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This paper aims to identify the data elements that social network sites (SNS) users consider important for shaping their digital identity and explore how users’ privacy concerns, self-esteem and the chosen SNS shape this process.

This study conducted an online survey with the participation of 759 individuals, to examine the influence of privacy concerns, self-esteem and the chosen SNS platform, on the shaping of the digital identity, through a classification of identity elements that users disclose when using a SNS, the Rosenberg self-esteem scale and relevant constructs from the literature.

Findings reveal that users consider the name, gender, picture, interests and job as most important elements for shaping their digital identity. They also demonstrate that privacy concerns do not seem to affect the amount of information users choose to publish when shaping their digital identity. Specific characteristics of SNS platforms are found to affect the way that users shape their digital identity and their privacy behavior. Finally, self-esteem was found to affect privacy concerns and digital identity formation.

To avoid a lengthy questionnaire and the risk of low participation, the respondents answered the questions for one SNS of their choice instead of answering the full questionnaire for each SNS that they use. The survey included the most popular SNSs at the time of the survey in terms of popularity.

The results contribute to the theory by furthering our knowledge on the elements that shape digital identity and by providing evidence with regard to the role of privacy and self-esteem within social networking. In practice, they can be useful for SNS providers, as well as for entities that design security and privacy awareness campaigns.

This paper identifies novel factors that influence digital identity formation, including the specific SNS used with its particular characteristics in combination with privacy concerns and self-esteem of the user.

This paper seeks to contribute to an understanding of the organizational culture of the spin‐off knowledge‐based enterprises, which operate within the science and technology parks in Greece. In this context, a critical number of questionnaires have been distributed to the spin‐offs to examine whether firms born within the parks have developed a functional organizational culture, one that provides a solid foundation for organizational effectiveness and business excellence.

The Organizational Culture Assessment Questionnaire (OCAQ) was developed by Sashkin to help people identify and understand the nature of the culture in their own organization, as a first step in identifying problems and defining the sort of culture they want (and the sort of culture that will help deal with organizational problems). The data for the present study were obtained by the OCAQ mailed to a sample of 33 spin‐off companies that operate within the aforementioned science and technological parks. The mailing consisted of the questionnaire itself, a cover letter, and a stamped pre‐addressed return envelope. Of the 90 questionnaires mailed after phone contact, 33 were received, representing a 37 percent response rate.

There seems to have been ascertained serious organizational culture weaknesses regarding management of change practices, goal and customer orientation, cultural strength and efficient team working. It is of critical importance for policy makers to set general principles, guidelines as well as organizational reform measures and priorities to achieve better efficiency and effectiveness of spin‐offs in Greece and approach business excellence. Finally, implications for theory, managers and future research are presented.

This paper provides useful information on organizational culture assessment.

The purpose of paper is twofold. First, it provides a consolidated overview of the existing literature on “big data” and second, it presents the current trends and opens up various future directions for researchers who wish to explore and contribute in this rapidly evolving field.

To achieve the objective of this study, the bibliographic and network techniques of citation and co-citation analysis was adopted. This analysis involved an assessment of 57 articles published over a period of five years (2011-2015) in ten selected journals.

The findings reveal that the number of articles devoted to the study of “big data” has increased rapidly in recent years. Moreover, the study identifies some of the most influential articles of this area. Finally, the paper highlights the new trends and discusses the challenges associated with big data.

This study focusses only on big data concepts, trends, and challenges and excludes research on its analytics. Thus, researchers may explore and extend this area of research.

To the knowledge of the authors, this is the first study to review the literature on big data by using citation and co-citation analysis.