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1 – 10 of 51Vita Glorieux, Salvatore Lo Bue and Martin Euwema
Crisis services personnel are frequently deployed around the globe under highly demanding conditions. This raises the need to better understand the deployment process and more…
Abstract
Purpose
Crisis services personnel are frequently deployed around the globe under highly demanding conditions. This raises the need to better understand the deployment process and more especially, sustainable reintegration after deployment. Despite recent research efforts, the study of the post-deployment stage, more specifically the reintegration process, remains fragmented and limited. To address these limitations, this review aims at (1) describing how reintegration is conceptualised and measured in the existing literature, (2) identifying what dimensions are associated with the reintegration process and (3) identifying what we know about the process of reintegration in terms of timing and phases.
Design/methodology/approach
Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, the authors identified 5,859 documents across several scientific databases published between 1995 and 2021. Based on predefined eligibility criteria, 104 documents were yielded.
Findings
Research has primarily focused on descriptive studies of negative individual and interpersonal outcomes after deployment. However, this review indicates that reintegration is dynamic, multi-sector, multidimensional and dual. Each of its phases and dimensions is associated with distinct challenges.
Originality/value
To the authors’ knowledge, this is the first research that investigates reintegration among different crisis services and provides an integrative social-ecological framework that identifies the different dimensions and challenges of this process.
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The International Network of Research Management Societies (INORMS) celebrated its 20th anniversary in 2021. It was established to increase communication among research management…
Abstract
The International Network of Research Management Societies (INORMS) celebrated its 20th anniversary in 2021. It was established to increase communication among research management societies. The need for a formal international research management community developed because there was (1) increased international funding of research, (2) the number of international research collaborations was growing, and (3) there was a need to understand research regulations in other countries. INORMS sought to address these issues through international congresses and by providing a forum for member societies to work more closely together on common issues. Membership in INORMS steadily increased over the years. The 20th anniversary meeting was highlighted with the signing of the Hiroshima Statement that described a research manager’s principles and responsibilities, which include collegiality, inclusiveness, professionalisation, innovation, and accountability. This chapter summarises the factors that led to the formation of INORMS and its history.
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Jan Svanberg, Tohid Ardeshiri, Isak Samsten, Peter Öhman, Presha E. Neidermeyer, Tarek Rana, Frank Maisano and Mats Danielson
The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted…
Abstract
Purpose
The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.
Design/methodology/approach
This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.
Findings
The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.
Practical implications
This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.
Social implications
The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.
Originality/value
To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.
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Marlene Janzen Le Ber, Rita A. Gardiner and Liza Howe-Walsh