The role of digitalization in business and management: a systematic literature review

Digitalization is a powerful engine for economic growth in the world. In 2018, digitally transformed firms represented 13.5 billion US dollars of global GDP and, towards the end of 2023, they are expected to represent 53.3 billion US dollars, over half of the general nominal GDP (Statista, Nominal GDP driven by digitally transformed and other enterprises worldwide 2018–2023. https://www.statista.com/statistics/1134766/nominal-gdp-driven-by-digitally-transformed-enterprises/, 2022). The main objective of this study is to provide information (highlighting principal research topics and research agendas) from the literature on state-of-the-art digitalization within firms through a Systematic Literature Review (SLR). In all, 119 review articles on the most mature functional areas of the firm are analyzed: management, marketing, and finance and accounting, published in the WOS over the period 2018-April 2022. In this study, key relevant tendencies are identified in the most mature areas of the firm, which are the impact of digital technologies on the analysis of consumer behavior; digitalization and green innovation within organizations; and blockchain technology applied to financial services. The main contributions of this work are as follows: (1) to provide the most complete and up-to-date review of digitalization from a global perspective, summarizing the current state of knowledge within an integrated framework; (2) to reduce the complexity of digitalization by offering structure and clarity; and (3) to offer links between digitalization and established points of view in the literature on management, marketing, finance, and accounting. The novelty of this paper is centered on a joint analysis of digitalization, digital transformation, and digital technologies, taking into account the most mature functional areas of the firm.

Privacy concerns in social media UGC communities: Understanding user behavior sentiments in complex networks

In a digital ecosystem where large amounts of data related to user actions are generated every day, important concerns have emerged about the collection, management, and analysis of these data and, according, about user privacy. In recent years, users have been accustomed to organizing in and relying on digital communities to support and achieve their goals. In this context, the present study aims to identify the main privacy concerns in user communities on social media, and how these affect users’ online behavior. In order to better understand online communities in social networks, privacy concerns, and their connection to user behavior, we developed an innovative and original methodology that combines elements of machine learning as a technical contribution. First, a complex network visualization algorithm known as ForceAtlas2 was used through the open-source software Gephi to visually identify the nodes that form the main communities belonging to the sample of UGC collected from Twitter. Then, a sentiment analysis was applied with Textblob, an algorithm that works with machine learning on which experiments were developed with support vector classifier (SVC), multinomial naïve Bayes (MNB), logistic regression (LR), random forest, and classifier (RFC) under the theoretical frameworks of computer-aided text analysis (CATA) and natural language processing (NLP). As a result, a total of 11 user communities were identified: the positive protection software and cybersecurity and eCommerce, the negative privacy settings, personal information and social engineering, and the neutral privacy concerns, hacking, false information, impersonation and cookies data. The paper concludes with a discussion of the results and their relation to user behavior in digital environments and an outline valuable and practical insights into some techniques and challenges related to users’ personal data.

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.

Towards a network-based view of effective entrepreneurial ecosystems

We conceptualize entrepreneurial ecosystems as fundamentally reliant on networks and explore how and under what conditions inter-organizational networks lead an entrepreneurial ecosystem to form and evolve. It is widely accepted that entrepreneurial ecosystems possess a variety of symbiotic relationships. Research has focused considerable efforts in refining the structure and content of resources found within these networked relationships. However, merely focusing on actor-level characterizations dilutes the notion that social relationships change and are complex. There has been little conceptual treatment of the behavioral and governance factors that underpin how quality interactions composing an entrepreneurial ecosystem develop and change over time. In response, we provide a longitudinal ethnographic study examining how ecosystems are managed and evolve in their relational configurations and governance at critical junctures. Using mixed methods and data collected over 3 years, we reveal a cyclical process of relational development central to the initiation, development, and maintenance phases of a valuable entrepreneurial ecosystem. We contribute to a conceptualization of effective ecosystems as reliant on networks, we reveal the behavior and governance characteristics at play in the entrepreneurial ecosystem during each phase of its evolution.

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.