thực hiện và người hướng dẫnBỘ GIÁO DỤC VÀ ĐÀO TẠO ĐẠI HỌC KINH TÊ THANH PHO HO CHI MINH BÁO CÁO TỐNG KẾT ĐÈ TÀI NGHIÊN cứu KHOA HỌC THAM GIA XÉT GIÁI THƯỞNG ‘’ NHÀ NGHIÊN CỨU TRẺ UEH ”
OVERVIEW
Reasons to choose a topic
In Vietnam, with the rise in living standards, the purchase has become an essential need in everyone’s life According to the information posted on VNETWORK
(2023), Vietnam has more than 77 million users of social networks and ranks 6th in the group of 30 countries with the potential and attractiveness of investment in the global retail sector.
Gen z generation consumers have become a potential customer for retailers worldwide due to their growing number and dominance in global markets (Tunsakul,
According to Kantar's MONITOR study (2021), 85% of Gen Z consumers in the United States actively engage in shopping on Amazon This generation is characterized by high levels of education, innovation, and technological proficiency, having grown up in a digital era They frequently utilize social networks, mobile devices, and online applications to research products and brands, presenting both opportunities and challenges for businesses aiming to connect with them.
In today's competitive landscape, businesses are shifting from mass marketing to personalized strategies to enhance customer experiences Personalized marketing entails a thorough understanding of individual customer needs and interests, allowing companies to deliver tailored messages and products As the economic, cultural, and industrial hub of Vietnam, Ho Chi Minh City presents a diverse consumer market For businesses aiming to thrive in this city, grasping the preferences of Gen Z and adopting personalized marketing approaches is crucial However, the effectiveness of personalized marketing specifically for Gen Z in Ho Chi Minh City remains underexplored and warrants further investigation.
The concept of "personalization" has garnered significant attention from researchers, including David Jingjun Xu (2006), who explored its impact on user attitudes, and P T Odoom from Ghana (2019), who examined the link between personalized image advertising and purchase intent However, the implications of personalization remain underexplored in Ho Chi Minh City, Vietnam, due to research limitations and a lack of recent studies To address this gap, the research topic "The Influence of Personalized Marketing on Gen Z's Purchase Decisions in Ho Chi Minh City" is proposed, aiming to contribute to the existing body of knowledge in this area.
Research objectives
The purpose of this study is to identify, analyze and clarify how the factors of personalized marketing affect the purchasing decisions of the young consumer group in
Ho Chi Minh City From there, give limitations and suggest ideas for personalized marketing to have a positive impact and promote purchasing.
4- Determine the influence of personalized marketing on factors affecting the purchasing decisions of the young consumer group in Ho Chi Minh City
4- Analyze and clarify the influence of personalized marketing on Gen Z's purchasing decisions in Ho Chi Minh City
4- Propose managerial implications for policies for individuals and businesses in the Marketing industry
Subject and scope of study
4- Research subject: factors that belong to personalized Marketing and its impact on consumer purchasing decisions.
4- Objects: Generation z objects (from 16 to 25 years old) in Ho Chi Minh City, Vietnam.
4- Spatial scope: research topics are carried out on the Ho Chi Minh City area.
4- Time scope: the information and data in the research paper that was searched is within the time frame from 1995 to the present.
Research methodology
The research topic uses a combination of both qualitative and quantitative research methods.
The analytical method employs a 5-level Likert scale (ranging from completely disagree to completely agree) to assess respondents' approval of survey comments SmartPLS statistical software is utilized to analyze key indicators, including quality, observation variables, scale reliability, and both convergence and discrimination validity Additionally, bootstrap analysis in SmartPLS is applied to test the significance of path coefficients and evaluate the relationships between various factors.
THE INFLUENCE OF PERSONALIZED MARKETING ON GEN Z'S
Usage theory
The theory of planned behavior, proposed by Ajzen in 1991, suggests that an individual's intention to engage in specific behaviors can be accurately predicted by their attitudes, subjective norms, and perceived behavioral control These intentions, along with the perception of control, significantly influence actual behavior Additionally, behavioral trends, including social attitudes and personality traits, are crucial in understanding and forecasting human behavior, as highlighted by Campbell (1963) and Sherman & Fazio (1983).
Beliefs about an object are shaped by connecting it to specific attributes, influencing our attitudes toward behaviors (Fishbein & Ajzen, 1975) Each belief links behavior to particular outcomes, leading to varying attitudes based on whether these attributes are viewed positively or negatively Consequently, we tend to endorse behaviors we believe will yield favorable results while developing a negative attitude toward those we associate with undesirable consequences.
An individual's attitude significantly influences their behavior, particularly in shopping Consumers' varying attitudes toward products and brands ultimately determine their purchasing decisions.
The concept of "personalized marketing" dates back to the 1870s and has evolved significantly over time Initially defined by Wind & Rangaswamy (2001) as the adjustment of products or services and the customization of message content, personalization now encompasses the integration of geolocation with customer data Peppers and Rogers (1998) emphasized that personalization involves transforming customer data into actionable insights for effective outreach Additionally, Imhoff et al (2001) highlighted its role in understanding customer needs and delivering tailored advertising messages With advancements in technology, personalization has increasingly been adopted in the online marketing landscape, becoming an essential strategy for marketers (Tran, 2017).
Personalized advertisements from e-commerce companies are defined as the process of creating tailored ads based on previous customer online activities (Tran et al., 2020) These ads offer advantages such as cost-effectiveness and relevance, as they target specific customer groups based on preferences and demographics, making them more informative and trustworthy than traditional advertising (Kim et al., 2001) However, excessive personalization can lead to discomfort and privacy concerns, as consumers may perceive it as an infringement on their privacy and data management (Malheiros et al., 2012; Tucker, 2014) Such privacy violations can result in negative consumer reactions, fostering feelings of avoidance and skepticism towards brands.
Formation of research hypothesis
Previous research has explored various viewpoints on how personalization affects customers For instance, Y.-Q Zhu and J Kanjanamekanant (2020) applied the theory of personalized cognition to examine the delicate balance between privacy and personalization.
Or V Setyani, et al (2019) explored the psychological mechanisms and built the fundamental response of users to personalized advertising on social networks, In this study, the authors wanted to provide an overview of both the positive and negative impact of personalized Marketing on purchase decisions Thus, from the previous solid theoretical foundations, became the premise that contributed to the formation of the following research hypotheses:
With the rapid growth of internet content, consumers often feel overwhelmed by the sheer volume of information available, making it challenging to identify useful patterns Consequently, they frequently navigate through multiple search results before locating the relevant information they need Research by Gao and Koufaris (2006) indicates that users value receiving crucial information to aid in their purchasing decisions.
Advertising primarily aims to disseminate information about specific goods or services (Kim & Han, 2014) Personalized advertising enhances this objective by tailoring content based on user data such as age, gender, and interests This approach not only delivers more relevant and targeted information that aligns with user needs (Chen & Hsieh, 2012) but also minimizes information overload by providing content directly related to individual preferences (Liang, Lai, & Ku, 2006) Ultimately, information gains value when it is pertinent and necessary, making higher levels of ad personalization increasingly beneficial for users.
So, from previous studies, the research hypothesis is formed:
HI Personalized Marketing helps customers receive useful and fast information.
Advertising's entertainment value lies in its ability to satisfy the audience's desire for enjoyment, aesthetics, and emotional release Individual tastes and preferences greatly influence how creativity and aesthetics are perceived, leading to a high level of personalization in advertising This tailored approach enhances consumer engagement by aligning with their unique aesthetic needs Additionally, the human interest factor in advertising significantly shapes overall attitudes toward it The enjoyment derived from interacting with digital media fosters a positive consumer mood, highlighting the importance of entertainment in advertising effectiveness.
So, from previous studies, the research hypothesis is formed:
H2: personalized Marketing helps customers entertain when experiencing the forms offered.
Digital media serves as the most effective platform for delivering personalized experiences to website users, as noted by Mikalef, Giannakos, and Pateli (2013) Marketers leverage consumer browsing history to tailor their strategies to individual preferences (Bleier and Eisenbeiss, 2015; Lambrecht and Tucker, 2013) However, this shift towards personalized marketing has faced significant criticism from scholars and critics alike, who argue that it infringes on user privacy by tracking and collecting personal data (Graeff and Harmon).
2002) Simultaneously with the help of survey data, Sutanto et al, 2013 emphasizes that consumers are worried about the infringement of their privacy by personalized ads displayed by mobile apps.
Research by Aguirre et al (2015) indicates that consumer click-through rates for online ads are low, as users tend to react negatively towards websites that compromise their privacy Furthermore, a study by Kumar et al (2016) reveals that consumers are less inclined to engage with a company's social media platforms when they prioritize their privacy.
So, from previous studies, the research hypothesis is formed:
H3: Personalized Marketing makes users concerned about their privacy violated.
Personalized marketing enhances user access to relevant information while minimizing exposure to irrelevant content (Liang et al., 2006) The availability of product information significantly influences user opinions and their purchasing intentions (Childers et al., 2001) Research indicates a positive correlation between informativeness and e-commerce buying motivation and attitudes (Burke, 1997; To et al., 2007) When users perceive tailored marketing as highly informative, they are more likely to respond positively, as it effectively meets their needs during the purchasing process.
Consequently, based on earlier study, the following research hypothesis is developed:
H4: Receiving useful and necessary' information has a positive impact on customer attitudes.
High levels of engagement and enjoyment in computer-based media enhance user mood and perceptions Enjoyment plays a key role in shaping individuals' overall views, while entertainment is essential for creating personalized experiences To capture consumer attention effectively, messages should be concise and lighthearted Additionally, incorporating games and rewards through text messages can significantly boost participation rates, as people naturally seek fun Research shows that a customer's perception of value is closely linked to the entertainment quality of personalized experiences.
Consequently, based on earlier study, the subsequent research hypothesis is:
H5: Experiential entertainment has a positive impact on customer attitudes
Consumers often feel privacy concerns when they believe their personal information may be disclosed without consent (Baek & Morimoto, 2012) To create effective personalized experiences, businesses must align with the specific tastes and demands of their target audience, which can heighten privacy issues (Tucker, 2014; Jung, 2017) Customers may not realize their activities are being tracked until they encounter relevant advertisements, such as those for products they have previously viewed or added to their wishlist (Aguirre et al., 2016) While this targeted advertising can enhance user experience, it also raises significant privacy concerns, as consumers become aware of the potential for unauthorized access to their personal information.
Privacy concerns have significantly reduced consumers' willingness to engage with behavioral advertising, as highlighted by Kim and Huh (2017) Research indicates a negative relationship between privacy worries and online purchasing behavior, with many internet users reluctant to make purchases due to fears regarding the security of their personal information and transactions (Dinev & Hart, 2005) Consequently, if the customization inherent in display advertising amplifies these privacy concerns, it is likely that consumers' interest in buying advertised products will decline.
Consequently, based on earlier study, we propose the following research hypothesis:
H6: Privacy concerns have a negative impact on customer attitudes.
According to I Ajzen's theory of planned behavior, attitudes are the primary motivators of behavioral intentions, significantly influencing customers' decision-making during purchases (A Chen, Y Lu, B Wang) Varying opinions on advertising lead to different levels of purchase intention (S.P Brown, D.M Stayman) An individual's intention to engage in a behavior is shaped by their attitude towards that behavior Kinnear and Taylor define attitude as a customer's approval or preference for a product's features, which are crucial factors in their purchasing choices Korzaan emphasizes that attitude plays a vital role in purchasing decisions and can predict online transactions While a person's mindset can directly influence their buying intentions, emotional responses may lead to decisions based solely on satisfaction rather than comprehensive brand information (G Biehal, D Stephens, E Curio) Additionally, brand attitude can indirectly affect purchase intention (S.B MacKenzie, R.J Lutz, G.E Belch).
Therefore, from previous studies, the following research hypothesis is formed:
H7: Customer's shopping attitude of satisfaction and excitement has a positive impact on purchasing decisions.
Proposed research model
Research design
- Preliminary research: Qualitative research methods
To explore the components of personal marketing, the research team first gathered information from textbooks, books, and previous studies by both local and international authors that influence purchasing decisions Subsequently, they conducted group interviews with students at Ho Chi Minh City University of Economics using Google Meet These discussions involved students familiar with personalized marketing, who responded to pre-prepared questions based on their personal experiences.
The team identified key variables to improve the measurement scales for their research ideas, refining the proposed research model It is essential to evaluate the questionnaire's content to ensure it aligns with the study's objectives.
This study employs quantitative research methods to evaluate the impact of customized marketing on consumer purchasing decisions, utilizing a modified scale derived from exploratory research A questionnaire survey was conducted to gather data from participants, with the survey designed and distributed via Google Forms Respondents completed the survey independently through an online link provided to them.
Table I: Variables in the model
01 Pl I feel like Personalized Marketing creates experiences that are relevant to me
02 P2 1 feel like the personalized marketing strategy is designed to reach me
03 P3 Personalized Marketing Strategy notifies me about newly launched products that I am interested in and curious about
04 P4 Personalized Marketing also alerts me to impending sales events for things I'm interested in.
05 II Personalized interactions provide me up- to-date knowledge about a good or service.
06 12 Personalized interactions provide me with precise product details.
07 13 Personalized experiences deliver the information I need
08 14 Personalized experiences for me are a good source of information
09 ENT1 1 feel that personalized experiences are very appealing
10 ENT2 1 feel the personalized experiences very interesting
11 ENT3 I feel the experiences personalized to me very entertaining
12 PCI When I receive information that is so personal to me, I worry about my privacy.
13 PC2 The fact that companies may access my personal information makes me uncomfortable.
14 PC3 1 am concerned that my information may be used in ways I cannot foresee
Source: Compilation of references from previous studies
CUSTOMER ATTITUDE ABOUT PERSONALIZED MARKETING
15 ATT1 In my opinion, the personalized marketing strategy is positive
16 ATT2 I like the idea of using a personalized marketing strategy
17 ATT3 Using a personalized marketing strategy to customers is a wise idea
18 PI1 I think products/services introduced through personalized marketing strategies are worth buying
Do-Hyung Park, Sara Kim, 2008
19 PI2 I have thoughts about purchasing products/services introduced through personalized marketing strategies
20 PI3 I will likely buy products/services introduced through personalized marketing strategies
21 PI4 I am willing to buy this product/service after being introduced through a personalized marketing strategy
Do-Hyung Park, Sara Kim, 2008
22 PI 5 I am willing to recommend this product/service to others after being introduced through a personalized marketing strategy
Do-Hyung Park, Sara Kim, 2008
The research population in this article is designated as Ho Chi Minh City’s Gen z consumers The age range of this group will be 16 to 25 years old.
According to Nguyen Dinh Tho (2012), a sample frame comprises individuals who meet the criteria for a research population Data from the Vietnam Industry and Trade Magazine suggests that by 2023, there will be approximately 14.4 million members of Generation Z in Vietnam, defined as those born between 1997 and 2012 This indicates a significant presence of Gen Z in the country, although the data may not be entirely precise As a result, the research team chose to define the sample frame as a collection of high schools, universities, and colleges.
Ho Chi Minh City (representing around 10% of the overall research) in order to simplify and lower the cost of the research.
This study uses SEM linear structural model analysis, According to Hair et al,
In 2010, the determination of the appropriate sample size was based on factor groups, specifically following the proposed research model For factor groups consisting of seven or fewer, each group should include three observed variables with a communality of 0.5 or higher, resulting in a minimum required sample size of 150.
The study focuses on the Gen Z population in Ho Chi Minh City, representing approximately 20% of Vietnam's total Gen Z demographic To ensure the sample size meets all necessary criteria, it is essential to collect 300 samples for the quantitative survey, thereby improving the accuracy of the research findings.
While probability sampling offers a more accurate representation of the population, it is often costly and time-consuming due to the lack of relevant secondary data Consequently, this study opted for a convenience (non-probability) sampling method to reduce costs, speed up participant responses, and improve overall convenience.
In this sampling method, data is collected by sending questionnaires to 300 random people of Gen z (aged 16-25 years old) living in Ho Chi Minh City.
The team meticulously cleans and filters the gathered data before proceeding with data input, coding, and statistical processing By thoroughly checking questionnaires, they eliminate erroneous or conflicting responses, ensuring that only high-quality data is prepared for further analysis This rigorous data preparation process culminates in a response table that meets the necessary quality standards for in-depth evaluation and study results.
The team meticulously verified the data entered into Microsoft Excel to eliminate any errors prior to analysis To define the characteristics of the study sample, descriptive statistical analysis was conducted using both Microsoft Excel and SmartPLS 4.0 software.
Next, the team evaluated the research model through two stages: (1) evaluating the measurement model and (2) evaluating the structural model.
Results
This study analyzed statistical parameters, focusing on the correlations between outcomes and various factors within the study population, including gender and occupation Utilizing descriptive statistical frequency analysis tools, the research effectively summarizes data by presenting minimum and maximum values.
A survey conducted using Google Forms gathered 302 responses regarding the influence of personalized marketing on the purchasing decisions of Gen Z in Ho Chi Minh City The collected data provides valuable insights into this demographic's shopping behavior and preferences.
02 were invalid, while 300 were valid and used for subsequent analysis
Figure 2: Gender structure of the sample
The survey results indicate that 51% of participants identified as female, 47% as male, and 2% as other genders, reflecting a fairly balanced representation of male and female respondents.
Figure 3: Occupation ratio of the sample
■ University student ■ student ■ Working people
The survey comprised a diverse group of participants, with university students making up 60% (180 individuals), working professionals at 24% (71 individuals), and other students at 16% (49 individuals) This varied demographic enhances the representativeness of the research Notably, the focus on students, who possess significant knowledge and insight into technology and online advertising, positions them as a crucial segment of the Gen Z population, making them ideal subjects for studying engagement with personalized marketing.
Figure 4: The most used form of personalization
Personalized products Personalized web content Personalized messages Personalized Email Marketing
The results indicate that 85 consumers, representing 28.1%, preferred "Personalized Advertising" due to its numerous benefits and positive experiences, making them more receptive to advertising messages Additionally, 77 individuals (25.5%) opted for "Personalized Email Marketing," while 41 (13.67%) chose "Personalized Messages." Furthermore, 28 people (9.3%) selected "Personalized Web Content," and 26 participants (8.6%) favored another option.
“Personalized products'’, 25 people (8.3%) chose “Personalized user interface”, 10 people (3.3%) chose “ Personalized customer program” and the remaining 8 people (2.6%) chose "Personalized shopping experience".
Table 2: Benefits customers receive from personalized marketing
Al Unique and customized experiences for each customer
A2 Save time and effort during the purchasing 191 31.8% 63.6%
Source: Author's calculation process to find suitable information or products
A3 Providing special offers and promotions based on preferences and purchasing behavior
A4 Create a positive interactive environment between customers and businesses
A5 1 do not see any benefits from the strategy 13 2.1% 4.3%
According to the frequency statistics, a total of 300 respondents provided 601 choices regarding their preferences The most popular response, chosen by 191 respondents (31.8%), was "Save time and effort during the purchasing process to find suitable information or products." This was followed by "Unique and customized experiences for each customer," selected by 170 respondents (28.3%) Additionally, 122 respondents (20.3%) preferred "Providing special offers and promotions based on preferences and purchasing behavior," while 105 respondents (17.5%) valued "Creating a positive interactive environment between customers and businesses." The least favored option was "I do not see any benefits from this strategy," with only 13 respondents (2.1%) selecting it.
5.2.3 Feeling after experiencing the personalized marketing
Table 3: How customers feel after experiencing personalized marketing
Bl Be comfortable and happy when provided with unique experiences or products that suit my needs
B2 Received attention and respect from the business
B4 Worry because my information can be used in ways that I cannot see
B5 Annoyed because I feel bothered too much by emails, messages, ads,
B6 Unsatisfied because products/services are personalized for me not really suitable
The survey, which included 300 respondents and yielded a total of 557 choices, revealed that the most favored option was "Be comfortable and happy when provided with unique experiences or products that suit your needs," selected by 226 participants, representing 40.6% of the total Following this, 152 respondents (27.3%) chose "Received attention and respect from the business," while 72 participants (12.9%) expressed concern about personal privacy.
A recent survey revealed that 52 respondents, representing 9.3%, expressed concern over their information potentially being used in unseen ways Additionally, 40 respondents, or 7.2%, reported feeling annoyed by the excessive volume of emails, messages, and ads they receive Conversely, the least common sentiment was dissatisfaction with personalized products and services, chosen by only 15 respondents, equating to 2.7%.
5.2.4 Forms of personalized marketing that consumers want to experience
Table 4: Forms of personalized marketing that consumers want to experience
Form N Percent Percent of Case
In a survey of 300 participants, a total of 861 preferences were recorded The most favored option was "Personalized product," selected by 156 respondents, representing 18.1% of the total Following closely, "Personalized Email Marketing" garnered 132 votes, accounting for 15.3%, while "Personalized web content" was chosen by 125 respondents, making up 14.5% of the choices.
"Personalized advertising" (accounting for 12.5%) 7%), 107 respondents chose
“Personalized User Interface*' (accounting for 12.4%), 92 respondents chose
"Personalized Customer Program" (accounting for 10.7%), there were 74 respondents chose "Personalized shopping experience" (accounting for 8.6%), and the lowest remaining 66 respondents chose "Personalized messages" (accounting for 7.7%).
The Outer Loading coefficient indicates the strength of the relationship between observed variables and underlying factors, with a higher coefficient signifying a stronger correlation As noted by Hair et al (2016), an Outer Loading value of 0.7 or higher suggests that the observed variable meets quality standards.
The analysis indicates that most observed variables satisfy the established criteria, with values ranging from 0.719 (ENT3) to 0.863 (PC2) However, the 14 scale recorded a value of 0.584, falling below the minimum threshold of 0.7 set by the group Consequently, this scale does not meet the quality standards, prompting the group to remove it to maintain overall variable quality.
Table 5: Result of analysis Outer Loading
The reliability of a test is assessed using Composite Reliability (CR), which should ideally range between 0.7 and 0.9 for acceptance A CR value exceeding 0.95 indicates potential issues due to overlapping observed variables, while a value below 0.6 suggests a lack of internal consistency reliability with the original variable, necessitating further examination (Hair et al., 2014).
■ Cronbach’s alpha Composite reliability (rho_a)
The CR scale value table indicates that both independent and dependent variable values range from 0.727 to 0.803, meeting the necessary criteria Consequently, these scales will be retained for further analysis in subsequent tests.
The convergent validity coefficient indicates the extent to which a measure is positively correlated with other measures of the same construct (Hair et al., 2017) To assess convergent validity, researchers utilize the average variance extracted (AVE) value.
Average Variance Extracted (AVE) represents the mean value of squared factor loadings associated with variables relevant to a research concept As noted by Hock and Ringle (2010), a scale demonstrates convergent validity when the AVE is 0.5 or greater.
Cronbach’s alpha Average variance extracted (AVE) 1 pENT
Discuss the results
The research indicates that the proposed study model effectively explains 62.5% and 53.7% of the factors affecting Gen Z consumers' satisfaction and purchasing decisions Notably, customer sentiments are enhanced by the elements of Entertainment and Information Reception, while Privacy Concern has a detrimental effect on these sentiments.
Personalization significantly enhances users' purchasing experiences, making them more enjoyable and satisfying Statistics reveal that tailored product recommendations and personalized customer programs lead to increased comfort and happiness among consumers This distinct approach to marketing sets personalized techniques apart from traditional methods, highlighting their unique effectiveness in engaging customers.
Although research indicates a minimal impact, the hypothesis linking Privacy Concerns to purchase decisions remains unconfirmed However, since most users express concerns about the security of their personal information during transactions, it is reasonable to assume a negative correlation between privacy issues and purchasing behavior (Diev & Hail, 2005) Additionally, existing customization methods have unintentionally heightened customers' privacy fears and concerns during their shopping experience.
The study reveals that the key factor enhancing consumer satisfaction with personalized experiences is the availability of useful information, which significantly reduces search times by providing timely and relevant content Childers et al (2001) emphasize that consumers highly value product information accessibility, which influences their attitudes and purchase intentions However, the 14: Personalization scale has been deemed unreliable, likely due to negative experiences where consumers receive unexpected, irrelevant, or inaccurate information.
CONCLUSION
General conclusion
Gen Z has emerged as a powerful consumer group, presenting both opportunities and challenges for businesses A personalized marketing approach fosters stronger relationships with these customers, enabling brands to offer tailored products and services that resonate with individual preferences The study titled “The Impact of Personalized Marketing on Gen Z Purchasing Decisions in Ho Chi Minh City” reveals significant effects of personalized strategies on the purchasing behavior of this demographic.
The research model developed by the team explores the connection between Generation Z's purchasing decisions and personalized marketing, incorporating intermediary variables such as experience satisfaction, entertainment, privacy concerns, and information reception This model is grounded in established theories and previous studies.
The study analyzed 300 samples from working adults, pupils, and students aged 16 to 25 living and working in Ho Chi Minh City The survey data was meticulously examined and coded into an Excel file before being imported into the SMART PLS 4 program for comprehensive analysis.
The study reveals that tailored marketing allows clients to receive entertaining and informative content, fostering a personal connection with them Consumer satisfaction is positively influenced by two key elements: entertainment and information Additionally, a negative correlation between privacy concerns and purchasing attitudes was identified, although its impact is minimal and primarily affects a small group of Gen Z participants This finding aligns with the "privacy paradox," where individuals express concern for their privacy but fail to take protective measures (Barth & De Jong, 2017).
Research shows that Gen Z consumers are notably drawn to personalized email marketing and customized products These insights could significantly impact individuals and businesses in the marketing industry, influencing their strategies and policies.
Implications
Empirical research methodologies were employed to explore how customized marketing affects Gen Z's purchasing preferences The study rigorously tested and refined a scale to align with personalization factors influencing purchase intention Findings indicate that personalization significantly enhances the shopping experience for consumers, thereby increasing their likelihood and intention to buy Effective utilization of customization elements fosters better consumer engagement and drives improved business performance.
The study utilized the "theory of planned behavior" to analyze how individual attitudes shape their purchasing decisions, revealing that Generation Z holds unique perspectives that influence their buying habits This understanding has led to the development of tailored strategies that effectively align with their preferences and support their choices.
The theory of personalization perception explains how personalized elements effectively engage customers, enhancing their shopping experience and fostering stronger relationships By addressing individual needs and desires, consumers are more likely to feel satisfied and inclined to make purchases.
Personalized marketing leverages information systems and services that adapt to individual needs, enhancing user experience and boosting purchase intent Key elements such as "Information Reception," "Entertainment," and "Privacy Concerns" significantly influence consumer attitudes and buying decisions By tailoring products to meet the unique preferences of each user, the purchasing process becomes more convenient It is essential to maintain ethical and privacy standards to ensure effective customization, protect user interests, and provide a comfortable shopping experience.
To effectively engage and retain Gen Z consumers, it's essential to adopt a personalized marketing strategy while identifying and optimizing key performance metrics that influence their purchasing decisions Develop marketing plans based on consumer feedback and ensure that ongoing initiatives yield the desired results, continually improving to adapt to the evolving preferences and demands of Gen Z.
To effectively engage Gen Z consumers, marketing strategies must be tailored to their unique preferences, needs, and situations Utilizing analytics, predictive modeling, and online behavioral data can help create personalized content, advertisements, and promotions By developing more compelling marketing initiatives, brands can attract Gen Z and positively influence their purchasing decisions.
To effectively engage Gen Z users on platforms like Twitter, Instagram, and TikTok, it's essential to create captivating content, including articles, videos, and advertisements Incorporating humor, intrigue, or unique elements can attract their attention and foster consumer interaction This approach not only enhances the experience of obtaining product information but also encourages purchase decisions and builds brand loyalty through entertaining connections.
Limitationsand recommendation
There are still certain restrictions on the study:
The survey targeted Gen Z individuals aged 16 to 25 in Ho Chi Minh City and was conducted online, leading to a high response rate despite its short duration However, the data collected is incomplete and does not adequately represent the broader population, limiting its overall reliability and insights into the issue.
Participants in the answering process had the option to choose misleading and irresponsible responses, potentially leading to inaccuracies in the data's relative connectivity.
+ Besides, customer behavior will vary among platforms and organizations that use each form, even if the research only employs a single assessment model.
The study found no significant impact of "privacy concerns" on customer satisfaction, as evidenced by the lack of statistical significance in the statement, "Personalized experiences for me are a good source of information." This suggests that the current model has limitations and necessitates further research.
Personalized marketing has gained immense popularity due to its ability to improve consumer experiences, boost campaign effectiveness, and build lasting customer relationships Our team recommends exploring the following development directions to further investigate this important issue.
To enhance the richness of information sources, it is essential to expand the national coverage of research by incorporating a larger participant pool and diverse survey methods, including in-person interviews This approach will uncover additional influential factors that were previously overlooked by the research group.
- Examine more in-depth individual outcomes for every type of tailored marketing to advance many areas.
To gain a deeper understanding, it is essential to explore the relationships between various factors and assess their impacts, as well as identify any additional variables that may be involved Subsequently, leverage these insights to develop and enhance new, more comprehensive models.
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21 Hair Jr., J F., Hult, G T M., Ringle, c., & Sarstedt, M (2016) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) Thousand Oaks, CA: Sage Publications.
22 Hair, J F., Risher, J J., Sarstedt, M., & Ringle, c M (2019) When to use and how to report the results of PLS-SEM European business review, 31(1), 2-24.
23 Henseler, C.M., Ringle, J and Sinkovics, R.R.R (2009) The Use of Partial Least Squares Path Modeling in International Marketing Advances in International Marketing, 20, 277-319.
24 Hock, M., & Ringle, c M (2010) Local strategic networks in the software industry:
An empirical analysis of the value continuum International Journal of Knowledge Management Studies, 4(2), 132-151.
25 Hoffman, D and Novak, T (1996) Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations Journal of Marketing, 60, 50-68.
26 Imhoff, c., Loftis, L., & Geiger, J G (2001) Building the customer-centric enterprise: Data warehousing techniques for supporting customer relationship management New York: Wiley.
27 Jahn, B., & Kunz, w J J o s M (2012) How to transform consumers into fans of your brand 23(3), 344-361.
28 Jung, A R (2017) The influence of perceived ad relevance on social media advertising: An empirical examination of a mediating role of privacy concern, Computers in Human Behavior, Volume 70, 303-309
29 Kim, H., & Huh, J (2017) Perceived relevance and privacy concern regarding online behavioral advertising (OBA) and their role in consumer responses Journal of Current Issues & Research in Advertising, 38(1), 92-105.
30 Kim, J w., Lee, B H., Shaw, M J., Chang, H.-L., & Nelson, M J I J o E c
(2001) Application of decision-tree induction techniques to personalized advertisements on internet storefronts 5(3), 45-62.
31 Kim, Y J., & Han, J (2014) Why smartphone advertising attracts customers: A model of Web advertising, flow, and personalization Computers in human behavior, 33, 256-269.
32 Korzaan, M L (2003) Going with the flow: Predicting online purchase intentions Journal of Computer Information Systems, 43(4), 25-31.
33 Kumar, A., Bezawada, R., Rishika R., Janakiraman, R., & Kannan, p J J o m
(2016) From social to sale: The effects of firm-generated content in social media on customer behavior 80(1), 7-25.
34 Lambrecht, A., & Tucker, c J J o M r (2013) When does retargeting work? Information specificity in online advertising 50(5), 561-576
35 Liang, T p., Lai, H J., & Ku, Y c (2006) Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings Journal of Management Information Systems, 23(3), 45-70.
36 MacKenzie, s B., Lutz, R J., & Belch, G E (1986) The role of attitude toward the ad as a mediator of advertising effectiveness: A test of competing explanations Journal of marketing research, 23(2), 130-143.
37 Malheiros, M., Jennett, c., Patel, s., Brostoff, s., & Sasse, M A (2012) Too close for comfort: A study of the effectiveness and acceptability of rich-media personalized advertising Paper presented at the Proceedings of the SIGCHI conference on human factors in computing systems.
38 McQuail, D (1987) Mass communication theory: An introduction Sage Publications, Inc.
39 Mikalef, p., Giannakos, M., Pateli, A J J o t., & research, a e c (2013) Shopping and word-of-mouth intentions on social media 8(1), 17-34.
40 Nguyen Đình Thọ (2012), Phương pháp nghiên cứu khoa học trong kinh doanh, Nhà xuât bàn Lao động - Xà hội, TP Hô Chí Minh.
41 Odoom, p T (2022) Personalised display advertising and online purchase intentions: The moderating effect of internet use motivation International Journal of E-Services and Mobile Applications (IJESMA), 14(1), 1-16.
42 Peppers, D., & Rogers, M (1998) Enterprise One-to-one: Tools for Building Unbreakable Customer Relationships in the Interactive Age: Piatkus.
43.Priporas, c V., Stylos, N., & Fotiadis, A K (2017) Generation z consumers' expectations of interactions in smart retailing: A future agenda Computers in human behavior, 77, 374-381.
44 Roberts, M L., & Zahay, D (2012) Internet marketing: Integrating online and offline strategies: Cengage Learning.
45 Ross, N J u p., Direct Marketing Association, New York, NY (1992) A history of direct marketing.
46 Setyani, V., Zhu, Y.-Q., Hidayanto, A N., Sandhyaduhita, p I., & Hsiao, B (2019) Exploring the psychological mechanisms from personalized advertisements to urge to buy impulsively on social media International Journal of Information Management, 48, 96-107.
47.Shavitt, s., Lowrey, p., & Haefner, J (1998) Public attitudes toward advertising: More favorable than you might think Journal of advertising research, 38(4), 7-22.
48 Sherman, S.J and Fazio, R.H., (1983) Parallels between attitudes and traits as predictors of behaviour, Journal of consumer research, 51,3, 308-345
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50 Tran, T p J J o R., & Services, c (2017) Personalized ads on Facebook: An effective marketing tool for online marketers 39, 230-242.
51 Tran, T p., van Solt, M., & Zemanek Jr, J E J J o c M (2020) How does personalization affect brand relationship in social commerce? A mediation perspective 37(5), 473-486.
52 Tucker, c E J J o m r (2014) Social networks, personalized advertising, and privacy controls 51(5), 546-562.
53 Tunsakul, K (2020) Gen z consumers' online shopping motives, attitude, and shopping intention Hum Behav Dev Soc, 21,7-16.
54 Wind, J., & Rangaswamy, A J J o i m (2001) Customerization: The next revolution in mass customization 15(1), 13-32.
55 Xu, D J (2006) The influence of personalization in affecting consumer attitudes toward mobile advertising in China Journal of computer information systems, 47(2), 9-19.
Figure 2: Gender structure of the sample 20
Figure 3: Occupation ratio of the sample 20
Figure 4: The most used form of personalization 21
Chúng tôi là sinh viên khóa 47 của Trường Đại học Kinh tế TP.HCM (UEH), hiện đang thực hiện dự án nghiên cứu về ảnh hưởng của
Marketing cá nhân hóa lên quyết định mua hàng của Gen z tại TP Hồ Chí Minh”
Dữ liệu từ cuộc khảo sát này chỉ nhằm mục đích nghiên cứu học thuật và không phục vụ cho mục đích thương mại nào khác Chúng tôi hy vọng bạn có thể dành ít phút để hoàn thành bảng khảo sát bằng cách chọn lựa đáp án phù hợp với mình.
Xin chân thành cảm ơn anh (chị).
Marketing cá nhân hóa là chiến lược tiếp thị nhằm tạo ra trải nghiệm độc đáo cho khách hàng, từ đó nâng cao mức độ tương tác tích cực giữa doanh nghiệp và người tiêu dùng.
Dưới đây là một số hình thức phố biến cua marketing cá nhân hóa:
Giao diện người dùng cá nhân hóa là yếu tố quan trọng trong việc tạo ra trải nghiệm độc đáo cho người dùng trên các ứng dụng di động và trang web Bằng cách tùy chỉnh giao diện, nội dung và chức năng theo sở thích cá nhân, người dùng sẽ cảm thấy thoải mái và hài lòng hơn khi sử dụng sản phẩm Việc áp dụng giao diện cá nhân hóa không chỉ nâng cao trải nghiệm người dùng mà còn góp phần tăng cường sự gắn bó và trung thành của họ với thương hiệu.
Quảng cáo cá nhân hóa là việc sử dụng dữ liệu khách hàng để tạo ra những quảng cáo phù hợp trên các nền tảng trực tuyến như Google AdWords và Facebook Ads.
- Email cá nhân hóa: Gưi email theo tên, sở thích hoặc hành vi mua hàng trước đó.
Sản phẩm cá nhân hóa là giải pháp tối ưu, cung cấp các sản phẩm hoặc dịch vụ được tùy chỉnh để đáp ứng nhu cầu riêng biệt của từng khách hàng, bao gồm việc in tên hoặc hình ảnh lên sản phẩm.
- Nội dung web cá nhân hỏa: Tạo nội dung web động dựa trên thông tin cá nhân của khách hàng (tên, địa chí, sờ thích)
Chương trình khách hàng cá nhân hóa là việc xây dựng các chương trình thành viên hoặc chương trình khách hàng đặc biệt, dựa trên thông tin và sở thích cá nhân của từng khách hàng Việc này giúp tăng cường mối quan hệ với khách hàng và nâng cao trải nghiệm của họ.
Tin nhắn cá nhân hóa là một phương pháp hiệu quả để giao tiếp với khách hàng thông qua SMS hoặc thông báo, giúp gửi đi những thông điệp được điều chỉnh dựa trên hành vi, lịch sử mua hàng và vị trí địa lý của họ.
QUESTIONNAIRE SURVEY
Chúng tôi là sinh viên khóa 47 của Trường Đại học Kinh tế TP.HCM (UEH) và hiện đang thực hiện dự án nghiên cứu về ảnh hưởng của
Marketing cá nhân hóa lên quyết định mua hàng của Gen z tại TP Hồ Chí Minh”
Dữ liệu từ cuộc khảo sát này chỉ nhằm mục đích nghiên cứu học thuật và không phục vụ cho bất kỳ mục đích thương mại nào khác Chúng tôi hy vọng bạn có thể dành ít phút để hoàn thành bảng khảo sát bằng cách lựa chọn các đáp án phù hợp với mình.
Xin chân thành cảm ơn anh (chị).
Marketing cá nhân hóa là chiến lược tiếp thị nhằm tạo ra trải nghiệm độc đáo cho từng khách hàng, từ đó nâng cao sự tương tác tích cực giữa doanh nghiệp và khách hàng.
Dưới đây là một số hình thức phố biến cua marketing cá nhân hóa:
Giao diện người dùng cá nhân hóa là yếu tố quan trọng trong việc tạo ra trải nghiệm độc đáo cho người dùng trên ứng dụng di động và trang web Bằng cách tùy chỉnh giao diện, nội dung và chức năng theo sở thích cá nhân, người dùng sẽ cảm thấy thoải mái và hài lòng hơn khi tương tác với sản phẩm Việc này không chỉ nâng cao sự hài lòng của người dùng mà còn tăng cường khả năng giữ chân khách hàng.
Quảng cáo cá nhân hóa là việc sử dụng dữ liệu khách hàng để tạo ra những quảng cáo phù hợp trên các nền tảng trực tuyến như Google AdWords và Facebook Ads.
- Email cá nhân hóa: Gưi email theo tên, sở thích hoặc hành vi mua hàng trước đó.
Sản phẩm cá nhân hóa là giải pháp tối ưu, cung cấp các sản phẩm hoặc dịch vụ được tùy chỉnh để đáp ứng nhu cầu riêng biệt của khách hàng, chẳng hạn như in tên hoặc hình ảnh lên sản phẩm.
- Nội dung web cá nhân hỏa: Tạo nội dung web động dựa trên thông tin cá nhân của khách hàng (tên, địa chí, sờ thích)
Chương trình khách hàng cá nhân hóa là việc xây dựng các chương trình thành viên hoặc chương trình khách hàng đặc biệt, dựa trên thông tin và sở thích cá nhân của từng khách hàng.
Tin nhắn cá nhân hóa là một công cụ hiệu quả giúp doanh nghiệp gửi thông điệp trực tiếp tới khách hàng thông qua SMS hoặc thông báo Bằng cách dựa trên hành vi, lịch sử mua hàng và vị trí địa lý của khách hàng, doanh nghiệp có thể tạo ra những trải nghiệm độc đáo và phù hợp, từ đó tăng cường sự gắn kết và nâng cao khả năng chuyển đổi.
Trải nghiệm mua sắm cá nhân hóa ngày càng trở nên quan trọng, với việc điều chỉnh sản phẩm, xây dựng danh sách mong muốn cá nhân và cung cấp gợi ý dựa trên sở thích của khách hàng.
Marketing cá nhân hóa có thể được áp dụng qua nhiều kênh và phương pháp khác nhau, tùy thuộc vào ngành nghề và mục tiêu của doanh nghiệp Các hình thức này chỉ là một số ví dụ điển hình.
1 Anh (chị) đà từng nghe qua, biết hoặc trài nghiệm về các chiến lược Marketing cá nhân hoá chưa?
□ Đà từng □ Chưa biết (Dừng kháo sát)
2 Anh (chị) thây hình thức nào được sử dụng nhiêu nhât ? (có thê chọn nhiêu đáp án)
□ Email Marketing cá nhân hóa
□ Quảng cáo cá nhân hóa
□ Sán phâm cá nhân hỏa
□ Tin nhăn cá nhân hỏa
□ Nội dung web cá nhân hóa
□ Giao diện người dùng cá nhân hóa
□ Chương trinh khách hàng cá nhân hóa
□ Trâi nghiệm mua hàng cá nhân hóa
3 Hỉnh thức /chiến dịch Marketing cá nhân hoá đem lại cho anh (chị) ? (có thể chọn nhiều đáp án)
□ Trái nghiệm độc đáo và tùy chinh dành riêng cho từng khách hàng
□ Tiết kiệm thời gian và nồ lực trong quá trình mua hàng đê tim kiếm thông tin hoặc sản phẩm phìi hợp
□ Cung cấp ưu đài và khuyến mài đặc biệt dựa trên sở thích và hành vi mua hàng
□ Tạo ra một môi trường lương tác tích cực giừa khách hàng và doanh nghiệp
□ Tôi không nhận thấy được lợi ích gì lừ chiến lược này
4 Anh (chị) cảm thấy như thế nào sau khi trái nghiệm hinh thức/chiến dịch Marketing cá nhân hoá? (có thê chọn nhiêu dáp án)
□ Thoái mái, vui vé khi được cung câp những trài nghiệm hay sán phâm cá biệt, phù hợp với nhu cầu của bân thân.
□ Nhận được sự quan tâm vả trân trọng từ doanh nghiệp
□ Lo ngại quyên riêng tư cá nhân
□ Lo lắng vì thông tin của mình có thê được sừ dụng theo nhừng cách mà minh không thấy được
□ Khỏ chịu vỉ cám thây bị làm phiên quá nhiêu do email, tin nhăn, quáng cáo
□ Không hài lòng vì các sàn phâm/dịch vụ được cá nhân hóa cho tôi chưa thật sự phù hợp
5 Anh/chị hình thức Marketing cá nhân hỏa nào sau đây đen khách hàng: (có the chọn nhiêu đáp án)
□ Email Marketing cá nhân hóa
□ Nội dung web cá nhân hóa
□ Quáng cáo cá nhân hóa
□ Giao diện người dùng cá nhân hóa
□ Sán phâm cá nhân hóa
□ Chương trinh khách hàng cá nhân hóa
□ Tin nhắn cá nhân hỏa
□ Trái nghiệm mua hàng cá nhân hỏa
6 Anh/chị mong muốn được trái nghiệm nhiều hơn các hình thức Marketing cá nhân hóa nào sau: (có thê chọn nhiêu đáp án)
□ Email Marketing cá nhân hóa
□ Nội dung web cá nhân hóa
□ Quảng cáo cá nhân hóa
□ Giao diện người dùng cá nhân hóa
□ Sán phâm cá nhân hóa
□ Chương trinh khách hàng cá nhân hóa
□ Tin nhắn cá nhân hóa
□ Trái nghiệm mua hàng cá nhân hỏa
Xin vui lòng cho biết mức độ đồng ý của bạn với các phát biểu dưới đây về các yếu tố thuộc Marketing cá nhân và tác động của chúng đến quyết định mua sắm Hãy chọn con số phù hợp nhất với bạn theo quy ước đã được đưa ra.
Không đồng ý Trung lập Đồng ý Hoàn toàn đồng ý
01 Pl Tôi cám thấy chiến lược Marketing cá nhân hóa tạo ra các trải nghiệm thích hợp với tôi
02 P2 Tôi câm thây chiên lược Marketing cá nhân hóa được tạo ra đô tiếp cận đến tôi
03 P3 Chiến lược Marketing cá nhân hóa thông báo cho tôi về nhưng sán phâm mới ra mắt mà tôi quan tâm, tò mò
04 P4 Chiến lược Marketing cá nhân hóa thông báo cho tôi về nhừng sự kiện giâm giá sắp tới của sán phâm mà tôi đang theo dõi.
05 11 Các trài nghiệm được cá nhân hóa cung câp cho tôi thông tin kịp thời về sản phấm hoặc dịch vụ
06 12 Các trai nghiệm được cá nhân hóa cung cấp cho tôi thông tin chính xác về sản phâm
07 13 Các trâi nghiệm được cá nhân hỏa cung câp thông tin tôi cần
08 14 Các trái nghiệm được cá nhân hóa cho tôi là một nguồn thông tin tốt
SỤ GIẢI TRÍ KHI TRẢI NGHIỆM
09 ENT1 Tôi cám thấy ràng các trải nghiệm được cá nhân hóa cho mình rất hấp dần
10 ENT2 Tôi cảm thấy các trải nghiệm được cá nhân hóa cho mình rất thú vị
11 ENT3 Tôi câm thay các trái nghiệm được cá nhân hóa cho mình rất giải trí
QUAN NGẠI VÈ QUYỀN RIÊNG TƯ
12 PCI Tôi lo ngại về quyền riêng tư cùa minh khi nhận được nhừng thông tin quá hiêu rõ tôi
13 PC2 Tôi cám thấy khỏ chịu khi doanh nghiệp có thê truy cập thông tin về tôi
14 PC3 Tôi lo ngại rằng thông tin của tôi có thế được sử dụng theo những cách tôi không thề biết trước
THÁI Độ KHÁCH HÀNG VÈ MARKETING CÁ NHÂN HÓA
15 ATT1 Theo tôi Marketing cá nhân hóa là tích cực 1 2 3 4 5
16 ATT2 Tôi thích ỷ tường sứ dụng chiến lược
17 ATT3 Sư dụng Marketing cá nhân hóa đen khách hàng là một ỷ tường khôn ngoan
18 PI1 Tôi nghĩ sãn phấm/dịch vụ được giới thiệu qua chiến lược Marketing cá nhân hỏa là đáng mua
19 PI2 Tôi cỏ suy nghi vê việc mua sán phâm/dịch vụ được giới thiệu qua chiến lược Marketing cá nhân hóa
20 PI3 Tôi cỏ thể sè mua sán phầm/dịch vụ được giới thiệu qua chiến lược Marketing cá nhân hóa
21 PI4 Tôi sằn sàng mua sán phấm/dịch vụ này sau khi được giới thiệu qua chiến lược Marketing cá nhân hóa
22 PI5 Tôi sẵn sàng giới thiệu sản phâm/dịch vụ với người khác sau khi được giới thiệu qua chiến lược Marketing cá nhân hóa
PHẦN 3 Thông tin đáp viên
1 Hiện nay, Anh (chị) bao nhiêu tuôi?
4 Anh (chị) có đang sinh sông/ học tập/ làm việc tại TP Hô Chí Minh hay không?