TY - JOUR
T1 - Evaluating women’s happiness levels with ARASsort
T2 - The case of Türkiye
AU - Bala, Semiha
AU - Temur, Gül Tekin
AU - Gül, Sait
N1 - Publisher Copyright:
© 2025 by the authors; licensee Growing Science, Canada.
PY - 2025/3/1
Y1 - 2025/3/1
N2 - The happiness levels of women exhibit variations attributable to a myriad of factors, encompassing economic, social, cultural, and demographic variables. Numerous governments incorporate the meas-urement of happiness levels as part of life-satisfaction analyses; nonetheless, these analyses lack a comprehensive framework for predicting happiness levels over specific periods. Notably, in develop-ing countries, women confront the adverse consequences of economic, social, cultural, and demo-graphic determinants to a greater extent than men. Paradoxically, they remain significantly underrepre-sented in both academic and industrial domains. In light of this, the primary objective of this study is to conduct an in-depth analysis of happiness levels and their underlying determinants from a gender-oriented perspective. Therefore, the pertinent literature has not dedicated a systematic approach to classify and forecast the happiness of women. The present paper initiates by elucidating the factors influencing women’s perceptions of happiness through a comprehensive review of the existing litera-ture. Then, a multiple attribute decision-making algorithm-based sorting methodology, ARASsort, is utilized to evaluate how women’s happiness levels are affected by life satisfaction components in a developing country, Türkiye. The selection of ARASsort is based on its performance over other tradi-tional sorting approaches in terms of time and effort attachment. Various factors affecting the happi-ness levels of women in different cities in the country sample were discussed and analyzed in detail in accordance with the main findings of the OECD Better Life Index (2020), through representative data selected from TÜI˙K’s life satisfaction dataset.
AB - The happiness levels of women exhibit variations attributable to a myriad of factors, encompassing economic, social, cultural, and demographic variables. Numerous governments incorporate the meas-urement of happiness levels as part of life-satisfaction analyses; nonetheless, these analyses lack a comprehensive framework for predicting happiness levels over specific periods. Notably, in develop-ing countries, women confront the adverse consequences of economic, social, cultural, and demo-graphic determinants to a greater extent than men. Paradoxically, they remain significantly underrepre-sented in both academic and industrial domains. In light of this, the primary objective of this study is to conduct an in-depth analysis of happiness levels and their underlying determinants from a gender-oriented perspective. Therefore, the pertinent literature has not dedicated a systematic approach to classify and forecast the happiness of women. The present paper initiates by elucidating the factors influencing women’s perceptions of happiness through a comprehensive review of the existing litera-ture. Then, a multiple attribute decision-making algorithm-based sorting methodology, ARASsort, is utilized to evaluate how women’s happiness levels are affected by life satisfaction components in a developing country, Türkiye. The selection of ARASsort is based on its performance over other tradi-tional sorting approaches in terms of time and effort attachment. Various factors affecting the happi-ness levels of women in different cities in the country sample were discussed and analyzed in detail in accordance with the main findings of the OECD Better Life Index (2020), through representative data selected from TÜI˙K’s life satisfaction dataset.
KW - ARASsort
KW - Happiness
KW - Life satisfaction
KW - Multiple attribute sorting
KW - Women’s happiness
UR - https://www.scopus.com/pages/publications/105008092681
U2 - 10.5267/j.dsl.2025.3.009
DO - 10.5267/j.dsl.2025.3.009
M3 - Article
AN - SCOPUS:105008092681
SN - 1929-5804
VL - 14
SP - 707
EP - 726
JO - Decision Science Letters
JF - Decision Science Letters
IS - 3
ER -