“Impact of online buying behavioral tendencies of Generation Z on their parents’ consumption behavior: Insight from Indonesia”

The generation gap has been present since the beginning of humanity and has symbol- ized one of the challenges of decision-making in families. It affects family members’ consumption behavior, namely buying decisions, and creates an interrelated impact on consumption behavior among family members. The aim of this study is to examine factors related to the parents’ perceptions of how the new online purchase behavior of their Generation Z children affected their consumption behavior. To meet the research objective, the paper has shed light on Generation Z’s new online purchase behavior. A survey was sent to 384 Indonesian parents of Generation Z children to collect their perceptions of consumption behavior. The data were then computed and processed us- ing factor analysis, reliability analysis, regression analysis, as well as correlation and a t-test. The research results indicate that the new online purchase behavior of Generation Z children significantly affected their parents’ consumption behavior through different factors, such as online purchase illiteracy and self-control of consumption behavior. The findings also asserted that family consumption behavior is easily influenced by factors associated with parents’ perceptions. Moreover, this study also discussed the implications of the findings and identified the areas for future research. of The results show two hypotheses consistent


INTRODUCTION
Generation Z represents more than 25% of the world population. Digital people or natives are the "native speakers" to the digital language of the internet, computers, video games, and multimedia platforms (Prensky, 2001). When connected from an early age, this generation is completely different from the other generations (Baby Boomers, Generations X or Y). This generation was born between 1993 and 2005 (Turner, 2015), and then grew up in the era of internet, which allowed them to become the massive consumers of technological products (Prensky, 2001). Belonging to a modern environment full of communication technologies offered young people access to a vast amount of information (Broniarczyk & Griffin, 2014). In addition, they are apparently familiar with the search engines, such as Google, to obtain and improve their skills by assessing the information found through websites (Wallis, 2010). Thus, they became the fastest generation when compared to the previous ones. According to Parry and Urwin (2011), generation is defined as the combination of historical events and related phenomena. Following Pilcher (1994), it is a form of social proximity of shared events or cultural phenomena resulting in a distinct generation gap. Homogeneity is also a factor, as a member of this Generation Z, globally showing similar behavioral patterns causing augmentation to the ways of working, traveling, learning, and consuming. Therefore, the age factor has been frequently used in marketing to segment the consumers' markets, either as "chronological age, generational cohort, or life cycle" (Dibb et al., 2001;McDonald & Dunbar, 1998). It helped marketers adopt this as a method to segment the consumer population (Noble & Schewe, 2003) and consider the consumer preferences based on age, cohort, generation, or particular chronological period effect. Furthermore, marketers usually ascertain the best strategy to persuade consumers to focus on the online platforms that the consumers used to connect through, forget to highlight how to fulfill their needs by focusing on the inter-consumer relationship in accordance with their age groups and generations. However, the unexpected phenomenon here was that the unknown reverse effect possibly occurs more precisely at the intergenerational level in society as measured at the level of consumer purchase behavior.
The huge amount of time Generation Z spent online, and there is the ease and availability of online purchasing options and their special online purchase behavior. This sense of belonging, relationship to brands, commitment, and consumption habits led them to be characterized by their unique online purchase behavior compared to all past generations. However, one should not underestimate or forget the parents of Generation Z and their role in the recent technological booms among Generation Z. They formed an environment containing numerous technological devices, such as television, DVD player, digital video recorder, CD player, radio, computer, and video-game console (Rideout et al., 2010).
According to Steyer et al. (1999), this enormous amount of technology consumption was behind the disruption, not only related to the neurological development but also to the lowering resistance of cognitive regions reacting to the stimulation of impulses. This purchase behavior change started to emerge in the Indonesian society due to the impact of online purchase behavior of Generation Z. Consequently, many age groups in the society have begun to behave in similar patterns with the Generation Z manifested in the appearance of rapidly spreading new online purchase behavior tendency.

LITERATURE REVIEW
In Indonesia, the age group ranging from 16 to 64 years old represents nearly 70% of the population. In fact, most young people aged 15-35 years old (60% of the Indonesian population) use technologies, such as smartphones. Nowadays, digital consumers have represented 70% of Indonesians and increased by 60 million people in only a brief period from 2018 to 2020 with an annual growth rate of 12%. Nearly 60% of social life starts online for Generation Z; and around half of respondents confirmed that they perceived more freedom and pleasant feelings when connected to online social life than real-life interactions (Palley, 2012). Furthermore, they considered being connected online as an escape from real life (Toronto, 2009). According to Anderson and Jiang (2018), 95% of Generation Z owned or had access to a technological device and spent nearly three hours a day on social media and apps. For Generation Z, technolog-ical devices, such as smartphones, are "everything" and are considered one entertainment hub (Palley, 2012), allowing them to figuratively own their feelings of having the world in their hands. The dramatic transformation in the purchase behavior in the Indonesian society is clearly manifested in the volume of e-money transactions, which increased from 41 million online transactions in 2011 to the highest volume of 5.2 billion online transactions in 2019 (Hanadian, 2021a). Furthermore, a research study developed by Rakuten Insight (Hanadian, 2021b) demonstrated how Indonesian consumers during the COVID-19 pandemic augmented their online purchases by 55% when compared to 2019. Moreover, 85% of Indonesian consumers confirmed that they tend to purchase things based on their impulsive behavior (Nielsen, 2007).
Nowadays, the influence of children, teenagers, and young people on purchase decisions increased in their families (Hawkins & Mothersbaugh, 2010).
In this context, various researchers have already endeavored to study the relationship between the appearance of new purchase behavior of children, teenagers, and young people on the consumption behavior of their parents. The long absence of parents from home as they are busy working outside has caused parents to feel guilty about their children and then allow their children to purchase what they desire and react to the purchasing decisions, including the family purchase decisions (Nicholls & Lee, 2006). Moreover, age and number of children affected parents' purchase behavior. It was found that the increasing age and number of children affected parents' purchase behavior (Pettigrew et al., 2016). Children use a negative attempt and influence their parents when they strongly desire to purchase a particular item (Flurry & Burns, 2005). This was usually associated with food purchases that encouraged children to influence their parent's purchase behavior, possibly with negative consequences (Bandyopadhyay et al., 2001).
On the other hand, children were found to possess an active social power resulting in positive attempts to influence their parents (Flurry & Burns, 2005). Family members' responses were positively correlated, and the analyses proved that children positively influenced their parent's purchase decisions (Tamara, 1991). As a result, parent-parent and parents-children disparities and conflicts started to appear in the same family from the beginning of children's participation in the family purchase decisions (Kotler & Keller, 2009). However, the findings are still inconclusive and contradict those of previous studies. For example, the purchase behavior of children, teenagers, and young people negatively affected the consumption behavior of their parents. At the same time, the other research supported that the purchase behavior of children, teenagers, and young people positively affected the consumption behavior of their parents. Although some previous studies have discussed the impact of purchase behavior of children, teenagers, and young people on the consumption behavior of their parents (Kotler & Keller, 2009), it is noticed that those were conducted in the developed countries and did not specifically target the Generation Z. Thus, this study tried to fill the gap by targeting not only children from Generation Z and their parents but also conducting research in a developing country (Indonesia) and focusing on online purchase behavior of Generation Z to demystify the ambiguity on the existing new online purchase behavior tendency of Generation Z in Indonesian society.
The theoretical framework of this study used two theories to understand Generation Z's online purchase behavior and consumption behavior of their parents. First, the Mannheim's theory of generations states that a generation rapidly changes in response to major events due to the influence of history and past generations. Both have a cause-effect basis (Mannheim, 1928). Therefore, the theory of generations explains that "the era in which a person was born affects the development of their view of the world" (Pilcher, 1994). Second, the paper used the theory of family purchase decisions confirming the family consumption result in their purchase decision. It explains how purchase decisions could be made either autonomously by one person or all family members and how these affect their purchase decisions (Sheth, 1974).
In summary, the paper hypothesizes the existence of the impact of Generation Z's online buying behavior on the changes in their parents' consumption behavior.

AIM AND HYPOTHESES
This study aimed to figure out the impact of new online purchase behavior of Generation Z children on consumption behavior of their parents.
Referring to the literature reviews on the impact of online purchase behavior of Generation Z on the consumption behavior of their parents, and in line with the research objectives and questions, 3 hypotheses were formulated as follows: H1: There is a relation between Generation Z's online buying behavior and their parents' consumption behavioral changes.
H2: There are positive effects of Generation Z's online buying behavior on their parents' consumption behavioral changes.
H3: There are negative effects of Generation Z's online buying behavior on their parents' consumption behavioral changes.

METHODS
A quantitative method was used in this study. First, the research data were obtained from the participants' responses collected from the distributed questionnaires. Collected data were further analyzed and discussed.
To achieve these goals, a questionnaire was designed and distributed among 385 respondents, namely the Indonesian parents having the Generation Z children born between 1993 and 2005 (Turner, 2015), while the members of Generation Z were those born between 1997 and 2015 (Anderson & Jiang, 2018). Thus, the age group of Generation Z children ranged 6-24 years old when the study was conducted.
The sample size was determined using an intermediate estimation technique since the population size was not clearly arranged. Thus, to determine the appropriate sample size based on the population proportion possessing a particular property within a specified margin of error, the sample size was formulated according to Daniel (1999) as follows, if the population is more than 10,000: where: Z -statistics for a confidence level (for a confidence level of 95%, which is conventional, and Z value of 1.96). P -expected prevalence or proportion. (P is considered 0.5). d -precision. (d is considered 0.05 to result in good precision and smaller estimation error).
As a result:

N =
In addition, the questionnaire was similarly designed based on previous studies and a five-point Likert scale varying from 1 (strongly disagree) to 5 (strongly agree). According to Sugiyono (2010), a Likert scale is used to measure attitudes, opinions, and perceptions of a person or a group of people related to social phenomena. The questionnaire analysis was performed using a Likert scale using the interval formula according to Darmadi (2011).
The research primary data were the questionnaire results based on the parents' perspectives on their Generation Z children related to the impact of Generation Z's online purchase tendency on their parents' consumption behavior. Following the theoretical framework, three hypotheses were developed to figure out the correlation between the online purchase behavior of Generation Z and the consumption behavior of their parents. The hypotheses were developed based on some previous studies related to the behavior of parents and Generation Z children, such as Flurry and Burns (2005) and Tamara (1991). The hypotheses were then tested using Pearson's correlation test.
A piloting test was conducted among 99 respondents to detect and correct any occurring problem before the actual survey. A questionnaire was developed in English and then translated into Indonesian.
The data were obtained from the participants' responses to the distributed survey and then statistically processed using the SPSS program. In addition, the SPSS software was used to develop a measurement model based on validity and reliability testing. Characteristics of respondents and general data descriptions were examined using statistical analysis; frequencies were used to determine the common method variance. Finally, multiple regression analyses were conducted to examine the impact of the online purchase behavior of Generation Z on consumption behavior of their parents.

RESULTS
The piloting study distributed among 99 respondents allowed estimating the questionnaire validity and reliability. The editing process was conducted due to the completed questionnaires, consistency, and relevant answers. Descriptive statistics of the respondents are described in Table 1.
Of 384 parents approached for participation, 378 (98%) completed the survey. The responses of 378 (98% of respondents) were then analyzed. All respondents were the parents of Generation Z children. Furthermore, the respondents' gender was somehow equal, with 54% of mothers (female parents) and 46% of fathers (male parents). 42% of re-spondents were 35-45 years old, and approximately half of the respondents graduated from higher education and were employed.

Validity test
The validity test was designed to explain how well the collected data covered the actual area of investigation (Ghauri & Gronhaug, 2005). Validity basically means measuring what is intended to be measured (Field, 2005). The construct validity was established by correlation with other measures and analysis of obtained results. Table 2 shows the rules defined to check the construct validity. Following the validity test results indicated in Table 2, it can be seen that the calculated R-value (ranging from 0.528 to 0.754) was greater than the level of significance alpha = 0.05 with the degree of freedom (n-2). As a result, all four variables on the questionnaire were considered valid.

Reliability test
The paper also measured the reliability of constructs, namely the internal consistency of indicators, or the extent to which a measurement shows a stable and consistent result (Carmines & Zeller, 1979). Reliability can also be associated with repeatability. A model is reliable when repeated measurements show the same results under the same conditions (Wilkins & Moser, 1959).
Therefore, the reliability check should be done before starting the hypothesis testing. Cronbach's alpha was used for these purposes (Hulin et al., 2001). Cronbach's alpha values of 0.6-0.7 mean good reliability and values equal or higher than 0.8 indicate very good reliability (Hulin et al., 2001). Thus, Table 3 shows the reliability testing results using Cronbach's alpha reliability coefficient. Following the results of the reliability test in Table 3, it can be seen that the calculated value of Cronbach's alpha (ranging from 0.750 to 0.839) was greater than the reliability coefficient of 0.7. Thus, the reliability test results indicated that all variables studied were considered reliable. This indicated that all question items were considered reliable.

Regression result
The regression analysis was used in this study to investigate the impact of the independent variable (online purchase behavior of Generation Z children) on the dependent variable (consumption behavior of their parents).
The analysis was divided into four parts. Model 1 was used for the online purchase behavior of Generation Z children and the online purchase behavior of their parents. Model 2 was for the online purchase behavior of Generation Z children and the online purchase knowledge of their parents. Model 3 was for the online purchase behavior of Generation Z children and the online purchase self-control of their parents. Finally, Model 4 was for the online purchase behavior of Generation Z children and the online purchase decision of their parents. The results obtained from Table 4 related to the regression models of parents' consumption behavior and its predicted variables showed that the F test value for Model 1 was 3.26 (p-value = 0.01), and the adjusted R-squared was 0.453. It means that the impact of online purchase behavior of Generation Z children on the online purchase behavior of their parents was significant at the confidence level of 95%.
For Model 2, the F value was 2.71 (p-value = 0.02), and the adjusted to R-squared was 0.396, meaning that the impact of online purchase behavior of Generation Z children on the online purchase knowledge of their parents was significant at the confidence level of 95%.
Meanwhile, for Model 3, the F test value was 2.34 (p-value = 0.04), and the adjusted R-squared was 0.017, meaning that the impact of online purchase behavior of Generation Z children on the online purchase self-control of their parents was significant at the confidence level of 95%. For Model 4, the F test value was 2.34 (p-value = 1.25), and the adjusted R-squared was 0.07, meaning that the impact of the online purchase behavior of Generation Z children on the online purchase decision of their parents was not significant at the confidence level of 95%. In order to examine the three hypotheses, a t-test was used to statistically check the significant differences between the online purchase behavior of Generation Z children and the consumption behavior of their parents.

Hypotheses testing
The first hypothesis testing results show a relationship between the online purchase behavior of Generation Z children with the consumption behavior of their parents. It is statistically significant at p < 0.05 with a mean = 3.881 and a t-test value of 11.839; thus, H1 is empirically supported.
The second hypothesis testing results indicate that it was also supported as shown in Table 5.
T-statistical result was larger than 1.96, equal to 15.387, and the p-value was less than 0.05, equal to 0.000.
To examine H3, a t-test was used to statistically check the significant differences between the online purchase behavior of Generation Z children and the consumption behavior of their parents. A t-test was smaller than 1.98 and the p-value was 1.16, greater than the acceptable value (p < 0.05), meaning that statistically there was no significant difference; thus, H3 was rejected.

DISCUSSION
The present study investigated the impact of online purchase behavior of Generation Z children on the consumption behavior of their parents. The quantitative investigation found that the presence of new online purchase behavior of Generation Z children affected the consumption behavior of their parents. The results show that two hypotheses received consistent support, revealing a relationship between the on-line purchase behavior of Generation Z children and the consumption behavior of their parents (H1). Furthermore, there were positive effects of the online purchase behavior of Generation Z children on the consumption behavior of their parents (H2). Following previous studies, the influence of children and adolescents on their parents' purchase behavior happened at different stages from research to the purchase decision stage (Beatty & Talpade, 1994;Belch et al., 1985;Shoham & Dalakas, 2005).
In addition, the findings were confirmed through the regression analysis results of the first 3 models respectively. Model 1 checked the online purchase behavior of Generation Z children and the online purchase behavior of their parents. Model 2 checked the online purchase behavior of Generation Z children and the online purchase knowledge of their parents. And Model 3 checked the online purchase behavior of Generation Z children and the online purchase self-control of their parents. It was shown that all F test values, P-values, and adjusted R-squared values proved that the impact of online purchase behavior of Generation Z children on the online purchase behavior of their parents was significant at the confident level of 95%. However, the predicted negative effects related to the impact of online purchase behavior of Generation Z children on the consumption behavior of their parents (H3) were not significant. The quantitative research found that the presence of new online purchase behavior of Generation Z children affected the consumption behavior of their parents via different factors, such as online purchase illiteracy and self-control behavior. The resulting impacts on the research respondents' perceptions naturally could be explained by the presence of online purchase knowledge of parents, in addition to the robust social relationship among Indonesian family members.
However, those findings were asserted by the chosen theories in the research model considering the theory of family purchase (Sheth, 1974). This study also indicated how children could become a knowledge providers to their parents (Ekström, 2007), more precisely related to online purchasing activities. In addition, the agreement with the previous findings was re-emphasized: Generation Z children affected the online purchase of their parents only for certain products, especially when they are the primary users of the products and when the products are relevant to them (Shoham & Dalakas, 2005).

CONCLUSION
Overall, this study has provided knowledge related to factors affecting the online purchase behavior of Generation Z children and its influence on the consumption behavior of their parents, such as online purchase illiteracy and self-control behavior. In the present study, the empirical results offer the readers and practitioners a comprehensive analysis of how parents' consumption behavior could be affected by their Generation Z children's online purchase behavior. The findings of this study have also provided a better understanding related to the family purchase behavior, especially the Indonesian parents and their Generation Z children. Although this study was limited to the perception of parents of Generation Z children, the perceptions of family members was different and could be considered as the other demographic variables of parents and their Generation Z children who could affect their online purchase behavior, such as parents' income and gender of parents and children. Furthermore, due to the dramatic shift to online purchase during the COVID-19 pandemic, the insight and implication of this study could be further improved when comparing the correlation between the online purchase behavior of Generation Z children and the consumption behavior changes of their parents before and after the pandemic. In addition, the results lay a foundation for future research that could explore the Generation Z children and their parent's perceptions of different demographic characteristics affecting the purchase behavior, such as parents' income and gender of children. Thus, this difference in perceptions and characteristics could lead to significant findings.