Organizational memory: the role of business intelligence to leverage the application of collective knowledge

Nowadays, the major challenge to organizations managers is that they must make appropriate decisions in a turbulent environment while it is hard to recognize whether information is good or bad, because actions resulting from wrong decisions may place the organization at risk of survive. That is why organizations managers try to avoid making wrong decisions. In order to improve this, managers should use collective knowledge and experiences shared through Organizational Memory (OM) effectively to reduce the rate of unsuccessful decision making. In this sense, Business Intelligence (BI) tools allow managers to improve the effectiveness of decision making and problem solving. In the light of these motivations, the aim of this chapter is to comprehend the role of BI systems in supporting OM effectively in real context of crowdsourcing academic initiative called CrowdUM.This work is financed by Fundos FEDER through the Programa Operacional Fatores de Competitividade - COMPETE and Fundos Nacionais through FCT – Fundação para a Ciência e Tecnologia under the Project: FCOMP-01-0124-FEDER-02267

An innovative management perspective for organizations through a reputation intelligence management model

Banking companies aiming to maintain their sustainability in financial markets need to develop an integrated management based on the most important intangibles assets of relational capital. Decision-makers need to analyze and understand a huge volume of opinions continuously generated in digital ecosystems about emotions and feelings that their stakeholders associate with the performance and communication of the brand. Current tools of management fail to consider transversal and holistic models, which study the frequency and value of existing relationships between the relational capital and intangible assets. In this research, an innovative management model based on reputation intelligence is proposed. This model incorporates methodology from business intelligence models, through OLAP and data mining techniques, to analyses the complex relationships among intangible assets experience, emotion and attitude. The proposed model was applied to companies in the banking sector and the results obtained permit a conclusion about the kinds of relationships for these intangibles in each bank.University of Malaga (Spain)FCT -Foundation for Science and Technology through project CIEO [UID/SOC/04020/2019]FCT -Foundation for Science and Technology through project CEFAGE [UID/ECO/04007/2019

Knowledge Transfer Openness Matrix facilitating accessibility in UK management education teaching

This is an empirical investigation considering how the Knowledge Transfer Openness Matrix (KTOM) could facilitate accessibility and Knowledge Transfer (KT) for the UK Higher Education (HE) Management Education Teaching when utilising learning technologies. Its focus is where learning technologies applications currently assist the KT process and support accessibility for the HE teacher and learner. It considers the philosophy of openness, focusing on its usefulness to support accessibility within UK HE Management Education Teaching. It discusses how the openness philosophy may assist the KT process for the HE teacher and learners using learning technologies. In particular, the potential to support accessibility within HE Management Education Teaching environments is appraised. There appear several implications for both teachers and learners. These are characterized in the proposed KTOM. The matrix organises KT events based on the principles of the openness philosophy. The role of learning technologies in events is illustrated with regard to teaching and learning accessibility

IDSSE-M: A Software System Engineering Methodology for Developing Intelligent Decision-Making Support Systems

Decision-making Support Systems (DMSSs) have been traditionally designed and built by using mainly the Waterfall method, Prototyping-Evolutive, or Adaptive approach in the last three decades. In this paper, the authors argue that while such approaches have guided to DMSS developers, they have been also demanded for adding ad-hoc, non-standardized activities and extra techniques based on their own expertise due to the scarcity of open-access available information of them. Additionally, from a Software Systems Engineering (SSE) viewpoint, such approaches cannot be considered as well-defined methodologies. This article contributes to the research stream of SSE-based DMSS development methodologies by reporting an initial empirical evaluation of IDSSE-M, a free-access methodology for designing and building Intelligent Decision Support Systems. IDSSE-M extends and adapts Turban and Aronson’s DSS Building Paradigm (open access), and Saxena’s Decision Support Engineering Methodology (proprietary). IDSSE-M offers DMSS developers at least a moderate level of usefulness, compatibility, and results demonstrability, which leads to a positive, good and beneficial attitude of using the methodology

Sustainable infrastructure project planning: progress in contemporary decision support tools

Most infrastructure project developments are complex in nature, particularly in the planning phase. During this stage, many vague alternatives are tabled - from the strategic to operational level. Human judgement and decision making are characterised by biases, errors and the use of heuristics. These factors are intangible and hard to measure because they are subjective and qualitative in nature. The problem with human judgement becomes more complex when a group of people are involved. The variety of different stakeholders may cause conflict due to differences in personal judgements. Hence, the available alternatives increase the complexities of the decision making process. Therefore, it is desirable to find ways of enhancing the efficiency of decision making to avoid misunderstandings and conflict within organisations. As a result, numerous attempts have been made to solve problems in this area by leveraging technologies such as decision support systems. However, most construction project management decision support systems only concentrate on model development and neglect fundamentals of computing such as requirement engineering, data communication, data management and human centred computing. Thus, decision support systems are complicated and are less efficient in supporting the decision making of project team members. It is desirable for decision support systems to be simpler, to provide a better collaborative platform, to allow for efficient data manipulation, and to adequately reflect user needs. In this chapter, a framework for a more desirable decision support system environment is presented. Some key issues related to decision support system implementation are also described