Data-driven strategies in operation management: mining user-generated content in Twitter.

In recent years, the business ecosystem has focused on understanding new ways of automating, collecting, and analyzing data in order to improve products and business models. These actions allow operations management to improve prediction, value creation, optimization, and automatization. In this study, we develop a novel methodology based on data-mining techniques and apply it to identify insights regarding the characteristics of new business models in operations management. The data analyzed in the present study are user-generated content from Twitter. The results are validated using the methods based on Computer-Aided Text Analysis. Specifically, a sentimental analysis with TextBlob on which experiments are performed using vector classifier, multinomial naïve Bayes, logistic regression, and random forest classifier is used. Then, a Latent Dirichlet Allocation is applied to separate the sample into topics based on sentiments to calculate keyness and p-value. Finally, these results are analyzed with a textual analysis developed in Python. Based on the results, we identify 8 topics, of which 5 are positive (Automation, Data, Forecasting, Mobile accessibility and Employee experiences), 1 topic is negative (Intelligence Security), and 2 topics are neutral (Operational CRM, Digital teams). The paper concludes with a discussion of the main characteristics of the business models in the OM sector that use DDI. In addition, we formulate 26 research questions to be explored in future studies.

The role of sense of community in harnessing the wisdom of crowds and creating collaborative knowledge during the COVID-19 pandemic.

This study investigates the role of sense of community in harnessing the wisdom of the crowd and creating collaborative knowledge during the COVID-19 pandemic. It also explores the impact of collaborative knowledge creation on the perceived value of social media crowdsourcing in such crises. PLS-SEM was used to analyze the data and test the research model. The results show that sense of community has a significant role in harnessing the wisdom of the crowd and creating collaborative knowledge. The results confirm a significant impact of sense of community, the wisdom of the crowd, and collaborative knowledge creation on the perceived value of social media crowdsourcing in responding to the COVID-19 crisis.

The Role of Emotions and Motivations in Sport Organizations.

In sport organizations, a stance aimed at creating a positive emotional and social climate may be necessary. This study examines athletes' individual psychosocial factors that are linked to sports practice and sports performance. These factors include individual motivation, emotions, and beliefs. The main objective is to create a hierarchy of emotional and motivational factors that sport organizations can use to increase athletes' commitment. The Analytic Hierarchy Process (AHP) is used to do so. This method enables analysis of priorities and criteria to support decision-making. The results show that motivation, defined here as the drive that leads individuals to develop plans to achieve their goals by balancing short- and long-term goals, and emotion regulation, defined as the capacity to be aware of and manage one's emotions to reach a balanced emotional state, are the most important criteria to generate this commitment within sport organizations.