Investigating the Gen-Z perception regarding data privacy ininteractions with brands in an Artificial Intelligence context
- Felipe Gonçalves
- 2 days ago
- 30 min read
FELIPE GONÇALVES (AUTHOR)
MARIA DO CARMO BARRADAS LEAL, PhD (ADVISOR)
UNL - UNIVERSIDADE NOVA DE LISBOA (INSTITUTION)
Master’s Degree Program in
Data-Driven Marketing
ABSTRACT: The multifaceted relationship between Generation Z (Gen-Z), brands, and Artificial Intelligence (AI) within the contemporary digital landscape has emerged as a significant theme. With the proliferation of AI and the rise of Gen-Z as the first fully digital-native generation, understanding their behavior, preferences, and concerns regarding privacy in the context of AI-driven branding strategies plays a crucial role. For this study, a review of literature on Generation Z was conducted, elucidating its characteristics, birth years, and the societal factors that have shaped its worldview. Furthermore, theoretical definitions of Brand and Artificial Intelligence were explored to illuminate their concepts precisely. Drawing upon theoretical frameworks and a quantitative descriptive Likert scale survey, several research questions were tested, including perceptions of data privacy and Gen-Z's response to AI-driven brand resources. The research collected data from young people in countries such as Brazil, Portugal, Germany, the United States of America, Italy, Spain, France, Ireland, the United Kingdom, and others. Most of the respondents expressed comfort with brands using AI features, however, there is a significant variation between Portuguese and English-speaking respondents in different sectors.
KEYWORDS: Gen Z. Data Privacy. Brands. Artificial Intelligence. Consumers. AI-driven market.
1. INTRODUCTION
With the advent of digital technologies and the increasing reliance on artificial intelligence in various a spects of our lives, concerns about data privacy have become more prevalent (Bartneck et al., 2020). In this context, understanding the perceptions of Generation Z regarding data privacy (Hossain, 2018) in their interactions with brands becomes quite relevant. Furthermore, understanding Gen-Z concerns regarding this topic is crucial for brands to create suitable strategies and gain competitive advantage (Pichler et al., 2021), especially in a transformative landscape driven by Artificial Intelligence advances.
Generation Z, also known as Gen-Z, is a key demographic in the consumer market due to their exceptional digital fluency and tech-savviness (Turner, 2015). They are often referred to as "digital natives" (Talmon, 2019) as they have grown up in a world surrounded by technology and are highly comfortable with using digital platforms and services.
The ever-changing market needs have a direct impact on brands, pushing them to enhance their AI capabilities to provide more personalized and improved customer experiences (Ho & Chow, 2023). This significantly boosts consumer-brand relationships and sets the brand apart from competitors. However, the increased reliance on AI technology raises concerns about data privacy, as it involves the collection and utilization of personal information (Huang, 2023).
As a result of this scenario, as Thomas (2022) states, it is pivotal delve deeper into generation Z concerns regarding the exploration of AI focused on the brands. The paradox of the benefits and disadvantages of this technology play role an important thematic in order to optimize the use of this tool and to confluence with the generation Z data privacy expectations (Cranier, 2022).
The aim of this research is to address how Gen-Z feels regarding this transformative landscape and expand the discussion to provide more information on the potential risks and implications of data privacy when interacting with brands in an artificial intelligence experience. For this purpose, this research gets deeper in concepts as Generation Z, its behavior, brands, artificial intelligence, and data privacy. All those will be explored by the research marketing literature review.
In this sense, the study involves conducting surveys with Gen-Z individuals, and research to gather their perceptions and concerns regarding data privacy in interactions with brands using AI technology. The findings of this research will contribute to a better understanding of Gen-Z's expectations and concerns regarding data privacy, thus providing valuable insights for brands to develop strategies that align with their target audience's preferences and values, ultimately fostering stronger consumer-brand relationships in the AI-driven market.
2. LITERATURE REVIEW
2.1 GENERATION Z
While each human possesses a unique individuality, the quest to draw broader observations and delineate groups of individuals has been a recurrent attempt (Kupperschmidt, 2000). An outcome of this endeavor is encapsulated in the conceptualization of "generations" or in “the theory of generations” (Ensari, 2017). Generations, within this context, denote discernible sets of individuals who share birth years, spatial-temporal dimensions of age (Kupperschmidt, 2000), and the intersection with crucial life events during formative developmental junctures such as economic and technological periods and even similar lifestyles (Ensari, 2017).
In a more profound manner, it is conceivable to state which the term "generation," describes its role as a fundamental factor in shaping historical development (Mannheim, 1952). Much is said about changes in behavior when referring to Generation Z, in short, it is made up of people born in the mid-90s until the end of the 2000s (Cilliers, 2017). In terms of numbers, only in the United States of America there were at least 69 million of people belonging to this generation in 2022 (Korhonen, 2023).
Therefore, generation Z is a term referred to denominate the ones who were born after the millennials (Cilliers, 2017), also known as Generation Y. Besides Gen-Z, they are also known by other names as Digital natives, Digital integrators, Zeds, Zees, Bubble-wrap kids, The new millennials, Screenagers, iGen, Generation M – multitasking, Generation C – Connected Generation, Teens, Tweens, Click'n go kids (Budac, 2014).
This generation is considered to be the first generation to grow up entirely in the digital period, with full access to technology and the internet from an early age. Thus, they are characterized by their ability to adapt quickly to new technologies, as well as a wide range of social media platforms (Turner, 2015). The digital age has not only shaped their technological proficiency but has also influenced their social and psychological development (Malysheva et al., 2019).
Growing up in a time of fast technological advancement has exposed Gen Z to a constant influx of information and even to a fear of missing out (FOMO) because of the overload of content on social media, making them adept at filtering through vast amounts of data (Herawati et al., 2022) and discerning credible sources from unreliable ones and this skill has also fostered their critical thinking capabilities, enabling them to question and challenge societal norms and traditional structures.
According to several authors, the exact age of this generation can vary (Dolot, 2018) and Table 1 illustrates the different ranges.
Table 1 - Z Generation age range according to authors
Age range | Author(s) |
born 1990 or later | Świerkosz-Hołysz (2016, p. 441); Żarczyńska-Dobiesz and Chomątowska (2014, p. 407); Wiktorowicz and Warwas (2016, p. 22); Wojtaszczyk (2013) |
between 1990 and 1999 | Half (2015) |
between 1991 and 2000 | Tulgan (2009, p. 5) |
between 1993 and 2012 | White (2017) |
between 1993 and 2005 | Turner (2013, p. 18) |
after 1995 | Opolska-Bielańska (2016, p. 37); Ensari (2017, p. 53); Dudek (2017, p. 144) |
Source: Dolot (2018).
2.2 THE GEN-Z BEHAVIOR
The main point that differentiates Generation Z from others is that this is the first one that is completely digitally native (Dolot, 2018). Generation Y (millennials), its predecessor, is characterized by its introduction to the online world, that is, a generation that knew the world before the consolidation of digital technologies.
Generation Z is completely the opposite of other generations before, as they are more realistic, and mistrust politicians, companies, and the media. Furthermore, they tend to keep their work and private life separate (Scholz, 2019). They are also highly conscious of privacy and have a more nuanced understanding of online security and the implications of sharing personal information (Alic & Sopic, 2023).
Furthermore, it is notable that Gen Z has been greatly influenced by the COVID-19 pandemic effects (Liu et al, 2021), which has shaped their experiences and behaviors (Haddad et al., 2021). This generation's unique experiences and upbringing in the digital age have greatly shaped their worldview and values and added to it, social media has given them a platform to express themselves, connect with others, and support important causes (Konstantinou & Jones, 2022).
In addition, this is the most recent generation, which observes firsthand the emergence of Artificial Intelligence, including its use for purchases. Given this landscape, it is possible to elaborate on the following research question:
RQ 1: To what extent does Generation Z feel comfortable with brands usingartificial intelligence features?
Regarding consumption patterns, it is possible to observe that people are increasingly discerning when it comes to consuming any type of product or service (Kotler & Keller, 2006).
This behavior is mainly enhanced by Gen-Z which is more concerned with social and sustainable consumption (Kara & Min, 2024). The prevalence of social media has given Gen Z
a space to express themselves, connect with others, and advocate for important causes and
their online activism has brought attention to a myriad of social justice issues, fueling meaningful change and sparking crucial conversations (Turner, 2015). The interconnectedness facilitated by technology has instilled a sense of global citizenship within Gen Z, making them aware of the world's interconnectedness and their role as global citizens (Igielnik, 2020).
On the other hand, that pattern has been gradually increasing since the beginning of the 2000s with the rise of the internet and social media, which has made it possible to access information with greater ease, precision, and agility, according to Kotler and Keller (2006). Nowadays, Generation Z dedicates a substantial amount of time to social media, with approximately 60% using it for at least 5 hours daily (Ahmed, 2019). This led us to investigate
the following research question:
RQ2: How much time, on average, does Generation Z spend on the internet daily?
2.3 BRANDS
In today's consumer-driven world, it is important to understand the difference between a mere product and a strong brand. Briefly, a brand is a name, term, symbol, sign, design or even a combination of all of them driven to recognize the service and products of one seller or a group and differentiate it from other competitors (Keller, 2023).
Marketing and brand are entirely interconnected pieces that form the bottom of contemporary business strategies. To that extent, according to Kotler and Keller (2019), marketing is a process of social construction through which individuals and groups seek to fulfill their needs and desires through the creation, offering and free exchange of valuable products and services with other people.
Therefore, a brand, as suggested by Keller (2018), is not merely a label but a strategic marketing asset. It serves as a channel through which the core values of a product or service are talking to their consumers. Deeper than that, a brand can be considered a promise of value to consumers (Kotler & Armstrong, 2017).
It means that a brand is not limited to being only an identifier of a product or service, but an emotional connection to its consumers or even an experience. It is not just about tangible characteristics, brands have become cultural symbols that communicate meanings and values (Kapferer, 2009). According to Kapferer (2009), brands establish significant emotional connections with consumers.
Another relevant definition that convergencies to the previous ones is that while a product refers to a tangible item that is manufactured, marketed, and sold to fulfill a specific need or want of the consumer, a brand goes beyond the physical attributes of the product and encompasses the emotions, values, and overall perception associated with it. A brand represents the reputation and image that a company has built over time through consistent messaging, quality, and customer experience (Herskovitz & Crystal, 2010). Table 2 demonstrates in topics the main differences between a brand and a product:
Table 2.2 - Brand Versus Product
Brand | Product |
Has characteristics that differentiate it in some authentic way from other products designed to satisfy the same need | Anything available in the market for use or consumption, that may satisfy a need |
Differentiated based on: - Packaging - Services provided (experience) - Customer advice - Financing - Delivery arrangements - Warehousing -Consumer perception | Categorized into five levels namely: - Core benefit level - Generic product level - Expected product level - Augmented product level -Potential product level |
Source: Adapted from Keller (2021)
Regarding the consumer decision-making process, the brand plays a crucial role. A strong brand influences consumer perceptions, preferences, and choices (Aaker, 2019). The brand acts as a hub for the qualities and attributes associated with a product, collaborating with the decision-making process. From goods and services, nowadays brands are in different natures (Veloutsou & Guzman, 2017) and they are valuable intangible assets for companies (Keller, 2021).
Furthermore, consumers often develop loyalty towards certain brands, trusting their quality and reliability (Yeh et al., 2014). They identify with the brand's values and feel a sense of connection and affiliation. This loyalty and emotional connection are not easily achieved with just a product (Keller, 1993).
2.4 ARTIFICIAL INTELLIGENCE
Artificial Intelligence (AI) is reshaping the technology we know (Russel, 2010) and it is a consequence of the search for the solution of issues in our life associated with techniques (Lozano, 2019).
According to the pioneers on this subject Russel and Norvig (2010), Artificial Intelligence is the capability of machines to execute tasks requiring human intelligence, including problem-solving, learning, perception, and language understanding. This foundational perspective provides the broad framework below for definitions according to different authors and experts on the subject. Table 3 illustrates different definitions of Artificial Intelligence.
Table 2.3 - Some definitions of Artificial Intelligence on different perspectives
Thinking Humanly | Thinking Rationally |
“The exciting new effort to make computers think . . . machines with minds, in the full and literal sense” (Haugeland, 1985). “[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning . . .” (Bellman, 1978) | “Iterative nature of AI algorithms, showcasing the ability to learn patterns and adapt to diverse datasets.” (Goodfellow, Bengio, Courville 2016 “The study of mental faculties through the use of computational models” (Charniak and McDermott, 1985). “The study of the computations that make it possible to perceive, reason, and act” (Winston, 1992) . |
Acting Humanly | Acting Rationally |
“It is not a replacement for human capabilities but as a tool that enhances human potential.” (Brynjolfsson, McAfee, 2017) “A tool capable of understanding, interpreting, and replicating human cognitive functions” (Kurzweil, 2013) “The study of how to make computers do things at which at the moment people are better.” (Rich and Knight, 1991) | “Computational Intelligence is the study of the design of intelligent agents.” (Poole et al., 1998) “AI ...is concerned with intelligent behavior in artifacts.” (Nilsson, 1998) “AI agents learn to make rational decisions through interactions with the environment.” (Sutton and Barton, 2018) |
Source: Russell and Norvig (2010).
2.4.1 THE CURRENT USE OF AI
Gradually, Artificial Intelligence is becoming more present in our daily (Marcus & David, 2019) and this landscape is being accelerated by Gen-Z (Vitezić & Perić, 2021). From the healthcare sector to the language, it is possible to notice the exponential development and capability of IA in several applications, systems, brands and even in human experiences.
The widespread use of artificial intelligence is transforming various industries and sectors, including branding and marketing. It is possible to observe which artificial intelligence is revolutionizing the way brands understand and engage with their customers (Kumar et al., 2019).
By analyzing huge volumes of data and using advanced algorithms, AI-powered systems can identify patterns and trends in consumer behavior (Huang & Rust, 2020), preferences, and purchase history. This enables brands to personalize their marketing strategies, adapting their messaging and offerings to each customer. In addition to that, the use of AI in marketing allows brands to deliver more targeted and relevant content, increasing the chances of customer engagement and conversion (G, P., 2023).
In the field of content marketing, AI plays a crucial role in making processes more effective (Bernazzani, 2017), since AI-powered tools are being used by brands to curate content, optimize search engine optimization, and enhance email marketing campaigns (Savaram, 2018). For instance, by using AI-powered chatbots and personal assistants (McLean et. al, 2021), brands can offer an instant response and solutions to customer queries, improving customer satisfaction and loyalty.
2.4.2 PRIVACY RISKS WHEN USING AI
It is important to acknowledge that the increasing reliance on artificial intelligence by brands also raises valid concerns about privacy and security (Elliott & Soifer, 2022). With AI systems collecting and analyzing vast amounts of data about individual consumers, there is a potential risk of privacy breaches and unauthorized use of personal information (Mishra et al., 2022). The use of AI in personalized marketing strategies also raises questions about the ethical implications (Thamik & Wu, 2022).
For example, the use of AI in healthcare has raised significant concerns about the privacy and the ethics AI in patient data (Alder, 2023). Access to patient medical data is often central to the use of AI in healthcare (Malek & Jain, 2022), as the exchange of medical information between patients, physicians, and the care team through AI products increases, protecting an individual's information and privacy becomes even more relevant.
According to that, it is possible to make the following research question:
RQ3 – How do individuals perceive the access and exchange of patient medical data through AI products?
One potential privacy risk when using AI is the collection and storage of large amounts of personal data since AI systems rely on machine learning algorithms that require access to vast amounts of data to provide accurate predictions and insights (Rhem, 2023). However, the collection and storage of such large datasets raise concerns about the security and privacy of this information, since companies and clients are potential targets of cybercriminals (D’adamo et al., 2021).
Another privacy risk associated with AI is the potential for data breaches and unauthorized access (Polemi, Praça, Kioskli, & Becue, 2024). As AI systems handle and process sensitive personal data, they become attractive targets for malicious actors who seek to exploit vulnerabilities to gain unauthorized access to the data (Murdoch, 2021).
Furthermore, AI systems are susceptible to attacks such as data poisoning and model inversion attacks, where adversaries manipulate the training data to introduce biases or extract sensitive information from the model (Biggio & Roli, 2018). These privacy risks can have serious consequences, as they can lead to the disclosure of confidential information, identity theft, and other forms of harm to individuals.
Additionally, the use of AI introduces the possibility of algorithmic bias and discrimination (Mehrabi et al., 2021). This occurs when AI algorithms perpetuate and amplify existing biases present in the data used for training. For example, if the training data used to train an AI system is biased against certain demographic groups, the system may make decisions or predictions that unfairly disadvantage those groups (Rhem, 2023).
Moreover, there is a concern regarding the transparency and explainability of AI systems (Duran & Jongsma, 2021) as AI systems become more complex and intricate, it becomes challenging to understand how they arrive at their decisions or predictions. This lack of transparency and explainability can be concerning in terms of privacy, since individuals may not have control or knowledge over how their data is being used and analyzed by AI systems (Rahwan et al., 2019).
Given the characteristics associated with Generation Z (Vitezić & Perić, 2021) and the growing potential of artificial intelligence models, it is possible to assume that this generation has a certain distrust of security and privacy issues linked to AI. In contrast, according to Steijn et al. (2016), the issue of privacy could be influenced by the lack of clarity around the term itself. This means that differences in concerns about privacy between younger and older individuals are partly influenced by their differing conceptions of privacy.
Going deeper on this subject, for instance, the use of AI-powered chatbots for customer support raise concerns about the security of sensitive information shared by customers of different profiles and backgrounds during their interactions with these systems, since there is a risk of data breaches and unauthorized access to personal and financial information (The Economic Times News, 2023). Having said that, it is possible to develop the following research questions:
RQ4: What level of security do Generation Z individuals feel when being assisted by a chatbot?
RQ4A: What is the perception of Generation Z individuals regarding the security measures implemented in AI-powered chatbots for customer service?
RQ4B: What is Generation Z's stance on data sharing when being assisted by chatbots?
2.4.3 ARTIFICIAL INTELLIGENCE APPLICATIONS IN VARIOUS SECTORS
In recent years, Artificial Intelligence has demonstrated remarkable capabilities across various domains, revolutionizing the way we interact with technology and enhancing efficiency in numerous tasks. One prominent area where AI excels is Natural Language Processing (NLP) and Language Understanding (Jurafsky & Martin, 2020). AI algorithms exhibit proficiency in comprehending and generating human language, exemplified by virtual assistants like Siri, Alexa, and Google Assistant.
Additionally, AI showcases its prowess in Image and Speech Recognition (LeCun et al, 2015). Through sophisticated algorithms, AI can discern patterns in images and speech, powering applications such as Google Lens and Amazon Go.
Furthermore, Machine Learning, a subset of AI, plays a pivotal role in healthcare, aiding diagnostics, treatment plans, and drug discovery (Topol, 2019). Companies like Siemens, Healthineers, Tempus, Zebra Medical Vision, Path IA, and Google Health leverage AI algorithms to enhance medical practices. Regarding this topic, it is pivotal to investigate the following research question:
RQ5: What is the comfort level of Generation Z individuals regarding an AI tool diagnosing their health?
Moreover, AI algorithms excel in providing smart suggestions, as seen in platforms like Spotify and Netflix (Gomez-Uribe & Hunt, 2021). This statement leads us to the following research question:
RQ5A: What does Generation Z think about streaming services predicting their behavior?
In the sector of transportation, AI powers Autonomous Vehicles (Thrun & Schwartz, 2006). Self-driving cars, facilitated by machine learning algorithms, offer a glimpse into the future of transportation, with notable examples including Waymo (Alphabet Inc), Tesla’s Autopilot, Uber ATG, and Cruise (General Motors).
Finally, AI also contributes to enhancing security in the financial sector through Fraud Detection (Baesens, Van Vlasselaer, & Verbeke, 2015). Companies such as Feedzai, Forter, ThetaRay, and Simility (A PayPal Service) utilize AI algorithms to detect and prevent fraudulent activities, safeguarding financial transactions.
3. METHODOLOGY
This research utilized a descriptive statistics method to explore how Generation Z perceives data privacy when engaging with brands in the context of Artificial Intelligence. This is a quantitative study, which is an effective method for analyzing various subjects. Quantitative research methods are particularly advantageous for examining trends, making comparisons, and generalizing results (Queirós, Faria, & Almeida, 2017).
3.1 THE RESEARCH INSTRUMENT
The study involves creating and distributing a questionnaire to collect relevant data. As detailed in Appendix B, a well-structured questionnaire containing eighteen questions was made to gather numerical information about Gen-Z's opinions and concerns regarding data privacy during brand interactions with AI. The survey included a Likert scale from 1 to 5, where 1 indicates total disagreement and 5 indicates total agreement. All the questions were developed to answer the research questions, which were based on a literature review and supported by inputs from industry experts.
Before the formal distribution of the survey, a pilot test was carried out using a small group of participants from the intended target. This aimed to evaluate the clarity, relevance, and suitability of the questionnaire's questions. Input from the pre-testing contributors was utilized to improve and complete the questionnaire.
For the data collection, the survey was disseminated digitally through Google Forms, and it was created two versions of the questionnaire (English and Portuguese). The English one, it was sent to individuals from United States of America (USA), Germany, France, Italy, Spain, United Kingdom (UK), Ireland among others. The Portuguese one it was developed in order to target individuals majority from Brazil (80,5%) and Portugal (15,9%). The total figure of respondents was 125, including 90 in the Portuguese survey and 35 in the English one. This variant of countries was utilized to guarantee inclusive representation across various geographic areas.
To obtain precise answers, the survey included a Likert scale from 1 to 5, where 1 indicates total disagreement, 2 disagreement, 3 neutrality, 4 agreement and 5 indicates total agreement.
3.2 SAMPLE CHARACTERIZATION
Below, in Table, it is possible to verify the gender characterization of the research sample.
Table 3.1 - Gender Distribution of Portuguese-Speaking Respondents
Portuguese survey | |
Male | Female |
55,6% | 44,4% |
Source: author (2024).
Table 3.2 - Gender Distribution of English-Speaking Respondents
English survey | |
Male | Female |
54,3% | 45,7% |
Source: author(2024)
In the questionnaire conducted in Portuguese, out of 90 respondents, 82 could continue answering as the third question concerned age, and only individuals between 18 and 29 years old were eligible to complete the survey. In the questionnaire conducted in English, all respondents were within this age range, as verified in the following graphs.
The survey was conducted among respondents with a high level of educational qualifications, with the majority having completed higher education. This indicates that the participants possess substantial knowledge and expertise, making their insights particularly valuable for this study. The emphasis on higher educational attainment ensures that the data collected is informed by a well-educated demographic, likely to have a nuanced understanding of the issues being investigated.
This demographic characteristic is critical for the reliability and depth of the findings, as it reflects a population that is not only knowledgeable but also likely engaged with and informed about contemporary issues such as data privacy and brand interactions with AI. The following graphs illustrate these points.
Furthermore, respondents were enlisted via social media platforms (Facebook, Instagram, and WhatsApp groups), email invites, and online communities commonly visited by members of Generation Z.
After the data collection process, data validation checks were conducted to ensure the reliability and accuracy of the collected information and responses with incomplete or inconsistent information were excluded from the analysis.
4. RESULTS AND DISCUSSION
After collecting the data, we could thoroughly analyze and discuss the research questions that form the core of this study. To enhance clarity and organization, we have decided to present the results by distinctly separating the responses obtained from the questionnaires conducted in Portuguese from those gathered from the questionnaires conducted in English. This approach allows for a more detailed and structured comparison between the different language groups, ensuring that any language-specific nuances or variations in responses can be accurately identified and examined.
4.1 GEN Z AND AI FEATURES
The crucial point of this research is to identify the extent to which individuals of Gen Z are comfortable with brands using AI features. Regarding this topic, the majority of respondents in the Portuguese survey (52.5%) feel comfortable (either "agree" or "strongly agree") with brands using AI resources as illustrated in Figure 5. A significant portion of respondents (29.3%) have a neutral stance, indicating neither comfort nor discomfort, and a smaller group (18.3%) feels uncomfortable (either "disagree" or "totally disagree").
This distribution suggests a general trend towards comfort with AI applications by brands, even if a notable portion remains neutral or uncomfortable, indicating potential areas for further investigation or improvement in AI application strategies by brands. The figures observed in the English respondents' sample are similar as shown in Figure 6.
Going further on the research question, we gave an example of the use of AI by a brand and the results indicate that most respondents in the Portuguese questionnaire (62.2%) feel comfortable (either "agree" or "strongly agree") with AI-based recommendations to improve their shopping experience as it is showed in Figure 7. A significant portion of respondents (19.5%) have a neutral stance, indicating neither comfort nor discomfort. In contrast, a smaller group (18.3%) feels uncomfortable (either "disagree" or "strongly disagree"). This suggests that while AI-based recommendations are generally well-received, there is room for improvement in addressing the concerns of those who are neutral or uncomfortable.
It can indicate that brands aiming to use AI in their marketing strategies should consider these variations in user comfort across different nationalities to tailor their approaches effectively. The graphs below illustrate the data collected.
On the other hand, the English survey, exhibit a higher level of discomfort (40%) comparing to the Portuguese (18.3%) one as it is illustrated in Figure 8. These differences might be influenced by cultural factors, familiarity with AI technologies, or previous experiences with AI-based recommendations.
Despite differences in sample sizes, the overall trends are equivalent, with most respondents leaning towards comfort, a significant portion remaining neutral, and a smaller group feeling discomfort. This consistency suggests that the overall sentiment across different regions and nationalities is relatively stable.
4.2 AVERAGE TIME PER DAY SPENT ON THE INTERNET
To answer research question 2 regarding the time spent by the Gen-Z daily on the internet, the data indicates that Portuguese speakers tend to spend more time online, with a large proportion spending more than 7 hours daily. This indicates a higher level of internet engagement among this group.
In contrast, English speakers have a more balanced distribution of internet usage, with the majority spending 4-5 hours daily, and fewer individuals spending more than 7 hours. Figures 9 and 10 illustrate the results.
4.3 MEDICAL DATA THROUGH AI PRODUCTS
Regarding research question 3 “How do individuals perceive the access and exchange of patient medical data through AI products”, the data presented in Figure 11, illustrates the distribution of responses in the Portuguese survey indicates a significant level of concern among respondents regarding the privacy of their medical data when analyzed by AI systems.
Nearly half of the respondents express concern, while a smaller group is either neutral or unconcerned. According to this data, it is possible to highlight the importance of addressing privacy issues and building trust in AI systems handling sensitive medical data.
Furthermore, a slightly higher percentage of English-speaking respondents (51,4%), as is portrayed in Figure 12, express concern about the privacy of their medical data when analyzed by AI systems compared to Portuguese-speaking (45,1%) respondents.
Going deeper into the topic, as is possible to verify in Figures 13 and 14, we asked the respondents if they believe that the healthcare brands that use AI resources have adequate measures to protect their data and 45,2% including the ones who have chosen level 1 and 2 of the Portuguese-speaking respondents and 42.9% of the English-speaking respondents expressed distrust in the protective measures of healthcare brands using AI tools. This suggests a considerable level of skepticism or concern about the reliability of AI in this context.
For healthcare brands and AI developers, it is crucial to address these concerns through transparent communication about the accuracy, safety, and benefits of AI diagnostic tools, as well as ensuring robust privacy measures to build trust among users (Fig. 11 to 14).
4.4 GEN Z & CHATBOTS
The relationship between Gen-Z and chatbots is the main topic of our RQ 4. The level of security of Gen-Z individuals when assisted by chatbots can vary significantly according to their country. As it is illustrated the Figures on this topic, the English-speaking respondents exhibit a higher level of discomfort compared to Portuguese-speaking respondents, 48,5% and 37,8%, respectively.
On the other hand, 36,6% of the Portuguese-speaking respondents feel comfortable providing their data using chatbots against only 28,6% compared to the English speaking respondents. These insights suggest that English-speaking respondents are generally more skeptical or uncomfortable with providing their data to chatbots.
Regarding the security measures implemented in AI-powered chatbots for customer service, as illustrated in Figure 15 and Figure 16, it is notable that both English-speaking and Portuguese-speaking respondents exhibit similar levels of distrust, with slightly lower percentages among English speakers. Although, English-speaking respondents (37,1%) tend to be more neutral compared to Portuguese-speaking respondents (26,8%).
In contrast, Portuguese-speaking respondents (29,3%) exhibit higher levels of trust compared to English speaking respondents (20%). These data suggest that while there is a significant level of skepticism in both groups, Portuguese-speaking respondents are slightly more trusting overall.
This indicates a need for brands to enhance transparency and communication about their access control practices, especially when targeting English-speaking users, to build trust and confidence in their chatbot services.
4.5 GEN Z & AI IN HEALTHCARE
When the Portuguese and English-speaking respondents are asked whether they would feel comfortable if a healthcare brand, such as a renowned hospital network, used AI resources to make a health diagnosis, the data illustrated in Figures 19 and 20 indicates that a significant level of discomfort among both groups of respondents regarding the use of AI tools for health diagnoses, with nearly half of the sample expressing discomfort.
A notable portion of respondents is neutral (28% in the Portuguese survey and 25,7% in the English survey), indicating uncertainty or lack of strong opinion on the matter. Only about a quarter of the respondents feel comfortable with AI-based health diagnoses.
4.6 GEN Z & STREAMING BRANDS
The general skepticism towards AI in healthcare can lead us to consider how Generation Z perceives the use of AI in other areas, such as entertainment. This brings us to the next research question: What does Generation Z think about streaming services predicting their behavior?
When inquiring Gen-Z about their concerns regarding data privacy when streaming brands employ behavior prediction algorithms, we observed that 46.6% of Portuguese speaking respondents and 42.9% of English-speaking respondents are not particularly concerned as illustrated in Figures 21 and 22. These figures account for those who selected levels 1 and 2. Conversely, 37.1% of English-speaking respondents expressed significant concern, compared to 26.8% of Portuguese-speaking respondents. These figures account for those who selected levels 4 and 5.
Discussing personalized recommendations from streaming brands, 68.3% of Portuguese-speaking respondents believe that such recommendations enhance their entertainment experience, as depicted in Figure 23.
In contrast, English-speaking respondents tend to be more moderate, with 51.5% expressing that personalized recommendations improve their entertainment experience, as shown in Figure 24. These figuresrepresent those who selected levels 4 and 5. All these data suggest that personalized recommendations are generally well-received by both groups of respondents, however, they are more positively viewed by Portuguese-speaking respondents.
When respondents were asked whether they believed that streaming brands should be more transparent about how they use data to predict viewing behavior, the results revealed similarly high levels of concern across both groups: 77.2% of English-speaking respondents and 73.2% of Portuguese-speaking respondents.
The percentages represent those who selected levels 4 and 5, as shown in Figures 25 and 26. These findings indicate a clear demand for streaming services to be more open and transparent about their data collection, processing, and utilization practices in predicting user behavior.
4.7 GENERATION Z AND INTERNET SAFETY
Finally, we have asked to the respondents the level of concerns regarding data leaks or security breaches related to personal information provided to streaming brands (Figures 19 and 20). The English-speaking respondents exhibit a higher level of concern (68.6%) compared to Portuguese-speaking respondents (56.1%) and these percentages represent those who selected levels 4 and 5, as illustrated in Figures 27 and 28.
Furthermore, Portuguese-speaking respondents tend to be more neutral (23.2%) compared to English-speaking respondents (14.3%), which might indicate a lack of awareness about the subject in Brazil and Portugal compared to Gen-Z individuals from Germany, the United States of America, France, Italy, Spain, Ireland, United Kingdom (UK), among others.
5. CONCLUSIONS
While Generation Z generally shows a positive inclination towards AI features in brand interactions, significant nuances and concerns must be addressed to fully leverage AI's potential. Brands must prioritize transparency, cultural tailoring, education, and robust privacy measures to build trust and effectively engage with this digital-native generation. By understanding and addressing the specific needs and concerns of Gen Z, brands can enhance their AI strategies and foster stronger, more trustful relationships with this critical demographic.
Some key findings must be considered:
Comfort with AI Features:
Most of respondents in both language groups expressed comfort with brands using AI features. However, a significant portion remained neutral or uncomfortable, highlighting areas for brands to address through education and reassurance about AI technologies.
AI-Based Recommendations:
They were generally well-received, especially among Portuguese-speaking respondents. English-speaking respondents exhibited higher levels of discomfort, suggesting cultural influences and varying familiarity with AI that brands should consider when tailoring their AI strategies.
Internet Usage Patterns:
Portuguese speakers tend to spend more time online compared to their English speaking counterparts, indicating higher internet engagement in Portuguese-speaking regions. This suggests that brands should emphasize digital and online marketing strategies in these areas.
Perceptions of AI in Healthcare:
Concerns about the privacy of medical data analyzed by AI systems were prevalent among both groups, with English-speaking respondents showing slightly higher levels of concern. This underscores the need for healthcare brands to prioritize data privacy and transparent communication to build trust.
Security and Chatbots:
There was a notable discomfort with AI-powered chatbots, particularly among English-speaking respondents. This indicates a need for enhanced transparency and security measures to build confidence in chatbot services, especially among English-speaking users.
AI in Entertainment:
Personalized recommendations from streaming services were positively viewed, more so by Portuguese-speaking respondents. However, there was a strong demand for transparency regarding data usage, with high levels of concern about data leaks and security breaches across both groups. Based on these findings, the following recommendations are proposed for brands aiming to integrate AI into their customer engagement strategies.
Table 5.1 - Recommendations based on this research findings
Enhance Transparency | Tailor AI Strategies | Educate and reassure |
Brands should communicate how AI technologies are used and ensure robust data privacy measures. | Consider cultural and regional differences when implementing AI features. | Provide education and reassurances about the benefits and safety of AI technologies to alleviate discomfort and build trust among users. |
Privacy First | Digital Engagement | Address Security Concerns |
In healthcare and other sensitive areas, emphasize strong privacy protections and transparent practices to address prevalent concerns about data security. | For regions with higher internet engagement, such as Portuguese-speaking countries, focus on digital marketing to capture the attention of Gen Z. | Implement and communicate robust security measures for AI powered services, particularly chatbots, to alleviate skepticism and build user confidence. |
Source: author (2024).
6. LIMITATIONS OF THE STUDY
This study presents some important limitations that should be considered when interpreting the results. Firstly, the sample size used is relatively small (125 respondents), which may limit the generalizability of the findings to the general population. Additionally, the sample was not stratified, meaning there was no analysis to ensure that different subgroups of the population were proportionally represented. This can potentially limit the ability to make accurate inferences about specific groups.
Another limiting aspect of the study is the type of statistical analysis employed. We opted to use descriptive analyses, which, although useful for providing an overview of the data, do not allow for causal inferences or the evaluation of more complex relationships between variables. This may partially limit the depth of the conclusions we can draw from the collected data.
Furthermore, this study covered specific sectors such as health and entertainment, failing to address other sectors that could provide a more diversified view of the subject matter.
7. FURTHER RESEARCH
The conclusions of this study highlight the need for future research that utilizes larger and stratified samples and applies more robust statistical analyses. This is especially important in demographic terms, considering larger samples of English-speaking respondents.
Additionally, the health and entertainment sectors can be further explored, along with the inclusion of other relevant sectors such as finance and education. In this way, we can obtain a more comprehensive and detailed understanding of the application of AI resources in brands from different sectors, minimizing biases and increasing the validity of the results.
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