Impact of the new middle class on consumer behavior: a case study of Delhi-NCR

Abstract

Purpose

The paper examines the increase in annual income of the new middle-class (The NMC) of Delhi-NCR and its impact on their investment habits, consumption habits and lifestyle. The paper aims to look into the transformation of the new middle-class into the NMC in emerging economies and its potential to the companies and investors.

Design/methodology/approach

This study draws insight from 558 new middle-class consumers in Delhi-NCR. ANOVA, post hoc tests , and hierarchical multiple linear regression model are applied to test the proposed hypotheses.

Findings

The NMC living in India's megacities imitates the lifestyle of their counterparts living in the West. To maintain their status and present themselves different from those living in middle or lower-middle-class categories, they spend audaciously, even though the income is low. When they enter the new middle class, their consumption, saving and lifestyle diversify positively.

Research limitations/implications

This study has limitations. First, the authors do not apply any behavioral theory or marketing model such as the theory of reasoned action (TRA), Engel-kollat-Blackwell (EKB) model or theory of normative model of target markets. Second, the research is limited to the NMC of only one emerging economy, i.e., India. Third, the research sample is limited to only one megacity of India, i.e., Delhi. Finally, this research used only one factor, i.e., AI, to study the consumption pattern.

Practical implications

The results suggest that considering the buying habits and lifestyle of Indian the NMC, consumers would prove helpful to the companies in product decision-making. Furthermore, understanding change in investment habits across different income levels would be advantageous to financial institutions, investment planners and marketers while designing their products to attract investment.

Originality/value

The research holds significance from the point of view of understanding Indian consumers encompassing the the NMC and predicting their implications on consumer goods-producing industries, which shall, in turn, facilitate producers and government in formulating policies and strategies.

Citation

Chaudhary, A. and Khatoon, S. (2022), "Impact of the new middle class on consumer behavior: a case study of Delhi-NCR", Journal of Asian Business and Economic Studies, Vol. 29 No. 3, pp. 222-237. https://doi.org/10.1108/JABES-07-2020-0080

1. Introduction

In the last 2 decades, the developing economies have witnessed a remarkable shift in their growth and per capita income. Based on the surge in per capita income, the World Development Report (WDR) in 1978 introduced a grouping of “low income” and “middle income” countries using a threshold of $250 per capita income (Prydz and Wadhwa, 2019). In 1989, a new category was introduced as a “high income” group (Prydz and Wadhwa, 2019), which led to a remarkable change in the world economic scenario. With the swell in FDI during 2001–2018, countries categorized as low-income countries (hereafter LIC) entered middle-income countries (hereafter MIC) status (Steinbach, 2019). Rapid economic growth, low poverty and increased real income trimmed the number of LIC to 31 in 2010 from 64 in 2001 (Ravallion, 2010) and augmented the high-income countries to 80 (Fantom and Serajuddin, 2016). The metamorphosis of LIC into MIC added new ranks between the rich and the poor by facilitating global mass consumption and shifted many households to the “middle class” (hereafter MC) in developing countries. Birdsall et al. (2000) encapsulate MC under the relative income approach as households with a per capita income range of 75 and 125% of the median household per capita income. Banerjee and Duflo (2008) wrap MC in developing countries under absolute approach as two groups of households: one with daily per capita expenditure between $2 and $4 and the other between $6 and $10. Besides income patterns, few scholars define MC as a class having access to different resources. Mainly concentrated in urban areas, MC consumers are well-educated and young (Cavusgil et al., 2018) whose motives are not accompanied by property but by other resources such as human capital (Fernandes and Heller, 2006), education, organizational position or ownership of some exceptional occupational skill (Dhawan, 2010).

1.1 New middle-class

Fernandes and Heller (2006) evidenced that the new middle class (hereafter the NMC) is a real and significant phenomenon and a “class-in-practice” whose boundaries are continually being defined and tested. Recently, there has been an eye-catching expansion in the radius of the NMC in emerging economies (hereafter EEs), particularly India and China, because of significant growth in annual disposable income (Ozturk, 2016). Parker (2009) estimated that the NMC increased from nearly a third of the population in 1990 to more than half today in EEs. In India, the national economic policy 1991 scaled-down rural poverty from 94% in 1985 to 61% in 2005 and is projected to reduce further to 26% by 2025 (Beinhocker et al., 2007). According to a study conducted by McKinsey Global Institute (MGI), if India continues the same economic growth, the average household income will triple in the next 2 decades and will become the fifth-largest consumer market in the world by 2025 [1]. The new economic reform resulted in a boom in jobs and entrepreneurial opportunities for middle-class Indians (Harriss, 2006) and improved disposable income. Not to mention, Delhi-NCR [2], too, achieved an increase in annual disposable income (hereafter AI) which resulted in a voluminous change in consumption, savings and investment pattern of the bourgeoisie living there. Prime Minister Narendra Modi dubbed them the “neo middle class” as they helped the government move to power (Mandhana, 2014). Delhi-NCR were chosen for the present study because Delhi is a “sui generis.” Being the national capital of India and a megacity with the highest share of inter-state migrants in its total population (Kawoosa, 2019), Delhi continues to dominate the economy (Beinhocker et al., 2007). Jeelani (2019) quotes the report of economic survey of Delhi, which estimated that the capital's economy has grown at a rate of 8.60% in 2018–2019, and per capita income has increased to Rs. 3,65,529 (5336.72 US$), which is three times the national average, i.e., Rs. 1,25,397 (1830.79 US$). NCR, the first master plan of Delhi (Nitesh, n.d.), is rising briskly as a global economic hub, embracing a considerable percentage of the new urban middle class. In recent years, because of the increase in household consumption and expansion in the NMC, Delhi-NCR has become a hub of large, medium and small enterprises.

Some researchers affirmed that in India, economic reform expanded the radius of consumption and new desires for education, investment, employment and leisure, which revived the middle class as the “new middle class” (Mathur, 2010; Varma, 2007) in megacities such as Delhi, Mumbai, Bangalore and Hyderabad, accounting for about 35% of the country's the NMC (Ahmed et al., 2016). In urban India, the NMC consists of the households having doctors, professors, engineers and entrepreneurs earning within a range of Rs. 55, 000.00 to Rs. 200,000.00 per month (US$ 781–US$ 2840) (Beinhocker et al., (2007) also support the researchers' observation), with at least one family member working abroad and at least one child studying in universities of the UK and US (Khandekar, 2013). They practice a Western lifestyle (Cavusgil and Kardes, 2013) and follow bourgeoisie values to distinguish themselves from those living in the middle-class. Parallel to this, there exists a group within the Indian the NMC that, in their lifestyle pattern, rejects bourgeoisie values and attitudes (Fadaee, 2014). Class and status relationship has always been an issue in India (Jaffrelot and van der Veer, 2008), leading to different consumption behavior across different income groups (Kardes, 2016). Therefore, it would be interesting to know how far the increased disposable income impacted the consumer behavior of the Indian the NMC residing in megacities.

In this study, we took variables used in analyzing consumer behavior, including annual disposable income, investment habits, buying/spending habits and lifestyle. These variables are also relevant in assessing emerging markets' economic growth (Belbağ et al., 2019; Cavusgil and Kardes, 2013; Cavusgil et al., 2018). Furthermore, income is one of the demographic factors which has a direct bearing on consumption patterns of different social classes (Ahmed et al., 2016; Durmaz and Durmaz, 2014; Isozaki et al., 2017) and directly influences the living standards and decisions whether to spend or save for the future good (Avadhani, 1968; Bishnoi, 2014; Kumarasinghe, 2017).

Overall, the study's objective is to examine the change in consumer behavior of the NMC due to an increase in their annual income. Therefore, the following research questions will be explored in this study accordingly:

RQ1.

What is the difference in investment habits (hereafter IH), buying/spending habits (hereafter B/SH) and lifestyle (hereafter LS) of the NMC of Delhi-NCR across various income levels?

RQ2.

What is the impact of an increase in AI on IH, B/SH and LS of the NMC living in Delhi-NCR ?

The present study is structured on a sample of the NMC of one of India's megacities, Delhi-NCR. The study has applicability in the ambiance that aims to analyze the changes in IH, B/SH and LS of the NMC of Delhi-NCR because of the increase in their AI that they achieved due to the economic reform of 1991. Various scholars in different academic fields have extensively studied the consumption patterns of the middle-class in EEs, including India. However, limited work has been conducted to investigate the consumption pattern of the NMC living in megacities of India such as Delhi, Mumbai and Bangalore (e.g. van Holstein, 2019; Jana and Sarkar, 2018; Kaur et al., 2016; Khandekar, 2013; Narayan et al., 2015; Nijman, 2006; Schindler, 2014). There is a paucity of literature providing insight into the impact of the change in AI on the consumption pattern of the NMC living in Delhi-NCR (e.g. Schindler, 2014). The purpose of the current study is to fill the gap and add to the existing literature by measuring the impact of an increase in AI on the consumption pattern of the NMC of Delhi-NCR.

The paper's remainder is structured as follows: after presenting different definitions of MC and the NMC, we reviewed (Section 2) the existing literature in the context of the NMC and its consumption pattern in EEs. In Section 3, we discuss the sampling and data framework adopted in this paper. Section 4 justifies the methodology. In Section 5, we present the results and discussion, and in the last (Section 6), we discuss the conclusion, practical implications, limitations and future direction.

2. Literature review

2.1 Characterizing the NMC in EEs

In EEs, the NMC is characterized by its Western lifestyle, high disposable income, discretionary consumption, consumption of leisure commodities, education, career orientation, profession/employment, cultural features and mentalities (Cavusgil et al., 2018; Chevalier, 2015; Fernandes, 2009; Iqani, 2017; Kharas and Gertz, 2010; Kaplan, 2013; Kapur et al., 2002; Kardes, 2016; Krishnan and Hatekar, 2017; Varma, 2007). the NMC in EEs, such as India, China and Indonesia, is considered the offspring of economic development of the 1980 and 1990s (Ansori, 2009; Tsang, 2014). They are often considered consumerist and politically anti-poor groups (Fadaee, 2014; Nijman, 2006). Most EEs are characterized by their educated, growing new middle-class households, which have experienced significant enhancements in living standards and economic situation (Belbağ et al., 2019). They play a vital role in economic growth, wealth creation (Goodman, 2012) and entrepreneurial development in EEs (Cavusgil and Kardes, 2013). In China, they are called “new social strata,” consisting of professionals, managers, self-employed and private business owners − few of them with higher education and higher income (Wu and Cheng, 2013). In Turkey, the NMC consists of the households that have attained increased disposable income and have experienced lifestyle changes since market liberalization reforms after the 1980s (Uner and Gungordu 2016). Even though they experienced a decline in wages in Brazil, their discretionary spending surpasses their counterparts living in India, China and Russia (Kardes and Chueke, 2017). In India, the economic growth allowed the NMC to adopt the change in lifestyle and learn to display the newly achieved wealth and confidence (Brosius, 2012).

2.2 Consumption Pattern and annual income of the NMC

Consumption patterns and lifestyles redefine class boundaries and differentiate one economic class from the other (Tsang, 2014). The NMC in EEs possess different consumption patterns and lifestyles than their counterparts in developed countries and are more concerned about product differentiation and branding, even if their income is low (Guarín and Knorringa, 2014). Brazilian NMC spends a large portion of its disposable income on discretionary items (Kardes and Chueke, 2017). In contrast, the NMC in other EEs such as China, India, Vietnam and Thailand exhibit an increased desire for gold and real estate as precautionary wealth (Kaul, 2015; Liu, 2016). Indian NMC is a divergent social class that depends on visibility or visuality, performativity and competence to decode them (Brosius, 2012) and shapes diversified consuming groups in megacities (Kardes, 2016). In Mumbai, the upper-middle-class possesses more lavish modern retail spending and relative preference than the middle-class (Mathur, 2010; Narayan et al., 2015). In Delhi, the NMC relies more on convenience food consumption (Kaur et al., 2016), whereas in Bangalore, the NMC grows organic vegetables for personal consumption and as a form of distinction (van Holstein, 2019). To summarize, given the income, the the NMC of one economy or city may differ in terms of consumer behavior from that of another economy or city. Therefore, the enhancement in AI after the economic reform serves as a strong base for measuring the change in consumption patterns and living standards of the NMC in India, and the authors propose the following hypothesis (Table 1 for sub-hypotheses);

H1.

There is a significant difference in the IH (H1a), B/SH (H1b) and LS (H1c) across income levels of the NMC of Delhi-NCR.

Income, wealth, social class differences, education, employment, possession of assets, attitude and preferences are the major players which influence discretionary consumption (Ahmed et al., 2016; Belbağ et al., 2019; Cavusgil and Kardes, 2013; Durmaz and Durmaz, 2014; Gilbertson, 2014; Isozaki et al., 2017) and saving behavior of the NMC households in EEs (Avadhani, 1968; Das and Kumar, 2016; Snyder, 1974). The NMC, with good jobs, prefer to invest in healthcare and save for their children's education (Banerjee and Duflo, 2008). Investment in the portfolio and real estate is becoming fashionable among the NMC of megacities such as Delhi and Mumbai, depending upon their AI and attitude toward risk (Kaul, 2015; McInish et al., 1993; Ramaswami et al., 1992). Thus, the increase in AI influences the decisions to spend or save for future well-being. The authors hypothesized that

H2.

There is a significant impact of change in AI (increase) on IH (H2a), B/SH (H2b) and LS (H2c) of the NMC.

3. Sampling and data

3.1 Inclusion criteria

The participants were randomly selected from banks, universities, professionals, self-employed groups, business people, etc., based on their annual income ranging from Rs. 4,50,000 to Rs. 25,00,000 (US$ 6391 to US$ 35508 as per 2019 average exchange rate) and the age group between 21 and 62 years. The selection criterion of income was based on the studies conducted by the National Council of Applied Economic Research (NCAER) [3], McKinsey Global Institute (2007) [4], Birdsall (2015) [5] and Isozaki et al. (2017) [6]. A total of 1,000 questionnaires were distributed, out of which 558 were found appropriate for analysis. Respondents were approached personally and through e-mail.

3.2 Measures

The constructs' definition and measurement were adapted mostly from existing literature to ensure the research quality. Each construct item was scored on a Five-Point Likert Scale (from “strongly disagree” to “strongly agree”). The questionnaire comprises two sections. The opening section included general questions to capture respondents' demographic profile, and the second section measured the constructs used in our study. Statements were modified as per the requirement of the current study (Table 2).

4. Methodology

ANOVA was applied to test H1. The Tukey's honestly significant difference ( Tukey’s HSD) test was performed to identify which specific income groups differ by comparing the income levels. Partial Eta-squared (η_p²) was used to determine the effect size of AI on IH, B/SH and LS. The benchmark of Eta squared in ANOVA is 0.01 (small), 0.06 (medium) and 0.14 (large) (Lakens, 2013). After meeting all assumptions, hierarchical multiple linear regression was performed to test H2. In H2. We took AI as an independent variable and IH, B/SH and LS as dependent variables and added gender and employment, along with AI as control variables. The control variables were selected based on the study of Jones and Zufryden (1980).

• Model 1: Y=β0+β1GENDER+β2EMPLOYMENT+ε

• Model 2: Y=β0+β1GENDER+β2EMPLOYMENT+β3ANNUAL INCOME+ε

5. Results and discussion

5.1 Reliability and validity

A preliminary data analysis was conducted first, wherein the accuracy of data, outliers, reliability and missing values of all the variables were checked. IBM-SPSS 20 has been used to derive the results, and the reliability of the measures was checked by using Cronbach's alpha (Table 3). The validity of the model was measured by structural equation modeling (SEM), using AMOS 20. Cronbach's alpha of B/SH and LS surpasses the minimum threshold of >0.70, indicating the scale's relatively high reliability. IH's value was a bit low; however, in many studies, researchers have considered and accepted reliability of between 0.50 and 0.60 as minimally sufficient (Peter, 1979; Shim et al., 1989). Composite reliability (CR) for IH, B/SH and LS surpasses the widely agreed threshold of >0.7 for satisfactory convergent validity. Average variance extracted for IH and B/SH is greater than 0.5, indicating acceptable convergent validity, but AVE for IH is below the threshold. Fornell and Larcker (1981) suggest that CR alone can be used to conclude that the convergent validity of the construct is adequate (Table 3). Correlation between factors is less than 0.7, indicating satisfactory discriminant validity.

5.2 Descriptive statistics

There are a total of 558 respondents, wherein 70.3% of the respondents are male, and 29.7% are female. Among them, 76.7% fall in the age group of 21–40 years. Further, 42.7% of respondents are graduates, 69% of them are in services, whereas 23.7% are self-employed. In the overall sample, 74.2% earn between Rs. 4,50,000–9,00,000 (US$ 6391–US$ 12783) annually, and 72% are married, while 28% are unmarried (Table 4).

5.3 H1: Measure of significant difference in the IH, B/SH and LS across various income levels

One way ANOVA results (Table 5) showed that all three hypotheses (H1a, H1b and H1c) were rejected at 5% level of significance, it was needed to work out a post hoc test. Post hoc test (Table 6) indicates that respondents of income category I and III and category II and III possess significantly different IH (p < 0.05) than their counterparts in other income groups. Respondents in income category II and III show significantly different B/SH (p < 0.05) in comparison to people in other income groups. People in income category I and II show significantly different LS (p < 0.05) than people in other income groups. To sum up, people in income category III have robust IH and B/SH than their counterparts in other income levels (Table 7). People in income category I follow strong lifestyle patterns than people in other income levels.

5.4 H2: Measure of the impact of change in AI (increase) on IH, B/SH and LS

For the development of the model, control variables are included in model 1 of IH, B/SH and LS (Table 8). For IH, the model is statistically significant and explains 1.6% of the variance. The introduction of income in step 2 explains an additional 1% of the variance in IH after controlling for gender and employment. In step 1 of B/SH, the model is statistically significant and explained a variance of 4.8%. In step 2, the introduction of income with gender and employment explains an additional 1.3% variance in B/SH after controlling for gender and employment. In step 1 of LS, the model is statistically significant and explains a variance of 3.4% in LS. In step 2, when income is included, the model as a whole explains an additional 0.9% of variance after controlling for gender and employment. Gender and income are the statistically significant predictors of IH (p < 0.05), B/SH (p < 0.001) and LS (p < 0.001), while employment is a non-significant predictor. Gender proves to be the best predictor of IH (β = −0.08), B/SH (β = −0.22) and LS (β = −0.19) than employment and income. To sum up, gender and AI have more impact on spending patterns and lifestyle of the NMC than on investment patterns.

5.5 Discussion

5.5.1 IH

As households persistently save a considerable portion of their disposable income, the sector always remains the net supplier of funds to the deficit sectors (RBI, 2018). Our results confirm that investment decisions of the NMC change with change in AI depending on the preferences and priorities. The NMC, with relatively higher income, prefers to save more than their counterparts with low income. When annual income increases, they shift their investment choices from financial assets, e.g., shares, mutual funds, LIC, etc., to real estate, house and gold. Avadhani (1968), Das (2012), and NSO (2019) support our results that the NMC households prefer to invest in small savings, bank deposits, real estate and gold, followed by government securities and other financial assets. The results are also consistent with the studies of Bishnoi (2014) and Samudra and Burghate (2012), which provided evidence that change in income brings inconsistency in investment behavior of the different income groups concerning the features of investment instruments.

5.5.2 B/SH.

The NMC consumers in megacities like Delhi are engaged in a shopping spree, spending on entertainment and merry-making. Respondents agreed that they prefer cars, brands or malls either because their peer groups did so or to symbolize their status in society. The rise in income affected their consumption of various categories of goods differently. They spend a large proportion of their increased income on “high-frequency” items, such as food, beverages, personal-care products, cars, entertainment, telecom products and services, in comparison to medium-frequency (apparel, home furnishings and tourism) and low-frequency items (such as consumer durables and appliances or any other luxury items) (Singhi et al., 2017). Hubacek et al. (2005) and later Narayan et al. (2015) reflected that economic success in countries like India and China has enhanced the middle class's annual income, resulting in a shift in consumption expenditure to more luxury goods. This shows evidence that the upper-middle class's preference in India's megacities such as Mumbai is mainly driven by credit card acceptance, shorter store distance and higher vehicle ownership. Thus, it is observed that as income increases, consumers get more open-handed, brand-conscious and aspire for anything that makes their life easier and comfortable.

5.5.3 LS.

There is a difference in the lifestyle of the NMC across AI, and our results verify that income impacts the lifestyle. Preference strengthens personal houses, branded cars, clothes, smart televisions, smart mobile phones, celebrity-endorsed products, etc. Respondents who moved to the layer of wealthy class plan vacations more vigorously. Our results are in conformity with the case study conducted by Brosius (2012) that vacations like a weekend trip to Thailand or Singapore, dining-out at five-star hotels and admitting children in elite private schools add to the quality of lifestyle. Hubacek et al. (2005) and Mathur (2010) show evidence that Indian NMC, within an AI range of Rs. 2,00,000- Rs. 10,00,000, prefer to enjoy higher-quality life as compared to their counterparts in a lower-income category. Many respondents favored imported products over domestic products, further supported by Lee et al. (2010), concluding that Indian consumers prefer American brands over local brands for their uniqueness and good quality. As we checked on education preferences, Indian NMC is spending a significant percentage of their disposable income on good quality schools and colleges for their children. Our finding is similar to previous studies' results and confirmed that the importance of education for Indian NMC has increased in past years (Roche et al., 2011; Varma, 2007).

6. Conclusions

It is evident that consumption pattern varies with change in AI across cities and economies. When people move to the upper-income bracket, they spend a large portion of their disposable income on improving the quality of living standards and save some for a better life for their offspring (Javalgi and Grossman, 2016). Given the lifestyle and consumption pattern, our results conform with the lines of Sheth (1999) quoted by Jaffrelot and van der Veer (2008) that membership of today's NMC is associated with new lifestyle patterns, ownership of economic assets and self-consciousness of belonging to the middle class. The NMC living in India's megacities enjoys a lifestyle similar to their counterparts living in developed countries. Based on the results, we could observe that high-income individuals in the NMC spend robustly on discretionary consumption and save a portion for the future than individuals with low income.

6.1 Practical implications

Our study's main contribution is that increase in AI after the economic reform resulted in a transformation of the MC to the bourgeois class and shifted their consumption behavior to discretionary items. The rapidly expanding radius of the NMC in EEs such as India has been attracting Western companies' focus (Belbağ et al., 2019; Kardes, 2016), which are well prepared to grab this augmented class to give tough competition to domestic companies. The positive factor with domestic companies is that they have the advantage of already established relations with local consumers and an understanding of their nature, which international companies might lack. Therefore, managers of domestic and international companies are required to strategize and innovate in the light of the diverse consumption behavior of the NMC in emerging economies (Cavusgil and Kardes, 2013; Cavusgil et al., 2018). In megacities, Delhi, in particular, there is a crisis of proper housing societies. There are mainly two reasons behind this crisis, either the houses are very distant from the workplace, or the prices are beyond the pocket of the NMC. At the same time, housing developers are also facing financial losses as the stock remains unsold. Therefore, to fill this gap, developers should come with housing schemes that match the priorities of the NMC. Our results should inspire corporate and policymakers. For a sustainable market economy, the policymakers cannot overlook the the NMC's role as entrepreneurs, teachers, professors, doctors, engineers, skilled workers and their role in bringing political parties into power. The policymakers should consider the effects of monetary and fiscal policies on those residing in the middle strata in EE. Hence, in the Indian context, the corporate sector and policymakers need to focus on two critical aspects: megacities' unique urbanization pattern and the fast-growing the NMC consumer market. Both play a pivotal role in reducing poverty and enhancing the country's economy.

6.2 Academic implications

For the academic implication, the researchers should consider that increase in income is one of the factors that directly influence the consumer behavior and lifestyle of people of different income categories. Therefore, researchers should be clear about making a distinction between the middle-class and new middle-class consumers. It should also be considered that an increase in income is an indicator of the growth of the NMC in EEs. Ozturk (2016), in his study, mentions that growth in consumption in EEs is due to an increase in income of the upper-middle class.

6.3 Limitations and direction for future research

Our study has limitations. First, we do not apply any behavioral theory or marketing model such as the theory of reasoned action (TRA), Engel-Kollat-Blackwell (EKB) model or theory of normative model of target markets, which may be applied to study consumer behavior of THE NMC in emerging markets. Furthermore, we do not apply any social class theory, such as the Marxist theory or Weber's theory of class. This may prove to be a potential direction for future research. Second, the research is limited to the NMC of only one EE, i.e., India. Third, the research sample is limited to only one megacity of India, i.e., Delhi. Future researchers could consider the NMC of other megacities like Mumbai, Kolkata, Bangalore and Chennai and might compare their consumption patterns. Moreover, the NMC living in the countryside could also be considered for future studies.

Finally, in our study, the value of R² is low. In our study, we added gender and employment as control variables, which did not contribute much to improve the model, but as per the study's requirement, we have to stick to the same predictors only. However, previous studies show that the value of R² might be low (e.g., Kara et al. (2018)). There may be other relevant variables that may be included in future studies as control variables to improve the value of R². The use of more than one predictor might improve the value of R² (Nau, 2014; Frost et al., 2017). However, other factors such as age, qualification and marital status might influence consumer behavior and could be considered as control variables for future studies.

Table 1

Hypotheses summary

		Sig. value	Null hypotheses
H1	There is a significant difference in the IH, B/SH and LS across income levels of the the NMC		Rejected
H1a	Significant difference in the IH across income levels	0.000	Rejected
H1b	Significant difference in the B/SH across income levels	0.018	Rejected
H1c	Significant difference in the LS across income levels	0.027	Rejected
H2	There is a significant impact of Change in AI (increase) on IH, B/SH and LS of the NMC		Rejected
H2a	Impact of the change in AI (increase) on IH	0.008*	Rejected
H2b	Impact of the change in AI (increase) on B/SH	0.000**	Rejected
H2c	Impact of the change in AI (increase) on LS	0.000**	Rejected

Note(s): *p < 0.05, **p < 0.001

Table 2

Scale

Construct	Items	Source
IH	2	Bishnoi (2014), Mittal and Vyas (2009), Ramaswami et al. (1992)
Q1. My investment decisions are influenced by an increase in income
Q2. Change in my income shifted my investment choices from financial assets (shares, bonds, mutual funds, LIC, etc.) to other forms of assets (real estate, gold, etc.)
B/SH	13	Ahmed et al. (2016), Durmaz (2014), Durmaz and Durmaz (2014), Srinivasan et al. (2014)
Q1. Increase in income has led to more spending (extravagance)
Q2. Rise in income has encouraged me toward more online shopping
Q3. With the increase in my income, I have started buying more green/ eco-friendly products (LED, solar power, wool footwear and coffee cups, etc.)
Q4. Increase in income has made me more brand-conscious
Q5. With an increase in income, I shifted toward premium brands
Q6. As my income increased, I shifted toward higher quality, high priced products in following cases:
(a) With the rising income, I aspire to own a luxury car
(b) With the rising income, I aspire to own a latest model TV in comparison to old versions
(c) With more income in my pocket, I prefer to own fully automatic washing machine
(d) With a better spending power, I prefer to own a dishwasher
Q7. Rising income affects spending in various categories differently:
(a) Expenditure has increased on high-frequency items (such as food and beverages, personal-care products, entertainment, telecom products, etc.)
(b) Expenditure has increased on medium-frequency items (apparel, home furnishings, etc.
(c) Expenditure has increased on low-frequency items (such as consumer durables, car and appliances)
Q8. As my income increases, I like to go on a big spending spree more often
LS	8	Isozaki et al. (2017)
Q1. As my income increases, I follow the latest trends and fashion
Q2. With an increase in income, I prefer to travel in my own car rather than public transport
Q3. With more money in my pocket, I can hire full time domestic help
Q4. With an increase in income, I prefer to spend more on comfort than on asset creation
Q5. Increased income encouraged me to own a house
Q6. With a rise in income, I aspire for international vacations
Q7. As my income increases, immediate gratification (pleasure, ego satisfaction) is becoming more important than asset creation
Q8. With more purchasing power, I prefer buying imported products rather than products made in India

Table 3

Reliability and validity

	Cronbach's alpha	N	CR	AVE
IH	0.688	2	0.86	0.52
B/SH	0.913	13	0.71	0.55
LS	0.819	8	0.85	0.46

Table 4

Demographic statistics

Demographic variables	Frequency	Percentage	M	Median	SD
Age
21–30	216	38.7	1.93	2.00	0.94
31–40	212	38.0
41–50	82	14.7
51–60	48	8.6
Gender
Male	392	70.3	1.29	1.30	0.45
Female	166	29.7
Qualification
Undergraduate	106	18.5	2.20	2.00	0.72
Graduate	238	42.7
Other	217	38.9
Income
Category I:
4,50,000–9,00,000 (US$ 6391**–US$ 12783)	414	74.2	1.31	1.00	0.57
Category II:
9,00,000–15,00,000 (US$ 12783–US$ 21305)	111	19.9
Category III:
15,00,000–25,00,000 (US$ 21305–US$ 35508)	33	5.9
Employment
Service	385	69.0	1.38	1.00	0.61
Business/Selfemployed	132	23.7
Homemaker	41	7.3
Marital Status
Married	402	72.0	1.27	1.00	0.44
Unmarried	156	28.0

Note(s): ^**Average exchange rate in 2019: 70.4059 INR

Table 5

ANOVA results

	df	F	p-value	Partial Eta squared (ηp2)
IH	2,555	9.18	0.000	0.032
B/SH	2,555	4.03	0.018	0.014
LS	2,555	3.63	0.027	0.013

Table 6

Post hoc results

		IH			B/SH			LS
Income group (I)	Income group (J)	Mean difference	SE	Sig.	MD	SE	Sig.	MD	SE	Sig.
Category I: 4,50,000–9,00,000	Category II	0.15	0.09	0.28	0.20	0.09	0.06	0.22*	0.08	0.02
	Category III	−0.63*	0.16	0.00	−0.22	0.15	0.31	0.022	0.14	0.98
Category II: 9,00,000–15,00,000	Category I	−0.15	0.09	0.28	−0.20	0.09	0.06	−0.22*	0.08	0.02
	Category III	−0.78*	0.18	0.00	−0.42*	0.16	0.03	−0.20	0.15	0.39
Category III: 15,00,000–25,00,000	Category I	0.63	0.16	0.00	0.22	0.15	0.31	−0.02	0.14	0.98
	Category II	0.78	0.18	0.00	0.42*	0.16	0.03	0.20	0.15	0.39

Note(s): ^*Significant p < 0.05

Table 7

Descriptive statistics

AI	IH			B/SH		LS
	M	SD	M	SD	M	SD
4,50,000–9,00,000	3.60	0.94	3.53	0.84	3.64	0.76
9,00,000–15,00,00	3.45	0.96	3.33	0.90	3.41	0.84
15,00,000–25,00,000	4.24	0.61	3.75	0.69	3.61	0.61
Total	3.61	0.94	3.50	0.85	3.59	0.78

Table 8

Regression results

	IH					B/SH					LS
Variable	β	R2	ΔR2	F	ΔF	β	R2	ΔR2	F	ΔF	β	R2	ΔR2	F	ΔF
Step 1		0.016	0.016	4.55*			0.048	0.048	13.86**			0.034	0.034	9.89**
Gender	−0.09*					−0.22**					−0.19**
Employment	−0.07					0.03					0.03
Step 2		0.026	0.01	4.01*	3.30		0.061	0.013	9.44**	4.6		0.043	0.009	8.24**	4.79
Gender	−0.08*					−0.22**					−0.19**
Employment	−0.07					0.03					0.03
Income	0.07*					0.03*					−0.09*

Note(s): ^*p < 0.05, ^**p < 0.001

Notes

1. See the full report, The “bird of gold”: The rise of India's consumer market. 2007. Available at https://www.mckinsey.com/featured-insights/asia-pacific/the-bird-of-gold

2. NCR stands for national capital egion, an urban industrial area, comprising 24 districts surrounded by 3 states of Haryana, Uttar Pradesh and Rajasthan.

3. Households with annual disposable income from Rs. 200, 000 to Rs. 1,000,000 ($4,380 to $21,890), with size 50 million people.

4. McKinsey Global Institute (2007) defines the middle class as households with annual disposable income ranging between Rs. 200,000 and Rs. 1,000,000 ($3,280 to $16,390) with size 32 million households.

5. Birdsall refers the NMC of developing countries as “income-secure” middle class, with daily per capita income of $10 or above (purchasing power parity).

6. India's middle class refers to “households living in Tier-I cities (urban cities) with annual income between Rs. 500,000 ($7,500) and $35,000.”