Abstract
In this paper, we explore the determinants of being satisfied with a job, starting from a SHARE-ERIC dataset (Wave 7), including responses collected from Romania. To explore and discover reliable predictors in this large amount of data, mostly because of the staggeringly high number of dimensions, we considered the triangulation principle in science by using many different approaches, techniques and applications to study such a complex phenomenon. For merging the data, cleaning it and doing further derivations, we comparatively used many methods based on spreadsheets and their easy-to-use functions, custom filters and auto-fill options, DAX and Open Refine expressions, traditional SQL queries and also powerful 1:1 merge statements in Stata. For data mining, we used in three consecutive rounds: Microsoft SQL Server Analysis Services and SQL DMX queries on models built involving both decision trees and naive Bayes algorithms applied on raw and memory consuming text data, three LASSO variable selection techniques in Stata on recoded variables followed by logistic and Poisson regressions with average marginal effects and generation of corresponding prediction nomograms operating directly in probabilistic terms, and finally the WEKA tool for an additional validation. We obtained three Romanian regional models with an excellent accuracy of classification (AUROC > 0.9) and found several peculiarities in them. More, we discovered that a good atmosphere in the workplace and receiving recognition as deserved for work done are the top two most reliable predictors (dual-core) of career satisfaction, confirmed in this order of importance by many robustness checks. This type of meritocratic recognition has a more powerful influence on job satisfaction for male respondents rather than female ones and for married individuals rather unmarried ones. When testing the dual-core on respondents aged 50 and over from most of the European countries (more than 75,000 observations), the positive surprise was that it undoubtedly resisted, confirming most of our hypotheses and also the working principles of support for replication of results, triangulation and the golden rule of robustness using cross-validation.
Highlights
This study engages in a scientific endeavour through an in-depth analysis of a broad sample of data on job satisfaction behaviour
In these first DM tests, we have involved the Microsoft algorithms based on both the decision trees and naive Bayes techniques, which belong to the traditional classifiers that have been commonly used in the context of classification problems [93]
The careful treatment of data, the principle of support for replication of the results through transparent approaches and methods, the triangulation principle in terms of the many approaches, techniques and tools used as a basic principle in research, together with the golden rule of cross-validation, as a reliable way of testing for data randomly and non-randomly divided in subsets, all these allowed us to better explore this complex phenomenon
Summary
This study engages in a scientific endeavour through an in-depth analysis of a broad sample of data on job satisfaction behaviour. A recent study starting from 18,247 responses of people from the Romanian business, engineering and pharmaceutical sectors [15], focused on the most desirable employers from 2019, revealed interesting facts relating to job satisfaction. In this direction, the research emphasized that most of these respondents are satisfied and choose a certain employer if they are well paid, the company is big and important enough on the market, if their career will bring new professional opportunities, there are role models within the corporation they could learn from and last but not least, the job is safe enough. The paper is structured as follows: we present the literature review related to this topic and, according to it, several hypotheses to be further tested; we present the data we have used and the methods, which are explained in detail, followed by the section where the main results are underlined; the paper ends with a discussion of the results and brief conclusions
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