Heritage or historic building information modelling (BIM), often referred to as HBIM, is becoming an established feature in both research and practice. The advancement of…
Heritage or historic building information modelling (BIM), often referred to as HBIM, is becoming an established feature in both research and practice. The advancement of data capture technologies such as laser scanning and improved photogrammetry, along with the continued power of BIM authoring tools, has provided the ability to generate more accurate digital representations of heritage buildings which can then be used during renovation and refurbishment projects. Very often these representations of HBIM are developed to support the design process. What appears to be often overlooked is the issue of conservation and how this can be linked to the BIM process to support the conservation management plan for the building once it is given a new lease of life following the refurbishment process. The paper aims to discuss these issues.
The paper presents a review of the context of conservation and HBIM, and then subsequently presents two case studies of how HBIM was applied to high-profile renovation and conservation projects in the UK. In presenting the case studies, a range of issues is identified which support findings from the literature noting that HBIM is predominantly a tool for the geometric modelling of historic fabric with less regard for the actual process of renovation and conservation in historic buildings.
Lessons learnt from the case studies and from existing literature are distilled to develop a framework for the implementation of HBIM on heritage renovation projects to support the ongoing conservation of the building as an integral part of a BIM-based asset management strategy. Five key areas are identified in the framework including value, significance, recording, data management and asset management. Building on this framework, a conceptual overlay is proposed to the current Level 2 BIM process to support conservation heritage projects.
This paper addresses the issue of HBIM application to conservation heritage projects. Whilst previous work in the field has identified conservation as a key area, there is very little work focusing on the process of conservation in the HBIM context. This work provides a framework and overlay which could be used by practitioners and researchers to ensure that HBIM is fully exploited and a more standardised method is employed which could be used on conservation heritage renovation projects.
It has often been said that a great part of the strength of Aslib lies in the fact that it brings together those whose experience has been gained in many widely differing fields but who have a common interest in the means by which information may be collected and disseminated to the greatest advantage. Lists of its members have, therefore, a more than ordinary value since they present, in miniature, a cross‐section of institutions and individuals who share this special interest.
Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI…
Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.
This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.
This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.
This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.
This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.