The motivational drivers underlying for-profit venture creation: Comparing social and commercial entrepreneurs

Abstract

Despite the increase in scholarship on social entrepreneurship over the years, there is a lack of large-scale empirical research from which generalizations about social entrepreneurs can be made. As such, our understanding of these individuals is limited. In particular, we know little about the drivers motivating social entrepreneurs in their quest to create for-profit ventures and whether they differ from those of commercial entrepreneurs. In response to a call to investigate the possibility of such differences, we analyse data from Panel Study of Entrepreneurial Dynamics II (PSED II) and find evidence to suggest that nascent entrepreneurs seeking to create for-profit social ventures have higher levels of entrepreneurial self-efficacy and more ambitious goals than their commercial counterparts. We conclude by discussing the implications of our findings for academics and practitioners.

Keywords

goals motivation self-efficacy social entrepreneurs venture creation

Introduction

Since the term ‘social entrepreneur’ was first used in the academic literature (Waddock and Post, 1991), scholarly interest in the area has flourished. However, after roughly two decades of scholarship, Short et al. (2009) lamented that a unified body of generalizable empirical research had yet to emerge. In their comprehensive review, the authors note that the social entrepreneurship literature has placed ‘an anecdotal emphasis on … heroes rather than generalizable details’ (p. 166) and argue that, as a result, a gap exists ‘between our current understanding of social entrepreneurship and an enhanced knowledge that could aid in researching and fostering this emerging field’ (p. 162). In response to the paucity of robust empirical evidence about social entrepreneurs, Short et al. (2009: 174) identify 10 important, yet unresolved, research questions, among them: ‘Are the motivations/drivers of social venture creation the same as or different from those of traditional venture creation?’

We find this question of particular interest, given Grimes et al.’s (2013) recent argument that social entrepreneurs’

emphasis on social missions (i.e., eliminating poverty) alongside market-based organizing and the embrace of a distinctive identity (i.e., social venture as a hybrid organisation with multiple bottom lines) separates them from those focused primarily on maximizing shareholder value. As a result of these distinctions, the burgeoning literature on social entrepreneurship points to the theoretical importance of founder motivation. (p. 460)

In other words, these authors suggest that the very purpose social and commercial entrepreneurs seek to fulfil through their ventures may reflect differences in their motivation. In support, the research on social entrepreneur motivation since Short et al.’s (2009) review (for recent examples, see Alcantara and Kshetri, 2013; Campin et al., 2013; Dey and Lehner, 2017; Dorado and Ventresca, 2013; Jayawarna et al., 2011; Renko, 2013; Ruskin et al., 2016; Ruvio and Shoham, 2011) suggests that social entrepreneurs tend to act on the basis of motives specific to their context. While such evidence might seem to provide an answer to Short et al.’s (2009) question, it is important to note that because these studies focus only on what motivates social entrepreneurs, the answers they provide are somewhat tautological. Thus, rather than focusing on what social entrepreneurs are motivated to accomplish, which if Grimes et al.’s (2013) conceptualization of social entrepreneurs is accurate ought to be distinct from their commercial counterparts, we focus on the motivational drivers underlying their behaviour. In other words, by seeking to answer Short et al.’s (2009) question by examining motivational drivers (as opposed to the motives) underlying venture creation, we can identify differences in the levels of motivation between social and commercial entrepreneurs while avoiding any implicit tautology in the process. We consider motivational drivers to be different from motives, in that drivers are the specific mechanisms that lead individuals to act (Locke, 1997), while motives are the underlying reason(s) for the actions.

In order to match our theoretical and empirical approach, we analyse data from the Panel Study of Entrepreneurial Dynamics II (PSED II), which contains data on the start-up efforts of 1214 entrepreneurs seeking to create for-profit ventures (Aldrich, 2000: 77). Our analysis suggests that social entrepreneurs have more ambitious goals and higher levels of entrepreneurial self-efficacy than commercial entrepreneurs. We believe that our findings can contribute to the literature in the following ways. First, the popular view that social entrepreneurs are distinct from commercial entrepreneurs (Grimes et al., 2013) has not been well supported with empirical evidence. By focusing on differences in the motivational drivers of social and commercial entrepreneurs, we believe we can provide compelling evidence that can be used in order to begin to evaluate the merits of this widely held view. Second, the psychological makeup of entrepreneurs (of which motivation is a key component) is argued to have the greatest impact on their ventures during their earliest phases (Frese and Gielnik, 2014). While many studies have explored entrepreneur motivation, most have focused on firms that are already operational (Arend, 2014; Baum et al., 2001; Baum and Locke, 2004; Bird, 1988; Bird and Jelinek, 1988; Boyd and Vozikis, 1994; Campin et al., 2013; Carsrud and Krueger, 1995; Ruvio and Shoham, 2011; Timmons, 2000). Of those studies that have examined entrepreneur motivation during the firm creation process (Edelman et al., 2010; Hechavarria et al., 2012; Hopp and Stephan, 2012; Liu, 2012; Schenkel et al., 2009; Zanakis et al., 2012), few have explored the motivations of nascent social entrepreneurs (see Renko, 2013 for a noteworthy example), and none have sought to examine whether and to what degree social entrepreneur motivations differ from their commercial counterparts. Thus, this article can take an initial step towards filling this gap by responding to Short et al.’s (2009) question. Third, although research in the area of work motivation has made strides towards recognising the importance of context in a general sense as interacting with individual personality traits to result in motivational processes that influence work outcomes (Barrick et al., 2013; Jayawarna et al., 2011; Kammeyer-Mueller et al., 2016; Shaw and de Bruin, 2013), more specific questions regarding the context of new venture creation (Baum and Locke, 2004; Wry and York, 2017; York et al., 2016) and the intention to start a new business have just began to be examined. We believe that highlighting how the motivations of social entrepreneurs differ from those of commercial entrepreneurs, coupled with the well-established role motivation plays in the venture creation process, places the work motivation in a meaningful context. Finally, in addition to the fact that recent exploratory research has echoed Short et al.’s (2009) call to focus on differences in the motivation of social versus commercial entrepreneurs (Jayawarna et al., 2011), Shepherd (2015) has gone so far as to argue that

the vitality of the field of entrepreneurship will be maintained and knowledge advanced through … research on the motivations and capabilities for creating new ventures (new organizations or new ventures in established organizations) that help others and/or preserve the natural environment. (p. 503)

We agree with Shepherd’s (2015) view and believe that the important role social entrepreneurship plays in the modern economy establishes it as a focal activity within the entrepreneurship field. Coupled with the challenges social entrepreneurs face (Kanter, 1999), our findings contribute not only to the scholarly understanding of the process by which social ventures are created but also to the ability of practicing entrepreneurs to actually create them.

Theory and hypotheses

We begin our article by acknowledging a recent call to exercise precision when defining boundaries in social entrepreneurship research (Newbert and Hill, 2014). Thus, we restrict our focus to for-profit ventures in order to eliminate the profit imperative as a potentially confounding driver of differences in motivation between social and commercial entrepreneurs, thereby facilitating our ability to theorise specifically about how and why the presence or absence of a social objective might result in such differences, precisely as Short et al. (2009) propose. Given this framing, we adopt Dees and Anderson’s (2003: 2) conceptualisations of for-profit social ventures as ‘entrepreneurial organisations that are [1] legally incorporated as for-profit entities … and [2] explicitly designed to serve a social purpose’ and the ‘social entrepreneurs’ who create them as individuals with ‘dual social and financial objectives that guide their managerial decision-making and determine their success’. In this way, our definitions take into account the instrumental role financial rewards play in serving the greater good (Alvord et al., 2004; Mair and Marti, 2006; Wallace, 1999).

Following the advice of Dacin, Dacin and Matear (2010) who advocate the application of existing theories as opposed to the development of new ones in order to predict and explain social mission-related phenomena, we ground our hypotheses in well-established theories of work motivation and contextualise our arguments, given the realities of the social and commercial milieux. Amid the plethora of possible conceptualisations of motivation in an entrepreneurial context, we focus on two seminal concepts from goal-setting theory within the work motivation literature: self-efficacy and goals (Bandura, 1997; Kanfer and Chen, 2016; Latham and Pinder, 2005; Locke and Latham, 1990). We consider whether there are differences in these two concepts for social and commercial entrepreneurs, which would indicate a fundamental difference regarding how motivated these two groups actually might be in practice. These two concepts form the core of Locke’s (1991) hub of work motivation and have been studied before in the context of entrepreneurial motivation (Baum et al., 2001; Baum and Locke, 2004; Hechavarria et al., 2012). Additionally, Miner’s (2003) quantitative review of theories in organizational behaviour found Locke and Latham’s (1990) goal-setting theory (which includes goals and self-efficacy as critical drivers of work motivation) to be the highest rated theory of the 73 theories included in terms of importance.

Work motivation has been defined as a ‘set of energetic forces that originate both within as well as beyond an individual’s being, to initiate work-related behaviour and to determine its form, direction, intensity, and duration’ (Latham and Pinder, 2005: 486). While there are many theories of work motivation, as noted above, scholars have identified Locke and Latham’s (1990) goal-setting theory, which includes goal setting and self-efficacy as critical concepts, as a premier theory in the management literature in terms of importance, scientific validity and usefulness in practice (cf. Miner, 2003). We, therefore, focus on the core concepts in this theory in order to understand whether social and commercial entrepreneurs differ in terms of how motivated they actually are as they approach their work. Notably, this approach is different from recent work that focuses on the actual motives, such as altruism and social justice, underlying the choice individuals make to be social entrepreneurs (Ruskin et al., 2016). Rather, in light of Locke and Latham’s (2004) argument that motivation can influence ‘how and to what extent [people] utilize their skills and abilities’, we contend that social and commercial entrepreneurs differ in terms of the underlying motivational mechanisms such as self-efficacy and goals, as they approach their work (p. 388).

Our subsequent discussion of motivation makes two assumptions. First, like Locke and Latham (1990) and Bandura (1997), we consider human agency to play a foundational role in the conscious motivation process. In other words, ‘people can exercise influence over what they do … the power to originate actions for given purposes is the key feature of personal agency’ (Bandura, 1997: 3). This is not to say that individuals have complete control over the outcomes that follow, since, as Davidson (1971) notes, actions intended to serve a certain purpose can result in different outcomes. However, like Bandura (1997), we recognize that people can bring their thoughts into being by the ‘intentional exercise of personal agency’ (p. 5).

Second, building on the previous literature, we recognize that motivation is a psychological process resulting from the interaction between the individual and the environment. While classic work in motivation theory has focused on the individual’s needs, traits and values (Maslow’s (1943) hierarchical need theory, Allport’s (1951) discussion of traits as drivers of behaviour, etc.), exogenous sources of motivation such as national culture, job design characteristics and person–environment fit have been identified as important drivers of behaviour in the recent literature (Kanfer and Chen, 2016; Korsgaard and Anderson, 2011; Latham and Pinder, 2005). Similarly, Bandura (1997) postulated in his discussion of triadic reciprocal causation that events are caused by three major determinants – internal personal factors, behaviour and environmental events – and that these three determinants have bi-directional influences upon each other.

While we acknowledge that the above-noted external sources of motivation do matter, we focus in this article on the internal drivers of motivation that may differentiate social and commercial entrepreneurs. In part, this is because we consider conscious drivers of motivation within the framework of goal-setting theory (Locke and Latham, 1990). Research within this tradition has included cognitive variables that allow individuals to interpret environmental antecedents and consequences. Extant work has highlighted the key roles of self-efficacy and goals as being the most proximal drivers of individual motivation (cf. Locke, 1997), as these are the two components of what Locke (1991) calls the ‘motivation hub’ – the critical heart of the goal-setting model of motivation. The theory suggests that self-efficacy influences goal choice and goal difficulty; moreover, the body of empirical research has found that goals and self-efficacy affect performance on a given task through their influence on direction, effort, persistence and task strategies or tactics associated with that particular task (Locke, 1997). Importantly, although previous research on goal setting has considered the effects of both assigned goals and self-set goals on performance, we focus on self-set goals for the purposes of this study. Self-set goals are determined by the focal individual, and there is no assumption in the previous literature that these goals are influenced in any way by any kind of external force. Rather, self-set goals are primarily driven by internal factors, such as self-efficacy, as Locke (1991, 1997) describes. In the sections below, we develop arguments underlying why nascent social and commercial entrepreneurs may have differing levels of these underlying motivational drivers.

Self-efficacy

A major concept in goal-setting theory is self-efficacy, which is formally defined as ‘beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments’ (Bandura, 1997: 3). In other words, self-efficacy is ‘the belief that one is able to perform a certain action effectively’ (Frese et al., 2007). According to social cognitive theory (Bandura, 1986, 1997) and goal-setting theory (Locke and Latham, 1990), self-efficacy is a major antecedent of motivation to take action. Over three decades of empirical research supports the causal chain positively linking self-efficacy to self-set goals and performance (Bandura, 1997; Latham and Pinder, 2005; Locke, 1997; Locke and Latham, 2006). In other words, higher levels of task-specific self-confidence can lead an individual to set higher goals, which, in turn, can result in higher levels of individual performance. While this relationship depends in part on the type and complexity of task involved, the feedback that is received and the ability of the individual (Locke, 1997), the practical implications of this set of relationships are far-reaching. In short, individuals must feel confident in their ability with respect to a given task in order to take the necessary action to accomplish it (Bandura, 1997; Locke and Latham, 1990).

Early research in this area tended to focus either on task-specific self-efficacy, which refers to the belief individuals have in their ability to carry out a particular task, or generalized self-efficacy, which reflects an overall assessment of an individual’s capability to handle life’s challenges (Sherer et al., 1982). However, given that many of the tasks facing nascent entrepreneurs are inter-related, more recent work in the entrepreneurship literature has focused on ‘domain-specific self-efficacy’, which is defined as ‘an individual’s confidence in abilities that apply to several related tasks within a domain’ (Cassar and Friedman, 2009: 243). This domain-specific form of self-efficacy has come to be known as ‘entrepreneurial self-efficacy’ (Boyd and Vozikis, 1994; Chen et al., 1998), which ‘refers to the strength of an individual’s belief that he or she is capable of successfully performing the roles and tasks of an entrepreneur’ (Chen et al., 1998: 301). Entrepreneurial self-efficacy is particularly salient in the context of new ventures, given that there is ‘a stronger nexus between the entrepreneur’s decisions and the new venture’s actions than exists in established firms where formal processes and other factors mediate the influence of managerial cognition on firm action’ (Cassar and Friedman, 2009: 243).

With this framing in mind, we turn to the self-efficacy literature for insights into how one might develop entrepreneurial self-efficacy in various ways – and why certain individuals may differ with respect to their levels of entrepreneurial self-efficacy. This is an important question, given that the entrepreneurial self-efficacy literature has identified the concept as a critical antecedent to entrepreneurial performance (Hockerts, 2017). According to Bandura (1997), there are four primary sources of information from which individuals develop their self-efficacy beliefs:

enactive mastery experiences [i.e., practice] that serve as indicators of capability; vicarious experiences that alter efficacy beliefs through the transmission of competencies and comparisons with the attainments of others; verbal persuasion … that one possesses certain capabilities; and physiological and affective states from which people partly judge their capableness, strength, and vulnerability to dysfunction. (p. 79)

Notably, Bandura points out that these sources are not ‘inherently enlightening’; rather, individuals must interpret the information they receive as they develop self-efficacy. Also, Bandura (1997) writes that ‘any given influence [on self-efficacy]… may operate through one or more of these sources of efficacy information’ (p. 78). In other words, not all four of the sources must be present and positive in order for there to be an impact upon self-efficacy, and the information received from each source must be cognitively processed.

We argue that the first three sources of efficacy, and their interpretation, are unlikely to differ in a systematic way between nascent social and commercial entrepreneurs. There may be a difference with respect to the fourth, however, and this may drive nascent social entrepreneurs to have higher levels of self-efficacy than nascent commercial entrepreneurs. While it is true that people with more of Bandura’s (1997) first source of efficacy beliefs – experience or practice (Bandura’s ‘enactive mastery experiences’) – may have higher levels of self-efficacy (Hockerts, 2017), there is no reason to believe that the nascent commercial and social entrepreneurs in the sample would differ systematically with respect to prior entrepreneurial experience or how it is interpreted. A nascent commercial entrepreneur might have had three prior experiences starting businesses, for example, but those experiences may have left him or her feeling less efficacious, for various reasons that only the individual in question would understand than a nascent social entrepreneur who has never tried to start a business. Similarly, with respect to vicarious influence, Bandura’s second source of efficacy information, there is no reason to believe that nascent commercial and social entrepreneurs would systematically differ. While both groups probably would have been exposed to more examples of commercial entrepreneurs in everyday life, a single example of a successful social entrepreneur (Blake Mycoskie of Tom’s Shoes) might provide more of an efficacy boost to a nascent social entrepreneur, depending on how the individual in question interprets the information. With respect to verbal influence, Bandura’s third source of efficacy information, once again, there is no reason to believe that nascent commercial and social entrepreneurs would systematically differ. Depending on the networks of which they are part and the social influences around them, nascent social and commercial entrepreneurs will certainly differ on this at an individual level, but there is no reason to believe that commercial entrepreneurs would be subject to any greater verbal influence than social entrepreneurs or vice versa.

In stark contrast to the above three sources of self-efficacy, there is theoretical reason to believe that physiological and affective states might differ between nascent social and commercial entrepreneurs. With respect to these states, previous research has found that ‘mood and efficacy beliefs are related both concurrently and predictively’ (Bandura, 1997: 113). Positive affect, that is, a general feeling of positivity, can help raise efficacy beliefs and lead to a positive spiral of performance (Bandura, 1997). While this work was not specifically conducted within the realm of entrepreneurship, it is relevant when considering what may influence the self-efficacy levels of nascent social versus commercial entrepreneurs. Given that self-efficacy ‘reflects the conclusion one draws about one’s capacity for performance attainment’ (Baum and Locke, 2004: 590), Bandura’s (1997) work suggests that the more closely connected a task is to an individual’s deeply held values, the more confident the individual will be in his or her ability to accomplish it. In other words, tasks that are more closely connected to an individual’s deeply held values are more meaningful and are, therefore, likely to result in more positive affective attachment to those tasks, which, in turn, would manifest in higher self-efficacy. In the context of new venture creation, while commercial entrepreneurs are likely to have a certain level of confidence in their abilities as they develop their business ideas and plans, social entrepreneurs likely harbour additional aspirations about the possibility of affecting stakeholders in a positive way, such as by targeting a specific geography, for example, in one’s neighbourhood or city, social problem (i.e. child nutrition) or both. Indeed, research suggests a close alignment between the mission of social ventures and the deeply held values of their founders (Dees, 1998), and that non-economic motivations are typically more powerful (Jayawarna et al., 2011). Thus, the greater affective attachment by social entrepreneurs to their ventures may result in greater confidence regarding success and, by extension, that they are capable of putting forth the effort in assuring that end (Frese et al., 2007; Krizan and Windschitl, 2007; Simon and Shrader, 2011):

Hypothesis 1. Social entrepreneurs will have higher levels of entrepreneurial self-efficacy than commercial entrepreneurs.

Goals

In an organizational setting, ‘[a] goal is what an employee is trying to accomplish on the job’ (Locke and Latham, 1984: 5). More generally, a goal is the object or aim that results from undertaking a particular course of action (Locke, 1997; Locke and Latham, 1984). As noted above, goals in this study are self-set, so set by the focal entrepreneur. Moreover, the goals considered in this study – like the goals considered in much of the literature on goal-setting theory (cf., Locke and Latham, 1990) – are task-related. In other words, the nascent entrepreneurs in this study set their own goals associated with the performance of their business. We expect that nascent social and commercial entrepreneurs may systematically select different goal levels in terms of business growth benchmarks due to the specific challenges that they face when trying to understand the contextual realities of their business situation. As a baseline, we note arguments that individuals are likely to have higher expectations of their performance on a given task such as starting a business, the more atypical the task (Hayward et al., 2006; Vallone et al., 1990). The reason for this overconfidence is that when individuals undertake such tasks, the data upon which they must rely in order to make decisions is often unavailable or ambiguous. In such cases, individuals are unlikely to comprehend accurately cause-and-effect relationships and, thus, tend to interpret any information they receive in the course of embarking on that task in ways that are highly consistent with their aspirations, irrespective of reality (Baron et al., 2016; Gartner and Thomas, 1993). This malleability in the interpretation of task-related feedback tends to result in the overestimation of an individual’s prospects for success (Simon and Shrader, 2011), which is likely to manifest in highly inflated goals.

While we expect this misalignment between expectation and reality to appear in most entrepreneur goals to some degree, due to their inherent disposition towards overconfidence (McCarthy et al., 1993) we expect the misalignment to be greatest for those seeking to create social ventures. This arises as the contexts in which social ventures operate tend to be more chaotic, with more poorly defined boundaries, comparatively weaker institutions (Khanna et al., 2005), more uncertain demand conditions (Kanter, 1999) and more limited or dubious information (Bloom and Dees, 2008) than traditional commercial markets. As such, the tasks necessary in order to succeed in social sector markets are not as well documented as they are in commercial markets, leading to significantly more ambiguity regarding the effectiveness of chosen and planned courses of action. Compounding matters is evidence that social entrepreneurs often lack the business education and experience of their commercial counterparts (Amin, 2009; Miller and Wesley, 2010), both of which are critical to understanding the implications of their start-up efforts. Thus, while interpreting task-related feedback is difficult enough for any entrepreneur due to the inherent uncertainty of the start-up process (Knight, 1921), it is especially challenging for social entrepreneurs due to the additional ambiguity characterizing the context in which they seek to create their ventures. Taken together, social entrepreneurs are more likely than commercial entrepreneurs to set goals based on a biased desire for success than an accurate, unbiased understanding of the results of their past action and their plans for future action. Therefore, we expect the goals social entrepreneurs set will be more likely to reflect what they hope to achieve, that is, goals based on their aspirations, than what they are actually likely to achieve, that is goals based on the reality of their situation, and thus, will be significantly higher than the goals set by commercial entrepreneurs:

Hypothesis 2. Social entrepreneurs will set more ambitious goals than commercial entrepreneurs.

Method

Sample

Given that we have defined social entrepreneurs as individuals seeking to create new ventures that create not just social value but also economic value, we test our hypotheses using PSED II. This panel study, coordinated by the University of Michigan Survey Research Center (UMSRC), is a longitudinal dataset of individuals involved in the process of starting for-profit businesses. Respondents were identified from a random telephone survey of 31,845 adults from a representative sample of households in the United States between 2005 and 2006. From this target population, a randomized sample of nascent entrepreneurs, or individuals who initiate ‘serious activities that are intended to culminate in a viable business startup’ (Aldrich, 2000: 77), were identified as those individuals who met the following criteria: individuals who (1) were trying to start their own business either for themselves or for their employer, (2) were expected to be owners or part owners of the new firm which would not be majority owned by another business, (3) were active in trying to start the new firm within the past 12 months and (4) were still in the start-up phase (Reynolds and Curtin, 2008). In all, 1214 respondents satisfied the above conditions and were immediately contacted for follow-up telephone interviews by researchers at the UMSRC in order to collect data on the nature of the new ventures and the kinds of activities entrepreneurs undertake during the start-up process. Although five additional telephone interviews were conducted with these individuals at annual intervals, we restrict our focus to data from the first interview, given that this was the only time at which data for two of our four model variables (namely, social vs commercial entrepreneur and entrepreneurial self-efficacy) were collected. Due to the randomized nature of the sample, PSED II is representative of the population of emerging for-profit businesses in the United States.

Despite the fact that our data come from the same survey instrument, we believe that common method variance will not bias the results of our statistical tests for the following reasons. To begin, Podsakoff et al. (2003) argue that when it is not possible to obtain data from different sources, the manner in which the survey is designed can sufficiently reduce common method variance. This is particularly germane to this study, given that data on organizations that are not yet operational do not exist from sources other than the entrepreneur (Bamford et al., 2000). Fortunately, several of Podsakoff et al.’s (2003) remedies were used when designing and administering PSED II. Specifically, the anonymity of all respondents was guaranteed by the survey designers, a procedure that Podsakoff et al. (2003) suggest reduces the likelihood of socially desirable responses, a main source of such variance. In addition, data on independent and dependent variables were both temporally and proximally separated, such that the relevant items were included in distinct sections of the hour-long telephone interview (Reynolds and Curtin, 2007), resulting in a good deal of time elapsing between their measurement, and required different response formats. According to Podsakoff et al. (2003), these techniques reduce common method variance as they

reduce biases in the retrieval stage of the response process by eliminating the saliency of any contextually provided retrieval cues … reduce the respondent’s ability and/or motivation to use previous answers to fill in gaps in what is recalled and/or to infer missing details …[and] reduce biases in the response reporting or editing stage of the response process by making prior responses less salient, available, or relevant. (p. 888)

Notwithstanding these procedural remedies, in the research by Malhotra, Kim and Patil (2006) and Meade, Watson and Kroustalis (2007), the distinction between common method variance and common method bias was noted. This empirical research suggests that even in cases where common method variance is present, it does not necessarily result in common method bias that might jeopardize the validity of the results of any subsequent statistical tests. Moreover, these scholars find that even when common method bias resulting from common method variance is present, the effect of this bias on the results tends to be either ‘trivially small’ (Meade et al., 2007) or ‘not significant’ (Malhotra et al., 2006). In light of these procedural and statistical issues, we believe that our analyses will not be confounded by common method bias.

Measurement

Social entrepreneurs are those individuals who seek to create social value (Dacin et al., 2010), either directly by exploiting opportunities to solve specific social problems or indirectly by improving local economic conditions (Mair, 2006). In pursuit of such ends, social entrepreneurs are believed to ‘make quite deliberate decisions to solve social problems, rather than simply stumbling into their work by accident or circumstance’ (Light, 2009: 22). Thus, social entrepreneurs exhibit a purposeful intention to fulfil some societal need. In PSED II, respondents were asked on two occasions to indicate the top two reasons why they wanted to start their new business, the responses to which were open ended and then classified by the interviewers at the UMSRC using an array of 44 categories for the first question and 62 for the second. Relying on these categories, we follow the lead of recent empirical research on social entrepreneurship using PSED II (Gras and Lumpkin, 2012; Newbert, 2012; Renko, 2013) and operationalizing social entrepreneurs as those whose responses to either of these two questions on either occasion on which they were asked them were classified as ‘to help others; help community’ so, for example, ‘create social value directly by solving specific social problems’ and/or ‘to aid in economy; economic development’ so, for example, create social value indirectly by improving economic conditions. Because there are many non-social reasons individuals seek to create new ventures, we restrict our classification of commercial entrepreneurs in PSED II to those respondents who made at least one specific reference to monetary gain as a reason for wanting to start a new business. Thus, we operationalize commercial entrepreneurs as respondents who indicated any of the following as the top two reasons for wanting to start their business: ‘income; to make money’, ‘extra income’, ‘need to supplement income’, ‘investment’, ‘financial independence; financial/job security’, ‘income for educational expenses’, ‘income for retirement’, ‘to leave business/money to children’ and/or ‘other income references’. Of the 1214 respondents in PSED II, 595 did not indicate a social or financial reason for wanting to create their ventures. In such cases, reasons included wanting to exploit a business opportunity, the need for employment, personal reasons, pursuit of desired lifestyles, possession of slack financial resources and/or encouragement by a mentor. The exclusion of these cases yielded a final sample size of 619. Based on the above classifications, we operationalize this variable as a dummy variable, with a value of 1 for social entrepreneurs (n = 94) and a value of 0 for commercial entrepreneurs (n = 525). Consistent with recent research on dual identity in entrepreneurs (Wry and York, 2017; York et al., 2016), it is to be noted that in 36 cases, respondents indicated wanting to start their business for both social and financial reasons, with 15 giving primacy to social objectives and 21 giving primacy to financial objectives. Given that these cases adhered to our definition of for-profit social ventures, as ventures pursuing both social and financial objectives, we classified them as social entrepreneurs. However, we also conduct a robustness check of our results, wherein we classify the 21 respondents that gave primacy to financial objectives as commercial entrepreneurs.

Entrepreneurial self-efficacy ‘refers to the strength of an individual’s belief that he or she is capable of successfully performing the roles and tasks of an entrepreneur’ (Chen et al., 1998: 301). Baum and Locke (2004) maintain that an entrepreneur’s ‘[s]elf-efficacy reflects not only past experience and attainment but, more important, the conclusion one draws about one’s capacity for performance attainment from these past experiences’ (p. 590). Unlike previous studies on entrepreneurial motivation which focus on established organizations, we situate our study in the context of the emergence process in which the most salient focal task of respondents in our sample is the creation of a new organization. We, therefore, follow Cassar and Friedman’s (2009) and operationalize entrepreneurial self-efficacy with the average of the responses to the following three questions, each measured with a five-point scale ranging from strongly disagree (value of 1) to strongly agree (value of 5): (1) my past experience will be very valuable in starting this new business; (2) overall, my skills and abilities will help me start this new business and (3) I am confident I can put in the effort needed to start this new business.1 These items demonstrate a high level of internal consistency (α = 0.71), suggesting that they are reliable indicators of the focal construct (Nunnally, 1978).

As noted above, goals refer to what the nascent entrepreneur is trying to accomplish by starting his or her business. Given that we are interested in the goals set by entrepreneurs seeking to create new ventures that create not just social value but economic as well, we begin by considering the economic goals they might set for themselves. Given that revenue streams are instrumental to profitability, we follow Baum and Locke (2004) and measure economic goals as the respondents expected sales growth. We operationalize this variable as the compound annual sales growth rate for the first five years of the organization’s life. The distribution of this variable was skewed in its raw form; thus, we normalized it by taking its log, plus one. In order to account for nascent entrepreneur goals regarding social value creation, we also consider the impact they expect their organization will ultimately have on society, given arguments and evidence that social entrepreneurs, in particular, often seek to grow or scale their ventures as large as possible (Bloom and Chatterji, 2008). Following Newbert (2012), we operationalize a nascent entrepreneur’s scaling goals as a dummy variable, with a value of 1 for respondents who indicated wanting to scale their business to be ‘as large as possible’ and a value of 0 for respondents who indicated wanting their new business to be of a size they ‘can manage themselves or with a few key employees’.

In order to account for effects that might otherwise influence entrepreneur motivation, we control for a host of individual, firm and environmental factors. Table 1 presents these variables, along with the manner in which they were operationalized.

Table 1.

Control variables.

Variable	Operationalization
Individual-level
Gender	Female = 1, male = 0
Race	Dummies for Black, White (other = reference group)
Educational attainment	Four-year college degree = 1, no college degree = 0
Age	Age of respondent in years (log-transformed)
Marital status	Married/living with a partner = 1, not married/living with a partner = 0
Household income	Most recent annual household income of the respondent (log-transformed)
Retired	Retired = 1, not retired = 0
Born in the United States	Born in the United States = 1, born outside the United States = 0
Start-up experience	Respondents who have started a new venture = 1, respondents who have not started a new venture = 0
Industry experience	Number of years the respondent worked in the industry (log-transformed)
Type of entrepreneurship	Dummies for opportunity-based, necessity-based (combination = reference group)
Firm-level
Technological focus	High-tech = 1, low-tech = 0
Environmental-level
Geographic location	Dummies for urban and suburban (rural = reference group)
Industry	Dummies for retail, manufacturing (services = reference group)

Analysis and results

We computed descriptive statistics and correlations for the model variables (see Table 2). The descriptive statistics, along with a visual inspection of the data, suggest that all continuous variables are normally distributed. Moreover, variance inflation factors (VIFs) for all correlation coefficients were computed and the maximum VIF was found to be 1.615, well below the VIF of 10 that might confound the results of our subsequent statistical tests (Kennedy, 1992).

Table 2.

Descriptives and correlations.

	Variable	Mean	Standard deviation	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21
1	Gender (female)	0.370	0.484
2	Race (Black)	0.197	0.398	0.052
3	Race (White)	0.590	0.492	−0.007	−0.592***
4	Education	0.340	0.475	0.099***	−0.049	0.145***
5	Age	3.611	0.332	0.067*	−0.100***	0.166***	0.208***
6	Marital status	0.530	0.499	0.122***	−0.122***	0.165***	0.118***	0.232***
7	Household income	10.870	0.895	−0.075*	−0.117***	0.193***	0.273***	0.082**	0.279***
8	Retired	0.080	0.275	−0.030	−0.009	0.054	0.045	0.373***	0.032	0.006
9	Born in the United States	0.930	0.260	0.015	−0.023	0.157***	−0.006	0.053	0.067*	−0.081**	0.014
10	Start-up experience	0.398	0.490	−0.008	−0.035	0.059*	0.164***	0.301***	0.106***	0.167***	0.109***	−0.042
11	Industry experience	1.595	1.154	−0.136***	−0.048	0.037	0.080***	0.306***	0.018	−0.004	0.071*	0.045	0.103***
12	Geography (urban)	0.353	0.478	−0.022	0.257***	−0.272***	−0.009	−0.016	−0.140***	−0.028	0.031	−0.051	−0.064*	0.01
13	Geography (suburb)	0.411	0.492	0.026	−0.085**	0.094***	0.116***	−0.012	0.103***	0.105***	−0.040	−0.057*	0.053	−0.027	−0.617***
14	Industry (retail)	0.140	0.347	0.116***	−0.065*	0.001	−0.059*	−0.064*	−0.053	−0.018	0.003	0.029	−0.020	−0.093***	0.003	−0.045
15	Industry (manufacturing)	0.041	0.199	0.002	−0.103***	0.077**	0.058	0.118***	0.081**	0.039	0.054	0.021	0.094***	0.047	−0.044	0.030	−0.083**
16	High-tech	0.240	0.427	−0.089**	0.027	−0.059*	−0.033	−0.032	−0.027	−0.03	0.039	−0.005	−0.035	0.031	0.062*	−0.004	−0.045	0.009
17	Necessity-based	0.130	0.336	−0.089**	−0.001	−0.073*	−0.129***	0.022	−0.091**	−0.22***	0.002	−0.008	−0.068*	0.009	0.043	−0.062*	−0.005	0.023	0.009
18	Opportunity-based	0.827	0.379	0.092**	0.014	0.096***	0.114***	−0.050	0.106***	0.256***	−0.032	0.006	0.035	−0.035	−0.032	0.061**	0.016	−0.016	0.010	−0.843***
19	Social entrepreneur	0.170	0.372	−0.073	0.055	−0.116**	−0.145***	−0.013	−0.113**	−0.128**	0.039	−0.060	−0.004	0.05	0.090*	−0.115**	−0.062	−0.063	0.092*	−0.009	0.015
20	Self-efficacy	4.462	0.548	−0.032	0.042	0.005	0.096***	0.070*	0.080**	0.100***	0.002	0.034	0.107***	0.319***	0.012	0.018	−0.046	0.005	0.048	−0.101***	0.074*	0.049
21	Goals (sales)	0.313	0.257	−0.056	0.068*	−0.043	0.012	−0.030	−0.025	0.027	0.018	0.008	0.080**	0.006	0.087**	0.004	0.021	0.018	0.196***	−0.081**	0.084**	0.119**	0.039
22	Goals (scale)	0.239	0.427	−0.049	0.097***	−0.126***	0.018	−0.144***	−0.041	0.029	−0.031	−0.057*	0.011	−0.094***	−0.021	0.092***	0.045	−0.016	0.112***	−0.076**	0.059*	0.143***	−0.039	0.183***

p < 0.05, **p < 0.01, ***p < 0.001.

We test our hypotheses with ordinary least squares (OLS) regression, weighting the data with the weights created by the UMSRC. Based on the March 2005 Current Population Survey conducted by the United States Census Bureau, these weights were created in order to correct for differences in selection probabilities and differential non-response rates so that the estimated results would be representative of and, therefore, generalizable to the entire United States population (Reynolds and Curtin, 2008). The results of these analyses (see Table 3) suggest not only that the full models fit the data well but also that the inclusion of the independent variable (social entrepreneur) to these models improves their fit beyond that of the control model. Specifically, the model statistics show significant F-statistics for the full models, along with significant increases in those F-statistics and increases in the adjusted R² from the control models to the full models.

Table 3.

Weighted hierarchical OLS and logistic regression results.

	Self-efficacy		Sales goals		Scaling goals
Variable
Constant	2.988***	2.893***	0.681**	0.652**	0.926	0.715
Gender (female)	0.027	0.040	−0.059*	−0.055*	−0.421	−0.404
Race (Black)	0.046	0.052	−0.014	−0.012	0.378	0.442
Race (White)	−0.192***	−0.182***	0.004	0.008	0.000	0.082
Educational attainment	0.087	0.109*	−0.049	−0.039	0.225	0.346
Age	−0.049	−0.062	−0.107*	−0.114**	−0.989*	−1.126**
Marital status	0.099*	0.101*	0.050*	0.050*	0.058	0.093
Household income	0.121***	0.130***	−0.022	−0.019	0.183	0.228
Retired	−0.066	−0.075	0.013	0.012	0.295	0.269
Born in the United States	0.039	0.047	0.006	0.012	−0.735*	−0.719*
Start-up experience	0.146**	0.140**	0.087***	0.083***	−0.025	−0.021
Industry experience	0.191***	0.191***	0.002	0.002	−0.244*	−0.251*
Necessity-based entrepreneurship	0.060	0.062	0.038	0.038	−0.260	−0.290
Opportunity-based entrepreneurship	0.052	0.019	0.139**	0.127*	−0.170	−0.341
High-tech	−0.006	−0.025	0.104***	0.098***	0.919***	0.856***
Geographic location (urban)	−0.096	−0.091	0.139***	0.143***	0.272	0.281
Geographic location (suburban)	−0.124*	−0.106	0.072*	0.080**	0.460	0.547
Industry (retail)	−0.024	−0.021	0.085**	0.086**	0.219	0.299
Industry (manufacturing)	−0.033***	−0.011	0.019**	0.030	−0.286	−0.164
Social entrepreneur		0.204***		0.089***		1.029***
Model statistics
F-statistic	9.117***	9.445***	5.225***	5.515***
Change in F-statistic		12.003***		9.181**
Adjusted R²	0.208	0.224	0.128	0.142
−2 log likelihood					556.464	543.452
Likelihood ratio test						13.012***
Cox and Snell R²					0.089	0.111
Nagelkerke R²					0.134	0.166

OLS: ordinary least squares.

Unstandardized coefficients are reported.

p < 0.05, **p < 0.01, ***p < 0.001.

The parameter estimates for the independent variable suggest strong support for our hypotheses. Specifically, the parameter estimate for social entrepreneur in the model predicting entrepreneurial self-efficacy is positive and significant (β = 0.204, p = 0.001). This finding suggests that social entrepreneurs have significantly higher levels of entrepreneurial self-efficacy than commercial entrepreneurs. Because this finding is consistent with expectations, we conclude support for Hypothesis 1. The parameter estimates for social entrepreneur in the models predicting both sales goals (β = 0.089, p = 0.003) and scaling goals (β = 1.029, p = 0.000) are positive and significant, which are also consistent with expectations, suggesting that social entrepreneurs have significantly more ambitious short-term goals for their organizations than commercial entrepreneurs. In light of this finding, we conclude support for Hypothesis 2.

Robustness check

As noted above, 21 of the respondents in PSED II indicated wanting to start their business for both social and financial reasons, but who gave primacy to the latter. While we classified these respondents as social entrepreneurs for the above analysis, given that they represented for-profit firms seeking to serve a social purpose, we recognize that their relative emphasis on financial concerns might somehow affect their motivations. Therefore, we conduct a robustness check of our results, wherein we reclassify these respondents as commercial entrepreneurs and reran our analyses. The results of these analyses are virtually identical to those reported in Table 2, with full models yielding significant F- and change in F-statistics, increases in adjusted R² values and positive and significant parameter estimates for our independent variable (social entrepreneur) in models predicting both dependent variables. These results add credibility to our previous conclusions in support of our hypotheses.

Discussion

Our main finding, that significant differences exist between the motivational drivers of social and commercial entrepreneurs seeking to create for-profit ventures, is important for several reasons. To begin, much has been written over the past decade regarding the perceived differences between social and commercial entrepreneurs (Dees, 1998; Grimes et al., 2013; Light, 2009), with little more than anecdotal evidence to support such claims (Short et al., 2009). If social and commercial entrepreneurs were identical in terms of their motivational drivers, then academic inquiry of social entrepreneurs would likely be relegated to issues largely exogenous to the individual (Bloom and Chatterji, 2008). However, we believe that our findings offer strong support for the assertion that social entrepreneurs are, indeed, different from commercial entrepreneurs, and that, as a result, the possibility of examining additional factors that make social entrepreneurs unique becomes quite salient. For example, research suggests that traits influence self-efficacy, which, in turn, affects goal setting (Baum and Locke, 2004). Thus, there is value in studying this causal chain of relationships in future research. In addition, given theoretical and empirical evidence that motivational drivers ultimately affect performance (Bandura, 1997; Baum and Locke, 2004; Locke and Latham, 1984), examinations of the manner in which the levels of self-efficacy and goals held by social entrepreneurs affect their ability to create and sustain viable for-profit ventures also add to our understanding of social entrepreneurship. While such avenues for research exceed the scope of this article, we believe that our finding that the motivational drivers behind social venture creation are different from those behind traditional venture creation, provides an important first step in this potentially fruitful line of inquiry.

Implications for theory

At a more fine-grained level, the results of our individual hypothesis tests have important implications for theory and practice. Specifically, we find that social entrepreneurs trying to create for-profit ventures exhibit greater entrepreneurial self-efficacy and seek greater sales in the early years of the venture life, as measured by their goals, than commercial entrepreneurs. Taken together, these findings suggest that social entrepreneurs may be able to do what they do and accomplish their objectives because their relatively higher proximal motivational drivers, that is, self-efficacy and goals, are actually driving them to be more motivated than commercial entrepreneurs. Of course, higher levels of motivation do not necessarily result in higher levels of performance, as exogenous factors must be taken into account, such that the contextual situations into which social entrepreneurs may be venturing are likely to be less predictable and more volatile than the analogous situations for commercial entrepreneurs. Additionally, the finding that social entrepreneurs envision significantly higher goals for their organizations than commercial entrepreneurs supports arguments regarding their desire to scale the impact their organizations have on society (Bloom and Chatterji, 2008; Vickers and Lyon, 2014). Future research could examine the entire motivational process of social and commercial entrepreneurs – from underlying values and long-term vision to new venture emergence and success – in order to identify more ways in which the process may differ with respect to the two groups.

We believe that distinctions between social and commercial entrepreneurs are also important, given Shepherd’s (2015) recent argument that

the vitality of the field of entrepreneurship will be maintained and knowledge advanced through … research on the motivations and capabilities for creating new ventures (i.e., new organizations or new ventures in established organizations) that help others and/or preserve the natural environment. (p. 503)

As noted above, while several studies have focused on entrepreneur motivation during the firm creation process, few (see Renko, 2013) have explored the motivations of nascent social entrepreneurs, and none have sought to examine whether and to what degree the motivations of social entrepreneurs differ from their commercial counterparts. Although this relative lack of focus on social entrepreneurs involved in the process of creating new organizations may be attributable, in part, to the difficulty of obtaining data on ventures before they become operational (Bamford et al., 2000), the end result is that we know very little about their motivational mechanisms during the time at which motivation may actually have its greatest influence on their ventures (Frese and Gielnik, 2014). Thus, we believe that our study adds to the small but important literature on social entrepreneur motivation and hints at the potentially important role it might play in venture creation.

The findings of this study may also be used to extend the literature on work motivation. As noted above, this literature has just begun to consider the more specific context of new venture creation (Baron et al., 2016; Baum and Locke, 2004; Estrin et al., 2016; Hechavarria et al., 2012; Wry and York, 2017). Our study of this important context aids in our understanding of how social and commercial entrepreneurs differ with respect to self-efficacy and goals. Additionally, previous research has begun to acknowledge the importance of individual values (Locke, 1997) and higher order implicit goals, for instance, more abstract, fundamental and distal motivational objectives such as achievement striving that individuals wish to attain (Barrick et al., 2013) in explaining work motivation. Future research could integrate these recent developments in motivation theory with a further examination of how and why commercial and social entrepreneurs might have different levels of self-efficacy and goals in order to develop a more comprehensive causal, explanatory model. This could also shed some light on whether the underlying motivational processes are similar or different for commercial and social entrepreneurs.

Finally, our finding that social entrepreneurs exhibit greater levels of entrepreneurial self-efficacy than commercial entrepreneurs might seem surprising, given evidence of the overconfidence of entrepreneurs, in general (Baron et al., 2016; McCarthy et al., 1993). Yet, although it is true that all nascent entrepreneurs face a good deal of risk and uncertainty, social entrepreneurs also face additional hurdles, in the form of institutional voids that characterize the contexts in which they tend to operate, such as inner cities and emerging economies. The quality of an institutional environment is a function of the stability of political and social systems, the degree of openness to investment, the makeup of the product markets, the availability of qualified labour and the sophistication of capital markets. In markets where the quality of institutions is low, entrepreneurs need to rethink how they do business in order to navigate the underdeveloped infrastructures (Khanna et al., 2005). According to Kanter (1999), social sector markets, in both developing and developed countries, are often plagued by institutional voids, making them exceptionally daunting and complex places to conduct business. These conditions render such markets more complex, leading to uncertainty on the part of social entrepreneurs regarding how to navigate them effectively (Townsend and Hart, 2008). Further complicating matters, traditional Western business models tend to be less effective in social sector markets as they tend to assume the presence of well-functioning institutions. Despite the daunting and complex nature of the contexts in which they seek to do business, social entrepreneurs seem to be quite aware of the challenges they face. According to Light (2009), social entrepreneurs ‘are often quite sober about their decision to attack a social problem, and they usually understand the consequences of challenging the status quo’ (p. 22). Thus, while all nascent entrepreneurs face risk and most believe they will succeed, it seems that the awareness of social entrepreneurs of the sizable barriers they face relative to commercial entrepreneurs may explain their comparatively higher levels of entrepreneurial self-efficacy when seeking to create viable new organizations.

Implications for practice

Given the above challenges, coupled with the high failure rates of emerging firms, in general (Kirchhoff, 1994), practicing social entrepreneurs would be unwise to undertake the task of creating a new organization without believing that they can and will succeed. Thus, prior to undertaking such an arduous task, social entrepreneurs should evaluate their experiences, innate abilities and confidence in themselves in order to ascertain their level of entrepreneurial self-efficacy. As Lindsley, Brass and Thomas (1995) theorised, self-efficacy can grow and become more robust in a positive, self-correcting manner that is a reflection of reality, or it can spiral downwards or upwards in unsustainable, deviation-amplifying loops. It is important for nascent social entrepreneurs to remain grounded in reality in order to make a true difference in the world. Only if they are able to conclude from a clear-eyed assessment that they can and will succeed, are they likely to see their ideas come to fruition and, in turn, make an appreciable difference in the world. In light of these findings, practicing social entrepreneurs would be wise to complement their confidence in themselves as entrepreneurs and their high goals with an eye on the economic health of their organizations. Indeed, as Invernizzi et al. (2016) find, the link between entrepreneurial hubris and failure is mitigated by skills acquired by the entrepreneur via education and the use of budgetary controls. More specifically, a growing body of empirical evidence suggests that a sound-earned income strategy is positively related to the effectiveness of social entrepreneurs (Emerson and Twersky, 1996; Foster and Bradach, 2005; Grimes, 2010; Miller and Wesley, 2010). It may be that social entrepreneurs who can combine their long-term visions for societal change with their own self-efficacy and ambitious short-term sales goals may be extremely well poised to create and grow their organizations.

Limitations and conclusion

While we believe that this article contributes to contemporary knowledge regarding social entrepreneurs, we identify limitations to the study and offer advice to scholars interested in this line of research to improve upon it in future research. First, despite the panel nature of PSED II, we employed a cross-sectional analysis of data from the first wave only due the nature of our theoretical model. As such, we are able to identify significant relationships but not causality. For this reason, we cannot predict on the basis of an individual’s motivation, whether or not he or she will ultimately become a social or commercial entrepreneur, but rather what levels of motivational drivers seem to be associated with this entrepreneurial choice. Given that such a predictive model would add to what we know about entrepreneurs, we encourage scholars to conduct longitudinal analyses on entrepreneurial motivational mechanisms, perhaps through experimental designs.

Second, the data from the first panel in PSED II was collected in 2005–2006. While PSED II is arguably the richest available data source for emerging ventures in the United States, the fact that more than a decade has passed since these data were collected, coupled with the growing interest and attention given to social entrepreneurship, suggests the possibility that some of the magnitude of the differences we have uncovered in our study may have evolved during that time. Thus, we ask readers to accept our results with this context in mind and call on scholars interested in this line of inquiry to consider collecting new data on nascent entrepreneurs.

Third, while we believe that our analysis has contributed to the literature by identifying how the motivations of social versus commercial entrepreneurs are different, it stops short of addressing why they differ. Although such a research question exceeds the scope of our study, we believe answering this ‘why’ question would also increase our understanding of the psychology of social entrepreneurs. Ruskin et al. (2016) have provided a start in this direction, and we encourage interested scholars to build on their efforts by collecting and analysing data on the determinants of these motivational factors.

Fourth, we acknowledge that there are several ways to conceptualise motivation. Given that we could not include all potential constructs in this one study, we have chosen to capture the motivations of entrepreneurs by focusing on two seminal concepts from goal-setting theory: self-efficacy and goals (Bandura, 1997; Locke and Latham, 1990). While we are confident that our approach has captured the essence of the entrepreneurial ‘motivational hub’ and provided meaningful insights into how the fundamental concepts of self-efficacy and goals might differ between social and commercial entrepreneurs, other conceptualizations of motivation may yield equally interesting findings (see Jayawarna et al.’s (2011) exploratory study of entrepreneur motivation profiles for an example). To this end, we encourage motivation scholars to examine the degree to which other motivational factors might differ among social and commercial entrepreneurs.

Notwithstanding these limitations, we believe that our findings add to the body of literature on social entrepreneurship by providing the first empirical evidence of significant differences in the levels of motivational mechanisms of social and commercial entrepreneurs. By applying the above-mentioned theoretical lenses to the context of nascent entrepreneurs, we hope that our findings enrich the extant knowledge regarding who are social entrepreneurs and what differentiates them from commercial entrepreneurs. Given the important role entrepreneurial motivation is argued to play in the start-up phase (Frese and Gielnik, 2014) and given its importance in defining social and commercial entrepreneurs (Grimes et al., 2013), we hope to have provided a rigorous first step in the longer process of better understanding how social entrepreneurs may differ from commercial entrepreneurs and, in so doing, contributed to the vitality and advancement of the field (Shepherd, 2015).

Footnotes

Author’s Note

Authors are listed alphabetically.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

Notes

Author biographies

Kevin D Clark is an Associate Professor of Management and Naclerio Scholar in Strategic Management at the Villanova School of Business (Villanova University, Pennsylvania, USA). He earned his PhD in strategic management from the Robert H. Smith School of Business at University of Maryland, College Park. His research interests include top management teams, strategic decision-making, executive and entrepreneurial networks, knowledge creation and innovation. His work has been published in the Academy of Management Journal, Journal of Management, Journal of Management Studies, Journal of Business Ethics and Organizational Dynamics, among others.

Scott L Newbert is a Professor of Management and the Lawrence N. Field Chair in Entrepreneurship at Baruch College, City University of New York. He received his Ph.D. in strategic management and entrepreneurship from Rutgers University. His research interests include the processes by which existing and nascent firms create value through the entrepreneurial use of resources, the determinants of firm creation and the socioeconomic impacts of entrepreneurial activity. His research on these and related topics has been published in the top entrepreneurship and management journals. He also serves on numerous editorial boards and as a field editor for Journal of Business Venturing.

Narda R Quigley is an Associate Professor of Management and Chair of the Department of Management and Operations at the Villanova School of Business (Villanova University, Pennsylvania, USA). She earned her PhD in organizational behaviour from the Robert H. Smith School of Business at University of Maryland, College Park. Her research interests include motivation, multilevel issues, knowledge sharing, groups and teams in organizations, and emergent and cross-cultural leadership. Her work has been published in Organization Science, Organizational Behavior and Human Decision Processes, Organizational Research Methods, Journal of International Business Studies and Journal of Business Venturing, among others.

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