1. INTRODUCTION:

The world has a growing realization that striving for environmental sustainability and climate control initiatives are the need of the hour as nothing will remain unscathed from the peril of the climate changes that loom over the planet earth now. People all over the world are being sensitized now on environmental sustainability and the overall wellbeing of the planet earth through various initiatives and campaigns from the Governments and the private sectors across the world. A shift towards sustainability demands changes at organizational as well as individual level. At the individual level, the Go green initiatives are impacted by almost anything and everything that we do – our purchase behaviour, consumption of products and services, mode of transportations, energy and water usage and even by our payment behaviour. The consumers of the world are getting nudged in all possible ways to shift towards the sustainable environment consumptions. However, the payment domain is relatively one of the less-explored areas where green persuasion is not employed strongly.

In India, the digital transactions are growing in leaps and bounds. With improving Financial Inclusion Index (FI-Index), it is expected that more and more rural population will participate in the digital payment transactions. With 73% market share of the total digital transactions in India, the UPI mobile applications are the largest contributors of digital transactions by volume¹. So, before introducing the carbon footprint or carbon tag in the UPI mobile applications, a study should be made to understand the climate control awareness and the green purchase intention of the UPI mobile application users in India. More exploratory researches should be conducted to find the suitable operational model to introduce the carbon footprint or carbon tag in the digital payment market in India.

2. LITERATURE REVIEW:

There are many studies across the world on the environmental impacts of the products and services that we produce and consume in our society. green products and services are on the rise as an alternative to ensure environmental sustainability and improved climate control efforts. A change in consumer purchase behaviour is anticipated considering the growing concerns of environmental wellness which are propagated to the consumers of the world across medias, channels and campaigns. At this stage, it is of paramount importance to gauge the consumers’ intention to move to the green products as a replacement to the existing not-so-environment-friendly products and services. green products and services are deemed to be costlier options than the existing products and services and often they give benefits in long term while it lacks in ensuring low hanging fruits of short term benefits².

2.1 Consumer awareness on Environmental Concerns:

Researchers had found that the ecological responsibility encourages the green purchase intention³. The knowledge on environment education and the impact of human activities on environmental sustainability can help inculcate a general awareness on environmental concerns which influences the consumer behaviour towards green products. The ecologically conscious behaviour is influenced by many factors which in turn convert into green purchase intention. There are studies that found that socialization agents like family and peers, environmental concerns and perceived consumer effectiveness had significant impact on the ecologically conscious consumer behaviour⁴. The socio-demographic variable like gender and family income had no significant impact on raising environmental concern. However, age and education qualification are significant predictors for ecologically conscious consumer behaviour amongst millennial.

Theory of Planned behaviour model (TPB) was introduced as a psychological model to explain the complex purchase behaviour of consumers⁵. That TPB model had been extended with three more variable - environmental concern, trust and convenience to examine the organic food purchase intention amongst the young population in the Delhi, NCR region in India⁶. Based on the data collected from the 334 young respondents, the SEM analysis showed that the TPB model holds effective when used along with environmental Concern, Trust and Convenience factors for predicting the organic food purchase behaviour. One of the key outcomes that was derived from this study was that the marketers should show more environment sustainability related information on production methods, ingredients, product labelling, packaging etc that help increate the positive attitudes towards the organic food. This is an important derivation as the same approach could be used for other green products as well. Also, information like carbon footprint, energy saving certifications could play a crucial role in persuading people towards the green products and services.

Some studies had found strong connection amongst environmental knowledge, environmental concern, green attitude and perceived behaviour and found that environment knowledge influences the environmental concern and green attitude⁷. The research implied that customer interest and green product demand can be increased by making the green products accessible and showing the green product related information to the right customer segment.

Media plays a significant role on creating environmental concerns amongst consumers⁸. Researches on impact of media on consumer buying behaviour had shown that publishing the information on the product environment-friendliness, draws more environment concerned customers. In this matter, any sort of information which conveys that the product or service is environment-friendly, impacts the consumers’ green purchase intention. Price sensitivity is another key aspect that influences the green purchase behaviour of the consumers. There are studies to find out the mediating role of price sensitivity on consumers⁹. The research result showed that the price sensitivity played a negative moderating role in between the relationship of environmental concern and purchase intention for green products. However, environmental responsibility and green purchase intention is not impacted by the price sensitivity.

2.2 Consumer purchase intention for green product:

A study was conducted in the US urban centers where responses were collected from 896 pro-environment-friendly responders to analyse their green purchase intention². The sample was demographically spread out in terms age, gender, marital status, education etc. By using the SEM based analysis, the research found that there was a significant relationship between customer value for green products, customer’s environmental awareness with green product purchase intention. Some researchers had found that the green purchase intention is driven by both internal and external factors. green purchase intention is driven by rational consumer actions and other factors like Social Influence, Product marketing and various individual factors¹⁰. Socio-demographic characteristics like gender, age, yearly income etc play a significant role in buying green food products. The researchers had suggested that the future research should consider different other factors like personal traits, positive environment to evaluate the green purchase intention. A research study on awareness level of green Product amongst the university students in the Haryana state in India, had theoretically considered the functional value, social value and emotional value as the driving factors for green purchase intention¹¹. Based on the 250 respondents’ data, the research outcome showed that the functional and social values are good predictors for green purchase intention whereas the emotional value does not significantly influence the purchase intention. Some researchers had created a key factor model for green purchase behaviour and intention for the South Africa millennial¹². The hypothesized model was analysed using 355 respondents’ data via a composite questionnaire which measures various constructs like relative advantage, compatibility, complexity, observability, subjective norm, perceived behavioural control, attitude and behaviour intention. The data analysis with the partial least squares structural equation modelling (PLS-SEM) showed that the attitude, subjective norm and perceived behavioural control influence the green purchase intention. The impact of customer guilt, self-monitoring, perceived consumer effectiveness on green purchase intention¹³ were also studied by some researchers. Using the purposive sampling, a group of 124 students with better environmental awareness were selected for this study from four universities in Indonesia. Their responses were collected via composite questionnaire and the collected data were analysed using the PLS-SEM model. The research outcome showed that the Customer guilt and self-monitoring influences the green purchase intention for the consumers with good environmental awareness. Advertising could be used to increase the Customer guilt and self-monitoring which may draw more attention towards the green purchase intention. Studies were conducted to find out how the purchase intention for green products is influenced by environmental awareness and consumer attitude. Based on 167 respondents’ data from the UAE location, regression analysis was conducted and the research results did show that both the environmental awareness and attitude have significant and positive influences on green purchase intention¹⁴. And attitude was not a mediator between environmental awareness and purchase intention. In another study, researchers had used 3 variables – environmental attitude, environmental concerns and care for green products to create a conceptual model to evaluate how the purchase intention for the green products is impacted by those¹⁵. A study was conducted in the Bushehr city of Iran where 315 responses were collected from the respondents. A SEM analysis on the collected data, shows that the environmental attitudes of the consumer has a significant and positive impact on environmental concerns and the purchase intention of green products is influenced by the consumers’ care for green products.

Since, the green purchase intention is a function of both internal and external driven factors, many researchers had found that showing the contextual environment related message or advertising can increase the green purchase intention amongst the buyers. green advertisement and green-word-of-mouth (GWOM) have influences on consumers’ green purchase intention. Researchers had found that green advertisement in social media can play a moderator role for green purchase intention^{16, 17}. These researches suggest that the suitable content marketing has significant positive impact on consumers to influence their green purchase intention. Environment-friendly attitude has association with well-marketed products but this relation is mediated by the availability of online information on greenhouse effect¹⁸. Consumers can be persuaded towards green purchase behaviour or green purchase intention if the related environment sustainability information is provided to them via online medias.

3. RESEARCH MODEL AND HYPOTHESES:

The Global Financial Industry, one of the major contributors for the indirect emission of GHG across the world, has been undertaking various initiatives to mitigate the environmental risks. carbon tagging against payment methods and purchase decisions is one of the innovative solutions, imbibed by the financial industry in the developed countries to create awareness amongst the individual users on how their payment and purchase behaviour has impacts on the environmental sustainability. India, being the third largest carbon emitter of the world, needs to focus on controlling the carbon emissions in all possible ways. But as of now, no significant green initiative from the financial sector is in place to influence the purchase and payment behaviour of the Indian consumers on a large scale. In this aspect, the leading third party mobile payment application providers in India can be encouraged to introduce the carbon tagging or carbon footprint calculator for UPI and mobile wallet transactions to make awareness amongst the Indian consumers. The carbon tagging against payments can raise awareness about the environmental impact of the purchases and help consumers make more informed decisions. Over time, the consumers will become more knowledgeable and will shift towards the products and payment methods with lesser negative impacts on the environment. But to achieve this feat, first we need to find out an operational working model that can be effective on a large scale for the large consumer segments with varied demographics in India. My proposed research intends to do an impact analysis on consumers’ purchase behaviour if such carbon tagging initiatives are introduced in the Indian digital payment market.

3.1 Green purchase intention of the UPI mobile application users:

In the area of digital transactions, India has witnessed revolutionary changes in the last 8 years. The volume of digital payments increased from 2 billion in FY 2017-18 to 88 billion in FY 2021-22 at a CAGR of 43.74%¹⁹. Based on the NPCI data, the digital transaction volume has reached to 114 billion in FY 2022-23. The Unified Payments Interface (UPI) has become the largest contributor in the digital payment landscape in India. There are 20 third party UPI applications listed in NPCI website and Phonepe, Google Pay, Amazon Pay, Cred are just to name a few only. Also, there are 40 UPI bank applications available for digital transactions. Evidently, the UPI mobile payment applications had become the most frequently used digital payment channel in India and hence one of the key research objectives was to analyse the green purchase intention amongst the UPI mobile application users. The users of the UPI mobile applications are from varied demographics. So the Hypotheses 1 can be proposed as,

H₀ 1: The Sociodemographic factors of the UPI mobile application users are not associated with their green purchase intention.

The sub-hypotheses can be stated as,

H₀1a: The Age of the UPI mobile application users is not associated with their green purchase intention.

H₀1b: The Family Income of the UPI mobile application users is not associated with their green purchase intention.

H₀1c: The Education of the UPI mobile application users is not associated with their green purchase intention.

H₀1d: The Occupation of the UPI mobile application users is not associated with their green purchase intention.

3.2 Climate control awareness of the UPI mobile application users:

Based on the extended Theory of Planned Behaviour TPB model⁶, the environmental concern and convenience are also important factors to generate the green purchase intention amongst the consumers. From the various studies as mentioned earlier, it was established that the environmental knowledge generates the environmental concerns (EC) and sustainable environmental behaviour or green attitude in the consumers. Environmental concerns and the related green attitude of the consumers can be denoted as the overall climate control awareness of the consumers. Considering the varied demographics of the UPI mobile application users, the Hypothesis 2 can be proposed as,

H₀2: The Sociodemographic factors of the UPI mobile application users are not associated with their climate control awareness.

H₀2a: The Age of the UPI mobile application users is not associated with their climate control awareness.

H₀2b: The Family Income of the UPI mobile application users is not associated with their climate control awareness.

H₀2c: The Education of the UPI mobile application users is not associated with their climate control awareness.

H₀2d: The Occupation of the UPI mobile application users is not associated with their climate control awareness.

Climate control awareness is projected by the consumers’ environmental concerns and their sustainable environment behaviour or green attitude. This research is focused on those consumers who use UPI mobile applications to do digital transactions. In this aspect of payment behaviour, individual users in India have plethora of option today to choose the payment method for any purchases that they made. They can make digital payments via Cards, Internet banking, UPI, mobile wallets or they can use non-digital methods like cash, cheque transaction. And each of these payment methods has its own potential to emit green House Gases (GHG), especially CO2 per each transaction. According to Compte CO₂²⁰, a green mobile neo-bank with the new CO₂currency Euro-green, has estimated that- approximately, a card transactions emits 3g, internet banking emits 4g, a cheque transaction emits 15g and a cash (40 Euro) transaction emits 22g of CO_2,after factoring both the direct and indirect emission of GHG over the entire life cycle of the payment. It is evident from various researches that the card and mobile payments are the least CO₂ emitting and the cash, cheque payments are the most CO₂ emitting payment methods. While digital carbon footprint itself is one of the great concerns of present time²¹, the UPI mobile applications are the better choice of payment method when it comes to environmental sustainability. A Worldline Belgium-based study²² and later a Fintech²³ times article confirms the same. More usage of UPI mobile applications implies more environment-friendly payment behaviour and more climate control awareness of the users. So here the Hypotheses 3 can be proposed as,

H₀3: The usage amount of the UPI mobile payment applications is not associated with UPI users’ climate control awareness.

The convenience of using the UPI mobile application can be a factor which impacts the high or low usage of the UPI applications by the users. The users who find the UPI mobile applications as a convenient method for making payments, will exhibit the sustainable environment behaviour by using the UPI applications for buying green products or services. So, the related Hypotheses can be proposed as,

H₀4: The convenience of using the UPI mobile applications is not associated with UPI users’ climate control awareness.

The purchase intention for green products can be motivated by the climate control awareness. So, the proposed Hypotheses is,

H₀5: The purchase intention for green products is associated with UPI mobile application users’ climate control awareness.

3.3 Users’ Preferences for carbon tagging or carbon footprint information in the UPI mobile applications:

Based on the literature review of the consumers’ purchase intention for green products, it has been observed that many researchers had found that presenting the contextual environment sustainability related information to the user is very important. As the consumers’ purchase intention can be drawn towards the green products by making the related environment sustainability related information available and accessible to the consumers. The UPI mobile application users use the app to do digital transactions to buy products or services. None of the UPI mobile applications has any carbon tagging functionality to inform the consumers on their carbon footprint for the digital transactions. The carbon footprint or carbon emission per UPI transaction can be calculated based on the transaction type itself as mentioned by the Compte CO₂²⁰, a green mobile neo-bank in Europe. The merchant category type, the purchased product / service type can also be factored to get the net carbon emission or carbon tag per transaction. By showing this carbon tag or carbon footprint per transaction to the UPI mobile application users, many consumers can be persuaded towards the alternative green products. This theoretical model needs to be tested first with the UPI mobile application users in India. So, here the Hypotheses is,

H₀6: UPI mobile application users’ preference to see the carbon footprint of the digital transaction is not associated with their climate control awareness.

The alternative green products are usually costlier than the regular not-so-environment-friendly products. So, buying power or purchase power may play a vital role in carbon footprint preferences. Hence the Hypotheses is,

H₀7: The Family Income of the UPI mobile application users does not influence their preference to see the carbon Tag or carbon footprint per transaction.

Also, the carbon footprint or carbon Tag is a relatively difficult construct to understand as it is derived from many things related to the transaction. So, here the Hypotheses is,

H₀8: The Education of the UPI mobile application users does not influence their preference to see the carbon Tag or carbon footprint per transaction.

The existing researches had shown that showing the environment sustainability related information to the consumers nudge them towards green purchase behaviour and green purchase intention.

So, the purchase intention of green products is assumed to be related with UPI mobile application users’ references to view the carbon tag or carbon footprint data. The proposed Hypotheses is,

H₀9: The purchase intention for green products is associated with UPI mobile application users’ preferences to see the carbon Tag or carbon footprint per transaction

The theoretical research model is shown in Fig 1.

4. METHODOLOGY:

4.1 Design of measurement variables for the study:

The study is focused on finding how the existing UPI mobile application users will accept and adopt the idea of viewing the carbon tag or carbon footprint information against each transaction payment in the UPI application. The theoretical model is constructed based on the understanding that the UPI application users who have climate control awareness would like the idea of viewing the carbon Emission data for each digital transaction in the UPI application. The climate control awareness should be measured by individual’s strength of agreement to the idea that each of our consumption choices has impact on the nature and the climate. We should also factor the behavioural attitude of the individual in terms of taking sustainable environment actions while making consumption choices. The Theory of Planned Behaviour (TPB) model was adopted to predict the climate control awareness and carbon footprint preferences in UPI applications. The TPB model was extended with situational behavioural questions²⁴. The scale to determine the UPI usage amount for each individual, is adopted from the UPI Ecosystem Statistics for high transaction categories²⁵. The scale to measure the convenience factor of using the UPI mobile applications in terms of information accessibility, is adopted from existing studies²⁶ where the researchers had used the Computer System Usability Questionnaire (CSUQ) as developed by IBM. Technology has strong influence on digital economy and digital wallets²⁷. Studies show that online payment has been very effective in India during and after pandemic situation²⁸. Green banking initiatives are taken by banks for their different digital channels²⁹. Customers may have different experiences while using those digital channels. Hence the questionnaire was created in a way that it addresses consumers’ generic attitude towards green payment and purchases. Since consumers purchase behaviour is impacted by socio-demographic factors³⁰, those variables are also factored in the questionnaire.

4.2 Data Collection:

A Questionnaire Survey was designed and used for the purpose of data collection. The survey questionnaire was created by using the Google Form application. The survey questionnaire was designed as a composite questionnaire factoring the scales to measure the Convenience of using UPI mobile applications, climate control awareness, green purchase intention and viewing preferences for the carbon Tag or carbon footprint in the UPI applications. Sociodemographic questions are used to capture the respondents’ profile information. The measurement scale related questions are on a 5 point Likert slate (0-4). Table 1 shows the overview of the questionnaire model, used for this research. The research is focused on the UPI application users in Kolkata and Greater Kolkata locations. So, simple random sampling technique was used to distribute the online survey questionnaire over the email. A snowball sampling is also used as the respondents were requested to share the questionnaire with others. While evaluating the responses, we filtered in only those responses which are from Kolkata and Greater Kolkata location. The questionnaire was distributed with around 250 people across channels like email, Whatsapp group, Facebook etc. We have received total 204 responses in 30 days and out of which some were not accepted as outside Kolkata location. So, in total, the considered response count was 204.

Table 1: Questionnaire items used for measuring the scales in study

Scale	Referred Scales	Codes	What IT Measures	Measurement Items
UPI Usage	[25]	UU1	Usage Amount in High Transaction categories	Grocery Item
		UU2		Fast Food / Eateries / Restaurants
		UU3		Utility Bills
		UU4		Fund Transfer
Convenience of UPI Usage	[26]	CO1	Accessibility to information and application functions	Look and feel
		CO2		Functional features
		CO3		Informational features
Climate Control Awareness	[24]	CC1	Environmental Concerns	Self-awareness
		CC2		Knowledge on Carbon emission related to UPI transactions
		CC3		Environmental Knowledge
		CC4	Green Attitude	Seeking environment related information while buying products
		CC5		Inclination to go green
		CC6		Sustainable Environment Actions

PS : UU represents UPI Usage,

CO represents Convenience of UPI Usage,

CC represents Climate Control Awareness.

5. RESEARCH OUTCOME:

5.1 Sampling Adequacy with Reliability and Validity test of the questionnaire:

The collected data are analysed using IBM SPSS software version 26. The scale reliability analysis in the SPSS software shows that the scale construct wise Cronbach's α values are all greater than .9.

The overall Cronbach's α value for all the measurement scale questions is .938. It indicates that the scale, used in the research study is reliable.

Table 2: Reliability analysis of the measurement scale

Scale	Referred Scales	Number	Cronbach’s α	Overall Cronbach’s α
UPI Usage	[25]	UU1	0.92	0.938
		UU2
		UU3
		UU4
Convenience of UPI Usage	[26]	CO1	0.967
		CO2
		CO3
Climate Control Awareness	[24]	CC1	0.945
		CC2
		CC3
		CC4
		CC5
		CC6

The sampling adequacy and the appropriateness of the data are tested by using the Kaiser-Meyer-Olkin Measure of Sampling Adequacy and the Bartlett Test of Sphericity. The KMO value is found to be .891 for the survey questionnaire built using the Likert scale. The KMO value indicates that the sample size is good enough for data analysis and study. The Bartlett’s test of Sphericity is evaluated by the Chi-Square value and the level of significance. The Chi-square is found to be significant at 0.000% level of significance. This indicates that the data is suitable for the measurement of the scale constructs.

Discriminant validity is assessed by evaluating the Heterotrait-Monotrait ratio (HTMT) between the latent variables present in the study. The threshold value is considered as less than 0.85. The result of the HTMT ratio is presented in the Table 3. The HTMT ratios of the latent variables are less than 0.85. Hence, the test result is verifying the discriminant validity between the latent variables present in this study.

Table 3 : Discriminant validity assessment using the Heterotrait-Monotrait ratio of correlation

Correlation Matrix
	UU1	UU2	UU3	UU4	CO1	CO2	CO3	CC1	CC2	CC3	CC4	CC5	CC6
UU1
UU2	0.82
UU3	0.65	0.77
UU4	0.74	0.77	0.70
CO1	0.41	0.50	0.63	0.55
CO2	0.39	0.46	0.57	0.50	0.90
CO3	0.40	0.51	0.57	0.52	0.91	0.90
CC1	0.35	0.44	0.44	0.39	0.51	0.51	0.50
CC2	0.17	0.33	0.29	0.20	0.37	0.32	0.28	0.64
CC3	0.28	0.38	0.35	0.36	0.46	0.47	0.45	0.85	0.68
CC4	0.36	0.45	0.47	0.38	0.53	0.49	0.52	0.85	0.62	0.82
CC5	0.46	0.53	0.55	0.54	0.59	0.61	0.59	0.82	0.48	0.75	0.80
CC6	0.43	0.48	0.47	0.42	0.52	0.51	0.52	0.84	0.56	0.76	0.85	0.85

Monotrait Correlation		HTMT Ratio
UU	0.74	Latent Variables	UU	CO	CC
CO	0.91	UU
CC	0.74	CO	0.6
Heterotrait Correlation		CC	0.53	0.59
UU - CO	0.50
UU - CC	0.40
CO - CC	0.49

5.2 Normality test and Demographic Information of participants:

The Shapiro-Wilk test is conducted to test the normality of the collected data. The Significant value of the collected data is below .05. Hence, the test shows that the collected data are not normally distributed.

The demographic analysis shows that amongst the participants, 62% are male and 38% are female. 29% of the participants have age between 18 to 30 years, 61% have age between 31 to 45 years and rest of them are over 45 years. So, the participants are mostly from the young and middle age groups. Education wise, 33% of the participants have undergraduate degrees and 66% have postgraduate degrees, while only 1 participant is below undergraduate level. So, the participants are mostly from the educated groups. Based on monthly family income, the participants are fairly distributed amongst low, medium and high income groups.

Fig 2 : Demographic Information of the participants

Monthly income up to Rs. 40000 is considered as low income, from Rs 40000 to Rs 100000 is considered as medium income and above Rs 100000 is considered high income groups. The demographic information of the participants is presented in Fig 2.

5.3 Questionnaire analysis using factor analysis:

Factor analysis using Principal component analysis (PCA) was implemented to minimize the dimensionality of the data and understanding the most important questions. The questionnaire for UPI Usage (UU), Convenience of using UP (CO) and Climate Control Awareness (CC) are analysed. Kaiser Meyer sampling adequacy value in the output is found as .891. The value is greater than .5 and hence acceptable. From the rotated component matrix table (Table 4) it can be determined that all the questions load almost equally in component 1. For component 2, there are 2 questions that load highly - "Using UPI application at grocery store." and "Using UPI application at restaurants or eateries." For component3, all the questions are almost equally loaded.

Table 4 : Rotated component matrix of questionnaire

	Component			Questions
Items	1	2	3	Questions
CC3	0.89			Concern about what we purchase or use and their impact on environment.
CC1	0.89			General concern about the ecological environment.
CC4	0.87			Using home appliances with maximum energy saving label.
CC6	0.83			Concern about consumption of natural resources like water, fuels.
CC2	0.77			UPI payment method causes lower carbon emission.
CC5	0.74			Strive for responsible consumption behaviour.
UU1		0.90		Using UPI application at grocery store.
UU2		0.88		Using UPI application at restaurants or eateries
UU4		0.83		Using UPI application to pay utility bills
UU3		0.74		Using UPI application for fund transfer
CO2			0.89	Easy to find all the features in UPI apps
CO3			0.89	Easy to find all the necessary information in UPI apps
CO1			0.88	UPI mobile application has pleasant UI.

5.4 Chi-Square Analysis:

The collected data from the respondents do not attain the Normality as per the Normality test conducted. So, a non-parametric model of Chi-Square analysis are conducted to find out how the green purchase intention, climate control awareness and carbon footprint view preferences in UPI applications are associated with different other factors of the participants. The Chi-Square analysis for green purchase intention shows that here is no significant association with the participants’ Sociodemographic factors. So the hypotheses H₀ 1a, H₀1b, H₀1c and H₀1d are not valid and hence they are rejected. The Chi-Square result shows significant association between the green purchase intention and climate control awareness, X² =50.259, p = .000.5. The Likelihood ratio is shown as 46.783 and the related p = .000. The Cramer’s V is found as .554 with p = .000. So the variables are moderately associated. Hence the hypothesis H₀ 5 is valid and not rejected. However, there is no significant association found between the green purchase intention and the carbon footprint preference in the UPI applications, X² = 2.817, p = .245.2. The Likelihood ratio was shown as 2.628 and the related p = .269. Hence, the hypothesis H₀ 9 is not valid and rejected.

The Chi-Square analysis for climate control awareness shows that it does not have any significant association with the Sociodemographic factors of the participants, like – family income, education and occupation. Hence the hypotheses H₀2b, H₀2c and H₀2d are not valid and rejected. However, the Chi-Square analysis shows that the climate control awareness is significantly associated with UPI Usage (UPI application usage) amount and the Convenience of using UPI application. The relation between climate control awareness and UPI Usage is found significant, X² =21.597, p = .000.3. The Cramer’s V is found as .363 with p = .000. Cramer’s V value is greater than .2 and less than .6. That means, these two variables are moderately associated. So, here the hypothesis H₀3 is not rejected. The relation between climate control awareness and Convenience of using UPI application is found significant, X² = 40.88, p = .000.4. The Likelihood ratio is shown as 31.444 and the related p = .000. The Cramer’s V is found as .499 with p = .000. Since the Cramer’s V value is greater than .2 and less than .6, these variables are moderately associated. So, the hypothesis H₀ 4 is valid and not rejected.

The Chi-Square tests are conducted for the preference to view the carbon footprint or carbon Tag in the UPI application with many other factors of the participants. The association between carbon footprint preference and the job industry to which the participants belong to, is significant, X² = 36.246, p = .014. The Likelihood ratio is shown as 34.765and the related p = .021.The Cramer’s V is found as .47 with p = .014. The association of carbon footprint preference and the climate control awareness is also significant, X² = 50.259, p = .000. The Likelihood ratio is shown as 46.783and the related p = .00. The Cramer’s V is found as .554 with p = .00. Hence the variables are moderately associated. So the hypothesis H₀ 6 is valid and not rejected.

5.6 Mann-Whitney U test:

The demographic analysis on education shows that there is only one participant in the Below Under Graduate category and most of the participants are in Under Graduate and Post Graduate categories. carbon footprint is a concept that participants need to understand as a theory before they agree or disagree to the idea of using it in the UPI mobile applications. So, to compare the carbon footprint preference in the Under Graduate and Post Graduate education groups, a Mann-Whitney U test is performed. The Mann-Whitney U test shows that there is a significant difference in carbon footprint or carbon Tag view preference in the UPI applications between the Under Graduate and Post Graduate education groups, Z = -2.034, p = .042. So the hypothesis H₀ 8 is valid and not rejected.

Fig 3 shows the Mann-Whitney U test result for the carbon footprint preference.

Fig. 3: Mann-Whitney U test result to compare the carbon footprint preferences between UG and PG education groups

From the demographic analysis, it was found that there were less than 5% participants from the Old age group (age over 45 years). So, a Mann-Whitney U test is performed to compare the climate control awareness between the age groups of Young and Middle age. The Mann-Whitney U test shows that there is a significant difference in the climate control awareness between the age groups of Young and Middle age, Z = -2.1, p = .036. Hence the hypothesis H₀ 2a is valid and not rejected. Fig 4 shows the Mann-Whitney U test result for the climate control awareness.

Fig. 4: Mann-Whitney U test result to compare the climate control awareness between Young and Middle age groups

5.7 Binary Logistic Regression for purchase intention of green products:

IBM SPSS version 26 is used to test the Logistic Regression models to predict the green purchase intention of the participants. The testing of the models suggests not using any Sociodemographic factors of the participants as predictors. The climate control awareness and carbon footprint preference are not good predictors according to the model testing. The Binary Logistic Regression model is statistically significant when the UPI Usage Amount (UU), Convenience of UPI (CO) are used as predictors along with other predictors like participants’ choice of UPI applications for High Transaction Categories – Grocery shopping, Foods and Eateries, Utility Bill and Fund Transfer. These predictors can be used to predict the green purchase intention of the UPI users. The Chi-Square test has p-value as .012 which is less than .05. So the model is statistically significant and it has predicting ability for green purchase intention which is the dichotomous dependent variable. The Hosmer and Lemeshow goodness of fit test is used to indicate the model fitness. Since its value is greater than .05, that indicates that the model can be considered as good fit. The model summary shows that the value of Nagelkerke R Square is .653 which means that the model has strong predicting ability.

6. DISCUSSIONS:

The test results indicate that most of the Sociodemographic factors do not have significant effect on the UPI mobile application users’ climate control awareness. However, the climate control awareness is significantly associated with the age of the UPI mobile application users. The climate control awareness is constructed by factoring both the environmental concerns and the exhibition of the sustainable environment behaviour. Demographic analysis shows that 95% of the participants in the study belong to the Young and Middle age group (18 – 45 years). The Theory of Planned Behaviour (TPB) model was extended with environmental knowledge and situational behavioural related questions to determine the climate control awareness level of the participants. So, the test result implies that depending on the age, the participants have different environmental knowledge or they behave differently in a situation. The amount of UPI application usage for different payment purposes and the convenience of using a UPI mobile application for payments, are significantly associated with climate control awareness from an environment-friendly payment behavioural perspective. The Chi-Square test shows that UPI usage amount is associated with the convenience of using UPI mobile applications. There are 20 third party UPI applications and 40 UPI bank applications available in the market. For the purpose of this study, the top 5 UPI mobile applications based on the UPI transaction volume, are selected. The selected UPI mobile applications for this study are -Paytm, PhonePe, Google Pay, Cred, Amazon Pay and Other UPI Applications¹. So the convenience of using a UPI mobile application depends on the choice of the UPI application also. So, this implies that – the choice of UPI application for payments drives the convenience of UPI factor which in turn impacts the UPI usage amount factor. And this series of associated factors have significant effect on the participants’ climate control awareness behaviour.

The Ch1-Square tests show that UPI application users’ monthly family income, education and climate control awareness has significant effect on carbon footprint view preferences in the UPI applications. It requires some environmental and educational knowledge to understand the concept of carbon footprint or carbon Tag and how it impacts the environmental sustainability. So, a significant association between education and carbon footprint preference is in sync with our hypothetical assumption. And the UPI application users who have high climate control awareness, would seek the environment impact related information like carbon footprint or carbon Tag in the UPI applications. Hence the test results are supporting the findings from the earlier studies¹⁸, where it was found that the consumers were persuaded towards the green purchase behaviour by providing them the related environment sustainability information.

The results of green purchase intention analysis show that the purchase intention for green products is not associated with UPI application users’ Sociodemographic parameters. But it is associated with the UPI application users’ climate control awareness. This result is similar to the findings of existing studies^14,15, where it was found that the environmental attitude had a significant positive impact on the green purchase intention. The binary logistic regression to predict the green purchase intention of the UPI application users, show that the crucial predictors are UPI application usage amount, Convenience of using the UPI application and the choice of the UPI applications used for the high transaction categories like – grocery shopping, restaurants and eateries, utility bill payments and fund transfer. It is observed that the UPI application users’ Sociodemographic data are not considered as good predictors by the binary logistic regression model in IBM SPSS version 26 software. It is also observed that the climate control awareness and carbon footprint preference of the UPI application users are not considered as good predictors for green purchase intention. As mentioned before, the good predictors, found in this binary logistic regression analysis are inherently derived from the UPI application itself. The test results imply that the choice of UPI application out of the available third party or bank provided UPI applications, drives the UPI application convenience level and UPI usage amount and in turn persuades the users with climate control awareness to buy green products by using that UPI application. This study has considered the top 5 UPI mobile applications based on the UPI transaction volume. The UPI application convenience level might be different for all of them and hence the UPI usage might be different as well. So, in order to persuade the UPI mobile application users towards the green purchase intention by using the UPI applications, we have to do more research and study on the relative performances of those UPI mobile applications.

7. CONCLUSION:

Digitization of retail payments has a has strong influence on Indian economy especially around digital economy³¹. Consumers are now familiar with digital payment systems. However, more studies are needed in rural areas to understand how rural population is preferring digital channels³² when the FI index has been improving. Similarly, more comparative studies are required on socio-demographic factors to understand how the different digital payment channels are utilized³³ in India. There are several factors that determine online purchase behaviour of a consumer including demographic and pre-purchase decision factors^{34, 35, 36}.

The purpose of studying the carbon Tag or carbon footprint impact analysis on consumers’ payment behaviour using the UPI mobile application, is to investigate the factors that drive them towards the green purchase behaviour. This study is an exploratory research in nature as it seeks to understand the potential effect of showing the carbon footprint information in the UPI applications for digital transactions. One of the key objectives of the study is to explore the ways in which the UPI mobile application users can be persuaded towards the green purchase behaviour. Since, the carbon tag or carbon footprint information is not yet introduced in the UPI mobile applications in India, this study intends to analyse how the different consumer segments will accept the idea of carbon neutral payment behaviour if introduced in the market. There are research studies which had found that providing the contextual environment sustainability related information to the consumers via online channels or medias can persuade them towards the green purchase intention and green purchase behaviour. The carbon footprint for digital transaction is considered as the contextual environment sustainability information in this study. But however, the key difference is there are many UPI mobile applications. The consumers have different experiences with different UPI mobile applications based on their choice of UPI mobile App. The test results also suggest that the consumers’ green purchase intention using the UPI mobile application, is driven by their choice of UPI mobile App. It is not in scope of this study - how the different UPI mobile applications are driving the consumers’ payment experience. The consumers who are more satisfied with their choice of UPI mobile applications, may be more receptive to new ideas like carbon footprint and carbon neutral payments. A future study is needed to understand how the different UPI mobile applications are driving the consumer satisfaction and in what aspects one application performs better than the others.

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Received on 29.11.2025 Revised on 09.01.2026

Accepted on 12.02.2026 Published on 20.07.2026

Available online from July 30, 2026

Asian Journal of Management. 2026;17(3):273-285.

DOI: 10.52711/2321-5763.2026.00042

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

Null hypothesis		Test	Sig.	Decision
1	UPI mobile application users' preference to see the carbon footprint of 1 the digital transaction is not associated with their climate control awareness.	Independent-Samples Kruskal-Wallis Test	0.00	Reject the null hypothesis.
2	The Family Income of the UPI mobile application users does not 2 influence their preference to see the carbon Tag or carbon footprint per transaction.	Independent-Samples Kruskal-Wallis Test	0.015	Reject the null hypothesis.
3	The Education of the UPI mobile application users does not influence 3 their preference to see the carbon Tag or carbon footprint per transaction	Independent-Samples Kruskal-Wallis Test	0.209	Retain the null hypothesis.