Giuseppina Uva, Francesco Porco, Andrea Fiore and Mauro Mezzina
The purpose of this paper is to collect the numerical elaboration of resistances measured on cubes made during the concrete casting and on cores extracted after the completion of…
Abstract
Purpose
The purpose of this paper is to collect the numerical elaboration of resistances measured on cubes made during the concrete casting and on cores extracted after the completion of the structure, for the concrete used in the construction of the “Esaro” Dam facilities (Cosenza, Italy). In addition to the statistical treatment of the sample, aimed at assessing the analytical congruence with the homogeneous classes provided in the design, the influence of compaction degree on in place strength value was qualitatively evaluated.
Design/methodology/approach
The reliability of the concrete during the construction phases was evaluated by two analytical control types according to Italian and European technical rules: “production controls” based on statistical processing of resistance values; “laying controls” that serve to assess the compaction degree with a statistical approach.
Findings
Results highlighted in the assessing of compliance checks of the mixture, the fundamental relation between statistical approach and concrete laying control. They become important when is necessary to quantify, especially in the case of great infrastructure, the gap between “potential” and “structural” concrete.
Originality/value
The advantage obtained by controlling the compaction degree in the construction phase is unquestionable. Specifically, it might allow a reduction of the drilling cores, and so minor structural damage, especially for relatively recent structures favouring extensive non-destructive tests.
Details
Keywords
Gianluca Solazzo, Gianluca Elia and Giuseppina Passiante
This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about its…
Abstract
Purpose
This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about its beneficial opportunities for practitioners. In the knowledge management (KM) domain, a clear characterization of the BSD paradigm can lead to more effective and efficient KM strategies, processes and systems that leverage a huge amount of structured and unstructured data sources.
Design/methodology/approach
The study adopts a systematic literature review (SLR) methodology based on a mixed analysis approach (unsupervised machine learning and human-based) applied to 199 research articles on BSD topics extracted from Scopus and Web of Science. In particular, machine learning processing has been implemented by using topic extraction and hierarchical clustering techniques.
Findings
The paper provides a threefold contribution: a conceptualization and a consensual definition of the BSD paradigm through the identification of four key conceptual pillars (i.e. sources, properties, technology and value exploitation); a characterization of the taxonomy of BSD data type that extends previous works on this topic; a research agenda for future research studies on BSD and its applications along with a KM perspective.
Research limitations/implications
The main limits of the research rely on the list of articles considered for the literature review that could be enlarged by considering further sources (in addition to Scopus and Web of Science) and/or further languages (in addition to English) and/or further years (the review considers papers published until 2018). Research implications concern the development of a research agenda organized along with five thematic issues, which can feed future research to deepen the paradigm of BSD and explore linkages with the KM field.
Practical implications
Practical implications concern the usage of the proposed definition of BSD to purposefully design applications and services based on BSD in knowledge-intensive domains to generate value for citizens, individuals, companies and territories.
Originality/value
The original contribution concerns the definition of the big data social paradigm built through an SLR the combines machine learning processing and human-based processing. Moreover, the research agenda deriving from the study contributes to investigate the BSD paradigm in the wider domain of KM.
Details
Keywords
Pasquale Del Vecchio, Gioconda Mele, Giuseppina Passiante, Demetris Vrontis and Cosimo Fanuli
This paper aims to demonstrate how the integration of netnography and business analytics can support companies in the process of value creation from social big data by leveraging…
Abstract
Purpose
This paper aims to demonstrate how the integration of netnography and business analytics can support companies in the process of value creation from social big data by leveraging on customer relationship management and customer knowledge management (CKM).
Design/methodology/approach
This paper adopts the methodology of a single case study by using desk analysis, netnography and business analytics. The context of analysis has been identified into the case of Aurora Company, a well-known producer of fountain pens.
Findings
The case demonstrates how the integration of big data analytics and netnography is relevant for the development of a customer relationship management strategy. The results obtained have been categorized according to the three main categories of customer knowledge, such as knowledge for, from and about customer.
Research limitations/implications
This paper presents implications for the advancement of the theory on CKM by demonstrating, as the acquisition, storage and management of data generated by customers on social media require the adoption of a cross-disciplinary approach resulting from the integration of qualitative and quantitative approaches. The framework is structured as methodological tool to detect knowledge in virtual community.
Practical implications
Practical implications arise for managers and entrepreneurs in terms of value creation from knowledge assets generated on social big data through the management of the customers’ relationship and data-driven innovation patterns.
Originality/value
This paper offers an original contribution of integration of well-established research streams. The focus on the knowledge under the perspectives of information assets for, from and about customers in the debate on value creation and management of big data is an element of value offered by this study in addition to the comprehension of strategies of social customer relationship management as actual initiative embraced by a company in the leveraging of innovation and tradition.