Networks in Assembly: Investigating Social Factors in Robotic Automation

Automation will be one of the shaping influences of the coming decades. The increased application of robots in assembly will undoubtedly change these work environments. However, studies which attempt to predict the effect on the labour market resulting from the automation of work processes and the replacement of jobs suffer from overly simplistic dichotomy between routine and non-routine tasks. In contrast, research at the micro-level of the shop floor has shown that even routine tasks draw heavily on informal knowledge and experience. This paper reviews the concepts which describe these work processes and the necessary forms of knowledge and experience. I then argue that the literature on social networks in organisations can provide useful conceptual and methodical tools to investigate how these kinds of knowledge and experience are transferred between workers. Social network research therefore can serve as a way to shed light on the social factors in robotic automation. The paper concludes with the opportunities which the application of network analysis to assembly can provide for social network research itself.Programa europeu Erasmus+info:eu-repo/semantics/publishedVersio

Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions

Social network usage is growing exponentially in the most up-to-date decade; though social networks are becoming increasingly popular every day, many users are continuously active social network users. Using Twitter, LinkedIn, Facebook, and other social media sites has become the most convenient way for people. There is an enormous quantity of data produced by users of social networks. The most common part of modern research analysis is instrumental for many social network analysis applications. However, people actively utilize social networking sites and diverse uses of these sites. social media sites handle an immense amount of knowledge and answer these three computational problems, noise, dynamism, and scale. Semantic comprehension of the document, image, and video exchanged in a social network was also an essential topic in network analysis. Utilizing data processing provides vast datasets such as averages, laws, and patterns to discover practical knowledge. Using social media, data analysis was primarily used for machine learning, analysis, information extraction, statistical modelling, data preprocessing, and data interpretation processes. This research intentions to deliver an inclusive overview of social network research and application analyze state-of-the-art social media data analysis methods by reviewing basic concepts, social networks and elements social network research is linked to. Semantic ways of manipulating text in social networks are then clarified, and literature discusses studies before on these themes. Next, the evolving methods in research on social network analysis are discussed, especially in analyzing semantic text on social networks. Finally, subjects and opportunities for future research directions are explained

The evaluation of social network analysis application's in the UK construction industry

The Social Network Analysis (SNA) has been adopted in the UK construction management research and there is a trend to apply it in large scale. As an effective tool, social network analysis has been used to analyse information and knowledge flow between construction project teams which is considered as foundation for collaborative working and subsequently improving overall performance. Social network analysis is based on an assumption of the importance of relationships among interacting units. The social network perspective encompasses theories, models and applications that are expressed in terms of relational concepts or processes. Many believe, moreover, that the success or failure of organisations often depends on the patterning of their internal structure. This paper reviewed existing literatures on SNA applications in the UK construction industry. From the review, the research proposed some improvement in the application of SNA in the construction industry

Time for a real shift to relations : appraisal of Social Network Analysis applications in the UK construction industry

The Social Network Analysis (SNA) has been adopted in the UK construction management research and generated meaningful insights in analysing project management organisations from network perspectives. As an effective tool, social network analysis has been used to analyse information and knowledge flows between construction project teams which are considered as the foundation for collaborative working and subsequently improving overall performance. Social network analysis is based on an assumption of the importance of relationships among interacting units. The social network perspective encompasses theories, models and applications that are expressed in terms of relational concepts or processes. Many believe, moreover, that the success or failure of organisations often depends on the patterning of their internal structure. This paper reviewes existing literatures on SNA applications in construction industry from three leading construction management journals. From the review, the research proposed some advance in the application of SNA in the construction industry

Time for a real shift to relations: Appraisal of social network analysis applications in the UK construction industry

The Social Network Analysis (SNA) has been adopted in the UK construction management research and generated meaningful insights in analysing project management organisations from network perspectives. As an effective tool, social network analysis has been used to analyse information and knowledge flow between construction project teams which is considered as foundation for collaborative working and subsequently improving overall performance. Social network analysis is based on an assumption of the importance of relationships among interacting units. The social network perspective encompasses theories, models and applications that are expressed in terms of relational concepts or processes. Many believe, moreover, that the success or failure of organisations often depends on the patterning of their internal structure. This paper reviewed existing literatures on SNA applications in construction industry from three leading construction management journals. From the review, the research proposed some advance in the application of SNA in the construction industry

Mining email to leverage knowledge networks in organizations

There is nothing new about the notion that in today‟s knowledge driven economy, knowledge is the key strategic asset for competitive advantage in an organization. Also, we have learned that knowledge is residing in the organization‟s informal network. Hence, to leverage business performance from a knowledge management perspective, focus should be on the informal network. A means to analyze and develop the informal network is by applying Social Network Analysis (SNA). By capturing network data in an organization, bottlenecks in knowledge processes can be identified and managed. But where network data can easily be captured by means of a survey in small organizations, in larger organizations this process is too complex and time-intensive. Mining e-mail data is more and more regarded as a suitable alternative as it automates the data capturing process and enables longitudinal research possibilities. An increasing amount of tools for mining e-mail data into social networks is available, but the question remains to what extent these tools are also capable of conducting knowledge network analysis: the analysis of networks from a knowledge perspective. It is argued that in order to perform knowledge network analysis, a tool is required that is capable of analyzing both the header data and the body data of e-mail messages. In this paper two e-mail mining tools are elaborated. One focuses on the analysis of e-mail header data and the other focuses on the analysis of e-mail body data. Both tools are embedded in their theoretical background and compared to other e-mail mining tools that address e-mail header data or e-mail body data. The aim of this paper is two-fold. The paper primarily aims at providing a detailed discussion of both tools. Continuing, from the in-depth review, the integration of both tools is proposed, concluding towards a single new tool that is capable of analyzing both e-mail header and body data. It is argued how this new tool nurtures the application of knowledge network analysis

A knowledge structures exploration on social network sites

Purpose – This paper aims to describe a method for combining perceived community support, relationship quality and the extended technology acceptance model in the same empirically derived associative network. The research also examines the moderating role of accumulation of knowledge (based on beliefs and opinions) derived from social interactions. Design/methodology/approach – The Path fi nder algorithm is a valid approach for determining network structures from relatedness data. Such a graphical representation provides managers with a comprehensible picture of how social behaviours relate to loyalty-based dimensions. Findings – As the bene fi ts of community participation and integration might be differently evaluated by new and long-term users, the research examines the associative network by levels of user familiarity. This study indeed contributes to the analysis of enduring social bonds with respect to individuals ’ decision-making processes, as it provides details representing speci fi c relationships between diverse concepts based on true- loyalty. Practical implications – The application of Path fi nder to the study of online social services and user behaviour appears to have potential for unveiling the structures of social network sites members and designing successful strategies for prospective community managers. Originality/value – This is the fi rst study to the author ’ s knowledge that empirically tests a theory- grounded framework for integrating individual characteristics and relational driver and focuses on associative structures evidenced as a representation of the most salient loyalty-based concepts by also studying the moderating effects of familiarity.Junta de Andalucía SEJ-580

TRANSVERSALITY, TECHNOLOGICAL TRANSFER NETWORKS AND POLICY IMPLICATIONS: THE CASE OF REGIONAL INNOVATION POLICIES IN TUSCANY REGION (SDP 2000-2006)

Recently at the European level the theme of innovation has been further fostered with the Smart Specialization Strategy underlined within the COM(2010) 553 “Regional policy contributing to smart growth in Europe 2020â€. The aim of this study is to investigate the co-evolutive dynamics of the technological transfer processes at regional level, and in particular the issue of transversality and bases of knowledge between networks according to an evolutionary perspective. Transversality is analysed considering networks’ differences and proximities in terms of industry of application, applied technology, and local dimensions of relationships. In order to analyze these phenomena, we apply the Social Network Analysis to investigate the structural features of the space of relations and relational flows, and to roles and attributes of the universe of the co-funded actors. The structural analysis of the relations’ system (centrality, closeness, betweenness, local dimension) has been analyzed across five regional initiatives, studying over 150 networks and over 1300 co-funded actors. Relations between and within networks have been normalized and the role of specific agents has been underlined with regards to transversality dynamics. As conclusion, policy implications can be drawn, in particular as far as supply-led and demand-led innovation policy. The study is structured as follows. After the introduction describing the context of regional innovation policies over the last Regional Planning period (SPD 2000-2006), the first paragraph describes the main characteristics of the concept of transversality, with connections to RIS model and innovation networks. The second paragraph describes the Social Networks Analysis methodology used to study the evolutionary process of agglomeration with regards to bases of knowledge and transversality. The third paragraph deals with the results of the analysis and the fourth paragraph presents conclusive remarks on policy implication in terms of industrial policies.

A Social Network-Guided Approach to Machine Learning for Metal-Organic Framework Property Prediction

The number of new materials and applications of these materials is experiencing rapid growth. ‎Today, increased computational power and the established use of automated machine learning ‎approaches make data science tools available, which provide an overview of the chemical space, ‎support the choice of appropriate materials, and predict specific properties of materials for the ‎desired application. Among the different data science tools, graph theory approaches, where data ‎generated from numerous real-world applications are represented as a graph (network) of ‎connected objects, has been widely used in a variety of scientific fields such as social sciences, ‎health informatics, biological sciences, agricultural sciences, and economics. In this work, we ‎describe applying a particular graph theory approach, social network analysis (SNA), to the metal-organic framework (MOF). To demonstrate MOF materials, we construct a social network called ‎MOFSocialNet from geometrical MOFs descriptors in the CoRE-MOFs database. The MOFSocialNet ‎is an undirected, weighted, and heterogeneous social network; following the construction of this ‎graph, a set of social network analysis processes is conducted to extract valuable knowledge from ‎the MOFs data using graph machine learning algorithms. Community detection is one of the well-known SNA techniques employed on the MOFSocialNet to extract the most similar MOF ‎communities. To evaluate whether the properties of new MOFs can be predicted using MOF ‎communities, we randomly chose three from the CoRE MOFs database. For these MOFs, we ‎excluded the crystal density as input during featurization and placed the MOFs within the ‎MOFSocialNet. The crystal density of the new MOFs is predicted by simply averaging the crystal ‎density of the ten nearest neighbors. ‎ Additionally, communities extracted from MOFSocialNet can be leveraged to predict MOF gas ‎adsorption properties for CO2 and CH4.