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Occupants are one of the most impacting factors in the overall energy performance of buildings, according to literature. Occupants’ behaviours and actions may impact the overall use of energy in more than 50%. In order to quantify the impact that occupant behaviour has in the use of energy, this study simulated interactions between occupants and the systems present in two actual buildings. The main aim was to compare the deviations due to occupant behaviour with the actual conditions and energy use of the two buildings.

The buildings used as a case study in this research were green buildings, rated according to the Australian Green Star certification system as a 6-star and a non-rated building. The two buildings are university buildings with similar characteristics, from Western Sydney University, in Sydney, Australia. A comparison was performed by means of building simulations among the use of energy in both buildings, aiming to understand if the green rating had any impact on the energy related to occupant behaviour. Therefore, to represent the actual buildings' conditions, the actual data related with climate, geometry, systems, internal loads, etc. were used as input variables in the simulation models of the green and the non-rated buildings. Both models were calibrated and validated, having as target the actual monitored use of electricity.

Occupants were categorized according to their levels of energy use as follows: saving, real and intensive energy users. Building simulations were performed to each building, with varying parameters related with lighting, plug loads, windows/doors opening, shading and air conditioning set points. Results show that occupant behaviour may impact the buildings' energy performance in a range of 72% between the two extremes. There is no significant relationship between the green rating and the way occupants behave in terms of the energy use.

This study intends to show the impact of different categories of occupant behaviour in the overall energy performance of two university buildings, a non-rated and a green-rated building, having as reference an actual representation of the buildings. Additionally, the study aims to understand the main differences between a green-rated and a non-rated building when accounting with the previous categories.

The purpose of this study is to adopt the perspective of congruence to explore how organizational unlearning facilitates knowledge transfer in cross-border mergers and acquisitions (M&A).

Drawing on the congruence theory, this study built a theoretical model and examined it with survey data from 212 firms in China.

Organizational unlearning has no direct influence on knowledge transfer. In contrast, it promotes knowledge and routine compatibility that facilitate knowledge transfer. Routine and knowledge compatibility have different mechanisms on knowledge transfer. Specifically, the higher routine compatibility, the more effective is knowledge transfer. When knowledge compatibility is at a medium level, the effectiveness of knowledge transfer is optimal.

Firms should regard organizational unlearning as a crucial facilitator to knowledge and routine compatibility that promote knowledge transfer.

This study provides a specific understanding of the relationships between organizational unlearning and knowledge transfer by focusing on knowledge and routine compatibility as the crucial links, and enriches existing literature regarding knowledge transfer.

The purpose of this paper is to formulate and test a theoretical model to explain inter-organizational cooperation behaviors among suppliers in automotive new product development (NPD) projects. This study aims to investigate the effects of cost and benefit factors on trust and inter-organizational cooperative behaviors among suppliers in automotive NPD projects from the perspective of social exchange theory (SET).

The structural equation modeling method is applied to test the proposed model, which is based on the analysis of survey data from 272 product managers of automotive part suppliers.

Knowledge sharing and coordination effort influence inter-organizational cooperation indirectly through trust. Specially, trust is negatively influenced by coordination effort but positively affected by knowledge sharing. Requirement uncertainty moderates the relationship between cost–benefit factors and trust differently. Specifically, requirement uncertainty increases the negative influence of coordination effort on trust but also strengthens the positive effect of knowledge sharing on trust.

This study provides a relatively comprehensive cost–benefit framework for further understanding the formation mechanism of inter-organizational cooperation among suppliers. It also contributes to SET by incorporating the contextual factor to explain the moderating effect of requirement uncertainty on the relationships between cost–benefit factors and trust in the context of automotive NPD projects.

Credit risk assessment has gained importance in recent years due to global financial crisis and credit crunch. Financial institutions therefore seek the support of credit rating agencies to predict the ability of creditors to meet financial persuasions. The purpose of this paper is to construct neural network (NN) and fuzzy support vector machine (FSVM) classifiers to discriminate good creditors from bad ones and identify a best classifier for credit risk assessment.

This study uses artificial neural network, the most popular AI technique used in the field of financial applications for classification and prediction and the new machine learning classification algorithm, FSVM to differentiate good creditors from bad. As membership value on data points influence the classification problem, this paper presents the new FSVM model. The instances membership is computed using fuzzy c-means by evolving a new membership. The FSVM model is also tested on different kernels and compared and the classifier with highest classification accuracy for a kernel is identified.

The paper identifies a standard AI model by comparing the performances of the NN model and FSVM model for a credit risk data set. This work proves that that FSVM model performs better than back propagation-neural network.

The proposed model can be used by financial institutions to accurately assess the credit risk pattern of customers and make better decisions.

This paper has developed a new membership for data points and has proposed a new FCM-based FSVM model for more accurate predictions.

This study aims to provide a series of drivers that prompt the blockchain technology (BT) adoption decisions in circular supply chain finance (SCF) and also assesses their degrees of influence and interrelationships, which leads to the construction of a theoretical model depicting the influence mechanism of BT adoption decisions in circular SCF.

This study mainly uses the technology-organization-environment (TOE) framework, which focuses on the aspects based on the nature of innovation, intra-organizational characteristics and extra environmental consideration, to identify the drivers of blockchain adoption in circular SCF context, while the significance and causality of the drivers are explained using interpreting structural models (ISMs) and the decision-making trial and evaluation laboratory (DEMATEL) method.

The findings of this study indicate that government policy and technological comparative advantage are the underlying reasons for BT adoption decisions, management commitment and financial expectations are the critical drivers of BT adoption decisions while other factors are the receivers of the mechanism.

This study provides theoretical references and empirical insights that influence the technology adoption decisions of both BT and circular SCF by practitioners.

The theoretical research contributes significantly to current research and knowledge in both BT and circular SCF fields, especially by extending the existing TOE model by combining relevant enablers from technological, organizational and external environmental aspects with the financial performance objectives of circular SCF services, which refer to the optimization of the financial resources flows and financing efficiency.