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

Exploring, modelling and predicting spatiotemporal variations in house prices

Hedonic price modelling has long been a powerful tool to estimate house prices in the real estate market. Increasingly, traditional global hedonic price models that largely ignore spatial effects are being superseded by models that deal with spatial dependency and spatial heterogeneity. In addition, many novel methods integrating spatial economics, statistics and geographical information science (GIScience) have been developed recently to incorporate temporal effects into hedonic house price modelling. Here, a local spatial modelling technique, geographically weighted regression (GWR), which accounts for spatial heterogeneity in housing utility functions is applied to a 19-year set of house price data in London (1980–1998) in order to explore spatiotemporal variations in the determinants of house prices. Further, based on the local parameter estimates derived from GWR, a new method integrating GWR and time series (TS) forecasting techniques, GWR–TS, is proposed to predict future local parameters and thus future house prices. The results obtained from GWR demonstrate variations in local parameter estimates over both space and time. The forecasted future values of local estimates as well as house prices indicate that the proposed GWR–TS method is a useful addition to hedonic price modelling

Using strategic management accounting practices to measure and manage intellectual capital: a proposal

The main purpose of this chapter is to examine the role of management accounting to measure and manage intellectual capital (IC), and more specifically to explore the potential role of strategic management accounting (SMA) in this process. In addition, this chapter is intended to link SMA practices and some IC resources. SMA practices enable the identification, measurement, and management of IC resources such as production processes and innovation capacity (e.g., target costing), quality management (e.g., quality costing), knowledge-based resources related to the organization´s external relationships (e.g., attribute costing, value chain costing, and target costing), and brand image (e.g., brand valuation/management). SMA practices, given its external orientation, enable, mainly, the identification and management of resources encompassed in relational capital. Therefore, this chapter contributes to the extant literature regarding the measurement and management of IC, highlighting the role of SMA, and provides some suggestions for further research.The authors would like to thank the Portuguese Foundation for Science and Technology within the project UID/GES/0463012019 and the Research Unit NECE I UBI for providing financial support for this research.info:eu-repo/semantics/publishedVersio