Knowledge is the most important resource in software development. The success of software development relies on knowledge sharing between software developers working across the globe. Global software development has brought many benefits to the software industry; however, at the same, time knowledge sharing across diverse team members is one of the main concerns of global software development organizations. This paper provides a systematic literature review of 42 studies on knowledge sharing barriers and facilitators from 2010 to 2017 and classifies them into five main categories: Individual, Organizational, Technological, Cultural, and Geographical. In order to synthesize and represent the complexity of the knowledge sharing factors in a more manageable and visual manner, this paper proposes concept maps for each category. The identified factors can be strategically used as the guidelines in the global software development organizations to boost the culture of knowledge sharing. INDEX TERMS Knowledge sharing barriers, knowledge sharing facilitators, global software development organizations, cultural barriers, geographical barriers.

Software development is a cooperative process, which relies upon the integration of knowledge spread across various domains. The shift from the industrial economy toward a decentralized knowledge-based economy has given knowledge more value and importance for organizations, which operate globally. Management of knowledge sharing activities becomes challenging and complex, specifically when operating in a globally distributed organization. The impact of ''personal factors'' and ''environmental factors'' on software developers with regard to knowledge sharing behavior in global software development organizations is an important subject, which is still not well discussed. The social cognitive theory has been utilized in the formation of the conceptual model with a focus on knowledge sharing behavior. This paper examines the impact of key factors, including ''time zone difference,'' ''geographic distance,'' ''linguistic distance,'' ''trust,'' ''motivation,'' and ''social interaction'' on knowledge sharing behavior. Self-administrative postal and online questionnaire were used as the medium of data collection. It was found that ''social interaction'' had the strongest impact on knowledge sharing behavior. INDEX TERMS Knowledge sharing behavior, global software development organization, social cognitive theory, personal factors, environmental factors.

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Unstructured text contains valuable information for a range of enterprise applications and informed decision making. Text analytics is used to extract valuable insights from unstructured big data. Among the most significant challenges of text analytics, quality and usability are critical affecting the outcome of the analytical process. The enhancement in usability is important for the exploitation of unstructured data. Most of the existing literature focuses on the usability of structured data as compared to unstructured data whereas big data usability has been discussed merely in context of its assessment. The existing approaches do not provide proper guidelines on usability enhancement of unstructured data. In this study, a rigorous systematic literature review, using PRISMA framework, has been conducted to develop a model enhancing the usability of unstructured data and bridging the research gap. The recent approaches and solutions for text analytics have been investigated thoroughly. Furthermore, it identifies the usability issues of unstructured text data and their consequences on data preparation for analytics. Defining the usability dimensions for unstructured big data, identification of the usability determinants, and developing a relationship between usability dimension and determinants to derive usability rules are the significant contribution of this research, and are integrated to formulate the model. The proposed usability enhancement model is the major outcome of the study. It would contribute to making unstructured data usable and facilitating the data preparation activities with more valuable data that eventually improves the analytical process.

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