Repercussions of the COVID-19 Pandemic on Consumer Lifestyles and Purchasing Dynamics: Evidence from an Emerging Market Context

A number of emergent studies have examined COVID-19-induced behavioural responses, including shifts in consumption patterns (Kansiime et al., 2021; Pakravan-Charvadeh et al., 2021), impulsive purchasing (Naeem, 2020), stockpiling and panic buying episodes (Billore & Anisimova, 2021; Keane & Neal, 2021; Naeem, 2020; Prentice et al., 2021), brand and product substitutions (Knowles et al., 2020), and alterations in purchasing channel preferences (Mehrolia et al., 2021; Pantano et al., 2020). Scholars have attributed such behavioural phenomena to the pandemic’s effects on consumers’ socio-economic conditions, lifestyle disruptions, and challenges to pre-existing beliefs (Milaković, 2021), as well as to changes in the retail environment including stock shortages and supply chain interruptions (Prentice et al., 2021). Furthermore, external triggers such as information dissemination and social media exposure have been identified as influential factors shaping consumer responses (Laato et al., 2020; Naeem, 2020). Concomitantly, widespread job losses (Montenovo et al., 2020) and declines in household income (Kansiime et al., 2021) have exacerbated financial pressures. The pandemic has undermined consumers’ disposable income and affordability (Mahmud & Riley, 2021), transformed lifestyles (Sánchez-Sánchez et al.,2020), and heightened health-related awareness (Li et al., 2021). Collectively, these dynamics have compelled consumers to re-evaluate and adjust their spending practices compared to their pre-COVID-19 norms.

The transformations in consumer purchasing behaviour witnessed during the COVID-19 pandemic align with the broader theoretical framework concerning shifts in consumer preferences and consumption patterns prompted by external disturbances—whether environmental, social, biological, cognitive, or behavioural (Mathur et al., 2006). Such large-scale disruptions typically drive consumers toward efforts to restore a sense of stability (Minton & Cabano, 2021), often leading to more deliberate and cautious behavioural tendencies (Sarmento et al., 2019). In times of economic uncertainty or downturn, this inclination toward stability frequently translates into austerity-driven behaviours, with heightened price sensitivity among consumers (Hampson & McGoldrick, 2013).Historical evidence reveals that pandemics, such as influenza outbreaks, have had marked impacts on economic functioning (Verikios et al., 2016). However, not all behavioural shifts can be solely ascribed to economic disruptions. For instance, during the Asian flu epidemic, consumers responded by engaging in risk-mitigation behaviours that influenced their consumption of poultry products (Yeung & Yee, 2012). Likewise, in the wake of Hurricane Katrina, individuals residing in the affected U.S. Gulf Coast regions exhibited a blend of compulsive and impulsive purchasing, triggered by stress (Sneath et al., 2009). These extreme events also led to increased expenditures on luxury and premium goods, marked by both cross-category indulgences (Mark et al., 2016) and spontaneous purchasing impulses (Kennett-Hensel et al., 2012).

The present study is situated within this growing body of literature, focusing on two interrelated phenomena observed during the COVID-19 crisis: shifts in consumption patterns and product substitution behaviours. However, it differs from prior work in that it attributes these behavioural changes not solely to economic stimuli, but to the broader, multidimensional transformation in consumers’ lifestyles triggered by the pandemic. COVID-19 has altered both the volume and nature of consumer demand (del Rio-Chanona et al., 2020), leading to greater consumption of items that were previously marginal or absent in consumers’ typical purchase baskets (Kirk & Rifkin, 2020). These alterations represent what we define as “new demand”—a substantial, pandemic-induced shift in market consumption patterns. Examples include heightened demand for disinfectants and personal hygiene products such as hand sanitizers and disinfectant sprays (Chaudhuri, 2020), as well as wellness-related goods like vitamins, health supplements, and immunity-boosting foods (Hess, 2020). Additional categories experiencing surges include packaged consumables, household cleaning agents, organic and fresh food items, personal care products (Knowles et al., 2020), and digital service platforms (Debroy, 2020).

In tandem with this emergent demand, consumers have also demonstrated noticeable substitution behaviour, replacing prior consumption patterns with alternative brands or categories during the pandemic (Knowles et al., 2020). These substitutions stem in part from modified lifestyles and heightened health consciousness. The existing literature on substitution behaviour attributes such changes to a mix of factors including availability, price sensitivity, risk perceptions, and changing preferences (Hamilton et al., 2014). However, current studies examining new demand and substitution behaviours in crisis contexts often focus predominantly on the economic impacts (Martin et al., 2020). This highlights a need to investigate the non-economic dimensions influencing such behaviours during large-scale disruptions like COVID-19.

Disruptions, in general, have profound consequences on individuals’ lives, displacing established routines and prompting shifts across various dimensions of life. Earlier research on the behavioural effects of disruptions has documented changes in mental health, lifestyle, information consumption, and awareness (Mathur et al., 2006; Sneath et al., 2009). The COVID-19 pandemic has similarly instigated widespread modifications in health consciousness and daily routines (Arora & Grey, 2020). Government-enforced lockdowns and fear of infection have curtailed physical mobility and reduced outdoor activity (Sánchez-Sánchez et al., 2020), altered dietary preferences and food consumption behaviour (Kansiime et al., 2021; Pakravan-Charvadeh et al., 2021), and even impacted sleep patterns (Chopra et al., 2020). These shifts have collectively driven significant growth in demand for health and wellness-related products (Baiano et al., 2020; Hess, 2020).

Nonetheless, the extent of these behavioural adaptations has not been consistent across all consumer groups. As previous studies have suggested, lifestyle changes, health-related awareness, and spending patterns have varied considerably depending on socio-economic status (Laato et al., 2020). Given the differentiated impact of the pandemic across various socio-economic segments, there exists a compelling rationale for further research into how distinct demographic groups have responded to COVID-19 in terms of their purchasing behaviour and product preferences.

3. THEORETICAL MODEL AND HYPOTHESIS DEVELOPMENT

To explore how COVID-19 has reshaped consumers’ lifestyles and purchasing behaviours, this research draws extensively on preliminary empirical studies, market analyses, and relevant scholarly literature addressing the pandemic’s impact. The study conceptualises three primary dimensions: (1) Consumers’ socio-economic background, (2) Consumers’ evolving way of life, and (3) Adaptation in consumer buying behaviour, as illustrated in Figure 1, which provides the theoretical framework underpinning this investigation. Consumers’ changing way of life is examined through indicators such as ‘Changes in affordability’, ‘Lifestyle modifications’, and ‘Heightened awareness of health and hygiene’ resulting from the pandemic, while Adaptation in consumer buying behaviour is assessed through the ‘Emergence of new demand for wellness and entertainment products’, ‘Emergence of new demand for health and hygiene products’, ‘Substitution of essential goods due to affordability constraints’, and ‘Substitution of essential goods prompted by increased health awareness’.

Model conceptualising the behavioural impacts of COVID-19 on consumers

3.1. Consumers’ socio-economic background and affordability

The COVID-19 crisis has markedly affected household and individual incomes, subsequently influencing spending capacities. Nevertheless, the magnitude and character of these financial strains have varied considerably by occupation, employment stability, and broader socio-demographic factors (Witteveen, 2020). The most pronounced negative outcomes have been observed among occupations characterised by lower educational attainment, reduced skill requirements, limited remote work feasibility (Adams-Prassl et al., 2020), and higher dependence on face-to-face interaction (Avdiu & Nayyar, 2020; Montenovo et al., 2020). Many individuals reported earning less than their typical wages during lockdown periods, while others experienced job loss altogether, diminishing their ability to maintain pre-pandemic household expenditures. Prior studies have demonstrated that household purchasing power was mediated by family income levels, accumulated savings, and occupational standing (Kansiime et al., 2021; Pakravan-Charvadeh et al., 2021; Piyapromdee & Spittal, 2020). Moreover, the number of income-contributing family members is a critical determinant of a household’s economic resilience and spending capacity (Addabbo, 2000). Based on these observations, we propose the following hypotheses:

Hypothesis 1a Occupation exerts a significant effect on consumers’ affordability.

Hypothesis 1bCurrent employment status significantly affects consumers’ affordability.

Hypothesis 1cFamily earning status has a significant influence on consumers’ affordability.

3.2. Consumers’ socio-economic background and lifestyle modifications

The pandemic has also precipitated profound transformations in consumers’ lifestyles, though the extent and nature of these changes differ across socio-economic groups. For instance, individuals working in sectors such as hospitality, travel, and Micro, Small, and Medium Enterprises (MSMEs) have experienced sharp declines in business activity and income. By contrast, employees in other sectors, particularly those able to work remotely, often perceived the period as an opportunity to step back from otherwise demanding routines. This suggests that occupational characteristics play an important role in shaping individuals’ daily schedules and lifestyle adjustments during crises. Prior

literature has long established the connection between occupational class, social status, and lifestyle patterns (García-Mayor et al., 2021). Furthermore, reductions in salary or complete job loss during lockdowns have been shown to influence consumers’ daily practices, psychological outlook, and social engagement (Khubchandani et al., 2020; PTI, 2020). In addition, families with multiple earners may experience lifestyle adaptations that differ substantially from households relying on a single income source (Pew Research, 2008). Accordingly, we advance the following hypotheses:

Hypothesis 2aOccupation significantly impacts the lifestyle changes experienced by consumers.

Hypothesis 2bCurrent employment status has a significant effect on consumers’ lifestyle modifications.

Hypothesis 2cFamily earning status significantly influences lifestyle changes among consumers.

3.3. Consumers’ socio-economic background and awareness of health and hygiene

The COVID-19 pandemic has heightened individuals’ consciousness regarding their personal health and hygiene practices (Baiano et al., 2020; Hess, 2020). Public health advisories and extensive campaigns advocating regular handwashing and mask usage have significantly raised the salience of hygiene among the general population. Nonetheless, awareness concerning health and hygiene is not uniform across all groups; rather, it is influenced by occupational categories, which are themselves associated with variations in educational attainment (Teisl et al., 1999). Similarly, consumers’ levels of health and hygiene awareness are shaped by their employment status and overall family earning capacity (Prasad et al., 2008). Drawing on these insights, the following hypotheses are advanced:

Hypothesis 3a

Occupation has a significant impact on consumers’ awareness of health and hygiene practices.

Hypothesis 3b

Current employment status has a significant impact on consumers’ awareness of health and hygiene practices.

Hypothesis 3c

Family earning status significantly influences consumers’ awareness of health and hygiene practices.

3.4. Affordability and consumers’ buying behaviour

With diminished purchasing power during the pandemic, many individuals have prioritised essential goods and healthcare-related items while reducing expenditures on discretionary products (Martin et al., 2020). Consequently, sales volumes for a wide range of non-essential categories have declined. At the same time, there has been a notable surge in demand for wellness and entertainment products, particularly those delivered via digital platforms (Bakhtiani, 2021; Madnani et al., 2020). Given that such purchases are inherently discretionary (Singh, 2020), it is reasonable to expect that reduced affordability has constrained the creation of new demand for these products. Conversely, improvements in affordability would likely exert a positive effect on the consumption of wellness and entertainment offerings (Bakhtiani, 2021; Madnani et al., 2020).

Prior research in economics and public health underscores that household income is a major determinant of demand for hygiene products and related behaviours (Aunger et al., 2016; Jacob et al., 2014). Furthermore, as a consequence of constrained resources, many consumers have actively sought out more affordable options, including private labels and value brands (Mishra & Balsara, 2020). On the basis of these considerations, we propose the following hypotheses:

Hypothesis 4a

The creation of new demand for wellness and entertainment products is significantly associated with changes in affordability.

Hypothesis 4b

The creation of new demand for products related to health and hygiene is significantly associated with changes in affordability.

Hypothesis 4c

The demand for affordable substitute products for daily necessities is significantly associated with changes in affordability.

3.5. Lifestyle changes and demand for wellness and entertainment products

The lifestyle shifts induced by COVID-19 have led consumers to become increasingly attentive to their fitness and well-being, contributing to a marked rise in demand for wellness-related goods and services (Ojha, 2020). A growing segment of consumers now prefer organic and herbal products and have subscribed to various fitness programmes and digital content channels to maintain their health (Wernau & Gasparro, 2020).

Moreover, the lockdown measures implemented by governments have confined people to their homes, encouraging them to spend more time with their families (Debroy, 2020). With conventional entertainment venues such as restaurants and cinemas remaining inaccessible, many consumers have transitioned to online platforms to fulfil their recreational needs. Even virtual yoga sessions have experienced unprecedented increases in participation as the pandemic has progressed (Debroy, 2020). In view of these developments, we propose the following hypothesis:

Hypothesis 5

The creation of new demand for wellness and entertainment products is positively associated with lifestyle changes.

3.6. Awareness of health and hygiene and demand for health and hygiene products

Marketing scholars have consistently underscored the role of raising consumer awareness to stimulate demand for relevant products (Baiano et al., 2020; Hess, 2020). The COVID-19 pandemic has significantly heightened individuals’ attention to their personal health and hygiene practices. As an essential component of a healthy lifestyle, frequent handwashing and the use of protective face masks have come to be regarded as critical defence measures against viral transmission. Consequently, ordinary consumers have allocated increased portions of their expenditures to healthcare-related products (Rakshit, 2020). Furthermore, there has been a remarkable shift in attitudes, as many consumers

have demonstrated a pronounced inclination to replace less healthy food items and daily essentials with healthier alternatives (Master, 2020; Renner et al., 2020). In view of these developments, the following hypotheses are proposed:

Hypothesis 6a

The creation of new demand for products related to health and hygiene is positively associated with consumers’ awareness of health and hygiene.

Hypothesis 6b

The demand for healthy substitute products for daily necessities is positively associated with consumers’ awareness of health and hygiene.

3.7. Consumers’ socio-economic background and creation of new demand for wellness and entertainment products

During the pandemic, there has been a notable surge in the popularity of fitness and wellness products as well as digital entertainment platforms such as Netflix (Debroy, 2020). However, the pattern of demand for wellness and entertainment offerings has not been uniform across consumers of different socio-economic strata. An individual’s occupation, employment situation, and family income have been shown to influence preferences for wellness-oriented products (Suresh & Ravichandran, 2011) and exert considerable effects on the generation of new demand for wellness and entertainment goods (Madnani et al., 2020). Accordingly, this study aims to examine in greater depth the relationship between socio-economic characteristics and the emergence of new demand in these categories. Based on the preceding discussion, the following hypotheses are advanced:

Hypothesis 7

Occupation significantly influences the creation of new demand for wellness and entertainment products.

Hypothesis 8

Current employment status significantly influences the creation of new demand for wellness and entertainment products.

Hypothesis 9

Family earning status significantly influences the creation of new demand for wellness and entertainment products.

3.8. Consumers’ socio-economic background and creation of new demand for health and hygiene products

The pandemic period has similarly been characterised by heightened demand for health and hygiene products (Dsouza, 2020). Individuals have increasingly allocated spending toward items such as hand sanitizers, disinfectants, and face masks in efforts to guard against infection. Given that certain occupations expose individuals and their households to different degrees of risk and vulnerability (Avdiu & Nayyar, 2020), it is reasonable to expect variation in purchasing patterns for these products across occupational categories (Riise et al., 2003). Existing research has also established a link between household income levels and consumer preferences for healthier food and hygiene-related goods (Galati et al., 2019; Pakravan-Charvadeh et al., 2021). Furthermore, reductions in income and widespread job losses have contributed to increased mental stress and diminished disposable income (Witteveen, 2020), both of which are likely to influence purchasing decisions concerning health and hygiene products (Khubchandani et al., 2020). Accordingly, the creation of new demand for such products is expected to vary in relation to occupational type, employment status, and family earning capacity. On this basis, we posit the following hypotheses:

Hypothesis 10

Occupation significantly influences the creation of new demand for products related to health and hygiene.

Hypothesis 11

Current employment status significantly influences the creation of new demand for products related to health and hygiene.

Hypothesis 12

Family earning status significantly influences the creation of new demand for products related to health and hygiene.

4. RESEARCH METHODOLOGY

4.1. Design of the Survey Instrument and Its Reliability

Paul and Bhukya (2021) have underscored that the impact of COVID-19 on consumer behaviour represents a critical and timely area of inquiry. However, our review of existing literature did not uncover any pre-validated questionnaire specifically aligned with the hypothesised research model presented in Figure 1 that could be directly employed for primary data collection. Although we identified numerous measurement items developed for other forms of disasters, many were determined to be relevant to our context. In addition, we systematically reviewed information disseminated through newspapers, electronic media, and social platforms regarding challenges faced by consumers—such as reductions in income, employment instability, health-related concerns, and rising demand for health and hygiene products—stemming from COVID-19.

Taking these observations into account, we initially constructed an open-ended questionnaire to develop a deeper understanding of the diverse challenges encountered by consumers and their impact on consumption-related behaviour. The initial questionnaire was translated into Hindi, Malayalam, and Bengali with assistance from three bilingual experts proficient in the respective languages as well as English. To capture a broad spectrum of perspectives, the instrument was administered among consumers from diverse linguistic and socio-economic backgrounds. Specifically, we selected five participants employed in Government or Public Sector undertakings, five from Multinational or Private Sector firms, and five from Micro, Small, and Medium Enterprises (MSMEs). Furthermore, we engaged three independent business owners and seven daily wage earners. Each participant was briefed comprehensively about the study’s objectives and provided their informed consent to participate. For the daily wage earners, an incentive of INR 100/- per participant was provided to encourage their engagement.

Among the selected respondents, certain individuals were proficient in Hindi, others in Malayalam, and a few in Bengali. For participants employed in the Public and Private Sectors, the questionnaire was distributed via email, requesting clear and prompt responses within one week. In the case of MSME employees and independent business owners, appointments were arranged telephonically, and one of the authors conducted face-to-face interviews while adhering strictly to social distancing protocols. Data collection in Delhi and Kozhikode was carried out independently by authors based in those respective locations. For the daily wage earners—including rickshaw-pullers, street vendors, and masons—the questions were posed verbally, and their responses were captured through audio recordings that were later transcribed for analysis.

Following the compilation of responses from this preliminary phase, we systematically synthesised the findings into thematic sections and designed a second open-ended questionnaire. The principal aim of this second iteration was to verify content coverage with domain experts and ensure that each item accurately represented its intended construct, thereby strengthening content validity. For example, items capturing dimensions of financial distress due to the pandemic were classified under the construct of ‘Affordability’. Expert reviewers were then asked to assess whether these items sufficiently conveyed the essence of affordability as experienced by consumers.

Experts consulted for this purpose were carefully selected to include those with extensive experience in the sale of essential commodities through both offline and online channels, as well as scholars specialising in consumer behaviour. This panel comprised a Professor of Marketing, two active researchers in consumer behaviour, a manager of an offline retail establishment dealing in essential goods, and an executive from an online retail firm. All these experts possessed substantial familiarity with the repercussions of COVID-19 on consumers’ lifestyles and purchasing patterns across socio-economic segments. Following their review, the experts recommended retaining most of the items and suggested removing only a few.

Drawing upon the expert feedback, we proceeded to develop a close-ended questionnaire to support the main empirical investigation. The final survey instrument was structured into three primary sections. The first section collected information about respondents’ socio-demographic characteristics and family income status. The second section included items assessing the factors driving changes in consumers’ way of life in response to COVID-19. The third section comprised items capturing behavioural adaptations in purchasing. Responses in Sections 2 and 3 were recorded using a five-point Likert scale ranging from 1 = Not at all True to 5 = Absolutely True, facilitating the quantification of attitudes and behaviours.

The completed questionnaire was then circulated among the same panel of experts to evaluate its clarity, ease of comprehension, and suitability for the target respondent groups. On their recommendation, several questions were further refined to improve their interpretability and ensure alignment with the constructs under study. This iterative validation process enhanced the content validity and ensured the instrument was robust for data collection. Table 1 displays the items included in the first section of the questionnaire, while Appendices 1 and 2 provide the detailed items from Sections 2 and 3, respectively.

TABLE 1.

Distribution of the respondents based on socio-demographic background (n = 425)

Variable	Percentage of respondents (%)	Variable	Percentage of respondents (%)
Gender		Job profile
Male	71.53	Government or Public Sector	22.35
Female	28.47	Private Firm	27.53
Age		Micro, Small and Medium Enterprises, contractors and Daily Wage-earners	28.00
24–35 years	54.59	Independent Businesses	7.06
45–55 years	33.65	Others	15.06
56–65 years	10.59	Employment status
66 years and above	1.18	Employed and getting full salary	51.53
Educational background		Employed and getting reduced salary	23.29
Graduates in a non- professional course	13.88	Lost job due to lockdown	12.47
		Others	12.50
Graduates in a professional course	56.00	Family earning status
		Sole Earning Member	29.88
School Board or No Formal Education	25.64	Multiple Earning Member	55.29
Others	4.47	Non-earning Member	14.82

 

Subsequently, the reliability of the developed questionnaire was assessed by administering it to a carefully selected sample of 30 respondents. The computed Cronbach’s alpha coefficient for the scale measuring Consumers’ changing way of life was 0.795, whereas the corresponding alpha value for the scale assessing Adaptation in consumers’ buying behaviour was found to be 0.895. Both scales demonstrated high corrected item-to-total correlations, which confirmed robust internal consistency. Since the Cronbach’s alpha for each construct exceeded the widely accepted minimum threshold of 0.7, these scales were deemed reliable for the purposes of this study (Hair et al., 2009).

4.2 Target respondents and data collection procedure

The survey was administered among individuals representing diverse socio-economic backgrounds across India. The questionnaire was disseminated to participants employed in government organisations, private sector enterprises, micro, small, and medium-sized enterprises (MSMEs), as well as to daily wage earners. Recognising the linguistic diversity within India, the survey was prepared and distributed in four languages—English, Hindi, Malayalam, and Bengali. These languages were selected given that a substantial proportion of India’s population is proficient in at least one of them. Additionally, measures were implemented to ensure that no more than one response was collected from a single household.

Given the lockdown measures and restrictions on physical mobility imposed during the pandemic, a combination of approaches was adopted to reach potential respondents. Specifically, both online and offline modes were utilised for administering the questionnaire. For the online distribution, the survey link was shared via popular social media platforms, namely LinkedIn, WhatsApp, and Facebook, appealing to users to participate in the study. These platforms were selected not only due to their widespread popularity in India but also because the research team maintained active professional and social networks on these channels.

For the offline component, certain participants received the questionnaire via email, whereas others were provided with printed copies in their preferred language. To facilitate in-person data collection, field workers were engaged and compensated for their efforts. These field workers were tasked with collecting responses directly from respondents either at their residences or in public venues such as shopping malls, popular eateries, and retail outlets. They were specifically instructed to clearly explain the purpose and content of the questionnaire and to refrain from completing the survey on behalf of the respondents under any circumstances.

The data collection phase extended over a two-month period, encompassing August and September 2020. During this interval, various regions of India were subject to differing levels of restrictions contingent upon the local severity of COVID-19 outbreaks. In total, 494 responses were gathered, out of which 69 were identified as incomplete or inconsistent. Consequently, 425 valid responses were retained for subsequent analysis.

4.3 Assessment of potential biases in the survey data

To examine the potential for non-response bias, an independent samples t-test was conducted comparing early and late respondents. This approach rests on the assumption that the views of late respondents are likely to approximate those of non-respondents (Krause et al., 2001). Of the total responses, 241 (56.7%) were collected in the first month (August 2020), while 184 (43.3%) were obtained in the second month (September 2020). Respondents who submitted their surveys in the first month were classified as early respondents, whereas those responding in the second month were categorised as late respondents. T-tests comparing these two groups across individual items revealed no significant differences for the majority of the measures. This suggests that non-response bias was unlikely to compromise the validity of the findings.

As the study relied on self-reported data from individual respondents, the potential influence of common method bias was also evaluated. To this end, Harman’s one-factor test was employed separately for the scale measuring Consumers’ changing way of life and the scale capturing Adaptation in consumers’ buying behaviour. Specifically, exploratory factor analyses without rotation were performed in IBM SPSS (version 25). All 13 items associated with Consumers’ changing way of life were loaded onto a single latent factor, while all 16 items pertaining to Adaptation in consumers’ buying behaviour were similarly loaded onto a separate single factor. The results indicated that the single-factor solution for Consumers’ changing way of life accounted for only 25% of the total variance, whereas the single factor corresponding to Adaptation in consumers’ buying behaviour explained 30.4% of the variance. As these proportions fell well below the 50% threshold, the analyses indicated that common method bias did not pose a significant threat to the integrity of the study’s results (Podsakoff et al., 2003).

5. DATA ANALYSIS AND INTERPRETATION

The dataset comprising 425 usable responses was meticulously examined for missing entries and inconsistencies. A comprehensive overview of respondents’ demographic attributes, descriptive statistics, the results of the Confirmatory Factor Analysis (CFA), and validation of the conceptual framework through Structural Equation Modelling (SEM) are detailed in the sections below. IBM SPSS (version 25) was utilised to compute descriptive statistics for all manifest variables and to profile respondents’ socio-demographic characteristics. Additionally, IBM SPSS AMOS (version 24) was employed to perform CFA and SEM. As part of the descriptive analysis, the minimum and maximum scores, means, and standard deviations for all items in both scales were calculated and presented in Appendices 1 and 2.

5.1. Demographic profile

The socio-economic characteristics of the 425 respondents indicated that the majority were of working age, with a considerable proportion (71.53%) identifying as male. Most respondents reported being currently employed (74.83%). However, a noteworthy share (35.76%) indicated either experiencing job loss or receiving reduced salaries following the onset of the lockdown. Regarding educational attainment, a substantial portion (69.88%) held an undergraduate degree, with 56% of this subgroup possessing qualifications in a professional discipline. Analysis of household earning arrangements revealed that 29.88% of respondents were the sole earners in their families. The detailed demographic breakdown is provided in Table 1.

5.2. Confirmatory factor analysis

The questionnaire, developed through an iterative, expert-validated process, facilitated the identification of underlying latent constructs. It was established that Consumers’ changing way of life comprised three constructs, whereas Adaptation in consumers’ buying behaviour encompassed four constructs. CFA was implemented to examine the degree to which the observed variables—namely, 13 items related to Consumers’ changing way of life

and 16 items capturing Adaptation in consumers’ buying behaviour during COVID-19—represented the hypothesised latent constructs.

In constructing the CFA model, all seven latent variables were allowed to correlate, thereby forming a comprehensive measurement framework to capture Consumers’ changing way of life and behavioural adaptations during the pandemic. The estimation of model parameters was conducted using the maximum likelihood (ML) method. Given that ML estimation requires the assumption of normality among endogenous variables (Kline, 2016), kurtosis values were computed to verify this assumption. The results showed that nearly all variables exhibited kurtosis values within the acceptable range of −7 to +7, alleviating concerns about potential non-normality (Mueller & Hancock, 2019).

Items were assessed according to multiple criteria, including standardised regression weights, squared multiple correlations, and standardised residual covariances. Furthermore, the theoretical relevance and practical significance of each item were considered during the refinement of the measurement model. This process led to the removal of five items from the Consumers’ changing way of life scale and three items from the Adaptation in consumers’ buying behaviour scale. Consequently, the final measurement model retained eight items representing Consumers’ changing way of life and 13 items reflecting Adaptation in buying behaviour. This refinement did not materially compromise the content validity of the scales; rather, it rendered the model more parsimonious and focused.

It was noted that the construct “lifestyle changes” ultimately consisted of only two items. Nevertheless, this did not result in an under-identification issue for the model. Prior studies, such as those by Das (2018) and Pullman et al. (2009), have similarly demonstrated that constructs containing two items can be adequately specified and do not inherently lead to identification problems.

The goodness-of-fit (GOF) indices of the refined measurement model were as follows: χ² = 338.939, degrees of freedom (df) = 162, p = .00; χ²/df = 2.092; Goodness of Fit Index (GFI) = 0.931; Adjusted Goodness of Fit Index (AGFI) = 0.902; Comparative Fit Index (CFI) = 0.951; Tucker-Lewis Index (TLI) = 0.937; Root Mean Square Error of Approximation (RMSEA) [90% CI] = 0.051 [0.043, 0.058]; and Standardised Root Mean Residual (SRMR) = 0.0512. According to Hair et al. (2009), satisfactory model fit is demonstrated when GFI, CFI, and TLI exceed 0.90 and RMSEA and SRMR are below 0.08. Given these benchmarks, the model exhibited an acceptable fit across all major indices.

Table 2 presents the detailed results of the measurement model, including descriptive statistics (means and standard deviations), reliability coefficients (Cronbach’s alpha) for each construct, and the inter-construct correlations reflecting Consumers’ changing way of life and Adaptation in buying behaviour.

TABLE 2.

Summary of Measurement Findings and Correlations Between Constructs

Construct	Mean	SD	Cronbach’s Alpha	1	2	3	4	5
1. Affordability	2.985	1.614	0.842	–
2. Life-style Changes	3.147	1.376	0.645	−0.282***	–
3. Awareness towards health & hygiene	4.458	0.862	0.736	−0.181**	0.567***	–
4. Creation of new demand for wellness & entertainment products	4.29	0.927	0.816	−0.102*	0.616***	0.281***	–
5. Creation of new demand for health & hygiene products	2.114	1.235	0.801	−0.170**	0.324***	0.405***	0.252***	–
6. Substitution of daily necessities due to affordability	2.239	1.118	0.803	−0.197***	0.321***	0.187**	0.408***	0.149*
7. Substitution of daily necessities	2.856	1.248	0.817	−0.169**	0.440***	0.197***	0.272***	0.243*

The table presented above demonstrates that the Cronbach’s alpha coefficients for six out of the seven constructs exceed the threshold of 0.7, signifying robust reliability of these constructs (Hair et al., 2009). The alpha coefficient for the remaining construct reflects an acceptable level of reliability, as it is above 0.6 (Hair et al., 2009). Furthermore, Table 2 illustrates that nearly all inter-construct correlations are statistically significant at either the 0.1% or 1% significance level. Only a single inter-construct correlation achieves significance at the 10% level. These inter-construct correlations contribute to the assessment of discriminant validity for all constructs, which is elaborated upon later in this section.

The model was rigorously assessed for Construct Reliability (CR), convergent validity, and discriminant validity to ensure the soundness of the constructs representing Consumers’ changing way of life and the Adaptation in consumers’ buying behaviour in response to COVID-19. In this study, we calculated the CR coefficients for each construct, the results of which are displayed in Table 3. A CR value ranging between 0.6 and 0.7 is considered acceptable, while a value exceeding 0.7 indicates a high degree of reliability (Hair et al., 2009). Accordingly, six of the constructs exhibit excellent reliability, whereas the remaining construct demonstrates an acceptable level of reliability.

TABLE 3.

Results of Reliability, Convergent and Discriminant validity of the consumers’ changing way of life and consumers’ buying behaviour

Construct	Observable item	Standardized Loading	T–value	AVE	CR
Affordability				0.648	0.846
	Restricted economic	0.752	15.256
	activity arising out of
	Covid-19 has resulted in
	significant reduction of
	my regular income
	Restricted economic	0.881	16.212
	activity arising out of
	Covid-19 has resulted in
	significant reduction of
	my savings
	Restricted economic	0.777	a
	activity arising out of
	Covid-19 has reduced
	my ability to meet the
	day-to-day household
	expenses
Lifestyle changes				0.477	0.646
	The spread of Covid-19	0.707	a
	has forced me and my
	family-members to do
	Yoga/Physical exercise
	on regular basis
	The spread of Covid-19	0.674	10.301
	has renewed our
	interest towards the
	importance of herbal

Construct	Observable item	Standardized Loading	T–value	AVE	CR
	products in our day-to-
	day life
Awareness towards				0.504	0.752
health & hygiene
	The spread of Covid-19	0.769	a
	has increased the level
	of awareness of the
	health of my family
	members including me
	The spread of Covid-19	0.712	9.573
	has increased the level
	of awareness of my
	family members
	including me about
	maintaining cleanliness
	and hygiene
	The spread of Covid-19	0.643	8.363
	has increased the level
	of awareness of my
	family members
	including me about the
	adoption of safety
	measures in terms of
	using masks and gloves
Creation of new				0.553	0.827
demand for wellness
& entertainment
products
	Creation of new demand	0.526	a
	for Herbal products for
	external use due to
	Covid-19

Construct	Observable item	Standardized Loading	T–value	AVE	CR
	Creation of new demand	0.792	9.865
	for subscription to
	channels of Art of living
	lessons due to Covid-19
	Creation of new demand	0.888	10.018
	for subscription to Yoga
	channels due to Covid-
	19
	Creation of new demand	0.720	9.515
	for subscription to
	Fitness channels due to
	Covid-19
Creation of new				0.605	0.820
demand for health &
hygiene products
	Creation of new demand	0.688	a
	for liquid hand-wash
	due to Covid-19
	Creation of new demand	0.854	13.821
	for hand sanitizer due to
	Covid-19
	Creation of new demand	0.782	13.614
	for masks due to Covid-
	19
Substitution of daily				0.612	0.823
necessities due to
affordability
	Substitution of	0.719	a
	Expensive staple food
	items with the
	Inexpensive staple food
	items

Construct	Observable item	Standardized Loading	T–value	AVE	CR
Substitution of daily necessities due to awareness towards health	Substitution of Expensive Fast moving consumer goods with the Inexpensive Fast moving consumer goods	0.934	15.521	0.640	0.839
	Substitution of Expensive packaged food with the Inexpensive packaged food	0.669	13.74
	Substitution of Conventional staple food items with the Healthy staple food items	0.793	a
	Substitution of Conventional Fast moving consumer goods with the Organic (Non- toxic) Fast moving consumer goods	0.931	18.147
	Substitution of Conventional Packaged food with the Organic food	0.651	14.671

Abbreviations: AVE, average variance extracted; CR, construct reliability.

Convergent validity refers to the extent to which the indicator variables of a construct share a substantial proportion of variance. It was evaluated in this study using two distinct approaches. The first approach entailed examining the estimated factor loadings of each item on the respective constructs within the final Confirmatory Factor Analysis (CFA) model (Anderson & Gerbing, 1988). The analysis revealed that the standardized loadings of all items exceeded 0.5 and were statistically significant (p < .001). The second approach assessed convergent validity by calculating the Average Variance Extracted (AVE). An AVE of 0.5 or higher signifies a high degree of convergent validity (Hair et al., 2009). As presented in Table 3, the AVE values for the seven constructs ranged between 0.477 and 0.648. Six constructs attained AVE scores above the 0.5 threshold, indicating strong convergent validity. Only the construct pertaining to lifestyle changes displayed an AVE marginally below the recommended cutoff. However, because this construct satisfied the convergent validity criteria based on the first approach, and given that its AVE value was only slightly under the threshold, it may still be regarded as possessing an acceptable degree of convergent validity.

Discriminant validity evaluates whether each construct is empirically distinct from other constructs within the model and whether it captures unique aspects of the underlying phenomena (Hair et al., 2009). Discriminant validity was also examined through two complementary methods. The first approach involved analyzing the correlations between each pair of constructs in the CFA model. When inter-construct correlations remain comfortably below 0.9, it reduces the likelihood that items significantly loading on one construct will simultaneously load on another (Kline, 2016). The observed correlations between constructs ranged from −0.282 to 0.616, well below the 0.9 threshold, as detailed in Table 2. The second approach compared the AVE of each construct with the shared variance between each pair of constructs. If the square root of a construct’s AVE exceeds the inter-construct correlation coefficients, it suggests that the construct explains a greater proportion of variance in its assigned items compared to any shared variance with other constructs (Fornell & Larcker, 1981). Table 3 indicates that the lowest AVE observed was 0.477, the square root of which is 0.690. This value is higher than the maximum inter-construct correlation of 0.616 reported in Table 2. Accordingly, the seven-construct CFA model demonstrated a satisfactory level of discriminant validity. This confirmation of validity and reliability provided a robust foundation for implementing Structural Equation Modelling (SEM) on the final measurement model to test the hypothesized relationships outlined in Section 3.

5.3. Structural Equation Modelling

The final measurement model served as the primary basis for constructing the structural model. Within this framework, the demographic characteristics of respondents—namely occupation, current employment status, and family earning status—were specified as exogenous variables. Conversely, Consumers’ changing way of life and Consumers’ buying behaviour in response to COVID-19 were designated as endogenous variables. Structural Equation Modelling (SEM) was employed to examine these relationships and test the hypotheses articulated earlier. The structural model was evaluated using the Maximum Likelihood (ML) estimation procedure.

The model’s Goodness of Fit (GOF) indices were as follows: χ² = 887.533, degrees of freedom (df) = 324, p = .00, χ²/df = 2.739, Goodness of Fit Index (GFI) = 0.878, Adjusted Goodness of Fit Index (AGFI) = 0.825, Tucker-Lewis Index (TLI) = 0.840, Comparative Fit Index (CFI) = 0.881, Root Mean Square Error of Approximation (RMSEA) [90% CI] = 0.064 [0.059, 0.069], and Standardized Root Mean Residual (SRMR) = 0.075. While the TLI and CFI were marginally below the generally accepted threshold of 0.9, the RMSEA and SRMR values remained well within the acceptable limit of 0.08 (Hair et al., 2009).

It is important to note that model complexity—including the number of observed variables and estimated parameters—can adversely affect fit indices such as GFI, AGFI, and CFI. Accordingly, applying universal cut-off criteria (e.g., requiring GFI or CFI to exceed 0.9) may not always be appropriate in the case of complex structural models (Baumgartner & Homburg, 1996). A comparable observation was made by Srinivasan et al. (2002), who reported that in their measurement models, CFI and TLI values also fell below 0.9. Nevertheless, because RMSEA and SRMR stayed within the recommended range, those models were still regarded as acceptably fitting. Drawing on this rationale, it may be inferred that the present model demonstrates an acceptable overall fit to the data. The final structural model is depicted in Figure 2. Only the statistically significant paths are presented in the figure, including both direct effects and total effects (encompassing direct and indirect effects). Their interpretation is elaborated upon in the subsequent section.

FIGURE 2.

Final model of the impact of COVID-19 on consumer behaviour

6. MAJOR FINDINGS

6.1. Influence of Occupation, Employment Status, and Earning Status on Affordability

The socio-demographic and economic characteristics of the respondents, summarized in Table 1, demonstrate notable differences in occupation, current employment status, and earning status. Respondents were classified into five occupational categories, labelled Job1 through Job5. Regarding employment status, four categories were defined and denoted as Emp1 through Emp4. Additionally, respondents were grouped into three categories based on their family earning capacity, designated Earn1 through Earn3. These categorical distinctions aredetailed in Table 4. Before incorporating these variables as exogenous predictors in the structural model, all categorical variables were individually converted into binary variables. Consistent with the approach outlined by Cohen et al. (2003), Job1, Emp1, and Earn2 were selected as the reference categories for occupation, employment status, and earning status, respectively. These reference categories were chosen as each represented the most prevalent group within their socio-economic classifications and were presumed to be the least adversely affected by the pandemic.

Among the 21 hypotheses formulated in Section 3, 15 hypothesized direct relationships, while six involved both direct and indirect (mediated) effects. Tables 4 and 5 display the results for the hypotheses reflecting direct effects, drawing on standardized regression weights (β), critical ratios (t-values), and p-values. Table 4 specifically presents the outcomes concerning the influence of Consumers’ socio-economic background on their changing way of life. Findings related to Hypothesis 1a, which examined the association between occupation and affordability, indicate that the affordability of individuals engaged in four occupational categories (Job2 through Job5) was negatively impacted by COVID-19 when compared to those in the reference group, Job1. However, this negative effect reached statistical significance only among respondents classified under Job3 and Job5. This pattern underscores that the lockdown measures disproportionately constrained the affordability of individuals employed in the unorganised sector relative to those in the organised sector.

In relation to Hypothesis 1b, which assessed the link between current employment status and affordability, the results reveal a significant negative impact on the affordability of individuals within employment categories Emp2 through Emp4 compared to respondents in Emp1. This provides clear evidence that individuals who had lost their employment or were receiving reduced salaries as a consequence of COVID 19 experienced substantially diminished affordability relative to their counterparts who continued to receive their full salaries. With respect to Hypothesis 1c, which explored the relationship between family earning status and affordability, the results indicate that respondents in Earn1 and Earn3 did not experience significantly different impacts on affordability relative to the reference category, Earn2. This suggests that whether a household had a single earning member,

multiple earners, or no earners did not result in meaningful differences in affordability due to the pandemic. The significant negative influence of occupation (particularly Job3 and Job5) and employment status (Emp2 through Emp4) on affordability is illustrated in the final structural model presented in Figure 2.

TABLE 4.

Results of structural model for socio‐economic factors (direct effects) (n = 425)

Hypothesis	Structural path	β	t‐value	p‐value	Comments
Hypothesis 1a	Job2 → Affordability	−0.040	−0.669	.503	Not supported
	Job3 → Affordability	−0.226	−3.387	***	Supported in opposite direction
	Job4 → Affordability	−0.013	−0.241	.809	Not supported
	Job5 → Affordability	−0.136	−2.060	.039*	Supported in opposite direction
Hypothesis 1b	Emp2 → Affordability	−0.261	−4.722	***	Supported in opposite direction
	Emp3 → Affordability	−0.368	−6.462	***	Supported in opposite direction
	Emp4 → Affordability	−0.212	−3.273	.001***	Supported in opposite direction
Hypothesis 1c	Earn1 → Affordability	0.029	0.577	.564	Not supported
	Earn3 → Affordability	0.052	0.900	.368	Not supported
Hypothesis 2a	Job2 → Lifestyle changes	−0.178	−2.301	.021*	Supported in opposite direction
	Job3 → Lifestyle changes	−0.198	−2.306	.021*	Supported in opposite direction
	Job4 → Lifestyle changes	−0.140	−1.969	.049*	Supported in opposite direction
	Job5 → Lifestyle changes	−0.141	−1.659	.097†	Supported in opposite direction
Hypothesis 2b	Emp2 → Lifestyle changes	0.190	2.676	.007**	Supported
	Emp3 → Lifestyle changes	0.251	3.469	***	Supported
	Emp4 → Lifestyle changes	0.054	0.658	.511	Not supported
Hypothesis 2c	Earn1 → Lifestyle changes	−0.087	−1.365	.172	Not supported
	Earn3 → Lifestyle changes	0.042	0.554	.579	Not supported
Hypothesis 3a	Job2 → Awareness towards health	−0.150	−2.024	.043*	Supported in opposite direction
	Job3 → Awareness towards health	−0.052	−0.641	.521	Not supported
	Job4 → Awareness towards health	−0.101	−1.489	.137	Not supported
	Job5 → Awareness towards health	−0.125	−1.537	.124	Not supported
Hypothesis 3b	Emp2 → Awareness towards health	0.084	1.253	.210	Not supported
	Emp3 → Awareness towards health	0.097	1.430	.153	Not supported
	Emp4 → Awareness towards health	0.030	0.380	.704	Not supported
Hypothesis 3c	Earn1 → Awareness towards health	−0.017	−0.276	.783	Not supported
	Earn3 → Awareness towards health	0.054	0.758	.449	Not supported

Job1: Respondents who are working in government or public sector jobs; Job2: Respondents who are working in private sector jobs; Job3: Respondents who are working in MSME sectors/ Contractors/ Daily wage earners;

Job4: Respondents who own their own business or startups; Job5: Respondents with other job profiles.

Emp1: Respondents who are currently employed and getting full salary; Emp2: Respondents who are currently employed but are getting reduced salary; Emp3: Respondents who have lost their jobs during lockdown; Emp4: Respondents with other employment status;

Earn1: Respondents who are the sole earners of the family; Earn2: Respondents who are one of the earning members of the family; Earn3: Respondents who are the non‐earning members of the family.

^† 

p < .10.

^*

 p < .05

^**

 p < .01

^***

 p < .001.

Hypothesis	Structuralpath	β	t–value	p–value	Comments
Hypothesis2c	Emp4 → Lifestyle changes	0.054	0.658	.511	Notsupported
	Earn1 → Lifestyle changes	−0.087	−1.365	.172	Notsupported
	Earn3 → Lifestyle changes	0.042	0.554	.579	Notsupported
Hypothesis3a	Job2 → Awareness towards health	−0.150	−2.024	.043*	Supported in opposite direction
	Job3 → Awareness towards health	−0.052	−0.641	.521	Notsupported
	Job4 → Awareness towards health	−0.101	−1.489	.137	Notsupported
	Job5 → Awareness towards health	−0.125	−1.537	.124	Notsupported
Hypothesis3b	Emp2 → Awareness towards health	0.084	1.253	.210	Notsupported
	Emp3 → Awareness towards health	0.097	1.430	.153	Notsupported
	Emp4 → Awareness towards health	0.030	0.380	.704	Notsupported
Hypothesis3c	Earn1 → Awareness towards health	−0.017	−0.276	.783	Notsupported
	Earn3 → Awareness towards health	0.054	0.758	.449	Notsupported

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Job1: Respondents who are working in government or public sector jobs; Job2: Respondents who are working in private sector jobs; Job3: Respondents who are working in MSME sectors/ Contractors/ Daily wage earners;

Job4: Respondents who own their own business or startups; Job5: Respondents with other

job profiles.

Emp1: Respondents who are currently employed and getting full salary; Emp2: Respondents who are currently employed but are getting reduced salary; Emp3: Respondents who have lost their jobs during lockdown; Emp4: Respondents with other employment status;Earn1: Respondents who are the sole earners of the family; Earn2: Respondents who areone of the earning members of the family; Earn3: Respondents who are the non-earning members of the family.

TABLE 5.

Results of structural model of consumers’ way of life (direct effects) (n = 425)

Hypothesis	Structural Path	β	t-value	p-value	Comments
Hypothesis 4a	Affordability → Demand for wellness products	−0.092	−1.559	.119	Not supported
Hypothesis 4b	Affordability → Demand for health products	−0.104	−1.645	.110	Not supported
Hypothesis 4c	Affordability → Substitution of affordable necessities	−0.167	−3.079	.002**	Supported
Hypothesis 5	Lifestyle changes → Demand for wellness products	0.635	6.434	***	Supported
Hypothesis 6a	Awareness towards health → Demand for health products	0.402	5.822	***	Supported
Hypothesis 6b	Awareness towards health → Substitution of healthy necessities	0.227	3.673	***	Supported

** p < .01

*** p < .001.

6.2. Influence of Occupation, Employment Status, and Earning Status on Lifestyle Changes

Applying a comparable analytical approach, we examined how occupation, current employment status, and earning status affected lifestyle changes among individuals in the context of COVID-19. The results pertaining to Hypothesis 2a, which explored the association between occupation and lifestyle adjustments, demonstrated that individuals categorized under Job2 through Job5 exhibited lifestyle changes that were significantly different in direction compared to those in the reference group, Job1. This pattern suggests that individuals outside government or public sector employment were less likely to adopt lifestyle modifications during the pandemic.In relation to Hypothesis 2b, which investigated the connection between employment status and lifestyle changes, findings indicated that respondents within Emp2 and Emp3 categories experienced significantly positive lifestyle changes relative to the reference group, Emp1. This evidence implies that individuals who either lost their employment or faced reduced income became more engaged in activities such as practicing yoga and integrating herbal products into their daily routines compared to those who continued to receive full salaries.

Regarding Hypothesis 2c, which assessed the relationship between family earning status and lifestyle adaptations, the analysis revealed no significant differences among respondents in Earn1 and Earn3 compared to those in Earn2. This outcome suggests that lifestyle changes during the pandemic were not distinguishable based on family earning configuration. The final structural model in Figure 2 highlights the significant associations observed between occupation (Job2 through Job5) and employment status (Emp2 and Emp3) and lifestyle changes.

6.3. Influence of Occupation, Employment Status, and Earning Status on Awareness Towards Health

For Hypothesis 3a, which assessed the effect of occupation on health awareness, results demonstrated that respondents in occupations categorized as Job2 through Job5 reported lower health awareness compared to those in the reference group, Job1. However, this negative relationship was statistically significant only for the Job2 category.

Hypothesis 3b, examining the impact of employment status on health awareness, revealed that the health awareness levels among respondents in Emp2, Emp3, and Emp4 categories did not significantly differ from those in Emp1. This outcome suggests that employment status did not play a decisive role in shaping health awareness during the pandemic.

Finally, Hypothesis 3c evaluated whether earning status influenced health awareness, showing no significant differences between respondents in Earn1 and Earn3 categories relative to the reference category, Earn2. This indicates that family earning status could not meaningfully differentiate individuals’ awareness of health practices. The significant effect of occupation—particularly in category Job2—on health awareness is also displayed in Figure 2.

6.4. Association of Affordability, Lifestyle Changes, and Health Awareness with Demand for Wellness and Health Products and Substitution of Affordable Necessities

Table 5 provides a comprehensive overview of the relationships between Consumers’ changing way of life and Adaptation in consumers’ buying behaviour. Hypothesis 4a investigated whether reduced affordability contributed to an increase in demand for wellness and entertainment products, though this relationship was not found to be statistically significant. Similarly, Hypothesis 4b examined whether diminished affordability was linked to heightened demand for health and hygiene products, and again, the effect was negative but not significant.

By contrast, Hypothesis 4c demonstrated that a decline in affordability had a significant impact on the demand for more affordable substitute products of daily necessities, suggesting that economic constraints compelled consumers to seek cost-effective alternatives.

Hypothesis 5 confirmed that lifestyle changes significantly and positively influenced the increased demand for wellness products, which helps to explain the widespread uptake of fitness and entertainment services during the pandemic.

Furthermore, heightened awareness toward health and hygiene practices had a strong positive effect on demand for both health-related products and healthier substitutes for everyday necessities, as evidenced by Hypotheses 6a and 6b, respectively. These significant findings, presented in Figure 2, substantiate many anecdotal accounts reported in market surveys and media coverage of how COVID-19 transformed consumer purchasing behaviours.

6.5. Influence of Occupation on Demand for Wellness Products

The analyses of the six remaining hypotheses, which incorporated both direct and indirect effects of socio-economic background on Consumers’ changing way of life and their consumption patterns, are presented in Tables 6 through 9. These tables report the direct, indirect, and total effects of the relationships. To estimate the specific indirect effects, we used the AMOS plugin developed by Gaskin and Lim (2018) within IBM SPSS AMOS (version 24).

Table 6 illustrates the findings related to Hypothesis 7, which explored the influence of occupation on demand for wellness and entertainment products. Using Job1 as the reference category, we compared outcomes across Job2 through Job5. The results indicated that respondents classified in Job3 experienced a significant negative effect on the creation of new demand for wellness and entertainment products compared to the reference group. This association was moderate in strength and mediated through two intervening variables: (1) changes in affordability and (2) lifestyle changes. Notably, this mediation was characterized as partial rather than full.

In contrast, the other occupational categories—Job2, Job4, and Job5—did not differ significantly from the reference group in their influence on demand for wellness and entertainment products. Table 6 presents detailed results for the Job3 category specifically, and the cumulative significant effect of Job3 on demand is illustrated in Figure 2 with a bold arrow, signifying its importance within the structural model.

TABLE 6.

Hypothesis 7 Influence of occupation on the demand for wellness products (direct, indirect and

total effects) (n = 425)

Structural path	Direct effect		Specific indirect effect		Total indirect effect		Total direct & indirect effect		Comments
	Β	p‐value	β	p‐value	β	p‐value	β	p‐value
Job3 → Demand for wellness product	−0.022	0.753							Direct effect is negative & insignificant while the total indirect effect is negative & significant at 10% level. Total direct and indirect effect is negative & significant at 10% level. (Partial mediation)
Job3 → Affordability → Demand for wellness product			0.021	0.095
Job3 → Lifestyle changes → Demand for wellness product			−0.126	.014	−0.105	.077	−0.127	.069

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TABLE 7.

Hypothesis 9 Influence of earning status on the demand for wellness products (direct, indirect and total effects) (n = 425)

Structural path	Direct effect		Specific indirect effect		Total indirect effect		Total direct & indirect effect		Comments
	Β	p‐value	β	p‐value	β	p‐value	β	p‐value
Earn1 → Demand for wellness product	−0.062	.233							Direct effect is negative and insignificant while total indirect effect is also negative and insignificant. However, total direct and indirect effect is negative and significant at 5% level (Full mediation)
Earn1 → Affordability → Demand for wellness product			−0.003	.393
Earn1 → Lifestyle changes Demand for wellness product			−0.056	.212	−0.059	0.214	−0.121	.047
Earn3 → Demand for wellness product	−0.074	.228							Direct effect is negative and insignificant while total indirect effect is positive and insignificant. However, total direct & indirect effect is negative and insignificant.
Earn3 → Affordability → Demand for wellness product			−0.005	.263
Earn3 → Lifestyle changes → Demand for wellness product			0.026	.58	0.021	0.652	−0.053	.409

 

TABLE 8.

Hypothesis 11 Influence of emp. Status on the creation of new demand for health products (direct, indirect and total effects) (n = 425)

Structural path	Direct effect		Specific indirect effect		Total indirect effect		Total direct & indirect effect		Comments
	β	p‐value	β	p‐value	β	p‐value	β	p‐value
Emp3 → Demand for health products	0.095	0.137							Direct effect is positive & insignificant while total indirect effect is positive & significant at 5% level. The total direct & indirect effect is positive and significant at 1% level. (Partial mediation)
Emp3 → Affordability → Demand for health product			0.038	.137
Emp3 → Awareness towards health → Demand for health product			0.039	.211	0.077	.049	0.172	.004

TABLE 9.

Hypothesis 12 Influence of earning status on the creation of new demand for health products (direct, indirect and total effects) (n = 425)

Structural path	Direct effect		Specific indirect effect		Total indirect effect		Total direct & indirect effect		Com
	β	p–value	β	p–value	β	p–value	β	p–value
Earn1 → Demand for health product	−0.076	.155			−0.010	.731	−0.086	.195	Dire is ne insig whil indir effec nega insig The direc indir effec nega insig
Earn1 → Affordability → Demand for health product			−0.003	.436
Earn1 → Awareness towards health → Demand for health product			−0.007	.767
Earn3 → Demand for health product	0.111	.081							Dire is po and signi 10% whil indir effec posi insig
Earn3 → Affordability → Demand for health product			−0.005	.263

Structural path	Direct effect		Specific indirect effect		Total indirect effect		Total direct & indirect effect		Com
	β	p–value	β	p–value	β	p–value	β	p–value
Earn3 → Awareness towards health → Demand for health product			0.022	.468	0.017	.546	0.128	.05	The direc indir effec posi signi 5% l (Par med

6.6. Influence of Employment Status and Earning Status on the Demand for Wellness Products

We examined the outcomes of Hypothesis 8, which assessed the impact of current employment status on the demand for wellness and entertainment products, treating Emp1 as the reference group. The analysis revealed that employment status categories Emp2, Emp3, and Emp4 did not exert any statistically significant influence on the creation of new demand for wellness and entertainment products relative to the reference category. Given the lack of significant findings across all employment status categories, we have chosen not to report these results in detail.

Turning to Hypothesis 9, we evaluated the role of family earning status in shaping demand for wellness products, with Earn2 designated as the reference category. These findings are presented in Table 7. The results demonstrated that respondents belonging to earning status category Earn1 exhibited a significant negative effect on the generation of new demand for wellness and entertainment products compared to the reference group. This relationship was mediated by two intervening constructs: (1) change in affordability and (2) lifestyle changes. Notably, the mediation was identified as full, implying that the indirect pathways entirely explained the association between earning status and demand.

In contrast, the earning status of individuals in category Earn3 did not have any significant impact on demand for wellness and entertainment products relative to the reference category. Figure 2 visually highlights the significant effect of Hypothesis 9, illustrating the influence of earning status category Earn1 on demand for wellness and entertainment products.

6.7. Influence of Occupation, Employment Status, and Earning Status on the Demand for Health Products

Next, we analysed the role of occupational categories in driving demand for health and hygiene products, again designating Job1 as the reference category. The analysis indicated that occupations classified under Job2 through Job5 did not demonstrate any significant effect on the creation of new demand for health and hygiene products compared to the reference group. Accordingly, we have opted not to report the detailed outcomes of Hypothesis 10.

Subsequently, we assessed Hypothesis 11, which investigated the influence of employment status on the emergence of demand for health and hygiene products, with Emp1 as the reference category. The results revealed that respondents in employment status category Emp3 experienced a significant positive effect on the creation of new demand for health and hygiene products relative to the reference group. This association was mediated by two constructs: (1) change in affordability and (2) increased awareness toward health and hygiene. The mediation was identified as partial, indicating that both direct and indirect pathways contributed to the observed relationship.

Conversely, employment statuses corresponding to categories Emp2 and Emp4 did not exhibit any significant impact on demand for health and hygiene products compared to Emp1. Table 8 details the findings for Hypothesis 11 regarding employment status category Emp3 exclusively. Furthermore, Figure 2 displays the cumulative significant effect of employment status category Emp3 on demand for health and hygiene products.

Finally, we examined Hypothesis 12, which focused on the impact of family earning status on the creation of demand for health and hygiene products, using Earn2 as the reference category. As shown in Table 9, the results indicated that respondents in earning status category Earn3 reported a significant positive effect on the emergence of new demand for health and hygiene products relative to the reference group. This association was mediated by (1) change in affordability and (2) heightened awareness toward health and hygiene, with mediation identified as partial. Figure 2 provides a graphical representation of the total significant effect of Hypothesis 12 for earning status category Earn3.

In contrast, individuals within earning status category Earn1 did not exhibit any significant influence on demand for health and hygiene products compared to the reference category.

8. CONCLUSION

This paper presented a questionnaire-based investigation aimed at examining the impact of COVID-19 on consumers’ affordability, lifestyle adjustments, and health awareness, as well as how these factors collectively shaped purchasing behaviour. The analysis of the collected data brought to light several noteworthy insights regarding the pandemic’s consequences and consumers’ responses. Among the principal findings are: (1) COVID-19 exerted a greater adverse impact on the affordability of individuals employed in unorganised sectors relative to those in organised sectors, (2) the type of occupation, current employment status, and family earning capacity each exerted varying degrees of influence on lifestyle transformations experienced by consumers, and (3) health awareness was found to be significantly higher among respondents who had either lost employment or whose families had lower earning potential.

The study further observed that demand for wellness and entertainment products was driven more by lifestyle changes than by affordability constraints, while demand for health and hygiene products was predominantly influenced by heightened health consciousness. Conversely, affordability primarily determined consumers’ propensity to seek affordable substitutes for daily necessities. As such, these findings can serve as a valuable reference point for exploring how disruptive events precipitate shifts in consumption and substitution behaviour. Moreover, the results provide actionable insights for organisations seeking to develop appropriate strategies to address changing consumer needs and preferences in the wake of a crisis such as the COVID-19 pandemic.

It is important to acknowledge several limitations inherent in this research. The staggered imposition of lockdowns across different Indian regions presented logistical challenges to administering the survey. Additionally, the vast geographic and cultural diversity of India constrained the study’s reach, limiting the inclusion of all social and demographic groups. A broader reach would likely have yielded further nuanced perspectives on consumer behaviour and opportunities for more detailed market segmentation. Furthermore, the research scope was confined to wellness, entertainment, and health-related products, as well as daily necessities. Future studies could extend the scope to encompass a broader range of product categories, which would enhance understanding of marketing strategies under conditions of disruption.

Drawing on the observations of Paul and Bhukya (2021), the present research invites several avenues for future exploration. These include: (1) conducting cross-national studies to examine how pandemic-related disruptions have influenced consumer behaviour across cultural, regional, and generational divides, (2) investigating how firms adapt their practices and offerings in response to evolving consumer needs during crises, and (3) assessing how shifts in consumption patterns inform retailers’ decisions regarding product assortments, channel strategies, promotional tactics, and discounting practices. It is anticipated that these strategies will vary according to factors such as a retailer’s geographic location, scale of operations, and target customer segments.

Finally, given that government interventions—such as relief schemes, subsidies, and other forms of aid—played a critical role in shaping consumers’ experiences during the pandemic, an important direction for future research would be to examine how such measures helped mitigate negative outcomes for households while also supporting the long-term viability of businesses.

APPENDIX 1. DESCRIPTIVE STATISTICS OF FACTORS INFLUENCING CONSUMERS’ CHANGING WAY OF LIFE

Factors influencing consumers’ changing way of life	Min. score	Max. score	Mean	SD
Affordability
(1) Not at all True (2) Scarcely True (3) Somewhat True (4) Considerably True (5) Absolutely True
Restricted economic activity has resulted in significant reduction in my regular income a	1	5	2.73	1.70
Restricted economic activity has resulted in significant reduction in my savings a	1	5	2.96	3.27
Restricted economic activity has reduced my ability to meet the day-to-day household expenses a	1	5	1.59	1.54
Lifestyle changes
Covid-19 has forced me and my family-members to change our daily routine b	1	5	3.87	1.19
Covid-19 has forced me and my family-members to do Yoga/Physical exercise on regular basis b	1	5	3.01	1.39
Covid-19 has renewed our understanding towards the importance of herbal products in our day-to- day life	1	5	3.28	1.37
I have more free time now than it used to be earlier b	1	5	3.45	1.48
Awareness towards health and hygiene
Covid-19 has increased the level of awareness of my own health and the health of my family members	1	5	4.21	1.03
Covid−19 has increased the level of awareness of me and my family members about cleanliness and hygiene	1	5	4.42	0.90
Covid-19 has increased the level of awareness of me and my family members about the adoption of	1	5	4.74	0.59

 

Factors influencing consumers’ changing way of life	Min. score	Max. score	Mean	SD
safety measures in terms of using masks and gloves Covid-19 has made me sensitive to what I should eat b	1	5	3.44	1.39
Covid-19 has allowed me to get online appointment of Doctor very easily b	1	5	3.55	1.56
Covid-19 has allowed me to get hassle-free online consultation of the Doctor through video-call b	1	5	2.29	1.24

 

Adaptation in consumers’ buying behaviour	Min. score	Max. score	Mean	SD
Creation of new demand for products relating to health and hygiene
(1) Very low (2) Low (3) Moderate (4) High (5) Very High
Liquid hand wash	1	5	4.13	0.98
Hand sanitizer	1	5	4.31	0.93
Masks	1	5	4.42	0.87
Gloves a	1	5	3.10	1.39
Immunity booster supplements a	1	5	3.13	1.41
(Vitamin C, Zinc, Ayurveda formulations etc.)
Creation of new demand for products relating to wellness and entertainment
(1) Never (2) Rarely (3) Sometimes (4) Often (5) Always
Herbal products for external use	1	5	2.55	1.29
Subscription to Art of living lessons	1	5	1.80	1.12
Subscription to Yoga channels	1	5	1.98	1.20
Subscription to Fitness channels	1	5	2.12	1.32
Subscription Web‐series channels a	1	5	2.77	1.59
Substitution due to affordability
(1) Very low (2) Low (3) Moderate (5) High (5) Very high
Substitution of Expensive staple food (Rice, Ata, Pulses, sugar, salt, edible oil, spices etc.) with the Inexpensive staple food	1	5	2.22	1.09
Substitution of Expensive Fast‐moving consumer goods (FMCG) (Soap, detergent, shampoo, toothpaste, disinfectants etc.) with the Inexpensive FMCG	1	5	2.28	1.10
Substitution of Expensive Packaged food (Noodles, pasta, pizza base, bread, canned soups, Tomato sauce, Frozen food, oats, soft drinks, biscuits etc.) with the Inexpensive one	1	5	2.22	1.17
Substitution due to awareness towards health
(1) Very low (2) Low (3) Moderate (5) High (5) Very high
Substitution of Conventional staple food (Rice, Ata, Pulses, sugar, salt, edible oil, spices etc.) with the Healthy staple food	1	5	2.84	1.20
Substitution of Conventional FMCG (Soap, detergent, shampoo, toothpaste, disinfectants etc.) with the Organic (Non‐toxic) FMCG	1	5	2.82	1.22
Substitution of Conventional Packaged food (Noodles, pasta, pizza base, bread, canned soups, Tomato sauce, Frozen food, oats, soft drinks, biscuits etc.) with the Healthy one	1	5	2.90	1.32

^a 

The items have been dropped while carrying out Confirmatory factor analysis (CFA).

Das, D. , Sarkar, A. , & Debroy, A. (2022). Impact of COVID-19 on changing consumer behaviour: Lessons from an emerging economy. International Journal of Consumer Studies, 46, 692–715. 10.1111/ijcs.12786

DATA AVAILABILITY STATEMENT

The authors declare that the data used in the paper is collected through a questionnaire survey and have not used any proprietary data from any source. The data collected through the primary survey may be made available on demand.

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