Consumer Intentions to Accept and Use Mobile Advertising

Mobilne naprave se vse bolj vključujejo v življenja ljudi. S tem so oglaševalci pridobili nove priložnosti za komuniciranje s svojimi strankami, podjetja pa možnosti za oglaševanje svojih produktov in storitev na personaliziran način. Mobilno oglaševanje je tako pritegnilo pozornost oglaševalcev in raziskovalcev. Kljub dejstvu, da potrošniki že dolgo za svojimi mobilnimi napravami preživijo veliko časa, pa tržniki še vedno iščejo načine, kako bi v celoti izkoristili vse prednosti tega medija. Raziskovalci so v zadnjem času izvedli več študij na področju presoje in napovedovanja odnosa potrošnikov do mobilnega oglaševanja, toda nekatere nejasnosti še vedno obstajajo. Slednje še posebej velja za Pakistan, kjer je bilo opravljenih zelo malo raziskav na področju mobilnega oglaševanja. Pričujoča disertacija zato teoretsko obravnava presojo namer potrošnikov o sprejetju in uporabi mobilnega oglaševanja in vključuje empirično študijo v Pakistanu. Tema je relativno mlada in še premalo raziskana, zato prinaša številne priložnosti za razvoj novih in razširitev obstoječih teorij. Pričujoča disertacija želi razširiti obstoječo teorijo, to je Združeno teorijo o tehnološkem sprejetju in uporabi. Za to je bil oblikovan mešan raziskovalni pristop (ang. Mixed Methods Approach), v katerem se rezultati ustvarijo s kvantitativno in kvalitativno analizo podatkov, z namenom identifikacije ključnih dejavnikov, ki lahko vplivajo na namero potrošnikov o sprejetju mobilnega oglaševanja. Cilj disertacije je premostiti vrzel v znanju na področju presoje namere potrošnikov in njihovega odnosa do uporabe mobilnega oglaševanja. Teoretski in empirični rezultati so izpostavili nekaj ključnih dejavnikov (kot na primer pričakovan trud, pričakovani učinek, zaznan užitek, zaznana nadležnost in personalizacija), ki vplivajo na namero potrošnikov o sprejetju mobilnega oglaševanja. Poleg tega so rezultati razkrili tudi odnos med potrošnikovo namero sprejetja in sprejetjem mobilnega oglaševanja.The world has witnessed a high level of mobile device penetration in people’s lives. This has not only created an opportunity for marketers to communicate with their customers but has also enabled companies to advertise their products and services in personalized way. Due to this, mobile advertising has got the attention of both marketers and researchers. However, marketers have been struggling to fully exploit the benefits of this medium, even though consumers spend a considerable amount of their time on mobile devices. Recently, researchers have conducted various studies into ways to assess and predict consumer attitudes towards mobile advertising, but there is still ambiguity in this area. This is particularly the case in Pakistan, where very little research has been conducted in the area of mobile advertising. The current thesis is targeted towards assessing consumer intentions to accept and use mobile advertising. This under-researched topic is still in its infancy and there are lots of opportunities to not only come up with new theories but also extend the existing ones. The current thesis attempts to extend the existing unified theory of acceptance and use of technology (UTAUT). To do this, a mixed methodological approach was adopted where both quantitative and qualitative analysis techniques were applied to the data in order to uncover crucial factors that can affect consumer intentions to accept and use mobile advertising. The goal of the current thesis is to bridge the knowledge gap by assessing consumer intentions and actual behaviour towards mobile advertising. The results highlight some of the crucial factors (such as effort expectancy, performance expectancy, perceived enjoyment, perceived irritation, and personalization) that can affect consumer intentions to accept and use mobile advertising. In addition to this, the results also highlight the relationship between consumer intentions to accept and use mobile advertising

Automata-based Model Counting String Constraint Solver for Vulnerability Analysis

Most common vulnerabilities in modern software applications are due to errors in string manipulation code. String constraint solvers are essential components of program analysis techniques for detecting and repairing vulnerabilities that are due to string manipulation errors. In this dissertation, we present an automata-based string constraint solver for vulnerability analysis of string manipulating programs.Given a string constraint, we generate an automaton that accepts all solutions that satisfy the constraint. Our string constraint solver can also map linear arithmetic constraints to automata in order to handle constraints on string lengths. By integrating our string constraint solver to a symbolic execution tool, we can check for string manipulation errors in programs. Recently, quantitative and probabilistic program analyses techniques have been proposed which require counting the number of solutions to string constraints. We extend our string constraint solver with model counting capability based on the observation that, using an automata-based constraint representation, model counting reduces to path counting, which can be solved precisely. Our approach is parameterized in the sense that, we do notassume a finite domain size during automata construction, resulting in a potentially infinite set of solutions, and our model counting approach works for arbitrarily large bounds.We have implemented our approach in a tool called ABC (Automata-Based model Counter) using a constraint language that is compatible with the SMTLIB language specification used by satifiabilty-modula-theories solvers. This SMTLIB interface facilitates integration of our constraint solver with existing symbolic execution tools. We demonstrate the effectiveness of ABC on a large set of string constraints extracted from real-world web applications.We also present automata-based testing techniques for string manipulating programs. A vulnerability signature is a characterization of all user inputs that can be used to exploit a vulnerability. Automata-based static string analysis techniques allow automated computation of vulnerability signatures represented as automata. Given a vulnerability signature represented as an automaton, we present algorithms for test case generation based on state, transition, and path coverage. These automaticallygenerated test cases can be used to test applications that are not analyzable statically, and to discover attack strings that demonstrate how the vulnerabilities can be exploited. We experimentally comparedifferent coverage criteria and demonstrate the effectiveness of our test generation approach

Visual configuration of mobile privacy policies

Mobile applications often require access to private user informtion, such as the user or device ID, the location or the contact list. Usage of such data varies across different applications. A notable example is advertising. For contextual advertising, some applications release precise data, such as the user’s exact address, while other applications release only the user’s country. Another dimension is the user. Some users are more privacy demanding than others. Existing solutions for privacy enforcement are neither app- nor user- sensitive, instead performing general tracking of private data into release points like the Internet. The main contribution of this paper is in refining privacy enforcement by letting the user configure privacy preferences through a visual interface that captures the application’s screens enriched with privacy-relevant information. We demonstrate the efficacy of our approach w.r.t. advertising and analytics, which are the main (third-party) consumers of private user information. We have implemented our approach for Android as the VisiDroid system. We demonstrate VisiDroid’s efficacy via both quantitative and qualitative experiments involving top-popular Google Play apps. Our experiments include objective metrics, such as the average number of configuration actions per app, as well as a user study to validate the usability of VisiDroid