There are many factors that may affect employees' job performance such as psychological, sociological, anthropological, demographic and similar. However, related literature was mainly focusing on psychological and demographic ones, which were often analyzed through different job performance measurement methods such as self-evaluation and supervisor (or superior) evaluation. The main goal of the current study is to define and compare the factors affecting employee's job performance according to the above mentioned measurement methods, as well as their level of importance. For the purpose of this study, data were collected through survey conducted in the Antalya region in Turkey among 305 participants coming from seven countries and consisting of both employees and supervisors working for a performing artists organization company. Data were analyzed by using CHAID analysis through classification algorithms. Results show there is a difference between variables explaining the job performance of the employees when they do self-evaluation of their own performance than when the same is done by their supervisors. Nationality is one of the factors affecting performance in both evaluation forms. While the performance of individuals with extraversion personality traits was high in case of self-evaluation, the performance of the men who were second-born or after was high in the evaluation by the supervisors. These results demonstrated the problematic nature of measuring job performance and making accurate evaluations based on it.
This research is a qualitative study based on systematic analysis of the articles on mixed methods via the bibliometric analysis using the R programming language. Thus , this study analyzed articles using mixed methods in journals in the Web of Science database. 1,623 articles, which was published in 1999 -2018 and included 'mixed methods research' in the article title, abstract or keywords, were analyzed as a whole. This analysis was the bibliometric analysis using the R programming language. At the same time, content analysis was used to show the relationships between the subdomains of the studies using mixed methods and the development of time. The study focused more on resource impacts, the most cited countries, keyword plus cloud, co -occurrence network, co-citation, author collaboration. The study results in a discussion about the use of mixed methods on the part of future studies.
Artificial Intelligence (AI) came up as an ambiguous concept from computer sciences and now it is being used in many areas of our life. It has stimulated academia's interest due to its alternative insights into complex problems. Therefore, a bibliometric method was applied in this study to observe the progress of AI in the tourism field. A total of 102 papers were collected from Scopus database. Key factors such as most productive authors, collaborations and institutions were identified, and research hotspots were determined using co-occurrence network and most common author keywords. Progress of AI was visualized with thematic evolution analysis. Findings indicate that there is a progressive interest in AI after 2017, and average citations signify that papers are highly cited. Since this is the first study conducting a bibliometric on AI in the tourism context, it could be considered useful for academics and tourism professionals as it provides general overview of AI, demonstrates research trends and popular papers.
davranışının altında yatan sebepler bu belirsizlikler ve risklere dair algılardır. Karar vermeye ilişkin geleneksel modellerin bu dönemlerdeki turist davranışını açıklamada yetersiz kalması yüzünden (Sönmez ve Graefe 1998), kriz duru-
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