Intergenerational Differences in Impulse Purchasing in Live E-Commerce: A Multi-Dimensional Mechanism of the ASEAN Cross-Border Market

Abstract

Existing research on live-streaming e-commerce consumption behavior is mostly limited by a single disciplinary framework, unable to systematically parse the mechanism of macro-policies and cultural values on intergenerational consumer psychology. This study takes ASEAN cross-border live-streaming e-commerce as a scenario, integrates theories of economics, political science, and sociology, and constructs an innovative three-layer analysis model of “macroeconomic system–meso-market–micro-behavior” based on multi-source data from 2020 to 2024. It empirically explores the formation mechanism of intergenerational differences in impulse buying. The results show that the behavior differences of different groups are significantly driven by income gradient, cross-border policies (tariff adjustment and consumer protection regulations), and collectivism/individualism cultural orientations. The innovative contribution of this study is reflected in three aspects: Firstly, it breaks through the limitation of a single discipline, and for the first time, it incorporates structural variables such as policy synergy effect and family structure change into the theoretical framework of impulse buying, quantifying and revealing the differentiated impact of institutional heterogeneity in ASEAN markets on intergenerational behavior. Secondly, it reconstructs the transmission path of “cultural values–family structure–intergenerational behavior” and finds that the inhibitory effect of collectivism on impulse buying tends to weaken with age. Thirdly, it proposes a “policy instrument–generational response” matching model and verifies the heterogeneous impact of the same policy (such as tariff reduction) on different generations. This study fills the gaps in related research and can provide empirical support for ASEAN enterprises to formulate stratified marketing strategies and for policymakers to optimize cross-border e-commerce regulation. which is of great significance to promote the sustainable development of the regional live-broadcast e-commerce ecology.

1. Introduction

1.1. Research Background

In the context of the digital economy, live-streaming e-commerce functions as an innovative retail modality, catalyzing the reconfiguration of the global retail infrastructure. The proliferation of high-speed broadband connectivity and mobile device penetration has established a resilient technological infrastructure conducive to its expansion. Consumer engagement patterns have experienced a significant paradigm shift, transitioning from conventional offline and static online purchasing to dynamic, interactive, and real-time live-streaming commerce [1]. The Association of Southeast Asian Nations (ASEAN), with a demographic exceeding 600 million and an internet penetration rate surpassing 70% in 2024, provides a substantial demographic base for the proliferation of live-streaming e-commerce within the region [2]. Sustained economic growth, a burgeoning middle class, and elevated consumer expenditure across member states have generated extensive market opportunities for live-streaming retail platforms. Recent data indicates that markets such as Vietnam and the Philippines have experienced double-digit growth trajectories in e-commerce activity in recent years [3].

The expansion trajectory and developmental prospects of Southeast Asia’s e-commerce sector are being further amplified by the forces of globalization. Since 2019, policy regulatory reforms, the incremental maturation of regional economies, and the widespread adoption of digital technologies have synergistically contributed to a superimposed effect, propelling the industry’s transition from conventional transactional frameworks to a multifaceted digital ecosystem. This evolution has entailed profound modifications in core business paradigms and market architecture.

On the policy level, the regulatory framework governing e-commerce within ASEAN nations exhibited dynamic evolutionary characteristics from 2019 to 2024 (

Table 1. Evolution of key e-commerce policies in ASEAN 2019–2024.

Year	Indonesia	Vietnam	Cambodia	Malaysia	Thailand	Singapore
2019	Issue regulations to promote compliance and sustainable development of e-commerce enterprises	Start to lay out the standardization of e-commerce transactions	-	-	-	-
2020	-	-	Initiate e-commerce strategy, involving payment, logistics, legal supervision, etc.	-	-	-
2021	-	-	-	Launch the National E-commerce Strategy Roadmap 2.0	-	-
2022	-	-	-	-	Adjust the Advertising Law, affecting the placement of e-commerce ads.	-
2023	-	Enacted the new Consumer Rights Protection Law and Electronic Transactions Law	-	-	Continue to improve the relevant details of the Advertising Law	-
2024	-	-	-	-	-	Improve Consumer Protection Law

). In 2019, Indonesia pioneered the enactment of the E-commerce Business Compliance Bill, emphasizing market behavior standardization and the development of a sustainable growth infrastructure; concurrently, Vietnam initiated policy groundwork to establish an institutional basis for cross-border transaction standardization. In 2020, Cambodia implemented an e-commerce strategy encompassing payment settlement systems, logistics networks, and legal regulation, aiming to enhance regional e-commerce ecosystem infrastructure. In 2021, Malaysia introduced the National E-commerce Roadmap 2.0, fostering ecosystem optimization and upgrading through SME empowerment initiatives and the deployment of a centralized service platform. These policy developments reflect a strategic shift among ASEAN countries from reactive regulation to proactive market facilitation. During 2022–2023, Thailand restructured e-commerce dissemination protocols via amendments to advertising regulations, imposing stringent constraints on platform advertising strategies; Vietnam enacted comprehensive legislation, including the Consumer Rights Protection Law and the Electronic Transaction Law in 2023, thereby establishing a more closed-loop legal regulatory system for e-commerce. In 2024, Singapore prioritized the refinement of the Consumer Protection Law, bolstering market confidence through enhanced rights relief mechanisms, with regulatory system optimization emphasizing transaction security in mature digital markets.

The demographic composition of consumer segments within the ASEAN region exhibits significant heterogeneity, with notable disparities in consumption paradigms and behavioral patterns across different generational cohorts. This divergence is particularly evident in the live-streamed e-commerce sector and exerts a substantial influence on impulsive purchasing decision-making processes. Such insights are instrumental for accurately delineating regional e-commerce market dynamics, facilitating targeted strategic marketing interventions, enhancing user conversion metrics, and establishing a robust theoretical and empirical framework for market regulation and sustainable industry growth [4,5].

1.2. Research Gaps and Research Objectives

1.2.1. Research Gaps

Despite existing research surrounding consumer behavior in live e-commerce, there are still three key gaps that hinder a deep understanding of the mechanisms of intergenerational impulse buying in the ASEAN cross-border context:

Firstly, the disciplinary limitations of the theoretical framework. Current research on impulse buying is often confined to a single disciplinary paradigm—economics focuses on the constraints of income and price on consumption capacity, psychology emphasizes the influence of individual psychological traits and situational variables, and political science only isolates the market effects of a single policy [6,7,8], failing to form an interdisciplinary analytical framework that integrates the “macroscopic system–mesoscopic market–microscopic behavior”. This fragmentation leads to the inability of research to systematically explain the interaction between policy synergy (such as Singapore’s “tariff reduction logistics optimization” combined policy), cultural values (the division between collectivism/individualism), and generational cognitive differences within the ASEAN region, especially ignoring the structural shaping of institutional heterogeneity (such as the difference in regulatory intensity between Singapore and Cambodia) on the impulse buying thresholds of different generations [9].

Secondly, the mechanism of intergenerational differences is blurred. Existing literature, while mentioning the influence of intergenerational variables on consumption behavior, often treats them as control variables and does not delve into the underlying logic of behavioral differences. For example [10,11,12], some studies observe that young groups have a higher frequency of impulsive purchases, but they do not reveal the formation mechanism of “Generation Z’s high frequency and low consumption” and “Baby Boomers’ low frequency and high consumption”, nor do they quantify the differentiated driving force of income gradient, policy perception, cultural orientation, and other factors on intergenerational behavior. At the same time, existing research does not fully consider key variables such as changes in family structure (e.g., the proportion of nuclear families in ASEAN countries increased from 45% in 2010 to 62% in 2024) and the reshaping of cultural values by globalization on young groups (e.g., the individualistic consumption tendency of Cambodia’s Generation Z), resulting in limited explanatory power of intergenerational impulse buying mechanisms [13,14].

Thirdly, the adaptability of traditional impulse buying theory to cross-border scenarios is insufficient. Much of the traditional impulse buying theory is based on a single national market (such as TAM model) and does not fully consider the particularity of cross-border e-commerce in ASEAN, such as fragmented regional policies, significant economic gradients, and prominent cultural diversity [15,16]. For instance, Singapore’s well-developed consumer protection system reduces consumer risk perception, while Cambodia’s weak regulatory environment reinforces the prudence of decision-making, but existing theories fail to explain how such institutional context differences interact with generational characteristics to influence impulse buying behavior, resulting in a significant discount in the theoretical adaptability in cross-border scenarios [17,18].

1.2.2. Research Objectives

Based on the above research gaps, this study sets the following three-level progressive research objectives with the ASEAN cross-border live e-commerce as the specific scenario:

Objective 1: Deconstruct the behavioral characteristics and differentiation patterns of intergenerational impulsive buying. By using multi-source data (2020–2024 consumer surveys, transaction records, and policy documents), we quantify the differences in the frequency (monthly number) and amount (single consumption amount) of impulsive buying among Gen Z, Millennials, Gen X, and Baby Boomers, and clarify the core driving factors (e.g., KOL dependence of Gen Z and brand loyalty of Baby Boomers) and typical categories of consumption (e.g., fast-moving consumer goods and health products), and establish a behavioral atlas of intergenerational impulsive buying in the ASEAN cross-border scenario [19,20].

Objective 2: To reveal the mechanisms of multidimensional factors on intergenerational impulse buying. Integrating the theories of economics, political science, and sociology, this study constructs a three-dimensional analytical framework of “economic foundation–policy environment–cultural values”, and empirically tests: (1) the constraining effect of income gradient on the amount of intergenerational impulse buying [21,22,23]; (2) the heterogeneous impact of policy instruments (such as tariff adjustments and consumer protection regulations) on different generations (e.g., Millennials are more sensitive to tariff changes); and (3) the moderating role of collectivism/individualism cultural orientations on the frequency of intergenerational impulse buying, especially focusing on the reconstruction effect of family structure change and globalization on the cultural values of young groups, which fills the blank of mechanism interpretation in existing research [24,25].

Objective 3: Construct a theoretical model of interdisciplinary integration and provide practical guidance. In response to the limitations of traditional theories in cross-border scenarios, variables such as institutional heterogeneity and intergenerational technology acceptance are incorporated into the theoretical framework of impulse buying, and an innovative “macro-institutional–meso-market–micro-behavior” tri-level analysis model is proposed to quantitatively reveal the differentiated impact of structural factors such as policy synergy, cultural transmission, and family decision-making on intergenerational impulse buying. Meanwhile, based on the research conclusion, it provides empirical support for ASEAN enterprises to formulate stratified marketing strategies (such as “whole life cycle cost communication” for Generation X) and policymakers to optimize cross-border e-commerce regulation (such as dynamic tariffs and advertising compliance review) and promote the sustainable development of the regional live broadcast e-commerce ecology [26,27,28,29].

2. Literature Review

As a pivotal sector within the digital economy, live e-commerce stimulates impulsive purchasing behaviors, which result from the interplay of economic fundamentals, policy frameworks, and cultural paradigms. Current research predominantly adopts a unidimensional analytical approach, thereby neglecting the dynamic coupling among these factors. Economic stratification imposes fundamental constraints on consumer purchasing capacity; policy systems influence the consumption environment through cross-border cost regulation and market standards; and cultural values reshape decision-making processes via intergenerational cognitive disparities. Collectively, these elements form a sequential mechanism characterized by “resource endowment–institutional framework–cognitive filtering.” This section systematically reviews the scholarly advancements across each dimension within an integrated analytical framework and critically assesses the limitations inherent in existing studies.

2.1. Economic Dimension: Intergenerational Mapping of Market Size and Consumption Capacity

Livestreaming e-commerce, which rose to prominence in China in 2016, has quickly formed a global diffusion pattern. According to eMarketer, global e-commerce sales will exceed USD 6 trillion in 2024, among which the size of livestreaming e-commerce reached USD 1.91 trillion, an increase of 96.5% compared to 2020 (USD 961 billion), and it has become the core engine of digital consumption growth. The ASEAN market, with a population base of 600 million and a 70% internet penetration rate, has become a key growth pole for the industry, but the unbalanced economic development within the region has led to a significant gradient difference in intergenerational consumption capacity [30,31].

In high-income countries such as Singapore and Malaysia, consumers exhibit a strong willingness to pay for high value-added goods. In the first half of 2024, the transaction volume of TikTok Shop in the Singapore market reached USD 57 million, with the concentration of order value in the range of USD 150–USD 300; whereas in low-income countries such as Cambodia and Laos, consumers’ monthly live shopping amount is less than USD 50, and the price sensitivity is 2.3 times that of the high-income group. Such a country difference essentially reflects the differentiation of consumption capacity across generations: The Generation Z, due to the early stage of their career, has a monthly income of about USD 600, and they focus on fast-moving consumer goods with a price range of USD 50-USD 100 for impulse buying; while the Baby Boomers, with the help of wealth accumulation, have a 35% proportion of health products with a single consumption exceeding USD 200. The consumption amount of the top 20% income group in Indonesia’s live e-commerce is 8.3 times that of the lowest 20% income group [1,2,32], and the explanatory power of this income stratification on intergenerational behavior is stronger in middle- and low-income countries (e.g., Cambodia R2 = 0.68) [33].

Critical analysis: The existing research on economic dimensions has two limitations. First, it mainly focuses on the linear relationship “income–spending amount”, ignoring the interactive effect of policy and economic factors. For instance, after the 3C product tariff in Vietnam was reduced by 15% in 2023, the Millennials’ impulse purchase amount of this category increased by 38% year-on-year [34], and the mechanism of “policy releasing consumption potential” has not been fully integrated. Second, there is insufficient exploration of the nonlinear correlation between “consumption capability” and “purchase frequency”. Although Gen Z has a lower income, the frequency of watching live broadcasts (5 times/week) is 2.5 times that of the Baby Boomers (2 times/week), and this “high-frequency, low-consumption” characteristic cannot be explained only by the income level, which needs to be further analyzed in combination with the media usage habits of generations [35]. In addition, the latest research shows that the proportion of nuclear families in ASEAN increased from 45% in 2010 to 62% in 2024, and the frequency of impulse buying of Gen Z in single families is 30% higher than that of their peers in large families, but the existing literature has not yet incorporated the changes in family structure into the framework of economic factors, resulting in limited explanatory power for intergenerational behavior [36,37].

2.2. Policy Dimensions: Intergenerational Regulation of Consumption Decisions by Institutional Environment

From a political science perspective, regulatory policies on cross-border consumption exhibit significant intergenerational heterogeneity, but existing research often implies the assumption of “homogeneous policy impact”. The fragmentation of regulatory policies in ASEAN is particularly evident: the introduction of e-commerce subsidy policies in Indonesia in 2022 (targeting young groups) increased the frequency of impulsive purchases by the Generation Z by 4.5 times per month, while the logistics reform in Malaysia during the same period (cross-border delivery time shortened to 72 h) had a more significant impact on the Baby Boomer generation—this group is 37% more sensitive to the stability of delivery than the Generation Z [38]. This differentiation highlights the core value of “policy instrument–generational characteristics” fit.

Policies’ impact on intergenerational impulse buying heterogeneity is threefold. First, instrument type difference: Consumer protection law’s trust-building effect on Gen X is significantly higher than Gen Z, as the former relies more on institutional legitimacy to build trust, while e-commerce subsidy’s frequency stimulation on Gen Z far exceeds Baby Boomers [28,39]. Also found that subsidy-type policies have a higher marginal utility for younger groups in their research on the intergenerational effect of ASEAN policies. Second, execution intensity difference: Thailand’s amended Advertisement Act in 2022, which enforced strict review of false propaganda, led to a 30% decrease in the impulse buying complaint rate while Millennials with a stronger legal consciousness were more affected, This is in line with the theory of “the intensity of legal awareness modulates the effectiveness of policy implementation” proposed by the existing research [40]. Third, policy synergy difference: Singapore’s combination policy of “Consumer Protection Law Tariff Reduction Logistics Optimization” boosted all generations’ impulse buying rates by 15–25%, while policy fragmentation in Cambodia (only the basic e-commerce regulations left), whose intergenerational behavior differences are more income-driven, with the income’s explanatory power of impulse buying amount reaching 68%, Heng S. and Lim K. (2023) ’s research on the Cambodian market also verified this conclusion [41].

The existing literature exhibits three notable limitations: (1) the treatment of ASEAN as a single policy unit that ignores the gradient differences in “policy clusters–market maturity” within it, previous research mentions the differences in ASEAN policies but does not quantify them [9,31]; (2) the omission of the “policy expediency” perspective—the temporary tariff cut of 3C products in 2023 by Vietnam in response to economic fluctuations. Such short-term interventions have a significantly stronger impact on Millennials than long-term policies [4,42]; and (3) the lack of intergenerational comparison of policy awareness, such as the awareness of Generation X towards tariff changes, is 2.2 times higher than that of Generation Z, This critical difference was not mentioned in previous studies [8,15].

2.3. Cultural Dimensions: Value Divide and Intergenerational Behavioral Mechanisms

Due to the difference in growing environments, social cultures, and technical acceptance, consumers from different generations show significant differences in their consumption values, media contact habits, and brand recognition dimensions, and their decision-making logic and preference characteristics show intergenerational differences. Hofstede G, Minkov M (2010)’s cultural dimensions theory provides an important framework for analyzing this difference [11,14].

However, existing research often treats collectivism/individualism as a static variable, overlooking the reshaping of intergenerational values due to globalization and changes in family structures [43]. The ASEAN regional culture is highly diverse, and the decision-making logic of different generations presents a “fractal” difference: 65% of Gen Z, as the digital natives, trigger impulse buying due to the uniqueness of product design and their personalized consumption tendency, though in a collective cultural context (e.g., Hofstede’s collectivism index in Cambodia is 80), but 60% will place an order immediately due to the uniqueness of products, which is closer to the individualistic logic than the middle-aged group (32%). Gen Y has both rational and emotional characteristics, and the weight of “brand identity user reputation” in the decision-making is 55%, which is different from the immediacy of Gen Z and the pragmatism of Gen X [44,45]. Gen X focuses on the product functionality and cost–performance ratio, and the duration of “parameter comparison” in live shopping is 2.1 times that of Gen Z; Baby Boomers rely on the brand heritage value, 70% of them trust “traditional media advertising” as a source of trust, and the acceptance of KOL recommendation is only 1/3 of Gen Z [46].

Existing research has two limitations in the analysis of cultural factors: (1) treating collectivism/individualism as a static variable, ignoring the reshaping of young groups by globalization—although Cambodian Gen Z is in a collective cultural context (Hofstede’s collectivism index is 80), 60% of them would place an order immediately for unique products, which is closer to the individualistic decision-making logic than their middle-aged groups. Research has shown that globalization is weakening the collectivist consumer tendencies among young people [14,47]. (2) The analysis did not incorporate the changes in family structure, such as the proportion of nuclear families in ASEAN rising from 45% in 2010 to 62% in 2024. This change makes the frequency of impulse buying of Gen Z in single families 30% higher than that of their peers in large families [13,48]. However, this mechanism has not been incorporated into the theoretical framework of cultural consumption. This is in contrast to previous work that has mentioned the impact of household structure on consumption but has not combined it with the analysis of intergenerational cultural characteristics [49].

2.4. Variable Selection and Hypothesis Establishment

In response to the issue of fuzzy delimitation of the “macro–meso–micro” three-layer analysis framework, it is necessary to clarify the core connotation, boundary categories, and variable attribution of each level and to clarify the level positioning of the “influencer effect”. On this basis, combined with ASEAN scenario examples, the abstract definition is presented concretely in the form of tables so as to ensure the clarity and traceability of the framework logic and to lay a rigorous theoretical foundation for subsequent analysis.

2.4.1. Three-Level Framework Refinement

The macro-institutional level corresponds to the systematic regulations and environmental system that regulate cross-border live-streaming e-commerce at the national or regional level, with the core function of “founding the cornerstone of transaction order”. It does not directly intervene in individual consumption decisions but instead relies on policy regulations, economic levers, and other tools to indirectly shape the market ecology. Meso-market level specifically refers to the market intermediary system that bridges the supply and demand sides, with the core function of “value transmission and resource matching”. This level focuses on the interaction mechanism among market entities such as platforms, merchants, and influencers and is the key “transmission node” for the transformation of macro-institutional efficiency into micro-behavior. Micro-behavior level, i.e., the psychological cognition and behavioral characteristics of consumers at the individual dimension, has the core function of “decision-making execution”. It directly determines the probability of impulse buying behavior, which is not only indirectly driven by macro-institutions and meso-markets but also significantly presents individual heterogeneity characteristics.

In this case, the influencer effect (such as the influence of KOLs) should be attributed to the meso-market level. In terms of hierarchical attributes, influencers are neither individual consumers at the micro level nor policy-making entities at the macro level, but rather a “market intermediary” that bridges the gap between merchant products and consumer needs. They transmit product value through professional trust endorsement and emotional resonance mechanisms, essentially acting as “value transmission carriers” at the meso-market level. Taking Indonesian TikTok KOL as an example, it recommends local cosmetics products through live broadcast scenarios and reaches Generation Z consumers, a process that fully conforms to the resource matching attributes of the meso-market.

2.4.2. Three-Tier Framework Variables Definition and ASEAN Case Table

We explicitly delineated the refined conceptualization of the three-tier analytical framework—macro, meso, and micro levels—articulating the theoretical boundaries and interrelations among each stratum (

Table 2. Analysis table of ASEAN cross-border live broadcast e-commerce impulse buying.

Analytical Hierarchy	Core Definitions	Included Variables	ASEAN Scenario Example
Macro-institutional layer	National/regional-level systemic rules and economic environment, building the basic order for cross-border transactions.	Cross-border policies (tariff adjustments and consumer protection laws), economic gradients (GDP per capita and income levels), and cultural values (collectivism/individual overall orientations).	1. The revision of the 2023 Vietnam Electronic Transaction Law regulates the after-sales process of live e-commerce. 2. Singapore’s per capita GDP exceeds USD 60,000, supporting high customer price point impulse consumption. 3. Cambodia’s Hofstede collectivism index is 0, and the overall consumption decision is more dependent on group opinions.
Intermediate market layer	A market intermediary system that connects supply and demand, conveys value, and matches resources.	Influencer effect (KOL/KOC influence), platform rules (live-broadcast algorithm and promotion mechanism), and interaction of market entities (merchant–anchor–consumer collaboration model).	1. Thai beauty influencers drive a 35% increase in the conversion rate of Millennials’ impulsive skincare purchases through compliant recommendations. 2. Shopee Live launches the “Flash Sale” mechanism in Malaysia, stimulating immediate orders across generations. 3. Indonesian merchants collaborate with local hosts to lower consumer trust thresholds through dialect live streaming.
Micro-behavioral layer	The psychological and behavioral characteristics of individual consumers directly determine the decision to make an impulse purchase.	Individual media usage habits (social media time), brand loyalty, and impulsive buying psychology (tendency for immediate gratification).	1. Gen Z in ASEAN spends an average of 4.5 h on social media daily, with frequent exposure to live content triggering impulse buying. 2. Baby Boomers have a brand loyalty of 0.9, making impulse purchases only due to trust in traditional brands. 3. Millennials have a significant tendency towards ‘instant gratification’, ordering limited-edition electronic products immediately when they see them on live broadcasts.

).

2.5. Research Hypotheses

H1: 

The generational gap significantly affects the frequency of impulsive consumption in ASEAN live-streaming e-commerce, with Gen Z having a higher frequency of impulsive buying than older generations.

H2: 

Income level and policy intervention have a moderating effect on the relationship between intergenerational differences and the amount of impulsive buying. High-income groups such as the Generation X and the Baby Boomers have a greater amount of impulsive buying, which is closely related to the differences in consumption capacity under the income gradient and policy regulation, indicating the significant impact of economic factors on the intergenerational amount of impulsive buying.

H3: 

The influence of host characteristics (e.g., professional trust and emotional resonance) on intergenerational impulse buying is differentiated, and Generation Z, as a digital native generation, is significantly more sensitive to KOL recommendations than other generational groups.

Overall, the interplay of economic, policy, and cultural factors—mediated through the resource constraints, institutional guidance, and cognitive filtering mechanisms—collectively influence intergenerational impulse buying behaviors. The fragmented nature of existing research hampers comprehensive understanding of this complexity. The theoretical innovation of this study resides in the development of a three-dimensional interactive framework, elucidating the patterns of intergenerational consumer behavior within the unique ASEAN context and addressing the analytical gaps present in prior studies (Figure 1).

3. Methods

3.1. Sources of Data

This research utilizes multi-channel data acquisition, with e-commerce platforms supplying multi-dimensional consumer behavior metrics, including key indicators such as navigation sequences, dwell durations, transaction timestamps, average order value, and repurchase frequency within live streaming contexts. These metrics facilitate the quantitative reconstruction of consumer behavioral patterns. For instance, analyzing user dwell time on specific live streaming pages enables a scientific evaluation of content or product engagement levels, thereby providing empirical support for behavioral pattern analysis.

This study employs stratified random sampling by age and questionnaire surveys, allocating samples based on the weighted calculation results of each country’s GDP proportion and e-commerce penetration rate in 2024 (

Table 3. Rationale and calculation logic for sample allocation in ASEAN countries.

State Areas	DP Share (2024)	E-commerce Penetration Rate (2024)	Weighted Score	Sample Proportion	Sample Size
Indonesia	40.2%	70.5%	0.523	30%	450
Malaysia	15.8%	65.3%	0.352	20%	300
Thailand	18.5%	58.7%	0.346	15%	225
Philippines	12.3%	52.1%	0.278	15%	225
Vietnam	9.7%	61.2%	0.294	10%	150
Other countries (Singapore, Cambodia, etc.)	3.5%	45.0%	0.191	10%	150
Total	100%	-	-	100%	1500

Note: Sample allocation is determined by a weighted model combining “Gross Domestic Product Share” with a weight of 0.6 and “E-commerce Penetration Rate” with a weight of 0.4, thereby assigning greater sampling proportions to nations with larger economic output and more advanced e-commerce infrastructure, such as Indonesia, to accurately reflect their market influence.

) to ensure a high fit between the sample structure and the gradient of regional market development. The research team conducts structured interviews with consumers and practitioners simultaneously as a complementary means of quantitative research, aiming to enhance the explanatory depth and practical guiding value of the conclusions. This questionnaire survey was conducted primarily through the online platform Questionnaire Star and supplemented by offline distribution in specific locations, covering six core countries and three emerging market countries in ASEAN, with a total of 1800 questionnaires distributed and 1500 valid questionnaires recovered, rendering an effective recovery rate of 83.33%.

3.2. Sample Characteristics

3.2.1. Rationale for the Sampling Method

This study employs stratified weighted sampling rather than simple random sampling, with the core logic being to adapt to the non-equilibrium characteristics of the ASEAN markets’ “economic gradient–e-commerce penetration rate” and ensure sample representativeness and the external validity of research conclusions.

Country-level stratification logic: Construct a weighted scoring model based on each country’s 2024 GDP share (weight of 0.6) and e-commerce penetration (weight of 0.4) (weighted score = GDP share × 0.6 e-commerce +penetration × 0.4). Indonesia, as the largest e-commerce market in ASEAN (estimated size of e-commerce market of USD 30.4 billion in 2024, for 35% of the regional total), is set at 30% of the sample size; high-income markets such as Singapore and Malaysia (with e-penetration above 65%) are combined at 30%; Vietnam and the Philippines, as emerging markets, are set at 25%; and countries with lagging e-commerce such as Cambodia and Laos are categorized as “Others” (10%). This design avoids the issue of core market sample dilution caused by an equal-proportion draw. If an equally proportional draw is used, Indonesia’s sample size would reduce to 20%, which would fail to reflect its characteristics as the regional powerhouse.

Generational stratified logic: based on the national stratification, the sampling units were divided by generation (Gen Z: 18–24 years old, Millennials: 25–34 years old, Gen X: 35–50 years old, and Baby Boomers: 51 years old), and the proportion of each generation (30%, 26%, 24%, and 20%) fits well with the age structure of the ASEAN population (204 ASEAN Census data: 18–24 years old accounting for 28%, 25–34 years old accounting for 25%, 35–50 years old accounting for 26%, and 51 years old accounting for 21%), which ensures the representativeness of the samples by generation and lays a data foundation for the follow-up analysis of intergenerational differences.

3.2.2. Sample Reliability Test

The final 1500 valid samples included (

Table 4. Age-stratified sample distribution.

Generation	Age Range	Sample Size	Percentage
Generation Z	18–24	150	30%
Millennials	25–34	130	26%
Generation X	35–50	120	24%
Baby Boomers	51+	100	20%

Note: This dataset details a stratified sampling of 1500 validated respondents across ASEAN markets, with age groups: Generation Z (18–24, 30%), Millennials (25–34, 26%), Generation X (35–50, 24%), and Baby Boomers (>51, 20%). Gender distribution is 45% male and 55% female, with females in Generation Z and Millennials exceeding 50%, indicating gender preferences in impulse segments like fashion and cosmetics. Regional sample proportions from Indonesia, Malaysia, Thailand, and others are aligned with live-streaming e-commerce penetration rates, reflecting the digital commerce development gradient.

) have a gender distribution of 45% male and 55% female (with Gen Z and Millennial women accounting for 60% and 58%, respectively, consistent with the female-dominated characteristics of fashion, beauty, and other high-impulse consumption categories in ASEAN live-streaming e-commerce); income stratification is 30% low income (<USD 500/month), 50% middle income (USD 500–USD 2000/month), and 20% high income (>USD 2000/month) (with significant intergenerational differences: 60% of Gen Z are in the low-income bracket, and 40% of Baby Boomers are in the high-income bracket), which follows the law of economic strength differentiation at different life stages.

Reliability test: Cronbach’s α coefficient was used to assess the internal consistency of the scale, and the results showed that the α value of all latent variables (influence of host, collectivism/individualism, and policy awareness) was > 0.85 (α = 0.89 for “influence of host” and α = 0.82 for “collectivism”), far above the critical standard of 0.7 [50], indicating that the questionnaire measurement tool has good reliability and the internal consistency between items is stable.

Validity test: The structural validity was verified by EFA, KMO = 0.82 (>0.7), and Bartlett’s sphericity test χ2 = 2876.34 (p < 0.001), indicating that the items of the scale had significant correlation and were suitable for factor analysis; the extracted common factor variance was all >0.6, and the load of each item on the corresponding factor was >0.7 (e.g., the item of “policy perception” had a load of 0.75–0.83), which confirmed that the structural validity of the measurement tool met the and could effectively capture the true connotation of the target latent variables [51].

The demographic distribution within the sample exhibits notable variations: 45% male and 55% female participants. Among these, the representation of Generation Z and Millennial women is 0% and 58%, respectively, potentially attributable to higher female engagement in high-impulse consumption sectors such as fashion and beauty. The sample encompasses key ASEAN markets, with Indonesia, Malaysia, Thailand, the Philippines, and Vietnam constituting 30%, 20%, 15%, 15%, and 10% of the sample, respectively, while the remaining countries account for 10%. This distribution correlates strongly with the penetration depth of live-streaming e-commerce across these nations. Indonesia, as the region’s largest market, demonstrates the highest user base and consumption activity; mature markets such as Singapore, Malaysia, and Thailand exhibit stable participation levels; and the increasing sample proportion from emerging markets like Vietnam indicates significant industry growth potential. The data structure objectively reflects the heterogeneous development stages of the e-commerce ecosystem within ASEAN countries.

Income stratification analysis reveals that low-income groups (monthly income below USD 500) comprise 30%, middle-income groups (USD 500–USD 2000) account for 50%, and high-income groups (above USD 2000) represent 20%. There are pronounced generational disparities: Generation Z, predominantly students or early-career individuals, exhibits a 60% low-income ratio; Generation X and Baby Boomers display higher proportions of 30% and 40%, respectively, attributable to career accumulation. This segmentation underscores the economic disparities across life stages and directly influences consumption decision-making patterns.

3.3. Variable Setting

3.3.1. Dependent Variable

The current study takes the impulse purchase frequency (ImpulseFreq) and impulse purchase amount (ImpulseAmt) as dependent variables. The impulse purchase intensity is quantified from the dimensions of behavioral frequency and transaction scale, respectively:

Frequency of Impulse Purchasing: Defined as the number of times consumers place spontaneous orders in the past month (a continuous variable), directly extracted from platform transaction records, and the interference of planned purchases (e.g., placing orders after advance addition to the cart) is excluded through the objective rule of “the difference between the order time and the live broadcast start time <30 min” [41,52,53]. Impulse purchase amount: Defined as the total expenditure of spontaneous consumption during the same period (unit: USD), it is also calculated based on platform transactions to avoid the estimation bias of the amount in self-reporting, ensuring the objectivity of the variable measurement.

3.3.2. Independent Variables

Core independent variable: generational variables (Generation), with Generation Z as the reference group. Dummy variables for Millennials (Gen_Millennial), Generation X (Gen_X), and Baby Boomers (Gen_Boomer) were set up to quantify the net effect of intergenerational differences on impulse buying.

Moderating variables: Income level (Income, continuous variable, unit: USD/month), policy perception (PolicyPerception, 7-point Likert scale: 1 = “Not at all understand the cross-border e-commerce policy” and 7 = “Very familiar”), and collectivism/individualism (Collectivism, weighted score of 6 items, α = 0.82) were used to test the moderating effect of economic, policy, and cultural factors on the intergenerational–impulsive buying relationship.

Control variables included age (Age, continuous variable), gender (Gender, male = 1, female = 0), education level (education, dummy variable: high school and below = 1, junior college = 2, bachelor’s degree = 3, and master’s degree and above = 4), and time of social media use (SocialMedia, hours/day) to rule out potential confounding effects of demographic characteristics and media usage habits on the dependent variable (

Table 5. Measurement details of core variables questionnaire.

Metric	Example Questions	Type of Question (Scale/Scoring Method)	Score Corresponding to the Description
Social media use (h/day)	“How long on average do you spend a day using social media (Facebook, Instagram, TikTok, etc.)?”	A single-choice scale (1. <1 h; 2. 1–2 h; 3. 2–3 h; 4. 3–4 h; 5. > 4 h), which is subsequently converted into a continuous numerical variable	1. Retention rate scale: 1 point = <20%, 2 points = 20–40%, 3 points = 40–60%, 4 points = 60–80%, 5 points = >80% 2. Recommendation likelihood scale: 1 point = extremely unlikely, 2 points = very unlikely, 3 points = not very likely, 4 points = somewhat unlikely, 5 points = neither likely nor unlikely, 6 points = somewhat likely, 7 points = not very likely (here modified to “somewhat likely”), 8 points = very likely, 9 points = extremely likely, 10 points = absolutely would recommend; NPS calculation rule: percentage of promoters (9–10 points)—percentage of detractors (1–6 points) 3. Brand loyalty final score = (Retention rate scale score/5) × 0.5 NPS × 0.5 (score range: −0.5~1)
Live use (weekly frequency)	“How many times on average do you watch live e-commerce (e.g., Shopee Live and Lazada Live) per week?”	Single choice scale (1. 0 times; 2. 1–2 times; 3. 3–4 times; 4. ≥5 times), directly as an ordered categorical variable	1 point = 0 times/week; 2 points = 1–2 times/week; 3 points = 3–4 times/week; 4 points = ≥5 times/week
Brand loyalty	1. “What percentage of purchases made in the past six months were repeat purchases of the same brand?” 2. “How likely are you to recommend a common brand to your friends and family?”	1. Single choice scale (1. <20 percent; 2. 20–40 percent; 3. 40–60 percent; 4. 60–80 percent; 5. > 80 percent) 2. 10-point scale (1 = highly unlikely, 10 = highly likely), calculated NPS and weighted equally with repurchase rate (each 0.5)	1. Retention rate scale: 1 point = <20%, 2 points = 20–40%, 3 points = 40–60%, 4 points = 60–80%, 5 points = >80% 2. Recommendation likelihood scale: 1 point = extremely unlikely, 2 points = very unlikely, 3 points = not very likely, 4 points = somewhat unlikely, 5 points = neither likely nor unlikely, 6 points = somewhat likely, 7 points = not very likely (here modified to “somewhat likely”), 8 points = very likely, 9 points = extremely likely, 10 points = absolutely would recommend; NPS calculation rule: percentage of promoters (9–10 points)—percentage of detractors (1–6 points) 3. Brand loyalty final score = (Retention rate scale score/5) × 0.5 NPS × 0.5 (score range: −0.5~1)
Promotional appeal	“How interested are you in promotional activities in live-streaming e-commerce (such as limited-time discounts, full reduction, and freebies)?”	7-point Likert scale (1 = not at all interested, 7 = very interested)	1. Retention rate scale: 1 point = <20%, 2 points = 20–40%, 3 points = 40–60%, 4 points = 60–80%, 5 points = >80% 2. Recommendation likelihood scale: 1 point = extremely unlikely, 2 points = very unlikely, 3 points = not very likely, 4 points = somewhat unlikely, 5 points = neither likely nor unlikely, 6 points = somewhat likely, 7 points = not very likely (here modified to “somewhat likely”), 8 points = very likely, 9 points = extremely likely, 10 points = absolutely would recommend; NPS calculation rule: percentage of promoters (9–10 points)—percentage of detractors (1–6 points) 3. Brand loyalty final score = (Retention rate scale score/5) × 0.5 NPS × 0.5 (score range: −0.5~1)
Anchor’s influence	“How much influence do the anchors’ product recommendations have on your decision to buy a product during the broadcast?”	7-point Likert scale (1 = no effect, 7 = very much)	1 point = no effect; 2 points = minimal effect; 3 points = slight effect; 4 points = moderate effect; 5 points = considerable effect; 6 points = high effect; 7 points = extreme effect
Product display effect	“How attractive do you think the presentation of products in the live broadcast (e.g., visual clarity, completeness of selling point explanation, and scenario-based demonstration) will be?”	7-point Likert scale (1 = extremely low attraction, 7 = extremely high attraction)	1 point = Extremely low attractiveness; 2 points = Low attractiveness; 3 points = Slightly low attractiveness; 4 points = Average; 5 points = Slightly high attractiveness; 6 points = High attractiveness; 7 points = Extremely high attractiveness.
Collectivism/individualism	“Do you prioritize the opinions of friends and family or the group over your personal preferences before purchasing important items?”	7-point Likert scale (1 = never, 7 = always), with 6 items $\alpha = 0.82$	Scoring of individual items: 1 point = not at all; 2 points = very little; 3 points = not very well; 4 points = average; 5 points = somewhat well; 6 points = very well; 7 points = definitely would; Total score = sum of scores on the 6 items/6 (Scoring range: 1~7, higher scores indicate a stronger tendency toward collectivism, and lower scores indicate a stronger tendency toward individualism).
Power distance	“Do you trust products recommended by authoritative experts or official institutions more than the evaluations of ordinary consumers?”	7-point Likert scale (1 = completely distrusting, 7 = very trusting), with 4 items $\alpha = 0.78$	Item Scoring: 1 point = Completely distrusting/disagree; 2 points = Very distrusting/disagree; 3 points = Somewhat distrusting/somewhat disagree; 4 points = Neutral; 5 points = Somewhat trust/somewhat agree; 6 points = Very trust/very agree; 7 points = Complete trust/agree; Total Score = sum of 4 item scores/4 (Scoring range: 1~7, higher scores represent a stronger power distance tendency)

).

3.3.3. Key Variable Measurement Logic

To present the questionnaire measurement logic of the core variables in a clear way, the following table presents the details of the questionnaire design for key consumer behavior variables and marketing factor variables in a structured format:

3.4. Model Construction and Method Effectiveness Demonstration

3.4.1. Model Setting

Considering that the dependent variable (frequency and amount of impulse purchase) is a continuous variable and the interaction effect of multiple variables needs to be analyzed, this study uses a multiple linear regression model (OLS) to construct an analysis framework. The specific form is as follows:

$$\mathrm{Im} pulseFreq={\beta }_0+{\beta }_{1}Ge{n}_{Millennial}+{\beta }_{2}Ge{n}_X+{\beta }_{3}Ge{n}_{Boomer}+\gamma Controls+\epsilon$$

(1)

The impulse buying amount model is constructed as:

$$\mathrm{Im} pulseAmt={\beta }_0+{\beta }_{1}Ge{n}_{Millennial}+{\beta }_{2}Ge{n}_X+{\beta }_{3}Ge{n}_{Boomer}+\delta Moderators\times Gen+\gamma Controls+\mu$$

(2)

In this model, ${\beta }_0$ is for regular items; ${\beta }_1 {\beta }_2$ and ${\beta }_3$ are for the intergenerational variable coefficient; $\gamma$ is used to control the variable coefficient; $\delta$ is used to adjust the coefficient of utility; and $\epsilon$ and $\mu$ are used for stochastic perturbation terms. Introducing the “generation × moderating variable” interaction term in model 2 (such a $Ge{n}_{Millennial}\times Income$) to examine the moderating effects of variables such as income, policy awareness, and so on, on the relationship between intergenerational and impulsive purchase amounts.

3.4.2. Rationale for Method Selection

Reasons why OLS is used instead of count/logistic models: (1) Excludes Poisson regression (count model)—although impulse purchase frequency is a count, the mean = 2.78 and variance = 3.12 are approximately equal, no overdispersion problem exists (overdispersion coefficient = 1.12 < 0.5), and OLS regression has higher estimation efficiency [2,31]; using Poisson regression would bias coefficient estimates (e.g., Boomers’ frequency coefficient is overestimated by 15% in absolute value). (2) Exclude the logistic model: This study focuses on “the intensity of impulse purchase (frequency/amount)” instead of “whether or not an impulse purchase occurs”, and the logistic model can only capture the binary outcome (0 = no impulse purchase; 1 = impulse purchase), failing to quantify the continuous differences like “Gen Z makes 3.2 more impulse purchases per month than Baby Boomers”, while O can directly output the marginal effect of each variable on the dependent variable, which is more consistent with the research objective.

Endogeneity treatment: The income variable may suffer from endogeneity (e.g., highly impulsive buying behavior may prompt consumers to increase their time income, leading to the causal confusion of “income–impulsive buying”). This study employs the instrumental variable method (2SLS) to address this: “The number of fiber ports per 10,000 people in each country” is selected as the instrumental variable (IV) for income, justified as follows:

(1)

The density of internet infrastructure is highly correlated with the level of income (correlation coefficient = 0.68, p < 0.001), because the better infrastructure is more developed in the digital economy, with more employment opportunities and higher income levels.

(2)

Exogeneity: The number of fiber ports is a national-level public infrastructure index that is not directly related to the individual’s impulsive buying behavior (e.g., the density in a country would not change due to whether the individual impulse shops or not), satisfying the exogeneity assumption of the instrumental variable. The 2SLS regression results show that the coefficient of the income variable is 0.0003 (p < 0.05), which is consistent with the direction of the benchmark OLS regression (coefficient = 0.0002, p < 0.05), indicating that the endogeneity does not significantly distort the estimation results.

Multiple collinearity test: The variance inflation factors (VIFs) of all independent variables were calculated, and the results showed that the maximum VIF value was 1.72 (education level—master’s degree and above), far below the critical value of 10, indicating that the model did not have a serious case of multiple collinearity [17,51,54,55], and the marginal effects of each variable could be identified independently, such as the effects of “national differences” and “income level” not being confused with each other.

3.4.3. Robustness Check Strategy

To validate the reliability of the research conclusion, this study designs three robustness tests to further strengthen the validity of the method: (1) Replace the measurement method of the dependent variable: Replace the frequency of impulse buying from “monthly number” to “quarterly number”, and the regression results show that the coefficient and significance of the generation variable remain consistent (e.g., the coefficient of Baby Boomers = −6.8, p < 0.01, corresponding to the monthly coefficient = −2.3, p < 0.01), which proves the stability of the impact of intergenerational differences on the frequency of impulse buying. (2) Sample regression: Divide the sample into high-income countries (Singapore and Malaysia) and middle–low-income countries (Indonesia, Vietnam, Cambodia, etc.) according to the economic development level, and the coefficient symbol and significance of the generation variable in both groups of samples are consistent with the overall sample (e.g., the coefficient of Generation X in middle–low-income countries is 38, p < 0.01; in high-income countries, it is 42, p < 0.01), which shows that the conclusion has universal applicability in countries with different economic levels. (3) Exclude extreme values: After excluding the extreme values of the top 1% and bottom 1% of income and impulse buying amount, there is no significant change in the model R2 (from 0.72 to 0.70) and the coefficient of the core variable (e.g., the coefficient of Generation X amount from 40 to 38, p < 0.01), which proves that the results are not affected by extreme observations and the method has good stability.

In response to the objection of “excluding the characteristic variables such as the host’s sense of humor and product knowledge, or leading to conclusion bias”, this study adds the “core independent variable replacement test” to further verify the rationality of the KOL influence as the core variable and balance the argumentation of other influencing factors, as follows:

The newly added inspection design replaced the original core independent variable “KOL influence” (7-level Likert scale measurement) with “Host comprehensive feature”. This index covers three dimensions: KOL influence, product knowledge, and sense of humor. The weights of each dimension are set according to the results of a meta-analysis of existing studies (KOL influence 0.5, product knowledge 0.3, sense of humor 0.2) [56], and the regression analysis of the impulse purchase frequency and amount model is re-analyzed to compare the consistency of the core conclusion.

The core conclusion of the intergenerational difference remained stable as the test results showed: the frequency coefficient of impulse buying of Baby Boomers remained at 2.3 (p < 0.01) and the amount coefficient remained at 60 (p < 0.01) compared to Gen Z, the reference group, and the significance level of the coefficients of each generation was consistent with that of the benchmark regression. In the “comprehensive host characteristics index”, the independent effect of KOL influence on impulse buying frequency (β = 0.38, p < 0.001) was significantly higher than that of product knowledge (β = 0.15, p < 0.05) and humor (β = 0.08, p > 0.1), which confirmed that even with other host characteristics included, KOL influence was still the core factor driving the intergenerational difference in impulse buying; the change in the model fit was minor, the adjusted R2 of the impulse buying frequency model increased from 0.65 to 0.66, and the amount model from 0.69 to 0.70, without improvement, further supporting the rationality of setting “KOL influence as the core independent variable”.

Supplementary argumentation from the theoretical logic level: This study focuses on the ASEAN cross-border live e-commerce scenario, where the core pain points are cross-border trust deficit and cultural cognitive differences. KOLs can simultaneously resolve these two pain points through professional endorsement and localized emotional resonance. However, humor only attracts short-term attention (average duration of influence 3.2 min), and product knowledge is limited by live duration, making it difficult to convey key information such as the compliance of cross-border goods and after-sales guarantees. The effectiveness of the two in solving the core contradictions of the cross-border scenario is relatively weak. For example, in the scenario of Thai advertising law compliance supervision, KOL compliance recommendation increases the conversion rate of Millennials by 35%, and after controlling the influence of KOLs, the effect of “presenter humor” is not significant at all (β = 0.06, p > 0.1), further confirming the rationality of variable exclusion.

This technology roadmap takes the “generational gap” as the core independent variable and divides the research object into four major groups: Gen Z (18–24 years old), Millennials (25–34 years old), Gen X (35–50 years old), and Baby Boomers (51 years old), consistent with the core classification framework of the ASEAN intergenerational consumption characteristics in the document (Figure 2). At the same time, a “culture–policy–economy” three-dimensional regulatory variable system is constructed: B1 is the cultural dimension, covering collectivism/individualism (e.g., Cambodia Hofstede collectivism index of 80; Singapore individualism index of 74) and the degree of exposure to globalization (e.g., the time of using social media by Gen Z); B2 is the policy dimension, including tariff adjustments, consumer protection regulations, and cross-border compliance regulations [3,57]; B3 is the economic dimension, involving income level (e.g., Gen Z monthly average of USD 600; Baby Boomers monthly average of USD 3000), per capita GDP and changes in family structure, ASEAN core family ratio from 45% in 2010 to 62% in 2024, covering all key external variables affecting intergenerational impulse buying. The route map further sets the influence of KOLs at the meso-market level, platform rules (such as Shopee Live’s “limited-time flash sale” mechanism), and the merchant–anchor collaboration model as mediating variables, achieving logical connection of the “macro–meso–micro” three-layer analysis framework. Overall, it follows the path of “generational group → intervention by economic B1/B2/B3 regulatory variables → transmission through mediating variables → pointing to impulse buying results”, focusing on two dependent variables, impulse buying frequency (times/month) and amount (USD/month), and completes model verification through multiple linear regression, robustness test, and country difference analysis, presenting the core research structure of “economic–policy–cultural synergistic driving intergenerational impulse buying differences” in its entirety.

3.5. Ethical Compliance

This study strictly adheres to the Helsinki Declaration and ethical standards for cross-border research, ensuring the rigor and compliance of the research process: (1) Informed consent: All respondents read and electronically signed the informed consent form before filling out the questionnaire, clearly informing them of the research purpose, data usage, and confidentiality commitment, with no coercion or inducement to participate. (2) De-identification: Questionnaire data and platform behavior data both remove personal identifying information (name, mobile number, and IP address), replacing it with a “sample number” to ensure individual privacy security. (3) Compliance with data storage: All data are stored on encrypted servers within the ASEAN region that meet GDPR standards, accessible only by research team members with permissions, and after the research is completed, it is kept for 5 years according to regulations and then permanently destroyed. (4) Ethical approval: Although this study strictly adheres to the Helsinki Declaration and ethical standards for cross-border research, it is a non-interventional survey (with no potential risks), and it has been filed for ethical review through the academic ethics review of the university where it is located (Approval No.: YZGL-2025035), ensuring that the research process complies with academic ethical standards.

4. Results

4.1. Descriptive Statistics

Descriptive statistics of different generations of consumers for each variable are shown in

Table 6. Descriptive statistics of different generations of consumers for each variable.

Variable	Z for Generations	Millennials	X for Generations	Baby Boomers
Impulse purchase frequency (times/month)	4.5	3.2	2.1	1.3
Impulse purchase amount (USD/month)	80	120	150	200
Age (years)	21	30	42	60
Sex (percentage male)	40%	42%	48%	52%
Income (USD/month)	600	1200	2500	3000
Time spent on social media (hours/day)	4.5	3.5	2.0	1.0
Frequency of live-streaming e-commerce (times/week)	5.0	4.0	3.0	2.0
Brand loyalty (frequency of repeat purchases)	0.6	0.7	0.8	0.9
Attractiveness of live-streaming promotion (1–5 points)	4.0	3.8	3.5	3.2
Influencer power (1–5)	4.2	4.0	3.6	3.4
Product display effect (1–5 points)	4.1	3.9	3.7	3.5

Note: This analysis categorizes consumer behavior by generation using descriptive statistics. Generation Z averages 4.5 impulsive purchases monthly, spending USD 80, and spends 4.5 h daily on social media, indicating frequent low-value transactions. In contrast, Baby Boomers spend USD 200 monthly, have a brand loyalty index of 0.9, and primarily trust traditional media. One-way ANOVA shows significant differences in impulsive purchase frequency (F = 28.50, p < 0.001) and expenditure (F = 32.00, p < 0.001), confirming younger consumers are more prone to spontaneous buying in live broadcasts, while older consumers’ higher spending relates to income and brand loyalty.

.

The impulse buying behavior characteristics of different generations in the context of live-streaming e-commerce can not only be reflected by statistical data but also be more intuitively presented with the help of diagrams (Figure 3). Specifically, generational differences are clearly shown in descriptive statistics. Generation Z exhibits an average daily social media engagement of 4.5 h, markedly exceeding other cohorts, thereby underscoring their status as digital natives with pronounced media reliance. The Baby Boomer cohort demonstrates elevated brand loyalty metrics, indicative of their predilection for traditional, well-established brands. Concerning impulsive purchasing behavior within live streaming contexts, Generation Z records an average of 4.5 spontaneous transactions per month, surpassing Generation X and Baby Boomers, who average 2.1 and 1.3 transactions, respectively, thus reflecting a greater propensity for spontaneous consumption among younger demographics in digital live commerce environments (

Table 7. Comparison of impulse buying characteristics of different generations of consumers.

Intergeneration	Impulse Purchase Frequency (Times/Month)	Impulse Purchase Amount (USD/Month)	Core Drivers	Typical Commodity Categories
Generation Z	4.5	80	Social media immersion, KOL influence, and personalized design	Trendy clothes, creative accessories, and low-price FMCG
Millennials	3.2	120	Brand tone, product quality, and user reputation	Electronics, fashion, and luxury goods
Generation X	2.1	150	Cost performance, functional utility, and life cycle cost considerations	Household and durable consumer goods
Baby Boomers	1.3	200	Brand loyalty, quality reliability, and trust in traditional media	Health care products, traditional clothing, and high-end durable goods

Note: This table compares impulse purchase behaviors among generations: Generation Z favors trend-driven apparel and accessories (USD 50–100), influenced by social media and KOLs; Millennials prioritize brand identity and electronic devices (USD 120); Generation X emphasizes cost–performance with functional household items; and Baby Boomers (USD 200/month) base health product choices on brand legacy and trust. The divergence reflects a shift from social entertainment motives in Generation Z to quality and brand reliability in Baby Boomers.

).

Interpretation and Academic Significance of Data Patterns: The “Generation Z, high-frequency, low-consumption–Baby Boomers, low-frequency, high-consumption” pattern revealed in Table 5 and Table 6 is not simply caused by age differences but is the result of the coupling of intergenerational resource endowments and the adaptability of the digital ecology. The Generation Z’s high frequency of impulse buying 4.5 times/month (Table 5), combined with their 4.5 h/day of social media immersion (Table 5), forms a behavioral loop that confirms the decision-making characteristics of the digital native generation’s “instant feedback–high frequency interaction,” providing cross-border scenario evidence for the correlation between “media exposure–impulse triggering” in consumer behavior theory; while the Baby Boomers’ high consumption amount of USD 200/month (Table 5), coupled with their 0.9 brand loyalty (Table 5) and USD 3000/month income level (Table 5), highlights the dual support of “economic strength–brand trust” for high ticket price impulse buying, filling the research gap in the mechanism of cross-border consumption for middle-aged and elderly people in ASEAN.

4.2. Regression Results Analysis

4.2.1. Impulse Purchase Frequency Model

The regression analysis demonstrates that generational cohort variables significantly affect impulsive purchasing frequency. Compared to Generation Z, Millennials exhibit a negative coefficient of −0.8 (p < 0.05), indicating a statistically significant reduction in impulsive buying frequency. Generation X shows a coefficient of −1.5 (p < 0.01), reflecting a markedly lower impulsive purchase rate relative to Generation Z. Baby Boomers have a coefficient of −2.3 (p < 0.01), representing the lowest impulsive buying frequency among the cohorts studied (

Table 8. Regression results of impulse purchase frequency model.

Variable		Coefficient	Standard Error	$\mathit{t}$-Value	$\mathit{p}$-Value	95% Confidence Interval	VIF
Intergenerational variables	Gen_Millennia	−0.8	0.35	−2.29	0.023	[−1.49, −0.11] [−1.49, −0.11]	1.25
	Gen_X	−1.5	0.42	−3.57	0.000	[−2.32, −0.68]	1.30
	Gen_Boomer	−2.3	0.51	−4.51	0.000	[−3.30, −1.30]	1.28
Controlled variables	Age	−0.12	0.03	−4.00	0.000	[−0.18, −0.06]	1.15
	Sex (male = 1)	−0.35	0.20	−1.75	0.080	[−0.74, 0.04]	1.05
	Income	0.0002	0.0001	2.00	0.045	[0.0000, 0.0004]	1.35
	Educational level (college)	0.15	0.25	0.60	0.549	[−0.34, 0.64]	1.40
	Educational level (undergraduate)	0.28	0.27	1.04	0.300	[−0.25, 0.81]	1.50
	Education (master’s degree or above)	0.32	0.31	1.03	0.304	[−0.29, 0.93]	1.45
Consumer behavior variables	Time spent using social media	0.25	0.08	3.13	0.002	[0.09, 0.41]	1.60
	Frequency of live-streaming e-commerce	0.40	0.06	6.67	0.000	[0.28, 0.52]	1.55
	Brand loyalty	−0.50	0.10	−5.00	0.000	[−0.70, −0.30]	1.30
Marketing factor variables	Promotional appeal	0.30	0.07	4.29	0.000	[0.16, 0.44]	1.40
	The influence of the anchor	0.28	0.06	4.67	0.000	[0.16, 0.40]	1.35
	Product display effect	0.22	0.05	4.40	0.000	[0.12, 0.32]	1.25
Constant term		5.80	0.60	9.67	0.000	[4.62, 6.98]	1.00
Model fitting	R squared	0.68
	Adjusted R-squared	0.65
	$F$-Value	28.50			0.000

Note: This regression analysis demonstrates that generational cohort significantly influences impulsive purchase frequency, with Generation Z exhibiting the highest impulsivity. Compared to Generation Z, Millennials show a coefficient of −0.8 (p < 0.05), Generation X −1.5 (p < 0.01), and Baby Boomers −2.3 (p < 0.01). Social media engagement (0.25, p < 0.01) and live broadcast interaction (0.40, p < 0.01) positively correlate with impulsivity, whereas brand loyalty (−0.50, p < 0.01) shows a negative correlation. These findings suggest that digital social immersion and real-time engagement are primary factors driving impulsive purchasing behavior among younger demographics.

). These findings empirically support Hypothesis 1, confirming that intergenerational differences substantially influence impulsive purchasing behavior, with Generation Z exhibiting the highest propensity, consistent with prior theoretical expectations.

4.2.2. Impulsive Purchase Amount Model

In the impulse purchase amount model, intergenerational cohort variables exert a statistically significant impact on purchase magnitudes. The analysis reveals that, relative to Generation Z, Millennials exhibit an impulse purchase coefficient of 25 (p < 0.05), suggesting a marginal increase in impulsive spending. Generation X demonstrates a coefficient of 40 (p < 0.01), indicating a markedly higher propensity for impulsive expenditures. The Baby Boomer cohort presents a coefficient of 60 (p < 0.01), reflecting the highest levels of impulsive purchase behavior among the groups studied (

Table 9. Regression results of impulse purchase amount model.

Variable		Coefficient	Standard Error	$\mathit{t}$-Value	$\mathit{p}$-Value
Intergenerational variables	Gen_Millennia	25	12	2.08	0.038
	Gen_X	40	15	2.67	0.008
	Gen_Boomer	60	18	3.33	0.001
Controlled variables	Age	2.5	0.8	3.13	0.002
	Sex (male = 1)	15	8	1.88	0.065
	Income	0.15	0.05	3.00	0.003
	Educational level (college)	10	10	1.00	0.317
	Educational level (undergraduate)	20	12	1.67	0.095
	Education (master’s degree or above)	30	15	2.00	0.045
Consumer behavior variables	Time spent using social media	−5	3	−1.67	0.095
	Frequency of live-streaming e-commerce	10	4	2.50	0.012
	Brand loyalty	30	8	3.75	0.000
Marketing factor variables	Promotional appeal	15	4	3.75	0.000
	The influence of the anchor	12	3	4.00	0.000
	Product display effect	10	3	3.33	0.001
Constant term		50	15	3.33	0.001
Model fitting	R squared	0.72
	Adjusted R-squared	0.69
	$F$-Value	32.00			0.000

Note: This model validated H2, demonstrating that intergenerational demographic variables significantly influence impulsive expenditure patterns. The regression coefficients for Millennials aged 25 (p < 0.05), Generation X aged 40 (p < 0.01), and Baby Boomers aged 60 (p < 0.01) indicate elevated one-time purchase amounts among higher-income cohorts. Income level (coefficient 0.15, p < 0.01), brand loyalty (coefficient 30, p < 0.01), and host influence (coefficient 12, p < 0.01) positively predict expenditure magnitude, whereas social media engagement duration (coefficient −5, p > 0.05) lacks statistical significance. These findings suggest that increased average order values in older demographic groups are predominantly driven by economic capacity and brand trust, rather than social media engagement.

). From the perspective of consumption power, Gen X and Baby Boomers have higher income levels. Once they make impulse purchase decisions, their single consumption amount often has a greater scale effect.

Interpretation and academic significance of data patterns: The step-like decrease in the coefficients of the generational variables in Table 8 (Millennials −0.8 → Gen X −1.5 → Baby Boomers −2.3) quantitatively validates the linear relationship “age growth–frequency of impulsiveness”, and its statistical significance (p < 0.05 to p < 0.01) indicates that the influence of intergenerational differences on the frequency of impulsive buying exceeds demographic variables (such as the gender coefficient −0.35, p = 0.08), providing a quantitative basis for the application of generational theory in cross-border e-commerce scenarios. The interaction between the coefficient of the Baby Boomers aged 60 in Table 9 (p < 0.01) and the positive effect of the income variable 0.15 (p < 0.01) reveals the intergenerational specific mechanism of “high income–high customer unit price”, which corrects the bias in traditional cognition that “impulsive buying is only related to young groups” and expands the age boundary research of impulsive consumption behavior.

4.3. Mechanism Analysis of Country Differences

The intergenerational disparities in impulsive purchasing behaviors across ASEAN nations are attributable to a complex interplay of economic factors, regulatory frameworks, and cultural values. This multidimensional interaction underpins the differentiation in generational consumer behavior.

In comparison to Cambodia, Singapore’s Generation Z exhibits an average purchase frequency of 4.2 transactions per month, with a predominant customer unit price ranging from USD 150 to USD 300. This phenomenon is driven by three mechanisms: firstly, the policy synergy effect—stringent regulations on false advertising in live broadcasts under the Consumer Protection Law (resulting in a 42% reduction in complaint rates pre- and post-regulation) enhance consumer trust, while reduced cross-border tariffs decrease luxury goods prices by 18% relative to domestic markets, thereby mitigating purchase decision risks; secondly, the influence of cultural values—an individualistic culture (Hofstede index of 74) legitimizes consumption as a form of self-expression, with 62% of Generation Z perceiving impulsive buying as an act of self-indulgence; and thirdly, economic factors—per capita GDP exceeding USD 60,000 provides the material basis for frequent and high-value impulsive purchases.

Conversely, Cambodian Generation Z demonstrates a lower impulse purchase frequency of 2.0 times per month, with average transaction values below USD 50, reflecting distinct underlying mechanisms. At the policy level, the regulatory infrastructure for cross-border e-commerce remains underdeveloped, with 73% of respondents citing concerns over inadequate post-sale protections as a deterrent. Culturally, within a collectivist context (Hofstede index of 20), 58% of Generation Z report abandoning impulsive purchases due to opposition from peers and family. Economically, with 75% of the population earning less than USD 300 monthly, limited disposable income constrains consumption capacity, rendering low-cost daily necessities the primary category of impulsive purchases, accounting for 81% of such transactions.

The differential regulatory mechanisms across jurisdictions are notably pronounced: Thailand’s advertising legislation mandates real-time promotional content registration, resulting in a 35% increase in Generation X consumers’ trust and propensity for impulsive household appliance purchases. Following a 15% tariff reduction in Vietnam in 2023, there was a 28% surge in Millennials’ impulsive acquisition of 3C electronic products, with urban centers possessing advanced internet infrastructure, such as Ho Chi Minh City, experiencing a 41% escalation. These findings substantiate the interaction effect among policy interventions, technological infrastructure, and generational consumer behavior.

4.4. Cross-Dimensional Comparative Analysis

4.4.1. Analysis of National Differences

Due to the disparities in economic development levels, cultural paradigms, and policy frameworks among ASEAN member states, intergenerational impulsive purchasing behaviors exhibit significant heterogeneity (

Table 10. Impulse shopping data of Southeast Asian countries.

Country	Impulse Purchase Frequency (Times/Month)	Average Order Value (USD)	Anchor Category	Typical Platform	Policy Impact
Singapore	3.8 (Gen Z 4.2)	150–300	Luxury goods and electronics	Shopee Live	Consumer protection laws increase trust by 20%
Indonesia	4.5 (Gen Z 5.1)	80–120	Fashion and FMCG	TikTok Shop	E-commerce subsidy programs are stimulating young consumption
Thailand	3.2 (Gen Z 3.5)	100–200	Home life and beauty	Lazada Live	The advertising law reduced the rate of complaints about false promotions by 30%
Cambodia	1.2 (Gen Z 2.0)	<50	Daily necessities and agricultural products	Local live-streaming platform	Cross-border policies need to be improved to restrict categories

Note: This table compares impulsive purchasing across ASEAN countries. Singapore’s Generation Z averages 4.2 transactions/month with USD 150–USD 300 spent, and the Consumer Protection Act increased confidence by 20%. Cambodia’s youth average 2.0 transactions/month, with below USD 50 per purchase, limited by income and cross-border trade. Indonesia’s impulsive purchase rate is 35%, mainly due to e-commerce promotions. The Thai Advertising Act reduced false advertising complaints by 30%, illustrating regulatory influence on consumer behavior and exemplifying “hard constraints and soft synergy” in policy enforcement.

). In advanced, high-income markets such as Singapore, Generation Z consumers are immersed in individualistic cultural orientations, with an average impulse purchase frequency of 4.2 transactions per month, a typical customer transaction value ranging from USD 150 to USD 300, and a predilection for luxury goods and smart hardware. Conversely, in less economically developed nations like Cambodia, Generation Z consumers are constrained by income limitations, with an impulse purchase frequency of approximately 2.0 transactions per month, a typical transaction value below USD 50, and a consumption pattern predominantly centered on daily necessities and other low-cost essential commodities. These disparities not only reflect stratified differences in consumption capacity across economies but also underscore the profound influence of cultural values on the underlying logic of consumer decision-making processes.

4.4.2. Industry Difference Analysis

The propensity for impulsive purchasing across various product categories is markedly influenced by generational consumption patterns and marketing strategies. In the segment of low-cost, fast-moving consumer goods (comprising 40% of total sales), Generation Z constitutes the primary consumer demographic, accounting for 65% of purchase volume, with individual transaction values below USD 50. Purchase triggers predominantly involve “limited-time discount scenarios and scenario-based sampling demonstrations.” For smart hardware products (representing 25% of total sales), impulsive buyers are predominantly Millennials, comprising 45% of the segment, with average transaction values ranging from USD 100 to USD 200, emphasizing marketing approaches centered on “technical parameter analysis and use-case scenario construction.” Conversely, high-end durable goods (constituting 5% of total sales) are chiefly driven by Baby Boomers, who represent 35% of purchasers, with per capita expenditure exceeding USD 200, relying on “brand heritage narratives and authoritative endorsements” as mechanisms for trust establishment.

4.4.3. Cross-Year Trend Analysis

From 2020 to 2024, the penetration rate of impulsive purchasing behavior via live e-commerce platforms in ASEAN experienced a significant increase from 12% to 63%, with a Compound Annual Growth Rate (CAGR) of 48%. The market size of impulsive transactions expanded exponentially, reaching USD 38 billion in 2024, representing a 14.2-fold growth relative to 2020 (USD 2.5 billion) (

Table 11. Cross-year trends in impulsive purchasing behavior during 2020–2024.

Year	Impulse Buying Permeates	Total Amount of Impulse Purchases (in USD Billions)	Key Driver Events
2020	12%	25	In the market cultivation period, the platform initially penetrated
2022	35%	120	TikTok Shop in the ASEAN market
2023	52%	250	Vietnam and Indonesia have eased cross-border e-commerce policies
2024	63%	380	The ASEAN Economic Community deepens trade liberalization

Note: Impulsive purchase conversion in ASEAN live e-commerce rose from 12% in 2020 to 63% in 2024, with consumer expenditure increasing from USD 2.5 billion to USD 38 billion (CAGR 48%). Tariff reductions in Vietnam and Indonesia’s deregulation in 2023 significantly boosted activity, with a 15% price drop in 3C segments and a 30% increase in Generation Y’s AOV, indicating rapid market adaptation to policy changes and a shift from “traffic bonus” to “stock competition” strategies.

). Following the implementation of tariff reduction policies in Vietnam in 2023, cross-border impulsive purchase volumes grew by 25% month-over-month, indicating the regulatory policy’s stimulatory influence on consumer engagement. This growth pattern not only underscores the industry’s scaling trajectory but also demonstrates the immediate responsiveness of consumer behavior to policy interventions. For a conceptual visualization of the curve variations, refer to Figure 4 for detailed analysis.

Interpretation and Academic Significance of Data Patterns: The country differences between Singapore and Cambodia (4.2 times/month vs. 2.0 times/month, USD 150–300 vs. <50) essentially embody the concrete manifestation of the tridimensional interaction of “policy synergy–cultural orientation–economic foundation”. The superposition of Singapore’s policy compliance (complaint rate dropped by 42%) and individualism culture (Hofstede index of 74) reduces the risk perception of impulse purchase, which provides new cases for the theoretical synergistic effect of “institutional environment–cultural values” on consumption decision-making; the 25% amount of month-on-month growth after the tariff reduction in Vietnam in 2023 in Table 9 quantifies the verification of the causal chain of “trade facilitation–cross-border impulse consumption” and provides micro-behavioral evidence for the formulation of regional e-commerce policies under the RCEP framework.

5. Discussion

5.1. Theoretical Contribution: Interdisciplinary Integration and Scenario-Based Expansion of Classic Theories

The core theoretical breakthrough of this study is the construction of a three-layer integrated framework of “macroscopic institutions–mesoscopic market–micro-behavior”, breaking the fragmented interpretation of the mechanism of impulse purchase by a single discipline. On the level of S-O-R model expansion, policy variables in the ASEAN cross-border scenario (such as tariff adjustment and consumer protection regulations) are included in the “stimulus” antecedent dimension, and the transmission chain of “Vietnam’s 3C product tariff reduction by 15% → improvement of market accessibility → 38% increase in the amount of impulse purchases by Millennials” is empirically revealed, which revises the limitations of the traditional S-O-R model that only focuses on micro-marketing stimuli and provides a theoretical supplement for the study of cross-border consumer behavior with an institutional embedded perspective [41,52].

In deepening intergenerational consumption theory, quantifying the mechanism of “collectivism inhibitory effect’s intergenerational attenuation”: The proportion of immediate purchases triggered by product uniqueness among Cambodia’s Gen Z (60%) is 87.5% higher than that of middle-aged groups (32%), despite being in a high collectivism cultural context (Hofstede index of 80) [14]. This finding breaks the static cognition of Hofstede’s cultural dimensions theory, reveals the reshaping effect of globalization on the values of the youth, and injects a dynamic perspective into the study of intergenerational cultural differences.

In response to generational fit in TAM models, this study reveals generational heterogeneous “perceived usefulness” of live stream e-commerce: Generation Z’s usefulness perception is highly correlated with entertainment value (r = 0.72, p < 0.001), while Baby Boomers focus on transaction security (r = 0.68, p < 0.001), and this modification makes the TAM model more congruent to generational differences in technology acceptance in ASEAN cross-border scenarios, filling a research gap in the association between “technology acceptance” and “generational behavior” in the digital consumption domain.

5.2. Exploration of Possible Alternative Theories and Mechanisms

The findings can be further complemented by two alternative theories. One is the theory of symbolic consumption. The Generation X in the high-level stratum of Malaysia spend more than 500 USD in a single impulse purchase in luxury live-streaming, and their decision-making mechanism relies on a dual mechanism of “brand symbol–live-streaming host endorsement”, while the same-level Generation Z prefers “limited interactive snatching”, transforming consumption into the accumulation of social capital—this suggests that the intergenerational differences in symbolic consumption may constitute another path of differentiation of impulsive buying behavior, complementary to this study’s “economic–policy–cultural” framework, which can be further verified by incorporating the “perception of symbolic value” variable.

Second is the theory of planned behavior (TPB): The conventional TPB model emphasizes the influence of “attitude–subjective norm–perceived behavioral control” on decision-making, and the finding of “family structure modulates the cultural effect” in this study (the frequency of impulse buying among single Gen Z is 30% higher than that of their peers in larger families) can be explained by the “subjective norm” dimension of TPB—individual decision-making in a nuclear family is constrained by the group to a lesser extent, and perceived behavioral control is enhanced, which in turn increases the tendency for impulse buying. This alternative mechanism suggests that future studies can integrate TPB and intergenerational theory to develop a more comprehensive decision-making model for impulse buying.

5.3. Limitations

5.3.1. Limitations of Sample Selection

The sampling framework for this study encompassed primary ASEAN markets—including Indonesia, Malaysia, and Thailand, among others—but exhibited distributional bias, with Indonesia constituting 30%, Malaysia 20%, Thailand and the Philippines each 15%, Vietnam 10%, and the remaining 10% collectively. While this distribution correlates to some extent with the respective e-commerce penetration rates across these nations, it hampers the capacity to accurately depict the comprehensive ASEAN digital commerce landscape, particularly given the limited sample sizes in countries with underdeveloped e-commerce ecosystems, thereby potentially compromising the external validity of the findings in these regions. This sampling limitation must be duly acknowledged when interpreting the study outcomes.

Furthermore, although the sample was stratified by generational cohorts, substantial heterogeneity exists within each group. For instance, within the Gen Z cohort, the 18–24 age subset displays variability in purchasing power and consumption attitudes, which was not further delineated, potentially diminishing the precision of intergenerational comparative analyses. Regarding gender distribution, females constitute a slight majority at 55%, with female representation among Gen Z and Millennial segments reaching 60% and 58%, respectively. This gender skew, possibly attributable to higher female engagement in impulse-driven categories such as food and beauty products, may influence the sample’s representativeness and warrants careful consideration in the contextual interpretation of the results.

5.3.2. Limitations of Research Methodology

This investigation employs a mixed-methods approach, integrating questionnaire-based surveys and semi-structured interviews, analyzed through multiple linear regression modeling. While survey instruments and interview protocols facilitate the collection of extensive primary data, they are susceptible to subjective biases, including social desirability effects and recall inaccuracies, which may compromise data validity and reliability. Although the multiple linear regression model effectively quantifies the influence of multiple independent variables on the dependent variable, its assumption of linearity may not fully capture the complex nonlinear mechanisms underlying impulse purchasing behavior, thereby constraining the model’s explanatory capacity and necessitating cautious interpretation of the results. Data acquisition primarily involved multi-source datasets spanning 2020 to 2024; however, the static nature of such datasets limits their capacity to reflect real-time market fluctuations. Given the rapid evolution of the e-commerce sector and the dynamic shifts in consumer preferences and behaviors, the temporal lag inherent in the data may restrict the contextual relevance and generalizability of the findings. Consequently, the temporal limitations impose boundaries on the applicability of the results within the current fast-paced market environment, underscoring the importance of integrating real-time market analytics for more adaptive and contextually valid insights.

5.3.3. Limitations of Variable Measurement

There are inherent limitations in the operationalization of variables within this study. For instance, brand loyalty is quantified via repurchase rate, which only partially encapsulates the construct, neglecting deeper dimensions such as brand recognition and emotional attachment, thereby potentially resulting in an incomplete assessment. The evaluation of product display effectiveness is based on dimensions including visual clarity, completeness of value proposition communication, and scenario appeal; however, the granularity of these dimensions is insufficient, impeding comprehensive and precise elucidation of their influence on impulse purchasing behavior. Such measurement constraints may diminish the explanatory power of variable relationships.

Among marketing variables, indicators such as promotional attractiveness and host influence rely on consumers’ subjective assessments, which are subject to individual heterogeneity. Variations in perceptions of discount magnitude and gift value can introduce systematic bias into the measurement outcomes. The reliance on subjective cognition inherently risks interference from individual experiential differences, potentially compromising the accuracy of the findings. Therefore, more sophisticated and refined measurement instruments are necessary in future research to enhance validity and reliability.

5.3.4. Limitations of the Scope of This Study

This investigation concentrates on the ASEAN cross-border live-streaming e-commerce sector; despite its substantial scholarly contribution, the generalizability of the results is constrained. The heterogeneity across regions in terms of cultural, economic, and policy contexts may engender distinct consumer impulsive purchasing behaviors. For example, the underlying logic guiding live-streaming shopping in European and American markets markedly diverges from that in ASEAN, rendering direct extrapolation of these findings to such markets problematic. This geographical limitation underscores the necessity for future research to employ cross-regional comparative methodologies to validate the universality of the proposed theoretical framework.

The dependent variables in this study are primarily centered on the frequency and monetary value of impulsive purchases, with dimensions such as post-impulse satisfaction and repurchase intentions yet to be integrated into the analytical model. As critical components within the impulsive buying process, these subsequent behavioral indicators are essential for a comprehensive understanding of the complete consumer decision-making cycle. The current scope’s limitations may hinder the full elucidation of the mechanism from purchase initiation to post-purchase feedback, thereby highlighting a promising avenue for subsequent scholarly inquiry.

5.4. Discussion on the Fitting of Hofstede’s Cultural Dimension Framework

This study introduces Hofstede’s cultural dimensions theory (with a focus on the collectivism/individualism dimension) as a foundational analytical tool for parsing the impact of ASEAN cultural contexts on generational impulse buying. For instance, cultural differences between Cambodia (with a score of 80 on the collectivism index) and Singapore (with a score of 74 on the individualism index) are quantified and contrasted relying on this framework, which preliminarily explains the cross-national differences in the frequency of Generation Z’s impulse buying in the two countries (2.0 times/month vs. 4.2 times/month). However, it should be noted objectively that this theory has two aspects of limitations, which are mismatched with this study’s context.

Firstly, the theory suffers from significant static and insufficient timeliness, with its core data stemming from the 20th century, unable to capture the dynamic reshaping of young generations’ values in the digital era of globalization—such as the Cambodian Gen Z, despite being raised in a high collectivist culture context, inclining to individualistic decision-making logic (60% purchase impulsively for unique products) more than their middle-aged groups (32%) due to the frequent exposure to global social platform content, a generational mutation within the culture that traditional frameworks failed to cover. Secondly, there exists cultural homogeneity assumption bias, as the theory often defines cultural traits on a country-by-country basis, overlooking the dual cultural heterogeneity within “country–generation” in ASEAN, for example. Gen X in Vietnam is significantly influenced by collectivism (20% less likely to purchase impulsively than Gen Z), while the local Gen Z already exhibits a clear individualistic consumption tendency. Such internal differences need to be further modified in the context of generational growth.

Based on this, the current study, when applying the framework, partially compensates for its lack of dynamic adaptability by introducing moderating variables such as “global exposure” (e.g., time spent using social media) and “family structure change” (an increase in the proportion of nuclear families). Future studies can further integrate “generational cultural values tracking data” to construct a dynamic cultural analysis model that is more in line with the digital consumption scenario so as to more precisely capture the evolution characteristics of the cultural dimension in the digital age and its influence mechanism on consumer behavior.

6. Conclusions

6.1. Main Findings

This study takes ASEAN cross-border live-streaming e-commerce as the scenario and reveals the formation mechanism of intergenerational differences in impulse buying by an integration framework of “macro-institution–meso-market–micro-behavior”. The results show that Gen Z shows the characteristics of “high frequency and low consumption” (4.5 times per month, USD 80/month), and their behavior is driven by social media immersion (4.5 h per day) and KOL influence (65% of impulse buying is triggered by KOL); Baby Boomers are characterized as “low frequency and high consumption” (1.3 times per month, USD 200/month), relying on brand loyalty (0.9) and traditional media trust. This difference is co-shaped by economic gradient (200% higher unit price for Gen Z in Singapore than their counterparts in Cambodia), policy regulation (Millennials’ unit price increased by 30% due to Vietnam’s tariff reduction), and cultural values (Generation X’s frequency of impulse buying is 20% lower under collectivism), and hypotheses H1 (generational differences impact impulse frequency), H2 (income and policy regulation amount differences), and H3 (presenter influence intergenerational heterogeneity) are verified.

The theoretical contribution of this study is reflected in three aspects: Firstly, it expands the institutional antecedent dimension of the S-O-R (Stimulus–Organism–Response Model) model, incorporates cross-border policy variables into the “stimulus–organism–response” chain, and quantitatively confirms the transmission mechanism of “policy synergy (Singapore)-reduced risk perception-increased frequency of impulse purchase”, which fills the explanatory gap of traditional models in cross-border scenarios. Secondly, it deepens the intergenerational consumption theory, reveals that the inhibitory effect of collectivism on impulse buying tends to decrease with the younger generation (the personalized consumption tendency of Generation Z in Cambodia is 87.5% higher than that of the middle-aged group), and provides empirical evidence for the dynamic evolution of cultural values. Thirdly, it revises the “perceived usefulness” dimension of the TAM model, finds that Generation Z focuses on entertainment value (r = 0.72) and Baby Boomers pay attention to transaction security (r = 0.68), and improves the adaptability of the theory in the digital consumption scenario.

6.2. Practical Implications

The research findings offer precise strategic guidance for optimizing enterprise marketing frameworks. Considering intergenerational variations in impulsive purchasing behavior, organizations should implement targeted operational protocols: For Generation Z, develop personalized product offerings aligned with individual preferences, integrating entertainment and interactive modules such as gamified sweepstakes and real-time question-and-answer sessions to enhance engagement; for Millennials, establish a “quality narrative data verification” marketing pipeline by deconstructing technical specifications and displaying user activity tracking data to align with their “brand recognition rational assessment” decision-making processes. When confronting Generation X, focus on the “cost–performance balance function verification” communication strategy, including presenting comparative performance parameter tables and comprehensive lifecycle cost analyses during live broadcasts; for Baby Boomers, prioritize the cultivation of brand credibility through the display of industry authoritative certifications, heritage brand live streams, and similar approaches to mitigate trust barriers in emerging consumer scenarios.

Simultaneously, policymakers can dynamically refine the regulatory architecture for e-commerce based on these insights. To address regulatory fragmentation across ASEAN nations, it is essential to promote regional harmonization of e-commerce legislation by leveraging the trade facilitation principles of the Regional Comprehensive Economic Partnership (RCEP), with tailored provisions for different generational cohorts: for Generation Z, enforce a mandatory 24-h cooling-off period for impulsive purchases exceeding USD 100, coupled with secondary confirmation via SMS, reflecting the study’s finding that 42% of Gen Z’s high-value impulsive purchases are unplanned and 70% experience regret within 24 h. For Baby Boomers, mandate the inclusion of standardized return policies—such as “This product supports a 7-day no-reason return; see details in the platform’s terms”—delivered via live audio, with hosts interpreting policies in regional dialects (e.g., Isan in Northeast Thailand). Given this demographic’s lower textual information processing efficiency, audio disclosures alone increased return policy recognition by 35%.

Furthermore, establish a cross-border e-commerce complaint resolution mechanism requiring member states to address intergenerational consumer disputes—such as Gen Z’s grievances regarding false advertising and Baby Boomers’ complaints about logistical delays—within 48 h. The efficiency of dispute resolution should be incorporated into the ASEAN Economic Community’s performance metrics. Through these refined regulatory measures, the development of a robust cross-border live broadcast e-commerce ecosystem can be sustained, providing institutional safeguards for the long-term, sound evolution of the ASEAN regional market and fostering industry growth aligned with established standards.

6.3. Research Limitations and Future Research Recommendations

Three limitations of this study are noted: the sample distribution is biased towards the core ASEAN markets (Indonesia and Malaysia accounting for 50%), and the samples from the lagging e-commerce countries (Lao PDR and Myanmar) are scarce, which may undermine the external validity of the findings; brand loyalty is only measured by repurchase rate, and the dimension of emotional identification is not covered, and policy awareness is based on subjective scales and lacks objective data on the frequency of policy exposure; this study has not incorporated sudden external shocks (such as regional economic fluctuations and platform algorithm adjustments), and it is difficult to capture the dynamic response changes of intergenerational behavior in a dynamic market. It should be noted objectively that this study focuses on the core role of KOL influence and has not yet deeply analyzed the differentiated effects of characteristics such as host humor and product knowledge. Although the above robustness test has confirmed that the core conclusion is not affected by variable exclusion, there is still room for expansion. This variable dimension limitation may lead to an incomplete understanding of “the impact of host characteristics on intergenerational impulse buying”, and further research needs to further improve this aspect of analysis.

Future research can be deepened from several aspects: First, the sample coverage can be expanded to all ASEAN member countries, segmenting sub-generations within generations (such as Gen Z students aged 18–21 and newcomers to the workplace aged 22–24), and combining mixed research methods (such as in-depth interviews and big data tracking) to enhance the representativeness of the sample and the richness of the data. Second, optimize the variable measurement tools, introduce the “Brand Emotional Identification Scale” to supplement the brand loyalty dimension, and combine policy document browsing records, awareness of tariff adjustments, and other objective indicators to correct subjective measurement bias. Third, incorporate variables such as “platform algorithm recommendation” and “sudden public events”, construct dynamic panel models, and analyze the short-term and long-term effects of technological intervention and external shocks on intergenerational impulse buying. Fourth, carry out cross-regional comparative research, comparing ASEAN with Europe, the United States, and East Asia’s cross-border live broadcast e-commerce scenarios, and test the cross-cultural adaptability of the “macroeconomic system–intergenerational behavior” mechanism while integrating the theory of symbolic consumption and the theory of planned behavior and introducing variables such as “perception of symbolic value” and “subjective normative strength” to build a more comprehensive impulse buying decision model. Fifth, construct a “host feature–generation fit” sub-model: Design differentiated host feature combinations for different generations, such as verifying the “attraction effect of a sense of humor on Gen Z” and the “trust enhancement effect of product knowledge on Gen X”, and refine the host marketing strategy. Sixth, incorporate “scene-moderating variables”: Analyze the interaction effect of humor, product knowledge, and KOL influence in different commodity categories (such as FMCG vs. durable goods). For example, In the durable goods scenario, product knowledge may form a complementary relationship with KOL influence, improving the conversion efficiency of impulse purchases.