The Contingent Value of Responsive and Proactive Market Orientations for New Product Program Performance*

While the benefits of being market oriented are largely accepted, a group of scholars and managers remain skeptical. Marketing scholars have sought to counter the criticisms leveled against market orientation (MO) by arguing that it has both responsive and proactive dimensions. However, few studies have empirically examined the complexity of the effects of these dimensions on firm performance. Drawing on theories of resource‐based advantage and organizational search behavior, this article advances understanding by arguing that responsive and proactive market orientations have curvilinear effects on product development performance, that their interaction may be positively related to product development performance, and that their effects on new product program performance are moderated differentially by the organizational implementation conditions and marketing function power. Survey results of 175 U.S. firms indicate support for most of the hypotheses. Specifically, whereas responsive MO has a U‐shaped relationship with new product program performance, proactive MO has an inverted U‐shaped relationship with new product program performance. Contrary to the arguments presented here, the interaction of both orientations is negatively related to new product program performance. This study finds that both orientations are needed; however, new product program performance is enhanced when one is at higher level and the other is at lower level. Finally, responsive MO is only positively related to new product program performance under specific conditions such as when strategic consensus among managers is high. On the other hand, the positive effect of proactive MO on new product program performance is further strengthened when learning orientation and marketing power are high. Overall, this study suggests that the effects of responsive and proactive MO on new product program performance are more complex than previously theoretically argued and empirically examined.     

Feature selection methods involving support vector machines for prediction of insolvency in non‐life insurance companies

We propose two novel approaches for feature selection and ranking tasks based on simulated annealing (SA) and Walsh analysis, which use a support vector machine as an underlying classifier. These approaches are inspired by one of the key problems in the insurance sector: predicting the insolvency of a non‐life insurance company. This prediction is based on accounting ratios, which measure the health of the companies. The approaches proposed provide a set of ratios (the SA approach) and a ranking of the ratios (the Walsh analysis ranking) that would allow a decision about the financial state of each company studied. The proposed feature selection methods are applied to the prediction the insolvency of several Spanish non‐life insurance companies, yielding state‐of‐the‐art results in the tests performed. Copyright © 2005 John Wiley & Sons, Ltd.     

Artificial neural networks with feature transformation based on domain knowledge for the prediction of stock index futures

A feature transformation method based on domain knowledge for arti?cial neural networks (ANNs) is proposed. The method of feature transformation based on domain knowledge converts continuous values into discrete values in accordance with the knowledge of experts in speci?c application domains. This approach effectively ?lters data, trains the classi?er, and extracts the rules from the classi?er. In addition, it reduces the dimensionality of the feature space, which not only decreases the cost and time in the operation but also enhances the generalizability of the classi?er. The experimental results of the proposed approach will be compared and tested statistically with the results of the linear transformation method. The results show that the method of feature transformation based on domain knowledge outperforms the linear transformation in modelling of ANNs. Copyright © 2004 John Wiley & Sons, Ltd.     

The service sector of Cuba: An analysis and critique

The Cuban economy has experienced a significant increase in foreign direct investment over the past decade due to the end of the Soviet Union and the establishment of more liberal trade policies. Although the U.S. embargo of trade with Cuba still exists, there has been movement lately that suggests the end of the four‐decades‐old policy. The purpose of this article is to analyze the current Cuban business environment and to identify potential entrepreneurial opportunities in the service sector. Major trade partners are also reviewed and managerial implications discussed. © 2006 Wiley Periodicals, Inc.     

Sources of Sectoral Growth in an Economy Wide Context: The Case of U.S. Agriculture

Growth in U.S. agriculture is linked to the non-farm economy through domestic terms of trade and factor market adjustments. With almost stable input growth, the relatively large contributions from growth in Total Factor Productivity (TFP) are passed on to intermediate and final consumers in the form of declining real prices for primary farm products. The resulting net growth in the real value of farm output (GDP) is relatively low (0.25% per annum). The decomposition of TFP suggests that public agricultural stock of knowledge and infrastructure are robustly associated with TFP growth, while spill-overs from private agricultural and economy wide research and development (R and D) are positive but, relatively small.     

This is what the leading indicators lead

We propose an optimal filter to transform the Conference Board Composite Leading Index (CLI) into recession probabilities in the US economy. We also analyse the CLI's accuracy at anticipating US output growth. We compare the predictive performance of linear, VAR extensions of smooth transition regression and switching regimes, probit, non‐parametric models and conclude that a combination of the switching regimes and non‐parametric forecasts is the best strategy at predicting both the NBER business cycle schedule and GDP growth. This confirms the usefulness of CLI, even in a real‐time analysis. Copyright © 2002 John Wiley & Sons, Ltd.     

The Band Pass Filter*

We develop optimal finite‐sample approximations for the band pass filter. These approximations include one‐sided filters that can be used in real time. Optimal approximations depend upon the details of the time series representation that generates the data. Fortunately, for U.S. macroeconomic data, getting the details exactly right is not crucial. A simple approach, based on the generally false assumption that the data are generated by a random walk, is nearly optimal. We use the tools discussed here to document a new fact: There has been a significant shift in the money–inflation relationship before and after 1960.