Search results

Expert profiling plays an important role in expert finding for collaborative innovation in research social networking platforms. Dynamic changes in scientific knowledge have posed significant challenges on expert profiling. Current approaches mostly rely on knowledge of other experts, contents of static web pages or their behavior and thus overlook the insight of big social data generated through crowdsourcing in research social networks and scientific data sources. In light of this deficiency, this research proposes a big data-based approach that harnesses collective intelligence of crowd in (research) social networking platforms and scientific databases for expert profiling.

A big data analytics approach which uses crowdsourcing is designed and developed for expert profiling. The proposed approach interconnects big data sources covering publication data, project data and data from social networks (i.e. posts, updates and endorsements collected through the crowdsourcing). Large volume of structured data representing scientific knowledge is available in Web of Science, Scopus, CNKI and ACM digital library; they are considered as publication data in this research context. Project data are located at the databases hosted by funding agencies. The authors follow the Map-Reduce strategy to extract real-time data from all these sources. Two main steps, features mining and profile consolidation (the details of which are outlined in the manuscript), are followed to generate comprehensive user profiles. The major tasks included in features mining are processing of big data sources to extract representational features of profiles, entity-profile generation and social-profile generation through crowd-opinion mining. At the profile consolidation, two profiles, namely, entity-profile and social-profile, are conflated.

(1) The integration of crowdsourcing techniques with big research data analytics has improved high graded relevance of the constructed profiles. (2) A system to construct experts’ profiles based on proposed methods has been incorporated into an operational system called ScholarMate (www.scholarmate.com).

One shortcoming is currently we have conducted experiments using sampling strategy. In the future we will perform controlled experiments of large scale and field tests to validate and comprehensively evaluate our design artifacts.

The business implication of this research work is that the developed methods and the system can be applied to streamline human capital management in organizations.

The proposed approach interconnects opinions of crowds on one’s expertise with corresponding expertise demonstrated in scientific knowledge bases to construct comprehensive profiles. This is a novel approach which alleviates problems associated with existing methods. The authors’ team has developed an expert profiling system operational in ScholarMate research social network (www.scholarmate.com), which is a professional research social network that connects people to research with the aim of “innovating smarter” and was launched in 2007.

In recent years, the rise of big data has led to an obvious shift in the competence profile expected from the controller and management accountant (MA). Among others, business analytics competences and information technology skills are considered a “must have” capability for the controlling and MA profession. As it still remains unclear if these requirements can be fulfilled by today’s employees, the purpose of this study is to examine the supply of business analytics competences in the current competence profiles of controlling professionals in an attempt to answer the question whether or not a skills gap exists.

Based on a set of 2,331 member profiles of German controlling professionals extracted from the business social network XING, a text analytics approach is conducted to discover patterns out of the semi-structured data. In doing so, the second purpose of this study is to encourage researchers and practitioners to integrate and advance big data analytics as a method of inquiry into their research process.

Apart from the mediating role of gender, company size and other variables, the results indicate that the current competence profiles of the controller do not comply with the recent requirements towards business analytics competences. However, the answer to the question whether a skills gap exist must be made cautiously by taking into account the specific organizational context such as level of IT adoption or the degree of job specialization.

Guided by the resource-based view of the firm, organizational theory and social cognitive theory, an explanatory model is developed that helps to explain the apparent skills gap, and thus, to enhance the understanding towards the rationales behind the observed findings. One major limitation to be mentioned is that the data sample integrated into this study is restricted to member profiles of German controlling professionals from foremost large companies.

The insights provided in this study extend the ongoing debate in accounting literature and business media on the skills changes of the controlling and MA profession in the big data era. The originality of this study lies in its explicit attempt to integrate recent advances in data analytics to explore the self-reported competence supplies of controlling professionals based on a comprehensive set of semi-structured data. A theoretically founded explanatory model is proposed that integrates empirically validated findings from extant research across various disciplines.

User profiling with big data raises significant issues regarding privacy. Privacy studies typically focus on individual privacy; however, in the era of big data analytics, users are also targeted as members of specific groups, thus challenging their collective privacy with unidentified implications. Overall, this paper aims to argue that in the age of big data, there is a need to consider the collective aspects of privacy as well and to develop new ways of calculating privacy risks and identify privacy threats that emerge.

Focusing on a collective level, the authors conducted an extensive literature review related to information privacy and concepts of social identity. They also examined numerous automated data-driven profiling techniques analyzing at the same time the involved privacy issues for groups.

This paper identifies privacy threats for collective entities that stem from data-driven profiling, and it argues that privacy-preserving mechanisms are required to protect the privacy interests of groups as entities, independently of the interests of their individual members. Moreover, this paper concludes that collective privacy threats may be different from threats for individuals when they are not members of a group.

Although research evidence indicates that in the age of big data privacy as a collective issue is becoming increasingly important, the pluralist character of privacy has not yet been adequately explored. This paper contributes to filling this gap and provides new insights with regard to threats for group privacy and their impact on collective entities and society.

Generally, the user requires customized information reflecting his/her current needs and interests that are stored in his/her profile. There are many sources which may provide beneficial information to enrich the user’s interests such as his/her social network for recommendation purposes. The proposed approach rests basically on predicting the reliability of the users’ profiles which may contain conflictual interests. The paper aims to discuss this issue.

This approach handles conflicts by detecting the reliability of neighbors’ profiles of a user. The authors consider that these profiles are dependent on one another as they may contain interests that are enriched from non-reliable profiles. The dependency relationship is determined between profiles, each of which contains interests that are structured based on k-means algorithm. This structure takes into consideration not only the evolutionary aspect of interests but also their semantic relationships.

The proposed approach was validated in a social-learning context as evaluations were conducted on learners who are members of Moodle e-learning system and Delicious social network. The quality of the created interest structure is assessed. Then, the result of the profile reliability is evaluated. The obtained results are satisfactory. These results could promote recommendation systems as the selection of interests that are considered of enrichment depends on the reliability of the profiles where they are stored.

Some specific limitations are recorded. As the quality of the created interest structure would evolve in order to improve the profile reliability result. In addition, as Delicious is used as a main data source for the learner’s interest enrichment, it was necessary to obtain interests from other sources, such as e-recruitement systems.

This research is among the pioneer papers to combine the semantic as well as the hierarchical structure of interests and conflict resolution based on a profile reliability approach.

This chapter explores the ethical complexities of researching location-aware social discovery Smartphone applications (apps) and how they mediate contemporary experiences of travel. We highlight the context-specific approach required to carrying out research on Tinder, a location-aware app that enables people to connect with others in close proximity to them. By journeying through the early stages of our research project, we demonstrate how ethical considerations and dilemmas began long before our project became a project. We discuss the pulls toward data extraction/mining of user-generated content (i.e., Tinder user profiles) within digital social research and the ethical challenges of using this data for research purposes. We focus particularly on issues of informed consent, privacy, and copyright, and the differences between manual and automated data mining/extraction techniques. Excerpts from our university ethics application are included to demonstrate how our research sits uneasily within standardized ethical protocols. Our moves away from a ‘big data’ approach to more ‘traditional’ and participatory methodologies are located within questions of epistemology and ontology including our commitment to practicing a feminist research ethic. Our chapter concludes with the lessons learned in the aim to push forward with research in challenging online spaces and with new data sources.

The paper aims to studiy social recruiting for finding suitable candidates on social networks. The main goal is to develop a methodological approach that would enable preselection of candidates using social network analysis. The research focus is on the automated collection of data using the web scraping method. Based on the information collected from the users' profiles, three clusters of skills and interests are created: technical, empirical and education-based. The identified clusters enable the recruiter to effectively search for suitable candidates.

This paper proposes a new methodological approach for the preselection of candidates based on social network analysis (SNA). The defined methodological approach includes the following phases: Social network selection according to the defined preselection goals; Automatic data collection from the selected social network using the web scraping method; Filtering, processing and statistical analysis of data. Data analysis to identify relevant information for the preselection of candidates using attributes clustering and SNA. Preselection of candidates is based on the information obtained.

It is possible to contribute to candidate preselection in the recruiting process by identifying key categories of skills and interests of candidates. Using a defined methodological approach allows recruiters to identify candidates who possess the skills and interests defined by the search. A defined method automates the verification of the existence, or absence, of a particular category of skills or interests on the profiles of the potential candidates. The primary intention is reflected in the screening and filtering of the skills and interests of potential candidates, which contributes to a more effective preselection process.

A small sample of the participants is present in the preliminary evaluation. A manual revision of the collected skills and interests is conducted. The recruiters should have basic knowledge of the SNA methodology in order to understand its application in the described method. The reliability of the collected data is assessed, because users provide data themselves when filling out their social network profiles.

The presented method could be applied on different social networks, such as GitHub or AngelList for clustering profile skills. For a different social network, only the web scraping instructions would change. This method is composed of mutually independent steps. This means that each step can be implemented differently, without changing the whole process. The results of a pilot project evaluation indicate that the HR experts are interested in the proposed method and that they would be willing to include it in their practice.

The social implication should be the determination of relevant skills and interests during the preselection phase of candidates in the process of social recruitment.

In contrast to previous studies that were discussed in the paper, this paper defines a method for automatic data collection using the web scraper tool. The described method allows the collection of more data in a shorter period. Additionally, it reduces the cost of creating an initial data set by removing the cost of hiring interviewers, questioners and people who collect data from social networks. A completely automated process of data collection from a particular social network stands out from this model from currently available solutions. Considering the method of data collection implemented in this paper, the proposed method provides opportunities to extend the scope of collected data to implicit data, which is not possible using the tools presented in other papers.

The purpose of this paper is to investigate the data profile of manufacturing small and medium enterprises (SMEs) with specific emphasis on understanding the data readiness of SMEs for discrete event simulation (DES) modelling.

Research was conducted through a review of literature and a survey research strategy of manufacturing SMEs.

This paper illustrates the data profile of manufacturing SMEs. Insight is provided on the types of data collected by SMEs, the collection methods used and how these data are stored by the SMEs. Additionally size and age effects are considered. Based on this data profile, conclusions are made regarding an indication of data readiness of manufacturing SMEs for DES modelling.

This research is focused specifically on manufacturing SMEs in Ireland, other countries and sectors are not investigated.

This paper provides owner-managers and senior management insight into the data profile of manufacturing SMEs and their potential for utilisation of DES for performance improvement and decision support.

This paper addresses the gaps that exist in the knowledge of the data profile of manufacturing SMEs and consequently the status of this profile with regard to the readiness of SMEs for DES modelling.

Organization development is focused on implementing a planned process of positive humanistic change in organizations through the use of social science theory, action research, and data-based feedback methods. The role of personality in that change process, however, has historically been ignored or relegated to a limited set of interventions. The purpose of this chapter is to provide a conceptual overview of the linkages between personality and OD, discuss the current state of personality in the field including key trends in talent management, and offer a new multi-level framework for conceptualizing applications of personality for different types of OD efforts. The chapter concludes with implications for research and practice.

This article examines the overlooked literature on algorithmic profiling in public employment services (APPES) in the field of public administration. More specifically, it aims to provide an overview and connections to identify directions for future research.

To understand the existing literature, this article conducts the first systematic literature review on APPES. Through inductive coding of the identified studies, the analysis identifies concepts and themes, and the relationships among them.

The literature review shows that APPES constitutes an emerging field of research encompassed by four strands and associated research disciplines. Further, the data analysis identifies 23 second-order themes, five dimensions and ten interrelationships, thus suggesting that the practices and effects of algorithmic profiling are multidimensional and dynamic.

The findings demonstrate the importance of future research on APPES undertaking a holistic approach. Studying certain dimensions and interrelationships in isolation risks overlooking mutually vital aspects, resulting in findings of limited relevance. A holistic approach entails considering both the technical and social effects of APPES.

This literature review contributes by connecting the existing literature across different research approaches and disciplines.

In the Web 2.0 era, users massively communicate through social networking services (SNS), often under false expectations that their communications and personal data are private. This paper aims to analyze privacy requirements of personal communications over a public medium.

This paper systematically analyzes SNS services as communication models and considers privacy as an attribute of users’ communication. A privacy threat analysis for each communication model is performed, based on misuse scenarios, to elicit privacy requirements per communication type.

This paper identifies all communication attributes and privacy threats and provides a comprehensive list of privacy requirements concerning all stakeholders: platform providers, users and third parties.

Elicitation of privacy requirements focuses on the protection of both the communication’s message and metadata and takes into account the public–private character of the medium (SNS platform). The paper proposes a model of SNS functionality as communication patterns, along with a method to analyze privacy threats. Moreover, a comprehensive set of privacy requirements for SNS designers, third parties and users involved in SNS is identified, including voluntary sharing of personal data, the role of the SNS platforms and the various types of communications instantiating in SNS.