Holistic and Sustainable Employee Well-being Management: The Impact of Context to Guide Employee Well-being and Well-being Support Strategies

Authors: Loreta Kaklauskiene, Renata Korsakiene, Arturas Kaklauskas

Abstract:

Advancing employee well-being (EWB) and performance has been shown to improve quality of life and contribute to enhancing national and organizational efficiency and sustainable development. This study adopts a holistic approach, integrating theoretical framework (complexity theory and ecological systems theory) with methods, including opinion mining, statistical modeling (linear regression, neural networks, principal component analysis), and multi-criteria technique. The study analyzes the interaction dynamics between a nation's macro-context and workplace management to enhance EWB. This analysis encompasses a comprehensive sample of 94 nations from 1990 to 2023. The study encompassed 33 macro-environmental indicators (social, environmental, economic, political, and cultural) evaluated as individual metrics and aggregated category. Additionally, two indicators of published scientific production were considered: the count of articles per 100,000 population and the count of their citations (per article). A well-being-related word count was extracted from Google using opinion mining. The aim of the study was to determine the impact of national macroeconomic conditions and specific factors on scholars' and societies' attention, attitudes, and priorities regarding employee well-being. To this end, the models were developed, encompassing country-level analyses of employee well-being (EWB) research outputs and their citation patterns. They also integrated 711 EW models employing linear regression and neural networks (NN) for context and content found on Google analysis. The EW models exhibit robust explanatory power, accounting for 77 percent and 67 percent of the variance in research outputs and citation counts, respectively. Furthermore, the median R2 values derived from NN models indicate that the density of EWB-related Google records explains 51 percent of the observed variance. This study provides empirical evidence of a statistically significant relationship between macro-environmental changes and EWB, as reflected in national research productivity and the prevalence of EWB-related terms in web platforms such as Google. Social and political factors were identified as dominant influences, with additional contributions from economic, environmental, and cultural dimensions. The findings make a novel contribution to the extant literature on workplace management and EWB by elucidating the explanatory capacity of macro-level variables and their interaction with country-specific pillars. These insights provide valuable implications for policymakers, organizational leaders, and other stakeholders by highlighting the intricate interdependencies between EWB and the broader macro-environment. Improved identification, prediction, and understanding of macro-environmental factors and their influence on EWB can facilitate the effective allocation of resources and the development of targeted managerial strategies, thereby mitigating potential societal and economic repercussions. The complex interrelations identified between macro-level indicators, research output volume, and the prevalence of EWB-related records offer a nuanced understanding of the determinants influencing EWB at the macro scale. This enhanced knowledge enables more precise predictive EWB. From a practical perspective, the models developed in this study can be applied across various contexts related to EWB to address specific challenges. By identifying optimal combinations of macro-environmental factors, these models can inform strategies to foster favorable conditions while minimizing adverse impacts on EWB. Ultimately, this approach aims to develop a more supportive macro-context, thereby advancing future outcomes in EWB management.

Keywords: employees’ well-being, management, holistic workplace, opinion mining, neural networks, linear regression, context, human-centered approach, human resource