Financial Inclusion in India: A Decade of Systematic Review

Dr. Manish Panchasara1 , Dr. Vivek Sharma2 , Dr. Ritu Joshi 3


Journal of Inclusive Finance and Social Policy

Vol. 1 No. 1, pp 21-38 | © Sahulat Microfinance Society

Financial Inclusion (FI) has an undeviating association with poverty alleviation. Despite its significance and noble intentions, FI has greatly remained an ignored area in behavioral science literature. This study depends on a systematic review of the literature on FI. This included 135 research articles published from 2007 to 2017 directly relevant to India. This survey aims to examine the existence of gaps in the literature of FI, particularly in assessing the involved behavioral aspects, and to raise awareness of the concept. This will subsequently create the foundation for behavioral theories and science to creep in. The study depends on content analysis revealing that authorities overlooked an approach towards behavior change, both at the beneficiaries and those stretching out benefits to the recipients. Finally, the implications of these results are addressed to policymakers, behavioral scientists, and marketers.

Keyword: Stock Market, Market Capitalization, Multiple Regression, Multicollinearity.


It is obligatory to conduct a review of the analyses to comprehend the scientific enhancement and accomplishment in the study area to understand the development of the knowledge (Williams & Plouffe, 2007). The progress made in the respective area of study can be better understood by systematically reviewing and identifying the gaps and further expanding prior studies (Creswell & Creswell,

  1. Co-Founder and Director, SkillsEdge, Ex-Officer, State Bank of India, FDPM, IIM Ahmedabad, India. Email:
  2. Associate Professor, Institute of Management Studies, Devi Ahilya Vishwavidyalaya, Indore, Madhya Pradesh, India.
  3. Professor, Institute of Management Studies, SAGE University, Kailod Kartal, Rau Byepass, Indore, Madhya Pradesh, India.

2017). It will further help in evaluating the attempts made by the researchers in examining the application of the theories, the adopted methodologies, and the domain in which the research is carried out (Krippendorff, 2018; Hesse-Biber, 2010). Although the process of systematic review is time-consuming, owing to the involvement of a sufficient amount of data and analysis, providing the prospect for further studies with respect to progress in theory and methods (Williams & Plouffe, 2007).

As noted by the World Bank (2018), “Financial Inclusion has emerged as a critical development challenge and is a hot topic among policymakers, development practitioners and the private sector.” The rationalization of FI is found in one of the seven “Sustainable Development Goals” of the United Nations. According to the latest World Bank report, about 1.7 billion adults are unbanked, i.e., without having a bank account at a financial institution and nearly half of the world population live in seven developing nations, including India. Possessing a bank account increases the probability of acquiring access to credit from financial institutions like bank (Ellis, Lemma, & Rud, 2010). It also helps in enabling the poor to mitigate economic shock related to meeting any unforeseen circumstances such as sickness or loss of employment (Collins et al., 2010). Interest in financial inclusion has increased the attention of scholars as it has a direct linkage with poverty alleviation and economic well-being (Beck, Demirgüç-Kun, & Levine, 2007; Inoue & Hamori, 2012).

Interest in FI led to scholars proposing many academic reviews. Friedline and Rauktis (2014) reviewed 45 years of the literature which included 60 studies covering the saving habits of adults and their role played in the family, the role played by institutional players, and the policy ramifications. Different domains of financial inclusion like habits of savings, insurance, financial literacy, extending banking services to excluded poor and protection of consumer rights were disclosed by Yawe and Prabhu (2015). Shah and Dubhashi (2015), in reviewing the selected papers, had focused on initiatives taken by the country government. Brief details of the systematic reviews mentioned above are provided in table 1 below

Table 1: Details of the Systematic Reviews

Study Focus of Review Explanations
Yawe & Prabhu, 2015 The study covered the areas of FI like extending the banking services to the excluded and consumer protection and emphasized innovative banking. The study praised the use of mobile banking and technological innovations for FI implementation, but it also expressed the concern about the directive under which the mobile service providers operate. It emphasized on the need for establishing an institutional framework that comprises of regulators of telecommunications and financial institutions for expanding financial inclusion. The study referenced the measures recommended by the Alliance for Financial Inclusion (an international network of policymakers for financial inclusion) for expanding financial services.
Friedline & Rauktis, 2014 This study summarizes the importance of youth, role of family and financial institutions. This study is based on four decades of research on FI, which proclaims the need to extend the bank saving facility at an early stage. To achieve this goal, a new concept of “child saving account” is introduced. Although the study is reticent about the features of the scheme, it emphasizes the establishment of a dialog between researchers and policymakers to provide opportunities for saving, especially among the youth.
Shah & Dubhashi, 2015 The primary objective of the study is to discover the contribution made by FI in the inclusive growth of the country. Secondly, assessing the combined efforts of the Government of India and Reserve Bank of India in enhancing the growth of FI. This study focused only on raising awareness of financial products among the masses of people. Furthermore, research recommended that banks and financial institutions should be emphasized that the responsibility lies with the banks and financial institutions. It emphasized the coordination of the efforts put forth to implement FI by microfinancial institutions, nongovernmental organizations and local communities. It also led to the importance of technology to improve the reach of financial institutions.

One such appellant perspective that is completely missing in previous reviews which targeted the FI, i.e., recommending an approach to social change that targets individuals and communities to change behavior. This contributes to the originality of the current research.


Criteria for Search and Selection Procedure

In order to meet the required criteria, the current research accessed EBSCO, JSTOR, Google Scholar and Scopus websites using the key words, “financial inclusion”, “poverty alleviation”, “economic growth”, “financial growth”, “micro finance”, and “savings habit among poor”. The criteria for the review are essential for the systematic phase. In the initial phase, 551 research articles were therefore found to be relevant, out of which 135 articles corresponded to the research idea of the author. Although the study area was extensive, the scope of the author’s research was still discernible, resulting in the final selection of 135 articles.

Coding Process

Each article selected by the author is reviewed by preparing a database based on scrutinizing the processes and methodologies applied in the articles. Thus resorting to the coding procedure helped in seeking the efforts made by the researchers especially in addressing the more integral aspects like awareness and behavior (of individuals) related to FI. Each article was sorted and coded with the help of research focus, authorship and publication, place of study, contribution by institution, subjects based on upstream or downstream influencers, statistical methods applied, and the sources of data. The main objectives of the study that were coded under “focus of study “are agriculture, awareness, financial literacy, gender, measuring FI, the status of financial inclusion, poverty, and technology. Under the dimension of the “Authorship and Publication,” authorship was coded as “co-authorship” and “country of Author’s residence”. The extent of the significance given to the subject in the publication to the listed journals, “category of journal” was coded as “A,” “B,” “C,” “UGC Listed” (University grant commission) and “Not categorised.” Coding for “place of study” was done either as a “Village or City”, and “Province / State.” With the inclusion of this dimension, focus was put forth on the academicians that mainly targeted the empirical studies. The author also contemplated focus on “upstream and downstream influencers” to understand the effect of FI on the population and the efforts made by implementing authorities. “Data Analytical Tool” took one of the following codes like Analysis of Variance (ANOVA), CHAID (chi-square automatic interaction detection) & CRT (classification and regression trees), chi-square, correlation, descriptive analysis, factor analysis, principal component analysis, regression and structural equation modeling (SEM). Finally, the dimension of data was coded as “data type” (either primary, or secondary), “sampling units” (Households, individuals, and bank officials), and “source of data” (central bank of the country, international organizations, census, and government departments).


Results obtained after the ultimate selection of 135 research articles are categorized as follows:

  1. Focus of Study
  2. Authorship and Publication
  3. Importance given to the place of Study and the Influencers
  4. Application of Data Analytical Tools
  5. Type of data, its collection technique, and its sources

Focus of Study

After reviewing the selected papers, the areas of focus that were found to be prominent are “Analyzing the Status of Financial Inclusion” (n=37), “Banking and Financial services” (n=30), and “Relative Importance of Measuring Financial Inclusion Index” (n=19). When combined, these areas together formed 63 percent of the studies carried out in all. Creating awareness among the beneficiaries and users forms (n=5) only 3 percent of total studies. Several exploratory studies have also emerged which has explained in detail the role of FI (Kapoor, 2014), factors influencing outreach (Ghosh, 2012), ways in which money is managed by the poor (Lahiri-Dutt & Samanta, 2013) and searching for a pattern in informal savings of poor (Goedecke et al., 2018). After the development of the index on financial inclusion (FII) (Sarma, 2008), which measures financial inclusion, the topic found a prominent place in the study among the researchers (n=19).

Table 2: Focus of Study

Focus of Study 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total
Cropping pattern &
Agriculture growth 0 0 0 0 0 0 1 1 0 0 0 2
Awareness among Household & beneficiaries 0 0 0 0 0 0 1 1 1 1 1 5
Banking and Financial Services 2 1 2 0 2 3 1 2 12 4 1 30
Challenges and opportunities exist 0 0 0 0 0 0 1 1 1 0 0 3
Exploring the factors influencing economic growth 0 0 0 0 0 2 2 1 3 1 4 13
Importance of Financial Literacy 0 0 0 0 0 1 0 1 1 1 0 4
Significance of Gender 0 0 0 0 0 1 1 0 0 0 3 5
Role of Government in taking various initiatives 0 0 0 0 0 0 0 1 1 1 0 3
Relative importance of Financial Inclusion Index 0 0 0 0 2 2 3 0 1 4 7 19
Poverty Alleviation 0 0 0 0 0 1 0 0 0 0 1 2
Analyzing the Status of financial inclusion 0 1 0 2 3 3 8 0 11 1 8 37
Application of Technology 0 0 0 1 1 1 1 1 3 2 2 12
TOTAL 2 2 2 3 8 14 19 9 34 15 27 135

Domains that explored and influenced the FI, along with the technological innovations, were assessed by the researchers. Registration of these topics enhanced since the year 2012. Topics like creating awareness, financial literacy, the role of government and poverty alleviation were, however, unable to find prominence in the studies. The majority of the studies (57 percent) were conceptual, and it was found that researchers reported less on the empirical studies.

Authorship and Publication

An interesting trend emerged when a journal was coded based on the category of the journal, when the author is academician, non-academician, or formed collegiality locally or with overseas authors. When collegiality with foreign authors is established, the probability of the paper getting published in the reputed journal (category “A,” “B” or “C” of ABDC list) got increased. This intellectual integration not only improved the research quality but was also helpful in getting the paper published in listed journals.

Table 3: Details of year-wise publications

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total
a 0 0 0 0 0 1 4 0 2 1 1 9
B 2 0 0 2 1 5 2 1 0 2 2 17
C 0 1 1 1 0 1 2 1 0 2 3 12
UGC, List 0 0 1 0 2 3 0 2 11 4 5 28
Not Categorised 0 1 0 0 5 4 11 5 21 6 16 69
Total 2 2 2 3 8 14 19 9 34 15 27 135

Out of 135 articles reviewed, 117 were academics, and 18 were non-academics. Owing to the compulsion of publishing involved, contribution by academicians formed the majority in publication. Non-academicians, however, are also interested in the subject, especially the non-academicians from the Reserve Bank of India (Ghosh, 2012; Ghosh & Günther, 2018; Ghosh, 2017; Ghosh & Vinod, 2017) contributed more. Articles that were academics formed collegiality with foreign authors in four incidences. Only one incidence was observed in non￾academics which a foreign author co-authored. There are 52 (40 percent) such incidences where the articles were singly authored, and 83 (60 percent) were co-authored. The basis for forming collegiality among the authors are empirically driven (40 percent); secondly, the authors belong to the same city (85 percent), and lastly, banking on different skills not only improves the quality of research but also increases the chances of getting funds for the project (Jones et al., 2007).

Importance given to the Place of Study and Influencers

Maximum studies that were carried out in the country were observed in the state of Tamil Nadu (n=10). Among the Southern states, seven studies were conducted in Andhra Pradesh. Researchers were attracted to Andhra Pradesh as a place of study because this state was known for its FI initiatives and flourishing microfinance institutions (MFI). One such exciting fact observed among the empirical studies that were conducted in Andhra Pradesh was that all the studies found a place in the ABDC list.

In most cases, the study was focused on the rural areas to ensure the status of the implementation of FI. The northeast region of India comprises the seven sister states, namely Arunachal Pradesh, Manipur, Mizoram, Meghalaya, Nagaland, Tripura, and Sikkim of the country jointly contributed to 13 numbers of the studies. Among these 13 studies, five studies were targeted in the state of Tripura. Studies in these states were concentrated on the factors influencing the extent of FI and explored the relationship between the awareness and the impact of FI. In Maharashtra, the city of Mumbai was found to be the most attractive to researchers due to the existence of the largest slum areas, which thereby helped in determining the impact of technology on FI. No studies were undertaken in the states of Chhattisgarh, Rajasthan, and Uttarakhand, and only minimal studies (1 or 2) were observed in the states of Bihar, Haryana, Himachal Pradesh, Karnataka, Madhya Pradesh, Orissa and Punjab.

Implementation of FI led to a broader view directed towards the excluded and policymakers, which were considered for coding. Goldberg (1995) has referred to this approach as “downstream influencers”, “midstream influencers” and “upstream influencers” respectively. Households and individuals formed the part of downstream influencers, implementing agencies like banks, financial institutions and quasi-government bodies are referred to as midstream influencers, whereas policymakers and government authorities are referred to as upstream influencers. Downstream influencers dominated the studies with 35 percent (n=47) incidences and midstream with only 9 percent (n=12), rest in 76 cases no influencers were reported.

Table 4: State-wise concentration of studies

1 A & N ISLANDS # 0
9 DAMAN & DIU # 0
10 Delhi 1
11 GOA 0
36 North-East 13

# Union Territory

The synergy between the type of data and analytical tools is also observed. Table 6 below revealed that the studies that were more descriptive in nature were based on secondary data sources. Descriptive studies, however, do not reflect the real case scenario. Moreover, empirical studies have the ability to capture the aspects of the concept that are not possible through secondary research. Since the demographic profile of Indian states is very different, it would be prudent for the researchers (both academicians and non-academicians) to conduct more empirical studies for capturing the prevailing social and cultural factors. Secondly, it will also serve the purpose of directing the studies on the respondents belonging to upstream influencers. Furthermore, it is necessary to record their motivations, attitudes and behavioral aspects that will lead to the success of the FI.

Table 5: Details of study involved type of influencers

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total
Upstream 1 0 0 1 1 0 1 0 4 4 0 12
Downstream 1 2 1 2 1 4 5 3 13 3 12 47
No Influencers 0 0 1 0 6 10 13 6 17 8 15 76
Total 2 2 2 3 8 14 19 9 34 15 27 135

Application of Data Analytical Tools

The conclusion of the hypothesis is obtained after applying the statistical tools to the collected data. These tools can be applied in many ways. Researchers, in their analysis for empirical studies, had applied several statistical tools. In the studies, the descriptive analysis (36 percent) was dominated among the analytical tools. Mere percentage analysis and graphical representation of data formed the basis for such analysis. Moreover, researchers also resorted to data analytical tool like Regression (16 percent) and analysis of variance (8 percent). However, more robust analytical tools like Structure Equation Modeling (SEM) (0.75 percent), 31 Correlation (4 percent), Factor Analysis (4 percent) and Principal Component Analysis (6 percent) generally form the basis for analysis in psychological and behavioral studies were least applied. Since 2011, an increase in the application of data analytical tools started picking up, and the trend has been most visible since then. Surprisingly, there were 31 studies where no tools were applied, marked as “Not Applied” in the table.

Table 6: Year-wise trend of data analytical tools applied on study

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total
ANOVA 0 0 0 0 1 1 0 0 4 3 2 11
CHAID 0 0 0 0 0 0 0 0 1 0 0 1
Chi-square 0 0 0 0 0 0 1 0 1 0 0 2
Correlation 1 0 0 0 0 1 1 0 1 1 0 5
Descriptive 1 0 1 2 5 3 11 5 12 4 4 48 1 0 1 2 5 3 11 5 12 4 4 48
FA 0 0 0 0 0 0 0 0 3 1 2 6
PCA 0 0 0 0 0 0 0 1 2 1 4 8
Regression 0 0 0 0 1 7 2 1 3 2 6 22
SEM 0 0 0 0 0 0 0 0 0 0 1 1
Not Applied 0 2 1 1 1 2 4 2 7 3 8 31 0 2 1 1 1 2 4 2 7 3 8 31
Total 2 2 2 3 8 14 19 9 34 15 27 135

Ignoring the “Descriptive analysis” and “Not Applied” category from the above table, the studies conducted from the year 2008 to 2010 did not use any other analytical tools. Similar case was identified with Factor and Principal Component Analysis where their applicability was missing till the year 2013. A sophisticated technique like SEM was observed only in one of the incidences in 2017. Descriptive and Regression remain the popular tools among researchers during this decade. Factor analysis, PCA and SEM are the statistical tools that are applied in evaluating the psychological and behavioral aspects of individuals. The application of these statistical tools observed in lower percentages indicates that the researchers were not motivated enough to target the behavioral aspects of the people.

Data collection and its sources

The studies used empirical analysis comprising of both primary and secondary data collection. These studies were led by secondary (52 percent) data followed by primary data (28 percent). Primary data collection is not easy, requiring time, effort, and cost. Finding a sponsor is also challenging. Within the empirical studies where primary data collection was used, sample units were FI beneficiaries, bank customers and households. Foreign authors relied mainly on secondary data, whereas the resident authors depended on the survey. Analysis based on secondary data found their significance in the listed journal, including the ABDC list. Due to the compulsions of publishing among the academicians and their easy availability of secondary data sources, researchers were attracted to secondary data. A list of different data sources relied upon by the researchers to conduct their study is listed in Table 7.

Table 7: Year-wise details of data sources

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total
Census 0 2 0 0 0 1 2 1 1 0 2 9
Financial Institution 0 0 0 0 0 1 4 2 4 2 1 14
Government 0 0 0 0 0 2 2 1 3 3 5 16
International Sources 0 0 0 0 4 2 2 2 3 2 1 16
IRDA 0 0 0 0 0 0 1 0 0 0 0 1
NABARD 0 1 1 0 2 1 4 0 4 1 0 14
NSSO 0 1 0 0 2 1 2 0 0 2 2 10
RBI 1 2 1 1 2 4 9 0 6 3 8 37
BSR 0 1 0 0 0 1 1 1 4 1 2 11
OTHERS 0 0 1 1 1 1 1 0 4 1 3 13
Total 1 7 3 2 11 14 28 7 29 15 24 141

Note: A study can have more than one data source.

Also, the studies from the secondary data tried to establish the relationships between FI, economic growth and inequality. However, establishing such relationships requires longitudinal data on financial inclusion measures. Until recently, data for the FI were not available at a comparable global level, thereby limiting their ability to evaluate their impact. Data of FI from financial institutions have been available to select economies as part of the International Monetary Fund (IMF) Financial Access Survey, beginning in the year 2004. A similar case was observed in India as more refined data was available after the banking operations were shifted to the digital platform. The relationship between these variables is not independent of the secondary data and this type of analysis also does not capture the integral aspects of individuals and culture.


The studies were mostly focused on documenting the status of the FIs that existed at the time. However, some were also designed to demonstrate the importance of the role performed by banking and financial institutions in the implementation of FIs. Similarly, the development of the FI index, which was based on Sarma’s (2008) seminal work, led to an increase in the number of research using such indexes. Furthermore, because economic activity has a significant correlation with FI implementation, it was obvious for researchers to broaden their focus to include areas relevant to economic growth, particularly agricultural and household. High levels of financial service digitalization, along with the agriculture sector’s falling contribution to GDP, pose constant hurdles to rapid FI growth. Interestingly, prior to 2015, there was little research on the impact of technology in FI adoption; however, this trend has since changed as researchers focus on the importance of technology

The researchers addressed challenges pertaining to the agriculture sector and households, but those pertaining to creating awareness and impact of the digitization were not addressed prominently. The dearth of capturing the upgrowing challenges in the research also didn’t find a place in quality journals. This was also a major factor that lacked quality publication, i.e., less than 30 percent of the papers were published in quality journals (Table 3). Further, the demographically based clustering of regions also played a prominent role in FI-related research. For example, research in the regions dominated by tribals, especially in the Northeast region, formed a larger number of studies. In some cases, the studies got the support of Local Governments. Similarly, the expansion of microfinance in the state of Andhra Pradesh piqued the scholars’ interest in the region. However, no comparable enthusiasm was seen for the other provinces of the country.

The process of FI has direct relevance to midstream (and upstream) influencers like implementing authorities. For the country like India this responsibility is mainly undertaken by local government bodies and financial institutions. However, the research directly targeting authorities was quite low, at less than 10%. Moreover, the studies measuring their effectiveness along with designing interventions to increase their efficacy were also very few. One of the pieces of evidence available in the study was related to the training of bank officers. Moreover, most of the studies targeted downstream influencers but lacked in designing interventions that can motivate an individual to lead a voluntary approach.

The generalization of results for the research is based on the quality of the data source and the robustness of the analytical tools applied. Furthermore, the data source decides the predictability and the authenticity of the results. ‘Descriptive analysis’ and ‘regressions’ were two major statistical tools applied in most of the studies. As compared to primary data, results obtained from secondary data impose challenges in terms of authenticity and specificity. However, adopting such sources (52 percent), indicated that the researchers opted for a ‘comfortable’ route, while failing to explore more on primary data (28 percent), which might have given a better understanding towards FI implementation.

Interestingly, after 2004 FI was included as the policy initiative of central bank of the country, which was done with the presumption of its efficacy in poverty alleviation. Also, as a part of the policy initiative, it was decided to outsource some of the banking operations and also to increase the pace of FI it was decided to allow financial institutions to authorize Business Correspondence (BC) to conduct some of the banking operations on their behalf. This successful business model resulted in more than five hundred thousand active BCs operating all over the country with the primary objective of delivering financial services to remote places. However, the existing poverty level of 25 percent and frauds that happened in the microfinance sector contrasted such efforts.

Additionally, some of the efforts, like introducing the social welfare scheme like Pradhan Mantri Jan Dhan Yojna (PMJDY), improved the FI position of the country. Since the inception of the scheme (2014), bank accounts of more than 410 million individuals have been opened under the said scheme. This scheme supported ‘Direct Benefit Transfer’ which also resulted in the reduction of corruption to a larger extent. However, despite the extended benefits, the challenges in terms of the sustainable operations in bank accounts still persist and thus, the insufficient financial transaction resulted in the account becoming ‘dormant’. This has also been endorsed by the report of World Bank (2018), indicating increased proportions of accounts opened lying ‘inactive’ due to insufficient financial activity. Moreover, dilution in policy norms (e.g., Know your customer) for such schemes is ineffective and fails to cover the larger interest of individual and society (Chin, Karkoviata, & Wilcox, 2011). Also, the credit facilities associated with such products may increase the non-performing assets of the financial institutions.

Additionally, the creation of financial data history is one of the resorts available to masses from becoming eligible to obtain credit from the organized financial sector such as Public Sector Banks and MFI. Moreover, only 8.10 percent of individuals obtained credit from financial institutions (World Bank, 2018). Exorbitant higher interest rates charged by MFIs have serious implications. It reduces the consumption levels of individuals; in some cases, supplier’s recovery model acts as a threat to the life of the beneficiaries, as was evident from the MFI crisis of 2010.

In lieu of the efforts both in terms of policy initiatives and social welfare schemes, it also becomes prudent to test and design the interventions both for midstream and downstream influencers to set the momentum for the voluntary approach. Thus, highlighting the cognitive factors involved in the FI process for both the downstream and midstream influencers will motivate policymakers and implementing authorities to find an innovative (and alternative) way to address the problem. Moreover, the implications of these measures will result in many benefits like transferring the onus of FI to the beneficiary; bringing the masses within the ambit of the organization financial sector; self-realization towards financial well-being; and undertaking self-employment activities.


Since FI-related articles are published in different journals, a more in-depth search could be carried out, particularly covering streams such as humanities and economics. Second, due to the extensive scope of the topic, articles may have been omitted. Third, the measurement of inter-coding reliability for the coding process is not applied. Last, financial literacy and financial education are related to FI but are omitted from the search criteria and their inclusion can provide a better analysis of their scope and implementation.


More efforts from the research community are required to think beyond conceptual reporting and focus more on raising awareness as well as changing behavior among the downstream and, more importantly, for the upstream influencers. The author strongly supports the application of intervention targeting behavior at all levels (midstream, and downstream) in FI implementation. Since most of the research has been carried out in the southern and NE provinces of the country, researchers are expected to concentrate more on survey analysis in remote areas where the level of exclusion is high. Studies focusing on human behavior are needed to create effective marketing mix models, and researchers are encouraged to use more robust statistical tools such as factor analysis and structural equation models (SEMs) to study them. This article intends to add another much-needed dimension to behavioral science by identifying the important area that needs attention for behavioral scientists, marketers, and policymakers.


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