Digital relational capital of a company
Purpose – This paper aims to examine how a company can build and develop its relational capital in a
digital environment. It searches for proxy-indicators for digital relational capital and explores their impact on
Design/methodology/approach – The paper is designed to sit in the cross-section of two concepts – Big
Data and Intellectual Capital.We analyze eight metrics of digital relational capital (SEMrush rank, Trust flow,
Domain authority, MozRank, Number of pages indexed in Yandex and Google, Thematic Citation Index by
Yandex, Alexa Rank) and examine their impact on company performance by conducting a two-stage fixedeffect
regression. The empirical part of the paper is based on a database of more than 1,000 Russian public
companies from 2010-2016.
Findings – The study justifies eight Big Data-based metrics that enable the estimation of the digital
relational capital of a company. Empirical evidence of a significant impact on corporate performance is
provided. Moreover, a U-shaped configuration of obtained relationships allows for a better understanding of
the phenomenon of digital relational capital and has managerial implications.
Originality/value – Companies can indirectly influence the proposed metrics. The study gives specific
recommendations regarding these metrics to allow companies to optimize their performance. In addition, to
the best of the authors’ knowledge, this is the first empirical research on relational capital through Big Data in
Pattern structures, an extension of FCA to data with complex descriptions, propose an alternative to conceptual scaling (binarization) by giving direct way to knowledge discovery in complex data such as logical formulas, graphs, strings, tuples of numerical intervals, etc. Whereas the approach to classification with pattern structures based on preceding generation of classifiers can lead to double exponent complexity, the combination of lazy evaluation with projection approximations of initial data, randomization and parallelization, results in reduction of algorithmic complexity to low degree polynomial, and thus is feasible for big data.
The proceedings of the 11th International Conference on Service-Oriented Computing (ICSOC 2013), held in Berlin, Germany, December 2–5, 2013, contain high-quality research papers that represent the latest results, ideas, and positions in the field of service-oriented computing. Since the first meeting more than ten years ago, ICSOC has grown to become the premier international forum for academics, industry researchers, and practitioners to share, report, and discuss their ground-breaking work. ICSOC 2013 continued along this tradition, in particular focusing on emerging trends at the intersection between service-oriented, cloud computing, and big data.
Full texts of third international conference on data analytics are presented.
The practical relevance of process mining is increasing as more and more event data become available. Process mining techniques aim to discover, monitor and improve real processes by extracting knowledge from event logs. The two most prominent process mining tasks are: (i) process discovery: learning a process model from example behavior recorded in an event log, and (ii) conformance checking: diagnosing and quantifying discrepancies between observed behavior and modeled behavior. The increasing volume of event data provides both opportunities and challenges for process mining. Existing process mining techniques have problems dealing with large event logs referring to many different activities. Therefore, we propose a generic approach to decompose process mining problems. The decomposition approach is generic and can be combined with different existing process discovery and conformance checking techniques. It is possible to split computationally challenging process mining problems into many smaller problems that can be analyzed easily and whose results can be combined into solutions for the original problems.
Capital structure is one of key company value factor, so it is important to identify its determinants. Existing studies of Russian companies dedicated to capital structure puzzle use only publicly available data therefore ignore a number of important factors, including behavioral ones. Therefore, the current paper uses survey of people responsible for financial decision-making. In addition to choose statistically significant determinants the paper discusses the possibility of using factor analysis to identify groups of factors influencing the choice of funding.
In 2015-2016 the Department of Communication, Media and Design of the National Research University “Higher School of Economics” in collaboration with non-profit organization ROCIT conducted research aimed to construct the Index of Digital Literacy in Russian Regions. This research was the priority and remain unmatched for the momentIn 2015-2016 the Department of Communication, Media and Design of the National Research University “Higher School of Economics” in collaboration with non-profit organization ROCIT conducted research aimed to construct the Index of Digital Literacy in Russian Regions. This research was the priority and remain unmatched for the moment
The article is dedicated to the analysis of Big Data perspective in jurisprudence. It is proved that Big Data have to be used as the explanatory and predictable tool. The author describes issues concerning Big Data application in legal research. The problems are technical (data access, technical imperfections, data verification) and informative (interpretation of data and correlations). It is concluded that there is the necessity to enhance Big Data investigations taking into account the abovementioned limits.
Smoking is a problem, bringing signifi cant social and economic costs to Russiansociety. However, ratifi cation of the World health organization Framework conventionon tobacco control makes it possible to improve Russian legislation accordingto the international standards. So, I describe some measures that should be taken bythe Russian authorities in the nearest future, and I examine their effi ciency. By studyingthe international evidence I analyze the impact of the smoke-free areas, advertisementand sponsorship bans, tax increases, etc. on the prevalence of smoking, cigaretteconsumption and some other indicators. I also investigate the obstacles confrontingthe Russian authorities when they introduce new policy measures and the public attitudetowards these measures. I conclude that there is a number of easy-to-implementanti-smoking activities that need no fi nancial resources but only a political will.
One of the most important indicators of company's success is the increase of its value. The article investigates traditional methods of company's value assessment and the evidence that the application of these methods is incorrect in the new stage of economy. So it is necessary to create a new method of valuation based on the new main sources of company's success that is its intellectual capital.