Federal capital expenditures and budget deficits: Gross national product and labor implications

The issue of separating capital from operating expenditures has been largely ignored in discussions of the U.S. Federal Government's deficit. This paper attempts to overcome this void by conducting a simulation experiment designed to assess the macroeconomic consequences of separating capital from current expenditures under several alternative balanced budget rules and financing arrangements. The alternatives considered are directly related to management accounting issues and thus the purpose of this paper can be seen as compatible with the literature on the relationship between accounting techniques and economic analyses. The results of the study reaffirm the need for federal capital investment planning.     

Twitter Presence and Experience Improve Corporate Social Responsibility Outcomes

Journal of Business Ethics - We investigate the role of social-media-triggered public pressure on corporate social responsibility (CSR) that includes expectations of transparency and accountability...     

Rio plus Ten: Politics,Poverty, and Environment

The purpose of this paper is to contrast the work of Douglass North and Karl Polanyi regarding financial institutions associated in market economies. Both implicitly acknowledge the synergy between public and private financial markets, which can serve to solidify an alliance between wealth holders and the state, potentially at the expense of “sociery” at large. This alliance may stimulate growth, according to North, as well as impose the strain of the market, according to Polanyi. Taken together, these insights undercut the classical story of the neutral role of money, as well as the notion of a clear distinction between public and private, politics and markets.     

Modeling Clustered Ordered Categorical Data: A Survey

This article surveys various strategies for modeling ordered categorical (ordinal) response variables when the data have some type of clustering, extending a similar survey for binary data by Pendergast, Gange, Newton, Lindstrom, Palta & Fisher (1996). An important special case is when repeated measurement occurs at various occasions for each subject, such as in longitudinal studies. A much greater variety of models and fitting methods are available than when a similar survey for repeated ordinal response data was prepared a decade ago (Agresti, 1989). The primary emphasis of the review is on two classes of models, marginal models for which effects are averaged over all clusters at particular levels of predictors, and cluster-specific models for which effects apply at the cluster level. We present the two types of models in the ordinal context, review the literature for each, and discuss connections between them. Then, we summarize some alternative modeling approaches and ways of estimating parameters, including a Bayesian approach. We also discuss applications and areas likely to be popular for future research, such as ways of handling missing data and ways of modeling agreement and evaluating the accuracy of diagnostic tests. Finally, we review the current availability of software for using the methods discussed in this article.     

Retailing in the 21st century: reflections and prologue to research

Retailing in the 21st century will no doubt be very different from retailing in the 20th century, just as retailing in the 20th century was very different from retailing in the 19th century. To understand and explain current retailing phenomena and retailing-related phenomena, and accurately predict such phenomena, it is necessary to have a coherent and consistent definition of retailing as well as comprehensive retailing theories based on this definition. This introduction to the special issue argues for such a definition and related comprehensive theories.     

Exploring the implications of m-commerce for markets and marketing

Business pundits have enthusiastically prognosticated about a seamless, mobile world where commerce occurs on an anywhere, anytime basis. This type of commerce has been referred to as mobile commerce or, more simply, m-commerce. However, there have been relatively few attempts to systematically explore the opportunities and challenges posed by m-commerce. This article investigates the implications of m-commerce for markets and marketing by means of a formal conceptualization of m-commerce, a space-time matrix that delineates the impact of mobile technologies, and a taxonomy of m-commerce applications. Sridhar Balasubramanian is assistant professor of marketing at the University of North Carolina at Chapel Hill. His interests generally lie in the areas of marketing strategy, e-business, and game theory. He has published in such journals asMarketing Science, Journal of Retailing, andJournal of the Academy of Marketing Science. His doctorate is from Yale University. Robert A. Peterson holds the John T. Stuart III Centennial Chair in Business Administration and Charles C. Hurwitz Fellowship at The University of Texas at Austin. He is a former editor of theJournal of Marketing Research and theJournal of the Academy of Marketing Science. His more than 150 publications include nearly one dozen books and award-winning articles. Sirkka L. Jarvenpaa holds the James L. Bayless/Rauscher Pierce Refsnes Chair in Business Administration at the McCombs School of Business, University of Texas at Austin, where she codirects the Center for Business, Technology & Law. She presently serves as the editor ofJournal of the Association for Information Systems. She is a founding member of the Global Round Table on Mobile Commerce Research, which held its inaugural meeting in Tokyo in May 2002.     

The effect of management control mechanisms through risk-taking incentives on asymmetric cost behavior

Review of Quantitative Finance and Accounting - We investigate the relationship between management control mechanisms, specifically risk-taking incentives targeted at mitigating moral hazard, and...     

Industry learning environments and the heterogeneity of firm performance

This paper characterizes interindustry heterogeneity in rates of learning‐by‐doing, and examines how industry learning rates are connected with firm performance. Using plant‐level data from the U.S. manufacturing sector, we measure the industry learning rate as the coefficient on cumulative output in a production function. We find that learning rates vary considerably among industries and are higher in industries with greater R&D, advertising, and capital intensity. More importantly, we find that higher rates of learning are associated with wider dispersion of Tobin's q and profitability among firms in the industry. These findings suggest that learning intensity represents an important characteristic of the industry environment that affects the range of firm performance. Copyright © 2009 John Wiley & Sons, Ltd.