ASU Scholarship Showcase

This paper studies an infinite-horizon repeated moral hazard problem where a single principal employs several agents. We assume that the principal cannot observe the agents' effort choices; however, agents can observe each other and can be contractually required to make observation reports to the principal. Observation reports, if truthful, can serve as a monitoring instrument to discipline the agents. However, reports are cheap talk so that it is also possible for agents to collude, i.e., where they shirk, earn rents, and report otherwise to the principal. The main result of the paper constructs a class of collusion-proof contracts with two properties. First, equilibrium payoffs to both the principal and the agents approach their first-best benchmarks as the discount factor tends to unity. These payoff bounds apply to all subgame perfect equilibria in the game induced by the contract. Second, while equilibria themselves depend on the discount factor, the contract that induces these equilibria is independent of the discount factor.

This paper traces the history of mortgage law in the United States. I explore the history of foreclosure procedures, redemption periods, restrictions on deficiency judgments, and foreclosure moratoria. The historical record shows that the most enduring aspects of mortgage law stem from case law rather than statute. In particular, the ability of creditors to foreclose nonjudicially is determined very early in states’ histories, usually before the Civil War, and usually in case law. In contrast, the aspects of mortgage law developed through statute change more frequently. This finding calls into question whether common law is inherently more flexible than the civil-law system used in some other countries. However, case law tends to be less responsive to populist pressures than statutes. My findings suggest that the reason common law favors financial development is unlikely to be its greater flexibility relative to law made by statute.

Persistent economic pressures in today's business landscape require organizations to be constantly vigilant about managing costs. Reducing headcount is one common but often controversial form of cost cutting. Recently Hewlett-Packard announced that it would be cutting an additional 11,000-16,000 jobs on top of an original plan to let as many as 34,000 workers go as part of a business restructuring and turnaround strategy. Chief Executive Officer (CEO) Meg Whitman said major shifts that are transforming how technology is paid for and consumed pose major challenges for HP, along with its competitors. To be successful in this new reality, she emphasized that HP needs to be lower-cost and more nimble. This is just one of a long list of examples of significant corporate workforce reductions in the face of mounting financial and competitive challenges faced by businesses across many industries.

As our discipline has matured, we have begun to develop theories of supply chain management. However, we submit that a major omission of theory development in the supply chain management discipline is that we have failed to develop a theory of what we are managing - a theory of the supply chain. Using a conceptual theory building approach, we introduce foundational premises about the structure and boundary of the supply chain, which can serve as the basis for much needed, additional development of the theory of the supply chain.

Information systems research is replete with examples of the importance of business processes defining IT adoption. Business processes are influenced by both organizational and operational concerns. We evaluate the comparative importance of operational and organizational influences for complementary IT systems. In the context of acute-care hospitals the analysis shows that an organizational approach to automating a process is related to different financial outcomes than an operational approach. Six complementary systems supporting a three-stage medication management process are studied: prescribing, dispensing, and administration. The analysis uses firm-level, panel data extracted from the HIMSS Analytics database spanning ten years of IT adoption for 140 hospitals. We have augmented the HIMSS dataset with matching demographic and financial details from the American Hospital Association and the Centers for Medicare and Medicaid Services. Using event sequence analysis we explore whether organizations are more likely to adopt organization boundary spanning systems and if the sequence of adoption follows the temporal ordering of the business process steps. The research also investigates if there is a relationship between the paths to IT adoption and financial performance. Comparison of the two measures suggests that the organizational model of adoption is observed more often in the data. Following the organizational model of adoption is associated with approximately $155 dollar increase in net income per patient day; whereas the operational model of adoption is associated with approximately $225 dollars decrease in net income per patient day. However, this effect diminishes with the adoption of each additional system thus demonstrating that the adoption path effects may only be relevant in the short-term.

Purpose: The purpose of this paper is to review what we know - and don't know - about Generation Y's use of social media and to assess the implications for individuals, firms and society.

Design/Methodology/Approach: The paper distinguishes Generation Y from other cohorts in terms of systematic differences in values, preferences and behavior that are stable over time (as opposed to maturational or other differences). It describes their social media use and highlights evidence of intra-generational variance arising from environmental factors (including economic, cultural, technological and political/legal factors) and individual factors. Individual factors include stable factors (including socio-economic status, age and lifecycle stage) and dynamic, endogenous factors (including goals, emotions, and social norms). The paper discusses how Generation Y's use of social media influences individuals, firms and society. It develops managerial implications and a research agenda.

Findings: Prior research on the social media use of Generation Y raises more questions than it answers. It: focuses primarily on the USA and/or (at most) one other country, ignoring other regions with large and fast-growing Generation Y populations where social-media use and its determinants may differ significantly; tends to study students whose behaviors may change over their life cycle stages; relies on self-reports by different age groups to infer Generation Y's social media use; and does not examine the drivers and outcomes of social-media use. This paper's conceptual framework yields a detailed set of research questions.

Originality/Value: This paper provides a conceptual framework for considering the antecedents and consequences of Generation Y's social media usage. It identifies unanswered questions about Generation Y's use of social media, as well as practical insights for managers.

Reliable estimates of the impacts and costs of biological invasions are critical to developing credible management, trade and regulatory policies. Worldwide, forests and urban trees provide important ecosystem services as well as economic and social benefits, but are threatened by non-native insects. More than 450 non-native forest insects are established in the United States but estimates of broad-scale economic impacts associated with these species are largely unavailable. We developed a novel modeling approach that maximizes the use of available data, accounts for multiple sources of uncertainty, and provides cost estimates for three major feeding guilds of non-native forest insects. For each guild, we calculated the economic damages for five cost categories and we estimated the probability of future introductions of damaging pests. We found that costs are largely borne by homeowners and municipal governments. Wood- and phloem-boring insects are anticipated to cause the largest economic impacts by annually inducing nearly $1.7 billion in local government expenditures and approximately $830 million in lost residential property values. Given observations of new species, there is a 32% chance that another highly destructive borer species will invade the U.S. in the next 10 years. Our damage estimates provide a crucial but previously missing component of cost-benefit analyses to evaluate policies and management options intended to reduce species introductions. The modeling approach we developed is highly flexible and could be similarly employed to estimate damages in other countries or natural resource sectors.

Although perceptions of physically, socially, and morally stigmatized occupations – ‘dirty work’ – are socially constructed, very little attention has been paid to how the context shapes those constructions. We explore the impact of historical trends (when), macro and micro cultures (where), and demographic characteristics (who) on the social construction of dirty work. Historically, the rise of hygiene, along with economic and technological development, resulted in greater societal distancing from dirty work, while the rise of liberalism has resulted in greater social acceptance of some morally stigmatized occupations. Culturally, masculinity tends to be preferred over femininity as an ideological discourse for dirty work, unless the occupation is female-dominated; members of collectivist cultures are generally better able than members of individualist cultures to combat the collective-level threat that stigma inherently represents; and members of high power-distance cultures tend to view dirty work more negatively than members of low power-distance cultures. Demographically, marginalized work tends to devolve to marginalized socioeconomic, gender, and racioethnic categories, creating a pernicious and entrapping recursive loop between ‘dirty work’ and being labeled as ‘dirty people.’

Theory suggests that human behavior has implications for disease spread. We examine the hypothesis that individuals engage in voluntary defensive behavior during an epidemic. We estimate the number of passengers missing previously purchased flights as a function of concern for swine flu or A/H1N1 influenza using 1.7 million detailed flight records, Google Trends, and the World Health Organization's FluNet data. We estimate that concern over “swine flu,” as measured by Google Trends, accounted for 0.34% of missed flights during the epidemic. The Google Trends data correlates strongly with media attention, but poorly (at times negatively) with reported cases in FluNet. Passengers show no response to reported cases. Passengers skipping their purchased trips forwent at least $50 M in travel related benefits. Responding to actual cases would have cut this estimate in half. Thus, people appear to respond to an epidemic by voluntarily engaging in self-protection behavior, but this behavior may not be responsive to objective measures of risk. Clearer risk communication could substantially reduce epidemic costs. People undertaking costly risk reduction behavior, for example, forgoing nonrefundable flights, suggests they may also make less costly behavior adjustments to avoid infection. Accounting for defensive behaviors may be important for forecasting epidemics, but linking behavior with epidemics likely requires consideration of risk communication.

With the advent of high-dimensional stored big data and streaming data, suddenly machine learning on a very large scale has become a critical need. Such machine learning should be extremely fast, should scale up easily with volume and dimension, should be able to learn from streaming data, should automatically perform dimension reduction for high-dimensional data, and should be deployable on hardware. Neural networks are well positioned to address these challenges of large scale machine learning. In this paper, we present a method that can effectively handle large scale, high-dimensional data. It is an online method that can be used for both streaming and large volumes of stored big data. It primarily uses Kohonen nets, although only a few selected neurons (nodes) from multiple Kohonen nets are actually retained in the end; we discard all Kohonen nets after training. We use Kohonen nets both for dimensionality reduction through feature selection and for building an ensemble of classifiers using single Kohonen neurons. The method is meant to exploit massive parallelism and should be easily deployable on hardware that implements Kohonen nets. Some initial computational results are presented.