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Influencing subjective well-being for business and sustainable development using big data and predictive regression analysis

Weerakkody, V., Sivarajah, U., Mahroof, K., Maruyama, T., Lu, S. (2021) Influencing subjective well-being for business and sustainable development using big data and predictive regression analysis. Journal of Business Research, 131 . pp. 520-538. (doi:10.1016/j.jbusres.2020.07.038) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:89961)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1016/j.jbusres.2020.07.038

Abstract

Business leaders and policymakers within service economies are placing greater emphasis on well-being, given the role of workers in such settings. Whilst people's well-being can lead to economic growth, it can also have the opposite effect if overlooked. Therefore, enhancing subjective well-being (SWB) is pertinent for all organisations for the sustainable development of an economy. While health conditions were previously deemed the most reliable predictors, the availability of data on people's personal lifestyles now offers a new dimension into well-being for organisations. Using open data available from the national Annual Population Survey in the UK, which measures SWB, this research uncovered that among several independent variables to predict varying levels of people's perceived well-being, long-term health conditions, one's marital status, and age played a key role in SWB. The proposed model provides the key indicators of measuring SWB for organisations using big data. © 2020

Item Type: Article
DOI/Identification number: 10.1016/j.jbusres.2020.07.038
Uncontrolled keywords: Big data; Regression; Well-being; UN SDG goals; Public sector
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Accounting and Finance
Depositing User: Shan Lu
Date Deposited: 31 Aug 2021 10:46 UTC
Last Modified: 03 Sep 2021 13:55 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/89961 (The current URI for this page, for reference purposes)
Lu, S.: https://orcid.org/0000-0002-7588-8599
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