Using Real Activities to Avoid Goodwill Impairment Losses: Evidence and Effect on Future Performance

We examine whether managers postpone the recognition of goodwill impairment by manipulating cash flows and the consequences of such a strategy on future performance. According to SFAS 142, an impairment loss must be recognized if the reporting unit's total fair value to which goodwill has been allocated is less than its book value. A growing body of empirical evidence shows that managers delay the recognition of goodwill impairment in accounting books. However, past literature is silent on how managers convince various gatekeepers (e.g., auditors, financial analysts) that recognizing an impairment loss is unnecessary although it seems economically justified. SFAS 142 requires managers to forecast future cash flows to justify the decision to recognize, or not, an impairment loss. Therefore, we predict that managers manipulate upward current cash flows to support their choice to avoid reporting an impairment loss. We also test whether or not this real earnings management is detrimental to future performance. Based on a sample of US firms over the period 2003–2011, we document that firms suspected of postponing goodwill impairment losses exhibit significantly positive discretionary cash flows compared to various control groups. We also find that this real activities manipulation is detrimental to future performance.     

Comparing employment interviews in Latin America with other countries

This study compares job interviews (n = 11,667) in Mexico with those in the following countries: Belgium, Russia, Taiwan, and the U.S. The findings support our hypotheses, which are based on a meta-cultural framework. The results reveal that in Mexico and Taiwan women are less likely to conduct interviews. In addition, interviewers asked different questions. Outside the U.S., interviewers asked applicants about their family, marital status, and children. In Russia and Taiwan, they asked about applicants' reasons for quitting their last job. In Belgium, Russia, and Taiwan, they asked about applicants' wage and salary expectations. In Belgium and Russia, they less often asked about applicants' values, opinions, and beliefs. This study suggests that in some countries employment interviews are more than a test of job-related knowledge, skills, and abilities. This report provides a taxonomy that is useful for comparing interview questions in Latin American and other countries as well as directions for future research.     

Data accuracy's impact on segmentation performance: Benchmarking RFM analysis,logistic regression,and decision trees

Companies greatly benefit from knowing how problems with data quality influence the performance of segmentation techniques and which techniques are more robust to these problems than others. This study investigates the influence of problems with data accuracy – an important dimension of data quality – on three prominent segmentation techniques for direct marketing: RFM (recency, frequency, and monetary value) analysis, logistic regression, and decision trees. For two real-life direct marketing data sets analyzed, the results demonstrate that (1) under optimal data accuracy, decision trees are preferred over RFM analysis and logistic regression; (2) the introduction of data accuracy problems deteriorates the performance of all three segmentation techniques; and (3) as data becomes less accurate, decision trees retain superior to logistic regression and RFM analysis. Overall, this study recommends the use of decision trees in the context of customer segmentation for direct marketing, even under the suspicion of data accuracy problems.     

Smart predictors in the heterogeneous agent model

We extend the original heterogeneous agent model by introducing the concept of smart traders. The idea of smart traders is based on the endeavor of market agents to estimate future price movements. The main result of the simulations is that the probability distribution functions of the price deviations change significantly with an increasing number of smart traders in the model. We also find that the Hurst exponent is significantly increasing with an increasing number of smart traders in the simulations. Hence the introduction of the smart traders concept into the model results in significantly higher persistence of the simulated price deviations.     

Retrospective and prospective views about the future of the multinational enterprise

This paper reflects upon Buckley and Casson's seminal volume The Future of the Multinational Enterprise (1976). Retrospectively, it examines the reasons for the impact of The Future on research in international business, on the one hand, while it reviews the merit of the different chapters in today's economic environment. The paper subsequently also introduces some prospective views on the use of internalization theory, which are elaborated upon in the other papers in this issue.     

The Firm Size Distribution and Inter-Industry Diversification

We show that the stylized facts of the Firm Size Distribution (FSD) by age cohorts, as shown in Cabral and Mata (2003), bind within 4-digit manufacturing industries in the UK and Belgium. This paper investigates whether the time to build a portfolio of products from inter-industry diversification is a mechanism that helps explain the stylized facts of the FSD by age cohorts. We find that the degree of inter-industry diversification has a separate impact on firm size when controlling for age, amongst other factors. Using the techniques of Cabral and Mata, we find that inter-industry diversification does shift the FSD to the right, and more so in older age groups.     

Analyzing Massive Data Sets: An Adaptive Fuzzy Neural Approach for Prediction,with a Real Estate Illustration

Drawing useful predictions from vast accumulations of data is becoming critical to the success of an enterprise. Organizations’ databases grow exponentially from transactions with external stakeholders in addition to their own internal activities. An important organizational computing issue is that, as they grow, the databases become potentially more valuable and also more difficult to analyze. One example is predicting the value of residential real estate based on past comparable sales transactions. This is critical to several important sectors of the US economy including the mortgage finance industry and local governments that collect property taxes. The common methodology for dealing with such property valuation is based on multiple regression, although this methodology has been found to be deficient. Data mining methods have been proposed and tested as an alternative, but the results are very mixed. This article introduces a novel approach for improving predictions using an adaptive, neuro-fuzzy inference model, and illustrates its application to real estate property price prediction through the use of comparable properties. Although neuro-fuzzy–based approaches have been found to be effective for classification and estimation in many fields, there is very little existing work that investigates their potential in a real estate context. In addition, this article addresses several common problems in existing studies, such as small sample size, lack of rigorous data sampling, and poor model validation and testing. Our model is tested with real sales data from the assessment office in a large US city. The results show that the neuro-fuzzy model is superior in all of the test scenarios. The article also discusses and refines a unique technique to defining comparable properties to improve accuracy. Test results show very promising potential for this technique in mass appraisal in real estate and similar contexts when used with the neuro-fuzzy model.     

Controls,service type and perceived supplier performance in interfirm service exchanges

Control offers a critical differentiator between successful and failed interfirm service exchanges. The application of informal control to improve supplier performance has been well established, but the effect of formal control appears profoundly equivocal. This study proposes that the actual effect of formal control depends on its mode (output vs. behavior) and its relationship with the service type (mass vs. professional) and informal control. With survey data from 252 service buying organizations, the results indicate that output control interacts with service type to determine perceived supplier performance (PSP). Buyers’ reliance on high output control has a positive effect on PSP in mass service exchanges; this effect becomes negative in professional service exchanges. The effect of the interaction of behavior control and service type also depends on the presence of informal control. Buyers’ reliance on high behavior control exerts a more positive effect on PSP in professional service exchanges than in mass service exchanges, but only in the presence of informal control. These findings have key implications for both theory and practice.