1,720,985 research outputs found
DNA Methylation in Nasal Epithelium: Strengths and Limitations of an Emergent Biomarker for Childhood Asthma.
Full text linkAsthma is one of the most widespread chronic respiratory conditions. This disease primarily develops in childhood and is influenced by different factors, mainly genetics and environmental factors. DNA methylation is an epigenetic mechanism which may represent a bridge between these two factors, providing a tool to comprehend the interaction between genetics and environment. Most epidemiological studies in this field have been conducted using blood samples, although DNA methylation marks in blood may not be reliable for drawing exhaustive conclusions about DNA methylation in the airways. Because of the role of nasal epithelium in asthma and the tissue specificity of DNA methylation, studying the relationship between DNA methylation and childhood asthma might reveal crucial information about this widespread respiratory disease. The purpose of this review is to describe current findings in this field of research. We will present a viewpoint of selected studies, consider strengths and limitations, and propose future research in this area
Digital Transformation: A Definition of Component (Canvas) and Process (Roadmap) View
No full textDigital transformation has gathered a significant interest within the research and industrial
communities, and has become an umbrella concept to address the multiple technology, strategic, human
resource and operations management dimensions involved into a digital-enabled organizational
renewal. Despite such increasing interest, a shared understanding of what is involved in digital
transformation and how a digital transformation initiative can be undertaken is still missing in the extant
literature. This article aims to contribute by identifying the multifaceted conceptual and applicative
dimensions of digital transformation, and to integrate the same into a single unifying framework. Based
on a design science approach and the review of large although fragmented literature, the article presents
a component-view or Digital Transformation Canvas and a process–view or Digital Transformation
Roadmap. The contribution is thus both in the academic and in the practitioner field
The ‘Big Social Data’ paradigm: definition, key features, and applicative contexts
No full textThe exponential growth of data existing on the web and generated by organizations and individuals, computer systems and digital devices, is opening new scenarios and opportunities for their exploitation, and new technological and managerial challenges are arising about the collection, transformation, storage, processing, usage, and visualization of such huge amount of data. The Big Data paradigm has therefore emerged as a socio-technical system that allows for offering innovative services in many data-intensive applications and domains. Within the Big Data field, the Big Social Data concept emerged as a relatively new phenomenon with multiple meanings and applications. According to the literature, the Big Social Data paradigm still lacks of a clear and shared definition. Thus, through a Systematic Literature Review, this paper aims at fulfilling this gap by providing a conceptualization of the Big Social Data paradigm that includes a possible definition, the distinguishing characteristics and properties, and some applications in real-life settings. Furthermore, by leveraging an existing taxonomy of data types, this paper proposes an extension that is specific for the Big Social Data domain, by introducing a new category of data type, namely “Digital Context Data”, which includes data related to the patterns of digital context dynamics. Finally, specifically for this new category of data type, two example applications in data-intensive domains (i.e. smart tourism and e-health) have been provided to demonstrate how the Big Social Data paradigm can describe, both explicitly and implicitly, the patterns of digital context dynamics
A multi-dimension framework for value creation through Big Data
No full textBig Data represents a promising area for value creation and frontier research. The potential to extract actionable insights from Big Data has gained increasing attention of both academics and practitioners operating in several industries. Marketing domain has become from the start a field for experiments with Big Data approaches, even if the adoption of Big Data solutions does not always generate effective value for the adopters. Therefore, the gap existing between the potential of value creation embedded in the Big Data paradigm and the current limited exploitation of this value represents an area of investigation that this paper aims to explore. In particular, by following a systematic literature review, this study aims at presenting a framework that outlines the multiple value directions that the Big Data paradigm can generate for the adopting organizations. Eleven distinct value directions have been identified and then grouped in five dimensions (Informational, Transactional, Transformational, Strategic, Infrastructural Value), which constitute the pillars of the proposed framework. Finally, the framework has been also preliminarily applied in three case studies conducted within three Italian based companies operating in different industries (e-commerce, fast-moving consumer goods, and banking) in the final aim to see its applicability in real business scenarios
A Human Resources Analytics Dashboard to support People-Centred Organizational Transformation
No full textHuman Resource (HR) analytics (or people analytics) includes a large family of methods and
applications aimed to analyse people-related data and build robust and effective HR-centred
organizational processes. The development of HR analytics is a relevant trend, of major interest for
scholars and practitioners, and this is particularly true in the post-pandemic scenario, characterized
by growing volatility, uncertainty and complexity. Such conditions are requiring organizations to
increasingly put human resources at the centre of their resilience building and transformation
processes. Advanced intelligence and decision support capabilities are crucial to build people-centred
organizations, and new theory contributions and practitioner advancements are thus needed to provide
robust conceptual frameworks and real-life applications. In such endeavour, we present
HUMANWISE, an integrated HR analytics system providing analytics tools to support workforce
status monitoring, competence re-allocation and development, and predictive analysis. We adopt an
interdisciplinary and multi-dimensional approach and a mixed research process, which includes a
systematic review of literature on HR analytics and a design science and group model building
activity, aimed to involve key stakeholders in the conceptualization and development effort. We
describe the conceptual architecture of the HR analytics system, with key design choices in terms of
data input, processing and output. Next, we formulate a set of corporate scenarios and an illustrative
dashboard to generate decision support functionalities for company managers and provide them with
insights useful to build more robust HR-centred transformation plans
Defining the big social data paradigm through a systematic literature review approach
No full textPurpose: 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
Integrating Large Language Models and Optimization in Semi- Structured Decision Making: Methodology and a Case Study
Full text linkSemi-structured decisions, which fall between highly structured and unstructured decision types, rely on human intuition and experience for the final choice, while using data and analytical models to generate tentative solutions. These processes are traditionally iterative and time-consuming, requiring cycles of data gathering, analysis, and option evaluation. In this study, we propose a novel framework that integrates Large Language Models (LLMs) with optimization techniques to streamline such decision-making processes. In our approach, LLMs leverage their capabilities in data interpretation, common-sense reasoning, and mathematical modeling to assist decision makers by reducing cognitive load. They achieve this by automating aspects of information processing and option evaluation, while preserving human oversight as a crucial component of the final decision-making process. Another significant strength of our framework lies in its potential to drive the evolution of a new generation of decision support systems (DSSs). Unlike traditional systems that rely on rigid and inflexible interfaces, our approach enables users to express their preferences in a more natural, intuitive, and adaptable manner, substantially enhancing both usability and accessibility. A case study on last-mile delivery system design in a smart city demonstrates the practical application of this framework. The results suggest that our approach has the potential to simplify the decision-making process and improve efficiency by reducing cognitive load, enhancing user experience, and facilitating more intuitive interactions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Assessing learners’ satisfaction in collaborative online courses through a big data approach
Full text linkMonitoring learners' satisfaction (LS) is a vital action for collecting precious information and design valuable online collaborative learning (CL) experiences. Today's CL platforms allow students for performing many online activities, thus generating a huge mass of data that can be processed to provide insights about the level of satisfaction on contents, services, community interactions, and effort. Big Data is a suitable paradigm for real-time processing of large data sets concerning the LS, in the final aim to provide valuable information that may improve the CL experience. Besides, the adoption of Big Data offers the opportunity to implement a non-intrusive and in-process evaluation strategy of online courses that complements the traditional and time-consuming ways to collect feedback (e.g. questionnaires or surveys). Although the application of Big Data in the CL domain is a recent explored research area with limited applications, it may have an important role in the future of online education. By adopting the design science research methodology, this article describes a novel method and approach to analyse individual students' contributions in online learning activities and assess the level of their satisfaction towards the course. A software artefact is also presented, which leverages Learning Analytics in a Big Data context, with the goal to provide in real-time valuable insights that people and systems can use to intervene properly in the program. The contribution of this paper can be of value for both researchers and practitioners: the former can be interested in the approach and method used for LS assessment; the latter can find of interest the system implemented and how it has been tested in a real online course
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