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.

Crowdlending: mapping the core literature and research frontiers

Peer-to-peer (P2P) lending uses two-sided platforms to link borrowers with a crowd of lenders. Despite considerable diversity in crowdlending research, studies in this area typically focus on several common research topics, including information asymmetries, social capital, communication channels, and rating-based models. This young research field is still expanding. However, its importance has increased considerably since 2018. This rise in importance suggests that P2P lending may offer a promising new scientific research field. This paper presents a bibliometric study based on keyword co-occurrence, author and reference co-citations, and bibliographic coupling. The paper thus maps the key features of P2P lending research. Although many of the most cited papers are purely financial, some focus on behavioral finance. The trend in this field is toward innovative finance based on new technologies. The conclusions of this study provide valuable insight for researchers, managers, and policymakers to understand the current and future status of this field. The variables that affect new financial contexts and the strategies that promote technology-based financial environments must be investigated in the future.

A discriminant analysis of high and low-innovative firms: the role of IT, human resources, innovation strategy, intellectual capital and environmental dynamism

Purpose

This paper aims to examine the effect of technological, organizational and environmental factors on the level of innovation outcomes in manufacturing small- and medium-sized enterprises (SMEs).

Design/methodology/approach

Drawing on the technology-organization-environment theory this paper conducts a discriminant analysis of firms’ innovation level based on a data set of manufacturing SMEs.

Findings

The results show that low- and high-innovative firms can be distinguished in terms of information technology (IT) knowledge and infrastructure, commitment-based human resources (HR) selection practices, exploitative innovation and organizational capital.

Practical implications

The study findings support the idea that innovation is a complex phenomenon explained by multiple factors. As a consequence, firms need to devote extra efforts to develop IT knowledge and infrastructure, commitment-based HR selection practices and organizational capital because these are crucial for obtaining greater innovation outcomes. In addition, the identification of exploitative innovation as a strong discriminant variable highlights that the most effective way to be a highly innovative SME is through incremental innovation, which permits the firm to capitalize as much as possible on previous exploratory efforts.

Originality/value

Although many studies have highlighted that innovation is more challenging for SMEs than for their larger counterparts, the vast majority of studies has been conducted in large companies. This paper extends prior literature by analyzing the discriminant variables that may distinguish between low- and high-innovative manufacturing SMEs.

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Controlling the coronavirus pandemic is triggering a cross-border strategy by which national governments attempt to control the spread of the COVID-19 pandemic. A response based on sharing facts about millions of private movements and a call to study information behavior during the global health crisis has been advised worldwide. The present study aims to identify the technologies to control the COVID-19 and future pandemics with massive data collection from users' mobile devices. This research undertakes a Systematic Literature Review (SLR) of the studies about the currently available methods, strategies, and actions to collect and analyze data from users' mobile devices. In a total of 76 relevant studies, 13 technologies that are classified based on the following aspect of data and data management have been identified: (1) security; (2) destruction; (3) voluntary access; (4) time span; and (5) storage. In addition, in order to understand how these technologies can affect user privacy, 25 data points that these technologies could have access to if installed through mobile applications have been detected. The paper concludes with a discussion of important theoretical and practical implications of preserving user privacy and curbing COVID-19 infections in the global public health emergency situation.

Challenges in the Business Model of Low-Cost Airlines

In recent decades, low-cost airlines have proliferated in the European market offering cheap tickets and increasing popularity. This business model, characterised by cost leadership, has been studied on numerous occasions. The case of the Irish airline Ryanair has presented different challenges over the last few years in relation to its stakeholders, who are shaping the sustainability of the current era of air travel. This business model should be adapted to the current demands of the market, such as corporate social responsibility or care for the environment. The functioning of low-cost airlines regarding the use they make of ERP management systems is also analysed. They aim to balance their cost strategy with the development of internal resources and capabilities for the company's long-term strategy. A major current challenge for low-cost airlines is the implementation of ERP management systems to make strategies oriented to the customer, sustainability, and corporate social responsibility.

The effect of online social networks and competency-based management on innovation capability

Purpose

The purpose of this paper is to explore the effect of online social networks and competency-based management on innovation capability.

Design/methodology/approach

The paper is theory-confirming. Theoretical relationships were tested using an empirical study of 289 firms from the Spanish biotechnology and telecommunications industries.

Findings

Results confirm that online social network use for internal cognitive processes (e.g. reading, searching and storing information) and external cognitive processes (e.g. sharing and co-creating knowledge) positively affects knowledge transfer. This knowledge helps firms to achieve superior competency in R&D to succeed in innovation programs.

Research Limitations/implications

All survey respondents were from Spain, which may limit the generalizability of findings. A longitudinal approach was not used. However, doing so would make it possible to explore time lags between online social network use, competency-based management and innovation.

Practical Implications

This paper highlights the potential as well as the limitations of online social networks and competency-based management in promoting innovation capability. Businesses must consciously manage the assimilation and use of online social networks to benefit from them.

Originality/value

The study contributes to the literature by identifying effects on innovation capability at the meso-level (i.e. online social networks). Findings highlight the need for a shift in focus away from collaborating and interacting in online social networks (micro-level) and organizational contexts (macro-level) so as to improve innovation capability.