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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.

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.

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.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

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.

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.

The history, successes, failures and future needs that relate to the allocation of functions to humans and/ or machines in manufacturing environments are presented. The various methodologies that have been proposed for performing function allocation are discussed. The basic process involves matching the capabilities and limitations of the particular human or automated system with the requirements imposed by the manufacturing operation. This process can range from a global, systems approach down to the delineation of specific capabilities of humans and automated systems. Both recent advances and obstacles to the effective allocation of tasks to humans or machines based on the capabilities of each are presented. The current status and the areas where future research and development are needed are discussed.

The emergence of Construction 4.0 technologies provides an impetus for radical change and rejuvenates the interest of stakeholders in addressing long-standing performance issues in the construction sector. However, construction firms struggle to implement Construction 4.0 technologies for performance measurement and improvement. Therefore, the purpose of this research is to develop a conceptual model of innovation management for implementing Construction 4.0 that guides and facilitates the strategic transformation of construction firms.

A conceptual model of innovation management is presented, and the findings are synthesised based on a literature review, 20 semi-structured interviews, two focus group discussions, three workshops, expert consultation and observations on three digitally-enabled projects. Data were inductively analysed using thematic analysis.

The analysis of empirical data revealed: (i) Four scenarios that could lead the industry to different futures, based on the extent of research and development, and the extent of integration/collaboration; (ii) Construction 4.0 capability stages for a sustained implementation route; (iii) Possible business model configurations derived from servitisation strategies; and (iv) Skills management challenges for organisations.

First, the empirical data was only collected in the UK with its unique industry context, which may limit the applicability of the results. Second, most of the research data comes from the private sector, without the views of public sector organisations. Third, the model needs to be further validated with specific data-driven use cases to address productivity and sustainability issues.

Successful Construction 4.0 transformation requires a concerted effort of stakeholders, including those in the supply chain, technology companies, innovation networks and government. Although a stakeholder’s action would depend on others’ actions, each stakeholder should undertake action that can influence the factors within their control (such as the extent of collaboration and investment) and the outcomes.

The conceptual model brings together and establishes the relationships between the scenarios, Construction 4.0 capability stages, business models and skills management. It provides the first step that guides the fuzzy front-end of Construction 4.0 implementation, underpins the transformation to the desired future and builds long-term innovation capabilities.

The purpose of this paper is to propose a maturity model to improve warehouse performance.

This paper will follow De Bruin et al’s (2005) suggested six relevant phases: scope, design, populate, test, deploy and maintain in developing the proposed maturity model. This study concentrates on the first five phases.

The proposed warehouse maturity model can be used as descriptive, benchmarking and a prescriptive with a road map for improvement.

The warehouse maturity model was proposed to let warehouse managers evaluate their practices and assess them by maturity level. Then, the proposed warehouse maturity model can be utilized to develop a set of plans for conducting projects to improve the warehouse practices, techniques and tools.

The proposed warehouse maturity model contributes to fill the shortages of maturity model addressing the warehouse environment. In particular, it provides a useful tool to establish the overall maturity level of a warehouse system. The proposed maturity model supports strategic decisions oriented toward improvement capabilities of the warehouse and to compete based on service level provided.