International overview on the legal framework for highly automated vehicles

The evolution of Autonomous and automated technologies during the last decades has been constant and maintained. All of us can remember an old film, in which they shown us a driverless car, and we thought it was just an unreal object born of filmmakers imagination. However, nowadays Highly Automated Vehicles are a reality, even not in our daily lives. Hardly a day we don’t have news about Tesla launching a new model or Google showing the new features of their autonomous car. But don’t have to travel far away from our borders. Here in Europe we also can find different companies trying, with more or less success depending on with, not to be lagged behind in this race. But today their biggest problem is not only the liability of their innovative technology, but also the legal framework for Highly Automated Vehicles. As a quick summary, in only a few countries they have testing licenses, which not allow them to freely drive, and to the contrary most nearly ban their use. The next milestone in autonomous driving is to build and homogeneous, safe and global legal framework. With this in mind, this paper presents an international overview on the legal framework for Highly Automated Vehicles. We also present de different issues that such technologies have to face to and which they have to overcome in the next years to be a real and daily technology

The use of social media by exporting B2B SMEs, implications for performance

Previously held under moratorium from 1st December 2016 until 1st December 2021.The evolution of social media enables the creation of virtual customer environments where online communications have impacted increasingly on the marketing environment. As such, 21st century firms need to consider the many opportunities that social media present. Given the scant empirical evaluation of social media use in the SMEs Business-to-Business (B2B) context, this thesis aims to “empirically investigate SMEs B2B firms’ actual use of social media and how it impacts their export performance”. This study contributes to the emerging SMEs B2B digital marketing literature by determining, firstly, the factors that affect SMEs B2B firms using social media and, secondly, the mechanism through which SMEs B2B firms can potentially benefits from using social media in their exporting efforts. A number of hypotheses were developed building on the available literature. These hypotheses were examined using data from a sample of 277 British firms from different industries. Structural equation modelling was employed to test the hypotheses. The results suggest that the usage of social media is affected significantly by perceived ease of use; perceived relative advantage; and subjective norms. Additionally, both the firm’s training and innovativeness enhance the relationship between subjective norms and social media use. The results reveal, also, that the social media use influences export performance indirectly through the quality of international business contacts; understanding customers’ views and preferences; and understanding competition in different markets. However, the link between social media use and export performance is not indirectly influenced by the number of international business contacts and brand awareness. Furthermore, customer engagement enhances the relationships between social media use and the aforementioned factors through which social media use indirectly influences export performance. Cultural adaptation enhances, also, the relationships between understanding customers’ views and preferences;understanding competition in different markets; and export performance. Important implications for how SMEs B2B firms may benefit best from using social media for their exporting efforts and future research are derived from the findings.The evolution of social media enables the creation of virtual customer environments where online communications have impacted increasingly on the marketing environment. As such, 21st century firms need to consider the many opportunities that social media present. Given the scant empirical evaluation of social media use in the SMEs Business-to-Business (B2B) context, this thesis aims to “empirically investigate SMEs B2B firms’ actual use of social media and how it impacts their export performance”. This study contributes to the emerging SMEs B2B digital marketing literature by determining, firstly, the factors that affect SMEs B2B firms using social media and, secondly, the mechanism through which SMEs B2B firms can potentially benefits from using social media in their exporting efforts. A number of hypotheses were developed building on the available literature. These hypotheses were examined using data from a sample of 277 British firms from different industries. Structural equation modelling was employed to test the hypotheses. The results suggest that the usage of social media is affected significantly by perceived ease of use; perceived relative advantage; and subjective norms. Additionally, both the firm’s training and innovativeness enhance the relationship between subjective norms and social media use. The results reveal, also, that the social media use influences export performance indirectly through the quality of international business contacts; understanding customers’ views and preferences; and understanding competition in different markets. However, the link between social media use and export performance is not indirectly influenced by the number of international business contacts and brand awareness. Furthermore, customer engagement enhances the relationships between social media use and the aforementioned factors through which social media use indirectly influences export performance. Cultural adaptation enhances, also, the relationships between understanding customers’ views and preferences;understanding competition in different markets; and export performance. Important implications for how SMEs B2B firms may benefit best from using social media for their exporting efforts and future research are derived from the findings

Social Media Analytics and Information Privacy Decisions: Impact of User Intimate Knowledge and Co-ownership Perceptions

Social media analytics has been recognized as a distinct research field in the analytics subdomain that is developed by processing social media content to generate important business knowledge. Understanding the factors that influence privacy decisions around its use is important as it is often perceived to be opaque and mismanaged. Social media users have been reported to have low intimate knowledge and co-ownership perception of social media analytics and its information privacy decisions. This deficiency leads them to perceive privacy violations if firms make privacy decisions that conflict with their expectations. Such perceived privacy violations often lead to business disruptions caused by user rebellions, regulatory interventions, firm reputation damage, and other business continuity threats. Existing research had developed theoretical frameworks for multi-level information privacy management and called for empirical testing of which constructs would increase user self-efficacy in negotiating with firms for joint social media analytics decision making. A response to this call was studied by measuring the constructs in the literature that lead to normative social media analytics and its information privacy decisions. The study model was developed by combining the relevant constructs from the theory of psychological ownership in organizations and the theory of multilevel information privacy. From psychological ownership theory, the impact that intimate knowledge had on co-ownership perception of social media analytics was added. From the theory of multi-level information privacy, the impact of co-ownership perception on the antecedents of information privacy decisions: the social identity assumed, and information privacy norms used were examined. In addition, the moderating role of the cost and benefits components of the privacy calculus on the relationship between information privacy norms and expected information privacy decisions was measured. A quantitative research approach was used to measure these factors. A web-based survey was developed using survey items obtained from prior studies that measured these constructs with only minor wording changes made. A pilot-study of 34 participants was conducted to test and finalize the instrument. The survey was distributed to adult social media users in the United States of America on a crowdsourcing marketplace using a commercial online survey service. 372 responses were accepted and analyzed. The partial least squares structural equation modeling method was used to assess the model and analyze the data using the Smart partial least squares 3 statistical software package. An increase in intimate knowledge of social media analytics led to higher co-ownership perception among social media users. Higher levels of co-ownership perception led to higher expectation of adoption of a salient social identity and higher expected information privacy norms. In addition, higher levels of expectation of social information privacy norm use led to normative privacy decisions. Higher levels of benefit estimation in the privacy calculus negatively moderated the relationship between social norms and privacy decision making. Co-ownership perception did not have a significant effect on the cost estimation in social media analytics privacy calculus. Similarly, the cost estimation in the privacy calculus did not have a significant effect on the relationship between information privacy norm adoption and the expectation of a normative information privacy decision. The findings of the study are a notable information systems literature contribution in both theory and practice. The study is one of the few to further develop multilevel information privacy theory by adding the intimate knowledge construct. The study model is a contribution to literature since its one of first to combine and validate elements of psychological ownership in organization theory to the theory of multilevel information privacy in order to understand what social media users expect when social media analytics information privacy decisions are made. The study also contributes by suggesting approaches practitioners can use to collaboratively manage their social media analytics information privacy decisions which was previously perceived to be opaque and under examined. Practical suggestions social media firms could use to decrease negative user affectations and engender deeper information privacy collaboration with users as they seek benefit from social media analytics were offered

Automatic Extraction and Assessment of Entities from the Web

The search for information about entities, such as people or movies, plays an increasingly important role on the Web. This information is still scattered across many Web pages, making it more time consuming for a user to find all relevant information about an entity. This thesis describes techniques to extract entities and information about these entities from the Web, such as facts, opinions, questions and answers, interactive multimedia objects, and events. The findings of this thesis are that it is possible to create a large knowledge base automatically using a manually-crafted ontology. The precision of the extracted information was found to be between 75–90 % (facts and entities respectively) after using assessment algorithms. The algorithms from this thesis can be used to create such a knowledge base, which can be used in various research fields, such as question answering, named entity recognition, and information retrieval

An ant-inspired, deniable routing approach in ad hoc question & answer networks

The ubiquity of the Internet facilitates electronic question and answering (Q&A) between real people with ease via community portals and social networking websites. It is a useful service which allows users to appeal to a broad range of answerers. In most cases however, Q&A services produce answers by presenting questions to the general public or associated digital community with little regard for the amount of time users spend examining and answering them. Ultimately, a question may receive large amounts of attention but still not be answered adequately. Several existing pieces of research investigate the reasons why questions do not receive answers on Q&A services and suggest that it may be associated with users being afraid of expressing themselves. Q&A works well for solving information needs, however, it rarely takes into account the privacy requirements of the users who form the service. This thesis was motivated by the need for a more targeted approach towards Q&A by distributing the service across ad hoc networks. The main contribution of this thesis is a novel routing technique and networking environment (distributed Q&A) which balances answer quality and user attention while protecting privacy through plausible deniability. Routing approaches are evaluated experimentally by statistics gained from peer-to-peer network simulations, composed of Q&A users modelled via features extracted from the analysis of a large Yahoo! Answers dataset. Suggestions for future directions to this work are presented from the knowledge gained from our results and conclusion

Machine learning techniques for identification using mobile and social media data

Networked access and mobile devices provide near constant data generation and collection. Users, environments, applications, each generate different types of data; from the voluntarily provided data posted in social networks to data collected by sensors on mobile devices, it is becoming trivial to access big data caches. Processing sufficiently large amounts of data results in inferences that can be characterized as privacy invasive. In order to address privacy risks we must understand the limits of the data exploring relationships between variables and how the user is reflected in them. In this dissertation we look at data collected from social networks and sensors to identify some aspect of the user or their surroundings. In particular, we find that from social media metadata we identify individual user accounts and from the magnetic field readings we identify both the (unique) cellphone device owned by the user and their course-grained location. In each project we collect real-world datasets and apply supervised learning techniques, particularly multi-class classification algorithms to test our hypotheses. We use both leave-one-out cross validation as well as k-fold cross validation to reduce any bias in the results. Throughout the dissertation we find that unprotected data reveals sensitive information about users. Each chapter also contains a discussion about possible obfuscation techniques or countermeasures and their effectiveness with regards to the conclusions we present. Overall our results show that deriving information about users is attainable and, with each of these results, users would have limited if any indication that any type of analysis was taking place

R3C3: Cryptographically secure Censorship Resistant Rendezvous using Cryptocurrencies

Cryptocurrencies and blockchains are set to play a major role in the financial and supply-chain systems. Their presence and acceptance across different geopolitical corridors, including in repressive regimes, have been one of their striking features. In this work, we leverage this popularity for bootstrapping censorship resistant (CR) communication. We formalize the notion of stego-bootstrapping scheme and formally describe the security notions of the scheme in terms of rareness and security against chosen-covertext attacks. We present R3C3, a Cryptographically secure Censorship-Resistant Rendezvous using Cryptocurrencies. R3C3 allows a censored user to interact with a decoder entity outside the censored region, through blockchain transactions as rendezvous, to obtain bootstrapping information such as a CR proxy and its public key. Unlike the usual bootstrapping approaches (e.g., emailing) with heuristic security if any, R3C3 employs public-key steganography over blockchain transactions to ensure cryptographic security, while the blockchain transaction costs may deter the entry-point harvesting attacks. We develop bootstrapping rendezvous over Bitcoin, Zcash, Monero and Ethereum as well as the typical mining process, and analyze their effectivity in terms of cryptocurrency network volume and introduced monetary cost. With its highly cryptographic structure, Zcash is an outright winner for normal users with 1168 byte bandwidth per transaction costing only 0.03 USD as the fee, while mining pool managers have a free, extremely high bandwidth rendezvous when they mine a block

“Deal of the Day” Platforms: What Drives Consumer Loyalty?

“Deal of the Day” (DoD) platforms have quickly become popular by offering savings on local services, products and vacations. For merchants, these platforms represent a new marketing channel to advertise their products and services and attract new customers. DoD platform providers, however, struggle to maintaining a stable market share and profitability, because entry and switching costs are low. To sustain a competitive market position, DoD providers are looking for ways to build a loyal customer base. However, research examining the determinants of user loyalty in this novel context is scarce. To fill this gap, this study employs Grounded Theory methodology to develop a conceptual model of customer loyalty to a DoD provider. In the next step, qualitative insights are enriched and validated using quantitative data from a survey of 202 DoD users. The authors find that customer loyalty is in large part driven by monetary incentives, but can be eroded if impressions from merchant encounters are below expectations. In addition, enhancing the share of deals relevant for consumers, i.e. signal-to-noise ratio, and mitigating perceived risks of a transaction emerge as challenges. Beyond theoretical value, the results offer practical insights into how customer loyalty to a DoD provider can be promoted

Privacy considerations for secure identification in social wireless networks

This thesis focuses on privacy aspects of identification and key exchange schemes for mobile social networks. In particular, we consider identification schemes that combine wide area mobile communication with short range communication such as Bluetooth, WiFi. The goal of the thesis is to identify possible security threats to personal information of users and to define a framework of security and privacy requirements in the context of mobile social networking. The main focus of the work is on security in closed groups and the procedures of secure registration, identification and invitation of users in mobile social networks. The thesis includes an evaluation of the proposed identification and key exchange schemes and a proposal for a series of modifications that augments its privacy-preserving capabilities. The ultimate design provides secure and effective identity management in the context of, and in respect to, the protection of user identity privacy in mobile social networks