneurial self-efficacy as a stable personal characteristic that differs between individuals, future research might want to also study dynamic changes in entrepreneurial self-efficacy—for instance due to individuals’ program participation and respective learning processes (Zhao, et al., 2005)—its interplay with varying uncertainty perceptions, and effects on dynamic changes in exploration and opportunity identification. In line with our theorizing, entrepreneurial self-efficacy owns important benefits by facilitating exploration in times when entrepreneurs perceive their environments to be more uncertain and unpredictable than usual. Yet, Hmielski and Baron (2008) showed that under certain conditions (i.e., when entrepreneurs’ dispositional optimism is perceived to be extremely high), entrepreneurial self-efficacy may exert negative effects on entrepreneurial outcomes. Being high in both entrepreneurial self-efficacy and dispositional optimism could result in entrepreneurs feeling overconfident that they will achieve positive outcomes irrespective of their effort and their behavioral approach. This could prevent them from trying to follow new ways and approaches, and consequently result in a decrease in exploration. Whether the interplay of perceived uncertainty and high entrepreneurial self-efficacy might also reveal potential detrimental effects on exploration—for instance, when it is combined with high dispositional optimism or other biases (Hmielski & Baron, 2008; Zhang & Cueto, 2015)— may be of interest for future research. Our conceptual model includes opportunity identification as one key outcome variable of the entrepreneurial process and exploration as an important mediator. Previous literature suggests that opportunities can also be seen as phenomena that are actively created by


34

entrepreneurs and emerge from social and creative thinking processes through effectuation (Sarason, et al., 2006; Sarasvathy, 2001). Effectuation may be another possible response to within-person changes in perceived uncertainty (Engel, Dimitrova, Khapova, & Elfring, 2014). For instance, Engel and colleagues (2014) showed that inexperienced entrepreneurs higher in entrepreneurial self-efficacy were more likely to use effectuation as a result of framing situations of uncertainty as opportunities and challenges. Our theoretical arguments are largely based on the opportunity recognition literature, yet we think that it would be a fruitful approach for future research to integrate and combine the knowledge on effectual reasoning with our perspective on exploration as both processes focus on learning through experimentation (Mom, et al., 2007; Sarasvathy, 2001) and may be integrated by early-stage entrepreneurs (Vaghely & Julien, 2010). Practical Implications Our results provide some practical implications, which highlight the benefits of increasing entrepreneurial self-efficacy and improving the exploration of entrepreneurial opportunities. Although our approximate effect size measures were small in terms of the proportion of variance explained, one also needs to consider both the risks and benefits when thinking about the implementation of interventions. Interventions that are based on weak effect sizes—which is often the case in psychology—but without known risks may, nevertheless, be valuable to some extent (Aguinis, et al., 2013; Ferguson, 2009; Rosnow & Rosenthal, 2003). Entrepreneurial self-efficacy is a moderately malleable personal characteristic that can be enhanced through specific interventions and learning experiences (Gielnik, et al., 2015; Zhao, et al., 2005). According to some recent literature, the motivational benefits related to self-efficacy can be attributed to dynamic self-regulation processes. Hence, educational interventions should not only aim at improving entrepreneurs’ task knowledge, abilities, and


35

skills, but also aim at increasing self-regulatory competencies, such as fostering selfmotivation under demanding circumstances (Bledow, 2013). For example, Gielnik and colleagues (2015) implemented a training intervention for undergraduate students by letting them experience starting a real business. Being engaged in running a real business within a structured program, where support and guidance is provided, enables people to learn and receive advice on how to manage changes in uncertainty and regulate the negative emotions that might come up in the process of starting a business. Further, evidence suggests that self-efficacy can be increased when entrepreneurs associate with role models that have successfully mastered difficult situations and adverse events related to running a business (Leatherbee & Eesley, 2014). It seems, therefore, that advice, mentoring, and listening to those who have successfully managed uncertainty might help early-stage entrepreneurs to develop their self-efficacy (Bullough, et al., 2014). These approaches could be implemented in entrepreneurship schooling (cf. Gonzalez-Uribe & Leatherbee, 2017) and support programs for early-stage entrepreneurs, such as in universities and ecosystem accelerators. CONCLUSION This study tackles the competing theoretical perspectives about the relationship between entrepreneurs’ varying perceptions of uncertainty and the identification of business opportunities. We develop and empirically support a moderated mediation model, highlighting the role of entrepreneurs’ exploration as a mechanism, which is activated under increasing uncertainty by entrepreneurs high in entrepreneurial self-efficacy. Whether or not entrepreneurs actively explore their environments and, in turn, develop more business opportunities in situations of high uncertainty was found to be influenced by the personal characteristic of entrepreneurial self-efficacy. The study contributes to previous research on entrepreneurship through a dynamic approach on monthly changes or within-person variation

RUNNING HEAD: DYNAMICS OF ENTREPRENEURIAL UNCERTAINTY AND OPPORTUNITY IDENTIFICATION in uncertainty, exploration, and business opportunity identification and pointing to entrepreneurial self-efficacy as a key boundary condition at the level of the entrepreneur.

36


37

REFERENCES Aguinis, H., Gottfredson, R. K., & Culpepper, S. A. (2013). Best-practice recommendations for estimating cross-level interaction effects using multilevel modeling. Journal of Management, 39, 1490-1528 Ardichvili, A., Cardozo, R., & Ray, S. (2003). A theory of entrepreneurial opportunity identification and development. Journal of Business Venturing, 18, 105-123. Baas, M., de Dreu, C. K. W., & Nijstad, B. A. (2011). When prevention promotes creativity: The role of mood, regulatory focus, and regulatory closure. Journal of Personality and Social Psychology, 100, 794-809. Bakker, A. B., Hakanen, J. J., Demerouti, E., & Xanthopoulou, D. (2007). Job resources boost work engagement, particularly, when job demands are high. Journal of Educational Psychology, 99, 274-284. doi: 10.1037/0022-0663.99.2.274 Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman. Baron, R. A. (2008). The role of affect in the entrepreneurial process. Academy of Management Review, 33, 328-340. Baum, J. R., & Locke, E. A. (2004). The relationship of entrepreneurial traits, skill, and motivation to subsequent venture growth. Journal of Applied Psychology, 89, 587598. Baumeister, R. F., Vohs, K. D., DeWall, C. N., & Zhang, L. (2007). How emotion shapes behavior: Feedback, anticipation, and reflection, rather than direct causation. Personality and Social Psychology Review, 11, 167-203. Behrens, J., Ernst, H., & Shepherd, D. A. (2014). The decision to exploit an R&D project: Divergent thinking across middle and senior managers. Journal of Product Innovation Management, 31, 144-158. Bledow, R. (2013). Demand-perception and self-motivation as opponent processes: A response to Bandura and Vancouver. Journal of Management, 39, 14-26. Boyd, N. G., & Vozikis, G. S. (1994). The influence of self-efficacy on the development of entrepreneurial intentions and actions. Entrepreneurship Theory and Practice, 18, 6363. Brinckmann, J., Grichnik, D., & Kapsa, D. (2010). Should entrepreneurs plan or just storm the castle? A meta-analysis on contextual factors impacting the business planning– performance relationship in small firms. Journal of Business Venturing, 25, 24-40. Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models: Application and data analysis methods. Newbury Park: CA: Sage. Bullough, A., Renko, M., & Myatt, T. (2014). Danger zone entrepreneurs: The importance of resilience and self-efficacy for entrepreneurial intentions. Entrepreneurship Theory and Practice, 38, 473–499. Butler, J. E., Doktor, R., & Lins, F. A. (2010). Linking international entrepreneurship to uncertainty, opportunity discovery, and cognition. Journal of International Entrepreneurship, 8, 121-134. Carver, C. S. (2004). Negative affects deriving from the behavioral approach system. Emotion, 4, 3-22. Cervone, D., Kopp, D. A., Schaumann, L., & Scott, W. D. (1994). Mood, self-efficacy, and performance standards: Lower moods induce higher standards for performance. Journal of Personality and Social Psychology, 67, 499-512.


38

Chen, J., Reilly, R. R., & Lynn, G. S. (2005). The impacts of speed-to-market on new product success: the moderating effects of uncertainty. Engineering Management, IEEE Transactions on, 52, 199-212. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Quantitative scales, curvilinear relationships, and transformations. In J. Cohen, P. Cohen, S. G. West & L. S. Aiken (Eds.), Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (pp. 193-254). Mahwah, New Jersey: Lawrence Erlbaum Associates, Publishers. Drnovšek, M., Wincent, J., & Cardon, M. S. (2010). Entrepreneurial self-efficacy and business start-up: developing a multi-dimensional definition. International Journal of Entrepreneurial Behaviour & Research, 16, 329-348. Duncan, R. B. (1972). Characteristics of organizational environments and perceived environmental uncertainty. Administrative Science Quarterly, 17, 313-327. Eckhardt, J. T., & Shane, S. (2003). Opportunities and entrepreneurship. Journal of Management, 29, 333-349. Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12, 121-138. Engel, Y., Dimitrova, N. G., Khapova, S. N., & Elfring, T. (2014). Uncertain but able: Entrepreneurial self-efficacy and novices‫ ׳‬use of expert decision-logic under uncertainty. Journal of Business Venturing Insights, 1, 12-17. Ferguson, C. J. (2009). An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 40, 532-538. Fisher, G. G. (2012). Effectuation, causation, and bricolage: a behavioral comparison of emerging theories in entrepreneurship research. Entrepreneurship Theory and Practice, 36, 1019-1051. Frese, M. (2009). Toward a psychology of entrepreneurship - An action theory perspective. Foundations and Trends in Entrepreneurship, 5, 435-494. Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19, 72-91. Gielnik, M. M., Frese, M., Graf, J. M., & Kampschulte, A. (2012). Creativity in the opportunity identification process and the moderating effect of diversity of information. Journal of Business Venturing, 27, 559-576. Gielnik, M. M., Frese, M., Kahara-Kawuki, A., Katono, I., Kyejjusa, S., Munene, J., . . . Orobia, L. (2015). Action and action-regulation in entrepreneurship: Evaluating a student training for promoting entrepreneurship. Academy of Management Learning & Education, 14, 69-94. Gielnik, M. M., Krämer, A.-C., Kappel, B., & Frese, M. (2014). Antecedents of business opportunity identification and innovation: Investigating the interplay of information processing and information acquisition. Applied Psychology: An International Review, 63, 344-381. Gonzalez-Uribe, J., & Leatherbee, M. (2017). The effects of business accelerators on venture performance: Evidence from Start-Up Chile. Retrieved from https://ssrn.com/abstract=2651158 Grant, A. M., & Ashford, S. J. (2008). The dynamics of proactivity at work. Research in Organizational Behavior, 28, 3-34. Griffin, M. A., Parker, S. K., & Neal, A. (2008). Is behavioral engagement a distinct and useful construct. Industrial and Organizational Psychology, 1, 48-51. Gruber, M., MacMillan, I. C., & Thompson, J. D. (2008). Look before you leap: Market opportunity identification in emerging technology firms. Management Science, 54, 1652-1665.


39

Gupta, A. K., Smith, K. G., & Shalley, C. E. (2006). The interplay between exploration and exploitation. Academy of Management Journal, 49, 693-706. Harper, D. A. (2008). Towards a theory of entrepreneurial teams. Journal of Business Venturing, 23, 613-626. Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153161. doi: 10.2307/1912352 Hirsh, J. B., Mar, R. A., & Peterson, J. B. (2012). Psychological entropy: a framework for understanding uncertainty-related anxiety. Psychological Review, 119, 304-320. Hmieleski, K. M., & Corbett, A. C. (2008). The contrasting interaction effects of improvisational behavior with entrepreneurial self-efficacy on new venture performance and entrepreneur work satisfaction. Journal of Business Venturing, 23, 482-496. Hmielski, K. M., & Baron, R. A. (2008). When does entrepreneurial self-efficacy enhance versus reduce firm performance? Strategic Entrepreneurship Journal, 2, 57–72. Howell, R. D., Breivik, E., & Wilcox, J. B. (2007). Reconsidering formative measurement. Psychological Methods, 12, 205-218. Jex, S. M., Bliese, P. D., Buzzell, S., & Primeau, J. (2001). The impact of self-efficacy on stressor–strain relations: Coping style as an explanatory mechanism. Journal of Applied Psychology, 86, 401-409. Johnson, R. E., Lanaj, K., & Barnes, C. M. (2014). The good and bad of being fair: Effects of procedural and interpersonal justice behaviors on regulatory resources. Journal of Applied Psychology. doi: 10.1037/a0035647 Kuhl, J. (2001). Motivation und Persönlichkeit: Interaktionen psychischer Systeme [Motivation and personality: Interactions of psychological systems]. Göttingen: Hogrefe. LaHuis, D. M., Hartman, M. J., Hakoyama, S., & Clark, P. C. (2014). Explained variance measures for multilevel models. Organizational Research Methods, 17, 433-451. Laureiro-Martínez, D., Brusoni, S., & Zollo, M. (2010). The neuroscientific foundations of the exploration− exploitation dilemma. Journal of Neuroscience, Psychology, and Economics, 3, 95-115. Leatherbee, M., & Eesley, C. E. (2014). Boulevard of broken behaviors: Socio-psychological mechanisms of entrepreneurship policies. Retrieved from http://ssrn.com/abstract=2488712 Leatherbee, M., & Katila, R. (2017). Stay the course or pivot? Antecedents of cognitive refinements of business models in young firms. Retrieved from https://ssrn.com/abstract=2902869 Liao, J., & Gartner, W. B. (2006). The effects of pre-venture plan timing and perceived environmental uncertainty on the persistence of emerging firms. Small Business Economics, 27, 23-40. Lyubomirsky, S., King, L., & Diener, E. (2005). The benefits of frequent positive affect: Does happiness lead to success? Psychological Bulletin, 131, 803-855. Mackinnon, A., Jorm, A. F., Christensen, H., Korten, A. E., Jacomb, P. A., & Rodgers, B. (1999). A short form of the Positive and Negative Affect Schedule: Evaluation of factorial validity and invariance across demographic variables in a community sample. Personality and Individual Differences, 27, 405-416. March, J. (1991). Exploration and exploitation in organizational learning. Organization Science, 2, 71-87. Markman, G. D., Baron, R. A., & Balkin, D. B. (2005). Are perseverance and self-efficacy costless? Assessing entrepreneurs' regretful thinking. Journal of Organizational Behavior, 26, 1-19.


40

Matthews, C. H., & Scott, S. G. (1995). Uncertainty and planning in small and entrepreneurial firms: An empirical assessment. Journal of Small Business Management, 33, 34-52. McKelvie, A., Haynie, J. M., & Gustavsson, V. (2011). Unpacking the uncertainty construct: Implications for entrepreneurial action. Journal of Business Venturing, 26, 273-292. McMullen, J. S., & Shepherd, D. A. (2006). Entrepreneurial action and the role of uncertainty in the theory of the entrepreneur. Academy of Management Review, 31, 132-152. Milliken, F. J. (1987). Three types of perceived uncertainty about the environment: State, effect, and response uncertainty. Academy of Management Review, 12, 133-143. Mom, T. J. M., Van Den Bosch, F. A. J., & Volberda, H. W. (2007). Investigating managers' exploration and exploitation activities: The influence of top-down, bottom-up, and horizontal knowledge inflows. Journal of Management Studies, 44, 910-931. Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus User’s Guide (Seventh Edition ed.). Los Angeles, CA: Muthén & Muthén. Nonaka, I., Von Krogh, G., & Voelpel, S. (2006). Organizational knowledge creation theory: Evolutionary paths and future advances. Organization Studies, 27, 1179-1208. Ozgen, E., & Baron, R. A. (2007). Social sources of information in opportunity recognition: Effects of mentors, industry networks, and professional forums. Journal of Business Venturing, 22, 174-192. Peidro, D., Mula, J., Jiménez, M., & del Mar Botella, M. (2010). A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. European Journal of Operational Research, 205, 65-80. Perrewé, P. L., & Zellars, K. L. (1999). An examination of attributions and emotions in the transactional approach to the organizational stress process. Journal of Organizational Behavior, 20, 739-752. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879-903. Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185-227. Ramoglou, S., & Tsang, E. (2016). A realist perspective of entrepreneurship: Opportunities as propensities. Academy of Management Review, 41, 410–434. Rosnow, R. L., & Rosenthal, R. (2003). Effect sizes for experimenting psychologists. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale, 57(3), 221-237. Rossiter, J. R. (2002). The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19, 305–335. Sarason, Y., Dean, T., & Dillard, J. F. (2006). Entrepreneurship as the nexus of individual and opportunity: A structuration view. Journal of Business Venturing, 21, 286-305. Sarasvathy, S. D. (2001). Causation and effectuation: Toward a theoretical shift from economic inevitability to entrepreneurial contingency. Academy of Management Review, 26, 243-263. Schumpeter, J. A. (1934). The theory of economic development. Cambridge, MA: Harvard University Press. Shane, S., & Venkataraman, S. (2000). The promise of entrepreneurship as a field of research. Academy of Management Review, 25, 217-226. Siemsen, E., Roth, A., & Oliveira, P. (2010). Common method bias in regression models with linear, quadratic, and interaction effects. Organizational Research Methods, 13, 456476.


41

Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage Publishers. Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed. ed.). Thousand Oaks, CA: Sage Publications. Song, M., & Montoya-Weiss, M. M. (2001). The effect of perceived technological uncertainty on Japanese new product development. Academy of Management Journal, 44, 61-80. Sonnentag, S., Arbeus, H., Mahn, C., & Fritz, C. (2014). Exhaustion and lack of psychological detachment from work during off-job time: Moderator effects of time pressure and leisure experiences. Journal of Occupational Health Psychology, 19, 206-216. Tang, J. (2008). Environmental munificence for entrepreneurs: entrepreneurial alertness and commitment. International Journal of Entrepreneurial Behavior & Research, 14, 128-151. Tang, J., Kacmar, K. M., & Busenitz, L. (2012). Entrepreneurial alertness in the pursuit of new opportunities. Journal of Business Venturing, 27, 77-94. Townsend, D. M., Busenitz, L. W., & Arthurs, J. D. (2010). To start or not to start: Outcome and ability expectations in the decision to start a new venture. Journal of Business Venturing, 25, 192-202. Tumasjan, A., & Braun, R. (2012). In the eye of the beholder: How regulatory focus and selfefficacy interact in influencing opportunity recognition. Journal of Business Venturing, 27, 622-636. Ucbasaran, D., Westhead, P., & Wright, M. (2008). Opportunity identification and pursuit: Does an entrepreneur's human capital matter? Small Business Economics, 30, 153173. Uy, M. A., Foo, M. D., & Aguinis, H. (2010). Using experience sampling methodology to advance entrepreneurship theory and research. Organizational Research Methods, 13, 31-54. Vaghely, I. P., & Julien, P.-A. (2010). Are opportunities recognized or constructed?: An information perspective on entrepreneurial opportunity identification. Journal of Business Venturing, 25, 73-86. van Gelderen, M., Frese, M., & Thurik, R. (2000). Strategies, uncertainty and performance of small business startups. Small Business Economics, 15, 165-181. Wanous, J. P., Reichers, A. E., & Hudy, M. J. (1997). Overall job satisfaction: How good are single-item measures? Journal of Applied Psychology, 82, 247-252. Watson, D., Wiese, D., Vaidya, J., & Tellegen, A. (1999). The two general activation systems of affect: Structural findings, evolutionary considerations, and psychobiological evidence. Journal of Personality and Social Psychology, 76, 820-838. Zhang, S. X., & Cueto, J. (2015). The study of bias in entrepreneurship. Entrepreneurship Theory and Practice. doi: 10.1111/etap.12212 Zhao, H., Seibert, S. E., & Hills, G. E. (2005). The mediating role of self-efficacy in the development of entrepreneurial intentions. Journal of Applied Psychology, 90, 12651272


42

Table 1 Means (M), Standard Deviations (SD), and Inter-Correlations of Study Variables Variable

M

SD

1

2

3

4

5

6

-.17

-

7

8

9

10

Person level 1. Gender 2. Age 3. Education 4. Entrepreneurial experience 5. Trait positive affect 6. Trait negative affect 7. Entrepreneurial selfefficacy

1.86 0.35 31.15 5.77 4.66 1.39

-.04 -.17 .27**

1.52

1.19

.06

4.18 2.15

0.53 0.78

-.01 -.24** -.01 .17 .00 -.22* -.24** -.11

4.01

0.59

-.05

-.17

-.11 .25** .53** -.11

8. Uncertainty 2.82 9. Exploration 3.15 10. Business opportunity 0.32 identification (log)

0.69 0.57

.19* -.02

.03 -.12

-.17 -.01 -.13 .10 .02 -.03 -.04 .33** -.05 .24**

0.17

.03

-.06

-.07

.03

-.13

-

-

Monthly level .09

.12* -.01 - .27**

.18* .31** -.19* .33** -.07 .24**

-

Note. Correlations above the diagonal represent the monthly level (N= 424-439 observations) and are based on within-person deviation scores such that individuals’ monthly scores were subtracted from their respective person-mean score (Snijders & Bosker, 1999). Correlations below the diagonal are based on the person level (N= 118-121). Gender was coded 1 = female and 2 = male. Education was coded 1 = less than high school


43

degree, 2 = high school degree, 3 = 2-year college degree, 4 = 4-year college degree, 5 = Master’s degree, 6 = PhD degree. Business opportunity identification was log-transformed. p < .05.

**

p < .01.


44

Table 2 Results of Multilevel Regression Analysis with Uncertainty as Independent Variable DV: Business opportunity identification (log) Model 3

Mediator: Exploration Model 1 B Main effects person level Gender Age Education Entrepreneurial experience Trait positive affect Trait negative affect Entrepreneurial self-efficacy Main effects monthly level Uncertainty Exploration Cross-level moderation Uncertainty x Entrepreneurial self-efficacy R12 (approx.)

SE

Model 2 t

B

SE

t

B

SE

t

0.01 -0.00 0.01 -0.06 0.28 0.01 0.11

0.15 0.01 0.04 0.04 0.11 0.07 0.10

0.09 -0.29 0.17 -1.34 2.61** 0.12 1.02

0.01 -0.00 0.01 -0.06 0.28 0.01 0.11

0.15 0.01 0.04 0.04 0.11 0.07 0.10

0.09 -0.29 0.17 -1.34 2.61** 0.12 1.02

0.01 0.00 -0.00 0.01 0.05 -0.02 0.05

0.03 0.00 0.01 0.01 0.03 0.02 0.03

0.31 0.24 -0.14 1.17 0.06 -0.96 1.68

0.07

0.05

1.42

0.06

0.05

1.23

0.01 0.08

0.01 0.02

0.91 4.89**

0.09

0.03

2.76**

0.0280

0.0305

0.0588


45

Note. Calculations are based on N = 121 at the person level and N = 439 at the monthly level. Business opportunity identification was logtransformed. R12 (approx.) = amount of variance explained at the within-person or monthly level by the predictors in the model based on a randomintercept model (LaHuis et al., 2014). The formula is R12 (approx.) = (σ2 null model - σ2 current

model)

/ σ2 null model. * p < .05; ** p < .01.


46

Table 3 Conditional Indirect Effects of Uncertainty on Log-Transformed Business Opportunity Identification through Exploration for Different Values of Entrepreneurial Self-efficacy Moderator: Entrepreneurial self-efficacy B

SE

t

- 1 SD

-0.01 0.01

-0.54

Mean

0.00

0.01

1.21

+ 1 SD

0.02

0.01

2.34*

Note. Calculations are based on N = 121 at the person level and N = 439 at the monthly level. Business opportunity identification as the dependent variable was log-transformed. * p < .05; ** p < .01.


Entrepreneurial self-efficacy Person level

Uncertainty

Exploration

Business opportunity identification Monthly level

Figure 1. Conceptual model.

47


48

4

Exploration

3,5

3

High Entrepreneurial Self-efficacy

2,5

Low Entrepreneurial Self-efficacy

2 Low Uncertainty

High Uncertainty

Figure 2. Entrepreneurial self-efficacy as a moderator of the within-person relationship between uncertainty and exploration. Uncertainty was centered at individuals’ mean values. Entrepreneurial self-efficacy was centered on the mean of the whole sample of early-stage entrepreneurs.

48