The Influence of Online Reviews on Generation Y’s Purchase Intention Essay

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Updated: Nov 17th, 2023

Introduction

Consumer behavior is a critical field of marketing because it helps companies to develop marketing strategies that align with the values and expectations of their clients. As an important tenet of marketing, consumer behavior plays a critical role in influencing product sales and revenue (Gupta et al., 2018). To avoid adverse outcomes, firms pay close attention to the needs and wants of their consumers by tracking their behaviors (Lee, 2009). Despite the significance of consumer behavior to corporate performance, few people understand the breadth and depth of purchasing intentions exhibited by consumers. The lack of depth is caused by several factors, among them being other people’s views and opinions about a product. Thus, it is important to appreciate the myriad of factors influencing consumer behavior.

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The current investigation aims to understand the impact of online reviews on consumer purchasing intention by focusing on three key areas of review – the desire to make repeat purchases, willingness to recommend products/services to other people, and brand commitment. These areas of assessment are relevant in assessing purchasing intentions and they form the guiding framework for the research questions underpinning the present investigation, which appear as follows:

How do online reviews influence consumers’ desire to make repeat purchases?
What is the extent that online reviews can affect customers’ intentions to recommend products or services to other people?
To what extent do online reviews affect consumer brand commitment?

Literature Review

The current state of the literature regarding the impact of online reviews on the purchasing intentions of consumers is generalized. This has happened because few scholars have examined nuanced behavioral changes brought by technology integration in marketing. In this chapter, the current knowledge on the research topic is reviewed to have an in-depth understanding of the current state of research on the present topic of investigation.

Methods Deployed

The current probe consists of a review of extant works of the literature that have investigated consumer behavior. To obtain credible and reliable sources of information, emphasis was made to review books, journals, and credible websites. These sources of data offer credible materials for review because of their unbiased nature (Lämsä and Keränen, 2020). Books were obtained from Google Books, Google Scholar, and school libraries, while journals were sourced from reputable datasets, including Emerald Insight, Sage Journals, and Springer.

The potential prejudice that emerged in the data collection process was the preference for studies that had a Chinese or Asian focus. Similarly, the researcher was biased in including publications that discussed the effects of generational differences in marketing. These biases were accommodated in the present reading because the current probe is a case study that investigates consumer behavior among Generation Y consumers within the Chinese business setting. Therefore, materials relating to relevant market and demographic consumer group profiles were preferable. Overall, the pieces of the literature analyzed were organized according to relevant themes, as shown below.

Generational Differences in Behavior

In this section of the review, generational differences that describe contemporary markets are defined with the intention to highlight unique qualities and expectations affecting consumer purchasing decisions. For purposes of this analysis, generational differences are explained in the context of four major market cohorts – Baby Boomers, Generation X, Generation Y, and Generation Z consumers.

Baby Boomers

The Baby Boomer generation is defined by the year they were born – between 1946 and 1968. Members of this demographic group were born after the Second World War in a period of prosperity (Brandt et al., 2022). The cold war era and the hippie movement of the 1960s and 1970s are equally significant events that shaped the life and expectations of Baby Boomers (Elliott, 2022). Rooted in these experiences, most members of the Baby Boomer population have developed a life philosophy of setting up their children for success, while giving them limited financial assistance (Airey et al., 2021; BMI, 2020). Therefore, although they may arguably be termed the wealthiest generation that lives today, they pay minimal attention to leaving an inheritance to their children (Airey et al., 2021; Gupta et al., 2018). This practice means that members of this population group believe that their children should fend for themselves in the way that they did when young.

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Baby Boomers equally distinguish themselves from their peers based on their financial habits. For example, researchers have shown that most of them are living beyond their retirement savings and consider their financial security after retirement a top concern (Brandt et al., 2022). Marketers who have sold products to this population group suggest that they are the biggest consumers of traditional media sources, such as television and magazines (Elliott, 2022). Despite their conservatism, statistics show that up to 90% of this population has a Facebook account (Alzougool, 2018). Based on their dalliance with social media, members of this population cohort use digital platforms to stay in touch with family and colleagues or to reconnect with old friends.

Generation X

Generation X members comprise people who were born between the years 1968 and 1980. They share similarities with the Baby Boomer generation based on their liking for traditional media sources, such as television and radio (Saleem, Shenbei and Hanif, 2020). However, unlike their predecessors, Generation X consumers are technologically savvy. Notably, they spend more time on Facebook than on any other social media platform (Beig and Khan, 2020). Researchers, such as García-Canal et al. (2018), have argued that the current body of literature that has investigated purchasing intentions of Generation X buyers is incomplete because it fails to account for the breadth of factors impacting their decision-making processes. Consequently, scholars have advanced a common theoretical basis for analyzing consumer behavior, which is depicting it as a function of the generation’s upbringing, socialization, and cultural expectations (Saleem, Shenbei and Hanif, 2020). Relative to this observation, significant events that shaped the behaviors of Generation X buyers included the end of the cold war era and the rise of personal computing (Chen and Pain, 2019). Generation X consumers equally have high levels of student debt and carry the burden of raising children and nurturing families at the same time.

Generation Y

Generation Y members form the bulk of the people in developing nations because they mainly comprise young people. Comparatively, most developed nations have a lower percentage of people who identify as Millennials due to a growing elderly population and stagnant birth rates (Alhmoud and Rjoub, 2020). Generation Y consumers have unique features and characteristic that distinguishes them from other people (Tang and Chan, 2017). For example, members of this demographic group are more career-oriented compared to previous generations (Al-Kwifi, Farha and Zaraket, 2020). They are likewise associated with delayed marriages until later into their adult years (Milkman, 2017). This population group is similarly associated with low levels of fertility with most of them having one, two, or no children (Ross, 2019). These insights suggest that Millennial buyers are individualistic and tend to engage in self-fulfilling activities more than the allure of family life.

The interest in Millennials, which forms the basis for reviewing consumer behavior in the current study, emanates from the significant presence of Generation Y consumers in the marketplace. Notably, this generation is the highest spender in the current global market with researchers estimating that their overall market potential is in excess of $1.3 trillion (Top Agency, 2020). The same analysts also predict that in the next 10-20 years, this generation is likely to inherit up to $30 trillion in wealth from their parents (JP Morgan, 2021)). Broadly, these statistics indicate that by the year 2030, Generation Y consumers are likely to be the wealthiest generation in the market.

A deeper analysis of the online behaviors of Generation Y consumers is critical to this review due to the importance of understanding the impact that online reviews have on people’s purchasing behavior. Stemming from this line of thought, researchers reveal that Generation Y buyers are comfortable with using social media to make purchases (Dalessandro, 2018). Additionally, it is believed that most Millennials have more than one social media account and may have a presence on several such platforms (Alhmoud and Rjoub, 2020). In terms of the medium of communication, researchers have demonstrated that members of this demographic group consume media using television but prefer online streaming platforms, such as Netflix, for entertainment (Wang et al., 2021; Wayne and Sienkiewicz, 2022). Additionally, most Generation Y customers who shop for goods online use smartphones but 30% of the same demographic would use computers to perform the same function (Lim et al., 2020). Key events that shape the lives of Generation Y consumers include terrorist attacks, expansion of the internet, and widespread social media adoption.

Generation Z

Generation Y consumers comprise people who were born between the years 1997 and 2012. Economic analysts estimate that this demographic group influences about $600 billion in family expenditure (Hansaj, 2022). This figure suggests that this population group is likely to surpass the current purchasing power of Generation Y customers due to their influences on parents and society. Additionally, members of this demographic group consume media using smartphones because most of them gained access to these devices early in their teenage years (McKinskey and Company, 2018). Indeed, it is believed that most of them gained access to their first mobile phones through their parents. Broadly, smartphones are their preferred mode of communication because it activates the hyper-connectivity of their communication environment.

Key events highlighted above that shaped the life experiences of Generation Y consumers affected Generation Z as well. For example, the global internet spread and proliferation of smartphones have characterized the life experiences of both sets of consumers (Nguyen et al., 2022). However, Generation Z customers are likely to be distinguished from other demographic groups because they have experienced unique life events, such as the COVID-19 pandemic, and witnessed the unique financial struggles of their parents.

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Observing the life events of Millennials and previous generations have made Generation Z consumers adopt unique financial behaviors that influence their purchasing decisions. For example, after witnessing the financial struggles that previous generations have experienced, most Generation Z buyers practice fiscal conservativeness in their budgetary plans (Hansaj, 2022). Scholars also suggest that this population group is debt-averse and prefers to use mobile banking as opposed to other forms of digital payments (Farrell and Phungsoonthorn, 2020). These observations indicate that members of the Generation Z population have a willingness to avoid some of the financial challenges that have affected their predecessors by acquiring more knowledge on personal finance and applying the same in their lives (Nguyen et al., 2022). This attitude explains why the average Generation Z buyer opened a savings account earlier than other consumer groups highlighted in this probe.

Overall, the above-mentioned findings suggest that each demographic group has unique qualities and life experiences that are likely to influence their purchasing intentions. It is important to recognize the impact that these influences may have on their decision-making processes. The goal is to develop marketing campaigns that align with their core beliefs and values. Overall, these insights demonstrate that variations in attitudes that generational cohorts have towards technological use create different levels of exposure and influence to online reviews.

Critique of the Literature

The main challenge associated with the current literature is the lack of specificity regarding aspects of digital media engagement that influence consumer purchasing intentions. This gap in the literature creates opportunities for exploring consumer purchasing intentions from different angles. The current investigation seeks to find out if online reviews affect the purchasing intentions of Generation Y consumers. This analysis is crucial because the current body of literature has oversimplified people’s behaviors based on their age and generational affiliation. This assessment approach has failed to create a link between behavior change and the factors that drive it. Furthermore, excluded from this debate is the failure to recognize that purchasing intention is a subjective aspect of human behavior (Williams and Preston, 2018). Therefore, the context of review a researcher chooses to adopt when examining purchasing intentions could vary with the social framework in question. Therefore, there is a need to undertake a context-specific understanding of factors that may impact people’s cognitive processes and decisions.

The importance of employing a context-specific framework of analysis is rooted in the inherent risk to blur the line between what can objectively be measured as changes in consumer behavior due to marketing and non-marketing factors. The first approach of analysis accommodates the moderating effects of generational attitudes towards purchasing intentions and the second one is dependent on the effectiveness of marketing strategies on human behaviors. This dichotomy is social because it depends on human behavior, which varies across different demographic groups (De Mooij, 2019). It may also have implications on the methodology researchers use to estimate changes to consumer behaviors or purchasing intentions due to the difficulty of achieving standardization of behavior across various market segments.

Hypotheses Development

Critiques have questioned the extent that consumers can be affected by other people’s decisions. They argue that people’s purchasing intentions are products of complex processes, some of which are moderated by one’s upbringing, gender, and culture (Ahmad, 2020). Therefore, an argument has been advanced, which suggests that consumer purchasing intentions should be viewed within the broader metrics of factors impacting their cognitive processes. In developing hypotheses guiding the present study, it is claimed that online reviews significantly influence the purchasing intentions of Generation Y consumers. This idea stems from the seminal works of researchers, such as Saleem, Shenbei and Hanif (2020) who opine that differences in life events, experiences, and exposures to technology influence peoples’ purchasing intentions. Therefore, in this review, generational differences form the basis for evaluating consumers’ purchasing intentions.

Based on the insights highlighted above, there is a need to adopt a context-specific framework for reviewing consumer purchasing intentions. The current probe investigates the research topic from the perspective of a Chinese customer. The present investigation could add to the growing body of knowledge regarding factors that affect the typical Chinese customer. Findings could be extrapolated from one generational cohort to another, or from the Chinese market to the larger Asian demographic. Therefore, the context-specific framework adopted for the investigation creates a platform for unpacking details about consumer online reviews that affect purchasing intentions. To recap, the hypotheses underpinning this study are relevant to three key areas of assessment – brand loyalty, product recommendations, and repeat purchases.

Hypothesis 1 – Repeat Purchases

Consumer behavior and purchasing intentions can be measured by examining repeat purchase trends. This link explains why recurrent purchases have been included in the present probe as a research variable. Scholars argue that customers who buy goods or services from one vendor multiple times are demonstrating their trust and confidence in the seller (Abasilim, Gberevbie and Osibanjo, 2019). In the context of this study, a relationship is a form of trusted partnership between a buyer and seller whereby the customer repetitively goes to one vendor with the expectation that they would get the same quality of service or product. Researchers associate this behavior with increased customer loyalty and satisfaction (Alhmoud and Rjoub, 2019). Online reviews are likely to create the same outcome due to the influence of customer loyalty and satisfaction on consumer behavior. Therefore, such reviews are likely to influence consumer decision-making processes in favor of making recurrent purchases or terminating a business relationship.

Based on the pieces of evidence espoused above, it is hypothesized that online reviews significantly influence repeat purchase intentions. The assumption herein states that customers who are exposed to negative online reviews will likely terminate their relationship with a vendor or refrain from being associated with them in the first place (Hamilton et al., 2021). Similarly, those who are exposed to positive views about a vendor are likely to consider buying products from trusted vendors, hence promoting the repeat purchase intentions outlined above. In this correlation, recurrent purchase intentions appear to be a product of the type of feedback consumers get when reviewing the experiences of other customers online. To this end, the first hypothesis that will be tested in this study appears below

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H1: Online reviews increase the frequency of repeat purchase intentions among Generation Y consumers

Hypothesis 2 – Product or Service Recommendations

A consumer’s purchasing intentions are influenced by several factors, some of which are intrinsic and others extrinsic. Intrinsic factors are linked to one’s values, beliefs, and attitudes, while extrinsic factors relate to the influences of other people on their buying intentions (Saleem, Shenbei and Hanif, 2020). The latter argument forms the basis for this review because analyzing a customer’s willingness to recommend a product or service to another person signifies their confidence in it. The action is an indicator of consumer purchasing intentions because recommending a product or service to another person suggests that they are willing to come back for more (Alhmoud and Rjoub, 2020). Based on this statement, scholars have linked product or service recommendations to word-of-mouth communications in marketing (Men, Qin and Jin, 2021). It is a powerful tool of marketing because it conveys one’s trust and commitment to a product or service.

Online reviews can reinforce or undermine a customer’s confidence in a product or service. For example, a hotel guest who has a pleasant stay at a hotel could experience confirmation bias when they go online and find people who share similar positive views about their experiences at the same hotel (Wang et al., 2021). They may go a step further and ignore any other information that may exist on the platform because it does not align with their preferred outcome (Wang et al., 2021). Therefore, online reviews could affirm or dissuade customers from purchasing a good or service based on the kind of feedback they receive on these platforms. Based on this analysis, the second hypothesis that will be tested in this study appears below.

H2: Online reviews moderate the willingness of Generation Y consumers to recommend products or services to potential customers

Hypothesis 3 – Consumer Brand Commitment

The concept of brand commitment is closely associated with that of brand loyalty. Its inclusion in this study stems from extensive works of the literature, which show that brand commitment is gaining increased interest in the field of consumer behavior (Abasilim, Gberevbie and Osibanjo, 2019). This statement explains the logic of using brand loyalty to understand the purchasing intentions of Generation Y consumers. Scholars argue that brand commitment symbolizes a significant level of emotional investment in a brand or product (Shapiro, 2020). Therefore, the more emotionally invested a customer is towards a product or service, the more likely they are to remain committed to it. Online reviews are included in the current probe because they have the potential to influence consumer brand commitment (Lee, 2009). Indeed, positive reviews boost consumer trust and enhance their commitment to a product or service (Shapiro, 2020). Similarly, negative reviews are likely to erode customers’ confidence in a product or service Based on these findings, the third hypothesis that will be tested in the current study appears below.

H3: Online reviews moderate consumer brand commitment among Generation Y buyers

Theoretical Framework

The theory of buyer behavior is adopted in the current probe as the theoretical framework for review. The justification for its use in this framework of review is rooted in its focus on factors that influence behavior, which, in the context of the present study, refers to purchasing intentions. The theory was selected for use in the present study because it seeks to explain defiant behaviors that can be examined by questioning dominant assumptions (Howard and Sheth, 2022). Proponents of the theory argue that purchasing intentions are predictable and repetitive (Howard and Sheth, 2022). Equally, they believe that people are generally lazy and tend to make purchasing decisions that save them time and money. Familiar purchasing routines emerge in this context and they can be reviewed for a better understanding of consumer behaviors.

The theory of buyer behavior was employed in the current study because it seeks to find out elements in consumer decision-making processes that affect buyers’ choices. It equally explores the motives for making purchasing decisions and any changes that may occur as a result (Howard and Sheth, 2022). The theory of buyer behavior can be contrasted with the theory of reasoned action because their proponents argue that consumer choice is informed by several factors and select motives of engagement as the most powerful force influencing purchasing intentions (Lewis, Ricard and Klijn, 2018). This framework of analysis draws attention to the information buyers consume before making their purchasing decisions. Consumers review such data from a needs perspective where they assess information based on how relevant it is to their needs and how well it could help them to fulfill these needs (Rendeci, 2022). Subject to this framework of engagement, the theory of buyer behavior provides a platform for assessing the impact of online reviews on consumer purchase intentions.

Summary

The findings of this study indicate that consumer behavior varies with generational differences. These differences stem from life events that members of each demographic group have encountered. The evidence gathered so far is elaborate but fails to indicate behavioral nuances across consumer groups and economic sectors. Notably, there is a gap in the literature, which has failed to explain the impact of Chinese media on purchasing intentions. It is important to explore this line of the probe because China’s social media landscape is dissimilar from that of the west. Therefore, it is critical to investigate whether the relationship between online reviews and consumer purchasing intentions among Chinese consumers is the same as that of their western counterparts.

Methods

As highlighted in this study, the focus of the current probe is highlighting the effects of online reviews on the purchasing intentions of Generation Y consumers. This chapter highlights techniques that the researcher used to investigate the study topic. The format of this chapter follows the research onion model, developed by Saunders, Lewis, and Thornhill (2009), which contains six levels of analysis. They include research philosophy, approach, strategies, choices, time horizon, and techniques. This framework of review outlines techniques the researcher used to meet the objectives of the study.

Research Philosophy

There are four major types of research philosophies in research – positivism, realism, interpretivism, and pragmatism. Positivism relies on objectivity to understand the social world, while realism assumes that the truth could exist independently of the human mind (Allibang, 2020). Comparatively, the pragmatism research philosophy states that researchers should choose techniques that suit their unique study focus (Raju and Prabhu, 2019). At the same time, the interpretivism research philosophy assumes that a researcher plays an instrumental role in observing a given phenomenon without interrupting its occurrence (Kornberger and Mantere, 2020). Based on the merits and demerits of these techniques, the positivism research philosophy emerged as the most suitable philosophy for the study. It was appropriate for the investigation because the researcher relied on objectivity to understand purchasing intentions. Thus, elements that would cause bias in the interpretation of findings were eliminated from the probe.

Research Approach

Inductive and deductive techniques are the two major approaches in academic studies. The inductive method is associated with the process of collecting various pieces of evidence to come up with a common conclusion (Saunders, Lewis and Thornhill, 2009). Alternatively, the inductive method aims to test whether a theory or argument is plausible (Conway, 2020). Given that the main hypothesis underpinning this study suggests that online reviews moderate the purchasing intention of Generation Y buyers, the deductive technique emerged as the most suitable approach for the study. It allowed the researcher to test whether the hypotheses developed were true or false.

Research Strategy

The major research strategies used in academic research include experiments, surveys, case studies, and archival research. The present investigation used a combination of the case study and archival research methods because both primary and secondary sets of data were included in the review. The case study format was selected in the investigation because the investigation focused on Generation Y consumers who used the Xiaohongshu platform. Therefore, the case study research strategy was effectively adopted as the mainstream method of analyzing the evidence. The archival technique was included in the investigation as a supplementary framework of review. The goal of including secondary data was to compare and contrast it with the primary findings obtained from the respondents. The goal was to identify areas of convergence or disparity of thought for further analysis.

Research Methods

There are two major research methods adopted in academic research – qualitative and quantitative. Researchers often use the qualitative method to measure subjective variables (Wang et al., 2020). Comparatively, quantitative research is associated with the collection of quantifiable data (Saunders, Lewis and Thornhill, 2009). A third method emerges from a combination of both techniques highlighted above – mixed methods. As its name suggests, this technique combines elements of both qualitative and quantitative research techniques to gain a comprehensive overview of the findings (Brannen, 2017). This technique was integrated into the current investigation because of the exploratory nature of the probe. Broadly, “online reviews” formed the independent variable while “purchasing intention” was the dependent variable.

Time Horizon

The time horizon analysis of a research project relates to the time sequence approach taken by a researcher when collecting data. Two main techniques are used to analyze the time series analysis – cross-sectional and longitudinal techniques (Saunders, Lewis and Thornhill, 2009). The cross-sectional method is associated with the collection of research evidence at one point in time (Melnikovas, 2018). Comparatively, the longitudinal time series analysis is linked with the collection of data across different time sequences (Patten & Newhart, 2017). The current investigation adopted the cross-sectional time series analysis because data were obtained in one event. Therefore, the findings captured purchasing intentions at one point in time.

Techniques and Procedures

The techniques and procedures used by the researcher to investigate the topic of study are highlighted in this part of the review. These methods relate to data collection and analytical processes employed by the researcher throughout different stages of the investigation. Information relating to the data collection and analysis stages of the research process is highlighted below.

Data Collection

As mentioned in the first section of this probe, the present study focused on examining the purchasing intentions of Generation Y consumers. To recap, this demographic group comprises people who were born between the mid-80s and mid-90s (Lim et al., 2020). In some scholarly works of literature, this population group is also known as Millennials (Miklian and Hoelscher, 2021). The researcher examined their purchasing intentions based on their exposure to product reviews. This target population was justifiably selected as the sample group for review due to their increased dominance in the labor and consumer market segments (Omilion-Hodges & Sugg, 2019). Indeed, Millennials have unique characteristics, which marketers examine to influence their purchasing intentions. Failing to identify these unique characteristics in the behaviors and characteristics of Generation Y consumers implies that companies will be missing the opportunity to monetize their market potential.

The current study investigated the purchasing intentions of Millennials because of their growing dominance in the market. Again, this focus of the study is informed by the familiarity of this population group with online shopping reviews. Indeed, unlike other demographic groups, Millennials have grown up with computers, the internet, smartphones, and other technological equipment in their lives (Doherty, 2019). Therefore, it is assumed that they have gathered immense experience operating these devices and shopping online. This expectation comes from the integrated nature of technology in the life of the average Millennial (Doherty, 2019). Consequently, it could be assumed that they have a wealth of experience, which the researcher used as a resource to understand the impact of online reviews on their purchasing intentions.

The selected sample of respondents comprised Chinese students who used Xiaohongshu, which is a popular social media e-commerce platform. This social media tool is commonly used in China more than in any other part of the world and works in the same manner as Instagram does (Wayne and Uribe Sandoval, 2021). The researcher selected this platform to contact 126 respondents who participated in reviewing cosmetic products before purchase. The researcher contacted the informants from Xiaohongshu by messaging and requesting them to take part in the investigation. The Xiaohongshu platform was selected as the preferred social media platform for assessing online reviews because of its high population of Generation Y users. Indeed, it is estimated that up to 90% of Xiaohongshu’s customers fit this profile (Statista, 2022). The social media platform acts as a convergence point for the views of influences and regular users when making product reviews.

Respondents were furnished with details regarding the research plan and scope of the investigation using a participant information sheet (see appendix 1). This document provided the informants with extensive information relating to the research plan, including procedures for collecting and treating data, as well as recording observations. The aim was to educate them about what to expect in the study. The inclusion criterion for this group of respondents was defined by their online shopping experiences. Therefore, those who were recruited in the investigation had a history of online shopping.

The researcher obtained data from the respondents by sending them a link to the structured questionnaire via their social media accounts. This link was shared with them after consenting to participate in the study (see appendix 2). As part of the researcher’s duty to inform potential participants of the risks and opportunities of taking part in the study, they were notified of the main thematic areas that were to be covered in the questionnaire surveys. The goal was to familiarize the informants with areas of interest that were to be included in the investigation without giving specific details about the actual questions to be asked in the probe.

The researcher used the purposeful sampling technique to recruit participants because it allows investigators to identify informants with unique characteristics. This feature made it possible for the researcher to differentiate respondents who were exposed to product reviews in the cosmetics sector from those in other industries (Kim et al., 2018). Therefore, the purposeful sampling method was critical to the development of the current study because the researcher intended to identify respondents who met the criterion of being part of the Generation Y market cohort. The desire to identify respondents who were exposed to product reviews in the cosmetics industry also contributed to the reasons for using the technique. The purposeful sampling method provided the researcher with the leeway to identify respondents who met these criteria.

The respondents’ views were measured using the Five-point Likert scale. As its name suggests, the scale has five levels of measuring the intensity of responses. They include “strongly agree,” “agree,” “neither agree nor disagree,” “disagree,” and “strongly disagree.” As highlighted in Appendix 3, these five layers of measurement were used to gauge the informants’ sentiments about 15 statements aimed at detecting changes in purchasing intentions. These questions are located in the second part of the questionnaire, under the “factor analysis” section, where three variables were measured to estimate changes in consumer purchasing behaviors. The three variables included brand commitment/loyalty, willingness to recommend products to potential customers, and frequency of repeat purchases. Each of these categories of assessment had five statements to comprise 15 statements in the factor analysis.

Data Analysis

The data analysis section of this study was undertaken using the Statistical Package for the Social Sciences (SPSS) software – version 25. The researcher used this automated technique to conduct the analysis because of the large number of respondents who participated in the investigation. The SPSS method is known as a proficient tool for analyzing such large volumes of data (Brooker, Barnett and Cribbin, 2018). Data analysis processes adopted using this technique are categorized into two groups – descriptive and inferential analyses. The descriptive analysis part involved the presentation of expressive data relating to the research variables, such as frequencies, mean, and mode (van Wingerde and van Ginkel, 2021). These details were used to estimate the number of respondents who fit a specific demographic profile. This information was later combined with the findings of the inferential analysis part, which sought to find out if there was a correlation between the data obtained and the demographic characteristics of the respondents, to develop the final results.

Reliability Tests

The researcher conducted reliability tests in the study to safeguard the credibility of the findings. To this end, the Cronbach alpha test was used to test the reliability of the questions posed to the respondents in the questionnaires. The purpose of conducting this test was to assess the internal consistency of the five questions associated with each of the variables examined (Raju and Prabhu, 2019). To recap, the three variables assessed in the analysis included “repeat purchases,” “product/service recommendations,” and “brand commitment.” All statements linked to the three variables were measured using the 5-Point Likert scale. Table 3.1 below shows the results for the Cronbach’s alpha co-efficient test.

Table 3.1: Reliability test findings


Scale	Cronbach’s Alpha	No. of Items
Repeat Purchase Intentions	0.87	5
Product/Service Recommendations	075	5
Brand Commitment	0.81	5

As highlighted above, all the three variables tested in the study reported values that were higher than 7, which is the standard ideal result for affirming internal consistency in a questionnaire (Patten & Newhart, 2017). The lowest internal consistency reported in the analysis related to “product/service recommendations” variable, which had a figure of 0.75. “Brand commitment” and “repeat purchase intentions” similarly met the same threshold with the latter having the highest value of 0.87. Broadly, based on the findings highlighted above, it can be assumed that each of the statements linked to the consumer behavior outcomes sampled in the study were correctly designed to address different areas of purchase intentions (Raju and Prabhu, 2019). Therefore, subject to this statement, it can be similarly assumed that the questionnaire items, which were positively formulated, had strong a strong internal consistency and reliability.

Ethical Considerations of Study

According to the researcher’s policy on research ethics, participants were required to sign an ethical approval form. The aim of following the university’s ethical procedures for conducting research was to avoid instances where activities related to the research process could bring disrepute to the university. Key ethical considerations in this study were anonymity, treatment of data, and withdrawal from the study.

Anonymity and Confidentiality: Protecting the identity of the respondents was a key priority for the researcher. This task aligns with the duty of researchers to “do no harm” to informants as they participated in the investigation (Kim et al., 2018). Consistent with this goal, the researcher refrained from asking questions or seeking details from the informants that would make it possible to identify them. Nonetheless, emphasis was made to understand the effects of the respondents’ characteristics, such as age, gender, and education qualifications, on their perspectives on the research topic. Stated differently, information concerning the informant’s educational qualifications, gender, time spent on social media, and marital status was included in the investigation. These details were integrated into the probe because extant works of literature have highlighted their role in moderating consumer behavior.

Withdrawal from the Study: As highlighted in Appendix 2 of this document, the researcher required the participants to sign an informed consent form that affirmed their voluntary participation in the investigation. Stated differently, the informed consent form helped to enlighten participants about conditions that regulated the relationship between the researcher and informants. In line with the objective to stay objective throughout the research process, respondents enjoyed the freedom to withdraw from the study without any repercussions. Therefore, their participation in the investigation was voluntary and the researcher did not offer monetary incentives to secure their commitment to take part in it.

Data Storage and Use: Data obtained from informants were stored in a computer and secured using a password. Equally, the information gathered in the probe was not sent overseas or used outside of the European Union (EU) due to changes in the scope and extent of protection law applications. The goal of setting up this limitation was to safeguard the rights of the respondents, which are secured within the EU legal framework (Osafo, Paros and Yawson, 2021). Therefore, transferring data outside of the jurisdiction would undermine the protection they enjoy under the law.

Limitations of Study

The findings of this study are limited to the characteristics of the sample population. This feature makes it difficult to extrapolate the findings beyond the main demographic group that participated in the investigation. Therefore, it could be assumed that the findings generated in this investigation were only indicative of the purchasing intentions of consumers who hailed from China and fit the characteristics of Generation Y users.

Results/Analysis

This section of the review highlights the findings obtained after implementing the techniques highlighted in section three above. As mentioned in this study, primary data was gathered from a group of Xiaohongshu users and the information was analyzed using SPSS. The descriptive and inferential analysis findings of the investigation are highlighted below.

Descriptive Analysis

The descriptive analysis part of the current probe related to the information provided by the respondents in the first part of the questionnaire, which captured their demographic characteristics. Notably, four variables were assessed in this part of the probe – gender, education qualifications, marital status, and time spent online. These variables were included in the probe because of their moderating effects on consumer purchase intentions.

Gender

In the first part of the questionnaire, respondents had to state their gender as being either male or female. As shown in Table 4.1 below, 84.9% of the informants were female.

Table 4.1: Distribution of respondents according to gender

What is your gender?
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Male	19	15.1	15.1	15.1
	Female	107	84.9	84.9	100.0
	Total	126	100.0	100.0

The above-mentioned findings are consistent with the percentage of female users on the Xiaohongshu platform because it is estimated that about 90% of them are female (Statista, 2022). Additionally, women comprise the majority of customers in the cosmetics industry. Therefore, it is no surprise for men to be minimally represented in the sample. Nonetheless, based on the distribution of the genders outlined above, it could be assumed that the statistics mainly represented the views of female customers.

Marital Status

The respondents’ marital status was the second variable assessed in the current probe. Its inclusion in the research study was informed by differences in purchasing behaviors among people who live in different household structures (Lamanna, Riedmann and Stewart, 2020). According to Table 4.2 below, most of the respondents (82.5%) were single. Those who were divorced and widowed formed only 5% of the total sample.

Table 4.2: Distribution of respondents according to marital status

What is your Marital Status?
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Single	104	82.5	82.5	82.5
	Married	16	12.7	12.7	95.2
	Divorced	3	2.4	2.4	97.6
	Widowed	2	1.6	1.6	99.2
	Other	1	.8	.8	100.0
	Total	126	100.0	100.0

The high percentage of respondents who were “single” is not surprising because the sample demographic mostly comprised of young people. Thus, it could be argued that Generation Y respondents who participated in the study were young people without family commitments.

Education Qualifications

The third demographic variable sampled in the investigation related to the educational qualifications of the respondents. As highlighted in Table 4.3 below, a majority of the respondents had a “high school or lower” education qualification.

Table 4.1: Distribution of respondents according to education qualifications

What is your highest educational qualification?
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	High School	88	69.8	69.8	69.8
	Diploma	14	11.1	11.1	81.0
	Undergraduate	20	15.9	15.9	96.8
	Masters	4	3.2	3.2	100.0
	Total	126	100.0	100.0

Based on the above findings, the high concentration of respondents on the “high school” education level could be explained by the relatively young age of the informants. Concisely, most of them were still in college and had not graduated yet.

Time Spent on Social Media

The last demographic variable sampled in this study was the time spent on social media. This variable was included in the investigation because research studies have suggested a correlation between the time people spend on social media and their purchasing behaviors (Eze, Chinedu-Eze and Awa, 2021). Table 4.4 below shows that most of the respondents (54.8%) spent less than 2 hours on social media. Comparatively, those who spent between two and five hours formed 23% of the total sample.

Table 4.4: Distribution of respondents according to hours spent on Xiaohongshu

How many hours do you spend on Xiaohongshu per day?
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Less than 2 hours	69	54.8	54.8	54.8
	2-5 hours	29	23.0	23.0	77.8
	5-7 hours	15	11.9	11.9	89.7
	7-10 hours	12	9.5	9.5	99.2
	More than 10 hours	1	.8	.8	100.0
	Total	126	100.0	100.0

The above-mentioned demographic findings will later be assessed in the inferential analysis section to determine if they affected the respondents’ feedback, or not.

Factor Analysis

The second part of the questionnaire contained a set of 15 statements addressing three critical areas of human behavior. These areas of analysis helped the researcher to predict consumer purchasing intentions. They included frequency of making purchases, willingness to recommend products to other buyers, and commitment to a brand. These critical areas of importance formed the basis for the development of the three research hypotheses underpinning this investigation. The first hypothesis claimed that online reviews increased the frequency of repeat purchase intentions among generation Y consumers and the findings are highlighted below.

Hypothesis 1 Findings

Table 4.5 below affirms H1 findings and they demonstrate that online reviews had a significant impact on repeat purchase intentions of the respondents. This view is supported by the fact that the average mean of the respondents was less than 3. This figure implies that the informants “agreed,” “strongly agreed,” or “neither agreed nor disagreed” with the statements posed to them. The low number of dissenting views explains the low mean.

Table 4.5: Hypothesis 1 findings

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
Repeat Purchases1	126	1	5	1.58	.794
Repeat Purchases2	126	1	5	2.56	1.395
Repeat Purchases3	126	1	5	2.60	1.345
Repeat Purchases4	126	1	4	2.62	1.186
Repeat Purchases5	126	1	5	2.86	1.205
Valid N (listwise)	126

The findings highlighted above are consistent with research investigations, which have affirmed a positive relationship between frequency of purchase and the medium of purchase (Klesse et al., 2019). Most of these studies claim that the uptake of digital commerce has made it convenient for most people to purchase goods from the comfort of their homes, thereby increasing their frequency of purchase (Luo and Suacamram, 2022; Klesse et al., 2019). This behavior has further been affirmed by the convenience brought by digital purchasing platforms on consumer buying patterns because they entice them to buy goods online due to the ability to deliver them to their homes. The integration of financial services with -ecommerce platforms has made this transformation of purchasing behaviors possible because people find it easier to buy good using credit cards of mobile money applications as opposed to making physical visits to a store.

Hypothesis 2 Findings

The second hypothesis examined in this investigation tested the respondents’ resolve to recommend products to potential clients based on their exposure to online feedback. H2 claimed that online reviews moderated the willingness of generation Y consumers to recommend products or services to potential customers. As highlighted in Table 4.6 below, the hypothesis stated above was affirmed because the mean values of all the five statements relating to this variable were less than 3.

Table 4.6: Hypothesis 2 findings

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
Product Recommendations1	126	1	4	1.73	.950
Product Recommendations2	126	1	5	2.61	1.073
Product Recommendations3	126	1	5	1.65	.783
Product Recommendations4	126	1	5	2.67	1.367
Product Recommendations5	126	1	5	1.87	1.012
Valid N (listwise)	126

The second hypothesis was affirmed by the findings highlighted above because a majority of the respondents sampled in the study agreed with the view that their exposure to online reviews influenced their willingness to recommend products or services to other buyers. This finding is consistent with those of other researchers who affirm a positive relationship between consumer loyalty and exposure to product recommendations (Luo and Suacamram, 2022). They suggest that the relationship between the two variables is moderated by the perceived consumer screening cost and decision-making quality of the purchasing process.

The perceived consumer screening cost is determined by the trouble a potential buyer would go through to determine the right product to buy. Frustration is likely to occur in situations where the consumer has no prior knowledge of the product or service they intend to purchase (Klesse et al., 2019). Therefore, they delegate this responsibility to other buyers who have purchased similar products. Their decisions or recommendations are henceforth deemed to influence the final decision to be made by the customer.

In this analysis, the convenience of avoiding the time and costs associated with determining the best product to buy is here-in referred to as the “consumer screening cost.” This value could be used to explain why online reviews influence the willingness of consumers to recommend products to their peers. It gives them an opportunity to avoid the cost of determining the right product to buy. Indeed, it helps them to avoid the trouble of having to know or be acquainted with people who have bought products of a similar nature (Luo and Suacamram, 2022). Concisely, online consumer forums give potential buyers a wealth of information about the impact that products have had on the experiences of other buyers.

The decision to recommend some products to buyers emerges as a positively self-reinforcing behavior in the purchasing process, while raising awareness about the negative attributes of a brand creates negative self-reinforcing actions. The positive and negative self-reinforcing behaviors have been secured by the emergence of online consumer communities, on various social media platforms, that share interests on brands or companies. For example, there are several online communities for car enthusiasts whose interests or commonality stems from their love for specific brands of automobiles (Luo and Suacamram, 2022). Therefore, although there could be superior and more affordable substitutes in the market, they would receive limited attention because of loyalty to a preferred brand. Specific tech-based companies, such as Apple, have built a loyal customer base this way (Klesse et al., 2019). It is likely to rate the company’s products positively even though the competition could offer substitute items of superior quality and at a lower price. This high level of brand loyalty creates reinforcing patterns of consumer behavior that could be used to explain the respondents’ claim that online reviews were likely to influence their willingness to recommend brands to other people.

Based on the evidence highlighted above, it can be deduced that the findings highlighted in Table 4.6 above are consistent with the existing body of evidence on consumer behavior. In other words, it indicates that online reviews are self-reinforcing and thus impact a consumer’s willingness to recommend products or services to other buyers. Therefore, the above statistics affirm the second hypothesis, which claims that online reviews moderate the willingness of Generation Y consumers to recommend products or services to potential customers.

Hypothesis 3 Findings

The third hypothesis tested in the present investigation suggested that online reviews moderate consumer brand commitment among generation Y buyers. The findings highlighted in Table 4.7 below are consistent with those of H1 and H2 findings mentioned above because they affirm H3. This outcome is supported by the fact that no mean values for the statements associated with this variable was greater than 2.7. This number means that the respondents “agreed” with the statements posed to them that sampled their views about the relationship between online reviews and brand commitment.

Table 4.7: Hypothesis 3 findings

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
Brand Commitment1	126	1	5	2.66	1.247
Brand Commitment2	126	1	4	2.48	.994
Brand Commitment3	126	1	4	1.52	.874
Brand Commitment4	126	1	4	1.78	.946
Brand Commitment5	126	1	4	1.92	1.009
Valid N (listwise)	126

The findings highlighted above are consistent with the current body of evidence explaining the relationship between consumer brand commitment and product reviews. This positive relationship has its history rooted in research studies, which analyzed the impact of product quality on brand loyalty (Gai and Klesse, 2019). They suggest that brand commitment increases with an escalation of perceptions about product quality (Klesse et al., 2019). In the context of this study, product quality is a function of consumers’ online reviews. Therefore, there is a direct correlation between perceived product quality and the type of review it attracts from users.

In line with the aforementioned relationship, poor brand quality is likely to be realized when consumers have negative views about a product or service. Similarly, positive reviews symbolize the presence of high quality products. Therefore, it is assumed that products which attract positive reviews are likely to reinforce consumer loyalty and brand commitment (Gai and Klesse, 2019). The opposite reaction is also true because products with negative reviews undermine the confidence that people have in them. Therefore, there is a self-reinforcing behavior that influences the manner people review products. Overall, based on the findings highlighted above, it could be argued that brand commitment is a function of the reviews that people have made regarding a product or service.

Inferential Analysis

It was important to undertake an inferential analysis of the findings to determine if any of the demographic variables sampled in this review affected the opinions of the informants. To this end, the impact of gender, marital status, education, and time spent on social media were appraised in this assessment to predict their impact on the findings.

Impact of Gender on Findings

The findings highlighted in Table 4.8 below highlight the outcome of the analysis investigating the impact of gender on the informants’ views.

Table 4.8: Impact of Gender on Purchasing Intentions

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Repeat Purchases1	Between Groups	.061	1	.061	.096	.757
	Within Groups	78.645	124	.634
	Total	78.706	125
Repeat Purchases2	Between Groups	1.913	1	1.913	.983	.323
	Within Groups	241.198	124	1.945
	Total	243.111	125
Repeat Purchases3	Between Groups	1.277	1	1.277	.704	.403
	Within Groups	224.881	124	1.814
	Total	226.159	125
Repeat Purchases4	Between Groups	6.496	1	6.496	4.760	.031
	Within Groups	169.218	124	1.365
	Total	175.714	125
Repeat Purchases5	Between Groups	2.794	1	2.794	1.939	.166
	Within Groups	178.635	124	1.441
	Total	181.429	125
Product Recommendations1	Between Groups	1.629	1	1.629	1.817	.180
	Within Groups	111.196	124	.897
	Total	112.825	125
Product Recommendations2	Between Groups	.354	1	.354	.305	.581
	Within Groups	143.591	124	1.158
	Total	143.944	125
Product Recommendations3	Between Groups	.166	1	.166	.269	.605
	Within Groups	76.469	124	.617
	Total	76.635	125
Product Recommendations4	Between Groups	.041	1	.041	.022	.882
	Within Groups	233.617	124	1.884
	Total	233.659	125
Product Recommendations5	Between Groups	.011	1	.011	.010	.920
	Within Groups	127.958	124	1.032
	Total	127.968	125
Brand Commitment1	Between Groups	.142	1	.142	.091	.763
	Within Groups	194.183	124	1.566
	Total	194.325	125
Brand Commitment2	Between Groups	.300	1	.300	.302	.584
	Within Groups	123.169	124	.993
	Total	123.468	125
Brand Commitment3	Between Groups	.000	1	.000	.000	.989
	Within Groups	95.428	124	.770
	Total	95.429	125
Brand Commitment4	Between Groups	.478	1	.478	.533	.467
	Within Groups	111.300	124	.898
	Total	111.778	125
Brand Commitment5	Between Groups	.138	1	.138	.135	.714
	Within Groups	127.068	124	1.025
	Total	127.206	125

The findings highlighted above demonstrate that gender had an insignificant impact on the respondents’ views because none of the 15 statements posed in the questionnaire met the p>0.05 threshold of significance. This finding is inconsistent with the current body of evidence, which suggests differences in purchasing behaviors between men and women. For example, Bryan, Pope and Rankin-Wright (2021) noted that both genders had different risk appetite levels that influenced the kind of goods or services they bought. Similarly, a study by Hermansen and Penner (2022) demonstrated that cultural differences in societies influenced the market behaviors of men and women. None of these variances sufficed in the study to demonstrate differences in purchasing behaviors between men and women.

Disparities between the purchasing intentions of men and women in western and eastern societies are apparent in current literature (Bryan, Pope and Rankin-Wright, 2021). This area of research is relevant to the current probe because the informants who participated in the study were Chinese. Nonetheless, cultural differences did not affect the findings highlighted in this report due to the low significance levels of the research statements posed to the respondents. These findings could mean that there are limited differences between the purchasing behaviors of men and women among generation Y consumers. Therefore, it could be deduced that the effects of culture and risk appetite differences are diminished among younger demographics of buyers.

Impact of Marital Status on Findings

As highlighted above, the findings of this study demonstrated an insignificant impact between gender and the informants’ views on the research topic. The same result was reported when investigating the impact of marital status on the informants’ purchasing behaviors. Table 4.9 below highlights this outcome because only one out of the 15 statements posed to the informants met the significance threshold of p>0.05.

Table 4.9: Impact of marital status on consumer purchasing intentions

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Repeat Purchases1	Between Groups	3.501	4	.875	1.408	.235
	Within Groups	75.205	121	.622
	Total	78.706	125
Repeat Purchases2	Between Groups	2.174	4	.543	.273	.895
	Within Groups	240.938	121	1.991
	Total	243.111	125
Repeat Purchases3	Between Groups	2.333	4	.583	.315	.867
	Within Groups	223.825	121	1.850
	Total	226.159	125
Repeat Purchases4	Between Groups	2.163	4	.541	.377	.825
	Within Groups	173.551	121	1.434
	Total	175.714	125
Repeat Purchases5	Between Groups	3.603	4	.901	.613	.654
	Within Groups	177.825	121	1.470
	Total	181.429	125
Product Recommendations1	Between Groups	5.024	4	1.256	1.410	.235
	Within Groups	107.801	121	.891
	Total	112.825	125
Product Recommendations2	Between Groups	11.456	4	2.864	2.616	.039
	Within Groups	132.489	121	1.095
	Total	143.944	125
Product Recommendations3	Between Groups	1.346	4	.337	.541	.706
	Within Groups	75.288	121	.622
	Total	76.635	125
Product Recommendations4	Between Groups	4.117	4	1.029	.543	.705
	Within Groups	229.542	121	1.897
	Total	233.659	125
Product Recommendations5	Between Groups	15.069	4	3.767	4.038	.004
	Within Groups	112.899	121	.933
	Total	127.968	125
Brand Commitment1	Between Groups	3.962	4	.990	.630	.642
	Within Groups	190.364	121	1.573
	Total	194.325	125
Brand Commitment2	Between Groups	5.403	4	1.351	1.384	.243
	Within Groups	118.066	121	.976
	Total	123.468	125
Brand Commitment3	Between Groups	9.145	4	2.286	3.206	.015
	Within Groups	86.284	121	.713
	Total	95.429	125
Brand Commitment4	Between Groups	7.340	4	1.835	2.126	.082
	Within Groups	104.437	121	.863
	Total	111.778	125
Brand Commitment5	Between Groups	4.569	4	1.142	1.127	.347
	Within Groups	122.638	121	1.014
	Total	127.206	125

The findings highlighted above are inconsistent with the current body of evidence describing the relationship between marital status and consumer shopping behaviors. Indeed, a majority of researchers indicate that people’s shopping behaviors vary with household types (Hartono et al., 2021). For example, families with both parents are more risk-averse compared to those which have one parent or are run by unmarried people (Ugaddan and Park, 2019). The same differences have been observed among consumers who hail from different generations (Hertz, Mattes and Shook, 2021). The findings of this study suggest that these differences did not affect consumers’ purchasing intentions. Thus, there is a disparity in responses between the current literature and the findings presented in this probe. This variance could be explained by the uniqueness of generation Y buyers.

Impact of Education Qualifications on Findings

The impact of education variables on the findings was more pronounced in the current probe compared to gender and marital status. This statement is plausible because three of the 15 statements posed to the respondents in the study met the significance threshold of p>0.05. However, 3 out of 15 statements was inadequate to affirm the influence of the respondents’ educational background on their views. Table 4.10 below summarizes the findings.

Table 4.10: Impact of education qualifications on purchasing intention

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Repeat Purchases1	Between Groups	2.198	3	.733	1.168	.325
	Within Groups	76.508	122	.627
	Total	78.706	125
Repeat Purchases2	Between Groups	13.611	3	4.537	2.412	.070
	Within Groups	229.500	122	1.881
	Total	243.111	125
Repeat Purchases3	Between Groups	5.472	3	1.824	1.008	.392
	Within Groups	220.687	122	1.809
	Total	226.159	125
Repeat Purchases4	Between Groups	1.093	3	.364	.254	.858
	Within Groups	174.622	122	1.431
	Total	175.714	125
Repeat Purchases5	Between Groups	16.874	3	5.625	4.170	.008
	Within Groups	164.555	122	1.349
	Total	181.429	125
Product Recommendations1	Between Groups	2.322	3	.774	.855	.467
	Within Groups	110.503	122	.906
	Total	112.825	125
Product Recommendations2	Between Groups	4.383	3	1.461	1.277	.285
	Within Groups	139.562	122	1.144
	Total	143.944	125
Product Recommendations3	Between Groups	.296	3	.099	.157	.925
	Within Groups	76.339	122	.626
	Total	76.635	125
Product Recommendations4	Between Groups	4.363	3	1.454	.774	.511
	Within Groups	229.296	122	1.879
	Total	233.659	125
Product Recommendations5	Between Groups	3.695	3	1.232	1.209	.309
	Within Groups	124.273	122	1.019
	Total	127.968	125
Brand Commitment1	Between Groups	3.093	3	1.031	.658	.580
	Within Groups	191.232	122	1.567
	Total	194.325	125
Brand Commitment2	Between Groups	6.999	3	2.333	2.444	.067
	Within Groups	116.469	122	.955
	Total	123.468	125
Brand Commitment3	Between Groups	95.429	3	31.810	.	.
	Within Groups	.000	122	.000
	Total	95.429	125
Brand Commitment4	Between Groups	65.414	3	21.805	57.376	.000
	Within Groups	46.364	122	.380
	Total	111.778	125
Brand Commitment5	Between Groups	56.395	3	18.798	32.387	.000
	Within Groups	70.812	122	.580
	Total	127.206	125

The low level of significance reported in the above findings was adequate to conclude that education did not affect the respondents’ views. Consequently, it could be assumed that this variable had an insignificant impact on the findings.

Impact of Time Spent On Social Media on Findings

The time spent on social media was the last variable analyzed in the inferential analysis part. As highlighted in Table 4.11, this factor of assessment had an insignificant impact on the findings because none of the statements sampled met the significance threshold of p>0.05.

Table 4.11: Impact of time spent on Xiaohongshu on purchasing intention

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Repeat Purchases1	Between Groups	4.598	4	1.150	1.877	.119
	Within Groups	74.108	121	.612
	Total	78.706	125
Repeat Purchases2	Between Groups	1.495	4	.374	.187	.945
	Within Groups	241.616	121	1.997
	Total	243.111	125
Repeat Purchases3	Between Groups	3.709	4	.927	.504	.733
	Within Groups	222.450	121	1.838
	Total	226.159	125
Repeat Purchases4	Between Groups	2.248	4	.562	.392	.814
	Within Groups	173.466	121	1.434
	Total	175.714	125
Repeat Purchases5	Between Groups	10.409	4	2.602	1.841	.125
	Within Groups	171.019	121	1.413
	Total	181.429	125
Product Recommendations1	Between Groups	.637	4	.159	.172	.952
	Within Groups	112.188	121	.927
	Total	112.825	125
Product Recommendations2	Between Groups	10.216	4	2.554	2.311	.062
	Within Groups	133.728	121	1.105
	Total	143.944	125
Product Recommendations3	Between Groups	2.297	4	.574	.935	.446
	Within Groups	74.338	121	.614
	Total	76.635	125
Product Recommendations4	Between Groups	2.314	4	.578	.303	.876
	Within Groups	231.345	121	1.912
	Total	233.659	125
Product Recommendations5	Between Groups	2.780	4	.695	.672	.613
	Within Groups	125.189	121	1.035
	Total	127.968	125
Brand Commitment1	Between Groups	5.564	4	1.391	.892	.471
	Within Groups	188.761	121	1.560
	Total	194.325	125
Brand Commitment2	Between Groups	7.695	4	1.924	2.011	.097
	Within Groups	115.773	121	.957
	Total	123.468	125
Brand Commitment3	Between Groups	8.004	4	2.001	2.770	.030
	Within Groups	87.424	121	.723
	Total	95.429	125
Brand Commitment4	Between Groups	7.127	4	1.782	2.060	.090
	Within Groups	104.651	121	.865
	Total	111.778	125
Brand Commitment5	Between Groups	2.841	4	.710	.691	.600
	Within Groups	124.366	121	1.028
	Total	127.206	125

The above-mentioned findings are consistent with the extant body of literature on consumer purchase intentions. Indeed, research studies have shown that Millennials have unique behaviors that align with the observations made in this study (Zheng et al., 2021). For example, low levels of trust have been associated with Millennials more than any other consumer group (Zhang, Jinpeng and Khan, 2020). This outcome is observed because they do not have a strong loyalty to a specific brand. Indeed, with the availability of multiple products in the market, most Generation Y consumers appear to be lost in endless choices. Therefore, they have a low affinity for staying loyal to a specific brand. Research studies have equally shown that this demographic group has little patience for poor quality services and inefficient service delivery processes (Zheng et al., 2021). Thus, they pay attention to products or brands that can assure them of top quality. In this regard, giant global firms with a reputation for innovation and quality products and services, such as Google and Apple, have reported increased brand values partly due to the attention they receive from Millennials.

The behaviors of Generation Y consumers were similarly observed through their financial behaviors, which ultimately influenced their purchasing intentions. For example, scholars have intimated that most Generation Y consumers are burdened by student debt and exhibit financial behaviors that reflect this problem (Sparre, 2020). Significant life purchases, such as homes, and impactful life events, such as weddings and marriages, have been postponed because of the financial insecurity that characterizes the Millennial generation. This behavior has been assimilated into their purchasing intentions with most of them preferring to pay for goods or services that would grant them access to resources as opposed to owning them. The appetite for on-demand services among Generation Y consumers affirms this observation.

Summary

The findings gathered in this study affirm the three hypotheses developed at the onset of this study. These findings have an implication on consumer purchasing behaviors because they demonstrate that online reviews can significantly alter the perception of generation Y consumers about their purchasing intentions. However, of interest to this investigation is the insignificant impact that the demographic profiles of the respondents had on these findings. This finding needs further probing because it contradicts the idea that consumer-purchasing intentions are subject to cultural and social factors.

Conclusion and Recommendations

Summary

The findings of the literature review chapter of this dissertation highlighted the importance of understanding the characteristics of a consumer as a tool to predict their future behaviors. This need outlines the premise for which the characteristics of Generation Y customers were evaluated in this study when examining their purchasing behaviors. For purposes of this review, these characteristics were confined to reviewing their social tendencies, cultural inclinations, and economic preferences.

Overall, this paper adopted a nuanced approach to addressing the research topic by limiting the scope of the investigation to the behaviors of Generation Y consumers and the cosmetics industry. Concisely, the findings of this study revealed that online reviews affect consumers’ frequency of making repeat purchases, willingness to recommend products to potential customers, and brand loyalty. Relative to the findings reported in section four of this study, the impact of online reviews on consumer behavior has been lost in discussions that have investigated the impact of technology or social media on consumer behavior.

The findings of this study have implications for the marketing strategies of multinational companies that intend to attract a young demographic of buyers. Similarly, analysts could use the findings of this study to predict future buying behaviors because Generation Y buyers are increasingly dominating the marketplace. Thus, it could be useful to predict associated market trends and exploit the opportunities that exist therein by developing products or services that appeal to the needs and requirements of younger buyers. These ideas have implications for the marketing strategies to be developed in the cosmetics industry and other economic sectors that target Generation Y buyers. However, it is equally important to recognize limitations to the findings because they are only relevant to the cosmetics industry and to the behaviors of a younger demographic of buyers. Therefore, attempting to extrapolate these findings to an older demographic may yield different results. Consequently, it is critical to recognize the limits of the study. At the same time, audiences intending to use the findings of this probe should understand that they are indicative. This means that they do not represent the actual views of consumers in the Chinese cosmetics sector.

Recommendations

The current study has focused on understanding the purchasing intentions of Generation Y customers based on their exposure to online reviews. Future studies may replace this target population with another demographic, such as Baby Boomers or Generation Z consumers, to find out if the findings will be consistent across different age groups. Similarly, future researchers may investigate the same research phenomenon using a group of non-Chinese respondents to establish if the findings will be consistent, or not. The same approach is applicable to the Xiaohongshu social media platform used to recruit participants because future research could explore the impact of online reviews on the purchasing intentions of other social media users, such as Facebook or Instagram subscribers. Broadly, these recommendations highlight the importance of adopting a holistic outlook on the research issue.

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Appendix

Appendix 1: Participant Information Sheet

Dear Participant,

You are invited to take part in a study that is being conducted for a research project as part of the Master’s degree in [insert program of study) at University. The purpose of the research is to understand the influence of online reviews on generation y’s purchase intention.

The interview will take roughly thirty minutes and can be held at a time and place that is convenient for you.

If you decide to take part in the study, you will be asked a series of questions, however, you do not have to answer any questions if you do not wish to and you are able to withdraw from the study at any time. Prior to the survey, you will be asked to sign a consent form that outlines more about your rights during the project and guarantees confidentiality of your information. The interview will be recorded and will later be transcribed. This will enable me to capture what is said in the interview without being distracted by the need to take notes. However, you can ask not to have your interview recorded or ask to have the recording device stopped at any time during the interview. Your transcribed interview will be kept for 12 months in a secure place and then destroyed.

Once all interviews have been conducted, a transcript/detailed notes will be analyzed and examined in an anonymous format, and compared with information from other interviewees. No information that could identify you as an individual will be used in writing up the research.

The research will be written up in the form of a dissertation to be submitted as a requirement for my MSc degree. Although the dissertation itself is not published, academic publications based on this work may also be developed. These too will be made anonymous, with respect to both individual interviewees and their organizations.

A copy of the transcribed interviews and the final dissertation can be made available to you upon request.

Kind Regards,

Researcher.

Appendix 2: Participant Consent Form

I have read the Information Sheet and have had the details of the study explained to me. My questions have been answered to my satisfaction, and I understand that I may ask further questions at any time. I have the right to withdraw from the study at any time and to decline to answer any particular questions. I agree to provide information to the researcher(s) on the understanding that my name will not be used without my permission (The information will be used only for this research and publications arising from this research project.)

I understand that I have the right to ask for the recording device to be turned off at any time during the interview.

Please tick:

I agreewith the interview being recorded.

I do notagree with the interview being recorded.

I agree to participate in this study under the conditions set out in the Information Sheet.

Name of Participant___________

Date______________________

Signature_______________________

Appendix 3: Questionnaire

Part A – Demographic Findings

[Please, (✔) tick on the appropriate boxes]

What is your gender?

☐Male
☐Female

What is your marital status?

☐ Single
☐ Married
☐ Widowed
☐ Divorced
☐ Other

What is your highest educational qualification?

☐ High school or lower
☐ Diploma
☐Undergraduate degree
☐Master’s Degree
☐ PhD or higher

How many hours do you spend on social media per day?

☐ Less than 2 hours
☐ 2-5 Hours
☐ 5-7 Hours
☐ 7-10 Hours
☐ More than 10 hours

Part B – Factor Analysis

In this part of the questionnaire, we seek your views on the effects that exposure to online comments has had on your purchasing intentions. Please, tick (✔) on the option that best describes your sentiments vis-à-vis the statements highlighted below.

Repeat Purchases

My exposure to online reviews has changed the frequency I purchase products

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

My exposure to online product reviews have made me change vendors

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

My exposure to product reviews has made me cancel a purchase

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

My exposure to product reviews has made me ask for a refund

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

My exposure to product reviews has made me change my mind about buying a product

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

Product/Service Recommendations

My exposure to product reviews has made me recommend a product to a friend or another buyer

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

I have felt guilty recommending a product to a friend after seeing negative reviews online

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

I would not recommend a product to a friend if I did not see positive reviews online

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

I have been discouraged from inquiring about a product because of the negative reviews

I saw online

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

I have bought a product that was recommended to me by a friend who saw positive online

Reviews

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

Brand Commitment/Loyalty

Seeing positive online reviews affirms my commitment to my favorite brands

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

I have lost trust in my vendor because of negative online feedback

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

I have bought goods from the competition because of negative online reviews about my favorite brands

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

I have discouraged other people from buying products that have negative reviews

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

My commitment to a product or brand can be shaken by consistent negative reviews

☐ Strongly Agree
☐ Agree
☐ Neither Agree nor Disagree
☐ Disagree
☐ Strongly Disagree

Thank you for participating in the study!

*The End*

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IvyPanda. (2023, November 17). The Influence of Online Reviews on Generation Y’s Purchase Intention. https://ivypanda.com/essays/the-influence-of-online-reviews-on-generation-ys-purchase-intention/

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"The Influence of Online Reviews on Generation Y’s Purchase Intention." IvyPanda, 17 Nov. 2023, ivypanda.com/essays/the-influence-of-online-reviews-on-generation-ys-purchase-intention/.

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IvyPanda. (2023) 'The Influence of Online Reviews on Generation Y’s Purchase Intention'. 17 November.

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IvyPanda. 2023. "The Influence of Online Reviews on Generation Y’s Purchase Intention." November 17, 2023. https://ivypanda.com/essays/the-influence-of-online-reviews-on-generation-ys-purchase-intention/.

1. IvyPanda. "The Influence of Online Reviews on Generation Y’s Purchase Intention." November 17, 2023. https://ivypanda.com/essays/the-influence-of-online-reviews-on-generation-ys-purchase-intention/.

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IvyPanda. "The Influence of Online Reviews on Generation Y’s Purchase Intention." November 17, 2023. https://ivypanda.com/essays/the-influence-of-online-reviews-on-generation-ys-purchase-intention/.

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