Abstract:
This quantitative descriptive study aims to determine the correlation between intercultural communication competence (ICC) and emotional labor strategies of call center agents in Company X. It also intends to identify the kinds of emotions frequently felt as well as the differences between males and females in terms of ICC and emotional labor. For the purpose of this research, ICC was operationalized using three components namely affective, behavioral, and cognitive. Emotional labor strategies, on the other hand, include deep acting, surface acting, and emotion suppression. Cross-sectional research design was adopted with the aid of adapted selfreport questionnaires. A total of 126 surveys were deemed valid for the study. ICC level of the agents is recorded at low to moderate. Additionally, it was discovered that the agents use deep acting most frequently when working. Positive emotions such as happiness, enthusiasm, pleasure, pride, optimism, enjoyment of something, and contentment are more commonly felt than negative emotions. Moreover, Spearman's Rank correlation results reveal that there is no significant correlation between ICC and all three emotional labor strategies. Instead, a very weak negative correlation was found between ICC and emotional labor which indicates that for high scores of ICC, there are corresponding low scores for emotional labor strategies. Lastly, Levene's test showed that although not significant, the mean scores for surface acting, deep acting, and suppression are higher for females than for males. In further exploring the relationship between ICC and emotional labor strategies, future researchers may consider using or developing more comprehensive ICC instruments for their study. A similar study may also improve generalizability of results by focus on not just one account or call center. Settings which resemble call centers may also be studied (e.g. ESL schools where teachers and studies are from different cultures). Use of qualitative design may also be initiated in order to gain more in-depth explanation to support quantitative data.
