Human Information Interaction

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Background and Aim: Considering the increasing number of users who interact with online social networks, it can be inferred that these networks have become an essential part of users' lives and play different roles in their everyday life. Therefore, the present study aims to explore the role of these networks in users' everyday-life information seeking.

Method: This research is an applied research with qualitative approach and it was conducted using thematic analysis method. This method includes a semi - structured interview with active users of online social networks.

Results: Results indicate that online social networks play different roles in the users' lives, such as entertainment, education, communication and interactions; accompanied by, news, favorite contents, and up-to-date information; but, these networks face some challenges that affect information seeking behavior of users which compels users to utilize active information seeking.

Conclusion: Richer social capital and diversity of users in an individual's social network leads to the access to more qualitative information which in turn increases the probability of finding the required information and achieving the expected results with the least effort.

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Background and Aim. The promising outlook of easy communication incurring minimum cost has caused social networks to face increasing number of active members each day. These members develop and expand international communication through information sharing including personal information. Thus, big data analysis of social networks provides companies, organizations and governments with ample and unique opportunities to reach their strategic goals and various methods have been proposed in order to accomplish this objective. Each method has its own advantages, disadvantages and application area which would require deep study and assessment to understand. Therefore, the aim of this study is to investigate the approaches and methods of data analysis in social networks and study the advantages, disadvantages and application area of each method.

Method. This research is an applied research with qualitative approach and it was conducted using thematic analysis method and the study population include 35related conference papers, journal articles and reports published during 2010-2017.

Results. Various methods are used for the analysis of social networks and these methods are classified into three categories: quantitative, qualitative and mixed methods.

Conclusion. Due to the complex and multidimensional nature of social networks, the best approach is a mixed approach. This means combination of qualitative and quantitative methods and exploring various aspects of networks.

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Background/aim. Considering the underlying role played by knowledge management in project-based organizations; and, the fact that knowledge audit is the most important step in supplying, maintaining and updating the content of knowledge management systems; this research effort is aimed at designing an appropriate knowledge audit model based on the requirements and factors of knowledge audit in project-based organizations.
Methodology.This research is an applied research with a mixed-methods approach (both quantitative and qualitative). To be more exact, several methods such as documentary study method, exploratory study method, the survey method, factor analysis and thematic analysis were employed to establish a weighted knowledge audit model based on the requirements, associated with project- based organizations.The statistical population of this study for collecting qualitative data were 13 experts of knowledge management and the statistical population of collecting quantitative data were 220 project managers, project control experts and knowledge managers from 4 project-based organizations (ICT Research Institute, Research Institute of Petroleum Industry, Niroo Research Institute; and, Iranian Space Research Center).
Findings. The findings of this analysis demonstrate that all five factors of knowledge audit in project- based organizations constitute the final structural model for knowledge audit, and the appropriate knowledge audit model for project-based organizations includes five factors: (1) knowledge need analysis; (2) knowledge inventory analysis; (3) knowledge valuation, (4) knowledge flow analysis; and, (5) knowledge application analysis. Among these factors, knowledge valuation with its factor loading of 0.9 is the most important factor.
Conclusion. 5 factors and 54 subfactors of Knowledge audit have highly-desired factor loading values and exert impact within the knowledge audit model for project-based organizations. Moreover, the final model displays a fair goodness-of-fit.