The sustainable development goals (SDGs) reflect grand challenges that the global community needs to address in order to ensure economic welfare, environmental quality, social cohesion and prosperity for future generations. In this respect, the role of the banking sector, among other critical business entities and key stakeholders, is vital. The purpose of our paper is to examine how comprehensively the reported performance of banks aligns with the endorsement of SDGs. We employ the well‐established framework of the Global Reporting Initiative (GRI) performance indicators for a comparative assessment of the nonfinancial performance disclosed in the annual sustainability reports. Focusing on a small sample of leading European banks, we find an overall low contribution to SDGs. Furthermore, each bank's contribution remains particularly heterogeneous towards most individual SDG goals. Likewise, bank‐specific strategies drive the most extensively addressed SDGs, overlooking any critical importance of certain GRI indicators with multifaceted impact across several SDGs. The study sets forth managerial implications for improving effective reporting of SDG performance. It concludes with emerging opportunities for enhancing disclosure of SDGs contribution and highlights future research perspectives towards industry‐wide shared‐value appraisal under the scope of these pressing grand challenges.

Label switching is a well-known problem occurring in MCMC outputs in Bayesian mixture modeling. In this article we propose a formal solution to this problem by considering the space of the artificial allocation variables. We show that there exist certain subsets of the allocation space leading to a class of nonsymmetric distributions that have the same support with the symmetric posterior distribution and can reproduce it by simply permuting the labels. Moreover, we select one of these distributions as a solution to the label switching problem using the simple matching distance between the artificial allocation variables. The proposed algorithm can be used in any mixture model and its computational cost depends on the length of the simulated chain but not on the parameter space dimension. Real and simulated data examples are provided in both univariate and multivariate settings. Supplemental material for this article is available online.

Progressive Type-II censoring, Adaptive censoring scheme, Progressively Type-II right censored order statistics, Exponential distribution, Pareto distribution, 62N01, 62E15,

No abstract