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The wear of a machine element, whether it is due to fatigue or abrasive wear, will add contaminants in the form of particulates to the system. If a machine element is starting to wear out it will produce a large amount of particles and it will finally break down. Since this can be very costly, one can establish the need to monitor the system so that one can foresee failure. There are many different ways to monitor a system, e.g. measurements of the temperature, pressure, vibrations and the degree of contamination. The purpose of contamination control is to extend the life of a component and thereby save money. When monitoring a system it is very important that the monitoring control instrument should give the right output. One important factor in achieving this is the withdrawal of a representative oil sample. In this paper an investigation of where and how to take a representative sample is performed using Stokes’ law and the migration of spheres in a channel. A generalised sedimentation chart for different oils and particles is introduced. Sampling routines for proper sample withdrawal are also presented.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

The purpose of this paper is to analyze the effect of the reputation of underwriters and sponsoring representatives on initial public offering (IPO) underwriting fees, and further investigates the role of ownership and political connection.

The methodology includes three models. Model 1 empirically investigates the effect of underwriter’s reputation on underwriting fee. Model 2 studies the effect of sponsoring representative’s reputation on underwriting fee. Model 3 further examines the effect of underwriter’s reputation and sponsoring representative reputation on the underwriting fee controlling for the impact of ultimate controlling ownership and political connection.

The study documents that underwriters’ and sponsoring representatives’ reputation can result in reputational premiums. In the IPO of state-owned enterprises (SOEs), the reputation of underwriters and sponsoring representatives does not significantly affect the underwriting fees. In the IPO of non-state-owned enterprises (NSOEs), there is a significantly positive correlation between underwriters’ and sponsoring representatives’ reputation and underwriting fees. Further research results show that, on the one hand, the effect of underwriters’ and sponsoring representatives’ reputation on underwriting fees is not significant in the IPO of NSOEs with political connection. On the other hand, underwriting fees are positively associated with underwriters’ and sponsoring representatives’ reputation in the IPO of NSOEs without political connection.

The sponsoring representative’s fee is not disclosed separately, which makes it difficult to distinguish the incremental effect from underwriter’s services and reputation.

NSOEs relative to SOEs are more likely to pay higher underwriting fees for hiring underwriter and sponsoring representative with better reputation during the process of IPO.

The reputation of underwriter and sponsoring representative does not matter to SOEs but does matter to NSOEs. However, NSOEs’ political connection affects underwriter fees.

This paper provides new evidence of sponsoring representatives’ reputation and political connection on the underwriting fees in the IPO in Chinese SOEs and NSOEs.

Cross-national studies of employees’ values and beliefs have extracted dimensions of national culture from diverse samples of employees. The purpose of this paper is to find out if this sample diversity impacts the nature of the extracted dimensions: is a given dimension replicable across diverse samples (such as managers vs skilled workers?).

The authors analyzed a set of values from the World Values Survey, comparing nation-level value structures from four types of samples in 46 countries: national representation, managers, experts without supervisory duties, and skilled workers. The authors analyzed the data with, and simultaneously compared, two data reduction methods: multidimensional scaling (MDS) plots (Shalom Schwartz’s preferred method) vs exploratory factor analysis (EFA).

MDS plots suggested structural similarity across the four samples, whereas EFA suggests divergence.

Whether dimensions of national culture replicate across different samples or not depends on the data reduction method. There is no one best method in an abstract sense. Researchers’ choice of method should be contingent on their research philosophy: theory-driven vs empirical.

No such study has been published previously.

The purpose of this empirical study is to make a contribution to career theory in general, and to the literature on high‐potential careers in particular, by examining the careers of real high potentials, taking place in the twenty‐first century world of work, from the perspectives of the high potentials themselves as well as those of their organizations.

A total of 34 interviews were conducted within three study samples: high potentials (n=14), organisational representatives employed by the same organisations that provided the high‐potential participants (n=8), and organisational representatives employed by organisations that did not allow for interviewing of their high potentials (n=12).

The current study suggests that high potentials still have organisational‐traditional careers. High upward mobility, low inter‐organisational mobility and career self‐management emerged as key features of real high‐potential careers.

Implications are spelled out with respect to the “streaming” of different types of employees in the workforce and the importance of expectations management.

Not only are the viewpoints of individuals largely absent in the literature on high‐potential careers, the majority of publications on the subject‐matter are also non‐empirical and take a rather normative stance. The interview study presented in this paper looks into the assumptions of real high‐potential careers from the perspectives of the high potentials themselves as well as those of their organizations, providing empirical data that are interpretive and descriptive rather than normative.

Global marketing expansion is being widely promoted in both the professional and domestic communities. Domestic markets alone cannot provide the revenue and growth opportunities required by many business organizations. Many business firms want to sell in international markets but lack the expertise and financial resources to sell overseas effectively. In domestic markets, manufacturers’ representatives currently provide the sales function for many such firms, especially small manufacturers. Investigates the potential role that manufacturers’ representatives will play in global marketing expansion. A survey of 119 representative agencies was made to identify and study their experiences and/or perceptions about expanding their client base to world markets. Results show that 60 per cent of representatives were optimistic about their future opportunities in a global economy but were hardly aggressive (up to now) when it comes to entering world markets. Over 80 per cent of the representatives had enquiries from foreign principals, but only 47 per cent initiated the contacts. Moreover, only 23 per cent sought global markets for their current principals’ products.

This paper will detail the development of a multi-objective mathematical programming model for audit sampling of balances for accounts receivable. The nonlinear nature of the model structure will require the use of a nonlinear solution algorithm, such as the GRG or the genetic algorithm embedded in a Solver spreadsheet modeling system, to obtain appropriate results.

Qualitative researchers have been criticised for not justifying sample size decisions in their research. This short paper addresses the issue of which sample sizes are appropriate and valid within different approaches to qualitative research.

The sparse literature on sample sizes in qualitative research is reviewed and discussed. This examination is informed by the personal experience of the author in terms of assessing, as an editor, reviewer comments as they relate to sample size in qualitative research. Also, the discussion is informed by the author’s own experience of undertaking commercial and academic qualitative research over the last 31 years.

In qualitative research, the determination of sample size is contextual and partially dependent upon the scientific paradigm under which investigation is taking place. For example, qualitative research which is oriented towards positivism, will require larger samples than in-depth qualitative research does, so that a representative picture of the whole population under review can be gained. Nonetheless, the paper also concludes that sample sizes involving one single case can be highly informative and meaningful as demonstrated in examples from management and medical research. Unique examples of research using a single sample or case but involving new areas or findings that are potentially highly relevant, can be worthy of publication. Theoretical saturation can also be useful as a guide in designing qualitative research, with practical research illustrating that samples of 12 may be cases where data saturation occurs among a relatively homogeneous population.

Sample sizes as low as one can be justified. Researchers and reviewers may find the discussion in this paper to be a useful guide to determining and critiquing sample size in qualitative research.

Sample size in qualitative research is always mentioned by reviewers of qualitative papers but discussion tends to be simplistic and relatively uninformed. The current paper draws attention to how sample sizes, at both ends of the size continuum, can be justified by researchers. This will also aid reviewers in their making of comments about the appropriateness of sample sizes in qualitative research.

The purpose of this paper is to compare two ways of collecting job quality data in Britain using a common set of questions. One way is through a short quiz taken by a self-selected sample and completed by clicking on a web link www.howgoodismyjob.com. The other way is via an invitation to take part in a long-running survey of working life – the Skills and Employment Survey. The survey takes much longer to complete, is carried out face-to-face and is based on random probability principles.

To be content-comparable, the quiz uses tried and tested questions contained in recent waves of the Skills and Employment Survey. Each survey comprises a nationally representative sample of workers in Britain aged 20–65 years. However, the quiz is based on uncontrolled convenience sampling prompted, in large part, by a Facebook advertising campaign, whereas survey participants are randomly selected. In this paper, the authors compare the profile of respondents and their responses to these two different modes of data collection and therefore shine a light on any biases in the samples and differences in the results respondents report.

The paper shows that while the number taking in the quiz is impressive, participation in the quiz – unlike the survey – is heavily skewed. Weighting can be used to correct some of these sample selection biases. But, even then, the picture painted by the quiz and survey data varies with the quiz under-reporting the intrinsic quality of jobs, while over-reporting on the extrinsic rewards. This suggests that how job quality data are collected can have a strong influence on the results produced.

The findings suggest that a number of biases are in operation, both in terms of those who take part and the answers they give. This makes comparison between data collected using radically different methods, at best, inadvisable and, at worst, misleading. Nevertheless, quizzes are a good way of engaging large numbers of people in public debates, gathering additional data, extending the reach of academic work and prompting action to improve working life. However, the limitation of this study is that it does not offer a true experiment of different ways of collecting the same data. The quiz and survey were, for example, not carried out at the same time, but were some 14 months apart.

Over 50,000 people took part in the quizzes reported in the paper and almost 1,300 investigated joining a trade union as a result. The reach of the quiz far exceeds the 3,306 people who took part in the Skills and Employment Survey 2017.

This paper focusses on how job quality data are collected and the consequences this has for the validity of the data gathered. This is a unique contribution to international debates about the measurement and monitoring of trends in job quality.