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Purpose: Higher education (HE) institutions create, disseminate, share and exchange knowledge through relationships among people, processes and technologies. Knowledge sharing (KS) in academia enables people within the institute to develop practices which allow them to collect and share what they know. This often leads to actions that improve services and outcomes. Despite the opportunities, this sharing is not without implications as it exposes the knowledge sharer and recipient to a number of vulnerabilities and risks, which hinders the knowledge-sharing process. The paper aims to discuss these issues. Design/methodology/approach: This paper adopted a constructivist approach, which utilised focus group discussions within three UK universities. These were identified to improve the usefulness and effectiveness of the knowledge-sharing process. Findings: The paper concluded by purporting that by developing an understanding of the risks and opportunities of sharing HE, institutions will continue to grow, regenerate and develop knowledge. The results highlighted the plethora of risks and opportunities resulting from KS. These were further grouped into the political, social and organisational culture. Originality/value: This paper addresses the gap of risks perceptions of KS in HE institutions.

Modeling land surface processes requires complete and reliable soil property information to understand soil hydraulic and heat dynamics and related processes, but currently, there is no data set of soil hydraulic and thermal parameters that can meet this demand for global use. In this study, we propose a fitting approach to obtain the optimal soil water retention parameters from ensemble pedotransfer functions (PTFs), which are evaluated using the global coverage National Cooperative Soil Survey Characterization Database and show better performance for global applications than our original ensemble estimations (median values of PTFs) as done in Dai et al. (2013, https://doi.org/10.1175/JHM‐D‐12‐0149.1). Soil hydraulic conductivities are still estimated as the median values of multiple PTFs, and the results are shown to perform comparably to the estimates from the existing precision‐advanced models. Soil thermal properties are estimated following the schemes identified by Dai et al. (2019a, http://arxiv.org/abs/1908.04579), which evaluated several highly recommended schemes based on their land modeling applications. Using these approaches, we develop two global high‐resolution data sets of soil hydraulic and thermal parameters based on Global Soil Dataset for Earth System Models (GSDE) and SoilGrids soil composition databases. The delivered variables include six basic soil properties, four soil hydraulic parameters in the Campbell (1974, https://doi.org/10.1097/00010694‐197406000‐00001) model, five soil hydraulic parameters in the van Genuchten (1980, https://doi.org/10.2136/sssaj1980.03615995004400050002x) model, and four soil thermal properties. The delivered data sets are available at a 30″ × 30″ geographical spatial resolution and provide four sets of vertical profiles following the resolutions of SoilGrids, Noah‐Land Surface Models (LSM), Joint UK Land Environment Simulator (JULES), and Common Land Model/Community Land Model (CoLM/CLM). The data sets can be used in both regional and global applications. Plain Language Summary: The parameters of soil hydraulic and thermal properties are essential to the modeling of land surface processes, but no data set is currently available for global use. Using some newly proposed and existing widely used schemes, we developed two global high‐resolution data sets of soil hydraulic and thermal parameters based on two commonly used soil composition databases (Global Soil Dataset for Earth System Models [GSDE] and SoilGrids). The delivered variables include multiple basic soil properties and all the required parameters for solving soil hydraulic and heat dynamics and related processes. The data sets follow the vertical profiles of SoilGrids data set and four land surface models (i.e., Noah‐Land Surface Models [LSM], Joint UK Land Environment Simulator [JULES], Common Land Model [CoLM], and Community Land Model [CLM]). The data sets can be used in both regional and global applications. Key Points: A new fitting approach is proposed to obtain the optimal soil water retention parameters from ensemble pedotransfer functionsThe adopted approaches for soil hydraulic parameter estimations are evaluated to be superior or comparative to other approachesGlobal maps of soil hydraulic and thermal parameters for use in land models are produced based on two soil composition data sets [ABSTRACT FROM AUTHOR]

Knowledge retention (k-retention) is vital for various enterprise resource planning (ERP) implementations in small and medium-sized enterprises (SMEs), though it is a highly demanding and challenging task. The aim of this paper is to investigate different types of approaches to k-retention and factors influencing k-retention in SMEs. Our study adopts a grounded theory approach with cases based on 12 ERP implementations in UK SMEs. We analyze our data using thematic analysis. The findings reveal specific elements that support the k-retention of ERP package knowledge and business process knowledge. These elements are: k-retention tools, documentation, human capital and the understanding of k-retention challenges. In addition to insights from these aspects, our study reveals two additional factors (project management and organizational culture) that influence ERP package k-retention and business process k-retention. Based on these findings, we develop an ERP k-retention (EKR) framework that can be utilized by SMEs which are considering implementing ERP systems, to facilitate knowledge retention during implementation. [ABSTRACT FROM AUTHOR]

Abstract: Whilst the concept of a learning curve is well established, within organisational settings the coordinates of points on the curve often remain unmeasured. Devon and Cornwall Police are a UK police force that is seeking to better allocate officer resource to high volumes of crime demand through understanding the relationship between levels of officer learning and development and the time required to resolve crimes. Devon and Cornwall Police subscribed to building a system dynamics simulation model of officer resourcing, within which an aggregate learning curve would convert levels of available officer experience into time required to resolve volume crime. Fragmented learning curve data meant that judgmental data needed to be elicited from expert practitioners in order to establish a consensus based measure of bivariate relationships. VISCONS is a judgmental method that was developed to help Devon and Cornwall Police quantify these relationships in a group setting. Three components are synergised into the VISCONS approach, namely: visual or graphical elicitation, parameterisation and consensus through group model building. The method brings together expert practitioners to arrive at a single shared view of the quantified bivariate relationship from a set of individual perspectives. Experts are involved in sketching their views onto acetates before overlaying them into a set of agreed parameter values. Experts do not require any knowledge of system dynamics simulation techniques, as they are facilitated in a single workshop to represent and amalgamate different views and perspectives on the parameterisation problem under consideration. The group is able to validate resulting bivariate parameter values using an efficient graphical method of data capture. VISCONS can be used for parameter capture beyond the scope of learning curves or system dynamics. [Copyright &y& Elsevier]