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The purpose of this paper is to identify factors influencing implementation of control charts on key performance indicators (KPIs).

Factors driving organizational change described in literature are analyzed inspired by the affinity-interrelationship method. A holistic multiple-case design is used to conduct six workshops to affect the usage of control charts on KPIs at a global company in the automotive industry. The theoretical factors are compared with the result from the case study.

The important factors for implementation success differ to some extent between the theoretical and empirical studies. High-level commitment and a clear definition of the goal of change could be most important when creating a motivation for change. Thereafter, having a dedicated change agent, choosing an important KPI and being able to describe the gain in financial terms becomes more important.

By using control charts on KPIs, the organization in the case study has become more proactive, addressing the right issues upstream in the process, in the right way, cross-functionally.

Factors affecting the implementation of already available solutions in the industry are highlighted. This potentially provides a basis for improved decision making, which has a significant value.

This paper aims to present the Difference-in-Differences (DiD) method in an accessible language to a broad research audience from a variety of management-related fields.

The paper describes the DiD method, starting with an intuitive explanation, goes through the main assumptions and the regression specification and covers the use of several robustness methods. Recurrent examples from the literature are used to illustrate the different concepts.

By providing an overview of the method, the authors cover the main issues involved when conducting DiD studies, including the fundamentals as well as some recent developments.

The paper can hopefully be of value to a broad range of management scholars interested in applying impact evaluation methods.

The purpose of this paper is to pinpoint and analyse ethical issues raised by the dual role of artificial intelligence (AI) in relation to climate change, that is, AI as a contributor to climate change and AI as a contributor to fighting climate change.

This paper consists of three main parts. The first part provides a short background on AI and climate change respectively, followed by a presentation of empirical findings on the contribution of AI to climate change. The second part presents proposals by various AI researchers and commentators on how AI companies may contribute to fighting climate change by reducing greenhouse gas emissions from training and use of AI and by providing AI assistance to various mitigation and adaptation measures. The final part investigates ethical issues raised by some of the options presented in the second part.

AI applications may lead to substantial emissions but may also play an important role in mitigation and adaptation. Given this dual role of AI, ethical considerations by AI companies and governments are of vital importance.

This paper pinpoints practical ethical issues that AI companies and governments should take into account.

Given the potential impact of AI on society, it is vital that AI companies and governments take seriously the ethical issues raised by the dual role of AI in relation to climate change.

AI has been the subject of substantial ethical investigation, and even more so has climate change. However, the relationship between AI and climate change has received only limited attention from an ethical perspective. This paper provides such considerations.

Resistance is expected to emerge with the implementation and use of new technologies in production systems. This work focuses on identifying sources of resistance to the use of Industry 4.0 technologies when managing production disturbances and suitable managerial approaches to deal with them.

A qualitative approach was chosen in this research. The authors conducted a literature review and a series of interviews. Thirty-one papers from the literature review were analysed, and 16 people from five different companies were interviewed.

The authors identified five different sources of resistance and three managerial approaches to dealing with them. The sources of resistance were based on (1) feelings of over-supervision, (2) unclear values, (3) feelings of inadequacy, (4) concerns about loss of power and jobs and (5) work overload. The three approaches to dealing with resistance are (1) communication, (2) participation and (3) training.

This work identifies the sources and strategies to deal with resistance to the use of Industry 4.0 technologies in the management of production disturbances. The managerial literature in this area is limited, and to the authors's knowledge, the specific sources for resistance and strategies to deal with that in this topic have not been systematically investigated before.