experts

The present study aimed to design a model of talent turnover from the perspective of knowledge workers who have resigned in the Iranian Offshore Oil Company. Considering the main objective of this research—explaining and presenting an interpretive structural model of the factors influencing talent turnover in the Iranian Offshore Oil Company—this study falls within the scope of applied research and adopts an exploratory mixed-methods approach. The statistical population in the qualitative section included managers from the Iranian Offshore Oil Company, academics, and experts from the company. Using a purposive sampling method, 12 participants were selected as the sample. In the quantitative section, data were also collected from experts in the Iranian Offshore Oil Company. The sample size in this section was determined using Cochran’s formula and simple random sampling. With a total population of 250 individuals, the Cochran formula identified a sample size of 152 participants. Data collection employed interviews and a researcher-developed questionnaire. Thematic analysis was used to develop the model, which was ultimately analyzed using structural equation modeling (SEM) with PLS software and the ISM technique. Results indicated that, among the model's components, livelihood status and skills and expertise were prioritized as the most critical factors by the experts, situated at the first level. Cultural and social environments and the political environment were ranked at the second level. Organizational policies, rules and processes, organizational climate, and organizational management were placed at the third level. Finally, influence and impact, along with being overlooked and undervalued, were categorized at the fourth level. From the managers' perspective, the most critical factors driving talent turnover included influence and impact, being overlooked and undervalued, livelihood status, and skills and expertise.

Community Question Answering has a crucial role in almost all societies nowadays. It is important for the owners of a community to be able to make it better and more reliable. One way to achieve this, is to find the users who have more knowledge, expertise, experience and skill and can well share their knowledge with others (which we call experts and aim to encourage them to be more active in the website). One method to use is to identify expert users, and whenever a new question is asked, we suggest this question to them to check and answer if its in their area of expertise. One way to encourage users to post replies, is to use gameplay techniques such as assigning points and badges to users. But as we will discuss, this method does not always detect expert users well, because some users will try to have small and insignificant but numerous activities that will make them gain a lot of points, however they are not experts. In this study, we examine the methods by which experts in a question-and-answer system can be found, and try to evaluate and compare these methods, use their ideas and positive points, and add our own new ideas to a new way of finding them. We used some ideas such as profile making for users, categorize users’ expertise, A-Priori algorithm and showed that neural networks method results the best for the purpose of expert detection.