The Resource‐Based View of the Firm (RBV) has become an important stream of literature in strategic management. RDV's main prescription is that strategic assets are crucial determinants of sustainable competitive advantage and thus firm performance. Unfortunately, little empirical research has been occasioned to substantiate that prescription. Part of the difficulty in empirically testing RBV's main prescription lies in identifying resources capable of being strategic assets. This article combines RBV logic, the definition of strategic assets, Hall's studies, and the logic embodied in several streams of management literature to explain why strategic assets are intangible in nature, to show that not all intangible resources are strategic assets, and to demonstrate that company reputation, product reputation, employee knowhow, and organizational culture possess the characteristics of strategic assets. That is the foundation for the proposed hypotheses and proposed conceptual model presented in this paper for testing RBV's main prescription. We also discuss the practical, theoretical and empirical implications of this paper and make suggestions regarding empirical testing.
Forecasting of time series that have seasonal and other variations remains an important problem for forecasters. This paper presents a neural network (NN) approach to forecasting quarterly time series. With a large data set of 756 quarterly time series from the M3 forecasting competition, we conduct a comprehensive investigation of the effectiveness of several data preprocessing and modeling approaches. We consider two data preprocessing methods and 48 NN models with different possible combinations of lagged observations, seasonal dummy variables, trigonometric variables, and time index as inputs to the NN. Both parametric and nonparametric statistical analyses are performed to identify the best models under different circumstances and categorize similar models. Results indicate that simpler models, in general, outperform more complex models. In addition, data preprocessing especially with deseasonalization and detrending is very helpful in improving NN performance. Practical guidelines are also provided.
In this study, we examine two methods for Multi-Step forecasting with neural networks: the Joint Method and the Independent Method. A subset of the M-3 Competition quarterly data series is used for the study. The methods are compared to each other, to a neural network Iterative Method, and to a baseline de-trended de-seasonalized naïve forecast. The operating characteristics of the three methods are also examined. Our findings suggest that for longer forecast horizons the Joint Method performs better, while for short forecast horizons the Independent Method performs better. In addition, the Independent Method always performed at least as well as or better than the baseline naïve and neural network Iterative Methods.
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