34 research outputs found
Distinctiveness Centrality in Social Networks
The determination of node centrality is a fundamental topic in social network
studies. As an addition to established metrics, which identify central nodes
based on their brokerage power, the number and weight of their connections, and
the ability to quickly reach all other nodes, we introduce five new measures of
Distinctiveness Centrality. These new metrics attribute a higher score to nodes
keeping a connection with the network periphery. They penalize links to
highly-connected nodes and serve the identification of social actors with more
distinctive network ties. We discuss some possible applications and properties
of these newly introduced metrics, such as their upper and lower bounds.
Distinctiveness centrality provides a viewpoint of centrality alternative to
that of established metrics
The identity of social impact venture capitalists: exploring social linguistic positioning and linguistic distinctiveness through text mining
Impact investing is gaining momentum as an investment practice that optimizes both financial and social outcomes. However, the market is still in its emerging stage, and there is ambiguity regarding the definition of players and practices. In this paper, we adopt an investor identity perspective and use a linguistic approach to explore how social impact venture capitalists (SIVCs) communicate their identities and actions to their external stakeholders. Through a text mining analysis of the websites of 195 investors worldwide, our results reveal four types of investors who differ in terms of their social linguistic positioning and linguistic distinctiveness. Finally, by training a tree boosting machine learning model, we assess the extent to which the use of different linguistic styles is associated with website traffic
quality management in the design of tlc call centers
immediate Abstract Call centres rely heavily on the self-service paradigm through the use of an automated IVR (Interactive Voice Response) system. The service time delivered by the IVR is a major component of the overall QoS (Quality of Service) delivered by the call centre. We analyse the structure and service times of IVR systems through a case study of five call centres in the telecommunications sector. The service trees of the call centres under survey are reconstructed by complete exploration and analysed through a set of metrics. The present design of service trees leads to service times typically larger than those spent waiting for a human agent and to excessively long announcements, with a negative impact on the overall QoS. Imbalances in the popularity of the services offered by the IVR can be exploited to reduce remarkably the average service time, by properly matching the most popular services with the shortest service times
Brand Network Booster: A New System for Improving Brand Connectivity
This paper presents a new decision support system offered for an in-depth
analysis of semantic networks, which can provide insights for a better
exploration of a brand's image and the improvement of its connectivity. In
terms of network analysis, we show that this goal is achieved by solving an
extended version of the Maximum Betweenness Improvement problem, which includes
the possibility of considering adversarial nodes, constrained budgets, and
weighted networks - where connectivity improvement can be obtained by adding
links or increasing the weight of existing connections. We present this new
system together with two case studies, also discussing its performance. Our
tool and approach are useful both for network scholars and for supporting the
strategic decision-making processes of marketing and communication managers
Evaluating and improving social awareness of energy communities through semantic network analysis of online news
The implementation of energy communities represents a cross-disciplinary
phenomenon that has the potential to support the energy transition while
fostering citizens' participation throughout the energy system and their
exploitation of renewables. An important role is played by online information
sources in engaging people in this process and increasing their awareness of
associated benefits. In this view, this work analyses online news data on
energy communities to understand people's awareness and the media importance of
this topic. We use the Semantic Brand Score (SBS) indicator as an innovative
measure of semantic importance, combining social network analysis and text
mining methods. Results show different importance trends for energy communities
and other energy and society-related topics, also allowing the identification
of their connections. Our approach gives evidence to information gaps and
possible actions that could be taken to promote a low-carbon energy transition
The language and social behavior of innovators
Innovators are creative people who can conjure the ground-breaking ideas that
represent the main engine of innovative organizations. Past research has
extensively investigated who innovators are and how they behave in work-related
activities. In this paper, we suggest that it is necessary to analyze how
innovators behave in other contexts, such as in informal communication spaces,
where knowledge is shared without formal structure, rules, and work
obligations. Drawing on communication and network theory, we analyze about
38,000 posts available in the intranet forum of a large multinational company.
From this, we explain how innovators differ from other employees in terms of
social network behavior and language characteristics. Through text mining, we
find that innovators write more, use a more complex language, introduce new
concepts/ideas, and use positive but factual-based language. Understanding how
innovators behave and communicate can support the decision-making processes of
managers who want to foster innovation
Forecasting consumer confidence through semantic network analysis of online news
This research studies the impact of online news on social and economic
consumer perceptions through semantic network analysis. Using over 1.8 million
online articles on Italian media covering four years, we calculate the semantic
importance of specific economic-related keywords to see if words appearing in
the articles could anticipate consumers' judgments about the economic situation
and the Consumer Confidence Index. We use an innovative approach to analyze big
textual data, combining methods and tools of text mining and social network
analysis. Results show a strong predictive power for the judgments about the
current households and national situation. Our indicator offers a complementary
approach to estimating consumer confidence, lessening the limitations of
traditional survey-based methods
Linguistic sleuthing for innovators
For centuries “innovation” has been a topic of book authors and academic researchers as documented by Ngram and Google Scholar search results. In contrast, “innovators” have had substantially less attention in both the popular domain and the academic domain. The purpose of this paper is to introduce a text analysis research methodology to linguistically identify “innovators” and “non-innovators” using Hebert F. Crovitz’s 42 relational words. Specifically, we demonstrate how to combine the use of two complementary text analysis software programs: Linguistic Inquiry and Word Count and WORDij to simply count the percent of use of these relational words and determine the statistical difference in use between “innovators” and “non-innovators.” We call this the “Crovitz Innovator Identification Method” in honor of Herbert F. Crovitz, who envisioned the possibility of using a small group of 42 words to signal “innovation” language. The Crovitz Innovator Identification Method is inexpensive, fast, scalable, and ready to be applied by others using this example as their guide. Nevertheless, this method does not confirm the viability of any innovation being created, used or implemented; it simply detects how a person’s language signals innovative thinking. We invite other scholars to join us in this linguistic sleuthing for innovators
Boosting advice and knowledge sharing among healthcare professionals
Purpose: This study investigates the dynamics of knowledge sharing in
healthcare, exploring some of the factors that are more likely to influence the
evolution of idea sharing and advice seeking in healthcare.
Design/methodology/approach: We engaged 50 pediatricians representing many
subspecialties at a mid-size US children's hospital using a social network
survey to map and measure advice seeking and idea sharing networks. Through the
application of Stochastic Actor-Oriented Models, we compared the structure of
the two networks prior to a leadership program and eight weeks post conclusion.
Findings: Our models indicate that healthcare professionals carefully and
intentionally choose with whom they share ideas and from whom to seek advice.
The process is fluid, non-hierarchical and open to changing partners.
Significant transitivity effects indicate that the processes of knowledge
sharing can be supported by mediation and brokerage. Originality: Hospital
administrators can use this method to assess knowledge-sharing dynamics, design
and evaluate professional development initiatives, and promote new
organizational structures that break down communication silos. Our work
contributes to the literature on knowledge sharing in healthcare by adopting a
social network approach, going beyond the dyadic level, and assessing the
indirect influence of peers' relationships on individual networks