Blueprints of Strategy – An Empirical Study of Business Models in Rural Cooperative Banks

Blueprints of Strategy – An Empirical Study of Business Models in Rural Cooperative Banks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Blueprints of Strategy – An Empirical Study of Business Models in Rural Cooperative Banks Shaily Jamuar, Samar Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6435956/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2026 Read the published version in Future Business Journal → Version 1 posted You are reading this latest preprint version Abstract This study explores the business models of hybrid institutions, rural cooperative banks in India which balance the dual primary tasks of cooperation and welfare with profit-making. We undertake an exploratory analysis of 351 Central Cooperative Banks (CCBs)in India from 2015 to 2021 by clustering the variables derived from their financial statements. We consider these variables to be representative of the managerial choices of these institutions in their quest for optimum performance in the given environment. We delineate three business models from the clusters: Retail Funded Investment Oriented, Retail Funded Traditional Lending and Wholesale Funded Traditional Lending. We study their risk profile and delve into the inherent heuristics-based logic of each model to understand the institutions' realized strategy. We then enquire into the changes in the business models with time and find a new business model component based on trading in securities to be emerging. A business model canvas mapping of key resources, activities, partners, channels and value propositions provides deeper insights into the models. Our study mobilizes the contextual differentiator of rural cooperative banks. This research substantiates that cooperative banks, while balancing dual missions, adopt different business models and give insights into how they do it while managing the risks involved and how the same changes over time. It contributes to the business model literature by suggesting new typologies of business models adopted by hybrid institutions in rural hinterlands of a developing economy. It offers recommendations for policymakers, supervisory authorities and practitioners. business model clustering cooperative bank migration developing economy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cooperatives have attracted considerable international attention as they “promote the fullest possible participation in the economic and social development of local communities”(A/c.3/78/L11, Agenda item 24(b) UN General Assembly, 10th October 10, 2023). The United Nations proclaimed the International Year of Cooperatives in 2025 with a theme that underscores the capacity of cooperatives to build a better world (IY 2025). Among the cooperatives, cooperative banks are cooperative financial institutions possessing banking licenses that enable them to render financial services to cooperatives. These financial institutions represent a crucial component of the global banking sector, enjoying significant popularity and serving as a “dominant organisational form in many European countries” (McKillop, French, Quinn, Sobiech, & Wilson, 2020 ). Their structure, status, products and business offerings exhibit variability across developed and developing nations (Cuevas & Fischer, 2006 , 2018). An examination of how these stakeholder banks, which are established as cooperatives with analogous ownership structures, may adopt diverse strategies for conducting their business is essential to glean novel insights into the demand and supply side of strategy. The configuration of a firm’s strategy, along with how resources are harnessed for value creation and capture, can be assessed through the lens of the business model (Zott & Amit, 2024 ). Business models have been regarded as blueprints or recipes (Demil & Lecocq, 2010 ; Osterwalder, Pigneur, & Tucci, 2005 ) in a static approach to strategy studies. We employ this perspective to outline the overarching frameworks and broad blueprints of a firm’s strategic approach. Business models are frameworks that illustrate the mechanisms of value creation and capture by the focal firm for its own benefit as well as for stakeholders (Zott, Amit, & Massa, 2011 ). The strategic decisions made by managers concerning policy, asset composition, and, crucially, governance aspects are encapsulated within the business model (Casadesus-Masanell & Ricart, 2010 ). The option a firm selects among its strategic possibility sets is reflected in its assets and liability portfolio (Bolívar, Duran, & Lozano-Vivas, 2023 ). Our study extends the discourse initiated by Ayadi and other scholars, positing that the ratios derived from financial statements reflect managerial decisions aimed at revenue generation and utilise the balance sheet ratios as the variables for clustering to ascertain business models (Ayadi, Challita, & Cucinelli, 2023 ) within cooperative credit institutions. As the discourse evolves, featuring a series of point and counterpoint exchanges regarding the insights that strategy might glean from the business model perspective (Bigelow & Barney, 2021 ; Lanzolla & Markides, 2021 ), our empirical investigation into the business model of rural cooperative banks in India aims to contribute to the discourse by elucidating various typologies as well as strategic trends exhibited by these stakeholder entities, in their efforts to balance social objectives with profit missions (Chen & Wang, 2024 ). Our research develops a more nuanced understanding of business models, enquiring into product and process innovation aimed at meeting the previously unmet needs of customers (McKillop et al., 2020 ) and identifying antecedents of change. Given their characteristics of stakeholder firms and unique three-tier structure, we have opted for district-level rural cooperative banks in India as our empirical sample. Globally, European and American cooperative banks have been studied (Lagasio & Quaranta, 2022 ) extensively. However, strategic management research in India has largely overlooked cooperatives as a distinct organizational form despite their substantial contributions to economic activities (Nair, Khobdeh, Oksoy, Guldiken, & Willis, 2022 ). Indian cooperative banks, which account for an 11% share in lending (RBI, 2023 ), significantly contribute to the Indian banking landscape and are deeply embedded within the local economy. We investigate the diverse strategies implemented by these banks by undertaking clustering of seven financial ratios derived from the liabilities and assets sides of balance sheets, positing them as manifestations of managerial decisions, employing both cross-sectional and longitudinal data obtained from banks’ financial statements at the end of the financial year for seven years and analyzed the transformations in the business models adopted by these 351 banks. We consider the value captured by these institutions as indicated in the breakup of their income derived from operationalizing their business models. We also delve into components of key resources, partners, activities, channels, value propositions, costs, revenues, customer segments and relationships (Osterwalder & Pigneur, 2010 ) to map out their generic business model. We identified three business models among the 351 central cooperative banks in India: Retail Funded Investment Oriented (RFIO), Retail Funded Traditional Lending (RFTL), and Wholesale Funded Traditional Lending (WFTL). This study adds to business model taxonomy literature, determinants of change in business models and the strategy literature by delineating aspects of resources, activities, partners, channels and value creation and capture. While this study elucidates RFIO, RFTL, and WFTL business models, with the emergence of trading in securities as a new component of the business model, thereby contributing to the academic discourse on business model typology, it also enhances the understanding of the supply-side theory of strategy (Lanzolla & Markides, 2021 ) by mapping the resources and the resultant impact of the adopted business model on the organization’s performance. Additionally, it aids policymakers and decision-makers in discerning industry trends by providing a framework in the form of business models that encapsulate business managers' interpretations of value creation. Such a framework can be utilized to evaluate the associated risks and sustainability of the models and accordingly formulate appropriate policies for the institutions under reference. This study also empowers practitioners to learn from exemplary practices, select suitable models for adoption, and undertake appropriate adjustments and course corrections. Literature Review, Theoretical Groundings and Research Gap The business model concept originated with technological innovations during the dot-com boom (Massa, Tucci, & Afuah, 2017), and scholars have extensively analyzed the business models of corporations and start-ups. The business model functions as a heuristic framework that connects ideas and technologies to tap into their potential and leverage the inherent technical capabilities for the realization of economic value thereby generating profits (Chesbrough & Rosenbloom, 2002). The concept does not have a universal definition yet. Business models have been described differently based on the theoretical worldview and the research question of the scholars studying the phenomenon. It explores the embeddedness of a firm in its contextual environment and its dynamic interactions with that environment (Amit & Zott, 2010; Teece, 2010; Zott & Amit, 2009; Zott & Amit, 2008; Shafer, Smith, & Linder, 2005). It serves as a good vector to communicate the components of strategy adopted by an organization (Bigelow & Barney, 2021). Although there is no unified business model theory (Snihur & Markman, 2023), this domain contributes to many theories of strategy due to its inter-disciplinary nature. Business model research has branched out into definition of business model, identification and taxonomy of business models, impact of business models on performance, profit (Bolívar et al., 2023), risk (Altunbas, Manganelli, & Marques-Ibanez, 2011), efficiency (Badunenko, Kumbhakar, & Lozano-Vivas, 2021), sustainability, etc. A stream of business model literature has researched the business models of financial institutions, such as banks, using quantitative methodology (Ayadi, Arbak, & De Groen, 2011; Farnè & Vouldis, 2021; Lagasio & Quaranta, 2022; Roengpitya, Tarashev, Tsatsaronis, & Villegas, 2017). Predominantly commercial banks in Europe were studied (Erins & Erina, 2013; Hryckiewicz & Kozlowski, 2017; Roengpitya et al., 2017). These studies are mainly data driven, with the choice of variables, from balance sheet or income expenditure/ profit and loss statements, forming the basis of identification of business models (Badunenko et al., 2021). The methodology has varied from Ward’s hierarchical clustering, K-means clustering to factor analysis, discriminant analysis, random forest method (Bolívar et al., 2023). However, these investigations were mainly on the business models of the commercial banks. Studies of cooperative banks’ business models have been undertaken on cooperative credit unions in the United States of America (Ayadi, Keoula, De Groen, Mathlouthi, & Sassi, 2017) and Shinkin Banks of Japan (Chronopoulos, Sobiech, & Wilson, 2020), which are developed countries. The business models of cooperative banks operating in the rural hinterlands in a developing economy are an understudied issue in the existing management literature (Mazzarol, Clark, Reboud, & Limnios, 2018), although these institutions are present in most countries and comprise a significant base as formal institutions for financial intermediation. Business models for organizations that cater to customers from the bottom of the pyramid (Prahlad & Hart, 2002; Seelos & Mair, 2007; Thompson & MacMillan, 2010) are different from those of regular business enterprises because they have different paying capacities and requirements (Yunus, Moingeon, & Lehmann-Ortega, 2010). Investigating such institutions within a developing economy is poised to yield novel insights that complement and enhance the existing body of business model literature. Cooperatives expedite economic development and improve members’ welfare (Nzowa, Nandonde, & Seimu, 2023). Given the global presence of cooperative banks, our study narrows down to the Indian context, an emerging economy with untapped research potential and a unique landscape of cooperative credit institutions. This research used a sample of Indian rural cooperative banks. These banks represent the hybrid model of cooperative credit unions and banking institutions at the ground level operating in rural ecosystems. These banks are registered as cooperative societies under the cooperative societies’ acts of their respective states and embody the principles of mutualism and cooperation. Being a member-driven, cooperative society has the welfare of members as its objective; being a banking firm, profit-making is needed to keep the firm sustainable and in compliance with regulatory stipulations. Thus, cooperative banks are hybrid institutions with objectives other than profit for shareholder value, and these institutions aim to create stakeholder value. CCBs are local banks, as their area of operations is restricted to one district or a cluster of contiguous districts. Their governance structure features a Board of Directors (BoDs) comprising elected representatives drawn from their member base. This local, member-driven BoD is a repository of insights regarding the clientele; additionally, the personnel manning these banks are recruited from the local community. These uniquely structured provincial-level institutions, which are cooperatives as well as banks, derive equity from their member clients as well as from state governments and contribute equity and elect directors to the higher tier institution, the State Cooperative bank. Thus, they constitute a pivotal middle layer in a three-tiered cooperative credit structure and are distinctive. Their open and voluntary membership, adherence to democratic principles of one member, one vote, and the trust established through their long-standing relationship with primary societies nurtured over numerous years, robust connections and strong ties with the local community are unparalleled and challenging for other commercial banks to duplicate. The peculiarities of rural cooperative banks in developing economies like India present a unique case that challenges the traditional business model theories, making them a critical area for deeper investigation. The unique characteristics of rural cooperative banks in developing economies, such as India, present a distinctive case that challenges prevailing business model theories, thereby rendering them a pivotal area for comprehensive exploration. India is an emerging economy with rich research potential. It has a vast network of cooperative credit institutions that serve clientele at the base of the economic pyramid. These cooperatives are financed by cooperative banks. Their scale of operations has grown phenomenally since their establishment in colonial times (Vaidyanathan, 2013), and these organizations have diversified their involvement in various economic activities beyond the realm of agricultural credit. A recent scoping review of Indian Strategic Management Research has identified significant gaps in the context of cooperatives within India, specifically regarding impact of shifts in the local institutional environment on cooperatives and the evolution of their roles to adapt to a more market-based economy and competitive landscape (Nair et al., 2022). This research gap is the focus of our inquiry, where we examine the business models of rural cooperative banks and their progression over a span of seven years. An inquiry into their business models shifts the focus of management science from traditional models to those that lie outside traditional strategy and organizational assumptions of profit, monetization, patents, etc. (Arend, 2013). Such investigative endeavours enrich the academic corpus by introducing diverse contexts, which “involves counterintuitive variant” having implications for firm value, business model design and organizational performance (Leppänen, George, & Alexy, 2023). Within the realm of cooperative banks, we specifically select district-level banks, the CCBs, due to their proximity and closer connection with grassroots rural cooperatives. These banks occupy a distinctive intermediary position between state-level cooperative banks and ground-level primary cooperative societies, enabling them to provide specialized and customized financial and non-financial services to rural farmers. Such services tend to be non-substitutable because of their access to soft information and the member-ownership structure of their clientele, which confers a competitive advantage over other banks. Consequently, their business models emerged and evolved accordingly. The interactions of CCBs with the ecosystem and managerial cognition lead to the evolution of their business models as configurations of integrated attributes (Snihur & Eisenhardt, 2022). Their business models which are “configured systems of interdependent elements”(Desyllas, Salter, & Alexy, 2022) manifest differences due to the distinct strategic choices (Ocasio & Radoynovska, 2016) made in response to their environmental context (Shipilov, 2005). Accordingly, our research questions have emerged and guided our study. Research Question 1: Is there a universal business model applicable to all rural cooperative banks? Research Question 2: Do the business models of rural cooperative banks exhibit stability over the years? Research Question 3: What are the determinants of value creation within rural cooperative banks? Methodology We adopt a positivist perspective, concentrating on the selection of pivotal resources and activities while employing clustering techniques to elucidate the business models of banks. We utilised Ward’s hierarchical procedure to establish the clusters and applied Calinski and Harabasz’s (1974) pseudo-F index as the stopping rule, drawing inspiration from Ayadi et al. (2011). Clustering is a quick tool for identifying business models and studying their sustainability, risk factors and other pertinent dimensions (Farnè & Vouldis, 2021). Previous researchers have implemented hierarchical clustering and discerned three business models among European banks (Ayadi et al., 2011), four business models among Baltic banks (Erins & Erina, 2013), and 63 business models among European banking landscape (Lueg, Schmaltz, & Tomkus, 2019). Similar studies have been undertaken (Ayadi, Bongini, Casu, & Cucinelli, 2021; Cernov & Urbano, 2018; Farnè & Vouldis, 2021; Köhler, 2015; Mergaerts & Vander Vennet, 2016; Roengpitya et al., 2017; Roengpitya, Tarashev, & Tsatsaronis, 2014). Additional methodologies have encompassed correlation analysis and simple linear regression (Jočienė, 2015), non-parametric tests, data envelopment analysis, truncated regression, bootstrap (Curi, Lozano-Vivas, & Zelenyuk, 2015), K-medoids clustering (Hryckiewicz & Kozlowski, 2017), a combination of hierarchical clustering with discriminant analysis (Roengpitya et al., 2017), and random forest methodology validated by discriminant analysis (Bolívar et al., 2023) to identify the business models of various banking institutions. We adhere to the methodological framework established by Ayadi et al. (2017), calculate multiple indicators derived from the balance sheets of banks, and apply statistical clustering to delineate patterns and groups within our dataset. Our sample is a panel of year-end data from the balance sheet and profit and loss statements of 351 CCBs for the years 2014-15 and 2020–2021. The data from the balance sheets were sourced from the ‘Key Statistics of Cooperative Banks (Short Term Cooperative Credit Structure)’ on cooperative banks published by the National Bank for Agriculture and Rural Development, which is the supervisory body for rural cooperative banks in India. We posit that the composition of a bank’s balance sheet is a reflection of its strategic decisions. Our analysis emphasises their principal banking operations and funding methodologies. While selecting the indicators, we compute ratios as percentages of total assets (from the balance sheet) to create a standardised metric for analysis. This approach also eliminates the necessity for scaling variables during the clustering process. The rationale for indicator selection was a significant contribution to the balance sheet and utility for rent earnings. We excluded categories that constitute, on average, less than 5% of the total assets. Expert judgment was employed to determine the variables pertinent to the identification of business models. We executed the clustering algorithm on the seven variables identified as critical resources and activities and then used descriptive statistics to study the features and logic of the clusters/business models. Additionally, we employed the same clustering algorithm to the components of banks’ income and operational expenditure, as well as a synthesis of the balance sheet variables and income and expenditure ratios to validate the clusters/business models derived. In the subsequent phase, we scrutinise the risk profile, performance metrics, and value captured as manifested in the profitability indicators and income variables. Analysing return on assets and net interest margin facilitates ascertaining value capture. We also take an interpretive approach and delineate the generic business model of rural cooperative banks on the business model canvas tool (Osterwalder & Pigneur, 2010) by studying their websites and annual reports. Sample selection The rural short-term cooperative credit structure in India comprises 34 State Cooperative banks, 351 Central Cooperative Banks (CCBs), and more than 95,000 Primary Agriculture Cooperative Credit Societies (PACS) as per the ‘Key Statistics of Cooperative Banks (Short Term Cooperative Credit Structure) March 31, 2021’ ( https://www.nabard.org/auth/writereaddata/tender/pub_060723112055643.pdf ). This middle layer of 351 CCBs, operational at the district level in 20 states in India, which purveys credit through their 13589 branches and affiliated PACS and other societies, is the sample selected for this study. We selected these banks as they were the financial institutions closest to the ground-level cooperative societies in cooperative banking. The rural cooperative banking framework in India is characterized by two- and three-tiered rural cooperative banking structures. We have selected entities that form the intermediary tier, specifically central cooperative banks, in the three-tiered cooperative credit structure and systematically mapped their resources and activities to identify their business models. These hybrid institutions fulfil the dual roles of profit generation and the enhancement of member welfare while providing essential financial services to rural farmers, artisans, small enterprises and others. Accordingly, these samples were deemed appropriate for the objectives of this study. Descriptive Statistics We aggregate the bank year observations from data as of March 31, 2015, March 31, 2016, March 31, 2017, March 31, 2018, March 31, 2019, March 31, 2020, and March 31, 2021, together in one large, combined dataset of seven years’ bank observations (2540 rows) of 351 CCBs. Source of data is from website of NABARD ( https://www.nabard.org ) publications, details of which are given at the end of the paper. The descriptive statistics of the bank year observations for input variables (Table 1 ) show that the variables exhibit a central tendency towards deposits as a source of funds, followed by borrowings, capital reserves, and balance of profit. Notably the variation in deposits is substantial. Among the various activities, short-term loans predominate, followed by fixed deposits with other banks and investments. There is also considerable variation in the loan parameter. All the variables are normalized against total assets and articulated as a percentage of total assets/ contribution to the balance sheet, thereby ensuring that the bank size does not distort the analytical findings. Table 1 Descriptive Statistics of the 2540 Bank-Year Observations for the years 2015–2021 (Position as of 31st March every year)(All figures as a ratio of Total assets) Balance-Sheet Heads Mean Maximum Minimum Standard Deviation Median Variable selection Capital 0.055 0.983 0.000 0.066 0.039 Selected as ‘Stable funding’ CapResP Reserves 0.026 0.332 0.000 0.029 0.019 Balance of Profit 0.004 0.211 0.000 0.009 0.002 Revaluation Reserve 0.010 0.204 0.000 0.020 0 Not considered as less than 5% Provisions 0.070 0.457 0.000 0.064 0.049 Not considered as neither income earning nor source of funds Other Liabilities 0.026 0.356 0.001 0.031 0.017 Not considered as less than 5% Cash 0.013 0.203 0.000 0.015 0.009 Not considered as less than 5% Balance in Current Account 0.041 0.494 0.000 0.035 0.032 Not considered as less than 5% Other Assets 0.034 0.458 -0.386 0.056 0.019 Not considered as less than 5% Fixed Assets 0.013 0.204 0.000 0.019 0.006 Not considered as less than 5% Deposits 0.542 0.936 0.003 0.162 0.539 Selected as ‘Deposits’ Borrowings 0.218 2.340 0.000 0.161 0.190 Selected as ‘Borrowings’ Short Term (ST) Loans 0.406 0.868 0.000 0.180 0.414 Selected as ‘STLoans’ Medium Term (MT Loans) 0.114 0.516 0.000 0.101 0.084 Selected as ‘MTLoans’ Investments 0.151 0.772 0.000 0.091 0.135 Selected as ‘Investments’ Fixed Deposits 0.164 0.852 0.000 0.128 0.137 Selected as ‘FixDeposits’ Source: The Authors Variables selected for clustering The variables selected for clustering in this study are elucidated below. Asset side (Ratio to Total Assets) Loans to customers and societies - identifies the proportion of customer loans reflecting a dependence on conventional banking operations. The gross loan amount was taken as a ratio of total assets (balance sheet size). The lending product mix included short- and medium-term loans. Lending constitutes a fundamental function of banks and the composition of short-term lending and medium-term lending products is determined by banks. Consequently, both indicators were treated as two distinct variables: STLoans and MTLoans. Deposits with other Banks (Fixed deposits balance with State Cooperative Bank in deposit accounts and balance with other banks in deposit accounts) (FixDeposits) – This indicator, identifies investments in liquid assets, such as keeping term deposits with other banks, specifically state cooperative banks and commercial banks. Given that the sums retained as fixed deposits/term deposits with other banks can be withdrawn prematurely, contingent upon the relinquishment of a certain portion of the interest income, these deposits were classified as liquid deposits and as a form of interest-earning investment. The quantum held in the current account with the Reserve Bank of India has been excluded from consideration as part of an input variable. Investments /Trading assets – This metric assesses the book values of the investments in bonds and government securities. These could be for maintaining the Statutory Liquidity Ratio (SLR) or only for investments and may include non-SLR securities. Large values indicate a prevalence of investment activities (prone to market and liquidity risks). The CCBs do not offer loans to other CCBs/banks, and no significant interbank lending is seen; therefore, the variable Loans to banks considered by scholars studying European banks are not computed by us for Indian cooperative banks. Further, we take Fixed/Term Deposits with Other Banks as an indicator of investments in liquid assets and, in part, connectedness with the banking network. These are not loans but deposits with other banks, shown as assets by the depositing bank and liabilities by the deposit-taking bank. Liabilities side (Ratio to Total Assets) Interbank borrowing (Deposits from Banks) This has been clubbed with wholesale debt, that is, bank liabilities or the amount shown under Borrowings/Refinance , and identifies the share of liabilities owed to higher-tier institutions, including refinances from State Cooperative Banks, National Bank for Agriculture and Rural Development (NABARD), and overdrafts with other banks. This measure highlights banks with greater interbank funding requirements, often due to excessive reliance on short-term funding. Stable funding indicators taken by Ayadi et al. ( 2017 ) and Roengpitya et al. ( 2017 ) have been modified. Instead of adding total customer deposits with long-term funds to arrive at stable funds, we take the summation of capital, reserves, and balance of profits or retained profits, as these are stable, long-term, and own sources of funds out of internal accrual from operations. A unique feature of cooperative banks is that augmentation of resources of the cooperative banks is done by the “share-linking” concept, whereby the loanee societies are required to contribute a small percentage of funds in proportion to the loan taken, towards the share capital of the bank; this share capital is eligible for dividend, although not for additional voting rights. Customer deposits. The indicator identifies the share of deposits from customers, including societies, in the total balance sheet, indicating a reliance on more traditional funding sources. Deposits (customer deposits) can be taken as CASA (Current Account and Savings Account (CASA) deposits and term deposits kept by customers and cooperative societies with the cooperative bank. Derivative exposures, though reckoned by Ayadi et al. ( 2017 ) and Roengpitya et al. ( 2017 ), are not taken by us in the Indian cooperative bank’s case, as these were not seen in cooperative banks in India. We examined the correlations between the variables selected to see their inter-relatedness, linear relationship, and direction of relationship, and to rule out collinearity (Fig. 1 ). It was observed that there exists a positive correlation between borrowings/refinances and short-term loans, while fixed deposits with banks, customer deposits, and investments exhibit positive correlations among themselves. Conversely, short-term loans and fixed deposits demonstrate a negative correlation, and a similar negative correlation is noted between deposits and borrowings. This observation suggests that the borrowing levels of banks tend to be lower when outstanding deposits are high, as banks may possess sufficient low-cost resources in the form of savings account deposits for their activities. The remaining parameters displayed a relative low correlation quantified as less than 65%. Therefore, it can be deduced that there exists a weak or low correlation among the selected variables. The correlation coefficient did not reach significantly high threshold, thus permitting further data analysis with the anticipation that the influence of individual variables or instruments remains unimpeded. Data Analysis The variables in Table 1 were employed for cluster analysis to delineate distinct business models. We implemented Ward’s hierarchical clustering and calculated the Calinski-Harabasz pseudo-F values to arrive at the ‘stopping rule’ (Calinski and Harbasz, 1974). The pseudo-F indices were highest at 1344.65 for the two cluster-solution, followed by 1022.10 for three-cluster solution, and displayed a consistent decline in value for the subsequent cluster combinations. Subsequently, we assessed various combinations of clusters and analyzed them utilising descriptive statistics. We modeled the two clusters, as suggested by the highest pseudo-F value; however, this did not yield significantly distinct business models. Therefore, we proceeded with modeling three clusters, based on the pseudo-F value, which resulted in the identification of three significant business models. We also modeled four and five clusters. The results for combinations of three, four, and five clusters yielded three distinct business models and one or two intermediate models. We compiled a similar panel of indicators derived from the profit and loss statements of these banks for the same fiscal years and executed Ward’s hierarchical clustering. The selected variables for clustering included all subheads of income and operational expenses. The income variables included interest earned from advances, interest earned from investments, income from commission and exchange, the profit realised from the trading or sale of investments, and miscellaneous income, all quantified as a percentage of total income. Operational expenses include the interest paid on deposits and borrowings, computed as percentages of total expenditures. Analyzing the components of income earned by CCBs as documented in their profit and loss statements for the respective fiscal years, we identified that the predominant sources of income for all cooperative banks were interest income on loans extended and interest earned on investments made in government securities, bonds, deposits kept with other banks, dividends earned on shares invested in by them, etc. Income from commissions and exchanges earned constituted the next category of income. Another notable area of value capture is observed mainly from 2018 onwards in the form of income from the sale of investments and trading in securities. By undertaking Ward’s hierarchical clustering on the same, we obtain three similar clusters/business models (Fig. 2). Consequently, three distinct clusters were identified, and the features of these three distinct business models were studied. Hence, we deduce that three business models were implemented: retail-funded traditional lending, wholesale-funded traditional lending, and retail-funded investment-oriented. Distinct Business Models and Their Characteristic s The clustering analysis yielded the following business models as outcomes (Fig. 3). Their attributes and associated risk profiles were meticulously analysed (Table 2 ). Retail Funded Traditional Lending (RFTL) Retail Funded Investment Oriented (RFIO) Wholesale Funded Traditional Lending (WFTL) Business model 1 – Retail Funded Traditional Lending (RFTL) This model represents the traditional intermediation role between savers and borrowers: aggregating low-cost deposits and financing short-term loans. The principal activity involves extending credit for agricultural cultivation, which, as working capital, is for a year's duration. Customer deposits and short-term advances portfolios constitute a significant component of their balance sheets on average. Banks adopting the RFTL model assume the intermediation role of banks in the true sense, as they transform maturities and utilize the deposits maintained with them for lending to borrowers. Banks operating under this model exhibit the most extensive penetration within rural areas. Their credit-to-deposit (CD) ratio is also high. These banks exhibit a pronounced level of risk cost, gross non-performing assets (GNPA), and net NPA percentage, suggesting that conventional business activities are associated with a relatively heightened credit risk (Table 2 ). Higher risk and higher return have been adopted as the logic in this business model. Profitability metrics, the mean value of return on assets (RoA) and return on equity (RoE), are comparatively low for this model, in contrast with more positive RoA and RoE observed in the other two models; median values are moderate. Value capture, as quantified by its net interest income, remains average, supplemented by small earnings from commissions and fees. Business Model 2 : Retail Funded Investment Oriented (RFIO) This model envisages mobilizing deposits and deploying them as investments in government securities, other approved securities, and term deposits with other banks. Their balance sheet size is among the highest within the CCBs, with the largest banks classified in this category, as evidenced by their median asset size and business per branch (Table 2 ). The RFIO business model is characterized by a significant dependence on customer deposits outstanding on the liabilities side and a substantial percentage of investments and term/fixed deposits with other banks on the assets side. The deposits maintained as current, savings, which entail minimal interest expenditure, and term deposits constitute a considerable proportion of their liabilities. This is nearly one standard deviation above the population mean and can be identified as a distinctive characteristic of this model. In addition to mandated investments in authorised securities, the RFIO model opts for investments considered as a relatively low-risk activity for income generation. RFIO capitalizes on customer deposits maintained with the bank and invests them in securities, mirroring the function of investment banks, as well as in term-deposits placed with other banks. The investment-to-deposit ratio is among the highest. A diversification into medium-term loans is also noted in some banks, and their non-agricultural loan portfolio surpassing that of the other two models. The risk aversion of the RFIO model is additionally manifested in the elevated balances maintained in cash and current accounts, thereby sustaining a high level of liquid assets, albeit these are non-earning assets (since no interest accrues on these balances). Their risk cost is among the lowest (Table 2 ), and the mean value of the ratio of risk-weighted assets to total assets is the least among the three models. The rationale underlying the RFIO model suggests a relatively low risk - low margins framework. Business model 3 – Wholesale Funded Traditional Lending (WFTL) This business model engages in borrowings to facilitate short-term lending operations. It predominantly relies on debt sourced from other banks, which is then utilized for short-term lending as the primary activity. Short-term loans dominate the asset portfolio in this model, as suggested by the same exceeding the population mean by one standard deviation, and the agricultural loans component is higher than those of other models. This framework has been adopted by small to medium-sized banks in India. In this model, banks leverage funds to enhance their asset portfolios. Given that wholesale funds are acquired by borrowing from higher-tier institutions that involve less administrative costs, this model demonstrates higher operational efficiency as indicated by the mean cost-to-income ratio (CIR) being the lowest among the three models; however, the median CIR is higher, suggesting that a greater number of banks fall within the higher CIR quadrant (Table 2 ). The borrowings of these banks surpass the population mean by nearly one standard deviation. The percentage of deposits mobilized by these banks is lower than those of all CCBs and other models. The rationale underpinning this business model is leveraging and augmenting available resources through borrowing from other banks, thereby increasing volume and lending to clients to generate higher interest income. This model dispenses larger amounts in loans, resulting in higher average business per branch and business per staff as compared to the RFTL model. Table 2 Risk Factors and Performance of the Business Models Parameter (Median values) RFTL RFIO WFTL Capital Adequacy & Quality Tier I Capital to Risk Weighted Assets % 9.68 10.39 10.06 Capital to Risk-weighted Assets Ratio % (CRAR) 11.31 11.85 11.15 Business Total Assets (Amount in million) 14305.5 20067.3 9654.5 Credit Deposit Ratio % (CD Ratio) 84.48 49.15 148.25 Agricultural Loans outstanding to Total Loans outstanding % 62.62 36.24 84.03 Asset Quality Gross Non-Performing Assets (NPAs) to Gross Loans & Advances % (GNPA) 13.40 9.62 2.39 Net NPAs to Net Advances % (NNPA) 3.78 3.61 2.39 Risk Cost 0.32 0.30 0.17 Profitability Parameters Net Interest Margin (NIM) 2.28 2.40 2.02 Return on Assets (RoA) 0.20 0.28 0.19 Return on Equity (RoE) 2.33 3.95 2.13 Efficiency Cost to Income Ratio (CIR) 0.74 1.12 0.72 Productivity parameters Business per Branch (Amount in million) 317.9 390.1 397.7 Business per Staff (Amount in million) 59.4 71.2 75.6 Source: The Authors A comparative analysis of the performance parameters exhibits that the WFTL model has higher productivity ratios due to the augmentation of their funds with borrowed resources, operational efficiency is higher in the RFIO model, which is comparatively more profitable as seen from their asset quality, risk profile and productivity ratios. The traditional RFTL model manages its risk profile moderately. Evolution of Business Models in CCBs over seven years We identified modifications in the business model over a span of seven years and alongside changes, if any, in the business model of CCBs. The business models of banks were not observed to be immutable, rather they displayed transformations over the years, which were termed migrations (Ayadi et al., 2021). Following the same methodology as Ayadi, we considered migration when a bank’s business model transitioned to an alternative model, persisted in that model for two years, and did not revert to the earlier one. The table 3 shows the migration matrix of business models. Table 3: Migrations matrix in business models From (column) To (row) RFTL RFIO WFTL RFTL 86.02% (2.15%) 7.53% 4.30% RFIO 11.11% 82.91% (3.42%) 2.56% WFTL 20.51% 0.64% 75.00% (3.85%) (Figures in bold represent unchanged business models, and in brackets represent those banks that migrated temporarily for a year or so and then returned to the original business model) While studying the evolution of business models, we find that banks’ business models are substantially stable. The highest level of persistence is seen in the RFTL model, where 86% of the banks adopting this model exhibit no migrations, and 2.15% oscillated to another model and reverted to RFTL. About 11.11% of banks transitioned from the RFIO model to RFTL, while 20.51% of WFTL migrated to RFTL. Therefore, these banks manifest a tendency towards more retail funding. A total of 77 banks experienced at least one transition to an alternative business model. The RFIO model exhibited better stability than the WFTL model. The transitions in business models also corroborate this. Further research can be undertaken to ascertain whether migrations were in search for stability, profitability, or liquidity. The trend in the year-wise migration of the banks' preferred business model was discerned (Figure 4). It is observed that the WFTL model, which was favoured in 2015, has experienced a decline over time, whereas the RFTL model has gained prominence among banks. Therefore, we infer that with the expansion of branches, more retail funds become available with CCBs as deposits, which enhances their low-cost current account and savings account (CASA) funds. The adoption of banking technology by these banks has led to the issuance of Automated Teller Machine (ATM) cards, subsequently enabling direct account opening by the members of the PACS with the CCBs. This may have also contributed to these banks' influx of retail deposits. The RFIO model also exhibited a rise in popularity until 2017, after which a decline was noted. Value capture was assessed in terms of components of income generated, which primarily consisted of income from loans and advances in the RFTL and WFTL models, as well as income from investments in the case of the RFIO model. An analysis of the income revealed that net interest income demonstrated minimal variation across the business models; however, the net interest margin was notably higher for the RFIO model, followed by the RFTL and WFTL models. The diversification into non-interest income in the form of income from commission, fees, etc., was limited, although marginally higher in RFIO model, succeeded by WFTL and RFTL models. Profitability indicators suggest that the strategy of borrowing or availing refinances to enhance banks’ resources effectively expands the business of the bank by augmenting its advances portfolio, which tends to support lower risk costs. A novel facet of value capture has been identified in 14 CCBs in the form of ‘income earned from trading and sale of investments’ (we consider the values as significant if this income formed more than 10% of their total income). This was prevalent in the WFTL business model, as eight banks had adopted this component in the model, while two each were associated with the RFTL and RFIO models. This finding is counterintuitive, as it was anticipated that RFIO business models would engage in investment trading as an ancillary activity. The emergence of this new component in model may have been influenced by the regulatory directives for cooperative banks to park their Statutory Liquidity Ratio (SLR) and Cash Reserve Ratio (CRR) in approved securities that were government-issued instead of as deposits with their higher tier institution, the State Cooperative Banks. Accordingly, CCBs have maintained SLR in Government securities, which are tradeable in the secondary market. This shift may have facilitated trading in excess (over statutory requirements) securities held, thereby emerging as an additional component of their business model. The process of value creation and capture by these firms, beyond mere financial value capture was then inquired by constructing a generalized business model using business model canvas tool. This generic business model mapped out using the business model canvas tool elucidates the contents, structure, governance and value logic by identifying key partners, channels, resources, activities, customers, value propositions, costs, revenue, etc. (Figure 5) Discussion and Findings We conducted an examination of the business models of these institutions through an analysis of their tangible resources and operational activities, based on variables deduced from their balance sheet data over a period of seven years, and analyzed the components of their income and expenditure on operations to capture their value. We had set out to answer whether a universal business model is adopted by the rural cooperative banks, which share similar characteristics in terms of ownership, mutualism, localism, stakeholder banking, and level of knowledge of customer base. We find that rural cooperative banks differ in their implementation of funding sources and their activities realized, and thus, they exhibit different strategies to achieve their objectives, despite their common objective of mutualism. Our cluster analysis based on the method given by Ayadi (2021) delineated three broad business models. It is observed that most banks have preferred retail funding. However, there has been a gradual shift towards diversification of activities as time progressed and the business models have exhibited some changes although remaining stable largely. A new activity of trading in securities has been observed in 14 banks. Value creation and capture is granularly delineated using the business model canvas tool and cooperative capital emerged as a common resource unique to cooperative banks. Contribution Our study contributes to the body of research on business models in two ways. First, it substantiates the proposition that business models can function as a unit of analysis within management studies (Zott et al., 2011). The identified clusters represent different business models adopted by rural cooperative banks by considering the choices made by them in assets, funding, capitalization, and diversification, which are reflected as realized strategies in their balance sheets. These models help in understanding the gestalt of these firms, by sharing their approach to business in their rural ecosystems. The business model represents a more accessible vector to convey strategy (Bigelow & Barney, 2021). Our business model framework study disaggregates the various combinations of financial resources raised by the focal firms, the rural cooperative banks. The resources in question influence performance by exploiting complementarity and economies of scope (Villani, Greco, & Phillips, 2017), thereby leading to a better understanding of specific dimensions of tangible resources that align with the supply side perspective of strategy. Our study highlights the activities undertaken by these firms, which lead to value generation for the customer. Second, our study makes robust empirical contributions to business model categorization and the distinctiveness of the business model construct in relation to organizational structure (Zott & Amit, 2013). The typologies discerned within Indian rural cooperative banks augment the existing taxonomy of business models (Ayadi, De Groen, et al., 2017). Moreover, by exploring the heuristics-based rationale underpinning each business model, this study reveals the adaptive emergent properties inherent in these complex organizations (Bettis & Prahalad, 1995), which are often latent and not readily observable. By analyzing the trends and transformations in business models over time, and their trajectory using suitable analytical tools (Snihur & Markman, 2023), the adoption and evolution of business models within new organizational contexts (Ahlgren Ode & Louche, 2022) in cooperative banks can be further explored and emerging risks can be analyzed. Conclusion We present a study, the first of its kind, which maps out the business models of the CCBs in India, based on granular data derived from the balance sheet of these institutions and using business model canvas tool. We have undertaken an exploratory analysis using a clustering methodology on variables derived from the balance sheets of 351 banks to delineate the banks' business models. We took snapshots of the focal firms over a period and used panel data for seven years, with year-end positions as 31 st March 31, 2015, to 31 st March 31, 2021. Then, taking a resource- and activity-based view, we consider these variables to be conscious managerial choices of these banks. We infer the existence of three distinct models viz. ‘Retail Funded Traditional Lending,’ ‘Retail Funded Investment Oriented,’ and ‘Wholesale Funded Traditional Lending,’ among CCBs. Their embedded environments may have influenced their choices. CCBs generally adopt the traditional intermediation role of converting maturities by collating retail deposits and lending to customers (RFTL). Many banks have leveraged low-cost borrowings to provide short-term crop loans at directed lending rates, exhibiting another business model (WFTL). However, a section of banks diversified to investments in securities and term deposits, in addition to the liquidity level needed for statutory and regulatory requirements; hence, a different business model has also been adopted (RFIO). With regulatory changes, another element of income from trading in securities is emerging, changing business models. Our investigations into performance variables, efficiency, and risks provide empirical evidence of significant differences across these business models. The evolution of business models over a period of seven years reflects changes towards retail funding, which could be ascribed to higher penetration in rural hinterlands through branch expansion and technology adoption. Investment-oriented (investment orientation) firms also gained traction due to the tendency towards less risk-taking by banks. Implications of Study Findings This study, the first of its kind with respect to Indian banking institutions, while exploratory in nature, holds significant implications for business model taxonomy, evolution, and strategic management. Academia : The empirical findings substantiate the business model as a pertinent unit of analysis. It maps the spectrum and selection of resources utilized by rural cooperative banks in their activities to create and capture value. The findings of this study contribute to the existing literature on business model taxonomy particularly focusing on hybrid institutions of cooperatives, which serve as the context differentiator. It reinforces the relevance of business model as a critical focal point of study within the realms of strategy and finance. A well-defined business model is a critical modality (Snihur and Markman, 2023) in how an enterprise creates and captures value; this study defines the elements of various business models in cooperative banks. Policy Makers : Business model identification maps out the anatomy of the rural cooperative banking sector, and helps in the creation of subsets for quick analysis of banks. This quantitative approach of evaluating the financial indicators derived from the banks' balance sheet, as compared to qualitative parameters that are more time-intensive, enhances comprehension of the decisions made by the banks, facilitating appropriate policy development. The Supervisory Review and Evaluation Process (SREP) conducted by the European Central Bank incorporates profitability assessment of business models employed by their supervised entities. Identifying the logic behind the choices that various banks make can be utilized for assessing risky or conservative behavioural trends and designing effective policies for the same. Supervisors in India can consider adopting business model sustainability analysis as part of the assessment of these banks. The findings have special significance in view of the renewed focus on cooperatives by the Government of India, as exemplified by establishment of a separate dedicated Union Ministry of Cooperation in 2021. The business model framework delineated in this study can be extrapolated to other cooperative banks and incorporated into the public policy relevant to cooperatives. Practicing Managers - Business models are valuable instruments for quick decision-making by practicing managers, for studying strategic choices made by their counterparts in the peer groups, and adopting best practices from them. These findings will enhance understanding of the business models in use and the requisite changes to fulfill the strategic objectives of the institution. Managers can analyze the components of their business models; assess their resources, leverage, business activities, operational efficiency, profitability, and risk profile on an ongoing basis; and chalk out a suitable course of action. This business model tool is useful for identifying specific concrete components of their business profile which may be either bolstering or adversely impacting their performance. Empirical studies have established a correlation between business models and performance metrics, risk profiles, profitability, and resilience during economic downturns, thus, making awareness of the business models and their implications vital for managers seeking to to adeptly adjust their strategies. Limitations of the Study and Biases : Being the first such study undertaken on hybrid institutions within India, we limit our analysis to the identification of the business models in this distinctive context elaborating on their charecteristics, underlying logic, broad risk profiles, evolution, and transitions. Our analysis employs panel data comprising financial statements spanning seven years, which may be susceptible to measurement and reporting biases. In addition to being grounded in data, the selection of variables and the analysis of the findings have also benefitted from expert qualitative judgment. The temporal scope of this study reflects the macroeconomic developments and policy decisions during that period; the migration analysis across business models could be extended further in time, complemented with a detailed analysis of their ecological contexts to obtain a deeper understanding of the dynamics of the banking sector. Suggestions for future research – Changes in business models can be studied over the years to assess changes in models, migration, mergers, profitability, performance, and other parameters. There is scope for assessing innovations undertaken in the business models of rural cooperative banks, especially given the technological advancements in banking. The business models of other financial intermediaries operating in this sector can be identified to determine further diversities that may exist. Business model design and elements can be decomposed to obtain insights into the influence of social and environmental factors on managerial decision making and strategic choice. Declarations Ethical Compliance The authors declare that the study complies with the ethical standards and the procedures did not involve human participants. Funding The authors received no support for research, authorship and/or publication of this article. Author Contribution S.J. wrote the main manuscript, collected the data, analysed the same and prepared the figures and tables. S.S. edited and reviewed the manuscript and guided the study. All authors reviewed the manuscript. Data Availability The data which support this study have been sourced from compendium of key statistics published by NABARD every year and for the years 2017-2021 is available at www.nabard.org. The specific URLs for each year are indicated below:https://www.nabard.org/auth/writereaddata/tender/pub_060723112055643.pdf, https://www.nabard.org/auth/writereaddata/tender/pub_060723112459644.pdfhttps://www.nabard.org/auth/writereaddata/tender/3001203948Key%20Statistics%20 of%20Cooperative%20Banks%2031%20March%202019.pdfhttps://www.nabard.org/auth/writereaddata/tender/3001203407Key%20Statistics%20of%20Cooperative%20Banks%2031%20March%202018.pdfhttps: //www.nabard.org/auth/writereaddata/tender/3001202337Key%20Statistics%20of%20Cooperative%20Banks%2031%20March%202017%20.pdfFor the years 2015 and 2016, the data has been compiled from similar compendiums published by NABARD and is available with the authors. References Ahlgren Ode, K., & Louche, C. (2022). A business model pattern arrives … and then? A translation perspective on business model innovation in established firms. 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The business model: Recent developments and future research. Journal of Management, 37 (4), 1019–1042. https://doi.org/10.1177/0149206311406265 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Jan, 2026 Read the published version in Future Business Journal → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6435956","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450162880,"identity":"0231f410-777e-4a1c-9d5c-4a4c9327547a","order_by":0,"name":"Shaily Jamuar","email":"data:image/png;base64,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","orcid":"","institution":"National Bank for Agriculture and Rural Development (NABARD)","correspondingAuthor":true,"prefix":"","firstName":"Shaily","middleName":"","lastName":"Jamuar","suffix":""},{"id":450162881,"identity":"426bd5a0-b77b-40ad-8e3d-3d3f6a2290ec","order_by":1,"name":"Samar Singh","email":"","orcid":"","institution":"Indian Institute of Management Raipur","correspondingAuthor":false,"prefix":"","firstName":"Samar","middleName":"","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2025-04-12 17:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6435956/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6435956/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s43093-026-00740-w","type":"published","date":"2026-01-20T15:58:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82006064,"identity":"f9df29f1-5e34-4efd-8628-e676798907b1","added_by":"auto","created_at":"2025-05-05 22:38:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39758,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Plot of Variables\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6435956/v1/42d75382e0b826de50b96e1f.jpg"},{"id":82006368,"identity":"9df17582-64a4-4b53-b4b3-d0781bcd12eb","added_by":"auto","created_at":"2025-05-05 22:46:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61945,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6435956/v1/a42d879326bcb53d5c7f7759.jpg"},{"id":82006066,"identity":"a907fb84-b598-4ecf-a69c-83218ac6ded5","added_by":"auto","created_at":"2025-05-05 22:38:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59415,"visible":true,"origin":"","legend":"\u003cp\u003eBusiness Models - Number of Standard Deviations from Mean\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6435956/v1/db1fefe4a287f48ada7f7ac6.jpg"},{"id":82006068,"identity":"a86153a5-40e2-4942-861f-fa9ad8b33f7c","added_by":"auto","created_at":"2025-05-05 22:38:11","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":76498,"visible":true,"origin":"","legend":"\u003cp\u003eBusiness Models in CCBs- Evolution\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6435956/v1/0d0c1b15e3af8d4bc0b29341.jpg"},{"id":82006076,"identity":"41c62c32-c70b-42be-95b0-efe68e9ae7a7","added_by":"auto","created_at":"2025-05-05 22:38:11","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":439280,"visible":true,"origin":"","legend":"\u003cp\u003eBusiness Model of Rural Cooperative Banks in India : A Generic View (Source: Authors)\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6435956/v1/62e9c8a7224ad30d6cc96345.jpg"},{"id":101151766,"identity":"f232e3e2-ee77-42f5-8f70-f6c173c38f64","added_by":"auto","created_at":"2026-01-26 16:04:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1844727,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6435956/v1/14a298f2-67f4-485b-98fe-251a04125c84.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Blueprints of Strategy – An Empirical Study of Business Models in Rural Cooperative Banks","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCooperatives have attracted considerable international attention as they \u0026ldquo;promote the fullest possible participation in the economic and social development of local communities\u0026rdquo;(A/c.3/78/L11, Agenda item 24(b) UN General Assembly, 10th October 10, 2023). The United Nations proclaimed the International Year of Cooperatives in 2025 with a theme that underscores the capacity of cooperatives to build a better world (IY 2025). Among the cooperatives, cooperative banks are cooperative financial institutions possessing banking licenses that enable them to render financial services to cooperatives. These financial institutions represent a crucial component of the global banking sector, enjoying significant popularity and serving as a \u0026ldquo;dominant organisational form in many European countries\u0026rdquo; (McKillop, French, Quinn, Sobiech, \u0026amp; Wilson, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Their structure, status, products and business offerings exhibit variability across developed and developing nations (Cuevas \u0026amp; Fischer, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, 2018). An examination of how these stakeholder banks, which are established as cooperatives with analogous ownership structures, may adopt diverse strategies for conducting their business is essential to glean novel insights into the demand and supply side of strategy. The configuration of a firm\u0026rsquo;s strategy, along with how resources are harnessed for value creation and capture, can be assessed through the lens of the business model (Zott \u0026amp; Amit, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Business models have been regarded as blueprints or recipes (Demil \u0026amp; Lecocq, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Osterwalder, Pigneur, \u0026amp; Tucci, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) in a static approach to strategy studies. We employ this perspective to outline the overarching frameworks and broad blueprints of a firm\u0026rsquo;s strategic approach.\u003c/p\u003e \u003cp\u003eBusiness models are frameworks that illustrate the mechanisms of value creation and capture by the focal firm for its own benefit as well as for stakeholders (Zott, Amit, \u0026amp; Massa, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The strategic decisions made by managers concerning policy, asset composition, and, crucially, governance aspects are encapsulated within the business model (Casadesus-Masanell \u0026amp; Ricart, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The option a firm selects among its strategic possibility sets is reflected in its assets and liability portfolio (Bol\u0026iacute;var, Duran, \u0026amp; Lozano-Vivas, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our study extends the discourse initiated by Ayadi and other scholars, positing that the ratios derived from financial statements reflect managerial decisions aimed at revenue generation and utilise the balance sheet ratios as the variables for clustering to ascertain business models (Ayadi, Challita, \u0026amp; Cucinelli, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) within cooperative credit institutions. As the discourse evolves, featuring a series of point and counterpoint exchanges regarding the insights that strategy might glean from the business model perspective (Bigelow \u0026amp; Barney, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lanzolla \u0026amp; Markides, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), our empirical investigation into the business model of rural cooperative banks in India aims to contribute to the discourse by elucidating various typologies as well as strategic trends exhibited by these stakeholder entities, in their efforts to balance social objectives with profit missions (Chen \u0026amp; Wang, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our research develops a more nuanced understanding of business models, enquiring into product and process innovation aimed at meeting the previously unmet needs of customers (McKillop et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and identifying antecedents of change.\u003c/p\u003e \u003cp\u003eGiven their characteristics of stakeholder firms and unique three-tier structure, we have opted for district-level rural cooperative banks in India as our empirical sample. Globally, European and American cooperative banks have been studied (Lagasio \u0026amp; Quaranta, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) extensively. However, strategic management research in India has largely overlooked cooperatives as a distinct organizational form despite their substantial contributions to economic activities (Nair, Khobdeh, Oksoy, Guldiken, \u0026amp; Willis, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Indian cooperative banks, which account for an 11% share in lending (RBI, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), significantly contribute to the Indian banking landscape and are deeply embedded within the local economy. We investigate the diverse strategies implemented by these banks by undertaking clustering of seven financial ratios derived from the liabilities and assets sides of balance sheets, positing them as manifestations of managerial decisions, employing both cross-sectional and longitudinal data obtained from banks\u0026rsquo; financial statements at the end of the financial year for seven years and analyzed the transformations in the business models adopted by these 351 banks. We consider the value captured by these institutions as indicated in the breakup of their income derived from operationalizing their business models. We also delve into components of key resources, partners, activities, channels, value propositions, costs, revenues, customer segments and relationships (Osterwalder \u0026amp; Pigneur, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) to map out their generic business model. We identified three business models among the 351 central cooperative banks in India: Retail Funded Investment Oriented (RFIO), Retail Funded Traditional Lending (RFTL), and Wholesale Funded Traditional Lending (WFTL). This study adds to business model taxonomy literature, determinants of change in business models and the strategy literature by delineating aspects of resources, activities, partners, channels and value creation and capture. While this study elucidates RFIO, RFTL, and WFTL business models, with the emergence of trading in securities as a new component of the business model, thereby contributing to the academic discourse on business model typology, it also enhances the understanding of the supply-side theory of strategy (Lanzolla \u0026amp; Markides, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) by mapping the resources and the resultant impact of the adopted business model on the organization\u0026rsquo;s performance. Additionally, it aids policymakers and decision-makers in discerning industry trends by providing a framework in the form of business models that encapsulate business managers' interpretations of value creation. Such a framework can be utilized to evaluate the associated risks and sustainability of the models and accordingly formulate appropriate policies for the institutions under reference. This study also empowers practitioners to learn from exemplary practices, select suitable models for adoption, and undertake appropriate adjustments and course corrections.\u003c/p\u003e"},{"header":"Literature Review, Theoretical Groundings and Research Gap","content":"\u003cp\u003eThe business model concept originated with technological innovations during the dot-com boom (Massa, Tucci, \u0026amp; Afuah, 2017), and scholars have extensively analyzed the business models of corporations and start-ups. The business model functions as a heuristic framework that connects ideas and technologies to tap into their potential and leverage the inherent technical capabilities for the realization of economic value thereby generating profits (Chesbrough \u0026amp; Rosenbloom, 2002). The concept does not have a universal definition yet. Business models have been described differently based on the theoretical worldview and the research question of the scholars studying the phenomenon. It explores the embeddedness of a firm in its contextual environment and its dynamic interactions with that environment (Amit \u0026amp; Zott, 2010; Teece, 2010; Zott \u0026amp; Amit, 2009; Zott \u0026amp; Amit, 2008; Shafer, Smith, \u0026amp; Linder, 2005). It serves as a good vector to communicate the components of strategy adopted by an organization (Bigelow \u0026amp; Barney, 2021). Although there is no unified business model theory (Snihur \u0026amp; Markman, 2023), this domain contributes to many theories of strategy due to its inter-disciplinary nature. Business model research has branched out into definition of business model, identification and taxonomy of business models, impact of business models on performance, profit (Bolívar et al., 2023), risk (Altunbas, Manganelli, \u0026amp; Marques-Ibanez, 2011), efficiency (Badunenko, Kumbhakar, \u0026amp; Lozano-Vivas, 2021), sustainability, etc.\u003c/p\u003e\n\u003cp\u003eA stream of business model literature has researched the business models of financial institutions, such as banks, using quantitative methodology (Ayadi, Arbak, \u0026amp; De Groen, 2011; Farnè \u0026amp; Vouldis, 2021; Lagasio \u0026amp; Quaranta, 2022; Roengpitya, Tarashev, Tsatsaronis, \u0026amp; Villegas, 2017). Predominantly commercial banks in Europe were studied (Erins \u0026amp; Erina, 2013; Hryckiewicz \u0026amp; Kozlowski, 2017; Roengpitya et al., 2017). These studies are mainly data driven, with the choice of variables, from balance sheet or income expenditure/ profit and loss statements, forming the basis of identification of business models (Badunenko et al., 2021). The methodology has varied from Ward’s hierarchical clustering, K-means clustering to factor analysis, discriminant analysis, random forest method (Bolívar et al., 2023). However, these investigations were mainly on the business models of the commercial banks.\u003c/p\u003e\n\u003cp\u003eStudies of cooperative banks’ business models have been undertaken on cooperative credit unions in the United States of America (Ayadi, Keoula, De Groen, Mathlouthi, \u0026amp; Sassi, 2017) and Shinkin Banks of Japan (Chronopoulos, Sobiech, \u0026amp; Wilson, 2020), which are developed countries. The business models of cooperative banks operating in the rural hinterlands in a developing economy are an understudied issue in the existing management literature (Mazzarol, Clark, Reboud, \u0026amp; Limnios, 2018), although these institutions are present in most countries and comprise a significant base as formal institutions for financial intermediation. Business models for organizations that cater to customers from the bottom of the pyramid (Prahlad \u0026amp; Hart, 2002; Seelos \u0026amp; Mair, 2007; Thompson \u0026amp; MacMillan, 2010) are different from those of regular business enterprises because they have different paying capacities and requirements (Yunus, Moingeon, \u0026amp; Lehmann-Ortega, 2010). Investigating such institutions within a developing economy is poised to yield novel insights that complement and enhance the existing body of business model literature. Cooperatives expedite economic development and improve members’ welfare (Nzowa, Nandonde, \u0026amp; Seimu, 2023). Given the global presence of cooperative banks, our study narrows down to the Indian context, an emerging economy with untapped research potential and a unique landscape of cooperative credit institutions. This research used a sample of Indian rural cooperative banks. These banks represent the hybrid model of cooperative credit unions and banking institutions at the ground level operating in rural ecosystems. These banks are registered as cooperative societies under the cooperative societies’ acts of their respective states and embody the principles of mutualism and cooperation. Being a member-driven, cooperative society has the welfare of members as its objective; being a banking firm, profit-making is needed to keep the firm sustainable and in compliance with regulatory stipulations. Thus, cooperative banks are hybrid institutions with objectives other than profit for shareholder value, and these institutions aim to create stakeholder value. CCBs are local banks, as their area of operations is restricted to one district or a cluster of contiguous districts. Their governance structure features a Board of Directors (BoDs) comprising elected representatives drawn from their member base. This local, member-driven BoD is a repository of insights regarding the clientele; additionally, the personnel manning these banks are recruited from the local community. These uniquely structured provincial-level institutions, which are cooperatives as well as banks, derive equity from their member clients as well as from state governments and contribute equity and elect directors to the higher tier institution, the State Cooperative bank. Thus, they constitute a pivotal middle layer in a three-tiered cooperative credit structure and are distinctive. Their open and voluntary membership, adherence to democratic principles of one member, one vote, and the trust established through their long-standing relationship with primary societies nurtured over numerous years, robust connections and strong ties with the local community are \u003cstrong\u003eunparalleled\u003c/strong\u003e and challenging for other commercial banks to duplicate. The peculiarities of rural cooperative banks in developing economies like India present a unique case that challenges the traditional business model theories, making them a critical area for deeper investigation. The unique characteristics of rural cooperative banks in developing economies, such as India, present a distinctive case that challenges prevailing business model theories, thereby rendering them a pivotal area for comprehensive exploration.\u003c/p\u003e\n\u003cp\u003eIndia is an emerging economy with rich research potential. It has a vast network of cooperative credit institutions that serve clientele at the base of the economic pyramid. These cooperatives are financed by cooperative banks. Their scale of operations has grown phenomenally since their establishment in colonial times (Vaidyanathan, 2013), and these organizations have diversified their involvement in various economic activities beyond the realm of agricultural credit. A recent scoping review of Indian Strategic Management Research has identified significant gaps in the context of cooperatives within India, specifically regarding impact of shifts in the local institutional environment on cooperatives and the evolution of their roles to adapt to a more market-based economy and competitive landscape (Nair et al., 2022). This research gap is the focus of our inquiry, where we examine the business models of rural cooperative banks and their progression over a span of seven years. An inquiry into their business models shifts the focus of management science from traditional models to those that lie outside traditional strategy and organizational assumptions of profit, monetization, patents, etc. (Arend, 2013). Such investigative endeavours enrich the academic corpus by introducing diverse contexts, which “involves counterintuitive variant” having implications for firm value, business model design and organizational performance (Leppänen, George, \u0026amp; Alexy, 2023).\u003c/p\u003e\n\u003cp\u003eWithin the realm of cooperative banks, we specifically select district-level banks, the CCBs, due to their proximity and closer connection with grassroots rural cooperatives. These banks occupy a distinctive intermediary position between state-level cooperative banks and ground-level primary cooperative societies, enabling them to provide specialized and customized financial and non-financial services to rural farmers. Such services tend to be non-substitutable because of their access to soft information and the member-ownership structure of their clientele, which confers a competitive advantage over other banks. Consequently, their business models emerged and evolved accordingly. The interactions of CCBs with the ecosystem and managerial cognition lead to the evolution of their business models as configurations of integrated attributes (Snihur \u0026amp; Eisenhardt, 2022). Their business models which are “configured systems of interdependent elements”(Desyllas, Salter, \u0026amp; Alexy, 2022) manifest differences due to the distinct strategic choices (Ocasio \u0026amp; Radoynovska, 2016) made in response to their environmental context (Shipilov, 2005). Accordingly, our research questions have emerged and guided our study.\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003cp\u003eResearch Question 1: Is there a universal business model applicable to all rural cooperative banks?\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eResearch Question 2: Do the business models of rural cooperative banks exhibit stability over the years?\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eResearch Question 3: What are the determinants of value creation within rural cooperative banks?\u003c/p\u003e\n"},{"header":"Methodology","content":"\u003cp\u003eWe adopt a positivist perspective, concentrating on the selection of pivotal resources and activities while employing clustering techniques to elucidate the business models of banks. We utilised Ward’s hierarchical procedure to establish the clusters and applied Calinski and Harabasz’s (1974) pseudo-F index as the stopping rule, drawing inspiration from Ayadi et al. (2011). Clustering is a quick tool for identifying business models and studying their sustainability, risk factors and other pertinent dimensions (Farnè \u0026amp; Vouldis, 2021). Previous researchers have implemented hierarchical clustering and discerned three business models among European banks (Ayadi et al., 2011), four business models among Baltic banks (Erins \u0026amp; Erina, 2013), and 63 business models among European banking landscape (Lueg, Schmaltz, \u0026amp; Tomkus, 2019). Similar studies have been undertaken (Ayadi, Bongini, Casu, \u0026amp; Cucinelli, 2021; Cernov \u0026amp; Urbano, 2018; Farnè \u0026amp; Vouldis, 2021; Köhler, 2015; Mergaerts \u0026amp; Vander Vennet, 2016; Roengpitya et al., 2017; Roengpitya, Tarashev, \u0026amp; Tsatsaronis, 2014). Additional methodologies have encompassed correlation analysis and simple linear regression (Jočienė, 2015), non-parametric tests, data envelopment analysis, truncated regression, bootstrap (Curi, Lozano-Vivas, \u0026amp; Zelenyuk, 2015), K-medoids clustering (Hryckiewicz \u0026amp; Kozlowski, 2017), a combination of hierarchical clustering with discriminant analysis (Roengpitya et al., 2017), and random forest methodology validated by discriminant analysis (Bolívar et al., 2023) to identify the business models of various banking institutions.\u003c/p\u003e\u003cp\u003eWe adhere to the methodological framework established by Ayadi et al. (2017), calculate multiple indicators derived from the balance sheets of banks, and apply statistical clustering to delineate patterns and groups within our dataset. Our sample is a panel of year-end data from the balance sheet and profit and loss statements of 351 CCBs for the years 2014-15 and 2020–2021. The data from the balance sheets were sourced from the ‘Key Statistics of Cooperative Banks (Short Term Cooperative Credit Structure)’ on cooperative banks published by the National Bank for Agriculture and Rural Development, which is the supervisory body for rural cooperative banks in India. We posit that the composition of a bank’s balance sheet is a reflection of its strategic decisions. Our analysis emphasises their principal banking operations and funding methodologies. While selecting the indicators, we compute ratios as percentages of total assets (from the balance sheet) to create a standardised metric for analysis. This approach also eliminates the necessity for scaling variables during the clustering process. The rationale for indicator selection was a significant contribution to the balance sheet and utility for rent earnings. We excluded categories that constitute, on average, less than 5% of the total assets. Expert judgment was employed to determine the variables pertinent to the identification of business models. We executed the clustering algorithm on the seven variables identified as critical resources and activities and then used descriptive statistics to study the features and logic of the clusters/business models. Additionally, we employed the same clustering algorithm to the components of banks’ income and operational expenditure, as well as a synthesis of the balance sheet variables and income and expenditure ratios to validate the clusters/business models derived. In the subsequent phase, we scrutinise the risk profile, performance metrics, and value captured as manifested in the profitability indicators and income variables. Analysing return on assets and net interest margin facilitates ascertaining value capture. We also take an interpretive approach and delineate the generic business model of rural cooperative banks on the business model canvas tool (Osterwalder \u0026amp; Pigneur, 2010) by studying their websites and annual reports.\u003c/p\u003e\n\u003ch3\u003eSample selection\u003c/h3\u003e\n\u003cp\u003eThe rural short-term cooperative credit structure in India comprises 34 State Cooperative banks, 351 Central Cooperative Banks (CCBs), and more than 95,000 Primary Agriculture Cooperative Credit Societies (PACS) as per the ‘Key Statistics of Cooperative Banks (Short Term Cooperative Credit Structure) March 31, 2021’ (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nabard.org/auth/writereaddata/tender/pub_060723112055643.pdf\u003c/span\u003e\u003cspan address=\"https://www.nabard.org/auth/writereaddata/tender/pub_060723112055643.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This middle layer of 351 CCBs, operational at the district level in 20 states in India, which purveys credit through their 13589 branches and affiliated PACS and other societies, is the sample selected for this study. We selected these banks as they were the financial institutions closest to the ground-level cooperative societies in cooperative banking. The rural cooperative banking framework in India is characterized by two- and three-tiered rural cooperative banking structures. We have selected entities that form the intermediary tier, specifically central cooperative banks, in the three-tiered cooperative credit structure and systematically mapped their resources and activities to identify their business models. These hybrid institutions fulfil the dual roles of profit generation and the enhancement of member welfare while providing essential financial services to rural farmers, artisans, small enterprises and others. Accordingly, these samples were deemed appropriate for the objectives of this study.\u003c/p\u003e\n\u003ch3\u003eDescriptive Statistics\u003c/h3\u003e\n\u003cp\u003eWe aggregate the bank year observations from data as of March 31, 2015, March 31, 2016, March 31, 2017, March 31, 2018, March 31, 2019, March 31, 2020, and March 31, 2021, together in one large, combined dataset of seven years’ bank observations (2540 rows) of 351 CCBs. Source of data is from website of NABARD (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nabard.org\u003c/span\u003e\u003cspan address=\"https://www.nabard.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) publications, details of which are given at the end of the paper. The descriptive statistics of the bank year observations for input variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) show that the variables exhibit a central tendency towards deposits as a source of funds, followed by borrowings, capital reserves, and balance of profit. Notably the variation in deposits is substantial. Among the various activities, short-term loans predominate, followed by fixed deposits with other banks and investments. There is also considerable variation in the loan parameter. All the variables are normalized against total assets and articulated as a percentage of total assets/ contribution to the balance sheet, thereby ensuring that the bank size does not distort the analytical findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of the 2540 Bank-Year Observations for the years 2015–2021 (Position as of 31st March every year)(All figures as a ratio of Total assets)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalance-Sheet Heads\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVariable selection\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapital\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSelected as ‘Stable funding’ CapResP\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReserves\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalance of Profit\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevaluation Reserve\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot considered as less than 5%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvisions\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot considered as neither income earning nor source of funds\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Liabilities\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot considered as less than 5%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCash\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot considered as less than 5%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalance in Current Account\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot considered as less than 5%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Assets\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.386\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot considered as less than 5%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Assets\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot considered as less than 5%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeposits\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected as ‘Deposits’\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorrowings\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.340\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected as ‘Borrowings’\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort Term (ST) Loans\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected as ‘STLoans’\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium Term (MT Loans)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected as ‘MTLoans’\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvestments\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected as ‘Investments’\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Deposits\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelected as ‘FixDeposits’\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eSource: The Authors\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVariables selected for clustering\u003c/h2\u003e \u003cp\u003eThe variables selected for clustering in this study are elucidated below.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAsset side (Ratio to Total Assets)\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eLoans to customers and societies\u003c/b\u003e- identifies the proportion of customer loans reflecting a dependence on conventional banking operations. The gross loan amount was taken as a ratio of total assets (balance sheet size). The lending product mix included short- and medium-term loans. Lending constitutes a fundamental function of banks and the composition of short-term lending and medium-term lending products is determined by banks. Consequently, both indicators were treated as two distinct variables: STLoans and MTLoans.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDeposits with other Banks (Fixed deposits balance with State Cooperative Bank in deposit accounts and balance with other banks in deposit accounts) (FixDeposits)\u003c/b\u003e – This indicator, identifies investments in liquid assets, such as keeping term deposits with other banks, specifically state cooperative banks and commercial banks. Given that the sums retained as fixed deposits/term deposits with other banks can be withdrawn prematurely, contingent upon the relinquishment of a certain portion of the interest income, these deposits were classified as liquid deposits and as a form of interest-earning investment. The quantum held in the current account with the Reserve Bank of India has been excluded from consideration as part of an input variable.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInvestments /Trading assets\u003c/b\u003e – This metric assesses the book values of the investments in bonds and government securities. These could be for maintaining the Statutory Liquidity Ratio (SLR) or only for investments and may include non-SLR securities. Large values indicate a prevalence of investment activities (prone to market and liquidity risks).\u003c/p\u003e \u003cp\u003eThe CCBs do not offer loans to other CCBs/banks, and no significant interbank lending is seen; therefore, the variable \u003cb\u003eLoans to banks\u003c/b\u003e considered by scholars studying European banks are not computed by us for Indian cooperative banks. Further, we take \u003cb\u003eFixed/Term Deposits with Other Banks\u003c/b\u003e as an indicator of investments in liquid assets and, in part, connectedness with the banking network. These are not loans but deposits with other banks, shown as assets by the depositing bank and liabilities by the deposit-taking bank.\u003c/p\u003e\n\u003ch3\u003eLiabilities side (Ratio to Total Assets)\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eInterbank borrowing (Deposits from Banks)\u003c/b\u003e This has been clubbed with wholesale debt, that is, bank liabilities or the amount shown under \u003cb\u003eBorrowings/Refinance\u003c/b\u003e, and identifies the share of liabilities owed to higher-tier institutions, including refinances from State Cooperative Banks, National Bank for Agriculture and Rural Development (NABARD), and overdrafts with other banks. This measure highlights banks with greater interbank funding requirements, often due to excessive reliance on short-term funding.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStable funding\u003c/b\u003e indicators taken by Ayadi et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Roengpitya et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) have been modified. Instead of adding total customer deposits with long-term funds to arrive at stable funds, we take the summation of \u003cb\u003ecapital, reserves, and balance of profits\u003c/b\u003e or retained profits, as these are stable, long-term, and own sources of funds out of internal accrual from operations. A unique feature of cooperative banks is that augmentation of resources of the cooperative banks is done by the “share-linking” concept, whereby the loanee societies are required to contribute a small percentage of funds in proportion to the loan taken, towards the share capital of the bank; this share capital is eligible for dividend, although not for additional voting rights.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCustomer deposits.\u003c/b\u003e The indicator identifies the share of deposits from customers, including societies, in the total balance sheet, indicating a reliance on more traditional funding sources. Deposits (customer deposits) can be taken as CASA (Current Account and Savings Account (CASA) deposits and term deposits kept by customers and cooperative societies with the cooperative bank. Derivative exposures, though reckoned by Ayadi et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Roengpitya et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), are not taken by us in the Indian cooperative bank’s case, as these were not seen in cooperative banks in India.\u003c/p\u003e \u003cp\u003eWe examined the correlations between the variables selected to see their inter-relatedness, linear relationship, and direction of relationship, and to rule out collinearity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt was observed that there exists a positive correlation between borrowings/refinances and short-term loans, while fixed deposits with banks, customer deposits, and investments exhibit positive correlations among themselves. Conversely, short-term loans and fixed deposits demonstrate a negative correlation, and a similar negative correlation is noted between deposits and borrowings. This observation suggests that the borrowing levels of banks tend to be lower when outstanding deposits are high, as banks may possess sufficient low-cost resources in the form of savings account deposits for their activities. The remaining parameters displayed a relative low correlation quantified as less than 65%. Therefore, it can be deduced that there exists a weak or low correlation among the selected variables. The correlation coefficient did not reach significantly high threshold, thus permitting further data analysis with the anticipation that the influence of individual variables or instruments remains unimpeded.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThe variables in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were employed for cluster analysis to delineate distinct business models. We implemented Ward’s hierarchical clustering and calculated the Calinski-Harabasz pseudo-F values to arrive at the ‘stopping rule’ (Calinski and Harbasz, 1974). The pseudo-F indices were highest at 1344.65 for the two cluster-solution, followed by 1022.10 for three-cluster solution, and displayed a consistent decline in value for the subsequent cluster combinations. Subsequently, we assessed various combinations of clusters and analyzed them utilising descriptive statistics. We modeled the two clusters, as suggested by the highest pseudo-F value; however, this did not yield significantly distinct business models. Therefore, we proceeded with modeling three clusters, based on the pseudo-F value, which resulted in the identification of three significant business models. We also modeled four and five clusters. The results for combinations of three, four, and five clusters yielded three distinct business models and one or two intermediate models.\u003c/p\u003e \u003cp\u003eWe compiled a similar panel of indicators derived from the profit and loss statements of these banks for the same fiscal years and executed Ward’s hierarchical clustering. The selected variables for clustering included all subheads of income and operational expenses. The income variables included interest earned from advances, interest earned from investments, income from commission and exchange, the profit realised from the trading or sale of investments, and miscellaneous income, all quantified as a percentage of total income. Operational expenses include the interest paid on deposits and borrowings, computed as percentages of total expenditures. Analyzing the components of income earned by CCBs as documented in their profit and loss statements for the respective fiscal years, we identified that the predominant sources of income for all cooperative banks were interest income on loans extended and interest earned on investments made in government securities, bonds, deposits kept with other banks, dividends earned on shares invested in by them, etc. Income from commissions and exchanges earned constituted the next category of income. Another notable area of value capture is observed mainly from 2018 onwards in the form of income from the sale of investments and trading in securities. By undertaking Ward’s hierarchical clustering on the same, we obtain three similar clusters/business models (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsequently, three distinct clusters were identified, and the features of these three distinct business models were studied. Hence, we deduce that three business models were implemented: retail-funded traditional lending, wholesale-funded traditional lending, and retail-funded investment-oriented.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDistinct Business Models and Their Characteristic\u003c/b\u003es\u003c/p\u003e \u003cp\u003eThe clustering analysis yielded the following business models as outcomes (Fig.\u0026nbsp;3). Their attributes and associated risk profiles were meticulously analysed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRetail Funded Traditional Lending (RFTL)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRetail Funded Investment Oriented (RFIO)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWholesale Funded Traditional Lending (WFTL)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBusiness model 1 – Retail Funded Traditional Lending (RFTL)\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis model represents the traditional intermediation role between savers and borrowers: aggregating low-cost deposits and financing short-term loans. The principal activity involves extending credit for agricultural cultivation, which, as working capital, is for a year's duration. Customer deposits and short-term advances portfolios constitute a significant component of their balance sheets on average. Banks adopting the RFTL model assume the intermediation role of banks in the true sense, as they transform maturities and utilize the deposits maintained with them for lending to borrowers. Banks operating under this model exhibit the most extensive penetration within rural areas. Their credit-to-deposit (CD) ratio is also high. These banks exhibit a pronounced level of risk cost, gross non-performing assets (GNPA), and net NPA percentage, suggesting that conventional business activities are associated with a relatively heightened credit risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cb\u003eHigher risk and higher return\u003c/b\u003e have been adopted as the logic in this business model. Profitability metrics, the mean value of return on assets (RoA) and return on equity (RoE), are comparatively low for this model, in contrast with more positive RoA and RoE observed in the other two models; median values are moderate. Value capture, as quantified by its net interest income, remains average, supplemented by small earnings from commissions and fees.\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBusiness Model 2 : Retail Funded Investment Oriented (RFIO)\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis model envisages mobilizing deposits and deploying them as investments in government securities, other approved securities, and term deposits with other banks. Their balance sheet size is among the highest within the CCBs, with the largest banks classified in this category, as evidenced by their median asset size and business per branch (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The RFIO business model is characterized by a significant dependence on customer deposits outstanding on the liabilities side and a substantial percentage of investments and term/fixed deposits with other banks on the assets side. The deposits maintained as current, savings, which entail minimal interest expenditure, and term deposits constitute a considerable proportion of their liabilities. This is nearly one standard deviation above the population mean and can be identified as a distinctive characteristic of this model. In addition to mandated investments in authorised securities, the RFIO model opts for investments considered as a relatively low-risk activity for income generation. RFIO capitalizes on customer deposits maintained with the bank and invests them in securities, mirroring the function of investment banks, as well as in term-deposits placed with other banks. The investment-to-deposit ratio is among the highest. A diversification into medium-term loans is also noted in some banks, and their non-agricultural loan portfolio surpassing that of the other two models. The risk aversion of the RFIO model is additionally manifested in the elevated balances maintained in cash and current accounts, thereby sustaining a high level of liquid assets, albeit these are non-earning assets (since no interest accrues on these balances). Their risk cost is among the lowest (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and the mean value of the ratio of risk-weighted assets to total assets is the least among the three models. The rationale underlying the RFIO model suggests a relatively \u003cb\u003elow risk - low margins framework.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBusiness model 3 – Wholesale Funded Traditional Lending (WFTL)\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis business model engages in borrowings to facilitate short-term lending operations. It predominantly relies on debt sourced from other banks, which is then utilized for short-term lending as the primary activity. Short-term loans dominate the asset portfolio in this model, as suggested by the same exceeding the population mean by one standard deviation, and the agricultural loans component is higher than those of other models. This framework has been adopted by small to medium-sized banks in India. In this model, banks leverage funds to enhance their asset portfolios. Given that wholesale funds are acquired by borrowing from higher-tier institutions that involve less administrative costs, this model demonstrates higher operational efficiency as indicated by the mean cost-to-income ratio (CIR) being the lowest among the three models; however, the median CIR is higher, suggesting that a greater number of banks fall within the higher CIR quadrant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The borrowings of these banks surpass the population mean by nearly one standard deviation. The percentage of deposits mobilized by these banks is lower than those of all CCBs and other models. The rationale underpinning this business model is \u003cb\u003eleveraging and augmenting\u003c/b\u003e available resources through borrowing from other banks, thereby increasing volume and lending to clients to generate higher interest income. This model dispenses larger amounts in loans, resulting in higher average business per branch and business per staff as compared to the RFTL model.\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk Factors and Performance of the Business Models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter (Median values)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRFTL\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRFIO\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWFTL\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapital Adequacy \u0026amp; Quality\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTier I Capital to Risk Weighted Assets %\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.06\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapital to Risk-weighted Assets Ratio % (CRAR)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.15\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBusiness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Assets (Amount in million)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14305.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20067.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9654.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCredit Deposit Ratio % (CD Ratio)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148.25\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural Loans outstanding to Total Loans outstanding %\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsset Quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Non-Performing Assets (NPAs) to Gross Loans \u0026amp; Advances % (GNPA)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet NPAs to Net Advances % (NNPA)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Cost\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfitability Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet Interest Margin (NIM)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReturn on Assets (RoA)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReturn on Equity (RoE)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEfficiency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost to Income Ratio (CIR)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProductivity parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness per Branch (Amount in million) \u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e390.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e397.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness per Staff (Amount in million)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSource: The Authors\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA comparative analysis of the performance parameters exhibits that the WFTL model has higher productivity ratios due to the augmentation of their funds with borrowed resources, operational efficiency is higher in the RFIO model, which is comparatively more profitable as seen from their asset quality, risk profile and productivity ratios. The traditional RFTL model manages its risk profile moderately.\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003cstrong\u003eEvolution of Business Models in CCBs over seven years\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified modifications in the business model over a span of seven years and alongside changes, if any, in the business model of CCBs. The business models of banks were not observed to be immutable, rather they displayed transformations over the years, which were termed migrations (Ayadi et al., 2021). Following the same methodology as Ayadi, we considered migration when a bank\u0026rsquo;s business model transitioned to an alternative model, persisted in that model for two years, and did not revert to the earlier one. The table 3 shows the migration matrix of business models.\u003c/p\u003e\n\u003cp\u003eTable 3: Migrations matrix in business models\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6307%;\"\u003e\n \u003cp\u003eFrom (column) To (row)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9965%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRFTL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6864%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRFIO\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6864%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWFTL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6307%;\"\u003e\n \u003cp\u003eRFTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9965%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e86.02%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(2.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6864%;\"\u003e\n \u003cp\u003e7.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6864%;\"\u003e\n \u003cp\u003e4.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6307%;\"\u003e\n \u003cp\u003eRFIO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9965%;\"\u003e\n \u003cp\u003e11.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6864%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e82.91%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(3.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6864%;\"\u003e\n \u003cp\u003e2.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6307%;\"\u003e\n \u003cp\u003eWFTL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9965%;\"\u003e\n \u003cp\u003e20.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6864%;\"\u003e\n \u003cp\u003e0.64%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.6864%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e75.00%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(3.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e(Figures in bold represent unchanged business models, and in brackets represent those banks that migrated temporarily for a year or so and then returned to the original business model)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhile studying the evolution of business models, we find that banks\u0026rsquo; business models are substantially stable. The highest level of persistence is seen in the RFTL model, where 86% of the banks adopting this model exhibit no migrations, and 2.15% oscillated to another model and reverted to RFTL. About 11.11% of banks transitioned from the RFIO model to RFTL, while 20.51% of WFTL migrated to RFTL. Therefore, these banks manifest a tendency towards more retail funding. A total of 77 banks experienced at least one transition to an alternative business model. The RFIO model exhibited better stability than the WFTL model. The transitions in business models also corroborate this. \u0026nbsp;Further research can be undertaken to ascertain whether migrations were in search for stability, profitability, or liquidity. The trend in the year-wise migration of the banks\u0026apos; preferred business model was discerned (Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt is observed that the WFTL model, which was favoured in 2015, has experienced a decline over time, whereas the RFTL model has gained prominence among banks. Therefore, we infer that with the expansion of branches, more retail funds become available with CCBs as deposits, which enhances their low-cost current account and savings account (CASA) funds. The adoption of banking technology by these banks has led to the issuance of Automated Teller Machine (ATM) cards, subsequently enabling direct account opening by the members of the PACS with the CCBs. This may have also contributed to these banks\u0026apos; influx of retail deposits. The RFIO model also exhibited a rise in popularity until 2017, after which a decline was noted.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eValue capture was assessed in terms of components of income generated, which primarily consisted of income from loans and advances in the RFTL and WFTL models, as well as income from investments in the case of the RFIO model. An analysis of the income revealed that net interest income demonstrated minimal variation across the business models; however, the net interest margin was notably higher for the RFIO model, followed by the RFTL and WFTL models. The diversification into non-interest income in the form of income from commission, fees, etc., was limited, although marginally higher in RFIO model, succeeded by WFTL and RFTL models. Profitability indicators suggest that the strategy of borrowing or availing refinances to enhance banks\u0026rsquo; resources effectively expands the business of the bank by augmenting its advances portfolio, which tends to support lower risk costs. A novel facet of value capture has been identified in 14 CCBs in the form of \u0026lsquo;income earned from trading and sale of investments\u0026rsquo; (we consider the values as significant if this income formed more than 10% of their total income). This was prevalent in the WFTL business model, as eight banks had adopted this component in the model, while two each were associated with the RFTL and RFIO models. This finding is counterintuitive, as it was anticipated that RFIO business models would engage in investment trading as an ancillary activity. The emergence of this new component in model may have been influenced by the regulatory directives for cooperative banks to park their Statutory Liquidity Ratio (SLR) and Cash Reserve Ratio (CRR) in approved securities that were government-issued instead of as deposits with their higher tier institution, the State Cooperative Banks. Accordingly, CCBs have maintained SLR in Government securities, which are tradeable in the secondary market. This shift may have facilitated trading in excess (over statutory requirements) securities held, thereby emerging as an additional component of their business model.\u003c/p\u003e\n\u003cp\u003eThe process of value creation and capture by these firms, beyond mere financial value capture was then inquired by constructing a generalized business model using business model canvas tool. This generic business model mapped out using the business model canvas tool elucidates the contents, structure, governance and value logic by identifying key partners, channels, resources, activities, customers, value propositions, costs, revenue, etc. (Figure 5)\u003c/p\u003e"},{"header":"Discussion and Findings","content":"\u003cp\u003eWe conducted an examination of the business models of these institutions through an analysis of their tangible resources and operational activities, based on variables deduced from their balance sheet data over a period of seven years, and analyzed the components of their income and expenditure on operations to capture their value. We had set out to answer whether a universal business model is adopted by the rural cooperative banks, which share similar characteristics in terms of ownership, mutualism, localism, stakeholder banking, and level of knowledge of customer base. We find that rural cooperative banks differ in their implementation of funding sources and their activities realized, and thus, they exhibit different strategies to achieve their objectives, despite their common objective of mutualism. Our cluster analysis based on the method given by Ayadi (2021) delineated three broad business models. It is observed that most banks have preferred retail funding. However, there has been a gradual shift towards diversification of activities as time progressed and the business models have exhibited some changes although remaining stable largely. A new activity of trading in securities has been observed in 14 banks. Value creation and capture is granularly delineated using the business model canvas tool and cooperative capital emerged as a common resource unique to cooperative banks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study contributes to the body of research on business models in two ways. First, it substantiates the proposition that business models can function as a unit of analysis within management studies (Zott et al., 2011). The identified clusters represent different business models adopted by rural cooperative banks by considering the choices made by them in assets, funding, capitalization, and diversification, which are reflected as realized strategies in their balance sheets. These models help in understanding the gestalt of these firms, by sharing their approach to business in their rural ecosystems. The business model represents a more accessible vector to convey strategy (Bigelow \u0026amp; Barney, 2021). Our business model framework study disaggregates the various combinations of financial resources raised by the focal firms, the rural cooperative banks. The resources in question influence performance by exploiting complementarity and economies of scope (Villani, Greco, \u0026amp; Phillips, 2017), thereby leading to a better understanding of specific dimensions of tangible resources that align with the supply side perspective of strategy. Our study highlights the activities undertaken by these firms, which lead to value generation for the customer. Second, our study makes robust empirical contributions to business model categorization and the distinctiveness of the business model construct in relation to organizational structure (Zott \u0026amp; Amit, 2013). The typologies discerned within Indian rural cooperative banks augment the existing taxonomy of business models (Ayadi, De Groen, et al., 2017). Moreover, by exploring the heuristics-based rationale underpinning each business model, this study reveals the adaptive emergent properties inherent in these complex organizations (Bettis \u0026amp; Prahalad, 1995), which are often latent and not readily observable. By analyzing the trends and transformations in business models over time, and their trajectory using suitable analytical tools (Snihur \u0026amp; Markman, 2023), the adoption and evolution of business models within new organizational contexts (Ahlgren Ode \u0026amp; Louche, 2022) in cooperative banks can be further explored and emerging risks can be analyzed.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe present a study, the first of its kind, which maps out the business models of the CCBs in India, based on granular data derived from the balance sheet of these institutions and using business model canvas tool. We have undertaken an exploratory analysis using a clustering methodology on variables derived from the balance sheets of 351 banks to delineate the banks\u0026apos; business models. We took snapshots of the focal firms over a period and used panel data for seven years, with year-end positions as 31\u003csup\u003est\u003c/sup\u003e March 31, 2015, to 31\u003csup\u003est\u003c/sup\u003e March\u0026nbsp;31, 2021. Then,\u0026nbsp;taking a resource- and\u0026nbsp;activity-based view,\u0026nbsp;we consider these\u0026nbsp;variables to be conscious managerial choices of these banks. We infer the existence of three distinct models viz. \u0026lsquo;Retail Funded Traditional Lending,\u0026rsquo;\u0026nbsp;\u0026lsquo;Retail Funded Investment Oriented,\u0026rsquo;\u0026nbsp;and \u0026lsquo;Wholesale Funded Traditional Lending,\u0026rsquo;\u0026nbsp;among CCBs. Their embedded environments\u0026nbsp;may have influenced their choices. CCBs generally adopt the traditional intermediation role of converting maturities by collating retail deposits and lending to customers (RFTL). Many banks\u0026nbsp;have leveraged low-cost borrowings to provide short-term\u0026nbsp;crop loans at directed lending rates, exhibiting another business model (WFTL). \u0026nbsp;However, a section of banks diversified to investments in securities and term deposits, in addition to the liquidity level needed for statutory and regulatory requirements; hence, a different business model has also been adopted (RFIO). With regulatory changes, another element of income from trading in securities is emerging, changing business models. Our investigations into performance variables, efficiency, and risks provide empirical evidence of significant differences across these business models.\u0026nbsp;The\u0026nbsp;evolution of business models over\u0026nbsp;a period of seven years reflects changes towards retail funding, which could be ascribed to higher penetration in rural hinterlands through branch expansion\u0026nbsp;and technology adoption. Investment-oriented (investment orientation)\u0026nbsp;firms also gained traction\u0026nbsp;due to\u0026nbsp;the tendency towards less\u0026nbsp;risk-taking by banks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications of Study Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study, the first of its kind with respect to Indian banking institutions, while exploratory in nature, holds significant implications for business model taxonomy, evolution, and strategic management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcademia\u003c/strong\u003e: The empirical findings substantiate the business model as a pertinent unit of analysis. It maps the spectrum and selection of resources utilized by rural cooperative banks in their activities to create and capture value. The findings of this study contribute to the existing literature on business model taxonomy particularly focusing on hybrid institutions of cooperatives, which serve as the context differentiator. It reinforces the relevance of business model as a critical focal point of study within the realms of strategy and finance. A well-defined business model is a critical modality (Snihur and Markman, 2023) in how an enterprise creates and captures value; this study defines the elements of various business models in cooperative banks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy Makers\u003c/strong\u003e: Business model identification maps out the anatomy of the rural cooperative banking sector, and helps in the creation of subsets for quick analysis of banks. This quantitative approach of evaluating the financial indicators derived from the banks\u0026apos; balance sheet, as compared to qualitative parameters that are more time-intensive, enhances comprehension of the decisions made by the banks, facilitating appropriate policy development. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Supervisory Review and Evaluation Process (SREP) conducted by the European Central Bank incorporates profitability assessment of business models employed by their supervised entities. Identifying the logic behind the choices that various banks make can be utilized for assessing risky or conservative behavioural trends and designing effective policies for the same. Supervisors in India can consider adopting business model sustainability analysis as part of the assessment of these banks. The findings have special significance in view of the renewed focus on cooperatives by the Government of India, as exemplified by establishment of a separate dedicated Union Ministry of Cooperation in 2021. The business model framework delineated in this study can be extrapolated to other cooperative banks and incorporated into the public policy relevant to cooperatives.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePracticing Managers\u003c/strong\u003e - Business models are valuable instruments for quick decision-making by practicing managers, for studying strategic choices made by their counterparts in the peer groups, and adopting best practices from them. These findings will enhance understanding of the business models in use and the requisite changes to fulfill the strategic objectives of the institution. Managers can analyze the components of their business models; assess their resources, leverage, business activities, operational efficiency, profitability, and risk profile on an ongoing basis; and chalk out a suitable course of action. This business model tool is useful for identifying specific concrete components of their business profile which may be either bolstering or adversely impacting their performance. Empirical studies have established a correlation between business models and performance metrics, risk profiles, profitability, and resilience during economic downturns, thus, making awareness of the business models and their implications vital for managers seeking to to adeptly adjust their strategies. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations of the Study and Biases\u003c/strong\u003e: Being the first such study undertaken on hybrid institutions within India, we limit our analysis to the identification of the business models in this distinctive context elaborating on their charecteristics, underlying logic, broad risk profiles, evolution, and transitions. Our analysis employs panel data comprising financial statements spanning seven years, which may be susceptible to measurement and reporting biases. In addition to being grounded in data, the selection of variables and the analysis of the findings have also benefitted from expert qualitative judgment. The temporal scope of this study reflects the macroeconomic developments and policy decisions during that period; the migration analysis across business models could be extended further in time, complemented with a detailed analysis of their ecological contexts to obtain a deeper understanding of the dynamics of the banking sector.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSuggestions for future research \u0026ndash;\u0026nbsp;\u003c/strong\u003eChanges in business models can be studied over the years to assess changes in models, migration, mergers, profitability, performance, and other parameters. There is scope for assessing innovations undertaken in the business models of rural cooperative banks, especially given the technological advancements in banking. The business models of other financial intermediaries operating in this sector can be identified to determine further diversities that may exist. Business model design and elements can be decomposed to obtain insights into the influence of social and environmental factors on managerial decision making and strategic choice.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Compliance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the study complies with the ethical standards and the procedures did not involve human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no support for research, authorship and/or publication of this article.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.J. wrote the main manuscript, collected the data, analysed the same and prepared the figures and tables. S.S. edited and reviewed the manuscript and guided the study. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data which support this study have been sourced from compendium of key statistics published by NABARD every year and for the years 2017-2021 is available at www.nabard.org. The specific URLs for each year are indicated below:https://www.nabard.org/auth/writereaddata/tender/pub_060723112055643.pdf, https://www.nabard.org/auth/writereaddata/tender/pub_060723112459644.pdfhttps://www.nabard.org/auth/writereaddata/tender/3001203948Key%20Statistics%20\u003cbr/\u003eof%20Cooperative%20Banks%2031%20March%202019.pdfhttps://www.nabard.org/auth/writereaddata/tender/3001203407Key%20Statistics%20of%20Cooperative%20Banks%2031%20March%202018.pdfhttps:\u003cbr/\u003e//www.nabard.org/auth/writereaddata/tender/3001202337Key%20Statistics%20of%20Cooperative%20Banks%2031%20March%202017%20.pdfFor the years 2015 and 2016, the data has been compiled from similar compendiums published by NABARD and is available with the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhlgren Ode, K., \u0026amp; Louche, C. (2022). A business model pattern arrives \u0026hellip; and then? 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Journal of Management, \u003cem\u003e37\u003c/em\u003e(4), 1019\u0026ndash;1042. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0149206311406265\u003c/span\u003e\u003cspan address=\"10.1177/0149206311406265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"business model, clustering, cooperative bank, migration, developing economy","lastPublishedDoi":"10.21203/rs.3.rs-6435956/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6435956/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores the business models of hybrid institutions, rural cooperative banks in India which balance the dual primary tasks of cooperation and welfare with profit-making. We undertake an exploratory analysis of 351 Central Cooperative Banks (CCBs)in India from 2015 to 2021 by clustering the variables derived from their financial statements. We consider these variables to be representative of the managerial choices of these institutions in their quest for optimum performance in the given environment. We delineate three business models from the clusters: Retail Funded Investment Oriented, Retail Funded Traditional Lending and Wholesale Funded Traditional Lending. We study their risk profile and delve into the inherent heuristics-based logic of each model to understand the institutions' realized strategy. We then enquire into the changes in the business models with time and find a new business model component based on trading in securities to be emerging. A business model canvas mapping of key resources, activities, partners, channels and value propositions provides deeper insights into the models. Our study mobilizes the contextual differentiator of rural cooperative banks. This research substantiates that cooperative banks, while balancing dual missions, adopt different business models and give insights into how they do it while managing the risks involved and how the same changes over time. It contributes to the business model literature by suggesting new typologies of business models adopted by hybrid institutions in rural hinterlands of a developing economy. It offers recommendations for policymakers, supervisory authorities and practitioners.\u003c/p\u003e","manuscriptTitle":"Blueprints of Strategy – An Empirical Study of Business Models in Rural Cooperative Banks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 22:38:07","doi":"10.21203/rs.3.rs-6435956/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"383aac7c-f9bd-41c0-9b77-6223efea15e6","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:01:34+00:00","versionOfRecord":{"articleIdentity":"rs-6435956","link":"https://doi.org/10.1186/s43093-026-00740-w","journal":{"identity":"future-business-journal","isVorOnly":false,"title":"Future Business Journal"},"publishedOn":"2026-01-20 15:58:27","publishedOnDateReadable":"January 20th, 2026"},"versionCreatedAt":"2025-05-05 22:38:07","video":"","vorDoi":"10.1186/s43093-026-00740-w","vorDoiUrl":"https://doi.org/10.1186/s43093-026-00740-w","workflowStages":[]},"version":"v1","identity":"rs-6435956","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6435956","identity":"rs-6435956","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}