Customer Perception Towards Pradhan Mantri Jan-Dhan Yojana (PMJDY): An Empirical Investigation from Slum-dwellers Perspective

Abstract

The government-sponsored welfare programmes are instrumental in balancing economic growth and reducing inequalities in society. Society becomes inclusive only when the deprived people have access to banking and financial services. The policymakers and financial players emphasized the importance of financial inclusion programmes because of their significant impact on economic growth and the financial health of the economy. The Pradhan Mantri Jan-Dhan Yojana (PMJDY) is a government-led intervention programme with a national mission to provide banking facilities to all deprived sections in the country. The study focuses to examine the determinants of customer perception towards PMJDY. The study includes the slum dwellers of Bhubaneswar as the target group of beneficiaries for empirical research. The determinants include the delivery process (SERVQUAL) and outcome attributes associated with the social scheme. As customer perception is a categorical variable, the multinomial logistic regression model is adopted to test the hypothesis. The study results indicate that the beneficiaries consider reliability, assurance, tangibility and social connect dimensions as the likely factors to obtain a higher level of perception towards the welfare programme.

Keywords

Social schemes government-led market intervention Pradhan Mantri Jan-Dhan Yojana customer perception multinomial logistic regression

1. Introduction

Public services including social welfare programmes often pose evaluation challenges owing to their assorted and intangible nature of outcomes. The programmes that achieve societal outcomes and attend to customers’ perceptions receive a higher level of satisfaction (Fraser & Wu, 2016). The government-sponsored social programmes are aimed at balancing economic growth and human welfare, especially in the vulnerable section of society. Therefore, the programme delivery needs to be transparent and enable ordinary citizens to assess the quality, reliability and effectiveness of basic services (Bhattacharya et al., 2016).

All successive governments in India have recognized the need for welfare programmes and are undertaking policy initiatives to facilitate the weaker sections of the society in different ways. The programmes are launched for different vulnerable groups in society including women, senior citizens, minorities, the physically challenged and slum dwellers. The Pradhan Mantri Jan-Dhan Yojana (hereafter, PMJDY) is one of the popular social welfare schemes launched by the Indian government to facilitate the financial inclusion mission.

The major sections of the population particularly in the developing world are detached from formal banking systems. The development and growth of the financial sector become inclusive only when the poorer communities have access to a wide range of financial services (World Bank, 2014). The choices of banking services including saving and credit opportunities ensure financial inclusion and promote economic prosperity and well-being among citizens (Beck et al., 2010; Bruhn & Love, 2014; Burgess & Pande, 2005). According to the Indian Census 2011, only 58.7% of households in the country had access to formal financial services. This comprises 67.7% and 54.5% of urban and rural households respectively who had access to any kind of financial services.¹ This low financial penetration in the country leads to sluggish economic growth and is an indication of failed economic policy interventions reaching directly to the deprived section of society. Keeping this in mind, the government of India initiated and launched a mega financial inclusion programme called PMJDY in 2014. The programme is a national mission to provide banking and financial facilities to all underprivileged citizens in the country. The programme has created the Guinness Book of World Record, by opening the largest number of bank accounts (18 million) in just one week as a part of the financial inclusion campaign. The PMJDY is a welfare programme that provides several facilities including opening bank accounts with zero balance. The account holders can access universal banking services such as credit and remittance facilities and social security comprising insurance and pension. The latest figures indicate that by end of March 2022, 450 million PMJDY accounts are opened and many Indian states including Odisha reported a full coverage of households under the scheme.²

In the context of social welfare programme evaluation, the satisfaction ought to be interpreted from the beneficiaries’ perspective with their perception and judgements. Their judgements are based on the acceptance of physical service quality and endorsement of service experience. In the available literature SERVQUAL is a prominent model used to evaluate the quality of several services (Ocampo et al., 2019). This model provides the framework for service quality assessment from multiple quality dimensions such as—tangibility, reliability, responsiveness, empathy and assurance. In the context of evaluating the quality of PMJDY as a social welfare service, these quality dimensions are undertaken in the present study along with other outcome factors. The quality of PMJDY service and derived customer satisfaction is assessed from slum dwellers’ perspective. The slum dwellers of Bhubaneswar, the capital city of Odisha, are the focus group in the study.

The remaining of the paper is structured with other sections. The next section reviews the literature on the financial inclusion programmes and their perform ances. The third section includes the theoretical framework, the rationale and the objectives of the study. Section four enumerates the data sources with research design and methods of data analysis. The analysis and discussion of findings are detailed in section five. The conclusion and future research scopes are summarized in the last section of the paper.

2. Literature Review

The socio-economic issues are closely associated with financial exclusion. They are ranging from acute poverty, bad health, elevated crime rate, high unemployment and unhygienic housing, etc. (Kempson et al., 2000). The financial exclusion very often leads to rising costs that are borne by the financially disadvantaged group (Aduda & Kalunda, 2012). As mentioned by United Nations; the financial exclusions affect earning capacity of individuals are unable to protect people during crisis times and also create uncertain prospects. Several prior studies have highlighted the importance of financial inclusion programmes. They suggested that the constant exclusion from the mainstream financial system resulted in larger inequality in the society and drive people towards the perpetual poverty cycle (Aghion & Bolton, 1997; Banerjee & Newman, 1993). Therefore, the financial inclusion interventions are the essential elements that facilitate socio-economic development in the country with sustainable growth and progress. The financial system that provides easy access to structured financial products and avenues ultimately helps people in the underprivileged communities to sustain better life and be a part of the social mainstream (Demirguc-kunt et al., 2008; Levine, 2005; Rastogi & Ragabiruntha, 2018). This is the reason; the financial inclusion-oriented systems are widely considered significant policy initiatives by several developing nations. Developing countries such as India, Bangladesh and the Philippines have demonstrated a positive association between financial inclusion programmes and economic growth (Mohan, 2006; Onaolapo et al., 2015; Sharma, 2016; Swamy, 2014). The banking regulators in many countries are impressed with the impact of financial inclusion programmes and are given priority in their policy documents. Demirguc-Kunt et al. (2013) and Sangwan, (2017) found that in 143 countries over 60% of banking regulators are strongly promoting financial inclusion programmes in their country.

The lack of a structured market for lending and borrowing in developing countries is the cause of low financial inclusion in remote areas. Commercial banks do not prefer to open branches in rural areas due to high operational costs. Even though banking facilities are made available in such areas, the lack of collateral and good credit history prevents people to take advantage. As a result, they are compelled to borrow from local moneylenders with a high rate of interest. Therefore, the real challenge lies in bringing the services to the socially and economically vulnerable people living in far-flung areas (Rastogi & Ragabiruntha, 2018). Government intervention programme like PMJDY is significant in bringing banking services to the unbanked areas. The periodic reviews of social welfare programmes are essential to make them more robust and achieve the targeted goal. In this direction, Sharma et al., (2016) conducted a study in 42 districts across 17 states and one Union Territory in India to assess the impact of PMJDY on beneficiaries. The study was funded by Bill & Melinda Gates Foundation and supervised by the Ministry of Finance, Government of India. The study reported that PMJDY is a flagship financial inclusion programme that has changed the financial service landscape in rural India. They found that the bank account penetration in India has changed after PMJDY but owing to logistic and technical challenges, 28% of these accounts are dormant. The banking correspondents are supportive in opening accounts under PMJDY, but they need to be trained with scheme features and aspects of customer service. They also suggested that the beneficiaries need to be protected from fraudulent cases. The fraudulent cases such as hidden charges on customers while opening accounts under the scheme are reported in a few states. Singh and Ghosh (2021) found a long-term equilibrium between PMJDY and economic activities. They investigated the relationship between PMJDY and economic activities in India during the pre and post-demonetization periods. They observed that during the pre-demonetization phase the improved economic activities cause increased banking activities in PMJDY accounts. However, the period after demonetization noticed a reverse causal relationship between economic activities and transactions under PMJDY. The PMJDY-based bank accounts are helpful during the period of crisis for direct money transfers from public authorities. The Indian government used the PMJDY accounts to transfer the COVID-19 relief package to poor households. However, some glitches such as the failure of Aadhaar linked payment system and mismatch or exclusion from databases create unhappiness among the users (Palepu, 2020; Pande et al., 2020; Somanchi, 2020). CRISIL (2018) conducted a compressive assessment and established an index taking all dimensions of PMJDY. They performed a region-wise comparative study in India from four dimensions, that is, branch penetration, credit penetration, deposit penetration and insurance penetration. Their findings highlighted that the south region ranked at the top in all dimensions compared to other regions in India. The north-east region is positioned at the bottom but improving fast in all performance parameters. They observed that besides infrastructure improvement and dealing with inactive accounts, financial literacy is essential to achieve the absolute success of PMJDY (Nimbrayan et al., 2018).

Service quality plays a significant role in determining customer satisfaction. Several quality dimensions influence the service quality. The importance of each dimension varies from service to service and influences the overall service quality and customer satisfaction. Parasuraman et al. (1985, 1988) promoted the SERVQUAL model, which is widely used in academic research to assess the quality of diverse services. SERVQUAL model includes five service quality dimensions, that is, (a) tangibility (physical appearance of the facilities, material and the appearance of personnel); (b) reliability (service performed accurately as desired); (c) responsiveness (ready to address customer issues and prompt services); (d) assurance (conveying trust and confidence through knowledge and skills) and (e) empathy (caring and personalized attention). These five dimensions form a service quality system that ensures better functional quality and service performance. Several studies adopted the multidimensional SERVQUAL model to investigate service quality and customer satisfaction under different contexts. The model is applied in research fields such as—banking services, healthcare services, education, hotel and transportation and logistics and e-governance services. Meesala and Paul (2018) investigated the hospital service quality factors in an Indian city, Hyderabad. They conducted the study in 40 private hospitals and found that reliability and responsiveness are the two service quality dimensions that drive customer satisfaction. However, Anbori et al. (2010) highlighted empathy and assurance as the significant quality dimensions of the SERVQUAL model that influence patients to visit the same hospital repeatedly. Wang et al., (2003) tried to find the service quality of banks. They observed that all the dimensions of the SERVQUAL model are significant and impact banks’ reputations. The reputed banks in turn influence customers’ attachment and their loyalty towards the bank. Abdullah et al. (2012) investigated the service quality of airlines. They reported that the tangibility, assurance and reliability dimensions of the service quality model are significant evaluation parameters of air travel quality. However, Pakdil and Aydın (2007) adopted a modified SERVQUAL model and found that airline passengers consider responsiveness as the most important dimension of airline service quality. Chuenyindee et al. (2022) examined the causal relationship between service quality dimensions and customer satisfaction of public utility vehicles during the COVID-19 pandemic in the Philippines. They observed that tangibility and assurance are the most powerful quality dimensions of public utility vehicle services to achieve customer satisfaction. Mikhaylov et al. (2015) assessed the public transportation service quality in Russia. They reported that tangibility is the most important determinant of service quality. In a similar line, Munim and Noor (2020) found tangibility and empathy variables positively influence customers’ satisfaction while investigating the perception of young people towards bus services. Nutsugbodo (2013) observed that the customers give more importance to reliability, tangibility and assurance factors when choosing the public transportation service quality. Sam et al. (2018) used the SERVQUAL methodology in evaluating the public transport service in Kumasi metropolis, Ghana. They found that reliability and responsiveness are the two key bus service quality determinants in the city. Alam and Mondal (2019) conducted a study in Khulna railway slums to investigate the service quality using the SERVQUAL model. They observed that tangibility and empathy factors mostly affect customer satisfaction towards railway services. Tiglao et al. (2020) studied the service quality of metro services in Manila. They found that reliability and responsiveness dimensions are significant factors that drive customer satisfaction in metro services. Chou et al. (2011) also viewed the importance of responsiveness of service providers towards customer issues in deriving higher customer satisfaction. Rahman et al. (2016) highlighted the importance of the reliability variable. Tumsekcali et al. (2021) conducted a study during the COVID-19 pandemic on service quality and customer satisfaction. They viewed that, during the pandemic, the service providers who build trust and confidence can generate the highest customer satisfaction. As per their assessment, assurance is found to be the key quality dimension of public services. Chuah and Hilmi (2011) also found a positive impact of assurance and empathy variables on service quality.

The SERVQUAL model is criticized by some researchers from the conceptual and empirical points of view. They suggested that the quality dimensions comprised in the SERVQUAL model are not complete and different for industries and services, and hence, cannot be generalized (Babakus & Boller, 1992; Carman, 1990; Cronin & Taylor, 1992, 1994). Cronin and Tylor (1992) developed a SERVPERF model that measures the performances of service quality parameters. Two other models, Gronroo’s (Reichel et al., 2000) and Kano’s (Tan & Pawitra, 2001), are developed to measure the service quality dimensions. Grönroos (1983) suggested that the customers’ quality experiences are associated with two main dimensions; functional and technical qualities. The functional dimensions include the service delivery traits, while technical dimensions refer to the outcome qualities of the services. On the other hand, Kano (1984) proposed a two-dimensional model that is based on Hertzberg’s motivation-hygiene theory. He recommended that the existence of quality factors may not be sufficient enough to the customers’ level of satisfaction. But the absence of certain quality aspects may also result in customer dissatisfaction with services. Jain and Gupta (2004) verified that SERVPERF (Cronin & Tylor, 1992) is more useful than SERVQUAL in the case of fast food restaurant service quality in India. Narayan et al. (2009) suggested a service quality assessment scale for the Indian tourism industry. They identified 10 service quality dimensions to measure the services in the tourism sector. In a similar line of work, Edward and George (2008) applied the attribute approach in assessing the Indian tourist destinations’ service quality. His study is based on the tourist destinations of Kerala state (India) and suggested 24 performance attributes to measure the service quality perceptions.

The existing literature applied service quality models to evaluate the service quality and customers’ perception of different commercial services. Nevertheless, the application of such models in public welfare schemes is rare. Public welfare services are non-profitable where choices are often constrained and mainly focus on the economically backward classes. Because of these reasons the quality measurement of welfare services poses greater challenges. The present study is one of its kind that aims to investigate the PMJDY service quality and customer satisfaction from slum dwellers’ perspective. To evaluate the service quality dimensions of PMJDY, the SERVQUAL model is adopted. Figure 1 shows the framework of the study that includes the SERVQUAL model dimensions: reliability, assurance, tangibility, empathy and responsiveness. In addition to these dimensions, three other outcome variables: convenience, social connect and economic connect are also added to assess the service quality of PMJDY and customer satisfaction.

Figure 1.

Source: The author.

3. Rational and Theoretical Framework of the Study

The current research investigates the perception of slum dwellers towards PMJDY. The slum dwellers are the depressed communities who are deprived of basic facilities for comfortable living. They reside in raw houses under unhygienic and dismal socio-economic infrastructure. The poor slum households depend on government facilities for better education, health, transportation, clean water and safe cooking gas. The PMJDY is a Government of India-sponsored financial inclusion programme targeted to provide banking and financial services for an inclusive social network. The present study investigates the service quality of PMJDY and customer satisfaction from Bhubaneswar slum dwellers’ perspective. Figure 1 summarizes the multidimensional framework adopted in the study. The framework includes the dimensions of the SERVQUAL model (reliability, assurance, tangibility, empathy and responsiveness) and other extended variables. Grönroos (1982, 1990) and Lehtinen and Lehtinen (1991) argued that the dimensions proposed by Parasuraman et al. (1985) under the SERVQUAL model primarily target the service delivery process. However, according to their view, the customer perception of the service quality comprises three major dimensions: (a) functional (delivery or process) dimension, technical (outcome) dimension and image. Grönroos (1982, 1990) and Rust and Oliver (1994) believe that the service quality evaluation should be performed only after the service is delivered. Therefore, they viewed that the technical or outcome dimensions are more significant in determining the customers’ perception of the service quality. In a similar line, Richard and Allaway (1993) argued that the customer behaviour prediction and service quality measurement are incomplete utilizing only the functional or service delivery attributes. Several other studies used the extended SERVQUAL model to evaluate service quality in different contexts. Prior studies found the predictive power of variables such as convenience, comfort, connection, flexibility, safety, information and affordability while evaluating the service quality and customer satisfaction (Cavana Robert et al., 2007; Paquette et al., 2009; Şimşekoğlu et al., 2015; Yaya et al., 2014).

The author believes that the beneficiaries’ experience of service performance significantly affects their perception of the service quality. The combined impact of both delivery and outcome dimensions is more effective in predicting service quality and customer perception (Ray, 2021). Therefore in the present study, the author undertakes both functional (delivery process) and technical (outcome) dimensions for assessing the service quality of PMJDY and customer satisfaction. Figure 1 depicts the dimensions used in the research. The first five attributes are identical to the SERVQUAL dimensions and satisfy the process or delivery functions of the social scheme. The subsequent three attributes comprise of outcome qualities of the programme. The outcome qualities include three dimensions: (a) convenience (comfortable living and productive use of resources), social connect (social impact and improved status and engagement) and economic connect (economic opportunities and prospects of a better standard of living). The adopted methodology in the study tries to test empirically that there exists a positive association between customer perception and determinants while analysing the service quality of PMJDY as a social scheme. Table 1 summarizes the questions included in each dimension. The questions are measured in a 5-point Likert scale including the customer perception parameter.

The present study is distinctive from two points of view. First, the study attempted to evaluate the service quality of PMJDY and customer satisfaction through the dimensions of SERVQUAL, a model usually adopted to assess commercial services. Second, the study tries to analyse empirically the probable factors of the scheme that may change the beneficiaries’ perception in the future.

Table 1.
Functional Determinants of PMJDY

Customer Perception Functional Determinants

Delivery Process Factors
Outcome Factors

Reliability Assurance Tangibility Empathy Responsiveness Convenience Social Connect Economic Connect

Perception towards the scheme
(PMJDY) Reliable service performed accurately as desired Conveying trust and confidence through knowledge & skills The physical appearance of the facilities, material etc. The caring and personalized attention Prompt services and ready to address issues Comfortable living and productive use of resources Social impact and improved status & engagement Economic opportunities and potential of better standard of living

• The scheme gives me fulfilment of life.
• Happy that the government has come with this scheme.
• The scheme is sustainable in future.
• Scheme is served as declared.
• Reliability of the service rendered.
• Services rendered without any mistake.
• Promptness in getting the desired service. • Courteous service.
• Genuine and safe transactions.
• The latest information of the scheme shared.
• The queries are resolved with confidence and knowledge. • Comfortable ambiance at the service centre
• Physical appearance of the deliverable is good
• Appearance of personnel at the service centres • Dealing in a caring fashion
• Having best interests of customer
• Convenient business hours to render service • Prompt services provided
• Helpful in handling requests
• Responsive to all our issues
• The scheme makes our life easy.
• Dedicating more time for productive work
• Enhanced aspiration of life
• Part of the main stream
• Government is concerned about our well-being
• Social status is improved.
• Referred the scheme to others
• Socially sustainable scheme
• Strong social impact • Financial benefit of the scheme is better than others.
• Reasonable cost
• Affordable own contribution
• Economically viable for future
• Personal budgetary control
• Scope to save for future

Customer Perception	Functional Determinants
Perception towards the scheme (PMJDY)	Reliable service performed accurately as desired	Conveying trust and confidence through knowledge & skills	The physical appearance of the facilities, material etc.	The caring and personalized attention	Prompt services and ready to address issues	Comfortable living and productive use of resources	Social impact and improved status & engagement	Economic opportunities and potential of better standard of living
• The scheme gives me fulfilment of life. • Happy that the government has come with this scheme. • The scheme is sustainable in future.	• Scheme is served as declared. • Reliability of the service rendered. • Services rendered without any mistake. • Promptness in getting the desired service.	• Courteous service. • Genuine and safe transactions. • The latest information of the scheme shared. • The queries are resolved with confidence and knowledge.	• Comfortable ambiance at the service centre • Physical appearance of the deliverable is good • Appearance of personnel at the service centres	• Dealing in a caring fashion • Having best interests of customer • Convenient business hours to render service	• Prompt services provided • Helpful in handling requests • Responsive to all our issues	• The scheme makes our life easy. • Dedicating more time for productive work • Enhanced aspiration of life	• Part of the main stream • Government is concerned about our well-being • Social status is improved. • Referred the scheme to others • Socially sustainable scheme • Strong social impact	• Financial benefit of the scheme is better than others. • Reasonable cost • Affordable own contribution • Economically viable for future • Personal budgetary control • Scope to save for future

Source: The author.

Note: The customer perception and functional factor questions are measured with 5-point Likert scale, where 1 and 5 are the strongly disagree and strongly agree, respectively.

4. Data and Methodology

The present study is a primary research from the slum dwellers’ perspective. The respondents for the empirical investigation include the slum households of Bhubaneswar. Bhubaneswar is the capital city of Odisha. The city is spread over a 186 sq km area and is the economic hub of the state. The city is divided into 67 wards and is maintained by Bhubaneswar Municipality Corporation (BMC). The city attracts regular migrants from other areas within the state as well as from adjoining states. The state government is constantly putting efforts to facilitate all the necessities such as adequate housing, subsidized food, clean drinking water, sanitation and child education for the weaker section of the society including the slum communities. Bhubaneswar has 436 identified slums in 67 wards accommodating 301,611 populations. The total slum population of the city is residing in 80,665 households. In terms of area coverage of slums, the smaller slums occupy 0.11 acres, and the bigger slums with 45.24 acres. The slums are mostly concentrated in the north, south–east and south–west zones of the city. In the central part of the city, the slums are much smaller in size and scattered in comparison to the other zones.³

Sampling and Data Collection

The focus of the research is to investigate the service quality of PMJDY based on the opinion of its beneficiaries. As the study is primary, the slum dwellers of the city are surveyed with the help of a structured questionnaire. Initially, a list of 300 PMJDY beneficiaries was collected from two oil marketing companies (Indian Oil Corporation Limited and Hindustan Petroleum Corporation Limited). The data of oil marketing companies have been relied upon because the government subsidies under Pradhan Mantri Ujjwala Yojana (PMUY) are transferred to the beneficiaries’ PMJDY accounts. PMUY is another social welfare scheme that helps deprived communities to get clean cooking gas (Liquefied petroleum gas). The investigators found that out of the list shared by two oil marketing companies, 120 PMJDY beneficiaries’ contact details are incorrect. Therefore, finally, a snowball sampling technique was adopted, where the beneficiaries contacted from the list assisted to identify the next beneficiary. The study comprises a total sample size of 630 women beneficiaries of PMJDY from different slum households. The sample includes not more than one women member of each household covering the slums in all four zones of the city (north, south–east and south–west and central). The research sample size is estimated with the number of city slum households (80,665), confidence level (95% and Z value 1.96), probability (0.5) and margin of error (0.04). The beneficiaries’ responses are collected through a structured questionnaire, where the questions are measured on a 5-point Likert scale. The strongly agree and strongly disagree opinions are denoted with 5 and 1 respectively.

The questionnaire is divided into three sections. The first section contains the demographic and questions related to socio-economic status. The beneficiaries’ PMJDY knowledge and operational experience-related questions are included in section two (summary included in Appendix I). The third section comprises the questions to evaluate the service quality of PMJDY and customer satisfaction. The functional qualities (delivery process) measured through the SERVQUAL model include 17 questions related to the service quality of PMJDY under five dimensions: reliability (four questions), assurance (four questions), tangibility (three questions), empathy (three questions) and responsiveness (three questions). The technical qualities (outcome) include 15 questions relating to the potential outcomes of PMJDY under three dimensions: convenience (three questions), social connect (six questions) and economic connect (six questions). Finally, customer satisfaction comprises three questions appropriate to the perception of beneficiaries towards PMJDY. The reliability of questionnaire items is measured with 30 pilot survey questionnaires following Cronbach’s alpha method (Cronbach, 1951). The Cronbach’s alpha of responses on customer satisfaction questions, and questions on functional dimensions (SERVQUAL) and technical dimensions are found to be 0.796, 0.918 and 0.834 respectively. The alpha values indicate a good sign of the reliability of the questionnaire items for further analysis. The author has given equal weight to questions while finding the composite score of each attribute included in the questionnaire. The final composite score is the average of all responses to questions under each attribute. The equal weighting technique is applied and offered remarkable results in several other research settings. Dawes (1979) and Ree et al. (1998) stated that the equal or unit-based composite scores are highly correlated with other weighting methods like regression-based weighting. Stillwell et al. (1983), Doverspike et al. (1996) and Bobko et al. (2007) reported that the unit-based weighting technique is extremely appropriate in many conditions for data analysis and interpretation point of view.

Methodology

The PMJDY is a financial inclusion initiative, where the underprivileged people including the slum dwellers are allowed to open bank accounts with zero balance in any commercial banks in the country. The benefits associated with the scheme build the customer perception and derive satisfaction. The PMJDY is a financial intervention scheme sponsored by the government. Hence, the continuity and the success or failure of the social scheme are independent of the customers’ level of satisfaction. Therefore, the scheme outcomes cannot be determined in a binary form like satisfied or dissatisfied. The author argues that the beneficiaries may perceive the scheme—as high, low or neutral. Thus, customer perception is categorical and undertaken as a dependent variable in the model. The categorical nature of the variable is defined by high, low or neutral perception levels. To test the hypothesis that a positive association exists between the customer perception and other explanatory factors (delivery and outcome determinants), multinomial logistic regression is applied.

The beneficiaries’ level of perception towards PMJDY is determined with the help of three questions presented in Table 1. The pertinent questions include—whether they are feeling an achievement in life after availing of the scheme; whether they are happy that the government is concerned and brought out a scheme for them and lastly, whether they feel that the scheme has better outcomes sustainable in future. All these questions are measured on a 5-point Likert scale, and the average score of each respondent is estimated to find their perception category. The scores are ranging between a minimum of 1 and a maximum of 5. The respondents’ average scores are classified into three categories, that is, 1.0 to 2.5, 2.5 to 3.5 and 3.5 to 5. The respondents who secured scores within 1.0 to 2.5 are categorized as the low perception group and represented as 1. Similarly, beneficiaries whose average scores obtained within the range of 2.5 to 3.5 and 3.5 to 5 are designated as neutral and higher customer perception groups respectively. The neutral and high customer perceptions are denoted with 2 and 3, respectively. These customer perception categories (1, 2 and 3) represent the dependent variable in the multinomial logistic regression model. The initial result shows that among respondents; the high category (3) counts 258 and is followed by neutral (2) and low category (1) with 202 and 170 respectively. This indicates that 41% of respondents are highly satisfied with the PMJDY and followed by neutral and not satisfied with 32% and 27 % respectively. The independent variables in the model represent the functional determinants comprising both delivery and outcome factors.

5. Analysis and Discussion

Table 2 summarizes the descriptive statistics of independent variables undertaken in the study. The results indicate a maximum score between 4.55 and 5 with a mean score above 3 across all the variables. However, the minimum score for all independent variables ranges from 1.75 to 2.45. The responsiveness variable shows the highest standard deviation value of 0.55 and the lowest for economic connect with 0.25.

Table 2.
Descriptive Statistics of Independent Variables

Des. Stat/Variables Reliability Assurance Tangibility Empathy Responsiveness Convenience Social Connect Economic Connect

Mean 3.90 3.87 4.21 3.81 3.84 3.88 3.84 3.73

Median 4 4 4 3.7 4.0 4.0 3.8 3.7

Std. deviation 0.45 0.39 0.32 0.42 0.55 0.39 0.39 0.25

Minimum 1.75 2.00 3.00 1.67 1.67 2.00 2.00 2.45

Maximum 5.00 5.00 5.00 5.00 5.00 5.00 5.00 4.55

Count 630 630 630 630 630 630 630 630

Des. Stat/Variables	Reliability	Assurance	Tangibility	Empathy	Responsiveness	Convenience	Social Connect	Economic Connect
Mean	3.90	3.87	4.21	3.81	3.84	3.88	3.84	3.73
Median	4	4	4	3.7	4.0	4.0	3.8	3.7
Std. deviation	0.45	0.39	0.32	0.42	0.55	0.39	0.39	0.25
Minimum	1.75	2.00	3.00	1.67	1.67	2.00	2.00	2.45
Maximum	5.00	5.00	5.00	5.00	5.00	5.00	5.00	4.55
Count	630	630	630	630	630	630	630	630

Source: The author.

The correlation matrix of all independent variables is summarized in Table 3. The results indicate that all the correlation coefficient values are positive and range from 0.20 to 0.73. Table 3 also includes the variance inflation factor (VIF) of independent variables. VIF indicates the correlation between the independent variables with their strength of correlation. The VIF values are between 1.3 and 2.9, and it suggests that a moderate correlation exists and there is no need for any corrective measure. Therefore, the independent variables are free from multicollinearity issues, and all are fit for inclusion in the subsequent models.

Table 3.

Correlation Matrix of Independent Variables (PMJDY)

Variables	Reliability	Assurance	Tangibility	Empathy	Responsiveness	Convenience	Social Connect	Economic Connect	Collinearity Statistics
Variables	Reliability	Assurance	Tangibility	Empathy	Responsiveness	Convenience	Social Connect	Economic Connect	Tolerance	VIF
Reliability	1.000	0.453	0.285	0.570	0.697	0.357	0.263	0.255	0.468	2.138
Assurance	0.453	1.000	0.200	0.420	0.405	0.311	0.354	0.248	0.716	1.397
Tangibility	0.285	0.200	1.000	0.390	0.368	0.382	0.368	0.296	0.765	1.307
Empathy	0.570	0.420	0.390	1.000	0.727	0.451	0.377	0.265	0.426	2.346
Responsiveness	0.697	0.405	0.368	0.727	1.000	0.448	0.367	0.250	0.342	2.926
Convenience	0.357	0.311	0.382	0.451	0.448	1.000	0.664	0.478	0.477	2.095
Social connect	0.263	0.354	0.368	0.377	0.367	0.664	1.000	0.504	0.483	2.070
Economic connect	0.255	0.248	0.296	0.265	0.250	0.478	0.504	1.000	0.693	1.443

Source: The author.

Note: The VIF between 1 and 5 suggests a moderate correlation and no action required for multicollinearity. However, VIF greater than 5 is critical and action required correcting the multicollinearity issues.

The present study aims to investigate the association between the functional determinants and customer perception of PMJDY. The multinomial logistic regression model is adopted to find the likelihood of association between the dependent and independent variables. The categorical customer perception and functional determinants are taken as the dependent and independent variables respectively. The model fitness information is reported in Table 4. The significant Chi-square value indicates that the model is fitted with the complete set of predictors undertaken in the study.

Table 4.

Model Fitting Information

Model	Model Fitting Criteria		Likelihood Ratio Tests			Goodness-of-Fit
Model	AIC	BIC	Chi-square	Df	Sig.		Chi-square	Df	Sig.
Intercept only	1,340.146	1,349.037				Pearson	1,195.604	1,170	0.295
Final	1,332.254	1,412.277	39.892	16	0.001*	Deviance	1,269.783	1,170	0.022*

Source: The author.

Note: *P value < 0.005.

The Chi-square test likelihood ratio results are summarized in Table 5. This indicates the effect of each independent variable in the model and its association with the preference of the social scheme. The results indicate that assurance, tangibility and social connect dimensions are significant and influence the customer preference towards the PMJDY scheme. The assurance and tangibility dimensions are comprised of process factors; however, social connect is a part of outcome determinants.

Table 5.

Chi-square Likelihood Ratio Tests

Predictors	Chi-square	Df	Sig.
Intercept	14.870	2	0.001
Reliability	3.684	2	0.159
Assurance	6.646	2	0.036*
Tangibility	8.928	2	0.012*
Empathy	2.046	2	0.359
Responsiveness	0.932	2	0.628
Convenience	2.427	2	0.297
Social connect	5.883	2	0.053**
Economic connect	1.799	2	0.407

Source: The author

Note: *P value < 0.005; ** P value <0.10.

To determine the explanatory variables that significantly predict the customer preference category of the social scheme against the reference category, the multinomial logistic regression model is applied. Customer preference is a categorical and dependent variable, and the process and outcome dimensions are the explanatory variables in the model. The regression slope in the multinomial logistic regression model is interpreted as the predicted change in the log-odds of the assessment category relative to the reference category as per unit increase of the predictor. In the present research, high customer preference is taken as the reference (i.e. baseline) category, and the other two customer preference levels (i.e. neutral and low) are undertaken as comparison/assessment categories. The model parameter estimation results are summarized in Table 6. The log-odds of the neutral customer preference show that reliability, assurance, tangibility and social connect predictors are significant. However, the results show that except for assurance the regression coefficients are negative for reliability, tangibility and social connect predictors. The results suggest that for each one-unit increase of the explanatory variables like reliability, tangibility and social connect, the log-odds (B) of a customer falling into the neutral category relative to the high preference category is predicted to decline by 0.581, 0.688 and 0.824 respectively. Table 5 also summarizes the relative risk ratios (Exp(B)) of explanatory variables. The relative risk ratios 0.301, 0.255 and 0.880 are depicted for the reliability, tangibility, and social connect predictors respectively. This indicates that, with the increased scores of reliability, tangibility and social connect predictors; there is a higher likelihood that a person will belong to the higher preference category with a change factor of 0.301, 0.255 and 0.880 respectively relative to the assessment category i.e. neutral. The assurance predictor shows a positive log-odd (0.746) with a higher relative risk ratio (2.109) for the neutral customer preference category against the reference group. This indicates that with increased values of assurance as an explanatory variable, the probability of a person remaining in the neutral category increases relative to the baseline category. The model results revealed that other predictors are insignificant under neutral customer preference levels relative to the high customer preference category.

Table 6.

Multinomial Logistic Regression Analysis for Different Level of Customer Preference

Customer Preference Category	Predictors	Log-odd (B)	Sig.	Relative Risk Ratio (Exp(B))	95% Confidence Interval for Exp(B)
Customer Preference Category	Predictors	Log-odd (B)	Sig.	Relative Risk Ratio (Exp(B))	Lower Bound	Upper Bound
Low-customer preference	Intercept	-5.388	0.006
	Reliability	-0.079	0.814	0.924	0.479	1.783
	Assurance	0.437	0.157	1.548	0.846	2.832
	Tangibility	0.437	0.216	1.548	0.774	3.093
	Empathy	-0.382	0.295	0.683	0.334	1.394
	Responsiveness	0.307	0.340	1.359	0.723	2.553
	Convenience	0.098	0.793	1.103	0.530	2.297
	Social connect	-0.118	0.749	0.888	0.430	1.836
	Economic connect	0.556	0.252	1.744	0.673	4.519
Neutral-customer preference	Intercept	2.339	0.198
	Reliability	-0.581	0.043*	0.560	0.301	1.041
	Assurance	0.746	0.012*	2.109	1.182	3.765
	Tangibility	-0.688	0.046*	0.503	0.255	0.989
	Empathy	-0.452	0.194	0.637	0.322	1.258
	Responsiveness	0.150	0.611	1.162	0.651	2.074
	Convenience	0.546	0.131	1.726	0.849	3.508
	Social connect	-0.824	0.020*	0.439	0.219	0.880
	Economic connect	0.518	0.281	1.678	0.654	4.307

Source: The author.

Notes: *P value < 0.005. The reference category is: High Customer Preference.

The log-odds of predictors under low customer preference relative to the high preference category are also summarized in Table 6. The results indicate that the coefficient values are positive for assurance, tangibility, responsiveness, convenience and economic connect explanatory variables and negative for reliability, empathy and social connect. However, none of these predictors’ log-odds values are significant. The results show that none of the functional factors associated with the social scheme (PMJDY) are likely to influence the beneficiary group those who poorly perceive the scheme outcomes.

Discussion

The present study focuses to find a connection between the beneficiaries’ preferences and associated factors of PMJDY, a welfare scheme. The associated factors encompass the service delivery and outcome attributes. The delivery attributes include the dimensions that emphasize the delivery process of the welfare scheme. This includes five attributes namely; reliability, assurance, tangibility, empathy and responsiveness. Alternatively, the outcome factors focus on the results of the scheme and comprise the elements such as convenience, social and economic connects. Customer preference is a categorical variable and manifests the impression of the slum dwellers towards PMJDY. The customer preferences towards the scheme i.e. low, neutral and high are affected by the associated service delivery and outcome factors of the scheme. The multinomial logistic regression model is applied to find the association between customer preference level and functional dimensions. The empirical results suggest that the reliability, assurance, tangibility and social connect elements are significant and may drive the beneficiaries towards a higher category of satisfaction. The findings indicate that those slum dwellers who are neutral towards the scheme at present may be highly satisfied in the future provided the scheme improve its reliability, tangibility and social connect factors. They believe that the scheme lacks some delivery process issues such as reliability and tangibility. The reliability and tangibility are process dimensions whereas social connect is included under outcome factors. The reliability factor highlights the promptness and accuracy of service as desired while tangibility emphasizes the comfortable and prompt service delivery. The beneficiaries believe that in the future if the authorities/the service providers improve the reliability and comfortable service delivery they may rate the welfare scheme under the higher preference category. The social connect element under the outcome factors of the scheme also prohibits the beneficiaries from being highly satisfied. They consider the social impact and sustainability of the scheme are important, and once these are improved in the future they will be highly satisfied. The assurance (i.e. conveying trust and confidence) as a delivery process dimension of the scheme also influences the beneficiaries. However, improvement of this element is unlikely to obtain higher satisfaction towards the scheme.

The empirical results show that 27% of slum beneficiaries dislike the financial inclusion intervention like PMJDY. However, it is found that none of the predictors can significantly influence this group of beneficiaries towards a higher level of preference. The findings suggest that the beneficiaries whose preference level towards the scheme is low at present are not impressed with the delivery and outcome dimensions of the welfare programme. Even the improved process and outcome factors may not affect them and influence them towards a higher degree of happiness. They have availed the PMJDY as a government scheme but believe that it fails to change any of their prospects.

The findings of the research are significant for policymaking and programme implementation. The welfare schemes launched by the public authorities are meant for underprivileged sections of society. The success and failure of the social schemes are based on stakeholders’ acceptance and factors influencing the same. The delivery process and outcome factors discussed in the study are the determinants of customer preference. The delivery process dimensions focus on the effective implementation of the social scheme on the ground by the service providers. However, the outcome factors emphasize the eventual results of the social initiative including economic prosperity. The empirical findings suggest that reliability, assurance and tangibility are the significant delivery process dimensions that influence the beneficiaries’ preference toward PMJDY. Similarly, social connect is a significant contributor to outcome factors that drive beneficiaries to obtain a higher level of satisfaction. The policymakers should consider these factors while designing and evaluating the performance of welfare schemes.

6. Conclusion

The study is an attempt to examine the impact of associated factors of the government-sponsored PMJDY welfare scheme on customer perception. The study investigates the slum dwellers’ perspective and includes the beneficiaries of the scheme from the city slums of Bhubaneswar for empirical analysis. The research design includes customer perception and functional determinants as the dependent and independent variables respectively. The combined factors on service delivery and scheme outcomes are symbolized as functional determinants. The service delivery process dimensions include—reliability, assurance, tangibility, empathy and responsiveness attributes. The outcome factors emphasized the results of the social scheme and comprise—convenience, social and economic connections. Since customer perception is a categorical variable with low, neutral and high levels of perceptions, a multinomial logistic regression is used for empirical analysis. The findings suggest that the factors such as reliability, tangibility and social connect are significantly differentiating the beneficiaries between the neutral and higher category of satisfaction. The beneficiaries believe that the improvement of these factors of PMJDY in the future may drive them towards a higher degree of satisfaction. However, slum households consider assurance as a significant factor in the welfare scheme delivery process but that may not change their perception. The empirical analysis also found that none of the functional factors significantly influence the beneficiaries who perceive the PMJDY as an inferior scheme. This group of beneficiaries within the slum dwellers is believed to be inflexible in their perception of the scheme although there is an improvement in the delivery process and scheme outcomes.

The model results hold the hypothesis that the associated factors of the welfare scheme manipulate the customer perception towards the social scheme like PMJDY. The findings indicate that the financial inclusion initiatives taken by the government should improve some elements to bring efficiency to the service delivery process and create long-term opportunities for the beneficiaries. PMJDY is an initiative to empower underprivileged people to grow economically and sustain a happy and comfortable living. A social scheme like PMJDY develops trust towards the government and support for economic growth and equality. The present study adopted a snowball sampling technique and included 630 slum dwellers for empirical investigation. Therefore the sample may not represent the complete slum population of Bhubaneswar. The study is important from a socio-economic perspective and generates larger scope for further research in the domain of social science. Further research may be extended to other cities in India with different focused groups. The customer perception study of all government-sponsored welfare schemes with comparative analysis may be a topic for further investigation. The empirical analysis of the impact of the social welfare programmes on economic development and inequality may also give insights for policy research and execution.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is a part of the sponsored research project funded by ICSSR New Delhi under Impactful Policy Research in Social Science (IMPRESS) scheme. The author acknowledges the contribution of ICSSR New Delhi.

Appendix I

Notes

References

Abdullah

, Jan

M. T.

, & Manaf

N. H. A.

(2012). A structural equation modelling approach to validate the dimensions of SERVPERF in airline industry of Malaysia. International Journal of Engineering and Management Sciences, 3(2), 134–141.

Aduda

, & Kalunda

(2012). Financial inclusion and financial sector stability with reference to Kenya: A review of literature. Journal of Applied Finance and Banking, 2(6), 95.

Aghion

, & Bolton

(1997). A theory of trickle-down growth and development. The Review of Economic Studies, 64(2), 151–172.

Alam

M. S.

, & Mondal

(2019). Assessment of sanitation service quality in urban slums of Khulna city based on SERVQUAL and AHP model: A case study of railway slum, Khulna, Bangladesh. Journal of Urban Management, 8(1), 20–27.

Anbori

, Ghani

S. N.

, Yadav

, Daher

A. M.

, & Su

T. T.

(2010). Patient satisfaction and loyalty to the private hospitals in Sana’a, Yemen. International Journal for Quality in Health Care, 22(4), 310–315.

Babakus

, & Boller

G. W.

(1992). An empirical assessment of the SERVQUAL scale. Journal of Business Research, 24(3), 253–268.

Banerjee

A. V.

, & Newman

A. F.

(1993). Occupational choice and the process of development. Journal of Political Economy, 101(2), 274–298.

Beck

, Levine

, & Levkov

(2010). Big bad banks? The winners and losers from bank deregulation in the United States. Journal of Finance, 65(5), 1637–1667.

Bhattacharya

, Saha

, & Banerjee

(2016). Income inequality and the quality of public services: A developing country perspective. Journal of Development Economics, 123, 1–17.

10.

Bobko

, Roth

P. L.

, & Buster

M. A.

(2007). The usefulness of unit weights in creating composite scores: A literature review, application to content validity, and meta-analysis. Organizational Research Methods, 10(4), 689–709.

11.

Bruhn

, & Love

(2014). The real impact of improved access to finance: Evidence from Mexico. Journal of Finance, 69(3), 1347–1376.

12.

Burgess

, & Pande

(2005). Do rural banks matter? Evidence from the Indian social banking experiment. The American Economic Review, 95(3), 780–795.

13.

Carman

J. M.

(1990). Consumer perceptions of service quality: An assessment of the SERVQUAL dimensions. Journal of Retailing, 66(1), 33–35.

14.

Cavana Robert

, Corbett Lawrence

, & Lo

Y. L.

(2007). Developing zones of tolerance for managing passenger rail service quality. International Journal of Quality & Reliability Management, 24(1), 7–31.

15.

Chou

C.-C.

, Liu

L.-J.

, Huang

S.-F.

, Yih

J.-M.

, & Han

T.-C.

(2011). An evaluation of airline service quality using the fuzzy weighted SERVQUAL method. Applied Soft Computing, 11(2), 2117–2128.

16.

Chuah

S. H.

, & Hilmi

M. F.

(2011). Exploring service quality, customer satisfaction and customer loyalty in the Malaysian mobile telecommunication industry. 011 IEEE Colloquium on Humanities, Science and Engineering, Research (CHUSER 2011), December 5–6, 2011, Penang. 10.1109/CHUSER.2011.6163832

17.

Chuenyindee

, Kester

S. O. A.

, Ramos

J. P.

, Prasetyo

Y. T.

, Nadlifatin

, Kurata

Y. B.

, & Sittiwatethanasiri

(2022). Public utility vehicle service quality and customer satisfaction in the Philippines during the COVID-19 pandemic. Utilities Policy, 75, 101336.

18.

CRISIL

(2018). Financial inclusion surges driven by Jan-Dhan Yojana. Vol. IV.

19.

Cronbach

L. J.

(1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.

20.

Cronin Jr

J. J.

, & Taylor

S. A.

(1992). Measuring service quality: A reexamination and extension. Journal of Marketing, 56(3), 55–68.

21.

Cronin Jr

J. J.

, & Taylor

S. A.

(1994). SERVPERF versus SERVQUAL: Reconciling performance-based and perceptions-minus-expectations measurement of service quality. Journal of Marketing, 58(1), 125–131.

22.

Dawes

R. M.

(1979). The robust beauty of improper linear models in decision making. American Psychologist, 34(7), 571.

23.

Demirguc-Kunt

, Beck

, & Honohan

(2008). Finance for all? Policies and pitfalls in expanding access. The World Bank.

24.

Demirguc-Kunt

, Klapper

, & Singer

(2013). Financial inclusion and legal discrimination against women: Evidence from developing countries. The World Bank.

25.

Doverspike

, Winter

, Healy

, & Barrett

(1996). Simulations as a method of illustrating the impact of differential weights on personnel selection outcomes. Human Performance, 9(3), 259–273.

26.

Edward

, & George

(2008). Tourism development in the state of Kerala, India: A study of destination attractiveness. European Journal of Tourism Research, 1(1), 16–38.

27.

Fraser

M. W.

, & Wu

(2016). Measures of consumer satisfaction in social welfare and behavioral health: A systematic review. Research on Social Work Practice, 26(7), 762–776.

28.

Grönroos

> (1982). Strategic management and marketing in service sector. Marketing Science Institute.

29.

Grönroos

(1983). Innovative marketing strategies and organization structures for service firms. Emerging Perspectives on Services Marketing, 1983, 9–21.

30.

Grönroos

(1990). Service management and marketing: Managing the moments of truth in service competition. Lexington Books

31.

Jain

S. K.

, & Gupta

(2004). Measuring service quality: SERVQUAL vs. SERVPERF scales. Vikalpa, 29(2), 25–38.

32.

Kano

(1984). Attractive quality and must-be quality. Hinshitsu (Quality, The Journal of Japanese Society for Quality Control), 14, 39–48.

33.

Kempson

, Whyley

, Caskey

, & Collard

(2000). In or out. Financial exclusion: a literature and research review. Consumer Policy and Research, Financial Services Authority.

34.

Lehtinen

, & Lehtinen

J. R.

(1991). Two approaches to service quality dimensions. Service Industries Journal, 11(3), 287–303.

35.

Levine

(2005). Finance and growth: Theory and evidence. Handbook of Economic Growth, 1, 865–934.

36.

Meesala

, & Paul

(2018). Service quality, consumer satisfaction and loyalty in hospitals: Thinking for the future. Journal of Retailing and Consumer Services, 40, 261–269.

37.

Mikhaylov

, Gumenyuk

, & Mikhaylova

(2015). The SERVQUAL model in measuring service quality of public transportation: Evidence from Russia. Quality - Access to Success, 16(144), 78–83.

38.

Mohan

(2006). Economic growth, financial deepening, and financial inclusion. Economic developments in India: Monthly Update, Volume-108 Analysis, Reports, Policy Documents, 41.

39.

Munim

Z. H.

, & Noor

(2020). Young people’s perceived service quality and environmental performance of hybrid electric bus service. Travel Behaviour and Society, 20, 133–143. 10.1016/j.tbs.2020.03.003

40.

Narayan

, Rajendran

, Sai

L. P.

, & Gopalan

(2009). Dimensions of service quality in tourism–an Indian perspective. Total Quality Management, 20(1), 61–89.

41.

Nimbrayan

P. K.

, Tanwar

, & Tripathi

R. K.

(2018). Pradhan Mantri Jan Dhan Yojana (PMJDY): The biggest financial inclusion initiative in the world. Economic Affairs, 63(2), 583–590.

42.

Nutsugbodo

R. Y.

(2013). Tourists’ perceptions of the quality of public transportation services in the Accra metropolis: A SERVQUAL approach. African Journal of Hospitality, Tourism and Leisure, 2(4), 1–8.

43.

Ocampo

, Alinsub

, Casul

R. A.

, Enquig

, Luar

, Panuncillon

, Bongo

, & Ocampo

C. O.

(2019). Public service quality evaluation with SERVQUAL and AHP-TOPSIS: A case of Philippine government agencies. Socio-Economic Planning Sciences, 68, 100604.

44.

Onaolapo

A. A.

, Kajola

S. O.

, & Nwidobie

M. B.

(2015). Determinants of capital structure: A study of Nigerian quoted companies. Methodology, 7, 23.

45.

Pakdil

, & Aydın

Ö.

(2007). Expectations and perceptions in airline services: An analysis using weighted SERVQUAL scores. Journal of Air Transport Management, 13(4), 229–237.

46.

Palepu

A. R.

(2020). Glitches in Aadhaar enabled payment systems amplified as transactions surge’. Bloomberg Quint, 6.

47.

Pande

, Schaner

, Moore

C. T.

, & Stacy

(2020). A majority of India’s poor women may miss COVID-19 PMJDY cash transfers. Brief, Economic Growth Center, Yale University. https://egc.yale.edu/sites/default/files/COVID%20Brief.pdf

48.

Paquette

, Cordeau

J.-F.

, & Laporte

(2009). Quality of service in dial-a-ride operations. Computers & Industrial Engineering, 56(4), 1721–1734.

49.

Parasuraman

, Zeithamal

, & Berry

(1985). A conceptual model of service quality and its implications for future research. The Journal of Marketing, 49(4), 41–50.

50.

Parasuraman

, Zeithamal

, & Berry

(1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.

51.

Rahman

, Das

, Hadiuzzaman

, & Hossain

(2016). Perceived service quality of paratransit in developing countries: A structural equation approach. Transportation Research Part A: Policy and Practice, 93, 23–38. 10.1016/j.tra.2016.08.008

52.

Rastogi

, & Ragabiruntha

(2018). Financial inclusion and socioeconomic development: Gaps and solution. International Journal of Social Economics, 45(7), 1122–1140.

53.

Ray

K. K.

(2021). Slum dwellers perception towards Ujjwala Yojana, a government-lead LPG intervention programme in India: An empirical investigation. Journal of Social Inclusion Studies, 7(2), 153–171.

54.

Ree

, Carretta

, & Earles

(1998). In top-down decisions, weighting variables does not matter: A consequence of Wilk’s theorem. Organizational Research Methods, 1(4), 407–420.

55.

Reichel

, Lowengart

, & Milman

(2000). Rural tourism in Israel: Service quality and orientation. Tourism Management, 21(5), 451–459.

56.

Richard

M. D.

, & Allaway

A. W.

(1993). Service quality attributes and choice behavior. Journal of Services Marketing, 7(1).

57.

Rust

R. T.

, & Oliver

R. L.

(1994). Service quality: Insights and managerial implications from the frontier. In Rust

R. T.

& Oliver

R. L.

(Eds.), Service quality: New directions in theory and practice (pp. 1–19), SAGE Publications.

58.

Sam

E. F.

, Hamidu

, & Daniels

(2018). SERVQUAL analysis of public bus transport services in Kumasi metropolis, Ghana: Core user perspectives. Case Studies on Transport Policy, 6(1), 25–31.

59.

Sangwan

S. S.

(2017). Implementation and impact of financial inclusion in India: Village studies in Punjab & Haryana. Prajnan, 46(1), 75–95.

60.

Sharma

(2016). Nexus between financial inclusion and economic growth: Evidence from the emerging Indian economy. Journal of Financial Economic Policy, 8(1).

61.

Sharma

, Giri

, & Chadha

(2016). Pradhan Mantri Jan Dhan Yojana (PMJDY) Wave III Assessment. MicroSave. http://www.microsave.net/files/pdf/PMJDY_Wave_III_Assessment_MicroSave.pdf

62.

Şimşekoğlu

Ö.

, Nordfjærn

, & Rundmo

(2015). The role of attitudes, transport priorities, and car use habit for travel mode use and intentions to use public transportation in an urban Norwegian public. Transport Policy, 42, 113–120.

63.

Singh

V. K.

, & Ghosh

(2021). Financial inclusion and economic growth in India amid demonetization: A case study based on panel cointegration and causality. Economic Analysis and Policy, 71, 674–693.

64.

Somanchi

(2020). Covid-19 relief: Are women Jan Dhan accounts the right choice for cash transfers. Ideas for India.

65.

Stillwell

, Barron

, & Edwards

(1983). Evaluating credit applications: A validation of multiattribute utility weight elicitation techniques. Organizational Behavior and Human Performance, 32(1), 87–108.

66.

Swamy

(2014). Financial inclusion, gender dimension, and economic impact on poor households. World Development, 56, 1–15.

67.

Tan

K. C.

, & Pawitra

T. A.

(2001). Integrating SERVQUAL and Kano’s model into QFD for service excellence development. Managing Service Quality: An International Journal, 11(6), 418–430.

68.

Tiglao

N. C.

, De Veyra

J. M.

, Tolentino

N. J.

, & Tacderas

M. A.

(2020). The perception of service quality among paratransit users in metro Manila using structural equations modelling (SEM) approach. Research in Transportation Economics, 83, 100955. 10.1016/j.retrec.2020.100955.

69.

Tumsekcali

, Ayyildiz

, & Taskin

(2021). Interval-valued intuitionistic fuzzy AHP-WASPAS based public transportation service quality evaluation by a new extension of SERVQUAL model: P-SERVQUAL 4.0. Expert System with Applications, 186, 115757. 10.1016/j.eswa.2021.115757

70.

Wang

, Lo

, & Hui

Y. V.

(2003). The antecedents of service quality and product quality and their influences on bank reputation: Evidence from the banking industry in China. Managing Service Quality, 13(1), 72–83.

71.

World Bank (2014). Global financial development report: Financial inclusion.

72.

Yaya

L. H. P.

, Fortià

M. F.

, Canals

C. S.

, & Marimon

(2014). Service quality assessment of public transport and the implication role of demographic characteristics. Public Transport, 1–20. 10.1007/s12469-014-0099-7

Customer Perception	Functional Determinants
	Delivery Process Factors					Outcome Factors
	Reliability	Assurance	Tangibility	Empathy	Responsiveness	Convenience	Social Connect	Economic Connect
Perception towards the scheme (PMJDY)	Reliable service performed accurately as desired	Conveying trust and confidence through knowledge & skills	The physical appearance of the facilities, material etc.	The caring and personalized attention	Prompt services and ready to address issues	Comfortable living and productive use of resources	Social impact and improved status & engagement	Economic opportunities and potential of better standard of living
• The scheme gives me fulfilment of life. • Happy that the government has come with this scheme. • The scheme is sustainable in future.	• Scheme is served as declared. • Reliability of the service rendered. • Services rendered without any mistake. • Promptness in getting the desired service.	• Courteous service. • Genuine and safe transactions. • The latest information of the scheme shared. • The queries are resolved with confidence and knowledge.	• Comfortable ambiance at the service centre • Physical appearance of the deliverable is good • Appearance of personnel at the service centres	• Dealing in a caring fashion • Having best interests of customer • Convenient business hours to render service	• Prompt services provided • Helpful in handling requests • Responsive to all our issues	• The scheme makes our life easy. • Dedicating more time for productive work • Enhanced aspiration of life	• Part of the main stream • Government is concerned about our well-being • Social status is improved. • Referred the scheme to others • Socially sustainable scheme • Strong social impact	• Financial benefit of the scheme is better than others. • Reasonable cost • Affordable own contribution • Economically viable for future • Personal budgetary control • Scope to save for future