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The purpose of this paper is to show that virtual mirroring-based learning allows members of an organization to see how they communicate with others in a visual way, by applying principles of “social quantum physics” (empathy, entanglement, reflect, reboot), to become better communicators and build a shared “DNA” within their organization.

E-mail based social network analysis creates virtual maps of communication – social landscapes – of organizations, similar to Google Maps, which creates geographical maps of a person’s surroundings.

Applying virtual mirroring-based learning at various mulitnational firms has significantly increased their organizational efficiency and performance, for instance increasing customer satisfaction by 18 per cent in a large services organization, increasing retention, making sales forecasts, and improving call center employee satisfaction.

To address concerns of individual privacy, the guiding principle is to give individual information to the individual and provide aggregated anonymized information to management.

Virtual mirroring-based learning offers a unique way of creating collective awareness within an organization by empowering the individual to take corrective action aligned with collective action, and improves their own communication behavior through analyzing and visualizing their e-mail archive in novel ways, while giving strategic insight to management and improving organizational culture.

The purpose of this paper is to identify the factors influencing the growth of healthcare virtual communities of practice (VCoPs) through a seven-year longitudinal study conducted using metrics from social-network and semantic analysis. By studying online communication along the three dimensions of social interactions (connectivity, interactivity and language use), the authors aim to provide VCoP managers with valuable insights to improve the success of their communities.

Communications over a period of seven years (April 2008 to April 2015) and between 14,000 members of 16 different healthcare VCoPs coexisting on the same web platform were analysed. 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.

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 centralised, leaders are more dynamic (they rotate more) and the language used in the posts is less complex.

The available data set included one Web platform and a limited number of control variables. To consolidate the findings of the present study, the experiment should be replicated on other healthcare VCoPs.

The study provides useful recommendations for setting up and nurturing the growth of professional communities, considering, at the same time, the interaction patterns among the 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, that can significantly influence community growth, such as rotating leadership.

This pilot study aimed to evaluate the impact of the big five personality traits on user engagement with chatbots at the early stages of artificial intelligence (AI) adoption.

The pilot study involved 62 participants segmented into two groups to measure variables including engagement duration, task performance and future AI usage intentions.

The findings advocate for the incorporation of psychological principles into technology design to facilitate more tailored and efficient human–AI collaboration.

This pilot research study highlights the relationship between the big five personality traits and chatbot usage and provides valuable insights for customizing chatbot development to align with specific user characteristics. This will serve to enhance both user satisfaction and task productivity.

The purpose of the research is to conduct an exploratory investigation of the material handling activities of an Italian logistics hub. Wearable sensors and other smart tools were used for collecting human and environmental features during working activities. These factors were correlated with workers' performance and well-being.

Human and environmental factors play an important role in operations management activities since they significantly influence employees' performance, well-being and safety. Surprisingly, empirical studies about the impact of such aspects on logistics operations are still very limited. Trying to fill this gap, the research empirically explores human and environmental factors affecting the performance of logistics workers exploiting smart tools.

Results suggest that human attitudes, interactions, emotions and environmental conditions remarkably influence workers' performance and well-being, however, showing different relationships depending on individual characteristics of each worker.

The authors' research opens up new avenues for profiling employees and adopting an individualized human resource management, providing managers with an operational system capable to potentially check and improve workers' well-being and performance.

The originality of the study comes from the in-depth exploration of human and environmental factors using body-worn sensors during work activities, by recording individual, collaborative and environmental data in real-time. To the best of the authors' knowledge, the current paper is the first time that such a detailed analysis has been carried out in real-world logistics operations.

This study investigates the relationships between team dynamics and performance in healthcare operations. Specifically, it explores, through wearable sensors, how team coordination mechanisms can influence the likelihood of surgical glitches during routine surgery.

Breast surgeries of a large Italian university hospital were monitored using Sociometric Badges – wearable sensors developed at MIT Media Lab – for collecting objective and systematic measures of individual and group behaviors in real time. Data retrieved were used to analyze team coordination mechanisms, as it evolved in the real settings, and finally to test the research hypotheses.

Findings highlight that a relevant portion of glitches in routine surgery is caused by improper team coordination practices. In particular, results show that the likelihood of glitches decreases when practitioners adopt implicit coordination mechanisms rather than explicit ones. In addition, team cohesion appears to be positively related with the surgical performance.

For the first time, direct, objective and real time measurements of team behaviors have enabled an in-depth evaluation of the team coordination mechanisms in surgery and the impact on surgical glitches. From a methodological perspective, this research also represents an early attempt to investigate coordination behaviors in dynamic and complex operating environments using wearable sensor tools.

Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importance.

Different types of forecasting models and their adaptation to the special circumstances of social media are analyzed and the most representative research conducted up to date is surveyed. Presentations of current research on techniques, methods, and empirical studies aimed at the prediction of future or current events from social media data are provided.

A taxonomy of prediction models is introduced, along with their relative advantages and the particular scenarios where they have been applied to. The main areas of prediction that have attracted research so far are described, and the main contributions made by the papers in this special issue are summarized. Finally, it is argued that statistical models seem to be the most fruitful approach to apply to make predictions from social media data.

This special issue raises important questions to be addressed in the field of social media-based prediction and forecasting, fills some gaps in current research, and outlines future lines of work.