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Discusses and empirically tests special cases of multiple‐chain mixed Markov latent class models with business data. The switches between negative and positive changes in earnings‐per‐share of firms are captured by alternative Markov models. The estimated response probabilities and state transition probabilities show interesting changes in the transformation patterns of the firms over time. Shows that Markov models can be valuable tools in predicting switches in profitability of firms.

The paper is presented in two parts. The first is concerned with the methodology of the competing transformation models and the second details the results of the empirical tests. In particular, it concentrates on empirical testing of the stability and cross‐sectional invariance of the factor patterns underlying the arbitrage pricing models of two neighbouring security markets. In previous studies, the method of transformation analysis has been used to address these issues at the individual asset level. In the present study three alternative transformation analysis models are used to study the stability and invariance problems. The tests are carried out at an aggregated level, such that subsets of asset returns are combined in equally weighted portfolios in the spirit of Fama and MacBeth. Portfolio formation is motivated by the observed anomalies of individual asset return series. Even if some anomalies may be present on the aggregated level also, their impact will be weaker. The amount of different issues of the same company in the database is varied in order to study the impact of parallel issues on the empirical results.

Presents a new powerful multiple‐criteria decision‐making (MCDM) framework with an approach that combines recent results from several related areas. The De Novo programming and external reconstruction approach (ERA) provides the overall structure for the algorithms and the sequence of r‐constrained linear programs generated by the ERA‐framework is solved. This is by a parallel implementation of a powerful interior point algorithm called the conjugate gradient method (CGM), selected because it is particularly suitable for parallel processing. Gives details of the proposed parallel implementation of the CGM, together with the algorithm. Presents an analysis of the parallel performance and discusses theoretical speed‐up.

Presents for the first time empirical evidence on the forecasting performance of multi‐layer neural nets in modelling multiple‐input vector time series processes. Compares the results produced by the neural net with those obtained by a robust VARMAX‐algorithm and a multiple‐input state space algorithm for vector‐valued time series processes. The neural net and the VARMAX‐algorithm were programmed in the C‐language and the state space algorithm was programmed in FORTRAN.

Evaluates an identification strategy, based on ridge regression, for mutliple‐input models. The corresponding computer algorithm was implemented on a VAX–8800 computer at the Computing Centre at A˚bo Akademi. The evaluation of the ridge‐regression method was carried out by simulations of different transfer‐function noise model structures. The models are essentially the same as those of Edlund, but a far greater number of replications, 1,000, is used in each of the 21 cases tested. Furthermore, uses actually identified and estimated ARIMA models of the residuals in the identification procedure of impulse response weights, unlike Edlund, who used only theoretical noise models in filtering the input and output series. A short discussion of the underlying theory is presented in Part I. The procedures and results of the empirical testing will be published in Part II in Kybernetes, Vol. 22 No. 7, 1993.

Considers the modelling of dynamic systems using biased regression and spectral methods. Provides evidence on the power of transfer function modelling for unravelling the empirical connection between endogenous and exogenous (control) variables in both regression type and spectral input‐output systems. The Multiple Input Transfer Function Noise Model – of specific value when the input variables are collinear – has previously been used to demonstrate the connection between macroeconomic forces and stock market pricing on a thin security market. Compares the adequacy of representative time and frequency domain algorithms for modelling observed data series. The estimations are done with the combined Transfer Function and Cartesian ARIMA Search algorithm of Östermark and Höglund and the CAPM/APM programs of Östermark.

Evaluates an identification strategy, based on ridge regression, for multiple‐input models. The models are essentially the same as those of Edlund, but a far greater number of replications, 1,000, is used in each of the 21 cases tested. Furthermore, users actually identified and estimated ARIMA models of the residuals in the identification procedure of impulse response weights, unlike Edlund, who used only theoretical noise models in filtering the input and output series. A short discussion of the underlying theory was presented in Part I, Kybernetes, Vol. 22 No. 4, 1993, pp. 47–53. In Part II the procedures and results of the empirical testing are published together with some concluding remarks.

This study aims to analyze the dynamic of front end development (discover, define and create/analysis and test phases) in the co-creation with multiple stakeholders taking into account the value creation and capture process in a strategic business, consulting project, adding knowledge sharing flows.

The first phase of the study consisted of a review of the narrative literature. The method chosen for analyzing the findings is the narrative synthesis, which sought to identify and show the complementarity between the areas studied. Then, a summary of the review presents a conceptual structure. At the third phase of the study consisted of a field survey characterized for being a descriptive-exploratory and qualitative investigation based on a single case study.

For the results, three phases of evolution in the relationships among stakeholders can be characterized, namely: formation, strengthening and integration. With regard to value capture, it is also possible to characterize three phases of simultaneous non-linear evolution: sharing of perceptions, ideas and experiences, self-reflection, and vision and solution. With regard to knowledge sharing, two important flows were characterized: solution development and socialization and communication, in which there is a greater relationship with the development of trust, commitment, tie strengthening and shared vision. And some insights from the perspective of integrating dynamic capabilities and front end with value co-creation with multiple stakeholders.

Practical and theorical results of the evolution of value co-creation on the front end phases dynamic.