Abstract
The aim of this study is to examine the impacts of board size, gender diversity and independence on ESG performance whilst also examining the impact of country-level social trust on such performance. We perform a panel data analysis and the least squares method for a sample of 75 European banks and a time span of 4 years from 2016 to 2019. We find that ESG performance is positively associated with board gender diversity and independence, and negatively associated with board size. Surprisingly, we find a negative relationship between country-level social trust and ESG performance. This is an important finding that we interpret as being related to the loss of confidence in the banking sector in the wake of the 2008 financial crisis. To regain such trust, the banking sector is likely to have suffered higher social pressure to engage in ESG activities in countries where social trust is lower. © 2023 by the authors.

Abstract
Purpose: This descriptive paper seeks to explore the role of Portuguese academics within corporate social responsibility and disclosure (CSR&D) research. It aims to give some insight into the nature and the extent of research on CSR&D being undertaken by Portuguese authors. Design/methodology/approach - This review specifically targeted international journals to see how much Portuguese research had penetrated the international business, management and accounting (BMA) academic discourse. The research that is reported in this paper is based on an electronic search of the online databases which include journals in these areas. The paper reviews journal articles published in BMA journals over the period 1998 to 2008. Findings: The paper reaches several conclusions: the volume of published research in international journals is still very low; most papers focus on CSR; research is overwhelmingly empirical in orientation; and most papers focus on Portugal. Hence, there is great scope for expanding the amount of research on CSR&D in Portugal, as well as improving the diversity of its content and its geographical coverage. Research limitations/implications : The paper is not expected to be a complete study, but rather a stimulus for future research. It is intended to provide a basis for comparison with further studies on CSR&D research in Portugal with studies from other geographical areas. Originality/value: The paper provides the first survey on CSR&D research in Portugal. It also provides the first opportunity to reflect on the current state of these still emerging academic fields. © Emerald Group Publishing Limited.

Abstract
In this paper we analyse the estimates of the matrices produced by the non-biased deterministic-stochastic subspace identification algorithms (NBDSSI) proposed by Van Overschee and De Moor ( 1996). First, an alternate expression is derived for the A and C estimates. It is shown that the Chiuso and Picci result ( Chiuso and Picci 2004) stating that the A and C estimates delivered by this algorithm robust version and by the Verhaegen's MOESP (Verhaegen and Dewilde 1992a, Verhaegen and Dewilde 1992b, Verhaegen 1993, Verhaegen 1994) are equal, can be obtained from this expression. An alternative approach for the estimation of matrices B and D in subspace identification is also described. It is shown that the least squares approach for the estimation of these matrices estimation can be just expressed as an orthogonal projection of the future outputs on a lower dimension subspace in the orthogonal complement of the column space of the extended observability matrix. Since this subspace has a dimension equal to the number of outputs, a simpler and numerically more efficient ( but equally accurate) new subspace algorithm is provided.

Abstract
In this paper we provide a different way to estimate matrices B and D, in subspace identification algorithms. The starting point was the method proposed by Van Overschee and De Moor [10] - the only one applying subspace ideas to the estimation of those matrices. We have derived new (and simpler) expressions and we found that the method proposed by Van Overschee and De Moor [10] can be rewritten as a weighted least squares problem, involving the future outputs and inputs.

Abstract
In this paper a recursive technique, based on the subspace state space identification methods, is presented for identification of time-varying systems. The main idea was to develop an iterative algorithm with most of the advantages of this kind of methods in order to deal with real-time applications and minimize the computational burden. As a subspace-based state space system identification technique, it has two main steps: first, a state vector sequence is estimated, using numerical linear algebra tools, then the state space model is obtained from a simple least squares model. © 2001 EUCA.

Abstract
Purpose - The purpose of this paper is to obtain pertinent information pertaining to benefits accomplished, main difficulties felt, and key lessons learned in the implementation of lean Six Sigma (LSS), which are likely to be helpful to financial services organizations wanting to implement the same tool. Design/methodology/approach - This paper reports the results of a case study from a financial services organization which has begun the implementation of LSS methodologies ten years ago, in the pursuit of service excellence. Findings - The benefits derived from LSS implementation, such as lowering the operational costs, improving processes and product quality, increased efficiency, which leads to the increase of productivity, the agility and versatility obtained by the organization, vastly outweigh the costs. At an international level, there are cultural differences pertaining mainly to internal resistance and openness to change. Research limitations/implications - This work focuses on a specific case study. Practical implications - The authors compiled the lessons learned and recommendations for future implementations of this methodology. The paper, therefore, will be of interest to managers of similar companies. Originality/value - The paper presents a successful application of LSS for a financial services company. © Emerald Uroup Publishing Limited.