Volume 28, No. 2 Spring, 2017

Editorial Staff

Managing Editor William T. Jackson

Editors Mary Jo Jackson Jeff Vanevenhoven

The Journal of Business and Entrepreneurship is published by the Association for Small Business and Entrepreneurship (ASBE) and Stetson University. All ASBE members receive one copy of the publication. Subscribe, order back issues or single copies at [email protected]. Submission guidelines can also be found at www.asbe.us.

ISSN: 1042-6337

©2017 Association for Small Business and Entrepreneurship

Hosted by:

Table of Contents

Volume 28, No.2

Spring, 2017

Modeling Business Failure among SMEs: An Artificial Neural Networks and Logistic Regression Analysis Densil A. Williams ...... 2

In or Out: Narcissism and Entrepreneurial Self-Efficacy and Intrapreneurial Intentions Reginald L. Tucker, Randall M. Croom, and Louis D. Marino ...... 28

The Relations Between Entrepreneurs’ Ethnicity, Familism Values, Beliefs, and Use of Financial Planning Dianna L. Stone, Julio C. Canedo, Teresa L. Harrison, and Kimberly M. Lukaszewski ...... 51

An Examination of Faculty Salaries Between the Fields of Strategic Management and Entrepreneurship Todd A. Finkle and Manasi Katragadda ...... 83

Different But Inseparable: The Contingent Association of Instrumental and Emotional Support Mette Søgaard Nielsen ...... 128

The Effects of Motivatio over Entrepreneurial Performance: The Case for Mexican Startups Elvira Anzola González and Carlos Canfield Rivera ...... 150

----2017-2018 Officers----

Association for Small Business & Entrepreneurship

Mary Jo Jackson, Stetson University

President

Courtney Kernek, Southeastern Oklahoma State University

President Elect

Janice Black, Costal Carolina University

Vice President - Programs

Eugenie Ardoin, University of Louisiana at Monroe

Vice-President Membership

Randall Croom, Stetson University

Treasurer & Secretary

Henry Cole, University of Louisiana at Monroe

Past President

----Editorial Review Board----

Joshua Abor Charles Fischer University of Stellenbosch Pittsburg State University

Joe Ballenger Donald W. Garland Stephen F. Austin State University New Mexico State University

Jurgita Baltrusaityte-Axelson William C. Green Stockholm School of Economics Sul Ross State University

Stephen S. Batory Walter E. Greene Bloomsburg University Greene and Associates

James A. Bell Marko Grünhagen University of Central Arkansas Eastern Illinois University

Josh Bendickson Robert D. Gulbro Eastern Carolina University Athens State University

Thomas M. Box Stephen C. Harper Pittsburg State University University of North Carolina ~ Wilmington

Susan Boyd E. Alan Hartman University of Tulsa University of Wisconsin ~ Oshkosh

Steve Brown Diana M. Hechavarria Eastern Kentucky University University of South Florida

Kent Byus Marilyn M. Helms Texas A&M ~ Corpus Christi Dalton State College

Thomas M. Cooney Colin Jones Dublin Institute of Technology University of Tasmania James A. DiGabriele DiGabriele, McNulty & Co. LL Minjoon Jun New Mexico State University Paul Dunn University of Louisiana ~ Monroe M. Riaz Khan University Massachusetts Lowell João J. M. Ferreira University of Beira Interior

Naresh Kumar NESH Training and Consultancy Joseph F. Singer University of Missouri Kansas City Agnieszka Kurczeska University of Lodz George Solomon George Washington University Vaidotas Lukosius Tennessee State University Tulus Tambunan University of Trisakti Keishiro Matsumoto University of the Virgin Islands Ayman El Tarabishy George Washington University Shaun McQuitty Athabasca University Leslie Toombs Texas A & M Commerce Teresa V. Menzies Brock University Raydel Tullous University of Texas ~ San Antonio Jay Nathan St. John’s University Jude Valdez University of Texas ~ San Antonio Barbara R. Oates Texas A&M ~ Kingsville Jeff Vanevenhoven University of Wisconsin White Water Linda Ann Riley Roger Williams University Rebecca J. White University of Tampa Philip T. Roundy University of Tennessee at Densil Williams Chattanooga University of West Indies Mona

Christopher M. Scalzo Phillip H. Wilson Morrisville State College Midwestern State University

Mark T. Schenkel Marilyn Young Belmont University University of Texas ~ Tyler

Philip Siegel Florida Atlantic University

Dear JBE Readership:

Welcome to the spring 2017 issue of the Journal of Business and Entrepreneurship. This year is shaping up to be a great year for the journal as well as the Association for Small Business and Entrepreneurship. As announced in the last issue our acceptance in the SCOPUS ranking system is already paying dividends with greater exposure and recognition.

First, all of the hard work in making this happen will result in providing our loyal authors—those that took a chance on using us as their publishing option—greater support in their promotion and tenure aspirations. It also provides considerable national and international exposure thus increasing the number and quality of the submissions to follow. Finally, it is a great reward for all of those that have given their time and energy to maintain the quality of our 27 year existence.

There is also additional news related to the Association for Small Business and Entrepreneurship (ASBE). The Executive Committee as endorsed by membership at the last conference have decided to join the Federation of Business Disciplines as a conditional member at that organization’s next conference (Albuquerque, March 2018). We are very excited to be a part of the long history of FBD and feel strongly that this will further advance JBE through this outreach effort.

Also, if you haven’t submitted a manuscript to JBE recently (or ever) please consider us as a potential research outlet for your next manuscript. While we are excited about being a part of SCOPUS, we see this as only the beginning of our future growth

William T. Jackson (Bill) Mary Jo Jackson Jeff Vanevenhoven Managing Editor Editor Editor

MODELING BUSINESS FAILURE AMONG SMEs: AN ARTIFICIAL NEURAL NETWORKS AND LOGISTIC REGRESSION ANALYSIS

Densil A. Williamsi Mona School of Business and Management

Since the 1980s, there has been a burgeoning literature on business failure. However, the results have been fragmented and inconsistent in most respects. This is mainly due to the less than rigorous analytical tools that have been used to conduct the analysis of the data. To address this problem, this research has applied the artificial neural networks model to predict business failure, a departure from the traditional methods. The results found that the neural networks method is a much more powerful analytical tool to predict business failure than the traditional logistic regression models. The results from the neural networks and their predictive accuracy are presented in the paper along with the predictive accuracy of the logistic regression model. The implications of the work for future research and managers in small firms are also presented.

Keywords: Business Failure, Neural Networks, Logistic Regression, SMEs.

INTRODUCTION

Since the 1980s, there has been a growing literature on business failure as a measure of firm performance (Campbell et al., 2012; Mellahi and Wilkinson, 2004; Thornhill and Amit, 2003; Whetten, 1980). The literature however, remains quite disperse with very few definitive conclusions as to the factors that have led to failure among firms, especially the small and medium-sized enterprises (Mellahi and Wilkinson,2004). This seems to be due mainly to: the less than rigorous and sophisticated methods used to model failure, the divergence in theoretical lenses used to analyze the problem, among other things (Mellahi and Wilkinson, 2004). This study therefore, will depart from the traditional simplified methods such as, linear regression models and qualitative case studies and use a more powerful tool, the Artificial Neural Networks model,

Spring 2017 Journal of Business & Entrepreneurship 1

which has never been used in the literature before, to predict the factors that impact on business failure. Neural network models are more flexible and less restrictive in their assumptions about the data and as such, will lead to more robust results. In addition to the Neural Networks model, this study will also apply the logistic regression model to the problem in order to validate the findings even more rigorously. This methodological approach to the problem will help to add new insights to the work and also, further advance the ability of theorists in the field to build a general theory on business failure. The work presented in this paper will try to answer the following questions: a) Which factors are most important in predicting business failure among SMEs? b) Can the neural networks models give a better predictive accuracy than the traditional logit model in predicting business failure? The first question draws on the resource-based view of the firm as the theoretical lens through which to analyze the research problem. Viewing business failure through the resource-based lens, it argues that the fewer resources that a firm possess is the greater the likelihood that it will fail. In essence, the argument is that there is a negative relationship between failure rates and the availability of resources (Anderson and Tushman, 2001; Barney, 1991; Williams, 2009). The empirical evidence however, is not always consistent. It is this inconsistency that has motivated the research reported in this study. Given the strength of the methods to be applied to the problem in this research, the work is expected to add greater clarity to the findings. Further, the results should help to enhance the external validity of the works in previous studies given that the variables used will be taken from previous studies on the subject. To shed greater light on the research problem, the remainder of the paper is organized as follows: the next section will give a brief overview of the theoretical lens that is used to focus the study. Following this review of the theory, the next section will present the research variables and hypothesis development. The subsequent sections will present the research method, the findings, a discussion of the findings and ends with some concluding remarks.

THE THEORETICAL LENS

Since Penrose (1959) conceptualization of the firm as a bundle of resources, a steady stream of work has evolved under the rubric, the resource- based view of the firm (Amit and Schoemaker, 1993; Barney, 1991; Cooper et al, 1994, Wernefelt, 1984). The basic idea is that the firm is viewed as a

2 Journal of Business & Entrepreneurship Spring 2017

heterogeneous bundle of resources (Thornhill and Amit, 2003). Indeed, Amit and Schoemaker have defined resources as the stocks of available factors that are owned and controlled by the firm. They further went on to identify the difference between resources and capabilities, which in most cases are conflated. They noted that capabilities are information based, tangible or intangible processes that are firm-specific and are developed over time through complex interactions among the firm’s resources (1993, ibid pp.35). The resource-based view of the firm becomes a useful lens through which business failure or success can be analyzed because, it is assumed that resources and capabilities provide the firm with a competitive advantage which helps it to deliver superior value to its customers and as such, will ensure its survival. Following this argument, the more resources the firm possesses, the greater the likelihood that it will have a stronger competitive advantage and as such, it can better serve its customers and therefore should be able to survive in the marketplace. However, for these resources to lead to a competitive advantage, they must be: hard to imitate (non-imitable), rare and valuable (Barney, 1991). It is not merely having excess resources in the firm that will enable it to derive a competitive advantage, but the resources have to possess these important characteristics in order to help the firm survive in the marketplace. Despite an almost general consensus that these characteristics are critical for resources to deliver competitive advantage to the firm, there have been some criticisms as to whether or not these characteristics are indeed attainable (Lado et al., 2006). For example, scholars have noted that the variables that are most interesting theoretically in the resource-based view argument, are least identifiable and measurable (Spender and Grant, 1996). However, if a resource is unobservable, it cannot be accurately measured and verified as such, trying to determine its contribution to sustained competitive advantage for the firm is a most difficult task (Lado et al., 2006). The criticisms of the scientific status of the resource-based view notwithstanding, the theory has some important elements that can be used to advance knowledge on firm failure or successes. Indeed, scholars have argued that the paradoxes in the resource-based view of the firm might reflect scientific anomalies that should be tolerated as long as the theory produces interesting insights (Lado et al., 2006: pp 125.) Indeed, these paradoxes can motivate greater scholarship and also advance our understanding of the subject matter. As such, the resource-based theory is still applicable in the present study despite its limitations. The variables presented in the next section are derivatives from the

Spring 2017 Journal of Business & Entrepreneurship 3

resource-based view of the firm. They evolve from previous works which have looked at the resource-based view and found surrogates for resources in the firm. This study has adopted some of these variables that are both theoretically sound and empirically feasible. That is, the selection of the variables was based on those that had common data for all the firms in the sample and, the literature has identified as theoretically sound.

THE RESEARCH VARIABLES

Factors associated with the performance of firms have been a major area of interest for researchers due in part to the financial and human costs associated with the business failure (Lechner et.al, 2006; Watson, 2007). However, the definition of business failure is not always consistent and clear in the literature. Various definitions have been used to operationalize failure with some researchers arguing that there has been a one-dimensional adoption of failure and performance as being synonymous (Gimeno et.al, 1997; Watson, 2007). Indeed, previous studies used the discontinuance of business or the discontinuance of ownership as a proxy for failure (Watson and Everett, 1996), while others have used various phrases such as: firm exit, death, termination, quitting (Yang and Aldrich, 2012:479). Shepherd et.al (2009: pp 134) defined failure to have occurred “when a decline in revenue and/or increase in expenses are of such magnitude that the firm becomes insolvent, and is unable to attract new debt or equity funding. Following Mellahi and Wilkinson, (2004) the definition that will be adopted for firm failure is whether or not the firm is active or inactive in the industry. Despite the lack of a consistent definition on failure, there appears to be some consensus that if a firm exits the industry that is a sign of failure and if it remains, that is a sign of success (Mellahi and Wilkinson, 2004). The work presented here will not deviate from this consensus. The variables discussed below which emanated from the resource-based view of the firm will be looked at in light of their relationship with the success or failure of the firms as captured under the rubric of firm performance.

Networks

Network relationships are seen as an important resource for firms to have in order to ensure their survival (Johanson and Vahlne, 2003). This is even more

4 Journal of Business & Entrepreneurship Spring 2017

important for the small firm which is resource poor and has to find ways to climb over the resource barrier in order to survive (Watson, 2007; Williams, 2009). The proponents of the Networking Theory argue that there is some relationship between the firm’s association with a network and performance (Watson, 2007). Networks are defined as: “a specific set of linkages between defined set of actors with the characteristic that the linkages as a whole may be used to interpret the social behaviours of the actors involved” (Lechner et.al, 2006:516). Watson has argued that whilst there has been postulation about this perceived relationship, there has been a paucity of previous empirical research conducted to substantiate this view. He further argues that of the research which has been done, the predominant approach has been to use cross-sectional research designs, which does not enable the researchers to determine whether a causal relationship exist. However, his own research employed a longitudinal methodology, accessing data on over 5000 small firms from the Australia Bureau of Statistics. His findings suggest that there is a relationship between networking and firm performance (which he operationalizes in terms of survival, growth and Return on Equity). He differentiates between formal and informal networks, arguing that while both formal and informal networks are associated with survival, only formal networks are associated with growth. He adds that the intensity of the firm’s network is more important than the range of the network (Watson, 2007). Other scholars have also added to the discussion by arguing that there must be a more extensive look on the way in which networks benefit small firms (Lechner et.al, 2006). They argue that there are different types of networksii and these are advantageous to firms at different stages of a firm’s development. In their study of 60 venture-capital financed start-ups of less than 10 years in Austria, Germany and Switzerland, they found that the ‘relational mix’ of the network was a major factor in explaining a firm development than sheer network size (Lechner et.al, 2006). Indeed, the results from the impact of networks on firm performance and especially firm failure are not as straightforward. There are still some inconsistencies in the findings on this variable and as such, it is difficult to generalize the results as they relate to the impact of network relationships on firm performance.

Spring 2017 Journal of Business & Entrepreneurship 5

Location

The Agglomeration Theory is a lens that is used to view the impact of location on the performance of a firm. It hypothesizes that there is a relationship between the geographical location of firms and their competitive positions (Folta et.al, 2006: 217). It is argued that the performance of geographically clustered firms improve with cluster size. The theory argues that the ‘economies of agglomeration’ enhances firm’s ability to innovate through patenting, attracting alliances partners and private equity partners. This suggests that these geographical links, such as those which exist in places like Silicon Valley, benefits small firms by improving the access to and use of information whether it relates to process and company strategy and knowledge as well as the ability to attract additional financial resources (Folta et.al, 2006:222). McGann and Folta further argue that firms do not benefit equally from clustering or networks as it is important to consider the knowledge stocks of the firms before entering the network as a key determinant of possible clustering performance than those with lesser knowledge stocks (McGann and Folta, 2011). Locational advantages can also be derived based on the stock of human capital resources available to the firm from being in a particular area. For small firms, the recruitment of skilled workers and access to capital are important resources that can determine their survival or failure. If a location possesses these resources in abundance, it may be easier for the firms there to access them. Williams and Jones (2010) in analyzing the longevity of small firms in Jamaica found that firms which are located in rural areas had a higher chance of survival than those in urban centres. It appears that urban centres, while having larger amounts of resources, especially human capital resources, the level of competition for market is more intense and as such, those firms that do not start with a high stock of resources, will eventually exit the marketplace. While it is expected that urban centres will have a greater stock of resources which small firms can access, the cost of accessing these resources may inhibit resource poor SMEs from actually gaining access. With the inability to gain access to these resources, it can make these firms unable to compete in a highly competitive market environment. Urban centres that are densely populated but possess a demand for resources (human capital especially) that outweighs the supply may not be a good place for resource-poor SMEs to be located. They may find it hard to compete with more resource-endowed firms and, as such, have to exit the market. Indeed, the impact of the geographical location on the performance of

6 Journal of Business & Entrepreneurship Spring 2017

the firm is still not certain as the context of the locational environment needs to be taken into consideration. It is not merely being located in an area that helps performance but it is more the quality of the resources in the area. This paper will try to understand the relationship, if any, between the location and the performance of the firm.

Firm Size

Firm size is used by many researchers as a surrogate for the resource stock of the firm (Bonaccorsi, 1992; Williams, 2011). Indeed, an extensive amount of work exists on the relationship between firm size and the performance of the firm (Calof, 1994; Hall and Tu, 2004; Williams, 2011). It is argued that firms with more employees tend to have a larger stock of resources and as such, can generate economies of scale and reduce the cost of doing business (Mittlestead et al., 2003). However, the more compelling argument for the importance of size as a critical proxy for firm resources is that, size provides a buffer for the firm to absorb the fixed cost of doing business. With larger size and presumably more resources, there are certain fixed costs of operations that the firm will be better able to absorb. Small firms do not have this latitude since absorbing large fixed costs can lead to a firm having to exit an industry (Hall and Tu, 2004). Indeed, a number of studies have noted a positive relationship between size and firm performance as measured by growth, profitability, survival or internationalization. For example, Watson (2007) and Calof (1994) noted that larger firms are more likely to survive than small ones. The positive relationship between size and performance of the firm seems overwhelming. The general consensus seems to be that, there is a positive relationship between firm size and firm performance. Using the resource-based view lens as the over-arching theoretical framework to analyze firm performance, it is expected that larger firms will have a higher stock of resources. These resources can be used as a buffer to absorb fixed cost and as such, helps the firm to overcome turbulent times in the market.

Governance Structure

The way a firm is organized is also a critical source of resources that can impact on the performance of that firm. For example, whether or not the firm is

Spring 2017 Journal of Business & Entrepreneurship 7

publicly or privately owned can impact on the amount of resources it has at its disposal. Firms that are publicly owned and listed on stock markets, are more likely to have access to cheaper sources of finance than firms that are privately owned and depend solely on the small networks of the owner and family members (Watson ,2007; Brusch 2002). As such, it is expected that firms that are publicly owned and listed, will have a larger stock of resources than firms that are privately owned. Following this logic and using the resource-based view lens to look at firm performance, it is expected that publicly-listed firms are more likely to survive than private firms, given that the former will likely have more avenues to gather additional resources than the latter.

Industry or Sector

The industry or sector in which the firm operates can also impact on its overall performance. Other researchers have identified this as an important variable impacting firm failure and also success (Campbell et al., 2012, Watson, 2007). Different industry or sector may allow the firm different levels of access to resources and as such, the performance outcome may differ across sector (Barney, 1991; Watson 2007). It is therefore important to take this into account when trying to understand the success or failure of an enterprise. The level of competition in the industry, the number of firms and the structure of the industry (Porter, 2008), are all factors that will determine whether or not a firm exits or remains in the sector.

Age

Age is seen as a good proxy for the stock of resources that a firm possesses (Williams, 2009). Researchers have used the age of the firm as a proxy for experience (Autio et al., 2000). Indeed, from a resource-based perspective of the firm, older firms will have considerable more resources than younger firms. This logic is based on the assumption that firms acquire resources over time (Autio, 2005). Because older firms will have a larger stock of resources than younger firms, the resource-based view explains that these firms will stand a better chance of survival than those with lower stock of resources. This is because the higher stock of resources will provide a stronger buffer for the firms to absorb shocks and unanticipated costs which can generally lead to business failure. Given this reasoning, it can be seen why the expectation that

8 Journal of Business & Entrepreneurship Spring 2017

older firms should be less likely to fail than younger firms, is promoted by some researchers. Indeed, Watson (2007) found evidence among established firms that the older ones had a greater chance of survival than the younger ones.

Financial Resources

Besides these non-financial surrogates for resources, a large number of financial measures, which are proxies for the amount of resources in the firm, are also important in explaining firm performance. A number of these measures were used in this study to capture the resource stock of the firms including net income, revenue, and return on assets. Caves (1998) argues that higher capitalisation normally suggests a greater belief in the viability of the business. Further, researchers have argued that lower capitalisation might suggest that the owner might want to learn from the business instead of wanting to necessarily grow the business, with the idea being that thinly capitalized business is a greater candidate for closure (Bates, 2005:351). However, this view is challenged by Gimeno et.al (1997: pp.751) who argue that organizational survival is not exclusively a function of economic profitability, but also depends on the firms ‘threshold for performance’. Internal characteristics such as firm size as well as other human capital attributes, such as the owner’s interests, are variables which help to define this threshold which therefore means that this varies across the different types of firms (Gimeno et.al, 1997:750). They argue that the dynamism in the relationship with firm performance is not only dependent on the interest of the owner but also on the influence of outside stakeholders such as shareholders, employees, customers, community members and the government (Gimeno, et.al, 1997:752). The strength of the influence of the external stakeholders tends to vary based on the size of the firm, with the owners of smaller firms having more control over decision-making, bearing in mind the fact that their financial and non-financial resources normally outweigh those of other stakeholders. Summarily, the above review of the literature suggests that, using the resource-based view lens as the guiding theoretical frame through which to analyze business failure, if a firm has a larger stock of financial and non- financial resources the likelihood of it succeeding is greater than if it has a smaller stock of resources. A large number of variables that serve as surrogates for resources were presented based on previous works and their relationship with firm performance highlighted. It is these variables that will be used to model the

Spring 2017 Journal of Business & Entrepreneurship 9

impact of resources on firm failure. The method section will shed further light on how this will be done.

METHOD

This research tries to understand which resources are more likely to impact on business failure which in this case is measured as active or inactive firms, that is, whether or not the firm remains open or closes. A large number of variables, which are surrogate for resources, do impact on failure as posited by the extant literature. As such, to motivate this study, a general model which captures business failure and resources will have to be identified. The theoretical model therefore becomes:

Yj = ∫ (X1, X2, X3….Xn) + εj (1)

Where: Y represents Business failure J is each individual firm. X1….Xn represent the vector of variables capturing the different types of resources both financial and non-financial that have been found to impact on business failure εj is the error term, accounting for all other factors that impact business failure but are not captured in this study.

Because the assumptions about normality and linearity are relaxed, this function is best modeled using the neural networks. Neural networks are flexible non-parametric modeling tools. They can be used to predict the probability of an outcome given certain information. The neural networks are found to provide much better results for problems which require classification based on a set of conditions, i.e. where the dependent variable is dichotomous (Zhang et al., 1999). It is more superior to conventional prediction models such as logit models, probit model and discriminant models because they generally depend on strict assumptions such as linearity, normality, independence among predictors among others. In essence, these methods provide more valid results when the assumptions are met. However, neural networks are not constrained by rigid assumptions and can therefore provide more robust results. Indeed, works have

10 Journal of Business & Entrepreneurship Spring 2017

shown that neural networks produce pattern recognition and classification that are more robust due to their non-linear, non-parametric properties (Zhang et al, 1999). The neural networks model will identify which of the resources in this study are most relevant in explaining failure among SMEs. As such, the non-linear model for estimation using the neural network becomes: y=f2(r2f1(r1x)) (2)

Where: x = (x1, x2,….xn) be an n-vector of the independent variables, y is the dependent variable or the output from the network, r1 and r2 are the weights of matrices linking input to hidden layer and hidden layer to output respectively f1 and f2 are the transfer functions for the hidden and output nodes respectively.

Indeed the form that these transfer functions take is as follows: -x -1 f1(x) =f2 (x) = (1+e )

These transfer functions reflect the relationship between the logit model and the neural networks.

Model Building for Neural Networks

In using the neural networks, the data were split into training and validation data sets. The first 31,552 records were allocated to training data and the remaining 31,551 were allocated to validation. The data was modelled in two main ways, firstly on the overall dataset using a macro model and secondly by modelling the data based on firm size. Given that firm size displayed the largest number of outliers, separating the data into sub-groups would also control for extreme values and outliers. Select nodes were used to separate the data into two (2) groups, small and medium based on the ‘Size’ variable. Medium firms were classified as size greater than 50 employees while Small Firms were classified based on size less than or equal to 50 employees. For each group, a series of models were developed to determine company status. When building the neural networks models, multilayer perceptron, a type of feed forward neural networks was used. This enabled information to flow from the input to the output using a supervised learning process. In the first

Spring 2017 Journal of Business & Entrepreneurship 11

instance, all identified independent variables were taken as inputs into the model to ensure the inclusion of important variables. The output of each neural network was evaluated and the least important predictor identified. A new neural network model was subsequently constructed which excluded the least important predictor and the model re-run with the aim of improving model accuracy. In doing so, a series of neural networks were constructed for each group. The figures below show the final neural networks diagrams that were used in running the model to determine which resources were most important in predicting business failure.

Figure 1: Artificial Neural Network for Macro-Model

12 Journal of Business & Entrepreneurship Spring 2017

Figure 2. Artificial Neural Network for Medium Firms

Spring 2017 Journal of Business & Entrepreneurship 13

Figure 3: Artificial Neural Network for Small Firms

For comparative purposes the models derived from neural networks were also estimated using the Logistic regression. This helps to identify the predictive accuracy of both models in determining the classification and the prediction of which resources best explain business failure. To estimate the logit model for comparative purposes, the theoretical model in Eqn. 1 has to be transformed into an operational model which can better capture the resources that are most important in explaining business failure. To develop this estimated model, from the vector of factors presented, specific variables have to be selected in order to estimate their impact on business failure. The variables selected in this research were a function of what is theoretically robust as reflected in the resource-based view of the firm and also empirically feasible, that is, good data could be collected on these variables. Taking these criteria into consideration, the choice was made to focus on variables such as: networks, locations, size, age, governance structure of the firms, industry sector and financial variables such as net income, revenue and, return on assets. It is these specific variables that are used to build a variant of the general theoretical model presented above. Therefore; the unbiased logit model for estimation becomes:

14 Journal of Business & Entrepreneurship Spring 2017

Ŷ= α+β1X1 + β2X2+β3X3 +β4X4+β5X5+β6X6+β7X7+β8X8+ β9X9+ɛj (2)

Where: Ŷ represents the unbiased estimator of the dependent variable, business failure which is dichotomous and measured by whether the firm is active in the industry, that is, it keeps open or if it’s inactive, that is, it closes its door.

X1 represents networks X2 represents location X3 represents size

X4 represents age

X5 represents governance structure

X6 represents industry sector

X7 represents net income

X8 represents revenue

X9 represents return on asset. ɛj represents the error term Variable Measures and Data Sources For this study, data were collected from the FAMEiiiiv database. The search was narrowed down to firms that are active or inactive in all industry sectors in the UK economy over the period, 1999-2008. This period was chosen because it represents a sort of halcyon period in the United Kingdom (UK) Economy in-terms of economic growth and stability coming out of the early 1990s. The average GDP growth over this period was 2.74%. Average inflation rate was 1.75% and interest rate as 4.79%. Also, the exchange rate variation was -0.82. Given the relative robustness of the economy, it is expected that macro- economic conditions are not strong determinants of business failure. In order to control for size, a maximum upper bound on the number of employees in the firm was placed at 250 employees. This upper bound of 250 employees represents the definition for SMEs in the UK. This search has led to over 60,000 firms that were deemed appropriate for the analysis. There number of inactive firms accounted for 32.8 percent of the sample while the number of active firms accounted for 67.2 percent of the sample. Similarly, nine variables that had full information and were used in previous studies as surrogate for

Spring 2017 Journal of Business & Entrepreneurship 15

resources have been collected from the database for analysis. These variables along with their operational measures are listed in the table below.

Table 1: Variable Measurements Variable Measurement Variable Previous Code Research Dependent Dichotomous variable with the CS Mellahi and variable following labels Wilkinson (2004) ( Output) Inactive = 1 Active = 0 Business Failure

Independent Dummy variable NW Semrau and Variables 1 = the firm has an advisor, such Werner, (2012) (Attribute as accountants or banks Watson (2007), variables) 0 = no advisors Networks Firm Size Latest number of employees Size Tang and Murphy (2012) Williams (2011) Firm Age Number of years since Age Semrau and incorporation Werner (2012) Autio et al. (2000) Location 1 = Urban centres, LC Williams and 0 = Rural areas Jones (2010) Governance 1 = Private limited liability GS Structure 2 = Public listed company Industry sector Ordinal IDS Williams and 1 = Services Jones, 2010 2 = Wholesale and retail 3 = Food 4 = Manufacturing Net Income Revenue minus cost NETI Bates, 2005 Revenue Sales figures Rev Bates, 2005 Return on Asset Total Asset divided by Profit ROA Bates, 2005

As suggested above, these variables were analyzed using both the artificial neural networks and the logistic regression model. The results from these analyses are presented below.

RESULTS The neural networks results show that the determinants of business failure vary based on the sample of firms used. When the overall model (macro-

16 Journal of Business & Entrepreneurship Spring 2017

model) was estimated, the results revealed that the most important determinant of business failure was net income. This was followed by firm size, firm age, revenue, return on assets, governance structure, industry sector and, location. Importantly, networks did not play a significant role in explaining business failure. Figure 4 below shows these results and their importance. Figure 4: Macro Model – Important Predictors of Business Failure

Further, when the model was restricted to analyze business failure among small and medium –sized firms separately, the result revealed a slight variation in the importance of the factors that determine business failure. For medium- sized firms, the factors are: revenue, net income, age, governance structure, location, return on assets and firm size. Networks and industry sectors were not found to be important predictors of failure. Figure 5 below shows these results.

Spring 2017 Journal of Business & Entrepreneurship 17

Figure 5: Micro Model (Medium Firms) – Important Predictors of Business Failure

Also, in relation to small firms, the neural network model predicts that the most important factors that determine business failure are: age, governance structure, return on asset, revenues, firm size, net income and industry sector. Again, network was not found to be important in predicting failure. In this case as well, the location of the firm was not important in predicting business failure among small firms. Figure 6 below shows this result.

18 Journal of Business & Entrepreneurship Spring 2017

Figure 6: Micro Model (Small Firms) – Important Predictors of Company Status

In order to determine the predictive accuracy of the models in all cases, a nodes analysis was carried out. Indeed, as more hidden nodes are used in the model, the overall classification rate in the training data set increased as the additional nodes increased the model’s flexibility and capacity to identify complex patterns in the data. It was revealed that for the overall model, when network is dropped as a predictor of business failure, the model has the highest predictive accuracy of 79.6%. Further, in the micro-models, in medium-sized firms, when network and industry sectors are dropped, the model has the highest predictive accuracy of 83.4 %. Also, in the smaller firms, when network and location are excluded from the model, the highest predictive accuracy is 78.2%. Table 2 below summarizes these results.

Spring 2017 Journal of Business & Entrepreneurship 19

Table 2: Model summary with predictive accuracy of business failure 1) Overall Firm Group Model Excluded Accuracy CS0 None 79.3% CS1 NW 79.6% CS2 NW, IDS 79.3% CS3 Predictors below 10% importance in model CS1 (NW, IDS, Size, 79.1% GS, LC) CS4 Non-metric predictors (LC, NW, IDS, GS) 78.8% CS5 Continuous predictors (Ag, Size, ROA, REV, NETI) 78.1% 2) Medium Firm Group Model Excluded Accuracy M-CS0 None 83.1% M-CS1 NW 83.3% M-CS2 NW, IDS 83.4% M-CS3 NW, IDS, GS 82.8% M-CS4 Predictors below 10% importance in model M-CS0 (LC, NW, IDS, 82.5% Size, GS) M-CS5 Non-metric predictors (LC, NW, IDS, GS) 82.5% M-CS6 Continuous predictors (Ag, Size, ROA, REV, NETI) 82.3% 3) Small Firm Group Model Excluded Accuracy S-CS0 None 78.1% S-CS1 NW 77.8% S-CS2 NW, LC 78.2% S-CS3 NW, LC, Size 77.8% S-CS4 Predictors below 10% importance in model M-CS0 (LC, NW, IDS, 78.0% Size) S-CS5 Non-metric predictors (LC, NW, IDS, GS) 77.4% S-CS6 Continuous predictors (Ag, Size, ROA, REV, NETI) 76.0%

Overall, the results suggest that the best models to be used to predict business failure are as follows: in the overall sample, model CS1 where network is excluded, in medium-sized firms only, model CS2 where network and industry sector are excluded and, in small firms only, the model CS2 where network and location are excluded should be used. These models provide the highest predictive accuracy of business failure. However, it must be noted that, when the selected models were tested on the validation dataset, there was a slight decrease in model accuracy for macro model from 79.6% to 72.5%. This was also the case for medium-sized firms which accuracy fell from 83.4% to 70.1% and for small firms with accuracy falling from 78.2% to 73.3%. It should be noted that these

20 Journal of Business & Entrepreneurship Spring 2017

slight decreases in model accuracy are considered normal during the validation process given that the validation model attempts to account for every possible trend including unique occurrences in the training data set, thereby overfitting the data and resulting in a lower accuracy. Further, for comparative purposes, the selected macro-model was estimated using the logistic regression model in order to determine which method provides the better predictive accuracy. The results revealed that the predictive accuracy for this model is 74.6 %. Similarly, when the models were restricted to medium firms and small firms only, the predictive accuracy were 82.5 and 74.1 respectively. These results are captured in the tables below.

Table 2a: Predictive accuracy of macro model- logit model Predicted Active or inactive firm status Percentage

Observed Inactive Active Correct (%) Step 1 Active or Inactive Firm Status Inactive 6289 9382 40.1 Active 3834 32542 89.5 Overall Percentage 74.6 a. The cut value is .500

Table 2b: Predictive accuracy of medium firms- logit model Selected Cases Active or inactive firm status Percentage Observed Correct (%) Inactive Active Step 0 Active or Inactive Firm Status Inactive 0 1878 .0 Active 0 8605 100.0 Overall Percentage 82.1

Spring 2017 Journal of Business & Entrepreneurship 21

Table 2c: Predictive accuracy of small firms- logit model Selected Cases Active or inactive firm status Percentage Observed Correct (%) Inactive Active Step 1 Active or Inactive Firm Status Inactive 6990 6826 50 .6 Active 3975 23935 85.8 Overall Percentage 74.1

Indeed, in all cases, the neural network models have provided higher predictive accuracy than the logistic regression model. These results are not unsurprising given the greater sophistication of the neural network models to predict outcomes in unknown populations. The next section of this paper will present a discussion on these results and provide some concluding remarks.

DISCUSSION AND CONCLUDING REMARKS

The aim of the work presented in this study was to determine, using a more sophisticated analytical tool, the artificial neural networks, which resources are more likely to predict business failures. The paper tried to do two things: a) determine which resources are better predictors of business failure and b) determine which analytical tool has the better predictive accuracy. Theoretically, it is suggested that businesses generally fail because they lack resources (Ahmad and Seet, 2009; Thornhill and Amit, 2003). However, it is not always clear which resources are more important in predicting failure as the results from this line of research are fragmented and inconclusive. Further, the analytical tools that are used to determine failure generally impose assumptions about the linearity of the relationships between failure and resources and also, about the normality of the population from which the data are derived (Campbell et al., 2012; Lee et al., 2012). Given the rigidity in the assumptions of these works, the models generally produce less accurate results. As such, this paper has opted for a more sophisticated model which has less rigid assumptions and a greater predictive accuracy, which will make the findings more credible. Using the artificial neural networks model to predict business failure, this work found that networks, a highly rated surrogate for resources in most studies on business failure (Watson, 2007) was not important in predicting whether or not a firm actually fails. This was found to be so at all levels of granularity of

22 Journal of Business & Entrepreneurship Spring 2017

the modeling process, that is, in both the macro and micro-models. This result is interesting as it goes against the traditional thinking in the resource-based view of the firm which says that within a network there should be resources that are available to the firm which, if it is outside of the network it would not have access to and as such, it should be better able to survive than firms outside of a network. The results in this paper have argued otherwise. One of the reasons for this difference seems to be how the variable is measured. Indeed, researchers looking at network as a surrogate for resources have argued that it is not merely being a part of a network that matters but the quality of the network (Semrau & Werner, 2012; Watson, 2007). For networks to be successful, the types of firms in the network matters, the quality of interaction matters, the length of time in the network matters among other things. The model in this case, was not able to pick up these granular characteristics of the network and as such, this could possibly explain the results obtained. The application of the neural networks in predicting business failure is an important addition to the literature in this area. Prior to now, no other studies have looked at neural networks as good analytical tools to classify firms and predict failure outcomes. The common model for doing this was the discriminant analysis or the probit and logit models but these have turned out to have less predictive accuracy than neural networks. This paper has shown that neural networks are indeed a more powerful tool to predict business failure and as such, the results from this analysis are more robust than those from previous works. Indeed, the neural networks provide a much better estimation of the classification rate for a population that is not fully defined and, unknown parts of the population (Zhang et al., 1999). The findings presented in this work have some implications for future researchers and managers in small firms. Future research on the subject should start applying neural networks as an alternative tool to analyze business failure. Besides its flexibility, the tool has less restrictive assumptions than other linear models and as such, can better handle data from a wide variety of populations and give greater predictive accuracy. This methodological sophistication is important for this literature as in most cases, the population from which the firms emanate is unknown and as such, the normality assumption of the variables will definitely be violated. Similarly, managers in small firms can use the results presented in this paper as a basis for determining which resources are better targeted in order to prevent business failure. The work has shown that a bundle of resources, e.g. net income, age, revenue, size among other things are

Spring 2017 Journal of Business & Entrepreneurship 23

important predictors of business failure. Managers in these small firms need to identify the ones that are most critical for causing failure and ensure that they cultivate these in their firms if they are to survive in the business environment.

REFERENCES

Ahmad, N.H., Seet, P, 2009. Dissecting Behaviours Associated with Business Failure: A Qualitative Study of SME Owners in Malaysia and Australia. Asian Social Science, 5 (9), 98-104.

Amit, R., Schoemaker, P.J., 1993. Strategic assets and organisational rent. Strategic Management Journal, 14, 33-46.

Anderson, P., Tushman, M.L., 2001. Organizational Environments and Industry Exit: The Effects of Uncertainty, Munificence and Complexity. Industrial and Corporate Change, 10 (3), 675-711.

Autio, E., 2005. Creative tension: The Significance of Ben Oviatt’s and Patricia Mc Dougall’s article a theory of international ventures. Journal of International Business Studies, 36, 1, 9-20

Autio, E., Sapienza, H.J &Almedia, J.G., 2000: Effects of age at entry, knowledge intensity and imitability on international growth. Academy of Management Journal, 43, 5, 909-924

Barney, J.B., 1991. Firm resources and sustained competitive advantage. Journal of Management, 17, 99-120.

Bates, T., 2005. Analysis of Young, Small Firms that have closed: delineating successful from unsuccessful closure. Journal of Business Venturing, 20, 343- 358.

Bonaccorsi, A., 1992. On the Relationship between Firm Size and Export Intensity. Journal of International Business Studies, 23 (4), 605-635.

Brush, C.G., 2002. Venture capital access in the new economy: Is gender an issue? In David Hart (ed), The emergence of entrepreneurship policy: governance, start-ups and growth in the Knowledge economy. London: Cambridge university press.

24 Journal of Business & Entrepreneurship Spring 2017

Calof, J., 1994. The Relationship between Firm Size and Export Behaviour Revisited. Journal of International Business Studies 25 (2), 367-387.

Campbell, N.D., Heriot, K.C., Jauregui,A., Mitchell, D.T., 2012. Which state policies lead to US firm exits? Analysis with the Economic Freedom Index. Journal of Small Business Management, 50, (1), 87-104.

Caves, R., 1998. Industrial Organization and New Findings on the Turnover and Mobility of Firms. Journal of Economic Literature, 36, 4, 1947-1982.

Folta, T., Cooper, A., Baik Y., 2006. Geographic cluster size and firm performance. Journal of Business Venturing, 21, 217-242.

Gimeno, J., Folta, T., Cooper, A., Woo, C., 1997. Survival of the Fittest? Entrepreneurial Human Capital and the persistence of Underperforming firms. Administrative Science Quarterly, 42, 740-783.

Hall, G.C., Tu, C., 2004. Internationalization and the Size, Age and Profitability in the United Kingdom, in: Dana, L., 2004. Handbook of Research on International Entrepreneurship, Edward Elgar, Chelthenham, UK, 596-613.

Johanson, J., Vahlne, J., 2003. Business Relationship Learning and Commitment in the Internationalization Process. Journal of International Entrepreneurship, 1 (1), 83-101.

Lado, A.A., Boyd, N.G., Wright, P., Kroll, M., 2006. Paradox and Theorizing Within the Resource-Based View. Academy of Management Review, 31 (1), 115-131.

Lechner, C., Dowling, M., Welke, I., 2006. Firm networks and firm development: The role of the relational mix. Journal of Business Venturing, 21, 514-540.

Lee, H., Kelley, D., Lee, J., Lee, S., 2012. SME survival: The impact of internationalization, technology, resources and alliances. Journal of Small Busines Management, 50, (1), 1-19

McCann, B., Folta. T., 2011. Performance differentials within geographic clusters. Journal of Business Venturing, 26, 104-123.

Spring 2017 Journal of Business & Entrepreneurship 25

Mellahi, K., Wilkinson, A., 2004. Organizational Failure: A Critique of Recent Research and a Proposed Integrative Framework. International Journal of Management Reviews, 5, 21-41.

Mittelstaedt, J.D., Harben, G.N., Ward, W.A., 2003. How Small is too small? Firm Size as Barrier to Exporting from the United States. Journal of Small Business Management, 41, 1, 68-84

Porter, M. E., 2008. The Five Competitive Forces That Shape Strategy. Harvard Business review, January, 79-93.

Semrau, T., Werner, A., 2012. The Two Sides of the Story: Network Investments and the New Venture Creation. Journal of Small Business Management, 50, 159- 180.

Shepherd, D., Wiklund, J., Haynie, J., 2009. Moving Forward: Balancing the financial and emotional costs of business failure. Journal of Business Venturing, 24, 134-148.

Tang. J and Murphy, P.J. (2012). Prior knowledge and new product and service introductions by entrepreneurial firms: the mediating role of technological innovation. Journal of Small Business Management, 50 (1), 41-62

Thornhill, S., Amit, R., 2003. Learning About Failure: Bankruptcy, Firm Age and the Resource-Based View. Organization Science, 14, 497-509.

Watson, J., 2007. Modeling the relationship between networking and firm performance. Journal of Business Venturing, 22, 852-874.

Watson, J., Everett, J., 1996. Small Business Failure Rates: Choice of Definition and the size effect. Journal of Entrepreneurial and Small Business Financing, 5, 3.

Whetten, D.A., 1980. Organizational Decline: A Neglected Topic in Organizational Science. The Academy of Management Review, 5 (4), 577-588.

Williams, D. A., 2009. Understanding exporting in the small and micro enterprise. Nova Science Publishers, New York: USA, 1-183

Williams, D. A., Jones, O., 2010. Factors that determine longevity of small family owned firms. International Journal of Entrepreneurship, 14, 37-58.

26 Journal of Business & Entrepreneurship Spring 2017

Williams, D. A., 2011. Impact of firm size and age on the export behaviour of small, locally-owned firms: Fresh insights. Journal of International Entrepreneurship 9 (2), 152- 174.

Williams, D.A., 2015. Predictors of Business Failure among High-technology Firms: A neural networks analysis. ICSB 2015. UAE University, Dubai, June 6- 9. Pp 1-29.

Yang, T., Aldrich, H., 2012. Out of Sight but not out of mind: Why failure to account for left truncation biases research of failure rates. Journal of Business Venturing, 27, 477-492.

Zhang, G., Hu,M. Y., Patuwo, B.E & Indro, D.C., 1999. Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research,116,16-32.

i Densil A. Williams is Professor of International Business and Executive Director of Mona School of Business and Management. He has published works on small firm internationalization behaviour, strategy and economic development. His works appear in major journals in North America, Europe and the Caribbean. ii They identify social networks, reputational networks, marketing information networks, and co- operative technology networks. . ivFAME means Financial Analysis Made Easy. It is a database with a significant amount of financial and company information on UK firms

Spring 2017 Journal of Business & Entrepreneurship 27

IN OR OUT: NARCISSISM AND ENTREPRENEURIAL SELF- EFFICACY AND INTRAPRENEURIAL INTENTIONS

Reginald L. Tucker Randall M. Croom East Carolina University Stetson University

Louis Marino University of Alabama

ABSTRACT

We suggest that examining intrapreneurial intentions through the lens of motivation can provide insight into why individuals with valuable entrepreneurial skills would choose to apply those skills inside organizations as intrapreneurs. In particular, we examine whether two variables, entrepreneurial self-efficacy and narcissism, motivate intrapreneurial intentions. In a study of 221 working professionals, we draw on expectancy theories of motivation and personality research to specify and test a model in which entrepreneurial self- efficacy and narcissism independently motivate intrapreneurial intentions, but that these variables interact such that when both entrepreneurial self-efficacy and narcissism are high, intrapreneurial intentions are diminished. We suggest that the reversal in the direction of the interaction is explained by how the self- focused, self-centered nature of narcissism influences motivation.

Key Words: Narcissism, intrapreneurship, self-efficacy, entrepreneurial self- efficacy, motivation, intrapreneurial intentions

INTRODUCTION

An entrepreneur is someone who sees new opportunities for products or services, launches a business to try to realize such opportunities, and engages in the process of taking risks to create a new enterprise and pursue opportunity (Kinicki & Williams, 2016, p. 29). Not only are entrepreneurs known as creators of new business enterprises, but they are also recognized as both agents of change and catalysts for industrial progress (Kent, Sexton, & Vesper, 1982). But

28 Journal of Business & Entrepreneurship Spring 2017

the skills that entrepreneurs possess are valued beyond the setting of completely new businesses—existing organizations values them as well. When employees pursue opportunities by undertaking new ventures within organizations, they are engaged in “intrapreneurship”—which is essentially entrepreneurship inside the corporation (Pinchot, 1985). Intrapreneurship is a means for corporations to enhance the innovative abilities of their employees while simultaneously increasing corporate success through the creation of new corporate ventures (Kuratko, Montagno, & Hornsby, 1990). As Kuratko and colleagues note, research in this area is important both practically and theoretically. On a practical level, organizations benefit from understanding the factors that lead to effective intrapreneuring strategies. On a theoretical level, researchers need to continually “reassess the components or dimensions which predict, explain, and shape the environment in which corporate entrepreneuring flourishes” (Kuratko et al, 1990). In our research, we present evidence that could help explain why some individuals with entrepreneurial skills and interests choose to apply those skills intrapreneurially within the organizations they work.

MOTIVATION AND ENTREPRENEURIAL INTENTIONS

While the factors leading to entrepreneurial choice are multifaceted, varied, and context-specific, we assert that examining intrapreneurial and entrepreneurial intentions through the lens of motivation could provide valuable insight into our central question: why would an individual with entrepreneurial skills choose to apply those skills inside the organization as an intrapreneur when they might be able to do so outside the organization as an entrepreneur? Expectancy theories of motivation may be able to explain entrepreneurial and intrapreneurial drive. Expectancy motivation, as conceptualized by Vroom (1964), is one of the most central motivation theories. Expectancy theory holds that motivation has three basic components: valence, instrumentality, and expectancy (Vroom, 1964). Specifically, expectancy theory predicts that individuals will be motivated to exert effort if they believe that those efforts will result in performance (expectancy), that this performance will lead to some outcomes (instrumentality), and if they assign a high positive valence to those outcomes. Next, we briefly explain these three components of motivation and how they might relate to entrepreneurs.

Spring 2017 Journal of Business & Entrepreneurship 29

Expectancy as described by Vroom (1964) refers to the subjective probability of an action or effort (e) leading to an outcome or performance (p) expressed as e → p. Some scholars have also measured expectancy as the perceived correlation between an action and an outcome (Van Eerde & Thierry, 1996). In an entrepreneurial context, we might think of expectancy in terms of an entrepreneur’s level of confidence that they will be able to start a new venture if they try to do so. While expectancy refers to the belief that effort will lead to performance, instrumentality refers to the perceived likelihood that performance will lead to rewards. While the expectancy component of motivation asks “Will I be able to do what I try to do?” instrumentality asks “Will performing get me what I want?” In an entrepreneurial context, we might imagine a potential entrepreneur who would like to have more free time than they currently experience working for a corporation. They may be confident that their efforts to start a new venture will result in the creation of a new venture, but they may not be confident that successfully starting a new venture will result in the free time they seek. Thus, a potential entrepreneur or intrapreneur who had high expectancy motivation for the success of their entrepreneurial efforts might not be sufficiently motivated to start a new venture if they do not perceive instrumentality. The final component of expectancy motivation, valence, describes what Victor Vroom called “the affective orientation towards particular outcomes” (Vroom, 1964, p.49). Outcomes that are desired are considered positively valent, outcomes that individuals seek to avoid are negatively valent. The implication is that “valences are scaled over a virtually unbounded range of positive or negative values” (Behling & Starke, 1973). The objective vales or usefulness of any outcome are not of primary concern in discussions of valence; what matters is what the level of satisfaction (or dissatisfaction) an individual expects to derive from a given level of effort (Vroom, 1964). In other words, valence really describes the value or importance an individual ascribes to achieving an outcome. For example, an entrepreneur might recognize that they might receive fame as a result of starting and managing a successful business, but their level of motivation to do so depends in part on how much they value fame to begin with. Summarily, potential entrepreneurs’ beliefs about their ability to perform well and the likelihood that they will be rewarded for their performance in a subjectively valuable way is likely to motivate intentions to engage in entrepreneurial—or intrapreneurial—behavior.

30 Journal of Business & Entrepreneurship Spring 2017

Our primary question of interest is the following: under what circumstances are individuals motivated to engage in intrapreneurial activity? Applying the motivational theory described above, we theorize that two variables of interest, narcissism and entrepreneurial self-efficacy, are related to intrapreneurial intentions, although for different reasons. In the coming sections we theorize about three things in particular. First, we first theorize that narcissism increases motivation to engage in intrapreneurship. The benefits that individuals can accrue to themselves through intrapreneurship are likely to be especially appealing to narcissists, given that appealing rewards suggest that the valence component of motivation is both positive and high. Consequently, we investigate whether narcissism is related to intrapreneurship intentions. Second, we theorize that entrepreneurial self-efficacy increases motivation for intrapreneurship, particularly expectancy motivation. Once again drawing upon expectancy theories of motivation, we suggest that individuals who believe that they have the skills to be effective in the creation of new ventures might be more motivated to create a new venture. Thus, we will also investigate whether entrepreneurial self-efficacy is related to intrapreneurship intentions. Third and finally, we theorize about how the motivational effects of self-efficacy in combination with the self-focused nature of narcissism suggest that the interaction between narcissism and entrepreneurial self-efficacy produce effects rather unique to the entrepreneurial context. Figure 1 below captures these theoretical ideas.

Figure 1. Hypothesized Model of the Relationship of Narcissism to Intrapreneurial Intentions Moderated by Entrepreneurial Self-Efficacy

Spring 2017 Journal of Business & Entrepreneurship 31

NARCISSISM AS A PREDICTOR OF INTRAPRENEURIAL INTENTIONS

There are several reasons that we expect narcissists to find intrapreneurial activities appealing. First, they may find intrapreneurial ventures appealing because they expect to succeed in them, as narcissists have been empirically demonstrated to have very high self-esteem (Emmons, 1984; Watson, Taylor, & Morris, 1987; Raskin, Novacek, & Hogan, 1991a, Raskin, Novacek, & Hogan, 1991b). Narcissists believe that they will be successful—and that they deserve to be (Raskin & Terry, 1988). Thus, a second reason that narcissists may be attracted to intrapreneurial activities is because they are an opportunity for glory. Narcissists regularly engage in self-enhancement (Raskin, Novacek, and Hogan, 1991a), even at the expense of others (Campbell, Reeder, Sedikides, & Elliot, 2000). Narcissists also have a strong desire to receive admiration from others, are preoccupied with success and power (Kernberg, 1975; APA, 2000), and are willing to expend much more effort when the opportunity to receive glory is high (Wallace & Baumeister, 2002). Leading a successful intrapreneurial venture can provide glory, success, and power—thus creating a high positive valence for intrapreneurial activities for individuals high in narcissism. A third reason narcissists may find the intrapreneurial context appealing is that performing well within an organizational context gives them an opportunity to self-enhance by comparing their presumably strong performance to that of others. As Elliot & Thrash (2001, p. 217) note:

…relational others for the narcissist primarily serve a feedback function, allowing them to clearly and emphatically demonstrate normative competence. Thus, in the world of the narcissist, social contexts are important because they are the arena in which all-important achievement processes take place and where narcissists are able to feed their strong desire for normative competence. This helps explain how narcissists can seem to care so much about social contexts but at the same time appear so callous in their actual social interactions.

Finally, narcissists’ tendency to self-enhance may also explain why, in the absence of other mitigating factors, narcissism increases the desire to engage in intrapreneurial activity rather than entrepreneurial activity. Narcissists work to look good, even at the expense of others (Campbell et al., 2000). Narcissists seek

32 Journal of Business & Entrepreneurship Spring 2017

attention (Buss & Chiodo, 1991) and are even willing to steal credit from others (Campbell, Reeder, Sedikides, & Elliot, 2000; Farwell & Wohlwend-Lloyd, 1998). Narcissists’ desire to enhance themselves also leads to a tendency to blame others or a situation for failure (Farwell & Wohlwend-Lloyd, 1998). Taken together, we see the foregoing as evidence that narcissists not only want to look good, but also want to avoid looking bad. Consequently, narcissists may want to avoid situations in which failure is likely, absolute, public, and directly associated with them. Entrepreneurship may be just such a situation: the majority of new ventures ultimately fail, and the leader of the organization—the entrepreneur—usually takes the brunt of the blame when they do. Consider the case of Elizabeth Holmes, founder and chief executive of blood-testing company Theranos. Once considered a bright and rising star in the world of business, Holmes topped the Forbes List of Richest Self Made Women with a net worth of $4.5 billion based on her 50% ownership stake in Theranos (Herper, 2016). But after allegations that the tests were inaccurate, Forbes revised her net worth down to zero, changed its valuation of the company’s valuation from $9B to $800M, and cast serious doubt about the viability of the company (Herper, 2016). It’s clear who regulators and the market believe bears much of the blame: federal regulators have barred Holmes from owning or operating a medical laboratory for at least two years and prohibited the company from taking Medicare or Medicaid payments (Pollack, 2016). Narcissism alone might not be enough to motivate an individual to face the risk of such harsh and damaging consequences. Conversely, within the confines of a corporation, individuals are likely to have safeguards against the harshest penalties faced by entrepreneurs: the risk of personal financial ruin and individualized public approbation are mitigated, resources are often available that could increase the chances of success, and individuals who engage in intrapreneurship may have other examples of success within the organization that they could point to as evidence of their capability if the latest venture proves fruitless. Thus, narcissists’ decision to engage in intrapreneurial activity may not reflect on their beliefs about their ability to be successful, but their beliefs about the situational likelihood of success internally versus externally. Self-efficacy, not just self-admiration, is likely to be required to motivate an individual to leave an organization to strike out on their own. Thus, we hypothesize the following:

H1 Narcissism will be positively associated with intrapreneurial intentions.

Spring 2017 Journal of Business & Entrepreneurship 33

ENTREPRENEURIAL SELF-EFFICACY AND INTRAPRENEURIAL INTENTIONS

While we believe narcissism predicts individuals’ intent to engage in intrapreneurial behavior, we suggest that another variable, entrepreneurial self- efficacy, is also related to intrapreneurial intentions. Entrepreneurial self-efficacy (ESE) is defined as a person’s belief in their ability to successfully launch an entrepreneurial venture (McGee, Peterson, Mueller, & Sequeira, 2009). Prior research demonstrates that high levels of entrepreneurial self-efficacy increases entrepreneurial intentions and subsequent entrepreneurial behavior (Sequeira, Mueller, & McGee, 2007). Prior research has found that founders of businesses have higher levels of entrepreneurial self-efficacy than non-founders (Chen, Greene, & Crick, 1998), that innovators have higher levels of ESE than other members of the population (Drnovsek & Glas, 2002), and that entrepreneurial self-efficacy is positively associated with nascent entrepreneurship (Arenius & Minniti, 2005). Entrepreneurial self-efficacy, then may be an important ingredient in entrepreneurship and the intention to engage in entrepreneurial behaviors. Seen through the lens of expectancy motivation, it is likely that beliefs about entrepreneurial self-efficacy motivate individuals to engage in intrapreneurial behavior. In general, self-efficacy is linked to expectancy motivation (Chiang & Jang 2008). Self-efficacy, the general belief in one’s ability to perform (Bandura, 1977), is also defined as a personal judgment of “how well one can execute courses of action required to deal with prospective situations” (Bandura, 1982, p. 122). Consistent with the well-established link between motivation and performance, self-efficacy is related to performance in a variety of contexts (Stajkovic and Luthans, 1998; Barling & Beattie, 1983; Taylor, Locke, Lee, & Gist, 1984; Wood, Bandura, & Bailey, 1990; Eden & Zuk, 1995; Hackett & Betz, 1989; Chemers, Hu, & Garcia, 2001). Thus, individuals who are high in self-efficacy—including self-efficacy related to their ability to successfully launch a new venture—are likely to perform better than individuals who are low in self-efficacy. Expectancy theories of motivation hold that individuals will be motivated to exert effort if they believe that their efforts will lead to performance. Individuals who are high in entrepreneurial self-efficacy—high in their belief in their ability to perform in entrepreneurial domains—are likely to be more motivated to engage in entrepreneurial and intrapreneurial activity. While prior

34 Journal of Business & Entrepreneurship Spring 2017

research finds that entrepreneurial self-efficacy is related to both entrepreneurial intentions and entrepreneurial behaviors (Sequeira et al., 2007), we hope to extend these findings by demonstrating that entrepreneurial self-efficacy is also related to intrapreneurial intentions. Logically, if many of the same skills required of entrepreneurs are required of intrapreneurs—i.e., the ability to recognize and seize opportunities, successfully navigate risk, catalyze change, and lead industrial progress—individuals high in entrepreneurial self-efficacy should believe that they are likely to be successful in intrapreneurial ventures as well. Thus, we hypothesize the following:

H2 Entrepreneurial self-efficacy is positively related to intrapreneurial intentions.

HOW THE INTERACTION OF NARCISSISM AND ENTREPRENEURIAL SELF-EFFICACY REDUCES INTRAPRENEURIAL INTENTIONS

We have proposed that narcissism and entrepreneurial self-efficacy are positively related to intrapreneurial intentions, in large part because they will increase motivation to engage in intrapreneurial behavior. But in studying narcissism, we faced a conundrum: could narcissists’ desire for glory and adulation cause them to seek entrepreneurship rather than intrapreneurship? After all, even though the price of failure for entrepreneurs may be steep, the spoils of great success are more than commensurate. Intrapreneurs, although rewarded for success, must share glory—and financial benefits—with the organization. Thus, we asked: even though narcissists would reap benefits of intrapreneurship, under what conditions would narcissists find intrapreneurship unattractive? One answer to that question presents itself in our other variable of interest, entrepreneurial self-efficacy. As we described earlier, individuals high in entrepreneurial self- efficacy a) believe they will be successful in launching a new venture b) are actually more likely to be successful. In other words, individuals high in entrepreneurial self-efficacy may have a lower risk of venture failure than individuals low in entrepreneurial self-efficacy. Thus, we must consider the case of an individual who is high in both narcissism and entrepreneurial self-efficacy. First, as we have theorized, narcissism can increase (in particular) the valence component of motivation to engage in entrepreneurial activity, and entrepreneurial self-efficacy can increase

Spring 2017 Journal of Business & Entrepreneurship 35

(in particular) the expectancy component of motivation to engage in entrepreneurial activity, an individual who is high in both narcissism and entrepreneurial self-efficacy has higher levels of entrepreneurial motivation overall. But second, an individual who is high in narcissism desires to accrue as many of the benefits of their entrepreneurial skills to themselves as possible. If a narcissistic individual is also high in entrepreneurial self-efficacy, they have reason to believe that their entrepreneurial activities outside the organization will be successful. Consequently, self-efficacious individuals high in narcissism could shift focus from inside the organization to outside the organization—and have higher overall motivation to do so. In summation, the combination of the rational confidence created by entrepreneurial self-efficacy and the self-serving nature of narcissism decrease the motivation to apply one’s entrepreneurial skill within the organization, and consequently have a negative relationship with intrapreneurial intentions.

H3 The interaction of narcissism and entrepreneurial self-efficacy reduce intrapreneurial intentions, such that individuals who are high in both narcissism and entrepreneurial self-efficacy are have lower intrapreneurial intentions.

METHOD

Procedure

Data collection spanned March 31, 2016 to April 30, 2016. On March 31, an email was sent to MBA alumni of a university in the Southeast region of the United States. The survey was distributed via the same email using the Qualtrics platform. A reminder email was sent on April 6, 2016 and a second reminder was sent on April 13, 2016.

Participants

We administered surveys to MBA alumni and allowed respondents approximately one month to respond to the survey. The email was sent to 540 alumni and we received 414 responses. We eliminated respondents from this study who were currently entrepreneurs in the capacity as a founder, owner,

36 Journal of Business & Entrepreneurship Spring 2017

operator, or self-employed. This resulted in sample of 208 respondents. The average age is 38.91 years (s.d. = 12.75) with work experience averaged at 15.89 years (s.d. = 12.42). Our sample was 65% female and 90% Caucasian. To test hypotheses all three of our hypotheses and our post-hoc test of narcissism and entrepreneurial self-efficacy’s effect on entrepreneurial intentions, we centered all continuous variables and performed multiple regression analyses using SPSS 24.

Measures

Narcissism

A 4-item scale developed by Jonason and Webster (2010) was used to measure narcissism. Respondents were asked to rate their level of agreement with the following items – (1) “I tend to want others to admire me,” (2) “I tend to want others to pay attention to me,” (3) “I tend to expect special favors from others,” and (4) “I tend to seek prestige or status.” The response choices ranged from 1 (strongly disagree) to 5 (strongly agree). The internal consistency reliability of this scale was .78.

Entrepreneurial Self-Efficacy

We used a 4-item measure developed by Cassar and Friedman (2009) to measure entrepreneurial self-efficacy. Items include “If I can work hard, I can successfully start a business,” “Overall, my skills and abilities will help me start a business,” “My past experience will be very valuable in starting a business,” and “I am confident I can put in the effort needed to start a business.” The response choices ranged from 1 (strongly disagree) to 5 (strongly agree). The internal consistency reliability of this scale was .79.

Intrapreneurial Intentions

We used a 3-item measure developed by Douglas and Fitzsimmons (2013) to measure intrapreneurial intentions. A sample of items include “how likely is it that you would want to manage (within your employer’s business) a new division (or branch) that is set up to exploit a radical innovation?” The

Spring 2017 Journal of Business & Entrepreneurship 37

response choices ranged from 1 (very unlikely) to 7 (very likely). The internal consistency reliability of this scale was .93.

Control Variables

Control variables include sex, age, race, years of work experience, and marital status. Extant literature indicates that women have a lower entrepreneurial propensity than men (e.g., Xavier, Kelley, Kew, Herrington, & Vorderwulbecke, 2012) and also exhibit a lower level of entrepreneurial intention (Schlaegel & Koenig, 2014). Sex was measures as a dichotomous variable (0 = female, 1 = male). Previous research shows that age has an influence on who starts new ventures and who does not, and thus, we include age (Shane, 2003). Age was measured as a continuous variable. Marital status was measured by asking respondents to indicate their relationship status (1 = single, 2 = married, 3 = divorced).

RESULTS

We present the means, standard deviations, reliabilities, and inter- correlations of the study variables in Table 1.

Table 1: Means, Standard Deviations, and Intercorrelations

Variables Mean SD 1 2 3 4 5 6 7 8

1. Sex .64 .48 -

2. Age 39.12 12.87 .16* -

3. Race 5.61 1.25 -.02 .06 -

4. Work Exp. 16.12 12.58 .12 .94** .07 -

5. Marital Status 1.82 .48 .11 .23** .12 .23 -

6 Narcissism 3.04 .79 .07 -.16 -.02 -.15* -.10 -

7. ESE 4.09 .68 .08 -.08 .02 -.04 -.04 .06 - - 8. Narcissism. x ESE 12.52 4.02 .09 .00 -.15* -.10 .84** .58** - .17*

38 Journal of Business & Entrepreneurship Spring 2017

Hypothesis Testing

To test our hypotheses, we entered the control variables into the equation for Model 1. Among the control variables, Sex and Age were the only variables significant (p < .05). We then entered narcissism and entrepreneurial self- efficacy as independent variables in Model 2. Here, only ESE was significant (p < .001). Next, we tested Hypothesis 1, Hypothesis 2, and Hypothesis 3 by creating and entering a two-way interaction term (Narcissism and ESE) into the regression equation in Model 3. The results of our regression analysis for Models 1,2, and 3 are presented in Table 2. Hypothesis 1 proposed that Narcissism would be positively related to intrapreneurial intentions. Results indicated that Narcissism had a significant positive relationship with intrapreneurial intentions (2.54, p < .01). Thus, Hypothesis 1 was supported. Hypothesis 2 proposed that entrepreneurial self-efficacy would be positively related to intrapreneurial intentions. Multiple regression analysis revealed that entrepreneurial self- efficacy is significantly positively related to intrapreneurial intentions (2.55, p < .001). Thus, Hypothesis 2 was supported. Finally, Hypothesis 3 supposed that because of the combination of the motivational effects of entrepreneurial self- efficacy and the self-centered, individualistic glory-seeking nature of Narcissism, individuals who were high in both entrepreneurial self-efficacy and narcissism would find intrapreneurship less appealing, such that the interaction between entrepreneurial self-efficacy and intrapreneurial intentions would be negative. As shown in Table 2, Model 3 included the interaction term for narcissism and entrepreneurial self-efficacy. The interaction between entrepreneurial self- efficacy and narcissism was also significant (-.62, p < .01). Thus, Hypothesis 3 was also supported. The interaction effects are also plotted in figure 2, which clearly shows a decreasing slope for individuals with high levels of narcissism and ESE.

Spring 2017 Journal of Business & Entrepreneurship 39

Figure 2. Entrepreneurial Self-Efficacy Moderating the Effect of Narcissism

Table 2: Results of Regression Analysis

Model 1: Model 2: Model 3: Predictors Controls Main Effects Interaction Sex .33** .28** .26** Age -1.05** -.86** - .80* Race -.07 -.08 - .06 Work experience .51 .35 .30 Marital status .01 .03 .03 Narcissism .02 2.54** ESE .67*** 2.55*** Narcissism X ESE - .62**

R2 .15 .23 .26 ΔR2 .08*** .32**

DISCUSSION

The results of our study show that both narcissism and ESE have a positive relationship with intrapreneurial intentions. While both of these variables have significant and positive relationships with intrapreneurial intentions, their interaction term was significant, but negative. The narcissism and intrapreneurial intentions relationship has a number of explanations. For one, individuals who desire to self-enhance are likely to

40 Journal of Business & Entrepreneurship Spring 2017

have intentions toward an activity that would allow them to self-enhance. This is in line with extant and recent research that finds a positive relationship between narcissism and entrepreneurial intentions (Mathieu & St-Jean, 2013). Intrapreneurship is an interesting outcome for individuals with narcissistic-like traits because they can garner praise and admiration for taking on new initiatives without the added obstacles of starting an entirely new venture. Additionally, intrapreneurship is much more common and viable than starting a new venture. For example, 70% of successful entrepreneurs incubated their business ideas while employed by someone else (Chamorro-Premuzic, 2012). Additionally, a recent study indicated that 58% of managers are either very willing or extremely willing to support employees who want to chase business opportunities (Schawbel, 2013). ESE’s relationship with intrapreneurial intentions is also supported by previous studies. While these studies essentially found that individuals who believe they have requisite abilities to start a new venture might intend to do so, we found that individuals with similar characteristics intend to do so inside a venture. While the interaction term has a statistically significant relationship with intrapreneurial intentions, the coefficient is negative. This suggest that individuals with high levels of narcissism and high levels of ESE are less likely to have intrapreneurial intentions.

THEORETICAL AND PRACTICAL IMPLICATIONS

There are three primary theoretical implications of the foregoing study. First, our results suggest that entrepreneurial self-efficacy is suitable for use as a predictor of intrapreneurial intentions. Future research might consider whether a measure of intrapreneurial self-efficacy adds any predictive value for intrapreneurial intentions and behavior beyond what entrepreneurial self-efficacy currently provides. Second, our findings suggest that investigating the motivational nature of dark traits further. While it may be natural to suspect that dark traits lead to dark motivations, we find in this case that narcissism leads to an intention that is, on its face, morally neutral and possibly beneficial to the organization. Future research might investigate how dark traits can motivate individuals to engage in behaviors that are adaptive rather than destructive. Third, we find that even though narcissism had positive main effects on intrapreneurial self-efficacy, it may interact with other traits in ways that are

Spring 2017 Journal of Business & Entrepreneurship 41

counter to the direction of the main effects. This suggests that narcissism—and perhaps other dark traits—may be variables that behave in interesting ways when they interact with other variables because of the nature rather than the level of the motivation they provide. Our work has practical implications as well. While it is well established that individuals with certain personality characteristics may be unpleasant to associate with both inside and outside the workplace (Judge, LePine, & Rich, 2006), other research suggests that these same individuals can be the employees who innovate, ideate, and bring in new revenue streams for their firms (Goncalo, Flynn, & Kim, 2010; Gino & Ariely, 2012). Thus, one practical implication is that despite the social costs and shortcomings of narcissistic individuals, firms and narcissists may mutually benefit from narcissistic individuals’ desires to engage in intrapreneurial behaviors. To reap the benefits of narcissistic intrapreneurial motivations (Wiklund & Shepherd, 2003; Wiklund & Shepherd, 2005) while mitigating the detriments (Judge, LePine, & Rich, 2006), a firm might consider allowing individuals with narcissistic tendencies to lead new initiatives, but at some point shift leadership of these ventures to individuals less likely to derail. Our results highlight that individuals who are high in both narcissism and entrepreneurial self-efficacy are less likely to have intrapreneurial intentions. It is possible that these individuals are not only less likely to have intrapreneurial intentions, but perhaps more likely to have a propensity towards entrepreneurship. Future studies might investigate populations of both entrepreneurs and intrapreneurs (or potential intrapreneurs) to determine the degree to which narcissism and entrepreneurial self-efficacy lead to entrepreneurial and intrapreneurial behaviors. Researchers should consider examining behavior—not just intentions—in the future given that extant research has highlighted the weak link between entrepreneurial intentions and subsequent behaviors (Schlaegel & Koenig, 2012). One possible reason is because of the both fluid definition of entrepreneurship and time associated with starting a new venture (Bird & Schjoedt, 2009; Kautonen, Gelderen, & Fink, 2015). However, we suspect that some of the concerns raised by previous researchers about the link between entrepreneurial intentions and entrepreneurial behavior are likely less of a concern in the specific context of intrapreneurship. It is likely that an individual is likely to know exactly what new initiative he or she will engage in and will be given a time frame to execute on that initiative by a superior (Hisrich, 1990).

42 Journal of Business & Entrepreneurship Spring 2017

STRENGTHS, LIMITATIONS AND FUTURE DIRECTIONS

The present study possesses several strengths that enabled us to examine the relationship between personality variables and intrapreneurship. First, our sample consist of working adults with several years (15.89) of working experience. Our respondents also had a mean score of 5.07 (out of 7) for intrapreneurial intentions indicating that these intrapreneurial intentions do in fact exist. Our study is not without its limitations. First, we acknowledge that our study used cross-sectional, self-reported data. We believe that these self-ratings are particularly appropriate for ESE and Intrapreneurial Intentions because the respondents’ perceptions of their ESE and intrapreneurial intentions are more valid than others’ reports (e.g., coworkers) because others may not have full knowledge of the focal participants’ attitudes and intentions (May, Chang, & Shao, 2015). In addition, we focus on the interaction effect in Model 3 and interaction effects are unlikely to be influenced by common method bias (Siemsen, Roth, & Oliveira, 2010). Another limitation in our study is the assumption that all individuals can engage in intrapreneurship. An individual’s intention to engage in intrapreneurship is less powerful if the opportunity to do so does not exist. Future studies should ask whether this opportunity even exist for respondents. The cross-sectional nature of our study speaks to future research directions. First, studies that follow should examine whether individuals with intentions actually engage in intrapreneurial endeavors. Intentions alone do not always precipitate behavior, particularly in entrepreneurship. Thus, interesting results may follow from studies that investigate intrapreneurial behaviors occur at later dates (e.g., 6 months, 1 year). Extant research finds that studies examining the intention-behavior link in entrepreneurship might rely on a lagged-time of one year (Kautonen et al., 2015). Other future directions should consider whether individuals high certain personality variables engage in intrapreneurship behaviors, entrepreneurship behaviors, both, or simply leave their respective firm all together. While we control for certain factors, future studies might examine whether other variables pull or push individuals into intrapreneurship, entrepreneurship, or turnover all together. For example, job stress, burnout, and depression are all factors that can influence certain job behaviors. Further, they might increase or decrease particular motivations associated with intrapreneurship or entrepreneurship.

Spring 2017 Journal of Business & Entrepreneurship 43

REFERENCES

American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (DSM-IV-TR-4th edition, Text Revision). Washington, DC: American Psychiatric Association.

Arenius, P., & Minniti, M. (2005). Perceptual variables and nascent entrepreneurship. Small Business Economics, 24(3), 233-247.

Barling, J., & Beattie, R. (1983). Self-efficacy beliefs and sales performance. Journal of Organizational Behavior Management, 5(1), 41-51.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215.

Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122-147.

Behling, O., & Starke, F.A. (1973). The postulates of expectancy theory. Academy of Management Journal, 16(3), 373-388.

Bird, B., & Schjoedt, L. (2009). Entrepreneurial behavior: Its nature, scope, recent research, and agenda for future research. In A. Carsrud and M. Brannback (Eds.), Understanding the Entrepreneurial Mind: Opening the Black Box, (pp. 327-358). Heidelberg: Springer.

Buss, D.M., & Chiodo, L.M. (1991). Narcissistic acts in everyday life. Journal of Personality, 59(2), 179-215.

Campbell, W.K., Reeder, G.D., Sedikides, C., & Elliot, A.J. (2000). Narcissism and comparative self-enhancement strategies. Journal of Research in Personality, 34(3), 329-347.

Cassar, G., & Friedman, H. (2009). Does self-efficacy affect entrepreneurial investment? Strategic Entrepreneurship Journal, 3(3), 241-260.

44 Journal of Business & Entrepreneurship Spring 2017

Chamorro-Premuzic, T. (2012). How bad leadership spurs entrepreneurship. Harvard Business Review, https://hbr.org/2012/09/how-bad-leadership-spurs- entrepreneurship.

Chemers, M.M., Hu, L.T., & Garcia, B.F. (2001). Academic self-efficacy and first year college student performance and adjustment. Journal of Educational Psychology, 93(1), 55-64.

Chen, C., Green, R., & Crick, A. (1998). The self-efficacy expectations and occupational preferences of females and males. Journal of Business Venturing, 13(4), 295-316.

Chiang, C.F., & Jang, S.S. (2008). An expectancy theory model for hotel employee motivation. International Journal of Hospitality Management, 27(2), 313-322.

Douglas, E.J., & Fitzsimmons, J.R. (2013). Intrapreneurial intentions versus entrepreneurial intentions: Distinct constructs with different antecedents. Small Business Economics, 41(1), 115-132.

Drnovsek, M., & Glas, M. (2002). The entrepreneurial self-efficacy of nascent entrepreneurs: The case of two economies in transition. Journal of Enterprising Culture, 10(2), 107-131.

Eden, D., & Zuk, Y. (1995). Seasickness as a self-fulfilling prophecy: Raising self-efficacy to boost performance at sea. Journal of Applied Psychology, 80(5), 628-635.

Elliot, A.J., & Thrash, T.M. (2001). Narcissism and motivation. Psychological Inquiry, 12(4), 216-219.

Emmons, R.A. (1984). Factor analysis and construct validity of the narcissistic personality inventory. Journal of Personality Assessment, 48(3), 291-300.

Farwell, L., & Wohlwend-Lloyd, R. (1998). Narcissistic processes: Optimistic expectations, favorable self-evaluations, and self-enhancing attributions. Journal of Personality, 66(1), 65-83.

Spring 2017 Journal of Business & Entrepreneurship 45

Gino, F., & Ariely, D. (2012). The dark side of creativity: Original thinkers can be more dishonest. Journal of Personality and Social Psychology, 102(3), 445- 459.

Goncalo, J., Flynn, F., & Kim, S. (2010). Are two narcissists better than one? The link between narcissism, perceived creativity, and creative performance. Personality and Social Psychology Bulletin, 36(11), 1484-1495.

Hackett, G., & Betz, N.E. (1989). An exploration of the mathematics self- efficacy/mathematics performance correspondence. Journal for Research in Mathematics Education, 20(3), 261-273.

Herper, M. (2016, May). Theranos’ last defense crumbles. Forbes. Retrieved from http://www.forbes.com/sites/matthewherper/2016/05/19/theranos-last- defense-crumbles/#2ca2a7a63b0f

Hisrich, R.D. (1990). Entrepreneurship/ intrapreneurship. American Psychologist, 45(2), 209-222.

Jonason, P.K., & Webster, G.D. (2010). The dirty dozen: A concise measure of the dark triad. Psychological Assessment, 22(2), 420-432.

Judge, T.A., LePine, J.A., & Rich, B.L. (2006). Loving yourself abundantly: Relationship of the narcissistic personality to self-and other perceptions of workplace deviance, leadership, and task and contextual performance. Journal of Applied Psychology, 91(4), 762-

Kautonen, T., Gelderen, M., & Fink, M. (2015). Robustness of the theory of planned behavior in predicting entrepreneurial intentions and actions. Entrepreneurship Theory and Practice, 39(3), 655-674.

Kent, C.A., Sexton, D.L., & Vesper, K.H. (1982). Encyclopedia of Entrepreneurship. Englewood Cliffs, NJ: Prentice-Hall.

Kernberg, O.F. (1975). Borderline conditions and pathological narcissism. New York: Jason Aronson.

46 Journal of Business & Entrepreneurship Spring 2017

Kinicki, A., & Williams, B. K. (2016). Management: a practical introduction (7th ed). New York: McGraw-Hill.

Kuratko, D.F., Montagno, R.V., & Hornsby, J.S. (1990). Developing an intrapreneurial assessment instrument for an effective corporate entrepreneurial environment. Strategic Management Journal, 11(5), 49-58.

May, D.R., Chang, Y.K., & Shao, R. (2015). Does ethical membership matter? Moral identification and its organizational implications. Journal of Applied Psychology, 100(3), 681-694.

McGee, J.E., Peterson, M., Mueller, S.L., & Sequeria, J.M. (2009). Entrepreneurial self-efficacy: Refining the measure. Entrepreneurship Theory and Practice, 33(4), 965-988.

Pinchot, G. (1985). Intrapreneuring: Why you don’t have to leave the corporation to become an entrepreneur. New York: Harper & Row.

Pollack, A. (2016, March). Report shows Theranos testing plagued by problems. New York Times. Retrieved from http://www.nytimes.com/2016/04/01/business/report-shows-theranos-testing- plagued-by-problems.html.

Raskin, R., & Terry, H. (1988). A principal-components analysis of the Narcissistic Personality Inventory and further evidence of its construct validity. Journal of Personality and Social Psychology, 54(5), 890-902.

Raskin, R., Novacek, J., & Hogan, R. (1991). Narcissistic self-esteem management. Journal of Personality and Social Psychology, 60(6), 911-918

Raskin, R., Novacek, J., & Hogan, R. (1991). Narcissism, self-esteem, and defensive self-enhancement. Journal of Personality, 59(1), 19-38.

Schawbel, D. (2013). 3 things you don’t know about intrapreneurship. Entrepreneur, https://www.entrepreneur.com/article/227725.

Spring 2017 Journal of Business & Entrepreneurship 47

Schlaegel, C., & Koenig, M. (2014). Determinants of entrepreneurial intent: A meta-analytic test and integration of competing models. Entrepreneurship Theory and Practice, 38(2), 291-332.

Sequeira, J., Mueller, S.L., & McGee, J.E. (2007). The influence of social ties and self-efficacy in forming entrepreneurial intentions and motivating nascent behavior. Journal of Developmental Entrepreneurship, 12(3), 275-293.

Shane, S. (2003). A general theory of entrepreneurship: The individual- opportunity nexus. Edward Elgar, Cheltenham, UK.

Siemsen, E., Roth, A., & Oliveira, P. (2010). Common method bias in regression models with linear, quadratic, and interaction effects. Organizational Research Methods, 13(3), 456-476.

Stajkovic, A.D., & Luthans, F. (1998). Self-efficacy and work-related performance: A meta-analysis. Psychological Bulletin, 124(2), 240-261.

Taylor, M.S., Locke, E.A., Lee, C., & Gist, M.E. (1984). Type A behavior and faculty research productivity: What are the mechanisms? Organizational Behavior and Human Performance, 34(3), 402-418.

Van Eerde, W., & Thierry, H. (1996). Vroom’s expectancy models and work- related criteria: A meta-analysis. Journal of Applied Psychology, 81(5), 575-586.

Vroom, V.H. (1964). Work and Motivation. New York: Wiley, 1964.

Wallace, H.M., & Baumeister, R.F. (2002). The performance of narcissists rises and falls with perceived opportunity for glory. Journal of Personality and Social Psychology, 82(5), 819-834.

Watson, P.J., Taylor, D., & Morris, R.J. (1987). Narcissism, sex roles, and self- functioning. Sex Roles, 16(7-8), 335-350.

Wiklund, J., & Shepherd, D. (2003). Knowledge-based resources, entrepreneurial orientation, and the performance of small and medium-sized businesses. Strategic Management Journal, 24(13), 1307-1314.

48 Journal of Business & Entrepreneurship Spring 2017

Wiklund, J., & Shepherd, D. (2005). Entrepreneurial orientation and small business performance: A configurational approach. Journal of Business Venturing, 20(1), 71-91.

Wood, R., Bandura, A., & Bailey, T. (1990). Mechanisms governing organizational performance in complex decision-making environments. Organizational Behavior and Human Decision Processes, 46(2), 181-201.

Xavier, S.R., Kelley, D., Kew, J., Herrington, M., & Vorderwulbecke, A. (2012). Global entrepreneurship monitor 2012 global report. Global Entrepreneurship Research Association (GERA).

Spring 2017 Journal of Business & Entrepreneurship 49

THE RELATIONS BETWEEN ENTREPRENEURS’ ETHNICITY, FAMILISM VALUES, BELIEFS, AND USE OF FINANCIAL PLANNING

Dianna L. Stone Kimberly M. Lukaszewski University of Albany & Virginia Tech Wright State University

Julio C. Canedo Mark Suazo University of Houston, Downtown Wright State University

Teresa L. Harrison Dianna Contreras Krueger University of the Incarnate Word Tarleton State University

ABSTRACT

Research indicated that ethnic entrepreneurs are more likely to fail than their counterparts, and use of financial planning may be an important determinant. Thus, this study presented a model of factors (e.g., familism, beliefs, knowledge) thought to be related to Hispanic and Anglo entrepreneurs’ use of financial planning. Results revealed that Hispanic entrepreneurs were less likely to use financial planning than Anglos, and familism values moderated the relations between ethnicity and the use of financial planning. Knowledge of planning and revenue levels were also related to use of planning, and entrepreneurs considered the needs of families when developing financial plans.

Keywords: Familism, Hispanic entrepreneurs, financial planning, financial knowledge

50 Journal of Business & Entrepreneurship Spring 2017

INTRODUCTION

Hispanic-Americans (Hispanics) are the fastest growing ethnic group in the U. S., and they have been starting small businesses at a very rapid rate. For instance, in the U.S., there are 3.3 million Hispanic-owned businesses, and the growth rate was 46.9 percent from 2007-2012 (U.S. Bureau of Census, 2015). Although there may be many reasons for this, one motive is that entrepreneurship “can provide ethnic minorities a springboard for economic advancement and social integration” (Wang & Li, 2007, p. 167).

In spite of this rapid growth, relatively little empirical research has focused on the factors affecting the success of Hispanic entrepreneurs (hereinafter referred to as HEs) (e.g., Bates, Jackson, & Johnson, 2007; Wang & Li, 2007). However, research revealed that ethnic minorities (e.g., Hispanics and African-Americans) were more likely to start small businesses, but were also more likely to fail than Anglos (Sullivan, 2007). Although there may be a number of reasons for the failure rate of minority-owned businesses, researchers argued that the biggest challenges facing ethnic entrepreneurs included: limited training, difficulties obtaining financial resources, inadequate managerial skills, and lack of financial planning (e.g., Bates et al., 2007; Chaganti & Green, 2002; Williams, Ortiz-Walters, & Gavino, 2012). Further, researchers argued that financial planning is a key factor affecting entrepreneurial success rates (Robinson & Pearce, 1984). Even though planning is important to entrepreneurial success, research indicated that entrepreneurs may be less likely to plan than managers of large businesses because they have short term time horizons, and few opportunities to plan (Robinson & Pearce, 1984).

Still other research found that the factors affecting the success of HEs may be different than those of Anglo-American entrepreneurs (hereinafter referred to as AEs), but little research has considered these differences (Aldrich & Waldinger, 1990). Researchers also argued that we need more research on ethnic entrepreneurs especially studies that compare their behaviors to those of majority group members (e.g., Abbey, 2002; Aldrich & Waldinger, 1990; Bates et al, 2007; Clark & Drinkwater, 2010; Serviere-Muniz, Hurt, & Miller, 2015). Thus, a better understanding of the factors affecting HEs’ use of financial planning is important for enhancing their effectiveness and survival rates.

Spring 2017 Journal of Business & Entrepreneurship 51

Given that the use of financial planning may be a key determinant of the success rates of HEs, we believe that research is needed to identify the factors that are related to their use of financial planning. Survey research showed that Hispanics had low levels of financial literacy, and did not use personal financial planning (Lusardi & Mitchell, 2011). Thus, they may not have the knowledge needed to conduct planning. In support of this argument, results of studies found that Hispanics were less likely to plan or save for retirement than Anglos (e.g., Lusardi & Mitchell, 2011). Further, lack of financial knowledge may also affect HEs’ beliefs about the benefits of financial planning. For instance, they may not be aware that financial planning will (a) enable them to take advantage of opportunities, (b) provide a cushion for economic downturns, and (c) keep their business on the growth track. However, research revealed that those who are financially savvy are more likely to use and prosper from financial planning than those who have less financial knowledge (Lusardi & Mitchell, 2011).

Although a lack of financial knowledge may be a key factor affecting HE’s use of financial planning, we believe, as do others, (e.g., Sussan, Weisfeld- Spolter, Rippe, Gould, & Yurova, 2016) that familism cultural values may also influence their use of planning. Familism is typically defined as a cultural value that emphasizes obligation, filial piety, family support and obedience to the family (Sabogal, Marín, Otero-Sabogal, Marín, & Perez-Stable, 1987), and research consistently revealed that Hispanics are, on average, higher in terms of familism than Anglos (Sabogal et al., 1987). Given the differences in familism, we contend that HEs who are high in terms of familism values should be more likely to use financial planning than those who are low in these values. The primary reason for this is that those who emphasize familism are likely to view financial planning as a means of providing support and care for their families. This argument is consistent with the life course perspective which contends that work and family spheres are linked, and those who stress familism will make decisions based, not only on business needs, but family needs as well (e.g., Bengtson & Allen; 1993; Szinovacz, DeViney, & Davey, 2001).

Indirect support for this argument comes from the research of Sussan et al. (2016) that found that HEs’ knowledge of finance, familism values, and perceived control predicted their financial decision making. Despite these findings, it merits noting that these researchers did not measure familism directly, but inferred it from ethnicity. Researchers have argued that there within

52 Journal of Business & Entrepreneurship Spring 2017

group differences in cultural values, and one cannot make accurate inferences about values based on ethnicity (Betancourt & Lopez, 1993). Researchers should measure these values directly rather than rely on ethnicity as an indirect measure.

Despite the importance of financial planning, we know of no research that has directly examined the relations between HEs’ familism cultural values, beliefs about financial planning, knowledge of financial planning, and its use. Therefore, the primary purposes of the present study were to (a) present a model of the factors that may be related to HEs’ use of financial planning, (b) relay the results of a study that examined these factors, and (c) consider the implications of the study’s results for future research and practice. It merits emphasis that in this study the term Hispanic is used to refer to individuals who are indigenous to the Americas, but trace their ancestors to Spain or Latin America (Marin & Marin, 1991).

THEORETICAL BACKGROUND FOR THE MODEL

Model of Factors Related To Hispanic Entrepreneurs’ Use of Financial Planning

Using a modified version of the Fishbein and Ajzen (1975) Theory of Reasoned Action, and Ajzen’s (1991) Theory of Planned Behavior, we developed a model of the factors thought to be related to HEs and AEs’ use of financial planning. A depiction of the model is noted in Figure 1.

Spring 2017 Journal of Business & Entrepreneurship 53

Figure 1: Model of Factors Related To Entrepreneurs’ Use of Financial Planning

Quite simply, Fishbein and Ajzen’s model suggests that individuals’ values and beliefs about the consequences of a behavior influences behavioral intentions, and actual behaviors.. Thus, our model predicts that the entrepreneurs’ ethnicity, familism cultural values, beliefs about the benefits of financial planning, beliefs about the ability to conduct financial planning (e.g., self-efficacy beliefs), knowledge about financial planning, and revenue should be related to their actual use of financial planning.

Entrepreneurs’ Ethnicity

First, the model predicts that there should be differences in HEs’ and AEs’ use of financial planning. One reason for this is that survey research has consistently revealed that Hispanics (a) had lower levels of financial literacy (Ariel/Hewitt, 2009), and (b) were less likely to use personal financial planning than Anglos (Lusardi & Mitchell, 2011). Surprisingly, this research did not assess the extent to which HEs were less likely to use financial planning for their

54 Journal of Business & Entrepreneurship Spring 2017

businesses than AEs. Therefore, one purpose of the study was to test following hypothesis.

H1: Hispanic entrepreneurs will be less likely to use financial planning than Anglo entrepreneurs.

Familism Cultural Values

Considerable research has focused on the personality of entrepreneurs (Baron & Henry, 2011; Rauch & Frese, 2007), but much less research has considered entrepreneurial values (Morris, Schindehutte, & Lesser, 2002). As a result, our model includes values as a predictor of entrepreneurial beliefs and behaviors. In particular, the model argues that familism cultural values should be related to HEs’ use of financial planning, but should have little or no relation to AEs’ use of it. One reason for this is that research has consistently revealed that Hispanics, on average, are more likely to value familism than Anglos (Marin & Marin, 1991; Stein et al., 2011). Therefore, we argue that familism values should affect HEs’ motivation to care for their families, and those who high levels of these values should be more likely to use financial planning to support their families than those who are low in these values. Indirect support for these arguments comes from the research on the relation between family capital and entrepreneurial outcomes (e.g., Dyer, Nenque, & Hill, 2014; Sussan et al., 2016). This research found that family social support contributed to venture preparedness and financial decision making (Hoffman, Hoelscher, & Sorenson, 2006), and family capital provided unique advantages that were positively related to entrepreneurial outcomes (Danes, Stafford, Haynes, & Amarapurkar, 2009; Dyer et al., 2014). Further, these arguments are consistent with the life course perspective which contends that those who emphasize familism make decisions not only on business needs, but family needs as well (e.g., Bengtson & Allen; Elder, 1995; Szinovacz et al., 2001).

Although most research refers to familism as a single value (e.g., Marin & Marin, 1991) research showed that it is actually comprised of

Spring 2017 Journal of Business & Entrepreneurship 55

multiple dimensions (Sabogal et al., 1987 (e.g., attitudinal familism, family obligation or duty, and interconnectedness). In the present study, we focused on two aspects of familism (i.e., attitudinal familism and family duty) (Sabogal et al., 1987).

Attitudinal familism. Attitudinal familism refers to the feelings of support, honor, and subjugation of the self to the family (Sabogal et al., 1987). Our model argues that attitudinal familism should be positively related to HEs’ use of financial planning. The primary reason for this is that attitudinal familism consists of values that one should support and honor families, and these values are more central to the Hispanic culture than the Anglo culture (Stein et al., 2014). As a result, we predict that HEs who have high levels of attitudinal familism will use financial planning, not only for business needs, but for their families’ needs too (e.g., Bengtson & Allen, 1993). However, Anglo culture typically stresses that work should be the priority in a person's life, and people should be willing to sacrifice their family life in the interests of achievement (Lobel & Kossek, 1996).Thus, we predict that attitudinal familism will moderate the relation between ethnicity and use of financial planning such that HEs who have high levels of attitudinal familism will be more likely to use financial planning than those who are low in terms of these values. However, attitudinal familism should have little or no relation to the AEs’ use of financial planning. Indirect support for these argument comes from research that revealed that Hispanics’ work motivation was positively related to their values about taking care of their families, and their concern for family security was the most important motive for developing a new business (Kuratke, Horsnsby, & Naffziger, 1997). In addition, survey research also found that HEs put a great deal of pressure on themselves to succeed in order to maintain their families’ well-being (Mass Mutual, 2011), and family security was the most important predictor of personal financial planning (Blue Shore, 2017). Despite these arguments, we know of no research on that has directly examined the arguments noted above. Therefore, we tested the following hypothesis.

H2: Attitudinal familism will moderate the relation between entrepreneurs’ ethnicity and their use of financial planning such that

56 Journal of Business & Entrepreneurship Spring 2017

Hispanic entrepreneurs who have high levels of attitudinal familism will be more likely to use financial planning, and those with low levels of these values will be less likely to use financial planning than their counterparts.

Family Duty Values. Family duty is another dimension of familism values that should moderate the relations between entrepreneurs’ ethnicity and their use of financial planning. Family duty values refer to the strong endorsement of family obligations, and values that emphasize support, respect, and duty to one’s family (Suarez-Orozco & Suarez-Orozco, 1995). Thus, we believe that HEs who place a great deal of emphasis on family duty values will be more likely to engage in financial planning in order to fulfill their duty to their families than those who have lower levels of these values. Given that Anglos typically stress work over family, we also contend that family duty values should have little or no relation to AEs’ use of financial planning.

In support of these arguments, research indicated that family duty is more likely to be valued in the Hispanic culture than in the Anglo culture (Stein et al., 2011). A sense of duty or obligation to one’s family is seen as imperative in many Hispanic families, and members are expected to consider their families when making decisions (e.g., Telzer, Gonzales & Fuligni, 2001). Similarly, research revealed that obligations to one’s family were positively related to the decision to remain working long after anticipated retirement dates (Szinovacz et al., 2001). This latter finding is consistent with the life course perspective that emphasizes that work and family spheres are interdependent (Bengtson & Allen, 1993; Elder, 1995; Moen, 1996). Further, research indicated that family social capital was positively related to the survival of new ventures (e.g.,Hoffman et al., 2006), and family capital helps entrepreneurs sustain their businesses (Danes et al., 2009; Hoffman et al., 2006). Family social capital refers to the issues associated with unique family relationships and communication channels among family members. It also includes family obligations, expectations, identity and a moral infrastructure (Hoffman et al., 2006).

In view of the arguments noted above, we believe that family duty values will moderate the relation between entrepreneurs’ ethnicity and their use of financial planning. In particular, HEs’ who place a great deal of emphasis family

Spring 2017 Journal of Business & Entrepreneurship 57

duty values will be more likely to use financial planning than those who place less emphasis on these values. However, family duty values will have little or no relation to AEs’ use of financial planning. We know of no research that has assessed these predictions so we tested the hypothesis below.

H3: Family duty values will moderate the relation between entrepreneurs’ ethnicity and their use of financial planning such that HEs’ who have high levels of family duty values will be more likely to use financial planning than those with low levels of these values.

Beliefs about Financial Planning

Our model argues that another factor that may be related to entrepreneurs’ use of financial planning is their beliefs about it. For instance, we contend that two beliefs should be related to their use of financial planning.

Beliefs about the benefits of financial planning. Our model argues that entrepreneurs’ beliefs about the benefits of financial planning should be related to their use of financial planning (see Figure 1). In particular, beliefs about the benefits of financial planning refer to the perceived consequences of conducting financial planning. For example, the more that entrepreneurs believe that conducting financial planning will result in positive consequences (e.g., financial security, expanding their businesses), the greater should be their motivation to gain knowledge about it and use it. In contrast, the more that they perceive that financial planning will result in negative consequences (e.g., divert attention away from business) the less they should be motivated to gain knowledge about it and use it.

In addition, our model predicts that entrepreneurs’ ethnicity should be related to their beliefs about the benefits of financial planning. The primary reason for this is that research suggested that HEs’ did not view financial planning as particularly beneficial (Mass Mutual, 2009). Although the study by

58 Journal of Business & Entrepreneurship Spring 2017

Mass Mutual (2011) surveyed HE’s beliefs about the benefits of planning, it did not compare these beliefs to those of AEs. Despite these arguments, we know of no research that has examined these predictions so we tested the following hypotheses:

H4: Hispanic entrepreneurs will be less likely to believe that financial planning is beneficial than Anglo entrepreneurs.

H5: Entrepreneurs’ beliefs that financial planning is beneficial will be positively related to their knowledge of financial planning.

Beliefs about the ability to conduct financial planning. Our model also hypothesizes that entrepreneurs’ beliefs about their ability to conduct financial planning will be positively related to their knowledge of financial planning. This prediction is consistent with social learning theory (Bandura, 1997) that argues that an individual’s capacity to perform a task or self-efficacy is positively related to their motivation and task performance (Gist, 1987). Indirect support for this argument also comes from research that revealed that entrepreneurs’ generalized self-efficacy was positively related to their overall business success (Rauch & Frese, 2007).

In addition, our model suggests that there will be ethnic differences in entrepreneurs’ beliefs about their ability to conduct financial planning. In particular, we argue that HEs should be less likely to believe that they have the ability to conduct financial planning than AEs. The primary reason for this is that research has repeatedly found that Hispanics have less knowledge about finance than Anglos (Ariel/Hewitt, 2012). In spite of these findings, research did not assess the difference between HEs’ and AEs’ in terms of ability beliefs. Thus, we know of no research that has examined the arguments noted above, and tested the following hypotheses.

H6: Hispanic entrepreneurs will be less likely to believe that they have the ability to conduct financial planning than Anglo entrepreneurs.

Spring 2017 Journal of Business & Entrepreneurship 59

H7: Entrepreneurs’ beliefs that they have the ability to conduct financial planning will be positively related to their knowledge of financial planning.

Relation Between Knowledge of Financial Planning and its Use

Consistent with the arguments just noted, our model also maintains that entrepreneurs’ knowledge of financial planning should be positively related to their use of financial planning. One reason for this is that those who have knowledge of financial planning should be more motivated to engage in it than those who do not have this knowledge. In support of this argument, research found that knowledge of finance predicted investment behaviors and financial decision making (Sussan et al., 2016).

Further, our model proposes that there will be ethnic differences in knowledge of financial planning, and these differences will be related to the use of planning. Based on the arguments and research noted above, we believe HEs should have lower levels of financial knowledge than AEs. Although there is some research on Hispanics’ knowledge of financial planning, research has not compared the knowledge of HEs and AEs. Therefore, we examined the following hypotheses:

H8: Hispanic entrepreneurs will have lower levels of financial knowledge than Anglo entrepreneurs.

H9: Entrepreneurs’ knowledge of financial planning will be positively related to their use of financial planning.

Relation Between Revenue and Use of Financial Planning

Finally, our model predicts that entrepreneurs’ level of business revenue will be positively related to their use of financial planning. Quite simply, we believe that the greater amount of revenue available, the more that entrepreneurs will perceive that they have control over their finances and the implementation of financial plans. Thus, they should they should be more motivated to engage in

60 Journal of Business & Entrepreneurship Spring 2017

financial planning when they have adequate levels of revenue than when they do not. This argument is consistent with Azjen’s extension of the Fishbein and Ajzen (1975) model which maintained that individuals must have control over a situation in order to engage in a behavior. In support of this argument, research by Schwenk and Shrader (1993) showed that levels of sales or revenue were positively related to business planning.

Our model also suggests that there will be ethnic differences in revenue levels, and HEs will have lower levels of revenue than AEs. Thus, one reason that HEs may be less likely to use financial planning is that they have lower revenue levels than AEs. Research revealed that Hispanics were less likely to engage in retirement planning because they fewer monetary resources to invest in retirement than Anglos (Lusardi & Mitchell, 2011). Although research has examined the relations between revenue and business planning and ethnic differences in retirement planning, we believe that these studies should be replicated in terms of financial planning. Thus, we tested the following hypothesis:

H10: Hispanic entrepreneurs’ will have lower levels of business revenue than Anglo entrepreneurs.

H11: Entrepreneurs’ level of business revenue will be positively related to their use of financial planning.

METHOD

Sample and Data Sources

Data were collected from 299 entrepreneurs in large Southwestern (213) and Northeastern (86) urban areas. One hundred and fifty HEs and 149 AEs participated in the study. Data were collected using two basic methods (210 completed paper and pencil questionnaires and 76 responded to an online

Spring 2017 Journal of Business & Entrepreneurship 61

survey). Additional details about questionnaire development, and data collection strategies can be obtained from the authors.

Participants

The overall sample consisted of 299 respondents (99 women, 181 men, 19 undeclared, 150 Hispanics and 149 Anglos). Their average age was 45.59 years, and 36 % had no college degree, 31.1% had an undergraduate degree, and 25.1% had a graduate degree (7.3% undeclared). Their average years in business were 10.95 years and they reported average revenue between $50,000 and $99,999.

Measures

Data for the present study were collected using six questionnaires plus a demographic and a business background questionnaire.

Beliefs about the benefits of financial planning. Three items were used to measure respondents’ beliefs about the benefits of financial planning. A sample item is “I know that developing a financial plan will increase my success.” It used a 7 point Likert-type scale with a strongly disagree to strongly agree response format. Items were summed to create scores for this construct. Higher scores reflected greater beliefs about the benefits. The coefficient alpha reliability estimate for this measure was .55.

Beliefs about the ability to conduct financial planning. Three items were used to assess participants’ beliefs about their ability to conduct financial planning. A sample item is “I feel confident that I can conduct financial planning.” It used a 7 point Likert-type scale with a strongly disagree to strongly

62 Journal of Business & Entrepreneurship Spring 2017

agree response format. Items were summed to create a score for beliefs about the ability to conduct financial planning. Higher scores indicated greater beliefs about the ability to conduct financial planning. The coefficient alpha reliability estimate for this measure was .55.

Knowledge of financial planning. A nine-item questionnaire was used to assess participants' knowledge of financial planning for their businesses. It used a 5 point Likert-type scale with a strongly disagree to strongly agree response format. Items were summed to form the overall measure. There were two parts to this measure. In Part 1, participants were asked "How knowledgeable are you about the following financial services for your business?" Specifically, three financial services included succession planning, business asset protection, and liability protection.

In Part 2, respondents were asked "How knowledgeable are you about the following three financial services for your employees?" Financial services included employee health insurance, retirement plans, and employee financial planning. Items were summed to create a score for knowledge of financial planning. Higher scores reflected greater knowledge of financial planning. The coefficient alpha reliability estimate for this scale was .94.

Use of financial planning services. An eight-item measure was used to assess participants' use of financial services for their businesses. Respondents were asked to select the financial planning services they used for their businesses. Services presented mirrored those presented in Questionnaire 3. Services used were coded as 1 and those not used were coded as 0. Items were summed to create an overall measure of use of financial planning. Higher scores reflected greater use of financial planning. The coefficient alpha estimate for this measure was .81.

Attitudinal familism. Three items were used to measure attitudinal familism values. A sample item was "My family is more important to me than anything else.” It used a 7 point Likert-type scale with a strongly disagree to

Spring 2017 Journal of Business & Entrepreneurship 63

strongly agree response format. Items were summed to form the overall measure. Higher scores reflected higher levels of attitudinal familism. The coefficient alpha reliability estimate for attitudinal familism was .61.

Family duty values. Four items were used to measure family duty values. A sample item is “I expect to care for my parents financially when they are older.” It used a 7 point Likert-type scale with a strongly disagree to strongly agree response format. Items were summed to form the overall measure. Higher scores reflected higher levels of family duty values. The coefficient alpha reliability estimate for family duty values was .50.

Business background and Demographic Questionnaires. Two questionnaires were used to assess participants’ business and demographic backgrounds.

Analyses

HEs were coded 1 and AEs were coded 2. All variables were standardized prior to conducting the analyses. Several demographic variables were included in the analyses (e.g., sex, educational level, and years in business) because these variables may be related to the use of financial planning. Our model did not imply causality so we gathered non-experimental data. As a result, there was no sound basis for testing a causal model using strategies like structural equation modeling (Stone-Romero & Rosopa, 2010). As a result, we used multiple regression analysis to test hypotheses regarding use of financial planning and entered all predictor variables simultaneously. We also used correlation analysis to test some specific hypotheses.

64 Journal of Business & Entrepreneurship Spring 2017

RESULTS

Table 1 presents descriptive statistics for the study’s variables, and Table 2 relays the results of correlational and reliability analyses for all variables. Further, Table 3 reports the results of the overall regression analyses for use of financial planning.

Table 1: Descriptive Statistics

Spring 2017 Journal of Business & Entrepreneurship 65

Table 2:

Correlations and Reliability Estimates

* p <.05 (1-tailed). ** p <.01 (1-tailed). α Reliability estimates (Cronbach’s alpha) for each measure

66 Journal of Business & Entrepreneurship Spring 2017

Table 3 Regression Results: Dependent Variable Use of Financial Planning (Hispanics & Anglos)

R2 = .46, F(12, 216) = 14.63, p = .00. * p < .05. ** p < .01

Overall Test of the Model

As can be seen in Table 3, the results of the overall regression equation was statistically significant [R² = .46, F (12, 216) = 14.63, p = .00], and revealed that there was considerable support for a number of predictions in the model.

Spring 2017 Journal of Business & Entrepreneurship 67

Hypothesis 1. Hypothesis 1 predicted that HEs would be less likely to use financial planning than AEs. The results of the regression analysis (Hispanic SBOs coded 1, Anglo SBOs coded 2) revealed that there was support for this hypothesis (β = .77, t = 2.20, p < .05). Means levels of use of financial planning were 2.08 for HEs, and 3.37 for AEs (see Table 1).

Hypothesis 2. Hypothesis 2 maintained that attitudinal familism values would moderate the relation between entrepreneurs’ ethnicity and their use of financial planning such that HEs with high levels of attitudinal familism would be more likely to use financial planning than those with low levels of attitudinal familism. In addition, we predicted that attitudinal familism would have little or no relation to AEs’ use of financial planning. The results supported the first part of this prediction, and the interaction of ethnicity and attitudinal familism was statistically significant (β =.-1.02, t = -2.57, p <.01). In order to understand this moderating relation we plotted the results in Figure 2. As can be seen in Figure 2, the plot of the results revealed that when HEs had high levels of attitudinal familism values they were more likely to use financial planning than when they had low levels of familism values. However, the plot also indicated that AEs’ attitudinal familism values was negatively related to their use of financial planning.

68 Journal of Business & Entrepreneurship Spring 2017

Figure 2: Attitudinal Familism Moderates The Relation Between Ethnicity And Use Of Financial Planning

Spring 2017 Journal of Business & Entrepreneurship 69

Figure 3: Family Duties Moderates the Relation Between Ethnicity and Use of Financial Planning

Hypothesis 3. This hypothesis predicted that family duty values would moderate the relation between entrepreneurs’ ethnicity and their use of financial planning such that HEs who have high levels of family duty values would be more likely to use financial planning than those with low levels of these values. However, family duty values would have no relation to AEs use of financial planning. As can be seen in Table 3, the regression coefficient representing the interaction of ethnicity and family duty values was statistically significant (β = .44, t=1.90, p < .05). In order to understand this interaction we plotted the data in Figure 3. The plot of the data revealed that family duty values moderated the relations between ethnicity and use of financial planning for both AEs and HEs. Specifically, the data indicated that when AEs and HEs had high levels of family duty values they were more likely to use financial planning than when they had low levels of family duty values. However, the results showed that when AEs

70 Journal of Business & Entrepreneurship Spring 2017

and HEs had high levels of family duty values, AEs were more likely to use financial planning than HEs.

Hypothesis 4. This hypothesis argued that HEs would be less likely to believe that financial planning was beneficial than AEs. Results of correlational analysis (see Table 2) indicated that there was no support for this hypothesis (r = .07, p > .05). Mean levels of beliefs about the benefits of planning were 17.87 for HEs and 18.31 for AEs.

Hypothesis 5. This prediction maintained that entrepreneurs’ beliefs about the benefits of financial planning would be positively related to their knowledge of financial planning. Results of correlational analyses (see Table 2) found that there was support for this prediction (r = .12, p < .05).

Hypothesis 6. This hypothesis claimed that HEs would be less likely to believe that they had the ability to conduct financial planning than AEs. Correlational analyses revealed that there was support for this hypothesis (r = .14, p < .05). Mean levels of beliefs about the ability to conduct planning were 13.18 for HEs and 14.25 AEs.

Hypothesis 7. This hypothesis asserted that entrepreneurs’ beliefs that they have the ability to conduct financial planning would be positively related to their knowledge of financial planning. The data from the present study provided support for this hypothesis. Correlational analyses indicated that there was support for this hypothesis. The correlation between entrepreneurs’ beliefs about their ability to conduct planning and their knowledge of planning was positive and statistically significant (r = .53, p < .01).

Hypothesis 8. This hypothesis stated that HEs would have lower levels of knowledge of financial planning than AEs. Results of correlational analyses

Spring 2017 Journal of Business & Entrepreneurship 71

specified that there was support for this prediction (r = .19, p < .01). The mean levels of knowledge of financial planning were 24.55 for HEs and 28.30 for AEs.

Hypothesis 9. This prediction forecasted that entrepreneurs’ knowledge of financial planning would be positively related to their use of financial planning. The results of regression analysis (see Table 2) found support for this hypothesis (β = .39, t = 5.71, p < .01).

Hypothesis 10. This hypothesis maintained that HEs would have lower levels of revenue than AEs. Correlational analyses found support for this hypothesis (r = .11, p < .05).

Hypothesis 11. This prediction argued that entrepreneurs’ level of revenue would be positively related to their use of financial planning. As can be seen in Table 3, the regression analysis revealed that the regression coefficient representing revenue was positive and statistically significant (β = .28, t = 2.65, p < .01).

Supplemental analyses. Although no hypotheses were presented, we examined the relations between several demographic variables (e.g., sex, educational level, years in business) and entrepreneurs’ use of financial planning. As can be seen in Table 3, the results of regression analysis indicated that there were no relations between sex (β = -.08, t = -1.42, p > .05), educational level (β =.07, t = 1.35, p > .05), or years in business (β = -.01, t = -.24, p > .05).

DISCUSSION

The results of the present study provided considerable support for a number of the relations in our model. For instance, the findings revealed that

72 Journal of Business & Entrepreneurship Spring 2017

there were ethnic differences in the use of financial planning such that Hispanic entrepreneurs were less likely to use financial planning than Anglo entrepreneurs. In addition, attitudinal familism and family duty values moderated the relations between ethnicity and use of financial planning. Further, knowledge of financial planning and revenue were positively related to the use of financial planning.

In addition, results indicated that Hispanic entrepreneurs were less likely to believe that they had the ability to conduct planning than Anglo entrepreneurs, and these beliefs were positively related to knowledge of financial planning. However, our findings revealed that there were no differences in Hispanic and Anglo entrepreneurs’ beliefs about the benefits of planning, but the data showed that these beliefs were positively related to the knowledge of financial planning. Taken together, we believe that these findings have important implications for future theory, research, and practice, and these will be considered below.

First, our findings revealed that Hispanic entrepreneurs were less likely to use financial planning than AEs, and we believe that the lack of planning may be one reason that ethnic entrepreneurs are more likely to fail than their Anglo counterparts. Second, our study found that entrepreneurs’ use of financial planning may depend on their familism values. Interestingly, our data indicated that Hispanic entrepreneurs considered their care and concern for families when making decisions about financial planning. However, both Hispanic entrepreneurs and Anglo entrepreneurs considered their obligations to families when determining whether to use financial planning. These findings support the life course view of interdependence between work and family which predicts that individuals consider, not only their business and individual needs, but the needs of their families when making important business decisions (Bengtson & Allen, 1993). Until recently, researchers argued that familism was primarily a Hispanic value, but our results found that family duty was valued by both HEs and AEs.

Second, our results revealed that knowledge of financial planning and revenue levels were positively related to entrepreneurs’ use of financial planning. In addition, our data were consistent with previous research that found that Hispanics were less likely to believe that they had the ability to conduct planning, and were less knowledgeable about financial planning than Anglos (Ariel/Hewitt 2012, Lusardi & Mitchell, 2011). Further, the findings showed that there were no differences in Hispanic entrepreneurs and Anglo entrepreneurs’

Spring 2017 Journal of Business & Entrepreneurship 73

beliefs about the benefits of planning, but beliefs about benefits and perceptions about the ability to conduct planning were positively related to knowledge of financial planning. As a result, these perceptions may be related to entrepreneurs’ knowledge, but not their actual use of financial planning.

Further, our results were consistent with previous research that found that Hispanic entrepreneurs had lower levels of revenue than Anglo entrepreneurs (Lusardi & Mitchell, 2011). In view of the fact that revenue levels were positively related to the use of financial planning, our data suggested that another reason that Hispanic entrepreneurs may be less likely to use planning is that they do not have the revenue levels needed to implement their plans.

IMPLICATIONS FOR FUTURE THEORY AND RESEARCH

Our findings suggested that models of entrepreneurial success should consider the extent to which a different set of factors may influence the effectiveness of Hispanic entrepreneurs and Anglo entrepreneurs. For instance, even though previous models of entrepreneurship (e.g., Baron & Henry, 2001; Gaskill et al., 1993) argued that individual characteristics (e.g., entrepreneurial behavior, and environmental characteristics affect entrepreneurial success), we believe that cultural values may also be related to entrepreneurial behaviors. For instance, our data indicated that different dimensions of familism moderated the relations between entrepreneurs’ ethnicity and the use of financial planning. In particular, HEs considered their care and concern for families, but both Hispanic and Anglo entrepreneurs felt that family obligations were important determinants of financial planning. Therefore, models of entrepreneurship might be expanded to include the influence of work-family interdependence on entrepreneurial decisions and behaviors.

In support of these arguments recent research in entrepreneurship considered the relations between family capital and entrepreneurial success (e.g., Danes et al., 2009; Hoffman et al., 2006), but little research examined the relations between familism values and use of financial planning (e.g., Sussan et al., 2016). Therefore, we believe that models of entrepreneurship should include entrepreneurs’ familism and other cultural values (e.g., collectivism, uncertainty

74 Journal of Business & Entrepreneurship Spring 2017

avoidance) as predictors of entrepreneurial decisions and behaviors. For instance, research found that Hispanics are often higher in uncertainty avoidance and view debt or mortgages as a threat to their families more than Anglos (Korzenny & Korzenny, 2005). Thus, Hispanic cultural values regarding uncertainty avoidance and debt may place limits on their acquisition of resources. Our results also indicated that knowledge of financial planning was related to the use of financial planning. In addition, the findings revealed that entrepreneurs’ beliefs about the benefits of planning, and perceptions about the ability to conduct financial planning were related to their knowledge of financial planning. However, the data indicated that there were no differences in Hispanic entrepreneurs and Anglo entrepreneurs’ beliefs about the benefits of planning, but Hispanic entrepreneurs were less likely to perceive that they had the ability to conduct planning than Anglo entrepreneurs. Although Hispanic entrepreneurs may perceive that they have lower financial planning ability levels than Anglo entrepreneurs, previous research revealed that Hispanics often have some unique abilities that may affect their effectiveness as entrepreneurs (Peterson, 1995). For example, Hispanic entrepreneurs’ social networks and ethnic enclaves often give them access to distinct customer bases, and knowledge of international markets that helps them identify new business opportunities (e.g., opportunities in international trade) (Peterson, 1995). Similarly, many Hispanics grow up in family businesses which provides them with role models, and knowledge that enhances their business-related skills (e.g., Peterson, 1995). Thus, we believe that models of entrepreneurship should be expanded to consider the unique skills and abilities of ethnic entrepreneurs and social networks that might influence their success rates.We believe our results also offer several important suggestions for practice.

Implications for Practice

Apart from the implications for future theory and research, we believe that our findings have a number of important practical implications for enhancing the success rates of entrepreneurs. First, our data revealed that Hispanic entrepreneurs’ care, concern, and duty to their families may influence their use of

Spring 2017 Journal of Business & Entrepreneurship 75

financial planning. However, not all Hispanic entrepreneurs may be aware that business financial planning will help them provide security for their families. Thus, training and mentoring programs might relay the reasons that financial planning is beneficial for both businesses and families.

In addition, our results suggested that Hispanic entrepreneurs were less likely to believe that they had the ability to conduct planning, and have knowledge of financial planning than Anglo entrepreneurs. Given that knowledge of planning is positively related to the use of financial planning, we believe that several interventions are needed to enhance Hispanic entrepreneurs’ knowledge and perceived ability to conduct planning. For example, online or face to face training could be provided by small business development programs or local communities to increase Hispanic entrepreneurs planning self-efficacy, and knowledge of financial planning. In addition, successful role models who have similar backgrounds (e.g., successful or experienced Hispanic entrepreneurs) could be used to motivate them to learn about and use financial planning. Support for this intervention comes from research that found that Hispanic entrepreneurs were more likely to trust advisors who are members of a Hispanic enclave or their family than Anglo entrepreneurs (Peterson, 1995).

Limitations of the Study

Although we believe that our findings are interesting, there are a number of potential limitations associated with the present study. First, the measures used to assess beliefs about the benefits of financial planning, beliefs about the ability to conduct financial planning, attitudinal familism, and family duty values had fairly low reliability levels. Thus, the low reliability levels of these measures may have placed limits on the ability to detect several relations in the model. In spite of these low reliability levels, results of correlational analyses detected relations between these variables and knowledge of financial planning. As a result, the low reliability levels may not have affected some of the study’s results.

Interestingly, although the reliability levels associated with measures of attitudinal familism and family duty values were low, the results of the study still

76 Journal of Business & Entrepreneurship Spring 2017

found that these variables moderated the relations between ethnicity and use of financial planning. However, research is needed to increase the reliability estimates for all of these measures so that they can be used in future research.

Another potential limitation of this study is that the sample may not be representative of all entrepreneurs in the U. S. or other nations. We collected our data from actual entrepreneurs in the Southwestern and Northeastern U.S, but the study needs to be replicated with entrepreneurs in different areas of the country (e.g., Southeast, Northwest, Midwest), and with different ethnic groups (e.g., African-American, Native American).

CONCLUSION

The success of entrepreneurs and small businesses has a major impact on the economy of our nation and other countries, and is often the source of new job opportunities in these societies. Furthermore, in the U. S. Hispanics are starting small businesses at a very rapid rate, but they often have higher failure rates than Anglo entrepreneurs (Sullivan, 2007). Thus, our study developed and tested a model of the factors affecting Hispanic and Anglo entrepreneurs’ use of financial planning because financial planning is a key factor thought to be a key determinant of entrepreneurial success rates. Our results provided support for a number of relations in the model. We hope that the findings of our study will be used to foster additional research on ethnic entrepreneurs, and identify interventions that can be used to enhance the success rates of all entrepreneurs.

REFERENCES

Abbey, A. (2002). Cross-cultural comparison of the motivation for entrepreneurship. Journal of Business and Entrepreneurship, 14(1), 69-82.

Ajzen, I. (1991). Theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.

Spring 2017 Journal of Business & Entrepreneurship 77

Aldrich, H. E., & Waldinger, R. (1990). Ethnicity and entrepreneurship. Annual review of sociology, 111-135.

Ariel/Hewitt (2009). 401K plans in living color. Retrieved July 10, 2015 from https://www.arielinvestments.com/images/stories/PDF/ariel-aonhewitt-2012.pdf

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

Baron, R.A. and Henry, R.A. (2011). Entrepreneurship: the genesis of organizations. In S. Zedeck (Ed.), Handbook of Industrial and Organizational Psychology (Vol 1, pp 241-273). Washington, DC: APA.

Bates, T., Jackson, W. E., & Johnson, J. H. (2007). Introduction: Advancing research on minority entrepreneurship. The Annals of the American Academy of Political and Social Science, 613, 10-17.

Bengtson, V. L., & Allen, K. R. (1993). The life course perspective applied to families over time. In P. Boss, W. Doherty, R. LaRossa, W. Schumm, & S. Steinmetz (Eds.), Sourcebook of family theories and methods: A contextual approach (pp. 469–498). New York: Plenum Press.

Betancourt, H., & Lopez, S. R. (1993). The study of culture, ethnicity, and race in American psychology, American Psychologist, 48, 629-637.

Blue Shore (2017). Ten reasons why financial planning is important. Retrieved October 21, 2016 from https://www.blueshorefinancial.com/ToolsAdvice/Articles/FinancialPlanning/Te nReasonsWhyFinancialPlanningIsImporta201nt/

Chaganti, R., & Green, P. G. (2002). Who are ethnic entrepreneurs? A study of entrepreneurs’ ethnic involvement and business characteristics. Journal of Small Business Management, 40, 126-143.

Clark, K., & Drinkwater, S. (2010). Recent trends in minority ethnic entrepreneurship in Britain. International Small Business Journal, 28(2), 136- 146.

Danes, S. M., Stafford, K., Haynes, G., & Amarapurkar, S. S. (2009). Family capital of family firms: Bridging human, social, and financial capital. Family

78 Journal of Business & Entrepreneurship Spring 2017

Business Review, 22, 195-215. Dyer, W. G., Nenque, E., & Hill, E. J. (2014). Toward a Theory of Family Capital and Entrepreneurship: Antecedents and Outcomes. Journal of Small Business Management, 52(2), 266-285.

Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research. Reading, MA: Addison-Wesley Publishing Company.

Gaskill, L. R., Van Auken, H. E., & Manning, R. A. (1993). A factor analytic study of the perceived causes of small business failure. Journal of Small Business Management, 31(4), 18-31.

Gist, M. E. (1987). Self-efficacy: Implications for organizational behavior and human resource management. Academy of Management Review, 12, 472-485.

Hoffman, J., Hoelscher, M., & Sorenson, R. (2006). Achieving sustained competitive advantage: A family capital theory. Family business review, 19(2), 135-145.

Korzenny, F., & Korzenny, B. A. (2005). Hispanic marketing: A cultural perspective. New York: Routledge.

Lobel, S. A., & Kossek, E. E. (1996). Human resources strategies to support diversity in work and personal lifestyles: Beyond the “family friendly” organization. In E. E. Kossek & S. Lobel (Eds.), Managing diversity: Human resource strategies for transforming the workplace (pp. 221-244). Cambridge, MA: Blackwell.

Lusardi, A., & Mitchell, O. S. (2011). Financial literacy and retirement planning in the United States. Journal of pension economics and finance, 10(04), 509-525.

Marin, G., & Marin, B. V. (1991). Research with Hispanic populations. Newbury Park, CA: Sage.

Spring 2017 Journal of Business & Entrepreneurship 79

Mass Mutual (2011). Family and community are the greatest motivations for Hispanics in pursuing the American dream of entrepreneurship. Retrieved January 20, 2017 from http://www.prnewswire.com/news-releases/family- and-community-are-the-greatest-motivations-for-hispanics-in-pursuing- the-american-dream-of-entrepreneurship-127297253.html

Morris, M., Schindehutte, M., & Lesser, J. (2002). Ethnic entrepreneurship: Do values matter?. New England Journal of Entrepreneurship, 5(2), 35-47.

Peterson, M.F. (1995). Leading Cuban-American entrepreneurs: the process of developing motives, abilities, and resources, Human Relations, 48 (10), 1193- 1215.

Rauch , A., & Frese, M., (2007) Born to be an entrepreneur? Revisiting the personality approach to entrepreneurship. In J. R. Baum, M. Frese, & R. A. Baron (Eds.), The psychology of entrepreneurship (pp. 41-62). Mahwah, NJ: Lawrence Erlbaum Associates.

Robinson, R. B., & Pearce, J. A. (1984). Research thrusts in small firm strategic planning. Academy of Management Review, 9, 128-137. within the family business context. Cooperative Regional Research Project.

Sabogal, F., Marín, G., Otero-Sabogal, R., Marín, B. V., & Perez-Stable, E. J. (1987). Hispanic familism and acculturation: What changes and what doesn't?. Hispanic Journal of Behavioral Sciences, 9(4), 397-412.

Schwenk, C. R., & Shrader, C. B. (1993). Effects of formal strategic planning on financial performance in small firms: A meta-analysis. Entrepreneurship: Theory and Practice, 17(3), 53-65.

Serviere-Munoz, L., Hurt, K. J., & Miller, R. (2015). Revising the Entrepreneur Opportunity Fit Model: Adressing the Moderating Role of Cultural Fit and Prior Start-Up Experience. Journal of Business and Entrepreneurship, 27(1), 59-73.

Stein, G. L., Cupito, A. M., Mendez, J. L., Prandoni, J., Huq, N., & Westerberg, D. (2014). Familism through a developmental lens. Journal of Latina/o Psychology, 2(4), 224-250.

80 Journal of Business & Entrepreneurship Spring 2017

Stone-Romero, E. F., & Rosopa, P. (2010). Research design options for testing mediation models and their implications for facets of validity. Journal of Managerial Psychology, 25, 697-712.

Suárez-Orozco, M. M., & Suarez-Orozco, C. E. (1995). The cultural patterning of achievement motivation: A comparison of Mexican, Mexican immigrant, Mexican American, and non-Latino White American students. California’s immigrant children: Theory, research, and implications for educational policy, 161-190.

Sullivan, D. (2007). Minority entrepreneurs: More likely to try, but less likely to succeed? The Academy of Management Perspectives, 21(1), 78-79.

Sussan, F., Weisfeld, S, Rippe, C. B., Gould, S., & Yurova, Y. (2016). A subregional cultural perspective on Hispanic financial planning. Proceedings of the AMA Summer Educators’ Café, C23-C25.

Szinovacz, M. E., DeViney, S., & Davey, A. (2001). Influences of family obligations and relationships on retirement variations by gender, race, and marital status. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 56(1), S20-S27.

Telzer, E. H., Gonzales, N., & Fuligni, A. J. (2014). Family obligation values and family assistance behaviors: protective and risk factors for Mexican– American adolescents’ substance use. Journal of youth and adolescence, 43(2), 270-283.

U.S. Census Bureau. (2015). Hispanic origin. Retrieved August 22, 2015 from http://www.census.gov/topics/population/hispanic-origin/about.html

Wang, Q., & Li, W. (2007). Entrepreneurship, ethnicity and local contexts: Hispanic entrepreneurs in three U.S. southern metropolitan areas. GeoJournal, 68(2-3), 167-182

Williams, D. E., Ortiz-Walters, R., & Gavino, M. C. (2012). Entrepreneurial Education and Latina Business Owner Preferences: Do Gender and Race Matter?. Journal of Women's Entrepreneurship and Education, (1-2), 74-98.

Spring 2017 Journal of Business & Entrepreneurship 81

An Examination of Faculty Salaries between the Fields of Strategic Management and Entrepreneurship

Todd A. Finkle Manasi Katragadda Gonzaga University University of Massachusetts Amherst

ABSTRACT

Research in compensation for faculty at universities is an understudied area. This study will contribute to our understanding of the trends in salaries for faculty in the areas of strategic management and entrepreneurship. Based on 325 AACSB schools of higher education in the United States from 2004- 2015, the study examines three separate variables: sex, rank, and type of institution. The results of this study will be beneficial to both doctoral students, faculty, and administrators as they will get a better understanding of the trends that are occurring in the marketplace. A discussion of the findings and implications are examined.

Keywords: Faculty, Strategic Management, Entrepreneurship, Trends, Salaries, Higher Education

INTRODUCTION

The purpose of this article is to assist faculty and administrators within schools of higher education with the trends in faculty salaries in the fields of strategic management and entrepreneurship. By understanding these trends, potential students, faculty, and administrators can make better informed decisions about their future. Specifically, this article will examine the trends in faculty salaries over the past 10 years. Using data on salaries of strategic management and entrepreneurship faculty, this article will answer the following questions: (1) What are the differences in salaries between strategic management and entrepreneurship faculty by rank at AACSB schools in the United States (US)? (2) What are the differences in salaries between strategic management and entrepreneurship faculty by sex at AACSB schools in the US? And (3) What

82 Journal of Business & Entrepreneurship Spring 2017

are the differences in salaries between strategic management and entrepreneurship faculty by type of school (Public versus private) at AACSB schools in the US? The results of this study will be beneficial to both doctoral students, faculty, and administrators as they will get a better understanding of the dynamic changes that are occurring in the marketplace. We provide a discussion our findings towards the end of the article.

A BRIEF HISTORY OF THE FIELD OF STRATEGIC MANAGEMENT

The field of Strategic Management is about the creation of a strategic plan that allows organizations to compete more effectively in their environment. The strategic plan can be broken down into two stages; The strategic and implementation stage. The strategic stage involves the vision, values, objectives, current strategies, SWOT (Strengths, weaknesses, opportunities, and threats) analysis, industry analysis, problems, opportunities, and strategic recommendations. The second portion is the implementation stage which involves the actual implementation of the recommendations. Typically, there are a number of steps in the implementations stage some of which include the structure of the organization, culture, people, rewards, assignments, timeline, and financial and strategic metrics. The implementation stage is the most difficult and least understudied aspect of the strategic process. The earliest modern definition of strategy was given by game theorists, Von Nuemann and Morgenstein (1947), who defined strategy as the “series of actions by a firm that are decided according to a particular situation” (Bracker, 1980). The historical evolution of strategic management research has been shaped by the influences of three main streams: the institutionalists or field researchers in the 1960s and early 1970s, the economists in the late 1970s and 1980s, followed by the behaviorists in the late 1980s and 1990s. Theories from economics, sociology and psychology have been mainly used in strategic management research (Bowman, Singh, & Thomas, 2002). While the origin of academic research in strategic management is as early as the 1960s, the broad concept of strategic management has been seen in Chinese military thinking such as The Art of War by Sun Tzu (2016 Reprint), which was written around the 5th century BC. Machiavelli, Napoleon, and Homer were also early writers (Bracker, 1980; Thomas, Wilson, & Leeds, 2013).

Spring 2017 Journal of Business & Entrepreneurship 83

The field of strategic management originated from business policy as early as the 1960s (Bowman et. al., 2002). In 1972, Schendel and Hatten proposed the emergence of business policy, then considered only to be a course, into a much broader discipline called strategic management. Business policy was renamed strategic management in 1979 (Schendel & Hofer, 1979). The evolution of key ideas in strategic management research began with the firm as the main focus in the mid-1960s. It transitioned on to the environment and its relationship to the firm in the 1970s. This was followed by industries, markets, and firm scope in the 1980s and finally firm capabilities and core competencies in the 1990s (Bowman et. al., 2001). Two paradigms have been identified within strategic management that have guided research- the industry based view and the resource based view (Cox, Daspit, McLaughlin, & Jones, 2012). Early literature in strategic management research until the mid-1970s focused on ‘organizational fit’- ‘strategy as integrating organizational functions’, or ‘strategy as a fit between the organization and the environment’ or ‘strategy as a planning perspective in terms of ends-means’ (Bowman et. al., 2002). In the late 1970s, operations research, game theory and economics led to the development of modeling tools such as the SWOT analysis. Mintzberg, Raisinghani, and Theoret’s (1976) “Structure of unstructured decision processes”, is an example of the evolutionary view of the strategy process in the literature. Porter’s five forces model (Porter, 1980), was based on the “strategy as a fit between the organization and the environment” thinking. Porter’s work in the mid-1990s focused on understanding the role of the top management teams in the fit between the organization and the environment. Other scholars in the 1990s focused on the firm as a rent-seeking mechanism. Around the 1990s, the resource-based view gained prominence in the strategic management literature (Bowman et. al., 2002). Institutionalism of strategic management first came about with Porter’s Competitive Strategy (1980). As strategic management became to be established as an academic discipline by AACSB, the MBA programs and courses in strategic management offered today by business schools are one example of institutionalism. Professional societies such as the Strategic management society and presences of journals such as Strategic Management Journal, Strategic Entrepreneurship Journal, the Global Strategy Journal and the Journal of Business Strategies are other examples of the institutionalization of the field of Strategic Management.

84 Journal of Business & Entrepreneurship Spring 2017

Strategic management research has acknowledged the increasing importance of entrepreneurship literature. For example, the Strategic Management Society in 2007, established and added to the Strategic Management Journal, the Strategic Entrepreneurship Journal (SEJ) to advance entrepreneurship research (Aldrich, 2012). The importance of strategic management in entrepreneurship research makes it relevant for entrepreneurship researchers to study the history of strategic management as a guide for researchers that want to shape the future of entrepreneurship research. As of 2016, the Academy of Management reported that there were 4,865 members in Business Policy and Strategy Division and 3228 members in the entrepreneurship division (AOMonline.com, 2016).

A BRIEF HISTORY OF THE FIELD OF ENTREPRENEURSHIP

Entrepreneurship is a subtopic of strategic management, but has morphed into its own dynamic field. Entrepreneurship plays a key role in society. The ability to be creative and entrepreneurial is a differentiating aspect for individuals, organizations, and countries in today’s increasingly competitive environment. Entrepreneurship is especially vital in the strategic management process and the creation of strategic plans. Strategists or strategic planners are involved in creating solutions to problems or finding new ways to take advantage of opportunities. They need to be forward thinking. It is not uncommon in larger organizations to have a whole department involved in the strategic planning of an organization where these key creative entrepreneurial thinkers form the future vision for the organization. However, in smaller organizations everyone may be involved in using their creativity and innovation to come up with entrepreneurial solutions or opportunities. Entrepreneurship research initiated as far back as the 1930’s under Joseph Schumpeter at Harvard when he wrote two books called The Theory of Economic Development (1936) and Business Cycles (1939). In 1958, the Small Business Administration (SBA) started a three-year research program, which led to the publication of the “The Small Business Research Series” (Finkle & Deeds, 2001). Katz (2003) gave a detailed outline of the history of the field of entrepreneurship through 1999. Some of the highlights of the article included the following: Myles Mace from Harvard offered the first entrepreneurship course in 1947 called The Management of

Spring 2017 Journal of Business & Entrepreneurship 85

New Enterprises. In 1953, Peter Drucker offered entrepreneurship courses at New York University. In 1968, Babson College created the first undergraduate major in entrepreneurship. The first entrepreneurship major at the MBA level was created in 1972 at the University of Southern California. In 1963 the Journal of Small Business Management was founded as the first academic journal dedicated to the publication of research on small business and entrepreneurship. In 1975, the forerunner of Entrepreneurship Theory and Practice was founded and in 1985 the Journal of Business Venturing was founded at Wharton (Finkle & Deeds, 2001). The addition of Family Business Review (1988) and Small Business Economics (1989) From the late 1970’s through today we have seen an increase in the emergence of entrepreneurship with the introduction of Entrepreneur (1977), Inc. (1979), Fast Company (1995), and Success, which was relaunched in 2008. Today, a few magazines have sections dedicated to entrepreneurship including Forbes, Fortune, Bloomberg BusinessWeek, CNN Money, and Black Enterprise. The combination of the academic journals, magazines in the press and the growth of the field in terms of the number of entrepreneurship centers, tenure track positions and candidates (see Kuratko, 2005; Finkle, Kuratko, & Goldsby, 2006; Finkle, Menzies, Kuratko, & Goldsby, 2010; 2012; 2013; Finkle, 2010; 2013; 2015; 2016). During the 1990s faculty in the field of entrepreneurship had to fight for the legitimacy of the research and to get tenure. Today’s entrepreneurship faculty have an easier road due to their groundwork. In the 1980s and 1990s, publishing in entrepreneurship journals was a high risk for tenure track faculty. Today, there are over 40 academic journals devoted solely to entrepreneurship research and we have established ourselves as a legitimate field at most universities.

METHOD

The data for this study was based on an annual survey of 325 American Association for Colleges and Schools of Business (AACSB) schools within the United States from 2004-2015. Faculty salaries include the base contract only and does not include summer pay, stipends, or other benefits. A list of the schools used to evaluate salaries can be seen in Appendix 1.

86 Journal of Business & Entrepreneurship Spring 2017

RESULTS

To evaluate the trends in salaries for strategic management and entrepreneurship faculty over the past 10 years, 5 tables and 12 figures were created. Table 1 and Figures 1-4 examine the salaries of faculty based on their rank (Professor, Associate Professor, Assistant Professor, Instructor, and New Doctorate). Tables 2 and 3 and Figures 5-8 examine the average salaries by rank for strategic management and entrepreneurship faculty broken down by gender at AACSB schools of higher education in the US. Tables 4 and 5 and Figures 9-12 evaluate strategic management and entrepreneurship faculty based on the type of school (private versus public).

Average Strategic Management versus Entrepreneurship Faculty Salaries at AACSB Schools 2004–2015

Table 1 and Figures 1-4 show the average salaries of strategic management and entrepreneurship faculty at AACSB schools in the US from 2004-2015. During the most recent year in the study, 2014/15, the average salaries for strategic management and entrepreneurship faculty at AACSB schools of higher education in the US were the following: Full Professor ($182,400 vs $162,000), Associate Professor ($136,700 vs $131,400), Assistant Professor ($129,300 vs $113,600), and Instructor ($91.500 vs $85,800). Strategic management full professors’ average salaries were $20,400 (13%) more than entrepreneurship full professors. Strategic management associate professors’ average salaries were only $5,300 (4%) more than entrepreneurship associate professors. Strategic management assistant professors’ average salaries were $15,700 (14%) more than entrepreneurship assistant professors. Finally, strategic management instructors’ average salaries were $6,500 (8%) more than entrepreneurship assistant professors. The salary trends over a 10-year period from 2004-05 to 2014-15 for each rank shows the following dollar and percentage increases for strategic management and entrepreneurship faculty: Full Professor ($62,500: 52%; 46,500: 40%), Associate Professor ($43,700: 47%; $36,500: 38%), Assistant Professor ($39,200: 44%; $26,500: 30%), and Instructor ($23,600: 35%; $27,600: 47%).

Spring 2017 Journal of Business & Entrepreneurship 87

Overall, the data show that if you are motivated by money, it is better to become a strategic management faculty member rather than an entrepreneurship faculty member.

Table 1: Average Strategic Management versus Entrepreneurship Faculty Salaries at AACSB Schools 2004–2015

Associate Assistant Year Full Professor Instructor Professor Professor

Strategy ENT Strategy ENT Strategy ENT Strategy ENT

04-05 $119,900 $115,500 $93,000 $94,900 $90,100 $87,100 $67,900 $58,200

05-06 $127,000 $123,900 $97,300 $96,200 $93,600 $90,900 $65,900 $70,100

06-07 $134,400 $131,400 $103,400 $101,800 $99,400 $94,800 $71,600 $73,500

07-08 $146,200 $140,200 $109,500 $104,500 $103,100 $97,800 $72,000 $80,000

08-09 $151,300 $148,100 $115,500 $110,200 $108,100 $100,600 $74,900 $78,500

09-10 $152,200 $154,600 $118,200 $111,600 $112,600 $103,100 $79,900 $76,400

10-11 $156,400 $153,300 $120,700 $113,700 $115,700 $106,400 $86,400 $78,900

11-12 $159,600 $156,800 $124,100 $119,300 $119,200 $109,600 $87,900 $81,500

$164,000 12-13 $166,200 $128,700 $123,900 $120,600 $111,000 $85,800 $79,400

13-14 $173,500 $165,800 $134,300 $125,000 $123,800 $113,700 $89,100 $79,400

14-15 $182,400 $162,000 $136,700 $131,400 $129,300 $113,600 $91,500 $85,000

Source: The data is based upon a controlled group of 325 US schools that completed an AACSB Salary Survey in each of the benchmarking years.

88 Journal of Business & Entrepreneurship Spring 2017

Spring 2017 Journal of Business & Entrepreneurship 89

Average Strategic Management Faculty Salaries by Gender at AACSB Schools 2004–2015

90 Journal of Business & Entrepreneurship Spring 2017

Table 2 and Figures 5-8 show the average salaries of strategic management faculty by gender at AACSB schools in the US from 2004-2015. During the most recent year, 2014/15, the average salaries for entrepreneurship faculty were: Full Professor (Male: $184,800; Female: $168,800), Associate Professor ($137,300; $134,600), Assistant Professor ($128,300; $121,300), and Instructor ($91,300; $92,300). What is most noteworthy from the data is the significant difference between male versus female Full Professors in 2014-15. The males made 10% more. Since 2013-14, the average salaries increased for male Professors ($6,900; 4.5%), Associate Professors ($1,700; 1.3%), Assistant Professors ($6,700; 5.5%), and Instructors ($3,900; 4.5%). Since 2013-14, average salaries increased for all ranks of female faculty members: Professors ($7,200; 5.5%), Associate Professors ($4,900; 3.8%), and Assistant Professors ($2,600; 2%). In 2014-15, the average difference in salary between a male Assistant and Associate Professor was $9,000 (7%). The average difference in salary between a male Associate Professor and Full Professor was $47,500 (35%). In 2014-15, the average difference in salary between a female Assistant and Associate Professor was $3,300 (2.5%). The average difference in salary between a female Associate Professor and Full Professor was $33,500 (25%). Salaries over a 10-year period from 2004-05 to 2014-15 broken down by gender show the following dollar and percentage increases: Full Professor (Male: $64,500: 53.6%; Female: $50,100: 42.5%), Associate Professor ($44,700: 48.3%; $40,300: 42.7%), Assistant Professor ($37,900: 42%; $41,800: 46.7%), and Instructor ($22,400: 32.5%; $29,100: 46%).

Spring 2017 Journal of Business & Entrepreneurship 91

Table 2: Average Strategic Management Faculty Salaries by Gender at AACSB Schools 2004–2015 Academic Full Associate Assistant Instructor Year Professor Professor Professor

Male Female Male Female Male Female Male Female

04-05 120,300 118,000 92,600 94,300 90,400 89,500 68,900 63,200

05-06 126,400 130,400 97,000 98,000 94,000 92,800 64,600 73,700

06-07 132,700 143,800 104,300 100,600 100,100 98,000 71,500 71,900

07-08 144,900 153,900 110,000 107,500 102,900 103,400 72,300 70,200

08-09 150,400 157,800 115,600 115,200 107,700 108,800 74,800 75,500

09-10 152,300 151,600 117,900 119,300 113,300 110,800 77,500 87,700

10-11 157,500 150,500 120,300 121,800 115,700 115,800 83,800 94,600

11-12 159,500 160,100 126,000 117,900 119,200 119,200 87,700 88,300

12-13 167,900 156,200 130,700 122,600 119,100 124,100 86,300 84,100

13-14 175,300 160,900 135,600 129,700 121,600 128,700 87,400 95,200

14-15 184,800 168,100 137,300 134,600 128,300 131,300 91,300 92,300

Source: The data is based upon a controlled group of 325 US schools that completed an AACSB Salary Survey in each of the benchmarking years (See Appendix 1).

Average Entrepreneurship Faculty Salaries by Gender at AACSB Schools 2004–2015

Table 3 and Figures 5-8 shows the average salaries of entrepreneurship faculty by gender at AACSB schools in the US from 2004-2015. During the most recent year, 2014/15, the average salaries for entrepreneurship faculty were: Full Professor (Male: $159,800; Female: $171,700), Associate Professor ($133,200; $125,600), Assistant Professor ($114,000; $112,500), and Instructor ($87,500; $77,700). For the most recent year male salaries were higher for every rank, except for Full Professors. Women made 7.5% more money.

92 Journal of Business & Entrepreneurship Spring 2017

Since 2013-14, average salaries decreased for male Professors ($6,900; 4.1%) and Assistant Professors ($500; .4%) while average salaries increased for male Associate Professors and Instructors $8,100 (6.5%) and $7,400 (9.2%). Since last academic year (2013-14), average salaries increased for all ranks of female faculty members: Professors ($11,100; 14.5%), Associate Professors ($900; .7%), Assistant Professors ($800; .7%), and Instructors ($2,200; 2.9%). In 2014-15, the average difference in salary between a male Assistant and Associate Professor was $19,200 (16.8%). The average difference in salary between a male Associate Professor and Full Professor was $26,600 (20%). In 2014-15, the average difference in salary between a female Assistant and Associate Professor was $13,100 (11.6%). The average difference in salary between a female Associate Professor and Full Professor was $46,100 (36.7%). Salaries over a 10-year period from 2004-05 to 2014-15 broken down by gender show the following dollar and percentage increases: Full Professor (Male: $43,400: 37.3%; Female: $63,100: 58%), Associate Professor ($36,100: 37%; $41,600: 49%), Assistant Professor ($27,500: 32%; $23,800: 27%), and Instructor ($25,900: 42%; $27,700: 55.4%).

Table 3: Average Entrepreneurship Faculty Salaries by Gender at AACSB Schools 2004–2015 Academic Associate Assistant Instructor Full Professor Year Professor Professor

Male Female Male Female Male Female Male Female

04-05 116,400 108,600 97,100 84,000 86,500 88,700 61,600 50,000

05-06 125,200 112,500 97,500 89,200 89,800 93,100 75,100 55,300

06-07 133,200 113,200 100,000 113,000 93,200 98,400 77,000 56,300

07-08 141,700 124,900 103,300 112,100 95,700 101,000 84,800 52,600

08-09 148,700 142,000 107,600 127,500 100,900 100,000 83,800 59,500

09-10 155,600 142,500 109,100 126,300 103,400 102,500 79,300 64,200

10-11 153,800 147,500 111,000 126,200 108,300 102,200 82,300 66,600

11-12 155,600 169,500 116,300 134,100 111,800 105,000 84,300 69,600

12-13 161,500 182,200 123,400 125,800 113,400 105,800 80,500 68,200

13-14 166,700 160,600 125,100 124,700 114,500 111,700 80,100 75,500

14-15 159,800 171,700 133,200 125,600 114,000 112,500 87,500 77,700

Source: The data is based upon a controlled group of 325 US schools that completed an AACSB Salary Survey in each of the benchmarking years (See Appendix 1).

Spring 2017 Journal of Business & Entrepreneurship 93

94 Journal of Business & Entrepreneurship Spring 2017

Average Strategic Management Faculty Salaries by Type of School at AACSB Schools 2004–2015

Spring 2017 Journal of Business & Entrepreneurship 95

Table 4 and Figures 9-12 show the average salaries of strategic management faculty by type of school from 2004-2015. During 2014/15, the average salaries for strategic management faculty at AACSB schools of higher education in the US were the following: Full Professor (Public: $174,300; Private: $196,600), Associate Professor ($131,300; $149,800), Assistant Professor ($126,200; $136,900), and Instructor ($81,300; $115,400). The numbers favor private schools in every category. Since 2013-14, the average salaries decreased for Full Professors at public schools by $8,800 or 5.3%. However, Full Professors at private schools saw their salaries jump $9,800 or 6%. Average salaries decreased for Associate Professors at public schools by $3,800 (3%) and private schools by $1,900 (1.3%). Average salaries increased for Assistant Professors at public schools by $500 (.5%). However, Assistant Professors at public and private schools saw their salaries $5,200 (4.3%) and $5,600 (4.3%). Finally, average salaries increased for Instructors at both public and private schools. Instructors at public schools saw their salaries increase by $6,000 (8%). Instructors at private schools saw their salaries decrease by $1,500 (1.3%). In 2014-15, the average difference in salary of an Assistant Professor and an Associate Professor at a public and private school was $5,100 (4%) and $13,100 (9.6%). The average difference in salary between an Associate Professor and a Full Professor at a public and private school was $43,000 (33%) and $46,800 (31.2%). Salary trends over a 10-year period from 2004-05 to 2014-15 broken down by type of school shows the following dollar and percentage increases: Full Professor (Public: $57,300: 49%; Private: $68,000: 53%), Associate Professor ($42,500: 48%; $46,600: 45%), Assistant Professor ($58,100: 66%; $42,700: 45%), and Instructor ($11,200: 16%; $54,000: 8.8%).

96 Journal of Business & Entrepreneurship Spring 2017

Table 4: Average Strategic Management Faculty Salaries by Type of School at AACSB Schools 2004–2015

Academic Associate Assistant Full Professor Instructor Year Professor Professor

Public Private Public Private Public Private Public Private

04-05 117,000 128,600 88,800 103,200 88,100 94,200 70,100 61,400

05-06 124,900 133,400 93,400 106,300 92,200 96,800 65,900 65,600

06-07 133,300 137,100 100,400 110,900 98,100 102,300 71,500 71,800

07-08 141,900 156,100 106,600 117,100 101,500 107,500 67,800 80,400

08-09 147,600 158,600 111,700 124,700 106,000 112,900 71,400 82,800

09-10 147,400 162,700 113,100 128,400 109,100 120,900 77,200 84,600

10-11 150,700 168,100 114,000 135,800 112,800 122,700 79,400 96,200

11-12 151,500 174,300 117,800 140,900 115,800 126,600 80,700 97,700

12-13 160,400 176,500 122,400 146,600 117,100 128,100 78,200 101,200

13-14 164,500 189,200 127,500 151,700 121,000 131,300 75,300 116,900

14-15 174,300 196,600 131,300 149,800 126,200 136,900 81,300 115,400

Source: The data is based upon a controlled group of 325 US schools that completed an AACSB Salary Survey in each of the benchmarking years (See Appendix 1).

Average Entrepreneurship Faculty Salaries by Type of School at AACSB Schools 2004–2015

Table 5 and Figures 9-12 show the average salaries of entrepreneurship faculty by type of school from 2004-2015. During 2014/15, the average salaries for entrepreneurship faculty at AACSB schools of higher education in the US were the following: Full Professor (Public: $157,100; Private: $173,000), Associate Professor ($129,500; $135,000), Assistant Professor ($112,400;

Spring 2017 Journal of Business & Entrepreneurship 97

$117,000), and Instructor ($79,300; $98,200). The numbers favor private schools in every category. Since 2013-14, the average salaries decreased for Full Professors at public schools by $8,800 or 5.3%. However, Full Professors at private schools saw their salaries jump $7,300 or 4.4%. Average salaries increased for Associate Professors at public schools by $10,200 (8.6%), however Associate Professors at private schools saw their salaries drop $1,100 (.8%). Average salaries increased for Assistant Professors at public schools by $500 (.5%). However, Assistant Professors at private schools saw their salaries drop $700 (.6%). Finally, average salaries increased for Instructors at both public and private schools. Instructors at public schools saw their salaries increase by $7,700 (10.8%) and Instructors at private schools saw their salaries increase $3,600 (3.8%). In 2014-15, the average difference in salary of an Assistant Professor and an Associate Professor at a public and private school was $17,100 (15.2%) and $18,000 (15.4%). The average difference in salary between an Associate Professor and a Full Professor at a public and private school was $27,600 (21%) and $38,000 (28.1%). Salary trends over a 10-year period from 2004-05 to 2014-15 broken down by type of school shows the following dollar and percentage increases: Full Professor (Public: $47,800: 43.7%; Private: $43,400: 33.5%), Associate Professor ($42,400: 48.7%; $31,800: 30.8%), Assistant Professor ($29,800: 36%; $21,800: 21.9%), and Instructor ($22,300: 39%; $34,100: 53.2%).

98 Journal of Business & Entrepreneurship Spring 2017

Table 5: Average Entrepreneurship Faculty Salaries by Type of School at AACSB Schools 2004–2015 Academic Associate Assistant Instructor Full Professor Year Professor Professor

Public Private Public Private Public Private Public Private

109,300 129,600 87,100 103,200 82,600 96,000 57,000 64,100 04-05 117,700 135,500 92,500 100,800 86,300 100,400 68,300 77,700 05-06 127,800 140,700 99,200 106,300 91,200 104,100 67,700 87,400 06-07 136,100 148,400 103,700 105,800 95,000 103,100 78,000 84,200 07-08 143,900 157,400 108,100 114,000 99,700 103,200 70,200 91,900 08-09 150,500 162,800 109,100 115,700 100,900 108,300 65,300 94,300 09-10 149,400 160,800 107,600 122,500 105,400 108,900 70,500 95,800 10-11 154,100 162,600 114,000 128,500 108,300 112,700 75,600 95,200 11-12 163,600 164,700 119,200 132,200 109,100 114,500 71,300 94,500 12-13 165,900 165,700 119,300 136,100 111,900 117,700 71,600 94,600 13-14 157,100 173,000 129,500 135,000 112,400 117,000 79,300 98,200 14-15

Source: The data is based upon a controlled group of 325 US schools that completed an AACSB Salary Survey in each of the benchmarking years (See Appendix 1).

Spring 2017 Journal of Business & Entrepreneurship 99

100 Journal of Business & Entrepreneurship Spring 2017

CONCLUSION AND IMPLICATIONS

The purpose of this study was to investigate the differences in salaries between strategic management and entrepreneurship faculty over the past 10 years. The article answers the following questions: (1) What are the differences in salaries between strategic management and entrepreneurship faculty by rank at AACSB schools in the US? (2) What are the differences in salaries between strategic management and entrepreneurship faculty by sex at AACSB schools in the US? And (3) What are the differences in salaries between strategic management and entrepreneurship faculty by type of school (Public versus private) at AACSB schools in the US? The results of this study will be beneficial to doctoral students, faculty, and administrators. Table 1 and Figures 1-4 evaluate research question number one: What are the differences in salaries between strategic management and entrepreneurship faculty by rank at AACSB schools in the US? In 2014/15, the average salaries for strategic management and entrepreneurship faculty at AACSB schools of higher education according to rank were the following: Full Professor ($182,400 vs $162,000), Associate Professor ($136,700 vs $131,400), Assistant Professor ($129,300 vs $113,600), and Instructor ($91,500 vs $85,800). Strategic management Full Professors’ average salaries were $20,400 (13%) higher than entrepreneurship Full Professors. Strategic management

Spring 2017 Journal of Business & Entrepreneurship 101

Associate Professors’ average salaries were only $5,300 (4%) more than entrepreneurship Associate Professors. Strategic management Assistant Professors’ average salaries were $15,700 (14%) more than entrepreneurship Assistant Professors. Finally, strategic management Instructors’ average salaries were $6,500 (8%) more than entrepreneurship Assistant Professors. In regards to the salary trends over the past 10-years 2004-05 to 2014-15, Strategic management shows a stronger increase in dollars and percentage increase versus entrepreneurship faculty for each rank: Full Professor ($62,500: 52%; 46,500: 40%), Associate Professor ($43,700: 47%; $36,500: 38%), Assistant Professor ($39,200: 44%; $26,500: 30%), and Instructor ($23,600: 35%; $27,600: 47%). The average salaries for both areas have increased from 2004-05 to 2014-15. However, entrepreneurship research (see Finkle, 2015) has shown that there is a decreasing trend in entrepreneurship tenure track positions. The percentage of advertised tenure track job openings in entrepreneurship over the last 10-year period has decreased from 92% to 64%. Tables 2 and 3 and Figures 5-8 answered research question number two: What are the differences in salaries between strategic management and entrepreneurship faculty by sex at AACSB schools in the US? In 2014/15, the average salaries for strategic management and entrepreneurship faculty at AACSB schools of higher education for males: Full Professor ($184,800 vs $159,800), Associate Professor ($137,300 vs $133,200), Assistant Professor ($128,300 vs $114,000), and Instructor ($91,300 vs $87,500). Similar to Table 1, at every rank, male strategic management faculty had higher salaries than male entrepreneurship faculty. For female faculty: Full Professor ($168,100 vs $171,700), Associate Professor ($134,600 vs $125,600), Assistant Professor ($131,300 vs $112,500), and Instructor ($92,300 vs $77,700). With the exception of the Full Professor rank, female strategic management faculty had higher salaries than female entrepreneurship faculty. At the Full Professor rank, female entrepreneurship faculty average salaries were slightly higher ($3600 or 2.1%) than female strategic management faculty. Within strategic management, in 2014/15, female Full Professors had average lower ($16,700 or 10%) salaries than male Full Professors. However, the difference in average salaries between gender was lower at the Associate Professor rank. Female Associate Professors salaries were $2700 (2%) lower than male Associate Professors. However, female Assistant Professors had

102 Journal of Business & Entrepreneurship Spring 2017

slightly higher average ($3000 or 2.3%) salaries than male Assistant Professors. Similarly, female instructors had slightly higher average ($1000 or 1.1%) salaries than male instructors. Within entrepreneurship, in 2014/15, female Full Professors had higher average ($11,900 or 7.4%) salaries than male Full Professors. However, at the three other levels, female faculty average salaries were lower than males. Female Associate Professors average salaries were $7600 (6%) lower than male Associate Professors, Female Assistant Professors average salaries were slightly lower ($1500 or 1.3%) than male Assistant Professors, female instructors average salaries were lower ($9800 or 12.6%) than male instructors. In regards to the salary trends over the past 10-years 2004-05 to 2014- 15, Strategic management and entrepreneurship have seen strong increases in salaries for male and female faculty at all ranks (Figures 5-8). For Strategic management compared to entrepreneurship, salaries over a 10-year period from 2004-05 to 2014-15 show the following dollar and percentage increases by gender: Full Professor (Male: $64,500: 53.6%; Female: $50,100: 42.5%) as opposed to Full Professor (Male: $43,400: 37.3%; Female: $63,100: 58%); Associate Professor (Male: $44,700: 48.3%; Female: $40,300: 42.7%) as opposed to Associate Professor (Male: $36,100: 37%; Female: $41,600: 49%); Assistant Professor (Male: $37,900: 42%; Female: $41,800: 46.7%) as opposed to Assistant Professor (Male: $27,500: 32%; Female: $23,800: 27%); Instructor (Male:$22,400: 32.5%; Female: $29,100: 46%) as opposed to Instructor ($25,900: 42%; Female: $27,700: 55.4%). It is good news that faculty salaries have increased for male and female faculty in both fields, which means there are ample opportunities for hiring both male and female faculty. However, given that there is a decreasing trend in the number of available tenure track positions (Finkle, 2015), it will mean more candidates will be competing for fewer advertised positions. Tables 4 and 5 and Figures 9-13 answered research question number three: What are the differences in salaries between strategic management and entrepreneurship faculty by type of school (Public versus private) at AACSB schools in the US? In 2014/15, the average salaries for strategic management and entrepreneurship faculty at AACSB schools of higher education by type of school (Public versus private) were the following: For public schools: Full

Spring 2017 Journal of Business & Entrepreneurship 103

Professor ($174,300 vs $157,100), Associate Professor ($131,300 vs $129,500), Assistant Professor ($126,200 vs $112,400), and Instructor ($81,300 vs $79,300). For private schools: Full Professor ($196,600 vs $173,000), Associate Professor ($149,800 vs $135,000), Assistant Professor ($136,900 vs $117,000), and Instructor ($115,400 vs $98,200). In regards to the salary trends over the past 10-years 2004-05 to 2014- 15, Strategic management and entrepreneurship have seen strong increases in salaries for faculty at all ranks by type of school (Figures 9-12). Comparing Strategic management to entrepreneurship salary trends over a 10-year period from 2004-05 to 2014-15 by the type of school shows the following dollar and percentage increases for: Full Professor (Public: $57,300: 49%; Private: $68,000: 53%) as opposed to Full Professor (Public: $47,800: 43.7%; Private: $43,400: 33.5%); Associate Professor ($42,500: 48%; $46,600: 45%) as opposed to Associate Professor ($42,400: 48.7%; $31,800: 30.8%); Assistant Professor ($58,100: 66%; $42,700: 45%) as opposed to Assistant Professor ($29,800: 36%; $21,800: 21.9%); Instructor ($11,200: 16%; $54,000: 8.8%) as opposed to Instructor ($22,300: 39%; $34,100:53.2%). The strongest increase was seen at the instructor level in entrepreneurship for both public and private schools compared to Strategic management. For all other ranks, Strategic management salaries ranked higher on average for both public and private schools. Results over a 10-year period from 2004-05 to 2014-15 showed strong increases in salaries for strategic management and entrepreneurship faculty in public and private schools. As strategic management and entrepreneurship courses gain popularity in business schools, this trend is reflective of institutionalism of both fields. Business schools have recognized the importance of hiring faculty for quality teaching and research in these fields. In this study, at every rank, strategic management faculty had higher salaries. Hence, doctoral students and faculty who are seeking higher salaries should seek positions in strategic management. It must be noted that these are merely averages and do not necessarily mean that your compensation will be as stated in the study. You may have a certain expertise that schools need (e.g., experience with creating an entrepreneurship center), which would command a higher salary. Or you may see lower salaries at regional public schools. Therefore, it is advised that you ferret out your opportunities through advertisements and your contacts within the fields.

104 Journal of Business & Entrepreneurship Spring 2017

For doctoral students, it might be a wise strategy to either double major in strategic management and entrepreneurship, which should create a nice niche for you. In baseball terminology, it would be equivalent to being able to bat left or right handed. There will always be a demand for strategic management faculty. Or at a minimum get a minor in entrepreneurship. These strategies should make you more marketable. The trends in salaries from this study may be attributed to several reasons. The field of strategic management was founded much earlier than the field of entrepreneurship. Strategic management has been much more legitimized within higher education. In almost every US school, a capstone course in strategic management is required, which increases the demand for faculty. Entrepreneurship, which came out of strategic management, is a much younger field that has faced difficulty with legitimacy and the acquisition of resources such as tenure track positions, money for centers for entrepreneurship, number of journals, quality of research, faculty earning tenure, etc. The hard work of faculty from the late 1980s through the 1990s paved the way for the field. They took the risk to study entrepreneurship when there were only a handful of PhD programs in entrepreneurship. Sometimes faculty would come from other disciplines like Psychology, Sociology, Education, etc. They would publish research in an area that was often laughed at by other long-standing fields. Faculty who taught entrepreneurship had to fight for the legitimacy of their research and the right to earn tenure. Despite this, today, entrepreneurship is still not fully institutionalization within schools of higher education. Institutional theory (see Dowling & Pfeffer, 1975; Meyer & Rowan, 1977) denotes that entrepreneurship has not been fully accepted at schools of higher education. If so, entrepreneurship would be a required course and/or program at most schools. There is nothing further from the truth right now. Entrepreneurship is still seen as an elective except at a few schools. Furthermore, there are only a few departments and fewer colleges. While the field is increasing (see Finkle, 2007, Finkle, 2010, Finkle, 2013a, Finkle, 2013b, Finkle 2015, Finkle Kuratko, and Goldsby, 2006; Finkle, Menzies, Kuratko, and Goldsby, 2012; 2013), there is not the demand for faculty like strategic management. Additionally, strategic management is a very difficult subject to teach. Strategic management requires a certain level of competency in most areas of business, including a certain competency in a variety of industries

Spring 2017 Journal of Business & Entrepreneurship 105

and technologies. This has become even more difficult with the emergence of the variety of new technologies and expectations of the millennial generation.

FUTURE RESEARCH

The purpose of this research was to examine the salaries of faculty within the fields of strategic management and entrepreneurship. This research sheds light on current and past trends in the remuneration of faculty. While this research limits itself to comparing entrepreneurship to strategic management, other studies could be useful as well. How does the field of entrepreneurship compare with other areas within the fields (e.g., Finance, Accounting, Marketing, Economics, International Business, Ethics, etc.)? These numbers would be extremely beneficial to doctoral students, faculty and administrators.

REFERENCES

Academy of Management (2016). Entrepreneurship. Accessed from:http://aom.org/Divisions-and-Interest- Groups/Entrepreneurtship/Entrepreneurship.aspx

AOMonline.com (2016). Academy of Management Placement Services. Briar Cliff, New York.

Aldrich, H. E. (2012). The emergence of entrepreneurship as an academic field: A personal essay on institutional entrepreneurship. Research Policy, 41(7), 1240- 1248.

Bowman, E. H., Singh, H., & Thomas, H. (2002). The domain of strategic management: History and evolution in Howard Thomas and Andrew Pettigrew, eds. Handbook of Strategy and Management. London: Sage.

Bracker, J. (1980). The historical development of the strategic management concept. Academy of management review, 5(2), 219-224.

Cox, M. Z., Daspit, J., McLaughlin, E., & Jones III, R. J. (2012). Strategic management: is it an academic discipline? Journal of Business Strategies, 29(1), 25.

106 Journal of Business & Entrepreneurship Spring 2017

Dowling, J., & Pfeffer, J. (1975). Organizational legitimacy: Social values and organizational behavior. Pacific Sociological Review 18: 122-136.

Finkle, T.A. (2007). Trends in the market for entrepreneurship faculty from 1989-2005. Journal of Entrepreneurship Education, 10, 1-25.

Finkle, T. A. (2010). Entrepreneurship education trends. Research in Business & Economics Journal, 1, 35-53.

Finkle, T. A. (2013a). Trends in the market for entrepreneurship faculty from 1989-2011. Journal of Entrepreneurship Education, 16(Special Issue), 59-76.

Finkle, T.A. (2013b). Job opportunities for faculty in the field of entrepreneurship. Journal of Applied Management & Entrepreneurship, 18, 3, 94-112.

Finkle, T. A. (2015). An examination of the job market for entrepreneurship faculty from 1989 to 2014. Journal of Business and Entrepreneurship, 26(3), 55- 78.

Finkle, T. A. (2016). A current look at salaries and faculty demand within the field of entrepreneurship. Journal of Applied Management & Entrepreneurship, 21(2), 45-69.

Finkle, T. A., & Deeds, D. (2001). Trends in the market for entrepreneurship faculty, 1989–1998. Journal of Business Venturing, 16(6), 613-630.

Finkle, T. A., Kuratko, D. F., & Goldsby, M. G. (2006). An examination of entrepreneurship centers in the United States: A national survey. Journal of Small Business Management, 44(2), 184-206.

Finkle, T. A., Menzies, T., Kuratko, D. F., & Goldsby, M. G. (2012). Financing activities of entrepreneurship centers in the United States. Journal of Business and Entrepreneurship, 23(2).

Finkle, T. A., Menzies, T. V., Kuratko, D. F., & Goldsby, M. G. (2013). An

Spring 2017 Journal of Business & Entrepreneurship 107

examination of the financial challenges of entrepreneurship centers throughout the world. Journal of Small Business & Entrepreneurship, 26(1), 67-85.

Hambrick, D. C., & Chen, M.-J. (2008). New academic fields as admittance- seeking social movements: The case of strategic management. Academy of Management Review, 33(1), 32-54.

Katz, J. (2003). The chronology and intellectual trajectory of American entrepreneurship education: 1876–1999. Journal of business venturing, 18(2), 283-300.

Meyer, J. W., & Rowan, B. (1977). Institutionalized organizations: Formal structure as myth and ceremony. American Journal of Sociology 83(2), 340-363.

Mintzberg, H. (1990). The design school: reconsidering the basic premises of strategic management. Strategic Management Journal, 11(3), 171-195.

Mintzberg, H., Raisinghani, D., & Theoret, A. (1976). The structure of “unstructured” decision processes. Administrative Science Quarterly, 1(2), 246- 275.

Pettigrew, A. M., Thomas, H., & Whittington, R. (2001). Handbook of Strategy and Management: Sage.

Porter, M. (1980). Competitive Strategy. Free Press, New York.

Schendel, D., & Hofer, C. W. (1979). Strategic Management: A new view of business policy and planning: Little, Brown.

Schendel, D. E., & Hatten, K. J. (1972). Business Policy or Strategic Management: A Broader View for an Emerging Discipline. Paper presented at the Academy of Management Proceedings.

Schumpeter, J. (1939). Business Cycles. New York, NY, McGraw-Hill Book Company.

Thomas, P., Wilson, J., & Leeds, O. (2013). Constructing ‘the history of strategic

108 Journal of Business & Entrepreneurship Spring 2017

management’: A critical analysis of the academic discourse. Business History, 55(7), 1119-1142.

Tzu, S. (2016 Reprint). The Art of War. Value Classic Reprints.

Von Nuemann, J. & Morgenstein, O. (1947). Theory of Games and Economic Behavior. Princeton, NJ. Princeton University Press.

Spring 2017 Journal of Business & Entrepreneurship 109

Appendix 1: AACSB Schools in the United States Used in Study School Name

Abilene Christian University, College of Business Administration (Abilene, TX)

Akron, University of, College of Business Administration (Akron, OH)

Alabama at Birmingham, University of, Collat School of Business (Birmingham, AL)

Alabama in Huntsville, University of, College of Business Administration (Huntsville, AL)

Alabama, University of, Culverhouse College of Commerce and Business Administration (Tuscaloosa, AL)

Alaska Anchorage, University of, College of Business and Public Policy (Anchorage, AK)

Alaska Fairbanks, University of, School of Management (Fairbanks, AK)

American University, Kogod School of Business (Washington, DC)

Appalachian State University, John A. Walker College of Business (Boone, NC)

Arizona State University, W. P. Carey School of Business (Tempe, AZ)

Arizona, University of, Eller College of Management (Tucson, AZ)

Arkansas at Fort Smith, University of, College of Business (Fort Smith, AR)

Arkansas Tech University, School of Business (Russellville, AR)

Arkansas, University of, Sam M. Walton College of Business (Fayetteville, AR)

Auburn University, Raymond J. Harbert College of Business (Auburn, AL)

Babson College, School of Management (Babson Park, MA)

Ball State University, Miller College of Business (Muncie, IN)

Baltimore, University of, Robert G. Merrick School of Business (Baltimore, MD)

Barry University, D. Inez Andreas School of Business (Miami Shores, FL)

110 Journal of Business & Entrepreneurship Spring 2017

Baruch College-City University of New York, Zicklin School of Business (New York, NY)

Baylor University, Hankamer School of Business (Waco, TX)

Bellarmine University, W. Fielding Rubel School of Business (Louisville, KY)

Bentley University, McCallum Graduate School of Business (Waltham, MA)

Berry College, Campbell School of Business (Mount Berry, GA)

Binghamton, State University of New York, School of Management (Binghamton, NY)

Bloomsburg University, College of Business (Bloomsburg, PA)

Boise State University, College of Business and Economics (Boise, ID)

Boston College, Wallace E. Carroll School of Management (Chestnut Hill, MA)

Boston University, School of Management (Boston, MA)

Bowling Green State University, College of Business Administration (Bowling Green, OH)

Bradley University, Foster College of Business (Peoria, IL)

Bucknell University, School of Management (Lewisburg, PA)

Buffalo, State University of New York at, School of Management (Buffalo, NY)

Butler University, College of Business Administration (Indianapolis, IN)

California, Berkeley, U of, Haas School of Business (Berkeley, CA)

California, Davis, University of, Graduate School of Management (Davis, CA)

California, Irvine, University of, Paul Merage School of Business (Irvine, CA)

California, Los Angeles, University of, UCLA Anderson School of Management (Los Angeles, CA)

Spring 2017 Journal of Business & Entrepreneurship 111

California Polytechnic State University, San Luis Obispo, Orfalea College of Business (San Luis Obispo, CA)

California State University, Bakersfield, School of Business and Public Administration (Bakersfield, CA)

California State University, East Bay, College of Business and Economics (Hayward, CA)

California State University, Fresno, Craig School of Business (Fresno, CA)

California State University, Fullerton, Steven G. Mihaylo College of Business and Economics (Fullerton, CA)

California State University, Northridge, David Nazarian College of Business and Economics (Northridge, CA)

California State University, Sacramento, College of Business Administration (Sacramento, CA)

California State University, San Bernardino, College of Business and Public Administration (San Bernardino, CA)

Central Arkansas, University of, College of Business Administration (Conway, AR)

Central Florida, University of, College of Business Administration (Orlando, FL)

Central Michigan University, College of Business Administration (Mount Pleasant, MI)

Central Missouri University of, Harmon College of Business and professional Studies (Warrensburg, MO)

Charleston, College of, School of Business (Charleston, SC)

Cincinnati, University of, Carl H. Lindner College of Business (Cincinnati, OH)

Clarion University of Pennsylvania, College of Business Administration (Clarion, PA)

Clark University, Graduate School of Management (Worcester, MA)

112 Journal of Business & Entrepreneurship Spring 2017

Cleveland State University, Ahuja College of Business Administration (Cleveland, OH)

Coastal Carolina University, E. Craig Wall Sr. College of Business Administration (Conway, SC)

Colorado at Boulder, University of, Leeds School of Business (Boulder, CO)

Colorado at Colorado Springs, University of, College of Bus and Admin and Grad School of Bus Admin (Colorado Springs, CO)

Colorado, Denver, University of, Business School (Denver, CO)

Columbus State University, D. Abbott Turner College of Business (Columbus, GA)

Connecticut, University of, School of Business (Storrs, CT)

Cornell University, Samuel Curtis Johnson Graduate School of Management (Ithaca, NY)

Creighton University, College of Business Administration (Omaha, NE)

Dalton State College, Division of Business Administration (Dalton, GA)

Dartmouth College, Tuck School of Business at Dartmouth (Hanover, NH)

Dayton, University of, School of Business Administration (Dayton, OH)

Delaware, University of, Alfred Lerner College of Business and Economics (Newark, DE)

Denver, University of, Daniels College of Business (Denver, CO)

DePaul University, Richard H. Driehaus College of Business (Chicago, IL)

Detroit Mercy, University of, College of Business Administration (Detroit, MI)

Drexel University, Bennett S. LeBow College of Business (Philadelphia, PA)

Duquesne University, A.J. Palumbo School of Bus Admin and John F. Donahue Grad School of Bus (Pittsburgh, PA)

East Carolina University, College of Business (Greenville, NC)

Spring 2017 Journal of Business & Entrepreneurship 113

Eastern Illinois University, School of Business (Charleston, IL)

Eastern Kentucky University, College of Business and Tech (Richmond, KY)

Eastern Michigan University, College of Business (Ypsilanti, MI)

Eastern Washington University, College of Business and Public Administration (Spokane, WA)

East Tennessee State University, College of Business and Tech (Johnson City, TN)

Emory University, Goizueta Business School (Atlanta, GA)

Emporia State University, School of Business (Emporia, KS)

Evansville, University of, The Schroeder Family School of Business Administration (Evansville, IN)

Fairfield University, Charles F. Dolan School of Business (Fairfield, CT)

Florida Atlantic University, College of Business (Boca Raton, FL)

Florida Gulf Coast University, College of Business (Fort Myers, FL)

Florida International University, College of Business (Miami, FL)

Florida State University, College of Business (Tallahassee, FL)

Florida, University of, Warrington College of Business Administration (Gainesville, FL)

Fordham University, Gabelli School of Business (New York, NY)

Fort Lewis College, School of Business Administration (Durango, CO)

Frostburg State University, College of Business (Frostburg, MD)

George Mason University, School of Business (Fairfax, VA)

Georgetown University, McDonough School of Business (Washington, DC)

Georgia College & State University, J. Whitney Bunting School of Business (Milledgeville, GA)

114 Journal of Business & Entrepreneurship Spring 2017

Georgia Institute of Tech, Ernest Scheller Jr. College of Business (Atlanta, GA)

Georgia Regents University, James M. Hull College of Business (Augusta, GA)

Georgia Southern University, College of Business Administration (Statesboro, GA)

Georgia State University, J. Mack Robinson College of Business (Atlanta, GA)

Georgia, The University of, Terry College of Business (Athens, GA)

Gonzaga University, School of Business Administration (Spokane, WA)

Hawaii at Hilo, University of, College of Business and Economics (Hilo, HI)

Hawaii at Manoa, University of, Shidler College of Business (Honolulu, HI)

Hofstra University, Frank G. Zarb School of Business (Hempstead, NY)

Houston-Clear Lake, University of, School of Business (Houston, TX)

Houston-Downtown, University of, College of Business (Houston, TX)

Houston, University of, C.T. Bauer College of Business (Houston, TX)

Houston-Victoria, University of, School of Business Administration (Victoria, TX)

Idaho State University, College of Business (Pocatello, ID)

Idaho, University of, College of Business and Economics (Moscow, ID)

Illinois at Springfield, University of, College of Business and Management (Springfield, IL)

Illinois at Urbana-Champaign, University of, College of Business (Champaign, IL)

Illinois State University, College of Business (Normal, IL)

Indiana State University, Scott College of Business (Terre Haute, IN)

Indiana University, Bloomington/Indianapolis, Kelley School of Business (Bloomington, IN)

Indiana University Northwest, School of Business and Economics (Gary, IN)

Spring 2017 Journal of Business & Entrepreneurship 115

Indiana University South Bend, School of Business and Economics (South Bend, IN)

Indiana University Southeast, School of Business (New Albany, IN)

Iona College, Hagan School of Business (New Rochelle, NY)

Iowa State University, College of Business (Ames, IA)

Iowa, University of, Henry B. Tippie College of Business (Iowa City, IA)

Ithaca College, School of Business (Ithaca, NY)

Jacksonville State University, College of Commerce and Business Administration (Jacksonville, AL)

James Madison University, College of Business (Harrisonburg, VA)

John Carroll University, John M. and Mary Jo Boler School of Business (University Heights, OH)

Kansas State University, College of Business Administration (Manhattan, KS)

Kennesaw State University, Coles College of Business (Kennesaw, GA)

Kent State University, College of Business Administration (Kent, OH)

Kentucky, University of, Carol Martin Gatton College of Business and Economics (Lexington, KY)

King's College, William G. McGowan School of Business (Wilkes-Barre, PA)

Lamar University, College of Business (Beaumont, TX)

La Salle University, School of Business (Philadelphia, PA)

Lehigh University, College of Business and Economics (Bethlehem, PA)

Long Island University- Post Campus, College of Management (Brookville, NY)

Louisiana at Monroe, University of, College of Business Administration (Monroe, LA)

Louisiana State University, E. J. Ourso College of Business (Baton Rouge, LA)

116 Journal of Business & Entrepreneurship Spring 2017

Louisiana State University in Shreveport, College of Business, Education, and Human Development (Shreveport, LA)

Louisiana Tech University, College of Business (Ruston, LA)

Louisville, University of, College of Business (Louisville, KY)

Loyola University Maryland, Sellinger School of Business and Management (Baltimore, MD)

Maine, University of, Maine Business School (Orono, ME)

Marist College, School of Management (Poughkeepsie, NY)

Marquette University, College of Business Administration (Milwaukee, WI)

Marshall University, Lewis College of Business (Huntington, WV)

Massachusetts, Amherst, University of, Eugene M. Isenberg School of Management (Amherst, MA)

Massachusetts Boston, University of, College of Management (Boston, MA)

Memphis, University of, Fogelman College of Business and Economics (Memphis, TN)

Meredith College, School of Business (Raleigh, NC)

Miami University, Farmer School of Business (Oxford, OH)

Miami, University of, School of Business Administration (Coral Gables, FL)

Michigan-Dearborn, University of, College of Business (Dearborn, MI)

Michigan-Flint, University of, School of Management (Flint, MI)

Michigan State University, Eli Broad College of Bus and Eli Broad Grad School of Mgt (East Lansing, MI)

Michigan Technological University, School of Business and Economics (Houghton, MI)

Michigan, University of, Stephen M. Ross School of Business (Ann Arbor, MI)

Spring 2017 Journal of Business & Entrepreneurship 117

Middle Tennessee State University, Jones College of Business (Murfreesboro, TN)

Midwestern State University, Dillard College of Business Administration (Wichita Falls, TX)

Minnesota, Duluth, University of, Labovitz School of Business and Economics (Duluth, MN)

Minnesota State University, Mankato, College of Business (Mankato, MN)

Minnesota, University of, Carlson School of Management (Minneapolis, MN)

Mississippi State University, College of Business (Mississippi State, MS)

Mississippi, University of, School of Business Administration (University, MS)

Missouri-Columbia, University of, Robert J. Trulaske, Sr. College of Business (Columbia, MO)

Missouri-Kansas City, University of, Bloch School of Management (Kansas City, MO)

Missouri State University, College of Business Administration (Springfield, MO)

Missouri-St. Louis, University of, College of Business Administration (St. Louis, MO)

Missouri Western State University, Steven L. Craig School of Business (St. Joseph, MO)

Montana State University - College of Business (Bozeman, MT)

Montclair State University, School of Business (Montclair, NJ)

Monterey Institute of International Studies, Robert L. and Marilyn J. Fisher Graduate School of International Business (Monterey, CA)

Morehead State University, College of Business (Morehead, KY)

Morgan State University, Earl G. Graves School of Business and Management (Baltimore, MD)

Nebraska at Kearney, University of, College of Business and Tech (Kearney, NE)

118 Journal of Business & Entrepreneurship Spring 2017

Nebraska at Omaha, University of, College of Business Administration (Omaha, NE)

Nebraska-Lincoln, University of, College of Business Administration (Lincoln, NE)

New Jersey, College of, School of Business (Ewing, NJ)

New Mexico State University, College of Business (Las Cruces, NM)

New Mexico, University of, Robert O. Anderson Schools of Management (Albuquerque, NM)

New York University, Leonard N. Stern School of Business (New York, NY)

Nicholls State University, College of Business Administration (Thibodaux, LA)

North Carolina at Asheville, University of, Department of Management and Accountancy (Asheville, NC)

North Carolina at Charlotte, University of, Belk College of Business (Charlotte, NC)

North Carolina A&T State University, School of Business and Economics (Greensboro, NC)

North Carolina Wilmington, University of, Cameron School of Business (Wilmington, NC)

North Dakota State University, College of Business Administration (Fargo, ND)

North Dakota, University of, College of Business and Public Administration (Grand Forks, ND)

Northeastern University, D'Amore-McKim School of Business (Boston, MA)

Northern Arizona University, College of Business Administration (Flagstaff, AZ)

Northern Colorado, University of, Kenneth W. Monfort College of Business (Greeley, CO)

Northern Illinois University, College of Business (DeKalb, IL)

Northern Kentucky University, College of Business (Highland Heights, KY)

North Florida, University of, Coggin College of Business (Jacksonville, FL)

Spring 2017 Journal of Business & Entrepreneurship 119

North Texas, University of, College of Business (Denton, TX)

Notre Dame, University of, Mendoza College of Business (Notre Dame, IN)

Oakland University, School of Business Administration (Rochester, MI)

Ohio Northern University, James F. Dicke College of Business Administration (Ada, OH)

Ohio State University, Max M. Fisher College of Business (Columbus, OH)

Oklahoma State University, Spears School of Business (Stillwater, OK)

Oklahoma, University of, Michael F. Price College of Business (Norman, OK)

Old Dominion University, Strome College of Business (Norfolk, VA)

Oregon State University, College of Business (Corvallis, OR)

Oregon, University of, Charles H. Lundquist College of Business (Eugene, OR)

Pace University, Lubin School of Business (New York, NY)

Pacific Lutheran University, School of Business (Tacoma, WA)

Pennsylvania State University at Erie, Behrend College, Sam and Irene Black School of Business (Erie, PA)

Pennsylvania State University at Harrisburg, School of Business Administration (Middletown, PA)

Pennsylvania State University, Mary Jean and Frank P. Smeal College of Business Administration (University Park, PA)

Pennsylvania, University of, Wharton School (Philadelphia, PA)

Pepperdine University, Graziadio School of Business and Management (Los Angeles, CA)

Pittsburgh, University of, Joseph M. Katz Graduate School of Business (Pittsburgh, PA)

Pittsburg State University, Gladys A. Kelce College of Business (Pittsburg, KS)

120 Journal of Business & Entrepreneurship Spring 2017

Portland State University - School of Business Administration (Portland, OR)

Prairie View A & M University, College of Business (Prairie View, TX)

Providence College, School of Business (Providence, RI)

Radford University, College of Business and Economics (Radford, VA)

Rensselaer Polytechnic Institute, Lally School of Management (Troy, NY)

Richmond, University of, Robins School of Business (Richmond, VA)

Rider University, College of Business Administration (Lawrenceville, NJ)

Rochester Institute of Tech, Saunders College of Business (Rochester, NY)

Roger Williams University, Gabelli School of Business (Bristol, RI)

Roy E. Crummer Graduate School of Business (Winter Park, FL)

Saginaw Valley State University, College of Business and Management (University Center, MI)

Saint Louis University, John Cook School of Business (St. Louis, MO)

Samford University, Brock School of Business (Birmingham, AL)

Sam Houston State University, College of Business Administration (Huntsville, TX)

San Diego State University, College of Business Administration (San Diego, CA)

San Diego, University of, School of Business Administration (San Diego, CA)

San Francisco, University of, School of Management (San Francisco, CA)

Santa Clara University, Leavey School of Business (Santa Clara, CA)

School of Hotel Administration, Cornell University (Ithaca, NY)

Seattle Pacific University, School of Business and Economics (Seattle, WA)

Seattle University, Albers School of Business and Economics (Seattle, WA)

Seton Hall University, Stillman School of Business (South Orange, NJ)

Spring 2017 Journal of Business & Entrepreneurship 121

Shippensburg University, John L. Grove College of Business (Shippensburg, PA)

Siena College, School of Business (Loudonville, NY)

South Alabama, University of, Mitchell College of Business (Mobile, AL)

South Carolina Aiken, University of, School of Business Administration (Aiken, SC)

South Carolina, University of, Darla Moore School of Business (Columbia, SC)

South Carolina Upstate, University of, School of Business Administration and Economics (Spartanburg, SC)

South Dakota, University of, School of Business (Vermillion, SD)

Southeast Missouri State University, Donald L. Harrison College of Business (Cape Girardeau, MO)

Southern Arkansas University, College of Business (Magnolia, AR)

Southern California, University of, Marshall School of Business (Los Angeles, CA)

Southern Illinois University Carbondale, College of Business and Administration (Carbondale, IL)

Southern Illinois University Edwardsville, School of Business (Edwardsville, IL)

Southern Indiana, University of, Romain College of Business (Evansville, IN)

Southern Maine, University of, College of Management and Human Service (Portland, ME)

Southern Methodist University, Edwin L. Cox School of Business (Dallas, TX)

Southern Utah University, School of Business (Cedar City, UT)

St. Cloud State University, Herberger Business School (Saint Cloud, MN)

Stephen F Austin State University, Nelson Rusche College of Business (Nacogdoches, TX)

St. John Fisher College, School of Business (Rochester, NY)

122 Journal of Business & Entrepreneurship Spring 2017

St. Joseph's University, Erivan K. Haub School of Business (Philadelphia, PA)

Stonehill College, Department of Business Administration (Easton, MA)

St. Thomas-Minnesota, University of, Opus College of Business (St. Paul, MN)

Suffolk University, Sawyer School of Management (Boston, MA)

SUNY at New Paltz, School of Business (New Paltz, NY)

SUNY College at Brockport, Department of Business Administration and Economics (Brockport, NY)

SUNY College at Geneseo, School of Business (Geneseo, NY)

SUNY College at Oneonta, School of Economics and Business (Oneonta, NY)

SUNY College at Oswego- School of Business (Oswego, NY)

Susquehanna University, Sigmund Weis School of Business (Selinsgrove, PA)

Temple University, Fox School of Business and Management (Philadelphia, PA)

Tennessee at Knoxville, University of, College of Business Administration (Knoxville, TN)

Tennessee at Martin, University of, College of Business and Public Affairs (Martin, TN)

Tennessee State University, College of Business (Nashville, TN)

Tennessee Tech University, College of Business (Cookeville, TN)

Texas A&M International University, College of Business Administration (Laredo, TX)

Texas A&M University-Commerce, College of Business and Entrepreneurship (Commerce, TX)

Texas A&M University-Corpus Christi, College of Business (Corpus Christi, TX)

Texas A&M University, Mays Business School (College Station, TX)

Spring 2017 Journal of Business & Entrepreneurship 123

Texas at Arlington, University of, College of Business Administration (Arlington, TX)

Texas at Dallas, University of, Naveen Jindal School of Management (Richardson, TX)

Texas at El Paso, University of, College of Business Administration (El Paso, TX)

Texas at San Antonio, University of, College of Business (San Antonio, TX)

Texas at Tyler, University of, College of Business and Tech (Tyler, TX)

Texas Christian University, Neeley School of Business (Fort Worth, TX)

Texas-Pan American, University of, College of Business Administration (Edinburg, TX)

Texas State University, Emmett and Miriam McCoy College of Business Administration (San Marcos, TX)

Texas Tech University, Jerry S. Rawls College of Business Administration (Lubbock, TX)

The Citadel, School of Business Administration (Charleston, SC)

Thunderbird School of Global Management (Glendale, AZ)

Toledo, University of, College of Business Administration (Toledo, OH)

Towson University, College of Business and Economics (Towson, MD)

Truman State University, School of Business (Kirksville, MO)

Tulane University, A. B. Freeman School of Business (New Orleans, LA)

Tulsa, University of, Collins College of Business (Tulsa, OK)

Union Graduate College, School of Management (Schenectady, NY)

Utah State University, Jon M. Huntsman School of Business (Logan, UT)

Valdosta State University, Harley Langdale, Jr. College of Business Administration (Valdosta, GA)

124 Journal of Business & Entrepreneurship Spring 2017

Valparaiso University, College of Business (Valparaiso, IN)

Vanderbilt University, Owen Graduate School of Management (Nashville, TN)

Vermont, University of, School of Business Administration (Burlington, VT)

Virginia Commonwealth University, School of Business (Richmond, VA)

Virginia-McIntire, University of, McIntire School of Commerce (Charlottesville, VA)

Virginia Polytechnic Institute and State University, Pamplin College of Business (Blacksburg, VA)

Wake Forest University-Schools of Business (Winston-Salem, NC)

Washburn University, School of Business (Topeka, KS)

Washington University in St. Louis, Olin School of Business (St. Louis, MO)

Washington, University of, Michael G. Foster School of Business (Seattle, WA)

Wayne State University, School of Business Administration (Detroit, MI)

Weber State University, John B. Goddard School of Business and Economics (Ogden, UT)

Western Carolina University, College of Business (Cullowhee, NC)

Western Illinois University, College of Business and Tech (Macomb, IL)

Western Kentucky University, Gordon Ford College of Business (Bowling Green, KY)

Western Michigan University, Haworth College of Business (Kalamazoo, MI)

Western Washington University, College of Business and Economics (Bellingham, WA)

West Florida, University of, College of Business (Pensacola, FL)

West Georgia, University of, Richards College of Business (Carrollton, GA)

West Virginia University, College of Business and Economics (Morgantown, WV)

Spring 2017 Journal of Business & Entrepreneurship 125

Wichita State University, W. Frank Barton School of Business (Wichita, KS)

Widener University, School of Business Administration (Chester, PA)

Willamette University, Atkinson Graduate School of Management (Salem, OR)

William and Mary, College of, Mason School of Business (Williamsburg, VA)

William Paterson University, Cotsakos College of Business (Wayne, NJ)

Winston-Salem State University, School of Business and Economics (Winston- Salem, NC)

Winthrop University, College of Business Administration (Rock Hill, SC)

Wisconsin-La Crosse, University of, College of Business Administration (La Crosse, WI)

Wisconsin-Madison, University of, School of Business (Madison, WI)

Wisconsin-Parkside, University of, School of Business and Technology (Kenosha, WI)

Wisconsin-River Falls, University of, College of Business and Economics (River Falls, WI)

Wisconsin-Whitewater, University of, College of Business and Economics (Whitewater, WI)

Worcester Polytech Institute, School of Business (Worcester, MA)

Wright State University, Raj Soin College of Business (Dayton, OH)

Wyoming, University of, College of Business (Laramie, WY)

Xavier University, Williams College of Business (Cincinnati, OH)

Youngstown State University, Warren P. Williamson, Jr. College of Business Administration (Youngstown, OH)

126 Journal of Business & Entrepreneurship Spring 2017

DIFFERENT BUT INSEPARABLE: THE CONTINGENT ASSOCIATION OF INSTRUMENTAL AND EMOTIONAL SUPPORT

Mette Søgaard Nielsen University of Southern Denmark

ABSTRACT

The purpose of this study is to investigate the extent to which, and in what way, instrumental and emotional support provided to nascent entrepreneurs are associated. Furthermore, it is investigated how this association may be contingent on the strength of the relationship between the resource provider and the nascent entrepreneur. In previous studies, entrepreneurs’ instrumental and emotional support have primarily been studied separately and thereby potential associations between the two support types have been ignored. The arguments and hypotheses are built and developed based on social support theory and theory of impression management. The hypotheses are tested on a dataset of individuals who provide social support to nascent entrepreneurs in Denmark (N=453). Key words: Nascent entrepreneur, social network, social support, emotional support, instrumental support

INTRODUCTION

Imagine you are sitting face to face with a person who has just pitched his potential new business idea. You can see that he is obviously excited about the idea, but you are skeptical about its viability. He now asks for your advice, so what do you do? Do you go straight ahead and tell him that you do not believe in the idea? Do you give him suggestions on how to improve the idea, but say nothing about your opinion of the idea? Or do you give him some suggestions, while assuring him that the idea does have some potential after a bit more work? There is no one correct answer to this question, and people will answer in different ways. However, the example illustrates some of the complexity of

Spring 2017 Journal of Business & Entrepreneurship 127

supporting nascent entrepreneurs, i.e. providing instrumental and emotional support, which is the focus of this paper. From one of the very first contributions within entrepreneurship research on social networks, the instrumental and emotional support that entrepreneurs receive from their network have been identified as important and necessary resources (Birley, 1986). Since then, a number of studies have been carried out into how entrepreneurs’ instrumental and emotional support influence different parts of the entrepreneurial process. In particular, the early studies of entrepreneurs’ social networks gave primary attention to instrumental support, focusing on how entrepreneurs obtain advice, assistance and finance from network contacts (Butler & Hansen, 1991; Cromie & Birley, 1992; Davidsson & Honig, 2003; Shane & Cable, 2002; Uzzi, 1999). Many of these studies seem to simply assume that the resources needed by entrepreneurs are instrumental (Butler & Hansen, 1991). At the same time, there is also a stream of research focusing on emotional support, and this is often done through studies of entrepreneurs’ close network relationships in terms of families, friends and spouses (Brüderl & Preisendörfer, 1998; Gudmunson, Danes, Werbel, & Loy, 2009; Van Auken & Werbel, 2006). However, despite the substantial number of studies focusing on entrepreneurs’ instrumental and emotional support, their main focus has been on the separate effects, and not on potential associations. This paper argues that because instrumental and emotional support are often provided by the same person at the same point in time, they are therefore likely to be embedded within each other. The theoretical consequences of this is that research needs to take both instrumental and emotional support into account in future studies and hence control for them, because the interpretation of their effects requires acknowledgement of their mutual dependency. The paper builds on the argument that when instrumental support is provided to entrepreneurs, it often carries an emotional meaning (Semmer et al., 2008). Based on this argument, the purpose of this study is to investigate to what extent, and in what manner, the provision of instrumental and emotional support to nascent entrepreneurs are embedded within each other. The paper argues that emotional support is an important component of instrumental support. Furthermore, that this association between emotional and instrumental support is dependent upon the strength of the relationship between the support provider and the nascent entrepreneur. Individuals are embedded within a social context that governs how they act in relation to other people, and they are therefore concerned with the impression that others form of them. It is argued that in weak

128 Journal of Business & Entrepreneurship Spring 2017

relationships between the support provider and the nascent entrepreneur, the support provider will try to manage the impression that others form of him in order to be accepted and that this can be done by ensuring a positive relationship with the nascent entrepreneur by not only providing instrumental support, but also emotional support. The argument is built on impression management theory (Baumeister, 1982; Leary & Kowalski, 1990; Schlenker, 1980; Westphal & Zajac, 2001; Zott & Huy, 2007). The best way to understand how instrumental and emotional support are embedded is through the perspective of the support provider, because the recipient of support will have difficulties evaluating this embeddedness. This perspective provides insight to the embeddedness of emotional and instrumental support, but not into the consequences of this embeddedness for the entrepreneur. In this study, the provision of support is the focus, and the study therefore involves those individuals who provide social support to nascent entrepreneurs to understand how instrumental and emotional support are associated. The current study contributes to existing research in two important ways. First, it moves current discussions in entrepreneurship research, which are now mainly focused on either emotional or instrumental support, to encompass the idea that the provision of these two are often associated and that instrumental support often has an emotional connotation to it. Secondly, and most importantly, it adds to existing knowledge about this association by arguing and demonstrating how the association is situational and contingent on the strength of the relationship between the provider and recipient of support, due to people’s efforts to manage how others perceive them.

THEORY

Social Support in Entrepreneurship

In contrast to the structural (Burt, 2001) and relational perspectives (Granovetter, 1973), the content perspective focuses on what is actually exchanged in a network on the dyadic level between two individuals (Hoang & Antoncic, 2003; Kim, Longest, & Aldrich, 2013). One way of addressing content in social relationships, is by studying the social support that is being exchanged (Kim et al., 2013). Research into social support often draws on House’s (1981) discussions, developed for studies on work stress, where he considers both direct effects from social support on work stress and health, and more interestingly,

Spring 2017 Journal of Business & Entrepreneurship 129

how social support functions as a buffering or moderating effect between work stress and health (House, 1981: 30). A number of studies have followed this line of work and shown how social support is important in relieving work stress (Cohen & Wills, 1985; King, Mattimore, King, & Adams, 1995). It is relevant to apply social support theory to studies of entrepreneurs, because the entrepreneurial process is also related to a great deal of stress. Entrepreneurs often struggle to acquire sufficient resources, they work long hours, and the entrepreneurial process itself is often related to a great deal of uncertainty and ambiguity (Baron, 2008). There are a number of ways to distinguish between different types of support. This study follows Pierce et al. (1996) by focusing on instrumental and emotional support as the simplest dichotomy to distinguish between social support types. In entrepreneurship research, instrumental support from the social network has been studied in a number of different ways, where the focus has been on how network relationships ease access to obtaining loans and lower interest rates (Uzzi, 1999), aid business development through advice and information (Cromie & Birley, 1992; Rooks, Klyver, & Sserwanga, 2014), and function as a mechanism through which investors acquire the information needed to decide on whether to support entrepreneurs or not (Shane & Cable, 2002). In general, much of the early research on entrepreneurs’ social networks equates the resources that entrepreneurs obtain from the network with instrumental resources (Butler & Hansen, 1991; Katz & Gartner, 1988). On the other hand, a number of studies address emotional support focusing on motivation, caring, sympathy and encouragement through the study of strong ties in the network (Granovetter, 1973). Studies have, for example, discussed how emotional support from family or spouses sustain entrepreneurs’ emotional stability (Brüderl & Preisendörfer, 1998; Klyver, 2007), how spousal support is related to business performance (Gudmunson et al., 2009), and how support from the family increases entry into entrepreneurship (Davidsson & Honig, 2003). There are also a number of studies that address both instrumental and emotional support, often in connection with discussions on weak and strong ties (Davidsson & Honig, 2003; Nelson, 1989), but also on how family, friends and spouses are providers of both instrumental and emotional support (Rogers, 2005; Van Auken & Werbel, 2006). However, despite the fact that these studies include both instrumental and emotional support and also recognize that the same person may provide different types of support, these support types are still treated as isolated concepts each having a separate effect on output. This leaves us in a situation where we know

130 Journal of Business & Entrepreneurship Spring 2017

almost nothing about how instrumental and emotional support are associated, or about the circumstances that determine this association. In the following section, three hypotheses will be developed that seek to uncover this association and its dependencies.

Hypothesis Development

The association between instrumental and emotional support In his seminal book on social support, House (1981) addresses how the different types of social support are closely linked: “Obviously, providing information may imply emotional support and may, at times, constitute instrumental support” (p.25). House’s thoughts here show how embedded these concepts really are. While some researchers continue to analyze the different types of support separately, others argue that some support types must be connected and that some support types carry more weight than others. There are at least three different ways of arguing for an association between instrumental and emotional support. In a comprehensive review, Sarason et al. (1996) states that the essence of social support is to feel accepted and loved by those close to you. They thereby indicate that emotional support is more important than other support types, but do not comment on potential associations. Both Barling (1988) and Tardy (1994) takes this one step further when they discuss how instrumental support also carries emotional meaning, which is a way of addressing a possible association. A third step is taken by Semmer et al. (2008) when they argue that emotional support is the decisive factor in determining whether instrumental support is perceived as useful or not. Despite their differences, these studies all touch upon the importance of emotional support. The purpose of this study is to understand the embeddedness of emotional and instrumental support rather than their causal relation. Based on these three different arguments for an association between instrumental and emotional support, it can be argued that it is possible to provide emotional support without instrumental support, i.e. you can show enthusiasm about an entrepreneur’s idea without providing advice, information or other types of instrumental support, but that the reverse situation, where instrumental support is provided without emotional support, is more difficult to imagine. Just the mere provision of instrumental support sends a signal that the support provider cares in some way about the entrepreneur and wants to help. Therefore, instrumental support carries an emotional meaning expressed through emotional support. The

Spring 2017 Journal of Business & Entrepreneurship 131

purpose of this is to ease the perception that the nascent entrepreneur forms of the instrumental support provided through its emotional meaning. It is therefore expected that individuals who provide instrumental support to nascent entrepreneurs are more likely to also provide emotional support. In this way, instrumental support carries an underlying emotional meaning.

H1: Individuals who provide nascent entrepreneurs with instrumental support are more likely to also provide emotional support.

Emotional support in strong relationships In a social network, people have different relationships and connections to each other, and according to Granovetter (1973), it is common to distinguish between strong and weak ties in the network; which is defined as “…a (probably linear) combination of the amount of time, the emotional intensity, the intimacy (mutual confiding), and the reciprocal services which characterize the tie” (Granovetter, 1973: 1361). Studies of entrepreneurs’ social networks often make use of Granovetter to show how entrepreneurs benefit from strong and weak relationships (e.g. Brüderl & Preisendörfer, 1998; Elfring & Hulsink, 2003; Jack, 2005; Ruef, Aldrich, & Carter, 2003). Several studies show how the majority of the support that entrepreneurs receive is from friends and family (Birley, 1986; Hanlon & Saunders, 2007; Klyver, 2007; Marsden & Campbell, 1984; Rooks et al., 2014), and that these strong relationships are often associated with emotional support, e.g. how emotional support from family, spouses and friends has a positive effect on entrepreneurial performance (Gudmunson et al., 2009; Menzies, Diochon, & Gasse, 2004; Van Auken & Werbel, 2006). Based hereon, it is argued that emotional support is often provided in strong relationships, because in a strong relationship people are more attentive to changes in behavior due to being familiar with that person’s behavior under normal conditions. Therefore, it is expected that individuals with a close relationship to the nascent entrepreneur are more likely to provide emotional support, because they have a better feeling of when this support is needed, than an individual with only a weak relationship to the entrepreneur.

H2: The stronger the relationship between the support provider and the nascent entrepreneur, the more emotional support will be provided to the nascent entrepreneur.

132 Journal of Business & Entrepreneurship Spring 2017

Managing impressions The question is whether the association between instrumental and emotional support is universal or contingent. The following section will argue how this association is expected to be dependent on individuals’ need to make a good impression of themselves on others. Being part of society implies being part of a social group with distinct rules and norms, and consequently there are certain expectations attached to the positions that individuals occupy (Biddle, 1986). These expectations are not only expressed by others, but are also felt by the individual who may feel a pressure to fit in. In order to do so they will try to manage how others perceive them: this is also referred to as impression management (Leary & Kowalski, 1990; Schlenker, 1980). Impression management is often equated with self-presentation and is defined as “the process by which individuals attempt to control the impression others form of them” (Leary & Kowalski, 1990: 34). The development of impression management theory represented a move away from explaining human behavior as simply psychic processes within the individual, to also encompassing the influence of the social context (Tetlock & Manstead, 1985). Leary and Kowalski (1990) distinguish between impression motivation and impression construction, where impression motivation constitutes the antecedents for wanting to control how others perceive you, and impression construction consists of the actions that are taken to manage the impression. There are a number of motivations for individuals wanting to manage and control the impression they convey of themselves to others, and these motivations can be both material and social. An example of a material reason is the situation of a job interview, where individuals will try to convey the best impression of themselves in order to be considered for a specific job (Baron, 1986). Less material reasons are interpersonal and more social; where one is trying to convey a specific image of oneself to obtain social approval (Leary & Kowalski, 1990), which can be done through acts of helping (Baumeister, 1982). This section argues how the expected association between instrumental and emotional support will be dependent on how close the support provider feels to the nascent entrepreneur. The argument builds on impression management theory (Baumeister, 1982; Leary & Kowalski, 1990; Schlenker, 1980), where it is argued that when two people have a weak relationship, there is a mutual expectation and desire to first establish and subsequently maintain a good relationship, and one way of meeting these expectations is by trying to manage the impression that others form of you. Therefore, when instrumental support is

Spring 2017 Journal of Business & Entrepreneurship 133

provided in a weak relationship, this instrumental support will likely be accompanied by emotional support, signaling understanding for the entrepreneur’s project and sustaining the expected role. Put differently, support providers want to ensure that others perceive them as intended, and a way to manage this perception is by establishing a good relationship with the nascent entrepreneur by providing emotional support simultaneously. However, in a strong relationship, the bond has already been established, and therefore efforts to manage self-impression are reduced, and there is less need for emotional support to accompany instrumental support at all times. There can be two reasons for a lower amount of emotional support. First, because there is a basic level of emotional support inherent in the relationship, which implies that emotional support is a latent and expected part of the relationship and does not need to be constantly expressed. Second, it could be that in a strong relationship, there is more room for honesty and actual instrumental support without constantly having to worry about the relationship and the impression that is conveyed. Because the relationship is strong and stable, it is possible to express an honest opinion without much concern as to how one is perceived. Based on this argumentation, it is therefore expected that in weak relationships between support providers and nascent entrepreneurs, the greater wish to manage how others perceive you will cause the association between instrumental and emotional support to be stronger. H3: The effect of instrumental support on the level of emotional support provided to nascent entrepreneurs is weaker when the relationship is stronger.

METHODOLOGY

Data: Danish Alter Study of Entrepreneurship

With Gartner’s (1988) seminal paper, entrepreneurship researchers started to study the emergence of the actual firm rather than just the entrepreneurs’ personality. Since then, a number of studies have focused on obtaining representative samples of nascent entrepreneurs as well as studying the firm’s emergence as it takes place, such as the Panel Studies of Entrepreneurial Dynamics I and II (PSED I and II), the Comprehensive Australian Study of Entrepreneurial Emergence (CAUSEE) and the Global Entrepreneurship Monitor (GEM). Each of them captures nascent entrepreneurs and follows their development over time. Studying the actual firm emergence decreases the risk of

134 Journal of Business & Entrepreneurship Spring 2017

retrospective bias. The focus of this study is to investigate the embeddedness of instrumental and emotional support in the firm emergence stage. This is best done through the support providers, because it would be difficult for the entrepreneur to evaluate this provision. This is done through the Danish Alter Study of Entrepreneurship (DASE) that consists of a representative sample of individuals in Denmark ‘who know someone in the process of starting a business or someone who just recently started a business’. With this screening question the trends of GEM, PSED and CAUSEE are followed by studying nascent entrepreneurship, but from the perspective of support providers. In the data collection, 16394 individuals were called, and of them 1742 individuals answered the screening question, which gives a response rate of 9.4 %. Out of these, 508 individuals answered that they know a nascent entrepreneur. Of these, 453 are useable for the current study.

Measures

Dependent variable The dependent variable, ‘provided emotional support’, is measured on a four-item scale with response categories of ‘yes’ and ‘no’ (exact questions are listed in Table 1). These questions are inspired by House’s (1981) definition of social support and reflect how the respondent provides and shows encouragement, excitement, understanding, sympathy and interest. Through a factor analysis, which also included the six questions on instrumental support, the reflective scale was confirmed valid with a Cronbach’s Alpha at 0.82 (see Table 1).

Independent and moderation variables The independent variable, ‘provided instrumental support’, is also a reflective measure with five items (Table 1) and a Cronbach’s Alpha at 0.76. The instrumental support scale encompasses both House’s (1981) definition of instrumental and informational support, as the respondents were asked if they have helped the nascent entrepreneur with information, advice and references. The final independent variable is the moderating variable ‘strength of relationship’. The respondents were asked how they would rate their personal relationship with the nascent entrepreneur on a 3-point scale ranging from ‘distanced’ (coded as 1), ‘somewhere in between’ (coded as 2) and ‘close’ (coded as 3).

Spring 2017 Journal of Business & Entrepreneurship 135

Table 1: Exploratory factor analysis of items constructing provided instrumental and emotional support

Factor 1: Instrumental Factor 2: Emotional Questions Support Support

Have you helped this person getting information related to the start-up? 0.76 Have you expressed your willingness to help this person with the start-up? 0.62 Have you given this person specific advice regarding the start-up? 0.76 Have you done anything for this person related to the start-up? 0.69 Have you referred this person to a contact relevant for the start-up? 0.65 Have you encouraged or backed up this person in relation to the start-up? 0.8 Have you explicitly expressed your excitement for the start-up? 0.82 Have you explicitly expressed your understanding and sympathy for the start-up? 0.88 Have you explicitly expressed your interest in how the start-up is progression? 0.68

Percentage of variance 30.7 28 Cronbach’s Alpha 0.76 0.82

Control variables A number of control variables are included in the analyses. First of all, the study is controlling for the gender of the respondent (the support provider) and the nascent entrepreneur, as research shows that there are differences in how women activate and provide social support compared to men (Barbee et al., 1993; Klyver, 2011). The age of both the respondent and the nascent entrepreneur is also controlled for by recoding actual age into 5 different age categories with age 16-25 years as the reference category. The third and last control variable is ‘family relationship’, which is a proxy for whether the nascent entrepreneur is the father, mother, sibling or partner of the resource provider. The reason for including this control is to separate the effect from the strength of the relationship from the family effect, because family members are often among the primary support providers. The focus of this paper is to study the association between instrumental and emotional support, i.e. the instances where instrumental and emotional support are provided simultaneously, without claiming causality.

FINDINGS

In Table 2, the Pearson correlations are shown along with means and standard deviations. The dependent variable, ‘provided emotional support’, is positively correlated with the two independent variables ‘provided instrumental support’ (p<0.01) and ‘strength of relationship’ (p<0.01), and positively correlated with the control variable ‘family relationship’.

136 Journal of Business & Entrepreneurship Spring 2017

The highest correlation in the table is between the age categories 16-25 years for ego and 16-25 for alters (r= .518). There is also a high correlation between emotional support and instrumental support (r = .432), which is consistent with my expectation of these two variables being associated. However, there is no indication of multicollinearity since the correlation is still below 0.7 (Knoke, Bohrnsted, & Mee, 2002). Because the data come solely from self-reported measures from the same respondent, there is always a potential risk of common method bias. There are a number of ways to overcome common method bias and to subsequently test for it. In the questionnaire, different anchors for the scales have been used, the different scales have been spread out, and the anonymity of the respondents has been protected. These are three of the four important ways to overcome common method bias as suggested by Podsakoff et al. (2003), but their last point about coding some items in reverse has not been applied for the measures used for the current study (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). A single-factor test (Harman, 1976) was performed with all the items. To rule out common method bias, more than one factor must be extracted, and no factor must account for a majority of the variance. An eight-factor extraction accounting for 65% of the total explained variance was obtained, where the first factor explained only 17% of the total variance. These results suggest that common method bias is not a problem for the results. In order to test the three hypotheses, four linear regressions predicting the emotional support provided to the nascent entrepreneur were carried out (Table 3). Model 1 is a linear regression of the control variables, where it can be seen that the control variable ‘family relationship’ has a significant effect on the

Spring 2017 Journal of Business & Entrepreneurship 137

provision of emotional support (p<.01). Furthermore, respondents in the two age categories of 26-35 years (p<.05) and 36-45 (p<0.1) years are significantly more likely to provide emotional support to a nascent entrepreneur than respondents in the reference category of 16-25. The first hypothesis, H1, expects that individuals who provide instrumental support are more likely to also provide emotional support, and this is tested by adding the independent variable, ‘provided instrumental support’ in Model 2 (Table 3). There is a significant positive association between the provision of instrumental and emotional support (β= .408, p<0.01), which supports hypothesis 1. Furthermore, a significant change in R square going from Model 1 to Model 2 (p<0.01) is noted. Furthermore, in a scatterplot (see Figure 1) it can be seen that instrumental support is most often provided together with emotional support, while emotional support is often provided without instrumental support. In Model 3 (Table 3), the second hypothesis, expecting that stronger relationships provide more emotional support, is tested by including ‘strength of relationship’ in Model 3, where a significant positive effect (β= .062, p<0.01) is found and thus provides support for hypothesis 2. Again, a significant change in R square (p<0.01) is noted, indicating that by including the variable ‘strength of relationship’ more of the variance can be explained here than in Model 2.

138 Journal of Business & Entrepreneurship Spring 2017

Spring 2017 Journal of Business & Entrepreneurship 139

Finally, hypothesis 3, expecting the association between instrumental and emotional support to be negatively dependent on the strength of the relationship, is supported. This can be seen in Model 4, where the interaction effect of ‘provided instrumental support’ and ‘strength of relationship’ is included, and a negative interaction effect (β= -.186, p<0.01) is found, thus in support for hypothesis 3. This result is further strengthened by a significant change in R square (p<0.01). Figure 2 shows the interaction effects graphically with an interaction plot.

DISCUSSION AND CONCLUSION

Summary and Interpretation of Results

With a point of departure in an initial interest in knowing more about the provision of instrumental and emotional support to nascent entrepreneurs, the focus of this paper has been to investigate the extent to which instrumental and emotional support are associated and what determines this association. To study this, data from the Danish Alter Study of Entrepreneurship were applied, consisting of a representative sample of individuals who know a nascent entrepreneur. Because instrumental and emotional support are likely to be provided simultaneously and by the same person, it was expected that an association between these two types of support would be found. The study is a reaction to other studies that actually cover both types of support, but still treat them as two isolated concepts and ignore possible links between them. The hypotheses were argued for based on previous research into how instrumental support also carries an emotional meaning (Barling et al., 1988; House, 1981; Semmer et al., 2008; Tardy, 1994), and the empirical analyses provided support for these arguments. Now, knowing that the provision of instrumental and emotional support are associated and hence tied together, it is interesting to consider and investigate what this association could look like. Instrumental support is tied more to emotional support than the reverse, i.e. it is possible to provide emotional support without instrumental support, but more difficult to provide instrumental support without some kind of emotional support; and this was shown graphically in a scatter-plot. This is an interesting finding, as it tells us that instrumental support is more tied to emotional support than the reverse. Emotional support does play an important role in support provision, and

140 Journal of Business & Entrepreneurship Spring 2017

may even be at the heart of the provision, as the results also suggest in how emotional support eases the perception of instrumental support. This underlines the complex nature of providing support and shows how relationships are essentially based on feelings. As individuals, we want to ensure that the support we provide is perceived as it was intended, and a way of ensuring this is by supplementing instrumental support with a more emotionally supportive component. It was argued that individuals with a strong connection will be better at perceiving when the other is feeling stressed and can therefore better provide the necessary emotional support. This was supported empirically with the result that the strength of the relationship had a positive influence on the provision of emotional support. This adds to existing research in how important it is for nascent entrepreneurs to have strong relationships in their network, as these will provide them with much needed emotional support. Finally, it was shown empirically how the association between the provision of instrumental and emotional support is dependent on how close the provider of social support feels to the nascent entrepreneur. The theoretical argument is built on impression management (Baumeister, 1982; Leary & Kowalski, 1990; Schlenker, 1980; Tetlock & Manstead, 1985; Westphal & Zajac, 2001; Zott & Huy, 2007), and it was argued that in a weak relationship there is a greater incentive to attempt to manage the impression that is formed. A way of managing this impression is by establishing and sustaining a positive relationship by showing interest and support for the nascent entrepreneur. Conversely, in a strong relationship, instrumental support does not need to be backed with emotional support in order to sustain the relationship, because the relationship is already established and so there is less of a need to take steps to build the relationship. With this result, it is stressed how the association between provision of instrumental and emotional support is not universal, but is instead a contingent association. As individuals, we do care about how others feel about us, even if we are not closely connected, and this consideration guides much of our behavior in that we try to manage the perception that others form of us. Impression management then seems to be more important in situations with weak relationships, where the status of the relationship has not yet been established and where concern for self-presentation is thus more prevalent. Effectively, this also implies that supportive behavior is not always triggered by an urgent need in those receiving support, but also by social processes in those providing the support.

Spring 2017 Journal of Business & Entrepreneurship 141

Limitations and Future Research

The most important limitation to the current study is the issue of causality, i.e. that it is not actually known whether it is instrumental support that has an emotional meaning, or emotional support that has an instrumental meaning. Another limitation to consider is the issue of sincerity. It was argued that emotional support is used to deliver the instrumental support in a weak relationship in order to obtain social approval, but we cannot be sure whether the emotional support is then simply an attempt to gain social approval rather than an expression of honest opinion and sincere support. This raises the question of whether emotional support still functions and has an actual influence when it is provided for reasons of self-interest and hence might be dishonest. A suggestion for future research would be to look more into the motivation for providing social support, i.e. gain a deeper understanding of why individuals choose to support nascent entrepreneurs. Social support could be provided to obtain financial gains, e.g. if the resource provider chooses to provide financial assistance, it is not certain that it is just an act of kindness; it could also be with an expectation of a future return. The combinations of associations between instrumental and emotional support could also reflect the resource providers’ abilities. If the entrepreneur needs assistance in an area, where the resource provider is inexperienced, it is likely that more emotional support will then be provided to compensate for the lack of instrumental support. Furthermore, it will be interesting to explore other contingent factors in the association between instrumental and emotional support apart from the strength of the relationship, which was the focus of this study. Possibly, there may be a difference in the association between men and women according to which gender places the greater value on social approval.

Theoretical Contributions and Implications

This study contributes to existing research in two important ways. First, current studies in entrepreneurship research on emotional and instrumental support have primarily treated them as isolated categories with less interest in their association. This study contributes by empirically showing that in the provision of instrumental and emotional support the two concepts are embedded within each other. With this important empirical finding, a trend in entrepreneurship is followed recognizing the important benefits of studying the

142 Journal of Business & Entrepreneurship Spring 2017

interaction of entrepreneurs’ resources (Brüderl & Preisendörfer, 1998; Chandler & Hanks, 1998; Klyver & Schenkel, 2013; Mosey & Wright, 2007; Piazza- Georgi, 2002; Rooks, Szirmai, & al., 2009), where results show that resources, in terms of human, social and financial capital do not simply function as isolated concepts, but are interacting and should be studied together. This calls for a loosening of the strict separation of support types, in order to be able to explore possible associations. For entrepreneurship researchers, the association between instrumental and emotional support is an important finding, because it underlines that both types of social support must be included in studies of entrepreneurs to gain a deeper understanding of how social support functions. If instrumental support is most often provided along with emotional support, we need to incorporate this into our studies of entrepreneurs and the social support they receive. Their close association means that we cannot interpret the effects of one of them without also taking the other into account, because they are mutually dependent. Second, existing research into associations between social support types is advanced by demonstrating that the proposed association is dependent on the strength of the relationship between the provider and the nascent entrepreneur. This is an important result as it stresses that the association is not universal, but instead is more or less likely to take place under certain circumstances. It was argued that, due to efforts to manage the impression that others form and to build a relationship, individuals with a weak relationship to the entrepreneur will be more likely to provide emotional support with instrumental support. This is an important contribution, as it shows how social support is not just provided to satisfy the recipient, but instead also serves the purpose of building a relationship and thus ensuring the social status of the provider. This demonstrates how complex social relationships really are, when acts of helping are motivated by several different reasons. The findings underline the importance of expanding studies of nascent entrepreneurs to encompass content, i.e. what is exchanged; but more importantly also those who provide social support. By taking the perspective of the support provider this study has been able to show that differences in the social support received by nascent entrepreneurs are not solely a function of their own personal characteristics or their position in the social network, but might as easily also be a function of those people by whom they are surrounded within their social network. In terms of practical implications, entrepreneurs can benefit from being aware that the social support they receive is not just provided because they need

Spring 2017 Journal of Business & Entrepreneurship 143

it, but also that it is likely to be provided with ulterior motives by the support provider. With this in mind, entrepreneurs may become more capable of evaluating the support they receive from their social network, and to judge whether a supportive act is sincere or whether it is simply an effort on the support provider’s side to improve his image.

REFERENCES

Barbee, A. P., Cunningham, M. R., Winstead, B. A., Derlega, V. J., Gulley, M. R., Yankeelov, P. A., et al. (1993). Effects of Gender Role Expectations on the Social Support Process. Journal of Social Issues, 49(3): 175–190.

Barling, J., MacEwen, K. E., & Pratt, L. I. (1988). Manipulating the type and source of social support: An experimental investigation. Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement, 20(2): 140–153.

Baron, R. (2008). The role of affect in the entrepreneurial process. Academy of Management Review.

Baron, R. A. (1986). Self-Presentation in Job Interviews: When There Can Be “Too Much of a Good Thing”1. Journal of Applied Social Psychology, 16(1): 16–28.

Baumeister, R. F. (1982). A self-presentational view of social phenomena. Psychological Bulletin, 91(1): 3–26.

Biddle, B. (1986). Recent development in role theory. Annual review of sociology.

Birley, S. (1986). The role of networks in the entrepreneurial process. Journal of business venturing, 1(1): 107–117.

Brüderl, J., & Preisendörfer, P. (1998). Network support and the success of newly founded business. Small Business Economics, 10(3): 213–225.

Burt, R. S. (2001). Structural holes versus network closure as social capital. Social capital: Theory and research, 31–56.

144 Journal of Business & Entrepreneurship Spring 2017

Butler, J. E., & Hansen, G. S. (1991). Network evolution, entrepreneurial success, and regional development. Entrepreneurship & Regional Development, 3(1): 1–16.

Chandler, G. N., & Hanks, S. H. (1998). An Examination of the Substitutability of Founders’ Human and Financial Capital in Emerging Business Ventures. Journal of business venturing, 13: 353–369.

Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98(2): 310–357.

Cromie, S., & Birley, S. (1992). Networking by female business owners in Northern Ireland. Journal of Business Venturing, 7(3): 237–251.

Davidsson, P., & Honig, B. (2003). The role of social and human capital among nascent entrepreneurs. Journal of business venturing, 18(3): 301–.

Elfring, T., & Hulsink, W. (2003). Networks in entrepreneurship: the case of high-technology firms. Small business economics, 21: 409–422.

Gartner, W. B. (1988). Who is an entrepreneur? Is the wrong question. American journal of small business, 12(4): 11–32.

Granovetter, M. S. (1973). The Strength of Weak Ties. (T. S. Huang, A. Nijholt, M. Pantic, & A. Pentland, Eds.)American Journal of Sociology, 78(6): 1360– 1380.

Gudmunson, C. G., Danes, S. M., Werbel, J. D., & Loy, J. T.-C. (2009). Spousal Support and Work--Family Balance in Launching a Family Business. Journal of Family Issues, 30(8): 1098–1121.

Hanlon, D., & Saunders, C. (2007). Marshaling Resources to Form Small New Ventures: Toward a More Holistic Understanding of Entrepreneurial Support. Entrepreneurship Theory and Practice, 31(4): 619–641.

Harman, H. (1976). Modern factor analysis. University of Chicago Press.

Hoang, H., & Antoncic, B. (2003). Network-based research in entrepreneurship: A critical review. Journal of business venturing, 18(2): 165–187.

House, J. S. (1981). Work stress and social support.

Spring 2017 Journal of Business & Entrepreneurship 145

Jack, S. (2005). The Role, Use and Activation of Strong and Weak Network Ties: A Qualitative Analysis*. Journal of Management Studies, 42(6): 1233– 1259.

Katz, J., & Gartner, W. B. (1988). Properties of Emerging Organizations. Academy of Management Review, 13(3): 429–441.

Kim, P. H., Longest, K. C., & Aldrich, H. E.( 2013). Can You Lend Me a Hand? Task-Role Alignment of Social Support for Aspiring Business Owners. Work and Occupations, 40(3): 213–249.

King, L. A., Mattimore, L. K., King, D. W., & Adams, G. A. (1995). Family Support Inventory for Workers: A new measure of perceived social support from family members. Journal of Organizational Behavior, 16(3): 235–258.

Klyver, K. (2007). Shifting family involvement during the entrepreneurial process. International Journal of Entrepreneurial Behaviour & Research, 13(5): 258–277.

Klyver, K. (2011). Gender differences in entrepreneurial networks: adding an alter perspective. Gender in Management: An International Journal, 26(5): 332– 350.

Klyver, K., & Schenkel, M. T. (2013). From Resource Access to Use: Exploring the Impact of Resource Combinations on Nascent Entrepreneurship. Journal of Small Business Management, 51(4): 539–556.

Knoke, D., Bohrnsted, G. W., & Mee, A. P. (2002). Statistics for Social Data Analysis. hing Sociology. Wadsworth Publishing Company.

Leary, M. R., & Kowalski, R. M. (1990). Impression management: A literature review and two-component model. Psychological Bulletin, 107(1): 34–47.

Marsden, P. V., & Campbell, K. E. (1984). Measuring Tie Strength. Social Forces, 63(2): 482–501.

Menzies, T., Diochon, M., & Gasse, Y. (2004). Examining venture-related myths concerning women entrepreneurs. Journal of Developmental Entrepreneurship, 9: 89–108.

146 Journal of Business & Entrepreneurship Spring 2017

Mosey, S., & Wright, M. (2007). From Human Capital to Social Capital: A longitudinal study of Technology-based Academic Entrepreneurs. Entrepreneurship Theory & Practice, 31: 909–935.

Nelson, G. (1989). Factors of friendship: relevance of significant others to female business owners. Entrepreneurship Theory and Practice, Summer: 7–18.

Piazza-Georgi, B. (2002). The Role of Human and Social Capital in Growth: Extending Our Understanding. Cambridge Journal of Economics, 26: 461–479.

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. The Journal of applied psychology, 88(5): 879–903.

Rogers, N. (2005). The impact of family support on the success of women business owners. In S. L. Fielden & M. J. Davidson (Eds.), International handbook of women and small business entrepreneurship: 91–102. Cheltenham, UK: Edward Elgar.

Rooks, G., Klyver, K., & Sserwanga, A. (2014). The Context of Social Capital: A Comparison of Rural and Urban Entrepreneurs in Uganda. Entrepreneurship Theory and Practice, n/a–n/a.

Rooks, G., Szirmai, A., & al., et. (2009). The Interplay of Human and Social Capital in Entrepreneurship in Developing Countries. World Institute for Development Economics Research.

Ruef, M., Aldrich, H., & Carter, N. (2003). The structure of founding teams: Homophily, strong ties, and isolation among US entrepreneurs. American sociological review, 68(2): 195–22.

Schlenker, B. R. (1980). No Title. Monterey, CA: Brooks/Cole Publishing Company.

Semmer, N. K., Elfering, A., Jacobshagen, N., Perrot, T., Beehr, T. A., & Boos, N. (2008). The emotional meaning of instrumental social support. International Journal of Stress Management, 15(3): 235–251.

Shane, S., & Cable, D. (2002). Network ties, reputation, and the financing of new ventures. Management Science, 48(3): 364–381.

Spring 2017 Journal of Business & Entrepreneurship 147

Tardy, C. H. (1994). Counteracting task-induced stress: Studies of instrumental and emotional support in problem-solving contexts. Communication of social support: Messages, interactions, relationships, and community: 71–87. Thousand Oaks: Sage Publications.

Tetlock, P. E., & Manstead, A. S. (1985). Impression management versus intrapsychic explanations in social psychology: A useful dichotomy? Psychological Review, 92(1): 59–77.

Uzzi, B. (1999). Embeddedness in the making of financial capital: How social relations and networks benefit firms seeking financing. American sociological review, 64(4): 481–505.

Van Auken, H., & Werbel, J. (2006). Family Dynamic and Family Business Financial Performance: Spousal Commitment. Family Business Review, 19(1): 49–63.

Westphal, J. D., & Zajac, E. J. (2001). Decoupling Policy from Practice: The Case of Stock Repurchase Programs. Administrative Science Quarterly, 46(2): 202.

Zott, C., & Huy, Q. N. (2007). How Entrepreneurs Use Symbolic Management to Acquire Resources. Administrative Science Quarterly, 52(1): 70–105.

148 Journal of Business & Entrepreneurship Spring 2017

THE EFFECTS OF MOTIVATION OVER ENTREPRENEURIAL PERFORMANCE: THE CASE FOR MEXICAN STARTUPSv

Elvira Anzola González Carlos Canfield Rivera Universidad Anáhuac, México Universidad Anáhuac, México

ABSTRACT

Motivation, the link between intention and entrepreneurial action, sustains this longitudinal study analyzing the impact of motivation and other contextual variables over the initial startup phases. With data from 29 teams participating in a Lean Startups workshop in Mexico, an exact logistic regression model revealed that Push motivation jointly with human capital attributes such as allocated time, entrepreneurial experience and diversity of thinking derived from greater female participation in teams, contribute to explain an increase of the success probabilities of teams in the early stages of startup formation. The originality of this research lies in the use of primary sources data and the authors´ involvement with startups. Scope, limitations and contributions to the development of public policies promoting startup formation in Mexico are discussed.

Keywords: Push-Pull motivation, lean startups, longitudinal perspective, human capital, entrepreneurial team performance, Mexico JEL Classification: M13; J24

INTRODUCTION

The current academic perspective understands entrepreneurship as an evolutionary process where participants decide to integrate into business teams and collectively make continuous adjustments to their products, adapting them to their customer´s needs (Kamm, Shuman, Seeger, & Nurick, 1990; Garud & Giuliani, 2013). This entrepreneurship vision is brought together by a common concept called the Lean Startup Process (LSP), defined as an approach to entrepreneurial and innovative activities that emphasizes placing resources into the creation of

Spring 2017 Journal of Business & Entrepreneurship 149

customer value, viewing all other activity as waste until a fit is found between the product and the intended market, concept also known as Product-Market-Fit (York & Danes, 2015). Provided that entrepreneurial activity can be considered as an intentionally planned behavior (Ajzen, 1991; Shapero & Sokol, 1982), and could provide founders with an expectation of some kind of reward (Vroom, 1964). some researchers recognized the importance of the link between ideas and action (Bird, 1989; Krueger & Carsrud, 1993). Further, authors like Krueger, Reilly & Carsrud (2000) emphasize the fact that the entrepreneurial choice is better predicted by the intention towards such conduct. There are several, mostly cross-sectional studies, analyzing the effects of motivation over the individual´s decision to create a new venture and its subsequent performance. To attain further understanding of factors that contribute to early startup success, the present quest, is based on arguments by Gartner (1985) pointing out the large differences that exist among entrepreneurs, almost ruling out the possibility of pinpointing individual´s characteristics as predictors of venture performance. In this perspective, entrepreneurship is here considered as an evolutive process and teams become the subjects of our attention. Firm survival is an indicator of business success (Bruderl & Schussler, 1990), in that sense endurance expresses the ability of organizations to react to rapid environmental conditions and adapt. This paper examines the role played by motivation in the initial phases of startup creation, period in which they face more restrictions and as a result become more vulnerable. Moreover, as it has also been pointed out, entrepreneurial inquiry has taken distance from the actual founders (Meyer, 2011). This paper advances on the contrary path by increasing researcher´s involvement and using first hand data collected from a sample of 29 teams that participated in a full immersion Lean Startup Training Workshop in Mexico 2015, where the authors participated as mentors and observers. With the aid of an exact logistic regression, three hypotheses would be validated in our study, model. The first one being: Push-Pull motivation categories modify the probabilities of success in the initial startup phases of teams in the sample. The second hypothesis is: In conjunction with push motivation, the probability of attaining a Minimum Viable Product (MVP) in the pre-startup phase is positively affected by contextual variables, mostly considered to be attributes of human capital, such as: time deployed in the

150 Journal of Business & Entrepreneurship Spring 2017

process and previous entrepreneurship experience. The third hypothesis is: as they relate to diversity of perspective and shared knowledge, variables such as the team´s gender composition and the possibility of increasing discussion within a larger group (team size), have a positive influence over the possibility of attaining the above said success. As a preview of our findings, evidence revealed that it is likely that the presence of necessity-based entrepreneurship in a team increases their probability of developing a MVP. It was also found that motivation, jointly with other related variables such as time deployed in the process and previous experience in entrepreneurship contributed to the explanation of improved team´s performance in the process of startup conformation. The latter indicates that besides motivation, there are additional factors elucidating success in the pre-startup phase. Interestingly enough, it was also found that another variable, diversity in the entrepreneurial perspective, as measured by the predominance of women in the gender composition of a team, partially contributed to behavior explanation in our sample. The analysis is confined to a particular sample that was collected in a LSP workshop convened nationwide by the National Institute for Entrepreneurship (INADEM) in the summer of 2015, in Mexico. However, it is considered that the participants represent the geographical diversity of the Mexican entrepreneurial ecosystem, particularly of those businesses identified with the programs of support and promotion sponsored by both private and public sectors. The selection criterion required for teams to be ambitious and favors the use of information technology for marketable applications. Given the high failure rate of startups, it is important for those involved with the entrepreneurial track, specifically those instances responsible of fostering the ecosystem, to identify what factors lead to the success of these new ventures and understand the processes that support their path. This study contributes to the study in emerging countries of the effects of motivation and other contextual variables over business performance, in a preliminary attempt to predict initial startup success. Future research requires better understanding of our dependent variable in latter entrepreneurial phases. Knowledge can be attained by a further follow up of this sample and the replication of the study, with the possible inclusion of additional relevant factors, in other developing countries, particularly in Latin America. The remainder of the article is organized as follows. Section 2 provides an overview of the existing literature and lays the foundation for the study´s

Spring 2017 Journal of Business & Entrepreneurship 151

hypotheses. The sample, variable operationalization, data collected, and estimation techniques are described in Section 3. Section 4 provides the study´s results and discussion and Section 5 concludes.

REVIEW OF THE LITERATURE AND HYPOTHESES STATEMENT

Based on inconclusive results, the academic narrative left behind the study of the individual characteristics of the founders as determinants of business performance (Naffziger, Hornsby & Kuratko, 1994), reorienting their vision towards the entrepreneurship processes (Gartner, 1988). Moreover, based on proposals made by researchers such as Gartner, Shaver, Gatewood & Katz (1994), favoring the collective nature of the processes of entrepreneurship, the attention of academics is now focused, on the one hand, on a better understanding of the procedures and conditions in which entrepreneurship activities are developed, and on the other, over the interaction of the members participating in the teams (Ensley Carland & Carland, 1998). Under this proposal, the present study analyses the characteristics and performance in Mexico, of entrepreneurs groups that are in the early stages of creation of its technology-based startups. Two alternative theoretical frameworks for studying entrepreneurial intentions, as derived from planned behavior are present in the literature: the entrepreneurial event theory (Shapero & Sokol, 1982) and the theory of planned behavior (Ajzen, 1991). There is empirical evidence showing that both theories have been successfully applied in the prediction of self-employment intentions Krueger et al. (2000). Provided that entrepreneurial activity can be considered as an intentionally planned behavior, some researchers recognized the importance of the link between ideas and action (Bird, 1989; Krueger & Carsrud, 1993). Moreover Krueger, Reilly and Carsrud (2000), emphasize the notion that entrepreneurial choice is better predicted by observing intentions toward such behavior and people with the intention to start a new venture have better success opportunities than those without it (Indarti, Rostiani, & Nastiti, 2010). Motivation constitutes a great aide in explaining a wide variety of business behaviors, in particular the entrepreneurial choice and the subsequent efforts to develop a successful business (Van Gelderen et al., 2005). According to Acs (2006), the entrepreneurial reasons based on necessity (PUSH) and opportunity (PULL) play an important role in the operation of new

152 Journal of Business & Entrepreneurship Spring 2017

developments. On one hand, PUSH motives arise when entrepreneurs are forced by conditions external to self-employment (Alstete 2002); Shane, Locke & Collins, 2003; Basu & Goswami, 1999; Grilo & Thurik, 2006) and on the other, PULL motives associate with those individuals that opt for self-employment and take advantage of specific business opportunities (Zhao, Seibert & Lumpkin, 2010; Shane & Venkataraman, 2000; Miller & Friesen, 1978; Van Gelderen & Jansen, 2006). Human capital attributes such as education, experience, knowledge and managerial skills are initially related with success in small businesses (Rauch & Frese, 2000), and their effect over new ventures has been studied (Rauch, Frese & Utsch, 2005). Investors normally assign a great weight to the experience of entrepreneurs in the evaluation of new businesses (Stuart & Abette, 1990), and similarly, in the provision of equity (Zacharakis & Meyer, 2000). In general, human capital is positively associated with business success (Cassar, 2006; Bruderl, Preisendörfer, & Ziegler, 1992), and relates to increased financial returns for the founders (Rauch et al., 2005; Unger, Rauch, Frese & Rosenbusch, 2011). For human capital to be profitable, the conditions of the new venture should be considered, since the possession of human capital alone does not translate directly or sufficiently in success. Thus, it is necessary to consider not only business opportunities, but the capabilities of entrepreneurial teams to detect and seize these opportunities (Shane & Venkataraman, 2000). Cassar (2006), found that once individuals have opted for self-employment, greater investments in human capital increases the success probabilities. This last argument lays the foundation for our second hypothesis. Business success requests from the entrepreneur, the capacity of recognizing and taking advantage of unique business opportunities (Shane, 2000), in order to obtain some kind of economic benefit (Grégoire & Shepherd, 2012; Christiansen, 1997). It can be argued that entrepreneurial motives are important but they require complementation by other internal and external factors. In this study the effect of contextual factors is controlled by a grouping variable that categorizes participants over two groups, depending on the existence of a real business opportunity to take advantage of, notion defined in the literature as the opportunity confidence (Dimov, 2010). In general, the effect of heterogeneity on the integration of the team and, in particular, the importance of diversity in its conformation, as well as the impact on their performance has not been widely established. Gartner (1985) argues in favor of a better performance from multiple capacities in the

Spring 2017 Journal of Business & Entrepreneurship 153

participants of the new business, while authors such as Ensley et al. (1998) found evidence that functional diversity negatively affects performance. In their study on technological ventures, Roure and Maidique (1986) found that both, the heterogeneity and the experience contributed to the good business performance. In his study of entrepreneurs in Germany, Bouncken (2004) found positive effects of cultural diversity over performance, among others ontro positive effects of cultural diversity on performance, among others, from better communication and creativity. Wright and Vanaelst (2009) highlight the increasing interest in analyzing the diversity and complexity of entrepreneurial teams over their potential for generating economic growth. The present research attempts to identify the effect of motivation over the initial success probabilities of startups in Mexico. Following authors such as Van Gelderen et al. (2005), pre-startup success strives over two venues: The first one tries to identify individual founders from non-entrepreneurs, while the second compares successful and non-successful entrepreneurs. In the present case, the analysis of success considers both venues. From observed variables in the sample, factors that are identified as possible predictors of good team´s performance, as measured by the probability of attaining a MPV, are tested. Under the firs venue, as cited above, research attempts to validate the inclusion of PULL-PUSH motives, jointly with other contextual factors as predictors of the expected business behavior. Under the second venue, and controlling for the existence of a real business opportunity, the study aims to validate the effect of factors normally associated with entrepreneurial success such as time deployed and previous experience are also verified (Watson, Hogarth-Scott & Wilson, 1998; Van Praag, 1999; Cassar, 2006; Parker & Van Praag, 2006). Another variable, diversity, measured by greater female participation in teams would also be tried as a predictor for startup success.

Hypotheses Statement

Motivation has been considered in the academic narrative as an important factor predicting initial success of startups (Van Gelderen et al., 2005). In that sense, our first hypothesis is: Motivation, as characterized by the Pull-Push dyad (Acs, 2006), contributes to explain initial startup success in our sample. As it relates to geographical and economic growth based country differences, necessity based entrepreneurship is considered to be an important motive for

154 Journal of Business & Entrepreneurship Spring 2017

self-employment in emerging nations (Valliere & Peterson, 2009). In Mexico, even though opportunity motives are very important (55.5% of entrepreneurial declared motives), the necessity based entrepreneurship accounts for 21% of total self-employment manifested intentions , whereas 22.2% of the respondents in the GEM survey for 2016 acknowledged both motives (GEM, 2016). For that matter a moderate or even a negative effect of the PUSH motive is to be expected. Teams with greater stocks of human capital exhibit less uncertainty about their efficacy and appear to learn faster about their economic surroundings thus reducing their probability of failure in their processes (Baptista et al. 2014). Human capital can be defined as the abilities, knowledge and competences acquired by individuals through their investment in education, training and other related experiences, and arguably it has been considered as a critical resource for new venture success (Unger et al. 2011). Based on earlier studies (Parker y Van Praag, 2006; Shrader & Siegel, 2007;Baptista, Karaöz, & Mendonça, 2014), the second hypothesis is: PUSH motivation, jointly with other factors associated with human capital attributes (Becker, 1975), have a positive effect over the initial entrepreneurial performance of teams in our sample The variables are: time dedicated to the startup´s activities and previous entrepreneurial experience (Cassar, 2006). There is extant literature dedicated to the study of the positive effect of factors associated with demographic diversity over entrepreneurial performance (Williams & O´Reilly III, 1998), and are used as proxies for informational or cognitive diversity (Zhou & Rosini, 2015). For that matter, our third hypothesis relates to the positive effect of variables related to diversity perspective in collective knowledge in the startups. In that sense, heterogeneity of thought favors discussion by increasing the cognitive ability in teams (Ensley & Hmieleski, 2005). This effects are operationalized as i) a significant female participation in teams ii) and a greater amount of human capital (Ucbasaran, Lockett, Wright & Westhead, 2003) being the number of team members, the observable variable in the model.

THE EMPIRICAL RESEARCH PHASE

The Sample

For the study, data was collected from a sample from the nationwide invitation workshop organized by (INADEM), the Mexican Institute for

Spring 2017 Journal of Business & Entrepreneurship 155

Entrepreneurship Promotion (SantanderPyme.com, 2015). The Lean Startup Mexico program was conducted, in the summer of 2015, using the methodology of Customer Development, a method of creating and testing assumptions regarding the end business model for the startup (Blank & Dorf, 2012). INADEM also launched a call for mentors, representing all instances of the Mexican entrepreneurial ecosystem. 40 of them were selected and received special training in the methodology. Thereafter 30 of them were directly responsible for aiding the same number of new ventures and the rest acted as observers. The course lasted 10 weeks and a continuing follow up of 4 extra weeks. After one year, a follow-up revision was completed, where only 29 teams survived the process. Teams included on average three members, considered to be young and ambitious entrepreneurs (M= 28 yrs., SD= 8.5 yrs.). All of the associates in the teams had college education, 43% in public universities in Mexico and with respect to gender, 70% of them were male. Given that the invitation was nationwide and open, the sample can be considered representative of the founder’s community associated with both public and private sector entrepreneurial promotion programs. In order to participate in the workshop, the teams were required to incorporate information technology applications and provide digital solutions to solve real-life problems that could potentially arrive to the market. The selected groups represented activities from the following sectors: Education (21%), health (14%), trade (14%), services (34%), and technology (17%).

Data Collection

Motivation, as the link between intention and entrepreneurial action is the major topic in our analysis. In an open question format, team members were asked to express and rank up to five of their most important motives for participating in the startup program and consequently the reasons behind their entrepreneurial choice. Information was analyzed and for this investigation, entrepreneurial motivation categories, similar to those found in the literature were formed, specifically those that induced opportunity and necessity based entrepreneurship (Acs, 2006). Similarly team members were asked about the time they have spent in their process and their history as entrepreneurs. As for this research, the open end questions relative to the motivation to become entrepreneurs and previous experience are shown in Table 1.

156 Journal of Business & Entrepreneurship Spring 2017

Table 1: Sample Responses to the Lean Startup Workshop Survey

Variable Question Sample responses Why have you elected to “Achievement”; “self-confidence” PULL participate in the “government support”; “knowledge” program? “opportunity”; “technology” Why have you elected to “Additional income” “Unemployment” PUSH participate in the “time availability”; “pure necessity” program? Number of previous new Serial “Number and type of previous projects” ventures? How long have you been Time “The time elapsed and the process itself” working in this venture?

The Operationalization of Success Factors

We are particularly interested in features that match the concept of the potential startup teams participating in our sample. The groups under examination have the following characteristics: 1) They are formed by ambitious and experienced entrepreneurs; 2) have already chosen to be entrepreneurs and are working on their startup; 3) they possess a college education level, and 4) are committed to the use of information and communication technologies, an essential feature in order to achieve startup´s business value in the new economy. Factors conducive to the explanation of initial startup´s sucess Motivation is regarded as the primary link between wanting to do and actually undertaking the project. It is conceptualized under the categories of necessity (PUSH) and opportunity (PULL). According to Acs (2006), necessity entrepreneurship reflects the voluntary action that leads to consider that self- employment is the best available individual opportunity, while opportunity entrepreneurship, and represents the discretionary participation of the person in a project. In the sample, the team´s members expressed their motives, which were classified under the following codes: 1. Solution to unemployment; 2. Identification of a unique business opportunity; 3. Having a specific knowledge, and 4. Internal Motivation internal for self-employment, close to the concept of emotion. Pull and Push" notation is used in this paper. Push motives include the category 1, while the Pull corresponds to categories 2 through 4 In this model, the reasons are mutually exclusive (Dawson & Henley, 2012) and are coded as 1

Spring 2017 Journal of Business & Entrepreneurship 157

when they are present and 0 otherwise. In addition to the pull and push dichotomy, a third category of motivation has been created, called opportunities (Grégoire & Shepherd, 2012), containing categories 2 and 3, thus separating the teams with entrepreneurial opportunities potential, from those with just the expressed manifestation of willingness to undertake the project. In this category the participants made explicit their desire to seize an opportunity or exploit specific business knowledge, unlike those that were compelled to self- employment due to external causes. Other contextual variables suggested by the literature and confirmed with both mentors and leading professionals in the field are: the business experience measured by means of Serial a dichotomous variable coded as 1, if the members of the team had participated in 3 or more entrepreneurial projects or 0 otherwise; The variable time, expressed in months dedicated to the creation of the startup; Two variables that represent multiple perspectives in the decision-making process of the teams that make up the sample. The first called Diversity, a Bernoulli variable defined as 1, if the gender composition, that is the number of women participating in the team represents 50% or more and 0 otherwise; the second is the team´s size as suggested by some studies (Baptista et al. 2014; Ucbasaran et al. 2003). These variables are intended to capture the diversity of thought and shared knowledge in the team (Song, Podoynitsyna, Van Der Bij & Halman, 2008; Vanaelst et al.,2006) The variables contributing to startup success and their Operationalization are exhibited in Table 2.

Table 2. Operationalization of startup success factors Influencing Code Operationalization Factors Classified as derived either from 1= solution to unemployment; 2 = Motivation business opportunity; 3 = Specific opportunity or necessity knowledge; 4 = internal motivation Those motives associated with Bernoulli variable: 1 if Motivation is 2, 3 PULL opportunity entrepreneurship and 4; 0 otherwise. Those motives associated with Bernoulli variable: 1 if Motivation is 1 ; 0 PUSH necessity entrepreneurship otherwise Time deployed in the new Measured in months Time venture Serial Number of previous ventures 1= 3 or more ventures; 0 otherwise.

158 Journal of Business & Entrepreneurship Spring 2017

Control variable separating Bernoulli variable : 1 if containing Advantages potential entrepreneurial Motivation categories 2 and 3; 0 opportunities otherwise Members in the team. Actual number of team´s members Team size Knowledge shared across the team. Diversity in team´s perspective: 1 = 50% or more female members and 0 Diversity Gender composition of the team otherwise.

Entrepreneurial Success as the Dependent Variable

The existence of multiple success determinants in the literature reviewed yields a great difficulty in specifying the factors to be used in the empirical research. The different perspectives of success in new business, makes the choice of a well thought and practical definition of initial startup success an essential task for the present quest. Given that our research is longitudinal and founded on an evolutive stage process, success in the pre-startup phase, as the dependent variable (DV) in our model, is operationalized as the probability of attaining a minimum viable product. In this investigation we will use the definition of MVP provided by Ries (2011), which is that version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort. After one year of working in the process, the teams considered in the sample were monitored as part of the program and their performance, by means of the possibility of attaining a MVP, was evaluated by mentors and observers. This perspective was reinforced and corroborated through self-expressed assessments by the team members themselves. This information was collected at semi-structured interviews conducted by the authors, and coded as 1 if based on the mentor´s perspectives the team showed progress therefore having a good probability of success and 0 if the teams either had failed or showed little progress at the time of the one year´s review.

Descriptive Statistics

From the 29 teams that survived after one year in the program, as shown on Table 3, on average 55% of the teams showed a good probability of developing a MVP according to their mentor´s assessment and the team´s

Spring 2017 Journal of Business & Entrepreneurship 159

expressed performance. As it relates to motivation, the teams expressed that their main motives concentrate around the opportunity category, also 40% of them revealed potential for success as conveyed by the mix of having a specific knowledge and being able to take advantage of a unique business opportunity.

Table 3. Descriptive statistics for variables in the study Variables M SD Range n MVP (DV) .55 .50 0-1 29 PULL .76 .43 0-1 29 PUSH .21 .41 0-1 29 Time 8.00 6.50 1-24 29 Serial .37 .49 0-1 29 Advantages .41 .50 0-1 29 Team size 3.65 1.47 1-8 29 Diversity .31 .47 0-1 29

Estimation Technique

Our hypotheses testing rely on the reduced form model: y = + , i = 1, … , n, Where is the expected value of given = x , = i πi x , … , = x , (Aguilera et al. 2006). In our case is the probability of εi πi 𝒴𝒴 �𝒳𝒳1 i1 𝒳𝒳2 attaining a MVP as a function of a set of available information about the teams. i2 𝒳𝒳p ip� 𝒴𝒴 The analysis of the determinants of success as they´re operationalized requires a technique that handles with adequacy the probabilities of attaining a MVP. Logistic regression is the appropriate technique when dealing with the relationship between a dichotomous outcome and a set of explanatory variables. When a binary response outcome is modeled using logistic regression, it is assumed that the logit transformation of the outcome has a linear relationship with the predictor variables. Thereby the relationship between the response variable and its covariates is interpreted through the odds ratio from the parameters of the models. ( , ,…, ) Log The logistic regression model can be written as: ( , ,…, ) = π Χ1 Χ2 Χk + + + …+ (1), with the binary response�1− variableπ Χ1 Χ2 Χk �being either 0 or 1, and E( | , , … , ).Then ( , , … , ) is interpreted as β0 β1Χ1 β2Χ2 βkΧk 𝒴𝒴 P( = 1) for a given combination of values of the predictor variables 𝒴𝒴1 Χ1i Χ21 Χk1 π Χ1 Χ2 Χk , , … , . The model then can be expressed as: = ( , , … , ) + , 𝒴𝒴 where , could only assume two values depending on whether is equal to zero Χ1 Χ2 Χk 𝒴𝒴 π Χ1 Χ2 Χk ϵ

160 ϵ Journal of Business & Entrepreneurship 𝒴𝒴 Spring 2017

or one. The left hand side of equation (1) is the log odds ratio, that is, the logarithm of the odds that will equal 1, for a given combination of the predictor variables. Maximum likelihood𝒴𝒴 (ML) is generally the estimation method of choice. Notwithstanding, it has been shown that the use of ML, which is heavily based on asymptotic methods might be inadequate when sample sizes are small or the data are sparse, skewed, or heavily tied, yielding poor results in terms of p-values and confidence intervals for (Mehta & Patel, 1995;King & Ryan, 2002). Exact conditional inference remains ivalid in such situation. Therefore, given the characteristics of our sampleβ exact logistic regression (ELR) is used for our estimations. The vector of sufficient statistics for is given by t = with denoting the ith predictor. It`s distribution is: f t , t , … , t = PnT = t , T = β ∑j=1 𝓎𝓎j𝓍𝓍j 𝓍𝓍ij ( ) ( t , T = t , … , T = t = with {S(0t) =1 { p, , … , 0 }: 0 1 ( ) ( ) � � � c t exp t`β 1 2 2 p p ( ) | 1| 2 | |n m, �= t∑u, ic =u exp1,2,u`…β , p} and c t = 𝓎𝓎S ,𝓎𝓎 with S𝓎𝓎 denoting the numbern of distinctn elements in S and the sum is over all u for which c(u) 1. ∑j=1 𝓎𝓎j= ∑j=1 𝓎𝓎j𝓍𝓍ij i t Assuming we wish to estimate , let f(t | , t , t , … , t = P(T = t |T = ( , , ,…, ) ( ) ≥ t , T = t , … , T = t p p p 1 2 p−1 p p 0 = β , , ,…, β, ( )) with the summation over c t0 t1 t2 tp exp βptp all0 values1 1 of u forp− which1 p −c1(t ∑,utc�,tt0 t,1…t2, t tp−1)u�exp1. βThepu ELR estimate of is the value that maximized this conditional likelihood. 0 1 2 p−1 ≥ βp

RESULTS

As per the literature, the factors that are associated with entrepreneurial success can be synthesized over two venues: the first one relates to the self- employment choice (Bird & Schjoedt, 2009) and the second one to the effectiveness of entrepreneurial actions. In the present study both settings would be addressed. The probability of attaining a MVP for a startup in the sample is estimated trough the model shown in the following equation 2: = + + + + + + (2). 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝐵𝐵𝑜𝑜 𝐵𝐵1𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝐵𝐵2𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐵𝐵3𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝐵𝐵4𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝐵𝐵5𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝜀𝜀𝑖𝑖 It has been argued that the creation of new enterprises involves an ex ante selection of business opportunities where entrepreneurs potentially assess their quality (Helfat & Lieberman, 2002; Audretsch & Thurik, 2001), as well as other

Spring 2017 Journal of Business & Entrepreneurship 161

conditions related to economic environment and a high potential for innovation and scalability (Santarelli & Vivarelli, 2007). The factor´s influence is controlled by grouping the teams over the real possibility of taking advantage of a business opportunity. For testing our hypotheses, in Table 4 we report the results from the exact logistic regression, having the probability of developing a MVP as our dependent variable (DV).

Table 4: Final Model: Summary of ELR Analysis for Variables determining the probability of developing a MVP Std. Errors B Exact Exact e of eB Exact Conf. Factor B (Odds 95% Prob Pr <= Int. Ratio) Prob Lower Upper

PUSH++ 2.56*** .02 .01 12.89 NA 1.63 +inf

Term_1** .0001 .05

Time .11* .001 .07 1.12 .09 .97 1.34

NA 1.97 Serial++ -.91 .19 .24 .40 0 8.48 233 Diversity 1.99* .12 .07 6.69 .57 NA 1.20 Team Size -.49 .33 .42 .61 -Inf Notes: *p <= .1; **p <= .05. , ***p <= .01. Group Control variable: Advantages; ++ Median unbiased estimates (MUE) without mid-p value; p = mid p value computed for the MUEs, probabilities and CIs. Term_1 shows joint test for multiple coefficients, in this case Time and Serial.

The overall model is statistically significant with a chi-squared score of 1.2e-06 and a significance of .018. All the estimated coefficients are significant at the 10% level, with the exception of PUSH, which is significant at the 1% level. A specification error test (linktest) was used to verify the assumption that the outcome logit was a linear combination of the independent variables, proving not to be significant at the 5% level The first hypothesis that would be tested is the effect of motivation being the link between intention and action and operationalized through the push-pull dyadic categories over success in the initial startup phases. Here B1 is statistically different from zero at a significance level of 1%, hence we reject the

162 Journal of Business & Entrepreneurship Spring 2017

null hypothesis and consider necessity motives influential in the probability of developing a MVP. Moreover, our results show that PUSH has a positive effect over our DV. Specifically, for this sample, having necessity as a motive for self- employment increases the odds of developing a MVP in 1,189%. The second hypothesis evaluates if jointly with motivation, the probability of attaining a MVP is positively affected by factors that have been considered in the literature as attributes of human capital, such as Time deployed and Serial, as a measure of previous entrepreneurial experience. As can be seen in Table 4, Time is positively associated with MVP while the variable Serial alone was not proven to be significant. Nevertheless, the joint hypothesis test of multiple coefficients, testing that together Time and Serial coefficients are 0 was rejected, with 5% significance. The third hypothesis is: cultural and perspective diversity in the team´s composition is positively associated with success in the pre-startup phase. On one hand, the gender composition of the team, considered to be a measure of diversity of perspectives in the sample, was proven to be significant at the 10% level. That is a team with 50% or more of the members being female increases the odds of achieving a MVP in 559%. On the other, team size was not proven to be significant in this sample.

DISCUSSION

This research is based on the argument that in the field of entrepreneurship, motivation is the missing link between the wanting to do and the actual involvement. In summary, conforming to expectations, the results imply that in the pre-configuration phase, as it relates to our sample, motivation is a significant contributor to startup success. The features considered to be attributes of human capital, as operationalized in our study as Time deployed and Serial (Stuart & Abette, 1990) , have jointly a strong positive effect over the probability of achieving a MVP, as expected (Unger et al. 2011), while those factors that relate to diversity of perspectives and knowledge sharing, only the gender composition of teams prove to be significant. The positive influence of the push motive over the probability of reaching a MVP can have two possible explanations: i) As indicated in the literature, entrepreneurial experience is deemed to increase the probability of early survival in the case of entrepreneurs that have declared push motives; ii) given that in our model we have controlled for teams with a real business opportunity, external factors that increase the pressure for self-employment have a strong influence on teams for attaining good results in the short term.

Spring 2017 Journal of Business & Entrepreneurship 163

Among the surprising results, the variable size of the team, considered important in previous studies (Ucbasaran et al. 2003; Ensley & Hmieleski, 2005) was not deemed significant in our sample. This may be due to several reasons, among them three stand out: the first relates to the s size of the sample itself, which may cause estimation problems; the second relates to the fact that heterogeneity in the team is not big enough to influence decisions and the third one, considers that the project is still in an incipient stage where the teams are in the process of customer discovery, so it is possible that shared knowledge has not been able to be expressed in this phase and the third one considers that heterogeneity in the team in this sample is not big enough to influence decisions. Contrary to earlier studies (Van Gelderen, et al. 2005), another unanticipated result was the positive influence of the Push variable over the possibility of developing a MPV. From our point of view, there are two possible explanations: on the one hand, as it is argued by Baptista et al. (2014), previous experience appears to increase the odds of early survival in the case of necessity induced entrepreneurship In this way, the two factors together seem to have a positive effect on our DV. On the other hand, given that our model has controlled for teams with real business opportunities to seize, the external factors that increase pressure for self-employment have a positive for developing a successful startup. In both cases, there are strong incentives for further clarification of these results. Given the nature of our sample it is not easy to generalize patterns in this research that are common with other studies about small firms. Further investigation need to consider the coexistence in the sample of differential behavior due to the fact that the teams might be in different stages of their entrepreneurial path.

CONCLUSIONS AND LIMITATIONS OF THE RESEARCH

This research explores the role of motivation, as well as that of other contextual variables related to attributes of human capital attributes and team´s characteristics in the process of achieving initial business success, in the case of Latin America, particularly Mexican startups. Endurance is considered as an important milestone in the pre- configuration phases of a startup. For that matter, in this study, success was operationalized as the probability of obtaining a MVP by teams in the sample.

164 Journal of Business & Entrepreneurship Spring 2017

The analysis of startups´ initial success strives on two different avenues: the comparison between entrepreneurs and non-entrepreneurs and the consequent comparison between successful and non-successful ones. Both venues are considered in our study. Based on the collected information we have analyzed the factors which, together with motivation, help explain the initial entrepreneurial performance. Under the first venue the effects of Push-Pull motives on performance were considered. The second examined the impact of variables related to survival that supposedly relate to successful entrepreneurs such as dedication and previous experience in the field of entrepreneurship. We also studied other variables related to the characteristics of teams, particularly those relating to the diversity of thinking (cognitive) and shared knowledge in the teams. In synthesis, as expected, it was found that motivation is conductive to an initial successful performance of the startups in the sample. Also factors related to attributes that characterize human capital such as the combined effect of previous experience and dedication to the project, also contributed to such good performance. Likewise, of the factors related to the team´s characteristics, such as the diversity of perspectives and knowledge sharing, only the first of them, defined under the concept of majority female participation was significant. The positive influence of the Push motives over the probability success, contrary to expectations can have two possible explanations: the first relates to previous results in the literature pointing to that entrepreneurial experience increases the likelihood of early survival in the case of entrepreneurs based necessity motivation and the second related to external pressures to increase success in the case of self-employment induced by external factors, in cases in which teams have expressed a real or potential possibility of exploiting a business opportunity. In both cases it is required to clarify the results both by expand the sample and by further replication of this research under different settings, particularly in other emerging countries. Among the surprising results, the variable size of the team was not significant, situation attributable, in the first instance to the sample; Also to the fact that the heterogeneity is still not big enough to change the decisions inside the startup and the possibility that teams are in the process of validating their products with customers, so shared knowledge has not yet been able to materialize. Contrary to current trends, this research is original to the extent that analyzes the interaction between the members of the team and with a

Spring 2017 Journal of Business & Entrepreneurship 165

longitudinal perspective focuses on study of the early phases of startups. In this way, the analysis favors a greater involvement with the founders, the subject of the study, and uses primary sources of information, enabling further advancement in the understanding of the specific role of motivation in conjunction with other behavior defining variables. Startups´ potential in the creation of jobs, productivity, growth, the diffusion of technology and innovation has aroused public interest. These results contribute significantly both to the advancement of the theory of entrepreneurship and entrepreneurial education and to those activities related to the entrepreneurs programs of support and promotion of the eco-systems in Mexico and Latin America, shedding light over those factors that lead to the good performance of start-ups

REFERENCES

Acs, Z. (2006). How is entepreneurship good for economic growth. Innovations, 1(1), 97-107.

Aguilera, A., Escabias, M., & Valderrama, M. (2006). Using principal components for estimating logistic regression with high-dimensional multicollinear data. Computational Statistics & Data Analysis, 50(8), 1905-1924.

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.

Alstete, J. (2002). On becoming an entrepreneur: an evolving typology. International Journal of Entrepreneurial Behavior & Research, 8(4), 222-234.

Audretsch, D., & Thurik, A. (2001). What's new about the new economy? Sources of growth in the managed and entrepreneurial economies. Industrial and corporate change, 10(1), 267-315.

Baptista, R., Karaöz, M., & Mendonça, J. (2014). The impact of human capital on the early success of necessity versus opportunity-based entrepreneurs. Small Business Economics, 42(4), 831-847.

Basu, A., & Goswami, A. (1999). Influencing Growth,. International Journal of Entrepreneurial Behaviour and Research, 5(5), 251-275.

Becker, G. (1975). Human capital. Chicago: University of Chicago Press.

166 Journal of Business & Entrepreneurship Spring 2017

Bird, B. (1989). Entrepreneurial Behavior. Glenview, IL: Scott Foresman.

Bird, B., & Schjoedt, L. (2009). Entrepreneurial behavior: Its nature, scope, recent research, and agenda for future research. En Understanding the entrepreneurial mind (págs. 327-358). New York: Springer.

Blank, S. (2014). What Drones and Crop Dusters Can Teach About Minimum Viable Product. Harvard Business Review Digital Articles, 2-4.

Blank, S., & Dorf, B. (2012). The startup owner's manual: the step-by-step guide for building a great company. Pescadero,CA: K&S Ranch.

Bouncken, R. (2004). Cultural diversity in entrepreneurial teams: findings of new ventures in Germany. Creativity and Innovation Management, 13(4), 240-253.

Brockhaus, R., & Howtz, P. (1982). The psychology of the entrepreneur. En C. Kent, D. Sexton, & K. Vesper (Edits.), The Encyclopedia of Entrepreneurship.

Brüderl, J., Preisendörfer, P., & Ziegler, R. (1992). Survival chances of newly founded business organizations. American sociological review, 227-242.

Bruderl, J., & Schussler, R. (1990). Organizational mortality: The liabilities of newness and adolescence. Administrative Science Quarterly, 530-547.

Cassar, G. (2006). Entrepreneur opportunity costs and intended venture growth. Journal of Business Venturing, 21(5), 610-632.

Christiansen, C. (1997). The innovators dilemma. Cambridge. Cambridge: Harvard Business School Press.

Dawson, C., & Henley, A. (2012). “Push” versus “pull” entrepreneurship: an ambiguous distinction? International Journal of Entrepreneurial Behavior & Research, 18(6), 697- 719.

Dimov, D. (2010). Nascent entrepreneurs and venture emergence: Opportunity confidence, human capital, and early planning. Journal of Management Studies, 47(6), 1123-1153., 47(6), 1123-1153.

Edelman, L., Brush, C., Manolova, T., & Greene, P. (2010). Start‐up motivations and growth intentions of minority nascent entrepreneurs. Journal of Small Business Management, 48(2), 174-196.

Spring 2017 Journal of Business & Entrepreneurship 167

Ensley, M., & Hmieleski, K. (2005). A comparative study of new venture top management team composition, dynamics and performance between university-based and independent start-ups. Research policy, 34(7), 1091-1105.

Ensley, M., Carland, J. W., & Carland, J. C. (1998). The effect of entrepreneurial team skill heterogeneity and functional diversity on new venture performance. Journal of Business and Entrepreneurship, 10(1), 1.

Gartner, W. (1985). A conceptual framework for describing the phenomenon of new venture creation. Academy of management review, 10(4), 696-706.

Gartner, W. (1988). "Who is an Entrepreneur?" Is the Wrong Question. American Journal Of Small Business, 12(4), 11-32.

Gartner, W., Shaver, K., Gatewood, E., & Katz, J. (1994). Finding the Entrepreneur in Entrepreneurship. Entrepreneurship. Entrepreneurship: Theory & Practice, 18(3), 5-9.

Garud, R., & Giuliani, A. (2013). A narrative perspective on entrepreneurial opportunities. Academy of Management Review, 38(1), 157-160.

Van Gelderen, M., & Jansen, P. (2006). Autonomy as a Start-up Motive. Journal of Small Business and Enterprise Development, 13(1), 23-32.

Van Gelderen, M., Thurik, R., & Bosma, N. (2005). Success and risk factors in the pre- startup phase. Small business economics, 24(4), 369-380.

GEM. (2016). Reporte Nacional México 2015-2016. México: ITESM.

Grilo, I., & Thurik, A. (2006). Entrepreneurship in the Old and New Europe,. En E. Santarelli (Ed.), Entrepreneurship, Growth, and Innovation: the Dynamics of Firms and Industries: (págs. 75-103). Berlin: Springer Science.

Helfat, C., & Lieberman, M. (2002). The birth of capabilities: market entry and the importance of pre‐history. Industrial and corporate change, 11(4), 725-760.

Indarti, N., Rostiani, R., & Nastiti, T. (2010). Underlying factors of entrepreneurial intentions among asian students. The South East Asian Journal of Management, 4(2), 143-159.

Kamm, J., Shuman, J., Seeger, J., & Nurick, A. (1990). Entrepreneurial teams in new venture creation: A research agenda. Entrepreneurship Theory and Practice, 14(4), 7- 17.

168 Journal of Business & Entrepreneurship Spring 2017

Katz, J., & Gartner, W. (1988). Properties of Emerging Organizations. Academy of management Review, 13(3), 429-441.

King, E., & Ryan, T. (2002). A preliminary investigation of maximum likelihood logistic regression versus exact logistic regression. The American Statistician, 56(3), 163-170.

Krueger, N., & Carsrud, A. (1993). Entrepreneurial intentions: applying the theory of planned behaviour. Entrepreneurship & Regional Development, 5(4), 315-330.

Krueger, N., Reilly, M., & Carsrud. (2000). Competing models of entrepreneurial intentions. Journal of business venturing, 15(5), 411-432.

Mehta, C., & Patel, N. (1995). Exact logistic regression: theory and examples. Statistics in medicine, 14(19), 2143-2160.

Meyer, G. (2011). The reinvention of academic entrepreneurship. Journal of Small Business Management, 49(1), 1-8.

Miller, D., & Friesen, P. (1978). Archetypes of Strategy Formulation. Management Science. 24 (9), 921-933, 24(9), 921-933.

Naffziger, D., Hornsby, J., & Kuratko, D. (1994). A proposed research model of entrepreneurial motivation. Entrepreneurship: Theory and Practice, 18(3), 29-43.

Parker, S., & Van Praag, C. (2006). Schooling, capital constraints, and entrepreneurial performance: The endogenous triangle. Journal of Business & Economic Statistics, 24(4), 416-431.

Rauch, A., & Frese, M. (2000). Psychological approaches to entrepreneurial success: A general model and an overview of findings. International review of industrial and organizational psychology, 15, 101-142.

Rauch, A., Frese, M., & Utsch, A. (2005). Effects of human capital and long‐term human resources development and utilization on employment growth of small‐scale businesses: a causal analysis. Entrepreneurship Theory and Practice, 29(6), 681-698.

Richard, O., Barnett, T., Dwyer, S., & Chadwick, K. (2004). . Cultural diversity in management, firm performance, and the moderating role of entrepreneurial orientation dimensions. Academy of management journal, 47(2), 255-266.

Ries, E. (2011). The lean startup: how today's entrepreneurs use continuous innovation to create radically successful businesse. New York: Crown Business.

Spring 2017 Journal of Business & Entrepreneurship 169

Roure, J., & Maidique, M. (1986). Linking prefunding factors and high-technology venture success: An exploratory study. Journal of Business Venturing, 1(3), 295-306.

SantanderPyme.com. (2015). lean-startups un metodo revolucionario para emprender. Recuperado el 28 de Marzo de 2017, de https://www.santanderpyme.com.mx/detalle- noticia/lean-startups-un-metodo-revolucionario-para-emprender.html

Santarelli, E., & Vivarelli, M. (2007). Entrepreneurship and the process of firms’ entry, survival and growth. Industrial and corporate change, 16(3), 455--488.

Shane, S. (2000). Prior knowledge and the discovery of entrepreneurial opportunities. Organization science, 11(4), 448-469.

Shane, S., & Venkataraman, S. (2000). The Promise of Entrepreneurship as a Field of Research. Academy of Management Review, 25(1), 217-226.

Shane, S., Locke, E., & Collins, C. (2003). Entrepreneurial motivation. Human resource management review, 13(2), 257-279.

. Shapero, A., & Sokol, L. (1982). The Social Dimensions of Entrepreneurship . Encyclopedia of Entrepreneurship, 72-90.

Shrader, R., & Siegel, D. (2007). Assessing the relationship between human capital and firm performance: evidence from technology‐based new ventures. Entrepreneurship Theory and Practice, 31(6), 893-908.

Song, M., Podoynitsyna, K., Van Der Bij, H., & Halman, J. (2008). Success factors in new ventures: A meta‐analysis. Journal of product innovation management, 25(1), 7-27.

Stuart, R., & Abette, P. (1990). Impact of entrepreneural and management experience on early performance. Journal of business venturing, 5, 151-162.

Ucbasaran, D., Lockett, A., Wright, M., & Westhead, P. (2003). Entrepreneurial founder teams: factors associated with member entry and exit. Entrepreneurship: Theory And Practice, 2, 107.

Unger, J., Rauch, A., Frese, M., & Rosenbusch, N. (2011). Human capital and entrepreneurial success: a meta-analytical review. Journal of Business Venturing, 26, 341-358.

Valliere, D., & Peterson, R. (2009). Entrepreneurship and economic growth: Evidence from emerging and developed countries. Entrepreneurship & Regional Development, 21(5/6), 458-480.

170 Journal of Business & Entrepreneurship Spring 2017

Van Praag, C. (1999). Some classic views on entrepreneurship. De Economist, 147(3), 311-335.

Vanaelst, I., Clarysse, B., Wright, M., Lockett, A., Moray, N., & S'Jegers, R. (2006). Entrepreneurial team development in academic spinouts: An examination of team heterogeneity. Entrepreneurship Theory and Practice, 30(2), 249-271.

Vroom, V. H. (1964). Work and motivation. New York: Wiley.

Watson, K., Hogarth-Scott, S., & Wilson, N. (1998). Small business start-ups: success factors and support implications. International Journal of Entrepreneurial Behavior & Research, 4(3), 217-238.

Williams, K., & O'Reilly III, C. (1998). Demography and Diversity in Organizations: A Review of 40 Years of Research. 20, 77-140.

Wright, M., & Vanaelst, I. (Edits.). (2009). Entrepreneurial teams and new business creation. Edward Elgar.

York, J., & Danes, J. (2015). Customer Development, Innovation, and Decision-Making Biases int he Lean Startup. Journal of Small Business Strategy, 24(2), 21-40.

Zacharakis, A., & Meyer, G. (2000). The potential of actuarial decision models: can they improve the venture capital investment decision? Journal of Business Venturing, 15(4), 323-246.

Zhou, W., & Rosini, E. (2015). Entrepreneurial team diversity and performance: Toward an integrated model. Entrepreneurship Research Journal, 5(1), 31-60.

v Authors wish to thank Universidad Anahuac, México, IDEARSE Anahuac Institute and INADEM for the aide provided in concluding the present research.

Spring 2017 Journal of Business & Entrepreneurship 171