User perception-based quantitative studies of Location Based Services of today and tomorrow

Modern Location Based Services (LBS) are not any more limited to navigation or routing services, but they have flourished in every sphere of life whether it is regular activity tracker or family finder. The continuous advancement of location technologies, such as GNSS and cellular in outdoors and WLAN in indoors, opens new challenges for the LBS providers. Due to the emergence of location-enabled smartphone technologies, the use of location based services and applications has increased remarkably in the last few years. This forces the LBS providers to think beyond the boundaries. Therefore, the analysis of the user needs, behavior, perception and preference becomes one of the key factors and eventually prerequisites for success in this sector. The thesis comprises a survey focusing on LBS from different perspectives, such as localization knowledge, privacy concerns and LBS usage, and an analysis based on the responses from 118 volunteer participants. The analysis begins with the classification of the users with respect to their “technical knowledge in localization”, “privacy concerns” and “LBS usage” based on the survey results, and it continues with the analysis of the correlation and similarity between the user classes. The user classes are compared based on the Mann-Whitney-Wilcoxon, Fligner-Policello and unpaired t-test in terms of preferences similarity. The user perceptions with respect to the cost and feature preferences are also analyzed per user class. The aim of the thesis is to illustrate how the LBS preferences differ among various user classes and how the user classes may correlate. The main findings of the analysis are that the user’s background class has a significant impact on the preferences. Moreover, the high-level knowledge users have similar preferences as the high-level usage users, even though the correlation among the user classes is very low. Another interesting finding of this analysis is that the high-level knowledge users are relatively less willing to pay for LBS applications in comparison to the other user classes. From the privacy-concern based classification, it is observed that most of the users have certain privacy concerns, which represents one of the barriers in the LBS development. Finally, it can be inferred that the statistical analysis and the comparative results justify the empirical user classification derived in this thesis