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This dissertation is comprised of three separate entrepreneurship papers. Paper 1, "Entrepreneurial Heuristics...", found that being mentored, rather than either having extensive higher education or more work experience, was primarily responsible for entrepreneurs acquiring the simplified decision rules (heuristics) that can be useful to them in their business pursuits. The study also found that entrepreneurs do not seem to switch their decision making processes from a "rational man" (thorough) mode to a more abbreviated, heuristic mode as some current thinking suggests. Also in Paper 1 this researcher presented and utilized a 27 item heuristics scale which was used to identify "use of heuristics" by the entrepreneurs studied. Paper 2, "Female Serial Entrepreneurs...", examined the characteristics of female serial entrepreneurs (SE's) as a group of growing size and importance. Three areas- business size, hours worked in the business, and amount and type of capital raised- were explored through contrasting female SE's with female non-SE's and male SE's. The primary findings were as follows: the businesses of female SE's are larger than those of female non-SE's, and female SE's in the professional, technical and scientific services industry borrow more debt than female SE's in this industry, but they do not work longer hours than female non-SE's. It was also found that female SE businesses, in the industries examined in the study, have come to rival male SE businesses in size, as measured in revenues. Paper 3, "The Serial Entrepreneur Dilemma...", explained a conundrum: why serial entrepreneurs do not seem to outperform novice entrepreneurs. A literature review is given consisting of the scholarly thinking about the causes of the conundrum, and then three hypotheses are tested to explore the dilemma. It was found that looking at serial entrepreneurs and novices over time, rather than cross-sectionally, helps to explain the conundrum: the SE's are willing to take losses early on (thus not performing higher than the novices) in expectation of future profits. It was also found that in slow-moving industries, serial entrepreneurs performed much better than novice entrepreneurs in revenues, whereas in fast-moving industries the difference between the two groups in performance was negligible. / Business Administration

Modern construction projects are delivered in complex, fast pace environments. Stakeholders are required to participate in dynamic project settings with resource constraints, information constraints, and time constraints. To overcome gaps in knowledge, to deliver decisions quickly, and to overcome human limits in cognitive ability, decision makers typically employ heuristics, or rules of thumb to arrive at relatively quick answers. Heuristics are cognitive shortcuts that an individual employs to arrive at quick decisions (Goodwin et al., 2004). These heuristics are used in a variety of ways, ranging from using the process of elimination (elimination heuristic) to applying different cognitive weights to options based on recent experience, reputation, or familiarity (Shah and Oppenheimer, 2008). This research aims to identify heuristics present in the implementation phase of construction. By summarizing the results of two studies conducted with a Mid-Atlantic Contractor, this thesis prescribes seven heuristics commonly used by construction stakeholders. / Master of Science / Modern construction provides a difficult decision making environment for workers. Construction stakeholders often work in environments with limited time, with limited information, and with limited knowledge. Decision makers in these environments typically use mental rules of thumb (formerly known as heuristics). These rules of thumb help decisions makers arrive at quick answers and often increase efficiency. They can be used in a variety of ways. An individual may use the process of elimination to find a solution. Others may base their decision off a company, person, or object ‘s reputation. Others may only choose an option that is recognizable. Rules of thumb take many forms and are used by all people. Studying rules of thumb can benefit an industry. This has already been proven in many industries, such as insurance (Handel & Kolstad, 2015), medicine (Martin et al., 2012), and economics (Grandori, 2010). The construction industry has begun to study rules of thumb that impact early stages of the construction process, but it still lacks rules of thumb that impact the process of physical construction. This paper aims to assist the construction industry in gaining a fuller view of decision making shortcuts used by its stakeholders. By summarizing the results of two studies conducted with a Mid-Atlantic Contractor, this thesis outlines seven heuristic used by construction workers.

In recent years, the expansion of online media has presented firms with rich and voluminous new datasets with profound business applications. Among these, online reviews provide nuanced details on consumers' interactions with products. Analysis of these reviews has enormous potential, but the enormity of the data and the nature of unstructured text make mining these insights challenging and time-consuming. This paper presents three studies examining this problem and suggesting techniques for automated extraction of vital insights. The first study examines the problem of identifying mentions of safety hazards in online reviews. Discussions of hazards may have profound importance for firms and regulators as they seek to protect consumers. However, as most online reviews do not pertain to safety hazards, identifying this small portion of reviews is a challenging problem. Much of the literature in this domain focuses on selecting "smoke terms," or specific words and phrases closely associated with the mentions of safety hazards. We first examine and evaluate prior techniques to identify these reviews, which incorporate substantial human opinion in curating smoke terms and thus vary in their effectiveness. We propose a new automated method that utilizes a heuristic to curate smoke terms, and we find that this method is far more efficient than the human-driven techniques. Finally, we incorporate consumers' star ratings in our analysis, further improving prediction of safety hazard-related discussions. The second study examines the identification of consumer-sourced innovation ideas and opportunities from online reviews. We build upon a widely-accepted attribute mapping framework from the entrepreneurship literature for evaluating and comparing product attributes. We first adapt this framework for use in the analysis of online reviews. Then, we develop analytical techniques based on smoke terms for automated identification of innovation opportunities mentioned in online reviews. These techniques can be used to profile products as to attributes that affect or have the potential to affect their competitive standing. In collaboration with a large countertop appliances manufacturer, we assess and validate the usefulness of these suggestions, tying together the theoretical value of the attribute mapping framework and the practical value of identifying innovation-related discussions in online reviews. The third study addresses safety hazard monitoring for use cases in which a higher yield of safety hazards detected is desirable. We note a trade-off between the efficiency of hazard techniques described in the first study and the depth of such techniques, as a high proportion of identified records refer to true hazards, but several important hazards may be undetected. We suggest several techniques for handling this trade-off, including alternate objective functions for heuristics and fuzzy term matching, which improve the total yield. We examine the efficacy of each of these techniques and contrast their merits with past techniques. Finally, we test the capability of these methods to generalize to online reviews across different product categories. / Doctor of Philosophy / This dissertation presents three studies that utilize text analytic methods to analyze and derive insights from online reviews. The first study aims to detect distinctive words and phrases particularly prevalent in online reviews that describe safety hazards. This study proposes algorithmic and heuristic methods for identifying words and phrases that are especially common in these reviews, allowing for an automated process to prioritize these reviews for practitioners more efficiently. The second study extends these methods for use in detecting mentions of product innovation opportunities in online reviews. We show that these techniques can used to profile products based on attributes that differentiate them from competition or have the potential to do so in the future. Additionally, we validate that product managers find this attribute profiling useful to their innovation processes. Finally, the third study examines automated safety hazard monitoring for situations in which the yield or total number of safety hazards detected is an important consideration in addition to efficiency. We propose a variety of new techniques for handling these situations and contrast them with the techniques used in prior studies. Lastly, we test these methods across diverse product categories.