Corporate Social Responsibility perception versus human values: a structural equation modeling approach

M. Rosario González-Rodríguez, M. Carmen Díaz Fernández and Biagio Simonetti

Journal of Applied Statistics, 2016, vol. 43, issue 13, 2396-2415

Abstract: In the business world, increasing importance is being given to Corporate Social Responsibility (CSR). Consumer perception of CSR is determinant on the success of CSR practices and this perception is directly influenced by individual value structures. Despite research efforts and the continued preoccupation of CSR role in business and Society, few studies to date have analyzed jointly CSR perception and the value structure. As a result, the paper brings new knowledge of the relationship between basic human values and CSR's perception under a particular social initiative carried out by a company. To reach our purpose a Hierarchical Component Model which includes the variable gender to control for heterogeneity is adopted. The model focuses on not only by analyzing the effects of human values on CSR but also analyzes the influence of values by gender on CSR perception. This approach to study the relationship of CSR versus values considering the Schwartz's higher-order values and the moderating role of gender constitutes a new perspective. The main results of this study reveal the influence of values on CSR, the strength of those relationships and the importance of analyzing the moderator effect to control for heterogeneity.

Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2016.1163528 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:43:y:2016:i:13:p:2396-2415

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2016.1163528

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().