Artificial neural network (ANN) based decision support model for alternative workplace arrangements (AWA):readiness assessment and type selection

Readiness Level Assessment Indicators (RLAI);Alternative Workplace Arrangements (AWA);Decision support model;Artificial Neural Network (ANN)

Kim, Jun Ha ; Building Construction

University:Georgia Institute of Technology

Department:Building Construction

关键词: Readiness Level Assessment Indicators (RLAI);    Alternative Workplace Arrangements (AWA);    Decision support model;    Artificial Neural Network (ANN);   

Others  :  https://smartech.gatech.edu/bitstream/1853/31830/1/Kim_JunHa_200912_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

A growing body of evidence shows that globalization and advances in information and communication technology (ICT) have prompted a revolution in the way work is produced. One of the most notable changes is the establishment of the alternative workplace arrangement (AWA), in which workers have more freedom in their work hours and workplaces. Just as all organizations are not good candidates for AWA adoption, all work types, all employees and all levels of facilities supports are not good candidates for AWA adoption. The main problem is that facility managers have no established tools to assess their readiness for AWA adoption or to select among the possible choices regarding which AWA type is most appropriate considering their organizations' business reasons or objectives of adoption and the current readiness levels. This dissertation resulted in the development of readiness level assessment indicators (RLAI), which measure the initial readiness of high-tech companies for adopting AWAs and the ANN based decision model, which allows facility managers to predict not only an appropriate AWA type, but also an anticipated satisfaction level considering the objectives and the current readiness level. This research has identified significant factors and relative attributes for facility managers to consider when measuring their organization's readiness for AWA adoption. Robust predictive performance of the ANN model shows that the main factors or key determinants have been correctly identified in RLAI and can be used to predict an appropriate AWA type as well as a high-tech company's satisfaction level regarding the AWA adoption.