Using Social Network Research in HRM: Scratching the surface of a fundamental basis of HRM

This article is the guest editors' introduction to a special issue on using Social Network Research in the field of Human Resource Management. The goals of the special issue are: (1) to draw attention to the points of integration between the two fields, (2) to showcase research that applies social network perspectives and methodology to issues relevant to HRM and (3) to identify common challenges where future collaborative efforts could contribute to advancements in both fields.

Performing knowledge requirements analysis for public organisations in a Virtual Learning Environment: a social network analysis approach

This paper describes an application of Social Network Analysis methods for identification of knowledge demands in public organisations. Affiliation networks established in a postgraduate programme were analysed. The course was executed in a distance education mode and its students worked on public agencies. Relations established among course participants were mediated through a virtual learning environment using Moodle. Data available in Moodle may be extracted using knowledge discovery in databases techniques. Potential degrees of closeness existing among different organisations and among researched subjects were assessed. This suggests how organisations could cooperate for knowledge management and also how to identify their common interests. The study points out that closeness among organisations and research topics may be assessed through affiliation networks. This opens up opportunities for applying knowledge management between organisations and creating communities of practice. Concepts of knowledge management and social network analysis provide the theoretical and methodological basis.

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

Students’ perception of privacy risks in using social networking sites for learning: a study of Uganda Christian University

Although social networking sites (SNSs) present a great deal of opportunities to support learning, the privacy risk is perceived by learners as a friction point that affects their full use for learning. Privacy risks in SNSs can be divided into risks that are posed by the SNS provider itself and risks that result from user’s social interactions. Using an online survey questionnaire, this study explored the students’ perception of the benefits in using social networking sites for learning purposes and their perceived privacy risks. A sample of 214 students from Uganda Christian University in Africa was studied. The results show that although 88 % of participants indicated the usefulness of SNSs for learning, they are also aware of the risks associated with these sites. Most of the participants are concerned with privacy risks such as identity theft, cyber bullying, and impersonation that might influence their online learning participation in SNSs.

Policy in the knowledge economy social network : a social capital redux

Purpose – The purpose of this paper is to suggest how enabling policy should be focused in a knowledge economy by developing the concept of a knowledge economy social network (KESN).

Design/methodology/approach – The paper employs an interdisciplinary approach in developing the KESN by drawing on concepts and methodology from economics, political science and social network theory.

Findings – The KESN's social capital is defined. As such, maintaining accountability, increasing cohesion and connections among knowledge actors are suggested as relevant guidelines for policy in the KESN.

Research limitations/implications – The knowledge economy should ideally be seen as having unique needs compared to the traditional economy in devising policy.

Practical implications – The paper suggests using the KESN as a basis for devising policy for a knowledge economy.

Originality/value – The paper uses an interdisciplinary approach to studying the knowledge economy and introduces the KESN.

Influence of social network structure on entrepreneurship participation — a study of 20 national

By investigating differences in social networks among entrepreneurs in 20 cultures, this paper contributes to the debate on whether there is universality in the process of entrepreneurial networking. Representative samples of entrepreneurs were identified in the same manner in 20 countries from 2000 to 2004 (N=€‰304,560). The sampling methodologies and the questions asked were similar across all countries. Logistic regression was used to test for significant regional interaction effects involving personally knowing an entrepreneur. Results are contrary to the existence of any mono-dimensional form of networking practice but do strongly support the existence of both variform universality (culture moderates the importance of networking) and functional universality (cultural similarities in networking practice exist).

Who will be an entrepreneur? How cultural mechanisms and social network structure together influence entrepreneurial participation

Using data collected from 35 countries over five years, this study provides an investigation of the combined influence of cultural factors and social network structure on whether or not an individual, anywhere in the world, becomes an entrepreneur. Results show that knowing someone who has started a business recently, across the world, has a significant impact on entrepreneurship participation. Regarding the potential cultural influences, it seems that importance attached to personally knowing entrepreneurs differs significantly between individuals operating in different cultures. In cultures with high power distance, personally knowing a person who recently started a business is relatively less important as a driver of entrepreneurship participation compared to cultures with low power distance. On the other hand, in cultures where the Hofstede’s ‘masculinity’construct predominates, it is more important than in cultures characterised by ‘femininity’.

The application of social network analysis to team sports

This article reviews how current social network analysis might be used to investigate individual and group behavior in sporting teams. Social network analysis methods permit researchers to explore social relations between team members and their individual-level qualities simultaneously. As such, social network analysis can be seen as augmenting existing approaches for the examination of intra-group relations among teams and provide detail of team members' informal connections to others within the team. Social network analysis is useful in addressing the issue of interdependencies in the data inherent in team structures. Social network terms are introduced and explained by way of an example team, software and resources are discussed, and a statistical approach to social network analysis is introduced.

A social network analysis model for analysing stakeholder-associated safety risks in infrastructure projects

An important strategy in the long-term blueprint for making Australia's 18 capital and major regional cities more productive, sustainable and liveable is to develop high quality public infrastructure systems to improve civic quality of life. Because of the unique features of construction activities, such as long period, complicated processes, and dynamic organizational structures, infrastructure projects normally involve multiple stakeholders and are subject to various risks, especially safety issues. Any negligence or mismanagement of critical safety risks will have huge impact on achieving project objectives and success. Although many previous studies have identified and assessed various safety risks in construction industry, a main research gap is that these studies ignored a fact that most risks are interrelated and associated with internal and external stakeholders of the projects. The lack of a theoretical foundation and appropriate methods for analysing stakeholder-associated safety risks and their interdependencies in infrastructure projects hinders effective risk management processes and the formulations of decision strategies. This research aims at enabling higher performance in strategic safety risk management in infrastructure projects through the development of a holistic risk analysis model using Stakeholder and Social Network Theories. The outcomes can broaden project managers' awareness of emerging influential safety risks and enhance their ability to perceive, understand, assess, and mitigate safety risks in an effective and efficient way; thereby higher performance in strategic risk management could be achieved in infrastructure projects.

Stakeholder analysis in urban development : the application of stakeholder circle methodology and social network analysis

The increasing research interest on stakeholder analysis in urban planning reflects a growing recognition that stakeholders can and should influence the decision-making. This paper concentrates on exploring the techniques for analysing stakeholders, especially the application of the Stakeholder Circle tool and Social Network Analysis. An urban renewal project and an infrastructure project in Australia are presented as case studies to verify the use of these two techniques. The stakeholders are identified and prioritized from two different points of view, namely, the attribute evaluations in the Stakeholder Circle tool, and the relationship network analysis. The paper ends with a discussion on the strengths and limitations of the techniques for stakeholder analysis. No method for stakeholder identification and prioritization is perfect. The selection of the approaches is an art with extensive considerations of ‘when, what, and how’ to choose methods to achieve the project objectives. Each method has its own strengths and limitations. Combining several methods when necessary is the best way to analyse stakeholders.