Review articles or literature reviews are a critical part of scientific research. While numerous guides on literature reviews exist, these are often limited to the philosophy of review procedures, protocols, and nomenclatures, triggering non-parsimonious reporting and confusion due to overlapping similarities. To address the aforementioned limitations, we adopt a pragmatic approach to demystify and shape the academic practice of conducting literature reviews. We concentrate on the types, focuses, considerations, methods, and contributions of literature reviews as independent, standalone studies. As such, our article serves as an overview that scholars can rely upon to navigate the fundamental elements of literature reviews as standalone and independent studies, without getting entangled in the complexities of review procedures, protocols, and nomenclatures.

The development of the Internet and the implementation of traditional marketing strategies have given rise to the emergence of digital marketing strategies exploited both by SMEs and large companies. These companies combine data sciences with digital marketing strategies to sell products, generate brand awareness, or access new markets. The present study aims to understand the role and use of data science by SMEs in their online marketing performance. The research method used in this study is a systematic literature review. The data were analyzed using multiple correspondence analysis (MCA) in the programming language R. Based on the results, we identify a total of seven state-of-the-art uses of data science in digital marketing used by SMEs in their online marketing strategies that are graphically represented and analyzed. In addition, four future lines of research are proposed and discussed to understand the direction of the next steps that SMEs should take to successfully develop their digital strategies. Finally, the review concludes with a discussion of the theoretical and practical implications of our findings for further research on the influence and use of data sciences in SMEs' online marketing performance.

Purpose: Technological advances in the last decade have caused both business and economic sectors to seek for new ways to adapt their business models to a connected data-centric era. Family businesses have also been forced to leave behind traditional strategies rooted in family stimuli and ties and to adapt their actions in digital environments. In this context, this study aims to identify major online marketing strategies, business models and technology applications developed to date by family firms. Methodology: Upon a systematic literature review, we develop a multiple correspondence analysis (MCA) under the homogeneity analysis of variance by means of alternating least squares (HOMALS) framework programmed in the R language. Based on the results, the analyzed contributions are visually analyzed in clusters.Design/methodology/approach: Upon a systematic literature review, we develop an MCA under the HOMALS framework programmed in the R language. Based on the results, the analyzed contributions are visually analyzed in clusters.Findings: Relevant indicators are identified for the successful development of digital family businesses classified in the following three categories: (1) digital business models, (2) digital marketing techniques and (3) technology applications. The first category consists of four digital business models: mobile marketing, e-commerce, cost per click, cost per mile and cost per acquisition. The second category includes six digital marketing techniques: search marketing (search engine optimization and search engine marketing (SEM) strategies), social media marketing, social ads, social selling, websites and online reputation optimization. Finally, the third category consists of the following aspects: digital innovation, digital tools, innovative marketing, knowledge discovery and online decision making. In addition, five research propositions are developed for further discussion and future research.Originality/value: To the best of our knowledge, this study is the first to cover this research topic applying the emerging programming language R for the development of an MCA under the HOMALS framework.

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.

Digital transformation (DT) and Big Data Analytics Capabilities (BDAC) enable SMEs to adapt to rapidly changing markets, innovate, and maintain relevance in the digital age. This research explores the impact of DT on SME performance through the lens of BDAC and innovation, from a multi-methods approach and applying the dynamic capabilities view. It asserts that simply investing in DT doesn't ensure enhanced performance. Analyzing 183 Spanish SMEs from various sectors, the study highlights the need for creating specific conditions that enable DT to positively impact performance. The integration of PLS-SEM and fsQCA methodologies provides a comprehensive analysis of BDAC as pivotal in optimizing SME performance through DT, emphasizing the necessity of strategic alignment with innovation. This nuanced approach, combining the predictive power of PLS-SEM and the configurational insights of fsQCA, demonstrates that investment in DT alone is insufficient without fostering conditions conducive to innovation. Our empirical insights offer actionable guidance for managers utilizing BDA or contemplating technological investments to elevate firm performance which go in the direction of increasing their innovation capabilities. Additionally, these findings equip policymakers with a nuanced understanding, enabling the design of tailored measures promoting DT in SMEs anchored in the nuances of BDAC and innovation capabilities.