Sustainable business models: integrating employees, customers and technology

This Special Issue of the Journal of Business & Industrial Marketing has the same title as the 23rd International Conference CBIM 2018 (June 18-20, 2018, Madrid, Spain) “Sustainable Business Models: Integrating Employees, Customers and Technology”. In this edition of International Conference, following a competitive blind review process, papers from 126 authors and 25 countries were ultimately accepted. The best papers of the Conference were invited to submit to this Special Issue and we were also open to direct submissions from other authors. We present here the 17 accepted papers for publication in this Special Issue

The detection of influential subsets in linear regression using an influence matrix

This paper presents a new method to identify influential subsets in linear regression problems. The procedure uses the eigenstructure of an influence matrix which is defined as the matrix of uncentered covariance of the effect on the whole data set of deleting each observation, normalized to include the univariate Cook's statistics in the diagonal. It is shown that points in an influential subset will appear with large weight in at least one of the eigenvector linked to the largest eigenvalues in this influence matrix. The method is illustrated with several well-known examples in the literature, and in all of them it succeeds in identifying the relevant influential subsets

DEBT REFINANCING AND CREDIT RISK

Many firms choose to refinance their debt. We investigate the long run effects of this extended practice on credit ratings and credit spreads. We find that debt refinancing generates systematic rating downgrades unless a minimum firm value growth is observed. Deviations from this growth path imply asymmetric results: A lower value growth generates downgrades and a higher value growth upgrades as expected. However, downgrades will tend to be higher in absolute terms. On the other hand, credit spreads will be independent of the risk free interest rate in the short run, but positively correlated with this rate in the long run.

The detection of influential subsets in linear regression using an influence matrix.

This paper presents a new method to identify influential subsets in linear regression problems. The procedure uses the eigenstructure of an influence matrix which is defined as the matrix of uncentered covariance of the effect on the whole data set of deleting each observation, normalized to include the univariate Cook's statistics in the diagonal. It is shown that points in an influential subset will appear with large weight in at least one of the eigenvector linked to the largest eigenvalues in this influence matrix. The method is illustrated with several well-known examples in the literature, and in all of them it succeeds in identifying the relevant influential subsets.Eigenvectors; Masking; Multivariate Influence; Outliers;

Robust estimation for ARMA models

This paper introduces a new class of robust estimates for ARMA models. They are M-estimates, but the residuals are computed so the effect of one outlier is limited to the period where it occurs. These estimates are closely related to those based on a robust filter, but they have two important advantages: they are consistent and the asymptotic theory is tractable. We perform a Monte Carlo where we show that these estimates compare favorably with respect to standard M-estimates and to estimates based on a diagnostic procedure.Comment: Published in at http://dx.doi.org/10.1214/07-AOS570 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org