Organizational and accounting change within the context of the environmental agenda: evidence from Portugal

Purpose – The purpose of this paper is to explore the process of organizational change undergone by a large Portuguese business group within the context of the environmental agenda and the role of accounting as a mechanism for change. Design/methodology/approach – The paper reports the results of a case study conducted between 2006 and 2009. Information was obtained from semi-structured interviews and secondary sources. Organizational changes were analyzed using Laughlin’s model in order to identify which category reflected most of the changes introduced to address environmental matters. Findings – This paper offers evidence that change is not a homogeneous phenomenon. Additionally, it confirms previous studies’ findings which found that accounting did not play a significant role in the process of organizational change within the context of the environmental agenda. Originality/value – This paper seeks to complement the research in this area by integrating observations from a case study into an existing model of levels of organizational change according to how a Portuguese business group incorporated environmental issues into its processes, policies and corporate culture.

On the discovery of business processes orchestration patterns

COORDINSPECTOR is a Software Tool aiming at extracting the coordination layer of a software system. Such a reverse engineering process provides a clear view of the actually invoked services as well as the logic behind such invocations. The analysis process is based on program slicing techniques and the generation of, System Dependence Graphs and Coordination Dependence Graphs. The tool analyzes Common Intermediate Language (CIL), the native language of the Microsoft .Net Framework, thus making suitable for processing systems developed in any .Net Framework compilable language. COORDINSPECTOR generates graphical representations of the coordination layer together with business process orchestrations specified in WSBPEL 2.0

Bananas, Dark Worlds, and AspectH: (Group 5 Discussion)

This report summarises our idea of code clone detection in Haskell code and refactorings based on identified clones as it evolved in our group-of-three discussion

The perceived potential of business social networking sites

Business social networking is a facilitator of several business activities, such as market studies, communication with clients, and identification of business partners. This paper traduces the results of a study undertaken with the purpose of getting to know how the potential of networking is perceived in the promotion of business by participants of the LinkedIn network, and presents two main contributions: (1) to disseminate within the business community which is the relevance given to social networking; and (2) which are the social networks best suitable to the promotion of business, to support the definition of strategies and approaches accordingly. The results confirm that LinkedIn is the most suitable network to answer the needs of those that look for professional contacts and for the promotion of business, while innovation is the most recognized factor in the promotion of business through social networking. This study contributes to a better understanding of the potential of different business social networking sites, to support organizations and professionals to align their strategies with the perceived potential of each network.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Economic analysis of occupational risk prevention: a case study in a textile company

Work accidents affect business and society as a whole. Fewer accidents mean fewer sick leaves, which results in lower costs and less disruption in the production process, with clear advantages for the employer. But workers and their households bear also a significant burden following a work accident, only partially compen-sated by insurance systems. Furthermore, the consequences of work accidents to the State and Society need also to be considered. When an organization performs an integrated risk analysis in evaluating its Occupational Health and Safety Management System, several steps are suggested to address the identified risk situations. Namely, to avoid risks, a series of preventive measures are identified. The organization should make a detailed analysis of the monetary impact (positive or negative) for the organization of each of the measures considered. Particularly, it is also important to consider the impact of each measure on society, involving an adequate eco-nomic cost-benefit analysis. In the present paper, a case study in a textile finishing company is presented. The study concentrates on the dyeing and printing sections. For each of the potential risks, several preventive measures have been identified and the corresponding costs and benefits have been estimated. Subsequently, the Benefit/Cost ratio (B/C) of these measures has been calculated, both in financial terms (from the organisa-tion’s perspective) and in economic terms (including the benefits for the worker and for the Society). Results show that, while the financial analysis in terms of the company does not justify the preventive measures, when the externalities are taken into account, the B/C ratio increases significantly and investments are fully justified.

IDENTIFYING ETHICAL DILEMMAS OF PORTUGUESE CHARTERED ACCOUNTANTS

The way professionals deal with ethical dilemmas and the decisions they make may be guided by a personal and individual ideology, but it is also strongly influenced by their professional group and society. This paper focuses in real situations as they are experienced by individuals in their day-to-day professional life. The data were collected using opened-end interviews. Respondents were asked to identify the ethical dilemmas they had been faced with during their professional life. Qualitative analysis shows that main dilemmas are about how to deal with “informal economy”, “false invoices” and “tax evasion”. This study aims to contribute to the discussion of ethical issues faced by Portuguese Chartered Account (TOC), thus promoting a large debate about the way the TOC can help to create a better society and consequently legitimating their existence as a professional organization of public interest. More than ever, understanding professionals’ behavior in their real context is essential for to build a culture conducive to the ethical development of society, and to ensure, at the same time, the desirable business sustainability. This study gives a broaden description of ethics dilemmas faced by chartered accounts and shows some inefficiency in the ethical control system made by professional bodies.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.