The ability of entrepreneurs to incubate new venture opportunity remains an under-researched area of entrepreneurship studies. When engaged actors are motivated to pursue such activity venturing action is invigorated. This bridges the gap between believing in an idea and developing personal capacity to translate that belief into viable venturing. However, it has remained unclear how successful entrepreneurs have navigated this complex phase. This study therefore advances empirical insight into the iterative character of new venture opportunity incubation as enacted by seasoned entrepreneurs. Building on existing scholarship the study promotes a lived experience-led conceptualisation of key constructs and their relationships. Longitudinal data gathering from purposively selected case studies enabled the capture of qualitative data. Computer aided data analysis and coding (CADAC) revealed underling themes, thus illuminating meaningful pattern recognition. Deductive analysis of cross sectional interview data substantiated findings. Triangulation analysis revealed the activities, cognitions and behaviours which characterise opportunity incubation, as the subjects converted ideas into new venture concepts. These findings contribute to existing knowledge at a theoretical level: firstly, by identifying and describing the micro-processes that constitute new venture opportunity incubation. Secondly, the granular level of activation that the study accessed, revealed entrepreneurs’ cognitive and behavioural competencies in driving enactment. Finally, the study identified the venture concept artefacts that seasoned entrepreneurs’ prioritise when shaping new venture concepts. / Thesis (DPhil)--University of Pretoria, 2020. / Gordon Institute of Business Science (GIBS) / DPhil / Unrestricted

Thesis advisor: Rui Albuquerque / Thesis advisor: Thomas J. Chemmanur / In my first essay, I develop a model of investor behavior around prescheduled macroeconomic announcements to analyze the optimal allocation of investor attention between systematic and idiosyncratic risk factors when a macroeconomic announcement is anticipated. Skilled investors, when producing information under a limited attention capacity, optimally allocate more of their attention to analyzing the idiosyncratic risk factor when they anticipate more precise public information about the systematic risk factor from the macroeconomic announcement. Consequently, my model predicts that, the more informative (precise) the macroeconomic announcement is expected to be about the underlying risk factors, ceteris paribus, the more uncertainty pre-announcement, the more resolution of uncertainty post-announcement, and the higher the trading volume around the announcement on the market index. My empirical analysis of trading by investors around both FOMC and CPI announcements support my model's predictions. In particular, my empirical findings are consistent with model predictions about the effect of the anticipated macroeconomic announcement precision on investor attention allocation, the effect of investor attention on the levels of pre-announcement and post-announcement trading volumes, and the effect of investor attention on the ratio of post-announcement trading volume over the pre-announcement trading volume. In my second essay, we analyze, theoretically and empirically, how investor attention affects the stock market reaction to innovation announcements. In a dynamic model with limited investor attention, we show that the immediate reaction to innovation announcements increases, while the post-announcement stock return drift decreases, in investor attention. We empirically confirm our model predictions using a matched sample of pharmaceutical industry patent grant and subsequent FDA drug approval announcements and also a general USPTO patent sample. We show that post-announcement drift has predictive power for firm growth, profitability, and productivity, drawing implications for enhancing measures of patents' economic value and for trading strategy. In my third essay, we analyze, theoretically and empirically, the implications of a fraction of investors in the equity market paying only delayed attention to SEO announcements. We first show theoretically that, in the above setting, the announcement effect of an SEO will be positively related to the fraction of investors paying attention to the announcement and that there will be a post-announcement stock-return drift that is negatively related to investor attention. In the second part of the paper, we test the above predictions using the media coverage of firms announcing SEOs as a proxy for investor attention, and find evidence consistent with the above predictions. In the third part of the paper, we develop and test various hypotheses relating investor attention paid to the issuing firm (between the announcement and the equity issue) to various SEO characteristics. We empirically show that SEO underpricing, institutional investor participation in SEOs, and the post-SEO equity market valuation of firms are all positively related to investor attention. The results of our identification tests show that the above results are causal. / Thesis (PhD) — Boston College, 2020. / Submitted to: Boston College. Carroll School of Management. / Discipline: Finance.

No description available.

Thesis advisor: Thomas Chemmanur / My Ph.D. dissertation consists of three essays. The first essay studies the economic consequence of the current patent screening process on firm performance using a machine-learning approach. Using USPTO patent application data, I apply a machine-learning algorithm to analyze how the current patent examination process in the U.S. can be improved in terms of granting higher quality patents. I make use of the quasi-random assignment of patent applications to examiners to show that screening decisions aided by a machine learning algorithm lead to a 15.5% gain in patent generality. To analyze the economic consequences of current patent screening on both public and private firms, I construct an ex-ante measure of past false acceptance rate for each examiner by exploiting the disagreement in patent screening decisions between the algorithm and current patent examiner. I first show that patents granted by examiners with higher false acceptance rates have lower announcement returns around patent grant news. Moreover, these patents are more likely to expire early. Next, I find that public firms whose patents are granted by such examiners are more likely to get sued in patent litigation cases. Consequently, these firms cut R&D investments and have worse operating performance. Lastly, I find that private firms whose patents are granted by such examiners are less likely to exit successfully by an IPO or an M&A. Overall, this study suggests that the social and economic cost of an inefficient patent screening system is large and can be mitigated with the help of a machine learning algorithm. The second essay studies how investor attention affects various aspects of SEOs. Models of seasoned equity offerings (SEOs) such as Myers and Majluf (1984) assume that all investors in the economy pay immediate attention to SEO announcements and the pricing of SEOs. In this paper, we analyze, theoretically and empirically, the implications of only a fraction of investors in the equity market paying immediate attention to SEO announcements. We first show theoretically that, in the above setting, the announcement effect of an SEO will be positively related to the fraction of investors paying attention to the announcement and that there will be a post-announcement stock-return drift that is negatively related to investor attention. In the second part of the paper, we test the above predictions using the media coverage of firms announcing SEOs as our main proxy for investor attention, and find evidence consistent with the above predictions. In the third part of the paper, we develop and test various hypotheses relating investor attention paid to an issuing firm to various SEO characteristics. We empirically show that institutional investor participation in SEOs, the post-SEO equity market valuation of firms, SEO underpricing, and SEO valuation are all positively related to investor attention. Lastly, we also use the number of SEC EDGAR file downloads as an alternative proxy for investor attention, and our findings are robust to this alternative investor attention measure. The results of our identification tests show that the above results are causal. The third essay studies how the location of a lead underwriter in its network of investment banks affects various aspects of seasoned equity offerings (SEOs). We hypothesize that investment banking networks perform an important economic role in the SEO underwriting process for SEOs, namely, that of information dissemination, where the lead underwriter uses its investment banking network to disseminate information about the SEO firm to institutional investors. Consistent with the above information dissemination role, we show that firms whose SEOs are underwritten by more central lead underwriters are associated with a smaller extent of information asymmetry in the equity market. We then develop testable hypotheses based on the information dissemination role of underwriter networks for the relationship between SEO underwriter centrality and various SEO characteristics, which we test in our empirical analysis. Consistent with the above hypotheses, we find that more central lead SEO underwriters are associated with less negative SEO announcement effects; smaller SEO offer price revisions; smaller SEO discounts and underpricing; higher immediate post-SEO equity valuations for issuing firms; and greater post-SEO long-run stock returns for issuing firms. We also find that SEOs with more central lead underwriters are associated with greater institutional investor participation. Our instrumental variable (IV) analysis using the industry-average bargaining power of underwriters relative to issuers as the instrument shows that the above results are causal. Consistent with greater value creation by more central lead underwriters, we find that more central lead underwriters receive greater compensation. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Carroll School of Management. / Discipline: Finance.

In food manufacturing, the quality control procedure is a critical activity that consists in organizing, measuring, tracking, and filing the conditions of the production process and the final product, with the goal of guaranteeing the designed quality standard. During the last 30 years, due to a mounting concern by both consumers and lawmakers, the definition of quality and the application of quality control improved drastically, and new methodologies have been developed to ensure better control of food production and to understand the effect of raw materials and the process condition on the final quality of the food product. This thesis discusses the approaches to quality control procedures in food manufacture, focusing on the relationship between the conditions of the process and the quality profile of the final product, testing in a real-case scenario of a complex production process advanced data analysis procedures. The statistical and analytical procedures proposed have been applied in a real case studio from Trentingrana cheese production, a dairy consortium in the northeast region of Italy producing a ripened semi-artisanal hard cheese under the Protected Denomination of Origin (PDO) of Grana Padano. The aim is developing tailored statistical procedures that infer the effect of the critical factors of production on quality properties of this PDO product considering its semi-artisanal production process and the presence of multiple confounding factors. The statistical analyses were applied to a dataset of measurements of physical, sensory, and chemical properties collected on cheese wheels sampled systematically to represent the variability of the production of the Trentingrana wheels over two years of production. In the first introductory chapter, after a review of the different definitions of quality, the most important quality parameters for a food product and the standard measurement techniques adopted in quality control are presented. Then, in the chapter 2, the standard procedures of data analysis are reviewed, as well as the new approaches derived from the context of the foodomic sciences and machine learning models for the analysis of quality control data in food manufacturing. Two implemented and tested practical statistical procedures in the context of the Trentingrana consortium are reported: the results are discussed according to the objectives of the quality control process, the type of data, and the organization of food production. In the first case, reported in chapter 3, Linear Mixed Model ANOVA Simultaneous Component Analysis (LMM-ASCA) was developed to investigate the effect of the dairy factory, the bimester of production, and the variability within a cheese wheel using colorimetric and textural measurements. In the second case, reported in chapter 4, a standard ASCA model with the addition of a blocking factor to include systematic error was developed to investigate the relationship between the dairy factory and bimester of production and the volatile organic compounds (VOCs) profile of Trentingrana cheese wheels. In addition, in chapter 5, an approach to relate physical measurements on Trentingrana samples with sensory evaluations of texture by a trained panel is presented. The objective of this procedure is to incorporate the quality control procedure information from different quality parameters. The development of the Partial Least Squares (PLS) predictive model, its validation, and the evaluation of its performances are discussed. In the last section (chapter 6), the development of an image analysis procedure to measure the visual quality of the rind thickness of cheese wheels is reported, comparing the performances of two different algorithms. The data analysis tools proposed in this thesis have been proved to be useful for exploring, inferring, and plotting the process quality properties and suitable for analyzing complex and unbalanced experimental designs. Furthermore, the data analysis procedures proposed improve quality control activity both at the process level and at the product level, increasing the information that is possible to extract from the measurement collected in a context where standard statistical approaches cannot infer significant information.

This thesis examines three important topics in corporate finance: the relation between the dividend-paying status of a firm and its investment and operating performance following a seasoned equity offering (SEO), the market's view on one-dollar CEO salary announcements, and the value of corporate social responsibility (CSR) in the event of a data breach. First, I provide an in-depth analysis of the connection between dividend payouts and corporate investment of SEO firms. Empirical studies have documented the decline in post-issue operating performance of SEO firms, and the potential overinvestment of SEO proceeds seems to be a critical factor. Studies on dividend payouts argue that the agency cost of overinvestment could be lowered when dividends are paid to reduce free cash flows held by managers. To examine the connection, I utilize two post-issue dividend policies, paying consecutive dividends or nothing, to separate my sample of SEO firms and compare the two groups' post-issue investment and operating performance. I find that non-dividend-paying SEO firms overinvest more, leading to the deterioration of asset turnover and worse post-issue operating performance compared with dividend-paying ones. The results suggest a beneficial effect of consistent dividend payouts on post-SEO business operations. Second, I examine the market reaction to the public announcement of a $1 CEO salary decision using explicit reasons for the decision and mechanisms for dealing with the base salary to disentangle possible explanations for the reaction. It shows that the market does not favour the so-called personal sacrifice when CEOs eliminate their salary to counter a downturn or crisis. When a firm is in a predicament or has poor performance, the market sees its CEO’s decision to give up the salary as a signal that the outlook for the firm is bleak and the CEO is attempting to save their position. However, when newly hired CEOs start with a $1 salary, the market reacts positively. The results ascertain that a $1 salary is not seen purely as a vehicle for interest alignment. Third, I investigate whether public firms' CSR activities pay off when they suffer a data breach that potentially harms their reputation and hurts firm value. I use a sample of US data breaches and two sources of environmental, social, and corporate governance (ESG) ratings to investigate whether CSR engagement by public firms mitigates the negative stock market reactions to their data breach announcements. I utilize pre-breach ESG scores to separate my sample of breached firms into high and low CSR groups. Using event study methodology, I find that the market reacts significantly negatively to only the low CSR group's announcements. Consistent with previous studies on how firms benefit from CSR activities when they face adversity and lose public trust, the results suggest that social performance protects firms against information leakage incidents. However, the extent to which the market assesses the ratings from different providers is still divergent, which is a concern for practitioners. / Thesis / Doctor of Philosophy (PhD)

No description available.

This dissertation consists of two essays. The first provides evidence that the recent revival of shelf equity offers is related to changes in how firms use shelf registration. During 1990-2003 firms that make shelf filings have no immediate intent and low probability of issuance, lower pre-filing returns relative to non-shelf issuers, and often have been certified in prior SEOs. The evidence indicates that the way firms now use shelf offerings resolves the under-certification problem responsible for the shelf demise in the 1980s (Denis, 1991) and results in smaller market penalties and lower underwriter fees relative to non-shelf offerings. This allows firms with greater uncertainty to take advantage of the shelf option to defer or abandon offers. Additionally, firms often use universal shelf filings and choose between debt and equity offerings based on the prevailing relative market conditions. The second essay examines offer price discounting of traditional and shelf-registered seasoned equity offerings (SEOs). The results indicate that relative to traditional SEOs, shelf discounting during 1982 - June 2004 is similar in magnitude, is influenced by the same factors, and has increased similarly over time. Prior studies attribute the time-series increase of seasoned offer discounting to pre-offer short sale constraints (Rule 10b-21; adopted in 1988). This study provides insights about the effect of Rule 10b-21 by exploiting the fact that shelf-registered offerings were exempt from this regulation until September 2004. The analysis uses the shelf exemption as a control in testing the Rule's effect, and the elimination of the exemption as an "out-of-sample" test. The results suggest that Rule 10b-21 is not associated with the increase in seasoned offer discounts. The gradual increase in discounting over the past two decades is largely due to a shift in the composition of issuers toward firms that have greater stock volatility and pre-offer price uncertainty. / Ph. D.

Yes / This study provides new evidence that the performance of issuing firms varies by issue type, based on survival analysis methods. Our non-parametric results show that firms raising capital through rights issues, and notably through cash offers, experience a greater risk of delisting following issuance, as compared to those issuing convertible bonds. Our Cox model analyses demonstrate that plain equity issues, in contrast to convertible issues, are subject to different degrees of regulatory discipline, obligations and incentives in shaping survival trajectory. Further, high ownership concentration, agency issues intrinsic to equity offerings, weak shareholders' protection, and corporate ownership and governance and corporate control development at the time of an offer markedly influence post-issue survival. Plain equity issues, notably cash offers, are strongly linked with the agency costs of free cash flows. A large and truly independent board, allied to a separation of CEO and chairman powers, acts as a primary restraint on managers' self-interested behaviour. Such a cohesive governance mechanism can restrain rent-seeking in the firm's fundraising initiative. These observations hold when we take into account information available before an issue, at the time of an issue, and after an issue, demonstrating the robustness of our findings.

本研究以羅吉斯迴歸模型探討影響上市櫃公司選擇公開募集或私募之因素。 實證結果發現採私募或現金增資的決策，和下列因素有關：(1)資訊不對稱程度愈高之公司愈傾向以私募發行新股，且上櫃公司較上市公司更傾向以私募發行新股；(2) 資訊透明度越低的公司，資金成本越高，籌資也較困難，會傾向以私募方式發行新股；(3) 投資機會愈多的公司，因為控制權考量，會選擇公開募集資金；(4) 當景氣愈好，公司愈傾向以公開募集發行新股，且上市公司較上櫃公司更易傾向以現金增資方式發行新股。 / This study applies logit model to examine the determinants of choice of private placements/seasoned equity offerings. The results indicate that firms being severe in information asymmetry tend to choose private placements. Contrast to listed firms, OTC firms prefer private placements to seasoned equity offerings. Firms with lower information transparency pay higher costs in seasoned equity offerings and therefore tend to choose private placements. Firms being abundant in investment opportunities prefer to choose seasoned equity offerings because of control considerations. In bull markets, more firms choose seasoned equity offerings other than private placements while listed firms have stronger tendency to choose seasoned equity offerings than OTC firms.