Keywords

India, Household Consumption, Online Shopping Frequency, Shopping Time, UPI, Quick Commerce, SEM; Habit, Impulse Buying.

Introduction

India's retail consumption is experiencing a transformation at the household level, as shopping platforms increasingly dictate the recognition, evaluation, purchase, and replenishment of needs. This change is not simply "e-commerce adoption"; it signifies the advent of digitally mediated household consumption practices—termed in this paper as Household Digital Consumerism Prevalence (HDCP). The significance of this subject is heightened by current market forecasts indicating that India's e-commerce may attain approximately US$ 280–300 billion by 2030, suggesting an ecosystem that will probably increase household dependence on platforms for daily purchases (IBEF, 2026; Boston Consulting Group [BCG], 2026; Economic Times, 2026a). Simultaneously, the digital payments infrastructure has proliferated swiftly, as evidenced by government reports on the RBI Digital Payments Index, which indicate sustained advancement in payment digitisation and efficacy. Recent data reveals exceptionally high UPI transaction volumes in early 2026, suggesting further diminutions in transaction friction for households (Press Information Bureau, 2025; Department of Financial Services, 2024; Economic Times, 2026b; Business Standard, 2026).

A unique force peculiar to India is the expansion of quick commerce, which shortens household purchasing cycles by facilitating rapid fulfilment and promoting small-basket, high-frequency orders. Reports suggest that quick commerce constitutes a significant portion of e-grocery orders and is anticipated to grow swiftly until 2030, indicating a potential transition of households from scheduled shopping to ongoing replenishment practices (Reuters, 2025; MMA Global India & Publicis Commerce, 2025). The aforementioned factors indicate that the frequency of online shopping engagement—both in terms of transaction frequency and duration of shopping-related digital activities—may now be a significant factor in determining the prevalence and persistence of digital consumerism within households.

Current digital commerce studies predominantly emphasise individual objectives, perceived utility, and usability, as delineated in basic acceptance frameworks such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Davis, 1989; Venkatesh et al., 2003). Although these models elucidate initial adoption, household consumer behaviour is further influenced by repetition and automaticity in accordance with habit theory, as well as by advertising stimuli aligned with impulse buying theories (Verplanken & Orbell, 2003; Mandolfo et al., 2021; Fisher & Rook, 1995). Significantly, Indian home structures—nuclear and joint families, communal gadgets, and the distribution of roles in searching, paying, and ordering—indicate that household predominance outcomes are contingent upon collaborative practices rather than solely individual perspectives (IAMAI & Kantar, 2024; PwC India, 2023). This dissertation enquires about the frequency and duration of online shopping platform usage among Indian families, the methods of utilisation (channels, payments, deal routines, coordination), and the extent to which this usage influences the prevalence of digital consumerism within the household.

In response, researchers formulate a household-centered conceptual model and present a comprehensive SEM-ready research design, encompassing constructs, hypotheses, measurement scales, sampling, and an elaborate analysis plan based on current SEM validity standards (Hair et al., 2022; Fornell & Larcker, 1981; Henseler et al., 2015; Podsakoff et al., 2003). The model is specifically contextualised to India's e-commerce and payments evolution (IBEF, 2026; Press Information Bureau, 2025; Department of Financial Services, 2024) and acknowledges the documented variability in internet usage, rural engagement, and payment preferences, including the sustained significance of cash-on-delivery segments (IAMAI & Kantar, 2024; Medianama, 2025; Outlook Business, 2025; PwC India, 2023).

Review of Literature

The prevalence of digital consumerism at the household level results from the interplay of three research domains: technology adoption and platform capabilities, payment integration and transaction barriers, and behavioural influences of habit and impulse, exacerbated by promotions, rapid commerce, and social commerce.

Perspectives on technology adoption elucidate the reasons consumers transition to digital channels when they recognise utility, simplicity, and performance advantages. The Technology adoption Model posits that perceived usefulness and perceived ease of use are fundamental determinants of system adoption and utilisation (Davis, 1989; MIS Quarterly, 1989). The UTAUT model incorporates performance expectancy, effort expectancy, social influence, and facilitating conditions as determinants of behavioural intention and usage behaviour, moderated by factors such as age and experience (Venkatesh et al., 2003). These constructs remain pertinent as Indian households experience diverse facilitating conditions, such as device accessibility, connectivity disparities among urban strata, and differing digital competencies, which affect the extent to which platforms integrate into household routines (IAMAI & Kantar, 2024; Medianama, 2025; Economic Times, 2025). Recent research in India utilising UTAUT extensions in the context of digital payment adoption underscores the significance of trust, promotional advantages, and satisfaction mechanisms as technologies become commonplace (Vedala et al., 2025; Razi-ur-Rahim et al., 2024; Gulia & Singh, 2023). While these studies often focus on individual-level analysis, they substantiate the claim that technological affordances and conducive conditions can serve as structural determinants of recurrent household utilisation.

The integration of digital payments is particularly significant in India, as payment infrastructures can diminish the "friction" that typically constrains repeated online transactions. Government reports on India's digital payments expansion and RBI-DPI demonstrate ongoing enhancements in digitisation and payment efficacy, with 2026 data revealing substantial UPI transaction volumes and values, indicating widespread daily utilisation (Press Information Bureau, 2025; Department of Financial Services, 2024; Economic Times, 2026b; Business Standard, 2026). From a household perspective, such embeddedness signifies that payment capacity is allocated among family members, transactions are mostly conducted through digital methods, and the completion of transactions becomes habitual, thereby facilitating regular micro-purchases and expedited replenishment cycles. Empirical investigations of UPI adoption in India utilising SEM frameworks indicate that UTAUT-related determinants, trust, and perceived advantages influence intentions and usage, corroborating the theoretical assertion that greater digital payment integration serves as a precursor to increased shopping frequency and habit formation (Razi-ur-Rahim et al., 2024; Vedala et al., 2025; Gulia & Singh, 2023).

Household digital consumption is contingent upon behavioural repetition and automaticity, in accordance with habit theory. The Self-Report Habit Index defines habit not merely by past frequency but as a psychological construct comprising automaticity and identity expression elements (Verplanken & Orbell, 2003; Verplanken & Orbell, 2006). In domestic procurement, habit materialises as a “online-first script,” wherein the primary reaction to a necessity is to consult a platform prior to contemplating offline purchasing. Habit is likely reinforced by platform design elements that minimise effort and enhance convenience, aligning with acceptance models' trajectories from perceived ease to continued usage (Davis, 1989; Venkatesh et al., 2003). As usage becomes routine, the prevalence of digital consumerism in households may increase, as platforms capture a greater number of categories and purchasing instances compared to traditional businesses.

Impulse activation offers an additional explanation for how browsing duration and promotional exposure might lead to unanticipated purchases and enhance the proportion of consumption facilitated by digital platforms. Research on impulse buying differentiates between a consistent impulsive buying disposition and momentary impulse actions, emphasising the influence of normative assessments and environmental stimuli on impulsive behaviour (Fisher & Rook, 1995; Mandolfo et al., 2021). Culturally relevant scale construction regarding impulsive purchase tendency in India offers measurement alternatives that can enhance classic scales and mitigate construct validity issues in Indian samples (Badgaiyan & Verma, 2016). Simultaneously, research on promotions and deal proneness reveals that consumers exhibit varying degrees of response to deals, coupons, and price comparisons, potentially resulting in increased browsing duration and heightened buy activation (Lichtenstein et al., 1997; Lichtenstein et al., 1995). In Indian families, where holiday sales and app-based flash discounts are ubiquitous, deal orientation may become a habitual practice, influencing both the duration (time allocated for seeking and comparing) and frequency (timed purchases aligned with sale calendars).

Quick commerce disrupts the conventional household buying pattern by facilitating swift and effortless restocking. Reports indicate that quick commerce constituted a significant amount of e-grocery orders and is acquiring an expanding share of e-retail expenditure, with anticipated rapid increase until 2030 (Reuters, 2025). Industry playbooks delineate the operational model of fast commerce and its proliferation across key participants, suggesting that platform ecosystems now significantly influence household purchasing behaviours (MMA Global India & Publicis Commerce, 2025). This is significant because rapid commerce can enhance household shopping frequency without a corresponding rise in basket size, therefore altering the predominance of digital consumerism by normalising frequent app-based purchases in everyday categories.

Ultimately, social commerce and creator-driven retail ecosystems impact household buy discovery and assessment by integrating purchasing signals into social platforms. The IAMAI–Kantar analysis reveals that social media shopping is present but may not be consistently increasing, showing varied adoption among households (IAMAI & Kantar, 2024). Comprehensive data regarding social commerce suggests that trust and attitude pathways are significant for purchase intentions, indicating that social influence can enhance household exposure to buying stimuli and product discovery methods (Elshaer et al., 2024). The increasing incorporation of buying functionalities in prominent platforms is evident in reports about the expansion of shopping affiliates, reinforcing the likelihood that household "methods of utilising" internet shopping encompass social discovery and influencer-driven prompts (Times of India, 2025).

The literature suggests that platform affordances and the integration of digital payments likely facilitate repeated usage; promotions, susceptibility to deals, and impulse triggers likely transform browsing into further purchases; and habit formation likely accounts for the widespread and enduring use within households. However, the absent component is a household-level Structural Equation Model (SEM) that concurrently analyses intensity measurements (frequency, length), usage modalities (channels, payments, coordination), and an outcome specifically characterised as the prevalence of household digital consumerism rather than individual intent. This publication fills the existing gap through an India-based conceptualisation, operationalisation, and SEM-ready design that adheres to best-practice measurement validation and CMV mitigation protocols (Hair et al., 2022; Fornell & Larcker, 1981; Henseler et al., 2015; Podsakoff et al., 2003).

Methodology

This research is structured as an explanatory quantitative analysis at the home level, employing a multi-informant survey alongside a brief diary regimen to enhance the precision of behavioural measurement. The family serves as the unit of study, as procurement decisions and expenditures are frequently coordinated among its members, and digital consumerism is regarded as a household condition rather than an individual perspective. The sampling frame encompasses Indian households in metropolitan, tier-2/3, and semi-urban areas, illustrating the documented variability in internet access and active user distribution, notably the significant presence of rural users within India's internet demographic (Economic Times, 2025; Medianama, 2025; IAMAI & Kantar, 2024).

A minimum sample of 500 houses is recommended, with two adult respondents per household (N = 1,000 persons), facilitating aggregation and cross-validation of household variables while mitigating single-source bias. Household recruitment may employ stratified methods through residential communities, businesses, educational institutions, and survey panels to guarantee representation across household composition (nuclear versus joint), income brackets, and geographic regions. The research design explicitly incorporates households with varying payment preferences, recognising that cash-on-delivery is still crucial for numerous online consumers, potentially influencing the impact of digital payment integration (IAMAI & Kantar, 2024; Outlook Business, 2025; PwC India, 2023).

The data gathering occurs in two steps. Phase 1 of the household survey identifies latent dimensions including platform affordances, promotion/deal orientation, role dispersion, habitual behaviour, impulsive activation, budgeting discipline, and the prevalence of digital consumerism within households. Phase 2 is a two-week diary documenting household online shopping experiences, detailing the quantity of orders and duration allocated to shopping-related activities, categorised as browsing/comparison, checkout/payment, and post-purchase tracking/returns. Diary-based collection is crucial as frequency and duration metrics are susceptible to recall bias and piling when assessed retrospectively, particularly in mobile-first environments where shopping sessions may be divided into brief intervals throughout the day. The diary may be executed using a daily WhatsApp form or a basic mobile form, documenting session durations and chronological events while excluding sensitive personal identifiers.

Ethical safeguards are instituted to preserve family privacy by prohibiting the acquisition of bank details, UPI IDs, order screenshots, or product lists that may disclose sensitive consumption information. Only aggregated counts, temporal measurements, and scaled replies are gathered, and participants are apprised of data utilisation and confidentiality. This method conforms to best practice guidelines that highlight procedural solutions to address method bias and respondent discomfort, such as anonymisation and the separation of predictors and outcomes in surveys (Podsakoff et al., 2003).

Analysis of Results

The study strategy designates PLS-SEM as the principal estimate method due to the model's incorporation of both reflecting latent variables and formative/composite indices for shopping modality complexity and behavioural intensity composites. PLS-SEM is highly endorsed for models that prioritise prediction, address non-normality, and facilitate formative measurement, while also allowing for stringent measurement validation (Hair et al., 2022). A robustness check using CB-SEM may be conducted on reflective-only components if desired; however, the primary household model advantages from the flexibility of PLS-SEM in managing composite structures.

The study commences with the examination of data for missing values, outliers, and distributional irregularities. Duration and frequency metrics collected from diaries often exhibit right skewness; modifications or robust bootstrapping are employed as necessary, while maintaining interpretability. Household-level aggregation is performed for variables designated as household properties, employing inter-rater agreement assessments to confirm that the perspectives of the two adult respondents are adequately matched prior to aggregation. When disagreement is informative, it can be represented as an indicator of household heterogeneity.

The assessment of measurement models for reflective constructs adheres to recognised standards of reliability and validity. Internal consistency is evaluated by Cronbach’s alpha and composite reliability, whereas convergent validity is determined by AVE criteria in accordance with traditional SEM guidelines (Fornell & Larcker, 1981). Discriminant validity is assessed by HTMT, which is advocated as a more dependable criterion than Fornell–Larcker in variance-based SEM situations (Henseler et al., 2015; Roemer et al., 2021). Indicator loadings are analysed, and theoretically justifiable items are preserved when loadings satisfy established levels in used SEM methodology (Hair et al., 2022).

Formative constructs, such as Shopping Mode Complexity, are assessed by collinearity checks (VIF), weight significance via bootstrapping, and content validity via redundancy analysis when a global criteria item is present. This method mitigates the potential of erroneously interpreting complicated behavioural modality indices as reflecting scales.

Common method variance is mitigated using both procedural and statistical solutions. The study employs multi-respondent households, diary enhancement for behavioural intensity, and a section arrangement that minimises immediate consistency patterns between predictors and outcomes. Statistical methods such as comprehensive collinearity assessments and marker-variable techniques can be employed to determine if a general factor influences outcomes, with interpretation informed by recognised CMV guidelines (Podsakoff et al., 2003; Podsakoff et al., 2024).

Structural model assessment examines proposed direct, indirect, and interaction effects. Path coefficients are computed using bootstrapped confidence intervals and p-values, whereas explanatory power is indicated by R² for significant endogenous constructs, namely HDCP, habit, and impulse activation. Effect sizes (f²) and predictive relevance (Q²) are presented in accordance with PLS-SEM reporting standards, facilitating the understanding of the extent to which platform usage accounts for the prevalence of household digital consumerism (Hair et al., 2022).

Mediation is evaluated for the impulse pathway from promotional and deal orientation to HDCP via impulse activation, and for the habitual pathway from platform affordances and digital payment integration to HDCP through habitual online-first scripts. These mediations align theoretically with impulse purchase theories and habit measuring frameworks that underscore automaticity and repetitive execution (Fisher & Rook, 1995; Mandolfo et al., 2021; Verplanken & Orbell, 2003). Moderation is evaluated by assessing interaction terms, wherein household budgeting discipline is anticipated to diminish the correlation between frequency and duration of use and the prevalence of digital consumerism. This suggests that budgeting systems may introduce friction or deliberation that mitigates impulsive and automatic behaviours, even amidst high platform engagement.

Ultimately, multi-group analyses examine path disparities across Indian household contexts, including metropolitan versus non-metropolitan, nuclear versus joint households, and those with high digital payment integration versus those reliant on cash on delivery, indicating that cash on delivery continues to be favoured by significant segments of online consumers and that internet adoption is markedly increasing beyond metropolitan areas (IAMAI & Kantar, 2024; Outlook Business, 2025; PwC India, 2023; Economic Times, 2025). This stage facilitates policy-relevant conclusions regarding which household types are most susceptible to elevated digital consumerism inside platform-driven purchasing ecosystems.

This Table 1 serves to substantiate that your constructs are measured with reliability and validity prior to interpreting any SEM pathways. In a publishable model, Cronbach’s alpha and Composite Reliability should generally surpass 0.70, signifying internal consistency among items within each construct. The Average Variation Extracted (AVE) must surpass 0.50, indicating that the construct accounts for over fifty percent of the variation in its indicators, hence demonstrating convergent validity. In the aforementioned template, all constructs satisfy the specified criteria, thereby rendering the measurement model acceptable for later structural analysis. In your current manuscript, please include a phrase indicating if any item had a loading below 0.70 and whether it was retained for content validity, along with a concise statement that HTMT values were below 0.85/0.90 to substantiate discriminant validity (HTMT can be included individually or as a brief note).

Table 1 Measurement Model Quality
Construct	Items (k)	Cronbach’s α	Composite Reliability (CR)	AVE	Notes on acceptable thresholds
Platform Affordances (PAF)	4	0.88	0.91	0.72	α/CR ≥ 0.70; AVE ≥ 0.50
Digital Payment Embeddedness (DPE)	4	0.90	0.93	0.76	Higher values indicate strong consistency
Promotion & Deal Orientation (PDO)	4	0.86	0.90	0.69	AVE confirms convergent validity
Household Role Dispersion (HRD)	4	0.84	0.89	0.67	Reflective block (loadings expected >0.70)
Habitual Online-First Script (HOF)	4	0.91	0.94	0.79	Usually one of the strongest constructs
Impulse Activation (IA)	4	0.87	0.91	0.72	Impulse often correlates with PDO; check HTMT
Household Budgeting Discipline (HBD)	4	0.83	0.88	0.64	Moderator (still validate like other constructs)
Household Digital Consumerism Prevalence (HDCP)	5	0.92	0.94	0.76	Main outcome; strong reliability expected

This Table 2 addresses your primary research inquiry regarding the correlation between online shopping intensity and the prevalence of digital consumerism within households. If your final estimates resemble the illustrative pattern, the primary direct determinant of HDCP is Online Shopping Frequency (OSF), indicating that households with a higher monthly order volume exhibit an increased online purchase share, enhanced platform centrality, and greater reliance on app-driven habits. The Online Shopping Duration (OSD) offers further explanatory capacity beyond mere frequency, suggesting that the time allocated to browsing and comparing enhances exposure to recommendations and promotions, hence reinforcing household digital consumption despite already elevated order counts. The Shopping Mode Complexity (SMC) suggests that households utilising various channel types (marketplaces, rapid commerce, brand applications, social commerce) exhibit a heightened prevalence of digital consumerism, as an increased number of buy instances and categories are included by digital platforms.

Table 2 Structural Model Results for Hypotheses Testing (Illustrative Results Format; Replace Numbers with your Estimated Values)
Hypothesis	Path	Std. β	t-value	p-value	Decision
H1	OSF → HDCP	0.32	6.80	<0.001	Supported
H2	OSD → HDCP	0.21	4.55	<0.001	Supported
H3	SMC → HDCP	0.17	3.20	0.001	Supported
H4a	PAF → OSF	0.24	4.90	<0.001	Supported
H4b	PAF → OSD	0.29	5.80	<0.001	Supported
H4c	PAF → HOF	0.31	6.40	<0.001	Supported
H5a	DPE → OSF	0.28	5.60	<0.001	Supported
H5b	DPE → HOF	0.22	4.10	<0.001	Supported
H6	PDO → OSD	0.35	7.10	<0.001	Supported
H7	PDO → IA	0.41	8.50	<0.001	Supported
H9a	HRD → OSD	0.19	3.70	<0.001	Supported
H9b	HRD → SMC	0.27	5.20	<0.001	Supported
H10	HOF → HDCP	0.26	5.10	<0.001	Supported
(Mechanism)	IA → HDCP	0.18	3.90	<0.001	—

Upstream data generally indicate that Platform Affordances (PAF) enhance frequency, duration, and habit development, suggesting that convenience and efficient discovery promote increased purchasing and browsing while also establishing a “online-first” routine. The enhancement of Digital Payment Embeddedness (DPE) inside OSF and HOF suggests that regular use of UPI/digital payments diminishes transaction friction and facilitates online purchasing for various family members, hence amplifying household-level engagement and habitual usage. The influence of Promotion & Deal Orientation (PDO) on duration and impulse activation is typically regarded as indicative that deal-seeking and involvement in sales enhance both time spent on platforms and unanticipated purchases, driving households towards increased digital consumerism. The results of Household Role Dispersion (HRD) indicate that when tasks such as searching, deciding, and paying are allocated among members, coordination enhances channel utilisation and prolongs engagement time, hence intensifying digital routines inside the household.

This Table 3 elucidates the reasons behind the escalation of household digital consumption and provides a mitigating household factor. An important indirect effect for PDO → IA → HDCP indicates that the "promotion pathway" is functioning: deal orientation and sales exposure not only extend shopping duration but also stimulate unplanned purchases, hence enhancing the prevalence of digital consumerism within the household. The indirect effects of PAF and DPE via HOF signify a "habit pathway": convenience and seamless payment facilitate not only singular transactions but also transform online shopping into an automatic household routine ("online first"), thereby enhancing the degree to which household consumption becomes platform-centric.

Table 3 Mediation and Moderation Summary
Effect type	Relationship tested	Indirect/Interaction	Bootstrapped 95% CI	p-value	Interpretation (one-line)
Mediation (H8)	PDO → IA → HDCP	0.07	[0.04, 0.10]	<0.001	Promotions raise HDCP partly by increasing impulse activation
Mediation (H11a)	PAF → HOF → HDCP	0.08	[0.05, 0.12]	<0.001	Convenience/personalization raise HDCP partly by strengthening online-first habit
Mediation (H11b)	DPE → HOF → HDCP	0.05	[0.02, 0.08]	0.001	Payment embeddedness increases HDCP partly through habit formation
Moderation (H12a)	OSF × HBD → HDCP	−0.06	[−0.10, −0.02]	0.004	Budgeting weakens how frequency translates into HDCP
Moderation (H12b)	OSD × HBD → HDCP	−0.05	[−0.09, −0.01]	0.012	Budgeting weakens how time-on-platform translates into HDCP

The adverse interaction terms for OSF × HBD and OSD × HBD signify moderation: as families exhibit greater budgetary discipline (rules, tracking, limits), the correlation between online purchasing intensity and the prevalence of digital consumerism diminishes. This indicates that two homes may exhibit comparable online shopping frequencies; but, the household with more robust budgeting practices is less inclined to transform such activity into widespread digital consumerism (for instance, it may adhere to scheduled purchases and limit impulsive additions). In your publication, you should include a verbal interpretation of the interaction results (e.g., “the OSF→HDCP slope is most pronounced at low HBD and least pronounced at high HBD”) and, if feasible, a figure depicting the interaction plot.

Findings

The anticipated results, based on India's digital commerce evolution, indicate that the frequency and length of online purchasing each uniquely influence the prevalence of household digital consumerism, albeit through partially distinct mechanisms. Frequency is anticipated to be the most significant direct influence on HDCP, as repeated ordering enhances the online proportion of household purchases and standardises platform-mediated procurement. This expectation aligns with India's projected growth in e-commerce and the swift normalisation of digital payments, which diminish transaction friction and facilitate more frequent purchasing behaviour (IBEF, 2026; BCG, 2026; Press Information Bureau, 2025; Department of Financial Services, 2024; Economic Times, 2026b).

The duration is anticipated to provide additional explanatory power beyond frequency, as the time allocated to browsing and comparing enhances exposure to suggestions, limited-time offers, and cross-category discoveries. According to impulse buying theory, exposure can enhance unexpected purchases, particularly when consumer norms allow for "treat" purchases or when promotional pressure is significant (Fisher & Rook, 1995; Mandolfo et al., 2021). In the Indian context, the presence of validated scales for impulsive buying tendencies and evidence of promotion-driven behaviours indicates that impulse activation can be reliably quantified and modelled, rather than dismissed as noise (Badgaiyan & Verma, 2016).

Promotion and deal orientation is anticipated to enhance both duration and impulse activation, resulting in a significant indirect influence on HDCP. This is consistent with established research indicating that deal proneness differs among persons and promotional categories, and that responsiveness to promotions might influence shopping behaviour patterns (Lichtenstein et al., 1997; Lichtenstein et al., 1995). In India's platform ecosystem, deal orientation may manifest at the family level, as families synchronise during sale periods and conduct app comparisons, thereby augmenting the household's reliance on platforms.

The integration of digital payments is anticipated to enhance frequency and reinforce habitual behaviour. The rationale is that when UPI and digital payments are the norm, households see fewer obstacles in finalising purchases, allowing more members to engage in transactions, hence facilitating distributed ordering responsibilities and increasing buy frequency. This expectation is substantiated by official evidence of enhanced digitisation and performance in payment systems, alongside recent reports of elevated UPI volumes (Press Information Bureau, 2025; Department of Financial Services, 2024; Economic Times, 2026b; Business Standard, 2026), as well as Indian SEM studies indicating that trust, facilitating conditions, and perceived benefits are predictors of UPI adoption and usage behaviour (Razi-ur-Rahim et al., 2024; Vedala et al., 2025; Gulia & Singh, 2023).

Platform affordances are anticipated to enhance both frequency and habit, since perceived convenience, time efficiency, and successful search/recommendations diminish effort and increase the likelihood of online-first routines. This aligns conceptually with the usefulness and ease-of-use principles of TAM and the performance and effort expectancy conceptions of UTAUT (Davis, 1989; Venkatesh et al., 2003).

Quick commerce is anticipated to function as a structural amplifier of frequency effects, particularly for groceries and basic necessities. Reports indicate that quick commerce has captured a significant portion of e-grocery orders and is anticipated to expand swiftly, implying that households utilising quick commerce will exhibit increased order frequency and potentially elevated HDCP, even if their prior traditional e-commerce behaviours were minimal (Reuters, 2025; MMA Global India & Publicis Commerce, 2025).

Discipline in household budgets is anticipated to diminish the correlation between intensity and prevalence, suggesting that governance and monitoring can avert excessive platform usage from evolving into widespread digital consumption. This aligns with impulse research highlighting the significance of self-control and normative assessment in impulse purchases (Fisher & Rook, 1995; Mandolfo et al., 2021) and suggests that household-level "financial routines" may serve as a protective factor.

Conclusion

This manuscript proposes a household-centric framework for digital consumerism in India by analysing the frequency of online shopping among families, the duration of their engagement in shopping-related digital activities, and their utilisation of platforms across various channels, promotions, and payment methods, while quantifying the extent to which these usage patterns influence the prevalence of digital consumerism within households. India's context is particularly relevant to this investigation due to market forecasts suggesting sustained robust growth of e-commerce until 2030 (IBEF, 2026; BCG, 2026), the proliferation of digital payments evidenced by definitive official metrics of payment digitisation and efficacy (Press Information Bureau, 2025; Department of Financial Services, 2024), and recent reports indicating substantial UPI transaction volumes in 2026 (Economic Times, 2026b; Business Standard, 2026). Simultaneously, cash on delivery (COD) retains significance for several online consumers, suggesting that domestic payment customs and variations in trust will perpetuate the segmentation of platform reliance (IAMAI & Kantar, 2024; Outlook Business, 2025; PwC India, 2023). The emergence of quick commerce alters household purchasing patterns by promoting frequent replenishment behaviours, thereby rendering frequency and habitual pathways crucial for comprehending the prevalence of digital consumerism in households (Reuters, 2025; MMA Global India & Publicis Commerce, 2025).

By incorporating acceptance theories (Davis, 1989; Venkatesh et al., 2003), habit formation frameworks (Verplanken & Orbell, 2003), susceptibility to promotions (Lichtenstein et al., 1997), and impulse purchasing dynamics (Fisher & Rook, 1995; Mandolfo et al., 2021) into a structural equation modelling (SEM) household framework with stringent measurement validation criteria (Fornell & Larcker, 1981; Henseler et al., 2015; Hair et al., 2022) and common method variance (CMV) controls (Podsakoff et al., 2003), the manuscript offers a comprehensive model for quantifying the extent to which household platform utilisation correlates with household digital consumer behaviour. The resultant evidence can guide household consumer literacy programs, platform design elements that facilitate conscientious purchase, and regulatory dialogues around transparency in promotions and recommendation-based purchasing stimuli within the swiftly digitising Indian retail landscape.

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Received: 02-Mar-2026, Manuscript No. AMSJ-26-16983; Editor assigned: 03-Mar-2026, PreQC No. AMSJ-26-16983(PQ); Reviewed: 10-Mar-2026, QC No. AMSJ-26-16983; Revised: 17-Mar-2026, Manuscript No. AMSJ-26-16983(R); Published: 24-Mar-2026