Smith Entrepreneurship Research Conference

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Date

Thursday, April 19 to Saturday, April 21, 2018

Advisory Board

Anil Gupta
Phone: 301-537-6738
Email: agupta@rhsmith.umd.edu

David Kirsh
Phone: 240-533-5047
Email: dkirsh@rhsmith.umd.edu

Conference Location

Van Munching Hall
Robert H. Smith School of Business
The University of Maryland
College Park, Md. 20742

Guest Lodging

The Hotel at The University of Maryland
7777 Baltimore Avenue
College Park, Md. 20740
301-277-7777

Supported by

Department of Management & Organization, University of Maryland
Ewing marion Kauffman Foundation
Dingman Center for Entrepreneurship, Robert H. Smith School of Business

Conference Program

Kauffman Doctoral Consortium
Thursday, April 19, 2018, Van Munching Hall

Time Activity Room
11 - Noon Registration and Lunch 2333
Noon - 2 p.m.

Though Leadership Talk

Introduction to the Best Methods Course, Ever
Evan Starr, Maryland

2333
2 - 2:15 p.m. Break  
2:15 - 3:30 p.m.

Roundtable Discussions - PHD Students' Research
Six roundtables, three students and two faculty per table

2333
3:30 - 3:45 p.m. Break  
3:45 - 4:45 p.m. Converstaions with Howard Aldrich, UNC and Kathy Eisenhardt, Stanford
Moderator: Anil Gupta, Maryland
2511
4:45 - 5 p.m. Break  
5 - 5:45 p.m. Job Search and Placement - Conversation with Recent Graduates 2511
6 p.m. Cocktails and Dinner
Third floor atrium
 

Research Conference
Friday, April 20, 2018, Van Munching Hall

Time Activity Room
8 - 8:30 a.m. Registration and Continental Breakfast  
8:30 - 10 a.m.

Session 1: Labor Markets - Impact of Supply and Demand Factors

The Effect of Firm Characteristics on Supply-side Labor Market Mechanisms
Mabel Abraham, Columbia
Vanesssa Burbano, Columbia
Discussant: Jim Wade, George Washington 

Trade-offs in Motivationg Volunteer Effort: Experimental Evidence on Voluntary Contributions to Science
Laurina Zhange, Georgia Tech
Elizabeth Lyons, UC San Diego
Discussant: Serguey Braguinsky, Maryland

2333
10 - 10:30 a.m. Break  
10:30 a.m. - Noon

Session 2: The Dilemma of Persistence vs Change

Keep Learning and Carry On: How Entrepreneurial Firms Learn From Choosing Not to Change Strategy
Jacqueline Kirtley, Wharton
Discussion: David Kirsch, Maryland

Decision Weaving: Effective Strategy Formation in Entrepreneurial Settings
Timothy E. Ott, UNC
Kathleen Eisenhardt, Stanford
Discussant: Mary Tripsas, Boston College

2333
Noon - 1:30 p.m. Lunch  
1:30 - 3 p.m.

Session 3: Panel Discussion - Gender and Entrepreneurship: Past, Present and Future

Mabel Abraham, Columbia
Dolly Oberoir, C-Founder & Chairman, C2 Technologies
David Ross, Florida
Rajshree Agarwal, Maryland - Session Chair and Moderator

Sponsored by
Dingman Center for Entrepreneurship
Ladies First Initiative

2333
3 - 3:30 p.m. Break  
3:30 - 5 p.m.

Session 4: Impact of Rivalry on Creativity and Innovation

Creativity Under Fire: The Effects of Competition on Creative Production
Daniel Gross, Harvard
Discussant: David Waguespack, Maryland

The Performance Effects of Errors in Mental Models in Novel Innovation
Jim Ostler, Michigan
Nile W. Hatch, Brigham Young
Discussant: Christine Beckman, Maryland

2333
5:30 - 6:45 p.m. Cocktails: The Hotel at The University of Maryland Lobby Bar 
7 - 9 p.m. Dinner: The Hotel at The University of Maryland
Keynote: Kathleen Eisenhardt, Stanford
Henson Room

Saturday, April 21, 2018, Van Munching Hall

Time Activity Room
8 - 8:30 a.m. Continental Breakfast  
8:30 - 10 a.m.

Session 5: Decision Making - Individuals and Algorithms

Social Norms as a Strategic Tool: Evidence from a Randomized Field Experiment
Bryan Stroube, LBS
Qiang "Johr" Li, HKUST
Bo Zhao, Asiar Development Banking
Discussant: Chris Rider, Georgetown 

Bias and Productivity in Humans and Algorithms: Theory and Evidence from Resume Screening
Bo Cowgill, Columbia
Discussant: Anil Gupta, Maryland

2333
10 - 10:30 a.m. Break  
10:30 a.m. - Noon

Session 6: Interfirm Collaboration Within Entrepreneurial Ecosystems

Startups and large Corporation Collaboration: Prey or Partners?
Daniel C. Fehder, USC
Yael V. Hochberg, Rice
Daniel J. Lee, Rice
Discussant: Sonali Shah, Illinois

How to Join the Club: Patterns of Embeddedness and Addition of New Members to Interorganizational Collaborations
Lei Zhang, USF
Isin Guler, UNC
Discussant: Waverly Ding, Maryland

2333
Noon Brown Bag Lunch & Close  

Paper Abstracts

The Effect of Firm Characteristics on Supply-side Labor Market Mechanisms
Mabel Abraham (Columbia) – with Vanessa Burbano (Columbia)

Gender inequality across a number of labor market outcomes, including hiring and wages, is persistent. In examinations of labor market inequality, research has commonly focused on two dominant categories of mechanisms. Demand-side explanations center on how organizational practices and employers’ biases lead to differential outcomes for certain groups, such as racial minorities and women (e.g., Bielby and Baron 1986; Pager et al. 2009; Fernandez & Mors 2008; Kmec 2005). For example, some studies have found that men and women are steered into different job openings by managers (Pager et al. 2009; Fernandez & Mors 2008). Supply-side explanations, on the other hand, focus on how the preferences and behaviors of job candidates may lead some groups to favor certain types of jobs or companies over others (e.g., Correll 2004; Fernandez and Friedrich 2011; Cech, Rubineau, Silbey & Seron 2011). Results from this line of research reveal that observed gender differences may in fact be due to men and women choosing different jobs, companies, or industries.

Though the majority of research looking at labor market inequality has focused on either supply or demand-side explanations, findings from some recent work highlights that these processes must be taken together as they are not independent. For example, expectations about discrimination have been found to affect the likelihood that job seekers apply for a given job, with women being especially deterred from jobs perceived to be more discriminatory (Barbulescu & Bidwell 2012; Fernandez-Mateo & Fernandez 2016; Pager & Pedulla, 2015). In this study we make progress on the intersection of demand and supply-side mechanisms by uncovering the conditions under which organizational characteristics affect the likelihood that particular job seekers enter into the pipeline, or apply, for a given job or to an organization. Generally, job seekers have imperfect information about what it will be like to work with a prospective employer. This is heightened for entrepreneurial firms where there is minimal information available to prospective employees, in terms of both the work environment and the prospective employer’s quality more generally. Therefore, informational signals influence a job seeker’s perceptions about an employer (Fombrun and Shanley 1990). We draw on gender role theory to argue that the gender of a firm’s leadership team and organizational claims about social responsibility and diversity are important characteristics from which job seekers may infer information about a firm.

One key challenge in understanding supply-side mechanisms is related to collecting appropriate data. Specifically, it is necessary to observe all possible applicants to job vacancies across organizations with various characteristics. We address this challenge using a field experimental design that allows us to track job seeker application behavior across firms that vary in terms of founder gender and stated commitment to issues around social responsibility and diversity. This approach allows us to deepen our understanding of how demand- and supply-side factors contribute to gender differences in the labor market through causal identification.

Bias and Productivity in Humans and Algorithms: Theory and Evidence from Resume Screening
Bo Cowgill (Columbia)

Where should algorithms improve decision-making? I formally model the advantages of human judgements and decision-making algorithms. I show that algorithms can remove human biases exhibited in the training data, but only if the human judgment is sufficiently noisy. The model suggests that decision-making algorithms have the biggest effects on productivity where human judgement is both biased and inconsistent. Where human decisions are biased and consistent, then algorithms trained on these judgments (and their outcomes) will codify the bias rather than reducing it. By contrast: Noise in human judgement facilitates de-biasing by contributing quasi-experimental variation into algorithms' training data. I test these predictions in a field experiment in applying machine learning for hiring workers for white-collar team-production jobs. The marginal candidate selected by the machine (rejected by human screeners) is a) +14\% more likely to pass a face-to-face interview with incumbent workers and receive a job offer, b) +18\% more likely to accept job offers when extended by the employer, and c) 0.2$\sigma$-0.4$\sigma$ more productive once hired as employees. They are also 12\% less likely to show evidence of competing job offers during salary negotiations. Estimates of heterogeneous effects suggest that the results are driven by non-traditional job applicants: Candidates from non-elite backgrounds, those who lack job referrals, those without prior experience, those with atypical credentials and those with strong non-cognitive soft-skills. Empirical evidence suggests that human evaluation of these candidates was both noisy and biased.

Startups and Large Corporation Collaboration: Prey or Partners?
Daniel C. Fehder (University of Southern California) - with Yael V. Hochberg (Rice) & Daniel J. Lee (Rice)

Despite their increasing importance for both sides, we understand little about the process generating alliances between startups and existing firms beyond corporate venture capital. While strategic investments are important, there are a broader set of relationships employed by firms (e.g. beta customer, strategic advisory, etc.) that are not as easily observed. We use observational data in combination with a field experiment in a prominent networking event for healthcare startups, payor networks and provider networks, to explore startup motivations and willingness to seek relationships with established corporations. While existing literature suggests that only larger and more established startups seek relationships with corporations due to fears of expropriation, we find that, in fact, extremely small and early stage startups also express demand for relationship with large corporations, though not necessarily through an investment channel. Our experimental evidence suggests that such small and early startups respond best to the possibility of gaining feedback from key corporate decision makers.

Creativity Under Fire: The Effects of Competition on Creative Production
Daniel Gross (Harvard)

Though fundamental to innovation and essential to many industries and occupations, individual creativity has received limited attention as an economic behavior and has historically proven difficult to study. This paper studies the incentive effects of competition on individuals' creative production. Using a sample of commercial logo design competitions, and a novel, content-based measure of originality, I find that intensifying competition induces agents to produce original, untested ideas over tweaking their earlier work, but heavy competition drives them to stop investing altogether. The results yield lessons for the management of creative workers and for the implementation of competitive procurement mechanisms for innovation.

Keep Learning and Carry On: How Entrepreneurial Firms Learn From Choosing Not to Change Strategy
Jacqueline Kirtley (Wharton)

Entrepreneurial technology firms exist in a dynamic and uncertain context about which they are advised to continually learn so that they can change their strategies to improve their likelihood for success. Yet, these firms are more likely to carry on with their current strategy than to change. Focus on the outcome of strategic change has limited our understanding of how these firms learn from and during the process of choosing not to change. With a longitudinal field study of seven entrepreneurial firms developing innovations in energy and cleantech, I examined 80 strategic decisions for which the firms chose to not change their strategy. I found that new learning augmented the accepted information and believed assumptions on which they based strategic decisions. They used that learning to clarify their goals, reassess tradeoffs, and refine their assumptions, which ultimately supported their choice to not change. Further, I found that the choice to not change strategy was not a choice for inaction, but for one of a set of purposefully selected options: to abstain from change, time shift the current strategy, or adjust to improve the current strategy.

The Performance Effects of Errors in Mental Models in Novel Innovation
Jim Ostler (Michigan) – with Nile W. Hatch (Brigham Young)

Cognitive representations of rivalry are difficult for managers in the best of times. When facing the uncertainties of innovation, managers must add the problem of competing against uncertainty to the problem of competing against rivals. This added complexity increases the likelihood of a misrepresentation of the competitive environment and errors in innovation decisions. We study the impact of differences in the representations of rivalry that firms may have when contemplating innovation and entry into a new market. We build a formal model of these firms and impose competing representations on them to evaluate the accuracy of their decisions and the profitability losses they suffer due to inaccurate representations of the rivalry they face. We find that under certain conditions incorrect mental models can outperform correct ones. Further, a firms performance is often more dependent upon the mental model of the competition, and in general it is better to play against a smart competitor than a naïve one.

Decision Weaving: Effective Strategy Formation in Entrepreneurial Settings
Timothy E. Ott (UNC) – with Kathleen M. Eisenhardt (Stanford)

Strategy formation is central to why some firms succeed in entrepreneurial settings while others do not. Prior research suggests that executives effectively form strategies by acting to learn about novel opportunities, and thinking to develop a holistic understanding of the complex set of activities that must fit together in a strategy. But it remains unclear how effective strategists actually combine these processes. So, we ask: How do executives effectively form strategies in entrepreneurial settings? Given limited theory and research, we use theory-building case methods to take a rare look at how 3 matched pairs of firms in distinct two-sided markets attempt to form strategies. Our key contribution is a novel and effective strategy formation process: Decision weaving. Decision weaving is two-pronged: Executives use sequential focus to intensely figure out strategy in a focal domain and then move to a new one at a learning plateau. Simultaneously, they use easy and low-resource stepping stones both to advance progress in background domains and to maintain a holistic view. Broadly, we add to the organizational learning, managerial cognition, and strategy formation literatures. Overall, we contribute a portrait of a cognitively sophisticated, yet realistic strategist.

Social Norms as a Strategic Tool: Evidence from a Randomized Field Experiment
Bryan Stroube (LBS) – with Qiang “John” Li (HKUST) & Bo Zhao (Asian Development Bank)

Strategy research has documented how firms are influenced by social norms, yet has generally overlooked how a firm itself might attempt to communicate social norms to stakeholders in order to improve its own performance. In this study, we conducted a randomized field experiment involving 96,065 users of an online lending platform to test whether the firm could strategically disclose information about collective behavior to users to increase firm performance. We found that the communication of even very simple social normative information (i.e., how others had behaved) affected (1) the likelihood that users made a desired decision, and (2) subsequent performance outcomes for the firm. Unlike economic incentives, the communication of social normative information is nearly costless, making it a particularly useful strategic tool for firms.

Trade-offs in Motivating Volunteer Effort: Experimental Evidence on Voluntary Contributions to Science
Laurina Zhang (Georgia Tech) - with Elizabeth Lyons (UC-San Diego)

Digitization has facilitated new opportunities for voluntary contributions to public goods, for instance, through the proliferation of crowd science. However, the incentives that affect participation and the outcomes of voluntary scientific contributions are unclear. In this paper, we examine non-pecuniary incentives for scientific contribution with a field experiment on the world's largest crowd science platform and with a survey experiment. In both experiments, we examine whether the salience of project outputs (i.e., project outcome) or project inputs (i.e., research hours) impacts the quantity and quality of crowd science contributions. We find that increasing the salience of both input and output value decreases voluntary participation in science, but increases the match quality between the task and the volunteer. Furthermore, we find that individuals that select out of volunteering in response to the type of information provided substitute volunteering time by donating money from wage work. We discuss implications for organizations, entrepreneurship, and policy.

How to join the club: Patterns of embeddedness and addition of new members to interorganizational collaborations
Lei Zhang (USF) - with Isin Guler (UNC)

We develop and test a theory of how group dynamics manifest in variable patterns of relational embeddedness influence the growth of interorganizational collaborations through new member additions. By building on theory on interorganizational collaborations, social exchange, and group-based research, we argue that group dynamics that develop among members in a collaboration, as well as between each member and potential newcomers, influence which new members join existing collaborations. For potential newcomers, we distinguish between the depth and breadth of embeddedness, which refer to the strength and spread of the newcomer’s prior ties with members of the collaboration, respectively. For existing members of a collaboration, we examine the strength of network faultlines, which refers to subgroups based on heterogeneous strength of past relationships among members. Using U.S. VC investment data (1985-2008) and a matched-sample framework, we find that, contingent on the strength of network faultlines within a collaboration, the depth and breadth of a potential newcomer’s embeddedness will have distinct influences on its likelihood of joining the collaboration. We also demonstrate that the strength of these influences varies with the newcomer’s attributes.