• Iberoamérica
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• v.22 no.1
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• pp.73-109
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• 2020

In the tourism industry, emotional labor-related problems are highly likely because workers cannot avoid frequent face-to-face contacts with customers. Emotional labor, however, is not always recognized in the same pattern by every worker in similar situations. As can be see in this research, emotional labor appears to vary depending on individual characteristics and sociocultural and economic conditions. In fact, there are so many factors affecting emotional labor, and the level of influence concerning these factors can vary depending on when and where they are. Psychological and mental pains and stress from emotional labor depend on a number of factors. The expression of emotional labor, depending on age, length of employment and gender, shows a clear difference. In particular, for those who are planning to participate in international labor migration to the United States in the future, emotional labor was often not recognized as a serious problem or rather a positive social and cultural experience. In fact, there are various types of tourism workers in Oaxaca, and their experiences vary greatly depending on individual and economic and sociocultural environments. The emotional labor of the workers in the tourism sector of Oaxaca also has various forms depending on various conditions. Positive or negative perceptions and consequences of emotional labor vary depending on the circumstances of the individual and working conditions. In other words, socioeconomic conditions, individual personality or experiences, sociocultural characteristics, and autonomy in the workplace, have a significant impact. As a result, the forms and effects of emotional labor expressed and understood by tourism workers vary considerably and may vary depending on the situations. Considering these problems, the way emotional labor emerges is greatly influenced by sociocultural or personal factors as well as economic factors. In addition, rather than considering the existence and seriousness of emotional labor as given depending on the nature of a particular industry or work, it should also be recognized that the expression of emotional labor differ considerably from individual to individual. Also, the seriousness of problems caused by emotional labor can be diversified. In this sense, it is necessary to clearly understand the meanings of the dynamism, diversity and multidimensionality of emotional labor from a new perspective.

• Journal of the Ergonomics Society of Korea
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• v.36 no.3
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• pp.183-196
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• 2017

Objective: The purpose of this research was to assess the agreement between job physical risk factor analysis by ergonomists using ergonomic methods and physical examinations made by occupational physicians on the presence of musculoskeletal disorders of the upper extremities. Background: Ergonomics is the systematic application of principles concerned with the design of devices and working conditions for enhancing human capabilities and optimizing working and living conditions. Proper ergonomic design is necessary to prevent injuries and physical and emotional stress. The major types of ergonomic injuries and incidents are cumulative trauma disorders (CTDs), acute strains, sprains, and system failures. Minimization of use of excessive force and awkward postures can help to prevent such injuries Method: Initial data were collected as part of a larger study by the University of Utah Ergonomics and Safety program field data collection teams and medical data collection teams from the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). Subjects included 173 male and female workers, 83 at Beehive Clothing (a clothing plant), 74 at Autoliv (a plant making air bags for vehicles), and 16 at Deseret Meat (a meat-processing plant). Posture and effort levels were analyzed using a software program developed at the University of Utah (Utah Ergonomic Analysis Tool). The Ergonomic Epicondylitis Model (EEM) was developed to assess the risk of epicondylitis from observable job physical factors. The model considers five job risk factors: (1) intensity of exertion, (2) forearm rotation, (3) wrist posture, (4) elbow compression, and (5) speed of work. Qualitative ratings of these physical factors were determined during video analysis. Personal variables were also investigated to study their relationship with epicondylitis. Logistic regression models were used to determine the association between risk factors and symptoms of epicondyle pain. Results: Results of this study indicate that gender, smoking status, and BMI do have an effect on the risk of epicondylitis but there is not a statistically significant relationship between EEM and epicondylitis. Conclusion: This research studied the relationship between an Ergonomic Epicondylitis Model (EEM) and the occurrence of epicondylitis. The model was not predictive for epicondylitis. However, it is clear that epicondylitis was associated with some individual risk factors such as smoking status, gender, and BMI. Based on the results, future research may discover risk factors that seem to increase the risk of epicondylitis. Application: Although this research used a combination of questionnaire, ergonomic job analysis, and medical job analysis to specifically verify risk factors related to epicondylitis, there are limitations. This research did not have a very large sample size because only 173 subjects were available for this study. Also, it was conducted in only 3 facilities, a plant making air bags for vehicles, a meat-processing plant, and a clothing plant in Utah. If working conditions in other kinds of facilities are considered, results may improve. Therefore, future research should perform analysis with additional subjects in different kinds of facilities. Repetition and duration of a task were not considered as risk factors in this research. These two factors could be associated with epicondylitis so it could be important to include these factors in future research. Psychosocial data and workplace conditions (e.g., low temperature) were also noted during data collection, and could be used to further study the prevalence of epicondylitis. Univariate analysis methods could be used for each variable of EEM. This research was performed using multivariate analysis. Therefore, it was difficult to recognize the different effect of each variable. Basically, the difference between univariate and multivariate analysis is that univariate analysis deals with one predictor variable at a time, whereas multivariate analysis deals with multiple predictor variables combined in a predetermined manner. The univariate analysis could show how each variable is associated with epicondyle pain. This may allow more appropriate weighting factors to be determined and therefore improve the performance of the EEM.