Özelleştirme Süresi Öncesi Yaşanan Kriz Döneminde Ilıca Şeker Fabrikasi Çalışanlarının Mesleki Stres Düzeylerinin İncelenmesi


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Kaya M. D., Buladi Çubukcu B., Güzel D., Haşıloğlu A., Demirtaş M. F.

Akademik Fener Balikesir Üniversitesi Bandirma İ.İ.B.F Dergisi, cilt.0, ss.85-93, 2010 (Hakemli Dergi)

Özet

Alternating with globalization the concept of economy has led to accelerate the studies of privatization. Many people were dismissed after the privatization, however, the great majority employees who did not lose their’ employments have faced with distrustful work situation. Therefore, privatization implementations which have been perceived as crisis process by organization’ employee, to generate reactions (reactions arises. Undoubtedly, organization’s success or failure is directly related to be employee’s stress-free. For this reason, it is necessary to investigate the work places where employees spend a signifi cant portion of their lives and to display organizational stress factors. Occupational stress, both for people in the organization and the organization itself poses an important problem. Occupational stress is a unwanted concept that work in cooperation with their own physical stress resources and get together insuffi cient fi ghting concludes the result of mental and physical illness. The case of uncertainty and changes in environment confront constantly organizations with unexpected dangers or opportunities. Such situations are perceived as crisis situations in organizations. In this study, operating in Erzurum and under the scope of privatization the Sugar Factory employees’ levels of occupational stress before privatization intended for measuring with survey was applied for 82 employees. Also taking into account the demographic characteristics take place in the survey, employees’ occupational stress levels was tried to designate with multiple regression analysis. Resulting regression equation related to occupational stress has been interpreted in terms of work place employees. In the next stage of the study, artifi cial intelligence learning techniques works will continue as an alternative of obtained regression models.