Culture, participative decision making and job satisfaction

Abstract

Abstract This study explores the impact of culture on participatory decision making (PDM) and job satisfaction (JS) using data obtained from the European Values Study (EVS). We parameterise two different cultural variables using principal components analysis: first a continuum based on survival versus self-expression values and second a continuum based on traditional versus secular-rational values. Application of ordered logistic regression to Likert scales of PDM and JS suggests that greater self-expression in the survival versus self-expression variable enhances both PDM and JS; and more traditional values in the traditional versus secular-rational continuum have the same effect. Keywords: culturejob satisfactionparticipatory decision making Notes 1. These studies are on a smaller scale to the empirical research presented in this article. For instance, analysis by Fargher et al. (Citation2008) covered 20 European countries, whereas the data employed in this article encompass 39 countries in Europe. 2. The dates of these empirical studies indicate the lack of contemporary research in this field. 3. Culture that promotes the welfare, interests and goals of the individual and his/her core family (Sagie and Aycan Citation2003). 4. Culture that advocates membership within communities or large groups; it considers the welfare, interests and goals of the group to be more important than that of the individual group member (Sagie and Aycan Citation2003). 5. Countries included in 2008 first release sample are Albania, Azerbaijan, Austria, Armenia, Belgium, Bosnia Herzegovina, Bulgaria, Belarus, Cyprus, Northern Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Ireland, Northern Ireland, Kosovo, Latvia, Lithuania, Luxembourg, Malta, Moldova, Montenegro, the Netherlands, Poland, Portugal, Romania, Russia, Serbia, Slovak Republic, Slovenia, Spain, Switzerland and Ukraine. 6. With the exception of Finland (Internet panel). 7. It needs to be acknowledged that Loo (Citation2002) and Rose (Citation2005) argue that a single-item measure of JS tends to overestimate the percentage of people satisfied in their jobs. The alternative multi-item measures can have the advantage of indicating particular areas for management to target (e.g. pay, job challenge and so on). However, Oshagbemi (Citation1999) explains that a single-item measure of JS is also useful in eliminating the unique characteristics of a specific job. 8. Calculated by estimating eβ (Tarling Citation2009). 9. The four categories of occupational status used in this article (professionals, skilled, less skilled and manual) correspond to the ISCO-08 classifications of major groups (1 and 2, 3 and 4, 5–7, 8 and 9); see ILO (Citation2010). 10. It is important to make note of a caveat that accompanies our empirical findings; this research does not relate to how much an employee participates in job-related decisions, but rather how much freedom an employee perceives he/she is granted for PDM. 11. The table in the appendix illustrates that gender itself is not an issue. However, the importance of specific traits does differ significantly across gender, as highlighted by shading. Effectively the variables not multiplied by ‘male’ correspond to females. If the variables differ significantly between genders, then this will be highlighted on the variable multiplied by male. For illustration, consider the impact of higher education on JS. For women this variable has a coefficient equal to − 0.569 and it is statistically significant at the 1% level. The same variable for males is 0.367, but the interpretation of this male coefficient should be relative to the female equivalent. Hence, the effect for males is equal to ( − 0.569+0.367) = − 0.202. This suggests that the effect of higher education on both males' and females' perception of JS is negative, but that the effect is more strongly negative for females than for males, which corroborates the results presented in Table 4.