Purpose
This paper aims to analyse the role of the code of ethics as a critical element of responsible management and posits it as a tool that integrates ethics, sustainability and attention to stakeholders. This proposed tool can be a facilitator of integrated management of these dimensions.
Design/methodology/approach
A survey was developed to answer the research questions, and descriptive and factor analyses were carried out. A non-probabilistic sampling technique, purposive sampling, was used. The survey, sent by e-mail, was addressed to managers and decision makers of Spanish companies belonging to associations explicitly committed to corporate social responsibility and ethics; 73 questionnaires were answered. Statistical analyses were performed with SPSS 26.0 software.
Findings
The findings highlight that companies that are showing leadership in ethical management are using their codes of ethics as a key instrument in the business ethics strategy. Codes of ethics go beyond being a guide to ethical conduct to being an instrument at the service of stakeholder relations, sustainability and ethics. The keys that these companies agree on are the design of participative processes of responsible management, the multidimensional content of their codes of ethics and a code management oriented to generate a proactive ethical culture in the company.
Practical implications
This paper proposes a series of recommendations that may be useful to all those companies that wish to promote effective and integrative ethical management through their code of ethics, as much as if they already have one, as they are developing it.
Originality/value
This research highlights the role of code of ethics as an integrative tool for ethics, sustainability and stakeholder responsibility. For that, the keys that these companies agree on are the design of participative processes of responsible management, the multidimensional content of their codes of ethics and code management oriented to generate a proactive ethical culture in the company.
In this work, spatial analysis was used to identify the locations of entrepreneurs supported by the regional government of Andalusia (Spain). The objective of this research is to study the effectiveness of the support work for entrepreneurship carried out by the Andalusians Entrepreneurship Centres (CADEs) in the autonomous community. As a first approach to this objective, the geographical situation of the supported entrepreneurs is determined, and how that situation influences the support for entrepreneurship is analysed. We use spatial pattern analysis techniques that allow us to assess the impact of these efforts. Attending to the areas of greater concentration as well as those of lower concentration, we conclude that CADEs are an effective instrument of the entrepreneurship policy in this region. In addition, by concentrating on rural and mountain areas, the work of CADEs contributes to the local development of these zones by supporting the development and sustainability of the business sector in areas with higher unemployment rates and a greater threat of depopulation. The study's conclusions are relevant in showing the role of public administration in the development of European Union (EU) Objective 1 regions and, more specifically, in the support of entrepreneurship.
By using a high-variability sample of real agrarian enterprises previously classified into two classes (efficient and inefficient), a comparative study was carried out to demonstrate the classification accuracy of logistic regression algorithms based on evolutionary productunit neural networks. Data envelopment analysis considering variable returns-to-scale (BBC-DEA) was chosen to classify selected farms (220 olive tree farms in dry farming) as efficient or inefficient by using surveyed socio-economic variables (agrarian year 2000). Once the sample was grouped by BCC-DEA, easy-to-collect descriptive variables (concerning the farm and farmer) were then used as independent variables in order to find a quick and reliable alternative for classifying agrarian enterprises as efficient or inefficient according to their technical efficiency. Results showed that our proposal is very promising for the classification of complex structures (farms).
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