Introduction: We analyse the privacy policies of 15 private corporations to understand if the data handling practices – data collection, storage, and sharing –described in the policies are ethical or unethical. The data we leave behind when we use the Internet are crucial for corporations. The data provides valuable insights into our lives, thus helping corporations improve targeted marketing campaigns and increase their revenue. Method: Extensive literature review of peer-reviewed articles, written between1993 and 2021, to examine how theoretical perspectives and empirical findings evolved over time; combined with empirical research to analyse the privacy policies and “dark patterns” of 15 companies. The companies were chosen at random and belong to different sectors to give a broader understanding of the current privacy and data handling practices. Analysis: Discourse analysis of the privacy policies to evaluate the type of language used, if it is clear, easy to understand, and if the policy informs users about how their data are collected, shared, and stored. But also, a visual analysis to understand if the company is implementing “dark patterns”. Results: The results indicate that most privacy policies use misleading terms, are not fully transparent about the company’s data handling practices, and often implement “dark patterns” to try to influence the users’ decisions. Conclusion: Most companies have privacy policies available on their websites due to a clear influence from the GDPR legislation, however, there appears to be a conflicting relationship between wanting to comply with the GDPR and wanting to gather as much information as possible.

by Lau Hon-Biu. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 45-49). / ABSTRACT --- p.iii / TABLE OF CONTENTS --- p.iii / LIST OF TABLES --- p.v / LIST OF FIGURES --- p.vi / ACKNOWLEDGEMENTS --- p.vii / Chapter / Chapter I --- OBJECTIVE AND SCOPE OF STUDY --- p.1 / Chapter II --- LITERATURE REVIEW --- p.4 / Ethical Issues in International Marketing --- p.4 / Safeguards for Ethical Behavior --- p.5 / Deontological vs. Teleological Philosophical Approaches --- p.7 / Relativism & Idealism --- p.7 / Individualism-Collectivism (IC) --- p.9 / Use of Scenarios --- p.10 / "Culture, Ethical Problem Perception & Ethical Decision Making" --- p.11 / Chapter III --- THEORETICAL CONSTRUCT OF STUDY --- p.12 / Chapter IV --- METHODOLOHY --- p.15 / Participants --- p.15 / Procedure --- p.15 / Measurements --- p.16 / Dependent Variables --- p.16 / Independent Variables --- p.17 / The shortened version of Individualism- Collectivism scales (INDCOL) --- p.17 / Ethics Position Questionnaire (EPQ) --- p.18 / Data Analysis Methods --- p.19 / Chapter V --- FINDINGS --- p.21 / Reliability --- p.21 / Perceived Ethical Problem --- p.21 / Ethical Decision Making --- p.22 / Categorization of the Subjects --- p.23 / Effects of Origin on Perceived Ethical Problem and Ethical Decision Making --- p.24 / "Effects of Origin on Idealism, Relativism, General Collectivism Index (GCI), Ingroup Solidarity and Social Obligation" --- p.24 / Effects of Cultural Orientation on Perceived Ethical Problem and Ethical Decision Making --- p.25 / Effects of Ethics Position on Perceived Ethical Problem and Ethical Decision Making --- p.26 / Chapter VI --- DISCUSSION --- p.28 / Reliability --- p.28 / Perceived Ethical Problem --- p.28 / Ethical Decision Making --- p.29 / "Effects of Origin on Age, Years of residence and full-time work" --- p.30 / "Effects of Origin on Collectivism, Idealism and Relativism, Perceived Ethical Problem and Ethical Decision Making" --- p.31 / Effects of Cultural Orientation on Perceived Ethical Problem and Ethical Decision Making --- p.32 / Effects of Ethics Position on Perceived Ethical Problem and Ethical Decision Making --- p.32 / Chapter VII --- CONCLUSION --- p.34

This PhD thesis examines the factors that have shaped entrepreneurial cognition and practice in entrepreneurs from within the world’s oldest monotheistic religious community; the Zarathustrian community. Zarathustrianism is the religion that was founded by a Prophet named Zarathustra in approximately 1200 BCE. Marketing and Entrepreneurship have, until quite recently, remained two quite independent scholarly domains. In 2002, Morris et al., provided a definition of Entrepreneurial Marketing as, "an integrative construct for conceptualising marketing in an era of change, complexity, chaos, contradiction, and diminishing resources, and one that will manifest itself differently as companies age and grow. It fuses key aspects of recent developments in marketing thought and practice with those in the entrepreneurship area into one comprehensive construct". Since then, research in this field has grown in significance across the globe. A recent book by Sethna, Jones and Harrigan (2013) presents important theoretical developments with regard to research at the Marketing and Entrepreneurship Interface and which addresses critical issues for businesses, both small and large, from global perspectives, and covers topics such as new venture creation, marketing in Small-to-Medium-Sized Enterprises (SMEs) as well as large companies, renewal of existing businesses facing market challenges, internationalization, innovative cost-effective marketing strategies and practices, along with recent exploration of entrepreneurship theory and entrepreneurial behaviour of individuals and, in organisations. Zarathustrianism has not only been instrumental in shaping nascent civilisation of ancient Iran, but has also wielded a considerable influence on Biblical religions and Greaco-Roman philosophical thought. Zarathustra gave his followers a basic and comprehensive ethical rule to live by, namely that they should think Good Thoughts, speak Good Words and perform Good Deeds (Humata, Hukhta, Hvarshta in the ancient Persian language called Avestan). This PhD thesis explores the impact of these basic tenets – Good Words, Good Thoughts and Good Deeds - on Zarathustrian entrepreneurship. The researcher takes the stance that the realities of the Entrepreneur/Owner-Manager (EOM) are socially constructed, using ‘thoughts, words and deeds’, rather than objectively determined. In doing so, this research is interested in understanding why things are happening to those Zarathustrian EOMs (actors) and how their different experiences eventually shape, nurture and affect the actors’ entrepreneurial behaviour. Thus, throughout this research study, a qualitative research design based on the Carson et al. (2005) perspectives on an ‘integrative multiple mix of methodologies’ is used, but primarily all centred around ethnographic form. The use of narrative theory and life story techniques is further overlaid with the use of the EMICO framework, a qualitative research model developed by Jones and Rowley (2009) as the basis for exploring ‘entrepreneurial marketing and the Zarathustrian entrepreneur’. The findings reveal that whilst the dimensions of the EMICO framework are both usable and valid for Zarathustrian entrepreneurs, when applied to these firms in the context of ‘ethnic’ entrepreneurs, the framework is lacking in two particular areas; Family Support and Religio-Cultural Identity and Influences of business practice. The thesis makes a significant contribution to the EM and ethnic entrepreneurship literature by first of all re-developing and re-naming the framework, 2e(EMICO), and secondly by further extending the knowledge in respect to Zarathustrian entrepreneurship, about which nothing currently exists in the EM literature.

Across three chapters of this doctoral dissertation, I explore how machine learning algorithms can be used to efficiently acquire personal information and responsibly use it in decision making, in marketing and beyond. In the first chapter, I show that machine learning on consumer facial images can reveal a variety of personal information. I provide evidence that such information can be profitably used by marketers. I also investigate the mechanism behind how facial images reveal personal information. In the second chapter, I propose a new self-supervised deep reinforcement learning approach to question prioritization and questionnaire shortening and show it is competitive against benchmark methods. I use the proposed method to show that typical consumer data sets can be reconstructed well based on relatively small select subsets of their columns. The reconstruction quality grows logarithmically in the relative size of the column subset, implying diminishing returns on measurement. Thus, many long questionnaires could be shortened with minimal information loss, increasing the consumer research efficiency and enabling previously impossible multi-scale omnibus studies. In the third chapter, I present a method to speed up ranking under constraints for live ethical content recommendations by predicting, rather than finding exactly, the solution to the underlying time-intensive optimization problem. The approach enables solving larger-than-previously-reported constrained content-ranking problems in real time, within 50 milliseconds, as required to avoid the perception of latency by the users. The approach could also help speed up general assignment and matching tasks.