To tackle the complex problem of providing business intelligence solutions based on business data, bioinspired deep learning has to be considered. This paper focuses on the application of artificial metaplasticity learning in business intelligence systems as an alternative paradigm of achieving a deeper information extraction and learning from arbitrary size data sets. As a case study, artificial metaplasticity multilayer perceptron applied to the automation of credit approval decision based on collected client data is analyzed, showing its potential and improvements over the state-of-the-art techniques. This paper successfully introduces the relevant novelty that the artificial neural network itself estimates the pdf of the input data to be used in the metaplasticity learning, so it is much closer to the biologic reality than previous implementations of artificial metaplasticity.

Numerous studies and reviews about University Knowledge Transfer Offices (UKTO) have been written, but there are few that focus on Social Responsibility (SR). We present a systematic review of the research on both fields. We consider not only logics from agency theory and resource-based view, but also the dynamic approach from institutional theory, as they aim to generate sustainable economic and social value. The evolution of Knowledge Transfer Offices depends on their role as brokers of collaborations among different stakeholders, according to their mission and capacity to confront the innovation gap. We follow the line of SR viewed as a response to the specific demands of large stakeholders. Building upon recent conceptualizations of different theories, we develop an integrative model for understanding the institutional effects of the UKTO on university social responsibility.

Purpose -The purpose of this paper is to analyze how team management affects team-learning activities. Design/methodology/approach -The authors empirically study 68 teams as they operate in the natural business context of a major Spanish bank. Quantitative research utilizing multiple regression analyses is used to test hypotheses. Findings -The leadership behaviour (consideration, initiation of structure) displayed by the team leader plays a key role in facilitating team learning. Team leader behaviour characterised by consideration and in particular by initiation of structure are both positively related to team-learning activities. Cross-training of team members also contributes to team-learning behaviour. Research limitations/implications -A specific setting may limit the generalizability of findings. Further research may accordingly investigate to what extent these results can be generalized to other settings or other aspects of team learning. Practical implications -The leadership style adopted by the team leader, as well as cross-training of members, affect team-learning activities. These results link leadership theory to collective learning in teams and organizations, and suggest ways leaders can contribute to improved learning. Originality/value -The study provides new insight into how management of teams facilitates team-learning activities. While consideration is somewhat related to team learning, initiation of structure as well as cross-training appear as key variables.

The conceptual model proposed in this study is used to serve a guide for Industry 4.0 to understand the effect of GIC (green intellectual capital) and GKM (green knowledge management) on sustainability. Green challenge in Industry 4.0 has increasingly become a hot topic in both academia and practice. Among the Industry 4.0 topics, digital chain monitoring has a great impact on the performance of the company. The study of the industrial digital chain is a great green challenge in the 21st century in order to understand and manage the flows of green information. Knowledge of the human, relational, and structural (including technological aspects) will help to better understand and management the effects of traceability on sustainability. Several of the concepts and variables in the suggested model can easily be managed by organizations if they carefully measure their green intangible nature with smart sensors.

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