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Job-related social networking websites (e.g. LinkedIn) are often used in the recruitment process because the profiles contain valuable information such as education level and work experience. The purpose of this paper is to investigate whether people can accurately infer a profile owner’s self-rated personality traits based on the profile on a job-related social networking site.

In two studies, raters inferred personality traits (the Big Five and self-presentation) from LinkedIn profiles (total n=275). The authors related those inferences to self-rated personality by the profile owner to test if the inferences were accurate.

Using information gained from a LinkedIn profile allowed for better inferences of extraversion and self-presentation of the profile owner (r’s of 0.24-0.29).

When using a LinkedIn profile to estimate trait extraversion or self-presentation, one becomes 1.5 times as likely to actually select the person with higher trait extraversion compared to the person with lower trait extraversion.

Although prior research tested whether profiles of social networking sites (such as Facebook) can be used to accurately infer self-rated personality, this was not yet tested for job-related social networking sites (such as LinkedIn). The results indicate that profiles at job-related social networks, in spite of containing only relatively standardized information, “leak” information about the owner’s personality.

This paper aims to develop a thorough understanding of industry-specific networking behaviour on the internationalization performance of Czech small and medium enterprises (SMEs).

The authors used a profile deviation-ideal profile methodology to explore the ideal networking behaviour profile of different industries. The authors argue that firms adhering to ideal profiles performed well in the international market, while firms deviating from the ideal profile performed poorly. Data were collected through an online questionnaire specifically targeted at Czech SME executives. The authors attempted to explore these issues by using four aspects of networking behaviour to test the ideal networking behaviour profile of five different industries (automotive, telecommunications, construction, audit and finance and transportation).

The authors have identified different ideal networking behaviour profile for three industries, which underpinned supported the hypothesis that each dimension of networking behaviour should be fine-tuned for each sector to achieve to attain maximum benefits and performance in the international market.

Although previous studies supported the role of networking behaviour in improving internationalization performance, multiple studies had also provided conflicting results on how networking affects different industries and it is unclear how and why networking affects these various industries differently. The authors believe that the results research provides empirical evidence in proving that different networking dimensions should be fine-tuned as per industry to achieve the highest performance in the international market. The authors believe that the findings broaden the current understanding of the role of networks in the internationalization. The authors believe that the findings extend the current understanding of the role of networks in the internationalization of SMEs.

As organizations increase their dependence on supply chain networks, they become more susceptible to their suppliers’ disaster risk profiles, as well as other categories of risk associated with supply chains. Therefore, it is imperative that supply chain network participants are capable of assessing the disaster risks associated with their supplier base. The purpose of this paper is to assess the supplier disaster risks, which are a key element of external risk in supply chains.

The study participants are 15 automotive casting suppliers who display a significant degree of disaster risks to a major US automotive company. Bayesian networks are used as a methodology for examining the supplier disaster risk profiles for these participants.

The results of this study show that Bayesian networks can be effectively used to assist managers in making decisions regarding current and prospective suppliers vis-à-vis their potential revenue impact as illustrated through their corresponding disaster risk profiles.

A limitation to the use of Bayesian networks for modeling disaster risk profiles is the proper identification of risk events and risk categories that can impact a supply chain.

The methodology used in this study can be adopted by managers to assist them in making decisions regarding current or prospective suppliers vis-à-vis their corresponding disaster risk profiles.

As part of a comprehensive supplier risk management program, organizations along with their suppliers can develop specific strategies and tactics to minimize the effects of supply chain disaster risk events.

The article aims to present a holistic approach to analysis of patterns on complex online profiles, demonstrated on profiles of European scientists.

An existing analytical framework was developed to incorporate a holistic understanding of online profiles. The framework was applied to a sample of 188 online profiles belonging to 48 European scientists. The profile data were studied on three levels (content‐unit level, profile‐instance level, and profile‐network level), using methods of the qualitative comparative analysis to derive profiling patterns.

The approach developed in this work generated profiling patterns for European scientists. The patterns exist on all three levels, forming a hierarchy. This pattern structure shows the variety of ways in which scientists can use the internet for self‐presentation.

The study was based on a holistic understanding of online self‐presentation, acknowledging that personal presentation can be spread across different platforms. The study presented shows how this understanding can be used when analysing online profiling behaviour. The profiling patterns of European scientists identified in this study supplement existing typologies. The study serves as a foundation to structure further research as well as to inform practitioners.

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.

The purpose of this paper is twofold. First, the author aims to explore if there is a curvilinear relationship between the network rent, generated as a combination of the relational performance of two dyads and the network profile of the triadic supply chains. Second, the author seek to recognize the ideal network profile, consisting of the properties at the node and relationship level, that provides the highest network rents, and thus enables to increase the competitive advantage of supply chains.

The paper opted for an exploratory study using a survey of triads forming supply chains. In order to reveal the capability of yielding the network rent in the examined triads, multiple regression analysis with interaction effects was employed. Having confirmed the existence of supernormal profit, the author investigated the relationship between the network rent and the network index. Finally, a cluster analysis was conducted to compare the network profile in the group of triads generating higher network rents with the cluster yielding relatively lower network rents.

The obtained findings show that a combination of the relational performance of two dyads contributes to generating the network rent, and thus ensures a more favorable competitive position of supply chains. The results of the study also indicate that there is a significant curvilinear, inverted U-shaped relationship between the network profile and the competitive advantage of triadic supply chains. In addition, the following network properties appear to be particularly important for yielding higher network rents: network centrality, betweenness, network density and network size.

The study contributes to the theory by testing if the network rent can be yielded as a combination of the relational performance of two dyads in the triadic supply chains. The research also indicates that there is a curvilinear relationship between the network rent and the network profile of examined supply chains. Moreover, the study also addresses the link between the network profile, consisting of the multiple network properties simultaneously, in relation to the competitive advantage of supply chains.

The purpose of this paper is to propose a new approach recommending friends to social networking users who are also using weight loss app in the context of social networks.

Social network has been recognized as an effective way to enhance overweight and obesity interventions in past studies. However, effective measures integrating social network with weight loss are very limited in the healthcare area. To bridge this gap, this study develops a measure for friend recommendation using the data obtained by weight loss apps; designs methods to model weight-gain-related behaviors (WGRB); constructs a novel “behavior network;” and develops two measurements in experiments to examine the proposed approach.

The approach for friend recommendation is based on Friend Recommendation for Health Weight (FRHW) algorithm. By running this algorithm on a real data set, the experiment results show that the algorithm can recommend a friend who has a healthy lifestyle to a target user. The advantages of the proposed mechanism have been well justified via comparisons with popular friend recommenders in past studies.

The conventional methods for friend recommenders in social networks are only concerned with similarities of pairs rather than interactions between people. The system cannot account for the potential influences among people. The method pioneers to model a WGRB as recommendation mechanism that allow recommended friends to simultaneously fulfill two criteria. They are: first, similarity to the target person; and second, ensuring the positive influence toward weight loss. The second criterion is obviously important in practice and thus the approach is valuable to the literature.

The purpose of this paper is to develop an algorithm, using PCA‐based neural network, to retrieve the vertical rainfall structure in a precipitating atmosphere. The algorithm is powered by a rigorous solution to the plane parallel radiative transfer equation for the atmosphere with thermodynamically consistent vertical profiles of humidity, temperature and cloud structures, together with “measured” vertical profiles of the rain structure derived from a radar.

The raining atmosphere is considered to be a plane parallel, radiatively participating medium. The atmospheric thermodynamic profiles such as pressure, temperature and relative humidity along with wind speed at sea surface and cloud parameters corresponding to Nargis, a category 4 tropical cyclone that made its landfall on May 2, 2008 at the Republic of Myanmar, are obtained by solving the flux form of Euler's equations in three‐dimensional form. The state‐of‐the‐art community software Weather Research and Forecasting has been used for solving the set of equations. The three‐dimensional rain profiles for the same cyclone at the same instant of time are obtained from National Aeronautics and Space Administration's space borne Tropical Rainfall Measuring Mission's precipitation radar over collocated pixels. An in‐house Micro‐Tropiques code is used to perform radiative transfer simulations for frequencies corresponding to a typical space borne radiometer, and hence to generate the database which is later used for training the neural network. The back propagation‐based neural network is optimized with reduced number of parameters using principal component analysis (PCA).

The results show that neural network is capable of retrieving the vertical rainfall structure with a correlation coefficient of over 0.99. Further, reducing the ill‐posedness in retrieving 56 parameters from just nine measurements using PCA has improved the root mean square error in the retrievals at reduced computational time.

The paper shows that combining numerically generated atmospheric profiles together with radar measurements to serve as input to a radiative transfer model brings in the much‐required synergy between numerical weather prediction, radar measurements and radiative transfer. This strategy can be gainfully used in satellite meteorology. Using principal components to reduce the ill‐posedness, thereby increasing the robustness in retrieving vertical rain structure, has been attempted for the first time. A well‐trained network can be used as one possible option for an operational algorithm for the proposed Indian climate research satellite Megha‐Tropiques, due to be launched in early 2011.

The purpose of this paper is to better understand why many researchers do not have a profile on social networking sites (SNS), and whether this is the result of conscious decisions.

Thematic analysis was conducted on a large qualitative data set from researchers across three levels of seniority, four countries and four disciplines to explore their attitudes toward and experiences with SNS.

The study found much greater scepticism toward adopting SNS than previously reported. Reasons behind researchers’ scepticism range from SNS being unimportant for their work to not belonging to their culture or habits. Some even felt that a profile presented people negatively and might harm their career. These concerns were mostly expressed by junior and midlevel researchers, showing that the largest opponents to SNS may unexpectedly be younger researchers.

A limitation of this study was that the authors did not conduct the interviews, and therefore reframing or adding questions to specifically unpack comments related to attitudes, feelings or the use of SNS in academia was not possible.

By studying implicit attitudes and experiences, this study shows that instead of being ignorant of SNS profiles, some researchers actively opt for a non-use of profiles on SNS.

Over the last years, many corporations have started to maintain profile pages on social networking sites (SNS), but research on how and why organizational stakeholders use these profile pages has not kept pace. The paper aims to discuss these issues.

The study applies a combined perspective of uses-and-gratifications (U & G) and social cognitive theory (SCT) to investigate the reasons why politicians and digital natives consume and interact with corporations on SNS. In total, 65 semi-structured interviews were conducted and analyzed using qualitative content analysis.

Results suggest that the two stakeholder groups differ in their motivations, as well as behavior to use corporate profile pages. Digital natives seem to prefer Facebook to interact with companies, politicians prefer Twitter. Corporate YouTube pages are almost not important to any of the groups.

The qualitative nature of the study does not allow for generalizations of the findings to larger populations. Suggestions for further research are addressed in the discussion section.

The study results have numerous implications for the practice of communication management. Fans on SNS do not tend to interact with corporations to a large extent, but are loyal followers. Once a connection between an individual and a company is established, it is likely to last. This enables corporations to gain rich information from their networks to be included in customer service, product development, issues management and recruiting.

This is the first study in the field of communication management, which applies a micro-level approach to interviewing users of corporate communication; in order to reveal the reasons why and how they use corporate social networking profile pages.