The Semantic Brand Score (SBS) is a new measure of brand importance calculated on text data, combining methods of social network and semantic analysis. This metric is flexible as it can be used in different contexts and across products, markets and languages. It is applicable not only to brands, but also to multiple sets of words. The SBS, described together with its three dimensions of brand prevalence, diversity and connectivity, represents a contribution to the research on brand equity and on word co-occurrence networks. It can be used to support decision-making processes within companies; for example, it can be applied to forecast a company's stock price or to assess brand importance with respect to competitors. On the one side, the SBS relates to familiar constructs of brand equity, on the other, it offers new perspectives for effective strategic management of brands in the era of big data.

Purpose This 7-year longitudinal study identifies factors influencing the growth of healthcare Virtual Communities of Practices (VCoPs) using metrics from social-network and semantic analysis. Studying online communication along the three dimensions of social interactions (connectivity, interactivity and language use) we aim to provide VCoPs managers with valuable insights to improve the success of their communities. Design/methodology/approach Communications over a period of 7 years (April 2008 to April 2015), and between 14,000 members of 16 different healthcare VCoPs coexisting on the same web-platform, were analyzed. Multilevel regression models were used to reveal the main determinants of community growth over time. Independent variables were derived from social network and semantic analysis measures. Findings Results show that structural and content-based variables predict the growth of the community. Progressively more people will join a community if: its structure is more centralized, leaders are more dynamic (they rotate more), and the language used in the posts is less complex. Research limitations/implications The available dataset included one web platform and a limited number of control variables. In order to consolidate the findings of the present study, the experiment should be replicated on other healthcare VCoPs. Originality/value The study provides useful recommendations for setting up and nurturing the growth of professional communities, considering at the same time the structure of the interaction patterns among community members, the dynamic evolution of these interactions and the use of language. New analytical tools are presented, together with the use of innovative interaction metrics which can significantly influence community growth, such as rotating leadership

We investigate the impact of a novel method called "virtual mirroring" to promote self-reflection and impact customer satisfaction. The method is based on measuring communication patterns, through social network and semantic analysis, and mirroring them back to the individual. Our goal is to demonstrate that self-reflection can trigger a change in communication behaviors. We illustrate and test our approach analyzing e-mails of a large global services company by comparing changes in customer satisfaction associated with team leaders exposed to virtual mirroring (the experimental group). We find an increase in customer satisfaction in the experimental group and a decrease in the control group (team leaders not involved in the virtual mirroring process). With regard to the individual communication indicators, we find that customer satisfaction is higher when employees are more responsive, use a simpler language, are embedded in less centralized communication networks, and show more stable leadership patterns.

Purpose-This paper examines the innovative capabilities of biotech start-ups in relation to geographic proximity and knowledge sharing interaction in the R&D network of a major hightech cluster. Design/methodology/approach-This study compares longitudinal informal communication networks of researchers at biotech start-ups with company patent applications in subsequent years. For a year, senior R&D staff members from over 70 biotech firms located in the Boston Biotech cluster were polled and communication information about interaction with peers, universities, and big pharmaceutical companies was collected, as well as their geo-location tags. Findings-Location influences the amount of communication between firms, but not their innovation success. Rather, what matters is communication intensity and recollection by others. In particular, there is evidence that rotating leadership-changing between a more active and passive communication style-is a predictor of innovative performance. Practical implications-Expensive real-estate investments can be replaced by maintaining social ties. A more dynamic communication style and more diverse social ties are beneficial to innovation. Originality/value-Compared to earlier work that has shown a connection between location, network, and firm performance, this paper offers a more differentiated view; including a novel measure of communication style, using a unique dataset, and providing new insights for firms who want to shape their communication patterns to improve innovation, independently of their location.

In this study we propose a method based on e-mail social network analysis to compare the communication behavior of managers who voluntarily quit their job and managers who decide to stay. Collecting 18 months of e-mail, we analyzed the communication behavior of 866 managers, out of which 111 left a large global service company. We compared differences in communication patterns by computing social network metrics, such as betweenness and closeness centrality, and content analysis indicators, such as emotionality and complexity of the language used. To study the emergence of managers' disengagement, we made a distinction based on the period of e-mail data examined. We observed communications during months 5 and 4 before managers left, and found significant variations in both their network structure and use of language. Results indicate that on average managers who quit had lower closeness centrality and less engaged conversations. In addition, managers who chose to quit tended to shift their communication behavior starting from 5 months before leaving, by increasing their degree and closeness centrality, as well as their oscillations in betweenness centrality and the number of "nudges" they need to send to peers before getting an answer.

This study looks for signals of economic awareness on online social media and tests their significance in economic predictions. The study analyses, over a period of 2 years, the relationship between the West Texas Intermediate daily crude oil price and multiple predictors extracted from Twitter; Google Trends; Wikipedia; and the Global Data on Events, Location and Tone (GDELT) database. Semantic analysis is applied to study the sentiment, emotionality and complexity of the language used. Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) models are used to make predictions and to confirm the value of the study variables. Results show that the combined analysis of the four media platforms carries valuable information in making financial forecasting. Twitter language complexity, GDELT number of articles and Wikipedia page reads have the highest predictive power. This study also allows a comparison of the different fore-sighting abilities of each platform, in terms of how many days ahead a platform can predict a price movement before it happens. In comparison with previous work, more media sources and more dimensions of the interaction and of the language used are combined in a joint analysis.

Forecasting tourism demand has important implications for both policy makers and companies operating in the tourism industry. In this research, we applied methods and tools of social network and semantic analysis to study user-generated content retrieved from online communities which interacted on the TripAdvisor travel forum. We analyzed the forums of 7 major European capital cities, over a period of 10 years, collecting more than 2,660,000 posts, written by about 147,000 users. We present a new methodology of analysis of tourism-related big data and a set of variables which could be integrated into traditional forecasting models. We implemented Factor Augmented Autoregressive and Bridge models with social network and semantic variables which often lead to a better forecasting performance than univariate models and models based on Google Trend data. Forum language complexity and the centralization of the communication network -i.e. the presence of eminent contributors -were the variables that contributed more to the forecasting of international airport arrivals.

Prior research has emphasised the importance of informal advice networks for knowledge sharing and peer learning. We use Social Network Analysis to detect individuals who play a strategic role in advice networks. Even if roles have been extensively described, how to identify people within them is still an open issue. Furthermore, we investigate whether an association between key players and the big five personality traits exists, by means of nonparametric statistics. To achieve this, we present a case study which involves roughly 180 university students. We found 21 of them playing a key role. Results give evidence of significant associations between key positions and Conscientiousness, Neuroticism and Agreeableness; whereas no evidence was found for a relationship with Extraversion or Openness to Experience. Consistently, personality emerges as a relevant indicator for predicting people who are more likely to play a strategic role, even when connection patterns are unknown.