A trust-based performance measurement modeling using t-norm and t-conorm operators

• Published in: Applied soft computing Vol. 30; pp. 491 - 500
• Main Authors: Azadeh, Ali, Pourmohammad Zia, Nadia, Saberi, Morteza, Khadeer Hussain, Farookh, Yoon, Jin Hee, Khadeer Hussain, Omar, Sadri, Shadi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2015
• Subjects: Auto regressive model; Data fusion; Performance assessment; t-Norm and t-conorm operators; Time series modeling; Trust; t-Norm and t-conorm operators; Auto regressive model; Data fusion; Trust; Performance assessment; Time series modeling
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2015.01.015

•This study presents an efficient model for analysing the outputs of performance measurement methodologies, by means of trust.•This is the first attempt to implement the concept of trust in terms of performance measurement.•Comprehensive synthetic data set is generated in fifteen scenarios to the applicability of the proposed model.•t-Norms, t-conorms and time series modeling are utilized for the modeling in this study. In today's highly dynamic economy and society, performance assessment of decision making units (DMUs) is a problem of great significance. In order to make efficient decisions and beneficial improvements, decision makers and top management need to have a comprehensive view over capabilities and performance of DMUs which could be defined as organizational units. This study presents an efficient model for analyzing the outputs of performance measurement methodologies, by means of trust that provides explicit qualitative scales rather than pure numerical data. To the best of our knowledge, this is the first attempt for implementing the concept of trust in terms of performance measurement. In order to develop the structure of our proposed framework, fifteen scenarios are established based on number of DMUs and timeslots. These scenarios form the basis of the proposed structure. The efficiency rate of current, previous and upcoming years, as well as the average efficiency and standard deviation, are five inputs for this model. The approach incorporates time series forecasting to predict the future efficiency rate. Furthermore, auto correlation function (ACF) is used as the input selection in time series context. The model utilizes t-norms and t-conorms as the final modeling tools. To show the applicability and superiority of the proposed model, it is applied to a data set which is provided by running a simulation structured by a unique logic. The results provided by this model are more user-centric and have been represented in a potentially more transparent and intelligible way for decision makers.