This study draws upon organizational learning in the management domain and analogical reasoning in the psychology arena to examine the antecedents of acquisition success in a study of 655 firms from 54 industries that conducted 2622 acquisitions from 1991 through 2005. Where previous work in this domain focused on cumulative abnormal returns (CAR) as the measure of performance and ordinary least squares as the method of analysis, this work extends the literature by introducing Tobins Q-ratio as the measure of performance and autoregressive, integrated, moving average (ARIMA) model with transfer functions as the methodological approach. Earlier research on the influence of prior experience on focal acquisition performance has yielded interesting insights despite variation in findings. However, CAR is a short-term measure that is dependent upon stockholder reaction and does not fully anticipate the long-term fitness of the acquisition event. Ex ante, this study expected to result in a meta-narrative applicable to all merger and acquisition activity that could guide management of an acquisition program through the identification of the antecedent conditions of success. Prior research suggests that the Q measure is more relevant to managerial understanding and strategic orientation than insights gained from investor opinion as measured from cumulative abnormal returns. This studys findings suggest that acquisition experience, timing, antecedent performance, and, interaction between experience, timing and performance are all related to focal acquisition results. Further, when ARIMA is used to analyze the data and Q is the dependent variable, additional details and richer insights about the influence of the independent measures are gained. These findings justify the additional effort and time necessary for managers to use Tobins Q. Additionally, while CAR does provide a particular set of actionable information, Tobins Q-ratio provides a more robust long-term indicator of acquisition performance especially where analogical reasoning is the process through which learning is demonstrated.

This dissertation studies foreign firms shareholder value and earnings-related information measures in relation with the implications of the Sarbanes-Oxley Act of 2002. Chapter One addresses the value implications and empirically tests the changes in market values of foreign firms around SOX related announcements, and how it varies across home country legislative characteristics. My findings on market reactions provide mixed evidence. SOX related announcements exhibit the expected sign, but statistical significant is limited. Results from cross-sectional analysis are partially aligned with the bonding hypothesis implications. My findings suggest that the market reaction for cross-listed firms from countries with common-law origin and, consequently, better investor protection is not statistically different than firms from civil law originated jurisdictions and, consequently, weaker investor protection. Chapter Two studies the earnings-related information environment for foreign firms following the enactment of SOX Act in comparison with earlier periods. In particular, I empirically analyze the change in forecast accuracy, dispersion among the analysts forecasts, and the informativeness of earnings announcements. My analysis suggests there was no significant improvement or deterioration in forecast accuracy and informativeness of earnings announcement in the post-SOX period relative to pre-SOX period. However, my findings suggest some improvement in forecast dispersion of foreign firms for the post-SOX period.

This set of two essays addresses questions about the impact of SFAS No. 131, which provides the current GAAP for segment reporting. Many firms enhanced their segment reports in compliance with SFAS No. 131, while some did not. The first essay documents the impact of the new standard on the market for stocks of firms that began reporting disclosure for multiple segments (Change firms) compared to that of firms that did not report multiple business segments (Control firms). Share volume and price increased abruptly following release of the new disclosure while volatility and the adverse selection component of the bid-ask spread decreased. These changes were significant for subsets of Change firms identified as being more likely to face information asymmetry problems (i.e., low capitalization and high bid-ask spread firms). The second essay compares firms segment disclosures from pre- to post-implementation of the standard. Newly-reported segments tend to be smaller than previously-disclosed segments, and they are often combined with larger segments in a related industry prior to implementation of the standard. Firms providing relatively aggregated segment reports tend to be large, to have international operations, and complex operating structures compared to firms providing more detailed disclosures in the pre-SFAS No. 131 period.

In this research, we provide a comprehensive examination regarding outsourcing policies and decisions by conducting a systematic analysis from macro to micro level. At the macro level, we utilize a BOCR-based (Benefit, Opportunity, Cost, Risk) Analytical Network Process (ANP) model to find the best government policy regarding outsourcing. At the micro level, we carry out a case study to demonstrate how firm level outsourcing decisions can be made. For such, we employ a comprehensive model that consists of the the BSC-based (Balanced Scorecard) ANP model to assess the case firms strategic alternatives and to identify the best option for the studied firm. After recommending the best outsourcing strategy, i.e. selective outsourcing, to the case firm, we then proposed an AHP ratings model for the firm to prioritize the various activities and identify the activities to outsource. Understanding the economic impact of outsourcing can help firms facing outsourcing options to make a better decision. To provide firms with this guidance and the decision-support tool, in chapter 5 of this dissertation we empirically examine the relationship between outsourcing contract size, as reported in news and trade journals, and the firms financial performance. Using the data from Compustat for those firms that conducted outsourcing, we are able to predict firms post-outsourcing financial performance, as measured by Tobins q and the changes in Tobins q. Besides predicting financial performance, our empirical models also reveal a number of important managerial implications. In the modeling process, we make use of both traditional Least Squares Regression and advanced Machine Learning techniques, such as Regression Tree and Neural Networks. In the research front, our empirical study offers three significant findings: (1) there is a non-linear relationship between outsourcing contract value and the change in Tobins q; (2) firms other accounting variables do interact with the outsourcing contract size in affecting the firms Tobins q; and (3) outsourcing affects firms differently. In summary, the body of this dissertation consists of the three key areas: macro- and micro-level outsourcing decision making, and financial performance evaluation and prediction.

Most profit oriented organizations are constantly striving to improve their revenues while keeping costs under control, in a continuous effort to meet customers‟ demand. After its proven success in the airline industry, the revenue management approach is implemented today in many industries and organizations that face the challenge of satisfying customers‟ uncertain demand with a relatively fixed amount of resources (Talluri and Van Ryzin 2004). Revenue management has the potential to complement existing scheduling and pricing policies, and help organizations reach important improvements in profitability through a better management of capacity and demand. The work presented in this thesis investigates the use of revenue management techniques in the service sector, when demand for service arrives from several competing customer classes and the amount of resource required to provide service for each customer is stochastic. We look into efficiently allocating a limited resource (i.e., time) among requests for service when facing variable resource usage per request, by deciding on the amount of resource to be protected for each customer and surgery class. The capacity allocation policies we develop lead to maximizing the organization‟s expected revenue over the planning horizon, while making no assumption about the order of customers‟ arrival. After the development of the theory in Chapter 3, we show how the mathematical model works by implementing it in the healthcare industry, more specifically in the operating room area, towards protecting time for elective procedures and patient classes. By doing this, we develop advance patient scheduling and capacity allocation policies and apply them to scheduling situations faced by operating rooms to determine optimal time allocations for various types of surgical procedures. The main contribution is the development of the methodology to handle random resource utilization in the context of revenue management, with focus in healthcare. We also develop a heuristics which could be used for larger size problems. We show how the optimal and heuristic-based solutions apply to real-life situations. Both the model and the heuristic find applications in healthcare where demand for service arrives randomly over time from various customer segments, and requires uncertain resource usage per request.

Product bundling is a business strategy that packages (either physically or logically), prices and sells groups of two or more distinct products or services as a single economic entity. This practice exploits variations in the reservation prices and the valuations of a bundle vis-à-vis its constituents. Bundling is an effective instrument for price discrimination, and presents opportunities for enhancing revenue without increasing resource availability. However, optimal bundling strategies are generally difficult to derive due to constraints on resource availability, product valuation and pricing relationships, the consumer purchase process, and the rapid growth of the number of possible alternatives. This dissertation investigates two different situationsvertically differentiated versus independently valued productsand develops two different approaches for revenue maximization opportunities using product bundling, when resource availability is limited. For the vertically differentiated market with two products, such as the TV market with prime time and non-prime time advertising, we derive optimal policies that dictate how the seller (that is, the broadcaster) can manage their limited advertising time inventories. We find that, unlike other markets, the revenue maximizing strategy may be to offer only the bundle, only the components, or various combinations of the bundle and the components. The optimality of these strategies critically depends on the availability of the two advertising time resources. We also show how the network should focus its programming quality improvement efforts, and investigate how the value of bundling, defined as the networks and the advertisers benefit from bundling, changes as the resource availabilities change. We then propose and study a bundling model for the duopolistic situation, and extend the results from the monopolistic to the duopolistic case. For the independently valued products, we develop stochastic mathematical programming models for pricing bundles of n components. Specializing this model for two components in a deterministic setting, we derive closed-form optimal product pricing policies when the demand functions are linear. Using the intuition garnered from these analytical results, we then investigate two procedures for solving large-scale problems: a greedy heuristic, and a decomposition method. We show the effectiveness of both methods through computational experiments.

Organizational control, defined as efforts to increase the chances that employees of an organization work toward achieving organizational goals, is believed to have positive effects on performance. However, few studies have tested this assumption. This research draws on theories of control and coordination to investigate the relationship between control and information systems (IS) development performance. It consists of analyses of two research models, one at the individual level of analysis, and the other at the team level of analysis. The individual-level model investigates how control affects individual effort toward task and individual coordination success, and proposes that these relationships are moderated by controlees perceptions of how effectively a controller can monitor their work behaviors and outcomes. The team-level model investigates two mediators through which control may affect IS development performance: team effort toward task, and team coordination success. A field survey was conducted, and completed matched survey responses from 106 managers and team members involved in 36 different IS development projects were used to test the hypotheses. The results suggest that control does have a positive relationship with effort toward task and coordination success. Specifically, clan control was positively associated with coordination success at the individual and team levels, and with team effort toward task. Outcome control was positively associated with individual effort toward task. In addition, the relationship between outcome control and individual effort toward task was moderated by team member perceptions of the difficulty of observing outcomes. At high levels of difficulty of observing outcomes, high levels of outcome control resulted in higher effort than at low levels of difficulty of observing outcomes. Control was also positively related to performance outcomes. Behavior control was associated with reduced overruns of resources such as time, budget, and systems and programming effort. Outcome control was positively associated with product performance, which represents the quality, ease-of-use, and functionality of the system developed or enhanced by the project. Clan control was associated with both improved product performance and reduced resource overruns. No support was found for mediation of the effects of control on performance by effort toward task or coordination success.

Electronic knowledge repositories represent one of the fundamental tools for knowledge management (KM) initiatives. Existing research, however, has largely focused on supply-side driven research questions, such as employee motivation to contribute knowledge to a repository. This research turns attention to the dynamic relationship between the supply-side issue of knowledge contribution and demand-side issue of knowledge usage, as repository systems are successful only to the extent that their content is actively utilized by organizational members to enhance their work performance. There are two primary objectives of this dissertation research. The first is to examine determinants of high quality knowledge contribution, the knowledge refinement process, and effective knowledge use, by drawing on organizational and dyadic factors. The second purpose is to expand the current understanding of knowledge contribution and use beyond conventional constructs that are based on quantity or frequency. New theoretical frameworks are proposed to conceptualize knowledge quality, knowledge refinement, and knowledge use. Towards these goals, informal qualitative interviews and a survey study with a matched-triad design were conducted with users of Eureka, a successful global knowledge repository system of the Xerox company. Results reveal that procedural justice significantly contributed to the quality of refined knowledge and the extent of knowledge use. However, procedural justice had little impact on the quality of knowledge contribution. In addition, expertise gap and communication frequency significantly influenced the quality of refined knowledge, whereas shared understanding made little contribution. These findings are discussed with respect to implications for knowledge management research and managerial practices.

My dissertation examines the impact of in-store slack on shoppers spending. In my first essay, I propose that consumers mental budgets for grocery trips are typically comprised of both an itemized portion and in-store slack. I conceptualize the itemized portion as the amount that the consumer has allocated to spend on items planned to the brand or product level and the in-store slack as the portion of the mental budget that is not assigned to be spent on any particular product but remains available for in-store decisions. Using a secondary data set and a field study, I find incidence of in-store slack. Moreover, I find support for my framework predicting that the relationship between in-store slack and budget deviation (the amount by which actual spending deviates from the mental trip budget) depends on factors related to desire and willpower. Building on my first essay, my second essay examines how the impact of promotions depends on whether the shopper still has in-store slack remaining in her mental budget. Specifically, I evaluate how the effect of promotional savings for both planned and unplanned items on spending varies as a function of whether the item is purchased before or after the shoppers in-store slack is depleted. Additionally, I examine how these relationships vary depending on income. To achieve these goals, I conducted a field study in which respondents used a hand held scanner to record the order of purchases. The results suggest that savings on planned items lead to stockpiling by higher income shoppers when the savings occur before the in-store slack has been depleted, but lead to increased purchase of unplanned items when they occur after in-store slack is depleted. I also show that promotions on unplanned grocery items do generate incremental spending at the basket level which increases with income, but only when the item is purchased after the in-store slack is exceeded. I conclude my dissertation with a discussion of future research opportunities within the field of shopper marketing.

Shareholder litigation is important because it is costly and can influence firms reporting behavior. Prior research finds that attorneys incentives and specific firm characteristics drive shareholder litigation. In this study, I control for these known drivers in order to examine whether an additional behavioral factor, investor retribution, also drives shareholder litigation. Retribution theory suggests that investors will initiate litigation to punish managers for misreporting even when there is no financial incentive to do so. My study uses experimental markets to examine whether retribution plays a role in investors litigation decisions, and if this, in turn, affects managers reporting decisions. Consistent with economic theory, I find that when investors do not have an option to initiate litigation, managers frequently misreport and investors do not find their reports credible. Further, when investors have an option to initiate litigation and a financial incentive to do so (similar to current securities laws), misreporting decreases and the credibility of managers reports increases. However, inconsistent with economic theory but consistent with retribution theory, when investors have an option to initiate litigation but have no financial incentive to do so, misreporting decreases and the credibility of managers reports increases to approximately the same level as when investors have a financial incentive to initiate litigation. These findings have important implications for regulators and managers who need to accurately anticipate investors litigation decisions in order to make optimal policy and reporting decisions.