Setting Privacy "by Default" in Social IoT: Theorizing the Challenges and Directions in Big Data Research

[EN] The social Internet of Things (SIoT) shares large amounts of data that are then processed by other Internet of Thing (IoT) devices, which results in the generation, collection, and treatment of databases to be analyzed afterwards with Big Data techniques. This paradigm has given rise to users' concerns about their privacy, particularly with regard to whether users have to use a smart handling (self-establishment and self-management) in order to correctly install the SIoT, ensuring the privacy of the SIot-generated content and data. In this context, the present study aims to identify and explore the main perspectives that define user privacy in the SIoT; our ultimate goal is to accumulate new knowledge on the adoption and use of the concept of privacy "by default" in the scientific literature. To this end, we undertake a literature review of the main contributions on the topic of privacy in SIoT and Big Data processing. Based on the results, we formulate the following five areas of application of SIoT, including 29 key points relative to the concept of privacy "by default": (i) SIoT data collection and privacy; (ii) SIoT security; (iii) threats for SIoT devices; (iv) SIoT devices mandatory functions; and (v) SIoT and Big Data processing and analytics. In addition, we outline six research propositions and discuss six challenges for the SIoT industry. The results are theorized for the future development of research on SIoT privacy by "default" and Big Data processing.In gratitude to the Ministry of Science, Innovation and Uni-versities and the European Regional Development Fund: RTI2018-096295-B-C22.Saura, JR.; Ribeiro-Soriano, D.; Palacios Marqués, D. (2021). Setting Privacy "by Default" in Social IoT: Theorizing the Challenges and Directions in Big Data Research. Big Data Research. 25:1-12. https://doi.org/10.1016/j.bdr.2021.100245S1122

Setting B2B digital marketing in artificial intelligence-based CRMs: A review and directions for future research

[EN] The new business challenges in the B2B sector are determined by connected ecosystems, where data-driven decision making is crucial for successful strategies. At the same time, the use of digital marketing as a communication and sales channel has led to the need and use of Customer Relationship Management (CRM) systems to correctly manage company information. The understanding of B2B traditional Marketing strategies that use CRMs that work with Artificial Intelligence (AI) has been studied, however, research focused on the understanding and application of these technologies in B2B digital marketing is scarce. To cover this gap in the literature, this study develops a literature review on the main academic contributions in this area. To visualize the outcomes of the literature review, the results are then analyzed using a statistical approach known as Multiple Correspondence Analysis (MCA) under the homogeneity analysis of variance by means of alternating least squares (HOMALS) framework programmed in the R language. The research results classify the types of CRMs and their typologies and explore the main techniques and uses of AI-based CRMs in B2B digital marketing. In addition, a discussion, directions and propositions for future research are presented.In gratitude to the Ministry of Science, Innovation and Universities and the European Regional Development Fund: RTI2018-096295-BC22.Saura, JR.; Ribeiro-Soriano, D.; Palacios Marqués, D. (2021). Setting B2B digital marketing in artificial intelligence-based CRMs: A review and directions for future research. Industrial Marketing Management. 98:161-178. https://doi.org/10.1016/j.indmarman.2021.08.006S1611789

From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets

[EN] In recent years, strategies focused on data-driven innovation (DDI) have led to the emergence and development of new products and business models in the digital market. However, these advances have given rise to the development of sophisticated strategies for data management, predicting user behavior, or analyzing their actions. Accordingly, the large-scale analysis of user-generated data (UGD) has led to the emergence of user privacy concerns about how companies manage user data. Although there are some studies on data security, privacy protection, and data-driven strategies, a systematic review on the subject that would focus on both UGD and DDI as main concepts is lacking. Therefore, the present study aims to provide a comprehensive understanding of the main challenges related to user privacy that affect DDI. The methodology used in the present study unfolds in the following three phases; (i) a systematic literature review (SLR); (ii) in-depth interviews framed in the perspectives of UGD and DDI on user privacy concerns, and finally, (iii) topic-modeling using a Latent Dirichlet allocation (LDA) model to extract insights related to the object of study. Based on the results, we identify 14 topics related to the study of DDI and UGD strategies. In addition, 14 future research questions and 7 research propositions are presented that should be consider for the study of UGD, DDI and user privacy in digital markets. The paper concludes with an important discussion regarding the role of user privacy in DDI in digital markets.Saura, JR.; Ribeiro-Soriano, D.; Palacios Marqués, D. (2021). From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets. International Journal of Information Management. 60:1-13. https://doi.org/10.1016/j.ijinfomgt.2021.102331S1136

Editorial: Innovative behavior in entrepreneurship: Analyzing new perspectives and challenges

In recent years, the relationship between behavior and innovation has come to be globally accepted as a prerequisite of business success (Li et al., 2022). Innovative behavior is seen as an introduction to the application and development of new ideas, processes, initiatives, or actions by qualified professionals (RoŽman and Štrukelj, 2021). Developed either individually or collectively, innovative behavior drives creativity and is directly linked to a multitude of behaviors that lead to the generation of new ideas, initiatives, and value for new companies’ products and services (Barbosa et al., 2022).info:eu-repo/semantics/publishedVersio

Consumer behavior in the digital age

In recent decades, the Internet, evolving technologies, and social media have led to the evolution of consumer behavior. The changes in customer behavior driven by digital developments provide many opportunities and challenges that businesses also need to deal with online. The better companies know about the behavior of their customers, the easier they can engage with them using strategies such as content marketing, User Experience (UX), influencers marketing, User-Generated Content (UGC), or Electronic Word of Mouth (eWOM). These strategies are essential to get more sales and to develop businesses online, as such strategies increase the engagement with users and influence their behavior. This Special Edition of JOSD focuses on the analysis of consumer behavior in the digital age and, by doing so, contributes to extant knowledge about digital marketing strategies, online consumer behavior, and new digital business models such as mobile applications or shared economy.FCT- Foundation for Science and TechnologyPortuguese Foundation for Science and Technology [UIDB/04020/2020]info:eu-repo/semantics/publishedVersio

USERS ACCEPTANCE OF LOCATION-BASED MARKETING APPS IN TOURISM SECTOR: AN EXPLORATORY ANALYSIS

Mobile devices are the most used technology tools to access the Internet since they allow access from anywhere. This possibility has prompted companies to focus, to a greater extent, strategies based on geolocation marketing. Geolocation is a tool through which people or places can be located and have very diverse functionalities and applications. Location-Based Services (LBS) allow businesses to incorporate these types of tools into their digital marketing strategies. Social networks based on location services (LBSNS or Location-Based Social Network System) allow businesses to access information on the location of customers in real time. The present study provides more information on LBS and geolocation marketing, also known as geomarketing, analyzing the utility and benefits that this tool has to digital marketing and social networks and the importance of its technological adoption. To achieve this objective, a thorough review of technology adoption literature was carried out and a series of interviews were made with experts and professionals in its two aspects: digital marketing and information technologies. The results show the way in the tourism sector, these tools are managed, the means in which they are active, the LBS systems used, the utility and benefits they perceive from them, as well as the importance and efforts that they dedicate to them. This study reaches relevant conclusions for tourism professionals interested in incorporating LBS and geomarketing strategies into their businesses, as well as researchers interested in the behavior in Location-Based Services

Clearance of ctDNA in triple-negative and HER2-positive breast cancer patients during neoadjuvant treatment is correlated with pathologic complete response

Breast cancer; Liquid biopsy; Neoadjuvant therapyCàncer de mama; Biòpsia líquida; Teràpia neoadjuvantCáncer de mama; Biopsia líquida; Terapia neoadyuvanteBackground: Although the standard of care is to perform surgery of primary breast cancer (BC) after neoadjuvant chemotherapy (NAC), for certain patients achieving clinical complete response (cCR) and pathologic complete response (pCR), omission of surgical treatment may be an option. Levels of circulating tumor DNA (ctDNA) during and after therapy could identify patients achieving minimal residual disease. In this study, we evaluated whether ctDNA clearance during NAC could be a correlate to effective response in human epidermal growth factor receptor 2 positive (HER2+) and triple-negative (TN) BC patients. Methods: A prospective study was conducted to identify patient-specific PIK3CA and TP53 mutations in tissue using next-generation sequencing, which could then be used to track the presence/absence of mutations prior to, during, and following NAC using Sysmex SafeSEQ technology. All patients underwent a surgical excision after NAC, and pCR was assessed. Results: A total of 29 TN and HER2+ BC patients were examined and 20 that carried mutations in the PIK3CA and/or TP53 genes were recruited. Overall, 19 of these 20 patients harbored at least one tumor-specific mutation in their plasma at baseline. After NAC, 15 patients (75.0%) achieved pCR according to the histopathologic evaluation of the surgical specimen, and 15 patients (75.0%) had a cCR; 18 of 20 patients (90.0%) had concordant pCR and cCR. The status of ‘no mutation detected’ (NMD) following NAC in cCR patients correctly identified the pCR in 14 of 15 patients (93.33%), as well as correctly ruled out pCR in three patients, with an accuracy of 89.47%. During the 12-month follow-up after surgery, 40 plasma samples collected from 15 patients all showed no detectable ctDNA (NMD), and no patient recurred. Conclusion: These findings prompt further research of the value of ctDNA for non-invasive prediction of clinical/pathological response, raising the possibility of sparing surgery following NAC in selected BC patients.The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by a grant from Sysmex Inostics, Inc. The sponsor coordinated data collection from study centers, and funded the statistical analysis and medical writing assistance

Comparing Data-Driven Methods for Extracting Knowledge from User Generated Content

This study aimed to compare two techniques of business knowledge extraction for the identification of insights related to the improvement of digital marketing strategies on a sample of 15,731 tweets. The sample was extracted from user generated content (UGC) from Twitter using two methods based on knowledge extraction techniques for business. In Method 1, an algorithm to detect communities in complex networks was applied; this algorithm, in which we applied data visualization techniques for complex networks analysis, used the modularity of nodes to discover topics. In Method 2, a three-phase process was developed for knowledge extraction that included the application of a latent Dirichlet allocation (LDA) model, a sentiment analysis (SA) that works with machine learning, and a data text mining (DTM) analysis technique. Finally, we compared the results of each of the two techniques to see whether or not the results yielded by these two methods regarding the analysis of companies’ digital marketing strategies were mutually complementary

A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis

The global development of the Internet, which has enabled the analysis of large amounts of data and the services linked to their use, has led companies to modify their business strategies in search of new ways to increase marketing productivity and profitability. Many strategies are based on business intelligence (BI) and marketing intelligence (MI) that make it possible to extract profitable knowledge and insights from large amounts of data generated by company customers in digital environments. In this context, the present study proposes a three-step research methodology based on data text mining (DTM). In further research, this methodology can be used for business intelligence analysis (BIA) strategies to analyze user generated content (UGC) in social networks and on digital platforms. The proposed methodology unfolds in the following three stages. First, a Latent Dirichlet Allocation (LDA) model that determines the database topic is used. Second, a sentiment analysis (SA) is proposed. This SA is applied to the LDA results to divide the topics identified in the sample into three sentiments. Thirdly, textual analysis (TA) with data text mining techniques is applied on the topics in each sentiment. The proposed methodology offers important advances in data text mining in terms of accuracy, reliability and insight generation for both researchers and practitioners seeking to improve the BIA processes in business and other sectors

Does User Generated Content Characterize Millennials’ Generation Behavior? Discussing the Relation between SNS and Open Innovation

The millennial generation plays a leading role in today’s connected world in which exists a confluence of numerous technologies and the internet in science, economy and innovation. This study aimed to identify the key factors that characterize the millennial generation within the online chatter on Twitter using an innovative approach. To this end, we analyzed the user generated content (UGC) in the social network (SNS) Twitter using a three-steps knowledge-based method for information management. In order to develop this method, we first used latent Dirichlet allocation (LDA), a state-of-the-art thematic modeling tool that works with Python, to analyze topics in our database. The data were collected by extracting tweets with the hashtag #Millennial, #Millennials and #MillennialGeneration on Twitter (n = 35,401 tweets). Secondly, sentiment analysis with a support vector machine (SVM) algorithm was also developed using machine-learning. Applying this method to the LDA results resulted in the categorization of the topics into those that contained negative, positive and neutral sentiments. Thirdly, in order to gather the final results, data text mining techniques were used. The negative factors that characterize the behavior of this generation are depression, loneliness and real-world relationship. The positive factors are body image, self-expression, travelers and digital life and the neutral factors are self-identity and anxiety. Practical implications can be used by public actors, companies or policy makers that are focused on the millennial generation as a target. The study has important theoretical applications as the topics discovered can be used to test quantitative models based on the findings and insights extracted from the UGC sample