Organizational Learning,Innovation and Internationalization: A Complex System Model

Research on organizational learning, innovation and internationalization has traditionally linked these concepts through linear causality, by considering any one of them as the cause of another, an approach that might be considered contradictory and static. This paper aims to clarify these relationships and proposes a dynamic theoretical model that has mutual causality at its core and is based on ideas originating in complexity theory. The final model results from case studies of two clothing sector firms. The authors consider that the three concepts constitute a complex system and can adapt and transcend, as any alteration can take the system to the edge of chaos. Adaptability is fostered by concentration, improvement and discussion. Transcendence is fostered by attention, dialogue and inquiry. The different paces of the two case study companies led their systems to two different models: the incremental complex adaptive system model and the global complex generative system model. The incremental model is characterized by adaptive learning, incremental innovation and low internationalization; and the global system is characterized by generative learning, radical innovation and global internationalization. The paper ends with an exploration of the academic and management implications of the model.     

Randomized singular spectrum analysis for long time series

ABSTRACT

Singular spectrum analysis (SSA) is a relatively new method for time series analysis and comes as a non-parametric alternative to the classical methods. This methodology has proven to be effective in analysing non-stationary and complex time series since it is a non-parametric method and do not require the classical assumptions over the stationarity or over the normality of the residuals. Although SSA have proved to provide advantages over traditional methods, the challenges that arise when long time series are considered, make the standard SSA very demanding computationally and often not suitable. In this paper we propose the randomized SSA which is an alternative to SSA for long time series without losing the quality of the analysis. The SSA and the randomized SSA are compared in terms of quality of the model fit and forecasting, and computational time. This is done by using Monte Carlo simulations and real data about the daily prices of five of the major world commodities.     

Explaining Investment Dynamics in U.S. Manufacturing: A Generalized (S,s) Approach

In this paper we derive a model of aggregate investment that builds from the lumpy microeconomic behavior of firms facing stochastic fixed adjustment costs. Instead of the standard sharp (S,s) bands, firms' adjustment policies take the form of a probability of adjustment (adjustment hazard) that responds smoothly to changes in firms' capacity gap. The model has appealing aggregation properties, and yields nonlinear aggregate time series processes. The passivity of normal times is, occasionally, more than offset by the brisk response to large accumulated shocks. Using within and out-of-sample criteria, we find that the model performs substantially better than the standard linear models of investment for postwar sectoral U.S. manufacturing equipment and structures investment data.     

Measuring residential segregation in multi-ethnic and unequal European cities

Immigration flows and social inequalities reflect increased social and multi-ethnic segregation in contemporary urban Europe. For a better understanding of these processes, the present study investigates the main strengths of the multi-group residential indices, testing sensitivity and reliability under different metropolitan contexts in five European countries. These indices focus on different research dimensions and approach multi-group residential segregation conceptually and mathematically in a different way. A multivariate exploratory data analysis was adopted to classify the observed segregation patterns into a few homogeneous types and to delineate the multivariate relationship between the indices. The results of principal component analysis demonstrate that the indices assessing uniformity and disproportionality of the social groups analysed (H and D) contribute largely to the diversification in today's multi-ethnic communities, clarifying the importance of the dimension of evenness. Our results highlight how segregation is more evident in economically disadvantaged metropolitan regions with high levels of social vulnerability.     

Predicting burrowing owl flight trajectories in urban environments

Urban Ecosystems - It is important to understand how animals respond to changes in landscape structure, especially when considering habitat alteration and urbanization. Using the burrowing owl...     

Liquidity in Asset Markets With Search Frictions

We develop a search‐theoretic model of financial intermediation in an over‐the‐counter market and study how trading frictions affect the distribution of asset holdings and standard measures of liquidity. A distinctive feature of our theory is that it allows for unrestricted asset holdings, so market participants can accommodate trading frictions by adjusting their asset positions. We show that these individual responses of asset demands constitute a fundamental feature of illiquid markets: they are a key determinant of trade volume, bid–ask spreads, and trading delays—the dimensions of market liquidity that search‐based theories seek to explain.     

THE RELATIONSHIP BETWEEN THE ANALYTIC HIERARCHY PROCESS AND THE ADDITIVE VALUE FUNCTION*

The relationship between the analytical hierarchy process (AHP), a multiple-criteria decision method based on the properties of the eigenvalues of positive reciprocal matrices, and the additive value function is studied. The AHP is compared with the standard method of building an additive value function. The advantages and disadvantages of each method are discussed and a new method that uses elements of both is proposed. The proposed method relies on the AHP for building the unidimensional value functions and incorporates the systematic use of pairwise comparisons into the standard method of estimating the weights of the additive value function. Subject Area: Decision Processes.     

WHAT MEASURE OF INFLATION SHOULD A CENTRAL BANK TARGET?

This paper assumes that a central bank commits itself to maintaining an inflation target and then asks what measure of the inflation rate the central bank should use if it wants to maximize economic stability. The paper first formalizes this problem and examines its microeconomic foundations. It then shows how the weight of a sector in the stability price index depends on the sector's characteristics, including size, cyclical sensitivity, sluggishness of price adjustment, and magnitude of sectoral shocks. When a numerical illustration of the problem is calibrated to U.S. data, one tentative conclusion is that a central bank that wants to achieve maximum stability of economic activity should use a price index that gives substantial weight to the level of nominal wages. (JEL: E42, E52, E58)