Comparing Employer Attractiveness of Public Sector Organizations to Nonprofit and Private Sector Organizations: An Experimental Study in Germany and the U.S.

Sector preferences in job choice have rarely been tested empirically across different administrative systems. We address this gap and apply a between-subject experimental design to examine the attractiveness of public, private, and nonprofit employers in two countries in different administrative traditions. Respondents ( n = 362) from an Anglo-Saxon (i.e., the U.S.) and continental European country (i.e., Germany) were exposed to job advertisements that only differed in the employer’s sector affiliation, with other job attributes, such as payment and working hours, held constant. Contrary to expectations, and consistently across the two country samples, respondents evaluated public sector jobs more positively compared to vacancies in the private sector. In contrast, we found no such comparative advantage of public over nonprofit employers. By providing counterevidence to the prevalence of negative attitudes toward public organizations, our study warns against overgeneralizing previous findings on negativity biases to the context of employer attractiveness.

Resource and Performance Modelling of Hadoop Clusters Using Machine Learning

<p>There is a huge and rapidly increasing amount of data being generated by social media, mobile applications and sensing devices. Big data is the term usually used to describe such data and is described in terms of the 3Vs - volume, variety and velocity. In order to process and mine such a massive amount of data, several approaches and platforms have been developed such as Hadoop. Hadoop is a popular open source distributed and parallel computing framework. It has a large number of configurable parameters which can be set before the execution of jobs to optimize the resource utilization and execution time of the clusters. These parameters have a significant impact on system resources and execution time. Optimizing the performance of a Hadoop cluster by tuning such a large number of parameters is a tedious task. Most current big data modeling approaches do not include the complex interaction between configuration parameters and the cluster environment changes such as use of different datasets or types of query. This makes it difficult to predict for example the execution time of a job or resource utilization of a cluster. Other attributes include configuration parameters, the structure of query, the dataset, number of nodes and the infrastructure used. Our first main objective was to design reliable experiments to understand the relationship between attributes. Before designing and implementing the actual experiment we applied Hazard and Operability (HAZOP) analysis to identify operational hazards. These hazards can affect normal working of cluster and execution of Hadoop jobs. This brainstorming activity improved the design and implementation of our experiments by improving the internal validity of the experiments. It also helped us to identify the considerations that must be taken into account for reliable results. After implementing our design, we characterized the relationship between different Hadoop configuration parameters, network and system performance measures. Our second main objective was to investigate the use of machine learning to model and predict the resource utilization and execution time of Hadoop jobs. Resource utilization and execution time of Hadoop jobs are affected by different attributes such as configuration parameters and structure of query. In order to estimate or predict either qualitatively or quantitatively the level of resource utilization and execution time, it is important to understand the impact of different combinations of these Hadoop job attributes. You could conduct experiments with many different combinations of parameters to uncover this but it is very difficult to run such a large number of jobs with different combinations of Hadoop job attributes and then interpret the data manually. It is very difficult to extract patterns from the data and give a model that can generalize for an unseen scenario. In order to automate the process of data extraction and modeling the complex behavior of different attributes of Hadoop job machine learning was used. Our decision tree based approach enabled us to systematically discover significant patterns in data. Our results showed that the decision tree models constructed for different resources and execution time were informative and robust. They were able to generalize over a wide range of minor and major environmental changes such as change in dataset, cluster size and infrastructure such as Amazon EC2. Moreover, the use of different correlation and regression techniques, such as M5P, Pearson's correlation and k-means clustering, confirmed our findings and provided further insight into the relationship of different attributes and with each other. M5P is a classification and regression technique that predicted the functional relationships among different job attributes. The use of k-means clustering allowed us to see the experimental runs that shows similar resource utilization and execution time. Statistical significance tests, were used to validate the significance of changes in results of different experimental runs, also showed the effectiveness of our resource and performance modelling and prediction method.</p>

Resource and Performance Modelling of Hadoop Clusters Using Machine Learning

<p>There is a huge and rapidly increasing amount of data being generated by social media, mobile applications and sensing devices. Big data is the term usually used to describe such data and is described in terms of the 3Vs - volume, variety and velocity. In order to process and mine such a massive amount of data, several approaches and platforms have been developed such as Hadoop. Hadoop is a popular open source distributed and parallel computing framework. It has a large number of configurable parameters which can be set before the execution of jobs to optimize the resource utilization and execution time of the clusters. These parameters have a significant impact on system resources and execution time. Optimizing the performance of a Hadoop cluster by tuning such a large number of parameters is a tedious task. Most current big data modeling approaches do not include the complex interaction between configuration parameters and the cluster environment changes such as use of different datasets or types of query. This makes it difficult to predict for example the execution time of a job or resource utilization of a cluster. Other attributes include configuration parameters, the structure of query, the dataset, number of nodes and the infrastructure used. Our first main objective was to design reliable experiments to understand the relationship between attributes. Before designing and implementing the actual experiment we applied Hazard and Operability (HAZOP) analysis to identify operational hazards. These hazards can affect normal working of cluster and execution of Hadoop jobs. This brainstorming activity improved the design and implementation of our experiments by improving the internal validity of the experiments. It also helped us to identify the considerations that must be taken into account for reliable results. After implementing our design, we characterized the relationship between different Hadoop configuration parameters, network and system performance measures. Our second main objective was to investigate the use of machine learning to model and predict the resource utilization and execution time of Hadoop jobs. Resource utilization and execution time of Hadoop jobs are affected by different attributes such as configuration parameters and structure of query. In order to estimate or predict either qualitatively or quantitatively the level of resource utilization and execution time, it is important to understand the impact of different combinations of these Hadoop job attributes. You could conduct experiments with many different combinations of parameters to uncover this but it is very difficult to run such a large number of jobs with different combinations of Hadoop job attributes and then interpret the data manually. It is very difficult to extract patterns from the data and give a model that can generalize for an unseen scenario. In order to automate the process of data extraction and modeling the complex behavior of different attributes of Hadoop job machine learning was used. Our decision tree based approach enabled us to systematically discover significant patterns in data. Our results showed that the decision tree models constructed for different resources and execution time were informative and robust. They were able to generalize over a wide range of minor and major environmental changes such as change in dataset, cluster size and infrastructure such as Amazon EC2. Moreover, the use of different correlation and regression techniques, such as M5P, Pearson's correlation and k-means clustering, confirmed our findings and provided further insight into the relationship of different attributes and with each other. M5P is a classification and regression technique that predicted the functional relationships among different job attributes. The use of k-means clustering allowed us to see the experimental runs that shows similar resource utilization and execution time. Statistical significance tests, were used to validate the significance of changes in results of different experimental runs, also showed the effectiveness of our resource and performance modelling and prediction method.</p>

Gender differences and similarities in work preferences: Results from a factorial survey experiment

Despite increased gender equality in many arenas in most of the Western world, women and men continue to choose different educational paths; this is one reason for the persistent gender segregation in the labour market. Cultural and economic explanations for occupational gender segregation both contend that gendered career choices reflect gendered preferences. By analysing data from a multifactorial survey experiment conducted in Norway, designed to isolate the preferences for occupations from preferences for job attributes with which occupation is often correlated: pay; type of position; and amount of work, this article examines whether and to what extent boys and girls who have not yet entered the labour market have different preferences for different work dimensions. The study shows some gender differences in occupational preferences, while also demonstrating similarities in boys’ and girls’ preferences for work dimensions, such as pay and working hours. This indicates that attributes tested by the experiment, which are typically associated with gendered occupations, cannot independently explain why boys and girls tend to have divergent occupational preferences. Importantly, however, the results suggest that boys’ reluctance to undertake some female-typed occupations might be reduced if they did not pay less than male-typed occupations requiring the same level of education.

The Relationship Between Job Satisfaction and the Congruence of Desired and Perceived Job Attributes: An Exploratory Study of IT Professionals

The main purpose of this study was to investigate whether employee job satisfaction is associated with the congruence between desired and perceived job attributes. The desired and perceived levels of 30 job attributes were measured on employees from a large Information Technology (IT) company based in Romania. Results indicate that employees who experience congruence between desired and perceived job attributes have higher levels of overall job satisfaction, confirming the assumptions of the value congruence theory. In addition, the results of this study show that employee job satisfaction is associated with both intrinsic and extrinsic factors i.e., job attributes. This indicates that extrinsic factors can also be a source of job satisfaction, the same as intrinsic factors, which is contrary to what Herzberg's motivation-hygiene theory assumes.

Preferences for work arrangements: A discrete choice experiment

This study investigates individual preferences for work arrangements in a discrete choice experiment. Based on sociological and economic literature, we identified six essential job attributes—earnings, job security, training opportunities, scheduling flexibility, prestige of the company, and gender composition of the work team—and mapped these into hypothetical job offers. Out of three job offers, with different specifications in the respective job attributes, respondents had to choose the offer they considered as most attractive. In 2017, we implemented our choice experiment in two large-scale surveys conducted in two countries: Germany (N = 2,659) and the Netherlands (N = 2,678). Our analyses revealed that respondents considered all six job attributes in their decision process but had different priorities for each. Moreover, we found gendered preferences. Women preferred scheduling flexibility and a company with a good reputation, whereas men preferred jobs with high earnings and a permanent contract. Despite different national labor market regulations, different target populations, and different sampling strategies for the two surveys, job preferences for German and Dutch respondents were largely parallel.