Author
AbstractTraditional assessment of economic performance has been based upon traditional production factors such as land, labour and capital but the importance of the knowledge-based assets’ role in firm’s performance increase undeniably. Knowledge assets or intellectual capital may be mentioned as the “hidden” assets of a firm which is based on Human capital. According to this statement selection of the human resource becomes a much more important case that has to be achieved for firms and other agents. The development of internet in 1990’s has caused a kind of revolution in labour market which provides significant cost advantage forming a candidate pool. For a Human Resources Manager (HRM), choosing an appropriate candidate for the suitable position is just as difficult as to click his/her PC’s mouse button. However, efficiency requires all labour forces to be employed under the assumption that the supplier (candidate) knows the true quality of him/herself whereas the HRMs (dealer) are unable to find the true quality of a specific candidate and adverse selection effect may cause the labour market to collapse entirely. My paper is trying to introduce these selection process problems by combining different methods, Lemon Markets, Bayesian Signalling Games, Moral Hazard, Adverse Selection and Principal-Agent problems. The term lemon will refer to the candidates who apply for any kind of job while the interviews form the signals between the candidate (sender) and the HRMs (receptor). Using these tools, the paper is basing all these microeconomic problems on factors such as immigration and/or gender. Although Akerlof showed that informational asymmetries can cause adverse selection on markets. Inspiring by Spence’s theory under certain conditions, well informed job applicants can improve their probability of taking the job by signalling their private information to poorly informed HRMs. In the first part of the paper, I will give a very brief explanation about theoretical background of these tools and establish the link between those theoretical explanations and candidate selection process. In this framework, the labour force is dividing into two group: one group belongs to the well educated-white/blue collar labour force and the other group belongs to unskilled-ordinary labour force. This distinction helps us to interpret the signals from our model much more correctly. Second part of the paper includes information about the selection and the real Human Resources Management examples. In this context, this part gives different selection problem cases. For instance, those inefficient choice techniques usually find the right CVs but wrong person. So choosing the good lemon among the others becomes more and more difficult when HRMs look at the wrong basket. Finally the last part gives a summary.
Suggested Citation
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:izm:prcdng:200601. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ayla Ogus Binatli (email available below). General contact details of provider: https://edirc.repec.org/data/deieutr.html .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.