Individuals have an increasing number of work-related role-identities (RIDs). Identity theory (IDT) helps explain how the psychological importance (i.e., centrality) of one’s RIDs—arranged in a relatively stable, cognitive schema—and perceived social cues from one’s leader interact to shape role-related behaviors and work-related attitudes. Despite the theory’s emphasis on dyadic interaction, the extant literature has focused primarily on only one side of the interaction at a time, either from the leader’s perspective (e.g., getting followers to identify more strongly with a team RID) or more commonly from the follower’s perspective (e.g., how a high-centrality RID influences positive emotional states). Furthermore, the literature has ignored how dyadic interactions relating to one focal RID may influence an individual’s other RIDs contained within the same cognitive hierarchy. This study extends the original interactional aspects of IDT by investigating first the effects of perceived leader-follower centrality congruence on follower performance and attitudes. Next, it seeks to further our understanding of whether the effects of perceived RID-centrality congruence differ, depending on a given RID’s relative position in the centrality hierarchy: congruence effects should be greater for more-central RIDs. Finally, this study expands our understanding IDT by examining how the perceptions of the leader’s centrality on the follower’s most-central RID moderates the effects of perceived congruence on separate RIDs contained in the follower’s cognitive centrality hierarchy. Specifically, I propose that for the follower’s most-central RID, perceptions of high leader centrality of this RID will mitigate the negative relationships of incongruence on followers’ least-central work-RID. I collected data from a sample of 442 respondents who were online panel participants, and I tested my hypotheses and research questions utilizing moderated polynomial regression with response surface analysis. Results indicate that RID-centrality congruence is an important variable relating to performance and work attitudes. Furthermore, my results suggest that the positive effects of perceived centrality congruence are stronger when congruence occurs on one’s most-central RID compared to RIDs that are less central to followers. Researchers and practitioners should therefore consider not only the centrality of a particular type of work-RID (e.g., Team identity), but they should account for a RID’s centrality relative to the centrality of other concurrent work-RIDs. My results further suggest that verification of one RID can mitigate the effects of incongruence on other, concurrent work-RIDs lower in followers’ centrality hierarchies.

Over the next 20 years the labor shortage in the U.S. is expected to grow to 25 million with skilled labor being in especially high demand (Employment Policy Foundation, 2001). As such, the firm's ability to recruit human capital will increase in importance. Research suggest that person-organization fit is an important predicator of early stage recruiting outcomes such as organizational attraction (e.g., Kristof-Brown, Zimmerman, & Johnson, 2005) As such, this dissertation seeks to increase our understanding of the causes of overall PO fit perceptions in the context of realistic early recruiting outcomes. Organizational brand image, individual affectivity, and measures of PO fit on specific work attributes are hypothesized to be related to job seekers perceptions of overall fit perceptions.

Mandatory stock ownership plans require executives to hold a minimum level of stock. I exploit these changes in managerial stock ownership to examine the relation between managerial ownership and manager-shareholder incentive alignment. In contrast to prior work that suggests equity incentives induce opportunistic managerial behavior, I find earnings management declines following the adoption of mandatory stock ownership plans relative to a propensity-matched control sample. I also posit and find a reduction in bid-ask spreads following plan adoptions, consistent with manager-shareholder incentive alignment improving market liquidity and decreasing information asymmetry. These findings are consistent with boards of directors contracting with managers to reduce the agency costs of equity.

Text mining and machine learning methodologies have been applied to biomedicine and business domains for new relationship and knowledge discovery. Company annual reports (or 10K filings), as one of the most important mandatory information disclosures, have remained untapped by the text mining and machine learning community. Previous research indicates that the narrative disclosures in company annual reports can be used to assess the company's short-term financial prospects. In this study, we apply text classification methods to 10K filings to systematically assess the predictive potential of company annual reports. We specify our research problem along five dimensions: financial performance indicators, choice of predictions, evaluation criteria, document representation, and experiment design. Different combinations of the choices we made along the five dimensions provide us with different perspectives and insights into the feasibility of using annual reports to predict company future performance. Our results confirm that predictive models can be successfully built using the textual content of annual reports. Mock portfolios constructed with firms predicted by the text-based model are shown to produce positive average stock return. Sub-sample experiments and post-hoc analysis further confirm that the text-based model is able to catch the textual differences among firms with different financial characteristics. We see a rich set of research questions with the promise of further insight in this research area.

This thesis consists of three essays and studies debt structure, cash holdings, and CEO compensation in empirical corporate finance. The first essay is sole-authored and is titled “How Do Firms Choose Their Debt Types?” The second essay, “Corporate Cash Holdings and Industry Risk,” is a joint work with Jaewoo Kim and Erik Lie. The third essay is titled “Does Managerial Incentive Influence a Firm’s Borrowing Diversity?” and is sole-authored. In the first essay, we empirically investigate the joint determinants of a firm’s debt types (i.e., bank debt, bonds-and-notes, capitalized leases, and convertible debt) using a simultaneous equation framework in which a firm’s choice of a debt source is endogenous to other choices of debt sources. We find that firms with high growth opportunities and few tangible assets are more likely to depend on bank debt, and that firms with high profitability tend to use more convertible debt. We further examine the interactions between debt choices within a firm. Our research suggests that among a firm’s debt components, bank debt has a complementary relation with bonds-and-notes, and that bank debt and convertible debt are substitutes. Finally, we examine the changes in composition of debt types across the market-to-book ratio and cash flow volatility quartiles. Our study shows that the proportion of firms using bank debt and convertible debt increases with firms’ high growth opportunities. The propensity of using capitalized leases and convertible debt increases as firms are financially constrained or have severe asymmetric information problems; meanwhile, the propensity of using bank debt decreases. In the second essay, we conjecture that a firm’s sensitivity to industry shocks escalates its need to retain a cash buffer. Consistent with our conjecture, we find that a one standard deviation increase in a firm’s industry risk exposure increases cash holdings by eight percent. In fact, industry risk has a greater effect on corporate cash holdings than economy-wide and idiosyncratic risk. The effect of industry risk is more pronounced for firms in competitive industries, firms with high leverage, and firms that are financially constrained. Lastly, in the third essay, we empirically investigate how the structure of managerial compensation and corresponding incentives affect firms’ borrowing diversity. We also explore which types of debt allow a CEO to have the flexibility to take more risks and provide more discretion in business decisions. We find that firms with higher CEO vega have lower borrowing diversity, and these firms increase the likelihood of convertible debt and capitalized leases issuances, relative to bank debt borrowing. Finally, after changes to the accounting standards (FAS 123R), we find that firms with higher CEO vega are more likely to issue capitalized leases and bonds-and-notes, but less likely to issue convertible debt. Our findings indicate that a CEO’s risk-taking incentives affect a firm’s debt structure and the adoption of FAS 123R has changed patterns of debt security choices.

Successful retailers like eBay and Amazon.com utilize consignment and/or wholesale contractual arrangements with their suppliers (also referred to as sellers) to manage the flow of goods, information and funds in their supply chains. Furthermore, today's pricing environment demands better, faster, and more frequent pricing decisions than ever before, and sellers' management of their inventory through dynamic pricing has contributed significantly to their financial success. The purpose of this study is two-fold. First, to investigate the decisions and channel performance under a consignment contractual arrangement and how they differ from a wholesale contractual arrangement. Second, to study a revenue management problem involving a seller of perishable products in an e-commerce setting where buyers are sensitive to both price and seller reputation. Research issues addressed by this study fall under four themes: evaluation of contract preferences of retailer and supplier; coordination of retailer and supplier decisions (i.e. channel coordination); investigation of the effects of product competition on channel decisions and profits; and finally, development of a dynamic pricing strategy. Our contributions to the existing body of literature are as follows: develop a better understanding of the factors contributing to the growing success of virtual retailers as a result of the increasing number of products that are being sold on consignment contractual arrangements; provide some insights on contractual preferences of managers to learn why they prefer consignment over wholesale contractual arrangement with certain sellers; develop incentives to promote better coordination between the supplier and retailer in a consignment contractual arrangement, thus creating a win-win situation for both players; develop a better understanding of the formation of reservation price, i.e. the amount a consumer is willing to pay for a product/service, from the perspective of consumer behavior. This work will unveil the underlying dynamics and relationships between reservation price and customer feedback rating of seller product/service quality which sellers can use to set product prices and maximize revenues.

Leadership research has traditionally explored the consequences of leadership behaviors while giving far less attention to understanding why leaders behave the way they do toward subordinates. Moreover, the few theoretical frameworks and studies that do address antecedents of leadership behavior have focused almost entirely on personality and other individual differences while largely overlooking situational determinants, particularly proximal situational determinants that would help explain why a variety of leadership behaviors exist at varying levels in the same organization. To fill these gaps in theory and research, this dissertation proposes that leaders' job demands may serve as a proximal situational determinant of "constructive" and "destructive" forms of leadership. Specifically, by integrating transactional stress theory and the challenging job assignments model, I introduce the construct of leadership challenge demands and propose a theoretical model that depicts the process whereby leadership challenge demands come to influence three types of leadership behavior: transformational leadership, abusive supervision, and passive leadership. In essence, the model depicts two competing reactions that leaders may have to leadership challenge demands. On one hand, leaders high on leadership self-efficacy are proposed to react favorably to leadership challenge demands through feelings of engagement and therefor exhibit transformational leadership. On the other hand, leaders low in leadership self-efficacy are proposed to react negatively to leadership challenge demands through feelings of burnout and therefore exhibit passive leadership or abusive supervision. The hypothesized model was tested in a sample of 153 managers and 631 direct reports at a Fortune 500 company. Results showed that while leadership challenge demands are related to higher engagement and thereby related to transformational leadership, leaders low in LSE react to leadership challenge demands with high burnout and consequently engage in passive leadership. Hypotheses regarding abusive supervision were not supported. All these results held after controlling for relevant dispositional, demographic, and experience-oriented factors. This study thus contributes to theory and research on leadership behavior, leadership development, and work stress by linking leadership challenge demands to leadership behaviors, illustrating the dual nature of developmental leadership challenges, identifying self-efficacy as a moderator of challenge demands, and exploring motivation and stress in the leadership role. It further suggests that organizations should be cautious about giving "stretch" assignments to leaders before they feel confident in their leadership ability.

While home services is a fast growing industry, little attention has been given to the management of its workforce. To maintain growth, a key challenge for home-service companies is managing their expensive and limited labor resources. In particular, the time an employee needs to provide high quality service often depends on his/her experience. Importantly, experience increases over time, thus gradually decreasing the time required to provide service. By accounting for employee experience and the accompanying learning, managers can take advantage of capacity increases that result from experience, improving efficiency and enabling further growth. We address the technician routing and scheduling problem from three perspectives, which in turn constitute the three parts of my dissertation. First, we introduce a model of technician routing that explicitly models individualized, experience-based learning. We convert the multi-day problem into a series of daily problems and approach the daily decision making in a myopic fashion. The results demonstrate that explicit modeling and the resulting ability to capture changes in productivity over time due to learning lead to significantly better and different solutions than those found when learning and workforce heterogeneity is ignored. We show that these differences result from the levels of specialization that occur in the workforce. In the second part, we design solution methods that account for the fact that serving today's demand has implications, in terms of learning, for serving tomorrow's demand. We integrate the future information in the decision process to overcome the drawback of the myopic algorithm. We introduce the multi-period technician scheduling problem with experience-based service times and stochastic customers. Then, we model the problem as a Markov decision process and introduce an approximate dynamic programming-based solution approach. The model can be adapted to handle cases of worker attrition and new task types. Using an extensive computational study, we demonstrate the value of our approach versus a myopic solution approach that views the problem as a single-period problem. In the final part, we continue exploring the value of integrating future information into the current period decision-making process for the Multi-period Dynamic Technician Scheduling Problems with Experience-based Service Times and Stochastic Customers discussed in Chapter 3. We propose an alternate approximate dynamic programming solution approach with basis function to approximate the value function by taking the advantage of the future information for the whole planning horizon. We turn to an offline simulation procedure to recursively update the coefficient vector of the basis function, which allows fast decision making within the execution phase. Our computational results demonstrate the value of the ADP solution approach with the basis function.

The social context has long been recognized as an influence on individual behavior, and research is increasingly recognizing the ways in which social contexts at work play a role in employee performance. In this dissertation, I apply social comparison theory to better understand an understudied aspect of social context: the performance of other people at work. Specifically, I argue that interactions with higher-performing colleagues and lower-performing colleagues provide the referents against which employees compare themselves to evaluate their own performance. These social comparisons are the basis upon which individuals construct an internal norm for performance, influencing their own motivation and performance. I posit that upward comparisons (to better performers) raise normative expectations of performance while downward comparisons (to worse performers) lower normative expectations for performance. I further test whether the relationships between upward and downward comparisons and motivation are moderated by goal orientation, a disposition that describes individuals’ propensity to set different kinds of goals in achievement situations. I examine my hypotheses in two studies: students in and introductory management course and corporate staff in a mid-size food processing company.

Finding the most interesting correlations among items is essential for problems in many commercial, medical, and scientific domains. For example, what kinds of items should be recommended with regard to what has been purchased by a customer? How to arrange the store shelf in order to increase sales? How to partition the whole social network into several communities for successful advertising campaigns? Which set of individuals on a social network should we target to convince in order to trigger a large cascade of further adoptions? When conducting correlation analysis, traditional methods have both effectiveness and efficiency problems, which will be addressed in this dissertation. Here, we explore the effectiveness problem in three ways. First, we expand the set of desirable properties and study the property satisfaction for different correlation measures. Second, we study different techniques to adjust original correlation measure, and propose two new correlation measures: the Simplified χ² with Continuity Correction and the Simplified χ² with Support. Third, we study the upper and lower bounds of different measures and categorize them by the bound differences. Combining with the above three directions, we provide guidelines for users to choose the proper measure according to their situations. With the proper correlation measure, we start to solve the efficiency problem for a large dataset. Here, we propose a fully-correlated itemset (FCI) framework to decouple the correlation measure from the need for efficient search. By wrapping the desired measure in our FCI framework, we take advantage of the desired measure's superiority in evaluating itemsets, eliminate itemsets with irrelevant items, and achieve good computational performance. In addition, we identify a 1-dimensional monotone property of the upper bound of any good correlation measure, and different 2-dimensional monotone properties for different types of correlation measures. We can either use the 2-dimensional search algorithm to retrieve correlated pairs above a certain threshold, or our new Token-Ring algorithm to find top-κ correlated pairs to prune many pairs without computing their correlations. In order to speed up FCI search, we build an enumeration tree to save the fully-correlated value (FCV) for all the FCIs under an initial threshold. We can either efficiently retrieve the desired FCIs for any given threshold above the initial threshold or incrementally grow the tree if the given threshold is below the initial threshold. With the theoretical analysis on correlation search, we applied our research to two typical applications: Market Basket Analysis and Network Community Detection.